Repository: tweag/monad-bayes Branch: master Commit: 86f13498dfbb Files: 134 Total size: 34.1 MB Directory structure: gitextract_x9tdq2je/ ├── .github/ │ ├── actions/ │ │ └── nix-common-setup/ │ │ └── action.yml │ ├── dependabot.yml │ └── workflows/ │ ├── ci.yml │ ├── nix.yml │ └── update-flake-lock.yml ├── .gitignore ├── .hlint.yaml ├── CHANGELOG.md ├── LICENSE.md ├── MAINTAINERS.md ├── README.md ├── Setup.hs ├── benchmark/ │ ├── SSM.hs │ ├── Single.hs │ └── Speed.hs ├── default.nix ├── docs/ │ ├── docs/ │ │ ├── examples.md │ │ ├── index.md │ │ ├── javascripts/ │ │ │ └── mathjax.js │ │ ├── notebooks/ │ │ │ ├── AdvancedSampling.html │ │ │ ├── Bayesian.html │ │ │ ├── ClassicalPhysics.html │ │ │ ├── Diagrams.html │ │ │ ├── Functional_PPLs.html │ │ │ ├── Histogram.html │ │ │ ├── Introduction.html │ │ │ ├── Ising.html │ │ │ ├── Lazy.html │ │ │ ├── Lenses.html │ │ │ ├── MCMC.html │ │ │ ├── Parsing.html │ │ │ ├── RealTimeInference.html │ │ │ ├── SMC.html │ │ │ ├── Sampling.html │ │ │ └── Streaming.html │ │ ├── probprog.md │ │ ├── tutorials.md │ │ └── usage.md │ ├── mkdocs.yml │ ├── netlify.toml │ ├── requirements.txt │ └── runtime.txt ├── flake.nix ├── kernels/ │ └── haskell.nix ├── models/ │ ├── BetaBin.hs │ ├── ConjugatePriors.hs │ ├── Dice.hs │ ├── HMM.hs │ ├── Helper.hs │ ├── LDA.hs │ ├── LogReg.hs │ ├── NestedInference.hs │ ├── NonlinearSSM/ │ │ └── Algorithms.hs │ ├── NonlinearSSM.hs │ ├── Sprinkler.hs │ └── StrictlySmallerSupport.hs ├── monad-bayes.cabal ├── notebooks/ │ ├── Implementation.ipynb │ ├── _build/ │ │ └── _page/ │ │ └── Introduction/ │ │ └── html/ │ │ └── _sphinx_design_static/ │ │ └── design-tabs.js │ ├── examples/ │ │ ├── ClassicalPhysics.ipynb │ │ ├── Diagrams.ipynb │ │ ├── Histogram.ipynb │ │ ├── Ising.ipynb │ │ ├── Lenses.ipynb │ │ ├── Parsing.ipynb │ │ └── Streaming.ipynb │ ├── file.json │ ├── models/ │ │ └── LDA.ipynb │ ├── plotting.hs │ └── tutorials/ │ ├── AdvancedSampling.ipynb │ ├── Bayesian.ipynb │ ├── Introduction.ipynb │ ├── Lazy.ipynb │ ├── MCMC.ipynb │ ├── SMC.ipynb │ └── Sampling.ipynb ├── plots.py ├── profile.sh ├── regenerate_notebooks.sh ├── shell.nix ├── src/ │ ├── Control/ │ │ ├── Applicative/ │ │ │ └── List.hs │ │ └── Monad/ │ │ └── Bayes/ │ │ ├── Class.hs │ │ ├── Density/ │ │ │ ├── Free.hs │ │ │ └── State.hs │ │ ├── Enumerator.hs │ │ ├── Inference/ │ │ │ ├── Lazy/ │ │ │ │ ├── MH.hs │ │ │ │ └── WIS.hs │ │ │ ├── MCMC.hs │ │ │ ├── PMMH.hs │ │ │ ├── RMSMC.hs │ │ │ ├── SMC.hs │ │ │ ├── SMC2.hs │ │ │ └── TUI.hs │ │ ├── Integrator.hs │ │ ├── Population.hs │ │ ├── Sampler/ │ │ │ ├── Lazy.hs │ │ │ └── Strict.hs │ │ ├── Sequential/ │ │ │ └── Coroutine.hs │ │ ├── Traced/ │ │ │ ├── Basic.hs │ │ │ ├── Common.hs │ │ │ ├── Dynamic.hs │ │ │ └── Static.hs │ │ ├── Traced.hs │ │ └── Weighted.hs │ └── Math/ │ └── Integrators/ │ └── StormerVerlet.hs └── test/ ├── Spec.hs ├── TestAdvanced.hs ├── TestBenchmarks.hs ├── TestDistribution.hs ├── TestEnumerator.hs ├── TestInference.hs ├── TestIntegrator.hs ├── TestPipes.hs ├── TestPopulation.hs ├── TestSSMFixtures.hs ├── TestSampler.hs ├── TestSequential.hs ├── TestStormerVerlet.hs ├── TestWeighted.hs └── fixtures/ ├── HMM10-MH.txt ├── HMM10-RMSMC.txt ├── HMM10-SMC.txt ├── LDA10-MH.txt ├── LDA10-RMSMC.txt ├── LDA10-SMC.txt ├── LR10-MH.txt ├── LR10-RMSMC.txt ├── LR10-SMC.txt ├── SSM-PMMH.txt ├── SSM-RMSMC.txt ├── SSM-RMSMCBasic.txt ├── SSM-RMSMCDynamic.txt ├── SSM-SMC.txt └── SSM-SMC2.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/actions/nix-common-setup/action.yml ================================================ name: Setup Nix Environment inputs: CACHIX_AUTH_TOKEN: required: true description: 'Cachix Auth Token' runs: using: "composite" steps: - name: Installing Nix uses: cachix/install-nix-action@v31 with: nix_path: nixpkgs=channel:nixos-unstable - uses: cachix/cachix-action@v17 with: name: tweag-monad-bayes authToken: "${{ inputs.CACHIX_AUTH_TOKEN }}" ================================================ FILE: .github/dependabot.yml ================================================ version: 2 updates: - package-ecosystem: github-actions directory: "/" schedule: interval: daily time: '00:00' timezone: UTC open-pull-requests-limit: 10 commit-message: prefix: "chore" include: "scope" # By default, when `package-ecosystem: github-actions` is set, # dependabot only looks in the `.github/workflows` directory, # even when setting `directory: "/"`. # But we need to keep updating the common nix setup as well. # Hopefully the following works. - package-ecosystem: github-actions directory: "/.github/actions/nix-common-setup" schedule: interval: daily time: '00:00' timezone: UTC open-pull-requests-limit: 10 commit-message: prefix: "chore-actions" include: "scope" ================================================ FILE: .github/workflows/ci.yml ================================================ name: ci on: push: branches: - newdocs - master permissions: contents: write jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - uses: actions/setup-python@v6 with: python-version: 3.x - run: pip install mkdocs-material # - run: mkdocs gh-deploy --force ================================================ FILE: .github/workflows/nix.yml ================================================ --- name: "nix-build" on: pull_request: push: branches: - master jobs: lint: runs-on: ubuntu-latest steps: - uses: actions/checkout@v6 - name: Setup Nix Environment uses: ./.github/actions/nix-common-setup with: CACHIX_AUTH_TOKEN: ${{ secrets.CACHIX_AUTH_TOKEN }} - name: Lint run: nix --print-build-logs build .#pre-commit build: needs: lint strategy: matrix: include: - os: ubuntu-latest system: x86_64-linux - os: macos-latest system: x86_64-darwin runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v6 - name: Setup Nix Environment uses: ./.github/actions/nix-common-setup with: CACHIX_AUTH_TOKEN: ${{ secrets.CACHIX_AUTH_TOKEN }} - name: Build run: nix --print-build-logs build .#packages.${{ matrix.system }}.monad-bayes - name: Development environment (package only) run: nix --print-build-logs develop .#packages.${{ matrix.system }}.monad-bayes --command echo Ready build-all-ghcs: needs: lint strategy: matrix: ghc: ["ghc90", "ghc92", "ghc94", "ghc96", "ghc98", "ghc910"] include: - os: ubuntu-latest system: x86_64-linux - os: macos-latest system: x86_64-darwin runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v6 - name: Setup Nix Environment uses: ./.github/actions/nix-common-setup with: CACHIX_AUTH_TOKEN: ${{ secrets.CACHIX_AUTH_TOKEN }} - name: Build run: nix --print-build-logs build .#packages.${{ matrix.system }}.monad-bayes-per-ghc.${{ matrix.ghc }} - name: Development environment (package only) run: nix --print-build-logs develop .#packages.${{ matrix.system }}.monad-bayes-per-ghc.${{ matrix.ghc }} --command echo Ready notebooks: needs: - build - build-all-ghcs strategy: matrix: include: - os: ubuntu-latest system: x86_64-linux # Jupyenv doesn't support Darwin yet, https://github.com/tweag/jupyenv/issues/388 # - os: macos-latest # system: x86_64-darwin runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v6 - name: Setup Nix Environment uses: ./.github/actions/nix-common-setup with: CACHIX_AUTH_TOKEN: ${{ secrets.CACHIX_AUTH_TOKEN }} - name: Development environment (complete) run: nix --print-build-logs develop --command echo Ready - name: Check whether notebook *.html files are up to date run: | ./regenerate_notebooks.sh git diff --exit-code || (echo "Please update notebooks by running regenerate_notebooks.sh, and inspecting and committing the result." && exit 1) ================================================ FILE: .github/workflows/update-flake-lock.yml ================================================ name: update-flake-lock on: workflow_dispatch: # allows manual triggering schedule: - cron: '0 0 * * 0' # runs weekly on Sunday at 00:00 jobs: lockfile: runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v6 - name: Install Nix uses: cachix/install-nix-action@v31 with: extra_nix_config: | access-tokens = github.com=${{ secrets.GITHUB_TOKEN }} - name: Update flake.lock uses: DeterminateSystems/update-flake-lock@v28 ================================================ FILE: .gitignore ================================================ src/data docs/site venv _cache docs/build *.csv *.pdf *.dat *.aux *.chi *.chs.h *.dyn_hi *.dyn_o *.eventlog *.hi *.hp stack.yaml.lock .vscode/* *.o *.prof .HTF/ .cabal-sandbox/ .ghc.environment.* .hpc .hsenv .vscode .envrc .stack-work/ cabal-dev cabal.project.local cabal.project.local~ cabal.sandbox.config dist dist-* /result /.pre-commit-config.yaml /.direnv venv/ .venv/ .ipynb_checkpoints/ .netlify/ .jupyter/ build/ cache/ ================================================ FILE: .hlint.yaml ================================================ - ignore: {} ================================================ FILE: CHANGELOG.md ================================================ # 1.3.0.4 - Allowed GHC 9.10 - Updated some version bounds # 1.3.0.3 - Relaxed some version bounds # 1.3.0.2 - Relaxed some version bounds # 1.3.0.1 - Support for GHC 9.8 # 1.3.0 - Support for GHC 9.6 - Replaced transformers' `ListT` by (https://github.com/tweag/monad-bayes/pull/295) - Naming fixes for `Sampler` and `SamplerT` # 1.2.0 - Renamed monad transformers idiomatically (https://github.com/tweag/monad-bayes/pull/295) # 1.1.1 - add fixture tests for benchmark models - extensive documentation improvements - add `poissonPdf` - Fix TUI inference - Fix flaky test - Support GHC 9.4 # 1.1.0 - extensive notebook improvements # 1.0.0 (2022-09-10) - host website from repo - host notebooks from repo - use histogram-fill # 0.2.0 (2022-07-26) - rename various functions to match the names of the corresponding types (e.g. `Enumerator` goes with `enumerator`) - add configs as arguments to inference methods `smc` and `mcmc` - add rudimentary tests for all inference methods - put `mcmc` as inference method in new module `Control.Monad.Bayes.Inference.MCMC` - update history of changelog in line with semantic versioning conventions - bumped to GHC 9.2.3 # 0.1.5 (2022-07-26) - Refactor of sampler to be parametric in the choice of a pair of IO monad and RNG # 0.1.4 (2022-06-15) Addition of new helper functions, plotting tools, tests, and Integrator monad. - helpers include: `toEmpirical` (list of samples to empirical distribution) and `toBins` (simple histogramming) - `Integrator` is an instance of `MonadDistribution` for numerical integration - `notebooks` now contains working notebook-based tutorials and examples - new tests, including with conjugate distributions to compare analytic solution against inferred posterior - `models` directory is cleaned up. New sequential models using `pipes` package to represent monadic streams # 0.1.3 (2022-06-08) Clean up of unused functions and broken code - remove unused functions in `Weighted` and `Population` - remove broken models in `models` - explicit imports - added some global language extensions # 0.1.2 (2022-06-08) Add documentation - docs written in markdown - docs built by sphinx # 0.1.1 (2020-04-08) - New exported function: `Control.Monad.Bayes.Class` now exports `discrete`. # 0.1.0 (2020-02-17) Initial release. ================================================ FILE: LICENSE.md ================================================ The MIT License (MIT) Copyright (c) 2015-2020 Adam Scibior Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: MAINTAINERS.md ================================================ # GHC compatibility and Cabal dependency version bounds ## Overview `monad-bayes` supports the three most recent [major versions][ghc-major] of GHC: - CI builds and tests against **all supported versions**. The CI setup is the source of truth for which GHC versions `monad-bayes` supports. - The local environment (e.g., stack.yaml) sets up **a supported version** of GHC. - The Cabal dependency version bounds for each dependency are as follows: The **lower bound** is taken from `cabal gen-bounds` run against the oldest supported GHC version. The **upper bound** is taken from `cabal gen-bounds` run against the newest supported GHC version. ## What to do when a new major GHC version has been released A **new major GHC version** has been released. Here's what you need to do: - **Add the new major GHC version** to the CI build matrix and **drop the oldest version** that was previously supported. - Make sure the the **local environment** (e.g., stack.yaml) still sets up a supported version of GHC. If not, update it. - Update the Cabal **dependency bounds** as described above. ## How to release a new version - Open a separate branch `release` and a merge request. On this branch, do the following: - Update the file `CHANGELOG.md`, using the diff to the last release. - Increment the version in `monad-bayes.cabal`. See the [Hackage Package Versioning Policy][hackage-pvp]. - Upload the package candidate sources: ```console $ dir=$(mktemp -d monad-bayes-sdist.XXXXXX) $ cabal v2-sdist --builddir="$dir" $ cabal upload --user= --password= "$dir/sdist/*.tar.gz" $ rm -rf "$dir" ``` - Upload the package candidate documentation: ```console $ dir=$(mktemp -d monad-bayes-docs.XXXXXX) $ cabal v2-haddock --builddir="$dir" --haddock-for-hackage --enable-doc $ cabal upload --documentation --user= --password= "$dir/*-docs.tar.gz" $ rm -rf "$dir" ``` - Check the candidate's Hackage page, make sure everything looks as expected. - When you're ready, and the CI passes for your merge request, repeat the above `cabal upload` commands (for sources and documentation), adding `--publish` so the uploads are no longer marked as candidates but as proper releases. - Merge the `release` branch. - Add a `git` tag in the form `vmajor-major-minor`, e.g. `v1.1.0`, to the commit that was uploaded: ```console git tag v1.2.3 git push --tags ``` [ghc-major]: https://gitlab.haskell.org/ghc/ghc/wikis/working-conventions/releases#major-releases [hackage-pvp]: https://pvp.haskell.org/ ## Documentation The docs are built with MkDocs. Serve locally with: `mkdocs serve`. Site is served online with Netlify. # Benchmarking ## Quick benchmark * Run `cabal run single -- -m MODEL -a ALG` * For `MODEL`, insert e.g. `LR100` * For `ALG`, insert e.g. `SMC` * See `benchmark/Single.hs` for details ## Extensive benchmark * Run `cabal bench speed-bench` * It will run several benchmarks of differing complexity, and try to plot them using Python Pandas * Look at `samples.pdf` ================================================ FILE: README.md ================================================ # [Monad-Bayes](https://monad-bayes.netlify.app/) A library for probabilistic programming in Haskell. [See the docs](https://monad-bayes.netlify.app/) for a user guide, notebook-style tutorials, an example gallery, and a detailed account of the implementation. Created by [Adam Scibior][adam-web] ([@adscib][adam-github]), documentation, website and newer features by [Reuben][reuben-web], maintained by [Tweag][tweagio]. ## Project status Now that `monad-bayes` has been released on Hackage, and the documentation and the API has been updated, we will focus on adding new features. See the Github issues to get a sense of what is being prepared, and please feel free to make requests. ## Background The basis for the code in this repository is the ICFP 2018 paper [2]. For the code associated with the Haskell2015 paper [1], see the [`haskell2015` tag][haskell2015-tag]. [1] Adam M. Ścibior, Zoubin Ghahramani, and Andrew D. Gordon. 2015. [Practical probabilistic programming with monads][haskell2015-doi]. In _Proceedings of the 2015 ACM SIGPLAN Symposium on Haskell_ (Haskell ’15), Association for Computing Machinery, Vancouver, BC, Canada, 165–176. [2] Adam M. Ścibior, Ohad Kammar, and Zoubin Ghahramani. 2018. [Functional programming for modular Bayesian inference][icfp2018-doi]. In _Proceedings of the ACM on Programming Languages_ Volume 2, ICFP (July 2018), 83:1–83:29. [3] Adam M. Ścibior. 2019. [Formally justified and modular Bayesian inference for probabilistic programs][thesis-doi]. Thesis. University of Cambridge. ## Hacking 1. Install `stack` by following [these instructions][stack-install]. 2. Clone the repository using one of these URLs: ``` git clone git@github.com:tweag/monad-bayes.git git clone https://github.com/tweag/monad-bayes.git ``` Now you can use `stack build`, `stack test` and `stack ghci`. **To view the notebooks, go to the website**. To use the notebooks interactively: 1. Compile the source: `stack build` 2. If you do not have `nix` [install it](https://nixos.org/download.html). 3. Run `nix develop --system x86_64-darwin --extra-experimental-features nix-command --extra-experimental-features flakes` - this should open a nix shell. For Linux use `x86_64-linux` for `--system` option instead. 4. Run `jupyter-lab` from the nix shell to load the notebooks. Your mileage may vary. [adam-github]: https://github.com/adscib [adam-web]: https://www.cs.ubc.ca/~ascibior/ [reuben-web]: https://reubencohngordon.com/ [haskell2015-doi]: https://doi.org/10.1145/2804302.2804317 [haskell2015-tag]: https://github.com/tweag/monad-bayes/tree/haskell2015 [icfp2018-doi]: https://doi.org/10.1145/3236778 [models]: https://github.com/tweag/monad-bayes/tree/master/models [stack-install]: https://docs.haskellstack.org/en/stable/install_and_upgrade/ [thesis-doi]: https://doi.org/10.17863/CAM.42233 [tweagio]: https://tweag.io ================================================ FILE: Setup.hs ================================================ import Distribution.Simple main = defaultMain ================================================ FILE: benchmark/SSM.hs ================================================ module Main where import Control.Monad (forM_) import Control.Monad.Bayes.Inference.MCMC import Control.Monad.Bayes.Inference.PMMH as PMMH (pmmh) import Control.Monad.Bayes.Inference.RMSMC (rmsmcDynamic) import Control.Monad.Bayes.Inference.SMC import Control.Monad.Bayes.Inference.SMC2 as SMC2 (smc2) import Control.Monad.Bayes.Population import Control.Monad.Bayes.Population (resampleMultinomial, runPopulationT) import Control.Monad.Bayes.Sampler.Strict (sampleIO, sampleIOfixed, sampleWith) import Control.Monad.Bayes.Weighted (unweighted) import Control.Monad.IO.Class (MonadIO (liftIO)) import NonlinearSSM (generateData, model, param) import NonlinearSSM.Algorithms import System.Random.Stateful (mkStdGen, newIOGenM) main :: IO () main = sampleIOfixed $ do dat <- generateData t let ys = map snd dat forM_ [SMC, RMSMCDynamic, PMMH, SMC2] $ \alg -> do liftIO $ print alg result <- runAlgFixed ys alg liftIO $ putStrLn result ================================================ FILE: benchmark/Single.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE ImportQualifiedPost #-} import Control.Applicative (Applicative (..)) import Control.Monad.Bayes.Sampler.Strict import Data.Time (diffUTCTime, getCurrentTime) import Helper import Options.Applicative ( ParserInfo, auto, execParser, fullDesc, help, info, long, maybeReader, option, short, ) -- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md -- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore import Prelude hiding (Applicative (..)) infer :: Model -> Alg -> IO () infer model alg = do x <- sampleIOfixed (runAlg model alg) print x opts :: ParserInfo (Model, Alg) opts = flip info fullDesc $ liftA2 (,) model alg where model = option (maybeReader parseModel) ( long "model" <> short 'm' <> help "Model" ) alg = option auto ( long "alg" <> short 'a' <> help "Inference algorithm" ) main :: IO () main = do (model, alg) <- execParser opts startTime <- getCurrentTime infer model alg endTime <- getCurrentTime print (diffUTCTime endTime startTime) ================================================ FILE: benchmark/Speed.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE ImportQualifiedPost #-} {-# OPTIONS_GHC -Wall #-} module Main (main) where import Control.Monad.Bayes.Class (MonadMeasure) import Control.Monad.Bayes.Inference.MCMC (MCMCConfig (MCMCConfig, numBurnIn, numMCMCSteps, proposal), Proposal (SingleSiteMH)) import Control.Monad.Bayes.Inference.RMSMC (rmsmcDynamic) import Control.Monad.Bayes.Inference.SMC (SMCConfig (SMCConfig, numParticles, numSteps, resampler), smc) import Control.Monad.Bayes.Population (resampleSystematic, runPopulationT) import Control.Monad.Bayes.Sampler.Strict (SamplerIO, sampleIOfixed) import Control.Monad.Bayes.Traced (mh) import Control.Monad.Bayes.Weighted (unweighted) import Criterion.Main ( Benchmark, Benchmarkable, bench, defaultConfig, defaultMainWith, nfIO, ) import Criterion.Types (Config (csvFile, rawDataFile)) import Data.Functor (void) import Data.Maybe (listToMaybe) import Data.Text qualified as T import HMM qualified import LDA qualified import LogReg qualified import System.Directory (removeFile) import System.IO.Error (catchIOError, isDoesNotExistError) import System.Process.Typed (runProcess) data ProbProgSys = MonadBayes deriving stock (Show) data Model = LR [(Double, Bool)] | HMM [Double] | LDA [[T.Text]] instance Show Model where show (LR xs) = "LR" ++ show (length xs) show (HMM xs) = "HMM" ++ show (length xs) show (LDA xs) = "LDA" ++ show (maybe 0 length $ listToMaybe xs) buildModel :: (MonadMeasure m) => Model -> m String buildModel (LR dataset) = show <$> LogReg.logisticRegression dataset buildModel (HMM dataset) = show <$> HMM.hmm dataset buildModel (LDA dataset) = show <$> LDA.lda dataset modelLength :: Model -> Int modelLength (LR xs) = length xs modelLength (HMM xs) = length xs modelLength (LDA xs) = sum (map length xs) data Alg = MH Int | SMC Int | RMSMC Int Int instance Show Alg where show (MH n) = "MH" ++ show n show (SMC n) = "SMC" ++ show n show (RMSMC n t) = "RMSMC" ++ show n ++ "-" ++ show t runAlg :: Model -> Alg -> SamplerIO String runAlg model (MH n) = show <$> unweighted (mh n (buildModel model)) runAlg model (SMC n) = show <$> runPopulationT (smc SMCConfig {numSteps = (modelLength model), numParticles = n, resampler = resampleSystematic} (buildModel model)) runAlg model (RMSMC n t) = show <$> runPopulationT ( rmsmcDynamic MCMCConfig {numMCMCSteps = t, numBurnIn = 0, proposal = SingleSiteMH} SMCConfig {numSteps = modelLength model, numParticles = n, resampler = resampleSystematic} (buildModel model) ) prepareBenchmarkable :: ProbProgSys -> Model -> Alg -> Benchmarkable prepareBenchmarkable MonadBayes model alg = nfIO $ sampleIOfixed (runAlg model alg) prepareBenchmark :: ProbProgSys -> Model -> Alg -> Benchmark prepareBenchmark MonadBayes model alg = bench (show MonadBayes ++ sep ++ show model ++ sep ++ show alg) $ prepareBenchmarkable MonadBayes model alg where sep = "_" :: String -- | Checks if the requested benchmark is implemented. supported :: (ProbProgSys, Model, Alg) -> Bool supported (_, _, RMSMC _ _) = True supported _ = True systems :: [ProbProgSys] systems = [ MonadBayes ] lengthBenchmarks :: [(Double, Bool)] -> [Double] -> [[T.Text]] -> [Benchmark] lengthBenchmarks lrData hmmData ldaData = benchmarks where lrLengths = 10 : map (* 100) [1 :: Int .. 10] hmmLengths = 10 : map (* 100) [1 :: Int .. 10] ldaLengths = 5 : map (* 50) [1 :: Int .. 10] models = map (LR . (`take` lrData)) lrLengths ++ map (HMM . (`take` hmmData)) hmmLengths ++ map (\n -> LDA $ map (take n) ldaData) ldaLengths algs = [ MH 100, SMC 100, RMSMC 10 1 ] benchmarks = map (uncurry3 (prepareBenchmark)) $ filter supported xs where uncurry3 f (x, y, z) = f x y z xs = do m <- models s <- systems a <- algs return (s, m, a) samplesBenchmarks :: [(Double, Bool)] -> [Double] -> [[T.Text]] -> [Benchmark] samplesBenchmarks lrData hmmData ldaData = benchmarks where lrLengths = [50 :: Int] hmmLengths = [20 :: Int] ldaLengths = [10 :: Int] models = map (LR . (`take` lrData)) lrLengths ++ map (HMM . (`take` hmmData)) hmmLengths ++ map (\n -> LDA $ map (take n) ldaData) ldaLengths algs = map (\x -> MH (100 * x)) [1 .. 10] ++ map (\x -> SMC (100 * x)) [1 .. 10] ++ map (\x -> RMSMC 10 (10 * x)) [1 .. 10] benchmarks = map (uncurry3 (prepareBenchmark)) $ filter supported xs where uncurry3 f (x, y, z) = f x y z xs = do a <- algs s <- systems m <- models return (s, m, a) speedLengthCSV :: FilePath speedLengthCSV = "speed-length.csv" speedSamplesCSV :: FilePath speedSamplesCSV = "speed-samples.csv" rawDAT :: FilePath rawDAT = "raw.dat" cleanupLastRun :: IO () cleanupLastRun = mapM_ removeIfExists [speedLengthCSV, speedSamplesCSV, rawDAT] removeIfExists :: FilePath -> IO () removeIfExists file = do putStrLn $ "Removing: " ++ file catchIOError (removeFile file) $ \e -> if isDoesNotExistError e then putStrLn "Didn't find file, not removing" else ioError e main :: IO () main = do cleanupLastRun lrData <- sampleIOfixed (LogReg.syntheticData 1000) hmmData <- sampleIOfixed (HMM.syntheticData 1000) ldaData <- sampleIOfixed (LDA.syntheticData 5 1000) let configLength = defaultConfig {csvFile = Just speedLengthCSV, rawDataFile = Just rawDAT} defaultMainWith configLength (lengthBenchmarks lrData hmmData ldaData) let configSamples = defaultConfig {csvFile = Just speedSamplesCSV, rawDataFile = Just rawDAT} defaultMainWith configSamples (samplesBenchmarks lrData hmmData ldaData) void $ runProcess "python plots.py" ================================================ FILE: default.nix ================================================ ( import ( let lock = builtins.fromJSON (builtins.readFile ./flake.lock); in fetchTarball { url = "https://github.com/edolstra/flake-compat/archive/${lock.nodes.flake-compat.locked.rev}.tar.gz"; sha256 = lock.nodes.flake-compat.locked.narHash; } ) { src = ./.; } ).defaultNix ================================================ FILE: docs/docs/examples.md ================================================ --- title: Example Gallery --- ## [Histograms](/notebooks/Histogram.html) ## [JSON (with `lens`)](/notebooks/Lenses.html) ## [Diagrams](/notebooks/Diagrams.html) ## [Probabilistic Parsing](/notebooks/Parsing.html) ## [Streams (with `pipes`)](/notebooks/Streaming.html) ## [Ising models](/notebooks/Ising.html) ## [Physics](/notebooks/ClassicalPhysics.html) ================================================ FILE: docs/docs/index.md ================================================ # Welcome to Monad-Bayes Monad-Bayes is a library for **probabilistic programming** written in **Haskell**. **Define distributions** [as programs](/notebooks/Introduction.html) **Perform inference** [with a variety of standard methods](tutorials.md) [defined compositionally](http://approximateinference.org/accepted/ScibiorGhahramani2016.pdf) **Integrate with Haskell code** [like this](examples.md) because Monad-Bayes is just a library, not a separate language ## Example ```haskell model :: Distribution Double model = do x <- bernoulli 0.5 normal (if x then (-3) else 3) 1 image :: Distribution Plot image = fmap (plot . histogram 200) (replicateM 100000 model) sampler image ``` The program `model` is a mixture of Gaussians. Its type `Distribution Double` represents a distribution over reals. `image` is a program too: as its type shows, it is a distribution over plots. In particular, plots that arise from forming a 200 bin histogram out of 100000 independent identically distributed (iid) draws from `model`. To sample from `image`, we simply write `sampler image`, with the result shown below: ================================================ FILE: docs/docs/javascripts/mathjax.js ================================================ window.MathJax = { tex: { inlineMath: [["\\(", "\\)"]], displayMath: [["\\[", "\\]"]], processEscapes: true, processEnvironments: true }, options: { ignoreHtmlClass: ".*|", processHtmlClass: "arithmatex" } }; document$.subscribe(() => { MathJax.typesetPromise() }) ================================================ FILE: docs/docs/notebooks/AdvancedSampling.html ================================================ AdvancedSampling ================================================ FILE: docs/docs/notebooks/Bayesian.html ================================================ Bayesian ================================================ FILE: docs/docs/notebooks/ClassicalPhysics.html ================================================ ClassicalPhysics ================================================ FILE: docs/docs/notebooks/Diagrams.html ================================================ Diagrams ================================================ FILE: docs/docs/notebooks/Functional_PPLs.html ================================================ Functional_PPLs ================================================ FILE: docs/docs/notebooks/Histogram.html ================================================ Histogram ================================================ FILE: docs/docs/notebooks/Introduction.html ================================================ Introduction ================================================ FILE: docs/docs/notebooks/Ising.html ================================================ Ising ================================================ FILE: docs/docs/notebooks/Lazy.html ================================================ Lazy ================================================ FILE: docs/docs/notebooks/Lenses.html ================================================ Lenses ================================================ FILE: docs/docs/notebooks/MCMC.html ================================================ MCMC ================================================ FILE: docs/docs/notebooks/Parsing.html ================================================ Parsing ================================================ FILE: docs/docs/notebooks/RealTimeInference.html ================================================ RealTimeInference

Real-time inference with Functional Reactive Probabilistic Programming

text

In the above gif, the green dots represents a latent variable, the unseen but true position of two agents, which are moving stochastically around a 2D environment. What the system actually sees are the noisy observations shown in red. The purple swarm is a population of particles from a particle filter.

The approach

In the simulation from which the above gif is made, the inference is performed in real-time, using a particle filter.

It is implemented using a combination of (1) a paradigm for representing Bayesian probability and inference known as probabilistic programming, and (2) a paradigm for representing real-time interactive systems known as (functional) reactive programming.

There are several appealing aspects to this approach:

  • it offers a natural representation for continuous time stochastic processes. Time is continuous in the sense that you write code that is agnostic to the final sampling rate of the whole system, and so can be arbitrarily fine-grained.
  • it decouples the specification of the prior and posterior from the inference algorithm. For example, in the code for the above gif, the model is described by a prior, which is a stochastic process, and the posterior is described by a function from one stochastic process to another, mapping from a stream of observations to a process representing the posterior.
  • inference methods can be designed compositionally in a similar manner to standard probabilistic programming languages. For example, we may want to add MH moves at various points, or to adaptively change the population size or resampling rate. These extensions fit naturally into the approach.
  • it allows for simulations to be run indefinitely long without concerns about time or space leaks.
  • it allows for observations to come from multiple streams, at different rates, or even from user input. For example, the frame rate of the simulation (say 60Hz) need not be the rate at which observations are received and processed. So if inference is slow at times, this can be handled gracefully.
  • it allows for action in the loop. For example, one might want to take actions in real time, based on the current belief state, which would in turn influence incoming data.

What the code looks like

The code to produce the above simulation is simple and declarative.

The prior

type Observation = V.Vector 2 Double
type Position = V.Vector 2 Double

prior :: StochasticProcess Position
prior = fmap V.fromTuple $ model1D &&& model1D where

    model1D = proc _ -> do
        acceleration <- constM (normal 0 4 ) -< ()
        velocity <- decayIntegral 2 -< acceleration -- Integral, dying off exponentially
        position <- decayIntegral 2 -< velocity
        returnA -< position

    decayIntegral timeConstant =  average timeConstant >>> arr (timeConstant *^)

prior describes a stochastic process that serves as the prior about how a (single) dot moves.

It is built by taking a constantly normally distributed stream of accelerations, and integrating it twice. That gives the circle's movement on one axis, so we join two of these together.

Note how this is an abstract mathematical object: it is not an event loop for example - it is simply a representation of a stochastic process. But of course we can convert it into something we can run.

Running it means sampling a single path (extending infinitely forward in time) for the dot, and showing that in real-time. This is also simple:

gloss :: IO ()
gloss = sampleIO $
        launchGlossThread defaultSettings
            { display = InWindow "rhine-bayes" (1024, 960) (10, 10) } 
        $ reactimateCl glossClock proc () -> do
            actualPosition <- prior -< ()
            (withSideEffect_ (lift clearIO) >>> visualisation) -< Result { latent = actualPosition }

text

Generative model

In standard probabilistic programming, a generative model is a conditional distribution on observations given the latent state, i.e. a function from the latent variable space to distributions over observations.

In the real-time setting, a generative model is a function from a process on the latent space to a process on observations. Ours looks like this (pointwise Gaussian noise), but significantly more complex examples are possible:

generativeModel :: StochasticProcessFunction Position Observation
generativeModel = proc p -> do
    n <- fmap V.fromTuple $ noise &&& noise -< ()
    returnA -< p + n
  where noise = constM (normal 0 std)

This generates:

text

The posterior

The posterior is a function from a process over observations to a process over the latent space.

posterior :: StochasticProcessFunction Observation Position
posterior = proc (V2 oX oY) -> do
  latent@(V2 trueX trueY) <- prior -< ()
  arrM factor -< normalPdf oY std trueY * normalPdf oX std trueX
  returnA -< latent

Also note that it is defined in a way that is familiar to probabilistic programming languages: we draw from the prior, and we reweight (this is the factor statement) in a manner that depends on the data.

However, the output stochastic process over the position is unnormalized. We need to perform inference to obtain a representation that we can sample from.

We do this with Sequential Monte Carlo.

inferredPosterior :: StochasticProcessFunction Position [(Observation, Weight)]
inferredPosterior = onlineSMC 20 resampleMultinomial posterior

Like posterior, inferredPosterior is a function from one stochastic process to another.

However, the output is now a process whose value at a given time is a list of pairs of observations and weights, i.e. a population of particles!

This is something we can sample from, and the swarm of purple dots is precisely a visualization of the population. So finally the code to produce the original gif:

gloss :: IO ()
gloss = sampleIO $
        launchGlossThread defaultSettings
            { display = InWindow "rhine-bayes" (1024, 960) (10, 10) } 
        $ reactimateCl glossClock proc () -> do
            actualPosition <- prior -< ()
            measuredPosition <- generativeModel -< actualPosition
            samples <- onlineSMC 100 resampleMultinomial posterior -< measuredPosition
            (withSideEffect_ (lift clearIO) >>> visualisation) -< Result { 
                                particles = samples
                                , measured = measuredPosition
                                , latent = actualPosition
                                }

This code generates a true trajectory of the particle (actualPosition) from the prior. It then uses the generativeModel to create the true observations. These are passed into model, to obtain samples from the particle filter, which are then passed to a visualizing function.

Under the hood

How does this actually work?

It is based on two libraries. First Monad-Bayes, which is a probabilistic programming library. Second Rhine, which is a functional reactive programming library.

Monad-bayes is great at handling distributions in a pure and functional manner, but doesn't have a good model of time. However, Rhine is superb at handling time in a pure and functional way, but don't know about distributions.

As such, it turns out that there is a beautiful synergy between the two libraries (or rather, the conceptual approaches to their domains that they represent).

More to come...

================================================ FILE: docs/docs/notebooks/SMC.html ================================================ SMC ================================================ FILE: docs/docs/notebooks/Sampling.html ================================================ Sampling ================================================ FILE: docs/docs/notebooks/Streaming.html ================================================ Streaming ================================================ FILE: docs/docs/probprog.md ================================================ # User Guide Probabilistic programming is all about being able to write probabilistic models as programs. For instance, here is a Bayesian linear regression model, which we would write equationally as: $$ \beta \sim \operatorname{normal}(0, 2) $$ $$ \alpha \sim \operatorname{normal}(0, 2) $$ $$ \sigma^2 \sim \operatorname{gamma}(4, 4) $$ $$ \epsilon_{n} \sim \operatorname{normal}(0, \sigma) $$ $$ y_{n}=\alpha+\beta x_{n}+\epsilon_{n} $$ but in code as: ```haskell paramPriorRegression :: Distribution (Double, Double, Double) paramPriorRegression = do slope <- normal 0 2 intercept <- normal 0 2 noise <- gamma 4 4 return (slope, intercept, noise) regression :: Kernel [(Double, Double)] (Double, Double, Double) regression xsys = do (slope, intercept, noise) <- paramPriorRegression forM_ xsys \(x, y) -> factor $ normalPdf (slope * x + intercept) (sqrt noise) y return (slope, intercept, noise) ``` `regression` takes observations (a list of pairs of x and y values), and using the prior expressed by `paramPriorRegression`, returns the posterior conditioned on the observations. This is the *model*. To perform *inference* , suppose we have a data set `xsys` like: ![](images/priorpred.png) To run the model ```haskell mhRunsRegression = sampler $ mcmc MCMCConfig {numMCMCSteps = 1500, proposal = SingleSiteMH, numBurnIn = 500} (regression xsys) ``` This yields 1000 samples from an MCMC walk using an MH kernel. `mh n` produces a distribution over chains of length `n`, along with the probability of that chain. Sampling a chain and plotting its final state gives: ![](/images/regress.png) Monad-bayes provides a variety of MCMC and SMC methods, and methods arising from the composition of the two. ## Resources Other probabilistic programming languages with fairly similar APIs include WebPPL and Gen. This cognitive-science oriented introduction to [WebPPL](https://probmods.org/) is an excellent resource for learning about probabilistic programming. The [tutorials for Gen](https://www.gen.dev/tutorials/) are also very good, particularly for learning about traces. ## Specifying distributions A distribution in monad-bayes over a set $X$, is of type: ```haskell MonadMeasure m => m X ``` For beginner friendliness, a synonym `type Measure a = forall m . MonadDistribution m => m a` is provided (as well as `Distribution` shown above, for normalized distributions, and `Kernel` for functions into distributions). Monad-bayes provides standard distributions, such as ```haskell random :: Distribution Double ``` which is distributed uniformly over $[0,1]$. The full set is listed at https://hackage.haskell.org/package/monad-bayes-0.1.1.0/docs/Control-Monad-Bayes-Class.html Note that these primitives already allows us to construct quite exotic distributions, like the uniform distribution over `(+) :: Int -> Int -> Int` and `(-) :: Int -> Int -> Int`: ```haskell distributionOverFunctions :: Distribution (Int -> Int -> Int) distributionOverFunctions = uniformD [(+), (-)] ``` ## Constructing distributions as programs monad-bayes also lets us construct new distributions out of these. `MonadMeasure m` implies `Monad m` and in turn `Functor m`, so we can do the following: ```haskell fmap (> 0.5) random :: MonadMeasure m => m Bool ``` This is the uniform distribution over $(0.5, 1]$. As an important special case, if `x :: MonadMeasure m => m (a,b)` is a joint distribution over two variables, then `fmap fst a :: MonadMeasure m => m a` **marginalizes** out the second variable. That is to say, `fmap fst a` is the distribution $p(a)$, where $p(a) = \int_b p(a,b)$. The above example use only the functor instance for `m`, but we also have the monad instance, as used in: ```haskell example :: MonadMeasure m => m Double example = bernoulli 0.5 >>= (\x -> if x then random else normal 0 1) ``` It's easiest to understand this distribution as a probabilistic program: it's the distribution you get by first sampling from `bernoulli 0.5`, then checking the result. If the result is `True`, then sample from `random`, else from `normal 0 1`. As a distribution, this has a PDF: $$ f(x) = 1[0\leq x \leq 1]*0.5 + \mathcal{N}(0,1)(x)*0.5 $$ Equivalently, we could write this in do-notation as: ```haskell example :: MonadMeasure m => m Double example = do bool <- bernoulli 0.5 if bool then random else normal 0 1 ``` **A technical note**: it is often tempting to read the line `bool <- bernoulli 0.5` as saying "take a sample from `bernoulli 0.5`. But although we'll see below that `example` can be interpreted as a sampler, there are many other interpretations, not least as a mathematical specification of a particular distribution. That said, it is often useful to think of probabilistic programs as specifying distributions over **program executation traces**. For example, one trace of `example` as defined above is (informally): `{bernoulli 0.5 : True, random : 0.7}`. ## Hard and soft conditioning monad-bayes provides a function `score :: MonadMeasure m => Log Double -> m ()`. (**Note**: `Log Double` is a wrapper for `Double` which stores doubles as their logarithm, and does multiplication by addition of logarithms.) ```haskell example :: MonadMeasure m => m Double example = do bool <- bernoulli 0.5 number <- if bool then random else normal 0 1 score number return bool ``` It's easiest to understand this in terms of the "program execution trace" perspective described above. What the score statement does is to multiple every trace by the value of `number` in that particular trace. `condition` in then defined as follows: ```haskell condition :: MonadMeasure m => Bool -> m () condition b = score $ if b then 1 else 0 ``` So `condition b` throws away every trace in which `b` is False, and keeps all traces in which `b` is True. For example: ```haskell example :: MonadMeasure m => m Int example = do n <- poisson 0.5 condition (n%2 == 0) return n ``` This describes a Poisson distribution in which all even values of the random variable are marginalized out. ## Inference methods To quote [this page](https://webppl.readthedocs.io/en/master/inference/), "marginal inference (or just inference) is the process of reifying the distribution on return values implicitly represented by a stochastic computation.". That is, a probabilistic program (stochastic computation) is an abstract object and inference transforms it into something concrete, like a histogram, a list of samples, or parameters of a known distribution. All inference methods in monad-bayes work with all distributions. The only exception is that exact inference only works with discrete distributions and will throw a runtime error on continuous distributions. The challenge of inference is that most distributions that are of interest are not as simple as `sprinkler`. They could have continuous random variables, a huge number of them, or even a number of them that is itself random. They could involve a series of observations, interspersed with other sources of randomness. Designing a language in which you can specify arbitrarily complex (computable) distributions as probabilistic programs turns out to be a largely solved problem. The tools given above are sufficient for that. The hard part is designing a language where you can specify how you want to do inference, because sophisticated, often approximate, inference methods are almost always necessary for the models involved in solving real world problems. Two of the large classes of inference methods are **sampling based methods** and **gradient based methods**. The latter only apply to continuous probability distributions, and are not the focus of monad-bayes. ## Exact inference ```haskell enumerator :: Ord a => Enumerator a -> [(a, Double)] ``` So `enumerator (bernoulli 0.7)` gives you ``` [(False,0.3),(True,0.7)] ``` This works for distributions with `factor` statements (i.e. an instance of `MonadMeasure`), as in: ```haskell model :: MonadMeasure m => m Bool model = do x <- bernoulli 0.5 y <- bernoulli 0.3 condition (x || y) return x enumerator model > [(True,0.7692307692307692),(False,0.23076923076923078)] ``` **Note: `enumerator` only works on finite discrete distributions** It will run forever on infinite distributions like `enumerator (poisson 0.7)` and will throw the following **runtime** error on continuous distributions as in `enumerator (normal 0 1)`: *"Exception: Infinitely supported random variables not supported in Enumerator"* **However**, it's totally fine to have the elements of the support themselves be infinite, as in: ```haskell fmap (\(ls,p) -> (take 4 ls, p)) $ enumerator $ uniformD [[1..], [2..]] ``` which gives ``` [([1,2,3,4],0.5),([2,3,4,5],0.5)] ``` ## Near exact inference for continuous distributions Monad-Bayes does not currently support exact inference (via symbolic solving) for continuous distributions. However, it *does* support numerical integration. For example, for the distribution defined by ```haskell model :: MonadDistribution m => m Double model = do var <- gamma 1 1 normal 0 (sqrt var) ``` you may run `probability (0, 1000) model` to obtain the probability in the range `(0,1000)`. As expected, this should be roughly $0.5$, since the PDF of `model` is symmetric around $0$. You can also try `expectation model`, `variance model`, `momentGeneratingFunction model n` or `cdf model n`. If the model has factor statements, as in ```haskell model :: MonadMeasure m => m Double model = do var <- gamma 1 1 n <- normal 0 (sqrt var) condition (n > 0) return var ``` we must first `normalize` the model, as in `probability (0, 0.1) (normalize model)`. ## Independent forward sampling For any probabilistic program `p` without any `condition` or `factor` statements, we may do `sampler p` or `sampleIOfixed p` (to run with a fixed seed) to obtain a sample in an ancestral fashion. For example, consider: ```haskell example = do x <- bernoulli 0.5 if x then normal 0 1 else normal 1 2 ``` `sampler example` will produce a sample from a Bernoulli distribution with $p=0.5$, and if it is $True$, return a sample from a standard normal, else from a normal with mean 1 and std 2. `(replicateM n . sampler) example` will produce a list of `n` independent samples. However, it is recommended to instead do `(sampler . replicateM n) example`, which will create a new model (`replicateM n example`) consisting of `n` independent draws from `example`. Because `sampler example` is totally pure, it is parallelizable. Monad-bayes gives the user freedom in the random number generator of the sampler: use `sampleWith` for full control. ## Independent weighted sampling To perform weighted sampling, use: ```haskell (sampler . runWeightedT) :: WeightedT SamplerIO a -> IO (a, Log Double) ``` `WeightedT SamplerIO` is an instance of `MonadMeasure`, so we can apply this to any distribution. For example, suppose we have the distribution: ```haskell example :: MonadMeasure m => m Bool example = do x <- bernoulli 0.5 condition x return x ``` Then: ```haskell run :: IO (Bool, Log Double) run = (sampler . runWeightedT) example ``` is an IO operation which when run, will display either `(False, 0.0)` or `(True, 1.0)` ## Lazy sampling If you want to forward sample from an infinite program, just as a distribution over infinite lists, you can use monad-bayes's lazy sampler, which is based on LazyPPL. For example, ```haskell import qualified Control.Monad.Bayes.Sampler.Lazy as Lazy example :: MonadDistribution m => m [Double] example = do x <- random fmap (x:) example infiniteList <- Lazy.sampler example take 4 infiniteList ``` To perform weighted sampling, use `lwis` from `Control.Monad.Bayes.Inference.Lazy.WIS` as in `lwis 10 example`. This takes 10 weighted samples, and produces an infinite stream of samples, regarding those 10 as an empirical distribution. LazyPPL's `mh` implementation is also available. ## Markov Chain Monte Carlo There are several versions of metropolis hastings MCMC defined in monad-bayes. The standard version is found in `Control.Monad.Bayes.Inference.MCMC`. You can use it as follows: ```haskell (sampler . mcmc (MCMCConfig { numMCMCSteps = 10, numBurnIn = 5, proposal = SingleSiteMH})) :: TracedT (WeightedT SamplerIO) a -> IO [a] ``` As you can see, `mcmc` takes a `MCMCConfig` object, which is where you specify the number of steps in the MCMC chain, and the number of burn in steps. `SingleSiteMH` refers to the proposal used in the chain. `TracedT (WeightedT SamplerIO)` is an instance of `MonadMeasure`, so we can apply this to any distribution. For instance: ```haskell example :: MonadMeasure m => m Bool example = do x <- bernoulli 0.5 condition x return x ``` Then ```haskell run :: IO [Bool] run = (sampler . mcmc (MCMCConfig { numMCMCSteps = 10, numBurnIn = 5, proposal = SingleSiteMH})) example ``` produces $5$ unbiased samples from the posterior, by using single-site trace MCMC with the Metropolis-Hastings (MH) method. This means that the random walk is over execution traces of the probabilistic program, and the proposal distribution modifies a single random variable as a time, and then uses MH for the accept-reject criterion. For example, from the above you'd get: ``` [True,True,True,True,True] ``` The final element of the chain is the head of the list, so you can drop samples from the end of the list for burn-in. ## Streaming MCMC You can also run `MCMC` using `mcmcP`. This creates an infinite chain, expressed as a stream or using the corresponding type from the `pipes` library, a `Producer`. This is a very natural representation of a random walk in Haskell. You can run this with a terminal user interface (TUI) by doing e.g. ```haskell tui 0 random noVisual ``` ## Sequential Monte Carlo (Particle Filtering) Run SMC with two resampling steps and two particles as follows, given a model `m`: ```haskell output = sampler $ runPopulationT $ smc (SMCConfig {numSteps = 2, numParticles = 2, resampler = resampleMultinomial} ) m ``` As a concrete example, here is a probabilistic program: ```haskell (sampler . runPopulationT . smc SMCConfig {numSteps = 2, numParticles = 2, resampler = resampleMultinomial} random) :: SequentialT (PopulationT SamplerIO) a -> IO [(a, Numeric.Log.Log Double)] ``` --> As a concrete example, here is a probabilistic program: ```haskell example :: MonadMeasure m => m Bool example = do x <- bernoulli 0.5 condition x return x ``` And here is the inference: ```haskell run :: IO [(Bool, Log Double)] run = (sampler . runPopulationT . smc (SMCConfig { numSteps = 2, numParticles = 4, resampler = resampleMultinomial})) example ``` The result: ``` [(True,6.25e-2),(True,6.25e-2),(True,6.25e-2),(True,6.25e-2)] ``` Each of these is a particle with a weight. In this simple case, there are all identical - obviously in general they won't be. `numSteps` is the number of steps that the `SMC` algorithm takes, i.e. how many times it resamples. Specify it as either `All` or `Only n` for `n` an integer. This should generally be the number of factor statements in the program. `numParticles` is the size of the population. Larger is better but slower. `resampler` is the mechanism used to resampling the population of particles after each `factor` statement. ## Resample Move Sequential Monte Carlo This is a fancier variant of SMC, which has the particles take an MCMC walk through the solution space in order to spread out. This can avoid a common failure mode of SMC, where the population concentrates its weight too heavily on one mode. ```haskell rmsmcBasic :: MonadDistribution m => MCMCConfig -> SMCConfig m -> -- | model SequentialT (TrBas.TracedT (PopulationT m)) a -> PopulationT m a ``` For instance: ```haskell run :: IO [(Bool, Log Double)] run = ( sampler . runPopulationT . rmsmcBasic MCMCConfig {numMCMCSteps = 4, proposal = SingleSiteMH, numBurnIn = 0} SMCConfig {numParticles = 4, numSteps = 4}) example ``` What this returns is a population of samples, just like plain `SMC`. More MCMC steps is of course better, but slower. ## Particle Marginal Metropolis Hastings This inference method takes a prior and a model separately, so it only applies to a (large) subset of probabilistic programs. Run it like this: ```haskell sampler $ pmmh mcmcConfig {numMCMCSteps = 100} smcConfig {numSteps = 0, numParticles = 100} random (normal 0) ``` The model is specified in two parts, the prior is `random` and the likelihood is `(\x -> normal 0 x)`. This produces a list, representing the MCMC chain. Each element in the chain is a population of particles. # Interoperating with other Haskell code Probabilistic programs in monad-bayes are Haskell programs. This contrasts to many probabilistic programming languages, which are deeply embedded and cannot smoothly interact with their host language. For example, we can use ordinary monadic combinators, as in: ```haskell example = do x <- bernoulli 0.5 when x (score 0.8) return x ``` or ```haskell example = whileM (bernoulli 0.99) (normal 0 1) ``` You may write distributions over artibrary types. For example, rather than drawing a sample from a distribution and then using the sample to construct a histogram or a plot, you can directly define a distribution over histograms or plots, and sample from that. You can have distributions over arbitrary data structures, such as trees, JSONs, databases, and so on. You can use `MonadMeasure m => m` as the monad in libraries which are parametric in a monad, such as `megaparsec` or `pipes`. # Tips on writing good probabilistic programs There are many ways to specify the same distribution, and some will lend themselves more readily to efficient inference than others. For instance, ```haskell mixture1 point = do cluster <- uniformD [1,5] prediction <- normal cluster 1 condition (prediction == point ) return cluster ``` is a piece of code to infer whether an observed point was generated from a Gaussian of mean $1$ or $5$. That is, `mixture1` is a conditional Bernoulli distribution over the mean given an observation. You're not going to be able to do much with `mixture1` though. Exact inference is impossible because of the sample from the normal, and as for sampling, there is zero probability of sampling the normal to exactly match the observed point, which is what the `condition` requires. However, the same conditional distribution is represented by ```haskell mixture2 point = do cluster <- uniformD [1,5] factor (normalPdf cluster 1 point) return cluster ``` This version, while *denotationally identical* (i.e. representing the same mathematical object), is perfectly amenable to exact inference: ```haskell enumerator $ mixture2 2 ``` yields ```haskell [(1.0, 0.98...), (5.0, 0.017...)] ``` as well as sampling methods. The local lesson here is that you shouldn't `condition` on samples from a continuous distribution and expect a sampling based inference method to work. But the more general lesson is that you aren't exempted from thinking about inference when specifying your model. Alas. As a second example of this general point, consider: ```haskell allAtOnce = do x <- bernoulli 0.5 y <- bernoulli 0.5 condition (x && y) return (x && y) ``` and ```haskell incremental = do x <- bernoulli 0.5 condition x y <- bernoulli 0.5 condition y return (x && y) ``` Like in the previous example, `allAtOnce` and `incremental` denote the same distribution, namely `[(True, 1.0), (False, 0.0)]`. However, any inference algorithm for `allAtOnce` will have to explore all 4 possible variable assignments (`[(True,True), (True, False), (False, True), (False, False)]`). Meanwhile, `incremental` opens the possibility for inference algorithms to first determine the value of `x` and then of `y`. In this example, the performance difference is negligible, but it's easy to extend this to models where it's the difference between something tractable and something intractable. # API docs For API docs in the normal Haskell style, see [hackage](https://hackage.haskell.org/package/monad-bayes). # Monad-Bayes vs other libraries Monad-bayes is a universal probabilistic programming system, in the sense that you can express any computable distribution. In this respect it differs from Stan, which focuses instead on handling inference on an important subset well. There is a variety of universal probabilistic programming libraries and/or languages, which include WebPPL, Gen, Pyro and Edward. **What other approaches have that Monad-Bayes lacks**: A lot of engineering work has been put into the above libraries and languages to make them practical for real-world problems. While monad-bayes' core is very nice, it doesn't come with a lot of the batteries you might want. (The author's PhD thesis contains this relevant paragraph: "our library implements basic versions of advanced sampling algorithms. However, their successful application in practice requires incorporating established heuristics, such as: adaptive proposal distributions, controlling resampling with effective sample size, tuning rejuvenation kernels based on population in SMC2, and so on.") **What Monad-Bayes has that is unique**: Monad-Bayes is just a library, unlike almost all other PPLs, which are separate languages written inside another language. As such, probabilistic programs in monad-bayes are first class programs in Haskell with no new special syntax or keywords. This allows all of Haskell's expressive power to be brought to bear. You can write distributions over any datatype (lists, trees, functions, histograms, JSON files, graphs, diagrams, etc). You can use powerful libraries like `pipes`, `lens` and `Parsec`. Everything is pure. Everything is strongly typed. You can make use of laziness. Models are monadic and inference is modular. Complex inference algorithms like RMSMC or PMMH are built out of simple composable pieces, and so are expressable extraordinarily simply. ================================================ FILE: docs/docs/tutorials.md ================================================ --- title: Tutorials --- ## [Introduction to Monad-Bayes](/notebooks/Introduction.html) ## [Sampling from a distribution](/notebooks/Sampling.html) ## [Bayesian models](/notebooks/Bayesian.html) ## [Markov Chain Monte Carlo](/notebooks/MCMC.html) ## [Sequential Monte Carlo](/notebooks/SMC.html) ## [Lazy Sampling](/notebooks/Lazy.html) ## [Advanced Inference Methods](/notebooks/AdvancedSampling.html) ================================================ FILE: docs/docs/usage.md ================================================ # Implementation guide This document assumes the reader is familiar with the basics of Bayesian probability theory, basic Haskell (the syntax, the type system, do-notation, monad transformers), and how to specify distributions in monad-bayes (see the [docs](probprog.md)) That's enough to understand the core ideas, but for the more advanced content, you'll also want to feel comfortable enough with Haskell's type system that free monads, free monad transformers, and coroutines aren't a barrier to entry. And of course, to understand how inference methods like MCMC and SMC are implemented, it doesn't hurt to understand how they work in a statistical sense. ## References Monad-Bayes is the codebase accompanying the theory of probabilistic programming described in [this paper](https://arxiv.org/pdf/1711.03219.pdf). ## The core typeclasses The library relies on two core typeclasses `MonadDistribution` and `MonadFactor`. `MonadMeasure` is simply the union of the two, that is: ```haskell (MonadDistribution m, MonadFactor m) => MonadMeasure m ``` You can find these in `Control.Monad.Bayes.Class`. ### MonadDistribution Here is `MonadDistribution`: ```haskell class Monad m => MonadDistribution m where random :: m Double ``` This one method, `random`, represents a uniform distribution over $[0,1]$. (`MonadDistribution` actually has a few other distributions, but that's not essential.) What comes next is clever: you can define any other distribution you like in terms of `random`. As an example: ```haskell bernoulli :: MonadDistribution m => m Bool bernoulli p = fmap (< p) random ``` That one is pretty simple. As a more complex example, here's how a normal distribution is defined: ```haskell normal m s = fmap (quantile (normalDistr m s)) random ``` `normalDistr` comes from a separate library `Statistics.Distribution.Normal` and `quantile (normalDistr m s) :: Double -> Double` is the inverse CDF of the normal, a deterministic function. Again, to emphasize: **all of our randomness can be reduced to draws from a uniform distribution over the interval $[0,1]$**. So we now have a way of constructing distributions in a monadic fashion. As a simple example: ```haskell example :: MonadDistribution m => m Double example = do x <- random y <- uniform 0 x return (x + y > 1.5) ``` Think of this as the procedure of first sampling uniformly from $[0,1]$, then from $[0,x]$, and then returning the Boolean $x + y > 1.5$. More precisely, this is the **marginal** probability of $x + y > 1.5$. **Technical note**: `MonadDistribution` actually contains a number of other distributions beyond `random`, which by default are defined in terms of `random`, but allow for different definitions when desired. For example, `SamplerT` (an instance of `MonadDistribution` in Control.Monad.Sampler) defines `normal` and other distributions independently of `random`. ### MonadFactor Here's `MonadFactor`. ```haskell class Monad m => MonadFactor m where score :: Log Double -> m () ``` `Log Double` is defined in `Numeric.Log`, and is a wrapper for `Double`s which does multiplication in log-space. It comes with `Exp :: a -> Log a` and `ln :: Log a -> a`. This is because we don't want numerical problems when multiplying tiny probabilities, and we want a hassle free typesafe separation of doubles and log-doubles. ## Inference transformers Now core idea of monad-bayes is that various monads will be made to be instances of `MonadDistribution`, `MonadFactor` or both (i.e. an instance of `MonadMeasure`), and different inference algorithms will be written using these instances. This separates the specification of the model (which happens abstractly in `MonadMeasure`) from the inference algorithm, which takes place in on of the concrete instances. The clever part of monad-bayes is that it allows this instances to be constructed in a modular way, using monad transformers. In the paper, these are termed *inference transformers* to emphasize that it doesn't really matter whether they satisfy the monad laws. For example, to run weighted rejection sampling on a probabilistic program `p`, we can write `(sampler . runWeightedT) p`. Here, `(sampler . runWeightedT) :: WeightedT SamplerIO a -> IO a`. So `p` gets understood as being of type `WeightedT SamplerIO`, a type we'll encounter soon. Some of these transformers are easy to understand (like `StateT Double`, while others (like the Church transformed Free monad transformer) lie on the more advanced side of things. The following tour of these types goes from easy to hard. ### Enumerator Summary of key info: - `Enumerator :: Type -> Type` - `instance MonadDistribution Enumerator` - `instance MonadFactor Enumerator` `Enumerator` is in `Control.Monad.Bayes.Enumerator`, defined as follows: ```haskell newtype Enumerator a = Enumerator (WriterT (Product (Log Double)) [] a) ``` This merits a little unpacking. First, `Product` is a wrapper from [Data.Monoid](https://hackage.haskell.org/package/base-4.16.0.0/docs/Data-Monoid.html) which makes the semigroup operator for numbers be multiplication, so that: ```haskell Product 2 <> Product 3 == Product 6` ``` Unpacking the definition of `Enumerator a`, it is isomorphic to: ```haskell [(a, (Product (Log Double)))] ``` So, a value of type `Enumerator Bool`, for instance, is a list of pairs of booleans along with a double, like: ```haskell [(False,0.8914),(True,0.1086)] ``` Also in `Control.Monad.Bayes.Enumerator` is a function `enumerator`, which has type: ```haskell enumerator :: Ord a => Enumerator a -> [(a, Double)] ``` We can write `enumerator sprinkler`. Why is this well typed? The idea is that `sprinkler` has type `forall m. MonadMeasure m => m Bool`, and we *instantiate* that `m` as `Enumerator`. But for this to be well-typed, we need `Enumerator` to be an instance of `MonadMeasure`. For that, we need `Enumerator` to be a `MonadDistribution`, and a `MonadFactor`. For that, we need it to be a `Monad`, and in turn, a `Functor`. In understanding these instance definition, we'll understand what what `Enumerator` is doing for us. `Enumerator` is a monad automatically, because `WriterT a m` is a monad for `a` a monoid and `m` a monad. As needed, `[]` is a monad and `Log Double` is a monoid. But what does that monad actually **do**? For instance, if I have a weighted list `l` like `[(False,0.8914),(True,0.1086)]` and a function `f :: Bool -> Enumerator a`, what is `l >>= f`? It takes each element in `l`, and passes it through `f`, to obtain a weighted list of weighted lists. Then it flattens it, by including each element in the inner lists in the resulting list, with the weight of the list multiplied by the weight of the element. As for `return x`, it gives the singleton list containing the pair of `x` and the unit of the product monoid, i.e. `[(x, 1.0)]`. This is the essence of propagating probability forward. What remains is to define the `MonadDistribution` and `MonadFactor` instances: ```haskell instance MonadDistribution Enumerator where random = error "Infinitely supported random variables not supported in Enumerator" bernoulli p = fromList [(True, (Exp . log) p), (False, (Exp . log) (1 - p))] ``` The first thing to notice is that `random` is actually not defined for `Enumerator`, and consequently, you can't handle any continuous distributions. This makes sense, because you can't represent continuous distributions (whose support is uncountably infinite) as a list of samples. So really `Enumerator` isn't very general purpose. It's best understood as a didactic tool, rather than a real world inference algorithm. But it can handle discrete distributions. It does this via `bernoulli` (as well as `categorical`, which I've omitted). `bernoulli p` constructs the weighted list corresponding to a `bernoulli` distribution with parameter `p`. And the `MonadFactor` instance: ```haskell instance MonadFactor Enumerator where score w = fromList [((), w)] ``` Finally, `enumerator` simply unwraps `Enumerator` to get a list of pairs of values and their weights, and then normalizes them into probabilities. It orders the list, which is why `enumerator` requires an `Ord` instance. To see how this all works together, consider: ```haskell example = do x <- bernoulli 0.5 condition x return x ``` From the way the `MonadDistribution` instance for `Enumerator` is defined, `bernoulli 0.5` is a list of two pairs: `[(True, 0.5), (False, 0.5)]`. Using the `Monad` instance, the next line multiplies each of the masses by a number (`0` for `True`, `1` for `False`). The final line multiplies both by `1.0`. And then `enumerator` normalizes the result. So the ensuing distribution from `enumerator example` is `{True : 1.0}`. ### SamplerIO Summary of key info on `SamplerIO`: - `SamplerIO :: Type -> Type` - `instance MonadDistribution SamplerIO` - **No** instance for `MonadFactor` Monad-Bayes actually provides a more general constructor: ```haskell newtype SamplerT g m a = SamplerT (ReaderT g m a) ``` `SamplerIO` just specializes `g`, which is the random number generator, and `m`, the IO-handling monad: ```haskell type SamplerIO = SamplerT (IOGenM StdGen) IO ``` But you can specialize many other ways (see the `random` package), depending on your use case. As the names suggest, `SamplerIO` instantiates an abstract distribution as a sampling procedure. We have ```haskell instance MonadDistribution (SamplerT g m) where random = SamplerT (ReaderT uniformDouble01M) uniform a b = SamplerT (ReaderT $ uniformRM (a, b)) normal m s = SamplerT (ReaderT (MWC.normal m s)) gamma shape scale = SamplerT (ReaderT $ MWC.gamma shape scale) beta a b = SamplerT (ReaderT $ MWC.beta a b) ... ``` Were the lines from `uniform` to `beta` commented out, they would be instantiated with the defaults defined in terms of `random` in the `MonadDistribution` class. The reason they are not is that there are more statistically efficient ways to sample. We can unpack values from `SamplerIO a` using `sampler :: SamperIO a -> IO a`. So for example: ```haskell print =<< sampler random ``` will print a sample from `random`. Similarly for other distributions. But note that `SamplerIO` only has a `MonadDistribution` instance. This means that `sampler sprinkler` does not type check and gives the type error: ```haskell No instance for (MonadMeasure SamplerIO) ``` This is to be expected. `SamplerIO` has no instance for `MonadFactor`. ### WeightedT m Summary of key info on `WeightedT`: - `WeightedT :: (Type -> Type) -> (Type -> Type)` - `instance MonadDistribution m => MonadMeasure (WeightedT m)` - `instance MonadFactor (WeightedT m)` ```haskell newtype WeightedT m a = WeightedT (StateT (Log Double) m a) ``` A **key** difference to `Enumerator` and `SamplerIO` is the **kind** of `WeightedT`: it takes a type `m :: Type -> Type` as an argument. `WeightedT m` is isomorphic to: ```haskell Log Double -> m (a, Log Double) ``` Some intuition for what this means comes from the `MonadFactor` instance: ```haskell instance Monad m => MonadFactor (WeightedT m) where score w = WeightedT (modify (* w)) ``` So if we write: ```haskell example :: MonadDistribution m => WeightedT m Bool example = do x <- bernoulli 0.5 score (if b then 1 else 0) return x ``` then the result is that first, we draw a sample from a Bernoulli distribution from the **underlying** distribution `m`, and then multiply the state (which is a `Log Double`) by a number which depends on that sample. For convenience, we write `condition b = score (if b then 1 else 0)`. To unpack from `WeightedT m a`, we use: ```haskell runWeightedT :: WeightedT m a -> m (a, Log Double) runWeightedT (WeightedT m) = runStateT m 1 ``` `WeightedT m` is not an instance of `MonadDistribution`, but only as instance of `MonadFactor` (and that, only when `m` is an instance of `Monad`). However, since `StateT` is a monad transformer, there is a function `lift :: m Double -> WeightedT m Double`. So if we take a `MonadDistribution` instance like `SamplerIO`, then `WeightedT SamplerIO` is an instance of both `MonadDistribution` and `MonadFactor`. Which means it is an instance of `MonadMeasure`. So we can successfully write `(sampler . runWeightedT) sprinkler` and get a program of type `IO (Bool, Log Double)`. When run, this will draw a sample from `sprinkler` along with an **unnormalized** density for that sample. It's worth stopping here to remark on what's going on. What has happened is that the `m` in `forall m. MonadMeasure m => m Bool` has been *instantiated* as `WeightedT SamplerIO`. This is an example of how the interpreters for inference can be composed in modular ways. Finally, there's a function ```haskell hoist :: (forall x. m x -> n x) -> WeightedT m a -> WeightedT n a ``` This takes a natural transformation `m ~> n` and lifts it into a natural transformation `WeightedT m ~> WeightedT n`. Most of the inference transformers have an analogous `hoist` function. ### PopulationT Summary of key info on `PopulationT`: - `PopulationT :: (Type -> Type) -> (Type -> Type)` - `instance MonadDistribution m => instance MonadDistribution (PopulationT m)` - `instance MonadFactor m => instance MonadFactor (PopulationT m)` ```haskell newtype PopulationT m a = PopulationT (WeightedT (ListT m) a) ``` So: ```haskell PopulationT m a ~ m [Log Double -> (a, Log Double)] ``` Note that while `ListT` isn't in general a valid monad transformer, we're not requiring it to be one here. `PopulationT` is used to represent a collection of particles (in the statistical sense), along with their weights. There are several useful functions associated with it: ```haskell spawn :: Monad m => Int -> PopulationT m () spawn n = fromWeightedList $ pure $ replicate n ((), 1 / fromIntegral n) ``` `spawn` spawns new particles. As an example: ```haskell enumerator $ runPopulationT (spawn 2) ``` gives ```haskell [([((),0.5),((),0.5)],1.0)] ``` Observe how here we have interpreted `(spawn 2)` as of type `PopulationT Enumerator ()`. `resampleGeneric` takes a function to probabilistically select a set of indices from a vector, and makes a new population by selecting those indices. ```haskell resampleGeneric :: MonadDistribution m => (V.Vector Double -> m [Int]) -> PopulationT m a -> PopulationT m a ``` `pushEvidence`, to quote the API docs, "normalizes the weights in the population, while at the same time incorporating the sum of the weights as a score in m." ```haskell pushEvidence :: MonadFactor m => PopulationT m a -> PopulationT m a ``` In other words, `pushEvidence` takes a `PopulationT m a` where `m` is a `MonadFactor` instance. It takes the sum of the weights, divides the weights by it, and then factors by the sum in `m`. ### SequentialT Summary of key info on `SequentialT`: - `SequentialT :: (Type -> Type) -> (Type -> Type)` - `instance MonadDistribution m => instance MonadDistribution (SequentialT m)` - `instance MonadFactor m => instance MonadFactor (SequentialT m)` ```haskell newtype SequentialT m a = SequentialT {runSequentialT :: Coroutine (Await ()) m a} ``` This is a wrapper for the `Coroutine` type applied to the `Await` constructor from `Control.Monad.Coroutine`, which is defined thus: ```haskell newtype Coroutine s m r = Coroutine { resume :: m (Either (s (Coroutine s m r)) r) } newtype Await x y = Await (x -> y) ``` Unpacking that: ```haskell SequentialT m a ~ m (Either (() -> SequentialT m a) a) ``` As usual, `m` is going to be some other probability monad, so understand `SequentialT m a` as representing a program which, after making a random choice or doing conditioning, we either obtain an `a` value, or a paused computation, which when resumed gets us back to a new `SequentialT m a`. (For more on coroutines, see the final article in: https://themonadreader.files.wordpress.com/2011/10/issue19.pdf.) The monad instance for coroutines is as follows: ```haskell instance (Functor s, Monad m) => Monad (Coroutine s m) where return = pure t >>= f = Coroutine (resume t >>= apply f) where apply fc (Right x) = resume (fc x) apply fc (Left s) = return (Left (fmap (>>= fc) s)) ``` The `MonadDistribution` instance follows from `lift : m a -> Coroutine (Await ()) m a`, defined as: ```haskell instance Functor s => MonadTrans (Coroutine s) where lift = Coroutine . liftM Right ``` The `MonadFactor` instance has less trivial content: ```haskell -- | Execution is 'suspend'ed after each 'score'. instance MonadFactor m => MonadFactor (SequentialT m) where score w = lift (score w) >> suspend ``` First you take a `score` in the underlying `MonadFactor` instance, and then you `suspend`, which means: ```haskell -- | A point where the computation is paused. suspend :: Monad m => SequentialT m () suspend = SequentialT (Coroutine (return (Left (Await return)))) ``` We can move to the next suspension point with: ```haskell advance :: Monad m => SequentialT m a -> SequentialT m a advance = SequentialT . bounce extract . runSequentialT ``` and move through all with: ```haskell -- | Remove the remaining suspension points. finish :: Monad m => SequentialT m a -> m a finish = pogoStick extract . runSequentialT ``` But most importantly, we can apply a natural transformation over the underlying monad `m` to only the current suspension. ```haskell hoistFirst :: (forall x. m x -> m x) -> SequentialT m a -> SequentialT m a hoistFirst f = SequentialT . Coroutine . f . resume . runSequentialT ``` When `m` is `PopulationT n` for some other `n`, then `resampleGeneric` gives us one example of the natural transformation we want. In other words, operating in `SequentialT (PopulationT n)` works, and not only works but does something statistically interesting: particle filtering (aka SMC). **Note**: the running of `SequentialT`, i.e. getting from `SequentialT m a` to `m a` is surprisingly subtle, and there are many incorrect ways to do it, such as plain folds of the recursive structure. These can result in a semantics in which the transformation gets applied an exponentially large number of times. ### DensityT Summary of key info on `DensityT`: - `DensityT :: (Type -> Type) -> (Type -> Type)` - `instance MonadDistribution (DensityT m)` - **No** instance for `MonadFactor` A *trace* of a program of type `MonadDistribution m => m a` is an execution of the program, so a choice for each of the random values. Recall that `random` underlies all of the random values in a program, so a trace for a program is fully specified by a list of `Double`s, giving the value of each call to `random`. With this in mind, a `DensityT m a` is an interpretation of a probabilistic program as a function from a trace to the *density* of that execution of the program. Monad-bayes offers two implementations, in `Control.Monad.Bayes.DensityT.State` and `Control.Monad.Bayes.DensityT.Free`. The first is slow but easy to understand, the second is more sophisticated, but faster. The former is relatively straightforward: the `MonadDistribution` instance implements `random` as `get`ting the trace (using `get` from `MonadState`), using (and removing) the first element (`put` from `MonadState`), and writing that element to the output (using `tell` from `MonadWriter`). If the trace is empty, the `random` from the underlying monad is used, but the result is still written with `tell`. The latter is best understood if you're familiar with the standard use of a free monad to construct a domain specific language. For probability in particular, see this [blog post](https://jtobin.io/simple-probabilistic-programming). Here's the definition: ```haskell newtype SamF a = Random (Double -> a) newtype DensityT m a = DensityT {getDensityT :: FT SamF m a} instance Monad m => MonadDistribution (DensityT m) where random = DensityT $ liftF (Random id) ``` The monad-bayes implementation uses a more efficient implementation of `FreeT`, namely `FT` from the `free` package, known as the *Church transformed Free monad*. This is a technique explained in https://begriffs.com/posts/2016-02-04-difference-lists-and-codennsity.html. But that only changes the operational semantics - performance aside, it works just the same as the standard `FreeT` datatype. If you unpack the definition, you get: ```haskell DensityT m a ~ m (Either a (Double -> (DensityT m a))) ``` Since `FreeT` is a transformer, we can use `lift` to get a `MonadDistribution` instance. `runDensityT` is then defined using the folding pattern `iterFT`, which interprets `SamF` in the appropriate way: ```haskell runDensityT :: MonadDistribution m => [Double] -> DensityT m a -> m (a, [Double]) runDensityT randomness (DensityT m) = runWriterT $ evalStateT (iterTM f $ hoistFT lift m) randomness where f (Random k) = do -- This block runs in StateT [Double] (WriterT [Double]) m. -- StateT propagates consumed randomness while WriterT records -- randomness used, whether old or new. xs <- get x <- case xs of [] -> random y : ys -> put ys >> return y tell [x] k x ``` This takes a list of `Double`s (a representation of a trace), and a probabilistic program like `example`, and gives back a `SamplerIO (Bool, [Double])`. At each call to `random` in `example`, the next double in the list is used. If the list of doubles runs out, calls are made to `random` using the underlying monad. ### TracedT Summary of key info on `TracedT`: - `TracedT :: (Type -> Type) -> (Type -> Type)` - `instance MonadDistribution m => MonadDistribution (TracedT m)` - `instance MonadFactor m => MonadFactor (TracedT m)` `TracedT m` is actually several related interpretations, each built on top of `DensityT`. These range in complexity. The reason traces are relevant here is that monad-bayes implements a version of Markov Chain Monte Carlo (MCMC) that operates on arbitrary probabilistic programs often referred to as *trace MCMC*. The idea is that the MCMC chain takes place on traces of the program. A step constitutes a change to this trace, i.e. to the list of `Double`s. For instance, the algorithm for an MH step goes as follows: - propose a new trace by randomly redrawing a single `Double` in the trace - accept or reject with the MH criterion It's convenient to specify a trace not just as a `[Double]` but also with the resulting output, and the density of that output. This is what monad-bayes does: ```haskell data Trace a = Trace { variables :: [Double], output :: a, runDensityT :: Log Double } ``` We also need a specification of the probabilistic program in question, free of any particular interpretation. That is precisely what `DensityT` is for. The simplest version of `TracedT` is in `Control.Monad.Bayes.TracedT.Basic` ```haskell TracedT m a ~ (DensityT Identity a, Log Double), m (Trace a)) ``` A `TracedT` interpretation of a model is a particular run of the model with its corresponding probability, alongside a distribution over `Trace` info, which records: the value of each call to `random`, the value of the final output, and the density of this program trace. This machinery allows us to implement MCMC (see inference methods below for details). ### Integrator Summary of key info: - `Integrator :: Type -> Type` - `instance MonadDistribution Integrator` `Integrator` is in `Control.Monad.Bayes.Integrator`, defined as follows: ```haskell newtype Integrator a = Integrator {getCont :: Cont Double a} ``` This `MonadDistribution` instance interprets a probabilistic program as a numerical integrator. For a nice explanation, see [this blog post](https://jtobin.io/giry-monad-implementation). `Integrator a` is isomorphic to `(a -> Double) -> Double`. A program `model` of type `Integrator a` will take a function `f` and calculate $E_{p}[f] = \int f(x)*p(x)$ where $p$ is the density of `model`. The integral for the expectation is performed by quadrature, using the tanh-sinh approach. For example, `random :: Integrator Double` is the program which takes a function `f` and integrates `f` over the $(0,1)$ range. We can calculate the probability for an interval $(a,b)4 of any model of type `Integrator Double` by setting `f` to be the function that returns $1$ for that range, else $0$. Similarly for the CDF, MGF and so on. ## Inference methods under the hood ### Exact inference Exact inference is nothing more than the use of the `Enumerator` instance of `MonadMeasure`. It should be noted that this is not a particularly efficient or clever version of exact inference. For example, consider: ```haskell example = replicateM 100 $ do x <- bernoulli 0.5 condition x return x ``` Doing `enumerator example` will create a list of $2^{100}$ entries, all but one of which have $0$ mass. (See below for a way to perform this inference efficiently). The main purpose of `Enumerator` is didactic, as a way to understand simple discrete distributions in full. In addition, you can use it in concert with transformers like `WeightedT`, to get a sense of how they work. For example, consider: ```haskell example = do x <- bernoulli 0.5 condition x return x ``` `(enumerator . runWeightedT) example` gives `[((False,0.0),0.5),((True,1.0),0.5)]`. This is quite edifying for understanding `(sampler . runWeightedT) example`. What it says is that there are precisely two ways the program will run, each with equal probability: either you get `False` with weight `0.0` or `True` with weight `1.0`. ### Quadrature As described on the section on `Integrator`, we can interpret our probabilistic program of type `MonadDistribution m => m a` as having concrete type `Integrator a`. This views our program as an integrator, allowing us to calculate expectations, probabilities and so on via quadrature (i.e. numerical approximation of an integral). This can also handle programs of type `MonadMeasure m => m a`, that is, programs with `factor` statements. For these cases, a function `normalize :: WeightedT Integrator a -> Integrator a` is employed. For example, ```haskell model :: MonadMeasure m => m Double model = do var <- gamma 1 1 n <- normal 0 (sqrt var) condition (n > 0) return var ``` is really an unnormalized measure, rather than a probability distribution. `normalize` views it as of type `WeightedT Integrator Double`, which is isomorphic to `(Double -> (Double, Log Double) -> Double)`. This can be used to compute the normalization constant, and divide the integrator's output by it, all within `Integrator`. ### Independent forward sampling For any program of type `p = MonadDistribution m => m a`, we may do `sampler p` or `runST $ sampleSTfixed p`. Note that if there are any calls to `factor` in the program, then it cannot have type `MonadDistribution m`. ### Independent weighted sampling Consider ```haskell example :: MonadMeasure m => m Bool example = do x <- bernoulli 0.5 condition x return x ``` `(runWeightedT . sampler) example :: IO (Bool, Log Double)` returns a tuple of a truth value and a probability mass (or more generally density). How does this work? Types are clarifying: ```haskell run = (sampler :: SamplerIO (a, Log Double) -> IO (a, Log Double) ) . (runWeightedT :: WeightedT SamplerIO a -> SamplerIO (a, Log Double) ``` In other words, the program is being interpreted in the `WeightedT SamplerIO` instance of `MonadMeasure`. ### Metropolis Hastings MCMC The version of MCMC in monad-bayes performs its random walk on program traces of type `Trace a`. The motivation for doing this is that **any** probabilistic program can then be used with MCMC entirely automatically. A single step in this chain (in Metropolis Hasting MCMC) looks like this: ```haskell mhTrans :: MonadDistribution m => (WeightedT (State.DensityT m)) a -> Trace a -> m (Trace a) mhTrans m t@Trace {variables = us, probDensity = p} = do let n = length us us' <- do i <- discrete $ discreteUniformAB 0 (n - 1) u' <- random case splitAt i us of (xs, _ : ys) -> return $ xs ++ (u' : ys) _ -> error "impossible" ((b, q), vs) <- State.runDensityT (runWeightedT m) us' let ratio = (exp . ln) $ min 1 (q * fromIntegral n / (p * fromIntegral (length vs))) accept <- bernoulli ratio return $ if accept then Trace vs b q else t ``` Our probabilistic program is interpreted in the type `WeightedT (DensityT m) a`, which is an instance of `MonadMeasure`. We use this to define our kernel on traces. We begin by perturbing the list of doubles contained in the trace by selecting a random position in the list and resampling there. We could do this *proposal* in a variety of ways, but here, we do so by choosing a double from the list at random and resampling it (hence, *single site* trace MCMC). We then run the program on this new list of doubles; `((b,q), vs)` is the outcome, probability, and result of all calls to `random`, respectively (recalling that the list of doubles may be shorter than the number of calls to `random`). The value of these is probabilistic in the underlying monad `m`. We then use the MH criterion to decide whether to accept the new list of doubles as our trace. MH is then easily defined as taking steps with this kernel, in the usual fashion. Note that it works for any probabilistic program whatsoever. **Warning**: the default proposal is single site, meaning that you change one random number in the trace at a time. As a result, the proposal may not be ergodic for models with hard constraints. As a simple example, suppose that you're doing a random walk, and trying to get from a trace corresponding to the output `(True, True)` to a trace corresponding to `(False, False)`. But if `(False, True)` and `(True, False)` have no probability, you have no hope of getting there. **Don't expect MCMC to be efficient without designing your own proposal, or even correct when the proposal is not ergodic**. ### Sequential Importance Sampling This is provided by ```haskell sequentially :: Monad m => -- | transformation (forall x. m x -> m x) -> -- | number of time steps Int -> SequentialT m a -> m a sequentially f k = finish . composeCopies k (advance . hoistFirst f) ``` in `Control.Monad.Bayes.Sequential.Coroutine`. You provide a natural transformation in the underlying monad `m`, and `sequentially` applies that natural transformation at each point of conditioning in your program. The main use case is in defining `smc`, below, but here is a nice didactic use case: Consider the program: ```haskell example = replicateM 100 $ do x <- bernoulli 0.5 condition x return x ``` Naive enumeration, as in `enumerator example` is enormously and needlessly inefficient, because it will create a $2^{100}$ size list of possible values. What we'd like to do is to throw away values of `x` that are `False` at each condition statement, rather than carrying them along forever. Suppose we have a function `removeZeros :: Enumerator a -> Enumerator a`, which removes values of the distribution with $0$ mass from `Enumerator`. We can then write `enumerator $ sequentially removeZeros 100 $ model` to run `removeZeros` at each of the 100 `condition` statements, making the algorithm run quickly. ### Sequential Monte Carlo Sequential importance resampling works by doing `sequentially` with a resampler of your choice, such as `resampleMultinomial`, after first spawning a set of `n` particles. ```haskell smc :: Monad m => -- | resampler (forall x. PopulationT m x -> PopulationT m x) -> -- | number of timesteps Int -> -- | population size Int -> -- | model SequentialT (PopulationT m) a -> PopulationT m a smc resampler k n = sequentially resampler k . Seq.hoistFirst (spawn n >>) ``` ### Particle Marginal Metropolis Hastings Quoting the monad-bayes paper: "PMMH is only applicable to models with a specific structure, namely the probabilistic program needs to decompose to a prior over the global parameters `m param` and the rest of the model `param -> m a`. Combining these using >>= would yield the complete model of type `m a`." "The idea is to do MH on the parameters of the model. Recall that for MH we need to compute the likelihood for the particular values of parameters but that involves integrating over the remaining random variables in the model which is intractable. Fortunately to obtain valid MH it is sufficient to have an unbiased estimator for the likelihood which is produced by a single sample from `WeightedT`. MH with such an estimator is referred to as pseudo-marginal MH. If instead of taking a single weight from `WeightedT` we take the sum of weights from `PopulationT` we obtain an unbiased estimator with lower variance. In particular if such a `PopulationT` is a result of smc the resulting algorithm is known as PMMH." Because of the modularity of monad-bayes, the implementation is remarkably simple, and directly linked to the algorithm: ```haskell pmmh mcmcConf smcConf param model = mcmc mcmcConf ( param >>= runPopulationT . pushEvidence . Pop.hoist lift . smc smcConf . model ) ``` There's a lot to unpack here. Here's the definition with more types. To shorten the signatures, the synonyms: `T = TracedT, S = SequentialT, P = PopulationT` are used: ```haskell pmmh :: MonadDistribution m => MCMCConfig -> SMCConfig (WeightedT m) -> TracedT (WeightedT m) a1 -> (a1 -> SequentialT (PopulationT (WeightedT m)) a2) -> m [[(a2, Log Double)]] pmmh mcmcConf smcConf param model = (mcmc mcmcConf :: T m [(a, Log Double)] -> m [[(a, Log Double)]]) ((param :: T m b) >>= (runPopulationT :: P (T m) a -> T m [(a, Log Double)]) . (pushEvidence :: P (T m) a -> P (T m) a) . Pop.hoist (lift :: forall x. m x -> T m x) . (smc smcConf :: S (P m) a -> P m a) . (model :: b -> S (P m) a)) ``` (Note that this uses the version of `mcmc` that uses the `TracedT` representation from `Control.Monad.Bayes.TracedT.Static`.) To understand this, note that the outer algorithm is just `mcmc`. But the probabilistic program that we pass to `mcmc` does the following: run `param` to get values for the parameters and then pass these to `model`. Then run `n` steps of SMC with `k` particles. Then lift the underlying monad `m` into `TracedT m`. Then calculate the sum of the weights of the particles and `factor` on this (this is what `pushEvidence` does). This `factor` takes place in `TracedT m`, so it is "visible" to `mcmc`. ### Resample-Move Sequential Monte Carlo The paper introducing monad-bayes has this to say about resample-move SMC: "A common problem with particle filters is that of particle degeneracy, where after resampling many particles are the same, effectively reducing the sample size. One way to ameliorate this problem is to introduce rejuvenation moves, where after each resampling we apply a number of MCMC transitions to each particle independently, thus spreading them around the space. If we use an MCMC kernel that preserves the target distribution at a given step, the resulting algorithm is correct" monad-bayes provides three versions of RMSMC, each of which uses one of the three `TracedT` implementations respectively. Here is the simplest, which I have annotated with types. To shorten the signatures, the synonyms: `T = TracedT, S = SequentialT, P = PopulationT` are used: ```haskell rmsmcBasic :: MonadDistribution m => MCMCConfig -> SMCConfig m -> -- | model S (T (P m)) a -> P m a rmsmc (MCMCConfig {..}) (SMCConfig {..}) = (TrBas.marginal :: T (P m) a -> P m a ) . sequentially ( (composeCopies numMCMCSteps TrBas.mhStep :: T (P m) a -> T (P m) a ) . TrBas.hoist (resampleSystematic :: P m a -> P m a ) ) numSteps . (S.hoistFirst (TrBas.hoist (spawn numParticles >>)) :: S (T (P m)) a -> S (T (P m)) a )) ``` What is this doing? Recall that `S m a` represents an `m` of coroutines over `a`. Recall that `TracedT m a` represents an `m` of traced computations of `a`. Recall that `P m a` represents an `m` of a list of `a`s. This means that an `S (T (P m)) a` is a program "interpreted as a population of traced coroutines". The paper adds that this "allows us to apply MH transitions to partially executed coroutines, which is exactly what we require for the rejuvenation steps." So the algorithm works by creating `n` particles, and at each of the first `k` calls to `factor`, first resampling the population and then for each particle in the population, doing an MH-MCMC walk for `t` steps to update it. ### Sequential Monte Carlo Squared This combines RMSMC and PMMH. That is, it is RMSMC, but for the MCMC rejuvenation procedure, PMMH is used instead of MH. There is one slight complication here, related to the fact that the MTL style effect system approach requires newtypes when more than one of a given effect appears in a stack, in order to differentiate them. ================================================ FILE: docs/mkdocs.yml ================================================ site_name: Probabilistic Programming in Haskell theme: name: material features: - content.code.annotate - content.tooltips - navigation.sections - navigation.top - navigation.tracking - search.highlight - search.share - search.suggest - toc.follow palette: - scheme: default primary: indigo accent: indigo toggle: icon: material/brightness-7 name: Switch to dark mode - scheme: slate primary: indigo accent: indigo toggle: icon: material/brightness-4 name: Switch to light mode extra: social: - icon: fontawesome/brands/github link: https://github.com/tweag/monad-bayes markdown_extensions: - pymdownx.arithmatex: generic: true - admonition - attr_list - pymdownx.details - pymdownx.emoji: emoji_index: !!python/name:materialx.emoji.twemoji emoji_generator: !!python/name:materialx.emoji.to_svg - pymdownx.highlight: anchor_linenums: true - pymdownx.inlinehilite - pymdownx.snippets - pymdownx.superfences - pymdownx.tabbed: alternate_style: true - toc: permalink: true extra_javascript: - javascripts/mathjax.js - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js nav: - 'index.md' - 'probprog.md' - 'tutorials.md' - 'examples.md' - 'usage.md' ================================================ FILE: docs/netlify.toml ================================================ [build] command = "mkdocs build" publish = "site" ================================================ FILE: docs/requirements.txt ================================================ mkdocs mkdocs-material pymdown-extensions ================================================ FILE: docs/runtime.txt ================================================ 3.8 ================================================ FILE: flake.nix ================================================ { description = "A library for probabilistic programming in Haskell."; nixConfig = { extra-substituters = [ "https://tweag-monad-bayes.cachix.org" "https://tweag-jupyter.cachix.org" ]; extra-trusted-public-keys = [ "tweag-monad-bayes.cachix.org-1:tmmTZ+WvtUMpYWD4LAkfSuNKqSuJyL3N8ZVm/qYtqdc=" "tweag-jupyter.cachix.org-1:UtNH4Zs6hVUFpFBTLaA4ejYavPo5EFFqgd7G7FxGW9g=" ]; }; inputs = { nixpkgs.url = "nixpkgs/nixos-unstable"; flake-compat = { url = "github:edolstra/flake-compat"; flake = false; }; flake-utils.url = "github:numtide/flake-utils"; pre-commit-hooks = { url = "github:cachix/pre-commit-hooks.nix"; inputs = { nixpkgs.follows = "nixpkgs"; }; }; jupyenv = { url = "github:tweag/jupyenv"; inputs = { flake-compat.follows = "flake-compat"; flake-utils.follows = "flake-utils"; }; }; }; outputs = { self , nixpkgs , jupyenv , flake-compat , flake-utils , pre-commit-hooks , } @ inputs: flake-utils.lib.eachSystem [ # Tier 1 - Tested in CI flake-utils.lib.system.x86_64-linux flake-utils.lib.system.x86_64-darwin # Tier 2 - Not tested in CI (at least for now) flake-utils.lib.system.aarch64-linux flake-utils.lib.system.aarch64-darwin ] ( system: let inherit (nixpkgs) lib; inherit (jupyenv.lib.${system}) mkJupyterlabNew; pkgs = import nixpkgs { inherit system; config.allowBroken = true; }; warnToUpdateNix = pkgs.lib.warn "Consider updating to Nix > 2.7 to remove this warning!"; src = lib.sourceByRegex self [ "^benchmark.*$" "^models.*$" "^monad-bayes\.cabal$" "^src.*$" "^test.*$" "^.*\.md" ]; # Always keep this up to date with the tested-with section in monad-bayes.cabal! # and the build-all-ghcs job in .github/workflows/nix.yml! ghcs = [ "ghc90" "ghc92" "ghc94" "ghc96" "ghc98" "ghc910" "default" ]; allHaskellPackages = lib.filterAttrs (ghcVersion: _: builtins.elem ghcVersion ghcs) (pkgs.haskell.packages // { default = pkgs.haskellPackages; }); monad-bayes-for = haskellPackages: haskellPackages.developPackage { name = "monad-bayes"; root = src; cabal2nixOptions = "--benchmark -fdev"; # https://github.com/tweag/monad-bayes/pull/256: Don't run tests on Mac because of machine precision issues modifier = drv: if system == "x86_64-linux" then drv else pkgs.haskell.lib.dontCheck drv; overrides = self: super: with pkgs.haskell.lib; { # Please check after flake.lock updates whether some of these overrides can be removed brick = super.callHackageDirect { pkg = "brick"; ver = "2.10"; sha256 = "sha256-m1PvPySOuTZbcnCm4j7M7AihK0w8OGKumyRR3jU5nfw="; } { }; vty = super.callHackageDirect { pkg = "vty"; ver = "6.4"; sha256 = "sha256-xHtMfRaJVk95UTwh2QU8VL3MgXuLQOzTwqTa5oevZ5U="; } { }; } // lib.optionalAttrs (lib.versionAtLeast super.ghc.version "9.10") { # Please check after flake.lock updates whether some of these overrides can be removed microstache = doJailbreak super.microstache; }; }; monad-bayes-per-ghc = lib.mapAttrs (_: monad-bayes-for) allHaskellPackages; monad-bayes = monad-bayes-per-ghc.default; monad-bayes-all-ghcs = pkgs.linkFarm "monad-bayes-all-ghcs" monad-bayes-per-ghc; jupyterEnvironment = mkJupyterlabNew { imports = [ (import ./kernels/haskell.nix { inherit monad-bayes; }) ]; }; pre-commit = pre-commit-hooks.lib.${system}.run { inherit src; hooks = { alejandra.enable = true; cabal-fmt.enable = true; hlint.enable = false; ormolu.enable = true; }; }; devShellFor = ghcVersion: haskellPackages: addJupyter: haskellPackages.shellFor { packages = hps: [ (monad-bayes-for haskellPackages) ]; nativeBuildInputs = with pre-commit-hooks.packages.${system}; [ alejandra cabal-fmt hlint ormolu ] ++ lib.optional addJupyter jupyterEnvironment ++ (with haskellPackages; [ haskell-language-server ]); }; in rec { packages = { inherit monad-bayes monad-bayes-per-ghc monad-bayes-all-ghcs pre-commit jupyterEnvironment; }; packages.default = packages.monad-bayes; checks = { inherit monad-bayes pre-commit; }; devShells = lib.concatMapAttrs (ghcVersion: haskellPackages: { "${ghcVersion}" = devShellFor ghcVersion haskellPackages false; "${ghcVersion}-jupyter" = devShellFor ghcVersion haskellPackages true; }) allHaskellPackages; # Needed for backwards compatibility with Nix versions <2.8 defaultPackage = warnToUpdateNix packages.default; devShell = warnToUpdateNix devShells.default; formatter = pkgs.nixpkgs-fmt; } ); } ================================================ FILE: kernels/haskell.nix ================================================ { monad-bayes }: { pkgs, ... }: { kernel.haskell.monad-bayes = { enable = true; name = "monad-bayes"; displayName = "monad-bayes"; extraHaskellPackages = p: [ p.hvega p.lens p.log-domain p.katip p.ihaskell-hvega p.ihaskell-diagrams p.diagrams p.diagrams-cairo p.aeson p.lens p.lens-aeson p.pretty-simple p.monad-loops # hamilton seems unmaintained, unclear whether we can keep it. See https://github.com/tweag/monad-bayes/issues/300 # p.hamilton p.hmatrix p.vector-sized p.linear p.recursion-schemes p.data-fix p.free p.comonad p.adjunctions p.distributive p.vector p.megaparsec p.histogram-fill # Strange build error which I can't fix: # > Configuring gloss-rendering-1.13.1.2... # > # > Setup: Encountered missing or private dependencies: # > bytestring >=0.11 && <0.12 # p.gloss monad-bayes ]; }; } ================================================ FILE: models/BetaBin.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} {-# OPTIONS_GHC -Wno-incomplete-uni-patterns #-} {-# OPTIONS_GHC -Wno-missing-export-lists #-} module BetaBin where -- The beta-binomial model in latent variable and urn model representations. -- The two formulations should be exactly equivalent, but only urn works with Dist. import Control.Monad (replicateM) import Control.Monad.Bayes.Class ( MonadDistribution (bernoulli, uniform), MonadMeasure, condition, ) import Control.Monad.State.Lazy (evalStateT, get, put) import Pipes ((<-<)) import Pipes.Prelude qualified as P hiding (show) -- | Beta-binomial model as an i.i.d. sequence conditionally on weight. latent :: (MonadDistribution m) => Int -> m [Bool] latent n = do weight <- uniform 0 1 replicateM n (bernoulli weight) -- | Beta-binomial as a random process. -- Equivalent to the above by De Finetti's theorem. urn :: (MonadDistribution m) => Int -> m [Bool] urn n = flip evalStateT (1, 1) $ do replicateM n do (a, b) <- get let weight = a / (a + b) outcome <- bernoulli weight let (a', b') = if outcome then (a + 1, b) else (a, b + 1) put (a', b') return outcome -- | Beta-binomial as a random process. -- This time using the Pipes library, for a more pure functional style urnP :: (MonadDistribution m) => Int -> m [Bool] urnP n = P.toListM $ P.take n <-< P.unfoldr toss (1, 1) where toss (a, b) = do let weight = a / (a + b) outcome <- bernoulli weight let (a', b') = if outcome then (a + 1, b) else (a, b + 1) return $ Right (outcome, (a', b')) -- | A beta-binomial model where the first three states are True,True,False. -- The resulting distribution is on the remaining outcomes. cond :: (MonadMeasure m) => m [Bool] -> m [Bool] cond d = do ~(first : second : third : rest) <- d condition first condition second condition (not third) return rest -- | The final conditional model, abstracting the representation. model :: (MonadMeasure m) => (Int -> m [Bool]) -> Int -> m Int model repr n = fmap count $ cond $ repr (n + 3) where -- Post-processing by counting the number of True values. count :: [Bool] -> Int count = length . filter id ================================================ FILE: models/ConjugatePriors.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} {-# LANGUAGE RecordWildCards #-} {-# LANGUAGE TypeFamilies #-} module ConjugatePriors where import Control.Applicative (Applicative (..)) import Control.Foldl (fold) import Control.Foldl qualified as F import Control.Monad.Bayes.Class (Bayesian (..), MonadDistribution (bernoulli, beta, gamma, normal), MonadMeasure, normalPdf) import Numeric.Log (Log (Exp)) -- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md -- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore import Prelude hiding (Applicative (..)) type GammaParams = (Double, Double) type BetaParams = (Double, Double) type NormalParams = (Double, Double) -- | Posterior on the precision of the normal after the points are observed gammaNormalAnalytic :: (MonadMeasure m, Foldable t, Functor t) => GammaParams -> t Double -> m Double -- | Exact posterior for the model. -- For derivation see Kevin Murphy's -- "Conjugate Bayesian analysis of the Gaussian distribution" -- section 4. gammaNormalAnalytic (a, b) points = gamma a' (recip b') where a' = a + fromIntegral (length points) / 2 b' = b + sum (fmap (** 2) points) / 2 -- | Posterior on beta after the bernoulli sample betaBernoulliAnalytic :: (MonadMeasure m, Foldable t) => BetaParams -> t Bool -> m Double betaBernoulliAnalytic (a, b) points = beta a' b' where (n, s) = fold (liftA2 (,) F.length (F.premap (\case True -> 1; False -> 0) F.sum)) points a' = a + s b' = b + fromIntegral n - s bernoulliPdf :: (Floating a) => a -> Bool -> Log a bernoulliPdf p x = let numBool = if x then 1.0 else 0 in Exp $ log (p ** numBool * (1 - p) ** (1 - numBool)) betaBernoulli' :: (MonadMeasure m) => (Double, Double) -> Bayesian m Double Bool betaBernoulli' (a, b) = Bayesian (beta a b) bernoulli bernoulliPdf normalNormal' :: (MonadMeasure m) => Double -> (Double, Double) -> Bayesian m Double Double normalNormal' var (mu0, var0) = Bayesian (normal mu0 (sqrt var0)) (`normal` (sqrt var)) (`normalPdf` (sqrt var)) gammaNormal' :: (MonadMeasure m) => (Double, Double) -> Bayesian m Double Double gammaNormal' (a, b) = Bayesian (gamma a (recip b)) (normal 0 . sqrt . recip) (normalPdf 0 . sqrt . recip) normalNormalAnalytic :: (MonadMeasure m, Foldable t) => Double -> NormalParams -> t Double -> m Double normalNormalAnalytic sigma_2 (mu0, sigma0_2) points = normal mu' (sqrt sigma_2') where (n, s) = fold (liftA2 (,) F.length F.sum) points mu' = sigma_2' * (mu0 / sigma0_2 + s / sigma_2) sigma_2' = recip (recip sigma0_2 + fromIntegral n / sigma_2) ================================================ FILE: models/Dice.hs ================================================ module Dice (diceHard, diceSoft) where -- A toy model for dice rolling from http://dl.acm.org/citation.cfm?id=2804317 -- Exact results can be obtained using Dist monad import Control.Applicative (Applicative (..)) import Control.Monad.Bayes.Class ( MonadDistribution (uniformD), MonadFactor (score), MonadMeasure, condition, ) -- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md -- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore import Prelude hiding (Applicative (..)) -- | A toss of a six-sided die. die :: (MonadDistribution m) => m Int die = uniformD [1 .. 6] -- | A sum of outcomes of n independent tosses of six-sided dice. dice :: (MonadDistribution m) => Int -> m Int dice 1 = die dice n = liftA2 (+) die (dice (n - 1)) -- | Toss of two dice where the output is greater than 4. diceHard :: (MonadMeasure m) => m Int diceHard = do result <- dice 2 condition (result > 4) return result -- | Toss of two dice with an artificial soft constraint. diceSoft :: (MonadMeasure m) => m Int diceSoft = do result <- dice 2 score (1 / fromIntegral result) return result ================================================ FILE: models/HMM.hs ================================================ -- HMM from Anglican (https://bitbucket.org/probprog/anglican-white-paper) module HMM where import Control.Monad (replicateM, when) import Control.Monad.Bayes.Class ( MonadDistribution (categorical, normal, uniformD), MonadFactor, MonadMeasure, factor, normalPdf, ) import Control.Monad.Bayes.Enumerator (enumerateToDistribution) import Data.Maybe (fromJust, isJust) import Data.Vector (fromList) import Pipes (MFunctor (hoist), MonadTrans (lift), each, yield, (>->)) import Pipes.Core (Producer) import Pipes.Prelude qualified as Pipes -- | Observed values values :: [Double] values = [ 0.9, 0.8, 0.7, 0, -0.025, -5, -2, -0.1, 0, 0.13, 0.45, 6, 0.2, 0.3, -1, -1 ] -- | The transition model. trans :: (MonadDistribution m) => Int -> m Int trans 0 = categorical $ fromList [0.1, 0.4, 0.5] trans 1 = categorical $ fromList [0.2, 0.6, 0.2] trans 2 = categorical $ fromList [0.15, 0.7, 0.15] trans _ = error "unreachable" -- | The emission model. emissionMean :: Int -> Double emissionMean 0 = -1 emissionMean 1 = 1 emissionMean 2 = 0 emissionMean _ = error "unreachable" -- | Initial state distribution start :: (MonadDistribution m) => m Int start = uniformD [0, 1, 2] -- | Example HMM from http://dl.acm.org/citation.cfm?id=2804317 hmm :: (MonadMeasure m) => [Double] -> m [Int] hmm dataset = f dataset (const . return) where expand x y = do x' <- trans x factor $ normalPdf (emissionMean x') 1 y return x' f [] k = start >>= k [] f (y : ys) k = f ys (\xs x -> expand x y >>= k (x : xs)) syntheticData :: (MonadDistribution m) => Int -> m [Double] syntheticData n = replicateM n syntheticPoint where syntheticPoint = uniformD [0, 1, 2] -- | Equivalent model, but using pipes for simplicity -- | Prior expressed as a stream hmmPrior :: (MonadDistribution m) => Producer Int m b hmmPrior = do x <- lift start yield x Pipes.unfoldr (fmap (Right . (\k -> (k, k))) . trans) x -- | Observations expressed as a stream hmmObservations :: (Functor m) => [a] -> Producer (Maybe a) m () hmmObservations dataset = each (Nothing : (Just <$> reverse dataset)) -- | Posterior expressed as a stream hmmPosterior :: (MonadMeasure m) => [Double] -> Producer Int m () hmmPosterior dataset = zipWithM hmmLikelihood hmmPrior (hmmObservations dataset) where hmmLikelihood :: (MonadFactor f) => (Int, Maybe Double) -> f () hmmLikelihood (l, o) = when (isJust o) (factor $ normalPdf (emissionMean l) 1 (fromJust o)) zipWithM f p1 p2 = Pipes.zip p1 p2 >-> Pipes.chain f >-> Pipes.map fst hmmPosteriorPredictive :: (MonadDistribution m) => [Double] -> Producer Double m () hmmPosteriorPredictive dataset = Pipes.hoist enumerateToDistribution (hmmPosterior dataset) >-> Pipes.mapM (\x -> normal (emissionMean x) 1) ================================================ FILE: models/Helper.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE ImportQualifiedPost #-} module Helper where import Control.Monad.Bayes.Class (MonadMeasure) import Control.Monad.Bayes.Inference.MCMC (MCMCConfig (..), Proposal (SingleSiteMH)) import Control.Monad.Bayes.Inference.RMSMC (rmsmcBasic) import Control.Monad.Bayes.Inference.SMC ( SMCConfig (SMCConfig, numParticles, numSteps, resampler), smc, ) import Control.Monad.Bayes.Population import Control.Monad.Bayes.Sampler.Strict import Control.Monad.Bayes.Traced hiding (model) import Control.Monad.Bayes.Weighted import Control.Monad.ST (runST) import HMM qualified import LDA qualified import LogReg qualified data Model = LR Int | HMM Int | LDA (Int, Int) deriving stock (Show, Read) parseModel :: String -> Maybe Model parseModel s = case s of 'L' : 'R' : n -> Just $ LR (read n) 'H' : 'M' : 'M' : n -> Just $ HMM (read n) 'L' : 'D' : 'A' : n -> Just $ LDA (5, read n) _ -> Nothing serializeModel :: Model -> Maybe String serializeModel (LR n) = Just $ "LR" ++ show n serializeModel (HMM n) = Just $ "HMM" ++ show n serializeModel (LDA (5, n)) = Just $ "LDA" ++ show n serializeModel (LDA _) = Nothing data Alg = SMC | MH | RMSMC deriving stock (Read, Show, Eq, Ord, Enum, Bounded) getModel :: (MonadMeasure m) => Model -> (Int, m String) getModel model = (size model, program model) where size (LR n) = n size (HMM n) = n size (LDA (d, w)) = d * w program (LR n) = show <$> (LogReg.logisticRegression (runST $ sampleSTfixed (LogReg.syntheticData n))) program (HMM n) = show <$> (HMM.hmm (runST $ sampleSTfixed (HMM.syntheticData n))) program (LDA (d, w)) = show <$> (LDA.lda (runST $ sampleSTfixed (LDA.syntheticData d w))) runAlg :: Model -> Alg -> SamplerIO String runAlg model alg = case alg of SMC -> let n = 100 (k, m) = getModel model in show <$> runPopulationT (smc SMCConfig {numSteps = k, numParticles = n, resampler = resampleSystematic} m) MH -> let t = 100 (_, m) = getModel model in show <$> unweighted (mh t m) RMSMC -> let n = 10 t = 1 (k, m) = getModel model in show <$> runPopulationT (rmsmcBasic MCMCConfig {numMCMCSteps = t, numBurnIn = 0, proposal = SingleSiteMH} (SMCConfig {numSteps = k, numParticles = n, resampler = resampleSystematic}) m) runAlgFixed :: Model -> Alg -> IO String runAlgFixed model alg = sampleIOfixed $ runAlg model alg ================================================ FILE: models/LDA.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} -- LDA model from Anglican -- (https://bitbucket.org/probprog/anglican-white-paper) -- This model is just a toy/reference implementation. -- A more serious one would not store documents as lists of words. -- The point is just to showcase the model module LDA where import Control.Monad qualified as List (replicateM) import Control.Monad.Bayes.Class ( MonadDistribution (categorical, dirichlet, uniformD), MonadMeasure, factor, ) import Control.Monad.Bayes.Sampler.Strict (sampleIOfixed) import Control.Monad.Bayes.Traced (mh) import Control.Monad.Bayes.Weighted (unweighted) import Data.Map qualified as Map import Data.Text (Text, words) import Data.Vector as V (Vector, replicate, (!)) import Data.Vector qualified as V hiding (length, mapM, mapM_) import Numeric.Log (Log (Exp)) import Text.Pretty.Simple (pPrint) import Prelude hiding (words) vocabulary :: [Text] vocabulary = ["bear", "wolf", "python", "prolog"] topics :: [Text] topics = ["topic1", "topic2"] type Documents = [[Text]] documents :: Documents documents = [ words "bear wolf bear wolf bear wolf python wolf bear wolf", words "python prolog python prolog python prolog python prolog python prolog", words "bear wolf bear wolf bear wolf bear wolf bear wolf", words "python prolog python prolog python prolog python prolog python prolog", words "bear wolf bear python bear wolf bear wolf bear wolf" ] wordDistPrior :: (MonadDistribution m) => m (V.Vector Double) wordDistPrior = dirichlet $ V.replicate (length vocabulary) 1 topicDistPrior :: (MonadDistribution m) => m (V.Vector Double) topicDistPrior = dirichlet $ V.replicate (length topics) 1 wordIndex :: Map.Map Text Int wordIndex = Map.fromList $ zip vocabulary [0 ..] lda :: (MonadMeasure m) => Documents -> m (Map.Map Text (V.Vector (Text, Double)), [(Text, V.Vector (Text, Double))]) lda docs = do word_dist_for_topic <- do ts <- List.replicateM (length topics) wordDistPrior return $ Map.fromList $ zip topics ts let obs doc = do topic_dist <- topicDistPrior let f word = do topic <- (fmap (topics !!) . categorical) topic_dist factor $ (Exp . log) $ (word_dist_for_topic Map.! topic) V.! (wordIndex Map.! word) mapM_ f doc return topic_dist td <- mapM obs docs return ( fmap (V.zip (V.fromList vocabulary)) word_dist_for_topic, zip (fmap (foldr1 (\x y -> x <> " " <> y)) docs) (fmap (V.zip $ V.fromList ["topic1", "topic2"]) td) ) syntheticData :: (MonadDistribution m) => Int -> Int -> m [[Text]] syntheticData d w = List.replicateM d (List.replicateM w syntheticWord) where syntheticWord = uniformD vocabulary runLDA :: IO () runLDA = do s <- sampleIOfixed $ unweighted $ mh 1000 $ lda documents pPrint $ take 1 s ================================================ FILE: models/LogReg.hs ================================================ {-# LANGUAGE BlockArguments #-} -- Logistic regression model from Anglican -- (https://bitbucket.org/probprog/anglican-white-paper) module LogReg (logisticRegression, syntheticData, xs, labels) where import Control.Monad (replicateM) import Control.Monad.Bayes.Class ( MonadDistribution (bernoulli, gamma, normal, uniform), MonadMeasure, factor, ) import Numeric.Log (Log (Exp)) logisticRegression :: (MonadMeasure m) => [(Double, Bool)] -> m Double logisticRegression dat = do m <- normal 0 1 b <- normal 0 1 sigma <- gamma 1 1 let y x = normal (m * x + b) sigma sigmoid x = y x >>= \t -> return $ 1 / (1 + exp (-t)) obs x label = do p <- sigmoid x factor $ (Exp . log) $ if label then p else 1 - p mapM_ (uncurry obs) dat sigmoid 8 -- make a synthetic dataset by randomly choosing input-label pairs syntheticData :: (MonadDistribution m) => Int -> m [(Double, Bool)] syntheticData n = replicateM n do x <- uniform (-1) 1 label <- bernoulli 0.5 return (x, label) -- a tiny test dataset, for sanity-checking xs :: [Double] xs = [-10, -5, 2, 6, 10] labels :: [Bool] labels = [False, False, True, True, True] ================================================ FILE: models/NestedInference.hs ================================================ {-# LANGUAGE BlockArguments #-} module NestedInference where import Control.Monad.Bayes.Class (MonadDistribution (uniformD), MonadMeasure, factor) import Control.Monad.Bayes.Enumerator (mass) import Numeric.Log (Log (Exp)) data Utterance = ASquare | AShape deriving (Eq, Show, Ord) data State = Square | Circle deriving (Eq, Show, Ord) data Action = Speak Utterance | DoNothing deriving (Eq, Show, Ord) -- | uniformly likely to say any true utterance to convey the given state truthfulAgent :: (MonadDistribution m) => State -> m Action truthfulAgent state = uniformD case state of Square -> [Speak ASquare, Speak AShape, DoNothing] Circle -> [Speak AShape, DoNothing] -- | a listener which applies Bayes rule to infer the state -- given an observed action of the other agent listener :: (MonadMeasure m) => Action -> m State listener observedAction = do state <- uniformD [Square, Circle] factor $ log $ Exp $ mass (truthfulAgent state) observedAction return state -- | an agent which produces an action by reasoning about -- how the listener would interpret it informativeAgent :: (MonadMeasure m) => State -> m Action informativeAgent state = do utterance <- uniformD [Speak ASquare, Speak AShape, DoNothing] factor $ log $ Exp $ mass (listener utterance) state return utterance ================================================ FILE: models/NonlinearSSM/Algorithms.hs ================================================ module NonlinearSSM.Algorithms where import Control.Monad.Bayes.Class (MonadDistribution) import Control.Monad.Bayes.Inference.MCMC import Control.Monad.Bayes.Inference.PMMH as PMMH (pmmh) import Control.Monad.Bayes.Inference.RMSMC (rmsmc, rmsmcBasic, rmsmcDynamic) import Control.Monad.Bayes.Inference.SMC import Control.Monad.Bayes.Inference.SMC2 as SMC2 (smc2) import Control.Monad.Bayes.Population import Control.Monad.Bayes.Weighted (unweighted) import NonlinearSSM data Alg = SMC | RMSMC | RMSMCDynamic | RMSMCBasic | PMMH | SMC2 deriving (Show, Read, Eq, Ord, Enum, Bounded) algs :: [Alg] algs = [minBound .. maxBound] type SSMData = [Double] t :: Int t = 5 -- FIXME refactor such that it can be reused in ssm benchmark runAlgFixed :: (MonadDistribution m) => SSMData -> Alg -> m String runAlgFixed ys SMC = fmap show $ runPopulationT $ smc SMCConfig {numSteps = t, numParticles = 10, resampler = resampleMultinomial} (param >>= model ys) runAlgFixed ys RMSMC = fmap show $ runPopulationT $ rmsmc MCMCConfig {numMCMCSteps = 10, numBurnIn = 0, proposal = SingleSiteMH} SMCConfig {numSteps = t, numParticles = 10, resampler = resampleSystematic} (param >>= model ys) runAlgFixed ys RMSMCBasic = fmap show $ runPopulationT $ rmsmcBasic MCMCConfig {numMCMCSteps = 10, numBurnIn = 0, proposal = SingleSiteMH} SMCConfig {numSteps = t, numParticles = 10, resampler = resampleSystematic} (param >>= model ys) runAlgFixed ys RMSMCDynamic = fmap show $ runPopulationT $ rmsmcDynamic MCMCConfig {numMCMCSteps = 10, numBurnIn = 0, proposal = SingleSiteMH} SMCConfig {numSteps = t, numParticles = 10, resampler = resampleSystematic} (param >>= model ys) runAlgFixed ys PMMH = fmap show $ unweighted $ pmmh MCMCConfig {numMCMCSteps = 2, numBurnIn = 0, proposal = SingleSiteMH} SMCConfig {numSteps = t, numParticles = 3, resampler = resampleSystematic} param (model ys) runAlgFixed ys SMC2 = fmap show $ runPopulationT $ smc2 t 3 2 1 param (model ys) ================================================ FILE: models/NonlinearSSM.hs ================================================ module NonlinearSSM where import Control.Monad.Bayes.Class ( MonadDistribution (gamma, normal), MonadMeasure, factor, normalPdf, ) param :: (MonadDistribution m) => m (Double, Double) param = do let a = 0.01 let b = 0.01 precX <- gamma a b let sigmaX = 1 / sqrt precX precY <- gamma a b let sigmaY = 1 / sqrt precY return (sigmaX, sigmaY) mean :: Double -> Int -> Double mean x n = 0.5 * x + 25 * x / (1 + x * x) + 8 * cos (1.2 * fromIntegral n) -- | A nonlinear series model from Doucet et al. (2000) -- "On sequential Monte Carlo sampling methods" section VI.B model :: (MonadMeasure m) => -- | observed data [Double] -> -- | prior on the parameters (Double, Double) -> -- | list of latent states from t=1 m [Double] model obs (sigmaX, sigmaY) = do let sq x = x * x simulate [] _ acc = return acc simulate (y : ys) x acc = do let n = length acc x' <- normal (mean x n) sigmaX factor $ normalPdf (sq x' / 20) sigmaY y simulate ys x' (x' : acc) x0 <- normal 0 (sqrt 5) xs <- simulate obs x0 [] return $ reverse xs generateData :: (MonadDistribution m) => -- | T Int -> -- | list of latent and observable states from t=1 m [(Double, Double)] generateData t = do (sigmaX, sigmaY) <- param let sq x = x * x simulate 0 _ acc = return acc simulate k x acc = do let n = length acc x' <- normal (mean x n) sigmaX y' <- normal (sq x' / 20) sigmaY simulate (k - 1) x' ((x', y') : acc) x0 <- normal 0 (sqrt 5) xys <- simulate t x0 [] return $ reverse xys ================================================ FILE: models/Sprinkler.hs ================================================ module Sprinkler (hard, soft) where import Control.Monad (when) import Control.Monad.Bayes.Class hard :: (MonadMeasure m) => m Bool hard = do rain <- bernoulli 0.3 sprinkler <- bernoulli $ if rain then 0.1 else 0.4 wet <- bernoulli $ case (rain, sprinkler) of (True, True) -> 0.98 (True, False) -> 0.8 (False, True) -> 0.9 (False, False) -> 0.0 condition (not wet) return rain soft :: (MonadMeasure m) => m Bool soft = do rain <- bernoulli 0.3 when rain (factor 0.2) sprinkler <- bernoulli $ if rain then 0.1 else 0.4 when sprinkler (factor 0.1) return rain ================================================ FILE: models/StrictlySmallerSupport.hs ================================================ -- A model in which a random value switches between -- two distributions, one with a support strictly -- smaller than the other. module StrictlySmallerSupport (model) where import Control.Monad.Bayes.Class model :: (MonadDistribution m) => m Bool model = do x <- bernoulli 0.5 _ <- uniformD (if x then [1, 2] else [1, 2, 3, 4] :: [Int]) return x ================================================ FILE: monad-bayes.cabal ================================================ cabal-version: 2.2 name: monad-bayes version: 1.3.0.5 license: MIT license-file: LICENSE.md copyright: 2015-2020 Adam Scibior maintainer: dominic.steinitz@tweag.io author: Adam Scibior stability: experimental tested-with: GHC ==9.0 || ==9.2 || ==9.4 || ==9.6 || ==9.8 || ==9.10 homepage: http://github.com/tweag/monad-bayes#readme bug-reports: https://github.com/tweag/monad-bayes/issues synopsis: A library for probabilistic programming. description: A library for probabilistic programming using probability monads. The emphasis is on composition of inference algorithms implemented in terms of monad transformers. Please refer to the [documentation](https://monad-bayes.netlify.app/) for a quick-start user guide and a reference overview of how it all works" and the included [README](#readme). category: Statistics build-type: Simple extra-doc-files: CHANGELOG.md README.md data-files: test/fixtures/*.txt source-repository head type: git location: https://github.com/tweag/monad-bayes.git flag dev description: Turn on development settings. default: False manual: True common deps build-depends: , base >=4.15 && <4.22 , brick ^>=2.10 , containers >=0.6 && <0.8 , foldl ^>=1.4 , free ^>=5.2 , histogram-fill ^>=0.9 , ieee754 ^>=0.8.0 , integration ^>=0.2 , lens >=5.2 && <5.4 , linear >=1.22 && <1.24 , log-domain >=0.12 && <0.14 , math-functions >=0.2.1 && <0.4 , matrix ^>=0.3 , monad-coroutine ^>=0.9.0 , monad-extras ^>=0.6 , mtl >=2.2.2 && <2.4 , mwc-random >=0.13.6 && <0.16 , pipes ^>=4.3 , pretty-simple ^>=4.1 , primitive >=0.8 && <0.10 , random ^>=1.2 , safe ^>=0.3.17 , scientific ^>=0.3 , statistics >=0.14.0 && <0.17 , text >=1.2 && <2.2 , transformers >=0.5.6 && <0.7 , vector >=0.12.0 && <0.14 , vty >=6.4 && <6.6 , vty-unix ^>=0.2.0.0 common test-deps build-depends: , abstract-par ^>=0.3 , criterion >=1.5 && <1.7 , directory ^>=1.3 , hspec >=2.10 && <2.12 , monad-bayes , optparse-applicative >=0.17 && <0.19 , process ^>=1.6 , QuickCheck >=2.14 && <2.16 , time >=1.9 && <1.13 , typed-process ^>=0.2 autogen-modules: Paths_monad_bayes other-modules: Paths_monad_bayes library import: deps exposed-modules: Control.Monad.Bayes.Class Control.Monad.Bayes.Density.Free Control.Monad.Bayes.Density.State Control.Monad.Bayes.Enumerator Control.Monad.Bayes.Inference.Lazy.MH Control.Monad.Bayes.Inference.Lazy.WIS Control.Monad.Bayes.Inference.MCMC Control.Monad.Bayes.Inference.PMMH Control.Monad.Bayes.Inference.RMSMC Control.Monad.Bayes.Inference.SMC Control.Monad.Bayes.Inference.SMC2 Control.Monad.Bayes.Inference.TUI Control.Monad.Bayes.Integrator Control.Monad.Bayes.Population Control.Monad.Bayes.Sampler.Lazy Control.Monad.Bayes.Sampler.Strict Control.Monad.Bayes.Sequential.Coroutine Control.Monad.Bayes.Traced Control.Monad.Bayes.Traced.Basic Control.Monad.Bayes.Traced.Dynamic Control.Monad.Bayes.Traced.Static Control.Monad.Bayes.Weighted Math.Integrators.StormerVerlet hs-source-dirs: src other-modules: Control.Monad.Bayes.Traced.Common default-language: Haskell2010 default-extensions: BlockArguments FlexibleContexts ImportQualifiedPost LambdaCase OverloadedStrings TupleSections if flag(dev) ghc-options: -Wall -Werror -Wno-missing-local-signatures -Wno-trustworthy-safe -Wno-missing-import-lists -Wno-implicit-prelude -Wno-name-shadowing -Wno-monomorphism-restriction -Wredundant-constraints else ghc-options: -Wall executable example import: deps, test-deps main-is: Single.hs hs-source-dirs: benchmark models other-modules: Dice Helper HMM LDA LogReg default-language: Haskell2010 if flag(dev) ghc-options: -Wall -Werror -Wcompat -Wincomplete-record-updates -Wincomplete-uni-patterns -Wnoncanonical-monad-instances else ghc-options: -Wall default-extensions: BlockArguments FlexibleContexts ImportQualifiedPost LambdaCase OverloadedStrings TupleSections test-suite monad-bayes-test import: deps, test-deps type: exitcode-stdio-1.0 main-is: Spec.hs hs-source-dirs: test models other-modules: BetaBin ConjugatePriors Helper HMM LDA LogReg NonlinearSSM NonlinearSSM.Algorithms Sprinkler TestAdvanced TestBenchmarks TestDistribution TestEnumerator TestInference TestIntegrator TestPipes TestPopulation TestSampler TestSequential TestSSMFixtures TestStormerVerlet TestWeighted default-language: Haskell2010 if flag(dev) ghc-options: -Wall -Werror -Wno-missing-local-signatures -Wno-unsafe -Wno-missing-import-lists -Wno-implicit-prelude else ghc-options: -Wall default-extensions: BlockArguments FlexibleContexts ImportQualifiedPost LambdaCase OverloadedStrings TupleSections benchmark ssm-bench import: deps, test-deps type: exitcode-stdio-1.0 main-is: SSM.hs hs-source-dirs: models benchmark other-modules: NonlinearSSM NonlinearSSM.Algorithms default-language: Haskell2010 build-depends: , base , monad-bayes , pretty-simple , random benchmark speed-bench import: deps, test-deps type: exitcode-stdio-1.0 main-is: Speed.hs hs-source-dirs: models benchmark other-modules: HMM LDA LogReg default-language: Haskell2010 if flag(dev) ghc-options: -Wall -Werror -Wno-missing-local-signatures -Wno-unsafe -Wno-missing-import-lists -Wno-implicit-prelude else ghc-options: -Wall default-extensions: BlockArguments FlexibleContexts ImportQualifiedPost LambdaCase OverloadedStrings TupleSections ================================================ FILE: notebooks/Implementation.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "6b982b5d-666e-4175-a3d1-a5ee502e68a9", "metadata": {}, "source": [ "# The design and implementation of Monad-Bayes\n", "\n", "*This notebook assumes the reader has a working knowledge of Haskell, or at least strongly typed functional programming*. In particular, it won't explain what a monad is, what a monad transformer is, or what a typeclass is.\n", "\n", "Monad-Bayes is, in typical Haskell fashion, very laconic. Its core capabilities are defined in very few lines of code, which belies its sophistication. The goal of this notebook is to explain how the library works.\n", "\n", "As usual with Haskell, the key to understanding things is to look at the types. \n" ] }, { "cell_type": "markdown", "id": "71734104-9ff6-4264-93c5-ce99087d65c8", "metadata": {}, "source": [ "# 1. Probability distributions form a monad\n", "\n", "It's easiest to see what the *monad of probability distributions* looks like in the discrete case. In the discrete case, a distribution is the following:\n", "\n", "`type Distribution a = Set (a, Double)`\n", "\n", "So for instance, a distribution over `String`s is a set of strings along with their probability mass. The masses should collectively sum to `1` and individually be non-negative. \n", "\n", "`Distribution` can be made an instance of the `Monad` typeclass. How?\n", "\n", "todo\n" ] }, { "cell_type": "markdown", "id": "1d5dfde5-3e9f-40ca-b877-e150cea063d2", "metadata": {}, "source": [ "# 2. There are many different probability monads\n", "\n", "The same idea of a distribution as a monad carries over to continuous distributions, but in this case, our concrete representation is not going to be a set of pairs of elements in the support and their weights.\n", "\n", "In fact, the **central idea of Monad-bayes** is that there is a variety of implementations of the probability monad, and the choice depends on the inference algorithm we want. Monad-bayes offers a typeclass of distributions `MonadDistribution` and a typeclass for unnormalized distributions `MonadMeasure`, and then offers many instances of both.\n", "\n", "To see this, we'll take one program and interpret it with different instances of `MonadDistribution` and/or `MonadMeasure`:" ] }, { "cell_type": "markdown", "id": "91763a0c-afd7-49a0-bb53-ef4e26719c00", "metadata": {}, "source": [ "# MonadDistribution\n", "\n", "the class definition\n", "\n", "So to be an instance of `MonadDistribution`, you need to say how you define a uniform distribution over the unit interval. " ] }, { "cell_type": "code", "execution_count": 3, "id": "6310e977-70c3-4240-9e17-15f96447c176", "metadata": {}, "outputs": [], "source": [ "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Population\n", "import Control.Monad.Bayes.Sampler\n", "import Control.Monad\n", "import Control.Monad.Bayes.Weighted\n", "import Data.List" ] }, { "cell_type": "code", "execution_count": 7, "id": "91820eb8-28fa-4bca-9e90-324bf8e32ff3", "metadata": {}, "outputs": [], "source": [ "program :: MonadDistribution m => m Double\n", "program = do \n", " x <- random\n", " normal x 1" ] }, { "cell_type": "markdown", "id": "1e3dd003-b1bc-4482-add0-49a60f4fab75", "metadata": {}, "source": [ "`program` represents a distribution, defined using monadic control flow. You might describe it as a hierarchical distribution, where you have a normal distribution whose mean parameter is drawn from a uniform unit interval which is marginalized out.\n", "\n", "The first instance of `MonadDistribution` we'll introduce is `SamplerIO`, which is a random number generator:" ] }, { "cell_type": "code", "execution_count": 8, "id": "bd3212d0-7c69-41f3-b7dc-cf14b45e45c6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.1194250440980824" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampleIO program" ] }, { "cell_type": "code", "execution_count": null, "id": "f2dabe42-125f-46e8-ba52-7192bc463306", "metadata": {}, "outputs": [], "source": [ "A second example of `MonadDistribution`:\n", " integrator\n", " \n", "transformers\n", "\n", "stateT. show traces\n" ] }, { "cell_type": "markdown", "id": "09c7c3c4-a88a-40c7-aaf4-f2e831a4426f", "metadata": {}, "source": [ "# MonadMeasure\n", "\n", "the typeclass\n", "\n", "`WeightedT`\n", "`PopulationT`\n", "`SequentialT`" ] }, { "cell_type": "code", "execution_count": 2, "id": "3d35d6a0-6b3c-4812-81cf-2d3bfcb98923", "metadata": {}, "outputs": [], "source": [ "program :: MonadMeasure m => m Double\n", "program = do\n", " x <- random\n", " factor (normalPdf 0 1 x)\n", " y <- random\n", " factor (normalPdf 0 1 y)\n", " return (x+y)" ] }, { "cell_type": "code", "execution_count": 46, "id": "d39aec4a-9670-4d0d-9571-6acfc0dad256", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.7186997078194988,0.13968662310676447)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampleIO $ runWeightedT program " ] }, { "cell_type": "code", "execution_count": 87, "id": "ca3b4501-4c6d-45ac-9e87-287aa5b887ca", "metadata": {}, "outputs": [], "source": [ "\n", "d = do\n", " x <- random\n", " factor (normalPdf 0 1 x)\n", " y <- random\n", " factor (normalPdf 0 1 (x+y))\n", " return (x+y) \n", " \n", " \n", "y <- sampleIO $ runPopulationT $ do \n", " x <- resampleMultinomial $ (spawn 10 >> d)\n", " factor (normalPdf 0 1 x)\n", " return x\n" ] }, { "cell_type": "code", "execution_count": 101, "id": "e7840f35-c91b-4a9f-b5ff-4e3cfdc74a4d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Redundant $
Found:
proper $ (spawn 2 >> random)
Why Not:
proper (spawn 2 >> random)
" ], "text/plain": [ "Line 1: Redundant $\n", "Found:\n", "proper $ (spawn 2 >> random)\n", "Why not:\n", "proper (spawn 2 >> random)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[(0.8860019925375079,1.0),(0.5033782588382129,1.0),(0.31137526868154797,1.0),(5.451319701546942e-2,1.0),(0.3872285285958025,1.0),(0.3834005992613564,1.0),(0.3141355914598035,1.0),(0.6585424700601658,1.0),(0.5176716807111276,1.0),(0.5625227391260524,1.0)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampleIO $ replicateM 10 $ runWeightedT $ proper $ (spawn 2 >> random)" ] }, { "cell_type": "markdown", "id": "7ad288a9-5962-4baf-a5af-2a6ea6f8ce59", "metadata": {}, "source": [ "# 3. We can construct probability monads modularly, with transformers" ] }, { "cell_type": "code", "execution_count": null, "id": "ca59cfbf-5f30-49c2-86e7-276a0baf3535", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "46b2d45c-fcde-4dbc-8545-dfed14d82abc", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/_build/_page/Introduction/html/_sphinx_design_static/design-tabs.js ================================================ var sd_labels_by_text = {}; function ready() { const li = document.getElementsByClassName("sd-tab-label"); for (const label of li) { syncId = label.getAttribute("data-sync-id"); if (syncId) { label.onclick = onLabelClick; if (!sd_labels_by_text[syncId]) { sd_labels_by_text[syncId] = []; } sd_labels_by_text[syncId].push(label); } } } function onLabelClick() { // Activate other inputs with the same sync id. syncId = this.getAttribute("data-sync-id"); for (label of sd_labels_by_text[syncId]) { if (label === this) continue; label.previousElementSibling.checked = true; } window.localStorage.setItem("sphinx-design-last-tab", syncId); } document.addEventListener("DOMContentLoaded", ready, false); ================================================ FILE: notebooks/examples/ClassicalPhysics.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "dcc50eb1-32fa-4619-b677-c1e1b81a9202", "metadata": {}, "source": [ "# Physics simulator with uncertainty" ] }, { "cell_type": "code", "execution_count": 1, "id": "dea0401b-5fdb-47a3-b3ae-5d1dff9d5898", "metadata": {}, "outputs": [], "source": [ "import Numeric.Hamilton (stepHam, System, Phase (Phs, phsPositions, phsMomenta), mkSystem', Config (cfgPositions, cfgVelocities, Cfg), toPhase)\n", "import GHC.TypeLits (KnownNat)\n", "import Numeric.LinearAlgebra.Static hiding ((#))\n", "import qualified Data.Vector.Sized as V\n", "import Control.Monad.Bayes.Class\n", "import Data.Maybe (fromMaybe)\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad\n", "import Diagrams.Prelude hiding (normal)\n", "import Diagrams.Prelude (V2(..), (#))\n", "import qualified Diagrams.Backend.Cairo as C\n", "import Data.List\n", "\n", "\n", ":e PatternSynonyms\n", ":e ViewPatterns\n", ":e RankNTypes\n", ":e DataKinds\n", ":e FlexibleContexts\n", ":e GADTs\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "30af4ead-38c3-4ca3-b5a9-d157edd00951", "metadata": {}, "outputs": [], "source": [ "\n", "pattern V1 :: a -> V.Vector 1 a\n", "pattern V1 x <- (V.head->x)\n", " where\n", " V1 x = V.singleton x\n", "\n", "type V2 = V.Vector 2\n", "pattern V2 :: a -> a -> V2 a\n", "pattern V2 x y <- (V.toList->[x,y])\n", " where\n", " V2 x y = V.fromTuple (x, y)\n", "\n", "fromDouble :: Double -> R 1\n", "fromDouble = fromList . (:[])\n", "\n", "-- couldn't find the right conversion function...\n", "toDouble :: R 1 -> Double \n", "toDouble = read . take 8 . tail . show" ] }, { "cell_type": "markdown", "id": "c8139623-cfd8-4423-97ad-2a88011d21b7", "metadata": {}, "source": [ "There's a number of ways a physics simulator could be used in a probabilistic context. The simplest is to have a prior over initial states, and evolve a system forward.\n", "\n", "As an example, here we use the `hamilton` classical physics engine in Haskell, which is a very simple, but coordinate independent, physics simulator.\n", "\n", "We can define a 1D harmonic oscillator like:" ] }, { "cell_type": "code", "execution_count": 3, "id": "3cae8752-1b16-4998-9b4c-0e0e6c0471ff", "metadata": {}, "outputs": [], "source": [ "system1 :: (System 1 1)\n", "system1 = mkSystem' 1 \n", " id \n", " \n", " (\\(V1 x) -> x ** 2 / 2)\n", " \n", "system2 :: (System 1 1)\n", "system2 = mkSystem' 1 \n", " id \n", " \n", " (\\(V1 x) -> x ** 2)\n", " \n", "-- we could have uncertainty over the Hamiltonian itself, but let's not for now\n", "system = uniformD [system1]" ] }, { "cell_type": "markdown", "id": "53bc8bc9-19a5-4b2b-bf81-e83e296f90b6", "metadata": {}, "source": [ "and an initial distribution over phase space:" ] }, { "cell_type": "code", "execution_count": 4, "id": "9406bef5-212c-4175-a56c-659aaa3f6657", "metadata": {}, "outputs": [], "source": [ "phase :: MonadDistribution m => m (Phase 1)\n", "phase = do\n", " pos <- (20*) <$> random\n", " return $ Phs {phsPositions = fromDouble pos, phsMomenta = 10}" ] }, { "cell_type": "markdown", "id": "91019986-f4d6-46ff-b7e1-8e9a148e30f0", "metadata": {}, "source": [ "`pushforward` is then a distribution over paths of the system, numerically evolved forward according to the oscillator Hamiltonian." ] }, { "cell_type": "code", "execution_count": 5, "id": "af70a4e0-4095-4e9f-91d0-4d8b10bb44dd", "metadata": {}, "outputs": [], "source": [ "pushforward :: MonadDistribution m => m [Double]\n", "pushforward = do\n", " initial <- phase\n", " sys <- system\n", " let new = [stepHam stepSize sys initial | stepSize <- [0.1,0.2..10]]\n", " return (toDouble . phsPositions <$> initial:new)\n", " \n", "pairs <- sampler $ replicateM 30 pushforward" ] }, { "cell_type": "code", "execution_count": 6, "id": "d3c167ca-fae2-4b69-bcdb-07f2f30eb9eb", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "toD x = translateX x $ circle 1\n", "diagram $ vsep 0.1 $ [mconcat $ toD <$> x | x <- transpose pairs]" ] }, { "cell_type": "markdown", "id": "19b4ab08-2274-42f9-a461-e1512f8dacf5", "metadata": {}, "source": [ "Time is the y-axis of this diagram, going downwards. Each circle is a sampled position of the oscillating particle. You can see that this has the nice property that all the uncertainty disappears at certain points, which is exactly what you'd expect from an oscillator." ] }, { "cell_type": "markdown", "id": "41685e86-7e6a-4d75-a376-7d1a2be262fa", "metadata": {}, "source": [ "" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/examples/Diagrams.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4a04aca6-ee17-4e97-89a2-682e428f216e", "metadata": {}, "outputs": [], "source": [ "import Diagrams.Prelude hiding (normal, Measure)\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Enumerator\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import qualified Control.Monad.Bayes.Sampler.Lazy as L\n", "import Control.Monad.Bayes.Traced.Static\n", "import Control.Monad.Bayes.Inference.MCMC\n", "import Numeric.Log\n", "import qualified Diagrams.Backend.Cairo as C\n", "import Control.Monad\n", "import qualified Control.Monad.Bayes.Weighted as Weighted\n", "import Control.Monad.Bayes.Density.Free\n", "import Control.Monad.Writer\n", "import Control.Monad.Loops\n", "\n", "{-# LANGUAGE NoMonomorphismRestriction #-}\n", "{-# LANGUAGE FlexibleContexts #-}\n", "{-# LANGUAGE TypeFamilies #-}" ] }, { "cell_type": "markdown", "id": "745dd187-2737-4075-a1e5-def36b220a77", "metadata": {}, "source": [ "\n", "\n", "`Diagram`s, from the `diagrams` package, are representations of 2D diagrams, which can be displayed using a variety of backends. In the spirit of Haskell, `Diagram`s compose in a variety of ways, so to create a complex diagrams, you build it up from simpler parts.\n", "\n", "Because `Diagram`s are first class values in Haskell, we can define distributions over them" ] }, { "cell_type": "code", "execution_count": 2, "id": "55e00c89-fa70-4c16-a9c5-a68fc1c49ed6", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "circles n = do \n", " x <- bernoulli 0.01\n", " if x then return $ mconcat (circle <$> [1..n]) else circles (n + 1)\n", " \n", "sampleIOfixed (diagram <$> circles 1)" ] }, { "cell_type": "markdown", "id": "b0c77024-41d0-4c82-8163-0f7d36faeb82", "metadata": {}, "source": [ "## Viewing traces\n", "\n", "A nice use of diagrams is to view probabilistic program execution traces. Given a `program :: MonadMeasure m => m X`, we can convert it into a sampler of traces and their weights using `getTrace` defined below:" ] }, { "cell_type": "code", "execution_count": 3, "id": "556f2c35-7561-47a1-9949-87b54513aff0", "metadata": {}, "outputs": [], "source": [ "getTrace :: WeightedT (DensityT SamplerIO) a -> SamplerIO ([Double], Log Double)\n", "getTrace m = fmap (\\((x,y),z) -> (z,y) ) $ runWriterT $ runWeightedT $ Weighted.hoist (WriterT . runDensityT []) m" ] }, { "cell_type": "code", "execution_count": 4, "id": "4339a75c-b28a-48fd-a498-15fdd07e6ca3", "metadata": {}, "outputs": [], "source": [ "program = do\n", " x <- beta 1 2\n", " b <- Control.Monad.Bayes.Class.normal (x**2) 1\n", " factor $ normalPdf 0 ( abs b) x\n", " return b" ] }, { "cell_type": "code", "execution_count": 5, "id": "3d9e47b6-fe71-4012-893f-fa8c9ff46bd2", "metadata": {}, "outputs": [], "source": [ "traceDiagram :: ([Double], Log Double) -> Diagram C.B\n", "traceDiagram (trace, weight) = fromVertices [p2 (i,y) | (y,i) <- zip trace [1..]] # opacity (ln $ exp weight) # scaleX 1\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "dcdb1f95-1b10-4db4-8a9b-c4fa8762b6bb", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAFhCAYAAABKyKDUAAAABmJLR0QA/wD/AP+gvaeTAAAgAElEQVR4nOy9W4xlaZbf9TsRkRF5z8j7vSqzqquqq6u7uqene9qe9gxjZhiwZcBcZBCSHxAPPPPqByRAyBIghBHmAQkhLBAyWGCEjMc2A8ae8dxnuqursrq6Lnm/Rt4iMuN+42GtH2udXZFV1bfpGU9u6SjinLPP3t/+9vf911r/9V/fHgEXgcf52ub59nx7vv1x20aD9xOfYZ/RMz77477PH/Y2xMztHT4bAVPAJLAr//r/bmBjCvhTwBbwBLiWr4UfW7Ofb8+3H9220yQcTtY/avvstN8Pss9PGoDgs4HQZ91n+1P2GR7rJ73PTr+Zyteuwd/Jts9avp/JY+wGdo2Afxd4AGwCs/nDeeA2AcoPgaX8/vn2k9/+qHskO+0z3O8H3ecnDT7PmrDDSfuj2Gen/X6S+zyPlmMbUd5sB9xd1PjcJvByPV/k93uAQ/l3BjgLvAQ8GQH/PbACXAV+h/CMTwLHgWngKXAfuJPfLRLA/BTY+DFdbN/+qHk0z72eHx8YDPf7Se/zHHz+5G4TjHu1HXAh5uYENWcn2ovcby9wANif/++hvOETwLnc78YI+Ku5035gHzAHfBv4iADik8DR/MEq4yC8lu8F6IX8TIsgUP/TwvX8JPeBoJD+sPZ5vj3f/iRserczBE0wTQDmbgqEBdzJ9r+A29+PKLphb/6dymPKC88AZwhHd5HA0isj4D8FDhPJugfAaQKU7wPvEoC8mAc7QYD1iABcgXZvnpD8bAVYztdS7rtGgHN/eYwtCri7l/2HDXTPt+fb8+2P9yYoCpp6trsJ7JIWECinGQdct20Cl8SkLQqnfK1TeLUfOAIczONPUsC8kfvuIcQRu4G7BAhPAXMj4K/kF0fzi9v5/kQe8AHwHnA9/1/Jhs/m36X8fD7fH8xG7c/3E9mYNcKjXstjaGF22gTlIXBv7PC5759vz7fn2z9dWwfVSQosJwaf76I82X0Efs0M/gqMgqkguk7g0QrlLK6112r+Zjt/r+phDwG8RwnedzL366zBYrbzIPA6wQdvAJfz77Hc98oI+AXgDeA8cIqgGT7Mhp3IE00QHvN1git+lCfZzO/25t8FAunnslG66Pvyr/vROmA5G7+cFysXM7wBdsBOFIZUyKeBtp+v89wjfr493/4wty7hGnqsfa4736cpb9XXJOMerKG/xxVoxRaBtEfnK5RDKChvtdcE41IzX7sJ8D1MOKHTlIO5SOCmDuZeCqSPEri6C3gfeItI0h0FbhFO7twI+AuEMmIKeA34ap7wbh58NQ9yKE8yTwDt/bxgk3dQFmszj3mXAG1Bb8Sng/MSn5wQ7DeuA3S/wV028lnAW3D2pgxDD98/B+/n25/0bafQv8+9odc65Eh70qv/rr8m2rl65LxBzMGhN7tEAO0i5d32udznrQZhmvCWdw1ebr395s+m8ljLFC7pdU9TdMdk+34m2/ft/OxP53m+S2DoWWAkR2zCbT4P8jqB+p1C0NKsUkA7T3jKtAYu5P+7KGB9QIDyTvrkHwU4f9pmh3Zw7gNj+PnUzocZGwTdy956xud/GKqS59vz7QfdhqA6BNIh2E637/2uvx96uzpB/TXMBxn2D19QnquiAKkDZWHPml+fFWzlb81VdcMhFsn3Crji1SQF+Lb5KYGHq4Ra4kj+f49I0L1O4Nh38/izeby7csRPCaCcbBf5Uu74NA96LN/vz5MuEJ6xALtMWYWh5djOBjzI39yhtMlD8tuO/HGD86dtDspPA+2+307bTnz3J9Emz/nu59sPsn3W0H/ooQqWQwBVWdNVA91THW7rjIf6mwQIbbSXALvRztU391sf/L/TnPhBwHaVwrfNbIPYIpe8l+B0DxLMgLizQeDNI4p6nSAwbi3/7iLw8WD+zn58mVBJ3Cfw6jiBa0rc5kfA/507fwj8Wh7wcJ7kSF7w5Wz8gTzoy4SsbS9hFe4CbwMfEJ713jyGF9PpgVGe42F7edO1jiovOrG+SYU1hgJ2uB29SIHzAgXOHn+bj0u3flTbTgN+p/DrR8V390H6nDL5p2f7fkJ/gaiPp5080e32nXOgjz0B0bHX9+1Rn5GfHuQK4+PR9sPO2lsYVyJ00N0pbzNi5zn/WcG2F1RMMC4r20N5vvspR1O8WqRo2Jv5Vww8kMd8wjjejfJYLxL5tf1E7m0P4/StSb3V/OzpiABQifEVwm3+1Tz5sTzoJPAOUfQxlxd+EfgpglM+mx3zJH//O4T0bTk7UOL6IGUJJvLCFgjX/RHh2Q4HXAez3vluEunu5z5blKXqnnMn6VfbcTpAbw4+d4B0IN/+hM8/6Vj9cyeU9M9O/FoP+Z5VkfZpfPdOtMlz8P7xb8+SUu0UwpvXmB583osFoEAVyssUJP3O8eD7Pm7690NJ1sbg706fMWjvsOjBTdB2LD6LTvhRgu3wuLsp2dpBil7YR2DSbP4/SeDDfQL3bhFRu1H+LJEjm8o2yEkfpRzSaeDVfO3J9s5kW+7m/ivUuj4redwLJDXxNymQPJoNW8sf/wEBvsez4Y8JsL0LfC//J7/7JvCV3HdE0BDfA36XAO9VCnAOtouzI54QgHyZKK2We94c/FVZ4WDwRkLdUAtUDlAWb7Idp8tVJPgd0H2A9gHdiX/bIjjCx0O30Q77/Ci2IR83rOqZaud3v52MhBnfodfdvZ5h0rIbn08yUDsZq77/H8fNfh4ax2Ek1LP6U4yDcacCulEejrPOow6jnj6ehuNv2MeC1xBUhwC7U5TYAXLo3XpO2z0EXIu6djrWZwFbjzEE3GdtjvVpYs4fzpfUgtSFNOfuPM9jQq57h5Lm2rZZwhE9SPSzNMsoj308jwOBfy/m+axEns7fPGVcsbGdx7xAgPg6cH0E/OcUGa3c4kw2GAIgLxMg+YAAScufHxIW5BE1WS8CPw18jqIubgDfIkAdamCpxztEec1mGe/k/tfyQj7r5mDuf6122UPcqM79yP+stpe0yFT7/dbg/5442Inv3Yn/lldyexZgf78A/0n7d89ruPJTfz9cHWp4vqGh8m+/9uH3n8R3fxbA/n4BfhiN7PSb3mc9yuj/d4G///ubIQXgpqc5NNjDSMXxstV+57F7u7rDMdyG/b5TnuFZADvcNC5DwJ1ubfJahqoF72+P6ORsh9xtH/c9ud2LuvSW+/jbaez7/W6KKrB+QXD0fktJjKjc0kMiEjcitg8sQ9Y53KCkb9v5+QHKeMwQZcoHCRrjRu73Qv5+gQDlBUpPPEsA8AHCCdwCVkfAf9g6dCkbc4xAbMXKJujW84TvEOC8lh2+RiXsVik64kI26kSe8FH+9vcIYHcwO+gmsoEn8ve7CVC8DFwidHhLjHuCn/T3k77T+PSEYOez5cBWKAvtTXtWQqR7pz0D3A1DH8TD//tnTloF5UPj8uP0MJ+V4HFCTbXPZyjPDz5On/QwuCdkuxfYgWOn43yS0emUzpDS6p91sPF3o8H/XWY5DN97O7vwf8ifyrF6jcO29ja5j9uwcmvIv9p/Aoy/GdIWnRf271CH21UQtk8w7Nf6LMAdJsc6LTH0lHuBhPf/0wzu0NhOEjhkFH2YojnFpuX8K1WpM6nOd57wUL0e59k0RZvq/Upjkp8JoBaMGG2vEZ71HsKBPZLne5dwUvdTDqZGYzcB0Mdp1MTfIAo1buWJoKrnJJwF1iN5QvngR4Tnep3SFi9SGuL9wCuEoPklIsl3JDvuHkFdXM39nfw9oysoH87PF/Jc380LnWfcGxuGYv2zzwJa8kou1iFAO3mH5dvLFNe7k1fVvc7P8veTZHM7eZ3DxIcvB3wPd58VKTzru0/7O9y8f0MN9+QnfN4BuxtOt+5d7gTAtN8MDWMHw+3B/yPGwX+zfdf3GQJC9xp9CWYCW7+ubpR3Apr+d3Pwu8+69ePCONjbD1NtX8eRnqjGfr0dy/P34ioNnPv0sWYU2atne5967s8auegkKfGSajhAFVLorS7lb1Uq7MlzLxKYdJvAKueEtMl+Au+O5e/WKLCWA54kMOt1AjQnqdySDqeOqmvxXAeu5Odn8prkg/dRErhTxHISK2RBx1/Lxn8X+D/zhC/ngSbyB3uzccfy4C4CtE5Nqo28COmE24QS4/3slAvZkENEJd8b+f8SYQSuUlyNCTitt787nW3YnRd9K4//AWGVPikU6xNumKAYftaBbovitnqduoP1RymlG1ETexgu7vS+A8Fo8OoeUweUZ3kcAvcnJfJ28nb1uoa/2SlKgHGjYwg8pEn6dQ4pALfupUoVWT7fqQABp796knbI3w5BrLe/j69OD/Ts/07vP4vH9yzvUCPUvx9R4b8GYDgOhl5tj0gcZ/b7DOM0Qjd4Q7qjUwnu88PkQ6Qw9BhVW81Sel2o0uEF4h5vDH67QTmG9whQlV7s89Z82FQe0xqHJQqgXyCK285SCgqPvUbg0RkCi0aUAZIH3p3tXMhzXqQc2n15/L3Zp7PA4gj4j/OAU3miv00s9HMoG6Jy4ggBrvP5+dfzIAvEzZ3NizRjuESA4yPCSrxHcChOruU85hsE2JPHukmA8tXszCkq4SZ/c4LwsOVaVrLtbxHUx/vZMUMJ0HDCPWsSftqm1Z5mvLKmh2QWyTzNPus6y24A4PvjgXfadyf+134eArf7dsnTkLrpoNPBfZtxUO2hfecah/RN7+8OQEOjpzHYCdg6eA2TpqN27M7fdmAaepl6oF2z2tcX0LNzcvUw/bPyrz/MZr8Oo6oh3/os7a0g1MG2g6/bdvvNTkmyH7TtwzEyRXG5zufZ/Gw2P3c8rlLg95iYP8v5nfTEnjz+PBFR38/35n/MCenUqWxQqSX1sI/AreNU+fJMnt9knlphKduLFMerYTApZ1LvAAG4qjWMrvdTc39vtv/vjoC/ng1xkK8SeuJL1KIVewngu5gXczcv/nR+5sDdzBMdzkbrspPfPWE8XFjIv9MEuB/P8xla3CToi7ezow1Z9E6PZrteI7z4Y3muRQLI3ycojO9ReuUOOgz+70DUeVAndg89hwkukwKWQ7rSkwNxi/EJbgJyyKH20G9IQwwpiv7ZJ3m7nTuEcWrAa7Bu34Er99v7ogP8sD22yVC3Jz77ADWE7aGxoPhpvP/QsHg/uoc9VCtMtPe04w054Wdx3G470UCG4ENdrNe2033ZieLphmzokcM4WA6BtwP0Thr74e9/VGDbN6m4Pn76PDCJNklxrJ3usyLtPoEPj4k5LPAeJZyvPZTa4X7+bld+rtcLNW/2UgvydK5a/NAozLTfzBO4tEQ4iu57kRAgHCfG8KVs637K8dqX35+m1uhxHvX57HmlWddG+Y/h9woBWHPA7xNJMieSZLkob+WIxPgtAlSVfuxj3AILTLuoCWsof4UA501CAG0HGG6tUlnJy0To0QeSA+EUAcqv5Y07SAxCLZdPHXlAWdrOaT1r2ylchI9zrcN9oQalFTyCc+ebVtoLPs4x6qX34w7D/T6pu6c7au/7sd2GCcQOIHr3va+Hnnw/roDfo4whOD5rG4be/Rz9+w789vUn6V6H0Qd8ejK3J6Q6VdS54KHKYEgL9XY7tnaKemCc7++qiv5/79MeefTxN0yMLVH374fl/zV8fU5PUeNbikDj2K+5t9m1IQS8R5RXCZUIO0ol7OWDn1L5K0HXdjs3rIybzfcm6taoBJ7XYN9stmuyMm5X/uYQoYx4Ic/1PiHJPUdRtKq+VGdoYJYJnFkgWIVzue8a8Nt5La8ByyOCF3ZJS8XJW3kBN6jFKa4TnvCubMB6du7FbOQBAuDu5f4T1JM+9lDeqDfMm+ggXs/ff0CAel/T08Tdruy0JcJY3MoLHSoNtqhqlzMEQB/i48BuUtE1Mu5TdILAuMw4AP6w24gKlfa3l16QnrPGQf21oLATCDgpe8g65Bad6HrnQ6+tc+jd2xsqBr4fnn2b8QTd9ODlhBpqc4cqjM3BsbuR+CTp4E7ff9bE7fe7aXSGdJUhcgdyJ2uPbLoRmBjs3yOP7nWvU7TJTknjz8rZdrqqe+VSIo7L3j4og9n7GGpuO/5WiPllJe084yoZ58NRiq6QvpQWECxH7bduM5Szs0FRDyutLQcoz/cptR7EYcJpU2Hhd1OEZ3uBANk5QoI7CbxJYJsO0hLh4G0Q9/oJhRvnCDnvYUp19lGe83Tuf3lEPKHjxWzIEwq5BU+5EumCeYoIf0x51F/IAz+lquW+nRdwEvgiAYbkzbFjnIwukmEi7lG+VihwXOTjXuUTgh9Wz0z7zsF0iNIZduJey63lMmMqEHUhtoPCZfSGID2cSEPqontYk5ShcdL1KiCttuCmd+OK/j1h0YsvhoqRDq59s329zZ1T3Yk7758NZUudAhgqGDq90qV63XPrRlTqYnvw2514/uGrKzM+yQMfgsengfcQwDsfPjSIbjtRGW56l3KZgpbt7oni7lHvFLUN6aFOldh3XbLnOUyGd7qte3PbFBB7jbZhux3XMauDZfJK6tEwv28m0HrybG/7rTI0q2DtB6WcJly95yPGjVO/J47DPi+U6Krg0tveyM/Mj/VneK7nb07k+eaISP5SHuOn8joe5vtjhGLsbL7XOLh2j7mt94D/dQT8B3kiK0UeZedYJTeRjbiTFzJJgYBP65DO0Lr0QfCY8HK/mx1wLn/n90pA+iQ+SIH2YypcfUoA7j3GQxEH1WK2Uw57o90USxUtbeyPRNFzGVFJRtcXdSCoJ/b/ThkMeUKBRY9hGEr73oHTw35f29RE6bz4j0OtATuD8rP+7wCn1zXkQt2nG4IRO4P9p9EWtOM+S90y/GxIEfWE3tCoPOv6Jgbfdw+zA/dwYfHNdp4RdR/dhpytIXLX2e60dSPXwd8xovfdx/T04BiOVZ0JuXvntuPOPtVYSo9IM0pH6GG6EuNDimboFJBlxccoENTzFXxdJKyDcDfQ9o2L8uhMQRnr7tTYVyvEPNlFAe1Uns8cmF65awl3esK5vEVgi/zwYYJamM3jXyHw7xSBc4ep9SjUJsuhXwP+EZHDmjRZt0Qkt47mTh/lj3+W4GwFqceMD+glyqIu5804QAD4eu53KE++TEnatike2ATOZu57jHEAmmw3SvAcUZ66nvPTPK5W+XFe7K38vt+oXXken8fnykvDx504yfXaemJNAbkZ087HLbaXVnCx7Wtbv9+th3GfdVU6Q9buKQ4B1r/Dbci/Dr0u//6oqJudPPGeUOyf9XYPud3PAuo9kea5VcN0RUn/TacA7HMlVOY/htywwGIE1cGvSwaf5YF3Sqc7Fd2b7VsHz6FHqePjuDFC7Hyz+3uNlg1baSeoPSKoRAu5eiSht2zSXr2uyfstSorWwXv4sqBCatKlFnq0rozNh3Tuy7Y8JRw2o+iX8nWCGif2gRTp7vb/KvX4uHlq2YeHBN35QrZljcCYlTz+eYIZmKJwwdUtTSDeIgQE8wT1MT0C/h2Cw9ibJ1WgPEeA88sEWG3kQW9QpLdAuZvyfhfz/UFqkQtXtj+SHasmb4kCwS55m6T0ykpeJilaRFJdi+okXKY8WSmPUWvHfLZPz9YqJdvgvt5gy6+nKG76MbWE5y6KYplhHJg6X+1k6Rnr/kRsXwL2UMa10yZAKe9zIPryuy3GC1A8/veb4PrjtA0Tb4KroNO1szNt354k7BEPfFyzTe7bVSL2nyAIH0/gdU9Zj1vDspP6o9Mzm1SyS820T6DoVaBGJDt5yrZdB6gnA60wE6gFTb05DfsG44bP+XOIAkvnhYqFZWLe3yUA8iEFVHrCQ+rFoovTebzt1rcCvedRTfGQmFsb2ZYvEhLZA9T9NPruxSid/nLFNYH8aPbNMcLbFWvm8vszwJcIGuJAXoc5tVuEY6uX/IBI+E0QuPsK8GgE/DcEdbCZDbxPoPSr2YGXKY3dVn7v/r3Szk5ZIEDWRYQc2FOD/6G8N7kfPYttCqg8jgm3KapqRg/dsEruZRdxkx9mZwpam619c9Qi9n6/jwqbHIR663LYXq8Jvwd5rvXWfuU5MJ791ksacptQk7pnvLvUa1j9NOQGBW33g5LPOCFp+/2413P+SW2dHxxyhW72l1unIsxb9GRhpz822vut9rudePZh8q4n4Dqn7P478cqddllv+5vfsA39mne145kDWWQ8QTtMnmowHBuPqRyJay1AOUGz+RIbDjMuA5N39SVn3Hnd4WYy/2S+jlDSLx9SIfUgJaJA4AFFRZ4FvkZUxB3MY0sxLFHGQKN2kADTmfz8I0rp8Gpe7xYlEDhE4JGFZiYZJ3Kftyh82SAMwVmqpmIPAdyH8hrujAieYiV/qJv9G9mZP5cdspw/MqF3E/jHBNH8+byIqWzwuexsF5rfncc2PHBRZAefwKDMRD5GDmotL+AJAYZnsxP0orsc5h4Bslu5z6k8xgolk+l61luEoblJVdcImIepSj7X01AG42pL1p6zwzkmKO9UTk1L3PlL/+pVuN5Fn9Ryera7e9B6+S4g0ukTGNe8di9GT6Zz4E/a6xF/dMG5qwp2kpHBuL4axiVjXnvfd8jZdqP2rMjg+6UNhknCobfbPednUS+9mkxvtyuMejTm9Rl2d68exjlux5Ta/l4K3DXBnX+F8XEoaHssx6Wqn6He2XYdJub1OWpBMSM5vXCvpTthjykt9WlCMPBlgh6YpgzKHSKSf4egR00QTuS+RvwPsj+c+wfzXJezT14ltMTmxVayvSbw7hLUq4+X26S8cWnZ/Xkc++jzwMII+M/yjZNRadpbRILtPMWjvJQN3CDA6/eBf5Kd+mo2SL7GbKSd/T5RKLKb4FfOUsT5/uxkNb9zuf8V4uZfyL8uKL9MWLvPU+tQbFOh10PCa7+W5zhHgXIHsxFVbHA/O9zn7OkJQxWp7KMomHt5jEOUTE9v3Kq6OQqct6h1UXvBh8BssqZPfiebnv5exsPGrl+VyxZAOh9p+WYPi6EmfA+Nd7V9XSbU6EQjYxs1EGuD/9Wt/rDbUFbVQddtqD/2uvSuflCwFTy62qX/1evs7ejFHL14wvvy/faJ3p9RluDlmOmyTa+/j5EuddSbhJ05ab27gxTNpVbX69Oodwqtfy+t53KPtmno3W9RUbTqBKPYuwQlasLPHJGKrSUql/M6AbyvMC4CuEd4njeJOX2bKsrYT839l6gyZXW/UqsC6xbhaJ7PfT7I45+j1h5ezfNcbvdlNyF4mCFw9HJe64n8/iiBX6vAb4+Av5QX8hUqk/hC7mgRBdn469l5X8sLegJ8B/jvsoG7CYvxYp4Uws3fR3DNjwmAv5wdfZhaA9mMqotl7MuLupvHvp43ZT9FC0iQv5K/1dK60v4TghT/dQIUX819LfbQg3Sg+ny9O4QRUBK3QWWLT+S5DlBJQ3lpvQfPP00N3EeMP5VEr7mrN+Tcu/bSsE4JjaDpbw9SCcZDFKcuyAvWDhABugv+1XNCeYtWKxla65HtlIDqSZoOjBo9wamv+9DBsIf5XdWw3V5bg5dgO+RsdwLbXtTg9klFCkOVA4PrXhv89dp+mG0IukN6a2hcYVz+1kHTTQPdeeuN/PwAMV5c20GjJQ+tF9qNtOPJc9pH3v+umulR32bua3LQgi/nzy1ivi3m/l2N4UJie4i5d57wfAXV6eyPhwTWvEUA+YM872kCI/YQc2gG+BlC7bCbmFsWeO0hnCoxxrZsUgvGH8x9dre+ep/wdi1nPkcYiTVCe3yL8ZzXC3ktJgPvjoD/kqo6eTEv+rfzB1+jFrBwMt4mwPnzVGj+CPh/ieKQ97LxL+UFn6Zqxn2YnhbVUEAqokuDJrLBL+YxJqlsrbrEhwRA3yEG5ReJgaVlf5Hiqe8Ry2/+Zh7jNAHKxymPt6+QpPj6NmE4PiKkJj4yxcKWI9m2VeoZfmvU2hun8hyGQlp3QXaRKneeoPSVvUbdCSnHpUffE3tDHlOg1vM2rPT/XgG1SU30PuFdXc7svYuW6HH1goOepFTe141GNx5dn+pk13vqXtNwUg8lY7a1a727GsJz2LdTjHvU/bgaDV+d0jGH8aPYhkk0o54uhVQlZFvMtmsc9dh2ol/8a19CJdJm23FgXOXjebrGvkdqArNJ63kqctQoddA+SCW3zrRz6/A8IkB2heKbex5Dj/l4vlwlbaad3wTgAgHkDxhfvMdkvuPgNcJRVNFgAt8ag/35uVXCo7wul0c4mG0ySp0kcOE+gScnqEj/PlFHYc7tNLXm+h5qTeQ50iP+K9nZu3OnL+ZJbhChvei/kJ1wlAKPM1Qou5K/eZ+wSpfyIt/IY+qh7ctOe5/inhw0hghOEMPq1bwZL1AZVGUr0hLz2V61egLBccKCfo4YjOt5wy4T3vJ9KjvtJJklLK7nkm64T1jbjwgv/QZFv0gfjCjpzD0qTBG0O4UhFeOAUIo3XLBarfVhasAIZoZUSuNW2u9Gg/87KKki2ENRT4J1l251MBL4rBzqRRzyl3q0UABv6Czd0oX2XULXqZLOoQ89Ytq9GhanGLbDOIjrzXYKRTDfCWi3Br/toL21w//uv9H+NwHeqwi7p93B33u4RRnMvka2+2t4yc+lujSsShsFVJPOG1T0ZpGF6iIpF5NsC+083hcdCBPTfesJvBOM54E0LE8Ip8YnYVj56pgRvE9THqMOlW2/TcwpH/CwnZ8po3X9iCMUlWjF3SHKweoFIz1xfTePt0VhhQUak5T663C26ffzt9+gjM2I8J7/gLiPYo/Kpkmqovc2gYN3R8C/mDtdIwbkEeAXiayea0+8kAddotQJkuDnCbAeUbK0uez4DwhA3qAoCFdOe0g82+4yxU+daBezQoCTXtZWvtfavUhpkdUD+tBTJSmPsy125pnslDNUBZ9c7hw1yJ5QVvFIduYrxCCxBNyVmW4RgKx17xVTesk3Cc9dS2+mtVcVbbdz95XrHvFxWc+IAtCeDBSETHJ0HXPP8O8E0l0MLzWh5+v90QBoAAV0GJyp4NQAACAASURBVAdJQVmw9Tu15sMVqzpI9mIIE1OdRtAwdW2xW/ege2FFT1yNGPciuyfei2o0Hv2Y/UU7jkZBoO3AOzRofclEE01DisRzrrd9urHvVXF9M1QWTB9Rks8FKmHbtc8wLtmU/tPIS411xUYfe6omOtWgQkND52cTlDMhyPrcNx9t5L1YpdYf/5CYwy8T836dWupSJ2WW8EZVb3id0nXbVGLRea8IQEMmvajXvch4IlbjsERE/icIL9u+2sj2Xs1++QqBH0pd1wkn7goBxPPE/LoxAv58XsBM7vR2Nu4N4F/IH38731/Im3EyO2QXYeEs3DD0dqEL8sIN71codcKfyQ64SwDZe1SJ4csEwFsGqBRks72/n51r2OKF6tGeozyC5dz/WrbxJLVoxzA8l3v2xslh3cn9LxDe8klKGqMk5iYVIm0x/uwss7I3s/0OUr0Y5X8ORqg6exfbVyq30zacIC5sbwjbwXkh/35a8qgXwAjSfUnS7knrhSmF0njoTWwzDqi073tVWq+o2smj7WDp/hvt+PZFpzz83EKDrhzpXnSXofn5aPB7r2un4pOeLNUI9kftTFBhbV+usXPByigt3FEnO0HRMnr0gp1KFyPVJca9dr1Fo9LuBeoVSxvoDKjxtc3+vhdOqKddoDhZ+9dx6zwwYX6M0uV2dURXXDwgMOMugStfJfBmgsCWNUqRpYJEB2mFikBdYMf8zAblIN7I63ktr/s+pcSaz8+OZX8cJKjY41TV7et5DqmQKeqRSNIQRsdK8G62azRHcxe4NCJAxWTa5/MHH+XNPki43SNCrqbHfCUv8p/P3+l2H8yb0RNYDjYLQtazwR8yPtkgrMnbBGDuIrzXLxCWZ7PdHEHsRn62lhd/Ltsnh3iAcdBdpzjuEZUNPZ7v9aS3qbJEPRkF6HJTRyjJzSnGQflh9tHlvKat7IdZigvXY5eGEFAc9CZfDHWhQqqdaIydtkkKOHuYBnWfnIxyfV0KtpNCQPpgWAgiteNvTfRZlSUY6EH3AgYBWvDoag2NetfAes6Nds4OnkMv3+922iYYB92e+NPT7VFA53Q7r+s41qvsErMOghqVznfL6e9qx1vn4xpcQbfr700Sdh5e71mj7HnlnlXwmOzabt9tU5RI57F7PkAFgxVn3lvHFNSj0oxypT6MhPTUBW/xQVrkJPV0nxHl4VuP0FUi3ieT/Sb6pNA0yFfypYTsKAHAdxlPPCoHtFL4K9lfi1QhyWS2SadnnWILdlH5rGOULFgDqEP0hMCP/SPg/yDA79fyi1eyU+VbDxNPaD6fny3myawYeYnSEZ8lgHOGKpy4S3l8dpqDXW7OY+3Nxl4HfovgmtcIQP56dt40tR6pYPRBnmc9O+ZstncPBdrThPU7Q3FLN/L/o3mO07lfD2WdNIZvd7Mf7lDZ3DN5zhfzf8u6VT4oEr+VbTds0mPRA5liXK0gJeBq/k6mziu6YJGvIY0B4wA1w7jCwhDRAd6P74AZAm5PDilFkvfdKVzeYhwUd6IVBFTbKPj18whiGkzb1Nfs6NK6XmlmP3SNdveILeboihGTQ16f1z4xeA3b3WWAPQHp+y4VdGW2zoN3Xr0nUvWCuzHy75Bqklc1KbVGGfjJ1pfkb6Sgevt7XqHnCTb5eK7CBXx0Sna333VKRu/RMdBzHOvZRsuhJyij02kmk9oCsH2ogZCCIfc36pbHPpTnu5z94xxz1TWTgacJ3NlHzTcTrdZNSGmogpog5rORtzQmFC1nZeB5stBjRBDOU/nlfcJiPKV4krnssJcJ0DXj6cU8pipL1P69xPiTM97LBrj0m2GNXo2LN7vAzyRFlH+XSKo9zYs+lxewl7rBLrjxAbWOw4hKHrhIs96DK/Jv5U26S6kJzlCZUSdGTwrpHa4SocZ3CMLdgX4s23cur9UV/8l9rAa6wfhCQsv5ucsEDkMv+TR5cQ3FUE61QHkrKxQV0Cu7upZYkOqaWQd211d2CVqnHT6JNvDaPmnrRRFeq/93pUX3cj23bfM8G+zs/Q71vXL1goGeq7z2kNcV1HuCroN6L5PuNIz/d/7XtQxs42o7JoyDbAf63gfepy4l05u2XzYYNxJek/2hCkLj1SMvr1sP3KIhHQUB+ABViWrVa4/YzE1IVWl4pQDvMo4vruA4wThoWVzSPU7pMb3luTxXB2UjTiker8lHuh0g5rvjA6pa9pv52k2tqXGQwBPvrZ63wKoixP7o6hIXClJk8IV87QbujahyvOuUVm+7XdzbBC2xSHik36TcfN31jwjg+UpezBYhXTubDXbxdzmSI1QRhB3ZkxlODGU0Zlxv5M0bUSsw9UE9T/DZv0aFSOQ+x6mV8wUf+eVVQpr2AVVtY1KvJ6PMHk9S+sv9xM29Qhi1b+V1blC6x88RCU8XGbLNw0Frck+gvpLfD7eDlILkOBVGaqH1uvrx71Eebq8gpPVfn7B6HZ0f7vIlE0HDZI6b3twPs+llGirrrerBOEYEACOG7r1NULSZ3r/jypDdTHoP+7uXZl+ZiFV3a1/Dx8ufe+JTw7c5OJeeVC8ecuuGsqtduppCbluPWVmlToUJVg2MBoT2v5GHXnnXfisj1fs1ipLqsv8swrpJFf30PtRBuEet+71OgO+fJgDpSJ7b8f+IAmCpPek9E/oPCOzp60FIR7hIkDztdcowrRGU7AnKWdmkuOFfJpiBLUp4YE7IKKLTY/bnBoELu/JY94l5PNfuy3FCCOGSEWvA7VF2ziQBcH8f+FuER/tVSjT9CPhV4B9kB/5zeaDvUtbnt7IjvpmfvQ/82Xwdzc65Q3DDDvjZvAkvU8qBRcJaSXwrRZG2WKWAS69RjbOewyIBqr9HhR9a2KnsjDNU9dHx1sYPCenJ7dZpLxGALedqAlD+snPNq9mnbxMllTdyn115jOOE0TpJDAQTNwrTnaxKfq5Ty4guU+Cjh+sCS3JVTvwtyts0E2wW3DWN+8pwclZ60gJQ3/q5rMAyhJX/Wm7HcyL3F4wnwT7Ld0NVgZ5Z1zSbMLQ/DR9NVHbvxBJZPUHP0RNT8ukdTE0GLbXjqbTR+Bkqd8mXi9HY1yYkYWdpXt/0/rwuqMjKZOAEFf25HspEu25ljdvteIb+KjikuYwKz1EqBb1eAXuDStAJqiPGjQ6U8TFyMQfiur6qM4YKp74Q1iaV63HRsHkq+hUgzTG4/sUGgTe38jgC9WT+RqyR6lok5vnPEgZngcChKWq9dtszQdGOCgQsVBlRnL4RgRGMWmOocbMN3BoR60pIJSgRe4so6rifN+RLeYAnBM3wITUhPmwd/bt5QV/LRn2PWM3+l6hKPfma+ewoCfGfJjjcF/Ki9dzM8G8RN1o1g17PBvVoFRMCStnMgN7KG/yI8WKFbUpdoLRmb353K6/1ap7rTL4MfUzWWWF4gwDgk8QA1sJbbn2PAgBDSj0bHxmuVriDjIBi9vcaAc6uYCUfptekPE6vbZ1K7JDfDRcAknuD8cSOHps85VBv6+TrCgDzAN1T6yu/dU98yNF2FULXFXeQksfueuJOWXgdPWmmse6JK/vF5FVP0njcXuhhsYjqFMX9amFh/MkvcxRQ96IHvbxnbaPW1p5MU/VigqtXeR6lxoiRrGH9iPEVDE2o2Yd7iLF6hCqgUk4muK0Q4+0K4VxcpoqWXIvFiNG+1rmQfz5PRXGuu7LeXv5eCkLdrn3Q+3eZMs46CxoAAfg6tU6F/L5zSkdOeucwMWcv5v7v5zHOZZu38pqdWw8JXDhCPWXD5Whd2nOO4pxPUSXYOgLej1XgwxHwCwTQ/kWCerC0WEvq0nWWCwqGcqfb1COsn+ZFzOWJDxKe5XSe5xBhRbV8U5RW+Um7IC3HFLWqkeG8VlfP+FYeb5nyCiTQ+yI6vVJJ9UZPAvWw8wA1UO5TGdslipJwgE5TchWTg05ANY67qEmiDM9QVDA26SGomIgYUQPGMFzJ16PsX6VtCvWtuFMTanUdjK/oZqgrTSI9pJfVCxAEDwFMtYUelWDTQ1lLrfVYlDXJaeuxde2v7eh/e0it1ynvLjBrKGzT0LtUf342X6epUnW9enXOVn0pM6SdU0NsSN7pDMG887c9IWk7vC5VD0Yvgpj76EFvUqBkfsP7ph5Wj9skr1SC/LV5E6vs3MeHJfQ1h43GrCy1LL9704LbZjunXLUcucsNWL0q8PSV2QRW+3KG8TzQEjHH3ycA7jDlEMkZ63A8oB5ZZHRjxaxjUK9cB8d5tS/bc4fK9eyl6gUUJFwmnM83KBrCBL8S2YcUDWFObIUA711UhbGO6bdGFMl+AvjLRJZwNjv4AZX0MtTSU7ibB7OEd4tap+HdbNBZwrq6tsLP53nm8mKuMK4BfEyBqGHoYrZRfkqdcF9oWlC+S+kB1RYLeLbfjLQTXS1v98D2Zmf1waPcRqWGVU1Qi2OrMz5GrcomT9nXAzCM73rQztkZ6uvJ9gm7TfGkDkbB0GSfYaPtEjQPtVcX7GuUDPOhQi85PyOHrihQp60Hs1MfC/w96dZVCL1owKSg0U9P2kF5sj05aSbeTcMlcO1rLw2c0jA9Xdvn2ica2w7S6j5Nhipv7F6dHnMPz72nXVkiSPcVzbq0zfvclSgm/jRg0hKO154j6PvS7k9Xzsi59wjIcT5PLYMpJWSkoxGxHFpj4zE2qOVk9frWKXrGiOEBxQUvEZzsm0R0Pk3M5beIKHuNwJmXqJUXvQ4jaxcD20c5ZC9Sy1U6PnUk7We10UbIGtIJqrT5SH7/ATF3zlPjQqdQo2kuQYA2Gpxg3As2T7BFVtZ9lQBNQ5h/jeJ1F7Mjfod6ZP1x6tHWW9nYER8Po94j6I0twiqsZsf/Yh5nI8/7IbF1eZsemsUbdyk+1MINjYiet96iIfzNPK7gbSijLMs6+SdU9tY1NQyxD1OLGM1Qk1Ga43Fen6XNEvS7CQN0gRo4/lYvdIZaXNpj9bUjXIrPMK5Pls12LAeeA8xrMuyWG+/LWz6igFdeVanQautbFRoTlMeiB6j3KmB0SZpgqyc20drc6Q4nuCH+LDWpNSBdBdKVBJ5HGkcPUFByMtnvDnxXwuuKgyHvLpB2ze9QudA3PfA+wbtBkuLQQKjQ0Knxt2b39SYFD4HVfhN8uwTMye0Y0rAbFWqAe3GIxtaElGPQ+9LnsxI4Vz5TN+yaDK7pe6x9Z5JYKnGRoAzUv58gwFfqc0TM23cJD3g6P5cW1Jj28dSlsPbnLmL+Hcr3OhsmFZ0bLxPOorSe/O80Ae6n8mUBmUm2CYoSUf9sZAcV4doWHbDe5xryLeDaCPjfs3O+Q7jgjwiv+BcIINjKTvlf8sc27uXsHAeOk8Hw08l7k6oH/5BI+v25PP4EYfV+M9ug/lewlyJ4QHBTq9RiOhZd6IkeoybgiFJ0uBCIcjRDVMFNK6lHf5lQPlyixNsnCRXIm9knhl8Cf+cPlexdpdaaOEqFKFrPlTyuYndXmnpMcdoP8jyK449Tk8lzG5prSI9QqgC9Fyg6yVBbSkaaRK7ViSdoC7p6p4bUgqRUSwdIJ4c8n0DXC0u64kCvWDWHbdODcz89YkN56Zt+rdImJuOcDH2BHUtaOx3U5VoaC/tDmkMNaX95zYKDfSo15ETcRQGmwNSjsu12jl70ohc6RUWFU21/2yatJtXlX/tOGughMc7uU5SiD1gwQblO3dstYr5foJJlRiOG3qepoq2+noVj0xzKfPb9eYKPfZEY/6tEdHyJKrg4S/Czykl3UQDW6YxbVB5ngqBXXUlyrvW1c1GVxC8ReSk93zvUE98tZpvO35lom6JyVgLw7WzPrrwWtcNLlKHU6VvKfnhKVaUuA++NiCd0HCAA6AMqUXUQ+GcJT3gvAZR/A/h/8gTniEq8L1LKioNE1vE05R0IloY488Ri9OvU6m7v5Ge/k785mTfhIuXxPia87Bv52zPE4NhLWTxv3gzj5cOCixNTzW7PbM7mTTxHhedP8kZYF75EDG7DnS5nM2SEApknVCn2OuUpy2M7sNapenmjAcFaL9v9pqnyTT1WLbBhpuG5HkQvjXVyOCgNzQXTXlQiCPSikTUKlI1Onrbv+7oWalhNonYOuhc3eDylUYJ3r07r4d4mH49O9Jppv/MlWEIVBmgQe4a+62n7q9MF/r5L2myrYOg1yM/uan+HFILRQ29fVzrQ+lnvvN+vbuQEaRhXQ1jwobGSz3eMGN0KbtsU/efqf+ZXvEaTlHqcnbZxHMhdLzO+zosO3DoBgNeI+diXiHQMrBLYc4laf0VjuZzXfSp/83r+L/UHZVxVmhwD/tXc7ynhKC4RWKbiSqdEIcFBKiezQS1ZcIPAyok83h5qTXMredfb/t+j6NfVvPa3gd0j4N8jHgV9JC/4W9loSewvZ+eYEfzbwP9GWdGjxLoRb2bDLxF8z88QQHmAWstYAN/fbpZyjkvEUpq/mjcFArQ+nx18Jjv+I8qC3cp9Ppc3zYq7F7I9es7TFPXhZO5KAz0pJ/UWBXRqDJeoR7J4k+WoDT8FYBUK0gV6BYbpVuWcJAaGUhrBtqslDP8FDgFHnXRXDXQP0gy5xuhA7u8xvAbP8SSvX7pF7aaDXPrDrLvP7jMhc5QKl7sqwcToMDu+k0ytV591AOvgY7gPdS9VgQgmji294+X2OyewwPv9bp2i8CUt4up6x6glVW2f19ETm16XSUbDXdUd8tlr7SVFt5fKEwiUAuEq46vcadBXKNrJ6FUvvkdUfd0YjYHnMeHlioHmMXRspFWcD+co4NXQyhl/SIDwDMUpOyYUAbxDFUlYD+B+Riu7qOUnVbk4lzU+u4glGb6c13OViPQtpYbADqMCoxApOxffmiQ87X+c1/8i4TA+Af4hgTNqkC3EWslrnaHKozcIh/VngIkR8G8TGtVvEB7qOoHcT6is6pvEZFQ/+xvA/5wX46Q5k8c4mo3W+5slJrMZyBUqU+tvT1Olyx8QoGw5sBbk8wTAakUdyPeoZ+S93C78SZ7nArX6UufoDMkNGVYoWkXeWDLdleH0MOVeraaD8j4Evi0qjOrlpguMly/PUiXSffU4s8qGN25SPibk9AZVfky03/SqNTPMR6lMs16CYXiXfknr3KfA9CCVnFUytU5xgcoD9YgFWEN9i3UMubvRcJK79QIfq9q6VE4j2BUmGi+pApNP9wjP5yYlK4NxMO3ee/dWh9/5u/2UfMyE2zD52FdAm6AMYm+j92noFXtOJVdyvMqkNJ638/pMCAmgcr+G0XrojnmNwH7qEfdywouMKyo0KN5vPWwprOV2LzSkUmT2l303R9Cg1/N6LlBFTqpu9II/Yvx5eFJ7eqIrBNB/g5hDa9SiPOtUtPEyQVfM5r14J/v6IkUliHc9V7JBrUVhJeEcgU/Hs+2TBMg+ovBPmdtuKtrquS8BWSXH/IiwChsEAD6gnt/0e9kZAsVPEUC4n7JWf5egFOYpC/hKHsPSwleo8JD83QpF7JO/fSN/t0mt03mN8ZD4WHbeRrbNYoVJqhx7guKLH+V+KxSIPsnvXqAeSGoY8l6eU4VA51/1/vZTFXpQoKLgXe9rkpKueY1SIteopwi4r3rii/k6yrg3+JTxUH6qHVvvUy9Fj1faweSm7djOPjhORQqG24KfyQY5z7Xs39uUAXAZRD3A/RQP7aQSZGxjLyZZaOft62coMVICZ0GKQCUQCJZGED2bLjCrfhius7BM8aBqP3syURpArtq1FI4zHh0sUYqAJSoJI2WlhE8g0vnYTSlzugFS5aNBcN5YmXaL0t9PUBWbs5Ru3f7X2BlJaCSVVqld17tUd92NBdRTLO5SSwiQf5WTeS3+1nHt/HxAOHz3qMeL6a2r9NmmPNU9BC16qJ1PZdReohDsVcIjnWbcIYGSwZ6n1Are4wlqofpVavGebcaNmjSbckHXhHEJBOfcXmIO6BTM5H4fUWsl6yCpytmf7ZwH3hkB/xFRZniG4lKdmB8Q7vajfP8akcg7Sz02+gqhrPgWUV78kACSM1QBw58lpCf788J/j7Ain8tjbueN+rl874Uov5JvWqIs+Db1fDkJ+fvZke/lb16kyhjVLR9gfI2LV7O9p/K7J3lN17KtyqzUJAp+XY5lKO/Ecx0M+aae3HHSuSiSOuihtGeSME5vUhSJIayifjW7emR651sUGAgyd6iBJJDpwe0mBsgJCjjknaFoAZOSgrxURq+AUmeqtysd0TPxJjMeUhHNIpX07eeW0jA5Krh2UO064l623f/v1U8qNEx+GcZDhbsC0672nZGC3Kcv2yj9oWfqvTBxpzEwspQ2GWWb9EwP5W8dt/eIsa3Do1JGFZCqGzWqpygDqxpEY2Xf6tnqMeuhQ40dIz/liwfbOYc8cY8+pqhChzkiIf+U8ExfoJ4tZ7WfjpperO3uUkXzKYcoY3g4+86xoyJpWMCySs2vDQKLjlCVcUfzPDcoisYI2rY+oObzUwIbb7Z7phe8j8K9w1QeR+fAueUcAFgeAf9J/mgmO0mh+MU80EMCZO/njTmRHXqUmnB6OK5+dJN68sVGvn+BAL2z2dgHBOBN53dmKDvn2yufesj1JC9csbXJDwfYWrbjozzPkewsJ4Whp96MSYLzrX1LBPGu53ojr0Pveh8VWsj7QMnDnHTyu/uoRXvUKRuGPSLAyJsPlQQxI2tZqMqMRcYn525q4WqB2QFgaNgn10MqieekEFTlKqeoJx5Y5uqAtx22X97+PgW8Cuk32jn0yM4yDvwmdtWi38v2dnWCSc+V9jKRBuOltfalnnOfDHLLeqkWnbgaXZcD2s96hD0D3pN7ygAPUMqMDrwaMpNwFlaY2FunnhF5lYo81ojxco5aFEdDL4irEpJ7NIrx+m2DtMaI0lkLWMPkpPx+54StRpOjlZe+n99JD+7P+/dOHuc8ca+lABYoyZtcumPR67EqzvqCw9QawB1one/mX8QJQdfIVsfrHFV4ZcRg9auFLUZl8sGuxzNJPQ36MOEovZ735Vhrt/SD89vIXcWTc8F5eXsE/JuUu3yIUjzczk59Iw/4IQFM83khL1OPUSI7626eQGJdXmSZ8DBXCGvt+bYIcFvNc09TKxS9kZ0t17VGTVovYCZv9PvEoJ1mPJTVi5TekAvtyRK1hVfz/SnCC/0yZSk/pJKEHxI6R1eKk787ShWwzGafKHG5SS0heij77hS1Opuh27cJ/mwx+3C2vVzgyISQCRUpmQ/y9YQYfKeoh5jqpXcvBspT0Nr3BKbenWG3k+0ABaJKDPe2YzrZrPrTaMo5ChadDlDs7loc6smNcm7nyyKDrXYuoxsn4lCJ0PW6ekfqN/WgBKghP28prQki5ZEd+PXovD9SAerVH1P8uslZx8cqpcs2qvAYan+PtmMrdTOK6XTLcM0KjR7t2lWgeA0WG+gBd329x5Wq0kCN8t7dyJcU1Rdyn5uML197ut1LDbseuiD4LiUTk946RYyz40R+6Ch1fw9SyxcIaMo5n+R+lvl3/vdo/l7qboFYI+ch8Kco/HHM3iPmo6B6N/vzjbzez1PrVgjARoLy/Pso9cwapSIx0roB/PoI+K+zMw2p5E/3UU8x/tnsGJUFWtWeGdZbeUS5+Lr2SmBcocmBIWf7djbyHDGx5Wl+OW/kteyQRxTdoOE4np1zl9JE3qcynCbGekHGKmXp9XoEnNv5+RGCsvnTeb4NKnll6eclPr46mqHzCUoTLT+tdf0OJZl7kYgUzjNewfOPqLU79HA8tmHsF/O3JkOhkmxmjTvnCFWGa5283p8hnuXmDip/J4AalRhWeWwnl4Dv4FXv/ZBaPW8pj+e4MPH0mMq8W2xytB3LUmIlTI4jwcetFzhY2TjVjtvLygUtE2t6QxNUAkolwEFqzd2LVIm0Ja6CvFGXHu/eHc5jdZ6UixN4ijK80xR/vkXx370AwUrE7hWa4+hUhMm1h9TSr3L2d6lHefXCKj1Ko8Q1ikaaJEDopWzvU2JObFIKGh0RKbyV9n6ZwIIRVfxktaxqEOmUVWLuOLacq+KQc9NFgC7m76WOPpf3bIt62LDRlzzyI4LSlPpZzfZ1ZcoZ6pl8OkLmbjTA83kfHNs9h7BNRa3y4e+SOuL/inpixiRFuiu6XiG8wYP52T3Cc5MzvUjpYk1uPSS4zwcU+X+UIujlWrTuTwkN8QNqLd8neQxlcHNEcvD/ojioLxBA9gZVFtknk/ykmd2+boATQM5mKtth57xDWa5vEtrDV/N4gski44/wHlZ+OfEvENz3m3kT13P/S5SnuZtIbP50XtPubPf3si3ut0GF1SpAZqkFVax6ggrfBSvDcsHPMFmP1EnooNJjMoKQV5Tfko5SF9zpA+/BNnH/TeYJWtbZdz237XVRGqV8Xt8ZyjN1AnYtsGJ7qCRqX1NAQNIblOu2/TttcoZHqXW3DS8t6ZX/lWqxDy18kZu3bb0Yw/NPUjSMEjw/l1ow79BL3/W8TlCPIjIihHGe2ZyHOSA94C5pm2jXZLJZsL5H3L+zFHVpxLvF+ELx9rnnWaDko0YyvdS7VxFKzz0h5tc6QQHMUICrQ3CFwIazhDLicF7nVWIu/DRFsf4KtYCXBRh7CcB9SqkooKKOrsrpeSGxy5qFubwX8sXmgFaIsWGCbkTM5e/mtVwEpkfAX8+dbgP/hAAHPa/TxPoQ5/Mke4kJsUZ4a9ezcacJ8DDJsEXc4LeBX6eqw05mZ1mJY3WJk+MqATxrlHd4iQDaL+X7Xwf+Zh6bvPDPA3+JsHx6aw7UJ9SggsqWGxY74PcTgHk+95sjKI+PqAWNLgL/MuEFbFGZ65Xsi98mBsZNimft22T+9ufzerTaN6iqxoXc702ioMb2PKHkSCbPXMfBDHf32iw11ZPUKltco8e5h+KxDUOV1untOXE0bL2wwZBWisNJqVzOKj/vsVV8yrIEjJ446hVyepAaWJNoekfSVb2gxfMIll1RIt+rJK978VgSIwAAIABJREFU1NIVVlNaNGSW3LaZ7LSPvEb7Qn2tCgO9ZQHWgoQZ6gECev2+pPx6NZ6qEu/7IeI+K++iXetlKscxQzgAU61PN4mx9fm8TpVDHlfqcC7Pf5IAO5PaWxSP3JOCUh8wrh7Ry95kPCKW03VTWfQugQVfISK/ibyWm1QeQQ/+a1Tlm/P9BcblZb+R13qWAlkBVWfG90YXOhI6GY4zVUcaGRViUrr/kOK1X8t2mAe4R61ZcY7Ag80R8NeyU07kyb5FALKC5sk82EuU1vaXiAFwjwArZSyuWCQFMEVY0l8jlBJX8jcv5DH35XG/RtESi/kbwXuSmDwagS3CmvwGsRD7VaoI4c8SAKaHKYdtRneZyqqOKJ3wFgEk7xNe+EuEd7qfWrHfpNhS9tUXqIcHGsJtUjzy+xTf1MtrDeMl+19mfJFoExCTrb2K3Z1I0iOG3no8N/KvK3adIQafkkKt8Uf5d4NaQFvwNhwWpJS5zVJJp+5ld4UD1CSTu7V/9ZRManq9RhF+p9KiV74JbqoDltoxpU96FaVtE8DUR4/aOZxkem7TVGm93LoJJI17L5WG0nJfI8bNdcafKtErq1STKME8TT1evdMKUkEwviqdyTlBVd5Yr9uy+g+IsTdJ8dE6Ro/y81cpflOPdj8xBy2wkid33PZsv2qm4foVRjQPqZDdHApUotVxJRDb/0+J8XuPGI9fyr8LeV33qSeF2A8nKW9VrfEsZSx0DjRE4pLJcKNW6TGjlwdUtGH+R3GC1Moc5cR8j6g6XiDWa/86Yeh2UwU1NyjprtWx68BvjIhn1u2mFquZzRv69yjeaCV/+Gae9LcI0Pu5fP9uXuAZAmBPUhZ2Oxt3m+BGlWwtUUqGe0Ry7AuUFfPcn6Oy6scphcV9gtN5h8raP8nzn6ZWdztBrcK0i5J/yblBAZeaypt5fleRkst+kL9RHnSWWh7PBJCTVL5onlrYRz5ZMFCudYLSIXfPVNmW3u4RKhx8TADATWoiTeX+htxOjN3EoBj2w83sw1vZLmVUis2hANFijFPE/XVSqlywgtHkmANbL1pOXjDsBQ0mmpYZlzCZjBJo9UbN1N+jpEJ6x+Yt5GblXgU0EygaRT045VOqSu5T91TAV1lyg3oeY69EHHrXvWpN7vJFilvuPK65ikd5boHdJNZLlFrJ+aCSoa8LoTTPMWnS0Qj3LBVO67H//4UFed4FyjCfpRwsjaxGtcsQu5RUfvcgJf3qEjyN0mQ79608ziyxFs1rVMJsnqomlBqQ5pJGkXZzn21q/OvUzDGu8jqfbdXZupnX0aN86SbHr+tbqKC6T8zDdQK/foZaj0ajpLxW+s+x95jA0l8bAf8FtfiGXtaZPPA/IdxsOakJIlQ4SHjOI+BfypP+bp7wFDFg9HiVIEmov0tsXpRA/Tb1CKBT2Xm3KDDenxc/nec8ld+ZALybHfIuAahfzAu9SXky56iMfw9lFZbrYTqQvJn+fj3Pc5kqI9WT0LvoZbNm6T22FWgWqLjJZSo9g+JYTaDZV6cIY/Vintvy8bcJ46Mm+BDFBfcqvK38/ZvEQFQ2+G1i8aXvUoPbxNRpikowSjpLgINRhwZAj5A81/1s13uUB2EGXhrFCi75TRNrvgRyJ9vQexYMXMxGr8Z7JOjNUDSLGlYrwUywSMuYYFumllcVfJWN9TYK+FCcsY6DVZNy9xrZx9TqgnqRK5R6Sb2r3p1gova6J55nKcCEGHsqBWap8auCyFyDSVojJCWW/lX2qJwMynCamFR2Jr+sQsAoTWOwn8AGo1TbdC+P/VOEF7yb8cS+hsfIU1phKvtXTbVe9mr2z5XWF16bVYaHs/+sHFW7/yK1frJSui6765GcUQZ5vy5Sc8Hcicar01fThIf/PxHz4vEI+PcJD9gsvCWE6mqvEiT3ApVM0LKbcPlGdsY7RFh0lADPl/KivdhD2bhvU48imqWysg6wzsUpmfMmPc19TOLJMXVC3eSSlTJXqRDIRIKegxl+EzkmgKyQkq+U0zOMuUIk6a5Qz8xyXQ6tsokbeT55J/nndwmQ6lVdB7J/pXjWsp9msk13sg9PZZ98lRLPL+S1/gGR/FQG5PoBuymJjzrdk0SoeiGv/w4RuXzIOA9qoYHG9WHem2vZn5/P4xynJrjKGZM+y8R9/5CilFy0ReMhSOvZdm+na5gFT1+GvYLyA0r7axLPzPfhdmwBxcnWk4hP8neWJstzQnHPcuXk8c8SVJB8qp6UXPc9AmA6Hz5DGb0Xsg+lLXpVnMZZ58GSb3l8J75RhAZNIN2gvNG+1gSML17Uy/bvU5yyOnyBbbl93vMV68R91vDuIcboYWpFwcOUrvoCMbf3Uw8wlhbSMTIKNUG9N/vJ8WANwVUij7RIYIQafykYOV7vweO8L9KvlqpbXSodZ+SmKuw6NZ5ebeexzfarEeFcO/8l4H+kloLYGAF/hZioH1FLYB7LC1ohspVrxEI/v0KFoBeAfyY75ArhYb1BPXl5igCULxEeqLyxIe08pS0eZUefoTK3DhKB905e2FlKM3iGmLBLVFLNiqk9lBc0n20ypPgoj6ds7xQRVryebT6eHeaNMtR8lO05SoGf0qzbbd9OZ2xQq0kp/7ICbTN/9y7Bd79DGRUlUoZOJlfOZt88JQbEQ2olvJepxM9aXudvEsB6ixgIJqHUtPYJeIZaWW6VGGx/kC9DRA3qKWqtZaOah9lvXyDA6BSlSFH9oCAfSuN9h1o/wIhBjrHLmQSdCaqkXkWDckYBVk/aBJFG1TLkBSpSMby/Qz1h4UG7n0ogPdeRvKddm60XqUcvd3+bAnaVNob3/lYjpDTNhPJDxp2K/e1cVmwJwPLdgpLXLwdqKD9FAaoJZaMGE4QajTUqOtTLVR5pufHjtt8spc23wEij4nHlwB9lO8/k8XUODlIPLtD4ycM6D8xHmTSbyna8S4z5owQ4SrdoyHQipLLIv2ep+Wm043GNrK4Tc+kmMS/fIIzmUcoQyX+rcLlHjOkrue85whN+i1oe4BywNiIAdi9loa5mJ5yjvCclbR8Qq6PNU+GXYmt5nNeyYVcoPug8AdxQKyFpPVQk6NHIUxuqexMf50XpgS1Qz8T7eh7vW8DfodaLeIHwUl+nPFHDKMORm4RnZ5JgMn/z9fydN1wudBfjE+oEVacucDvAp7MPrmR7T1ISnLt5nW9QCZJ5qnjkJrUoyxlKNnc379U56im1VuatUWBnnx/KdkrdyNPJqSstc3EXebvz+fk2tdCJumkpGYHBqjsVMJbvSm8cpWRrj/L61bsKXF3dYAJqjnpk0YP8vUnYF4kJZDGG35l8tLBmp1Jo5YzLjD8w1ftm6C2YKamT6xtRJbbnKEpFhYqJxfvUOgqGtSav5OLlso0ijIK6h+t7uWr3m2zHklbpVYhQPKUvZWhWa5okHxFG46M8zkmqtL4rjaRyoDxvdbHq141gdDzU/KqVP0x4nyco8DfZ1otuHmd7FrJNX6A081Ih662Pb1OrAS7mdXovj1BUlRpgDadAr7zVyNExeYuKTA9Q42Ijzz1JOVaWSt9q/fVT2de/SzhF0lYXs11LI8IL02po5R14ejNqTc3yXSJAeTHfz+aBvfgLFA/yQd6UE4R3vJEdto8CEq2EvMp0NtAM4xcZlxAtU3XmN7JzvpDf9+o3r+skAXhbBCdzO9ukFyV9ILc4R8lyzlMJSMHEhJ9VcyoVlIuZ3DpDEf5qDU2E7CEm64d5zBdy38OUDOcaVe5rie48VeduZlyOepLiqE3cOVHO5e87UHRpm/kBwVWv/yxVVqvhvERFIFIuAqGc5C5qQfBrefwjVAGAelKB/xTjC+mo1e6qhg5QJoZ2Zx/o4fdkjSG0cj1BTC9ZegAKyHQSnMAmIg3J7VP3W6EWl7pCRQ7T1II48sQWOkgZSItYzDJHLeijZ6ZhsRRar9YSbA2I5eb2zUPGK1J1bIyGvFYBz+ozQ+wlikaxAELlhgbJ6EDu+XTeRx2dSYq7foEqZjhI8cly9QK2VMGV7J+fJqIrKS/7DcoBmSNUVMeI8aXqyLJ1gdt7bFJ5k6rcNdLToKqs6DkfuW6jMcHcsWmfdeXX5/L9r2Rbz+f1nMx2nQEWR3nBE1R2fys7zEyj7r813YZQNwgu5j1Kz/cqcXPvU1nXu9QKb3sIb3OKSC4tUQ8KXSY82m51T1FifRN4Cqmh+LS72Tl/Jo/ltTzM/RTG65FaemqyQysqB6WFVMh+K99bWCB/J2UBBbSG73MEHXI/O/urhIetTEd+dg+l+1ykVvmXyzQJJlcnb/WYmPzXKVG+CS8TTSOK75vIY5+nMuDK3uRenVBmkfWyrbZ8kxhYB4gJdI0w5B8S48FIQVrhWN43vQUpBxOnLxATo3Po0gzqLnuiccR4+a/evIUCJnTk29V5PqUWfDpAqVQsQDDyU2M8T01aDYuTVFXMKkUfDDXOhvM6MgJuf9lPVk7C+AL4gi6Me8mrVESlpM4k0F3KcJuUPU+t82JBjImjLiczN3KHGLffohZNF+DlPR0PUmCzeWxVDEZ6L1Ars1m9qcOjBE5p4SPGn6jzhTzGkXZcx/QSEQ2/R+DI3wf+dWL+uwiW3LR0j+oUqAI2nbhTVLk+ea55gh24S9G2Jymn7DgxTqEiLMfpVcoAW3TieaRdLMKZBm6MCNL4TH4pNzRH3MQTlAbOMGSdkm5tEUDwAbUWhfXxhgmvUXzJFqVoUCZnXbeZz+vEhDXbOEtlQrfzgq9Q6xhcIEDOaq1vZkfdIMKAh9Tqalpwb4aW/SG1UJFGRa5Kbq1XXumpGOI4mPW29Ji9MeqT1WRfoJJX09RTQfQErlIKCUNwM+dOHie8FXJKckzKCFaqC9aoRMhT6km528Tku5x9IGBN5LVKM2gAZwk66k1qARW99D8ggNnSeMNpPaIXsk0bFOht5/WdY3zpUeWERg/2bZe2ddWE0ZT0kSWmetMaV1UoTvhpwovSY+seda+A0yu2r1cpWV0vTjA8NXGo2khg7VSDIb6Oh1VlnboTsOcIw6es0/s7oqiPRWLsv04YzLNUklZqpqsZ7lPjw7HaKyXlz23DBlVxe5Jasc9Et7zuMapgS8ejU1Ia2q7HVsN7mCp31mO3xFsVziXGi5p+Ia/VBJ/euGPEv0o+n1LRp8ZTZ9S++Vb+v4eoc7AvVajYZ1J+bxF5nhXCKPwM5XBdpHT8T6nko8n7hyMCrAS5BQIwLEiYIKq7BF0X1jEMepEAtnnqIYIWguynXHQ79FIef4uweK8R1uYuFUKfpDzag5RXcpgaoJvExL+a+z0lBp7hxC8TE3uZoCh+N2+gHuFrxIBxAEBN0PeJ4pPbVHbXsPcElVgxEegiPGahtykr3KuqrKC7n+frelA59/PZLr2bq/maz30v5HVJEfUkjZPBv/J6PYIwrJujFC4nCMN4Mo95Lft2k+JZLTjR6qujJe/jz1IZZzk1B6lyKUtAdxOG/yUKSNzHaOwiVVihYsEqTAsKHlNaXzPna5SXanWbYaTVoipIpEekKaQdZtq5zMRbsv8+VWRziLjvxyl+XCBW8tRVOFZOGuF1aZMVkYbFKjjksaFUAiZIbZf00AzF1cvtrlFFMA8JEDdpa4j+ID/X+5VOOEF5fpZPy5FqMI0spTbMNfj7CYobN7Eq4FmN+Z3sm4uEQ/Va9oNRSjd4qlj2Ux7yS/m+l8ZbtAXltd/O69Y7f5SfaTAsRX9ARASTlMOo967CZ4uYA8uEZ/5bhBPyFeDfIjz57jRIYc7nNZ6kootN4O0R8A8onaiDyRDlPSJTeCoPMEutKqXedQ8BKssE0N7Iz/cRwOFNs+pFlYGyoGPE5H+b4u1OEjf3HxMT2EKRL1OWeD+VcDIpcCzPfSv3v5jvr+e1fIfywl8nDImVXsrZ9PrvZyd/QAHL/bwGxd6qFDQWnR81vNzTziH/KRf4kKpM02hBgO0sRcXM5/6383+LSs5STwHQiLyd+0pxHKfKnQ1LpURW8pi3KOmVig+1xb+dfTBivAJPvaoJtiNE+PsGJdvSYOupyLlZKbdBKTCOM15tZfKmV/PNU162SdNb1IL+05T0TUmX4GwS0CjLJJfJoy6Xs5rPije5RivINLDmFBayLzyeob9e+GrbX1AU9NzXAiDbKgDvp7x1efcF6rFGzgfL0QXf28S4X6QAmHaN8r8LjCdfrcaTmrGy80yeQ065J/72ESD0CrXyouqUNUpCd4eYU9uUWsbo6qU8vl7pI+pxYjonanCNXKXJLBnvi0TJ8VucI+VjJeQa9TxOjfrvZT+9kdd8kwDls4w/fehenu8fEY93myWedPTLxDjWkD4gnNoP8jp/KtuxRElF3wGeqCPWfT9EPYNJQHpKgNe3qQdfQgx+xfn7qKofyxQVgO+nHl+0RK0qJXcmGKtfVn2gRblKhNNrue/LVL27najq4XuUJ/JdIqT4C7nvLeoZVnpBZkf7BNfrNTliUYWJP0tmFeVvUeujdpnQdNtPb0COcaIdV+H5POVBXM/fnKYeC24VoMdcpvj0WUqut0kMoN8hLLWAdyCPJ3d1gfF1Ym8Shuqj7N8vUTrNJ9QjefRAVdoIZAKKAKre0ySGoal9Zh9ppEYU5TBJVY5ZXWaSzIq1Scpr25d9eZUIKb9DrVdyPK9T46OuF8b5XLeuCrBNu6mCFnlc74Ngb6LaSEY+X+/QCi3HgXrWp5SSRXXICjVm9uX7B9Qyql5LNxD+/gnjK6CNqEQvFOc+T4XlRm9QNIwUj0mlOcLru0zN+y8TtMDr2U7xwqITKaP7xPhRMzyd7T+X7TRPYNS0h/HlZHWClgiwPEFU3+3PY9pXjjdFBAuUOkdKxiTjIWrMvEeMe9tzhwDYbeDfIAyMzplR469kO/4isULjSapu4R1iWYcPibn0TQLczSstE3ikOuWugAXjq2otU5NCxQR585TuTBEA8g+pbPdsNnqbCncm87PP5X5qN+WdFvJ3X8mOtHDEwaGKQ/7K9hjK7aeSTMpJVDR8Jy/2l3M/eewHBHicp7K8c5ToWvmTk0eJjR6tiSAzyCZ6FijAVp94lOI9DVWkMeTd9SDuZdvmqDUDTCTKXcqXOVFOUMDnjZWjW6UeN76U1yCIb1EFKp8jjK996URSs+n5HUTyhXpyerpGJtImm1QiRJmZ3OtEe28Iv0R56IK2ipZ5yquVotIYyb2/SXn0gpfXbTWVlWp7qEk7Ry3W9Cjbe5oqgZ1o+z6iChROUo6FiTQprnvEHDCBa7JGSkrDqmOgkVeSpqRsi0o2qWu177eoNXOvUAAsP36MWs+497vGxDaoMOmc8N7sK7XuqioM++2fvZREU7DV0O6ltLs6KhpyIz7lcUbNRs5KPz+gHo+0TAD/RcrbXW/9qdoESoq2TMwjKxZ1HK1iNAozR/MWgRHfIMBzipoLE8T93E94uMcpY3eHkM7+rWzvFwhn5ucJb394j6VfbgG/O6K44C3qZneOS6rChJTiciutHAwWNDygtJF6qx/lcb9OyZemqRDCUNDw6yNqwfB1asGO+1SWeUQNMvWivQ7eTX5HwJrPi79LJeZOU08dkB4x8aPXZOhnMk4eUo/X70wq9Ox990j0GE2i2Ke7BvuZXb6a98hkiPpWI4oFKokml36w/bWPlenMt77pYNg1rnqIJh2dPPKmm3kvrlK8txPoGMVVClR7GS+P1qA9ouRRcqhmu606W2rXrBepeH4ftfaFUYHXvo/ibOW271JVk0/acQ8O2qo3bkLRSXyWolA2KB36UypBeIwCZ8eE13uDMrAqcSaIsfkNwpCcpZ5npoZZPlWeskcpenh6xqqcoBQWcxQH38G4F3ZMZ9uNmvZR6xdbmafG34rYDWqBeMf6EsWlev1GPvL9OlkjxvXOy23/J5TW3sTdeSr6maOqV9XJP6GijEXC05WyVCpnXYDjXqrnUh7jq8Cfp9aQ0WjZRh0AHaIlQjr33xI4KM99HPjF7E/72eteyXPdzs/WR4QLroRkxPgz0BxMTj69UtUVB/KkWlw1d3aq2fHblCdqqGhFVl8DYI56LPYNYqJrkfZTxPl8++0hKgGjrEVPzgyu/NwreQx5154hFnwMy55SGlvLZScIasTjkG3+MF+b1IIzLkJzgMrcC2QOAhUKXVfpTTakNNPek1Nd52qGX9nZ02yXYK206Fj+7lH20e8TdJNJVrWpJgXfJDwPlRedEjDxZbJL3tdSYTeNkWPGfpYyUNIkn6Yn+YBaaWwfFZVY2qpsbVc7lx6rSS5DXJNHM1TEdCP3U6/eeVpf3idBRHWNygU98xnGl6zczGOeoeiBrvpRKqaRl6/uBlMt7WNq9S+rTqG8/F5BaJLQcaRiw7ltlCKfqoc3Qz0UVf54nvKCpSHMDzgeHhEOk0Vbzk9pk812LjloddeOTTHDPtGA2X6T3lNUYtcCHOfUEpUfMFms6sqo5yoxZl6ljOIitUbNFeoByW9QTwvSW+7KnEUKs1YIw/p2fnaEWn7gq5TSyWMYwT0i7rO65xVD8jVqgD2gii3kYdVfzlAL41jJco0Azy+1C7dk1tAHyiI9bjfHhE73CJ0gVsfMUWHJGqWhfZ+YUJuE1XmF4LcNlVYpfale5lOKd3tCURWPqOIIrZyhpkk3PWXLrbcJjuzLVNXOLcKy3qaA9CDlQZgAcfLrLZkZnmV8FTXpIiirrKRJT8/BobG5QXBUv5/3RuNwnFA3/Km8X2avL1NrPxwkvDNDQyMB+1EP020Ppcm1QEPP+zbjUYNaYTnjJ9S4uUAZI69rnVKcOJH1qqFogMft+vWaBWiplMX8TMneESoSu00Ayncoj1nvUJrDJJq5hCfUgi8P8lyvEPTa57L/HuZxr1F89kvtpb56K895Jfedo1Y7lLPcnZ/peUpxSWPY/46N+5SSwVB4KtvV+XyBfCWv7xoxbi7nMV/Ltl6g1oNZIQz5R/ndRcYfl/WY0pb/NBHCT1PjVAdP6ai0guCnkeo5B6We3ybm3ovUErW3KRmp+RCluB/lffhm9ncvM7+d/XSCWob3eN5LaZvlPMYl6rme5ygaxNzFK+26vpJ959x2Xs5TjtIatTQtwLUR8aiknyNA4HGeWGmG1WRDcTvtglepG29m9X7eVHWgJij05AwPu2VURmZmdjvPv0FYLnWHI8Ytp7xk59ImKS+sezZ2jnzqOjEB3s/2blMLTE9QYYjJAAecYGDoDlWyarXabUqhIZjqCRsKyln2QoFNSqPcPTW9TfW0nSZSVbBFJYJ6WCuwqWJQkqc80Psi132DylzLbfe1Cgwz91DgutX20YOUN79LyYvWKUCQCrCyTjVGrzzTk5C3NmM9Q9ESGjZzGfaHHqoUjDKi65RRP0h504bXD/L+XSFASa9yL0X/qKF1MZt1AiTeIkDoGOGBvU7x5Eabcv16vY5dvWsNn5Vf9xmP8qSwBAuNyXVKo6oHa6S3QSXqNOR6rFtUdHiKWiP7QB5Dh+kjYkwfJ6RdGjSN5508vgC+K+/Xd7OPT+VnFsx4n/W0jWR6dDqZ1/8+tezsBLVUaOfOHWsmePdn/4sphyl55V5qUTIpDnMH69nmXyXmgjkScUdaRYnliDDAGh09aNUlKmcsLHolj7VK4M7fHwH/Q354gRg8K9Q6v4qRrYkXiLR+S1R48SQvfpSdtUjIz64z/kiUM9SCOQKS3tADqgR4Ko97gaInXJPCGzBLeU0S4AcowbiKDydip1ksizVcUu3xgPJ+DXHuUEkS1RZ6TEPZlbSE1WQPCA/iUvbFEhUeqR8+TGk1Te6tUiGpCQYBuBd2bDA+ue9R1tuoRmNkdCBgOYlcf8OQCyqMUgfqvVJup0dq4hLGixeMPGhtlvfVc5OfW6AM5HFiws1Sa1fQ+neyfWY/KO6fpOi03W0f76/UmhSQUZMSqRElibtGGSTvvcbmCLUamGHnFkWVGU7riQqsOi5de7uLSoIZ4s9Qk3elncP1JSxQgKIjpikKxGj1LpWE3qb02TNUkU43rib6VDLpsfd7ZC5iRPHHGkt11buoQqaZdj9ViHhfjFJmqFXyDlIc7za1St/XCU94g5hHm5Q81mjFKEVMMFku9XmVksVJm75CAKjChCuEZHeOemr5EWpNEbl1S8mV7h2iaBB19hoJnagvUqqRZar8/3sj4K/mF6eoskSrYd4iiGgX8hCAXFPgFLVpxRwsx6mqq1vZWaob1qiwXQ/pApWltSpribDCZ/IliJn1XKW8sjtUtn0X9ZSNdcKwQE3sJwS3s05UwZykeFnBQq9OoFFMvkZxqnqnJh61lmpmTSQqz9FYSc8oulePaiJSsHQSWS1lueiBdr516mGaGqcR5R2YjJmi1nBQJmTI772yD84QA+8gtayh/KLRjJvGYYLyrvW8LTAxkapSxvtvYktDpqTPkFq6Rl6yJxbtN5OxGpSuyPE+umaGhRoWwAhwUg1rjOtnN9v1qn0XAM2AT1E5CqVwRgv9uns1GwQgWnKsN+x4UCK5m49z452SekxVU/bCHfXXGtpe5CNtp5HuY1QVjWAjJWOiSR7Z/leJYSRqf1rmL62n57dGVYl25cptatw/pu69DsWL1BrFHxHz4Rwxzm5RRmqCGnMvU46AWmrngc7Lq3lc+8X5142gHrPFYleo5+F9k8BL8hpuZv85f1eyzbvzXHrBl4j6jQ+IJO3xEaGH+5DQX97JH/wr+XdEDJx3KM/gAVX+62uGKk7oi2aYJFL+o2dkpZmAdDsbdZSSIJk43ENViCmZe4sYFE+pzKrcsQN5FwHEXyYGy7fzc62zwvDH1GL31unrKRygLJ/VTEp85D+1xEphThIT/zSVkBLsDMcMXZ5Q3pRRg96qHixUtdhyO4ZyHz0XvVQ51RmKWrlLPcNMPbB9f4TgTT+XL42hQGoUoVpEj1IvTJpGjlGDYBXVFuOlr0cpuZeAqb57m3HvfVc7vmstzZzpAAAgAElEQVRJ6I2Y+de4OpluUaueyeE68ZRRyrtKRbiWgkZgm/KC9HydkGa9LTAQkD2WfKserzmTXZScqxu3Ltsy+eq4V8ViuPyAKgQyt/Iwr/cu9Ty3RephmspGbctF4n7LudvH9uUmVU5tsvMUH18aoCeglXI+ovS+jnsjjxez3x3vE9TiRD0BuI+ad7ZRbfwWlbRUr+zCPwLyBaK82Go756YqJbXdqi0WqChrqR1viYpEVdJId7xIrbHd55zOxDwB/HcJg/v57OObhLb/Zn72dWJOLIwIkN2TN+oyAXSrhEXR5V6hlmXsHK6AoCzG6ifD5lvZ8K9RhQN6T7rsVpls5v63qKTV1dznS9RqUUeyrZeI7WHudyLbOk94Ho/z+5cIy3WACJW0Zk6ux3nObxA32UU77uf51BlfoTj0q9QgFQgECr1hPbiT+fJ86ognqPDfySSp33l5vTlDXsFKemijnV+rradooY4yts6hClx9UhtmWlBhgkWBvYUuKgguZ1/sy/63dt+svNl22y6fL6fse69bsJBqUZRvwukE9bDMEfUElusUqBm2dw5egHlCTDLHshyvPL19qvEx6uhGUS/Q+7zePvNeQGX+TVbqrTn2perkDnVULFw5QkV+V7Kf71DG3+u1+qwv5mNyTF59F5HF18nRW1bG5TU6JmYoSafcu7SC8jhB6A6hGrian79EAJXXBjEGNvI6LIPWk9dZWKTWJl6iOFm9eufDFmEkvkU4ZH3thq8RhmeNynM531SVdO2zv71LqMf+HlXY8ecIhuA0Ffm+kG0yp+I9Ve3yHlHIcTN/9+ep52xeI+bMwWyDtNYW8HREAFoPZfqEULh+IDvyQwK4eyLJclYnjWH/dWJCWnb7F4jn3B2jQr/9VPJuiwpjFimu8UkeyyqvbYo+Ue0gb3Sfymheohai2QP8ZcKw/CbxiJJXCYB/iVo57RcIIfYGVU68TRH5Jt3miIHwAZVU1Hjo8eg9amEfUSHoEeqRTXpCD6kJuUx5VfKMs9TiQE5yQ3fDSQe5CS89yJ59lkYxUSJwuhbB9fyNXgOUpK574RYmkG1ZoCgHQ1tL5W9QHKOApmTJEtoDlGd1m1oq9TSVdNKLlj+caffFxaoWWj8LIBYaaMj0hKSHzlCr46lV7bSCiwTJpevx/3+UvdeT5Wlynvecqq6202a6p/1Mm+mZHrd+tQsQCxAeFEQZkkKIEbqQbhi6Em8U+g+k0AUvJIaCipAYihAVcuQFAEqiEQGJMNwFsN7Nzs7smDbTvrq72ldXl9NFfo/ePGdrF9CJqOiuU+f8zGcy33zzzfx1DtxIrGvQLRk20jHqEVnPGm6jgk2yxr45xnCeoDMVNDqX26RZzotkDShDVSGxSfpEKwsUbT8hevSeDJQzV4+rvvhjao8tk8hXtZTyT+WQ18dxPjXGRAcqjfn8uG4jZ6kSmC4CukcZ85tMVyeuERWK63A3KaRaHMexEOQCSSr/GfDPxr2eoh528RmmZXgqoJwv6S1I0cllQre+RO1vuWC5dJ2CKhDGsW5OqFJYVQYmRK6PgbbI4k3yPKYH1AK5OS7mHrVZzpMSWwXg10kI9iMi4XHTKb/ZQyr6VDeYbTw6LvhD0mP0MUE6LtKTZDOY7PiYcgRfG4P/S1SV3cfA3ycVfa+MY75DhQy/Po75HqnFV0pkEkxEvzSO/T3KSb09flfcr5pCPlCUtZ1IjwyV5ZHvk/B3kTwrbaMd6wBBlm4A/9UxaIzWCQqT714hxlu0LkduscAiCZdp86p6Yw8pcrCAwjBf+sBNrjZYnbOJ31luWCXDPFEi/Iha7EZbJsBc9G+QTP8OEmJKUcjvik4PU5l9E6ECifsEYT8a55CeEHiYCJIuciwEFurIJ8SpGo67mU1yQqq6zMArq1sj9JJ8o+OqA/Fc6oulDuW795IkvMcSdbtP5UKlgra38300xn7PGOdVkox6Rp4K03XeOgfzK1fHcTWCHzP9aLYXSQ5lQtaE1+l922yqU4Mb4zhfIJKxruT6cJxP+7RCouclqip4kTzt/OfJXOsUdASQ3AJjjt6jgN4lYiu/QNmTTeKAdpO2mRMioxUwPZpQDwjVqEGUAXeprmVfGzfwKcqQnRoT8n1K4qHnOkgZbHlRYbvIUKT8DkFl3uT8+L4bXZ5VvsoEyOXx9xeIN91NNKtvEaH1KcJTirKvjGs/N67nn473TozPr5Gy6L8+jv11akHeITXrV6nN/+r4riW8IlnDHsPIxwRRGY7sJkba0F698TyRvyyRij3lR2q/5YchCURD2xeoRf48SbzcIE7MyMFjHaOMk3rRCTGi5gc0BEq9aHO2TG1IHZa8rVGEagtDYRUP8uOiN0i4bnFGp3CUe+0kCTHHVD3qUZKY6wkckZ6qFZNd6pzlCneQvgkmQjUS20nirPcAcQ50ZB5Pw6qsSj7TzWj4u0jtRaMsudJDJPF8iiQG5XdFhBAU7FhuI4k1nbYRlsoceW3pRXXIGroXx1iaKHuXWjeHyBNs1AIvUvvJMvtN0jGu2wJlrROSJ1A9YoSgY5QHvkStfbX9rxN9s/TAKqlevEftpZ8j/c6fjjHSKasEkjo0urZCEGKMpa6WKRvxOxTwWh7z9xlKo3+WRGK9aGsX6Vt9jDRDWwMuTIC/QxIRJ0l2WZLcOnKJasl+N/IFQkncGIN8hvCglgbLPVqOqNd3IiTnn1CE9kPK8L9BkgK7SONqUaF83mUqzDhHTfLdMbivMk3UQ7SKy1Sy8nfH396gFvqPxkT+++NYX6c6LV0cg75CIe3TpGLtzJiM40zLu3Q0MK15VoMsZ84Y013kwZNdUiWyM5mkIZuM+zM03EnaaF4dc3SEWmwLJBGjnEn9pk7yyjj3z1LeXSQvT2a4Z0LiPWqTXCNN9HeQWvsX231o3JRkPU+Mmby15e6OlwhcxC/tIVeuegWmVQIifQsYPL9Aw893RUivRtMwiPY0sqKujXa+btT8vvmSO0zTIgKIo+OePqa4ybdJ/xAlYlJNRiRGIRqN7ZRxfmXMmTRVN7SeT+mfc6DSwi50rjPVEK7FVfKILo3TadLK0cKGK2OczhDF0SHSEVGUfIwUqBiFG005xreowhKrW6+RKsTT5FmOzxNn9Jjan0vj/XNMP+BCh29RmTbARKEOyPtnXMd2yoZsp/bI3x3ztYNC2b9CgdTPjfs1CvScsgbvjms6S6Ilo6nFCdW+7dVx0F0EbbgA95DWemZL1c8ujPd+OH6uUOHAJlVVc3xMlNDcxM/+caOGoSa3ICWmP6QIdBMAxwm14SBZbEC7zvfH79uoxf2HlGETjR2lFu5rlLHYQ9CyfQVWqWjgG1T3pVfGfX19DOqL41wfELmKUrPj1GLUIYjIDal7OavhoYkrjZChojSE5L5GyB8TbBrFHxF5lIkzo4UDlGE8Sy2qRSqsMrOrY1trP4ZVB5huFj8hGsteLSmfZiLDf53njgJM9n1IMtc2uzlI+EslcTqxp+P6Rc1XSYa/90HQ+bo+RMNypt0BbieKHjP6rkfVI3K7Gn6dYS+28d7UU2u0LTBZZRo9/pBCmV3Pvdb+NZm6OsbjBLVPX6HWmGG01+9auDHGVM2zCbplan3cJm1NLQpRBy0PDHmUl/Nzhtp/1hCI/JWpiQSNupSj3RjXaUWnFNRB0m2vU1T3CYI2YjMSEdUfJ0qm+9Q6Nmmndn6FtJZ9RtmDK9QeOEPaH1wnTm+TRJ2nKHvxCPgnwH9JrbcdVBHcX6IUGp8mtKxRsE7lfUKB7iD9NLzfu8B3NMTvjwv6PCGqnyNlh0rOrjH6Z44b+TxlJOZJq7qrREYjElPAf4A0P+nlvHpyewi4aVbIkyNEvk9IdtUKIfkhHcafjAF9a7z3DgnllP9o9H6LUlUonzHxpZb6I1L1dY8KS/T+/j5PqA05akjzk+dJg5RvUIt7P/HuLxG+0vP3l15aikZkLwITeVu44FxYsLBOuprtocK6l0krwGvUnGr8lP3coJzNu+Nz5ynnpYTI+3NDaJBg2hCrFBBlev0i9+9T8sI10qXsJEk8WSDjOrSq6mEbE0j42bl1k1OHmK6esnCiI2hlY4bMRiyQkFUqQ9RkpZ3fE6mqBOrIy8/otDTcFph4TtUOO8gz5tR6O67y4PZKMDn4POnJLKK9QK07ZZ9folDcfmL4pTGsLLw4/n6OaS7d3g5q+Lt87fq4vzMEZa4RZNxVP10XvjbO+a1x/9KbOwkVo4TS/Iql5haBaUNE45eofXaaoHr3mTmBW+MaXhrHfn+M0+tURLiTWmf/57i39fHZz5Hue+a7blBrWMrpzrjGXxn37jpaIC01TcYvTcZJthPR+oQkDtRqyt1YxvjOuMldVKOMz47/G0IbZio7OULkRi5WZWCd1/Qij5AuWs9RBuwC0emKQvcQg3GYOJBn1EL6LlF1yLU+TzgndadvUAk6KJSyj1qAR8cY3CDoYoWgBPnZK2MC5qjJfXv8e5v0DfgiJa85S9oBPmyfcdMaZqsnNfMMMbwqSpSTyZHqZeU6RVkmMRepxfYx6SVwepzrJrUIv8O0M3EzWGEoZ/8WeUJLL0/W6ZqkMnlnElJlwGnKeR0nyRqRzd323V61dpzaCG5USOLsDnm6zLvUWjPJJCcO4Yt3Mv2wSHl88wh7qflX8G/jJ6sNdS7y6DvGuUxeidDlUN07okuVHSqW5LQt3uil3hprDfUKQfCi0QUiP7S8/31qDzymEmOfH+O+lxQCQVoHLI8xNAEuNaMy5cG4vhNE7SMFoybdSMl9bgK5UyXu981xDSqQ3hzXt9DuTRpIRKwiqZe6e31PqP30vXHsTxF54TEiMzPZa5L57hinPRS63TfG4qsUJTlHrffPE4rP/IMKklskWfmYJHmVtUpZ3CRteJ37+xPgfxgX76C+Rxmjw4R8PkQKIYT8oq510qBEmdsDIrju1TFWlkh/PCKPT7LIwMq110gT+P3jmH9CelpYC394XPc745hvkizx+rhGs7BWNjkBcmIWS3xiXNsfjTF4mcqkql3eR3qLXiZeUuO8SRqAKOXSe3fxv5ly5XqG1w9INtVEj5pt5Vr+DWKkRWWG3Sb3TOCZIDMJZ1TyHaL/fInIBX9EHvj6jDzO/PyYx16e7jWJeEWvK+1Ho2zVHSSBtECeeHJwfEYHJSe5jaBMDbOKj66vltt08yp9/JA09JGf3t+Oa/LOqEP6wQZGGkHVEW4g52Ef0byaRLUkFsITS6eYyDnM9IMIlMRJxXRHvUbolp5U7BV0ne7SAGlMpUSkL5YJLfCw/a522KhFyaCFEVYb3iES12OE2nL8NcRytNYLGG1KCXS55HZStHKwvee8rJJoewdptWkCT0nrMfKkDCOZ66QgymKth8BvU0j8b1OGdjsF4v5w3OtnKZuyn6wxr+nWuB7GPJrHchwgMk2pnF4DoIRuZUIZ3lUKRVwglvvD8fsJykscI0hZ9HuVQraPx42dowyRWXpR33vjgk5T6FNpiFn/LvMx9Lw8/j1KhPcL41jfIlrckyRDq9xGPllP+ZBkRleJwTcEf0gah5wZA/ptKpG3C/grlKGySOKzTHd5M6y7QhoUWTprRtsJNFTXiFkI0cO+r46fuyS8d5PI1xpG7yQ1/n0Dibr6Sz5aHfca04/lMeOuEfqQSHN8zZGeGHtI8qcbQVFd1+KanLQQwevzeuz8dorI+URtFynHaEHDw/G9g9R8fYpykCZvNEbqqEXNS6SPxDtU5PLBuI7PUhHLCeLAb5NKS5Nkewn9oU7YakGbAc2N736HMjQHSN7gJdI8xkhFJ6A6RHWA6iERvWhXp+zYq9JxDhjjL6XgmFg6rR5ZWeOe9j0LVTTsq+N9+XMVM2ukJ4W0S4/E/I5UjsDD++y6+W3ts0rynPubBK3fJSoEKZd71NxrJH9pjJvAx8Rpr32wCver1Hp7lVByF6h1sY+iKFRo3aTm9zBpm3CLVB2aPDTCskAHYnClY2jXu8GorPtdyuKbqLlGHkN/n+KEv0u6FZ0gPYMXqM36f4/vnKYSQp+gNtJ1sqi+M27yCFXYcZAyWvvGd3aQxwUppLYHgPyxRQSS8Gph9xAv9JQyiIZ+lkf+M2oBfWncg15Ykv1x+44O4vG4v8vjXveOwb9LIeXnSTs9Cw9EPRYNXBhjanJoD+HeIVl8N7LnFikrFr9JGSKpCXWhtOPK4SpBU1BujwgRpvymuuZHRPFwahxD+Z90kYUUJoPkCeVXO71gn4tewruPmvOOAC2McBxU3lwf/3+RNJ0SDYuINfpu0MsUmJij1p8SIek1DQHEKKu33RxjcIFy5E+Y5sCl6VRVHCSOTx7UAhojAL9rdAZBkvdIw5vjpEjHpJnGwryHxncnUUwoO4MYP52HzhlS3bdKtOtSESbKL4yxWydJLCMbEWyXH84zrXGWGjNxq8rkWTuGyhjVEv54r0ZuHkPnYmUm4zhW810naiE57jPjeNcoG/C9caxPEEpwQs3xKtW43fGUVl0mrWAFlM+IysXEOONzVss6pkYi3tMm06oef6Qh54HlCcULrpGetRYW3CMbDWJgeqLDwVuhEMafjIt+k/Imh0h4tUZtzm+O73yWMtwK9j9JbX4Xu4k7K/Ym7bz7SDXd94kxMDzyehYoiuNAO/81shj16lIuhnSqFuRGTe7JRz0Yx391TNDS+F2aRgQgBaJ3F91dpAz8fYIMDTV3UJvzHDGoq6RxutIb0YcLfIlEKC7SV8YYSxuJxP2uCNqQ98m4trvjfb97gCRlpCs0yqKWy0yHXlYR7SCtUC0qMR9gYugaSQjuHfMo529jGq9bhPuQPNrI0N0IRyBxlISvjv0qtT4t99UILxKqinYPOiIpIn+30tCIxiSRUs475Mk19mE5Svp6yHPPt3MZMTnG98kjwOQaPa+6aHXci8TRzZGHe6rRhqiidCrfpzokHqFyJGcIet4gGX5lb5uEc7fxlNersxdpPyOO3gTlw3E/SuEsozb5KIVhjuEN0ujIHMVVaj2dan9zD7sPFsf976TWb5fLPRrnlTYycpJvlkq0JmA/aaS1NI4h1dNVOZOZH8EORFHTlTb9b48mFNrtTXUOM/3Ms1vjoszkuxDMZnoxyjUsHZVcl6jvmWI1m8+RAgb1jtvbeUR/qiHukrBKstsKumfteAfGDd6gQloF5tbg3yaG6TYp1lBk3rksUYPGeB/TWmE3hES8XJgCfsfU8ZOvvjf+bnj8/viOEyhC/iSV6PsE05VXhvmWF8vHqjdWHSIHK68uoul0gMhM2skQ9BIVgl0j4bnls3NEqiNiMjnV5WO9eEOFxtepmvy1cX+ieKMaqQTXklVmFhf0jX17jOF3xjW9QJCKG687twOkauxDQg9J73jNovsn4z2RvEhHo7lKnixyj2w2UaKUlMnn3pZULr8bCWkg5VQ6QOm7W8QgXaP2xaepXimCkBWi0YYoIzSWiyQ38CZ5RPzV8f5Vao2qTno27v81pnMRygdvUXvsFLEHosHl8ZnvjTlaGfPwM+NYFpM8a589SGRyOjcLXV4mVI3J9x1kbT0m0Ya8rvNhgtX3RKrSLzDdP8S5Esn3SKCj3En7vmDFyE19udpiE8RGwQsMQ/yPxi8/oKronlAL7pdJw/gH1GK3KuYJZTjuUl5LLkVDdpFCuSco728oLnGt4X00LtCQYZF4rH1Mt3C0dHGZoAiN4CbptrWdSJ56IkbPDunsL+8qmW4CSm5sP/F+XrMNdfYQOZ1SP8MxSKSgp1dJYtKnh3miMkN6uWGY3oiGkzqU69QCvTPGyKSRvRlE+CLnIyQJssCPJ2SsIjIJ2jngxfbzlPR9tljDqEoj7TUbUkvtmIySH1WF0MdNPs0CCg3u+9Rm2076Wivtss/AexRV9qfj+2co9Ps6heaUOalpvsP0c/mkqXaQRuReswlJ50yw4HWqD3VjOs/SDEZ3FkB8h9p3tO+dJdl5pXZKpB7x4xpvVUHSS2qBO/UlQnM9LhLd9hPSL9tk9BmSoH5KHvtkFKlUzV4vJ4nCxLERFd8lyP4p6TltlLfRPmsVoVRQN2Q6SNcWJPn4DvCV8dk3KK5/N2lo5fqTqjRSE307R16L68M1qAHvBleZps5UW+Uemo0ojHZVDUnfPGD0I/6blC7u3LiwPyOd6Q8Cf2P8HKQ2wb8cF/Aa4ar0lhLi24kCY5Hpx57r9cyiC/dPUwvoMbXhDFENs+bHe/cplHufSKBeJLrfK6SI5DRlLOSbN0il3/I4z32mdaB7CW8pIjKhIW9kYtHmMCLUw+MYVug42MqNNHzyQzqYhXYseTfDYUt9HzBdMmzobxLHcPh+O1fnr9Qn72K6paMGUaphkTSwf3nMqzKcjuK6fMqo5RpRwcwRSsMwbjfpfCeHrXG6SdBPly49JfI0td9yph5fZ3OEVIaqZnlEGQHLbyEtOd+igIKRhskdHb1GRSNLGy+jEENvN6H5Abun7Wzfs5HTVWJIXQNy6PMk+nJs19u97W1j6nE1OEY0KmMeMM3tP6SM3XmSG+nVnSJTE4iHKWfnPD0myURBjzpjpVyWjZsUl8qUftlBmkypOlDmemKMQZfg7SSSO8dDNKvCZzuhsky6XWa6HetC+7E+YUI63JkUfIs4NxPlzr1gyujbvS3C1kEI6kyoS1+YpBaI6MgeTSij+zZl3N6gJvshhY6/Oi7yFGWwf2Ec/A7lyZVEydcdojy69MYGFf796XjvDfIoIjcX1CJRU/zGuHiNiplfF5Jli9corzzfJuH5MVDfpPjqy1QI9EtEj2yW9QxJVEkxrI7jyrfJ/SmfceO4qe6QCrinYwzOjPfeJRlzFz2E8hH1Hmjn0QN7PifRCTfZ82Aca57QAVYyKdlRISFKm5U2bbRjyPUrzephpYbd5kz9mkQsVp8ZpVgRdZEffxSQ4ZiOxIwzpCTYzazBNcEpt98R6TJRRxhSSk/0DLY0jpI5UaqbTUcrkvR46rBtInOHFCkYFu9s33cMDFF1bhcIBabqxDkWjRn1OMa9ytGIxfOJ3K2+1LE+otbdFUI3QhC+hucKaWilCkd9rMZeffUVymgKTJ6n9tC5cTzn+24bb/s1XBtj/zxlRyz0kLJxP/So0mjvANHtShGsje88JtSbewzyFI7XmY5kIOXTd8f4S3u5h3r1qGBD/vvKuJe3iD2yYMpIWqepBLAbcNeZsj0L2bYBDyfAP6eM41PKGE9I381l8lDRB9QC+dfGTc6Pv32d6PJMKB2hvNjr4ziLwD+gDOSvUJznCSIXsYTRzOV50tPBphpWz4lQRQ7vt2s7Rgz9HbJxLF440L6zSi2kvaRZ+sskC3uHIKFHlFGxgm4/kdkp+XttXMMiebK1xugx6Zcgl2ZRxz3yENYL43MaxOcIf2mxShffy4tdIKXNlmW6MDWmy0w37pGCUB4ofaRDEFFrpPX6kAXmItfIi6TcYJ5fmZLI7O64ZznVo5QDPsX0OjCvoEZ0q38tglBTq3E1AQMpTe7XapjuOXQWIvFJ+5zXoBOQM3Qt9gSk51bmJbLV8HaO0o1pxOPrLnmixBxlxIzuNoiOVu23Ek0LZhyTnm8wLL9DoT+pApHoYZJQFeHr+JXrmTQ0wmVcp/ztXmJorxJV1Mvj+KpbFogKYYHodEXoOggBiLmnneN4d6j1c7IdZ4Xab/vGezoyUfytcYxTpPjIiNIo1KjIojSjXyPsU2SdS8FBchYQKaBJTBP/RhVGe67TCQMRf20c3JBdCZGexqq0j6gNbx35zxLN8NtUJzMlLxZ7PE/JvH52XNgfA/8FZaB/lTR8t5Uk43OLY2JeHwOj1vPTY0JXxmetDb8xrruXeEo/LBJUd5I8W+oKecDp0XH+36YW2VvkySA7KeM+Txnw28RgOykXxmffGvfzMbVJXiZlsW66y1SC9BpJLiqRkvs1DO+odp1oFV8g/YzlPOW1e3LABWZVlPppw2f5w16YIToyIw5ZMGav1QprfExguahhOjMsmldGBdM0h4h1hfSLECVrOP1XRzFrNNW5Svt47Ssz3+/ndZN3xYUFA6tUdHeCKImWSGmv9IrSNCkU5U5y8l2+5kbsEc4SUQKtkzyJ46sBf0RK1F8jWloNl47+ANMqk87X90IPSIQC01Hek/YZE5/KEI0anpF8Qa+K1dA7TuZgblDGcBsptHmVasC+h0QDRtkdmYqgn1CR7lUSOYvuD1B5rUNMJ1pnxwOyFlwPJh6/RoCfqPvhGG8j/O5wXbtSFRrYHilaiGRS0uSu8sh1Ronz26QEUyJbtYQLwtD6A8qY/j/jYGeA/4gytmtURdr/RigIyegXqW71L47ff3+c9zPjGHr6Dwm/aD8BM7UfUGHHJ6lkxjZqISjkX6Q4aT2XxzWB1o1TrzC7Oa7JY/x9yum8Pj7zg3GuXx7X8mxcx6uUsdWLLo5Je5Uykr3669E4zhWSrNQgKK1aIuqOrly4QS1aHcoBIr/R8e0nWX9RbA+tNGAQNCu6NfT21Q3pXPu/+taeiBAFWDIqwlFV4ML0XkVZcoiQDLOFBCY3LK1VtuWP9Mqzdlyv2++7ybwepWBqU6Gc+CniAL3HfsxevCC3rlHtVI9IX02xoe4Ofny8BQeLRBljfmOdH+8lrSPfJAmzp9Qa/IgkMveR5xoayZnIftzGV9rrONNNmKR3NBpPxud1LOZwLDxSeqmRNzq04Y1UliXirsd1ap2qYKGNo/vTl+XTvRDLNSE1oMxVGZzRnsbXl5SdqohJ+/si0xW3RpyqMvre6cBBA2z0+YyoWV4iOSOTukskGujy06UJ8PcIFeDmF1ZbgWR9+aHxmR9QWcovk2ZBf5vSBj+jOOFvkAKQJ5SR/TTFM++kSPbfGwP2KlmQ9ymDquzp6rieo9Ti+wq1wH6LSrTcGgPx5hiMK6T8ct/4jO01XTyG/F2Ebfi5RtEJl4hu9cK45+cp1CvqP0Ea5hbykMYAACAASURBVDtmhpN72nGttrKoQhRjyLd7TKxcnBl8K7XcgJP2fR2LpZLKn0QPfvcgoTVsGqOB1UEpJ1wkIZxjpQpD4+DGk0s0XFdbqxFYafem1NGoS/Rlgk5nIr/qgpcvFwV3FKsjsNeIxT3qjp8jnPta+46fe64dU/md9yeakaKRf/XelJx19GfV4yHS0WyJ8IcqCnTOhrYaIcNZSDQhWusJ0RsUKJEq2dXGVt5Y53qdPKTgJOV4LHSRBrg1Pi8q71GGtI2A5glpzu48QhJWOj7R+QZl4IxYd5HuhBacPCDPm9tJtP6qSb7U5uhcG5eTW1wvbY50MNJDSmNN3AlWVMg4xxaU6Ehox7VazrUhcnesH5Kou9NOt8eYnSWRnmvvFvCtCYVi5RBPjYMbzm0fF/X2GMzjhD9+RqqZfjg+/yrw18YJH1B9O/+Uas78KkmM/Ty1IFYpQ3+JdMeyT4PKiBdJmCQftDSOdZLpBvJOzBUKRU5IA5/DRLHxmFQfPU/CjMekvNYmSNtIJrYnjxbG75Ck0INxbosDzhADeoY4GsNYEfkHTKsXzKAr+erJLRNWqg0+oEq+36EMw0nKsZ4nD8qUI3Xju/B8X3Qnl92NtcUT8ry9PPZ6u18X1u1xXUepdWBo+pQkbF4nSEN5nL0lTL6ZPNR4S590Q2gIrdEWbYtO58Z4HSFdzJQi9uSelI6G+ll73zJ1OW+vRYmTx3Pj6RBX2zlUbKjKcZ3PEypNWkf05f1pDEWsSvV6AlajKi11i1oPE2r+j4/vqUsWAJickvP+mNrn9i95bfwr8u1SMx2iL8fLeoCnpMGNWnrRvnK790m17SPKljjmp0k7BHMA2yk74v33c/ex6MlZyPpTWWJSf0KKNZ6QZKqIdaN9X5XHSrsG6wGWiYKHdu/fHcf+AtNPvN+gbN5Xx1gvTYD/jDTkeToGzGRSL7BQDuPAdPnTDVLRNU8Znc8QgfmPxk2cHN+5Sm2QN8d57HtqTXjnS6+RyeuZY5Ub65RO9BDlGDYoJP6QQs+XxzV9DvjL47tvU3zQJmUsXiEi8csk/L89BtMs8QLpy7B/fEeU5mLRCyvNMZP9PqXgODL+Jj+rHlfe0xBM3k+07Wa7TSrKRDqnSVgF0Tur37R0XGS7u/2IrEzS9DDz3XENasQNZUXLhs9ynSvjep5vx5tvx19hWpakcdxBEsMfkJ6vOqedpGrMkuCd7fjSHl2PbZLpKLUe1aZqLLwmSEgs3z3fjutnOtWjzE0KRme22T5rMlDUaNbfknOTg4a2hsQqHZT8iUw18A/Iw2ZVq3jtT6i9uEmtB8GNqNxE2nobI6vURMTywRbumAcx0ds1sT0hJn3SVS0m17Qljo334ThLzZnQcp2adBTBdt5YR2UE4FjKyTofOnojZMfTdW8F3RwBS55DXvoecermY4w+HX+pPI3wGknsC/IWxn1+k3LOO6i9cn9CoSk9pNyktfK724mUvbxNbRiTUcqmhPV3yPPJDlDeQ2P7R9QiPEWMhXXgKh2ukiKO3WOQvkZCk01S7WdJ7odUQvCL47gXKSS9a9zfv6DojsPAf0J5qAdjQOSwF8hjXUwEnRjXcmkMpM1I3qeaAl0nD1dVd/sKEeIrBBdBq+7YRh5Hs0zUFLvb99zwVhQukkSChRRmwrtcTTQnhyXKs2/HvXG8a0SKdGz8nCLa0X1j3hXvfzR+bpMEjsUzlhIrqzKkVv5k0YSoRTmQKPhTlBpHzeoT8mw0r3+ZSMeOUJvmGNObXANmqK4RfTrOdYn0+dXg+h05SgsONEoQXtEx1qiZwHtCHKrjYlQhpWEyEcKb9+SRFavdsagOUJKnPv8OKSdeGP+/TM3pZMzHSWLozhKDYNR2lRgtlU4qCvzuk/G5r1FgylLitwgF43ybZNMpqSaRbxWtS8OopZV6NAI6z9aI13XzLcrQvUyeriF4c06kyYyMRLner/PS7Z7XahTjnpntJeFa8KdTZ1I9SgyN1IwqLlN24xHpU74CvDOhCjTkxZbbiQ3t9Ix6N5Hqh9Qi3EvKWRUp/4iStV0df/+18bNvXOyHRH9r+epskurhuFF523eoBfEKtVDksK36+s44169Ti+q98f3TY+L+hEJ4t8a1/K0xeT8ax/oUkci54G3mM6GQ8dsktLlD1BJuwCvU4n2RdH6znPqTJCRXO+u4mxTS6bnRO6LsIbkI1ESSP10WZugsF2mN/+UxXhAjMU8t1POUgXuBPDPNENpNfIva8PfHe3LIGrfVcW0i0pdIKOfClOeW67b73WHymCivW/rmY6YbPWkU1SIfJOjSDeem6qj1cTueyWhLwO1UprG7TnhuZYHORddjy3lfodbIPVL1Z6MeOcfubFWwWJ1pXsQMu3OvQ3xIyrgFSaK+7vykadS0Tqg5/5Ba73vHvJig7IlfC6DuEK5aCknljvtcHa/5CAuW/PfeOJ8Uk/SKBn4vlVeyCZRr3ki7l/Iroz1LrdE5piVoyxQAe5cy1h9SDuM/oNZUj1rketW2mycyirw73j/B9IMMBDy2fNCZKylUqqghFiQsU5GetuKFdrzdjDaYF0lIZCWOIbP6PA+u53tuHOgGZaB2kEbIvdLoEbVxl8YAnKQWgFnP90i57B4S/hri229Aj7VMccfqefcy/bDD90iD6U+Mz18iYeklsgCOU4UeR6kF8w0iOVpu93+B9Gn4JtUk5QRRUHxMEnIvkkW7QDa2IasoYYP0tpBP6+WWGg6/a/JOftL3O68s58X43UTrd8ccnKU4tzOkxNlF70JSy/zu+H1+jOUvjPF0zF3UT0ivh6+TJ3u/Nc6zbxz73jiXEYf19iLOzu9q4Ny0fTwM7W8SHl40tb8d1411lVqjy+RRQ6fHNQgq3GQmd9Rnm+kX3a4Q50sbC1H/I6ZVJAIJy6Steuvcs05ihTTE+jK15s+QXuBSJifJ06phOvvf0bUGRkrLarhlprvQKVnsob+5B3Xy0g0vEF5UekmaznL6FZKA+ur491dJwlxu3DntEQNj/FQX7SZFOZDkdU+4OS/XqGhNbv4oBXoOE9rA67NM3J4Zz0jPdMdZ59evS73+qXFdRltSFPvb93WID6hHLP0+Bf4+Na5fRZqU8M0JNfnbmE4CSfzLe8rryF+ZZFCT+x5FOyyMQX+ZiLtXSOu4x9QieIMyyD3UMiwU0Qj3RTFPCAVilct+Qis8JtydMq8TpKUmBF3LC/WmOBPypFgpCZN9Zvb3jWO/PY53jiAEN0DvtWAyQy9qFr0vRhMDbmblP1JEllNDJF3z5PE7Hktjr/C+Z/hFyx+TLLmC/57gghg2Q75e2bdAqvj2EhTaUbPSMpF7b5YiWpM6uUciL9G0qoDnKIP+IikX1mhCkPUj4uBcbyL2XSSiMyTtiUqIUTFhvY+0eZWzv08ZJefoBrUpPxjH/AK1yY4TeZvNbzQYPVx+Qjhhk3Hen453iRQarVHUzXmi6xe1uV88h6qMSbuvXgDj2OlkNglCtzoOUvVpA6ftTKuCIOvLKjclp3fHeHyC0Ax9LZpUg6wLC390VEoKe2S1t31eZK28TtWTDllVhJG+ztVOgEY1kzZH7hnvTfneTdLH5RmhCq+SXicbbazWyROIzhFw9zZVQPeF8eP+eTghbQQvkqdQbBu//yvy+JAzRFFhjb0dt+x09TbJ1p4mLei8oauUwf6I2hz/NpGzXQT+4bjRXyRPuhDlPBvf0zjepcKPnZRhPzLOo1fUqx8Zg7/e3pMDUoaym3T2ekIoFyfTRIuRwga1Sa4TDyjC2Ucy2yYznFydgMkFUYyUyH1qk98kxR72DhCRWdWzqx3DUMhyWReWxrmHrZ0OMdHhJvV4hvSqGDbb32e1wTrMXrmmQZaDUxVxm2TmNyhn/iLhP3uVmhu3GxqjAo3c9vbZx5TR+phUJxrqK1dz8z6kDIYl9hZO7GnHukAeenmCtAM1OutKiWXyQM5jhJLokYM9VaQnOq+t8em6b53VhLS2vEJFKy+QR/9cJY/m+llSBPUHpInXPuCvUzJTlROuBVHzFZID2EHt3yNkbWr8NeA9D+K9+H+5YogxNOTfTh4k6/zY70K9e+/joEKlKzekCNzr7kF1y0bkInT3sKoGj9GlkrTjr5MK3MOUE9ig1oOacp30lfE3c2HXKVT/OlF0XSc9d84REOD5lyZUccYr5GnG9kDQyl+inlSxRMHy18ZEWQhhye4pogM0TD0+LuhIG/Rn1Ab4PkUzLFPhy+fGzf2QKqo4AvwGEX2/NCboNmW07XHxe2MwPt8+b8juxtxNGWt10BYDuEGUKB0jDxJcHONgOKaBUlmijE7Ji5VOluZuJzXwLlTDpMfkMVIK060yfEhKnkVwHh+COj6mNs4dUsV0nEKRr485fZ40YfIauioAYnQ7anDRdmNipKR29tYYJw2TSMWOdW4KebkuIdJwuyFEcrvbOR2nm+ShlMr37K5naCoqVhHSHYLSrE57KPESbakAECHqrOeZ7r/tta6M90ycWuxheO+5u6TKsbZI5xFB8B5XdOaPNI6GQS74NrXZv0WtoV+luNYJpRT678dxX6SeNPwbpI+2FagnqHVsCE2bA+fSsbhGrVcTbpDIwUrM2QSlCo4PqTV+kDJWXeblPhCcHGf6mXe9IMbxc/4eEEeisMB515nRPuMa9j59r1MjnZ9W5gnTIEtwJSXY1SX3iGO4Txnyjyjj+1kSqXRaZhm4MqFKk81Yyn18THnTg5QSYSe1cC9Ri0dvuU5KF+coI2ZV2feB36E20QlKmH2e6a5Rj8bfFcR/ekzG5nj/PWoxHSYbw5u5STivW1RS4MG4hiNE7L9IOYa7lLH+1Dj/BfIMK0POPZRjeYs8ZFR98g4Sos2RRJaFEI+JzM4wSYTXNbz9ZbJHdC6tcZ/aaF9nekGIFruHFy0YfnbEtq39rcu1zFh3RUdHByparo5xujnm5Di1mUzmiUx0fNIOohC5Xsa5bJLTDZrjsjbOeWn8TRWC2vANovs0ArEd6UnSGa9r4EXsjvVdosSwt4Jh8V2SgDw67rUXhZjYkn/tygvHrifPVAp0nlk5nBSZydWPx3EMm3eS0ma/J61jqbqFCOYZ7O9whTi789R+gCQAjRSkBW8S3bCadSMpuVUjIoGB/KnFDeaWdD6dQuu0oHPt+GyQpNuudkxIxKEj1YhKAXb6suc6VDL0aLD/0K5zMvNZ97IO3fuTR5eiVW30HNNzDtPyU/NpUmOuHYjzmWNQE3+XQrPPEQRoMuAC1RbzRcpIbhArv53yti+3C5Q/6vXxfwz8N9QGewP49yiNsQbEYoyLpLvaF6mNohTtDrWo5HbkRdcI8X2ZcipvU47i18aAykW+Sxm2c8C/SW3eJ5SxfzTO/Q6FNl6iKJPPkiIM+XGNW8+MT4hmVATcNaQivU5xdF2kodTmzOfukWbXbmoXq7yi6FAHIKd5kyQx5TcXyGN+VDp0XahGz+vr3K6GwCjixjiPlIh/vzf+9oQy2MfJ8wM7UnQj9GjD8ZIvXCC9J3aPv3nuCdMJFzdhLws3r3CImm/pJ8NcN7VOy/c0LovjfOpnLUFX2qd6xWZXH5PinM9QiU6TzCI3taaWQhvt6IggpeqqEy5SjvkJKQ9+mRgLaZ875Dl6R8axrFRzLl5o1/7BGB/1rjpijSBk7rvsS1SqY+rJPtFld1yONyRKkA7YNvO+9MYsvTBpn9f4+X9/74ZVWZnnpX3WY3kNjv0CUW6ZM9BZ3x0/B6l1tI1o7t2fS9ScnGQ6yurOWp7dsd0OTCbAfzpO+jqVEJDfcgKWqfDij0kibpkyxu+MC/+1cXGQ51BZ7qnW9Q61IMygH2x/c4O/N87zNmXk/9a4Kfnn+9QiFOWcJVVw18bN3iFavlNEjqPR/h7lYM5RyHf3uL9Foge+QBm5V8Y5DhAEt53oeCFeTnXAdZKUNEmp3OUEtUGcDKuyXAAmEPYQx3iXaC0PkMSVi0y0ps7VBIRhv8kKw3JD+XWCsB1/JT1Wf3VVQ/+eCMaEyhOC6t2QFr88GN+bEFqhh7MeX/Tgxnbh0u6tIxmpEtGh2tr9pGmL9+v4avSMKh6T6sBe2m/RzBMqAvgklSMxg6/MCaKQ+D6lFHAjrpK+KW+M79pnQkmbGfgdZL+YXF6koryrxDDOkYf4GhWIWFUCmDyV+tHJ6vRsHrQ+rmtP+5vGDWLMpHcc867sgURnnRawYKLztRCH6//n2t8m7T2NvYixr9ul8fnDTDee6goMHYLoujuJLu2bECpOhc9VyumdogCdkcuEPCTAtWP09YDkrhwDJaqdruo/HaGvT4iw/21Ka/s8FcKfJTycKOE2tUDU/Vm+adLqpfEd2skWiOG4Q6k0PqQW0GHKASgvc6JuUgj2JmXgf3Z85jrFgU0ornrnGKyT45x2z7pEGfRbVOb2MyTpJ1p9MK7tNNEz32kT2RMlSlMgCR/Rm+jAv1+nNuW3x2Tcp6Rx6+P8n6OQNqSpvuGvCM/QVNF8RyDKcCx/FZFJl/REiMjCRalB9j78kY/ryNdzqPSQ29Lom4iTu7UbmXyxi7A7WlFKp0ncSBqC3lzH69EYiKTczN5/l1k9I316e0XmTQo8fJsCEIepSsfXx/kXqajKisWXqc14cJzLZPNFCqycJs80lBt3bV0mssY1Irc7RDl3dcAaAOV0Gh2TV3KIVgJKVci1Gz11w6gD09D0v+nYOsqFoEa/95hycKo7rKjUMEpJyaEbtkvHSK9IWfl3KchO62iMuurn7jiHlMudcT2QFgSCEveqElmr84wWpVqkDBQkLJBc0VNqj/7PlNP9D8fx71AMwALpyHiHVBE/JT08pPsOEdlcv7++17r6Z5OhI/42kXlJMbiw7XkggjDrb8jbUZHhMOPkIrDdTIvF56iE3O9QxvA8ZZzeJOER5Lli3x2TcYZyEIdIIs7wfIMy2JZpXyX85gVqoctt6kU1JhPSk1fDISem0VXl4OJ5RnpEPCZ8m8T9IuVI9KyHSBP5F8a9nKMWpH0vNDy9pNlEndTHx+TJ2Ibd2yhn+sG4piukodDDcb3HKOP/aVJxuDbObZjsuTSE8spuGmmZHjpC0Ko9IlxsLjjD7y4R6khrvf0u6npKnhTyiKCTLjty00mviMSsiLpNCkC6A75CbdqeoT/J9COXRM3MXMsDIjmSX1X/rvzT9SVSetjOJeUyTzTVGtdH5CnS0i6Hx3w9T9aj4ybyEgV25Esb61lkaDTpy2NaJXiPGBCVC5ep/eba3EaAwgGiHZ4jQMy1p+qiO+CuWZZDh/R7cKw+GmOvSkQVR6fC7o5zea9em5GefVI2SMQhfbdMGdqvj+P+5jjPY+pxW+8Df4WyTduIXFKHpHrqgzEOXyKgratGeoLX8e/ztzIB/vG42O3kCa0QMnm+DYDhsIk2+yk4CPIkSpGUkU1IGLaX1GtfoB5z/5VxrHNUZdzLpAJtB5FQQRIL24knXB2fs8rKbLTJkI/G9+1GZkLLjLUVQ26mJapSz/D9IrWBj45rfInQFapMrAzUiGkwzDJvkmoyK8rUnd4mGmR54m2E6/zMOPeDcS/2oVgZx14dn/s6hfZujLE4TRl9Oa2eWJQ7VZL1kCC5DdKD41WiDvC6IIa2l4BqoLsR6EbXcLMbapF7V1eYnRdViNJEYV23rJHrZcdqqR+TJ03vJtp3EfgDEiFsJ70EDOEtkZVK2Ekc/RKp5NNY3yPqG+VuEAO8g5RPe/1qYVVwmEjrGmHvs2u2e6TAGDuTZgfaZwUPtM+ZhHtG0Jk0j8frMjodpNWcBwlVo666R0DKAeX1IesG0vjfyMkCpD0UKFQl0tUMh0mEo4HdJGoJmHb0AgypDw3zTaJs0JmrfFmj9voPqUj6XPvcCtM0kNSEyFslSL8GI7iuUhKdq8y4C/yTCfB3KERwkhgRxocvUl7h3XHB/y4VzrlYRb2WzxryaGjkrDTeXXlgeLlCejc8G38/OyZEsrv3wPB7e0glnzylnk8Evk5Kqq3iU/EhVbE6rv0C08/0mlBo/ArlRJTxSKccJeWjdmZyw6qj7MZY42DPB43RUco5PSL8uaL6A+Ozl8a4n6I2/D+nvPA+0tjecEgqwTmU77w5zmGhyxFS6eSGVZBulteGJ3a7MzKw+Y4IQ1VAL2DohtXwvScnZ5M7swYcspi7Ad9gGk1oNB8RnnlH+74Nc+TzIAVMs9etPNG1NCHc8jzJjMuHWs7/IUHuSjXlHg8ThYaOSr5Tw6k8ShXLUZI/oI2b42rkIR0lEt6qMMGXDlHlhZ0F7fewnQAD1TjaAikWDZiOrOcV5tvfuoTP69ZpPBrvGQEqRdM4mUCUsuz3p0F8MnMeAdOsw1E80NdyTzpbIi2ldY/Qgkov3b+CA/evPTmkXfuYq/TQeXapmvTnXaribgM4MqGM4B2KI7k4JsPKIknpu5TH+sPx+f+YeoadGkeRpZtVdOjgde5Rst+F2TfTjyht5ENCJ0hXiBTkkC5RxvMspbKQ55UrOkw4IkgXJhUQT8aPOlERvpynIXzvY6FqRMH2q6Rx/VNqES0RVYIJKyUv98fESa3sHtd4coy7qoMFYjj2kpajJ4lK5SJ5lP1zpPLPMeo8rsjAY85qeu0r4VhpKNy4OkNlSZ1qgKAmOexlEgkYks63HxFWl7eJakVwOrLZzbU583v/TA+zu7HXELgG3PhGRptj3D8i/a2Pk+bpRoa9+UtXf3gtjtsyQer+KzdqAlMq78Px724SvZjU7H0zenJLw9MjFOdidqycjw2iBJlQ6/MelbzuyfWT5FFgt8ga0/G6RvqagiTmfDknFiJdpKLT/YS/typXOzBPFAfzpPmUSHulHbf/7rl12nfH/+Xw77fx7xGQUcn9MS6dotSJL1F7z7zWZ6hI0bHvNN3GOPcNyi4JaFbHfUqb/SkF9n6FIRCYAP8VZUz2jAt6l3Cyb4yD3afQ4CZ50Oca8FcpEtvSYz3B5XEx6k1FHS58UcECyZTrUUwAisRXSJMMeViRsOWpz43BOUJCCbtZKZNz8+iVpDPkKZUZvTt+JhQ3dH5cw7fGNZwaf7s/znuYoOC5cT0fM60F7SGKoRukcs3FLJ/W+d85pit5TJBAwks3Xdcpa+Q6OvXzIjv5Xhe1FNBeEt6KzEQWIptucEUMGsYeTptUlDoSbXVKQlTkvH9AQvwT4195235sUbRobdvM8TXC3cDP8tTyd2vtfcfH+1SBcIdywveo+T5F9MaOb9fJPiXtRu9TtJENfpZIT4U3yWPkpbUekW5zXW3S+y30JFtfA13Op7F8hzK6h0nx1HamH5i6Oc773XF9Gl+YljJCOrH1kn6YRn+WO8upim43KcP0u5TtOU8M4DPSD8TrsuGRTtY9IdrUKZhEdxzdGyb9Om3a5Zbz45y2kTX6vTM+e4y05vUeuwFep8QO/5iS575O9sr7JOlpIvMY2c+3gK9PyHOkOtT3pp6MEymi1+Mbptl05SiRwfiy4Y7VJ5Y6bxIENhuqTtp7SmWsiDJr7Dlm5TFuTAcagtLlcgxfpCNuEaM2IUje8M1Eghlx5WlOir0gTLbtad9fJAkCubIeaj8mSg3r5SHhjsUFenErktSLfmJcg7RKd2y9WKFv1k4PGGo9aedRUqbhMXKww93iOJeVbC5ctdoPyJo5SDnyT5EnlCyP+xJpmQk/Srpc9XDzAakktBvaASppq/pAwbyJxr42jX50flIvXZanAROFzypNjCxsAC7q7gmZrmCAaT3sxhizi8Qg7aGQ71lS5OJxewsBHcqs49CB65ycYymLNQoIuR7dXyJNi0LMS7j/LeQxQhVQfYKgQBNsXbbZ5Zcm2S6R/hDmEXT61ymjaFGY5zk7zqU0dJU8bceoQCpMwKXzhzhb7ZfA6367T4tDblHO6UuUI5BDtkBtnlSJem9dVul83KdA2qkxRq7f96l5f32Mm3PlutJpbAc2J5SHl4ifJ4u7J1B6Bl9FhF7XmzZD2mVHtGObhJIXEvVppDR6fUPQzt9J7+6JujzEjSuKNoMrz7qP2vT7x/u3Kcmehs6KGbXKJlDM2u4memEpFhdvT/Z5n6Ka7eSx20tt/OSLrlEJAjW81qnfIt2+PhrX//OkS9VvjDH/BvAPxvlMJp6n0KQO1PadhteqIRw/FTGPSFmxv1+haKPHlKMwiukI1PUC05V8OnY5aOfj+rjvy+R5YV+g6J4TpGDHn84pW4rtuD7XzqWhk+MU4dpc6lukL/Zfoja+uZGDpF2lG060r1GGaZnZ03ZdOoQDpILQijONh4CjFyZAjL9oF6bplEWiPe9AwH0k3fSYUHPmDJYoZ/gCSZD2BJzKA5UOkFL+82NMHMeN9hlfm9R8/rdU97i/SRVEWUfgS2mhORzpHWmWTjuYwNTQGp31irrOnWvgrLi0HkBe+wNqfl4b/75MHmmk6uMbJKn/IlGM9WRbjwjtRXKMaXu3QmwATNsrI0/X7jaGIf6H4yI86TOSjJGf0dNskpDGG+9JE0NnB3+OoIRHVMj+lEheblJG7TRlIBfa8R3oLjB3IbjhRBx6aK9JotzsswjPcFHO0wSNyQuTa2uk+bleWcN7fPz7cLwnSrdDlyhxDzXRV4D/etz/b5L+yOqrDYkfUj09fjCu5RRlaBXgH2tj/Mvjnt6j+gycHfd1aRxbOc8LRITeuUz1nRpaEZjGWUTkfYvgLOmU3hB1yrmqTLAM/SmJGm4Qeus4KZQxUtHBXRn379ieIPp0ow1bYSr3ekBKdFWB7CM8r2oHiz4U9D8Z1/kyCRfl+H5ErbG3yFNBlKXJvduf424bQxG3kZTgBMIXz/K63SAbCVwn6/oA4f87Uhf09BJzQdP6GM8NogpwDK6TxlIvj8/fIx0Sj1L0hXJTuX8lhjBtsN8B/icq6ffvUDJJHaG2w37NAj0Tpn5uljLq/HunXroGumvUL1Fr0ZzSwvi/NJHOYKDarQAAIABJREFUSwPo9VvUo7BgO9NG12jJMTDiesp0JapOsyPmTgnaq6P3dxc9P5CPnLSDd08jf+qCg3ChJlc8kWhBPanhyBxpPSjHfGO89/x4T9QgLzkrQzGx4sB9QPpenCCFD/JWbmwXTl/8hn9yTnJuq6TCSq7MZBoknH6OPLfPasA10nxahGLYplFy8StfMkR3EzgP71LJwQUKjfzcGPMlshgeU8LzgwSNHxp/u0YQmE5mjTQh+ZB0wHLRGnEwxs4S2d4bQhR7nVTQudjNIIuAZg2LHLBh4vVxTBMoUjWPKWd2jyQF947fLxId72dJI38r4W5Q6HqVPKFjuc3bQdJW07ERPWu4vA4d9gOiGDlAKDg5eykpDY78p5y1aN7QVMMpqpL6uTfOp1xR+Zt0V1d4dHWJ+8kcg8DF9a1u3bB7tf2YTzBxqIxLCWinp7oD8fqlHk2uf46E4StMO+qL417eItGGxq0rtTr9ohrDdWQE5udEnnZUNIp2XrRpm+0cXUKnQ3/KtK2ClFVLSQiW5kldgUVo7kmjL5PP8uSCrsPtHuSa/dwjw7xOBciP2rRCJCx/cnFclJrgjpI6j6IRlEvaMXM8wygXz2PKEKqBdYN3KZVyEPnaH44bPUCes2coKYLSiRwgiZU5El7YFNqBvU9Vxn1MLbSnlHTsBOXx/xGFlv4NKgTbS9EGXyHOYIla1CIDpWIaItGSOlIRk5NottaCE1GWKF8tpdIbF7AI/jpl0K8R5CRPf50yWM7xXmqDnCZaWTd9l471MFkxvIvcJKPJ03vksVe3x7HPkqcI29f1NkHx28jz98yo36ASR1+j1t0pKolsJHGAomKM3iCUz7uUJPLSOOfLhFp7lTLkvfvY5TFe+4heVURktOVcSfH0hk4w/RQNKZNuZFzzbmbpBIs5JlRWXoCiIZ60Y4iilqh1Z0GTSFHFho4D0idBjlXppgZd8CWH7Ll0GBpDDbOVd7fG/b3KNK8reBMh7mc6VIfpfBBtLJ4wzbdrH0Sd5jZm/yZoVCzwI4qGUIjQnVhPbDu2aozvUxGYvT5M+D+lgIz9Q2YVPO5d942OxOvr5/LlMZ5OgP+ObHYRgyjIRFKX7ixTi+YdasOeHj9erFrYLnHyIq2y69IRi0O8qF5WK4LoCNP2d7aiXKTKmf9gfPcTpIXnBoUqjxKEc4QYHT2T4YuhiddxdQz+d8c9fZGaoPeoZkirVJ+Nt8axvzXO+RJJ2J0a97qX9CD4ePz99Lg/k1BKBl0od0lY5f0afqoyWKGMk4UMx8a436YWpNyj6E/t8xRHRaIakZqht4vKRSQPqhFSR36FMmRLJFtu+GkVFkzznadIRLNAkqjXx70eJ1LAXaTM/ip5GnaXuel4N8b1qZNWLmip6kvjWq6P4x0ij0F6SBLYJ0leYL2dS8cEARhGCJ37leL5gCq2+RaRism/en/HiF6+a3NhWqXyMfA/Av878EukxeKLpKBqftzjE8ogPSJcvtpcf1f62A2la7C/RKFGTwKsngfQuBkx98Rnp8Zo7wn8Fsex5I43CAAReUp16fBNeIsyTRSruuilxJ6zr+c5ItN7Sq1Fi6rW2/flfGe58dnXbPKQ9nvniRUgGB08mgD/K3lMjnKZn6G4xyPE+5p5tEzY8P3++L+loYZGhtqGcRBPukIy3CJkG8eIrG5Rm0T5ktyfSZze92IXtam+SaGonVQGc4FCUrfII+YNQ6RBdAor5LE6rxE+0h68hh+GiVaiLZMubOsUAvsO4fM0KiYlDY0vjev/0ri/B8T4uABEvj0JIyfmQhZhnSEyKo3kHUqytERUEYfHPTykJDfqWOWKTd4wPn+U2tSvkQ5m+2euzU0q3WK4Z2e4vlZWiZPuyEfhfC/ekGLQuGk8RKvq1nUimzPHMFS1Ab39HOTHuwphccyLjkEFgIUiCyQxZ7SloelGxpBX7vEC4SBPEt5V2kWOtN+P3KP3JuLVud8g+RcTz+9Tc/omtRaMDJQVGv0YGRmx7mrn837kXjWI3cB0PbivCT/dSDnORsjudyWHcrd9LEWwEBSu8ewOw/D/6hjbN9laMaSD63PW1S1bodV+f7N/69TnJqGHtH83x3UcIY7EKmCjHKWutyeUuFjjIjJU27qDCLxdwDbkkDZYJzXqhm29qYYcVC8rNksrveFA2eDEEO8qRT+8TxnRT5Mw1CSI6G3XGBDD2auUsTVLemdci55S3ucQCTVVSrxPGZ9zhE+c1XXquW+TfrK2a1ykMrA2GLlBFAcnKSfhdWwSJGVJriGt4n+bxuwgLQsNJw3H7pKGP+vEAT6gjPHFcQ+XqJD25BifS+O79hgxZJaX/wTFR79FVBh3KANjtCQnbRJOB2570w+JEZM/7Pd7k2SrPzd+DhPDKpenMzQpZzj8hDzC/AjhckX9Gm/XmS//b/it4fM8Jug0Ql1va4LsPnGMvYBGzthCGx1OR4rKK2+Ne+ytBMyuuyfVHZ+kuFgpwVXyOHjLt+VPnUsIhbVEIfR5ilI4yjQYepdaH8cpg+4YuJ46wp1F/iLanrByTLusVIqlvzT2swUSIvDJzOf6XHb+vB9vq//rKCGUhS8d3k+iEjS+jqlgbIGyIbeoqOcsBWZ7ZzuvVcCiKmUPsDYB/h7ZKMdJlt1qFrPcZ0iNfNfnGv7ZHOQj0hf1zDjZdqJzVavbaYg+SBpWB1ou8T1q858haHeDaf51O+nIZBLEMG/XuOYfkAoeZU4QhOLgqY0VhU3Gff1fFJ1zHPgtitODND05Pe7RjbnJdIJC7+gCliNcG5+7RzUcWRv3Kf+6TJ5hto2Uo9rfQtSsUqDLoB5SLRp/jzx5ZJUy6k8pmuUSUZmcpgzwedKremnMwVfHNdjoZo5aM2+Sxjy9sMSM+TXKOepYPmJ63X16nHM3iUJWSegsStUh2s1tH+Uwz5HmOCvj2lS+KGu6RaK71TFPLxIdc0/m0OZJdKWBk7uH6Qz5fHtPqqJroyFoqnOKnad1XfTCEJsHCQA0JoKlrlV/SAGDh0wrAZ6OcZ8QxUpXE92g9ob67+7svUeNiQZJ5DwhtsH7FMmb1PfV+WYjCHn4zqdrvDoC7uM9aT/91ZGqv6t8+u4Yr88RSZ7X00UKWx1zvX1+hdiGnaQt6l5CIXYqwl4afzDu8d8iEfQqsDwhz6l6n+KT7lMLWO/MONFdalJfI+W/kMops8JPKYNhNr9TAM9I5v1FCnVa9bLGtKzDxeqiMwMu/fAJwrGKUpUp7Sbl1yaWFGgvkOY5yqPki11oeuVV8iiX58fPZIzXlylDco5K/OwjT5xQSuUxXWwaZ8tg54mxhjxue0JFAitjvF00GhO5exG1zuIaZVTXqPLJN0jf1qekIMNCmbskwyyX6Lw4x1aT/QFJQuogDwN/mUJoSoVsfnOPFP+cJY5D7l1juptkpy3yeEi06TbWmVBrUv2n4nyN3SZB5WqdpW6ukUrNB6RfyFnSBErZ5SwiU030lCREe6VeR4h+XgMCMdwaFFUIEIMtZ+vGFM1qHNba56XvLGTpRSRPCWeqwZBeWSCcdzdsm+1nNpnVKQqpHxNQXe8/145hQleOtxtXr9MKROesK21E1xC6olMSfX46H2sUZ17JqOUeKW55iUQGs9ztrPH1mDqaufa7921S0hxaN+Yb1Bp/h6IAz1PtfG0SZc3CGvD2hNo40gjL4/cr5OGJdheys7+tE4+RRayhsazX4y2Ni1miNpclkyZeJuO4yqikMh4SWZEowwypG/AGNWEvkCcc0K7nNkkmaMDvUBv5PMncq3+V93OSerhjAmttvGdS5R6RTL04ruMpZWgej99fGH+/QBnuLqFaIw6qqyDcYI/Hffbsq7TAVWLEpT7Mkl8YY/5Zqgrt9BjPRxSi/T+IkuQMVSTyJkFIz8aY/ZB6gKyJFJ3lGZIgWqPoh29TiNnuVi+T6rcJaWxzhuh295JQTfnZnXEOUarOzTLhjyjEd5hygCfHsY4R+dzGON8itZ7UNxu9ubF8aRweUJHBbdIcSTlkV0eI5J6170ofHSXaZY1zN0KiNfeMGm8TVsrVTBrJrUoN9MIWkVo3bjrcJRIVvMB0ebJr3O+LyLtDmbRzaIS7tr+H8Jszx51FqqJUq0dn1QQQieRH41pN6uuojVZ0VtJHa5S9cv4haNzilVnHs5Uh7/fg+lfG9iG1X94kktHb1No7MXMv0mk2jepa41neWad7a0KhBMXO3aPJXd2iEIWt7vaRh3C6YaxGcpC8KPnTlfFZJWiGz+o3TaiIDDTK0hkiZJvniAYU5W+SMFnkIse63gbDZJR/t9zym5TneoXyWjYk6r1pOwLqSYBVCln+YNzvkXFfH1GZ8rUxgWepzfb7415/nXRe6yXgXWc6T036D8f5T4zPr5D2ng/HtVgSfWD8/XvUhvwk5XhOjvM8IwZ2jkLcB0mLyIfjux9R83yGtMuUupmjDObblGzvy+Pe1sc9vUwiBB3iOdLE/yjp69tljSI5VRg2qDGhNk8Z/3MEFctfupZM7OjkeqFKDxdNer5HVdrZX+STVLR1lukmPxqeWQRsMYvlsErIblBrcT/pfdCNl6G4FJ3ARC2z93+WQnI9ctARuN/ukSeG7CGFMt34QhCn/9qioEehGqe1cdxNko+ZjQB60srfOxWj43lErVfVQyZguy7Z9eOYd+Omg1lp51BNQfus54TpOeuov783u5c9V6cgRNmQVqwWyMxy0iJd82EdHQua3Of3iTP+eELxkVZ52Whjrh1YDaIFAe+QarjTZEOJXBVXH6A2nQqDxXERz41Bt55bwXmX0OgJIdSCiLXr9LriwUSTyY4NpiuPRBUb41q+R/SW28c1fTiO8TPU4nfSdrT/u4F6GOp936Q2NuN+7lMbap7iWs+Oe5SnPEttVOU49ynjtpNCe8cJatQZypcukM5QjqPa4ONEZvcH4x7PElrJYocLlKO8T83xRYKsJuOaXyaGusu77pMElgoIo5FHpP/CcVIZ1pvadIPZQ71V0ozGTfxwXJeUk8bbyEEO0OYqOtCFdlzphW6IjHDc+CZNO7LU0fWSZ6WV+0gzeWmHu2MONIZPqOTYH43r/A3KMRsVSq8oSbSgRb3zEaZLyFeJYmI2nJ7lTrsB6ut1jXC37pVZWsN133liiAG3am99XPMCtcbk5CHGXKXAc+N953iTWn83Kd52tphLflU0rmRulif2urbijqUTtrf3tqIheuRiRNA11V7DVmjaMe3Uic7yAQF1c0SEYFL4FLBjQj3Y8w0SqlvcYCgqFXCVoJJnFIL4U8KTfobS2Yp0HchewWXiQV7PwgUrgZ4yrTmVYxYZLpOQ7R61wJ+Sp0evE/XGfsIb7SYc8/7xeRHzMtNl26Jmkzlu9M5Xy4mboFBC1hMf8xSa3N++2w3XXaqv8NUxbnayWhhj+40xlqcopD6hwv/LFGo7R6iIm+Oa7ZfxA8oIniQGRRWIhsz5+CHwh5RjeoE8ymkv08+b65VmSr92EWpABKbaAKbDZpHbR6RQRrrLBX+IVC26qPcw3Q6yZ6AhG6KHfxpSk4SrpKJRx9yTcPKPT6k18m0qsXN4nEO+e98Ye42jTvwZ2R+fJtrxh+QhqwdJW1YTwfNMP9usqxK2KvuFacNjYltU25NEs6+eQNWwKD8Uic4RFcaENOzp0jlVLHcpUPYeKWs2Keg4bpA90BOZvryn2ft0fvoYzEYi/l+0bURtYlA06zF6b5t+DZ3qsPRdRYqRfFdXzDqAntD1mqynEEAeJHSFCfZOdTEB/iXRkop6nlCLTYHzIVIIYNZbgtokktrT54lWVYWFYZecjllz+wwo8l+iUNocZZyUnllEYLJvdVzTOnm+3QPS8EZJnfejMT5ADIIbww1omGZiaIXaQC5yF68ZcyfEZiSGMrZNVO3xkBQFuIg3SWP5W2MsjpAFMDfG4X8Z9/BZaqHvJcUkRyjEdHZ8ZoWoVSyusVxZZ7rOdEmuYaEyKQhXKvJ6MK5F+dxOUvVoQky+VEpJBYiCfLmwDaabBOmgzK5Li60QDXOvhFQS5qZ206pPt0TaKG4HSe5ZLGLydY0kEXeO+3yfkh/ZW0M6q5fqKxPbQ9qmfg3414FfJMqXq+Nz8vNdkTHrTGCap50NeUWzqigWyaPCTpFHnXUUDDFG25nubeDY+a/r+jopSNrdPm8LSZ2jBsTIpRcFWUOw0P4mWu0l3t0Beh0WjHQj1Q1wTxiuUntLRdT2dhxD/o5kZ2kalTQ3qPW9i9pPOvxO7TgH/XpXSD3EHLVuv0etsZcoB/RCu19fE6avYxO4MKH4PQXeq+P/NqYwZHTB9wntyThDJbmtd6iFeIxCc2baNynjo6G4RpIzeh4ziXepTWWiUKOyh2y4Pe1YltWukseei2QXSEh9kDJoPlXjPLXp5sb9vE026V7Ca8uhOuFOdk9gOFa2joSoE660Cdk23ttDbdSHFE99nMrmi97WxhjZiEl51iPKaDwl1JAL3mY5RiHqgTXGhmkaY/kvaSijot54ScRgwlL0YI8MW2A+Hd87RpKIalbPk0hEFGJyV/rlKdMPIhUBd+PtWtSxuMnVQd8hXPk2ak6/TUVv18dYfJ48wfs2RcmsUt3fzhO6i3Fuw2qjI8djjlATKmDk6l2bPVmkEezoyffdH70woq8l0bJdySxh7pVnAgEpiN7CcZMy4lJ/HaH2c88iT3lZHa7FKlJNos1LFKCAPF7L4gYjGqOV2YSjBlhDD9POSiPYDWN/eZzeDEo6TCfVjZ9qijskYpf68LocU/dMb+WwRuyg+2Gu/RjlOX5PxrEFsxDgNoF6Zp0GhfaBTur3G1XEvp0Q7nrC+4RPk++6Qcp3TxE9rdpXF4fGwhuFhPyXqc38PKnyOkJUB25USEXe4hjk50nyzCoupVE7qAX+h5Q2+K9RUqxdFG/1lXHdXxznnUXBht8bxAjPJgj04C5qwxMLAKRG7Hx3nTyOychCuZ06xd5sfnF83gIHE50aZBeJSaNdhG/V2fYFLr97kxR6GCJCjJOFFCZZRP37CJ99fYz9SRLNSHWsjevV0dwZY/8LpIGTBsGNKd+moVWS5xhrYKRRblFO9V9Qm+41KrKQNjAKYoy1fLz30WVebigdkVGF6793sVsgks0u3erSyI7W+roS+bsunrZz2RTJ9q4dYXt8HRNMr8Vn7VhGHpaH94Smr3WSpF9m2jg7pyZHr4zrOsaPdyMTvfq7BtXflVH2vsnuacfK+3CsTcZ3xG5UZH8cFRdGfb7cg4+JTZjlfbV9rrNj/HjVoNehIqxHSfY8uUatu+NEOdSvo9MZ/9+NQxaAm0VjoZUXsW6Sx9bLAR0lhtG2iHJMkIojvYITP9tUSMWFRn+eTKxe02vqde4aCLPQorfZUMeNbdZShL5BniOnksFzulm3mlgXWRelz4Yejq2Lphvmnp31+1IMkATYNvKQxmcEDck33iYGRAkaZAOJ1ObGZzTibkLPI/V0mXCrPSGlMVTa8xzRYTuHjrdGyEjE/hMqBPZTeYW3CNKfpTnWyOPUH41rfJ5QP1IbypRmN7p0mEarV875cgw8RteQey/y+53H7XPoex1pQoyj8+H99feVBErbqTP24ZhzY5xF+B4HphU26zPHNqF8i1o3ctHmZA4T1Njn6isU3XKCig4cb69DvnSZmkvRZDfWnVboVEnndW+RfiDPmDboFkV0hOx8GGFskLzRNqIq6efuL+kx97HjaPLR1/VxjDPtc37WCGW5/U3wsn/8CKbeIsnJfoytOPynEyokNitu2KimWJQrulOacYCa0D+m0OQ9qvnNb1Ih/TxlHL5NFQqcpBCJiEl06qY7QRmWTWqzPiVhj2GAHvsJ0TLbz2BWpK7Rc2ErHTFT3MPFLpbvDYhUcTgePSvakcv29jm5SaVebnCvxcSXi6zzpl6rkcZj0ldYY3yXQqrrFJ1ymHCwtygjeWqc3znTmLjgVkgEsUQ2Tg83NVY9kSHPZjKu832GwpvtPVGixQxW4vXvdiTiOWcjCvljHXavbHNT6WD7/Ey2OEdHjrMboofE3Zh0Q0t7r2fZO3foS9TpPBqaawx6QcYmeaJ0N3qzIfVshKqeHpJYN++yTlEFFt7Id0sFdUXAPWovW5Z/mlLtnCI6/Z4I1Mn1MelUi/83LNfJ36FooBeZ5u1XKMOn0qVHCT1a7+tCQKBk9CRxprP0SqddOududOOa1anPylSNiETdi6Qh16kxVt6vfS62emn4OwoH2JhQDWp2kh4Sp8bBOid0v/3dzmjHxgDco/i3r42be4UKM8+TMNNjqJjo3JCc6zzRyWoURchuDuUr/aZEAXKeDtxsWKtz6aS7yM3jiKrsgbCf9GY10y2HKs2iysJNv0xtgHtUKHy4nc/N8zExsDb+OE5tzCtjXM+R8uBN8vgj2th4z46nLTyvkeTfdYKiTW5CUTzPUej3PpXUUgUhT28rSyvkjhGlgDTQFUILeA0b1Do6TVqPdrpBo6hz3yC9CJzvzsH3jWM4rWF2o80a3M63zlIds9GJjuAB0xpzIzedmIlWDWmXd3luy1+XibJGVN2z+G580ezOdqwOFHRGGmijgO4g3AOO3RKl+lCu2J2Vn9lox7dc2sh3toCl87KO9ayxnEV7ApfurOfb7zr63sPC49P+P3tMawNU7exsn9kKbfZjdUfsfnUNmpybPYZ6c5sUORd7mH4A8k86tzTGKrFtP/aaUAbzb1BZ372Utb9PEe56UgdSWYn0xDoJW81izhOOrSd8vAAXpIPRJ1luzA5pk/Z56QcNac+Wy0lCSfE6KpRvEm2/TVWXrVHhw1nK69uCUvT1mJJ2fWd87oskI35x/F3eWnTb6RYbtRi+945XXr/JLcgzrLaNcyjml0p4RhJAfl/Ndg+J1bLeIlIZm9NYXvlnlDpgH6XGUHf7GjE+vRmTyNhEjZVLnnuzXVtPUM1u2I12PA1B18POUjadqjByka6wcYr1/krWdLY61LuEcjpJuFsjnn6PXpdGTorA8NfvWb2noZLu6t0IDeE1uKJ3k9ye23swwdo7G5qVnx/f+f4435ukn7VjCAELE9LO1vvqSpO+73qCD6afLzdrgOHHDabzIrKXS1ft0tFzj556W8lOX8y+nEeLS3aQaKLblZ/0cvxdEz0BbXJ4VpNt7kM073rWkXV+/qcZ4G7jZv829b0J1dd0gzIqp0mbPkNAvY4DrWHrXJwLF1JccYuEZPJQhrU7SUWacjR7OdhAyOy/Eikf5XKZOBBJcAf5IUWF3BjfPUcZs75h3ES2bHxC+jGIUrvnvk1VyH0wjveJ8bl3qGKYNynZ0gFSUNGTke9T8sDPjx+RRm8Zut4+bzJ0c4yBidFN8uiig+Qx77fG3ww5NXyiLekEmEYCPWTrqLOHZH5PJ/yYyLlmWyf6mg0nNa7doIuWRGdmpiHhrP9ujHvtCTZ14r2ZzUUiZ5pQ83mEhMGbRDXg2GugdCLOTUdOtLHy/1JdztEHlHN7Rq2HN0j/Eo8loKGNw4QkajuaVZ65jfTmWCB5gUfjfneRR1hpLGbpmD4nJjY9z9IYCxNNW0UtPd/g/XeE6xrw8z9JESIdNOt4O5ru1IGR5deo/WxjqTfJOp81gv2a71NA6uK4r8+PsTIymKWbfElDCBY1/jqN2QrJrV5bIeSVdt7Zz1yTArAC5uq4AcPTDfKInyMks9xv2IHtshmz4V+mOOil8d03qfB2B7UAj5PmPD7ZYZ0kJmz+vJMYSxNtt4kgfvt4z65aa9QkXhvX85cptNebwRseiahtwuEis+vX3LiOLwO/PY7961Qf4TkqufGvqCd2/AJpVr84jvfCGCv7JL9OUG4P6U1w7iBGdIOoRs6PsTfZcpo0wFcfe5xasG5s76UrCkTk/dUXRTc6mzOf3Yr76gaqv+dCtvDgETWXIq4esnUaRBVNR5+3KQpknVqbL5C5Y9z3y/z4M9C8Fq9DVKMhNElqiCqfbU8Ktc4mQPu1bZIyVR28EYjyN1UB3usm6dFsOb9j15GzOQijjg4MlM8ttPe30h93A7rWjtUjlG4Qt5rbWYPpe5sz7yvDEuTYV9m/b2P6idNyvFZkev1GP516M8r0Pr32rdawduzCOOcJUkTjmPwkbvspZTNWSON+AekaKYT6816zkUNXgPh3/6YEc2VCLXD5OQjFsEba6LlQ3dR6CG/exdrDGd+3usmSWI2wjXo6NzUhDxmEdIDbJJV3EMN7hdq8L5ON6DknJGTvZbsWJNg9zslfIqjqIIXWP6I2uUb8PiV3+31qUn+OQsPbqFD/NvBXKVpngXBL8slyjROi850wLXVyU/bow8Sgia7Vca41ssCfjPe2k1ajLm4RpoZN2sRm+6oC5AZd6D8t7OrX2VG2cyrSF9X2pu7d0agJFzG4fnyq8DMKHb7EtLqCNn5bIaq+sUXbHc3NGuHNNr7K/zYJL2/UsodQM51L7a0BTFTOk2jS/MYsVdeNnMalI+d+L4bFs6FxP8ZWyHQ24efasnFOR4cdPS+N6zUBJVqUJtSA6lBuj+9oQO17IQhYpfaQKp1ZdcVW1+i4uCZn72WdAouXqPl7ifDyRhmPiWzW73k9KxSIUfKm8muFSEhn5WtbvVT69OjTUve+Rv3sU/LQhHWpiTeJKF2UK3IwYw7pOXyVtJDsj8CxmsmkzqzEQ8rCpIeSD5HHDqZbUsq5LZBQVCQr3/OMZDDlbC0G6EmBXm3Te0+IkEVwlkxuUgvGROWL40dKwHaN20lnrw2CWHU2tPvtxR89jOzJQhMyokUdyhpFcXydVNl9NMbmM2MevLcNQkm4cI0k7Gs7IXy8RtuwrS92jRdML27IAhP1qtF+SqGSayR83k8at3hcVScvEn30XPvpBkJK4AEx2ipWts98TvWLPB/EGYqsnAMVOa79h4Rn9X11rp5nq/DfjTaLurqT6O+53txrvt859G54usGobfmgAAAgAElEQVRdJcBnc+a4s2i3710jRimRnuhy7JapEvkNKnqzd0SXiHZ+1++JujX8jrWOyQceyJ3383r8O1TkaCHXGaYf1tpfHex1J6Fe2iisg5JOEXWO/AEp2pilaPr52OK6H7XvQK23jykw5jk6Ol5mWta2CaxOKB5FZGVI1jnGrsF1092mKIeL1AZ7nfJEVptYpbWDSKygFsENijd9nwz6C5RRf0KVCT6ikmOfIRv4zriG40S37ID2cM6JMXHUEZSLpasptrXv9wHvi9r77tlrJ9YJl/iXPjG7azbWDaQhE6EbEos0dpPO/RBnKBoyrNYAbbZzaSgsObdb3mw417PVsxu5qxO8PxHeHHG8lhTL3b9AnjoyT5re9IoqjYzH3CQb9iHlxB4zzT96zb3yslfZuU5XCbreGOe3kXpP7PT7nTVCfZxmP9vXwk/iNh1bDapG3/e79M5evX19yZf30t1Vpp9x5h7tioZZRNn5bWmfZ4QP7o7LlwjOJ+d0uV3vHCZw6eOh8+myL9eSEe9sUlmg9UMqmvxwvP+r1N53fLaiTBynO9Tau0DN8eeIFNS8Qo/yfElFmITt0eO2LT7fv+fLZLn05TKJzre65o2f8D6MkylvkryHaV2xWkhDehMdEG9iGObNO4Bdg2urwgkJBy6TRxiZoTxJTdjS+IyGXt5YZDmbnfdcyow0juqeXbwaSa9Tcfkhyhn07k49PHJRSgkoIFcP3ct5P0ma6msge4a+ox5RV0dTzs1WiY9Z7+pnTDD0lpBKyQy7aMeRz4cY8l559ZjQUJ5rk6AGpXDqvNV17iPVXwvtPB1l9HC4G42+ULvD0hHNlpf3oh7pBY+jyqFzq66d2aIMv7MVAvKloVbFYyHILDXQ1SJ+T+PsnHYgIGh5PK7rEKEDaZ+fpQ76upilXLry5wq1JnqkKFJz3G5Tyee3KGeqMzcaFZV2dO4YdUc0Sy/0gptebLVEdaP7MrVPPkX1xD7a7nUrQ7hMqZ7s4TJP5UrOEMehQ5jVYHe6T7D2jAKEO6gczKx6ot+L9+3esIGU9NtzP+G7f5HX5oRqIiOnsk4tisvjpo8S9KkXgvQDcCGuMR3S9T4VbjaRS6+AMYw0G99DDc9pyLtA1AJdVqNTEGFCwmMN8yahCvTKXpNUwB1KHrRAOoBplERcGqq7VNh9kDyqyUUuL9kXg7y1hreHnDYbmq0u68bJsdyYOT5Mc4eijG2EaurIraOoWUPjGM6Gtp1v7JSJ4akRQQ/x54jczfm1D8heptsd9k0+63R6gs1xFLU4dvfH+XskMSvqn32J9E0Gi45Mypj0m4y/LxJZ4B5KibODcH22d50NoWc3Zjf+JqR01D3HshV32p2wv2vYjDBEza4FeW/H7hkBBOrfewsCKxaVVWrQ+3j2MXR/brbPbZKHsW4jPSc22998kIGU47E2bls5xHvAf05RJi9RxvcXqYh518x3Zikq99Ku8fsihVxPkN4ns6+tIiFtnHbBc2xFQ/3/fa1NqA5fE8qgnCFPOeh81azsol9EN7ga68sEaUvcQ/haK+deIhU2lyla4io1MXKyNqXuBR7ydV3Xt5UmsVMScoZ9sbvQRGhyvPdIWelsoxGNA0QILyUhbbFBmuGskvJSDZ3nNQxyQ8n9Tcjz/NbJJu+drmZD41kesqNrf+8IQbSpNM3FZoWX5xX9diRp0rbzs318dC6OvaqCjoJdX10uRDtPp5x0It1Yb0Ul9A3hZ6RaRELmKjaI84T0h3AdKBvbRmgW77dzf/2confvxXXpPM86wNmoy+Otk2bvliFrJJ+M+1ikwvLnqH1iQyznUaRvolI1h9foHG9jmvPta2SWroPMr5FQ54KlqnaS0mjnwafDLFHyvnNM94twX/SxeTa+s0g52YMk/9Q/657sEZYG1vF8TOZ8K+M7+5qlJXtU0s/3Fz3OT/3bhOJ5fbrvTWoj7qGUAseY7jKkQbUVn71/TxJ5R898yy8ZMgn9NZ7+28M8jYEaX7kpyx9tUP8BhdbPkUfBiEw6LSLSVjIlB6nhdDGaMRdFqhLpFV+zG74bve4AXAwdYXqfsxt3tX3eiKOXlPbQFqaRE/z4xvbzIiC/84iKfnycj0qN/n37z/Z7cewcL997TCiibUTTLD/vfc8mWL02DSRE0mYipV9TR4gWlCyQoqG+pmbHoRtrmFYhOLazKoR+bbOl2P0Y/Z46BQIxvjrcbmz8jNFNz1Nskoejym9PqHWhXHOdys9corSx54kaxk5t3VEdJMlZo0b/32kS2ndm1Qlb0TjMfK9L5LrRMsKDrXXKswbYl3SY82Vrgn4t0h4/KcLr/PVPM5yderDMfp6yQ1bRQUDFTzuOgMcK3K0+07+/AtyZUP0gDhFOSb7K4gSrt1xoBynjp+BcLmmDPAHaMLqHtZ60ayt3MP1oFG+yG3JLozeoiTDba5hzmyTKDEt7wxARnBrRbuA222dEhTv4ce5vNoPtq39GA6P37IkY2me6fEln4Vh0uqZvYs/FzDF7FNAz7T1svTnG25aJlnSqqjAa8NhemyW/oidDTxu1O8eiWSVMKid8GspmO75lojYPnyM5B4s1lBl1ZYmGTwlcdwoad6VRHxLE+zplhMza6zQ2KVTluhc5O6Y6Ts/dx76Pf///Rntvq02vY9aZzBFpl8ChG3KPqaP2PaPM2cIIk3y9A1znSWejxK2M6VYgozvl7tDWKHT7vXHuV0jl4mxUNruGvdetOFlBkLajJ85c06Lxngh1/1rh+ucVXnSgsUABuxWKJ/+Q9Nz+i7wEe3v+vA+28ypSmDDka5fbh0S/T4j2V4mTT5BYIa0LD5FH72yQjWtfhIXx/YfUYP2/7L15tGfZddf3+dWrqau7hq6ururu6qF6VLfUbcu2sGUZGWMwgzGSHSAYHMJgkmBwQsiKTRLWckISVgQOAQIJEGJjQEaxjTGJ8QDGFkJGsi1blmzL6kmtrqG7qrrmeXhT/tjn471/5937e79XXcJyVs5ab733fr97zz3n3HP2+e7vHk49gt0Br9blKozr4DshRbgKzBq4IJKZkMKlR47L5acKQ31tz5J5fp8geDMRnRyi6mmdTDu65/TCtz6zCicni8bLbaRwWGXatQnGOc9+rOrCcXwqOqa0X8RSN7ZlMnT7Kple8D6m80Y4dt6vUDEqzoVmCkeNqBPS2OHRMRNSA1tg2rq/XOqqKqh+2h4Z9Yn2nLeQiVj0Ed1HRm/2FI+lFxx1wxsad6+pi72ng64Ra0UuVc+FXvuC3HiG0KfvacL0GllhehPo10815N0kDcwVRAwJrLo+tZWYBU+udQIcIiMtt5f7qzbSayw9AnZsfZ9by49z8Ggbu/vLGOlnXqmKIcFb3538t+9ZJL+V6XUzT6mh77NKr7X2myATwnIpB2VcvVm4IBfn8fZj9NZOciFKusu/KFRrrglL3XkrB6MgdXepri7ujKskojAqpQYKrJBGOie3EXLV00P0oKXawBWFyGvEDrmZTF5TM1bZpsoLugH0CKKi+3NkTgl5tJrzANbyXapDlR91rOqYipIqf+l3m8rfS11d/WIZQv31OVI2FcVrtBGNmVPhePnM63uqYhuZGrVSVpWqsa1qCRpO/DlKBqg8SghdNz9pnppUp/KyjonvTypAw10faFNdDx2r6tIIaawy+Y8n1vTz8HXCF1z3zQlJDWqfgNSaHAtKP2oOmHqqBF1bqx2lll7o2Ec/N7ezm6P9rhFxs4RfpYl6/9x6zU3iXW4qz3Gt7iz3Vsqz+u8OaSAXyTw4Hya46QeZppHWCMWRMovrfbP33JgQUWJGuzxCRoKIPOtkEzUZ6qmaWjlVSNToAqhqQuVD5eGqJ4MDVHfjnt/S5eYFgi/7JeJlmfHtIsGhye/cTQRBPEOqDy7AGqNfUarX9GpoRRE9ZzxkIKtohfJ/pWHqPQqJKsRVvftn9nzeEKrxd71nyNpbf2yn/LnIwUNgzUOrO6PITrVxpfxvnueaSN+NWs3FzXcTeYCmbblMzMm7W32/RCyqu4l3fZkQYma620oaPXXls48uwJqe0mx0d5LCV0OS887IxOuk1491VoPrFaYPhryH9Bqo2oMbmWvDOS11VgWphrFKuXm9/sH7yc3c9+um5Wf9HOsRfg0w8d2p5tesZHX+9XXCtB3AMauHFtS2VI2xj1cYKtfab/1+++J8MaDK8dOWUK/bqFDdSNlw/RNCmIkEDe3TpUgXJHM73CAm1952r4cq3ksmDNra1V//3sRa40rdMWGau5WDvFo+d3KoBmk4utzaeifp5lbVOXnF6m1h6fnivjixquCsfRia5EMqaxWGdeL2wtnPdf7fTLqOSeNU/qwXuCtdXZVDFhV5unDNyqZb0SHCMX1CUDWXyROo9QYxqdMe0m9aH8srpLV/QlJTakyOQ80rrCAT4S2S/siQGdc8ukhhocCpY+/4CQ40iCkgNWpNCArGtlZXKDnnl4mAASMY7yrtNPDBI3ogk5pLV7l+qvq7lbUHSPZcqEZrk1/V6D/7XV2phtTq/jM1tyEPCcfOse+NY/0m7r1u0B8iqKF3E4BHjnoIMTsfjU+oVIk2BIW/z9nU/V+LqUe1E9mXfh3UslFEPE/RBgQbE8arE/KwToVeRbtapusuXV/EEO3Q74zmHKhuY1X4atiqO3itqxcolT/rJ6FozMUn4qp8l5tBT5tYrwa31XL/pH0uT1z9jxWOToIxA0TPDVWkW/ugYOo9BWxvDVwQ2fk+3GiMPPOZ1RCkRuM4SDntJnnhU4SDv/7In2jXfgMhHC8SKvVLZMj0V5NH6EBu7Bpi61FaLiqzp3ksjZnmDImGaX9k++TRUQvkKQ+VnpKzVoV2Dvi+nHc1xWF9H2cJb6KXSZCxp937GTJTISRNZv4EEd4RIhz9KTIkvYZJ95u17b7Wnn2EcO98gtTifPeVtoO1wKZHlUN/90BIGqh6gNwgjaRD3hC6+B1t7XRj6q+rz/ZZzgtan4+2z+5n7RrqQRNMUzPO/TFeepGY0/tKHUO5hzdabgeyXgUuTQieqvp5Voutpd+NnDRav+U1NxELxnSNBjq4+y2Qx17XgeiFexWQCvTaDj93olTub6nUV0OaFRDVZcyIt4os9eE8SibteJjpY59OkEcJeRrzhETqfZ4HUWrvElc9HBZJt73j7XmPlvFTgGwr9Xg/5TlVhVQIL5VrpAREhwoGNYoJIeReAP4fIu3ne9tzXyP5zGVCYDm+J0l+zyONRCeQ7nQaPKvV2zb77m8QEWEfJiKp3iAE4g3iGJ89xMJ/N2GU20EGjrgRWb9I+1JrlykjdZXT68PxMty8CjzbNCEjPBfLvaJVkbb5BxRMvg9IX1s9fBQegpEKFio/3dNQQ6XSWpWWqHNkCAUbRu04mUf7YXIea8hf7e4dQ6kr3TX1Op/5BrHpPE2Gzg9pmd7j58oTN9/6ffX5PQP8Y+A9rS9VI5xVbPcVct333/cb3zylXnsT+Ae09J4uQL/wHCqdvY2WWyZTOhou60s2qMGdU4NBr9bYENFZdU+qCFnB4ULY0l3jgNeJttjVqUDWiKErliHPcnyejnGstfkxMhT0GiEQXyNTWn4FeTy9ZajdItPK6UIKZDcx6Q4FrcEkpwnEeYBI4WmqT9VSfbMNTfbIGL0/thMTz3PJzPzmJNUVTcOXbfeoJ9Xe7aRgMlHJkfaML2j1/grw/taeZwhk/CTJCW5i+j37bmoOA9/jeQKF/jyxQI36epz0QHiaiKp6kEyGVP1NBQYiJAWlAtF6jrZ+PEaeIn2C1BC3lPHbTgYWef5f9WN285ab1q+3F1DVd7dqdn53pf1djXqwdg5RxtK/LxLzxzSzjseQau47MCfJFmJ+38E4ml0hBfUSITgNIOmF81B7V8hUCAKKLeW+obFSqx0TzhBjfph4d8+RGRgvk5458xQpGQ/m3Tzj2o0I32qotL0vEO/4kPVNgB8iJtw+cgKpkldDRL+zDvG8y+X/imL73Vme7gaJjhRI7syLpY5qFKpostYN00EMm8gsaedJimWFmEwnCDXzZ4EPtna9mzh37wsJFamigF6oWGpfbcMVYnJcISarG5joW2HwChk1tJ/kKNVMdBPSy2KBtBSbZMS0ndeBjxAoYAexKB8kkOOjTB9vvkxuFGoKbiJ1I4H0Ivnl9pwnW/sNE91PbjCV86xjU5HadULIvt6u1a5wBvhJIufzAiF8fxMhWA4SGQIf7NpraLycrq5P2jw0PqlBePLJFmLT2Nvqea29i6utP6Y/rN4c1lFPBLlIUDgvAV/WxllhZlHNl1ZQIBmYstjG4ighSB5lOqS4X/CV0qsAZZZhrt5b3Q1746DX9z+1L70MqEi1N177TIMkqofDmFB9lczsOLSBTMhw80ViQxjyl15PUHqdGd+eZWMCdqjt1SDoGFetRk2jBnqsAOcmJGUwNJCVb/VhRv7oOibHa3KYU4SAMwm4gsvJbUCGvM3HCbX2McL/U+v168RgbyWQoZtFRTZ1slZjh5PNzyEmw2mSt/TaGtCgZbrmDKiTz0WgxuBiWCARjXlsbxDC+PX2/14yiMBFXlHbaqlLHk064g0Cia62PtwDvKPVd5XwHLlKCC9pBX0s7ySTuRhwA6l+b2Itryc1sEjmb1AYrTBthXberBJz42hrw2YSNe0o9/tMz7w7TpxucY70VlggBP7jpAbmM6rmpnHwFDHBTeZdqS6j0q62uh4i3rHpTpdI/2I9O2o4tnYTPTDq5iJyrkJQjU9j268SQn4bmc/kKSLDmOrzA8RGs4Np4eb4Qs67FaaPIYK1XjM9JeD/0nKT0u5a1Jqs037ZHz+vgntow6gIeRaa9X2eYdq419fj/bV91aUTUrMbimazb9ajEXWJYeph3jIk9M2Ls71cM0SJ1HuX5Ih9yVeJQTlCBmbsbp+/Siyw/a1TLxAq5D0ExDY3w0OtIYeBnyLQgqdrXCcm+NPAlxO70E5iwn6KUAsfJ0I3jdzzZdnGSflcn2FdlerJt7161RP3J1pf+gikOunqJFMFNiNVzbMgElcg6zRuSr7jxBFORgAeIjPciUjkHPUQMTLN0w5WyIT3J4EfI9DT7yU2qisEf6oa7tlyuvDcTyDLXUwn+7et8px1zNzV64LU31O19xRBTzxFCN3D7edhwqC1g+SeDat2475GImOfs5NMzlQXuO5aR9s9JuK5n5gz8rYa8Mxvu0jmYnDeaFyttgERb/VOGKK7FLRmoxMdK+TOkIZMPTNc+PeSyHmFDHoxWMrnVC2srs3eda4KAK+9Vq6dEPP1Y8TGeoBYd/uYfqdy5H20nv0dMsz3gnUjPOl61w+het+RwHEX8z2zR+PzIuZZRR7fskRo1oeINQYJ1Pqw6No3x3ZpQpwJ9QaxuCtHBckfP0bs2ptJSsGTHU4QQnmBVFNXiUm3o33/b4jz3eSXJsSkeI44Yuid5HlclfO1A+6ap1p7tpCBEJCoXUu4kUuihuqwX5GzgyrCqVy1LklnWz07yZORNay5+0k7bGUtMq+biOHaZqnbQ6JkF6JcnALYM/ukamqaz4+1sX8vscAUCtILGscWy32rrU5Dnt1ITJ/Y+3MaqCFdIh/6860vD7bPXidQ3kMkb/exNiZPkG5iChJPKjG16A7yJOsqHD2s1aOm/DE9o4tM2wRM+7NXo11Pp2k7OEcIJ33oNfIuk15Ezg1Tth4gD/DUyPpRMnR1N3nKwyEyYyFkCkcP3u2LzxbF1egt34lcv4L0HJlz2HX6Omn4fYB065yQGoXpZLVb+DwpylkC680Is3mKPLG2npoPeb3iHLhd/sNDWoqnpusVY3G910hDi9ktny7fr0yAv0ZMthcIZHOJXBT3ER2Xy/1igj7waPZlktw+T7z0rYRgUQBua9erbldXLymAquIrBJyMCiA506MEel4gszEZev1G68dpYvJ/OWn5nfUCNNT8ErmJiN6kOVz0Ik1V6Al5Aq/+vnKAK2TuBH1I9VZwcYvSDJWWl95UrqXcK7+rgBFJ925rbj4a8C6UurzOJElbynU+Y2fp8yK5UVfr/0vtvneRc0JDjIbc80y73l1o4+HJCTXJuVrOCWIunSY24PsJgV9tCbpX3sU0OlFgKnSrx0GlXgzXXSA3JT1BjDBVkGtUFJGqrSiAj7W/HyK0gAUy57X3Go3au2nWdlfaw8/caJbbO/CAXd+xG6wbmHXXNWRYs3VWrxvnjy6dFUz0WiUD7d5I6evx3egOKmo1wf9WplOSbuQZQwj0Vor3Vx/hCwQ4eIoEFrAWKVuWgH9OCO0/TqDmKcQ/Ab6OMIo8QEzAjwM/TkzU/QS/eJCM975CWMy/lDwmfjMZlihyvZdQGbXaV16xeg4YrXWGPDJ7OyHMPbNOgaJxYUL6107IFJG9f2UdyMq1+exlpi3cFYUZbFAF8DlyQSpEN7e2f5AQIO8iNAhpicskP1kt01IdCkmF6V0kOuv58CEVp6qkF9o9Ovq7EBXOJiE/2trqRH+ijfeE6bBikbCo/HC7t75XNRZVcEO53VSukRztjvacarj0WZC+x/ZLmsVNSBql0grSN76bO8gz1lT9pC3OEAbND7U6vok8X9CcEFUQav8wP4pCqx5jpcueGo1jIbXSC94qjCDfbS+EpUWqh0WljqrBrYYxT8p31R11idgwjpPA5Whr+yFiLVeBV411QzzweqX2U1QrArxY+nSF2MAmxLpbYW2ay3mKQR0HNnjfrFI39FWi3XqQDY1FL/SNo1CmjfZJ9fga6Xr0dmIBvEpM2tPtwU8TyERhtYNYwI+QJ9LWcGbVXSeXQkBkppHmFwgkLlreRAiiZ1q9dwO/leS2VJ3r5HZB+PKXyfDa06QBTSQoL3dXua8mGbGI4uVtnegiikutfv2HdYe7m0Di1cglXyht4ku6RkZi9TmN9RKxTdVH2v+lXOqYW2dNTF99vfUcuUgGd+wiJrFCS6u0zvcHmc54ZpG3M7T3/jKudbwcv94dSWFqnQokn2Mwh2i0d0PTF1qBJepeJJNRvUFoO/+qte0/ITQ7Xc8ukqHbzo16zH31VKhc6RiqnbBWgA0JYIWRWoi0A6Qwqiq2CLg3hom0q1B3Xl8l7BOfJQTe3nad9MkQVVdpwY2UFaaPRhIISH1I5VX0vhHEOnRt7x62kXvHrjMSc1L+7znyvg39dxvp19IE+Muk4WBCCJCnyCOLTGBymQzNlFKwAfr7Qu7OuoqdIVJtfpaYVMcIFeteEjmeICbLq6SXxGMEUn+O9OU0gU/1LVbIv9Ha6dlmtu8MERjwA+2Z7yEMJk5CBTDEJDJD1R3lGZVHVkgoSBVCV0mi/nDrz5cS6NHTMlz0N8kkSdVI6E/lqn0vFSFTrnFx1nwd5ieQY655EyrqMidHRaFvIeieurAXut/1+b1hdKl8VtVEr79GbrrV66Z3P6qGwj67le+hIkn/r1Z9OUaNuh56YB1y/R5iWrWqIRRY+1R5XLUaIwMrFdAjTG0GvaDz+35ue0311LBs6upww1VL8Sir+8jUpRtFtrPKKgGoPkishXcS6FqgJEVzkuizkZdjAs06e+3BIKhaeo+R9eqy9G5mFn3yhzj7ectGaRDprVcmhMCA5M0UlBBCRDW0cpQinEvEQj7S7pW3g5j8W8iomXOEVV8/TjnSg8TLu5tEBwvtf0+20NVGoav/6DZS0ECGhxqe+wAh1LXCy83aftWhz7a+PEGqvXLQIlhVPgVl5bNXSc50odWznTSkQCLS6iXhy6+hnhoG+xBsBeuE6WdXiz5MI4MhA5UCvgpWhRKlzh4p9aVfLG+Wi+vr7hFkFdBSBFfIRD2iLiPsRJeLhNamp8gSocUYFWjQhppHLxwXyXkvrWOujUVizh8gNElPpagbuMdEmedbVHqz1bNEJsavfLnzRK2yV22rVvQ6MXcOklGAAoz6juvG2fdzvXdX38dNgvP8e60P7yS5T8HF0dbnAwS/P6aWXycjDTV46WNtcqkhzWMeaqAWUe5G7hkqs55dtc5Z5TjwfxPBS18N3DMhDF81WKKqrzWvgpzhBWISvk4MoqGJpwlk6LHZD5A84l4C4e4mjW4uJl2h5A6rj6/CysWgIKx+otICfTx8jy7tk/7EhsPKEeuWpTW+hnxXb4I68L6UHhXWKDvpDTm8upP7v5uPC1gfRzNfiQYqqq19rc9fKdfaHv9fb8L5znsfTesRBfqMnsrphWcNdBlCzmoVBl5sLr+roHHj1DAlAr2PzMp2hkyus6f8VBdDkW+feKrOF5/zU0SAyXsJIfkyOW/2EdqOOTF6g+MSsa5+jpj797Zr95MeJfpoTwiBtbNdKxAQMFQDrO/AcTnXnq23h3OvTyBUtYV5hE6dQ9brpvQJ4H0EYHs38K3E5uJcHtIkallt/T1GjN3D5Njpnlk1xXrfrDpvJ8pnzvqWCOeACUGlzrpnldicfoAQxG8ltPO9wPMTpk8C9ob6t/ymGbTcaauB6CJJUUAasfS3nZCeBHuIXbIeMOqzlkudIurNTOec3c10mLXJVjxTzPqcfE4qwzNdKEcIX9yrRArFLyct/zeIheFGUvnnMR/lnv907JbL/SKrSh/UQIFJ6bMBIuZw0Oe5jtcYV1n7XVVpv3NhyaVfav2VwtDw8RrTfq5a6xdJvrZHzvZzSJCLWE0urjuSm1I1ti6V+zT4KSiNVFwh58pdTM+LiqqHjChqdoIN1dWThLvlQQI8LJOHRuqCVp9RN3j56B8lgMUTBGe+q9Vjgvi7yOx3VwgD736Gw3nrBqjK7+dV+NdNsG7W62k29f6e+rAo9F9ufX+Y3EQgwUaNyKx1G35/htDA393ulybQftKfcHE7Beytlp7/FZgeJ96xmi8Mbx4QoPVTZBL7t5JRnZshvCZ+mQwTNQT2RUK9v0m4rD3bbnRRaHATcbrILhA73avtwa+2hn4F4S/8FJnwuqa+07D2GtNZWwoAACAASURBVMEXa/wzP8Q9xAJwQRpHvoc0LH26XfskIVgPkFyZk/+O8sxN5Xv9a2vuVReqFIoCsnLiPRpUyFZjYqUD5HP1JX6t/X1/+6mqpBpKpR1gbQRSNd7ZjrowNZIcBT5JaCpvJ4V65SAVFNvLfZvIE7CrB4DCvmoclaqRO3UsL5IIT83I92Af1ZCOkfmG/d5E4PUYc+/vuc+60fi/mtA5MmjDpPS6qB0jBOOjpD9ufY90fZT+sP8e+HmAaf9lhb2g5ghBh72LoP7GvCp6imaIguoR3Bj90JdqW1B41mjRvi2QhkR59t5GUMtNgjveTvjZQsy59SLg5im1z6tk7o/1KIG+VATff66WZh/NsOcY6VE01H8F7MeAv9/a+OXA15AOD/WepQnhmH+E2MWPEcaxryLQwBZiYUhD7CKTUFff0DPExNK9aBex4xkH7q5RVfVzhAC9g4zv1yfPPAKiWoXhtlKXRxstkakQXWAuzuoI7+BC+u5WY44+q1rbd5IO//pp2k5RvotClOIzzNlrtJ+bTvWZprwMUaL91BtiO6maViSkg7top2ZSqwKgCg/7e57gsu8k3rFCvVIjlYv2mZNSrxuUG4/vVZSrcbK6lnnAa/W8qMalCakl1FOLRVmVrqo83JAAM7z4FDEvX2baD9TDcR9q9bzR2niIoBBq5KbCqrodatS9WfrkHJHbdYE6vxyvVebzjZVf1qjZa2IG6dSjj8a4S8fJ97FE0hm+v9624PmVHyXkwh9sYzNU6jvU33kL8c6rUK/tGxOCtQ09wNC3u27e8yL+Ie2sN9o537zG9+1nS8ScMgFUXxTay+UHUjNhpK2LE2LCVvcnBZx5HuqR6i4qVdNdJFqqfGFFDHUiVl7Vjq52P1XFNyDgcqvbY5smJAeo/6aL5yIxcTSm7CQW3L72/auEFiByrsd+Xycm0bHWBpF4FT4u8Jqj4x5CXRPt10m5Wq5TcFU0XYX3G2Q0lq6ANYet171MuGL9BPDvExunqSB9X7ZDgddzhL7rBZKTq/yeKH6x/OjGZxpN6/SsMQ2n0kV1TlTO3vuq4FeV1gi8TFJDME0zOOF748t1QjN6kUxhuJtUj+8g5sKDTB9yCbnZrRJzzvs1FF8l1FHn8dVW151M2yz02JHHvos8cmkMPTo2lQKD6fXkNW4o6xmeetQ4hF57lVrDn7x9PSOuf05FtvUZvVCv7Vol1nK1e9Qy5gZmP95MMRhMt73aLsdUb56tJG3ipqSn05DR0Xqcu0ZOmgZ4rFj34QkhWH2xVUUybPIk8XK2kclRDC+uBqzl1pF6Uoa5FFxsp4mEQC+RfK8LYqVdaximQtDgiYsEYr1KJsNRWJ0j1O6fbZ891uo1jedm4gW8neRn5PYgdywXwg0yvNnsaZvJhEf1SPfd5Ts3qT2kG5M7pMhGbv0KqRpXi32deCKqM0SgzQq5+J3M5iHQXfBVgla6B/jtJCqpC7wKxIqwHROFnG0XsTvuCsfjhEb1GJGEqAZa1D7U51b3rSr8V5j2QfUz06xuY/rQzxqoIp32k+SpIabi1Bj8GBm0oNeKz1MFta+uhavtfjehLSTNpSeAqFhtbKjvY8JEgVbpF+kbhYDvZYy7NcNfRXI911uBjmBgc3eP7RjbLLzGe4faU/t7mVg/JnofQqDzlFsRxHUMzhLvbD/jQlRNfpaQHSrVcHqUdJGsmsBQfdKe0NbthLA8XyEm2iHCx/Y9BDesYaKv1EWj2q4Rzxytm9rnhwnBu0j6JhulpYpXPRr8zATb8rs7mD4O/hyxOfwMoTrdQyQKeoC0jtd8svtaPxV6dVDkiN0JVTVt09XWj5fJIInzRIDAy4SP9GsEpfNO0tVvtT3/bYQQ2FXqrRO2osPK1xrmuVzarVqu+iuqNZxag1VFkU7IsbBVrzF4oyLfe5n2p/Y0jePtWY8yzbtXz4OFgWf0kWIaL+Xfq81Bge37qJ/ZpitE8vpPEhvBs61d8rFuhhMyYrCiyYrCXVRmnnuVTMzk2HpOX59YaqjUse+vWyQSA20h5qwC/SqZeH5IaMNad69e5Z7iHksfe9tIX++Y8K02oZovA6blgp4tx1pdj7Dx6Lih50+YLQxrcf44nhNm55oY+1/g1K/Tel39u2p9Y3SLdJlZKfeW+1blLe8kdq7HCNT4JYQRwSgpd3pphtrhSkEo6W+Wzy6Rfo2ed3cHufg10BjqfJaYaJ8kBNwhgkIwX7LqwXUyAYtc6n7SkqnQq2rXNdI1THVb7lOXugWmEYneHm4OkvSvEi5O/4I0YiwQwvgb2xhqtIFE8NUgODa5qlZygUwTqDD3mt5Vz81MQae7zD8k8ix/G0Gh9J4qdcFWVKeK2vOedYEowPV48UDZOjeqEagargzW2NzV7bwwCMb+QLwbDbRH28+9hKazh9w0LhEgYNK+v5ukvJZKfbS/TUp/nfR2qOfKVaTbl16Y2ec6Zm7qR0nPA/sHw2jReipfP/bs+nzDwaVVhtwRx+pSA/swsf6eITa5ewaud+0aUj/PM9Z7fuVV5y3e45yb1QbneqX7/L+2Y55+VPBRbT/1Wa8SVOhmwuXxQHc/wIUJ8NcJtPoAaxPAnCKTGR8kM6pV9bA2yHy8Lh4t5AZwnCAsqR9p991PqNpVwIhgjYKSJzYnrCf6VuQHqVqasMX2GT12un33EuHfuZ9A/28hjztSCJwkjDyPECquB0YeJk8scUM5S2waK8Qmdj/TB5dqQIN0V3OzqAYy21+9LnoDiqrzFQKVvkGoyAdYG1knj+0GKAXkhnKQNDrWUlGs4csnyXzKNezXjcwfBYub6k0yKKYu0iocKG326PSaw+INQiA8Srz3JQJ1LRFzRw+Pk8RclVeXr3djEhm7cJzDCmQpl+olobbCwDhB0nG+K20INQjIzdF6hhbsrNKjr6F2VN5fNbuO9Sw1GXLNrpAntBxkLT8/hhDfbNF3faN1Vo57HrTLjGvmpUDUVhdJuqy/R3vaVTJ51tA6M4viSxNCYksaG22kmi6NYAO1YNcEPkY4Gbo7IYSa6rS5D2yIuY0/3TpziEyRuJNE4JtIg091dfNHzws5PNusj/BH2s99xK4ujyayONH6+wTJu14mBDaEENadb4HYqHa1vpwlBMDdpCp5keApXyJRut8faM/QJar2oyLbyhHDtFCUCqq5LTaRATYLpHP/ATLfQ6U7PPZItzjHuE5Gg2gUaL2xrfqo9sYkE+d4oKYBF17bBxpcJg913FHqqUnrq0Cz/ZvLdSI/yPfiXBqi1Sra1zhqetP+HfT/i+b1nddeorCtfvMbUakdD/2aa+6MMXe0uoksEuPo2qwHG/TPqAYxs/H5XsY8HPzM0o/RrRbpprou572vUgIbfSZMrwu1+6G+OF5XCHB2iphfz7KW9lDbluLs63OjO0UeuvsQsGlChgNrtBB1mdbyAtnpi4T69jFC2DxFcMlfQCAWT99wkZwlCeyar9dJoupt5Nm2cp0T0zPc5Gd1GfM6OdIaLKBKeJFAq0faQD9FCCmNiJcIrvdn2/XvbH3ZS+bhNZBCoaBR7DyhchwjUPUXkhZ022OgSaUiHEuFmgZOmN7Zq5sXTCduh2netfLMQwiu5w216OunfT+Z8Ke2sy52SJXXtku1iGh9h9INtf1VmK6Q9AUk77ml3FtdttyIPNFjhTQc6x5ZBc9Y3+W/NfJWNF/7W9tqcEr1DoBpYQkbFwqzhHT1ougFsOvT70yzKYAZqqunN3x2L9idm34vfaTR9hTJkd9qUVPcqGZQ7x/aJIbQetV+qi2qGq9nvYMJaes6R55pWe+5TKRu0Dtrx3Q1U/OqegcJElZpfsSfIHirl9vP8+TxLc8QaOUG8RKuEULqcYLj2tf+rxFNPtwHXyQQ8CVi8XkGWX/0vBbgU4Qh42hr8H3tu+cJobq7tctz7O4nVUFDheWDRSrmxDjWOq8w/rWBaAOkn7C5WT1k8jjJZ3q6rYZHJ+xmpjnF+iLs2xskr3mePML+bhINGeGmAe4hMhNY9Vd2ojih5Hr1e6y+tDvJvM1DqK9yzNULZsgX0ndbPRb0CKmCw35LOSy35+/orpOnU0hKTfgcDXQV3fboRZtF/b/fVGoZU1NrXW5uVWCtlL9vBQnWja3OEcfpXPv+Ptb6y54j5o8pA0xQNIR83UT6zbt+f5VYT5eIwJJd5bs6BwQkunHCraPgWcasvoiW++AR29UDjiqA3ah6MDCE+McoDO0MNxhOuKVtpG7qtSh7qs9/n89EIX1lQsbnm0jHXXFTqWhSHlZj/d1l5fJOtAfo4/sEwWHW2HG9FDR8mduhBiI4kY6TPqEPtI6YcEUHehf5QTLL1N2kW5noHNIP90Lrm5vINnJn+yiZk8B8GbruHSKDVIZUGTcfF7N80sVW/2GCkjlB7K6PtzbvIZGGY6BA9x1o7KsCSa8S1SHfUTWm9qijombH2gCUC2TOYG0BlY4YQrpDHjV10ShoJ91PRZ+1DjeUPnKxljGBMKZaVgQ4dE3l31dJZF6R8iwBUtvTc+waeyckeKA963XSpuFBDPW51W/ebH3rGbGG+FyBxioxF18i1ssjrN0QtMU4T26H8FWgzkNpKIt6P2/fkWBgCEzo+9sDoVq3G1N1f+w3az1s+kxxjqVzpO/DErGOzrb/lUVDZ/FVMLU8IXgPEa2UgDuhp94eJ4TUS6SAerQ94DLxcu8hXq5O9DXblRPBl+LuL+d5iUCGT5G8qoaTlXbdEQLR6jN7mRDIk9YeBZnC7yaBJp8ghbKD7WZwltAIfppAuW9r9YnM3kagZNF+XWQuXHdfJ4AL7BUyn7Jo0nHZRHoGuCFpub2r/RYlis4hBdwW1k64XmhV9KUQcZfWRXAT00llRL/2t7eE98Kzct211ElX6QURtEii5mzoUYLzZhtr2zBLKNR35CZSaQg/q3MA0sg6j7pchXMtl4j3eQfTJ5RX4bpIctM7mD4z0TZ7cojaw1gbegFSx2iRmIe6lLqhq3FYnBOT7vNbLVX1r4Kv+mwPlUUCob+NaepjvZDoIcFuUNeDZJ4P2yEnXQGBCPscmQmvf4Y0Ta9h6EN8jMwGuZfUYmsdusKa6VFPlKUJgQDPlsp2E2jtINPJXiqFoLBTiKySfJqIUyOaCNqz3x5vjTDiRA51pQ3EGWKibiMmzUECie4snTlFRgRWvtnQTXchea3XWvtFygp5kXgveOrgOVF1uTK8dYXkQ6ubUL/oRIOifIl6Xe4gXfEuk/SNIcEV0YgWV1n7oq2/56Bo7T1LeBacIieVKOkQsRHZHhemPxVFOy7e6+ezXLx6QS160TtEwVa9SIbqqfX1wrCqvXWz6DfQ+ns9d6uhDab/zLVwigz/r7zqRZIi0Npeg33qc9brr4JtSJWu/18l5kAVXmP3zEsT9Nf3Y68XQf3c9d8fZ1XLIiGDnmPaR1lvlF5wV22rlhuE7eqjRBzEW5ge15qzu8qyBfJ4uKGIwLqZ1KK31Hli7TzK8MkdZ4HvbT+/B/gWYm37/E3AqQmB3OT4XGA1WbbuFzWzWeXvjHjzxAdDQc35epWMajJGewtpUZfTNQPTZtKIJQ8jDXEf6TZW3agUlFrVDTzQQd4DEjX4Oeh14SpUKiJT8LqxGPFXD/oUYRktJzenv/NFwqj3M6QRSiPZY4RLYKVVav8XmHbnso2ieymeejyQ78c2VZ9JUZEnWxgZOKSGV/WpCskxYTsmfOsY+X6HFtIYZbBKJsl5B8OuQPO0Y71n1esreqvXSnO5iQgwNOhKw3gq+SOEpufi34hngABHYdoL0p7ntH2wVlhbeh/aoaIQ77WwoeI66b0H9Gzp3S/rM28C/4Twt39L+fws0+6sY8+V7jhPuMQ+T5xorlarkDNsuXoxiJArUh56Rj9GV4l5uEzKIu+v6wfCLvYiMRb3MH3AqDkzrhGutN/rxLFjNcS0Lhi/v0xGvOn6VKOM5GB1hZN+UBW7j6ALjFby6CRpkUvt/91k0qCt5WehPOc0geK3E4iuWv1FRHVCVu7Zs/JUkaVRauSW7k+UcdCoYga4ra2OI4Sh80Z7OYZT7mv9kNs+SSYSN5WoxjmfZTsNPlklM8dVftZFJVqtE9cNRNdBjYlqH6rLLqRzxDvbzPSRRBWdisSHTljoOdIxhDckJCfdbzeRjQquoWdUgTUmoCvKHQp1lSr4NDFnniW1uUod9OhpFrqdtekoxHokODSu1XgI4+9lVnug+bIS60hUuh4FZJKp+v8SmZLAZ/b1LBOeSg8wHdxwk7UbXz+mpix4kFhLP0h4ff0BIrWmoE6QtMT0ScpDvPBQG+vzVwk5s0xmSIQ0CEMK1lUiFcEPtHb+DmJzcDPVkH6+tf1HCJm4OiH9MCsa1K3sMIHmXiOEishWiy3kYhdtVTJd9dsd+BJhqHqBmNg6j3vG2zPkgaSG2ary6xWh7/ADrROVCK9RUBWx6h51pV23helQVVGfnO1JcifT9c7Br3yxASwuHJHseTLxx74yZv1CqQjqCBEJdoFAV8+Rqq47vJtm5YflOy+SeaG1qveRRo5lFT5VSPXGs1nCZL3ST+6xid7/3SPxHr05R3taoaLCVYbVTMo1Q2p+v2kbJSroGKMy6vXSVPW7MXSqMKvUT0Wg/fj43vRtnwe1eu9qd80Sgdr/NvDHiOCmIaG03v/2QSHnpj+08Z0nfelnld4r4hix7p9tdXysPfc5MoPeUrn/GmuPt699N3fJ0Lvx2urtIN1jKtY7WzuuEjJokdgQfqnV8RzhBlsF9R0EU/CrpBw6RsjXX5y0m1VH7msdqKhHCuICwYN5Iq/q+kFSXXdXMBjASamabNa2Wr/C6AYhpI8RL1bqwlwD1iW1AWkAdKJdb+3Ssd9AkR4ZiwT1FjBh/I72TCPjDFC4SiDbXaW97m7VGly9TGr0WP3xWj1NLjCNdGtCep+lcOyNam6eHh81IRMy1b72YbqO+xDf1wssF8UCawXDJtZO5iEedazUcTtNGJceJ8d5PS50jCax32P8Xr2nR5mzkGBFpZC01RJpiO3rr0UDbOXvfZ++pzGtwv+rljL2HNsGaX/ZRLpjfQT4DuDPAN/MsBCf5WrmGLn+TO7eGxe9zrUyC3nWfkpFmh/7/vbZK8R4HyLAzYTpIJg3aAiTtXNTWmnIB3pIo7vcnvcGAWze3vr7SeI9P9Pa8sPEsUfPAN/QrvPZ0ievEekQPtrq3dz68Fir6/iEkMjyv6rDqucuasN0fTFSECYcP1Pu2UfmqPAlOBFU81RDjEQyj2tvrHEAh1TdqnLWxe8CFEFKt0j818neq/iQEWw6/hugcoFAytsJY2NNRF03laHJWzeaE4SwP0Duym5elfuV1pGSWCZTkxpqDpkrt0440ckYV1ipKDWX3iBnmQfVOifMKNdbixU2lfrpVfgh1Fvf8XoUw1gZu2/W9X3R6KSgMZrUENdZRr8hOkGE9WFiXL6K3PyHaLXaj1nF91nTOF4C/i2hzt8geNm3An+Y4U3KdzUr0uwN0uJ/nBCKs/jgsVI5Wu07u4mxPd/qvQp8iBBmX0MIMDWay+36V0lk3M/hWd4atZ2LrZ4PEzTjO4jshbsIsPpJApF/EcET/wMiFe2XAX+I4LorSNtGMAr/mshQaBSjm7DJlPbR8hH/z8Sgqqrrj7qPFBa6W5m8HBI595FddlCBsNp937siWVbK334/JBjqwtaQZ84DEWrlP3XDO9YG5ipBgzxHbBi2X7SiX67tqQtDgefC9JBKyEWpyliRr5uWfpo3yOPrNZY5Xhoe+0liv4Y8OxyrKnD0sqj8tka6XusZQ5a1+BxRv9fb1mrMrfSG98rd9Yt/PVQ+S8CNodr1ytizYDqARBrAwIlquFwPNQsozgO/QIz3IWIe/NN27TcT68wxGZrvVSOq7Z0MfK+heImIFv1lwnj1JCE8FoiT0cdoDA2/QwL69daXtxKA4HSrd2wc1IL7U5F7lKov9VOt/SfIcfpHhLD6E4RQNu7gHCGzPktmZezbIQqfVW4Q9Mx3EW6sv53gm6VAfqI9/2sIw+vHgb8BfIDYPP8icfKGdelJ9dPA+4l1a+rVJdKJYYlMYDYBTkyAHycnnsfTGPVWnY6rm9YQ11lhfRWqQ2ihRzv9d9U6Xa91V6v+sTAtRKu1uWbbukiGbFfH+s1kZjPv2cRagWYo8ivEmWZHCZTxFcTE0bjngjXw5AIxrnpFVMOjfRgSDKtkhrjqKVHbdLn16fVW9zOEGtUbWi09uqxlCAm64UmFTMgAkNOtH0Y19vl4h57dUxvWKzIa4hb7uuYVtrB2ngzRKH5ew5+3lu9ru9d79irpS/w8IUieJgw2WwnbyBLwW8ijp4ba7HN7vnjonV0hIyY/Q8zNDxJW+q8jvX/ePqP9F1mbUtP6P0uMyQPEfN7B7CPnddnc3n1e23+ZGJtLwJ9un3+W8J64QpwWtK21f0Kspf3EBrCXQMe7mOaB5y2LhKfCdxHA7Engt7VnbSO0hx8k5OCfJMDaJwhN4qeJlA7/OQHmIN1oFwiK4gMk6DpFyKMn2899xBw/Rbyr14hNZGVCCAqluaj3LOn/qO+tvrl1sc1adLWsdNf0E1w06IL0/wvEDnm4tWlCcNLugjVZuIT9lXbPJwh1YjNBnL+DDEKBNDKK0mo4qJTFdaa9LMy5XBPZeJ39cxL2R9islHoV7HKYNQ9yPzaQmeuOlOtVfx5kLQ/el6rqw1r/2l5g3WzP+llivJ8sz/XUa7p7+mfV72YJ/TG6Y9b3XjM0/xQ8m5g29M0CAwpix1CL+Lzq9Qlica0Qau2PEULlPQTinRDztA8U6EullYb4d9t9kczz8SKx+L+LAAdvAf57MlH//oH77bM+50PlOInW1chmlcpz90WvghPA/9Su/W+JdfQCMb+uEhvIQULdP0Nosc+Sp7+cJ9fWRssRQsD+EDF+Xwv8KUKgHm2ff1/7/M8Q7+ufAX+HADp/lNg0DAU/T56K893Ad7b20tr6ECGnVlq/zxCC+DkymONKa9enJqSaWYVh9aBQLe/Vzn4RVFV+tbu+qiIa/l4jdsET7ft9hFB5kHSLMytTdUeRPzpNokHTLfpThVM1mKlW61tYI/dOEZvQHmLymmbTOoZySNS+i8hXus90Cat+jI5JX599HON3qzfBpoFrdHs7Rrzg7cTmcx9rLflqGzWvg8YXaZ5a/1DfKd8PCeD+mr6McYlD9YwJpd6Nr/Zz6P46Vy8TPN9XEfNmrK1DQn+RDD7aTPrTP05oSEMpRofqvUomXdfzZggpu+HfRQqO7ybABu153wz8V2Q06FAf/D3maTH0fMdrU/eZgnyon8vEHDxEAKm/TozZnyfW50uEui+N8hQhvH6RQIu/i9Qwh0KXZxX7tkog3O8hvBV+C/D7CD73JsEG/B1iHnwr8PWt3d8N/LX23G8nkLEI/ywhIy4Tgvo7CXpjSKPcSmwsDxJjepaQe3e1/j7b+nx2wvSZdWOUQ1/kXrVY+lD9jyck6rtAcDqrhJFJftKi0egsec6ciMTdz1y4Coc6mRQ+ZnEzXLum6qwqdo0IM3y4IqEajq23RuVZfab11o2qIs0efdYxrUIccoPSXUohvtD6IwfWvwuNgOYYXmDaLa/n7FcHPu8n0Cwqqf7U9gxZymF9QeT1kPNoc/d53+9eSNcxnIxc7091STKwYZav8pAAMomUYf1VkxlrXz8O18hkTDWabOjaanT7Z4RK/5NMv7evBP4xoUbPGvOxja+/xnlyjExZYKmHjw6Vm4Rgejth/PrLhCB+H0GXHCGR4qcJ6mZL69tHCZT6JGtdKdcrbsrmU/8QISS3ExzvHyLkyKcJZPwhYpP4euB3EuDu/cRBDzsIY+Z7SIrWyMnXCOH9o+3/3eSBvCfIbHi1bCaE8WPEevYkb411OxSYMM6dKRSuk6dnTAiprlB1Mmts0iik+n2NTGF4lkyg8xDT54g5CazjHPEyj7X77yd40AfbANRcBX1R8FSDYaUJLLbZXbQuTv2pTxIL4W5i8KVFxpz3x6ibijwpfa7P732h5y21nkpvaFytG86YULcNakrm9VAj0fPEMVQDqBnV+uL8gbWpKofaMSQsjLy8Xj5z4zrAWrcp66l/aywc8w7xul6jG2pX5TqvkxGi/fX62PouNATX6MCKVOk+XyVU979BCNqr3TN+L/D3yJDZWy2rxFoT6f0wgR4fLNeoivdI27JMCLm3EWv2fYRA/kutrmPkOJ0kKIHLwPcT6P6/JDSTeTaL2u4qsBcJb4bvAN5LUAzPEprKDwN/ixCAvxP4j4gN7PX2/A8Qc/u/JgT3hEzis49A6X+TGG81gS0EuHyMkEcXCRvSWaZzEzsHlgm56UEUNwjB/lovlKrgrd4CFiewxjGFrRyq3gDmhdhEqo0uaBGcO0ednIbcml92FsKpG4gTuQqDfjMRDYs+T5I+1G8lNoaaI7f2v6clelRbVT4/czPRkqoQHFKf+8VY+zmGrPvvFZR1vByT2laNjmZbu4PpDHm+2+VyT+W7xxZK1U5OtOt3kg78Y5znWFkhNu5/TdAHJsn5WmKxS0MNaXE9IlkPJdbfYxu7IfdqP2OBJjXfcv18aKNaJBb4E0xvUiKs7yD8T50vRpF+O6FiV41v3nGtRTvJHsK4+K9avfd318zih1eJ930/QTW+j/BT/m+B308gSe04l4l39jLBaf+rdv1Xb6D99X0JECBQ7vcTp5q/lxizDwL/J5Fe4ABhWP8PCbT+aWJz+xcEUv+zhNCGjJO4n3gP39va+zrT8lLgZAyGCcaukfEW1wgBfZ4EJBBj/g6CM16SmtCPFNYuZEhBJgqpKrkNcqFV1b8K08ppDqmaln5jGPpcCsG8Fs8Dv9I6q1vYc4TaY+Y1y0L3u6cShjSD2h4RU+2HdVR0PZQrtudhK/c+63neV7O0VQ+W/j43G7+Xz/T0kdX2vwvbwJn6g8SsYgAAIABJREFUrtYrjlnlxnWf6nN5zKMO90mAVsjIRTe03eThrBPWHuUzJvDGnumcGvOnnSVEKxqDWLims7SM0XrPE0a2300anq4T/CgE7/tD7bkHCJ7xXYSr1N5S1ywBvEoml+pRrP7GECr2SUIVr14IQ+uhcq8T8gTpVwka4IMEsv2j5Enn+jRvJTbV7yHmxl8kNOJ5i7EHluvE2H2EcNP7OmKcXiI43r/f+rUN+IPAXyB42U8SZzj+IHEQxP9AUCEQsnCJGOOPA3+F2AgNLrtKIOrTTMsMA9juIo9/WyAzV7rmdxKg5wFCI/DdH50QzsabyPPldJNyUbj714VlqYK6GpJ6Y9OYqk73v0jyEqEqHWvt+0wbREMaTYSjt4P1ivbkirYynUC795vtUWw/6USHK0znqLWOvg+1KAz1Z/T5Q25ldJ9VhGz01iLpWmipWoEbwUr5/zKh2m4l1CddpnqfY5/ZbzB9u4baXd9bPz71+qE5oB2hcv8aj4eSmos2fefVWLQe6h17z0PfW/r5UDdGPzOfQU+71GI+j58jFvPXk0LlJiHAzD/yKpmPdzexUd7HWkPZ2GZ5nECG72339u3Y3K75KUIQiB5r6ev3PqkWv3+VMGr9CEEDfEv5TuF/nUC+Zwg/3YMDbZ/Vn6ukjUSk+RAhE64R2tFVAtn+IDHGbtx/kDgw9z7CMPc+Qmj/aeDPkZTOKeJ97CICOv6PVv9qe56o3jS/usJ6JqSapGlxV8mUDQ+TEbvaA84T2sOnCe+uyxPy4Ms3iB35F4gX83jr5FPk6Q4uNAXKpPu/lqGFobC4Qaa8NMubeYhNSNNTARUVCvFrtNzYghzj3lxEtS9+3vevpx96TUFB3QvZSbmu/l/742GgpgnVXXBI9b1CcFDHiJ1X9LVIukdVf+Pe51t6ZlK+G0NVvZZQ/5c7u8T04aDeN0vrWSSMNxALQSGmB4sUS0+pVOoFxgVvfb89tTbUnll1icot1SWuag5jVI2HLkDmqrUsE5vkZmKDvUSelvMI8JuZRr+zyiohWL+NQKa/beD7CZGj4UWCG50HjUornia4UMthwgviBwlV/9vIk9kdH1MNePjC0BhdZdylz4RcEJTAZwmN4CIxZncRqPUfE54RAo87gD9C5M/YTgTP/KXW5v+MoHV0QXuDzP/9g0Rw22lC2DrfPJzW1Lv3EhucudhfJo+Eu4N4d0+Qxyq5NpSvHyOjNB9tfbpX9QKmo7DqAtS32ATw20keV8+DHhHUxaDqCikoeuFX7xFdOdGrRbpf3LMWY/93FZ50ffV5Q+q0bVpmut7eqFMF0JDrmXWYn1kf6IoiHS+j60SM18lcBvX6XujZF9tZx3gIBdf+1TZWqqlSMQY9bGH6jLQhNbbWS2vXYSLJzM8QG/w3kakta+a8m2TSpJoFb6iMIdxaqhfMLEFum2t0XRW4bhTO9bGxrDTL0KZ0ikTBWwitb4Hgvx8b6+hAWSIW958gBMx/MNCmGwQi3UII6DHqpkboOQ8+TdhPfM/HCFX+HxL0w18kNtNVckzUaIbmKSSFNSSAHXvzY3yS2Kx+H7k+TxAGue8iaIgJIX++lEDl7211fD+BbJeB/5gQzqYrOEKAy2XCU+L7CGBhpsgFMn+OEbV3kID1LOkr/AAh2O8k/a71D36ptf8CwUN/CXmIxVvJJGdnJu0GX447gGqTAqEmHNc/sqrbMB3ddp54iS8QCO/B9ntHa7BpHXtr9zxcoqWizFlCua93PeEtUvawymOkanaVGMT7yKCSOrHrZlLb6XjVZ1VvFMNoq3VVQWxayn4xV36yFzyzkNoqsRkcJ7QSI6XcWG2XvJfCpGod9ZlDyNffRv9dIA27S+Rx9/e2Z14hz0vcR6CE9fIO17JIaAp3EFpC3XRmzY8xeqhuRjDtTTJ0/Up3XQ8yLEuEynuS8L6R7jJPQl/vWP89S/E7idwHf2rg/mtEJNizxJiM1WfuE/OeLBK88W8hVemThP/v9wJ/HPjvSG1sbD6skjyu/wsI+lK/u0akkbxCbDDbiPfxU4THwg8xPc5fTPgAfyOhYX+AiHC7i/D//Vpi3i4RHgoHiXX8t4nxO9Pq2Uemr71I0kOmedC/f1e71vm7l3iHFwlf5Z8mZOpBAmwcar+t+wAZZfc64RHzXRNCHdIdq6rMNaDCgTKXg5/XgbS4mIcmVjU86XUhz6llvs9JWuvtPx9Tf4d4vp7v0/C4ibVHoNvGnn6hXLNMBpZsJl5Kf6ZY5RMdw+oHXUs/nnWB18ldJ2FFxLPUa4XrG63dJuivbofyWwtkUqftZMh0XWhDyLfaCLxeDcCQTzcv23WdUC9fIQxX987oR1+qptKP09gc8r6aIc86jFSs2sxGBbhzX7W2BkfpKVQT8ff3j20afqYl/ifa729hrUHYI5IeYnxeLBMU5NtIm8NNAmW+g8hHAbGJ/pV27e8nkOkupktd66L97QPXDZXa1wuEAL6rPcsgmb/b2mXgy8XW/ncRhsHfQ8yf/5VAwE8Bf5X0xLjexuNRQui+nxB+rzHtxbDQxuIhQmiutOtp97qh3UXSTi8QVMNZInr3SwhqwsOAF8n0to+2fj1JyNx/SgChPwzskZqoSKcuKJhGy7Os6dXdyXqGhIl8ap3wCgJP1/C3LnHbCFXB/Lw9heIimBXm2/erqtVLxMAuM0271DKLV3QxexafWaGM7uvRZN92iyi4+usOPbunJPp2nCbHzfdW04f2HgoaFa+1Oj3JesyTRqppgbUblCktV8jTVLxXymEL6So4S2BWBNXPJYWd/w8VNyA1t0pL+V19Rg1JHyuOXY+4V8r/y93f1t33zzL2POfCWTKI5CThb9sLYN/9rEQ31whk+W5SUN4g0OEDRMKbCfEOP0zM5ycJg1PNHdFvFieJTf6trNUQ19vUjhDBHO8k6AUIIfkdBAVhfdsJGfBewgf4rYRA+2sEUv8KguN1EzlLoP23tvreTwjqa6Q7nkdX6ZCwhdAQHyFog8fbc0218AaZ6EvviEPEXNblzcMujhCC+qsIw+D9hKbx94mE8d9EboIrTpQhATWEegwR9oie19pgLLYGPUTm7e35z1qcNCZhP0nu9lrG9xAqgHVW1Nob5iqa0OviMGHVpQ3sI6RPa4+SZ6GeIUqguvH0/OsYSh/63/rHFuUYCu3rXCQW6YuEVXgvMXZ6i+wmhWI9Kbpyp9Yl+p909R9pnx0kw05NRK+L3CLx7j2jTKTZe4yM9aMifovaCaQA7TWyobqqRmPuZ0N/h4DG2Bzw/QxtftJY/SbbazC9b3qvovdCSjXYU12MmBtLzLOeFnGVEAJfQ0bJ3SSDE/40OQfOt5+7mT5+y+L7OUdQSY8QQlw6q2/TmEfEKwSH+ptJgXSCoEA+0N1/BxG+/eeITeFjBAL+J0RQy/tIbv0kIVceJzjmv0F4TOxrz1F2bSNkjPlTTMx1dxuTi4R8kx3YTcyfQ6Tme5KQMycIY+KLBPo+2Nr6za1+IyG/kmmvJ1r9r4uI+8k2ZiTrhUQ/4cwFcZkQhh5tcqZ1bEKopw+3gREtVUu5R/n0rl71Gb37kBmvNDzuK/fXxWB2/SEk1gv2/rtZpRq0hhbKCtPZ5NQwagj1rE2h1gXTPsVqF46JyPY8uaEpfBUIvYfH0AayREysl4mF9gzplO8JxZCeAaYU9dDYu5h9KnLVCHqhX9vjWPXvZh7VvkeqkGcjjnHQlZqyLUNa2FgbLDXctqeSxvrhZ9cJN6wnyWRbs8qYML4B/EsClekjvNzqPkNEmFV7kBvzCtMCw/pvtHs3EQi2zmWLvuQr3edee7Q961Hy0M4zBO/8PV0b9hL877e2cfhRwlj4qfbZt5OeJUfaMx4hvCj+SrteebaTPH3+fjKAzJQIlwgKwayC+4g4BAGNlM+vtjF4hYxZcN49TXhr/DZS4xrSrq+RaUV3A29MCGmuUPBmI6s03t1RvodpDrXmqagCvXKT5u5VePhzB5lgp39pFXn2390kXt5xwpi2SkZx7W6DvJdpFG2ZdPXIOW0mEWQ/eEOCdh6BWX8rjHXZu4PpE0jGEF6tw3qq0KnW7iFkPrbg+2sUvpfb3+ampj3Dk4r1mjFj3AoxoSCz0g0VXd7k6xSEVSMY0wCGBFgtlXKYdR2MC19RaN28J+X/ofvHNuxZG3lF6vUzx6GeWbceAPBZ/XV6HDzNtBB/gxA4DzONXo8TiM40qrVuCB7/KJEV7X6mXcssNVtbv3aUM/vJgJYn2vd/m8jIdoEc90MEovyTrb7vB/5HYu58e/tOrvZw+3sfIXjfRwjKCZk98j5CQD9IauyQgBECsDzW2n6DELTPk/6+p9vY3Feu2UVw0t9IGDfHePFV8ixPaT/Icz0PT8hkK/1k09BylHA3+gliJ/oMGfO/iyDN30OQ1Q+Rbk11AouwN5f/67P8PbR4bMdp4mUtkLl9RXmzDHkKKy32ou7eT3fI6FO1BDcn+1IFY0VsVXUeWkjrCXCF3uvkeYHVMVzapz5fuqZfBOst5Kpl6JpWU4N62KjaUa27Cq6hei8RE3gHgUQ0xm4u98O05jULJdpWmFb1PfBTzUpkNwuNW5ZbO22bz67t6f9WWPaazFj7LVV1t1RXw1dbm9dL3GNddexrOz0Xrm7snjpzT3fPywQYeevAM48RPPG9hOGr9quOya8SqvhuputwXuqO9gKxObyTEIgfIGiI18kx+ALCAPeHiLn4PUTSoEUCCf8x0oPh+VbPLiKg5G8R0bW0fj5OCF/PnDT60/Y8Qno8XGrte4F4DydId12F7VOkf/AXksb5/p3WUt/LBabz9Owh6bufcvIOVaABzaPpXyUE8c8Swvluwkr4DPni7yJeygNMZzTTrU20DakS9K4+FQ3LpdVEOOsh0h7N1M/r7/pMEb4C+wKxw18q3x0gEztDuvkpDM6TLmF+tptwL5pncfXtrOqxk2KBDOve09q0j+GTle3jPM/y2tXyI0rr+c+xskjs8C8RC+4RwgJfDX/WXd3C5H+HBDLluor0K4UBaei9SAh+qamhomayQhpohsrQ2PQCcN53OlS36+4zZADVeqWnWSr/6sbYbwz95gWpkfVoeplwv3qZ4F73k5pxzwF/kljnQ/mOafVeJAIlPkEYp76UyHr2be0z2/cVBNL9PQTv+ncJI9xegmL4BnLuP0/InE0ER/xXifX61vZzL3nQsDJjC3m00zIhv14m1tEpQm49SnLE+wng8xC5SVdj3nrFQ44NWNlPgrmXiBw3RwiZ+ovAixPC2fshkotS6I0FJfSfKcAutJ/XCXeXDxGD/VsJxPxOYoH03gDuUh57JH1xJ0mmj7UF1k68eUoVdqqlV4gX82r7zhOcDbwQCa2Wv2sIs+k6x4yUq91PXUyVhpnl/eG9MKyGz7pnlTyA8XXCdem+0obeLjBPUVu5THLVq2TS8p4X1dm/12LGOHY37joH+o20bthDnCRMz4shwVTbUO9X89Gf3nbMGu95yw2SEhwr/byobVvv2XrOHOw+d73WIJnrxJrdTAhLx7MPaYYQhLuZzqlRyyqx7r+bMKp9G+H29nEiV/JHSK+FryYQ8JcRG9LfIVzV3kJQDF/Z+nmGQKu/qfXp+wh/4r2tf/vJwJ8dJNrU8+owmflxQiBpz9b0x/lqvyetHo2bbkh9X9WITYVg3y4QQvfl9vtVwuZiDutlYvP9amDrBPjnxK7wc4Qa/BAhNJ8jdrz+9Id+coueVG3Pky4iRoOJfCGNSfJgvdAa4wb7AagDYQIgXdBcLFW41GtENneTwQx3MU4p+BwncaUpaoTZ2KbgIndRnyLG/fva/7+bcEw/QKYXVcPoj1W6Fd7Q51ajqAa/PipyvSLadwOqRsChsVsuf1ftR8NqTV4EsQjOE333CCD7oLGzcvrrUT0uiOdYy2sKIHTz69+/ba/+1huhI/q2DFEeQ0Vvk6Ew97H7FJZX23W9dd7gnZpJzcCrLySRXgUKpqzdRAiPFXLz7ssVItz4bxIb87cRbmYniBBjPTS2Er6zf57gsH+RoBW+jxC87yMoCgjheZgQwJcIIf4CIXjNu6LrmWDqROuXa3U7IcMUuGrpugJ6eshBApx4Er0awNB8vsl04v8LBFtwihC0r7SfLWQA2H5iAzhApgWQzjk+IZCrarenNVwnJLgL1Rd8pl1zf7vekNsayjmGXP17XmFS75HesA2HCVRnMTrmAMHlmIB+hdwR3ZlukIaiK6QP5F5Cffpi8gW7ILaQvPJ6vGPtc1UlFWDHCLXuKMGvvUgshp3Av0dw7g+SE616esx6lqVH2lI0t4LeNDKoCVTh7QY2hvyr21r9Tg2o962Wr10i3d+GOOjeGl+NlWN9gNwAqq92RdmV9qhaR1+Hpfpijz2r50x7DaEvGo/7/ML6QvdtqAKDge/H2n6REMyPlPZYj9GkbyNV/BXWbmKWV4mjmb633f+twH/T7vlOwhjngbV/hEg3+QDBP/9Dgur8OuIcuAOtzuPEvHu8tfUoMTf0qFok1u4ZQvhdb33YQQo7gdhVEo0eJyjRxwna7FkSlKgBzVNWiE3qeUKm1DzREzKgRbe4B8mIzxXy+DcDQn50QhDdRwgJfrrd4AnH1wmD0b9oD/yi9nOwfX+o/e3C6ctGUIL/u9idAE4SJ88Z0gL/anvulxECWOF7jlTBF0gUsNDqNj3dBUIN+u2EJiCCutTquo+1eQ4W2/fVOFaNUH3fK3rvkXzl4etR8xrJ6iIfUrOHxq7SDHTfz4umxz7vN9H6zCGjqXWJco8Sxg5VR+/X5UlNpn/OmIYytMGoIVWk7tgvEnN8mfRYGaORhuwM1t9/plDeRG7ct6O4mfXlCqHKv4Xpg07Xe8e9ULYvNwhwcIJwddtFanFDAGCVeJf/C4FoTxJBCn+BADQ/APxvxDq5n/Aq+P3E3P63RADH64Qm+AdI6u84mWxddHuFaVuVdMle0vi4iZhfBl3oHXIn4QL4NGFo+7UACuYDVCvkyTd3EnLjJwmZeZEEKQda/W8jgNzTpIA/THDCP9fG6iUyRkKN7/yEdPH4HYRz9d2kM7PHXD/eOq7zs65h81rondizEFqlGUQtVR2V26tnzu0m81V4CsiR9vs0sbOfbwPzcOub0TT7W9/2lLYMqYLXiN1vgUx4b9CJGoGfyS1V9WeRPDW6CivRmc+dlc5xCGHPGu/qXjgr8KHv57VyvehjI0haV0VD4SdMB43U9+4mVGmbmsukb9+YYPT7imocf0pdnjgigpZym6fo4F/nis+1KIDnXeSzyphAvUgIsq9kmnrQi6POi1lUiP+rEZ4nBNbOkevrfZ8h8gqbf/cgsSHsJATpTxMuXxCo8ze3+/45gYCvEG5p30C6P5pMrKJd11g19ELakvTwuUICo73EutZFbR7twOKa2UKsd90sdxDa9McJQX8PyS8/QB4Ou0yeSfdau/8EmZ/bADZPdHmQPAj57ISAx5taJSZe2UJCdw1Ws9TbahCSRnCR9a5iIqDThNBUXXiI5EirSlsNMpAItK/PAJIrxIuFzPZ2mtiZ9hDGiP2s9TSw/fr7QfJFl9qgLpFEf82nYOCKeRU+SxzN8rF23TVicvxWIrz0WeIlDDl7WxQsML55VWGzUeqhIvSe/4dMfFTdzeSrx4RhLba/0lb95/WeWXWutzk5Ni7ePocIDPPX8zxDFbd+pteB5VZon6FS/aH7zxUStmFCut5V1DzkPVHvcbO8TAKSeQJGzhIaqJucyYr2kuj1JrFBaHtRyBrM1efT7jeLaodRyLqeDR82+nYHAa4eJo11tQjqeu2wjrGRvfcQsuFI++xpYu7/MqFZ7yM2mwdJe9MbBLo16OllcnOw7fsJEGumtcfbmEnzmBDrlJO3IrX1kK00wVkypNVQSK3cIg99Ummfm33N5zlAV4md9mPEUeSHCcL+dxK0w8H2nF5A+9LqYqo8ny96KMm491dPDX1ml8gco5vJzHGOkX0828ZCF5c3iEim7yY57P2EtvFuwgj6EBkiWRecm021ko8JB5PzjAmXodJPerp7e8E8purWjc/DWqvv+Fj9Q+25FeTYUzOVHtGQaiTUZ4lxfpDh8OChumfZMeqzDWq4HUJ4yAjo84ZQ7ZDAlkceQr5LhFqvmq+dZz1XLAXwxwm1/CYhlIwZ0OZxkQwB3tXaco48nbr3a67zQ0+eVwih+Bp52DBkWoUnCFr0LQQSrfSPdOYqaw28Rt2qxX+WTIr/ta29zxNr/H6ScjBizsMVPk14liicPXBCflqB+3T726g82ngca/3aRNIqtnXFiTxWVlqjfpkg13WEfoQ8wdQwTslzhfAlMqHzJRJpPkzwKE+R+UErryc1ocC52r7XN/QcsfveSR7aZ5RX9cKYhXDofhsmfbLVv6v1r3p8yP/Jdx4hvS9OES/qODHpDpDHoTxJGgerZqAAcdeelZfDMdoI99hTQXTPVnCNua7NEgr1M6+t9BGMC9n1hPOt8tj1fZq2VY1ovfqXiIWyj+nk7UP3zNvGecrF1tb9I8/pefCxjXGMmz9BoLZTra4drD1uaVbbjpPzZ5E8peImwfO+n9Cc/xgZsnyZaS+kunloC5i0tj3f2qZGafL1e4hz5d5GgrDaNz2AtpXPnIOe7rPc7t1GCPePErLoiwgefMK0MwLt/+eJtezPYXIdiYzfRmwMjxCarnNGmeV7W8890XuOjQniIXQ0dI1qYM/HVeHhwKmOSmJfI9Ut03D2KLE+y3tUPxfKvbOQyRg66/s9D+2yXK7VAqo7nhNjzABU6xuiGG6FXhhqp+0b48F6bnpsXPp+m6D+TtbmYf6NWgwuGvKf7lHn7RTAs0r1i76V57n2tLNAaoXzGqg8hd0x2EGuserz3VM2tVQqSloLkuN1DdfUArO0wLHNsMogXdl8bv28P/Wn9letul8/lSWYR5vy3vpZpVRr2xfJA2nXCOJ5VMr1ypBg71Xi/vPfSGVW/+a9dz2BOU+pG92E4Z3XiVBdrYaeWemdOvm830XpwqmLoPKoG6FK5i19H/oJfSsCy7EbikTTB776Do8Z9cY43VspQwjYz+fpn31Si9Sl6l7W54Bp94mA1QINZtpM0oC9N0oVhHXczhMa8AME0nyB0JClAOSLh5C8ny0yDRoWSE8G01GavGdCBljBMB25XhmiphxT27JKBnBtKdd4Mrq5a+4ic3o4p1YJTvlnyMCxTbSkP/+I2PEeJ/MZCNvlfF2ALlh3RrnK6mPbewa4iFSD62cXSH+6O8iDQZ04C61Nd7F2MorURGt6V2wls42ZMekTBJl+N0GaH2I6K9ksVMjA92OfDbWvulK9WdS7XpuGrh27TpoFpoW4graflEOI0UQtuxl3cxoq87Zd9D4p9fueq9o3D42yXqlayXpjfLvQcVWzYVpDmcf7om6UNVHXArNzate2XyY8MbYQ9ILrT62n2npExYcJF7VfJLwf3kMKmout7Z56fIw07PU0UW3LGUJIXSHW5gNkAIaBWHoq1SCqsXGx7krdeJCDfs3GTkBQGq8QlONF0tvhEOmOBmEH+hRBcxwlue1DRJrRryI2meutHXeQvszHWh3b2ueeCnLVhkLubFcIjkXy3wZordSSrsBbJS2wTmJVke2tMztJa6lHL3nc9h6mT7YYM1rIHQvpdYmqR7dbFgkO6gVydzIrm/SBuYlV4zx9dcJ0bDmsr5703POQ6jYkyGapoKIzQ2E1Nnmk92utP4fabw0Wnoent4t1ueCr2lU3C9t4nZhgN4gxO8jGjy0ygup2bDy1/LuiB95MmUfrm0UjKYDH+rpCgAttGTXXyEbGe4VYI1eZRp0GQ2mQ1od2mUiq88Pt2b+XcE3bQsyVVwnbyJ4ZbYcEYR5CMGZE1d3rYmvXLtYi++sEr3yMmLM7Ca+oe0jPFumneijCCTIQxA3GuS61cY0QsGZ4fK3ds0Ae9WUA2cOtfevNTykZ5YtlacI0RJZvrXySHIq7bjXyVLV0tXxWXbuuE7vIy8QLfAt5ZlN1vamCbLHdozO1lnD9ht3tfeak+796HlDqtdgf/QVfIAyS9xA+ms+QVtExztrnWKpgHQpuqG0Ye1luhqqHkIj+M2QE4TuIsNSa5HtCnhgrMvk0keXqRcKi6ymz7yYQSv+eFfJHiYn2RayNqBqbbH0oc+3nkMrnM8+Q6Muz+3p3yaF712tPHetZ13y+C/ZaqgCvZaPUSA8aYHoNVWOYRraam3nWYa5Dz3KtuO4s1uWm8BIh8PYQLp4PkPLhBjGPf4RA4ida3e8icll8MWs9M0S5b7TPjJk4RBoApRX0fjBfxSzDuEmj1BAEonta/36BODdviUDTX0J4mgzRh2eAf9pzxJ9PpU6WuiClI+T45I0UhL2hbBaHWIX2eohXNF7d7pxksP6pEWOlF872b4nYuD5FROacIl7oO8iIv54zvUQ423+ImCSm89tDTK5nCMRjuOh+Qv3bz7RL3Xqqvgv4OoGSNhNWZE+9XU8A93NOA5HPrsl25uX6hpDoULsrWJhVV+WGbxcNcStUxyqZP2JCgiHzRmykbUOC/HOhtTjO1Vg4C5zM6nv1/ul/Zj0fZm9QAj7PalTjV+vXA8S8KtsJEPMUsVZ0XdXT5iIBmm4Sa+wAw5rNVQKUfpDYUHYDOys1oWp7oVVaM4upwptsZbX8bajoCkE3yIl6ZttQY+rL6ndkBVtd0L4MQ59Xy+cKx8qv9cnqa/2wvuCtbRwSJL+RUFQtCvg6mTfqi9zXtZG8vM6h3iA49Px5eeT+2v6z2ub16rxBLD7VzDEvnr4N1xkOc3+zpRec88zZecetzutqzNUu1J8cPlS0E10i5IbyQtVbClAX10vE+9ftFaZzhShX7PO82QCHwIwnBUm1XiA3eV1G64n1grgq63YQ8s1zLKvGKuBbT8u17qq995vJjQmh8urqoaCDPPa+CjV507GTL2Yhz5u6JXQ2AAAgAElEQVQEUX6CdPL2JfXBAHZO7tbn9yqrgrw69Pu/vrm2v1p6x7g3T/74DLG77STUmLvbeEgFmNW/njIxT1kld18nX03xOCFftoisXxCOj9fUjcbP5ZVdAMbnqwo6FvLuQ8K0b3ctY5SLf/fCyyi9IXT7G3VTg/kW4a9nca71QkMqaGgtjH1WD938WSKU+VGCZlRQbSXWxPZy362MjdqQ52MqAwR99boThI/wrxL5Mn6FoDAqbVjLhMznspPknt08FgjN83cQeZI3EVTDYcJ/+Bmmg2eq5qR2fYHIFPd+wkPiDsKI93UElWIuD9f8zQmR/3O1NeIAwZNK1p8hCOtLZDTI3a2i+0haAHLxnSdP+XUwF0ijXb/o6+7sQFWVfwi9zhIMGxGKlwgH7teJF3wXmdXfyQp5xtQyeaT2PM9xo1gkqIXDpC/0JvIcrQPE5F3Ps0JB3mdDq54rvZGsp3U8smorwxFPCudfmyQkHbNKCnj7N4YYV4m58zKxYLcT471KzJGaVLx/t2MIV3polt/4RlzK9AHvw4THinNVH/hqaPqNxjd/PpSqddayHv2g7cnk6x555IavMb/Sm+fXVvNra3FCRF8+R2ws20kPi92sPdnEdomON7frP07Y3Exp8CRB2fXh17WsAosTgsyuD6iw3CTn/jgINmSpPfgosWA1pl0kjF9HCNL9y8hsQzcJofQr7b697RoX6klisT9A7lbyoVrj3blu1W91iBfvF7+LHtLhfL2yTPRNi/a29reJ07eRk0hLsJrBJtLoNnRaxRi9M+aZ4WamA72c4pibWVUJV4h38RqxSdxNCvglMnpqjKOsiOZGq2dCvGvdj+rErgbX2ueK4la770Uis9TDMeG42vqgP2j9fKieeYv9rhrYPPccI9MF7GB9WmBWv4aotDdbqn1kKArzKiG0rpE5yE0JYFkmgN0RYuw9+GEv0W/li/Topvb95nJ/9Trox1dN8gaJohXMcsEL3X2e3HEvaTTsM/dZhtbb7SqrE2ISOBAOdJ3g9aW7QE+Sp5nqQqVx6CbhGWHCIAhh9DOE68tHCYv9VxNhjJsIofwLhCfAVxG70x5ScFRu13boqrWJeOkKir69viBfkhm+eo8BkaBJn+fNPTxUFC5OhJqBrPpcyy/WPBcaZ0wgcpJIvfcq8LsI6+sB0ge6f2aPMoYmVb3H0gs9P/Pzyp/2VvMqwM3g5sY5Kf2ti1ejU4146n15h4TKm0GetyJofUerzBaQs+qexZlXfnbIy+dzgbLHePMhfn1Wf71+bINUY7tMulXeR27ox8jTMzSOPUOcgqyw/xRxXNIHyJiGg4RHjyknTVxU0926zhfI9WdfFPJSjRr+h/r4Zuaa8/gKCW63DFyzIgLu0ZdFd5MrZNTIArHjiVSXyo+L1MTynyIsgz/T7t9P8C9f3K690Or5AvK4EgVHXZwXWhvkibeXa+tArRCuKp8mE4XsJp2zzfi1Un7XoIZ5hO8ymf9CYh/ypTtB64Rcj4qhfEf3v8bUirL6idVPmAnDO7ptrRuD9FJ/7FPfnvUQWnVpNE3h3UzTCEPot2+/rlOV17wdwqj658LGhM0QwqwLjYHvvWZokdd3Ok/f5h2DWZrBEqH9niBU8L3lu/VQ9Kx66b4bqq9u8K6fE4SmpAb8mwhA55i+QCSb/1ECbe8k5lP/s5/Qtj3cQq1CY+FG586sTROm4ypOtmfdy/R81tX3DtbKk77+FVr2NZ25PcLnLClEpAEUgFIUquq9Z4O+ua8QBq+fJ7jV6nZlEMhOYleqEUA1qORFQohvJXa/B8hTV3vhpcvJMolma4SYyVXceFQx7mLtkTJ9qbypg3iz9evHW3u/tNV1g0zPt7c9w4RI0g7y5HURVyRI97tuGKpX5r29Qfp/94hqVn/6RQPTqAamo6n6e9YrY0LpdqE7x6QXZB4Y4NhTruuT49TTn/t2uSaGkuszcP1G2z60yP18Xi2sbqwKerWRmm+hGpaHKK/1+lI1LGnLCcO5G1a7nxrxZ98qmFglhWZfxzz0irLCbJCnmI72fIxMN9vX1c+HHjxZZAEMQNlCxhjMuzkObcarhAx5Gfj4BPgTpK/qfQQ6vZvkR99OZGx6lKQLVDuukSdamDW/HutT1UxGOr5Yfs4SC2lLa0t9Xi3LhDrzIWKQv4SgO0SkJom/QMaiq/bvKdeNDeRSu/cyyZXXhOMaa6Q3dOW7o33/CrGReHjjY6w9laKf1NUKaxY7NQ7Vml3k5O99fitC8yTqze2e2t8hTrkXrvMKmtspXOcpplfcy9ocsxdILx/LmNC7FQ61ju8s+oGB76V2qsY1tAlutFQKrPqxXyRSyq4SdF81NnnfUB9q0MVFYl3L4w7l5Vgm3ofHW0mv3cr4Gm1nTnETvl9v3xsV67zfwfT7rmBxXre3viwTSP3nCa+Q84Qs/H2EFjGUNtQNZ5Vh7tpi4MjLBFvwMmEjOwd8dkLA68qPXmq/95A+dJXzqTkKqkdDnaQOxhDnZaOciBLpnpkljzKEUi4R3PQSsTlsbfcqdD9D5Bp9gIiB76P3xoqo9yIhQF8lqJK3kIhZg4R5V/tk96oqh1v/9pPcud4Chlf3OQUM+zacUoOex8M/S1IsphesqpKLwoTUdRyH1N9ZqGg94TpLaH8uec2xMstz483U4wI7RrzHWbk0XifG/hHWRmT14/lmeceh0ms482pG5hZfIQXpvHRJX4+GuJuE4NdXWLmxQqxFgQFM57KZkClmr5ERbm4OGgz1jthenjN2wILBUWPHYfUap4BqFqWh3cb0pXczvEFtJmTSh4hN8ToxxlIoB8jUvi9MiBdRfXnHXkRVo2tHHIAxrrPnZM1ipHVZz4wxoV0Xty/VTEfnyWiYTa2DDzPtazhWbM9VYiEda/XeSwpgvQ4ut3vub2016fhn2/8emaKaJSUhD+tGYH0XSrt17avawQIxYR2batxzLBzTc639d5IZrdZDBEPCwTLrPfp9r7r1nw8J5B6JVnX3dgdCvNkypqb6He1zT2owOf6tPms94Vl9yw2YsA23+sz+/lViLegeKCWlwftm+bvOrwk5Tmosv0wApm1EMrEvJzOkrRCC7COE3+9JQph+ORmdSatrJwlcRLvzbrquNem1mnOiup3V68eQ/Hqb5432s4sYww8RSZF+nMydbpDQBTIz3g4ipcJ9k/ZFzd3g7nWRQHcvthsPEsjgURIlKjhUseUvFeb9Qh7iQefpcA2XrPxg9fCYp1QUfprgoH+EMO6dAv4L4kDDu0gE64R0Rz5PUDnPtzp2AV9PbAD2qw+B1nqs/y/kuBn5s40YW+mHefpkvZR7+lNMpE4qgtZAd5HY4fczjfikQ2pY6iyBNOuzjZRFgtbZzFoUOkv1h+mw9yGj3O0uG+FYe5/4X+9SE0DZD43ki4SXzg8RQuTrieQ+e5gGJhNyA9J2VPlfgx4ERTcJxAy5pq4RSPo06dFkwvX1fG/7Td6f2peh+2qQ2Ho5mj0f08CqanfQV1k3vRukl1Sdqx56eppMYGSCffO9nJsQSFAXjyUyG9M2MvMWxEv5FIG+NBLJQRoEsrM1TIH1CjHoT7e6howFln5Bq7JXoTsLsa9XKqKvExGGUyqaplMBepUQmAZ9qGLV/uhNUV0BFf4bSRZf/+/bfZmgMF4i1KLHmPa3tg83Cav0LgJx1NB00bQHn9YJBhsb440IpLFrVCGltWoxuGSoXc7ZN+tTLtIc+77//1Y40I22aV56aJ52VCElDbZM0hGOgXP1CqFO/18El3kG+Cbi6DLdz3Q3kx/W62gv6RdvW/Wi8f1Vo3//PmFciEpfQgabbSVQ95BArdr4ppFr6rXVKK/Bv1IYniByX+vzaQL9G8jhJqVxbw9hv3oXmedlyMB5UXVCisFBUX0+3yo1eqvPBernu8mX82r77nEy+q7vrN4NflYngfzoDTIv6pCnxDyI2joVpPJhhmnvZNqi6oTtUfssFD/Uhvp3RR7XyQQuK8QGtrW0s3Lt9ce0oebi3U5uBHWCVeReg1DWE5azNkXI91NtBefI0zpqPRWF2/axNJq95bpv09jnl4lN/hcJ4XA/G4uo+1wUo/QEG0OukNJO83K4t1Poj2kwlX64RvKv5h83FeRpYmO/l9j8lQOGIR8kBY1CU5qjygCBwHo0qKBQ6q4fM9Pc3kNEsFXkfY3pzaCv2zVmFjXXVEX0tk1DnMZQ+3aZDDARLOjBpGFx3qCeC5VIr5FVPW9VHc7rdz39sFEUVSdbrWOjxoIhQ0tN2QnJ1c6DnMYEl8/q6Zi6kVVhWNG1XhVj0WB93oi+bLTNFUnDWve01e7zoQV6mTQwjgmGfvz1NJllPXY8hlx6zIsxJrgt/y7V/Fn8ofOsb7Pjoro/i7d3zlwkuNV7CBV9nnSTY+CjrtGhe/oypqnWe6qcWM+mtFKud3zGkO56mtgisRl8stX3DKGVrucnvEIemvpAa4McrfluVsnwZ3OU90e7zSobocA8dPcUscGZOvPchEibqPrgIZ+byNNI+7wKCh3/HlqYtWFXCLc0VSKPm95FWA4NBe45n1kCed7Ou9vVhD+1bhH4ertzVe0mDPd7SLjeaumpGescQlmznjm0Sc6j9tZNuKdJ+gU7GbhnqE4NPvOenTarfbdzrPu6a53zPMP3NCuCcazoqugBCkMgQXvGDkLzXCUW8UkCmZoXRrV66OzHIXqlf19uKOulc52HMtnoO6khyCLbijLX00jrvDQqdTNrc6nMuld/4RsE5beFzGdhPhJPL5lVZwU6vXwYK8sT4H8nhOFeQr3w9OIFpvNy7iGtmLMcw3t03Xd4ntSCQ0UkJTddn2c8uZyhkX9LhApzsl2zj1BjRfjSI1qEbds1MrgFEq3eTaCVWXkWhtpdhbYGtl6wDiERx2yW8P1cCKTPl1LD2CfEPL0VOmOsVF5QQ7Njejs52vXaMLSJ3SAQ8t2ku+K/ISzx/ymxVk8Rvqhb2v+6UmksM4LtDoI2qG5eInC9E6pd483YYWYJy+qKtkqiZNukraAaiW9HqVrCEIi6XWWl/D22efTU5YR2QsePEbkdTHxRB1PXkVnqvKhN7nUjyU7GGunnomiYDiG+TqhwNwhUYCit97h5bCE3j0oJOBHW2xSM3FH1Fv1UNfw808esLBMuTduIzW2FQDQ/QRg7v55QrbaRqugx0un+xdbOryU8MYYc8VXhYRrt36oj++0oY2h5rPR+0LO0qs9l2ajwvh3PG6N4JmRu4/peNRhrL/kFwmvnC4m5tJlUe02M5dy+TngFXSSCtTxCa0jQVX9dwcqbMYT+Riuz5p2bm8Y4yMjj7UzHK1TeuNKXY+XapLvBMi8f6S7njj7EN3rdcneNn9dJKbd7ttW7j+nEzQZ+1ITO8xTbK1KvRqxLpEfIhwke6johYB8mXHdEYk+QXNOu9vcW0sA4IX2CzdHxY8ThjN9IhGpvIiyt/5I8tPUeMvLqi0kV1OLYXCNUpd4x/vNB+Pr/LIqiOtivEu5M+0nf0ReBbwH+LJEPtvqD364+VqpnI/X276Pm6/j1HP8x4fH/JcFZ3T81tM9r66ngoLeLbLQMjXWVYbUMAQs3OulZg7Y+LTfS31iLLlPVCdlBUL0fI+vHVIDbgUJ6Q1zlfGuugDro9ZmL5TqFvGqqi+wK6QIm4jXc8zIZ5ry9/f8GmeLRCKFHSL/kS4Tw8b49TGeZm1Uqiq+lqupDfO3YQh1axPNuwOtpMn3p58IQXXOceAf3k/lI3swcqW1aIbSOTa3+jfCGY4bFX89SNaM3K1zeTBtudRP4fKPTqk1GzxH9jO8s16hpmwP5OgEWHyYNqxcIuugIQYmeZTpnuDJpH5kC9LXKMVpEd2fbg3e3i92FbucL741ns/ilWsYEwdAL7tXl9cq8Rq1bKWPc4thO23s81PclXbKR9s6ze9+O4jNEMKrc58jTtC0bzUT261k2SqH0bn+/HjTIevSQ629s/EWj5rPYiFvWRss88kBq8RJh6JTK2UuGDAuwZqHmIXnQo+eez4VpIFSvq/75Y6WXRdI/x10sNXvZncx2P6plyHfTgdJ1p/rmXSLyQZhg6EvIwauNvUnsOPIvOlafInaajxEhkiuEKm/c9hcSXLeRY6JWKRhfWvVLXC+2fKhUnrkOrgPr/yKqaqicJ/JrzLgwL5KoiAnSY6GGW89Tx0aTnP//5XNfPh/Q5EaR+HpGPFGo2mTto/7Im1jrIjgEyIY+kz8X4VZhqtHy/6XuzWI0Ta/7vt9Xve89vc5098x0D2fhOhyKFClKlEkrtA1bthQpUYwsjiMZ2YAkF0ZgBDFg6CoIkJtcZQGMBLGdBHK8SopkyYolWZJJkZS4zgxn6+npfavqrbq2riUX5/nlnO/p96uq7hlZzgsUqur73uV5n+Us//M/51kllYzrtM77KmRdG2bSKa+uEfz2twlZZflgKyc+IFK5f4yo2HiIsK5/c0TUS/DhB0mCvkwEBaFCxQbXARC/cU87X0BGg9sELRD7PtWUWgXGHFlsSAzuDoHf3m3nuKOFmWQ9g2LoWCWTORzk++0796GrtTZ0UQyOyCtcItznrxMT5jTJDXaX5FWi498mginXCGjicwQWDEnq7xNdqqv5qPjlB308iuU2JBh6qt8fxXM/qEPPon+PjfDuzR4f1Dtt1uMZ8nLZ5LWbOW+j44O8j2sR0iB41Dk1dN+N1tiQZetn9f0qJDjUrkmevO+mFT2tpQgh9LSajpCaao1MR/RYJLKafoco6Pw5EitWYN8hidzmoz9OB+rSugurgToxVheRWLaJFFaEutLus40QjLNE9Pgj5J5jalw7F7LjewH5QdNq/L2Rizjp+jq40rDWiPdcJRTNEDPBw4ptKrbN7kb8r4Jl1h9/XNl1fTF7Xf7Nsg2GIKP1LEjH+i4RwzhHBI6PktmOZjN+kIpAF9yA6xZi/lxlfNf2es3Q+/QCzXYqO2Bj63e9o1aIrElcQ8KyUs6EYa4Ruw99g5AfzxB1YE4RnvxWwsK+QuDBlxkvEv+DRPlg96L0OcalLNzke98ZEdaqhbQt1yhNxmONFK5LZF2J9zPAQ3xjzf3KxFDovl/3uBccNdXSe88RnbtMlrp0YliPY0SSutfI0qHWD9ZiN/jXb6/TuzjusWXe/xCmPSrX+v+oXE+59o9SQD6ulVgLg/f3+5chyOtzaj0DP5ea5Hyrbaqc7/6ohV/WO7z/zu7zSe9foSnXw3q7xzxOH64Xd6kWWx23aiQY1K6BzGWCqrlCxgJqv/fGjPO31mfx/M3OsYpze++agLW1O6++7yTrurdc5wmlZ5EsFZEwrvNG+GQP6eUvt+92dfetMuDaJDfmgz7qC80SlqpJF5B8XfFhuYyH248v6z5utRLaFRIHeoeABd4iIJAvEAU3TpIsCCGU/e3Z8wRt6ncImObz7dpVggVBa9dxMhlEi/ONdt4r5P58nt8fleXhPapr87jHWvd7vey2vqjJH1cQ6YM6huCEXunZH5ZMPV7O0ULp029VsJtlS9REhEfh0Sucqhfm+NTx1HMV+nvcsVpPkfo836UGvHoFMnSt83t7+czdemaImtonSc/Vc+vO41630fv5rEmCu66JhdaG3yeTV6xhY3F5d6evjLAPEsqxP4VwnXda4O9UQbxGWHYWJt9KmtomKjjJFJxuS+P+Vx4KvbeIoNo5QvjeIrDVM4R2+QNCeB4kEjOOEVsqfZZwtba2877ZGnyQ6LSaEjkiLPUZwio8QeCxu9s1t8mg05OtfRagfocU+FJJ+iI8lRr3uLDEkCauRz+oFRbpXTetsD7I+P9HQbqZo/bNpEU6lLZed/+15q1sjUWyfkatbWH/1l3L+7Z4b+e4cNlGcI7ba32PmHeWQjxOzmsFwaN6HNWCHvI86nk1db5CDWbT1nlXrxt1vz0mWfQ1b8D/F8hNPWFyLKF6xCqh2+0zSyLUZ9VjvXVQvQzhi1rgzGqLZhkfICG7+v5T3WfLjL+rwTl3k95KyiyLKOmJH6TtWXedYCLcIQSYE3aF3CFC1oICYKjAiZjVRSKg9ZX2kksE/HGMYDWMCFzrvXbtdkJAfpHYvfWJdq9pcvsbtzqq6ZG12I4L4VGEkVu9+066FtUS8b2qZTQkRHVv+++1Kh7XiqlCqJYEHXU//aH2rami7yfxYJ4YL5X1LCFAzjDslldL9HGs7d4irEYA5btZMmbg/KyBYt3C+vwVwrMyMaC2+TaZ7ts/i02+R8VQ/f82sRDd7qqHPox/7ObhamX1sF8UID1s17dPz7F6Pn1SVb3vRph2H5Ooz1wtf/9RGQVCpMIDpoEbfDfDTXigFpOvito+rNu0OW7WB5ca9wSxUcQRxitGwjhMYx2du2S/16xg64zbJgX1ZeBbo/bHYcY3pJRFMSkZwoZ4/kJ7+H1iUpnkYL3iuoX8Zg8hiipsqksPKVwsL6m21aq9085z77d7ZInIusXKo0ycOgh98kgvGIeE9s32zEPt+wdEH1aaXnW7NmPp1MM29X200WHREwXFGqGkbxFBio2svvcbKFsjBJaWmVhff7/qllahW/t+vXoU/b0uE4vuOdYvRm4bJ1mMVQnU+9dz9CQd7908TJ2s88ttwM63a/aSNSXWq2bms023/z4ZTHO3i10kXWyz8/+DiD8MCfP17tlDb/5dx8K/jfvUz+DhmhY9Vqts8GeOmAsniD7rnw/Zv7WMsEfdy1Pvy9K/rvddxJjspdUjrprsUY5Kv1ggBIyRSi2Rra1RJofsIvdreoLUTGq2NRK3edwAnXVMDXAskxoOQlMeYTxF2PfR7bftCjG5wO6Rd6v9VLypr7MxaXFtdPSCfiNLRRzsPNFvx3g069dobu/uef0SManOETsKVIz1cQ/vL8vja4SAOUFM6FoA3OMK4UmdJLD6vp2TDt3QSfv3bXSslyGqIeJceUCks58nFO2B9vspctPaevSCXfxUi+8BWfVrN+PQy9Bc04Wf4uGdhh/Vsq/vOMTo2UzfVwGp4tSjreO7TMoIITeDW1sYVzoqPb0CNzmtRpGKTq9H/rB/P+oGpz6zQoa9lU2536POseUe7/GobvbQTR91IN2aRItvkSyYc5FYYOLRB9p1ewlhLSyxROBrr5F7XB0hIZGbxGA66afI6nELpCZSy+mySnOzNqiuaY1Wr7T2TrG5zUg9ekvJSekEsybFXPvMCafwHdp7z4kgp3szXOrNwgNLwG8SRWI+RfTVFkL47lnnuvoc1nlWFcBbiY1ebxIbvQov1OvXiLG5R8yFg0w+qnDSyhGSeZxj0rusEXPwnxLbar1EYoIKm6G91YYsea3kCmH1Y1mFdW1PtQYrpqpysA7vkzy8/daQZd9bnPbnowis/j2HFF9tt7V5PV9miTvBq0g08HYxnsMwZCVXS9Vki76cwYjo6wNMLiBf71vvDw+PxaQ+9Luh9VevWa4DWV3aynHzhRZJt+oW4fJI2arR3Nvt+6eImsbPtnvKBRa+MOnBvdpOEIvtOGF5bW3PukRk4i0RGSlHSlvmiIHc2z6XLmNgQPxHlsSjYLVr5f5bCctmZ/c9DGOhdZJXq8Aap0vl8+o+WTVrqI11An8QGFy/kOuW4BsVMYfxd50jLbZJz4FUHLeJ8ZOFAsNW8GbeoQbmNnM+E9rp9yoJBs59HKuynxc+QwVd+1oBslb+1rq1wtqIWDNLhOExQ25PVHdl2az3VWldGgiPCtUNwTX1+9oe322e3EX6AqE4dpMFcXYQ72Rt4KvEXnrnCa/vp4kt7g2mDa0/yH6sDJkeEhqiwA0ZqI6duQ3XidjJxfb9s+3nIFkmYoWY57W4VZ0DI1qw7k2SSuZiqlvU3yZoWtdJCGGeEJ5WDvP8BeA0QWjeSbAmvtLuo3C91jr9CeCjwIvkRFKIOhnXGN/YFDY3SYQ7DORVLFHGxDJJznaDwG2E4P8eYan9SdIVv9OuOUAM5ioxGAr4av26gCCDTfKTt5CaXa3/qALId9FyXu/o+22hPdvJVy209e7h4qgp62JpG12rsDRodYTJ4/iAZDn0mxI8zmHQRGz0j+qYJKSrRTUJW9YQ0njYQkIL9R5T7ZxrxNwziLxErKdTrJ+80+Otj+tKDwlX/688+cp6WGif1wSLGnyfJVhUv9uuvdve6zSB359u15wlWFTi5Z9q35k0Vtvj/J4UoNSgmyUMrunWPrd9Uljbzhoc1Ug1Icr96pYImXCUcYrs2+397pJJc0+3n/sjUmBBcnNvElL+/yFKNuoq3CYF7XFiIa4Q1uyn2nl3CaFzl1ikMjAMArrx3ma3ganYj/UqrrYOOdxe6g4xOZfa86aISSlEMUtgd8eJjDrTkRVk90h6jPtuqUENPkp9u0kM2vOkAHaw5hkv0XeBYJDsbx2/i9CMBi8fJYPNv4eYAJOuexwh1luwuryPgg3+cRx9uzQatpDw1nrX+p4b3RdyPgsL1aQBjYj6Oe1zXfHpdg+36pknFI/wy24y8m6QW+NCTNokAeuqaMn3Hppz5j6ZhNC/HwPvOOm7SQpHLF6Dplr/ri2NKqGGWrmxBvNrarPWrDCTWPwlAiK61555gMx8O8TD5IBFIjD7q4SwP0AYgqfbdXoU1dgw8F/RgTnSe6t1nyuH3Hngjs41y1KWln2xStsqaZ7UonXQxC21fJTiaur1JrZCzEDPGiF8627CSyQ2VAMp8wRk8b3W+OeJifkm8EvEovpJwgXwhW3/HKEUjrV7zRGczaNkgMdOkeIl3GBEm9JGsbVFQrnMExZ8tSDnycmn9n8T+CrhMTxNDPIhNmd5/ssUckOwgX9Psrb/uNqoALhPJhmYcKPyXCXmzV9r5/08QYnc6P4wLGxq3yh4K/5bObkufOf2fLmP5H371L+FqQ6SwuM2SQ9cJfD6+4TR8SxZIrRn62z0TpCxjmWSKte/d1W4s+T6NQP3FA9vSkt33SS4okt0ZvAAACAASURBVGezVIaD287rnVchqgC8QyhXrz9PJGrsBf4CkYjV17mRfSW9bIFYy79O7FJ9s527k0hlPk2s2RcIQa3XLzxU52LtN+M9jrt1aGoQv4cqra9zYeimvoS40STsc4XQSu8SmkZBrRDc0f6v9SCcuGp6hddS69TfJnCV51sHHCRTiJ9gfBcRP9fSPsg4RixFZAg/8nfFaOsEqf1iPwzxWD0Xspap7rqT/HEs14oh1YX/KPBMf1Qoos9C+lfFwq1tqVbQGqFQF4nFYXLQc+S7WB92F+tT7e4TwnovoVR7WKj2cd83jkulQjmHnNtSOQ14684KUVnlUPpa5ejDuNu7SrrHGi6b3fOvKokKB6x3vodsqMrV79cPE+7Xe3CeV+MRvZdVDz1LGKcw1j6v/Pj+WmNQ7gAtq6lCnLZHyqa1sKs8qO+4m1BCBxlf1xv1hX0wpJjq9fMOcN9h9e8hXKv+7q+b1JhJjVU71Pv17lXVoGJ+ayR5uxcsQxNVeppaaCu5jVK1cPr7DAnJOikrf/b9CDTv5ZbsG6WVPsp94eFgyRBu9i/zqP3WJ4DY92vEQrlPKGG9nIodK9j6mh79oUW1n4cDi8YO1uP01nPlnUr6F2qyKqDnK9BgPGnIuSLGqECuR11nG42T8MZdwqJ2A4M9TM4QrErF99JinyQw6D7TCqxuuevDfqo0L8/R691CGFxi933uQi9rhvIaajvukWUUVHBueW+8SCt1lRTQy4Qx+TuEMbidMAY/QqRmHyQ55xfb/x8lvdxq6db22s4qv5R1K+WauRHwd4gBe6KdvJMIxEnxUoDVqkhVU9ag1BoxGd4mFs8xwsU6QMIOQh/TJPPiDAEdiK8qcAXy5wmL6LX2rM8SjAzbpwXeWzMPCIv99wm4YI6oP/EjpDt7o333NImfVc7nJDfrgzp6K2Ezi4512uLW4weIBS5T4y4x+HX7qfdzrMevHTpPYWfkejuJqVehupN4x/eIcflce5eezQAJfVnCdUhp3mn3HGK8+HdVxL7TIuGdGYRZJRS3Ht4qKfhMk32HmNPPENZ6taAOMb4RaBV0tmOzClKFLRVSvHK98RBqWCY9R7cEM6VbD+wgad2b4PAc45CI8SEFlOUFFKrKg4qlV0tWKPG75P6O4ugn2/+T2DsKebMSLcewrXxukR4VYY3LeE9xd6myYtta1hcIT30nAcmcJGNQbuk2S8zPyk82CO/718L6NWXaPlkcAf9t6/jnyc0FIRMxfKELhDv4JIHFnCD3Z7tFbqhnirSCUeYFrTFXCK1yl3ANX2gNu0fgNffbix9ujbxC4sVfIINk8PBAKXwtQH+pff8UMZGM1jsJzCTTEvqjsBAnYZC1qtsQ0X8jD2OSi7zZd3jU8x/lGtsnVibjwkIvrxMTez8JM7mA54mJq/Ku9xyRin47MXfWY0KIE+4q/w8JqzVCgDrHrQewSAjf4yRT5glCaRs/cEPJ++3/OTIb7kWCYnWCdINlzNR32ujoYbRJnuqQZzsL/AZhjFjv4gzwc2Rh8q0kO2iBrKFxgJhjC0RCz98nLNmfJSxCBeJuQlZcafe0ToxtukMoVuloxpoU1sI4fYJVFdoVZ+9hRJO4pMRV/LyW8NX793ODcRW26T3etdIn7xLJR79G1NCZJwp+/Sng3yTw5VrDxBIKpk9bHsL2CGeeE0+tFsF9YhJdJwbtcuu0V9qPXL+vAn+PWBRfJnh9HyItBUtqVk0j7uWEmSOE5U0SyzHt8nL77gxRo0J629AiWi2/q0vUc2K1ysxY2iz/tLrR/ee6c317elet/3voGf29NnsMYZk1SeRRkhomCdv1hLDPmyeDGtvI/d5obTFyX2lZtd2yVBQAnmOk/T7JAe2zyzyWiPn0BCmAhw5rqbxOJLFUYWWs43j7fZqY+0+RlqM1UMQetSBNFLCAz0YezKRjI/xRq+1me+aTpIFiNultYjxWCUF5m1zve4l1ZbLUuXbtmXaf+4Rgtd7Czvb3dcJTWSEKdE0R/XiQZDPsI/pqH2ksWOXwJqGEj7efKmyr8NXqv0nIkpopuNb+FmYY8iLEmm+Tnrc7+QjZ9F7JUF9XC/kcMV9mCYXzLBlgPd/aeZTYeahmuErJnSPk2mutn14hlNaNEQER3AT+ObGT8cHW4A8TE9Aoqfzb99oDnyIsAzfGNMLqliRHSCyuTw2sLzlpwXvupAl8n5gAuiHnycjuD7V3MIqpVW11t43qx/btobRjUtrxZqybIby2/j/pmnrvSTi7GYKwOfhhM+2VijWJLncf+LvtnJ8gU9Nr4oFY6KSqYgqJe8T4CA8pxGuG3KQaElpM8zxcJ9v3VHBfJyiFVxkvpu5iu04sEnnjKpUjJAtG6EpMs+KgTOir/nBx9jGOSQqmMgyqYVA9ouvALxPe4MeINbqHWAsKRWGaB8R62UZCJiskR/lW66ML7bw/QWQR7iQtPNu/lViDc2QS1s7WZ6dIQV4NPjm3O0kuul5DTXahnXe3tUnWyV0iT+E24ck/R9JsjbH0wcaKd/e4t1asFShvEvLvEBmL6i1nDT8pdncIQXueZICcJgS27fFHg/UW8OoI+F/aP0cI4XuGdOEf8PDAO/l6d6kevrCC0MUkZWORGBw7TrdjitRyQ4tpgbDA3RHAxIyT7WUPkJQVD12g/hgSRL6rVrMTxPJ4R3g8i/X9HpPgjfp/jbIPXb+ecOiDZfIkJ11zHvirBEH9vwe+REw6r68WuBCKc0LFvEpYBt8n3PcPd+e8n8OFe5hU9jcIZezcsfaAMYwqVO0L8eFZkmuu8DpKCHKF3UZt1mLuayd49J7TLGFA6PLfIYJHLzNu6bsGDVouEILwm+0eJ4j+fb70jYrS+S10qOV6n+Tr3m3nv0kYYZ8nrG/X9H4yqcn7qxSPk0aPlcgMjjvWs6SArUExLesDJAfftblU/r5NeDS/Bvx54MdJKKmu+2oMauVqpdofeuV1/k6V/+3r1fJjiQLhld4DH5FeXOUSC1nMAa+LuVkgY5WkftQqUSeIySYHz+LGNSvMZArTEc+SJHXxlTeIQMbnCOtH6GAv4y5sPXzZW+23A0K7t5jMZvHOOuErpGE1fRelNKTN1JodeobWVC88K2wxdF1P7xlSFtY2Vcm5B+BmsOUe/5pEZRu613uEC7+fLLjvUa3em8A/BH6G8epVJqNcI9LW9xNu3FD/rpCV6tbLxOuvka7l+cvlx9ojN4g5ah2Li8T4H233sCzli8R83UssphOEENqMMnZO3WrX98HE2r8rrQ2vEWvqJWItzbXzdpPBdOePXH8ZIzW5yHGwH3TxF0h32noUUkBXSaaFdbx3lvP0AqbbvY+1590ja5JXo8fxu9m+O0rKGI0Ghb5cfCuSbW33PUd67AuEp2s2rF5SX8ypYs4ajH6+RIz72+SuONYdllc9JMCdO5UPrJDtqXSj7sej92qrUmBEpvlVjVXJyBbVeVC+86UUCNvaizwg3KM1YpE5iLfb/8+Xju4XVv1f4btGcgrF3Ca5uBsd9cVr0KYPCvRtGbqPWlmrsa/P/H6PSZjvNXI/sn47q/WOyjhY7x3Xw4c93wiwrpaptvsIAfbfAH+JoP1ApnefIhTxrxBJFp8YaIMWHeX+Q0c/fmbQVVrbfGubgbRZEmbZQvTnNCEAr5b3PkVgd88SgnejeaFidG6uB8N4CIEYDNxFBpxUAkIgFVZZJebA3yPYBp8kGU4mMtWMLa0+rb0VshiQFpq8/vvtZx/p3WhsjQiheIOsXHiAEIri/TUD1vmiULMsZDVCZDFIBdTKtfCWhkU1ChcIOSAePmnN2RYZG1u63+t5ljWG1Z/n70nwWP3RKPD/Krsq0wxgdQT8OeAvEynKz7QOuksMuHCB7Agxjbl2g/3ExN1GTCy1o6T5WSKy/QoxSWC8+EZvxl9ojd5HRhj77J+hY8gS6jvJ43EE5grhLt0jXdmNCPKwMcVLoS6boA86rNduA5KTFny1QNc71hMy1XrRC5F+U4OLupgqcO9bC/lUhfcobfB7obKt5e9vEXPmz5BBqXcIgb+DpDTpeYn11UJVzjMFhMLLbMmhQ5fd1FrbbRnVoX0KFXRu7zXX/r5MWLs/TAhT2Rh1fLXSp0krbJaALY6SVp045RTjzCXn2QxhXV4l68UcJ4VyxVQhA6Tea1T6xOSUOq5by/eOqUbVDEkVs7+rDHC+1vTnup7HLMhyVLig9yJrGyokASkUneN3CZjsq4Rncp+QiZ8hPJRDjAtSBTs8LLCrEpQ6er7d99uk8ltsz9g7IjE8CeoO1llyb6l3yUI9/vwgMXFuERPLgtW697rLtcqQQHpt+F3C5T3arlnPklDg1gGZJHzh0QRwrxkN3ujC9GmXG10/6Ry5mZeId3mBZIoMnV8nmOmv1u0deqYBj34niM0ea4T18xrRtwcIrPUZhi3/jSCOR332kLVSP5cCOSLdVF1kaZH2mSwcMToJ/FX5O5/WCwzbFgbOGaJe6cldbP8fJgst1UI3Bs+GlJvvqjWsgKntqMEvP/c9FATSpobYQ+56XuldKpehcpO66Vrt1rvY0X2vt1iFss80Yck2rpV7DBkhdZ772XJruxuVmqSjoK11PoxzaVBW/N9KkTsJS1xPwPGBVC61/31P5UP1BnaQbBrvJVXWIOjN1i4zhc/qclj4+zohiLcTgnWl/a+2GxEa9DRJpj9MCNADrYEK8TXCvTrJw0JssxCAgnczBYLqdZtdUEMLv06AD+pwUSgghqwtB1h3qlZ+ckug9fBwNftGFcZmGU8AqMc845ihbTlNMmfk02p9HR24j+3ZSHEZKBGu6r+v910ijAETDhTA10mlYXDHYlMKXfFa+bEK5SH3dpJb2lv0Q3NHN9vFb3+OWrvcE3Gq/NayFD6wX+dInNeEhVqQprrAWmp1zdR2+1PvV4Uy5d3q55MUbG/tL5LbT1WhDAkFVcu8blVV+3QINvBeQ3CZ60qIFFIw7iR3MvF8+2MIOuohS587SyjU7xAy8jQRoHUdGC97gqSsuTP7wfbjuA2t2WUafe05wmxeJUjaXyAG/RpZCOMwIYBfLi8nZvuANLvvEdbaJ8mtgB71UOOqVWVWrJGZKJMGZj0BJai/WWttvfN8vljxUNR8aEHXSV+PyhqpJTWFLMygelzlsEb06RaGy1ZeJIjql0n3eRn4z4nxd7HLk7WAt8eQVTh0iBe/SfTZJ3iYStj3uwkTxiGqFUg5twpIF2f1xBQIQ+M0ZOXazmPEGpm0N597nFW33cVpctFFYu5+nlBcWnQrZNWve4QB8zpZJ8FnP0um4mqt1U0EFEp9+my/+HWJIQVhhWSWSNhHvvAewn2uZWr16mbbtTvIAN8UuRGq51tbuGfm1DaqMP1+VH5UchoxdaxVPP13fl8FcGVOuN76tHYt9wp1uhZXy3l9lqDXVmZZvW8Pl4x53yMian2YEMI7CHzNgiovts9fJrSAXMNpwrU+2xr9mXb+RparGgDSGqn8zWvtO6OZ7paxHg46yRqzUx5FeDlBhga1Ps9JtJ3hNlTXZaW9z07GNW4P/sPDg7QRxryZ9zGJYshSNgPRqnNCR6eJOVHbsVkK4NCxCHyjPes0w1zuPqD4B8D/SEBg/y6pQOozbxHzUDaN0W9rN2xkkZvFp7UpC0Ue6pD1u0Z4fV8lhLXcaYXNUaLvKhaqAeAir1iqtLZa5nJEWM9PtM/fbu96kFiTWwkhKKOgCqr9JF6+wjg+XtlJa2SZRkge+m1irVsNbkTWaKnvYtLI64TX/BzJkHGLKNeu67GHJqug1Bip2XOOX2/FD825qpAMUMo51pCp1FsFrMK1YuQe/Xqtysp2VIHcr23bVY2GXnivADMj4G8QVqzV5D9DENefIoMO1b3ZzCagCrQbRO3PPyB21/gx0sLTrHePrr0Ml4ocIvE/rqDt72t69nniXU/zsHXbL8L1rOT+3KHsuqGjv45HOH/S/7WQe//9HBGYsPi2TAhL/z0O3ut5M2S0f5Goaf1qu/eXedgir0J+lVjY0wTr4lA5b55IOnqdxNjOEALgQ8TYrUcLVOBJQxIv9nt4GIPt21mpVtKvZsn+WyR3JX+6nWOyium9JqnIz/X520k2zIhxDPTJ9t1VYi39k/bdD7TnHCPWzz3Cih0RCuyZ1ocKQ9sKGSReIHe4qUaP1vcdIiA6IhSMG5DWhAvXu4LqTvv/GGkJbzSH1rrf/Xe9MJta51zKuf2h7KjFw5RxKtQ6H/X2qtxbK5+PuvP1DK6SRq0K9R0iwHyUGLtPE2M0MyIG7jDjaaWPc1R8+Hxr0McI93NbabgvsrVcZzR8vedvViCsd72umRNsmujg53j8amd1ggy5RfXzGnQYwtuWSNemb/uIzQl0q3m54KolfIeowfoe8c7PkbsEDBXF2ai/tV5GhGA/TWap/a/A3ya4xD/HxtXR6j3r5L5HCPI1sqyqAm3SYRTctN4R8X6HyMBZPdfiQz1mvEQYEwukRXafLPaia7uTEDrPtHOuEYvwMqGAXm73vk3CPsJOR8hgrXgxJKPA4OtVAv67QgpDhd9xAr44TlL4lon1eIG03E+SFrLYpUJVIVKtPMg5WjHjSeO40dycdH7/ncGvakFWo6bOu9qe6sH1eHllZzjHVknlrBe7Sm5grGJaIWsZV2Wpcn6bFLAfIsf6HKE4/0G77geIPn+LyO6cJQyOExWX6TunP4Yw2KoJ6wvXnPDekpVUv4XxjBwGzn+co95DIa8SsEMF8ydV7OoFZ++aLxOL4h3gi4xvVioRfqNNCevzhjLZVByVqD+pnb7bFmKQVwkX1u+X2+cr5L6ALnKxsPpuk2CZe4zXUBgRymwnGXG+QwiqJ3i4cM/Q8YDos1XSnYXkAe8v9x461gih+z1iM9KvkIkQpwl65p8irWuTJ14nC4BXSE18+AIhSO+SdEsLuO8hjRfpZtZNeaN9/wIh+OZbu36p3evl1qYXiQW9SMBy20m2Aoy7zb0rW+l1dezqNVqRFRJZY5yhUC3N6nZrFA3RszZzVOtW619MnHZ/A6bGmWba/86BKnB9Z9+h0g8nBd5UalZo0+s2CawqGkhZMUfuULSFUHDHyODcTULobiE8lZohbOLMSvnM53+XmAM3COG7g5hfd4ClHs+oR00gOMLD2JIRdAVI7Qy1+IjU3PNkAaBHPYaUQH1WDwMM4a8baWvPrf0hruU5N4jCMLsJbrSV6lxMBin6e3uIiVfLYsgFnsR+WC/Y9ybR5zVTTQu5avCh62FyHQddcMfaSf42IYCfbdfca589RVbymnQsEzsknCWEpBX4NnOsEXPzbSLL75cJD+w5QinK/Txezl8jFs8vEhbLjzMOe9Du9xpJ07QMrNdLN3IrHoOXVvyaIqloW8hUYvFKcfrt5X4qwZr6WgtjaeBIBetpbBUyrHhr7fstjAvVte6anjvc9/VGh8K2VgPsMd41csNNU6CFcdz9Qqvc960Uvro7SsVqvZeK0oJTlRHkehOasjiVsJ2B1d2kYVitZwXzLZIqt41MeReHtsCSVQWrgjBway7GFTIxC+B7I7JQ+hphZv8+YVL/TLtp1Rgu1iEc15KZu3m8mrdDOJ3/D50nkF7xmSqQPdeKUPeIBV8TDlYYnyw1Cl0/v8V4FaqNrFMnu+3TIoHJEEi1gPtjUraWrhEMb7RZ27VCUqvW27bJ958nSv09QxDafaerrR1WzhpShAy0hXbP/4GY6D/N+Pyqz6/Xa4mfI+onaG2fISzaQ2QQao206jQAjH0MVWPTQ3IN1NKd1WqrQtB5I7xg3MTFr5AwW2w36TFZpWuZyDI1+/QeMb8U3DvIHWeqp2Q2q/PT9stikQXRB7eqYVLd+LrOhvq9Ggz1Hrr0N9u7GNiVISEObpXD7eXefSyltsUMv3fa/3LsIQvqCN1YE0ILX4qsSsf3t13+SJ+Tby7Mo/FYvQdT0y2roGVurMC5aZmGBwTsd40Y31OEl9PXrxAGcTyvjgh+3BoxQU4RmO7QIu2jmWb7bGOyEGDg8/XOmQRNyKW14ye5Ip4rRjbTPjvJuEKpA6SwHGpLZXj0bXbRbuPhCT/p/77NTsBaj7d+v8jDufQQi+BVQuE9w3DfzxAT4gYx2G6cuh62qmI61/5+jhzrG62NT5Tz7R8XXsXy+vYsMDlLsh93AylmqikIXWA1cFL52X2iwKRgrh7MNBmf2EEusgUCWnqOEDKzhMXspp/bibViTW6V/fda/5iJNdX6ZbrdQ2hjrX32DuEC7yc8i6NkVqmCyT6m9FGPm1Zl6LXVMxwN/O91FRtVgfWWd39tD4ktlv+Nc1geQTz9COkRD0Gidd4o6Kfb/dzRW8qo9YFvtd+O4SFyY2AxdrMCew+g7wNIGMXynwrnfYRSkGpYlbI/vZFaKW+VmVGfrWJ4b0S6CPVw4dwn6TmPghNVTFXrwc5woWyUIlwF0ZAFfpOwMPaRWWRaC9ZCqBH6Pjr/KGyLirv5ezOFgIYglSp0agqwxzIJc1RLoj5/qN+WiUI7/7hd90UCpvgID/dfVQIqLQutrPAwtttDGpXi934pdrbdgNRZMpJ/kNyZVxfwLtE/bxEK48NEicYRYUzsJZSO7mCt/vUd4LcYtyrth1NEf50mFq/WzjRZXnQHw9AEjFvOGinLjO9faPEqOd27GC9cZWXCWiFNQ6EGEzUAFDbVkNBi7eec/azA8Kdau95HNohe1LvtRwH3bOun46RhVIWO/GIhh6pA6nM9vG7Una+Aqx5mXTsWIbtClsW1XKnJM/ZbhRt6JTB0fyERv3dshJe8d72v1wkxmb1oWQiFs/LP3d1vDWHEk6zSeqwSQtAiIVYrM6Lo5NRyqVhrfU4vpOp3tZCKh2C6mJuDVi2gKrzqpKyCZ+ioW7qsESwAixWdIge0arxeANluGF80dOdVzqyHg/c4BYR6vNG29G3byPOoh+moJlJAeh1TZHLHJIXkWO2ZcI4ZZOfJnXllNpxp18wRufn/nNhL7PuEEPh3gD9LjItWo1lpzkn7YYaIUK+QBWUcY8gAzr52ziVCwN9i3O2VkrZEeCOvEt7IR4nFBLnYtLAr59b5WQWNwktX2evNorQPITPFJu0m7HoSrrpIzN/dZMbhHsbXQQ2qKzQ0wC6QuOeBdq4V34S4HhDe5ovlnD5VmfKMSfGRulbruq/vNMQjrlatUIhKtldcVdlofFSDcK3cw+evEnNUb05j0mcLLXivEQmB1TFeLNe4Riu09N6QIK7HKjFBpsmCP4skTWNvd9P6okOCAIYFwBqZSriLmPBVoFwjLCDxNheTE1stpYvidUMQRi8Qr5I1C+aIDQTvEFSTuo/eeveo3xlQGHrXIQG4XgCtf8Z6acC9VTF0ztDzJeprlb1KBNJ+kChEo4VXXdX12modglqg3ecuEjjvDUKI6a46cXXVDKCIq/vsXSRLYbX87umPq0Tw8tcIYWLg+Vo758tEBbiPtvsKK3yHmIPvEYvvZ4B/g1AOZsjdI+aL2wG5b902MpmiCt+hDCzbuNZ9bhKQFt7x1j8WL7IvtaxmCQ/ibOubwyRbxXlrgIo2JlIbLxMBzCliTe0nhd0xEiqrVK/KwtDysx6JZTKHjBN/y1u+S1aA05PocfYao5kjFPYOQvbU3a+r11A912qcrDHe19WjNZlGLLuup+rVS18zxuPcVPFsIcfPNbWbDN6qIGfb++vJ7wbuDmlVcRI3Tlxof99onfAK4xQTzfVHteK0BL7VnvcJQgDbYUvEAvkXRKc9SwjG3eS2KXUwJh1D3zuZ1LrieDNkRt96wmaVh2GTSdbvo7Srfuci20XgU/25swRHUR708U3c0wVl4MggA4QV+I+IcfgTDAcNN3P/etQFJVezFoMxMKKLb+Cpt2xOEIud8g56XQo+F7C4uLShq+3/44Tg/RjRZ+cJStG1do+DZN0UKWqQReGll8mJr/i187/ikHUtXSXw4BGxhg6U+zsPd5EZfr3rvkLS494moJk97V2OlPNr38I4J32hvauFfZbafe60ez1JCmkhsx2k9ya1rmL11bqvQroaF/VdqtVr+V3HUWF6hPENSKvXO0fWvtnLeDCzWrWmYN9s73mMxKdtT40r2P5R92Ob/ZknA4ZmytpXyiWxced0zZQ07VvLW+/n/oiMgl8Bfo/kweli3yGsoz9LFnjZCBeUslMtuOoemE65m4fxSE35eRIbE+KYZGEOCcBJArgOmK7HFWLxnSTdyaFrZwms7Ajjha7dwnwzOzX0R1UKWjni9gcHzp0mFtRpMpV1Eryj1WEwbQvJsRU/l/4zpEy1LObZuJqbfXa9/bxITkqFwyy5Mee7hICUg1yL4Wwjos6nGefVTrfnuMOvQmKeEFIzZPUrSfm+v22sEfSKF1aKmPMDxilZChBIPrXKZpXwGH+3/bze7neq/b5CzJOTwI+SmW9aTB7VjRdqsUqagmN7ua7iro5BFV6+p+13o18tu0uEMXSbiCecbM94QO7LR3um2wb169WdMm6077R0VS72f007r9arcaR5Yoy3kp6PnpDzw/N9trLI+1fYSeH9LiFAT7V3sAY0JMvGQOBMa4O03VpmQdl3j5jHQlAaFHoXFT6i+90L+FXg3ojQsOcJCV4F3gHC0tpsOcUhzNcJ7cSwsxWuVTDqiroY18MeV7vrN7LS/P0eYZk81f6/S1ibT0y43kl8i3Bdz5B7+K2QRdFVJutZjL1yut/Od8+/Gm2uz3eS9MrGPusTQXRb4eE+3AhWqG19mxivjzKZ6ubWP1Nkxb0PkcLMDLOz7X53ycUh/UnBtoeAC9wN1/6cI4NuFhL33bSqrN9g/1WMVkGhYdHTAE2McH46DiomXc0dZPDJQ89IjNdnOFZVKVQ61Wo5x8/Fgy04U+slV/bHCpndN9POMUGnCp6vtOe7iecBYu4eJIWZNTXsJxVb9fRqf1Vo4RaxlhVsAWXJowAAIABJREFUJjRcbuf6zvbBFDnn5WirwJzjFVetQcVqbGlgfJNkNJhYY/8eYtyq1ntfLj/CWcoB32V7OdcUdGNfPfxVdyry89r2SqmUHjkisXTl4tyIWChqCAHpzbr6Vfhqma2RyRxWy3KS7Wdcc/fWnFFqLWUHy4j6QUJLOYl6QWubKP8vExPz98gdDfr37LFWlZJZVuJuLlR5hkOZeX1fvUe4Rga8zETbM3DuUHvq97pmiwRUU7Wuk6YX5kPtE4qRr+rzbhAp0K8AP8Iwxr5GeBFO/IrDjYix+j2CnWC5wwNkeUoXzg6yut5Joo/rZFaBe+8aWFGhz5HbBe0hF6DCarZ8P1vOP0jMg6PlfRbJhajwrUkCkHPDutCrJO6q0PQdKsZZLdOacHCH3CFkrj3f0olaYt6jWro1AUTo8Ca5xnwP29HXHKk0vwop+H3d6FN2gvNe2Aiy1q4Bvb0kPHS+naNHcqq902L73vfdQwpC65hXq9u15rsotCHrccy293PzWb0b5dK90pa+DyA9m/nSv5Cyxb5xbfXK6Soxjk+QMk4obj+pFAyE3iMV9yFgtsIM9eit2yo4R+W7hfKQm0QyyEmSizzEw7Xxbp9yiSzD+TnSNVppn18irLJDDNNyqhat7sNt4A+JPO+fItzBWuSlZgJBTiqVh5a7eNpqedeNhC/tXpXzbL2CaqXWNnv0NLtvAX+LsMh/Gvj3GYdArHlQF89Qm0aEZfp3ib2/vlQ+X273qWyDeq3jXel2KySt6ygxuV4nxrQq9qr0XBwGZ3oOtovcfrtD0ummSIEDSW9zjBRK98gECQNmCvFDhPXo1vD3SfdZa3KNcct3gbDyLpJbG7k5Ql3svotZVpfIZIej7We1jcFVMpizgyT9j8jgmt7QHsYpcI5H7/ZWoVotTHFM164R/kp/6618C/2ocE0VVnipjO62c66T3tNn2jWzpMJyvR0n4kDi7GtkOU7bauZbpZ7VQKG4cU9zs+3+7zraUT4zu03vW2+oKkdh0bpOR+W7upVXVZZLBKHgMjHPNNxmSEWkkDYuoiJ51ZtvL50tcXoS/iptSVfE4jnPMJzYUYNDFwnr9AGhFV8n8q5/ktBmo9ZIccSPk1jOJGvRRUZr/1VyF9oTjO8uXHFnP6uAu+89BAVsdHj+UHLGpPOrFb5W3mOJqE/wX7Q2/8dEbeAKE60Hg/j9NCEIXgX+N6LGwZfa9xu919D9jfyaQak17dxZLuctl88XSPzsKOMZbu4+XPdac0FpDc8T88fMLfFf6WYmW2jZ6LYb1PpCe2/z+0fE3NC9rAkiVSDcJi1vBa1WssJFN91nPklgrbV4uOVQtcSqpatgkKdqpL0GAMWmxfJrgksVyN7LDVI1BkxycAzkvyrUDWxtaf26TKzB7xJr9Ll2nyVi/LaUPrNgv5tKnAA+SyoeYTKVw07GCy8593urd627bgiCrDirdEspgEIhC4QQfIJc1/4YJ7KNGgeQXsBy68sbjHsaI7K0aDVglsldOHyPS8R6fkBuveQaWAGmR63zrDRUhe9quXmlOEnbqMB77Zgq3JfJLcylNS0TmuNpAg90Z49FMuDyCVJYbkYQaklV4nb9bp5xK2eV0Ea6YZPSsSfh3r0FOxo4b+helt3rAx5ic6ZIfo1Y4D9KJCxMwmh9P61I2zFNauTvEIvj6YFrNwq61vMk+Qs1jMjiKPMkRFEFlpPUesx1flX6zwPCArtGCLLT7Z7fJXd7foGAlmiff719d5+MPgt7HCf67SVCwYvjWgOgZl96rdijbrdWUqVRSk+aJ8tIrpFKyV2MFYg+py5Sf4QrxGkVvA9IGEXhbiLLRYJL/R4xl54ilMzz5H53eiHiqzWg53vokVqGVu/AIJXwgErtDWING0h/lsSAD7dnSIm7S8yBDxPerc9XxkDmAkBa61XeGIysGLlth3Evsiruqticj8oWeeTbu3tVJeB80DpeLfeBhMzEymeIMbnZ3vUVUunZn8JXBpcXyUCmymyuhybWw1r9zMlo+T/P0Qpyt4FLpGsF4+mpWnYKfDl6aq3NCF8xU12s2l4XylTrhKogbhCTZyjd1g78LmHV1CDeRhaoxyIxOIfa9eLMVpYalfPMBIKHJ8HQoWWppSU+5/n2h67fpL5c7zsYx3vPE0LyEEGX2k0suNfJugh6HbroB9q7H2Y8s/IuycMdEckav9rO+Rli8V4gahgvEhDKx1tbrxFK/TeJ4jxubls9tQME7/ffJiwyd3kZUpwwPm9r0MjzagGe/nutW+fRAlmHVwEwTXiA18it60+SdZ9rjQYTA6xTIQ3vPCF4Z0iWjCUxz7d3/tMEq+nT5HY9jqGCR4XtejOBRD6rwrtCF1rcVbmYKeZ6c+MB4R3Tjo8SimIvWaP4RruXMmE/yd6pClDq6qV23Q7CqrUKmp6DlrWWrlCUMNVdEnq508ZiiphTlv2tXnDvYUAaDVrZyq3KmV5lXGj3CTcVThpac6v15P7QghMbUgsYaKkayYbPEp29QnSyk7dqgOqKqCEk/29WAPu8ZVLbuz262Uc+owYphu4l2D8iuLQvEtZFPWejdk0TgukCQUt6vl1nZlOFKgw09QXSJ0EhdSKo+Hq2xEaWbcXQeutabLF6Eu4DN0tY0meI8bxHKJWbJD54pd3/WWKCv0jWcpATeo20jF8ntoP/dUKA/BWiv88SgvYYkUyzvd3jDWJO7SHpRNVKFdaQ6D/07lqfFdPVYlUQ3yPrc1xuv90OaI2w0j9JKIsT5LhU99j14o7NJnnIEpJ7Kkf8t4iKcG5LZv3ul9p1Bhc1INwj8nlCyIlT25YpxusyLJJJWLX2sLi6a9xgpVxYjZZRuc8a6T1aOKcqIq1Y1+U1AjK6R1iiu8k1eojcXLiyjm4RiTjL7Rx3ILGo0SvEfNxdxgzGN/d1vGnX3S5t2Fa+F+Kp7AZjBL5nX15hKJ62kWyoAr2uw+pdzfSCWPdsmhSQEIN1lPEUyYX28reJhfUPiQ76M8Rk2UVM2mfIgTH7xgl2is3RqTxWSA1pAXJL6x0ngzYb3XOVgEcgtWyfQbfR9Qb07pK4YXV7Noste78qSL3eWrhPkG4rjCdETIKHVJB9wLWe18Mp9whv4M323acJwXAd+DtEHQvrj3ySqGfxGUIIPyAsta2ExaslvJMskfkWMW9eJjyO7aSgsLaGqbbiqgoZla6e2B7GaWH1WCShjucIoaV7b7EdA3DvEpaXluy+co+n2zt+qbyTAlx6k2Pgb62mGgDy2TPtfYXq7pbPpUKZTm273QjV31qnBo0qVU+esMWJhBjuEVb1A7LspMJLha7FPFU+V8EYB5BVoNATZllr7/QmCV3sI5T0RUI5nyKr5NlGC/KskRQ0LWLL7JqYc6mN2zwxfz5KprVXul+1VIcgjJojUVk4eprCL9X7qXslKshrkH+NnIc9jAnjCmK+XG8fXrIR04Sb+LX20I+TtIsXGN8IUPfzMuEm/g4xYV5uA3CYmEg7CC3+RrvH/vKSG20sWoWGwnaWGEzxKF9+Er4rvnWY8U68TuJrvdu60WG7lgkFBFlcZL1jSNC6mPv6FVPkXmzPk9lN1lPo2zLp/n3Ao14npOFxjeBlurvH84QVvEhYqZfIQvrHCMtUj0dlsY8ktyuYjJxrHbqXmfCRsNIcIaS/RQQWDTI9SYyTdR5kF5hMUY95Yr5tIwvp+JyzRFHuXyf2mquFeo4SStwg1pcIY8KEFD23JcI6myEEzCWSdrWfgBxeIlk/usZyjB1rLVbnrQLQYj9mtKlsPE/ak8Hp/a1teqFVqUyRWYIfa32nkrtHrh0Dg76n3G63u7L2brUeVfC0d7pMCEjxV6EHqYrzhAyQkbSL8Chcx9sYT4fvKYuuUa3IB911PearsDVVvjJEfIepcr+aBFLXUYWm7PNqdVc4ywCdu4TLDNITsq+3lOcslXvcHBGFuacJl/rPtw492Qav53A+IBbm14hFs5NwIz9LLBgDRBfa4HyYmOg2fiOBZYeKud0nJ4Nk6PWs1uqWDCUzwKMJXq/TCtSKsjDNZizoqj3niUW8SuJnEBP/ALEIdNknpSyvB2H0lLz+0EX1sM6vwVWFkt/dISPdktrXSAzeCVafN0+mxEtDeqr92Mbt7btvEYkH19r7HyEgAGEskzjcIslJbr/oImt5m23mjh1fbW2SXyw7QIt0B2Hx/zBZH1gFJrzyNmHpufOKMY4Tra+sU6JBc4mY/9vIAJ5W330C1xWqeZ4Qlu5CInQx39pqzdv9bWwqNKWlqDXtgheOeLO1/WK71xcIoWzCx3R714PEOn25vc82kjssBbEGzIR5avKHRhqMsxIU+LTnmnxjLGMLOVcsxq8y15swHmVNZ72LC62NJ9uPQrIGJrXmPSo8oFDUQlYg64X5vWwMK6idYBwpuEvKxunW3+7tt6P8rJGKWLmqgTU9Av4rorbAEUL4uruCmmAbMXl+kYhSuw/YTxF0KAW2xUqmCcvYNNTNYL81eLQZgVkHWjhCi8O2V+GwWXbA0DN0HyqQ37d9S3edlJ4dRD/ME67hLkIgKQyt8GUSQV9Aum/P0POXCYHmnmlDRx+0uktYni6oJ8ii+RfIfQyfab9XicXyHrH4P0x4J5BBvVcJq9aYwgkC0ztGjodC3CBbtS6kZ20hYQf7smcvmAp7j7TQ54j5+S65pZNzwII4x4nxkKVQ+2SZ5M+eI+tfi62a7mvpx2VCUF8iy0RaX+AyGaQ7RKyHVwio50OkZQ9pnVlrQ6Wyn1QMWly3SPiA0i/3ybrTVpJzvqo0TXp4sl0nNmx8wM17Ffg+t26fNMSU8qiURXm6MjMs6GOuwfcJQ+1FwpATP97X+sfyAT5LOMj1oQVvCvgy4zt3G3hToZqdaXbrznKOcSv7tbJXanq85+iVqPStjyIUZalTyx5UL3EHac2b5LUCXBy1waj54FUQWjTFRpoJYnUmF4XawipM6w2YhwtwCOPz+96Vrxl7PVMCcoH3kc/+fps9FAS6S/13FWPVFXfSHGMcj6oBziqINnPYhhqxXyIoXE8QeGsPS0AKRYXYXYKRYHWvp4idtU8QguN1wtL/CLFQ/xnwC4Rl+XPAf0IW33lAWLO/QCTNXCeE1F8D/jLJONF9FheUDbBMCHOj4eLgLuSeVii9cZpYqM+2z2+Q3HHrVuxhPOBiX1f80H6dJYvfyFwQBtpHzGffeZZMkz9L0vlM8NCy1bp1bhu0cxycI0skk2Sq9YP1XMy603rd2p7xFJmR5jpTIBsAkssu7AA5h6tQdXyquyxsY+BOK7QyA6qBonutkFQYazkrGIVbNJzkTkMKxopJC3NoEM63cbpNzE3Ley4RgeVXCSFusN0+0lrVc7hDePQ3yJIFBnrXSBaQ8JJzQSu28rf9LeSqB7PQxu7brQ3PklDeHhIL//8owdUCXSs3dYt5y8PJkzxCako1jNZWXwR66HDQh6zTimv6+x5pKVlisD9cPEN1UB8FjugxVwbuBwns13d4UJ5VcUXZEd7HTMTNWv5uslrZACuEAFwiAkk9vgyxuA2AQEyKnyWwuiOEdfZXiDTmG4RAOdPu+R1iAn2HWPh/jrDktDC+TySHvEVgoi8QlvMrJP3QNnyTqJ53hayC9RJhBT3fzpXHWrHRmurs4jMZRPe77uDh5Be+kk7lAlJQLRGC7WuE1SsTQytSQ0L38QGxcO8x7opX5abFpfAz3VdMsEIoBudW2vOEXBSKCjGt2ErHMhurRvYrvquAqAyi94i1rHu8h1CWzxDrqWZ+1vlYKX0KYtNyhQ1kwWg9ugYrI8OjervVSJIueoPEwPW8LrVnfLb0+SoxHwyQ7yOErvNOBpTWsrXEzcqdJmmy84ThARmsh8R5T5JKEdJYXWF8q6WjJEtFpeH7yuyYJqEqDVhx5BEtxdkLdZ/fINNKnyYjt7pushTcefejpDWz0bGee63m1Prdus51Ts5JXNjeWl3v6KGF+nnFuMTAqlWq9aa14eJd4uFKbBtZ5FX4v0do8E+V71YIzu0y8BOML17/NkAhhnWPyMr7P9v/R4D/jkiTFqtdIgTv7xNVw64QcYP/gIQf5gnBdZV0m1fb78Mk1r1ETO7fJGGD08QcOUEuvhq9ljZlQHiNdO2sUeECuNPa8SqhEG6SWxp9mpjo1sg+TEz0acYTEkxx1uJ108g6zmKk0tBmy4/B6r3kTs4yZvQQb7e23Wh98kT50dKkPeMGyZftC9erbIRzzGq82p4xRSoOXWV36T5OBtxVHAqLOgZby+cPyg+k614zzipd1XtqlFQM1jVqAFAqp3GiGvySfbRIet5a1CNi7lQWxwyhUM+RNTb2EgrmAalcVcZCLltJ6MhAqH1p8lF9T6mnyr6djJfp1ANZKe+mAFZ26DnMtPauMr735W6aRSzt5HfbSz1NAPufbJ11hcDNzpMprVuJ4J4l8iYdavK+kFDVlv3ny+X7SawGJ1JvgT8OFtzf18lgp65156i8KlygS9YHE9cLrBkM0R08QNYY/gKZAQbwfxGT7S8xnoKpFref/X8Z+PtEAR+5p/8paUEvE0HBXyBoh6sEU+Cvk+NuG6fJXRnEsU8SWKNK6RapwE8Qk7VSHaV6KXS0wM+UNpuZJdPlWWJRfYOARq4TbJ6XyCyyen9xTsj9zNxG3fnS71YxRSYbKDi3k8wGrTuhDedWTUWfI1k9C6QQq5u6ymBYJaCZd9rPFZK+d5tMlDhGuNnPMR7AqtBbDZgeIdkl/U4Z1eK1aI9W/O3WnhGJnRsXsoRo9VSENqog14hS2FeBXD3MBTIVXPxbT6UGr6oVboq8bVIhC29oTTv3za60rZUnLCSjwpQDruC3PonWvf87dlIt32r98SKZFKL3W1ke9q/ZhzvbZxeIIOocyaV+Y0RES/cB/xaR3fQUmbon0fsYWdloqJ7E4x5idLcIDXeLsAJPM24RrxIW0DeAzxMTtLo/76c9q0Snv0W83wuM5+FDdrDp0k7OzWK8FVd7QPIVq/CcJt1UvZTfbtd9sTxLapcTvta2WAX+d+DnCYX6rxPjaoLKbcL6vdne1d2QTVP1Xa8TilfLZAsZDLS9SwROOkN6Ti4QLXO3mbHtPrPm2Vsr4hmSiTJLzj0DZbX2gkE9rT0VqBs/3iLhmSNksR9xP/mtF0h80/65QkIhZ4gF9yxpsWuh3m39eJOEL9wk86MEzq7nIFPjHLktlMJBrNS2Vm6vVvsexnFvrxUiFEJUsKlE58isPjFm52314kZkDQ/nUoV/brZ+eUCsTQO4N4l1e7a16wipRNw8dRtprTtnnfs1AKiB4zup/Fw79lm1wissVy3xHgLxt+d4nXCI69356nmXicJhiwSM91Lr48tEgHwPMda1umJVVg+I8b5G1hffRtblvtze+d6I0MxnSPzFal66bE7AzRxDgrGHER4Q4PqbZGGUncSE39ldJxVnK+tvAb/ZttkurYkZohrZrwB/kxDCttcEhdPkNjn7yY0s+3t6VC9A7Fj3pqfHOGDVtdPagXHecG/t9//fIIJ3wgFHyv1cTDXIoMXnc68QO3S83dp6kLCOf4RMZlD7rzBORTS6rgVTSyZqEVT8uCYkaHWukEXxFTgqGou3TBECdXf7/xIB47zd7iVmeJxMf5VO+S+IrZO+U+4n9e04Ufnv8yQXWHfyNZIBIWfYym/S154lg2gyiG6QuzdL85NiViHB6q7bbwpK3XPXwmL7XLxcaqfrapaYs84fLUxI3HQXuUW8brN1LS6QNTl2EApJKt729k7fb9/LtX6CDFQLaSrUFcCQglUZUbm0FT6pwUOv8XqPCqV4npCDFd+0bLX2haFUdHoOS63PtPJnyQ0GpON+g4grPEd4q2dae+XJbyEtcYN9WuNV8ejtu+4d76sjEsDeXm7wfgQeJMYmqVmw/5vEpP48MdkFueuxnpXrS/UQwEaH97QQ+W6is3+FsDY/Wc5z4Z4hO24j/vMSASl8myhktJcYoCeJRbmn3VMXr6Z/VwEsxUgB3Acwhw4jyhXHg1zsbvWj1XSknLdIUq+mWruPklXrhp67Vn4UwDVwazEfGQf2nYp+mhRaNYtJV/c2YY2/0z5/kQgE7iQF42+RSuNjhFD4ETIAaCLReTLbzwSCm63dxkBqcRotyMp3re9p4FQYwDTjNdJCdsst++QuqWCsGqeVa1lEg91aq465gtrraqlTx92gkQvftXadnBdCG/OEQfFaO8d6INb+9j1utbadJjdVldXxRBnTmrBQhYvrVEqpOPD98hwhgxXGM3l3kGtcS9VniFdXOEPFL+QwQ4y56dG3ycJmevaH23UHSfqthZYqj9v8gQOEofK3iXX+CaIOyodIiMw2uR6OkUZDlXF1/WhkvK17U4VaLb6xniXcC8w1xqu0QWpz71ld6qFgmo1zUNRSfrcRj7F+J+VulZh498lta863e/1A1x473nfqA3b9866S+PkPEYM6S5YS3EoMoBpzUmac1aj83loS/Tv1fOvah7WtJhfMEBNBzNbnzhOT1Ai4EfRJyk04QAVbaUsVwtEaqIHK+4SlogCbIgNh1iN4mxAcu4jkio8QymCqvcc3CZz4u+0enyCE7ofa+94hlOAb5J5iu0nc8EkC+niGWBzi/4vkwj1LjKPuqXxejZND5FbtB9q1rxMu6jfaNYfa9y+QFKpqtTofFxh3xWvB8GrVC1Vp8XqOHFc9SMdeoVuDRta91SJz7ljmcVSu21muXSrf72rtvtPu5TOtg7GL9J5pbZS7O9vG7AzJJVZAuimA5SUVaFLOVO41m/F8e97nCVhgB5kSv4OUGzJZqncmr3k3mTA1TSbVVPqfVrX9eqT1T/USDO7Zh+LeKuB5kp9d2VI15rAMXPePavlNEnSTmApaDJXn2tO/Vsu19f4ucEirfGfrBN2VjWCRyhrwPmrarxCD9wwx4E6ej5VrqkCc9I71WCW3gxHjPVHaoVDS0zDKWp/h332Ab76cW58/FIRcJSy/c2SG2Aq57ZVWZ33uEgkxVGxu6JgnrNJ3yYls0W5d5qX2fi+3HxWwlpVWttaSFtm59nOhff8KEYg7RfbvecIyc9HIKNAaE6s12OlCq4R7ub3OczPf3iYgjTXSEt1C1qK42O71HKGsv9DOMzPyIpkN+CSZ5KFVVpkONaC4UD4zeHiUFARS3rQs624OwkEqGLFix7E+owrYmtgg80NmiKyQrYxvXmqgy5ToK2QR8+8B/7R9d4aY+2ZeHm/jaBal2LM47IvtfU2isJ9g3KIXQpsjA2jOn0uEUv4GofB+jvReDXZW+qH0V8o4i0HrsdXvK5ZsP9kuDR0DhauMryOZSDJ3jF2Ih1dutPDTFmCxPrQ/Kpah5dULYOkvk7DhIeEDiTOqTXr3ewhwX88995629w0S+7bwiUGDSW3s36tnZKyRRe1N9T1FDo73ers9/wdIygxk51cLUqWk4unrZtj31VK16pkVpYy8WkOgbu0Diceukousd5VMfV4mcOb/mxBUsiX0aGzPYaLs4k8Sgsh30+rSlVwlN5W0cLvYW6VqCaHIXNhKBoZNDJGdMNPudZ/xOrPiwlbyqkG5S4RwV3BtIxWJ2PMRYmG/1O6lVa8XUIvi1Ej5nfajIq6MBco76y1UN7unbGoBusDtc5+l9a4iEEaZInBqqXn1uYfJ2iAVd/d5c61vTLIRTpDtUet2aBAstWdfb/fTIlbA7iEz9IRXXE+OgXN6mZjTFv63f+wzlZJyQqF4jaBcvkN4UXLdrTdSq0SKF+tRqjD1erTkFciVlaViHZX7ia1bv9r3rMplP1kdT8Ol0gIV0iNaPeIqiKtAdULsLN/VIAPl3HrI99QackG6UB2MGnQyw8Zz1VxDRy88a/tdWE7CPpNq0uFCMOFiW/fdVUIwnSKDJbUW8xph3b1GwBMvMZyp2B/iaTu6z50Ildt6trVhlVggHyYmvLhWxXSFH0y2Mdhie+60+7lBrLS5b7dzzMpSqHgPM9oMBPpeNQC5RtYOFufdQVhOZwjccVtr99X2M0vybA+T1sxNMiA3TUz+fYTAOdHOlTNt0Z8ZkrJmUMo+rdiksIUlGp0vlZalcu8PBVJNw/X+NYLvtTXKXw2a+hy9DrnCt8l02NpevTp3xBACEP56knSFXUOO/67yubxkfy4QSvgCabmLq2r1i2mL1d4lDI8dxLiqDOrag8SyrxGekLEKfyo+P0tyrX1n+0dP22SOKWL+fL219TPt2gvtcxWq61mYQyNmJzGv9rb21918FMDOFxNtrFrn2nQj1xoPE4671/rkSXINqQgV9KvAhYo12gCtsx2sL0QgJ5IRy68SrskPtu+rVumF5512jVZMzTQZOnq8tFrsFhSRDdDzlocOXVWFv/UN6rFIbvkufqY2te+uEdS3XYR7XgN7kyzuGcarydV3rIpjpd3boNEekkOru6NFpUCUCVArfK2093izffcJQqjeJ4TcNXLCaanp2ioge2/C+2ohmK47QzIiZBeY8KOb6x5nxxnfOknupQEWlbLWRd1Qti7OCoUp7OrP1vL3qPupfS8OrjCG8bWxWs5zYarw7A8txMpprZS/aUJIXCbWgNc6t2uq8ByJVdL66iQhHB1fjSYtLml0YqPWgJGjvZ9Yox9t99J6l4VwhcyufJvMsD1JzO9PE1CfsMw+km9uzoGxFo+6Geo9gnmxi4AIhWacX9IQ1xhnOtQEDymnkPUazhGGkKyXiwRXXhZRzZ5UZmn5V89KfrlzQ7aKgbh3iTVzmhCye1q/SROsCU6zrT/WWp+J3wvXOW43fakVMnAzSfgutZd9hyx3uZ3EQNSEQxBDtRrMDHJhiVcOCU6xxt2kUKkC2ILutcTcUNvr5/Otc1wg8it7LPWd1i+nGF9MCuA6QDVVdNKxSkyOW8Rk2dl9X9uoFf46WYHu40SfD51fM3mqEJcXe4vkAe8i6xhcIidlQ+DxAAAgAElEQVSh82AvucmjJUSroKvUOgX52Xafo8TiPEYm/9wj935TOOn264XYl5AUIGGU2n8uxtoeF81U+WzI09Ni9zvxQzHVymE1eObeY9dIvvBOYk48S1qACsXKINCimiELzZvKa9U0rXcD1FPEXDxGZoMpzLcxjotL85MmOU3Ma8dmC+NzXFxzmsRSTxGMoQ+Rlp5Bs0oPg/FUcS25WuZ0jpAPZjwuEkHX58gxN6FmlVQOCvSnSItbSOsSydZw7rseKpwg1CGmLQxkHGWOnCsGYD1/vn0uhu6OJSpvoTO3gNrR7vdtYkzPtHfc0579Jpm5d7SdP0MotJPtPZbIuiJTFIt40uEAX2sNP8rDRX2GMFf5ox52yNdaA14g8bJJrt89HsY7fYbCfRKe61EtyUVCYMieENfqrSKxSrN/eitd+GONXAi2i4F2yEedZxzD89DaUdFYRMlFVC3RHjunfK6VKETyBrkYvkhM+K8C/xMxDvJVzxBC/ocIrE1s70Frh/zfW+Qk+l3gNwjL6RPE3no/VL4/Syw0F5F88TVyVwLdX+lZNWIP42672VvV9VeYVvzac/WMvLeLqQ/oiR3qRVD60T5VeejWa5GrSCj/Gyy7Q3KVpcFVyERLzOCj8I2lRlUsvq+WtopLqpUR/q3kXDZZwKpkFiWqFdV0p98l1uWTxNi/TFp0Wm/261x7LxMRtpElIb1vTykTA62QiB7LVmKdvdPuu41QbFrHeklXCQ/qJLFut5LGgLufVE8IxmEoSOW6SCpY+wyyvvkMwcw5TAhX17+GljtWu0YuE/z0xdZ3z7f3rOVQbxJr7zAZIH6RDJx+Htg+JIhXW0OvMB59NiukHrqHdoZC0Qmqq/FW+/wZhot612dfIqu79YeLT+FYA2BVOM20th1t579b7nuS8UpYTva7BNa0hcjus/6p51wkqpGtEoG4l0nLpw66h4LM/buqq2bgzz6xn6wuJ5fYPqjsCoMMD0i3zKyty+28/cRgP93+v97a/4CsDqagqpofxqtvaQ2N2rsYJNpP1hjeRvJzTV4Qd4UUHNJ5VF7+1vK6TdYysaiL6b3CVrqmCkTnQ+VJW9fYhfUqYb28Q+666wLW9dUatJi5bAstS8dY72FUnq/gFXKw/5ybh8hYCSQUYRDKmMrN1r/nyUW6TM6Dg2S6rSnY9o0eRU3C8nNTddfIIkN7yhhVT7NCYyo5WQcG0Gomo/h0rbVR38dKaXOk5+hz95P7SQrRTBFr5QnSoq/JLiulDSpS+1nh6/fVaJkq5xs/uUXGkxT6evRXSOqnQUgV5TIxzguEYbVKyLd3CLhCAV6zHi8T613IdJnwQlYJwf+tKohvEZjQPGk26w70bvNbxGL5JOPFOC6Si2GZsMB+myg8UzPS+mONsOKeZVxg9UfFiWUCVItxjujEMyR97Z+19/nTjBdzd9KMiEV0mZgkJxhPQni9vYNUpmMT2uNxm1j8q+QGkfU93yMmyklSmVwkhKUFbHoYBjKo4maj3yN2R/kuGRg9Q9SW/kh7t3dJjWz6rJNyisTDrIPr/z5XS6hSC51Msg76SLQMCGs9wDjnUhdZnqdzqlbYgmRgaMUqnO6RJTUNNn6czP57l0yHv0RmtdmPBrTcSeY44zg4pGK41dpg9UEXqtstnSezskzvPUQKSlkhFX5T8WiZz5U+lH96g8wSmys/9tsaiTVa8Efho9A7SVIY95FbJFVrUwPCxJrKIlF5SPe63dp0nwyq7iS9HHMIVHAmSdxhvMCSEI7naik71hodwp6LpGUqW2OFhDHsL70M+6zWgjBJTSNGD6YmIEEyufQEHJdqbU8xbmlryBifkUXRJ4GpeKWA6m3uoW2VdLZddIy0Ot5qnXiazBmfJzLRrgD/IWmtXSSKy5wiMpx2EJPzawTFyVKMHr0Lb8cMYaz1XAfh2+2aH2e8AM5rJD/4e4Tr/DIROJTS5KEAXmz3m2/n1ajpXUJo7iYW7F7S4ncA6j2/QiQefIYMVtbvDaKIra8QeexbCMHZJ3t4SC2DDHCeJ6kzK4TS/BihRJZI1oqWwkx7n51kCcS7hKDaTRaU1xIy6iweeozErW+Q1pUR7cvEPFgkJtcpsvqZC0Bsd637v9LIhC1kEGhNuVh2kDi0kegl0uK/Qlp/lU70gCyJaIU2x3qFpKFZbH5veYc1YnGfa895QAoVBYlcVFkPd9o1lr00eLadEPwmMYxIHNnECIV4rUcipOExIoWkZTdtk7xoaWIe3seMM/vsPbKOxG4ykGXc52USC99O7tAufPdM+6k1kvXeXJ9VwApPQArGakiJy98i5tgDwqA7QLjxB9t5N9v1pmPPk3VYaO93nzBOjEftIqEZ4cxFkh1m0HmeDM4Jm/gDCS+phLaT6e0K2ynGee++e4Xe9LDuj8jssD8ggOaPElsmmXkyTwi5FSJiqtXyDvA/Eyb2T7WGGOgaKgw0yYWfdBjoOUcMvILsL5JZYlpXWgSC9XSd4KHbuJUQ6t8nrPqj5Zx3iapkJ4Avt98zZCprH203hbZavvX7WwTk8TIhPHTFTGv1XbUyTcV1oU23dr5F5uVLzztDaH4z+RRmN0lq00FijJzU19r9nyLhAy31d4g+lZS/SCi1y+15HyLG7xqhDLa1+59ivBaIcMQMSSm70X6ukJaDi0eBUtN/nfiHCeztWTIz7x7JY9U6qjQ02S21QA4k22GG3K7dVN/j7f67yjMqzqqFqBJRWRjws5buzTIOKpAajPP6reRefAqIaXJPRSlaWoNCA3KrTcDQU6nYq4q8UuPE3GuAVGGp9/OgPMN+GzG+VVZvXdYAqjBAz+aBrDx2llTkGiVrJGzkuXo9QgPvtZ8XiXmoEeCWUsZeNE52k8Hkm62vnyeU4Cq5VdRUa9c3CGPmeXI9bWu/3far1qpYIuGN2fZ9hVXs4xOMx9Xqj97gtRHwX7dO+WECG1UTO9mmSPdft/Pd9jJnSBcBhoXviPEBWe9Q8JuyeJck9z/JOKdZt8HgipHJ04wXvIFcgAahXm+d9fHS9nNEAaCPEu79PpJep0XaB+0U9JXo73EF+FuEknphoB8cKAMGVRCfJyCVXyUmyRZiQD9MTJYfIusjLJK0IQuePEVYKafbvd8ilOlThMWupXSVtMo/R0wYC9ZMMb5foAsXMilCi1N8800Clnmv9d11Er+V0rbRsYUIAH4R+JNkOcyz7ecC4xlpCiIDm2LfCgI38JSVsZ3cMVg6HIwzKIS9dJl12/vEgGWS7y3TQ6tHRbPAuIsrTKGiEaO0/dUNdq5UDFQX14CiASi5x0JY82TxeeEJlZzsjmoc2W7v7/vblpXuM8/XAFjo+to+0TKUojpNCL2vt7/1oHa2vz/cxuYBYbCI7R8gxv432jO/TG5yLAwmDVWIzWCtBaL08j/U2lflywMiwH2FWDsnGN+aqUILBiD1hIwTzDG+28gisS5mSKqf0J5Q035gddRuYBEag2yUDjcgcb11xCVyC/VKB9qsoB0S1tJD6mK1I935w8NJ7aTxx0len2FQQe1u1PPFcs+7hOtzon2+lRgMAxqT3kMeqYvIY5FMv6wV/iE6/+ut/T/ctfcuwUJ4gxi4EySFzDRpf0atjb9GZBfdJyy6HwW+REza94gC7SOi0PuJ1oYHxOSYInnMLqQ+JVbrwr5cIksfvkooNGk9/vQsl3oIN2gh+vdOYlF9iphbe9t3YrAW34ZMgoF0CcUTLRx/kZirN1rffYbwfF5ofaoFaRtsh8GV+jlkcE4rW6tPOpOfGVSs6cd1Lsp+qIK2elheW9dUpepV4VmTQBSSVVirJHSBq0LwXWosxHdVmYifCouoeKy7bIzB9pqOrYGxSnJ37Z9T5Gard0nWhrEC2t/Pkhxnoa95YiyXiLG1RsRz7fNFckMBITINSFkvi8T8WCDm2l4yOPwUMS/uEAaFsTKDtip4yQhVWUEakWYI1jWkVyu/+knCiN1GyKNvj8hIe91axokwTWKCU2QygQ+GjQXwJCFdaTFOxnquLoBHpXmJY5lOu4dxAe3L10jnXRK39H6vEgP2dHv29fb+bgnVH7eJQZolcOB95TuDMDVt28/fIybA88RkrN9JB7JKm1F7cfEZYoI82675KlH0/WJ7nx8F/jViIt0mhPlZQvh8hBSsRoy17p0skAvbwFz9X2tbJbwZy1ZrqAqvyrPdRcyl08SklKHiuPrjgq5CzvavklvWaAFOkVmBwhX7Sh9otRmIU2gYp1CQKUSrG6lhYgR9RznHPqy4t3GI6q67aEflumphKsBrQEvhW/F1yr0VChoGNfBm3wg59Na4gUzXhILWfvf/GcY3LlUxOZ+ElMRYLQ7kjiYmoMiEeZaMPQnj1LGYJgvtuGa1Ou1rMwt3tfudaufpicyQvHgVmlbsfWI9Wmlwof1/gAyii1PbXwvEGjhAGGzKHMe5Bi3vkQFc59V2MmHJmICc+6tqsloVDcKVnSG3HDnUPndRTBK+TpZJ7Ih6jm6eOK9CzP2+PNTkvngt7LGNmGxyKFfIguaSpX0PheYD4JeIwfsUKaQhBr1/t2UCC/9HBPPiP+LhAOQyGQWtNLA/JAbl06RwXyME2rfbuxndHpGZOOeICf0pwnKeJTb9/B1iQH+w3fN0e+crZLH3ZxjnNotHqqHte4NEFkly4d0grFDrOoj91awzBXV1zSvh30OuptH9U+1da+EXU1/Pt2duIeGDWudgkYyI++yKKWtImPywQqaavkNyyG2vKbQVW3XRWX+gClL7TQNAoaSyV9CJU4tVqxh8jnNAS7cXrlqnFWKwTyAFvUwUg3Q1pRkeXqeOr5im7BNpcipI+84gmv9LZ/P97bOaOm+/1Wtsr/dyfVhTudZpsJ+NNdUAX82o1HO5Qcyb6+25L7X7eo0JLJZ33Vru5ZwST14jd8p2myUzG5U/O8jNB46TymREUjIra2iaZFBY0EvqnUHTLcDIBWYHvN1OOkEsBIF9J2x/1CBTnWQwOVjnhNYi80WdVF5XtyqxkxSwI8IyfZ7UzF8nFuoJYrEvkJxJyGye3a2jlwhL9SqxQWHPW14F/gmhhb9ACMw+0Djb7vESOcGuAf8lYan+RcZ3PX6NGEiFstlzbxACY5aoq/sTBE56h6CnXSeE2DOEBp0ircEFEgO07+q4aAmvkhYTJPZ5q/zoVlWYqka6699aAxUzVWBU3qX8V/vhFhEA/CpRQnIf4ZGcJqmBejTTJP9YCp5joDV3iKz2dbf15QVyCyD5qvvIEp11QVZs1L7rk0kUYltJYVMVj+8qt1eLswqNaqRUC/Rua++bhHJ2HsuGsEDScwS08hTp7tv34vcyORQyBulso0lKVWiLiUuRWyGVmWu6BuBgPOi4Uu7lHKvK23iPgc05QoCadPICMddN268cZ9tmG7aQQXiFsoLRGhXPlP6pSVIKQr1+30VPVmPlfuv/yiIR9pGGt0oWp3JeL5T/a5ytJhn5v4aPuQavj8og6OJ7wqSst/UE7dDnPlyhKxRyj1g0P0yY+lUAXycs1hFhlU0RwnOFWMTPkxbuOWJRnyH3QOthDTtjJ0ny30dMgCEFc50Y4Ge7+3hIn3mmfHafWEgvMA5t+N5mM+ny3GvvZcrlSUIY7SEtuTlCOFnM3eIsr7bPPkni2ApfPQgjyuKDtwjr4XVCIXyHmMBOtCpkq7tcfw6QtXJPkKmpTm6FjFsiySfXKr3RnnWYzDLU7Vsg50mNVWjB6WZaaU2a0m0yKHi7faYA0W1fHvjbowqaynfViq1WbV+CUsPC+ghVoT0gdz7fS/KEb7V+0MBR2Jq44XmL3Y99VAWsgW3jNzfbtScIwX2UtPb0QE3AOE6mDkvpc83X81fKdSqTmpJerXv73fk2ImEnOdPCS8ad7rS+fIGYT4skW+JpMrFIq1OhplIV3rhEUEi/Qaylp9t5Vp67RRg1l4iA4CvtHDnPlPfXSFE2SkNUCR9r15jYcaI9S46wXGrnhzLENSL9zb8vV0tgM8G2euhK1EGoh9zMncTEUWvtJQblMGFJVnL0TaLjJIPrJgpJSKGB6KxLJK7aY7O2UVfUrC0FxNBRrcBq6Vft6fcC9Qawnuz6YJVkE7hIIQW/AvBDxMTYRUzM77XPP0JMqFmi/uv/QQz6Xycs+Posra6axXaNENjfKz+zbHyo9Ex28LcMBfvpJjERZ4jF9mEClz7W2vM2KXBOkJBJtdZqmU3bfp8UBvsIZWjQcoqEKIxfKBw8dP10a7ViYZxi5TUV/4Vxq357uVbhaNaYRV7uE/PpWHtPPTZhF9ORV4i5bfS8KvgKjSgUhCjukXUpfGeDQEIs1k4QR5WpIqYtJU94QUHj/azHcIjE7p8h14puuVae7a3p/jILtpFrtCaJHWrPvtF+m+W30NqxRozzR9pzfceDjDOLamafjAwTbZbJtOla+OgQuS+jWagXiPX1sfZca6MIldi/MmdkpGhsHCEZGSYX7WI8KGsfzZD4fM1OFNpaqFBChReGjhqNFd+z4+thFo4ukhpF92g741is7oAundxL+YWa/PU5CsxJtLn6/c32//F13g0yHfM4uW/fnu6cG4xvh16Di2vl/zcITFehso2kgq0QA/Y8wRTYRliobxGT/xWiX78C/IP2+c8ScEXlRQs7aGmfJ3exeLW14QGTjy2E0LCObxW8CgOpadYbuUrWHflc+/kRQhBYeU2LS8tyjfHsMDFPhZ/KTYGppWNW1BJZhvRca0vFDxV6azxszdcU6hoQrnNqJwlfqPSXSAzvDhn02kssWrNOnyKZA7IYRowXcNeChrQshX9U8lqgUr+kjOrZeY7PMKhafypDovKkhZv0FFfK9cskrUxrvQbmDrR3lQ75DJnIA+O4r4JGRSdstIPMBzAgvaV9ppe1hWQ03W7Xadica+2UKLCFcehiG1kcqVqcFZ+eb/2nTHEOyEE+UO5R2SkGpoWgLD8KCVUIFbrZrYJb2WgZgjcIlgitD06RCuSuZvh6wTUl+lZyo8Blsji6gmie0H7SPeaIiTBH5sifYHxB3CInXBXAPqN2im1154ca7YVxC9b29KmcQ4ea7iRJxeqz3O7xcNqibTIAtoN0we+TLty98rOdCLL9cGvrN9s7faY98yxRUOc14McIIVeVgYtRq+w7ZLLHO4xnotUgiUVHTMM9SmZ2qQi18q6W3zOl714iqHFfJiCRXaSAo/SvAaG7jFu7Rva1hsWr95GcYOEmAx3X228Fq7CFzJj6I5+2Brrut/uZkGA5TV1LyEQMcUsF73bSQjtDZpfplVV6Z6VT2p5aZe0uKRj8zhq9UsHEgiGVq27uMdI61VKXVWN/VFioeo5L7X6W01RgTrVrjcVAegMqBUuSTre2jFp7TH450O41Typg4SMpYwZdNbIqI0ZhWDFUrdgVMltPWpgQmCyErWRB++eJeaRnXOmHegS+uwprkdxw9X57p1NkLRKFb62R/IBQDlfKfUyAcn0KtwmVHWntdc363vbP2WoR9zSz6+2i061xU61Bh7pzF4msPCkqBk1ukprNFGOvsXylVdFqB9WFLZNijnDPdxH1HmoNjOpmQi6IOcb3TusPF+9eYsHownmoEWuktz5His1+YlB+m9B40nbuktbNC8BfIFgQMiPEfm8SwvdrRBLDF3jYEl8iubvvkjSynhYmi0CM098KMN0ocUzdWNM0d5d7HG3tfo4scWpfqygrvFDve5tUPuKm9t9xwrISctja2nWV3H1C/BHSgqyWpG1YIGs2LLQ2uk+cAlf6mt6ZvFEtdIj5+VT5kS3kAlaAqljtNy1I60vLRjCr0SCVi1nX1gQHt+qpXoMW+nFSyGksrHVtc57I2pluz15q35uYYPqt76C1JvVM2pnxBSmUFtYfot059y2YpMHU48leVyl/ntMnQ8iWUA4stfNrcNL5/oBkyZiuv51QmlrHq2QM4WJ7xlOETNrNOAPmCrEGzxHQ32cJubebLC1gLGcbobT/sD3DtPhPlrYebP3m9lsaP6tkcom719yoL2uH3WsNepGcDHM8vKvvamv0WnuIC+Nq60jdmGqNar4fK52tZhZznSJ3kZUNcY6oY+G2OVKy+kNXZVKQsbqmuwh3XtJ5PSpnuR6zrW92EFbtWeDngX/Mw27xViKR4j8jlIeL8xSxqH6LCJx9iQga9qyNBSL494uktQtZrF2rw8LrupjiqFrGvos4npa9f2u5VEFcdzVwcdwjxvYKMSnfIrG9miWmVbar3P8ouRuDCnem3e9S+62lLDZY04LlENu3u9v7WrPXCme1upUwiIkGLjpdVAvX1BoJ4qxayb7nJTJD6g5pNVfF7NhUi1RXvVrLlT620tp8oPzoWqscFdoGuKwDYcBqX/ldA+wyYmZJ/q6WWeUk+51MEEiB7XwWYqlc6LnSH1rDUrhgvIaM2Oj+MlYmR3g/DYXK4NBC1+OkvP+ecq68++vkbi8nSLhJA2KG8CAXyW2gfK4UwEVivO8ThsJTrd13CE91RFjfTxBj+AfEmoCko5qyfprcz9IEo0+3/r7R7rkFeMcJoov9h4Sloomve99joecIIfJxsuDGdUKwfa41BlIAC/LbqTCOH7tAL5OVri4SGOlPl/vVTJZ6rJXvesteq9p2SP3S2qlHhWnqPb7b2nIK+Pfa+/5NoiaFlqGCA6Ig0V8lLMpbJPb0u62fPklo7l6ZLAO/DPwNAuYRtz3Rfp4lrRuDJ9LTar/Kq9WK3UIKW8gFZdGaPu11ngx6nCX3ZHNhueAM0FQreZnEB02vXSO3ZzpLjLNWWU34ME5Qx0CrXshLC2219bnehwwRa50YDLIgjgKyZq6pYG4Si+n/be/MYvW8rvP8nImHg8RRJCWSIiVRcw1bcW3DcW3HbYM0RYMOSNPhok1vUnS4LIL2Jnc10F4EBlK0aG+CNmlqdADaJG3cuhdJ7VieZFuWB4mWKIrzPJMiD8/Ui7WfrPVt/ofnULPss4Ef//D937y/d6/1rnet/Sr5MKuvte/CMFljfsT3+h8YcsWVUoC08G+WbY1xuzbaddaQBeh146s0DpITh7zHUk9y+JCDpNdQlZRF7tXxq8oQ/OrAXj3IOdLal28XP1RK6KkZYLNcpwE47+9i2d4p4p6MlXXuJfXMDlB1UDtBpjtPEgbhNnI2c5VY59tycySsK+I1Fyc2kDWaFQicIRVEE4R1fJQsWWBehsHxXW39G239jQTGnmvrHR8jEw/WEBHEdeS03j0AHyWi908TnCGkLOqptj4MeeAZhh1wkZzyfDfRIYxG72gne6odR6UgeitX+sGoaOWHK/fdqyh68HXbWiH3lv/+L+DX27n+c+Km/CvgX7TrprTJAOTfAv4hAQC6URYM2cLtxZDUYB4FvkSA75Z2XWpxboM51T2vOlfdSpfbDIAJFFqre0ituNTACWKANeIs2Avg7tN3QcB4gYG+XWSGk0VwLDTU60/rtgyOan354BoUluc0MGWgSBdfi02gdnn1EE6TJUePteNTGVGlW/01dqDRuqsDx2T5XFPQ/U2rTyD2mEapXrTide3nunXt15Uzr95D1YmrwDBwqucEw8HDa2O24VXSApWT1kKeJQdh76VekNZ/raBnHOglwph5naSNdhJKm0fIGNE0Wa61KjXOkXPAbSUs1Q3kzC8723ktkDNoLJDgaT9WwjpN1lTROlZZZU1wr7n3/gkSePWYTLu+hywd8Er7/y6i/9YsxW3kzDbT7fvrwJfGgN8EfrFdIOVlPfBdIFzp44RLLre1wDAlt7r/Z0iS3kCautVdZMEMQd+R6ly7QXV7Nl1aAdB6qLbKedf13E4Vn9t8wL2Z88B/J8D2GUIutg/4HeCfkm6IbR/wzwhlQx9E6TPN3L4PoXzjjbKefKLSJ6PYusMGN5TamKygnrdSBKoQ5KO1zgS2o4S7dZRhgE+rygdQKshtGy22HoaR4VMEkL9CWDImY3g+Bq3MqjL5ZhdpYeudCWYCpfKgWmlvrh2n99rrP9/2eZZUPMhBVjCttEelawSWalX6QAp6Ao4aYhUPcp/1uwNllXqNl+3rqUyU/xpMqsBrxuFWhtNZafE5aBq8E7jrIC7IOkBImegdXC33ynKOVdF0iawwJxBrRXp8FlSS7xYcDxN02/myn81kZULBfSdJi9bkh/NEXz3W1nuG6PsXyz3z2ft+++8HyWxdwVHPwr4N+WyYHq1EjnZM59q+LKurPNd+qnewQM7y4/3VCNIzkR69h5CvXtESnCEf0DpSXyYLr+wjb3gl7KseE5KTsnPNEeB1hUxYMOihW3yl7Vs3oW8zxIj6GKmZ9MGolMco6uIU6erZdNdOEyOy1+HLBN2wn6ia9ggBKv+HeKghQWo/8PMkgLusBiWrte3NmG3H9DJZ31fXWA7Rcp26qZsJ4NtBWinVxfYaODjpelZQMSDxYnsdZZggUJMH+u/K/x4naBUTcE4TD9b32jaVNmp1bCPTmi2+rvXh8ff6XUhZj3pgs53kLe34te6J9YQNDpq2DkMLUGASXKvV6z793eY+7ddW9dIKH++2Jeh5HXsuuTYDZnoDdSD1nhpz0FLUCPL6VYNFusMB3WCswHGGnE1ljOhTSvI2kMF2eXHTyteRRe/1ogTrPhHI89pI9tv95JRZ14i+4yBtcFF9v8bJfnKCToOmUkrniedHQwOS89YAOkv0T5NVFhgGOv3PzfLb3nZ+VXqqJ2gdjX1tn+ZHyOX7X++fno3egv1b5Yjn+SfWg3+26eZSLu58+T7N0AWCuOFaFBAd8AUCyD/RboIRYKVEy2mXZwk97l7CSq4SKSUskBXkaiDxOYJUF9ylSSaJm7iz/P514PNEZ/zLxCh6hihFeZx86LcTtIwuCmUbPfgK9loTJ9v2/ogEc/knAaoG4Cq/qwvpA+aD7cPpOWvx6YKeIzriVwnqw2Ij1RJYqu0kQPeDhOXhAPoiCbxqQS1TugZFsl0AACAASURBVLNdoz3tZafXhRdU6n2S6zRAo6Wk/nWatGKM7l8lC+Qfb58FPS0igVAq4+1oBrIEUQNryp+8P1p1dT2DbZX7Vv5VEz6U6lmPW7WGJU+VpUFSEZvJIKSDmtvx2hhs08tSkbGDuI9qwC+RtapPkam/1noxnqC+exR/7kC3kehTz7R9qWKxut4kCdyzbb+XiXjK02RSzy1y0HLA0fjwWRknedkXSBprLdFXN5KqqtOkxheCGtxUjmsHGczUCJxu92mepFp3kEHVm919XCzrGVuxj75UXfn65ypzudZ+20pyHrMkl6p5rnVyheAaXyUkILtI10hurW+9JT5L8NZrybnhIEckb8hlMhDmdg63fRplNVq6rR17VSccJGiIWaI2xE+TpSqPEUByHwEqOxnqiG2O0FpG59pxbCcu9GvtmOYY1iLQ2oFhckaVfam00HMwJRXSPbZ2hgkrJwm37HnSSq2BIQNQuq9aEPcSHstT7WWND/m5o+1dmkChv8Ww5bPr4C7VIv9ogfhT5CwgF9v/TCp5kKypYYe9zFDjLFeswVBd/8qTvpnmtZFfXFe+18w7ZYIOnHV9AasWAnL9qmcVFD2/E8Rz5L3SA9CLsK9Iqzk496qdjWRRKeWlNbNLb9iApRbuZFvnYeJeKMXSkj3W3gUjvc1aCnNUs0+MkZrdDQwVLmqdd7bzvUz0aSsQ7mvn4aBrfMDt10xI8xQuAV8kwFZJ4IdI7+6Bdu4HyVRmq/ddIei7rYRHbqynSkTFmDMkB+5AAtGv1d1fI70gPahLAvEsqXuVHzE3/KdIEDtHlomz8wvAVhyCtOx0n2z1s0LzzQytSTOvKmAK7k+S3HK14AXD6e63g0Rn6q3u84QK4ixRZP3PtH0ebuchAe8o2Fvsc2QREV2tKyRfq1b4GBkV9qK/TrpVp4iHTlnYOtKafJCc5nycTFueJsDvEXKetmPt+hwhrnmtleAo3lvwWpvO+KDncIUYRA+3c5TPVYIm7QBpyaobP9rO43R7P0IW7qkD/kZisNxNPFh7SdA9R1i5h9tLSqgCoMewtnyu90h9ah/A1L2tICuYrivf1zLaS6sJBwKBXL0JAHqFRs61BmtyTx0QdV+rZNBzUItc6Q1BWeA3SULr2ApzfVtD6qR3kAWxahDwIql+Ul41RtyrJ8gZLsZJS/lFUmO7vR1X5Zsd8B0g6ud1xP2/n3Tla+DW1GRB7bW2nyfJer5m8Srh1Ht1wJHnnWnH6TOuasIA8+62ziXieVoklRoQFOVzbZ+qIDaTg9UUOUXcFcII3dLW+xoB+M+Qxux8O7d5mkX8eeKhXkNaMq8RereHyWig3ImdypFH6dqdZoWtloKSo54PdoSumXSXCXf+MwynKrJVDpayn/MMp0ryfzPtohwmRsOn2n+08quV2KsrrBplZtFNosOOEZbkZLsOBwhAqskDdf4yAyDriYfiQdLitkCKo7m1DNYQHcX7dJWIRH+bcLtUj/SueW/9biJG9WcIhYtBV8FASZsifflHB03F8SfJ0psn27EaqTawoiU+Rup95SMdpC8TfUvFxhGWT8seZZUa4VcStaG8DMzUpuZdrq5al7r8NcNLw2CKlFBpMVWDQLC9ssT7ck2Vi9I1Ab3GZXoP1gHW7EWtUs9plHcwTVatU2ZocoZUxiVyQD5Buv9bCUB+tK1vnsExwvA5TyoW5hkG95ZqVuB7gAzam0F7P2EM7iP6xtG2vbVkshkMZ4o2Ica6HjX12udS/vgQGQD1WVxo+znYtmV8bIbo/2fICVnHyzXZQKo8jpBBy+sEiJ8gcgpUnN1s25gbI/jDjUSHvthuykPtYK4w7MhVdWCn21SW6+pMMgRkQVALuoJpTZ4QuAU5RdlLtZukFQKZmmrgkbIvI7UWETFCbVqzov7aLCC9lhiUxshKXwrkbxKg+A1SCF5dRV31aZLqMDtKS+QM0ekPtf2NE9HZD5EV4o4SN/MFAux9IKuV1r8mCV33J4nO/DDDwEe1nJQDXiQ70tF2PCoA6vkY1Fro3jcTg4aR7/tJnluL6xwZJOmztiqPrKpBK6rqlvsAXLVUBdiaIGIzCCfo1foWFQBdPk0OKjMMMwcr0F5iGOB7K9sYwwpwFaRNWqiegXRHDc6ptumbVNM2UgqoUaDxc4HomxoZc+RAdD8JVBvIkghnGSbSqP29we0qFS18M3G3tOWmq6userS9mzimQajqSisesn8/SOQ7KHnTw9To9NxqBqe02yxh0Z4kMzWVrzk4OZitJQwls5Dl9Q0g3iKepZfasX6YLG50bIx0GxfbjVggZzHVBa0crhaWvysZUSPosko3SCXU36raws8+RN6cUU3pxzw52eAcafX5H/el+7xAEvo+nHLW1fpdIED7Jkm+O33K6wRnvaV9/zzwX9vyGiCSA9tFWKCPk3P7XSWtwMPt8xTRWT5GAOf2dozfB54lpjw6QVqaldvdTFIZ9xNg+yiZmKOHoSJDKZwBilfJspjmxvfNiG+1RuUH95Z9qwuHLHN4mszAMoByD9kf+oh7VfKoboBhWqyWv/zwPEMrvLfM5VntVwJapZ+qnMuA5mzZhjym/VkLVLB/qznqlTYHDC3BGvytg65yPiWigqRUACS4ah1rONxPzpThdkxsMHlHT2E7WVazGmheM8H4dYb0TA3yqes2MOYAeLHtazep+51hWBpVSsS+rIppHzlNlgOW1uwEqbZxIDtNlnm9TACyMSapwuOEIXuOeO42E4bTz5NTk6mAcrA4TjzT32jH9GHgmqOIPMyFdmGrLKiCp0VKHEUXuuW9dEsA7+kEL5CdWjek1gbtW30gp7rfqnRHML5FFmJ/nLiodgg1nPW4HWXNyrpAEPxfIwDyl9r6vwd8jrgBNusy7CO8iYfIYMwxQqp2mADvc22dLQQ3/SlCVbKe6AjfaPv8NjlY3Ed0CFUJcmdb28uHR0t/keTBjpKWzPF2XpdIy1IwU5erlVw1rvKo28kqXFoCnqcxAkXxVQ7mth2w5ZXV+FaZ1VJSLy3imlBRdcAODDVduPKGNfW9qjSulZdyoh7MNT58GZjVqtPir310KaC+Vf7Xc6Z035dbxhL/9fO9pPclbSPtNlteHpP3i3aODlYCvYOo3qMepQWaTL32uVYlYF/Sm6qZuMfITDj3rYLCQJxe0gKpGHmwnZt1hscIl/8D7Xz1TI0T3CSeH7lbZbUnCEXGfjIQutjWPdI+P9au22lS++w9Vj//g3Zcm4jn/8OEhWzyyAIxgLjuWcJCvkyZKsnAkxZmbfIya8lcdUighSHY6v5UmVKVdEG65FdIiU3f/K9g7s2p2xOotLjtAN9t6z1VzqlqjHVnp8mL+0j7/avEzBxrgb9NgPhxIrj3u8QDu424qFqi60jO80cE8L7a3qs7uJ8A3k8TlvUiAZLPEi7LDbLgukkY28mHxw65huFMBtJIL7ftPEfcszPkwFNdf91ws9esjaC2EhLYNpb/GfWWGz5MpixrpVbg8T6NsqalkKo3Ml+207/fItUdG5d4r9sR7CvQm4Axxu2JG/2xTZbj9j8V/OurBkUd5GsShs9C1cHWhA2vl8e3lNyuJtv0MjGpsH7fXn9lgAaUq8Uq+Hh/q6TQCnoeq4OplrD64xsEuBwm08R7JY0abKu3Gbg6RRhNep213UNa4xNlm9fa8e5rvx1p21hHeIQPM5yNw3TqxbZ8f/vtFeAP2jl+jMCBHaTyQb3/Y2Th+uNkvz3ZfqvW+ELb/lPkTEcOIusJ7NhAKl6OCGajrFDLWm4ip26HYfCm6uR0yftt9WDsKLqV4UNoE1TNUbez1+0YwOoVDVocRl3r9mfI2Zm3ExdXa+lbwG8TgPiLwK8QIPdHhLztR8SNVeqiRWGq7LG2vb4TTRI3/acIymFnO36VBUplFLJrhcnJ2vl1+XwfIzOVDhCj8Wky0FNBtQ6IXt/eRZRrM8ixm6w3cLUdr/Kc0+Tg0lvUPsQ+sLC8VVelWdYhNnFA6ZcDhw+Swbbr3esGaQH15/1G2ijgsx9XHltr0n7qMzBR/uvL+1LpkOrC10QQo+uCsxb6YrfdSiFqiXrM3p96nzRuBOgqE5Tr1tutfcp7VRUhUhta3xva71IYUgoaBKqtlHM+Rhahv0ECqskkeiM1RVtlkDWJ7yH6zExb9xwBphYY8h5NE8/xJsKA+lMEyF8kJuT9dtn2J9trOwG8L7RrIc3oPZkino2D5OQX1q14APizbV96WfL9eifXaVMl9RIzdZ/yRDZTDA+2g9nefrfTWJvCCzVRPo+THWSq/GZTr7qNzMzrJUn+Z4qspl9dzRPtu2Q5bZ8vEoD1M+Q07eva//8L8B+Jke4fAX+xXZhnCata5cUCw+LkL5Nui21NuyYfIC7+RwhXyRx3XV8LlOgaG0iys9TUZAvWmF56mrj+h9r+dfkNhPjg9mmtdgLdeC2SXe11H3G/pKfOEw/RSZJmEJQ8PgfCqo+t7j/cLur3OKoMTamdAZsKIg4SNfBWZWPuA9L99P9+91WDe/2x9WBbwezNNi3TPkClm9/z1SYa9Lpj6Q+Pt2Y+vl5eDrAOVJUnF6h7VYsAKw0BmTl3mSz8oyfmcW4lAE/pnUbTdPnNgd+EFFUpymVVEm0lgPFRUid8kjAGT5AZftIs3l+VCWtIFcNZwjiZaNu1b42RE0U81l5/vu13gqA4rHb4arsWv0SotuYIQJ4lE2akNJXLXSELFVkadYaQyD7Z9nGSeG52k7K9Q5Uu8GHrM58uEAB0kgC5D5Gj4Q2GxYGkCmpzm1oTvf73QNuuWTG6MnX9HxI39mFuHzQOtd+14BYJsPxvhJvxV0jr4RbwFYJieJmwfv9qu6gXGM5qYMbW8XZhLZUnP3sfydkaCNhCukDXCAvyOHFzqvZ5inQPtaK0kowCG2BRY6wiQ4tQ8KgPWQUSv68hReZbSKoDUptsevD5cm2rt1G3r9XbUxHqpCGtaykDdeWCjX1O78h6GrUQuZzjnVp1VUe92/TiRlEeK8kyrM0BpIKrv60t36fK76PaPEPeuFqteix+7wOOlbMVGHXdHdAdTKrKRIsaRtOBZgpqBQuuN8g6y1I93vNZ0qvbSpZVFYwddCplMkPWYzhFZuYZJN9D1sJeT3Kqxjyukh6F8kMDlD6bVwkQP0bGMmjHqVd6mbDGf5bAgn3tt6+TGaQHiADcL7TjP9K29SDx/N9LGkHSVxPtnH5EDmAPkbWMjdvsJaSkt7SIbVdJCcphUv+6iZBTeZO1eAXgUQkWtlmyFkQPot54T6ImHcwRM1hYBrLywzWgUAeB3yVcjH9MBMJs3wO+QAD6RwmL9UkyvfFFIhOtVp7StakgYsS0t3K0+C+TMz0Ign0CxAwZFJJCkQO7Reoxf0TcdEG11kOoQdD6XgN7lqA08DpLdHoDJEfJIjw1el2tJcp2a6BM/rhemyr/qhI309qtjWsARe3zmu5V21LSPKmPpbhUWBk3rfVWU5BrHEPwmGR5UNUrEVgrJdD/Ln/+VjWt2jogrCThpQ7YvTcwSfLvBj29t+YCeG71mlXtc/Wa1Ph6nBvJfnWDnI35JGH4HCGexz0EiJlmbCDNolgqWrSa7f8PEf3xOPHcz5NzzGk9myNxkQjY/T1iZpw17Rh+0Ja/2Lb1qXYeL5Dzbj5NAKqUkzOzWFv6RFt/mkxEkU/+QdvPxjGCH11PkMvzRLBninSb5CRhmEAh+Izi4vzddXp52C2ysr6yOcr/L7X/TDGsFTFKHTFDuBOXiUI9pgBfJeRfJ0meezfRuWbaBT5EclW149Tj1I26SWqBd5Iyt4Nk0WdraHicRpRPM4yoPkXcvDHCyniRrIJGd72WasrxtpFzz21v18UsqZq5d4kEbS1w96PEqTaBqz7QUgnqtLW2avH1/lX5ZK3mHvArf1atTMF/JUCtx1WDaFMMAaoCgSnLk+VVpYx9EE3X3wpaBgLvpPR4r7Q3AtReV+keyGw4KQy5eg2LGXLgMsVarl9Dxv157deT9X6lZAwUqoM+RWrxDVjDkFadI7M7D7fzfYR43sYIXDtIphpfITMMrxPAP0/MpPN3iQHgImGgvVzW/TiBAYcJDLnavj9KTh67nixqpFrrAGFcPU4mU4218/uiliRkOu5W8iGsagctUa3QHizqqCjB3kfEz5FTrJ8tF8n2GiEZe4SwWie69XVrBJMvEMD6SeLCL5LAp8xOd3yKYeDJzqbLZSDqOFkrYwcxCn6G5JGOk+6RWkUjxifbdvy8laByPtLep4gO8H1iVH2OLNbdtxp913KW091LJmcYuHOfnh8MAWlU4GqKDIbJSddByeusltNU2gpIVbBf+WotLhitrukHcN3mGoyCfKgFYoM0tXh61QLX1id4yFPWEpaCjYDjw/1WKT3e6yANdw/UDuQmmfhcXiEG/vOktQpD69r+5SBrnzA+cl952b+NsbxOXO9KnRgEhJTV3SCe5XNk7GZb+10JqcbKHEnXnSee7wcJyvIT7biOEM/rd9o5fpAw6lSQ3CD6jdUpd5AKi8Pk9Ew3iGd/nrCOt7ZreFKL+BARWJJr7YNtBri8CaNajZJXmdgYMZqoeb3O7TK5bxIBMhMaRoH8CfJGHicuei10fYUs1ryezHybJ7iir5DqAqVXp8kJJmnb/FnCPflEW3+u/e8IAc492J4ha2xsJIJ1HyRSxM0lf7md4wttG8s9nONt31rxe8mJV2+R+sfX2uvaqI2QAF4tE13NzQx5xaqtNdDjZ2mUaj2uKev620T53Mu6YFj+0uMT7KU8qgqh0iU1KOUxqf+tCR6QLndvTVegHqWh7hUgS7V3g5t+N9udgFoZpZXkpG+spX2aBGYHZ9Uxet1auaoqpDOlMJw7z+0a8DOJpUoEBcL1bTumWENYuZtIwL3e9neJTPWebN+nyOd4Yzt2KZPL5GwiF0ivfpLh7Dg72rIX2jofI0D/EvEMS3+eGAP+CfCrhGur7MeHQatyKfCdJTNS9pBVmAzaXSADUz1HTDupEwRhPYp/u0pOM38fKbVzO444guo2Qip2P2G1fh741yRnW0X30i4fIqqufZSMbEr0f4dIrpB6UJdrmyCs+g+29Z8geefvEjfgeW6XtfXtXhJ0d7Vtej0Mlp4gg6aVRuj1vvX9nnKtFkgrtk/Pdd6x5VrPjRvtrxSGVpKSpwrW1QqyCcQwBOCqbqg0SOXJq8RPK71K82rtDblPj6d6HAJor+TpgbkH7VHtTtZ0davhx8OahgTq6oGaJl15XZUPqmAgOWQHaLEHchCWrzcFex1ZYuE0SZmsJenEDWTGp3Sd8R+ls8cIyuB7xPM1Rzx/e8gEkicJAN3XjvEQSX1sJe7hkXYc68gJa51p5P72v0tEstYcYayZqr0GuKxLoDvm9+sMZ2/t21mCc7lKztjhhasKiVFBOkiN8BTJnfaBvPPlZOo2FgiQ+xxh5m8H/gaRfLGW0P7+NgGgPnhqEJXbPEFw4rvab7PEaPfNtr4VlAxkVPDt6/QaAPg+QTV8h6wYNqpNkPn5uwjw3dGW3SQrj/muhb+F24FWa8B2p6IzljOtx9HLquzMoxQBFgEa1XTvK3AtF7xaqlVgrHSAD+zaskzpmw+yFnAN4hrorCCnhV3lcH0iUp+0oQFQVSqV9pDasFraUtbunazpqhjSmhb0RwH2e7mNkXyylp+GmqqJOVJXq3V9L+lV6KnZZy6TheitHleDfYtkoPAmmdK9iZy5ebIdy0NkJcnrBCi/QODa9bbOBuI+SwU+SWDSawS+zBE4MNl+e4Hw/veRIgMTWD7StnmCAP57CMpimpZZN08Ga1Q/jHrgFttBfpaIFP4yOamnPM14O4m1pKqiZsS5nbPt953dvlQNOKI4MIy1m/AbBABfJ2Rpf5+gEk4D/x74D8RN3tEuglzNBlK3t4fMU/8uMSvH18gCNP2gMU3ctA+QAvB5AiR/0C7qD8kofpV6zRFgubdtQ+A1Sn+ubecoWZdXF+9uKnxJI+jSLRWgqjrWPvBluxtZ1duhAHgjrQbmapJIjeD3iS5j5b0mS9Rzq/cT0trVQquZef1zY7Cvvrx+6pqlUHz2fFak92oAsadWPN5R1vRNhl7Te6HpuVmLQuv1KuFpWihrLUOJqEkZymKl52oqvTK5G2Sa/YayH+MDVcc/QTyXHyDjLQuEx6mSQlHB+nbclvp8oC1/lsCAjxC4otrsfxPP5cPttYUwLB8lqNe1ZH3tzcBVg3VVBtW3RcIc/zdt+a8ynJrEwN1VhkG+PqCnZvUeAhDrvq4B/68d9FPlYisj+Q3g37WD/mViivo9BAB+kXD/FZZDPjzbiNFpfzueowTwfomwXkdZLePtgj1DjHZPEw/EVbI4zgESuLWWaMcrt7urneda0jWzOLqTHqq1tBP4QNZEhJrMoJLkbrSq76as6r3azOLTsq7F3WsChfdXtcd899kApR5lzayrQD01YpuVYhmlKOmB1O9a5pWDr5mLVYZWFR+Vsqlqlnejef3V/RqXUntvWdVx0jBxAgJldGKNMSxB02qQPm/+bpBVmakSzkVCyfA4QWVsIuVwR0m1xmYCuM1w3dr2c4DAknHCsFQB9jwRl7pJaKIN9l8jcGE/ce8uAq9qcfbN364RVuMkwb1Ol+Vaz3aQibJeBVmJ6YcZAoYd5rl2oGbqWeHoK8C/JAD6M8A/AH6u7e+HhBV7ilRCnCVlJB9u+7tFUAXfICqYHR5xrhAX+WmC5/3TZGbPAZLrPditUwNq+9r+dpPpn9VlNSPNh1L+0QFDF7TXCtfkDS0sI7UGP2pBmb7IyU8CqL7VrQfSSs8I2jUwWVVFVUolQMPtHDcMi/bUwVxQr1z5XFl/vvynqmoMdsrdO1BXT4GyD+kauXRpFquiVX1xHShqf6zfVbvcbTPtWyvYeNJ1MtHjOplGbeKIgT6fK0g1zCJpTctRz5DxKuWLFvo6RNIR1szeQFrrlr5dR9AT+0lw39iO8QT5/G0hDLEbZN3wi6Rs1fPbRmDP5QrENVFAoJCEr/zsVTIjDEZb0RA38Fo7uZukeyUVcp6cjseRahz4Y7K4zl8gEjN2kjNJ/y4hQdGd+QBRROfT5IyuzxJW71cZqiJsa9oFlet9hAyyPUdwxS8yDMhYIPrxdkEfIaPEduoKhnZWM3+kD+z4NQW5BpXqq+ptfVjvVEOhPqg1HbZacaPok9W28tYD9SirtwYC4fYyAoJ25YQr3aFRU3XR8tN1UK79qQYTq5frsVQ52vryvdaO0LLuufCalbdcUHcUSNfvi8ssW0OAoLPkaNScba9rJKBak8SazNUIOkNWGTRw7b26hyzsvpkMZAu29VrNk1JVJ3rYQxh7Ar0a5lNt+8fbfXiaMDAX2zGdbNvb145hllaO1gtetXyU9wrKx8gp2/sL30e+adv9JjG6PMgweDLWTm4jOQD8gADQBdI6XU9Yvr9JcMBzhNX5aULf+xGi47xKAO+XCet1FD9mkE1N7xripiq0PkB0bIsCPdRee9p5K0+ZJdM8TZqQl7JA+Fkyz78PXqk5fDNNftOHvr5XfrH/z3IAXh+KUYC+CuDLt1GZiPWzrQekqtmv97eCfK3xUa3dakELmL2npLVov6zW60qVHuJE31fqQO//7H91MOgt+KX6o03qzhKexqCk+KwfbDDb4GBVUJhCL4D7WmRYQc6aMFfaNveQFjrleo6RMrzdbb/GHc4RA8A4ofZ6lix0L/c9T0559nhb9ifUhGBoRpwj+a12sm6otlmG3JTtVeD3CUv1z3G7GmKGzCqZJ3TMX2gX5WcIAL4O/E/gPxFAt5/gbD9KgOItwmL9VjtZZXJa8EpL9hNUxRNkxttZwo04R07MuYnkoNQBGrU+S/JJB9v5KdmrRXveD20UgPe63/HuP5Mjt7S09b2cRf6TTJfcyZquWX0wtEB7bnqRIT0iUKskMXhVFTBu2wFAYNGDq9TE9bJPlVArUXrAnSV5o5QekwwHmD7+Uc+t1uFeR2ZWmi5vXYua6KOHYTad1It0haC6kbDC1xNG4quEcbWZoBk2kAXqF8s2HiAMze0Ejlgi9r62/v8lOewzBLDva+uea+cypWWq1KRGhy3mYfPBu0XWiKjLngd+DfjrwN9hCNBO1/JE+/0K8G+B3yLSkn+F4E6eJ5QPf0iY/j/d3q3deYDgh+VrKwk/SdYJfpgYicbbhT9HWq3eeCPouh8Ku5WNHSUAe1RATwvGzz2f63VcXGJ5v6wuZ8T/+2X193eiCeA9aC9lkb8ZAB9Fo/ykAPhKrWkYAvWotHGbMsWajGG/V/6nrMt7tli202ce1lKz9sNKy0wyHAjcbk/NCGQes6qjUc1+Ufu+gTklnLPE8y2vvIbkk1UfWUfH2ajNSxgnAXkzEbN6gARLJXMqufyvQUb101Y0vEnkIWwjBpDDBLZMkzOYbyZFD0erdAdG64ZnSa53htupiUuEbvdDhKa4PoAC5y+QmuHPEdbs3yQrGj1LBNSmiUpHe8k87B+2/7/KMMpPO5EnyckMHUxOt/8fY5gSqYujbOYwof8TdG8wzPrSFauf67J+OSP+X5eN+m0prfZK2zs9KIz6/6hBYTn6ZCmL/G7477ny+cedPrkba7ren3luv/e1/9btaFHq2qvo6bPfKvfdW+tVmy2Q1yDlQlm/3rcFhrUtLL1ZVUOj2gQpK9OiXSBB+SJppG0jNc3TZDBOOe0YafVCKB32kson8xIE6AsEpjxEgLAD0BNt+wfafzeTcjWTSc6389pGmyqpBhLqRdJC3EEqJGw+FN9rn59h2BGs8vVhUvr1n9v7XyK4kQtEublT7cCdSO+H5Dxtl8jiLJLrT5ABNjnm0wTPe5CcadkLbyc8x+3TtL+XrKyVAPfYHZaNGijudhB5s4MCrMya75fXZTAEY8G5BpP8T13Wq01s7xf+Wy9CUF2KV11qmUktWr4CDdJznwAACElJREFUrANfbVqkfcagRk4fRPNVlSIG3yuQ9/2p3g/jUVWa5z2E4f2t/dz7UUF6Kd20fPF9ZIbbIlly80I7VkF7N1md8CyBRRZxmic893myopuTrG5o536GNDa3EB78I8TA8AgBwMeJwUCv/hA5wemx9n59jDC7RXwfAFOUe3dCoJYb3dwtnyEszMfIVOHfIsD0r7WDfZmwVq3de76dyEsEeNeReLyd+M52YXe0Y7hIWMmvEJasvIsR0GtkbeFDLD9N+2obtvfjoNA/yFpvVZUAtz/8owBct9/3UXSJ61U5mU2g9LcKmD3YriRo5TH5qoDWu+2jlmkFVyWG8ZSlgoij6I5R+mZbpUD6Vw/SXvd63eo1rcHmUTI8t1UHDKV4NTAp92udlfG27DwBhotkBtweAnuuE4bd2W7b7lMjbx8B5OMEzrxC4Np9hKpqN4GDu8j5I6V8bxKDwoW2zktjbce6HV6YHoAvECa1N1EJWm2H2sEpWv69ts7PtZ1rgc6TRTeOESOV23Rku7edwL528W+SVcWuteMzK2+hbU8t8RmWrmjWW0s991j54GrJjVp2p/+O2s/bse47yRO/W+3NDgrVcuwf6Ora12QOlxskqskZNZjdt2rpC44VyKrMrAa1DMj1gbn6e/UYVuJZjKKnRsU74I0HEe/ETffbr9tdCqhtxmx6/bTNe1cDkwbm9ArGy3pew0mSdpkjY0Ln237uJ+vajJF88+VyLJNkZuRWghrdSdxLC4Hdar8pGNhHZvdpaZt8dpmmmqi8EQwt49fIfGgI4KyzUNAO1JrBM4Rk7fFycBb7eJWwfL9F1vG0zu8DBMcr5TBJDBAnCevXGwYBxEfIKelPMBxZqxXk99qRxrrvvYX2dq37Vrj9K21LufxwO/CP4oPrdt6OdZcafAROGCYsqBKoXHLvmi+1bCWtWlZ90LC3LH247Qtj5aUx4/GoBKgBq/El1hVoKoD2MZzF8ttSoLrS872bOAEkxeF9qVLJ+v++PkZNw65JLvX4BeoKzD1g1yZQ9696X9Qjy3GrolA3vZnUII+TVuplUtm1rR3HDEFbnCdLcY4TuHadsKYfbdu3Bvktsj7MQwSlqmV8jaBHpU/PVI64AvD5ctCLhMZ3PaFG8ML1mXUX28FPkFMY/SGRIfclYrRwxKya3mfaxdLyruUXvQDyukdYvpLZ+6G9U4PFqP++VetWF3uifK4Pah+wu9OylQ5Uo/je3hWfXWKZllvNaFzKO1nOUxkFYqO2U9etvDblnHvLfSX6b+9TLwusx1Tdfcq5vlV0k6+qeZ5k6cGwcvE9/VELJY2y5vVo6sBWB14Y9oHKeVd+WQ5cb8esvr2EQXhP22ZNm7Y+xiJhQAqm3tdLBIA/0La1QGDZ1XZc69qyT7T/O1vN1z1XR+J6UyfJTnOZsEqfbP+bKMvUHN8iq+q/TEzG+T8IDtdW6/R+nOBPpsnZL5yw8r0eUHu/tvrQ2Jl7S/Juli0lTav7g3wobAJhtSz9PFf+W7/X/lY9N/ez0oHmToNb/70foPr/vp1tFOBXpQMM7wvlc7W0J7ttVR68Jn7UQapy4Fq2VZ1Cty3I61IVBf7uwGNmX68TrlXW6vmacDJX3hfK/ybK+1KBw/qq23d7lfuvgcspspSnoKxcz3yDTe149Pg9Z4sQeSxyy5ATH3+ire/gcwj48hjDkdT56eYJ139vW6EWu7YYjw/IGUIR8VmC5PYCPUSoJj5DyECUlVxs67xCUB8n2vsRVgNqcDtojuI6V7JsJZKw2vqgT29l9st6t33Ush/X9lYB/ptZdyVeVOW06+ep8rn2n17vW1sdOKvkTOCeG/Gf5TT4gnU9vhpE1GN3/5WLllvvvRzPvV4Xz9fEFLdv/RDpL4+nJrLcS3LRi2QSmNSFktk5MrdinsBIj3FN+/9VAv8eI4QHDlQnxgjL87tEyvBGwsw2SaJOkVIlbPNE1bNfIzhfCNP7U4SE4xliVJkkrNqjbR8HyKmInNXi/d7sRG8GNOvntwM0l1v246S3XW1vvPVUQz+g21e1bHs9eG1ardV6rjRKrzjxc6VSfCYq114/18Gnbm8UXbXceddg4kT5buag9S02kuCrle4x+dt0OwaZAgt0Pdx+u0hk4wnOh8aAPyCVDQskqvd0BQR98OtEScoJwsz+FDkrxjgtCkgA9NcJEDax4t1uS4Hm+BtY9k7IjkYtWwXN1fZebKM4/2p43E32JQwTdZZKn4ehl1DVLaMkeVrVlYbxubqTNzJBpmEbBNxBzq1XJ0SVPagG1zzBAhwkwPwBgpbYS6gqjqtrk7yuATv5Eq3mzxJA/HEi+21f28nrhAb4a0TltJd4awJqFQjvJkp+p2XLtR407xRBv9OyGixa5bhX22pbulUAr/rhN5s+X2kK1/e7GNdb0KMkeQL4Um2CnIhiF8Oayc54fm97zRKc8BkCW63js12OuAJwtYCPEKUndxIBtilC1fAykZL8x4RcrUqNJrgdFN9t2dFKlr3ReqqrbbWttne+VZxZKn2+t8h7vr1fVsG6BoyV4amksEBSn4U5RtAN2wnqYRs55dJGsmjRDBFPWyCo4BdrsK6C8XWCyx0jhcrPE6B7iNR19gGDpVp1J+prrltWvy+3bLWtttW22u6mLUWf9BZ5VXcY3Os5cxgqMCyIVNOvtZb3kGoL5820DO8t4GA18cfIOeOOEXV9nRboEkOuZo7RwDpq2SporrbVttreC62XSa609eBtgaTKCzsn333lv5BqtNcJAJbb3k1WeBtTHnKTqAn8O8S0HodZddNX22pbbavtbps0bQXo+poiLOM9RH7Gw8D1/w+Izzp2irz8ZwAAAABJRU5ErkJggg==", "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampleIOfixed $ fmap (diagram . mconcat . fmap traceDiagram) $ replicateM 1000 $ getTrace program" ] }, { "cell_type": "code", "execution_count": 7, "id": "cabe0c55-57cc-4f3e-896c-cbda3ef14703", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "program :: Measure Double\n", "program = do\n", " x <- random\n", " y <- normal 0 (x**2)\n", " factor (Exp $ log y)\n", " return y\n", "\n", "sampleIOfixed $ fmap (diagram . mconcat . fmap traceDiagram) $ replicateM 100 $ getTrace program\n" ] }, { "cell_type": "code", "execution_count": 8, "id": "6dad6b23-bf92-4a8b-8fd4-74e91aac1959", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import Data.List (sort)\n", "\n", "program :: Measure ()\n", "program = do\n", " xs <- replicateM 3 random\n", " factor $ if xs == sort xs then 1 else 0.1\n", "\n", "sampleIOfixed $ fmap (diagram . mconcat . fmap traceDiagram) $ replicateM 1000 $ getTrace program\n" ] }, { "cell_type": "markdown", "id": "3f330c7a-6dce-4781-ae1a-e60bfa6eb07c", "metadata": {}, "source": [ "# Displaying random walks\n", "\n", "Another nice use of `diagrams` is to produce a distribution over a random walk, and then convert it into a distribution over diagrams of random walks:" ] }, { "cell_type": "code", "execution_count": 9, "id": "156c8dbb-2810-4ca9-beff-0a6bff04f8b3", "metadata": {}, "outputs": [], "source": [ "import qualified Control.Monad.Bayes.Sampler.Lazy as Lazy" ] }, { "cell_type": "code", "execution_count": 10, "id": "4191634c-9c6a-443a-a2b6-208ac45565de", "metadata": {}, "outputs": [], "source": [ "kernel :: MonadDistribution m => V2 Double -> m (V2 Double)\n", "kernel (V2 x y) = do\n", " newX <- normal x 0.01\n", " newY <- normal y 0.01\n", " return (V2 newX newY)\n", "\n", "randomWalk :: Distribution [V2 Double]\n", "randomWalk = unfoldrM (fmap (Just . (\\x -> (x,x))) . kernel) 0" ] }, { "cell_type": "code", "execution_count": 11, "id": "e686067d-0f1a-4ca2-b373-fbc96e0dafe2", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "convertPointToDiagram :: V2 Double -> Diagram C.B\n", "convertPointToDiagram point = circle 0.001 # translate point\n", "\n", "convertListofNPointsToDiagram :: Int -> [V2 Double] -> Diagram C.B\n", "convertListofNPointsToDiagram n = mconcat . take n . fmap convertPointToDiagram\n", "\n", "diagramList :: Distribution (Diagram C.B)\n", "diagramList = fmap (convertListofNPointsToDiagram 10000) randomWalk\n", "\n", "d <- L.sampler diagramList\n", "diagram d" ] } ], "metadata": { "kernelspec": { "display_name": "custom haskell", "language": "haskell", "name": "custom-haskell" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/examples/Histogram.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "d9f736cc-ee3f-4567-966e-9c15a5122ea6", "metadata": {}, "outputs": [], "source": [ "import Prelude \n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Enumerator\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad\n", "import Numeric.Log\n", "\n", ":l ../plotting.hs" ] }, { "cell_type": "markdown", "id": "5005eab6-3193-4bfc-b1be-e9e3748d2554", "metadata": {}, "source": [ "# Distributions over **anything**\n", "\n", "In Monad-Bayes, you can have distributions over absolutely any kind of value, of any type. Below, you can see a distribution over histograms, and a distribution over plots." ] }, { "cell_type": "code", "execution_count": 2, "id": "01e82e4f-e5a2-4fbb-8fad-5e4ab3a253c6", "metadata": {}, "outputs": [], "source": [ "unnormalizedDistribution :: Measure Double\n", "unnormalizedDistribution = do\n", " x <- bernoulli 0.5\n", " y <- normal (if x then (-2) else 2) 1\n", " factor (normalPdf 0 4 y)\n", " return y\n", " \n", "distributionOverWeightedSamples :: Distribution (Double, Log Double)\n", "distributionOverWeightedSamples = runWeightedT unnormalizedDistribution\n", " \n", "iidDistribution :: Distribution [(Double, Log Double)]\n", "iidDistribution = replicateM 100000 distributionOverWeightedSamples\n", " \n", "histogramDistribution :: Distribution Histogram\n", "histogramDistribution = histogram 500 <$> iidDistribution\n", " \n", "plotDistribution :: Distribution Plot\n", "plotDistribution = plot . histogramToList <$> histogramDistribution" ] }, { "cell_type": "markdown", "id": "2cb70027-b2fd-45c8-8434-22f2c92e0933", "metadata": {}, "source": [ "`unnormalizedDistribution` is an (unnormalized) distribution that we want to plot. \n", "\n", "`distributionOverWeightedSamples` is a distribution over pairs of samples from `unnormalizedDistribution` and their density. \n", "\n", "`iidDistribution` is a distribution over 100000 iid draws from `distributionOverWeightedSamples`.\n", "\n", "`histogramDistribution` is a distribution over histograms, each corresponding to a possible list of iid samples.\n", "\n", "`plotDistribution` is a distribution over plots (i.e. 2D visualizations that can be rendered in a notebook), each corresponding to a possible histogram. " ] }, { "cell_type": "code", "execution_count": 3, "id": "ace53904-ff25-419c-95b8-e3ee6debea48", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -6.0089217245146305, "Y": 0.06436598001308692 }, { "X": -5.984213001382199, "Y": 0 }, { "X": -5.959504278249766, "Y": 0 }, { "X": -5.934795555117335, "Y": 0 }, { "X": -5.910086831984902, "Y": 0 }, { "X": -5.88537810885247, "Y": 0.03381346439252048 }, { "X": -5.860669385720038, "Y": 0.03402809986227849 }, { "X": -5.835960662587606, "Y": 0 }, { "X": -5.811251939455174, "Y": 0 }, { "X": -5.786543216322741, "Y": 0.03488223661543553 }, { "X": -5.761834493190309, "Y": 0 }, { "X": -5.737125770057877, "Y": 0 }, { "X": -5.712417046925445, "Y": 0 }, { "X": -5.687708323793013, "Y": 0 }, { "X": -5.662999600660581, "Y": 0.07312172445509142 }, { "X": -5.638290877528148, "Y": 0.03701486949431892 }, { "X": -5.613582154395717, "Y": 0 }, { "X": -5.588873431263284, "Y": 0.03767435032354156 }, { "X": -5.5641647081308525, "Y": 0.03785147087512243 }, { "X": -5.53945598499842, "Y": 0 }, { "X": -5.514747261865988, "Y": 0.0384696777234303 }, { "X": -5.490038538733556, "Y": 0.11661872267593157 }, { "X": -5.465329815601124, "Y": 0.039270242090076096 }, { "X": -5.4406210924686915, "Y": 0 }, { "X": -5.41591236933626, "Y": 0.07990075373803746 }, { "X": -5.391203646203827, "Y": 0 }, { "X": -5.366494923071395, "Y": 0.040419268105091756 }, { "X": -5.341786199938963, "Y": 0.20440327709755488 }, { "X": -5.317077476806531, "Y": 0.041354106989514576 }, { "X": -5.292368753674099, "Y": 0.041434399423424625 }, { "X": -5.267660030541666, "Y": 0.08372306230707435 }, { "X": -5.242951307409235, "Y": 0.08457562303617844 }, { "X": -5.218242584276802, "Y": 0.08523723655480925 }, { "X": -5.19353386114437, "Y": 0.12877744959499762 }, { "X": -5.168825138011938, "Y": 0.08653480981517722 }, { "X": -5.144116414879506, "Y": 0.13085913124718732 }, { "X": -5.119407691747074, "Y": 0.21991689735537875 }, { "X": -5.094698968614642, "Y": 0.13329962214517627 }, { "X": -5.0699902454822094, "Y": 0.17882863911129349 }, { "X": -5.045281522349778, "Y": 0.22482062332073816 }, { "X": -5.020572799217345, "Y": 0.22726637416827394 }, { "X": -4.995864076084914, "Y": 0.4574552608509581 }, { "X": -4.971155352952481, "Y": 0.32240866425919873 }, { "X": -4.9464466298200485, "Y": 0.2788537026617831 }, { "X": -4.921737906687617, "Y": 0.23393766276029404 }, { "X": -4.897029183555185, "Y": 0.2829440671161367 }, { "X": -4.872320460422753, "Y": 0.6176798364386836 }, { "X": -4.84761173729032, "Y": 0.43073495842312703 }, { "X": -4.822903014157888, "Y": 0.48226483881993976 }, { "X": -4.798194291025457, "Y": 0.4372585825231104 }, { "X": -4.773485567893024, "Y": 0.7826008435036651 }, { "X": -4.748776844760592, "Y": 0.8381534673176434 }, { "X": -4.72406812162816, "Y": 0.5954658645746935 }, { "X": -4.699359398495727, "Y": 0.9999561137530477 }, { "X": -4.674650675363296, "Y": 0.45333519712117726 }, { "X": -4.649941952230863, "Y": 0.5074102223392255 }, { "X": -4.6252332290984315, "Y": 0.6129652478836873 }, { "X": -4.600524505965999, "Y": 1.1842068875087786 }, { "X": -4.575815782833567, "Y": 1.5034646007474188 }, { "X": -4.551107059701135, "Y": 0.8352834634117816 }, { "X": -4.526398336568702, "Y": 0.6838353903450933 }, { "X": -4.5016896134362705, "Y": 0.6360055015870401 }, { "X": -4.476980890303839, "Y": 1.5999409916174339 }, { "X": -4.452272167171406, "Y": 1.288165994720481 }, { "X": -4.427563444038974, "Y": 1.2979763280993106 }, { "X": -4.402854720906542, "Y": 1.4698085557273868 }, { "X": -4.37814599777411, "Y": 1.2595928108087364 }, { "X": -4.353437274641678, "Y": 0.9928624048520462 }, { "X": -4.328728551509245, "Y": 1.9990366504068187 }, { "X": -4.304019828376814, "Y": 1.84500102131843 }, { "X": -4.279311105244381, "Y": 2.025959763445529 }, { "X": -4.254602382111949, "Y": 2.7747059540724304 }, { "X": -4.229893658979517, "Y": 3.076564644184636 }, { "X": -4.205184935847085, "Y": 2.4109956991091437 }, { "X": -4.180476212714653, "Y": 3.0616982722314785 }, { "X": -4.155767489582221, "Y": 2.848197050837875 }, { "X": -4.131058766449788, "Y": 3.3342832656704653 }, { "X": -4.106350043317357, "Y": 3.3569294399548846 }, { "X": -4.081641320184924, "Y": 2.9032128971049196 }, { "X": -4.0569325970524925, "Y": 3.8169788834649814 }, { "X": -4.03222387392006, "Y": 4.201371325047379 }, { "X": -4.007515150787627, "Y": 3.9840333463296838 }, { "X": -3.9828064276551958, "Y": 3.9489778441664853 }, { "X": -3.958097704522763, "Y": 4.156704196012278 }, { "X": -3.933388981390331, "Y": 4.6123139761388785 }, { "X": -3.908680258257899, "Y": 4.70214497014048 }, { "X": -3.883971535125467, "Y": 4.5434450060589455 }, { "X": -3.859262811993035, "Y": 5.38591672043316 }, { "X": -3.8345540888606027, "Y": 5.040160881745403 }, { "X": -3.8098453657281706, "Y": 7.035579792925246 }, { "X": -3.7851366425957385, "Y": 7.139017030800116 }, { "X": -3.7604279194633063, "Y": 6.85914712502999 }, { "X": -3.7357191963308742, "Y": 5.738960543301927 }, { "X": -3.711010473198442, "Y": 7.716352402288785 }, { "X": -3.68630175006601, "Y": 7.306405861414238 }, { "X": -3.661593026933578, "Y": 8.721136763109758 }, { "X": -3.636884303801146, "Y": 8.511739596213928 }, { "X": -3.6121755806687137, "Y": 8.890840075929876 }, { "X": -3.5874668575362816, "Y": 9.871036008772927 }, { "X": -3.5627581344038495, "Y": 9.995338147689537 }, { "X": -3.5380494112714174, "Y": 10.790580378811898 }, { "X": -3.513340688138985, "Y": 11.25420201077188 }, { "X": -3.4886319650065527, "Y": 10.842958109350546 }, { "X": -3.4639232418741206, "Y": 10.693119391390733 }, { "X": -3.4392145187416885, "Y": 11.509223095174304 }, { "X": -3.4145057956092564, "Y": 12.885926892225067 }, { "X": -3.3897970724768243, "Y": 14.834659783499115 }, { "X": -3.365088349344392, "Y": 12.88318522791812 }, { "X": -3.34037962621196, "Y": 15.412690473016786 }, { "X": -3.315670903079528, "Y": 15.138308346093456 }, { "X": -3.290962179947096, "Y": 16.20853817121795 }, { "X": -3.2662534568146637, "Y": 15.292758528347138 }, { "X": -3.2415447336822316, "Y": 16.590805039539784 }, { "X": -3.2168360105497995, "Y": 17.89863372327899 }, { "X": -3.1921272874173674, "Y": 16.827809192395033 }, { "X": -3.1674185642849353, "Y": 20.3369597900328 }, { "X": -3.142709841152503, "Y": 20.95155871482908 }, { "X": -3.118001118020071, "Y": 16.71057479730506 }, { "X": -3.093292394887639, "Y": 22.854022743543087 }, { "X": -3.0685836717552064, "Y": 20.136975448774074 }, { "X": -3.0438749486227743, "Y": 24.5649111982752 }, { "X": -3.019166225490342, "Y": 22.576577437801525 }, { "X": -2.99445750235791, "Y": 23.287474948497493 }, { "X": -2.969748779225478, "Y": 21.87985300329724 }, { "X": -2.945040056093046, "Y": 21.067267485794222 }, { "X": -2.9203313329606138, "Y": 24.14089196366951 }, { "X": -2.8956226098281816, "Y": 24.943065367538527 }, { "X": -2.8709138866957495, "Y": 27.59668064881158 }, { "X": -2.8462051635633174, "Y": 27.563539921799702 }, { "X": -2.8214964404308853, "Y": 27.762674642006974 }, { "X": -2.796787717298453, "Y": 28.3552641035962 }, { "X": -2.772078994166021, "Y": 27.61554494524911 }, { "X": -2.747370271033589, "Y": 27.491825083118652 }, { "X": -2.722661547901157, "Y": 31.16939716705034 }, { "X": -2.6979528247687248, "Y": 31.698918822755846 }, { "X": -2.6732441016362927, "Y": 31.032978548996606 }, { "X": -2.6485353785038606, "Y": 30.275604084134358 }, { "X": -2.6238266553714285, "Y": 33.70003427593482 }, { "X": -2.599117932238996, "Y": 32.6255873396766 }, { "X": -2.574409209106564, "Y": 35.91598835002564 }, { "X": -2.5497004859741317, "Y": 35.49170531458677 }, { "X": -2.5249917628416996, "Y": 33.66900235337601 }, { "X": -2.5002830397092675, "Y": 35.6860556759161 }, { "X": -2.4755743165768354, "Y": 36.733054851028335 }, { "X": -2.4508655934444032, "Y": 38.687907424956826 }, { "X": -2.426156870311971, "Y": 35.18213033497522 }, { "X": -2.401448147179539, "Y": 38.06039818072502 }, { "X": -2.376739424047107, "Y": 38.284337326437964 }, { "X": -2.352030700914675, "Y": 38.93067696807831 }, { "X": -2.3273219777822427, "Y": 39.157214178906074 }, { "X": -2.3026132546498106, "Y": 40.56392806402764 }, { "X": -2.2779045315173785, "Y": 36.97874892989157 }, { "X": -2.2531958083849464, "Y": 41.53106387501214 }, { "X": -2.2284870852525143, "Y": 41.58793843001898 }, { "X": -2.203778362120082, "Y": 40.530791203657735 }, { "X": -2.17906963898765, "Y": 40.151957407194196 }, { "X": -2.1543609158552175, "Y": 41.494816646642036 }, { "X": -2.1296521927227854, "Y": 39.03287868279963 }, { "X": -2.1049434695903533, "Y": 42.553403993065594 }, { "X": -2.080234746457921, "Y": 44.2571184357169 }, { "X": -2.055526023325489, "Y": 42.0383366984788 }, { "X": -2.030817300193057, "Y": 46.20515263888747 }, { "X": -2.0061085770606253, "Y": 41.60274850303092 }, { "X": -1.9813998539281927, "Y": 46.57795759499732 }, { "X": -1.956691130795761, "Y": 43.97862459558296 }, { "X": -1.9319824076633285, "Y": 41.45142958991336 }, { "X": -1.907273684530896, "Y": 42.37106635130894 }, { "X": -1.8825649613984643, "Y": 40.35456770129033 }, { "X": -1.8578562382660317, "Y": 42.17380852821673 }, { "X": -1.8331475151336, "Y": 41.84178703939828 }, { "X": -1.8084387920011675, "Y": 42.59211805228891 }, { "X": -1.7837300688687359, "Y": 47.405854298680914 }, { "X": -1.7590213457363033, "Y": 43.00930475544739 }, { "X": -1.7343126226038716, "Y": 43.75849991929209 }, { "X": -1.709603899471439, "Y": 43.60548944355928 }, { "X": -1.6848951763390074, "Y": 42.25884476956666 }, { "X": -1.6601864532065749, "Y": 39.35010466305151 }, { "X": -1.6354777300741432, "Y": 42.47297728685894 }, { "X": -1.6107690069417107, "Y": 43.68639483133674 }, { "X": -1.586060283809279, "Y": 45.72856663508428 }, { "X": -1.5613515606768464, "Y": 41.30986273656901 }, { "X": -1.5366428375444148, "Y": 41.1304248029407 }, { "X": -1.5119341144119822, "Y": 42.25187354985236 }, { "X": -1.4872253912795497, "Y": 41.23212143330253 }, { "X": -1.462516668147118, "Y": 40.859918837080194 }, { "X": -1.4378079450146855, "Y": 36.65164696662195 }, { "X": -1.4130992218822538, "Y": 40.1050037650212 }, { "X": -1.3883904987498212, "Y": 37.93853455831329 }, { "X": -1.3636817756173896, "Y": 34.348208534381726 }, { "X": -1.338973052484957, "Y": 38.75716258299296 }, { "X": -1.3142643293525254, "Y": 37.88955040067198 }, { "X": -1.2895556062200928, "Y": 39.86070850689678 }, { "X": -1.2648468830876611, "Y": 38.23273946873289 }, { "X": -1.2401381599552286, "Y": 33.557585166775816 }, { "X": -1.215429436822797, "Y": 35.23717666400324 }, { "X": -1.1907207136903644, "Y": 33.58551178623465 }, { "X": -1.1660119905579327, "Y": 34.793521127343226 }, { "X": -1.1413032674255001, "Y": 35.430188122956096 }, { "X": -1.1165945442930685, "Y": 34.43581863760561 }, { "X": -1.091885821160636, "Y": 27.479547684611145 }, { "X": -1.0671770980282043, "Y": 32.916509313259375 }, { "X": -1.0424683748957717, "Y": 28.728904663104153 }, { "X": -1.0177596517633392, "Y": 28.38802374454083 }, { "X": -0.9930509286309075, "Y": 29.3994608523828 }, { "X": -0.9683422054984749, "Y": 26.827841843728525 }, { "X": -0.9436334823660433, "Y": 28.033131035649372 }, { "X": -0.9189247592336107, "Y": 30.111513213303322 }, { "X": -0.8942160361011791, "Y": 23.054244882436777 }, { "X": -0.8695073129687465, "Y": 26.591356821779318 }, { "X": -0.8447985898363148, "Y": 23.213259972908197 }, { "X": -0.8200898667038823, "Y": 26.56321827054447 }, { "X": -0.7953811435714506, "Y": 24.250544769621243 }, { "X": -0.7706724204390181, "Y": 25.748289755039313 }, { "X": -0.7459636973065864, "Y": 19.406938425852527 }, { "X": -0.7212549741741539, "Y": 19.919744765406744 }, { "X": -0.6965462510417222, "Y": 24.363043134507507 }, { "X": -0.6718375279092896, "Y": 21.438079944820213 }, { "X": -0.647128804776858, "Y": 20.573601210118362 }, { "X": -0.6224200816444254, "Y": 18.918916692685112 }, { "X": -0.5977113585119938, "Y": 19.13325318729233 }, { "X": -0.5730026353795612, "Y": 21.026674571077024 }, { "X": -0.5482939122471286, "Y": 17.3895040570883 }, { "X": -0.523585189114697, "Y": 19.777124174906994 }, { "X": -0.4988764659822644, "Y": 19.59468331416895 }, { "X": -0.47416774284983276, "Y": 18.817876198053412 }, { "X": -0.4494590197174002, "Y": 18.830225946265397 }, { "X": -0.42475029658496855, "Y": 18.34735206432481 }, { "X": -0.400041573452536, "Y": 14.984596740620747 }, { "X": -0.3753328503201043, "Y": 15.788197044792451 }, { "X": -0.3506241271876718, "Y": 13.611674886973216 }, { "X": -0.3259154040552401, "Y": 18.091802484621546 }, { "X": -0.30120668092280756, "Y": 14.818512075454704 }, { "X": -0.2764979577903759, "Y": 16.217640138002437 }, { "X": -0.25178923465794334, "Y": 13.736165466321435 }, { "X": -0.22708051152551167, "Y": 12.845261325322944 }, { "X": -0.20237178839307912, "Y": 12.45114475728478 }, { "X": -0.17766306526064746, "Y": 13.749899003544389 }, { "X": -0.1529543421282149, "Y": 12.457766191281154 }, { "X": -0.12824561899578235, "Y": 13.756365096801856 }, { "X": -0.10353689586335069, "Y": 15.453807662812105 }, { "X": -0.07882817273091813, "Y": 13.860481449965748 }, { "X": -0.05411944959848647, "Y": 12.366075142974426 }, { "X": -0.029410726466053916, "Y": 11.668709800566038 }, { "X": -0.004702003333622251, "Y": 12.167710931914211 }, { "X": 0.020006719798810302, "Y": 12.466791141838392 }, { "X": 0.04471544293124197, "Y": 12.067211905131938 }, { "X": 0.06942416606367452, "Y": 13.960896702696354 }, { "X": 0.09413288919610618, "Y": 11.865261195563276 }, { "X": 0.11884161232853874, "Y": 11.564050741480928 }, { "X": 0.1435503354609704, "Y": 12.857572528726678 }, { "X": 0.16825905859340295, "Y": 13.352719676018049 }, { "X": 0.19296778172583462, "Y": 15.440982337563588 }, { "X": 0.21767650485826717, "Y": 14.440130541994257 }, { "X": 0.24238522799069884, "Y": 15.828956376536942 }, { "X": 0.2670939511231314, "Y": 13.235154351059416 }, { "X": 0.29180267425556305, "Y": 14.025251751115862 }, { "X": 0.3165113973879956, "Y": 16.902014935313414 }, { "X": 0.34122012052042816, "Y": 15.800337940474277 }, { "X": 0.3659288436528598, "Y": 14.401327431787465 }, { "X": 0.3906375667852924, "Y": 14.69032526696409 }, { "X": 0.41534628991772404, "Y": 18.252962173456933 }, { "X": 0.4400550130501566, "Y": 16.951993454187967 }, { "X": 0.46476373618258826, "Y": 17.930328777706404 }, { "X": 0.4894724593150208, "Y": 17.817470586180736 }, { "X": 0.5141811824474525, "Y": 17.606304226434567 }, { "X": 0.538889905579885, "Y": 18.580780192817073 }, { "X": 0.5635986287123167, "Y": 18.169714894803352 }, { "X": 0.5883073518447492, "Y": 20.423736600946068 }, { "X": 0.6130160749771809, "Y": 21.390367042951908 }, { "X": 0.6377247981096135, "Y": 22.059028614949636 }, { "X": 0.6624335212420451, "Y": 18.790554913407508 }, { "X": 0.6871422443744777, "Y": 25.453572219350956 }, { "X": 0.7118509675069093, "Y": 21.49885893227534 }, { "X": 0.7365596906393419, "Y": 24.5145203865934 }, { "X": 0.7612684137717736, "Y": 24.093883013929812 }, { "X": 0.7859771369042061, "Y": 24.555446625123626 }, { "X": 0.8106858600366387, "Y": 26.77101735805807 }, { "X": 0.8353945831690703, "Y": 25.956483763332653 }, { "X": 0.8601033063015029, "Y": 26.995666527863623 }, { "X": 0.8848120294339346, "Y": 28.418751724327752 }, { "X": 0.9095207525663671, "Y": 28.573628001957204 }, { "X": 0.9342294756987988, "Y": 26.592006210915198 }, { "X": 0.9589381988312313, "Y": 29.363481718047122 }, { "X": 0.983646921963663, "Y": 30.77263570444857 }, { "X": 1.0083556450960955, "Y": 34.10524577296647 }, { "X": 1.0330643682285272, "Y": 33.18389020228303 }, { "X": 1.0577730913609598, "Y": 28.41207315183007 }, { "X": 1.0824818144933914, "Y": 32.497886561682655 }, { "X": 1.107190537625824, "Y": 31.293101526249888 }, { "X": 1.1318992607582556, "Y": 32.09961236828386 }, { "X": 1.1566079838906882, "Y": 32.617213192350064 }, { "X": 1.1813167070231199, "Y": 32.65455367445013 }, { "X": 1.2060254301555524, "Y": 35.64343175520338 }, { "X": 1.230734153287985, "Y": 37.00326441405618 }, { "X": 1.2554428764204166, "Y": 34.36876794917463 }, { "X": 1.2801515995528492, "Y": 35.059277567338874 }, { "X": 1.3048603226852808, "Y": 35.275134453488086 }, { "X": 1.3295690458177134, "Y": 37.561691206518574 }, { "X": 1.354277768950145, "Y": 40.02723320608505 }, { "X": 1.3789864920825776, "Y": 35.90010254061527 }, { "X": 1.4036952152150093, "Y": 37.69978802635619 }, { "X": 1.4284039383474418, "Y": 37.15052604791056 }, { "X": 1.4531126614798735, "Y": 40.52138229762324 }, { "X": 1.477821384612306, "Y": 43.41073041676884 }, { "X": 1.5025301077447377, "Y": 38.75612453662198 }, { "X": 1.5272388308771703, "Y": 40.98344553136436 }, { "X": 1.551947554009602, "Y": 41.44036181189422 }, { "X": 1.5766562771420345, "Y": 39.86634779451997 }, { "X": 1.6013650002744662, "Y": 43.44864979628387 }, { "X": 1.6260737234068987, "Y": 39.67101693490139 }, { "X": 1.6507824465393304, "Y": 42.5937350666885 }, { "X": 1.675491169671763, "Y": 40.47217472111512 }, { "X": 1.7001998928041955, "Y": 43.46481264630419 }, { "X": 1.7249086159366271, "Y": 45.34750953692863 }, { "X": 1.7496173390690597, "Y": 44.14066354776209 }, { "X": 1.7743260622014914, "Y": 41.31025208295274 }, { "X": 1.799034785333924, "Y": 42.549872329658406 }, { "X": 1.8237435084663556, "Y": 45.30560513479073 }, { "X": 1.8484522315987881, "Y": 40.60423709891016 }, { "X": 1.8731609547312198, "Y": 48.08626034992968 }, { "X": 1.8978696778636523, "Y": 43.224826464056704 }, { "X": 1.922578400996084, "Y": 40.34057931829437 }, { "X": 1.9472871241285166, "Y": 42.608809057408564 }, { "X": 1.9719958472609482, "Y": 42.57151596214939 }, { "X": 1.996704570393379, "Y": 41.90927744857683 }, { "X": 2.0214132935258116, "Y": 47.139104271536056 }, { "X": 2.046122016658244, "Y": 41.21687024608036 }, { "X": 2.0708307397906767, "Y": 47.27830543190963 }, { "X": 2.0955394629231074, "Y": 40.77812886179572 }, { "X": 2.12024818605554, "Y": 41.1636472710846 }, { "X": 2.1449569091879725, "Y": 42.49586991468964 }, { "X": 2.169665632320405, "Y": 44.68300367505257 }, { "X": 2.1943743554528377, "Y": 39.03998069328269 }, { "X": 2.2190830785852684, "Y": 39.50559694567912 }, { "X": 2.243791801717701, "Y": 40.90097987632673 }, { "X": 2.2685005248501335, "Y": 40.50494923004692 }, { "X": 2.293209247982566, "Y": 38.844598068670805 }, { "X": 2.317917971114997, "Y": 35.49881129306099 }, { "X": 2.3426266942474294, "Y": 38.8174963806082 }, { "X": 2.367335417379862, "Y": 38.42442190377412 }, { "X": 2.3920441405122945, "Y": 35.61591395221525 }, { "X": 2.4167528636447253, "Y": 36.8119095980132 }, { "X": 2.441461586777158, "Y": 39.324418684459346 }, { "X": 2.4661703099095904, "Y": 36.12165694940774 }, { "X": 2.490879033042023, "Y": 36.393609100321925 }, { "X": 2.5155877561744537, "Y": 34.6163408119633 }, { "X": 2.5402964793068863, "Y": 33.505408030175836 }, { "X": 2.565005202439319, "Y": 33.12670324762529 }, { "X": 2.5897139255717514, "Y": 34.8593585128976 }, { "X": 2.614422648704184, "Y": 34.153818914416455 }, { "X": 2.6391313718366147, "Y": 33.21392167940385 }, { "X": 2.6638400949690473, "Y": 30.836743930317578 }, { "X": 2.68854881810148, "Y": 28.806209372333864 }, { "X": 2.7132575412339124, "Y": 31.296477151941016 }, { "X": 2.737966264366343, "Y": 31.643197874526276 }, { "X": 2.7626749874987757, "Y": 26.55703059283822 }, { "X": 2.7873837106312083, "Y": 29.49196682551053 }, { "X": 2.812092433763641, "Y": 26.72085569288447 }, { "X": 2.8368011568960716, "Y": 27.919077439795384 }, { "X": 2.861509880028504, "Y": 27.104875869832124 }, { "X": 2.8862186031609367, "Y": 25.370006052765703 }, { "X": 2.9109273262933693, "Y": 24.25978072379728 }, { "X": 2.9356360494258, "Y": 22.703777984892895 }, { "X": 2.9603447725582326, "Y": 24.801621942554924 }, { "X": 2.985053495690665, "Y": 24.308690238087927 }, { "X": 3.0097622188230977, "Y": 22.47034794981525 }, { "X": 3.0344709419555302, "Y": 22.889271748557594 }, { "X": 3.059179665087961, "Y": 19.431354345341376 }, { "X": 3.0838883882203936, "Y": 18.74741818720601 }, { "X": 3.108597111352826, "Y": 20.500139452618384 }, { "X": 3.1333058344852587, "Y": 21.28247222306949 }, { "X": 3.1580145576176895, "Y": 18.18608657242754 }, { "X": 3.182723280750122, "Y": 17.078076913326182 }, { "X": 3.2074320038825546, "Y": 16.8508199454337 }, { "X": 3.232140727014987, "Y": 15.037914536562653 }, { "X": 3.256849450147418, "Y": 17.256052906867986 }, { "X": 3.2815581732798504, "Y": 15.672981084366228 }, { "X": 3.306266896412283, "Y": 13.398850310609918 }, { "X": 3.3309756195447155, "Y": 16.148101155892927 }, { "X": 3.3556843426771463, "Y": 12.13597825228821 }, { "X": 3.380393065809579, "Y": 13.397254095247922 }, { "X": 3.4051017889420114, "Y": 11.802531731536972 }, { "X": 3.429810512074444, "Y": 10.912992512203857 }, { "X": 3.4545192352068748, "Y": 10.85282144974084 }, { "X": 3.4792279583393073, "Y": 10.930609345459231 }, { "X": 3.50393668147174, "Y": 11.824815770673192 }, { "X": 3.5286454046041724, "Y": 10.34068389929586 }, { "X": 3.553354127736605, "Y": 9.345963931705008 }, { "X": 3.5780628508690357, "Y": 9.492216845231425 }, { "X": 3.6027715740014683, "Y": 9.440930020517115 }, { "X": 3.627480297133901, "Y": 9.518899206079858 }, { "X": 3.6521890202663334, "Y": 9.069608974439529 }, { "X": 3.676897743398764, "Y": 6.928533488001969 }, { "X": 3.7016064665311967, "Y": 8.319881722173866 }, { "X": 3.7263151896636293, "Y": 8.858689592366929 }, { "X": 3.751023912796062, "Y": 6.039902148152036 }, { "X": 3.7757326359284926, "Y": 6.1316927313421985 }, { "X": 3.800441359060925, "Y": 6.288586585659372 }, { "X": 3.8251500821933577, "Y": 5.2404328883115765 }, { "X": 3.8498588053257903, "Y": 5.3985602745501335 }, { "X": 3.874567528458221, "Y": 5.3034702579686135 }, { "X": 3.8992762515906536, "Y": 5.024706108803253 }, { "X": 3.923984974723086, "Y": 4.931420948987622 }, { "X": 3.9486936978555187, "Y": 4.227747442314353 }, { "X": 3.9734024209879513, "Y": 4.323527900117694 }, { "X": 3.998111144120382, "Y": 4.357543081507386 }, { "X": 4.022819867252815, "Y": 3.6091789441314703 }, { "X": 4.047528590385247, "Y": 2.988294060985391 }, { "X": 4.07223731351768, "Y": 3.148300871712172 }, { "X": 4.0969460366501105, "Y": 2.8329079365225875 }, { "X": 4.121654759782543, "Y": 2.9325614842188776 }, { "X": 4.146363482914976, "Y": 3.2629437821121843 }, { "X": 4.171072206047408, "Y": 2.954371156114901 }, { "X": 4.195780929179839, "Y": 2.531483962913992 }, { "X": 4.2204896523122715, "Y": 3.0305027475425863 }, { "X": 4.245198375444704, "Y": 2.0439133083911427 }, { "X": 4.269907098577137, "Y": 2.256153398292313 }, { "X": 4.294615821709567, "Y": 1.960402271412433 }, { "X": 4.319324544842, "Y": 1.7819633813070974 }, { "X": 4.3440332679744325, "Y": 1.7704301144955796 }, { "X": 4.368741991106865, "Y": 2.3634161802700913 }, { "X": 4.393450714239298, "Y": 1.5284886204721733 }, { "X": 4.418159437371728, "Y": 1.7880265295021038 }, { "X": 4.442868160504161, "Y": 2.0984750651885 }, { "X": 4.467576883636593, "Y": 1.4961955053488782 }, { "X": 4.492285606769026, "Y": 1.114393148198418 }, { "X": 4.516994329901457, "Y": 1.1599262806697297 }, { "X": 4.541703053033889, "Y": 0.8376504137825302 }, { "X": 4.566411776166322, "Y": 0.779316077022603 }, { "X": 4.591120499298754, "Y": 0.7744169363636016 }, { "X": 4.615829222431185, "Y": 0.973044435569106 }, { "X": 4.640537945563618, "Y": 0.7645531346633573 }, { "X": 4.66524666869605, "Y": 0.7572904114936728 }, { "X": 4.689955391828483, "Y": 0.6523412182503231 }, { "X": 4.714664114960914, "Y": 0.4482146575712663 }, { "X": 4.739372838093346, "Y": 0.34577191394521717 }, { "X": 4.764081561225779, "Y": 0.39242475294685053 }, { "X": 4.788790284358211, "Y": 0.5360607695099395 }, { "X": 4.813499007490642, "Y": 0.5798776765417563 }, { "X": 4.838207730623075, "Y": 0.5280358317393693 }, { "X": 4.862916453755507, "Y": 0.571334176912555 }, { "X": 4.88762517688794, "Y": 0.47209046981529823 }, { "X": 4.912333900020372, "Y": 0.09359050325590149 }, { "X": 4.937042623152803, "Y": 0.32620438660229956 }, { "X": 4.961751346285236, "Y": 0.2772999714701909 }, { "X": 4.986460069417668, "Y": 0.3667903556694632 }, { "X": 5.011168792550101, "Y": 0.18152001255089312 }, { "X": 5.0358775156825315, "Y": 0.36129055415867073 }, { "X": 5.060586238814964, "Y": 0.22377753284027765 }, { "X": 5.085294961947397, "Y": 0.17773777288222908 }, { "X": 5.110003685079829, "Y": 0.17645646525135483 }, { "X": 5.13471240821226, "Y": 0.08780727066687444 }, { "X": 5.1594211313446925, "Y": 0.21696349156720374 }, { "X": 5.184129854477125, "Y": 0.17250174801045592 }, { "X": 5.208838577609558, "Y": 0.0854566950118135 }, { "X": 5.233547300741988, "Y": 0.042410519512412204 }, { "X": 5.258256023874421, "Y": 0.16800592410150014 }, { "X": 5.2829647470068535, "Y": 0.16701472768429942 }, { "X": 5.307673470139286, "Y": 0.04126799341410359 }, { "X": 5.332382193271719, "Y": 0.08218399757197797 }, { "X": 5.357090916404149, "Y": 0.04072532339385404 }, { "X": 5.381799639536582, "Y": 0.08056434530933945 }, { "X": 5.4065083626690145, "Y": 0.12010785818091799 }, { "X": 5.431217085801447, "Y": 0 }, { "X": 5.455925808933878, "Y": 0 }, { "X": 5.48063453206631, "Y": 0 }, { "X": 5.505343255198743, "Y": 0.07729224513886634 }, { "X": 5.5300519783311755, "Y": 0 }, { "X": 5.554760701463606, "Y": 0.07606712333441693 }, { "X": 5.579469424596039, "Y": 0.037661211181885514 }, { "X": 5.604178147728471, "Y": 0 }, { "X": 5.628886870860904, "Y": 0.037179537497664714 }, { "X": 5.653595593993335, "Y": 0.0366313605933591 }, { "X": 5.678304317125767, "Y": 0.07277069223740595 }, { "X": 5.7030130402582, "Y": 0 }, { "X": 5.727721763390632, "Y": 0 }, { "X": 5.752430486523065, "Y": 0 }, { "X": 5.777139209655496, "Y": 0 }, { "X": 5.801847932787928, "Y": 0.034978286174887394 }, { "X": 5.826556655920361, "Y": 0 }, { "X": 5.851265379052793, "Y": 0.034278347052329225 }, { "X": 5.875974102185224, "Y": 0 }, { "X": 5.900682825317657, "Y": 0 }, { "X": 5.925391548450089, "Y": 0.03339867929760542 }, { "X": 5.950100271582522, "Y": 0 }, { "X": 5.9748089947149525, "Y": 0 }, { "X": 5.999517717847385, "Y": 0 }, { "X": 6.024226440979818, "Y": 0 }, { "X": 6.04893516411225, "Y": 0 }, { "X": 6.073643887244681, "Y": 0 }, { "X": 6.0983526103771135, "Y": 0 }, { "X": 6.123061333509546, "Y": 0 }, { "X": 6.147770056641979, "Y": 0.06139316984479559 }, { "X": 6.172478779774409, "Y": 0 }, { "X": 6.197187502906842, "Y": 0 }, { "X": 6.2218962260392745, "Y": 0 }, { "X": 6.246604949171707, "Y": 0 }, { "X": 6.27131367230414, "Y": 0 }, { "X": 6.29602239543657, "Y": 0 }, { "X": 6.320731118569003, "Y": 0 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler plotDistribution" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/examples/Ising.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "de51b2ba-b424-47c1-9825-4e8087914f69", "metadata": {}, "outputs": [], "source": [ ":e TypeFamilies\n", ":e GeneralizedNewtypeDeriving\n", ":e DeriveTraversable\n", ":e TypeSynonymInstances\n", ":e FlexibleInstances\n", ":e ApplicativeDo\n", ":e LambdaCase\n", ":e TupleSections\n", "\n", "import Graphics.Gloss.Interface.IO.Simulate (simulateIO, Display (FullScreen))\n", "import Graphics.Gloss (white, Picture (Text, Polygon, Color, Circle, Pictures), simulate, Rectangle (Rectangle), black, red, translate)\n", "import Control.Monad.Bayes.Class (MonadDistribution(bernoulli, uniformD, random, normal), factor, MonadMeasure)\n", "import Control.Monad.Bayes.Sampler.Strict (sampler, sampleIOfixed)\n", "import Control.Monad.Bayes.Traced (mh, mhStep)\n", "import Control.Monad.Bayes.Weighted\n", "import Data.Functor.Compose (Compose(..))\n", "import qualified Data.Vector as V\n", "import Data.Bool (bool)\n", "import Data.Distributive (Distributive(..))\n", "import Data.Functor.Rep (Representable(..), distributeRep, ifoldMapRep)\n", "import Data.Functor.Identity (Identity(..))\n", "import Control.Arrow ((***), Arrow (first, second))\n", "import Control.Comonad.Representable.Store (Store(..), StoreT(..), store, experiment, runStore)\n", "import Control.Comonad (Comonad(..), (=>>))\n", "import Graphics.Gloss.Data.Color (Color)\n", "import Control.Monad (filterM)\n", "import Debug.Trace (trace)\n", "import Data.Monoid (Sum(Sum, getSum))\n", "import Control.Monad.Bayes.Inference.SMC \n", "import Control.Monad.Bayes.Population \n", "import qualified Pipes.Prelude as P\n", "import Pipes ((>->))\n", "import Graphics.Gloss.Interface.IO.Animate (animateIO)\n", "import Data.IORef (newIORef, writeIORef)\n", "import GHC.IORef (readIORef)\n", "import qualified Pipes as P\n", "import Control.Concurrent (forkIO, threadDelay)\n", "import Control.Monad.IO.Class\n", "import Control.Monad.Bayes.Sampler.Strict\n" ] }, { "cell_type": "markdown", "id": "66f4ec05-fc58-4f99-ac3b-6c5015e19c60", "metadata": {}, "source": [ "The Ising model is a famous model in statistical mechanics. See the wonderful https://jaan.io/how-does-physics-connect-machine-learning/. Basically, it's a big grid of squares, each of which can be in one of two states. We can assign an \"interaction energy\" between adjacent states, which says how much they like to be in similar or different states. A configuration of the whole grid (i.e. choosing a setting for each square) determines a global energy, and in turn, a distribution (the Boltzmann distribution) over grids.\n", "\n", "The code borrows heavily from the excellent https://chrispenner.ca/posts/conways-game-of-life which uses a representable store comonad to perform efficient local updates to a grid. The approach should extend easily to similar models with different topologies." ] }, { "cell_type": "code", "execution_count": 2, "id": "0d9a571f-c793-49a1-9414-c118449353b0", "metadata": {}, "outputs": [], "source": [ "\n", "\n", "------\n", "-- Define a board as a length 20 array of length 20 arrays\n", "-- Define a grid as a board and a pointer to a square\n", "------\n", "\n", "-- | Grid a ~ ([[a]], (Int, Int))\n", "type Grid a = Store (Compose VBounded VBounded) a\n", "\n", "type Table = Compose VBounded VBounded\n", "\n", "-- | VBounded a ~ [a]\n", "newtype VBounded a = VBounded (V.Vector a)\n", " deriving (Eq, Show, Functor, Foldable, Traversable)\n", "\n", "instance Distributive VBounded where\n", " distribute = distributeRep\n", "\n", "-- | size of the Ising grid\n", "gridSize :: Int\n", "gridSize = 40\n", "\n", "-- | you can view a grid as a function from indices to square values\n", "-- or as an array of square values.\n", "-- These two structures are isomorphic, as witnessed by: \n", "-- index . tabulate === id === tabulate . index\n", "instance Representable VBounded where\n", " type Rep VBounded = Int\n", " index (VBounded v) i = v V.! (i `mod` gridSize)\n", " tabulate desc = VBounded $ V.generate gridSize desc\n", "\n", "-- | make a grid\n", "mkGrid :: [(Int, Int)] -> Grid Bool\n", "mkGrid xs = store (`elem` xs) (0, 0)\n", "\n" ] }, { "cell_type": "markdown", "id": "caa4f57b-2fe1-4158-b662-910d3229decb", "metadata": {}, "source": [ "# Approach 1: calculate the energy and perform Markov Chain Monte Carlo\n", "\n", "There are two ways we can simulate an Ising model. The first is to define a distribution over grids directly, by drawing from a uniform prior, and adding a factor statement for the energy. One can then sample from this distribution using MCMC, and indeed, the MCMC chain will look like a time-evolving Ising model. Which is very cool!\n", "\n", "Let's do that first:" ] }, { "cell_type": "code", "execution_count": 3, "id": "eb37114e-94c1-438e-baa2-8515fe98248c", "metadata": {}, "outputs": [], "source": [ "-- | calculate the energy of a particular square in the grid \n", "-- (which depends on the rest of the grid, in particular its neighbours)\n", "interactionEnergy :: Grid Bool -> Int\n", "interactionEnergy g = numNeighboursSame\n", " where\n", " numNeighboursSame = length (filter (==color) neighbours)\n", " color = extract g\n", " neighbours = experiment (\\s -> addCoords s <$> neighbourCoords) g\n", " addCoords (x, y) (x', y') = (x + x', y + y')\n", " neighbourCoords = [(x, y) | x <- [-1, 0, 1], y <- [-1, 0, 1], (x, y) /= (0, 0)]" ] }, { "cell_type": "code", "execution_count": 4, "id": "7a15c7b6-bdf2-4ff3-abb3-94311346a716", "metadata": {}, "outputs": [], "source": [ "-- | The prior is uniform over all configurations of the grid\n", "-- We score states based on their energy\n", "model :: MonadMeasure m => m (Grid Bool)\n", "model = do\n", " let allCoords = [(x,y) | x <- [0..gridSize-1], y <- [0..gridSize-1]]\n", " coords <- filterM (const $ bernoulli 0.5) allCoords\n", " let grid = mkGrid coords\n", " StoreT (Identity energy) _ = extend interactionEnergy grid\n", " score = getSum $ foldMap Sum energy\n", " factor $ exp $ fromIntegral score\n", " return grid\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "e0d27328-ff1f-4fb6-b1f2-e6b323e4dde6", "metadata": {}, "outputs": [], "source": [ "---\n", "-- render a grid into ascii using an indexed fold\n", "---\n", "\n", "display :: Grid Bool -> String\n", "display (StoreT (Identity grid) _) = ifoldMapRep match grid where\n", " match (_,n) b \n", " | n==0 = \"\\n|\" <> showB b\n", " | n==gridSize-1 = \"|\" <> showB b <> \"|\"\n", " | otherwise = \"|\" <> showB b\n", " showB True = \"X\"\n", " showB False = \"O\"\n", " \n", "samples = do\n", " s <- sampler $ unweighted $ mh 2 model\n", " mapM_ (\\x -> putStrLn (\"\\n\\n\" <> display x)) $ reverse $ take 2 s\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "e532c2fd-b74e-40bb-b487-4f04a6dc4d62", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "\n", "|X|O|O|X|X|O|X|X|X|O|X|O|X|O|O|X|O|O|X|X|X|O|O|X|X|O|O|X|O|O|X|O|O|X|X|X|X|O|O|X|\n", "|X|O|X|O|X|O|X|O|X|O|O|X|O|O|X|X|X|X|X|O|X|O|X|X|O|X|X|X|X|X|X|O|X|O|X|O|X|X|O|X|\n", "|O|O|O|X|X|X|X|O|X|O|O|X|X|O|O|X|O|X|X|X|O|X|X|O|X|O|O|O|O|O|O|X|O|O|X|O|O|X|X|O|\n", "|X|X|X|X|X|O|O|O|O|O|O|X|O|X|X|X|X|X|O|O|O|O|X|X|X|X|X|O|X|O|X|O|X|X|X|X|X|X|O|O|\n", "|X|O|O|X|X|O|X|O|O|O|O|O|O|O|X|X|X|O|O|X|O|X|O|O|O|X|O|X|X|O|X|O|X|O|O|X|O|O|X|O|\n", "|O|O|O|O|X|O|O|X|X|O|X|X|X|X|O|O|X|O|O|X|X|O|O|X|O|X|X|O|X|X|X|O|X|X|X|X|O|O|O|O|\n", "|O|O|O|O|X|O|X|O|X|O|O|O|X|O|O|O|X|O|X|O|O|X|X|X|O|X|O|O|X|O|O|O|O|X|X|X|X|X|O|X|\n", "|X|O|O|O|X|X|X|X|X|X|O|X|X|X|X|O|O|O|X|X|X|X|O|O|O|X|X|O|O|X|O|X|X|O|X|O|O|O|X|O|\n", "|O|X|O|O|X|X|O|X|O|X|O|O|X|X|O|X|X|X|O|X|X|X|O|X|O|X|O|O|X|X|O|O|O|O|O|O|X|O|X|O|\n", "|O|X|O|X|O|O|O|O|O|X|O|X|O|O|O|O|O|O|X|O|X|X|X|O|O|O|O|O|X|X|O|O|O|O|X|O|X|X|O|O|\n", "|O|O|O|X|O|X|O|O|O|O|X|X|X|X|X|O|X|X|X|X|X|X|X|O|X|O|O|O|X|X|X|O|X|O|X|O|O|X|X|X|\n", "|X|O|X|O|O|X|O|O|O|X|X|X|X|X|X|O|X|X|O|O|X|O|O|X|X|X|X|O|O|O|O|X|X|X|O|X|X|X|X|O|\n", "|O|X|X|O|O|X|X|X|O|X|O|O|O|O|O|X|O|O|X|X|O|O|O|X|O|O|X|X|O|O|X|O|O|X|X|O|X|X|X|X|\n", "|O|O|X|O|X|O|X|O|X|X|X|O|O|X|O|O|X|X|X|X|X|X|O|X|X|O|X|O|X|O|X|O|O|X|O|O|O|X|X|X|\n", "|O|X|X|X|O|O|O|O|O|X|O|X|X|O|X|O|X|O|O|X|O|X|X|O|O|X|O|X|X|O|O|X|O|X|O|O|O|X|X|O|\n", "|O|O|X|O|O|X|O|O|O|X|O|X|X|O|X|O|X|O|X|X|X|X|O|X|X|X|O|X|X|X|X|X|X|X|X|O|O|O|X|X|\n", "|O|O|X|X|O|O|O|X|O|O|X|O|O|X|X|X|X|O|X|X|O|O|X|O|O|O|X|X|X|X|X|O|O|O|O|O|O|O|X|X|\n", "|X|O|O|X|O|O|X|X|O|O|X|X|X|X|X|X|X|O|O|O|O|O|X|X|O|O|O|O|O|O|O|O|X|X|O|X|O|X|O|X|\n", "|O|O|O|O|O|O|O|X|X|O|O|X|X|X|X|X|O|X|O|O|X|X|O|O|O|O|X|O|X|O|X|O|X|O|X|O|X|O|O|O|\n", "|X|X|X|O|X|O|X|X|O|O|X|O|O|O|X|O|O|O|X|X|O|X|X|O|O|O|X|O|O|O|X|X|O|O|X|O|O|O|O|O|\n", "|X|X|O|O|O|X|O|O|O|X|O|O|X|X|O|O|X|X|O|X|X|X|O|X|O|O|X|X|O|O|X|X|X|O|X|X|X|X|X|O|\n", "|X|O|X|X|O|X|O|X|O|O|O|X|X|O|O|X|O|O|X|O|O|O|O|O|X|X|X|X|O|X|X|O|O|X|O|O|O|X|X|O|\n", "|X|O|X|X|X|X|O|O|X|X|O|X|O|X|O|X|X|X|O|X|X|O|O|X|O|X|O|X|X|X|O|X|X|X|X|O|X|O|O|O|\n", "|X|X|X|X|X|O|X|X|X|X|X|O|X|O|O|X|X|X|X|O|X|O|O|O|O|X|X|X|O|X|O|O|O|O|O|O|X|X|X|O|\n", "|O|X|O|X|O|O|X|X|O|O|O|X|O|X|O|X|O|O|O|X|O|X|O|X|X|X|O|O|X|O|O|O|O|O|O|O|O|O|O|X|\n", "|X|O|X|O|X|X|X|X|O|X|O|O|X|O|O|X|X|X|X|X|X|O|O|O|X|O|O|X|O|X|X|O|X|X|O|O|O|O|O|O|\n", "|O|O|X|X|X|X|O|O|O|O|O|O|O|O|X|X|O|X|X|O|X|X|X|X|X|X|X|O|X|X|O|O|X|O|X|X|X|X|X|X|\n", "|X|O|O|X|X|X|X|X|X|O|X|O|O|O|O|X|O|O|O|O|X|X|O|O|O|O|O|X|X|O|X|X|O|O|X|X|X|X|O|O|\n", "|O|O|X|X|O|O|X|O|O|O|O|O|X|X|X|O|O|O|O|X|O|X|X|X|O|O|X|O|X|X|X|O|X|X|O|O|O|X|O|O|\n", "|X|O|O|X|O|O|O|O|X|X|X|O|X|X|X|O|X|O|O|X|O|X|O|X|O|O|X|X|O|O|O|X|O|X|O|X|O|O|O|X|\n", "|O|X|O|X|X|X|O|X|X|X|O|O|O|X|X|O|X|O|X|O|O|X|X|X|O|X|O|O|O|X|X|X|O|X|O|O|O|X|O|X|\n", "|O|O|O|X|O|O|O|O|O|O|X|O|O|X|X|X|O|O|O|O|O|O|X|X|O|X|X|X|X|O|X|X|O|O|O|X|O|O|O|O|\n", "|X|O|O|O|X|X|X|X|O|X|X|O|O|O|X|X|X|X|O|O|O|X|X|X|O|X|O|O|X|X|X|O|X|X|X|X|X|O|O|O|\n", "|X|O|X|O|X|O|X|O|O|X|O|O|X|X|O|X|X|O|X|X|O|X|X|O|O|O|O|X|O|X|O|O|X|O|O|O|X|O|O|O|\n", "|O|X|O|O|O|O|O|O|X|X|O|X|X|X|O|X|X|X|X|X|X|O|O|O|X|X|X|X|O|O|O|X|O|O|O|O|O|O|X|O|\n", "|O|O|X|X|X|O|O|O|X|O|O|X|X|X|O|X|O|O|O|O|X|X|X|X|O|O|O|X|O|O|O|O|X|O|X|O|O|X|O|O|\n", "|O|O|X|X|O|X|O|X|O|X|O|X|O|X|O|X|O|X|O|X|O|O|O|O|X|X|X|O|X|O|X|O|O|X|X|O|O|O|X|X|\n", "|X|X|X|O|O|X|X|X|O|O|O|O|X|X|X|X|O|X|X|O|O|O|X|O|O|X|X|X|X|X|X|X|O|O|O|X|O|O|O|O|\n", "|X|O|O|X|X|X|O|O|O|O|O|X|X|X|X|X|X|O|O|X|O|X|X|X|X|O|O|X|O|O|X|X|X|X|X|O|X|X|O|O|\n", "|O|O|X|O|O|O|X|X|O|X|O|X|O|X|X|X|X|O|X|X|O|X|O|X|X|X|O|X|O|O|O|O|X|X|X|X|X|O|O|X|\n", "\n", "\n", "\n", "|X|O|O|X|X|O|X|X|X|O|X|O|X|O|O|X|O|O|X|X|X|O|O|X|X|O|O|X|O|O|X|O|O|X|X|X|X|O|O|X|\n", "|X|O|X|O|X|O|X|O|X|O|O|X|O|O|X|X|X|X|X|O|X|O|X|X|O|X|X|X|X|X|X|O|X|O|X|O|X|X|O|X|\n", "|O|O|O|X|X|X|X|X|X|O|O|X|X|O|O|X|O|X|X|X|O|X|X|O|X|O|O|O|O|O|O|X|O|O|X|O|O|X|X|O|\n", "|X|X|X|X|X|O|O|O|O|O|O|X|O|X|X|X|X|X|O|O|O|O|X|X|X|X|X|O|X|O|X|O|X|X|X|X|X|X|O|O|\n", "|X|O|O|X|X|O|X|O|O|O|O|O|O|O|X|X|X|O|O|X|O|X|O|O|O|X|O|X|X|O|X|O|X|O|O|X|O|O|X|O|\n", "|O|O|O|O|X|O|O|X|X|O|X|X|X|X|O|O|X|O|O|X|X|O|O|X|O|X|X|O|X|X|X|O|X|X|X|X|O|O|O|O|\n", "|O|O|O|O|X|O|X|O|X|O|O|O|X|O|O|O|X|O|X|O|O|X|X|X|O|X|O|O|X|O|O|O|O|X|X|X|X|X|O|X|\n", "|X|O|O|O|X|X|X|X|X|X|O|X|X|X|X|O|O|O|X|X|X|X|O|O|O|X|X|O|O|X|O|X|X|O|X|O|O|O|X|O|\n", "|O|X|O|O|X|X|O|X|O|X|O|O|X|X|O|X|X|X|O|X|X|X|O|X|O|X|O|O|X|X|O|O|O|O|O|O|X|O|X|O|\n", "|O|X|O|X|O|O|O|O|O|X|O|X|O|O|O|O|O|O|X|O|X|X|X|O|O|O|O|O|X|X|O|O|O|O|X|O|X|X|O|O|\n", "|O|O|O|X|O|X|O|O|O|O|X|X|X|X|X|O|X|X|X|X|X|X|X|O|X|O|O|O|X|X|X|O|X|O|X|O|O|X|X|X|\n", "|X|O|X|O|O|X|O|O|O|X|X|X|X|X|X|O|X|X|O|O|X|O|O|X|X|X|X|O|O|O|O|X|X|X|O|X|X|X|X|O|\n", "|O|X|X|O|O|X|X|X|O|X|O|O|O|O|O|X|O|O|X|X|O|O|O|X|O|O|X|X|O|O|X|O|O|X|X|O|X|X|X|X|\n", "|O|O|X|O|X|O|X|O|X|X|X|O|O|X|O|O|X|X|X|X|X|X|O|X|X|O|X|O|X|O|X|O|O|X|O|O|O|X|X|X|\n", "|O|X|X|X|O|O|O|O|O|X|O|X|X|O|X|O|X|O|O|X|O|X|X|O|O|X|O|X|X|O|O|X|O|X|O|O|O|X|X|O|\n", "|O|O|X|O|O|X|O|O|O|X|O|X|X|O|X|O|X|O|X|X|X|X|O|X|X|X|O|X|X|X|X|X|X|X|X|O|O|O|X|X|\n", "|O|O|X|X|O|O|O|X|O|O|X|O|O|X|X|X|X|O|X|X|O|O|X|O|O|O|X|X|X|X|X|O|O|O|O|O|O|O|X|X|\n", "|X|O|O|X|O|O|X|X|O|O|X|X|X|X|X|X|X|O|O|O|O|O|X|X|O|O|O|O|O|O|O|O|X|X|O|X|O|X|O|X|\n", "|O|O|O|O|O|O|O|X|X|O|O|X|X|X|X|X|O|X|O|O|X|X|O|O|O|O|X|O|X|O|X|O|X|O|X|O|X|O|O|O|\n", "|X|X|X|O|X|O|X|X|O|O|X|O|O|O|X|O|O|O|X|X|O|X|X|O|O|O|X|O|O|O|X|X|O|O|X|O|O|O|O|O|\n", "|X|X|O|O|O|X|O|O|O|X|O|O|X|X|O|O|X|X|O|X|X|X|O|X|O|O|X|X|O|O|X|X|X|O|X|X|X|X|X|O|\n", "|X|O|X|X|O|X|O|X|O|O|O|X|X|O|O|X|O|O|X|O|O|O|O|O|X|X|X|X|O|X|X|O|O|X|O|O|O|X|X|O|\n", "|X|O|X|X|X|X|O|O|X|X|O|X|O|X|O|X|X|X|O|X|X|O|O|X|O|X|O|X|X|X|O|X|X|X|X|O|X|O|O|O|\n", "|X|X|X|X|X|O|X|X|X|X|X|O|X|O|O|X|X|X|X|O|X|O|O|O|O|X|X|X|O|X|O|O|O|O|O|O|X|X|X|O|\n", "|O|X|O|X|O|O|X|X|O|O|O|X|O|X|O|X|O|O|O|X|O|X|O|X|X|X|O|O|X|O|O|O|O|O|O|O|O|O|O|X|\n", "|X|O|X|O|X|X|X|X|O|X|O|O|X|O|O|X|X|X|X|X|X|O|O|O|X|O|O|X|O|X|X|O|X|X|O|O|O|O|O|O|\n", "|O|O|X|X|X|X|O|O|O|O|O|O|O|O|X|X|O|X|X|O|X|X|X|X|X|X|X|O|X|X|O|O|X|O|X|X|X|X|X|X|\n", "|X|O|O|X|X|X|X|X|X|O|X|O|O|O|O|X|O|O|O|O|X|X|O|O|O|O|O|X|X|O|X|X|O|O|X|X|X|X|O|O|\n", "|O|O|X|X|O|O|X|O|O|O|O|O|X|X|X|O|O|O|O|X|O|X|X|X|O|O|X|O|X|X|X|O|X|X|O|O|O|X|O|O|\n", "|X|O|O|X|O|O|O|O|X|X|X|O|X|X|X|O|X|O|O|X|O|X|O|X|O|O|X|X|O|O|O|X|O|X|O|X|O|O|O|X|\n", "|O|X|O|X|X|X|O|X|X|X|O|O|O|X|X|O|X|O|X|O|O|X|X|X|O|X|O|O|O|X|X|X|O|X|O|O|O|X|O|X|\n", "|O|O|O|X|O|O|O|O|O|O|X|O|O|X|X|X|O|O|O|O|O|O|X|X|O|X|X|X|X|O|X|X|O|O|O|X|O|O|O|O|\n", "|X|O|O|O|X|X|X|X|O|X|X|O|O|O|X|X|X|X|O|O|O|X|X|X|O|X|O|O|X|X|X|O|X|X|X|X|X|O|O|O|\n", "|X|O|X|O|X|O|X|O|O|X|O|O|X|X|O|X|X|O|X|X|O|X|X|O|O|O|O|X|O|X|O|O|X|O|O|O|X|O|O|O|\n", "|O|X|O|O|O|O|O|O|X|X|O|X|X|X|O|X|X|X|X|X|X|O|O|O|X|X|X|X|O|O|O|X|O|O|O|O|O|O|X|O|\n", "|O|O|X|X|X|O|O|O|X|O|O|X|X|X|O|X|O|O|O|O|X|X|X|X|O|O|O|X|O|O|O|O|X|O|X|O|O|X|O|O|\n", "|O|O|X|X|O|X|O|X|O|X|O|X|O|X|O|X|O|X|O|X|O|O|O|O|X|X|X|O|X|O|X|O|O|X|X|O|O|O|X|X|\n", "|X|X|X|O|O|X|X|X|O|O|O|O|X|X|X|X|O|X|X|O|O|O|X|O|O|X|X|X|X|X|X|X|O|O|O|X|O|O|O|O|\n", "|X|O|O|X|X|X|O|O|O|O|O|X|X|X|X|X|X|O|O|X|O|X|X|X|X|O|O|X|O|O|X|X|X|X|X|O|X|X|O|O|\n", "|O|O|X|O|O|O|X|X|O|X|O|X|O|X|X|X|X|O|X|X|O|X|O|X|X|X|O|X|O|O|O|O|X|X|X|X|X|O|O|X|" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "samples" ] }, { "cell_type": "markdown", "id": "8e6c9193-9fa3-4d31-9af5-b1512c3c6d1f", "metadata": {}, "source": [ "# Approach 2: perform the stochastic evolution directly\n", "\n", "Instead of indirectly obtaining the Ising model time evolution as a Markov Chain over a model, we can also just directly write the kernel which moves from one state of a grid to the next, stochastically. We express the chain as a stream (a `Producer`) and display that.\n", "\n", "The one subtlety is that we need a function `Store (m a) -> m (Store a)`, i.e. a `Traversable` instance for `Store`, so that we can expand local stochasticity to global stochasticity. " ] }, { "cell_type": "code", "execution_count": 7, "id": "f9dfa532-e417-4775-a2bf-d3d1f1486d87", "metadata": {}, "outputs": [], "source": [ "instance Foldable (Store g)\n", "instance (Traversable g, Representable g, Functor g) => Traversable (Store g) where\n", " sequenceA g = fmap (uncurry store . first index) (distributed g) where\n", " distributed :: (Representable g, Traversable g, Applicative m) => Store g (m a) -> m (g a, Rep g)\n", " distributed g = grab $\n", " first (sequenceA . tabulate) $\n", " runStore g\n", " grab (ma, b) = (,b) <$> ma\n", "\n", "step :: MonadDistribution m => Grid Bool -> m (Grid Bool)\n", "step = sequence . extend rule \n", "\n", "rule :: MonadDistribution m => Grid Bool -> m Bool\n", "rule = experiment (uniformD . neighbours)\n", " where\n", " neighbours s = addCoords s <$> neighbourCoords\n", " addCoords (x, y) (x', y') = (x + x', y + y')\n", " neighbourCoords = [(x, y) | x <- [-1, 0, 1], y <- [-1, 0, 1], (x, y) /= (0, 0)]" ] }, { "cell_type": "code", "execution_count": 8, "id": "570a3655-5b2b-49d2-94af-ecb35227cc23", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "|X|X|O|O|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|\n", "|X|O|O|O|O|O|O|O|O|O|X|O|O|X|O|X|X|X|O|X|O|X|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|X|O|X|X|O|O|X|X|X|X|X|X|X|X|X|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|X|O|O|O|O|X|O|X|X|O|X|X|X|X|O|O|O|O|X|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|\n", "|O|O|O|O|O|X|X|X|X|X|X|X|O|O|O|X|X|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|X|O|\n", "|O|O|O|O|X|X|X|O|X|X|X|X|X|X|X|O|O|X|X|X|O|O|O|O|O|O|O|O|O|X|O|X|X|O|O|O|X|O|O|O|\n", "|O|O|O|O|X|X|O|X|O|X|X|X|X|X|X|O|X|X|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|\n", "|O|O|X|O|X|X|X|X|X|X|X|X|X|O|X|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|X|O|O|O|O|O|\n", "|O|O|O|X|O|O|X|X|X|X|X|X|O|X|X|X|O|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|\n", "|O|O|X|O|O|O|O|O|O|O|O|X|X|O|O|X|X|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|X|X|X|X|X|X|X|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|\n", "|O|O|O|O|O|O|O|O|O|O|X|O|O|X|X|X|O|O|X|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|X|X|O|X|X|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|\n", "|X|O|O|O|O|O|O|O|O|O|X|O|O|O|X|X|X|O|X|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|X|O|O|O|O|O|O|O|O|O|X|O|X|X|X|X|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|X|O|X|O|O|O|O|O|O|O|O|X|O|X|X|O|X|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|\n", "|O|X|O|O|O|O|O|O|O|O|O|O|O|O|X|O|X|O|O|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|X|X|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|X|X|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|X|X|X|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|O|X|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|X|O|O|X|O|X|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|X|O|O|O|O|O|O|O|O|X|O|O|O|O|O|X|X|O|O|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|X|X|X|O|O|O|O|O|O|O|O|X|O|X|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "\n", "|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|X|X|O|O|O|X|O|O|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|X|\n", "|O|X|O|O|O|O|X|O|O|X|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|\n", "|O|X|O|O|O|O|O|X|O|X|O|X|X|O|O|O|O|X|O|O|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|X|\n", "|O|O|O|O|X|O|X|O|X|X|O|X|X|X|O|X|X|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|\n", "|O|O|O|O|O|O|X|X|O|O|O|X|X|O|X|O|O|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|X|O|X|\n", "|O|O|O|O|X|X|X|X|O|X|X|X|O|O|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|O|X|X|X|O|\n", "|O|O|O|O|O|X|X|O|X|X|X|X|X|X|X|X|O|X|X|O|O|O|O|O|O|O|O|O|O|O|X|O|O|X|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|X|X|X|O|X|X|O|X|X|X|X|O|X|X|O|O|X|O|O|O|O|O|O|O|O|O|O|X|O|X|O|O|O|O|\n", "|O|O|O|O|O|O|X|X|X|X|X|X|X|X|X|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|X|O|X|O|O|X|O|X|X|X|X|O|X|O|X|O|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|\n", "|O|O|O|X|O|O|O|O|X|O|X|X|O|X|X|X|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|\n", "|O|O|O|O|O|O|O|O|O|X|O|X|X|X|X|X|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|\n", "|X|O|O|O|O|O|O|O|O|O|X|O|O|X|X|X|X|O|O|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|X|X|O|X|O|X|O|O|X|X|X|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|X|O|X|O|O|O|O|O|O|O|O|O|X|O|X|X|X|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|\n", "|O|O|O|O|O|O|O|O|O|O|X|X|O|O|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|\n", "|O|X|O|X|O|O|O|O|O|O|O|O|O|X|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|X|\n", "|O|O|O|O|O|O|O|O|O|X|O|X|X|O|O|X|X|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|X|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|X|X|O|X|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|X|X|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|X|X|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|X|X|O|X|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|O|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|X|X|X|O|O|O|X|X|X|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|O|O|X|O|O|O|O|O|X|X|X|X|O|O|O|O|O|X|O|O|X|O|X|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|\n", "|X|O|O|O|O|O|O|O|O|X|O|O|O|O|O|O|O|O|O|X|O|O|X|O|X|O|X|O|O|O|O|O|O|O|O|O|O|O|O|O|" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "steps :: MonadDistribution m => P.Producer (Grid Bool) m ()\n", "steps = P.unfoldr\n", " (fmap (Right . (\\y -> (y,y))) . step)\n", " (mkGrid [(m,n) | m <- [0..10], n <- [0..20]])\n", " \n", "\n", "sampler $ P.runEffect (steps >-> P.drop 50 >-> P.mapM (liftIO . putStrLn . display) >-> P.take 2 >-> P.drain)" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/examples/Lenses.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "46d2f1ea-9998-43d3-9e85-6ea21ea32956", "metadata": {}, "source": [ "# Lenses and probability" ] }, { "cell_type": "code", "execution_count": 1, "id": "51f00092-2847-4e33-8976-733d2b6db324", "metadata": {}, "outputs": [], "source": [ ":e BlockArguments\n", ":e LambdaCase\n", ":e OverloadedStrings\n", "\n", "import Data.Aeson.Lens\n", "import Control.Lens hiding (nth, (#))\n", "import Data.Aeson\n", "import Data.Maybe (fromMaybe)\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad.Bayes.Weighted\n", "import Data.Monoid \n", "import qualified Data.ByteString.Lazy as B\n", "import Text.Pretty.Simple\n", "import qualified Data.Text as T\n", "import Control.Monad" ] }, { "cell_type": "markdown", "id": "c7eba945-6bda-41ff-9a77-7e9962f3b739", "metadata": {}, "source": [ "Lenses are *getters* and *setters*, i.e. ways of extracting a value from inside a data structure or updating that value.\n", "\n", "Because of their type in the Haskell `lens` library, namely `forall f. (a -> f b) -> (c -> f d)`, we can specialize `f` to `MonadDistribution m => m`, and so use any lens to **probabilistically update** a data structure's elements in complex ways.\n", "\n", "As an example of how easy and extensible this, we provide some examples using lenses to manipulate a JSON file. This relies on the `aeson` and `lens-aeson` packages." ] }, { "cell_type": "code", "execution_count": 2, "id": "834e9c3d-87b4-4ef5-ae61-0f411f5199f2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Object\n", " ( fromList\n", " [\n", " ( \"address\"\n", " , Object\n", " ( fromList\n", " [ ( \"id\", Number 5.4 ), ( \"streetAddress\", String \"21 2nd Street\" ) ]\n", " )\n", " )\n", " ,\n", " ( \"age\", Number 27.0 )\n", " ,\n", " ( \"height\", Number 1.5 )\n", " ,\n", " ( \"isAlive\", Bool True )\n", " ,\n", " ( \"name\", String \"John\" )\n", " ]\n", " )" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "json <- fromMaybe undefined . decode <$> B.readFile \"../file.json\" :: IO Value\n", "\n", "pPrintCustom = pPrintOpt CheckColorTty defaultOutputOptionsNoColor {outputOptionsCompact = True, outputOptionsIndentAmount = 2} \n", "pPrintCustom json" ] }, { "cell_type": "markdown", "id": "1f862322-d071-44d4-a874-0a7002550b56", "metadata": {}, "source": [ "For example, if I have a function that randomly flips a boolean or a function that puts noise into a string, like" ] }, { "cell_type": "code", "execution_count": 3, "id": "52b5b2e7-6faf-41a9-9e96-b57a6dc30a48", "metadata": {}, "outputs": [], "source": [ "randomlyFlip :: MonadDistribution m => Bool -> m Bool\n", "randomlyFlip True = bernoulli 0.01\n", "randomlyFlip False = bernoulli 0.9" ] }, { "cell_type": "code", "execution_count": 4, "id": "514ffd97-35fb-470d-8bf9-8aa38eec0119", "metadata": {}, "outputs": [], "source": [ "noisifyString :: MonadDistribution m => T.Text -> m T.Text\n", "noisifyString t = fmap T.pack $ forM (T.unpack t) $ \\letter -> do\n", " x <- bernoulli 0.2\n", " if x then uniformD \"abcdefghijklmnopqrstuvwxyz\" else return letter" ] }, { "cell_type": "markdown", "id": "60d0dabd-52b6-4919-8520-b982efe0a979", "metadata": {}, "source": [ "then I can use it to update the \"isAlive\" value as follows, using the lens `(_Object . traverse . _Bool)` which points to the json object's list of element's boolean element:" ] }, { "cell_type": "code", "execution_count": 5, "id": "0ce3c580-a097-47ff-8292-f8570ed6aebb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Object\n", " ( fromList\n", " [\n", " ( \"address\"\n", " , Object\n", " ( fromList\n", " [ ( \"id\", Number 5.4 ), ( \"streetAddress\", String \"21 2nd Street\" ) ]\n", " )\n", " )\n", " ,\n", " ( \"age\", Number 27.0 )\n", " ,\n", " ( \"height\", Number 1.5 )\n", " ,\n", " ( \"isAlive\", Bool False )\n", " ,\n", " ( \"name\", String \"John\" )\n", " ]\n", " )" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pPrintCustom =<< sampleIOfixed ((_Object . traverse . _Bool) randomlyFlip json)" ] }, { "cell_type": "markdown", "id": "5ffa03fb-8a44-40dd-a034-79c2bf3e3054", "metadata": {}, "source": [ "Using the `Plated` instance of json values, I can do more powerful operations, like updating all doubles, bools and strings at arbitrarily nested positions in the json" ] }, { "cell_type": "code", "execution_count": 6, "id": "ef68efea-f0c9-4161-9b1f-510678195c6e", "metadata": {}, "outputs": [], "source": [ "jsonDist :: Distribution Value\n", "jsonDist = \n", " ((transformM . _Double) (\\case x -> normal x 0.001) >=>\n", " (transformM . _Bool) randomlyFlip >=>\n", " (transformM . _String) noisifyString\n", " )\n", " json" ] }, { "cell_type": "code", "execution_count": 7, "id": "abd5a2af-f4a6-4cbf-b242-9d79bc18004e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Object\n", " ( fromList\n", " [\n", " ( \"address\"\n", " , Object\n", " ( fromList\n", " [\n", " ( \"id\", Number 5.398324403347256605911752558313310146331787109375 )\n", " ,\n", " ( \"streetAddress\", String \"21 fnd Stredt\" )\n", " ]\n", " )\n", " )\n", " ,\n", " ( \"age\", Number 27.001264842576262026341282762587070465087890625 )\n", " ,\n", " ( \"height\"\n", " , Number 1.5021398495523310412380624256911687552928924560546875\n", " )\n", " ,\n", " ( \"isAlive\", Bool False )\n", " ,\n", " ( \"name\", String \"lohn\" )\n", " ]\n", " )" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pPrintCustom =<< sampleIOfixed (unweighted jsonDist)" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/examples/Parsing.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "2a798ffe-30c0-43ec-9614-c84c2a433da5", "metadata": {}, "source": [ "# Probabilistic Parser Combinators" ] }, { "cell_type": "code", "execution_count": 1, "id": "b14be2d4-ec16-46d3-8cd9-810a25f70495", "metadata": {}, "outputs": [], "source": [ "import Text.Megaparsec\n", "import Text.Megaparsec.Char\n", "import Data.Char\n", "import qualified Data.Text as T\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Population\n", "import Control.Monad.Bayes.Enumerator\n", "import Control.Monad.Bayes.Inference.SMC\n", "import Control.Monad.Trans (lift)\n", "import Control.Monad (join, replicateM)\n", "import Data.Void\n", "import Control.Monad.Bayes.Enumerator\n", "import Text.Pretty.Simple\n", "\n", ":e OverloadedStrings\n", ":e FlexibleContexts\n", ":e GADTs\n", ":e LambdaCase" ] }, { "cell_type": "markdown", "id": "97f7ae67-5a7f-4650-8530-72385d84b019", "metadata": {}, "source": [ "Probability interfaces nicely with parser combinators from libraries like `megaparsec`. A parser in this setting is roughly a function `[Char] -> m (a, [Char])`, in other words a function which (monadically) strips off a prefix of the input string and returns a result. \n", "\n", "To make this probabilistic, we simply set `m` to a probability monad. The result of parsing is then a distribution over possible parses (and possible parse failures).\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "5aec194f-b147-456b-8be0-46c12f4ed495", "metadata": {}, "outputs": [], "source": [ "alphabet = map (: []) ['h', 'i', 'x']\n", "\n", "noise x = do\n", " perturb <- lift $ bernoulli 0.1\n", " if perturb then lift $ uniformD alphabet else return x\n", "\n", "letter = do\n", " true <- lift $ uniformD [\"h\", \"i\",\"x\"]\n", " predicted <- noise true\n", " observed <- lookAhead (foldr1 (<|>) [\"h\",\"i\", \"x\"])\n", " lift . condition $ predicted == observed\n", " string observed\n", " return $ head true \n", " \n", "word = (do \n", " wd <- some letter\n", " lift $ factor (if wd `elem` [\"hi\", \"goodbye\"] then 100 else 1)\n", " return wd\n", " ) <* eof\n", "\n", "errorBundlePretty' :: (TraversableStream s, VisualStream s) => ParseErrorBundle s Void -> String \n", "errorBundlePretty' = errorBundlePretty\n", "\n", "\n", "run parser input = either (T.pack . errorBundlePretty' ) (T.pack . show) <$> runParserT parser \"\" input" ] }, { "cell_type": "code", "execution_count": 3, "id": "e64782da-ddcc-41bd-aa29-b8f2dcd27184", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[\n", " ( \"\"hi\"\", 0.7563333333333333 )\n", ",\n", " ( \"\"hx\"\", 0.20799999999999993 )\n", ",\n", " ( \"\"xx\"\", 1.5000000000000038 e- 2 )\n", ",\n", " ( \"\"hh\"\", 1.06666666666667 e- 2 )\n", ",\n", " ( \"\"ix\"\", 1.0000000000000014 e- 2 )\n", "]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pPrintCustom = pPrintOpt CheckColorTty defaultOutputOptionsNoColor {outputOptionsCompact = True, outputOptionsIndentAmount = 2} \n", "\n", "runWordParser w = do\n", " x <- sampler \n", " . runPopulationT \n", " . smc SMCConfig {numSteps = 5, numParticles = 3000, resampler = resampleMultinomial} \n", " $ run word w\n", " pPrintCustom $ toEmpiricalWeighted x\n", " \n", "runWordParser \"hx\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "bff38aec-9249-4d0e-b173-20ccecda9ab3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[\n", " ( \"\"hi\"\", 0.7813333333333331 )\n", ",\n", " ( \"\"ii\"\", 0.2046666666666667 )\n", ",\n", " ( \"\"xi\"\", 6.66666666666668 e- 3 )\n", ",\n", " ( \"\"ix\"\", 4.333333333333346 e- 3 )\n", ",\n", " ( \"\"ih\"\", 1.6666666666666711 e- 3 )\n", ",\n", " ( \"\"xh\"\", 1.0000000000000028 e- 3 )\n", ",\n", " ( \"\"xx\"\", 3.333333333333342 e- 4 )\n", "]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "runWordParser \"ii\"" ] }, { "cell_type": "code", "execution_count": 5, "id": "7ad652ee-3e29-4cce-8d50-e1fd2e5c26ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[\n", " ( \"\"hii\"\", 0.8063333333333336 )\n", ",\n", " ( \"\"xii\"\", 3.9333333333333186 e- 2 )\n", ",\n", " ( \"\"hhi\"\", 3.533333333333321 e- 2 )\n", ",\n", " ( \"\"hix\"\", 2.966666666666659 e- 2 )\n", ",\n", " ( \"\"hih\"\", 2.8999999999999908 e- 2 )\n", ",\n", " ( \"\"hxi\"\", 2.633333333333325 e- 2 )\n", ",\n", " ( \"\"iii\"\", 1.666666666666663 e- 2 )\n", ",\n", " ( \"\"hxx\"\", 4.333333333333327 e- 3 )\n", ",\n", " ( \"\"xih\"\", 3.999999999999995 e- 3 )\n", ",\n", " ( \"\"ixi\"\", 3.6666666666666636 e- 3 )\n", ",\n", " ( \"\"hhx\"\", 2.666666666666665 e- 3 )\n", ",\n", " ( \"\"xxi\"\", 2.3333333333333314 e- 3 )\n", ",\n", " ( \"\"hhh\"\", 3.333333333333324 e- 4 )\n", "]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "runWordParser \"hii\"" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/examples/Streaming.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "8fbdbe43-ea41-4693-9e33-01be36977a43", "metadata": {}, "source": [ "# Probabilistic Streaming\n", "\n", "Streaming libraries in Haskell exist to represent streams of data where each incoming datapoint may involve a side-effect. \n", "\n", "In this notebook, we'll use the `pipes` library, which is not the fastest streaming library, but is simple and easy to use." ] }, { "cell_type": "code", "execution_count": 1, "id": "748a7e61-e214-4a92-9d84-0ab8fc62da22", "metadata": {}, "outputs": [], "source": [ ":e ImportQualifiedPost\n", ":e FlexibleContexts\n", ":e BlockArguments\n", ":e TupleSections\n", ":e FlexibleContexts\n", ":e OverloadedStrings\n", ":e LambdaCase\n", "\n", "\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Enumerator\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad.Bayes.Density.Free\n", "import Control.Monad.Bayes.Population\n", "import Control.Monad.Bayes.Sequential.Coroutine\n", "import Control.Monad.Bayes.Inference.SMC\n", "\n", "import qualified Graphics.Vega.VegaLite as VL\n", "import IHaskell.Display.Hvega (vlShow)\n", "import Control.Applicative\n", "import qualified Data.Text as T\n", "import Pipes (Producer, (>->))\n", "import qualified Pipes as P\n", "import Pipes.Prelude (unfoldr)\n", "import qualified Pipes.Prelude as P\n", "import Data.Ord\n", "import Data.List\n", "import Control.Monad\n", "import Control.Arrow (first)\n", "import Numeric.Log\n", "import Data.Vector qualified as V\n", "\n", ":e PackageImports\n", "import \"matrix\" Data.Matrix hiding ((!))\n", "import Data.Vector.Generic as VG (Vector, map, mapM, null, sum, (!))\n", "\n", ":l ../plotting.hs" ] }, { "cell_type": "markdown", "id": "feb44c2e-d5ff-46d3-b393-37fb3b316d12", "metadata": {}, "source": [ "A `Producer` represents a stream of data, and a `Pipe` maps one stream to another. Both are parametrized by a monad `m`, which in our case will be `MonadDistribution m => m`. \n", "\n", "For example, a random walk with a Gaussian `transition` kernel can be written as follows:" ] }, { "cell_type": "code", "execution_count": 2, "id": "44083303-1d6c-4ba8-9cec-40f5af9455a4", "metadata": {}, "outputs": [], "source": [ "-- how to move from one latent state to the next\n", "transition :: MonadDistribution m => V.Vector Double -> m (V.Vector Double)\n", "transition vector = mvNormal (fmap (+0.5) vector) (fromLists [[1,0], [0,1]])\n", " -- (normal (x+0.5) 1) (normal (y+0.5) 1)\n", "\n", "-- a Markovian random walk starting at (0,0), with latentTransition as the kernel\n", "-- a Producer is an infinite stream of values\n", "prior :: MonadDistribution m => Producer (V.Vector Double) m r\n", "prior = flip P.unfoldr (V.fromList [0,0]) $ \\s ->\n", " Right <$> do\n", " new <- transition s\n", " return (new, new)" ] }, { "cell_type": "markdown", "id": "e2138945-588b-4956-81a1-91a0072506f2", "metadata": {}, "source": [ "It's straightforward to take the first `n` steps of this infinite stream, and convert it into a distribution over finite list, from which we can then sample:" ] }, { "cell_type": "code", "execution_count": 3, "id": "22553f70-df63-4819-8836-7f420a4ca451", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"Latent\"", "X": -0.976725852798596, "Y": 1.6137193880196077 }, { "Outlier": "\"Latent\"", "X": 0.20408138974710444, "Y": 1.7919034094222825 }, { "Outlier": "\"Latent\"", "X": 0.4831200523092556, "Y": 1.907099116274424 }, { "Outlier": "\"Latent\"", "X": -0.09176882753590254, "Y": 1.8955141804642897 }, { "Outlier": "\"Latent\"", "X": -1.2218589977848304, "Y": 2.1822412112642686 }, { "Outlier": "\"Latent\"", "X": -0.7668458940561108, "Y": 2.212530442551486 }, { "Outlier": "\"Latent\"", "X": -0.6690157871149474, "Y": 3.6065228326525913 }, { "Outlier": "\"Latent\"", "X": 0.11244333089173442, "Y": 2.9159986959761524 }, { "Outlier": "\"Latent\"", "X": 1.2687408682854557, "Y": 3.1217227285265796 }, { "Outlier": "\"Latent\"", "X": 2.9374928178780935, "Y": 4.807186806397708 }, { "Outlier": "\"Latent\"", "X": 1.7506468484445976, "Y": 6.528258479316207 }, { "Outlier": "\"Latent\"", "X": 2.736589222593742, "Y": 6.949265904332787 }, { "Outlier": "\"Latent\"", "X": 3.641180710652565, "Y": 6.681148574533908 }, { "Outlier": "\"Latent\"", "X": 3.256520244565894, "Y": 7.874942228007868 }, { "Outlier": "\"Latent\"", "X": 3.532287597906861, "Y": 8.830585157823975 }, { "Outlier": "\"Latent\"", "X": 2.1806733831668645, "Y": 8.017622208601368 }, { "Outlier": "\"Latent\"", "X": 2.009761220323491, "Y": 7.84268801368802 }, { "Outlier": "\"Latent\"", "X": 2.141654233585815, "Y": 7.1758683184915455 }, { "Outlier": "\"Latent\"", "X": 2.109134026193262, "Y": 6.295985176823156 }, { "Outlier": "\"Latent\"", "X": 2.405745472992563, "Y": 6.200693454492153 }, { "Outlier": "\"Latent\"", "X": -0.01763593390225182, "Y": 6.26472827677569 }, { "Outlier": "\"Latent\"", "X": -0.7129309873545127, "Y": 6.073043706411563 }, { "Outlier": "\"Latent\"", "X": -1.3881353873773867, "Y": 5.511813869639958 }, { "Outlier": "\"Latent\"", "X": -2.0112168014112006, "Y": 4.863763667368105 }, { "Outlier": "\"Latent\"", "X": -2.6313526403453142, "Y": 6.526587675025231 }, { "Outlier": "\"Latent\"", "X": -3.647308978418825, "Y": 9.059358389741346 }, { "Outlier": "\"Latent\"", "X": -3.522248228389822, "Y": 10.81130328807151 }, { "Outlier": "\"Latent\"", "X": -3.1756973573574925, "Y": 11.004220161318386 }, { "Outlier": "\"Latent\"", "X": 0.11010814466808982, "Y": 10.321234798745154 }, { "Outlier": "\"Latent\"", "X": -0.39187980161433855, "Y": 12.364011379280585 }, { "Outlier": "\"Latent\"", "X": 0.03081207681047786, "Y": 12.291837576109366 }, { "Outlier": "\"Latent\"", "X": -0.14240533533610533, "Y": 14.912448411461696 }, { "Outlier": "\"Latent\"", "X": 0.7434588861709679, "Y": 15.226819972736129 }, { "Outlier": "\"Latent\"", "X": -0.08616212989934202, "Y": 16.246907651854926 }, { "Outlier": "\"Latent\"", "X": -1.2965615882985957, "Y": 16.938548777168933 }, { "Outlier": "\"Latent\"", "X": -1.0233599219588343, "Y": 18.172882376939775 }, { "Outlier": "\"Latent\"", "X": -1.593063313624189, "Y": 17.761804204307506 }, { "Outlier": "\"Latent\"", "X": -0.9071992020452398, "Y": 16.80832237549457 }, { "Outlier": "\"Latent\"", "X": 1.537278546495295, "Y": 18.239554653824978 }, { "Outlier": "\"Latent\"", "X": 1.293986040876521, "Y": 18.531479436575104 }, { "Outlier": "\"Latent\"", "X": 0.33734706739165166, "Y": 19.17952773388102 }, { "Outlier": "\"Latent\"", "X": 1.5526873356861643, "Y": 19.89809726940538 }, { "Outlier": "\"Latent\"", "X": 0.9653041361138308, "Y": 18.978715147896484 }, { "Outlier": "\"Latent\"", "X": 2.1754309252424315, "Y": 20.733465934111884 }, { "Outlier": "\"Latent\"", "X": 1.9042944402780204, "Y": 21.96238995944239 }, { "Outlier": "\"Latent\"", "X": 1.1670764368639701, "Y": 20.42119386858742 }, { "Outlier": "\"Latent\"", "X": 1.3725692433059047, "Y": 20.078416515233386 }, { "Outlier": "\"Latent\"", "X": 2.685773076792982, "Y": 19.780304063644362 }, { "Outlier": "\"Latent\"", "X": 2.304412655375019, "Y": 18.3267445924497 }, { "Outlier": "\"Latent\"", "X": 3.3005017878405236, "Y": 19.282099858122923 }, { "Outlier": "\"Latent\"", "X": 5.293682460243824, "Y": 18.09075163407331 }, { "Outlier": "\"Latent\"", "X": 5.48617010881213, "Y": 18.88912742921528 }, { "Outlier": "\"Latent\"", "X": 5.460178753240708, "Y": 20.05661588255413 }, { "Outlier": "\"Latent\"", "X": 6.694373042710004, "Y": 21.28417138017432 }, { "Outlier": "\"Latent\"", "X": 7.176262338621055, "Y": 23.610823147215058 }, { "Outlier": "\"Latent\"", "X": 9.263967259279326, "Y": 23.793471620769253 }, { "Outlier": "\"Latent\"", "X": 9.570548244239397, "Y": 22.80525709268169 }, { "Outlier": "\"Latent\"", "X": 12.224205146716235, "Y": 23.467270848547056 }, { "Outlier": "\"Latent\"", "X": 12.998028549367675, "Y": 24.976179113989346 }, { "Outlier": "\"Latent\"", "X": 13.469190861482701, "Y": 25.033371366639106 }, { "Outlier": "\"Latent\"", "X": 15.202761232337336, "Y": 23.705490410204654 }, { "Outlier": "\"Latent\"", "X": 15.45147544674246, "Y": 23.62245393809943 }, { "Outlier": "\"Latent\"", "X": 15.948845956272837, "Y": 23.316292902745392 }, { "Outlier": "\"Latent\"", "X": 17.16913170577638, "Y": 24.777353392198744 }, { "Outlier": "\"Latent\"", "X": 17.210423055711384, "Y": 26.565764557732845 }, { "Outlier": "\"Latent\"", "X": 17.498127213076074, "Y": 26.891514119480085 }, { "Outlier": "\"Latent\"", "X": 16.154587096331557, "Y": 26.96269886332814 }, { "Outlier": "\"Latent\"", "X": 17.365461967568603, "Y": 27.809691065821152 }, { "Outlier": "\"Latent\"", "X": 17.798709082450195, "Y": 26.878326604248347 }, { "Outlier": "\"Latent\"", "X": 18.711785330404158, "Y": 27.40681326568193 }, { "Outlier": "\"Latent\"", "X": 19.69126789860091, "Y": 29.24772788318292 }, { "Outlier": "\"Latent\"", "X": 18.611842559840028, "Y": 29.623895863932383 }, { "Outlier": "\"Latent\"", "X": 18.839415485737106, "Y": 30.176998016059404 }, { "Outlier": "\"Latent\"", "X": 17.872071800282136, "Y": 31.31449032366575 }, { "Outlier": "\"Latent\"", "X": 17.234433457755895, "Y": 31.92462627820767 }, { "Outlier": "\"Latent\"", "X": 18.316980583807805, "Y": 31.971459033847758 }, { "Outlier": "\"Latent\"", "X": 18.20614845743506, "Y": 33.38337742068083 }, { "Outlier": "\"Latent\"", "X": 19.469906680607902, "Y": 35.00768028137768 }, { "Outlier": "\"Latent\"", "X": 21.166533013229724, "Y": 36.2275881565787 }, { "Outlier": "\"Latent\"", "X": 20.934551476362326, "Y": 37.71788456932134 }, { "Outlier": "\"Latent\"", "X": 23.091390514827275, "Y": 37.35456321648551 }, { "Outlier": "\"Latent\"", "X": 23.202579247316468, "Y": 38.33182657807645 }, { "Outlier": "\"Latent\"", "X": 24.814982095085746, "Y": 39.896259204044675 }, { "Outlier": "\"Latent\"", "X": 26.862289553534435, "Y": 41.594015899000084 }, { "Outlier": "\"Latent\"", "X": 29.13108856747485, "Y": 43.24237558248829 }, { "Outlier": "\"Latent\"", "X": 29.96573917639689, "Y": 43.08564704913662 }, { "Outlier": "\"Latent\"", "X": 30.38129760060132, "Y": 42.60313904655006 }, { "Outlier": "\"Latent\"", "X": 30.127208744625026, "Y": 43.46925607531138 }, { "Outlier": "\"Latent\"", "X": 30.55395052376439, "Y": 44.113573468167885 }, { "Outlier": "\"Latent\"", "X": 31.939391547518845, "Y": 43.2108558967726 }, { "Outlier": "\"Latent\"", "X": 32.61564963693414, "Y": 44.322174851055706 }, { "Outlier": "\"Latent\"", "X": 33.968317987343596, "Y": 43.58458105865007 }, { "Outlier": "\"Latent\"", "X": 33.869346145845, "Y": 44.815191784044025 }, { "Outlier": "\"Latent\"", "X": 35.656369696990275, "Y": 45.33590548300918 }, { "Outlier": "\"Latent\"", "X": 36.36554153078791, "Y": 43.511280010066635 }, { "Outlier": "\"Latent\"", "X": 38.89653663422507, "Y": 42.455163627123405 }, { "Outlier": "\"Latent\"", "X": 40.62441756480252, "Y": 41.727621228442274 }, { "Outlier": "\"Latent\"", "X": 41.91827352178923, "Y": 41.3009998512681 }, { "Outlier": "\"Latent\"", "X": 41.57668178338979, "Y": 41.469615373408814 }, { "Outlier": "\"Latent\"", "X": 41.42459856678652, "Y": 42.382041619310584 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "-- convert the stream to a list, taking only the first 100 steps\n", "toList :: Monad m => P.Producer a m () -> m [a]\n", "toList prod = P.toListM (prod >-> P.take 100)\n", "\n", "trueVectors <- sampleIOfixed $ toList prior\n", "\n", "plot \n", " (zip \n", " (fmap (\\v -> (v V.! 0, v V.! 1)) $ trueVectors) \n", " (replicate 100 (T.pack \"Latent\")))" ] }, { "cell_type": "markdown", "id": "618f1d07-cd05-41da-929c-94499e1e856c", "metadata": {}, "source": [ "We can also produce a stream of observations from `prior`, each a noisy perturbation of the datapoint in question, by using a `Pipe`:" ] }, { "cell_type": "code", "execution_count": 4, "id": "8ff7849d-4acc-48be-83ab-a3ecd5392094", "metadata": {}, "outputs": [], "source": [ "observationModel = (`mvNormal` fromLists [[20,0],[0,20]])\n", "\n", "observations :: MonadDistribution m => P.Pipe (V.Vector Double) (V.Vector Double) m ()\n", "observations = P.mapM observationModel" ] }, { "cell_type": "code", "execution_count": 5, "id": "dad1d7a2-784f-481d-89a5-5ce14c9dcc9b", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"Latent\"", "X": -0.976725852798596, "Y": 1.6137193880196077 }, { "Outlier": "\"Latent\"", "X": 0.20408138974710444, "Y": 1.7919034094222825 }, { "Outlier": "\"Latent\"", "X": 0.4831200523092556, "Y": 1.907099116274424 }, { "Outlier": "\"Latent\"", "X": -0.09176882753590254, "Y": 1.8955141804642897 }, { "Outlier": "\"Latent\"", "X": -1.2218589977848304, "Y": 2.1822412112642686 }, { "Outlier": "\"Latent\"", "X": -0.7668458940561108, "Y": 2.212530442551486 }, { "Outlier": "\"Latent\"", "X": -0.6690157871149474, "Y": 3.6065228326525913 }, { "Outlier": "\"Latent\"", "X": 0.11244333089173442, "Y": 2.9159986959761524 }, { "Outlier": "\"Latent\"", "X": 1.2687408682854557, "Y": 3.1217227285265796 }, { "Outlier": "\"Latent\"", "X": 2.9374928178780935, "Y": 4.807186806397708 }, { "Outlier": "\"Latent\"", "X": 1.7506468484445976, "Y": 6.528258479316207 }, { "Outlier": "\"Latent\"", "X": 2.736589222593742, "Y": 6.949265904332787 }, { "Outlier": "\"Latent\"", "X": 3.641180710652565, "Y": 6.681148574533908 }, { "Outlier": "\"Latent\"", "X": 3.256520244565894, "Y": 7.874942228007868 }, { "Outlier": "\"Latent\"", "X": 3.532287597906861, "Y": 8.830585157823975 }, { "Outlier": "\"Latent\"", "X": 2.1806733831668645, "Y": 8.017622208601368 }, { "Outlier": "\"Latent\"", "X": 2.009761220323491, "Y": 7.84268801368802 }, { "Outlier": "\"Latent\"", "X": 2.141654233585815, "Y": 7.1758683184915455 }, { "Outlier": "\"Latent\"", "X": 2.109134026193262, "Y": 6.295985176823156 }, { "Outlier": "\"Latent\"", "X": 2.405745472992563, "Y": 6.200693454492153 }, { "Outlier": "\"Latent\"", "X": -0.01763593390225182, "Y": 6.26472827677569 }, { "Outlier": "\"Latent\"", "X": -0.7129309873545127, "Y": 6.073043706411563 }, { "Outlier": "\"Latent\"", "X": -1.3881353873773867, "Y": 5.511813869639958 }, { "Outlier": "\"Latent\"", "X": -2.0112168014112006, "Y": 4.863763667368105 }, { "Outlier": "\"Latent\"", "X": -2.6313526403453142, "Y": 6.526587675025231 }, { "Outlier": "\"Latent\"", "X": -3.647308978418825, "Y": 9.059358389741346 }, { "Outlier": "\"Latent\"", "X": -3.522248228389822, "Y": 10.81130328807151 }, { "Outlier": "\"Latent\"", "X": -3.1756973573574925, "Y": 11.004220161318386 }, { "Outlier": "\"Latent\"", "X": 0.11010814466808982, "Y": 10.321234798745154 }, { "Outlier": "\"Latent\"", "X": -0.39187980161433855, "Y": 12.364011379280585 }, { "Outlier": "\"Latent\"", "X": 0.03081207681047786, "Y": 12.291837576109366 }, { "Outlier": "\"Latent\"", "X": -0.14240533533610533, "Y": 14.912448411461696 }, { "Outlier": "\"Latent\"", "X": 0.7434588861709679, "Y": 15.226819972736129 }, { "Outlier": "\"Latent\"", "X": -0.08616212989934202, "Y": 16.246907651854926 }, { "Outlier": "\"Latent\"", "X": -1.2965615882985957, "Y": 16.938548777168933 }, { "Outlier": "\"Latent\"", "X": -1.0233599219588343, "Y": 18.172882376939775 }, { "Outlier": "\"Latent\"", "X": -1.593063313624189, "Y": 17.761804204307506 }, { "Outlier": "\"Latent\"", "X": -0.9071992020452398, "Y": 16.80832237549457 }, { "Outlier": "\"Latent\"", "X": 1.537278546495295, "Y": 18.239554653824978 }, { "Outlier": "\"Latent\"", "X": 1.293986040876521, "Y": 18.531479436575104 }, { "Outlier": "\"Latent\"", "X": 0.33734706739165166, "Y": 19.17952773388102 }, { "Outlier": "\"Latent\"", "X": 1.5526873356861643, "Y": 19.89809726940538 }, { "Outlier": "\"Latent\"", "X": 0.9653041361138308, "Y": 18.978715147896484 }, { "Outlier": "\"Latent\"", "X": 2.1754309252424315, "Y": 20.733465934111884 }, { "Outlier": "\"Latent\"", "X": 1.9042944402780204, "Y": 21.96238995944239 }, { "Outlier": "\"Latent\"", "X": 1.1670764368639701, "Y": 20.42119386858742 }, { "Outlier": "\"Latent\"", "X": 1.3725692433059047, "Y": 20.078416515233386 }, { "Outlier": "\"Latent\"", "X": 2.685773076792982, "Y": 19.780304063644362 }, { "Outlier": "\"Latent\"", "X": 2.304412655375019, "Y": 18.3267445924497 }, { "Outlier": "\"Latent\"", "X": 3.3005017878405236, "Y": 19.282099858122923 }, { "Outlier": "\"Latent\"", "X": 5.293682460243824, "Y": 18.09075163407331 }, { "Outlier": "\"Latent\"", "X": 5.48617010881213, "Y": 18.88912742921528 }, { "Outlier": "\"Latent\"", "X": 5.460178753240708, "Y": 20.05661588255413 }, { "Outlier": "\"Latent\"", "X": 6.694373042710004, "Y": 21.28417138017432 }, { "Outlier": "\"Latent\"", "X": 7.176262338621055, "Y": 23.610823147215058 }, { "Outlier": "\"Latent\"", "X": 9.263967259279326, "Y": 23.793471620769253 }, { "Outlier": "\"Latent\"", "X": 9.570548244239397, "Y": 22.80525709268169 }, { "Outlier": "\"Latent\"", "X": 12.224205146716235, "Y": 23.467270848547056 }, { "Outlier": "\"Latent\"", "X": 12.998028549367675, "Y": 24.976179113989346 }, { "Outlier": "\"Latent\"", "X": 13.469190861482701, "Y": 25.033371366639106 }, { "Outlier": "\"Latent\"", "X": 15.202761232337336, "Y": 23.705490410204654 }, { "Outlier": "\"Latent\"", "X": 15.45147544674246, "Y": 23.62245393809943 }, { "Outlier": "\"Latent\"", "X": 15.948845956272837, "Y": 23.316292902745392 }, { "Outlier": "\"Latent\"", "X": 17.16913170577638, "Y": 24.777353392198744 }, { "Outlier": "\"Latent\"", "X": 17.210423055711384, "Y": 26.565764557732845 }, { "Outlier": "\"Latent\"", "X": 17.498127213076074, "Y": 26.891514119480085 }, { "Outlier": "\"Latent\"", "X": 16.154587096331557, "Y": 26.96269886332814 }, { "Outlier": "\"Latent\"", "X": 17.365461967568603, "Y": 27.809691065821152 }, { "Outlier": "\"Latent\"", "X": 17.798709082450195, "Y": 26.878326604248347 }, { "Outlier": "\"Latent\"", "X": 18.711785330404158, "Y": 27.40681326568193 }, { "Outlier": "\"Latent\"", "X": 19.69126789860091, "Y": 29.24772788318292 }, { "Outlier": "\"Latent\"", "X": 18.611842559840028, "Y": 29.623895863932383 }, { "Outlier": "\"Latent\"", "X": 18.839415485737106, "Y": 30.176998016059404 }, { "Outlier": "\"Latent\"", "X": 17.872071800282136, "Y": 31.31449032366575 }, { "Outlier": "\"Latent\"", "X": 17.234433457755895, "Y": 31.92462627820767 }, { "Outlier": "\"Latent\"", "X": 18.316980583807805, "Y": 31.971459033847758 }, { "Outlier": "\"Latent\"", "X": 18.20614845743506, "Y": 33.38337742068083 }, { "Outlier": "\"Latent\"", "X": 19.469906680607902, "Y": 35.00768028137768 }, { "Outlier": "\"Latent\"", "X": 21.166533013229724, "Y": 36.2275881565787 }, { "Outlier": "\"Latent\"", "X": 20.934551476362326, "Y": 37.71788456932134 }, { "Outlier": "\"Latent\"", "X": 23.091390514827275, "Y": 37.35456321648551 }, { "Outlier": "\"Latent\"", "X": 23.202579247316468, "Y": 38.33182657807645 }, { "Outlier": "\"Latent\"", "X": 24.814982095085746, "Y": 39.896259204044675 }, { "Outlier": "\"Latent\"", "X": 26.862289553534435, "Y": 41.594015899000084 }, { "Outlier": "\"Latent\"", "X": 29.13108856747485, "Y": 43.24237558248829 }, { "Outlier": "\"Latent\"", "X": 29.96573917639689, "Y": 43.08564704913662 }, { "Outlier": "\"Latent\"", "X": 30.38129760060132, "Y": 42.60313904655006 }, { "Outlier": "\"Latent\"", "X": 30.127208744625026, "Y": 43.46925607531138 }, { "Outlier": "\"Latent\"", "X": 30.55395052376439, "Y": 44.113573468167885 }, { "Outlier": "\"Latent\"", "X": 31.939391547518845, "Y": 43.2108558967726 }, { "Outlier": "\"Latent\"", "X": 32.61564963693414, "Y": 44.322174851055706 }, { "Outlier": "\"Latent\"", "X": 33.968317987343596, "Y": 43.58458105865007 }, { "Outlier": "\"Latent\"", "X": 33.869346145845, "Y": 44.815191784044025 }, { "Outlier": "\"Latent\"", "X": 35.656369696990275, "Y": 45.33590548300918 }, { "Outlier": "\"Latent\"", "X": 36.36554153078791, "Y": 43.511280010066635 }, { "Outlier": "\"Latent\"", "X": 38.89653663422507, "Y": 42.455163627123405 }, { "Outlier": "\"Latent\"", "X": 40.62441756480252, "Y": 41.727621228442274 }, { "Outlier": "\"Latent\"", "X": 41.91827352178923, "Y": 41.3009998512681 }, { "Outlier": "\"Latent\"", "X": 41.57668178338979, "Y": 41.469615373408814 }, { "Outlier": "\"Latent\"", "X": 41.42459856678652, "Y": 42.382041619310584 }, { "Outlier": "\"Observed\"", "X": -7.580844634776614, "Y": 6.5944239069622235 }, { "Outlier": "\"Observed\"", "X": 3.248743937559851, "Y": 0.3526986006438093 }, { "Outlier": "\"Observed\"", "X": -0.5050490895113429, "Y": 0.18620200124968855 }, { "Outlier": "\"Observed\"", "X": -4.898818034720657, "Y": -0.3923632050083641 }, { "Outlier": "\"Observed\"", "X": -8.511843858046447, "Y": 1.2284554974753363 }, { "Outlier": "\"Observed\"", "X": -0.9680334103747406, "Y": 0.11192002534055767 }, { "Outlier": "\"Observed\"", "X": -2.467574225881706, "Y": 7.604578343919755 }, { "Outlier": "\"Observed\"", "X": 1.3711667723918857, "Y": -2.408187100949384 }, { "Outlier": "\"Observed\"", "X": 4.203792682441598, "Y": 1.8056805938030605 }, { "Outlier": "\"Observed\"", "X": 8.164310434127184, "Y": 10.108743332405604 }, { "Outlier": "\"Observed\"", "X": -5.79315766200506, "Y": 11.989057011406512 }, { "Outlier": "\"Observed\"", "X": 4.909789586083988, "Y": 6.596000369571432 }, { "Outlier": "\"Observed\"", "X": 5.450568851487211, "Y": 3.2460234462820723 }, { "Outlier": "\"Observed\"", "X": -0.6998016337869939, "Y": 10.977681771059281 }, { "Outlier": "\"Observed\"", "X": 2.5294887164982844, "Y": 10.868282286895937 }, { "Outlier": "\"Observed\"", "X": -6.099997121360188, "Y": 2.145873395800658 }, { "Outlier": "\"Observed\"", "X": -0.9906491857748931, "Y": 4.824290533357343 }, { "Outlier": "\"Observed\"", "X": 0.49542974290970143, "Y": 1.9576920066017385 }, { "Outlier": "\"Observed\"", "X": -0.2723687400508079, "Y": 0.12496016527017417 }, { "Outlier": "\"Observed\"", "X": 1.4961642113883717, "Y": 3.538467939342046 }, { "Outlier": "\"Observed\"", "X": -13.091395033853809, "Y": 4.31503273038212 }, { "Outlier": "\"Observed\"", "X": -6.058452972731513, "Y": 2.979736269767715 }, { "Outlier": "\"Observed\"", "X": -6.643809239193391, "Y": 0.7658497600953291 }, { "Outlier": "\"Observed\"", "X": -7.033789573503589, "Y": -0.27047292035638915 }, { "Outlier": "\"Observed\"", "X": -7.640752400126181, "Y": 11.726894729005371 }, { "Outlier": "\"Observed\"", "X": -10.42687182412687, "Y": 18.150185391293476 }, { "Outlier": "\"Observed\"", "X": -5.199027529125694, "Y": 16.410171081572134 }, { "Outlier": "\"Observed\"", "X": -3.8619427242771804, "Y": 9.630902668992045 }, { "Outlier": "\"Observed\"", "X": 12.56860909391235, "Y": 5.030763424543189 }, { "Outlier": "\"Observed\"", "X": -4.872906122660174, "Y": 19.263517995624394 }, { "Outlier": "\"Observed\"", "X": -0.3149203532993794, "Y": 9.732998538438505 }, { "Outlier": "\"Observed\"", "X": -3.1531251297286103, "Y": 24.396108374802544 }, { "Outlier": "\"Observed\"", "X": 2.469096144920672, "Y": 14.396664357641086 }, { "Outlier": "\"Observed\"", "X": -6.0324080823904485, "Y": 18.57281046139439 }, { "Outlier": "\"Observed\"", "X": -8.945700503617706, "Y": 17.795593944142286 }, { "Outlier": "\"Observed\"", "X": -2.037632904454779, "Y": 21.456922071439237 }, { "Outlier": "\"Observed\"", "X": -6.376922312675819, "Y": 13.687338750663415 }, { "Outlier": "\"Observed\"", "X": -0.07598962590896796, "Y": 10.308154028921695 }, { "Outlier": "\"Observed\"", "X": 10.233247399440053, "Y": 22.404152008202583 }, { "Outlier": "\"Observed\"", "X": -2.030119098582926, "Y": 17.60093877616759 }, { "Outlier": "\"Observed\"", "X": -6.176940459383712, "Y": 19.84161984733927 }, { "Outlier": "\"Observed\"", "X": 4.751786269585101, "Y": 20.875569947891428 }, { "Outlier": "\"Observed\"", "X": -3.897621367556085, "Y": 12.631045328412961 }, { "Outlier": "\"Observed\"", "X": 5.351214471512851, "Y": 26.344882039709773 }, { "Outlier": "\"Observed\"", "X": -1.5443327603433157, "Y": 25.222237301585974 }, { "Outlier": "\"Observed\"", "X": -4.365930680376797, "Y": 11.292687439470313 }, { "Outlier": "\"Observed\"", "X": 0.05549303398885996, "Y": 16.30940161123943 }, { "Outlier": "\"Observed\"", "X": 6.32253117927403, "Y": 16.21103667276023 }, { "Outlier": "\"Observed\"", "X": -1.6371509745618358, "Y": 9.59016104109009 }, { "Outlier": "\"Observed\"", "X": 5.5190798340240566, "Y": 21.318510514038525 }, { "Outlier": "\"Observed\"", "X": 11.971389432609072, "Y": 10.526812428876351 }, { "Outlier": "\"Observed\"", "X": 4.110933065367986, "Y": 20.22350455077127 }, { "Outlier": "\"Observed\"", "X": 3.1078738999707833, "Y": 23.041714994277868 }, { "Outlier": "\"Observed\"", "X": 9.977789722601011, "Y": 24.537898480339187 }, { "Outlier": "\"Observed\"", "X": 7.095268807694508, "Y": 31.77985819186145 }, { "Outlier": "\"Observed\"", "X": 16.364399520884938, "Y": 22.374232448976972 }, { "Outlier": "\"Observed\"", "X": 8.705552112698728, "Y": 16.149759392868564 }, { "Outlier": "\"Observed\"", "X": 21.85565161501593, "Y": 24.191818391357085 }, { "Outlier": "\"Observed\"", "X": 14.222604033685503, "Y": 29.48815404317007 }, { "Outlier": "\"Observed\"", "X": 13.340224800633258, "Y": 23.05307491856172 }, { "Outlier": "\"Observed\"", "X": 20.719455640858513, "Y": 15.53095826347512 }, { "Outlier": "\"Observed\"", "X": 14.327691250003301, "Y": 21.01503556812154 }, { "Outlier": "\"Observed\"", "X": 15.93708651740031, "Y": 19.711031151018922 }, { "Outlier": "\"Observed\"", "X": 20.390347504004996, "Y": 29.07534656201258 }, { "Outlier": "\"Observed\"", "X": 15.1590156088864, "Y": 32.32771445594081 }, { "Outlier": "\"Observed\"", "X": 16.54871134212977, "Y": 26.11224246939548 }, { "Outlier": "\"Observed\"", "X": 7.910025055754277, "Y": 25.044978738238672 }, { "Outlier": "\"Observed\"", "X": 20.544591038733497, "Y": 29.361487370694647 }, { "Outlier": "\"Observed\"", "X": 17.500181104812206, "Y": 20.477070130939993 }, { "Outlier": "\"Observed\"", "X": 20.559118471035397, "Y": 27.53420948851696 }, { "Outlier": "\"Observed\"", "X": 21.83557913162914, "Y": 35.244480356693614 }, { "Outlier": "\"Observed\"", "X": 11.548437714130095, "Y": 29.070102238261843 }, { "Outlier": "\"Observed\"", "X": 17.621084572526097, "Y": 30.41447805987451 }, { "Outlier": "\"Observed\"", "X": 11.309911346217373, "Y": 34.16544259354775 }, { "Outlier": "\"Observed\"", "X": 12.146760122358163, "Y": 32.41716924045278 }, { "Outlier": "\"Observed\"", "X": 20.922210531706227, "Y": 29.944833506717718 }, { "Outlier": "\"Observed\"", "X": 15.474424142614655, "Y": 37.46160042646224 }, { "Outlier": "\"Observed\"", "X": 22.88553729138576, "Y": 40.03571552900896 }, { "Outlier": "\"Observed\"", "X": 26.518008660047066, "Y": 39.44711404955252 }, { "Outlier": "\"Observed\"", "X": 17.661030526941786, "Y": 42.146624762854835 }, { "Outlier": "\"Observed\"", "X": 30.5009999503933, "Y": 33.49367275374949 }, { "Outlier": "\"Observed\"", "X": 21.463762398172424, "Y": 40.46621321745128 }, { "Outlier": "\"Observed\"", "X": 29.789798867038662, "Y": 44.656546622311794 }, { "Outlier": "\"Observed\"", "X": 33.782058871901825, "Y": 46.950546679851634 }, { "Outlier": "\"Observed\"", "X": 37.04139823488559, "Y": 48.37799621228787 }, { "Outlier": "\"Observed\"", "X": 31.46234219691965, "Y": 40.148667762460455 }, { "Outlier": "\"Observed\"", "X": 30.003663393389132, "Y": 38.20922968210791 }, { "Outlier": "\"Observed\"", "X": 26.754820858548936, "Y": 45.106581203372485 }, { "Outlier": "\"Observed\"", "X": 30.226329800254234, "Y": 44.75898046969328 }, { "Outlier": "\"Observed\"", "X": 35.89920418588279, "Y": 36.93771221102605 }, { "Outlier": "\"Observed\"", "X": 33.40389977596779, "Y": 47.05607632647793 }, { "Outlier": "\"Observed\"", "X": 37.781566774899915, "Y": 38.04989336194857 }, { "Outlier": "\"Observed\"", "X": 31.19066263744683, "Y": 48.08258227818665 }, { "Outlier": "\"Observed\"", "X": 41.4121139949983, "Y": 45.42853996091228 }, { "Outlier": "\"Observed\"", "X": 37.300986409487486, "Y": 33.11523885061237 }, { "Outlier": "\"Observed\"", "X": 47.97942286073449, "Y": 35.49599960079908 }, { "Outlier": "\"Observed\"", "X": 46.11566802289627, "Y": 36.23788473101395 }, { "Outlier": "\"Observed\"", "X": 45.468505290120085, "Y": 37.15702307373626 }, { "Outlier": "\"Observed\"", "X": 37.81296911066304, "Y": 39.98761893504553 }, { "Outlier": "\"Observed\"", "X": 38.50839376816329, "Y": 44.226467862393385 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "observedVectors <- sampleIOfixed $ toList $ P.each trueVectors >-> observations\n", "\n", "\n", "plot \n", " (zip \n", " (fmap (\\v -> (v V.! 0, v V.! 1)) $ trueVectors <> observedVectors) \n", " (replicate 100 (T.pack \"Latent\") <> replicate 100 (T.pack \"Observed\")))" ] }, { "cell_type": "markdown", "id": "e48ded8f-26c7-4b8c-a423-072381fbf741", "metadata": {}, "source": [ "## Conditioning\n", "\n", "But most interestingly, we can update our prior, based on a stream of incoming observations. `conditioning` below takes two `Producer`s, the stream corresponding to the prior (see `prior` above) and the stream corresponding to the observations (`P.each observedVectors`, where `P.each` lifts a list to a stream), and a likelihood, and returns a posterior, also expressed as a stream. " ] }, { "cell_type": "code", "execution_count": 6, "id": "29063963-1733-48f5-b71e-05dd727ee664", "metadata": {}, "outputs": [], "source": [ "-- take the original random walk as a prior and condition on the observations\n", "-- to obtain a posterior random walk\n", "conditioning :: MonadMeasure m => \n", " P.Producer (V.Vector Double) m () -> \n", " P.Producer (V.Vector Double) m () -> \n", " ((V.Vector Double, V.Vector Double) -> m ()) ->\n", " P.Producer (V.Vector Double) m ()\n", "conditioning prior observations observationModel = \n", " P.zip prior observations\n", " >-> P.chain observationModel\n", " >-> P.map fst \n", "\n", "\n", "posterior :: MonadMeasure m => m [V.Vector Double]\n", "posterior = toList $ conditioning \n", " prior\n", " (P.each observedVectors)\n", " (\\(v, v') -> do\n", " prediction <- observationModel v\n", " let (x, y, x', y') = (prediction V.! 0, prediction V.! 1, v' V.! 0, v' V.! 1)\n", " factor $ normalPdf x 2 x' * normalPdf y 2 y' )\n" ] }, { "cell_type": "markdown", "id": "53c196ce-a76b-486a-af09-07dad9100abe", "metadata": {}, "source": [ "We can then use an inference method of our liking:" ] }, { "cell_type": "code", "execution_count": 7, "id": "38c2337a-4c9f-4ef8-85b3-691fc691ff51", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "particles <- sampleIOfixed $ runPopulationT $ \n", " smc SMCConfig {\n", " numSteps = 100, \n", " numParticles = 1000, \n", " resampler = resampleMultinomial} \n", " posterior" ] }, { "cell_type": "code", "execution_count": 8, "id": "ddd48970-cf80-4945-9b13-ceb33b5ef362", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"True\"", "X": -0.976725852798596, "Y": 1.6137193880196077 }, { "Outlier": "\"True\"", "X": 0.20408138974710444, "Y": 1.7919034094222825 }, { "Outlier": "\"True\"", "X": 0.4831200523092556, "Y": 1.907099116274424 }, { "Outlier": "\"True\"", "X": -0.09176882753590254, "Y": 1.8955141804642897 }, { "Outlier": "\"True\"", "X": -1.2218589977848304, "Y": 2.1822412112642686 }, { "Outlier": "\"True\"", "X": -0.7668458940561108, "Y": 2.212530442551486 }, { "Outlier": "\"True\"", "X": -0.6690157871149474, "Y": 3.6065228326525913 }, { "Outlier": "\"True\"", "X": 0.11244333089173442, "Y": 2.9159986959761524 }, { "Outlier": "\"True\"", "X": 1.2687408682854557, "Y": 3.1217227285265796 }, { "Outlier": "\"True\"", "X": 2.9374928178780935, "Y": 4.807186806397708 }, { "Outlier": "\"True\"", "X": 1.7506468484445976, "Y": 6.528258479316207 }, { "Outlier": "\"True\"", "X": 2.736589222593742, "Y": 6.949265904332787 }, { "Outlier": "\"True\"", "X": 3.641180710652565, "Y": 6.681148574533908 }, { "Outlier": "\"True\"", "X": 3.256520244565894, "Y": 7.874942228007868 }, { "Outlier": "\"True\"", "X": 3.532287597906861, "Y": 8.830585157823975 }, { "Outlier": "\"True\"", "X": 2.1806733831668645, "Y": 8.017622208601368 }, { "Outlier": "\"True\"", "X": 2.009761220323491, "Y": 7.84268801368802 }, { "Outlier": "\"True\"", "X": 2.141654233585815, "Y": 7.1758683184915455 }, { "Outlier": "\"True\"", "X": 2.109134026193262, "Y": 6.295985176823156 }, { "Outlier": "\"True\"", "X": 2.405745472992563, "Y": 6.200693454492153 }, { "Outlier": "\"True\"", "X": -0.01763593390225182, "Y": 6.26472827677569 }, { "Outlier": "\"True\"", "X": -0.7129309873545127, "Y": 6.073043706411563 }, { "Outlier": "\"True\"", "X": -1.3881353873773867, "Y": 5.511813869639958 }, { "Outlier": "\"True\"", "X": -2.0112168014112006, "Y": 4.863763667368105 }, { "Outlier": "\"True\"", "X": -2.6313526403453142, "Y": 6.526587675025231 }, { "Outlier": "\"True\"", "X": -3.647308978418825, "Y": 9.059358389741346 }, { "Outlier": "\"True\"", "X": -3.522248228389822, "Y": 10.81130328807151 }, { "Outlier": "\"True\"", "X": -3.1756973573574925, "Y": 11.004220161318386 }, { "Outlier": "\"True\"", "X": 0.11010814466808982, "Y": 10.321234798745154 }, { "Outlier": "\"True\"", "X": -0.39187980161433855, "Y": 12.364011379280585 }, { "Outlier": "\"True\"", "X": 0.03081207681047786, "Y": 12.291837576109366 }, { "Outlier": "\"True\"", "X": -0.14240533533610533, "Y": 14.912448411461696 }, { "Outlier": "\"True\"", "X": 0.7434588861709679, "Y": 15.226819972736129 }, { "Outlier": "\"True\"", "X": -0.08616212989934202, "Y": 16.246907651854926 }, { "Outlier": "\"True\"", "X": -1.2965615882985957, "Y": 16.938548777168933 }, { "Outlier": "\"True\"", "X": -1.0233599219588343, "Y": 18.172882376939775 }, { "Outlier": "\"True\"", "X": -1.593063313624189, "Y": 17.761804204307506 }, { "Outlier": "\"True\"", "X": -0.9071992020452398, "Y": 16.80832237549457 }, { "Outlier": "\"True\"", "X": 1.537278546495295, "Y": 18.239554653824978 }, { "Outlier": "\"True\"", "X": 1.293986040876521, "Y": 18.531479436575104 }, { "Outlier": "\"True\"", "X": 0.33734706739165166, "Y": 19.17952773388102 }, { "Outlier": "\"True\"", "X": 1.5526873356861643, "Y": 19.89809726940538 }, { "Outlier": "\"True\"", "X": 0.9653041361138308, "Y": 18.978715147896484 }, { "Outlier": "\"True\"", "X": 2.1754309252424315, "Y": 20.733465934111884 }, { "Outlier": "\"True\"", "X": 1.9042944402780204, "Y": 21.96238995944239 }, { "Outlier": "\"True\"", "X": 1.1670764368639701, "Y": 20.42119386858742 }, { "Outlier": "\"True\"", "X": 1.3725692433059047, "Y": 20.078416515233386 }, { "Outlier": "\"True\"", "X": 2.685773076792982, "Y": 19.780304063644362 }, { "Outlier": "\"True\"", "X": 2.304412655375019, "Y": 18.3267445924497 }, { "Outlier": "\"True\"", "X": 3.3005017878405236, "Y": 19.282099858122923 }, { "Outlier": "\"True\"", "X": 5.293682460243824, "Y": 18.09075163407331 }, { "Outlier": "\"True\"", "X": 5.48617010881213, "Y": 18.88912742921528 }, { "Outlier": "\"True\"", "X": 5.460178753240708, "Y": 20.05661588255413 }, { "Outlier": "\"True\"", "X": 6.694373042710004, "Y": 21.28417138017432 }, { "Outlier": "\"True\"", "X": 7.176262338621055, "Y": 23.610823147215058 }, { "Outlier": "\"True\"", "X": 9.263967259279326, "Y": 23.793471620769253 }, { "Outlier": "\"True\"", "X": 9.570548244239397, "Y": 22.80525709268169 }, { "Outlier": "\"True\"", "X": 12.224205146716235, "Y": 23.467270848547056 }, { "Outlier": "\"True\"", "X": 12.998028549367675, "Y": 24.976179113989346 }, { "Outlier": "\"True\"", "X": 13.469190861482701, "Y": 25.033371366639106 }, { "Outlier": "\"True\"", "X": 15.202761232337336, "Y": 23.705490410204654 }, { "Outlier": "\"True\"", "X": 15.45147544674246, "Y": 23.62245393809943 }, { "Outlier": "\"True\"", "X": 15.948845956272837, "Y": 23.316292902745392 }, { "Outlier": "\"True\"", "X": 17.16913170577638, "Y": 24.777353392198744 }, { "Outlier": "\"True\"", "X": 17.210423055711384, "Y": 26.565764557732845 }, { "Outlier": "\"True\"", "X": 17.498127213076074, "Y": 26.891514119480085 }, { "Outlier": "\"True\"", "X": 16.154587096331557, "Y": 26.96269886332814 }, { "Outlier": "\"True\"", "X": 17.365461967568603, "Y": 27.809691065821152 }, { "Outlier": "\"True\"", "X": 17.798709082450195, "Y": 26.878326604248347 }, { "Outlier": "\"True\"", "X": 18.711785330404158, "Y": 27.40681326568193 }, { "Outlier": "\"True\"", "X": 19.69126789860091, "Y": 29.24772788318292 }, { "Outlier": "\"True\"", "X": 18.611842559840028, "Y": 29.623895863932383 }, { "Outlier": "\"True\"", "X": 18.839415485737106, "Y": 30.176998016059404 }, { "Outlier": "\"True\"", "X": 17.872071800282136, "Y": 31.31449032366575 }, { "Outlier": "\"True\"", "X": 17.234433457755895, "Y": 31.92462627820767 }, { "Outlier": "\"True\"", "X": 18.316980583807805, "Y": 31.971459033847758 }, { "Outlier": "\"True\"", "X": 18.20614845743506, "Y": 33.38337742068083 }, { "Outlier": "\"True\"", "X": 19.469906680607902, "Y": 35.00768028137768 }, { "Outlier": "\"True\"", "X": 21.166533013229724, "Y": 36.2275881565787 }, { "Outlier": "\"True\"", "X": 20.934551476362326, "Y": 37.71788456932134 }, { "Outlier": "\"True\"", "X": 23.091390514827275, "Y": 37.35456321648551 }, { "Outlier": "\"True\"", "X": 23.202579247316468, "Y": 38.33182657807645 }, { "Outlier": "\"True\"", "X": 24.814982095085746, "Y": 39.896259204044675 }, { "Outlier": "\"True\"", "X": 26.862289553534435, "Y": 41.594015899000084 }, { "Outlier": "\"True\"", "X": 29.13108856747485, "Y": 43.24237558248829 }, { "Outlier": "\"True\"", "X": 29.96573917639689, "Y": 43.08564704913662 }, { "Outlier": "\"True\"", "X": 30.38129760060132, "Y": 42.60313904655006 }, { "Outlier": "\"True\"", "X": 30.127208744625026, "Y": 43.46925607531138 }, { "Outlier": "\"True\"", "X": 30.55395052376439, "Y": 44.113573468167885 }, { "Outlier": "\"True\"", "X": 31.939391547518845, "Y": 43.2108558967726 }, { "Outlier": "\"True\"", "X": 32.61564963693414, "Y": 44.322174851055706 }, { "Outlier": "\"True\"", "X": 33.968317987343596, "Y": 43.58458105865007 }, { "Outlier": "\"True\"", "X": 33.869346145845, "Y": 44.815191784044025 }, { "Outlier": "\"True\"", "X": 35.656369696990275, "Y": 45.33590548300918 }, { "Outlier": "\"True\"", "X": 36.36554153078791, "Y": 43.511280010066635 }, { "Outlier": "\"True\"", "X": 38.89653663422507, "Y": 42.455163627123405 }, { "Outlier": "\"True\"", "X": 40.62441756480252, "Y": 41.727621228442274 }, { "Outlier": "\"True\"", "X": 41.91827352178923, "Y": 41.3009998512681 }, { "Outlier": "\"True\"", "X": 41.57668178338979, "Y": 41.469615373408814 }, { "Outlier": "\"True\"", "X": 41.42459856678652, "Y": 42.382041619310584 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": 0.25037506894952266, "Y": 18.291326001784068 }, { "Outlier": "\"Inferred\"", "X": 0.4904983920693601, "Y": 17.539516184689955 }, { "Outlier": "\"Inferred\"", "X": 2.718080349400857, "Y": 18.155677211910415 }, { "Outlier": "\"Inferred\"", "X": 3.3596451377280476, "Y": 18.220928966901553 }, { "Outlier": "\"Inferred\"", "X": 3.3573249852732205, "Y": 17.474933199455286 }, { "Outlier": "\"Inferred\"", "X": 4.407756003985588, "Y": 19.226227061730217 }, { "Outlier": "\"Inferred\"", "X": 4.025326796779369, "Y": 20.335740872647136 }, { "Outlier": "\"Inferred\"", "X": 4.136009675859851, "Y": 20.372922008281094 }, { "Outlier": "\"Inferred\"", "X": 5.113886810674396, "Y": 21.700707769054723 }, { "Outlier": "\"Inferred\"", "X": 6.363757913621467, "Y": 21.700884276542915 }, { "Outlier": "\"Inferred\"", "X": 8.380513986552057, "Y": 22.53312193721249 }, { "Outlier": "\"Inferred\"", "X": 9.507495481737594, "Y": 23.947165773969388 }, { "Outlier": "\"Inferred\"", "X": 10.705051024174601, "Y": 23.72928746015251 }, { "Outlier": "\"Inferred\"", "X": 11.765533446012132, "Y": 24.62943086593668 }, { "Outlier": "\"Inferred\"", "X": 10.817092153370208, "Y": 24.983992747919615 }, { "Outlier": "\"Inferred\"", "X": 12.839903740647154, "Y": 24.945670025253797 }, { "Outlier": "\"Inferred\"", "X": 12.855560416110526, "Y": 24.66026185817934 }, { "Outlier": "\"Inferred\"", "X": 14.891630492065383, "Y": 25.0091091682573 }, { "Outlier": "\"Inferred\"", "X": 15.110979638937462, "Y": 25.382025977889217 }, { "Outlier": "\"Inferred\"", "X": 15.175315840723284, "Y": 25.279189038539325 }, { "Outlier": "\"Inferred\"", "X": 14.852563449400975, "Y": 26.018139606634307 }, { "Outlier": "\"Inferred\"", "X": 15.917435832583863, "Y": 27.146710919741135 }, { "Outlier": "\"Inferred\"", "X": 14.7687201873816, "Y": 26.501965148446903 }, { "Outlier": "\"Inferred\"", "X": 14.335633120183564, "Y": 27.417137424621757 }, { "Outlier": "\"Inferred\"", "X": 15.89298811356995, "Y": 29.928059673411973 }, { "Outlier": "\"Inferred\"", "X": 14.932265711934539, "Y": 30.17466234904125 }, { "Outlier": "\"Inferred\"", "X": 12.594058543048224, "Y": 29.645958436715073 }, { "Outlier": "\"Inferred\"", "X": 14.420555656807428, "Y": 29.81173518026478 }, { "Outlier": "\"Inferred\"", "X": 15.421965217753142, "Y": 31.288824931313332 }, { "Outlier": "\"Inferred\"", "X": 16.719885419922207, "Y": 31.768289549055897 }, { "Outlier": "\"Inferred\"", "X": 16.977312283523595, "Y": 33.692246596923425 }, { "Outlier": "\"Inferred\"", "X": 17.24846897951622, "Y": 33.579691656810056 }, { "Outlier": "\"Inferred\"", "X": 17.55970934625396, "Y": 34.71815458258916 }, { "Outlier": "\"Inferred\"", "X": 18.857404869889844, "Y": 37.086689719432165 }, { "Outlier": "\"Inferred\"", "X": 20.167204798700876, "Y": 37.04991765209162 }, { "Outlier": "\"Inferred\"", "X": 20.45696123231277, "Y": 38.41671597334564 }, { "Outlier": "\"Inferred\"", "X": 21.33683630179494, "Y": 37.52199058052792 }, { "Outlier": "\"Inferred\"", "X": 24.379793053083965, "Y": 38.510208714498376 }, { "Outlier": "\"Inferred\"", "X": 25.9118914603695, "Y": 40.00458672337111 }, { "Outlier": "\"Inferred\"", "X": 26.472021004401107, "Y": 41.16434149906598 }, { "Outlier": "\"Inferred\"", "X": 28.00143245255418, "Y": 41.34628621369594 }, { "Outlier": "\"Inferred\"", "X": 28.623048075222574, "Y": 41.24911218583916 }, { "Outlier": "\"Inferred\"", "X": 29.093924172752153, "Y": 43.33013794916392 }, { "Outlier": "\"Inferred\"", "X": 29.465159428401815, "Y": 43.13527487211388 }, { "Outlier": "\"Inferred\"", "X": 30.847921308558476, "Y": 43.6008633497382 }, { "Outlier": "\"Inferred\"", "X": 32.57186656276913, "Y": 42.62515709471449 }, { "Outlier": "\"Inferred\"", "X": 34.482945010545, "Y": 42.518897466883935 }, { "Outlier": "\"Inferred\"", "X": 34.17363173007262, "Y": 41.861459922001316 }, { "Outlier": "\"Inferred\"", "X": 35.27052242714312, "Y": 42.76006934967258 }, { "Outlier": "\"Inferred\"", "X": 35.98082394808923, "Y": 42.90941628061991 }, { "Outlier": "\"Inferred\"", "X": 38.027476041690825, "Y": 43.141604485103805 }, { "Outlier": "\"Inferred\"", "X": 39.41165420651421, "Y": 41.57184901265951 }, { "Outlier": "\"Inferred\"", "X": 39.650445873702225, "Y": 41.23521114741483 }, { "Outlier": "\"Inferred\"", "X": 39.099143849909865, "Y": 41.108285841788685 }, { "Outlier": "\"Inferred\"", "X": 40.276439391113776, "Y": 42.94205135331792 }, { "Outlier": "\"Inferred\"", "X": 39.48522961995993, "Y": 42.081363043446665 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": -0.7061071858531587, "Y": 17.365038139967538 }, { "Outlier": "\"Inferred\"", "X": 1.0408194778375142, "Y": 16.453328413776028 }, { "Outlier": "\"Inferred\"", "X": 1.5405091660502246, "Y": 17.549414850528695 }, { "Outlier": "\"Inferred\"", "X": 3.9338660225548883, "Y": 18.810585449009046 }, { "Outlier": "\"Inferred\"", "X": 5.934341937148822, "Y": 19.291644715457366 }, { "Outlier": "\"Inferred\"", "X": 5.860698325850149, "Y": 18.481355238954116 }, { "Outlier": "\"Inferred\"", "X": 5.705451214097312, "Y": 19.045493557223 }, { "Outlier": "\"Inferred\"", "X": 6.967486968545026, "Y": 20.43685475506132 }, { "Outlier": "\"Inferred\"", "X": 7.850876988134879, "Y": 20.85970235656663 }, { "Outlier": "\"Inferred\"", "X": 8.392808772137586, "Y": 23.32121753848475 }, { "Outlier": "\"Inferred\"", "X": 8.372631457732883, "Y": 23.550505259601916 }, { "Outlier": "\"Inferred\"", "X": 9.871662866734457, "Y": 23.307490436256916 }, { "Outlier": "\"Inferred\"", "X": 10.314173898117193, "Y": 22.949374682117536 }, { "Outlier": "\"Inferred\"", "X": 10.32563322222216, "Y": 22.80572929728597 }, { "Outlier": "\"Inferred\"", "X": 11.975182162138363, "Y": 22.756945025950817 }, { "Outlier": "\"Inferred\"", "X": 13.534798358225013, "Y": 23.922399218838915 }, { "Outlier": "\"Inferred\"", "X": 13.939544608950607, "Y": 24.9043680997103 }, { "Outlier": "\"Inferred\"", "X": 14.852997836030292, "Y": 24.505991407121257 }, { "Outlier": "\"Inferred\"", "X": 14.722821358543685, "Y": 24.594075915773587 }, { "Outlier": "\"Inferred\"", "X": 14.103015251522454, "Y": 25.337305991383168 }, { "Outlier": "\"Inferred\"", "X": 12.613805810396606, "Y": 24.313422106066636 }, { "Outlier": "\"Inferred\"", "X": 13.080486829217792, "Y": 25.777670422612843 }, { "Outlier": "\"Inferred\"", "X": 11.503005763094693, "Y": 26.751491443530828 }, { "Outlier": "\"Inferred\"", "X": 12.268003621078462, "Y": 27.23493393620551 }, { "Outlier": "\"Inferred\"", "X": 13.107978224639371, "Y": 26.92306508382835 }, { "Outlier": "\"Inferred\"", "X": 13.28221444777585, "Y": 29.395602706911507 }, { "Outlier": "\"Inferred\"", "X": 14.801724593495212, "Y": 31.014010268316014 }, { "Outlier": "\"Inferred\"", "X": 14.27581330599868, "Y": 32.454234349787356 }, { "Outlier": "\"Inferred\"", "X": 14.029469444313813, "Y": 32.87117959294212 }, { "Outlier": "\"Inferred\"", "X": 15.19015709218286, "Y": 32.86952476370945 }, { "Outlier": "\"Inferred\"", "X": 15.773016432325253, "Y": 32.73681053907679 }, { "Outlier": "\"Inferred\"", "X": 17.51929695827333, "Y": 34.48234688075527 }, { "Outlier": "\"Inferred\"", "X": 18.517892728110063, "Y": 36.48035168551525 }, { "Outlier": "\"Inferred\"", "X": 19.204987885038665, "Y": 39.88678266817718 }, { "Outlier": "\"Inferred\"", "X": 21.442417253877316, "Y": 41.08072121114131 }, { "Outlier": "\"Inferred\"", "X": 23.00537003178552, "Y": 40.67876253519744 }, { "Outlier": "\"Inferred\"", "X": 24.77068463689155, "Y": 39.644754539327785 }, { "Outlier": "\"Inferred\"", "X": 26.841222405457636, "Y": 40.610556540600946 }, { "Outlier": "\"Inferred\"", "X": 27.866274921957416, "Y": 40.70386345163607 }, { "Outlier": "\"Inferred\"", "X": 28.440999181920027, "Y": 41.26333519355627 }, { "Outlier": "\"Inferred\"", "X": 29.415990440563707, "Y": 41.53555764534253 }, { "Outlier": "\"Inferred\"", "X": 29.311058090188595, "Y": 42.16967838673546 }, { "Outlier": "\"Inferred\"", "X": 30.282831663472386, "Y": 43.0424291894592 }, { "Outlier": "\"Inferred\"", "X": 30.293365662434958, "Y": 42.438015235063844 }, { "Outlier": "\"Inferred\"", "X": 31.068088386419674, "Y": 42.2623529357851 }, { "Outlier": "\"Inferred\"", "X": 30.43196031201329, "Y": 39.82537011905435 }, { "Outlier": "\"Inferred\"", "X": 32.64091621401201, "Y": 40.45746585540844 }, { "Outlier": "\"Inferred\"", "X": 34.41602785548827, "Y": 40.00534774705635 }, { "Outlier": "\"Inferred\"", "X": 35.08714623791322, "Y": 41.56836269107095 }, { "Outlier": "\"Inferred\"", "X": 35.32292828665068, "Y": 41.9525104837474 }, { "Outlier": "\"Inferred\"", "X": 35.32395767902641, "Y": 42.46841137513841 }, { "Outlier": "\"Inferred\"", "X": 36.34356272388683, "Y": 42.603824984142584 }, { "Outlier": "\"Inferred\"", "X": 37.2337481055561, "Y": 41.48813504823372 }, { "Outlier": "\"Inferred\"", "X": 36.767323082024035, "Y": 43.16868445004919 }, { "Outlier": "\"Inferred\"", "X": 37.33470777201462, "Y": 42.692084291783516 }, { "Outlier": "\"Inferred\"", "X": 38.36005025576521, "Y": 44.10347570499716 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": -0.7061071858531587, "Y": 17.365038139967538 }, { "Outlier": "\"Inferred\"", "X": 1.0408194778375142, "Y": 16.453328413776028 }, { "Outlier": "\"Inferred\"", "X": 1.5405091660502246, "Y": 17.549414850528695 }, { "Outlier": "\"Inferred\"", "X": 3.9338660225548883, "Y": 18.810585449009046 }, { "Outlier": "\"Inferred\"", "X": 5.934341937148822, "Y": 19.291644715457366 }, { "Outlier": "\"Inferred\"", "X": 5.860698325850149, "Y": 18.481355238954116 }, { "Outlier": "\"Inferred\"", "X": 5.705451214097312, "Y": 19.045493557223 }, { "Outlier": "\"Inferred\"", "X": 6.967486968545026, "Y": 20.43685475506132 }, { "Outlier": "\"Inferred\"", "X": 7.850876988134879, "Y": 20.85970235656663 }, { "Outlier": "\"Inferred\"", "X": 8.392808772137586, "Y": 23.32121753848475 }, { "Outlier": "\"Inferred\"", "X": 8.372631457732883, "Y": 23.550505259601916 }, { "Outlier": "\"Inferred\"", "X": 9.871662866734457, "Y": 23.307490436256916 }, { "Outlier": "\"Inferred\"", "X": 10.314173898117193, "Y": 22.949374682117536 }, { "Outlier": "\"Inferred\"", "X": 10.32563322222216, "Y": 22.80572929728597 }, { "Outlier": "\"Inferred\"", "X": 11.975182162138363, "Y": 22.756945025950817 }, { "Outlier": "\"Inferred\"", "X": 13.534798358225013, "Y": 23.922399218838915 }, { "Outlier": "\"Inferred\"", "X": 13.939544608950607, "Y": 24.9043680997103 }, { "Outlier": "\"Inferred\"", "X": 14.852997836030292, "Y": 24.505991407121257 }, { "Outlier": "\"Inferred\"", "X": 14.722821358543685, "Y": 24.594075915773587 }, { "Outlier": "\"Inferred\"", "X": 14.103015251522454, "Y": 25.337305991383168 }, { "Outlier": "\"Inferred\"", "X": 12.613805810396606, "Y": 24.313422106066636 }, { "Outlier": "\"Inferred\"", "X": 13.080486829217792, "Y": 25.777670422612843 }, { "Outlier": "\"Inferred\"", "X": 11.503005763094693, "Y": 26.751491443530828 }, { "Outlier": "\"Inferred\"", "X": 12.268003621078462, "Y": 27.23493393620551 }, { "Outlier": "\"Inferred\"", "X": 13.107978224639371, "Y": 26.92306508382835 }, { "Outlier": "\"Inferred\"", "X": 13.28221444777585, "Y": 29.395602706911507 }, { "Outlier": "\"Inferred\"", "X": 14.801724593495212, "Y": 31.014010268316014 }, { "Outlier": "\"Inferred\"", "X": 14.27581330599868, "Y": 32.454234349787356 }, { "Outlier": "\"Inferred\"", "X": 14.029469444313813, "Y": 32.87117959294212 }, { "Outlier": "\"Inferred\"", "X": 15.19015709218286, "Y": 32.86952476370945 }, { "Outlier": "\"Inferred\"", "X": 15.773016432325253, "Y": 32.73681053907679 }, { "Outlier": "\"Inferred\"", "X": 17.51929695827333, "Y": 34.48234688075527 }, { "Outlier": "\"Inferred\"", "X": 18.517892728110063, "Y": 36.48035168551525 }, { "Outlier": "\"Inferred\"", "X": 19.204987885038665, "Y": 39.88678266817718 }, { "Outlier": "\"Inferred\"", "X": 21.442417253877316, "Y": 41.08072121114131 }, { "Outlier": "\"Inferred\"", "X": 23.00537003178552, "Y": 40.67876253519744 }, { "Outlier": "\"Inferred\"", "X": 24.77068463689155, "Y": 39.644754539327785 }, { "Outlier": "\"Inferred\"", "X": 26.841222405457636, "Y": 40.610556540600946 }, { "Outlier": "\"Inferred\"", "X": 27.866274921957416, "Y": 40.70386345163607 }, { "Outlier": "\"Inferred\"", "X": 28.916950242086248, "Y": 41.28456229329697 }, { "Outlier": "\"Inferred\"", "X": 30.94026908912259, "Y": 40.70140875415903 }, { "Outlier": "\"Inferred\"", "X": 30.038866630751038, "Y": 39.818984693557866 }, { "Outlier": "\"Inferred\"", "X": 31.22226611843761, "Y": 40.73133733059518 }, { "Outlier": "\"Inferred\"", "X": 32.59389188003861, "Y": 39.65332936503432 }, { "Outlier": "\"Inferred\"", "X": 32.4663340160892, "Y": 40.09740529655399 }, { "Outlier": "\"Inferred\"", "X": 33.37503831069919, "Y": 41.42950937314977 }, { "Outlier": "\"Inferred\"", "X": 34.73118899157602, "Y": 42.78168258862689 }, { "Outlier": "\"Inferred\"", "X": 35.66436590227478, "Y": 42.83057238033694 }, { "Outlier": "\"Inferred\"", "X": 38.553890594846315, "Y": 43.39277641890018 }, { "Outlier": "\"Inferred\"", "X": 38.70345011625045, "Y": 44.54318027243104 }, { "Outlier": "\"Inferred\"", "X": 41.23517242356548, "Y": 44.7490902467863 }, { "Outlier": "\"Inferred\"", "X": 41.163655561388815, "Y": 43.21644303021634 }, { "Outlier": "\"Inferred\"", "X": 42.42113565444212, "Y": 43.02494482051242 }, { "Outlier": "\"Inferred\"", "X": 42.99773197244849, "Y": 42.20056737363797 }, { "Outlier": "\"Inferred\"", "X": 41.690277858644414, "Y": 41.44598697156346 }, { "Outlier": "\"Inferred\"", "X": 42.51786496796608, "Y": 41.74514370591297 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": -0.7061071858531587, "Y": 17.365038139967538 }, { "Outlier": "\"Inferred\"", "X": 1.0408194778375142, "Y": 16.453328413776028 }, { "Outlier": "\"Inferred\"", "X": 1.5405091660502246, "Y": 17.549414850528695 }, { "Outlier": "\"Inferred\"", "X": 3.9338660225548883, "Y": 18.810585449009046 }, { "Outlier": "\"Inferred\"", "X": 5.934341937148822, "Y": 19.291644715457366 }, { "Outlier": "\"Inferred\"", "X": 5.860698325850149, "Y": 18.481355238954116 }, { "Outlier": "\"Inferred\"", "X": 5.705451214097312, "Y": 19.045493557223 }, { "Outlier": "\"Inferred\"", "X": 6.967486968545026, "Y": 20.43685475506132 }, { "Outlier": "\"Inferred\"", "X": 7.850876988134879, "Y": 20.85970235656663 }, { "Outlier": "\"Inferred\"", "X": 8.392808772137586, "Y": 23.32121753848475 }, { "Outlier": "\"Inferred\"", "X": 8.372631457732883, "Y": 23.550505259601916 }, { "Outlier": "\"Inferred\"", "X": 9.871662866734457, "Y": 23.307490436256916 }, { "Outlier": "\"Inferred\"", "X": 10.314173898117193, "Y": 22.949374682117536 }, { "Outlier": "\"Inferred\"", "X": 10.32563322222216, "Y": 22.80572929728597 }, { "Outlier": "\"Inferred\"", "X": 11.975182162138363, "Y": 22.756945025950817 }, { "Outlier": "\"Inferred\"", "X": 13.534798358225013, "Y": 23.922399218838915 }, { "Outlier": "\"Inferred\"", "X": 13.939544608950607, "Y": 24.9043680997103 }, { "Outlier": "\"Inferred\"", "X": 14.852997836030292, "Y": 24.505991407121257 }, { "Outlier": "\"Inferred\"", "X": 14.722821358543685, "Y": 24.594075915773587 }, { "Outlier": "\"Inferred\"", "X": 14.103015251522454, "Y": 25.337305991383168 }, { "Outlier": "\"Inferred\"", "X": 12.613805810396606, "Y": 24.313422106066636 }, { "Outlier": "\"Inferred\"", "X": 13.080486829217792, "Y": 25.777670422612843 }, { "Outlier": "\"Inferred\"", "X": 11.503005763094693, "Y": 26.751491443530828 }, { "Outlier": "\"Inferred\"", "X": 12.268003621078462, "Y": 27.23493393620551 }, { "Outlier": "\"Inferred\"", "X": 13.107978224639371, "Y": 26.92306508382835 }, { "Outlier": "\"Inferred\"", "X": 13.28221444777585, "Y": 29.395602706911507 }, { "Outlier": "\"Inferred\"", "X": 14.801724593495212, "Y": 31.014010268316014 }, { "Outlier": "\"Inferred\"", "X": 14.27581330599868, "Y": 32.454234349787356 }, { "Outlier": "\"Inferred\"", "X": 14.029469444313813, "Y": 32.87117959294212 }, { "Outlier": "\"Inferred\"", "X": 15.19015709218286, "Y": 32.86952476370945 }, { "Outlier": "\"Inferred\"", "X": 15.773016432325253, "Y": 32.73681053907679 }, { "Outlier": "\"Inferred\"", "X": 17.51929695827333, "Y": 34.48234688075527 }, { "Outlier": "\"Inferred\"", "X": 20.037100194311396, "Y": 34.946070338043754 }, { "Outlier": "\"Inferred\"", "X": 21.818139030888776, "Y": 35.85262831787663 }, { "Outlier": "\"Inferred\"", "X": 21.732369533022062, "Y": 38.285815587214415 }, { "Outlier": "\"Inferred\"", "X": 22.57036154565432, "Y": 39.26475493081164 }, { "Outlier": "\"Inferred\"", "X": 22.65428937960439, "Y": 38.16420139841805 }, { "Outlier": "\"Inferred\"", "X": 24.780585362838643, "Y": 39.91054354562683 }, { "Outlier": "\"Inferred\"", "X": 25.38354438220824, "Y": 39.24486197722906 }, { "Outlier": "\"Inferred\"", "X": 27.164442953314957, "Y": 40.251417386922114 }, { "Outlier": "\"Inferred\"", "X": 28.28179394365139, "Y": 40.646152553616645 }, { "Outlier": "\"Inferred\"", "X": 28.30913626904519, "Y": 41.02315352290143 }, { "Outlier": "\"Inferred\"", "X": 29.883021734981654, "Y": 41.74567007600542 }, { "Outlier": "\"Inferred\"", "X": 31.777263046995003, "Y": 41.57505095385209 }, { "Outlier": "\"Inferred\"", "X": 32.06844181937742, "Y": 42.277030586291964 }, { "Outlier": "\"Inferred\"", "X": 34.001286580375854, "Y": 44.064459055471616 }, { "Outlier": "\"Inferred\"", "X": 35.46263684098056, "Y": 44.39313681002709 }, { "Outlier": "\"Inferred\"", "X": 35.91657596164125, "Y": 43.94958775308008 }, { "Outlier": "\"Inferred\"", "X": 36.54973787601623, "Y": 42.1580021668003 }, { "Outlier": "\"Inferred\"", "X": 37.3930776154532, "Y": 41.48314238427116 }, { "Outlier": "\"Inferred\"", "X": 38.38432519062528, "Y": 42.29679000879268 }, { "Outlier": "\"Inferred\"", "X": 38.91123441323717, "Y": 41.26209411757374 }, { "Outlier": "\"Inferred\"", "X": 40.26594826553113, "Y": 41.8782209368717 }, { "Outlier": "\"Inferred\"", "X": 39.4284901166666, "Y": 41.68026687276266 }, { "Outlier": "\"Inferred\"", "X": 40.12485821299953, "Y": 40.82370707767209 }, { "Outlier": "\"Inferred\"", "X": 40.09959115483099, "Y": 40.61801492575557 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": -0.7061071858531587, "Y": 17.365038139967538 }, { "Outlier": "\"Inferred\"", "X": 1.0408194778375142, "Y": 16.453328413776028 }, { "Outlier": "\"Inferred\"", "X": 1.5405091660502246, "Y": 17.549414850528695 }, { "Outlier": "\"Inferred\"", "X": 3.9338660225548883, "Y": 18.810585449009046 }, { "Outlier": "\"Inferred\"", "X": 5.934341937148822, "Y": 19.291644715457366 }, { "Outlier": "\"Inferred\"", "X": 5.860698325850149, "Y": 18.481355238954116 }, { "Outlier": "\"Inferred\"", "X": 5.705451214097312, "Y": 19.045493557223 }, { "Outlier": "\"Inferred\"", "X": 6.967486968545026, "Y": 20.43685475506132 }, { "Outlier": "\"Inferred\"", "X": 7.850876988134879, "Y": 20.85970235656663 }, { "Outlier": "\"Inferred\"", "X": 8.392808772137586, "Y": 23.32121753848475 }, { "Outlier": "\"Inferred\"", "X": 8.372631457732883, "Y": 23.550505259601916 }, { "Outlier": "\"Inferred\"", "X": 9.871662866734457, "Y": 23.307490436256916 }, { "Outlier": "\"Inferred\"", "X": 10.314173898117193, "Y": 22.949374682117536 }, { "Outlier": "\"Inferred\"", "X": 10.32563322222216, "Y": 22.80572929728597 }, { "Outlier": "\"Inferred\"", "X": 11.975182162138363, "Y": 22.756945025950817 }, { "Outlier": "\"Inferred\"", "X": 13.534798358225013, "Y": 23.922399218838915 }, { "Outlier": "\"Inferred\"", "X": 13.939544608950607, "Y": 24.9043680997103 }, { "Outlier": "\"Inferred\"", "X": 14.852997836030292, "Y": 24.505991407121257 }, { "Outlier": "\"Inferred\"", "X": 14.722821358543685, "Y": 24.594075915773587 }, { "Outlier": "\"Inferred\"", "X": 14.103015251522454, "Y": 25.337305991383168 }, { "Outlier": "\"Inferred\"", "X": 12.613805810396606, "Y": 24.313422106066636 }, { "Outlier": "\"Inferred\"", "X": 13.080486829217792, "Y": 25.777670422612843 }, { "Outlier": "\"Inferred\"", "X": 11.503005763094693, "Y": 26.751491443530828 }, { "Outlier": "\"Inferred\"", "X": 12.268003621078462, "Y": 27.23493393620551 }, { "Outlier": "\"Inferred\"", "X": 13.107978224639371, "Y": 26.92306508382835 }, { "Outlier": "\"Inferred\"", "X": 13.28221444777585, "Y": 29.395602706911507 }, { "Outlier": "\"Inferred\"", "X": 14.801724593495212, "Y": 31.014010268316014 }, { "Outlier": "\"Inferred\"", "X": 14.27581330599868, "Y": 32.454234349787356 }, { "Outlier": "\"Inferred\"", "X": 14.029469444313813, "Y": 32.87117959294212 }, { "Outlier": "\"Inferred\"", "X": 15.19015709218286, "Y": 32.86952476370945 }, { "Outlier": "\"Inferred\"", "X": 15.773016432325253, "Y": 32.73681053907679 }, { "Outlier": "\"Inferred\"", "X": 17.51929695827333, "Y": 34.48234688075527 }, { "Outlier": "\"Inferred\"", "X": 20.037100194311396, "Y": 34.946070338043754 }, { "Outlier": "\"Inferred\"", "X": 21.818139030888776, "Y": 35.85262831787663 }, { "Outlier": "\"Inferred\"", "X": 21.732369533022062, "Y": 38.285815587214415 }, { "Outlier": "\"Inferred\"", "X": 22.57036154565432, "Y": 39.26475493081164 }, { "Outlier": "\"Inferred\"", "X": 22.65428937960439, "Y": 38.16420139841805 }, { "Outlier": "\"Inferred\"", "X": 24.780585362838643, "Y": 39.91054354562683 }, { "Outlier": "\"Inferred\"", "X": 25.38354438220824, "Y": 39.24486197722906 }, { "Outlier": "\"Inferred\"", "X": 27.164442953314957, "Y": 40.251417386922114 }, { "Outlier": "\"Inferred\"", "X": 28.28179394365139, "Y": 40.646152553616645 }, { "Outlier": "\"Inferred\"", "X": 28.30913626904519, "Y": 41.02315352290143 }, { "Outlier": "\"Inferred\"", "X": 29.883021734981654, "Y": 41.74567007600542 }, { "Outlier": "\"Inferred\"", "X": 31.777263046995003, "Y": 41.57505095385209 }, { "Outlier": "\"Inferred\"", "X": 32.06844181937742, "Y": 42.277030586291964 }, { "Outlier": "\"Inferred\"", "X": 34.001286580375854, "Y": 44.064459055471616 }, { "Outlier": "\"Inferred\"", "X": 35.46263684098056, "Y": 44.39313681002709 }, { "Outlier": "\"Inferred\"", "X": 35.91657596164125, "Y": 43.94958775308008 }, { "Outlier": "\"Inferred\"", "X": 36.54973787601623, "Y": 42.1580021668003 }, { "Outlier": "\"Inferred\"", "X": 37.3930776154532, "Y": 41.48314238427116 }, { "Outlier": "\"Inferred\"", "X": 38.38432519062528, "Y": 42.29679000879268 }, { "Outlier": "\"Inferred\"", "X": 38.91123441323717, "Y": 41.26209411757374 }, { "Outlier": "\"Inferred\"", "X": 40.166021193732696, "Y": 40.759603208035806 }, { "Outlier": "\"Inferred\"", "X": 40.50605107857945, "Y": 41.098319086440625 }, { "Outlier": "\"Inferred\"", "X": 41.47828444799129, "Y": 42.79761600201785 }, { "Outlier": "\"Inferred\"", "X": 41.18248674870844, "Y": 42.65921546057904 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": -0.7061071858531587, "Y": 17.365038139967538 }, { "Outlier": "\"Inferred\"", "X": 1.0408194778375142, "Y": 16.453328413776028 }, { "Outlier": "\"Inferred\"", "X": 1.5405091660502246, "Y": 17.549414850528695 }, { "Outlier": "\"Inferred\"", "X": 3.9338660225548883, "Y": 18.810585449009046 }, { "Outlier": "\"Inferred\"", "X": 5.934341937148822, "Y": 19.291644715457366 }, { "Outlier": "\"Inferred\"", "X": 5.860698325850149, "Y": 18.481355238954116 }, { "Outlier": "\"Inferred\"", "X": 5.705451214097312, "Y": 19.045493557223 }, { "Outlier": "\"Inferred\"", "X": 6.967486968545026, "Y": 20.43685475506132 }, { "Outlier": "\"Inferred\"", "X": 7.850876988134879, "Y": 20.85970235656663 }, { "Outlier": "\"Inferred\"", "X": 8.392808772137586, "Y": 23.32121753848475 }, { "Outlier": "\"Inferred\"", "X": 8.372631457732883, "Y": 23.550505259601916 }, { "Outlier": "\"Inferred\"", "X": 9.871662866734457, "Y": 23.307490436256916 }, { "Outlier": "\"Inferred\"", "X": 10.314173898117193, "Y": 22.949374682117536 }, { "Outlier": "\"Inferred\"", "X": 10.32563322222216, "Y": 22.80572929728597 }, { "Outlier": "\"Inferred\"", "X": 11.975182162138363, "Y": 22.756945025950817 }, { "Outlier": "\"Inferred\"", "X": 13.534798358225013, "Y": 23.922399218838915 }, { "Outlier": "\"Inferred\"", "X": 13.939544608950607, "Y": 24.9043680997103 }, { "Outlier": "\"Inferred\"", "X": 14.852997836030292, "Y": 24.505991407121257 }, { "Outlier": "\"Inferred\"", "X": 14.722821358543685, "Y": 24.594075915773587 }, { "Outlier": "\"Inferred\"", "X": 14.103015251522454, "Y": 25.337305991383168 }, { "Outlier": "\"Inferred\"", "X": 12.613805810396606, "Y": 24.313422106066636 }, { "Outlier": "\"Inferred\"", "X": 13.080486829217792, "Y": 25.777670422612843 }, { "Outlier": "\"Inferred\"", "X": 11.503005763094693, "Y": 26.751491443530828 }, { "Outlier": "\"Inferred\"", "X": 12.268003621078462, "Y": 27.23493393620551 }, { "Outlier": "\"Inferred\"", "X": 13.107978224639371, "Y": 26.92306508382835 }, { "Outlier": "\"Inferred\"", "X": 13.28221444777585, "Y": 29.395602706911507 }, { "Outlier": "\"Inferred\"", "X": 14.801724593495212, "Y": 31.014010268316014 }, { "Outlier": "\"Inferred\"", "X": 14.27581330599868, "Y": 32.454234349787356 }, { "Outlier": "\"Inferred\"", "X": 14.029469444313813, "Y": 32.87117959294212 }, { "Outlier": "\"Inferred\"", "X": 15.19015709218286, "Y": 32.86952476370945 }, { "Outlier": "\"Inferred\"", "X": 15.773016432325253, "Y": 32.73681053907679 }, { "Outlier": "\"Inferred\"", "X": 17.51929695827333, "Y": 34.48234688075527 }, { "Outlier": "\"Inferred\"", "X": 20.037100194311396, "Y": 34.946070338043754 }, { "Outlier": "\"Inferred\"", "X": 21.818139030888776, "Y": 35.85262831787663 }, { "Outlier": "\"Inferred\"", "X": 21.732369533022062, "Y": 38.285815587214415 }, { "Outlier": "\"Inferred\"", "X": 22.57036154565432, "Y": 39.26475493081164 }, { "Outlier": "\"Inferred\"", "X": 22.65428937960439, "Y": 38.16420139841805 }, { "Outlier": "\"Inferred\"", "X": 24.780585362838643, "Y": 39.91054354562683 }, { "Outlier": "\"Inferred\"", "X": 25.38354438220824, "Y": 39.24486197722906 }, { "Outlier": "\"Inferred\"", "X": 27.164442953314957, "Y": 40.251417386922114 }, { "Outlier": "\"Inferred\"", "X": 28.28179394365139, "Y": 40.646152553616645 }, { "Outlier": "\"Inferred\"", "X": 28.30913626904519, "Y": 41.02315352290143 }, { "Outlier": "\"Inferred\"", "X": 29.883021734981654, "Y": 41.74567007600542 }, { "Outlier": "\"Inferred\"", "X": 31.777263046995003, "Y": 41.57505095385209 }, { "Outlier": "\"Inferred\"", "X": 32.06844181937742, "Y": 42.277030586291964 }, { "Outlier": "\"Inferred\"", "X": 34.001286580375854, "Y": 44.064459055471616 }, { "Outlier": "\"Inferred\"", "X": 35.46263684098056, "Y": 44.39313681002709 }, { "Outlier": "\"Inferred\"", "X": 35.91657596164125, "Y": 43.94958775308008 }, { "Outlier": "\"Inferred\"", "X": 36.54973787601623, "Y": 42.1580021668003 }, { "Outlier": "\"Inferred\"", "X": 37.3930776154532, "Y": 41.48314238427116 }, { "Outlier": "\"Inferred\"", "X": 38.38432519062528, "Y": 42.29679000879268 }, { "Outlier": "\"Inferred\"", "X": 38.91123441323717, "Y": 41.26209411757374 }, { "Outlier": "\"Inferred\"", "X": 40.166021193732696, "Y": 40.759603208035806 }, { "Outlier": "\"Inferred\"", "X": 40.50605107857945, "Y": 41.098319086440625 }, { "Outlier": "\"Inferred\"", "X": 41.47828444799129, "Y": 42.79761600201785 }, { "Outlier": "\"Inferred\"", "X": 41.953536690486416, "Y": 44.50060661126623 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": -0.7061071858531587, "Y": 17.365038139967538 }, { "Outlier": "\"Inferred\"", "X": 1.0408194778375142, "Y": 16.453328413776028 }, { "Outlier": "\"Inferred\"", "X": 1.5405091660502246, "Y": 17.549414850528695 }, { "Outlier": "\"Inferred\"", "X": 3.9338660225548883, "Y": 18.810585449009046 }, { "Outlier": "\"Inferred\"", "X": 5.934341937148822, "Y": 19.291644715457366 }, { "Outlier": "\"Inferred\"", "X": 5.860698325850149, "Y": 18.481355238954116 }, { "Outlier": "\"Inferred\"", "X": 5.705451214097312, "Y": 19.045493557223 }, { "Outlier": "\"Inferred\"", "X": 6.967486968545026, "Y": 20.43685475506132 }, { "Outlier": "\"Inferred\"", "X": 7.850876988134879, "Y": 20.85970235656663 }, { "Outlier": "\"Inferred\"", "X": 8.392808772137586, "Y": 23.32121753848475 }, { "Outlier": "\"Inferred\"", "X": 8.372631457732883, "Y": 23.550505259601916 }, { "Outlier": "\"Inferred\"", "X": 9.871662866734457, "Y": 23.307490436256916 }, { "Outlier": "\"Inferred\"", "X": 10.314173898117193, "Y": 22.949374682117536 }, { "Outlier": "\"Inferred\"", "X": 10.32563322222216, "Y": 22.80572929728597 }, { "Outlier": "\"Inferred\"", "X": 11.975182162138363, "Y": 22.756945025950817 }, { "Outlier": "\"Inferred\"", "X": 13.534798358225013, "Y": 23.922399218838915 }, { "Outlier": "\"Inferred\"", "X": 13.939544608950607, "Y": 24.9043680997103 }, { "Outlier": "\"Inferred\"", "X": 14.852997836030292, "Y": 24.505991407121257 }, { "Outlier": "\"Inferred\"", "X": 14.722821358543685, "Y": 24.594075915773587 }, { "Outlier": "\"Inferred\"", "X": 14.103015251522454, "Y": 25.337305991383168 }, { "Outlier": "\"Inferred\"", "X": 12.613805810396606, "Y": 24.313422106066636 }, { "Outlier": "\"Inferred\"", "X": 13.080486829217792, "Y": 25.777670422612843 }, { "Outlier": "\"Inferred\"", "X": 11.503005763094693, "Y": 26.751491443530828 }, { "Outlier": "\"Inferred\"", "X": 12.268003621078462, "Y": 27.23493393620551 }, { "Outlier": "\"Inferred\"", "X": 13.107978224639371, "Y": 26.92306508382835 }, { "Outlier": "\"Inferred\"", "X": 13.28221444777585, "Y": 29.395602706911507 }, { "Outlier": "\"Inferred\"", "X": 14.801724593495212, "Y": 31.014010268316014 }, { "Outlier": "\"Inferred\"", "X": 14.27581330599868, "Y": 32.454234349787356 }, { "Outlier": "\"Inferred\"", "X": 14.029469444313813, "Y": 32.87117959294212 }, { "Outlier": "\"Inferred\"", "X": 15.19015709218286, "Y": 32.86952476370945 }, { "Outlier": "\"Inferred\"", "X": 15.773016432325253, "Y": 32.73681053907679 }, { "Outlier": "\"Inferred\"", "X": 17.51929695827333, "Y": 34.48234688075527 }, { "Outlier": "\"Inferred\"", "X": 20.037100194311396, "Y": 34.946070338043754 }, { "Outlier": "\"Inferred\"", "X": 21.818139030888776, "Y": 35.85262831787663 }, { "Outlier": "\"Inferred\"", "X": 21.732369533022062, "Y": 38.285815587214415 }, { "Outlier": "\"Inferred\"", "X": 22.57036154565432, "Y": 39.26475493081164 }, { "Outlier": "\"Inferred\"", "X": 22.65428937960439, "Y": 38.16420139841805 }, { "Outlier": "\"Inferred\"", "X": 24.780585362838643, "Y": 39.91054354562683 }, { "Outlier": "\"Inferred\"", "X": 25.38354438220824, "Y": 39.24486197722906 }, { "Outlier": "\"Inferred\"", "X": 27.164442953314957, "Y": 40.251417386922114 }, { "Outlier": "\"Inferred\"", "X": 28.28179394365139, "Y": 40.646152553616645 }, { "Outlier": "\"Inferred\"", "X": 28.30913626904519, "Y": 41.02315352290143 }, { "Outlier": "\"Inferred\"", "X": 29.883021734981654, "Y": 41.74567007600542 }, { "Outlier": "\"Inferred\"", "X": 31.777263046995003, "Y": 41.57505095385209 }, { "Outlier": "\"Inferred\"", "X": 32.06844181937742, "Y": 42.277030586291964 }, { "Outlier": "\"Inferred\"", "X": 34.001286580375854, "Y": 44.064459055471616 }, { "Outlier": "\"Inferred\"", "X": 35.46263684098056, "Y": 44.39313681002709 }, { "Outlier": "\"Inferred\"", "X": 35.91657596164125, "Y": 43.94958775308008 }, { "Outlier": "\"Inferred\"", "X": 36.54973787601623, "Y": 42.1580021668003 }, { "Outlier": "\"Inferred\"", "X": 37.3930776154532, "Y": 41.48314238427116 }, { "Outlier": "\"Inferred\"", "X": 38.38432519062528, "Y": 42.29679000879268 }, { "Outlier": "\"Inferred\"", "X": 38.91123441323717, "Y": 41.26209411757374 }, { "Outlier": "\"Inferred\"", "X": 39.52098004401935, "Y": 40.99541891009436 }, { "Outlier": "\"Inferred\"", "X": 40.31056007737459, "Y": 41.773538622098734 }, { "Outlier": "\"Inferred\"", "X": 40.49512318630572, "Y": 41.23779202277633 }, { "Outlier": "\"Inferred\"", "X": 41.97362812691924, "Y": 40.64697990298612 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": -0.7061071858531587, "Y": 17.365038139967538 }, { "Outlier": "\"Inferred\"", "X": 1.0408194778375142, "Y": 16.453328413776028 }, { "Outlier": "\"Inferred\"", "X": 1.5405091660502246, "Y": 17.549414850528695 }, { "Outlier": "\"Inferred\"", "X": 3.9338660225548883, "Y": 18.810585449009046 }, { "Outlier": "\"Inferred\"", "X": 5.934341937148822, "Y": 19.291644715457366 }, { "Outlier": "\"Inferred\"", "X": 5.860698325850149, "Y": 18.481355238954116 }, { "Outlier": "\"Inferred\"", "X": 5.705451214097312, "Y": 19.045493557223 }, { "Outlier": "\"Inferred\"", "X": 6.967486968545026, "Y": 20.43685475506132 }, { "Outlier": "\"Inferred\"", "X": 7.850876988134879, "Y": 20.85970235656663 }, { "Outlier": "\"Inferred\"", "X": 8.392808772137586, "Y": 23.32121753848475 }, { "Outlier": "\"Inferred\"", "X": 8.372631457732883, "Y": 23.550505259601916 }, { "Outlier": "\"Inferred\"", "X": 9.871662866734457, "Y": 23.307490436256916 }, { "Outlier": "\"Inferred\"", "X": 10.314173898117193, "Y": 22.949374682117536 }, { "Outlier": "\"Inferred\"", "X": 10.32563322222216, "Y": 22.80572929728597 }, { "Outlier": "\"Inferred\"", "X": 11.975182162138363, "Y": 22.756945025950817 }, { "Outlier": "\"Inferred\"", "X": 13.534798358225013, "Y": 23.922399218838915 }, { "Outlier": "\"Inferred\"", "X": 13.939544608950607, "Y": 24.9043680997103 }, { "Outlier": "\"Inferred\"", "X": 14.852997836030292, "Y": 24.505991407121257 }, { "Outlier": "\"Inferred\"", "X": 14.722821358543685, "Y": 24.594075915773587 }, { "Outlier": "\"Inferred\"", "X": 14.103015251522454, "Y": 25.337305991383168 }, { "Outlier": "\"Inferred\"", "X": 12.613805810396606, "Y": 24.313422106066636 }, { "Outlier": "\"Inferred\"", "X": 13.080486829217792, "Y": 25.777670422612843 }, { "Outlier": "\"Inferred\"", "X": 11.503005763094693, "Y": 26.751491443530828 }, { "Outlier": "\"Inferred\"", "X": 12.268003621078462, "Y": 27.23493393620551 }, { "Outlier": "\"Inferred\"", "X": 13.107978224639371, "Y": 26.92306508382835 }, { "Outlier": "\"Inferred\"", "X": 13.28221444777585, "Y": 29.395602706911507 }, { "Outlier": "\"Inferred\"", "X": 14.801724593495212, "Y": 31.014010268316014 }, { "Outlier": "\"Inferred\"", "X": 14.27581330599868, "Y": 32.454234349787356 }, { "Outlier": "\"Inferred\"", "X": 14.029469444313813, "Y": 32.87117959294212 }, { "Outlier": "\"Inferred\"", "X": 15.19015709218286, "Y": 32.86952476370945 }, { "Outlier": "\"Inferred\"", "X": 15.773016432325253, "Y": 32.73681053907679 }, { "Outlier": "\"Inferred\"", "X": 17.51929695827333, "Y": 34.48234688075527 }, { "Outlier": "\"Inferred\"", "X": 20.037100194311396, "Y": 34.946070338043754 }, { "Outlier": "\"Inferred\"", "X": 21.818139030888776, "Y": 35.85262831787663 }, { "Outlier": "\"Inferred\"", "X": 21.732369533022062, "Y": 38.285815587214415 }, { "Outlier": "\"Inferred\"", "X": 22.57036154565432, "Y": 39.26475493081164 }, { "Outlier": "\"Inferred\"", "X": 22.65428937960439, "Y": 38.16420139841805 }, { "Outlier": "\"Inferred\"", "X": 24.780585362838643, "Y": 39.91054354562683 }, { "Outlier": "\"Inferred\"", "X": 25.38354438220824, "Y": 39.24486197722906 }, { "Outlier": "\"Inferred\"", "X": 27.164442953314957, "Y": 40.251417386922114 }, { "Outlier": "\"Inferred\"", "X": 26.560669601746632, "Y": 39.735256602104684 }, { "Outlier": "\"Inferred\"", "X": 27.68676596745725, "Y": 39.29922370466575 }, { "Outlier": "\"Inferred\"", "X": 28.952704273061375, "Y": 38.57956299703587 }, { "Outlier": "\"Inferred\"", "X": 30.85994523760473, "Y": 38.58780878001739 }, { "Outlier": "\"Inferred\"", "X": 31.71173182195131, "Y": 41.00963032424547 }, { "Outlier": "\"Inferred\"", "X": 32.25996671802696, "Y": 42.62987372674458 }, { "Outlier": "\"Inferred\"", "X": 32.13901643341855, "Y": 42.64956953142973 }, { "Outlier": "\"Inferred\"", "X": 32.51223380321943, "Y": 42.002610122649294 }, { "Outlier": "\"Inferred\"", "X": 34.35299124605617, "Y": 41.24064784804826 }, { "Outlier": "\"Inferred\"", "X": 35.844122036634836, "Y": 42.1810784481577 }, { "Outlier": "\"Inferred\"", "X": 37.9081438014521, "Y": 42.22351955942152 }, { "Outlier": "\"Inferred\"", "X": 38.894510433125106, "Y": 43.06239427926112 }, { "Outlier": "\"Inferred\"", "X": 38.91558178103346, "Y": 42.185826893594445 }, { "Outlier": "\"Inferred\"", "X": 39.14616660606475, "Y": 41.82351594693451 }, { "Outlier": "\"Inferred\"", "X": 39.517857227969024, "Y": 43.242523449616634 }, { "Outlier": "\"Inferred\"", "X": 39.33222642368304, "Y": 44.853212976568145 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": -0.7061071858531587, "Y": 17.365038139967538 }, { "Outlier": "\"Inferred\"", "X": 1.0408194778375142, "Y": 16.453328413776028 }, { "Outlier": "\"Inferred\"", "X": 1.5405091660502246, "Y": 17.549414850528695 }, { "Outlier": "\"Inferred\"", "X": 3.9338660225548883, "Y": 18.810585449009046 }, { "Outlier": "\"Inferred\"", "X": 5.934341937148822, "Y": 19.291644715457366 }, { "Outlier": "\"Inferred\"", "X": 5.860698325850149, "Y": 18.481355238954116 }, { "Outlier": "\"Inferred\"", "X": 5.705451214097312, "Y": 19.045493557223 }, { "Outlier": "\"Inferred\"", "X": 6.967486968545026, "Y": 20.43685475506132 }, { "Outlier": "\"Inferred\"", "X": 7.850876988134879, "Y": 20.85970235656663 }, { "Outlier": "\"Inferred\"", "X": 8.392808772137586, "Y": 23.32121753848475 }, { "Outlier": "\"Inferred\"", "X": 8.372631457732883, "Y": 23.550505259601916 }, { "Outlier": "\"Inferred\"", "X": 9.871662866734457, "Y": 23.307490436256916 }, { "Outlier": "\"Inferred\"", "X": 10.314173898117193, "Y": 22.949374682117536 }, { "Outlier": "\"Inferred\"", "X": 10.32563322222216, "Y": 22.80572929728597 }, { "Outlier": "\"Inferred\"", "X": 11.975182162138363, "Y": 22.756945025950817 }, { "Outlier": "\"Inferred\"", "X": 13.534798358225013, "Y": 23.922399218838915 }, { "Outlier": "\"Inferred\"", "X": 13.939544608950607, "Y": 24.9043680997103 }, { "Outlier": "\"Inferred\"", "X": 14.852997836030292, "Y": 24.505991407121257 }, { "Outlier": "\"Inferred\"", "X": 14.722821358543685, "Y": 24.594075915773587 }, { "Outlier": "\"Inferred\"", "X": 14.103015251522454, "Y": 25.337305991383168 }, { "Outlier": "\"Inferred\"", "X": 12.613805810396606, "Y": 24.313422106066636 }, { "Outlier": "\"Inferred\"", "X": 13.080486829217792, "Y": 25.777670422612843 }, { "Outlier": "\"Inferred\"", "X": 11.503005763094693, "Y": 26.751491443530828 }, { "Outlier": "\"Inferred\"", "X": 12.268003621078462, "Y": 27.23493393620551 }, { "Outlier": "\"Inferred\"", "X": 13.107978224639371, "Y": 26.92306508382835 }, { "Outlier": "\"Inferred\"", "X": 13.28221444777585, "Y": 29.395602706911507 }, { "Outlier": "\"Inferred\"", "X": 14.801724593495212, "Y": 31.014010268316014 }, { "Outlier": "\"Inferred\"", "X": 14.27581330599868, "Y": 32.454234349787356 }, { "Outlier": "\"Inferred\"", "X": 14.029469444313813, "Y": 32.87117959294212 }, { "Outlier": "\"Inferred\"", "X": 15.19015709218286, "Y": 32.86952476370945 }, { "Outlier": "\"Inferred\"", "X": 15.773016432325253, "Y": 32.73681053907679 }, { "Outlier": "\"Inferred\"", "X": 17.51929695827333, "Y": 34.48234688075527 }, { "Outlier": "\"Inferred\"", "X": 20.037100194311396, "Y": 34.946070338043754 }, { "Outlier": "\"Inferred\"", "X": 21.818139030888776, "Y": 35.85262831787663 }, { "Outlier": "\"Inferred\"", "X": 21.732369533022062, "Y": 38.285815587214415 }, { "Outlier": "\"Inferred\"", "X": 22.57036154565432, "Y": 39.26475493081164 }, { "Outlier": "\"Inferred\"", "X": 22.65428937960439, "Y": 38.16420139841805 }, { "Outlier": "\"Inferred\"", "X": 24.780585362838643, "Y": 39.91054354562683 }, { "Outlier": "\"Inferred\"", "X": 25.38354438220824, "Y": 39.24486197722906 }, { "Outlier": "\"Inferred\"", "X": 27.164442953314957, "Y": 40.251417386922114 }, { "Outlier": "\"Inferred\"", "X": 28.28179394365139, "Y": 40.646152553616645 }, { "Outlier": "\"Inferred\"", "X": 28.30913626904519, "Y": 41.02315352290143 }, { "Outlier": "\"Inferred\"", "X": 29.883021734981654, "Y": 41.74567007600542 }, { "Outlier": "\"Inferred\"", "X": 31.777263046995003, "Y": 41.57505095385209 }, { "Outlier": "\"Inferred\"", "X": 32.06844181937742, "Y": 42.277030586291964 }, { "Outlier": "\"Inferred\"", "X": 34.001286580375854, "Y": 44.064459055471616 }, { "Outlier": "\"Inferred\"", "X": 35.46263684098056, "Y": 44.39313681002709 }, { "Outlier": "\"Inferred\"", "X": 35.91657596164125, "Y": 43.94958775308008 }, { "Outlier": "\"Inferred\"", "X": 36.54973787601623, "Y": 42.1580021668003 }, { "Outlier": "\"Inferred\"", "X": 37.3930776154532, "Y": 41.48314238427116 }, { "Outlier": "\"Inferred\"", "X": 38.38432519062528, "Y": 42.29679000879268 }, { "Outlier": "\"Inferred\"", "X": 39.898957888172944, "Y": 42.09404055571683 }, { "Outlier": "\"Inferred\"", "X": 41.46989633031488, "Y": 42.94971882423167 }, { "Outlier": "\"Inferred\"", "X": 41.64092592263666, "Y": 42.01241395939619 }, { "Outlier": "\"Inferred\"", "X": 41.17027081443124, "Y": 43.06694784486476 }, { "Outlier": "\"Inferred\"", "X": 41.634263968098416, "Y": 41.98798231318356 }, { "Outlier": "\"Inferred\"", "X": -1.3672224100432464, "Y": 0.48810911351361935 }, { "Outlier": "\"Inferred\"", "X": -1.362481961988402, "Y": 0.7076446081740262 }, { "Outlier": "\"Inferred\"", "X": -0.8715888597202504, "Y": 0.5387869363321125 }, { "Outlier": "\"Inferred\"", "X": -1.7846730243034472, "Y": 0.10506846798668501 }, { "Outlier": "\"Inferred\"", "X": -1.7248963513441793, "Y": 1.7527964620207235 }, { "Outlier": "\"Inferred\"", "X": -2.4584252971562393, "Y": 2.228366920830615 }, { "Outlier": "\"Inferred\"", "X": -1.1279224722940642, "Y": 3.443210568179678 }, { "Outlier": "\"Inferred\"", "X": -1.479008906435281, "Y": 3.6908288290815645 }, { "Outlier": "\"Inferred\"", "X": 0.3615054073081352, "Y": 4.27972153425087 }, { "Outlier": "\"Inferred\"", "X": 0.8486611255806581, "Y": 4.592720222548362 }, { "Outlier": "\"Inferred\"", "X": 0.32944348639832954, "Y": 4.596809693227079 }, { "Outlier": "\"Inferred\"", "X": -0.21925626392848585, "Y": 5.163907931661737 }, { "Outlier": "\"Inferred\"", "X": -0.21992193392391401, "Y": 4.118185761295895 }, { "Outlier": "\"Inferred\"", "X": 0.4150919011751638, "Y": 5.538117360209408 }, { "Outlier": "\"Inferred\"", "X": -0.7892140966783031, "Y": 5.695449583765478 }, { "Outlier": "\"Inferred\"", "X": 1.1404372527748952, "Y": 5.866232913939554 }, { "Outlier": "\"Inferred\"", "X": 1.8380893328893904, "Y": 6.364770313736374 }, { "Outlier": "\"Inferred\"", "X": 1.649053221222315, "Y": 6.512174927857027 }, { "Outlier": "\"Inferred\"", "X": 1.737182974826292, "Y": 5.77836967516248 }, { "Outlier": "\"Inferred\"", "X": -0.11560529994996305, "Y": 7.5367062800404945 }, { "Outlier": "\"Inferred\"", "X": -0.2298973114533206, "Y": 7.989511766138773 }, { "Outlier": "\"Inferred\"", "X": 1.57161291446925, "Y": 6.901917929677629 }, { "Outlier": "\"Inferred\"", "X": 2.1801023491411033, "Y": 8.860615665751432 }, { "Outlier": "\"Inferred\"", "X": -0.1784410881273133, "Y": 9.923272270828765 }, { "Outlier": "\"Inferred\"", "X": -0.7917192002889752, "Y": 11.753085534646807 }, { "Outlier": "\"Inferred\"", "X": -1.7648020508226134, "Y": 10.25041393399106 }, { "Outlier": "\"Inferred\"", "X": -1.5234743284293881, "Y": 11.775350954803978 }, { "Outlier": "\"Inferred\"", "X": 0.05357856804105787, "Y": 12.024349478829999 }, { "Outlier": "\"Inferred\"", "X": 0.4184332014913338, "Y": 14.340893261404778 }, { "Outlier": "\"Inferred\"", "X": 0.7969379235979688, "Y": 14.997540083518635 }, { "Outlier": "\"Inferred\"", "X": -0.7096426760664147, "Y": 14.162334925784382 }, { "Outlier": "\"Inferred\"", "X": -1.1397135872644253, "Y": 14.5137914003321 }, { "Outlier": "\"Inferred\"", "X": 0.31644446775231205, "Y": 17.969278373643117 }, { "Outlier": "\"Inferred\"", "X": 0.6438733418570133, "Y": 18.422304921183187 }, { "Outlier": "\"Inferred\"", "X": 1.1175523451407152, "Y": 18.713873184030778 }, { "Outlier": "\"Inferred\"", "X": 1.9686962872665608, "Y": 18.74605534641339 }, { "Outlier": "\"Inferred\"", "X": 2.001348574224465, "Y": 18.21984280700007 }, { "Outlier": "\"Inferred\"", "X": 1.2367693402425317, "Y": 17.359669921367168 }, { "Outlier": "\"Inferred\"", "X": 0.2995047254719734, "Y": 17.419198206704273 }, { "Outlier": "\"Inferred\"", "X": 0.8622826020639092, "Y": 18.53931366111848 }, { "Outlier": "\"Inferred\"", "X": 1.0735971282483163, "Y": 17.975531013733278 }, { "Outlier": "\"Inferred\"", "X": 1.2451588531584312, "Y": 18.79352133226893 }, { "Outlier": "\"Inferred\"", "X": 0.7834409223822317, "Y": 17.519317572506424 }, { "Outlier": "\"Inferred\"", "X": 0.022324380796899135, "Y": 18.666677816733223 }, { "Outlier": "\"Inferred\"", "X": -0.7061071858531587, "Y": 17.365038139967538 }, { "Outlier": "\"Inferred\"", "X": 1.0408194778375142, "Y": 16.453328413776028 }, { "Outlier": "\"Inferred\"", "X": 1.5405091660502246, "Y": 17.549414850528695 }, { "Outlier": "\"Inferred\"", "X": 3.9338660225548883, "Y": 18.810585449009046 }, { "Outlier": "\"Inferred\"", "X": 5.934341937148822, "Y": 19.291644715457366 }, { "Outlier": "\"Inferred\"", "X": 5.860698325850149, "Y": 18.481355238954116 }, { "Outlier": "\"Inferred\"", "X": 5.705451214097312, "Y": 19.045493557223 }, { "Outlier": "\"Inferred\"", "X": 6.967486968545026, "Y": 20.43685475506132 }, { "Outlier": "\"Inferred\"", "X": 7.850876988134879, "Y": 20.85970235656663 }, { "Outlier": "\"Inferred\"", "X": 8.392808772137586, "Y": 23.32121753848475 }, { "Outlier": "\"Inferred\"", "X": 8.372631457732883, "Y": 23.550505259601916 }, { "Outlier": "\"Inferred\"", "X": 9.871662866734457, "Y": 23.307490436256916 }, { "Outlier": "\"Inferred\"", "X": 10.314173898117193, "Y": 22.949374682117536 }, { "Outlier": "\"Inferred\"", "X": 10.32563322222216, "Y": 22.80572929728597 }, { "Outlier": "\"Inferred\"", "X": 11.975182162138363, "Y": 22.756945025950817 }, { "Outlier": "\"Inferred\"", "X": 13.534798358225013, "Y": 23.922399218838915 }, { "Outlier": "\"Inferred\"", "X": 13.939544608950607, "Y": 24.9043680997103 }, { "Outlier": "\"Inferred\"", "X": 14.852997836030292, "Y": 24.505991407121257 }, { "Outlier": "\"Inferred\"", "X": 14.722821358543685, "Y": 24.594075915773587 }, { "Outlier": "\"Inferred\"", "X": 14.103015251522454, "Y": 25.337305991383168 }, { "Outlier": "\"Inferred\"", "X": 12.613805810396606, "Y": 24.313422106066636 }, { "Outlier": "\"Inferred\"", "X": 13.080486829217792, "Y": 25.777670422612843 }, { "Outlier": "\"Inferred\"", "X": 11.503005763094693, "Y": 26.751491443530828 }, { "Outlier": "\"Inferred\"", "X": 12.268003621078462, "Y": 27.23493393620551 }, { "Outlier": "\"Inferred\"", "X": 13.107978224639371, "Y": 26.92306508382835 }, { "Outlier": "\"Inferred\"", "X": 13.28221444777585, "Y": 29.395602706911507 }, { "Outlier": "\"Inferred\"", "X": 14.801724593495212, "Y": 31.014010268316014 }, { "Outlier": "\"Inferred\"", "X": 14.27581330599868, "Y": 32.454234349787356 }, { "Outlier": "\"Inferred\"", "X": 14.029469444313813, "Y": 32.87117959294212 }, { "Outlier": "\"Inferred\"", "X": 15.19015709218286, "Y": 32.86952476370945 }, { "Outlier": "\"Inferred\"", "X": 15.773016432325253, "Y": 32.73681053907679 }, { "Outlier": "\"Inferred\"", "X": 17.51929695827333, "Y": 34.48234688075527 }, { "Outlier": "\"Inferred\"", "X": 18.517892728110063, "Y": 36.48035168551525 }, { "Outlier": "\"Inferred\"", "X": 19.204987885038665, "Y": 39.88678266817718 }, { "Outlier": "\"Inferred\"", "X": 21.442417253877316, "Y": 41.08072121114131 }, { "Outlier": "\"Inferred\"", "X": 23.00537003178552, "Y": 40.67876253519744 }, { "Outlier": "\"Inferred\"", "X": 24.77068463689155, "Y": 39.644754539327785 }, { "Outlier": "\"Inferred\"", "X": 26.841222405457636, "Y": 40.610556540600946 }, { "Outlier": "\"Inferred\"", "X": 27.866274921957416, "Y": 40.70386345163607 }, { "Outlier": "\"Inferred\"", "X": 28.440999181920027, "Y": 41.26333519355627 }, { "Outlier": "\"Inferred\"", "X": 29.415990440563707, "Y": 41.53555764534253 }, { "Outlier": "\"Inferred\"", "X": 29.311058090188595, "Y": 42.16967838673546 }, { "Outlier": "\"Inferred\"", "X": 30.282831663472386, "Y": 43.0424291894592 }, { "Outlier": "\"Inferred\"", "X": 30.293365662434958, "Y": 42.438015235063844 }, { "Outlier": "\"Inferred\"", "X": 31.068088386419674, "Y": 42.2623529357851 }, { "Outlier": "\"Inferred\"", "X": 30.43196031201329, "Y": 39.82537011905435 }, { "Outlier": "\"Inferred\"", "X": 32.64091621401201, "Y": 40.45746585540844 }, { "Outlier": "\"Inferred\"", "X": 34.41602785548827, "Y": 40.00534774705635 }, { "Outlier": "\"Inferred\"", "X": 35.08714623791322, "Y": 41.56836269107095 }, { "Outlier": "\"Inferred\"", "X": 35.85003087316117, "Y": 42.44067187392689 }, { "Outlier": "\"Inferred\"", "X": 35.37660646007298, "Y": 40.60848049122916 }, { "Outlier": "\"Inferred\"", "X": 37.356787408662605, "Y": 39.767933934314186 }, { "Outlier": "\"Inferred\"", "X": 39.74513735869365, "Y": 38.194836342145834 }, { "Outlier": "\"Inferred\"", "X": 37.94004385048179, "Y": 38.336382378021625 }, { "Outlier": "\"Inferred\"", "X": 37.758549528236486, "Y": 38.273403209139026 }, { "Outlier": "\"Inferred\"", "X": 37.048338160833765, "Y": 40.01044901766141 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "inferredVectors = fst =<< particles\n", "\n", "\n", "plot \n", " (zip \n", " (fmap (\\v -> (v V.! 0, v V.! 1)) $ trueVectors <> inferredVectors) \n", " (replicate 100 (T.pack \"True\") <> replicate 1000 (T.pack \"Inferred\")))\n" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/file.json ================================================ { "name": "John", "isAlive": true, "age": 27, "height": 1.5, "address": { "streetAddress": "21 2nd Street", "id" : 5.4 } } ================================================ FILE: notebooks/models/LDA.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 4, "id": "01d81119-f1fc-4bd1-9e31-c3477b8edebc", "metadata": {}, "outputs": [], "source": [ ":e OverloadedStrings\n", ":l ../models/LDA.hs" ] }, { "cell_type": "code", "execution_count": 7, "id": "635da03a-4d61-453f-bbd1-cc81b1315200", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "( fromList\n", " [\n", " ( \"topic1\"\n", " ,\n", " [\n", " ( \"bear\"\n", " , 0.34365781935415857\n", " )\n", " ,\n", " ( \"wolf\"\n", " , 0.2556245339526619\n", " )\n", " ,\n", " ( \"python\"\n", " , 0.24562951016031953\n", " )\n", " ,\n", " ( \"prolog\"\n", " , 0.15508813653285997\n", " )\n", " ]\n", " )\n", " ,\n", " ( \"topic2\"\n", " ,\n", " [\n", " ( \"bear\"\n", " , 3.910578485111217 e- 2\n", " )\n", " ,\n", " ( \"wolf\"\n", " , 0.4304465204440071\n", " )\n", " ,\n", " ( \"python\"\n", " , 0.1486640274645592\n", " )\n", " ,\n", " ( \"prolog\"\n", " , 0.3817836672403215\n", " )\n", " ]\n", " )\n", " ]\n", ",\n", " [\n", " ( \"bear wolf bear wolf bear wolf python wolf bear wolf\"\n", " ,\n", " [\n", " ( \"topic1\"\n", " , 0.9047101354542013\n", " )\n", " ,\n", " ( \"topic2\"\n", " , 9.528986454579876 e- 2\n", " )\n", " ]\n", " )\n", " ,\n", " ( \"python prolog python prolog python prolog python prolog python prolog\"\n", " ,\n", " [\n", " ( \"topic1\"\n", " , 9.413029740935884 e- 2\n", " )\n", " ,\n", " ( \"topic2\"\n", " , 0.9058697025906413\n", " )\n", " ]\n", " )\n", " ,\n", " ( \"bear wolf bear wolf bear wolf bear wolf bear wolf\"\n", " ,\n", " [\n", " ( \"topic1\"\n", " , 0.6835490762786085\n", " )\n", " ,\n", " ( \"topic2\"\n", " , 0.31645092372139155\n", " )\n", " ]\n", " )\n", " ,\n", " ( \"python prolog python prolog python prolog python prolog python prolog\"\n", " ,\n", " [\n", " ( \"topic1\"\n", " , 3.698500977423746 e- 2\n", " )\n", " ,\n", " ( \"topic2\"\n", " , 0.9630149902257626\n", " )\n", " ]\n", " )\n", " ,\n", " ( \"bear wolf bear python bear wolf bear wolf bear wolf\"\n", " ,\n", " [\n", " ( \"topic1\"\n", " , 0.9101537223185177\n", " )\n", " ,\n", " ( \"topic2\"\n", " , 8.98462776814825 e- 2\n", " )\n", " ]\n", " )\n", " ]\n", ")" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "runLDA" ] }, { "cell_type": "code", "execution_count": null, "id": "ce857de6-2b0c-44ce-893f-a67ad39f3040", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" }, "toc-autonumbering": false }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/plotting.hs ================================================ {-# LANGUAGE FlexibleContexts #-} {-# LANGUAGE FlexibleInstances #-} {-# LANGUAGE OverloadedStrings #-} module Plotting where import Control.Arrow (first, second) import qualified Data.Text as T import Graphics.Vega.VegaLite hiding (filter, length) import IHaskell.Display.Hvega hist (xs, ys) = let enc = encoding . position X [PName "X", PmType Quantitative] . position Y [PName "Y", PmType Quantitative] dat = ( dataFromColumns [] . dataColumn "X" (Numbers xs) . dataColumn "Y" (Numbers ys) ) [] in toVegaLite [ dat, mark Bar [], enc [], width 400, height 400 ] barplot (xs, ys) = let enc = encoding . position X [PName "X", PmType Nominal] . position Y [PName "Y", PmType Quantitative] dat = ( dataFromColumns [] . dataColumn "X" (Strings xs) . dataColumn "Y" (Numbers ys) ) [] in toVegaLite [ dat, mark Bar [], enc [], width 400, height 400 ] scatterplot ((xs, ys), cs) cE f mode = let enc = encoding . position X [PName "X", PmType Quantitative] . position Y [PName "Y", PmType Quantitative] . cE dat = ( dataFromColumns [] . dataColumn "X" (Numbers xs) . dataColumn "Y" (Numbers ys) . dataColumn "Outlier" (f cs) ) [] in toVegaLite [ dat, mark mode [], enc [], width 400, height 400 ] class Plottable a where plot :: a -> VegaLiteLab instance Plottable [((Double, Double), T.Text)] where plot ls = vlShow $ scatterplot (first unzip $ unzip ls) (color [MName "Outlier"]) (\cs -> (Strings (T.pack . show <$> cs))) Circle instance Plottable [((Double, Double), Double)] where plot ls = vlShow $ scatterplot (first unzip $ unzip (ls)) ( color [ MName "Outlier", MmType Quantitative, MScale [ SScheme "viridis" [] ] ] ) Numbers Circle instance Plottable ([Double], (Double, Double)) where plot ls = let cs = take (length $ fst ls) $ Prelude.repeat 1 xs = fst ls (slope, intercept) = snd ls ys = (+ intercept) . (* slope) <$> xs in vlShow $ scatterplot ((xs, ys), cs) (color []) Numbers Line instance Plottable [(T.Text, Double)] where plot ls = vlShow $ barplot $ unzip ls instance Plottable [(Double, Double)] where plot ls = vlShow $ hist $ unzip ls instance Plottable ([Double], [Double]) where plot (xs, ys) = vlShow $ scatterplot ((xs, ys), replicate (length xs) 1) (color []) Numbers Line type Plot = VegaLiteLab ================================================ FILE: notebooks/tutorials/AdvancedSampling.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ ":e ImportQualifiedPost\n", ":e FlexibleContexts\n", ":e BlockArguments\n", ":e TupleSections\n", ":e FlexibleContexts\n", ":e OverloadedStrings\n", ":e LambdaCase\n", "\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Enumerator\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad.Bayes.Density.Free\n", "import Control.Monad.Bayes.Population\n", "import Control.Monad.Bayes.Sequential.Coroutine\n", "import Control.Monad.Bayes.Inference.SMC\n", "import Control.Monad.Bayes.Inference.RMSMC\n", "import Control.Monad.Bayes.Inference.PMMH\n", "import Control.Monad.Bayes.Inference.MCMC\n", "import qualified Data.Text as T\n", "import Numeric.Log\n", "import Control.Arrow (first,second)\n", "import Control.Monad\n", "import Control.Applicative\n", "import Control.Monad.Bayes.Class \n", "import Control.Monad.Bayes.Traced\n", "import Control.Monad.Bayes.Weighted\n", "import Graphics.Vega.VegaLite hiding (runDensityT)\n", "import qualified Graphics.Vega.VegaLite as VL\n", "import IHaskell.Display.Hvega (vlShow)\n", "\n", "import qualified Pipes.Prelude as P\n", "import qualified Pipes as P\n", "import Pipes (Producer, (>->), MonadTrans (lift))\n", "\n", ":l ../plotting.hs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Combining SMC and MCMC\n", "\n", "Monad-bayes offers several more advanced inference methods, which are modular combinations of `SMC` and `MCMC`. \n", "\n", "`Sampling`, `MCMC` and `SMC` notebooks describe those methods in more detail." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resample Move Sequential Monte Carlo (RMSMC)\n", "\n", "RMSMC is fundamentally an SMC technique. It creates and updates a population of weighted samples. The clever part is that after resampling, the update step uses MCMC to perform a walk on each particle, which updates the population efficiently.\n", "\n", "To motivate this more sophisticated inference method, let's pick a relatively hard inference problem: inferring the position of a moving point mass from measurements of its bearings.\n", "\n", "Here it is in practice:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "-- calculate the bearing (the angle) of a point\n", "bearing (x, y) = atan2 y x\n", "\n", "prior :: MonadDistribution m => Producer (Double,Double) m ()\n", "prior = do\n", " let velocity_var = 1e-5\n", " initialPosition <- lift $ liftA2 (,) (normal 0.01 0.01) (normal 0.95 0.01)\n", " initialVelocity <- lift $ liftA2 (,) (normal 0.002 0.01) (normal (-0.013) 0.01)\n", " -- step is performed for a unit of time\n", " let step ((qx,qy), (vx,vy)) = do\n", " vxNew <- normal vx (sqrt velocity_var)\n", " vyNew <- normal vy (sqrt velocity_var)\n", " return ((qx + vx, qy + vy), (vxNew, vyNew))\n", " P.unfoldr (\\x -> Right <$> dup (step x)) (initialPosition, initialVelocity) >-> P.map fst\n", " \n", " where \n", " dup :: Monad m => m b -> m (b, b)\n", " dup mx = do\n", " x <- mx\n", " return (x,x)\n", "\n", "measurementNoise = 0.005\n", "\n", "-- observationModel :: MonadDistribution m => P.Pipe (Double, Double) Double m ()\n", "observationModel position = normal (bearing position) measurementNoise\n", "\n", "\n", "numPoints = 40\n", "\n", "model :: MonadMeasure m => [Double] -> Producer (Double, Double) m ()\n", "model observations = zipWithM likelihood prior observationStream\n", " where \n", " observationStream = P.each observations\n", " measurement_noise = 0.005\n", " likelihood (pos, obs) = do\n", " o <- observationModel pos \n", " factor $ normalPdf o measurement_noise obs\n", " zipWithM f p1 p2 = P.zip p1 p2 >-> P.chain f >-> P.map fst" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "positions <- sampleIOfixed $ P.toListM (prior >-> P.take numPoints)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"prior\"", "X": 0.004040813897471044, "Y": 0.9449190340942227 }, { "Outlier": "\"prior\"", "X": 0.012150145221787412, "Y": 0.9274840162884911 }, { "Outlier": "\"prior\"", "X": 0.016860379454206022, "Y": 0.9084312248689684 }, { "Outlier": "\"prior\"", "X": 0.01641581595718638, "Y": 0.8887040051034267 }, { "Outlier": "\"prior\"", "X": 0.015828991403087755, "Y": 0.8674914294672441 }, { "Outlier": "\"prior\"", "X": 0.013970393980576864, "Y": 0.8491059059946388 }, { "Outlier": "\"prior\"", "X": 0.013001848439189199, "Y": 0.8269556146407302 }, { "Outlier": "\"prior\"", "X": 0.01410869793872522, "Y": 0.8038747409690314 }, { "Outlier": "\"prior\"", "X": 0.01891146561873628, "Y": 0.7845426338677167 }, { "Outlier": "\"prior\"", "X": 0.018379957973462722, "Y": 0.7690718944391366 }, { "Outlier": "\"prior\"", "X": 0.019385135042090187, "Y": 0.7533513585553672 }, { "Outlier": "\"prior\"", "X": 0.021669742734900348, "Y": 0.7352018223991866 }, { "Outlier": "\"prior\"", "X": 0.021156808398970484, "Y": 0.7192462544141534 }, { "Outlier": "\"prior\"", "X": 0.01993478817383033, "Y": 0.7047315558870912 }, { "Outlier": "\"prior\"", "X": 0.012857449682167671, "Y": 0.6860649039570736 }, { "Outlier": "\"prior\"", "X": 0.0036585006460101623, "Y": 0.6652639227003978 }, { "Outlier": "\"prior\"", "X": -0.006704504890859797, "Y": 0.6407731335881577 }, { "Outlier": "\"prior\"", "X": -0.01875148718315546, "Y": 0.6119187708433766 }, { "Outlier": "\"prior\"", "X": -0.03144164055357166, "Y": 0.5811819303837852 }, { "Outlier": "\"prior\"", "X": -0.05337633763916294, "Y": 0.5490664469820896 }, { "Outlier": "\"prior\"", "X": -0.07909088956959608, "Y": 0.5147636649156484 }, { "Outlier": "\"prior\"", "X": -0.10852176412035312, "Y": 0.4771049794440802 }, { "Outlier": "\"prior\"", "X": -0.1415041339372596, "Y": 0.4358158404651159 }, { "Outlier": "\"prior\"", "X": -0.17802868429398142, "Y": 0.3982038738682732 }, { "Outlier": "\"prior\"", "X": -0.21934710951238376, "Y": 0.3670200926908218 }, { "Outlier": "\"prior\"", "X": -0.26185119674488966, "Y": 0.3397953088971217 }, { "Outlier": "\"prior\"", "X": -0.3048405327299023, "Y": 0.3115994429918755 }, { "Outlier": "\"prior\"", "X": -0.3390203782102853, "Y": 0.2796626489022578 }, { "Outlier": "\"prior\"", "X": -0.3763687877889552, "Y": 0.25260454272789823 }, { "Outlier": "\"prior\"", "X": -0.4139616671134319, "Y": 0.22373706411803673 }, { "Outlier": "\"prior\"", "X": -0.4536834468207761, "Y": 0.20157554577872092 }, { "Outlier": "\"prior\"", "X": -0.49218501672059023, "Y": 0.1788270187745313 }, { "Outlier": "\"prior\"", "X": -0.5348912174560139, "Y": 0.1577231534193479 }, { "Outlier": "\"prior\"", "X": -0.5830061761886975, "Y": 0.1372253105135145 }, { "Outlier": "\"prior\"", "X": -0.6318383342252785, "Y": 0.11904963434534747 }, { "Outlier": "\"prior\"", "X": -0.684053191400329, "Y": 0.09799287602519839 }, { "Outlier": "\"prior\"", "X": -0.7356802946475065, "Y": 0.07233980458833347 }, { "Outlier": "\"prior\"", "X": -0.7811584193497797, "Y": 0.04963154818166052 }, { "Outlier": "\"prior\"", "X": -0.8289870413375418, "Y": 0.026265300163843604 }, { "Outlier": "\"prior\"", "X": -0.8814219602100856, "Y": 0.0033672219692231498 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot ( zip positions $ Prelude.repeat $ T.pack \"prior\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "observations <- sampleIOfixed $ mapM observationModel positions" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sMCMC <- sampleIOfixed $ unweighted $ mcmc MCMCConfig {numMCMCSteps = 10000, numBurnIn = 1000, proposal = SingleSiteMH} $ \n", " P.toListM $ model observations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MCMC inference arrived returned a path which is divergent in the begining (starting at point (0,0.95)) from prior path." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"prior\"", "X": 0.004040813897471044, "Y": 0.9449190340942227 }, { "Outlier": "\"prior\"", "X": 0.012150145221787412, "Y": 0.9274840162884911 }, { "Outlier": "\"prior\"", "X": 0.016860379454206022, "Y": 0.9084312248689684 }, { "Outlier": "\"prior\"", "X": 0.01641581595718638, "Y": 0.8887040051034267 }, { "Outlier": "\"prior\"", "X": 0.015828991403087755, "Y": 0.8674914294672441 }, { "Outlier": "\"prior\"", "X": 0.013970393980576864, "Y": 0.8491059059946388 }, { "Outlier": "\"prior\"", "X": 0.013001848439189199, "Y": 0.8269556146407302 }, { "Outlier": "\"prior\"", "X": 0.01410869793872522, "Y": 0.8038747409690314 }, { "Outlier": "\"prior\"", "X": 0.01891146561873628, "Y": 0.7845426338677167 }, { "Outlier": "\"prior\"", "X": 0.018379957973462722, "Y": 0.7690718944391366 }, { "Outlier": "\"prior\"", "X": 0.019385135042090187, "Y": 0.7533513585553672 }, { "Outlier": "\"prior\"", "X": 0.021669742734900348, "Y": 0.7352018223991866 }, { "Outlier": "\"prior\"", "X": 0.021156808398970484, "Y": 0.7192462544141534 }, { "Outlier": "\"prior\"", "X": 0.01993478817383033, "Y": 0.7047315558870912 }, { "Outlier": "\"prior\"", "X": 0.012857449682167671, "Y": 0.6860649039570736 }, { "Outlier": "\"prior\"", "X": 0.0036585006460101623, "Y": 0.6652639227003978 }, { "Outlier": "\"prior\"", "X": -0.006704504890859797, "Y": 0.6407731335881577 }, { "Outlier": "\"prior\"", "X": -0.01875148718315546, "Y": 0.6119187708433766 }, { "Outlier": "\"prior\"", "X": -0.03144164055357166, "Y": 0.5811819303837852 }, { "Outlier": "\"prior\"", "X": -0.05337633763916294, "Y": 0.5490664469820896 }, { "Outlier": "\"prior\"", "X": -0.07909088956959608, "Y": 0.5147636649156484 }, { "Outlier": "\"prior\"", "X": -0.10852176412035312, "Y": 0.4771049794440802 }, { "Outlier": "\"prior\"", "X": -0.1415041339372596, "Y": 0.4358158404651159 }, { "Outlier": "\"prior\"", "X": -0.17802868429398142, "Y": 0.3982038738682732 }, { "Outlier": "\"prior\"", "X": -0.21934710951238376, "Y": 0.3670200926908218 }, { "Outlier": "\"prior\"", "X": -0.26185119674488966, "Y": 0.3397953088971217 }, { "Outlier": "\"prior\"", "X": -0.3048405327299023, "Y": 0.3115994429918755 }, { "Outlier": "\"prior\"", "X": -0.3390203782102853, "Y": 0.2796626489022578 }, { "Outlier": "\"prior\"", "X": -0.3763687877889552, "Y": 0.25260454272789823 }, { "Outlier": "\"prior\"", "X": -0.4139616671134319, "Y": 0.22373706411803673 }, { "Outlier": "\"prior\"", "X": -0.4536834468207761, "Y": 0.20157554577872092 }, { "Outlier": "\"prior\"", "X": -0.49218501672059023, "Y": 0.1788270187745313 }, { "Outlier": "\"prior\"", "X": -0.5348912174560139, "Y": 0.1577231534193479 }, { "Outlier": "\"prior\"", "X": -0.5830061761886975, "Y": 0.1372253105135145 }, { "Outlier": "\"prior\"", "X": -0.6318383342252785, "Y": 0.11904963434534747 }, { "Outlier": "\"prior\"", "X": -0.684053191400329, "Y": 0.09799287602519839 }, { "Outlier": "\"prior\"", "X": -0.7356802946475065, "Y": 0.07233980458833347 }, { "Outlier": "\"prior\"", "X": -0.7811584193497797, "Y": 0.04963154818166052 }, { "Outlier": "\"prior\"", "X": -0.8289870413375418, "Y": 0.026265300163843604 }, { "Outlier": "\"prior\"", "X": -0.8814219602100856, "Y": 0.0033672219692231498 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.009522606824511044, "Y": 0.9507967170703003 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.030715754715736746, "Y": 0.9359462721413535 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.04692266318358654, "Y": 0.9212877564229398 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.06259269174502599, "Y": 0.9100069051435712 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.07409952810746244, "Y": 0.898098257118873 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.08040726133570095, "Y": 0.883049600147868 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.07892336644873973, "Y": 0.8675075966703154 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.07225244694630924, "Y": 0.8511196920262338 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.06572498864778742, "Y": 0.8376221023853665 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.05143791095247105, "Y": 0.8210472908965629 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.04285280594715003, "Y": 0.802706317200674 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.03552054619819507, "Y": 0.7820548562357635 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.02697061907391134, "Y": 0.7647106837538756 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.014572110285940316, "Y": 0.7456511472472253 }, { "Outlier": "\"inferred: MCMC\"", "X": 0.0021130810418944195, "Y": 0.7285400944333256 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.010860568224164573, "Y": 0.7122968247477851 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.02356788043336675, "Y": 0.6932974286925321 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.041643788735266765, "Y": 0.6755885608853368 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.06353076508451232, "Y": 0.65658650963109 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.08805380951449007, "Y": 0.6372642355851278 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.11651582565803084, "Y": 0.6135799742551241 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.1501164008693634, "Y": 0.5868560622602802 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.18985408507545656, "Y": 0.5540535538274423 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.2342509367894407, "Y": 0.5233366455303043 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.28478081820773254, "Y": 0.48887115593987873 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.3427190297393583, "Y": 0.46338987069019677 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.40728348129548897, "Y": 0.43170607420912593 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.4745863636296226, "Y": 0.4039591275741752 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.5404920487503329, "Y": 0.3723629285755027 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.6026372899710436, "Y": 0.33724158407782906 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.6639896015993596, "Y": 0.3008302546507704 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.7202638821878264, "Y": 0.2637154738525396 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.7779704001260951, "Y": 0.23025433100914067 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.8454376222886029, "Y": 0.19890029220903532 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.9148160949875993, "Y": 0.16774932459725583 }, { "Outlier": "\"inferred: MCMC\"", "X": -0.9840269862747459, "Y": 0.1367335911191669 }, { "Outlier": "\"inferred: MCMC\"", "X": -1.052693761413397, "Y": 0.10593147256721763 }, { "Outlier": "\"inferred: MCMC\"", "X": -1.1208170895211886, "Y": 0.07359904936818655 }, { "Outlier": "\"inferred: MCMC\"", "X": -1.1930381207683638, "Y": 0.041103437433913446 }, { "Outlier": "\"inferred: MCMC\"", "X": -1.261504703232232, "Y": 0.013537981021413618 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot $\n", " zip positions (Prelude.repeat $ T.pack \"prior\") <> \n", " zip (head sMCMC) (Prelude.repeat $ T.pack \"inferred: MCMC\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sSMC <- sampleIOfixed $ runPopulationT $ smc \n", " SMCConfig {numSteps = numPoints, numParticles = 10000, resampler = resampleSystematic} \n", " $ P.toListM $ model observations\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SMC on the ther hand provided many paths close to the prior at the path's beginning and diverging as the path progresses." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"prior\"", "X": 0.004040813897471044, "Y": 0.9449190340942227 }, { "Outlier": "\"prior\"", "X": 0.012150145221787412, "Y": 0.9274840162884911 }, { "Outlier": "\"prior\"", "X": 0.016860379454206022, "Y": 0.9084312248689684 }, { "Outlier": "\"prior\"", "X": 0.01641581595718638, "Y": 0.8887040051034267 }, { "Outlier": "\"prior\"", "X": 0.015828991403087755, "Y": 0.8674914294672441 }, { "Outlier": "\"prior\"", "X": 0.013970393980576864, "Y": 0.8491059059946388 }, { "Outlier": "\"prior\"", "X": 0.013001848439189199, "Y": 0.8269556146407302 }, { "Outlier": "\"prior\"", "X": 0.01410869793872522, "Y": 0.8038747409690314 }, { "Outlier": "\"prior\"", "X": 0.01891146561873628, "Y": 0.7845426338677167 }, { "Outlier": "\"prior\"", "X": 0.018379957973462722, "Y": 0.7690718944391366 }, { "Outlier": "\"prior\"", "X": 0.019385135042090187, "Y": 0.7533513585553672 }, { "Outlier": "\"prior\"", "X": 0.021669742734900348, "Y": 0.7352018223991866 }, { "Outlier": "\"prior\"", "X": 0.021156808398970484, "Y": 0.7192462544141534 }, { "Outlier": "\"prior\"", "X": 0.01993478817383033, "Y": 0.7047315558870912 }, { "Outlier": "\"prior\"", "X": 0.012857449682167671, "Y": 0.6860649039570736 }, { "Outlier": "\"prior\"", "X": 0.0036585006460101623, "Y": 0.6652639227003978 }, { "Outlier": "\"prior\"", "X": -0.006704504890859797, "Y": 0.6407731335881577 }, { "Outlier": "\"prior\"", "X": -0.01875148718315546, "Y": 0.6119187708433766 }, { "Outlier": "\"prior\"", "X": -0.03144164055357166, "Y": 0.5811819303837852 }, { "Outlier": "\"prior\"", "X": -0.05337633763916294, "Y": 0.5490664469820896 }, { "Outlier": "\"prior\"", "X": -0.07909088956959608, "Y": 0.5147636649156484 }, { "Outlier": "\"prior\"", "X": -0.10852176412035312, "Y": 0.4771049794440802 }, { "Outlier": "\"prior\"", "X": -0.1415041339372596, "Y": 0.4358158404651159 }, { "Outlier": "\"prior\"", "X": -0.17802868429398142, "Y": 0.3982038738682732 }, { "Outlier": "\"prior\"", "X": -0.21934710951238376, "Y": 0.3670200926908218 }, { "Outlier": "\"prior\"", "X": -0.26185119674488966, "Y": 0.3397953088971217 }, { "Outlier": "\"prior\"", "X": -0.3048405327299023, "Y": 0.3115994429918755 }, { "Outlier": "\"prior\"", "X": -0.3390203782102853, "Y": 0.2796626489022578 }, { "Outlier": "\"prior\"", "X": -0.3763687877889552, "Y": 0.25260454272789823 }, { "Outlier": "\"prior\"", "X": -0.4139616671134319, "Y": 0.22373706411803673 }, { "Outlier": "\"prior\"", "X": -0.4536834468207761, "Y": 0.20157554577872092 }, { "Outlier": "\"prior\"", "X": -0.49218501672059023, "Y": 0.1788270187745313 }, { "Outlier": "\"prior\"", "X": -0.5348912174560139, "Y": 0.1577231534193479 }, { "Outlier": "\"prior\"", "X": -0.5830061761886975, "Y": 0.1372253105135145 }, { "Outlier": "\"prior\"", "X": -0.6318383342252785, "Y": 0.11904963434534747 }, { "Outlier": "\"prior\"", "X": -0.684053191400329, "Y": 0.09799287602519839 }, { "Outlier": "\"prior\"", "X": -0.7356802946475065, "Y": 0.07233980458833347 }, { "Outlier": "\"prior\"", "X": -0.7811584193497797, "Y": 0.04963154818166052 }, { "Outlier": "\"prior\"", "X": -0.8289870413375418, "Y": 0.026265300163843604 }, { "Outlier": "\"prior\"", "X": -0.8814219602100856, "Y": 0.0033672219692231498 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1470299080949992, "Y": 0.32546980397473285 }, { "Outlier": "\"inferred: SMC\"", "X": -0.16731251286754456, "Y": 0.28043416488829437 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18560897724432393, "Y": 0.24096350414027412 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2042139432854838, "Y": 0.20741903899779135 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2231812098936276, "Y": 0.18343075644211632 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24054151773340796, "Y": 0.1594861418520304 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2529400756199515, "Y": 0.139813861151296 }, { "Outlier": "\"inferred: SMC\"", "X": -0.26502270156481317, "Y": 0.12025843263195456 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2816291175493261, "Y": 0.10243088574452486 }, { "Outlier": "\"inferred: SMC\"", "X": -0.29295240656029375, "Y": 0.08734038146741441 }, { "Outlier": "\"inferred: SMC\"", "X": -0.30587282873915017, "Y": 0.07224082263624831 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31913869468557965, "Y": 0.06141227935709128 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33919864790040305, "Y": 0.047529134272641334 }, { "Outlier": "\"inferred: SMC\"", "X": -0.352388546477684, "Y": 0.035168574648325936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3656999857298576, "Y": 0.022899997645560256 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3804967404175305, "Y": 0.01335145996772475 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3947674308695043, "Y": 0.0006792850988576841 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1470299080949992, "Y": 0.32546980397473285 }, { "Outlier": "\"inferred: SMC\"", "X": -0.16731251286754456, "Y": 0.28043416488829437 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18560897724432393, "Y": 0.24096350414027412 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2042139432854838, "Y": 0.20741903899779135 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2231812098936276, "Y": 0.18343075644211632 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24054151773340796, "Y": 0.1594861418520304 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2529400756199515, "Y": 0.139813861151296 }, { "Outlier": "\"inferred: SMC\"", "X": -0.26502270156481317, "Y": 0.12025843263195456 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2816291175493261, "Y": 0.10243088574452486 }, { "Outlier": "\"inferred: SMC\"", "X": -0.29295240656029375, "Y": 0.08734038146741441 }, { "Outlier": "\"inferred: SMC\"", "X": -0.30587282873915017, "Y": 0.07224082263624831 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31913869468557965, "Y": 0.06141227935709128 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33919864790040305, "Y": 0.047529134272641334 }, { "Outlier": "\"inferred: SMC\"", "X": -0.352388546477684, "Y": 0.035168574648325936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3656999857298576, "Y": 0.022899997645560256 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3804967404175305, "Y": 0.01335145996772475 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3947674308695043, "Y": 0.0006792850988576841 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1470299080949992, "Y": 0.32546980397473285 }, { "Outlier": "\"inferred: SMC\"", "X": -0.16731251286754456, "Y": 0.28043416488829437 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18560897724432393, "Y": 0.24096350414027412 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2042139432854838, "Y": 0.20741903899779135 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2231812098936276, "Y": 0.18343075644211632 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24054151773340796, "Y": 0.1594861418520304 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2529400756199515, "Y": 0.139813861151296 }, { "Outlier": "\"inferred: SMC\"", "X": -0.26502270156481317, "Y": 0.12025843263195456 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2816291175493261, "Y": 0.10243088574452486 }, { "Outlier": "\"inferred: SMC\"", "X": -0.29295240656029375, "Y": 0.08734038146741441 }, { "Outlier": "\"inferred: SMC\"", "X": -0.30587282873915017, "Y": 0.07224082263624831 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31913869468557965, "Y": 0.06141227935709128 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33919864790040305, "Y": 0.047529134272641334 }, { "Outlier": "\"inferred: SMC\"", "X": -0.352388546477684, "Y": 0.035168574648325936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3656999857298576, "Y": 0.022899997645560256 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3804967404175305, "Y": 0.01335145996772475 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3947674308695043, "Y": 0.0006792850988576841 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37146228338370496, "Y": 0.16788696195433328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.406496564566301, "Y": 0.15156305204064338 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44103033905325845, "Y": 0.13395267488189772 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46990206166806425, "Y": 0.11283092923708206 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49820987192566185, "Y": 0.0933413613640384 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5268048865276319, "Y": 0.0750778042677162 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5544316698307336, "Y": 0.059303740262610746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5788701921163963, "Y": 0.04212873549002538 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5959402621420208, "Y": 0.024110995529660662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6084726987787488, "Y": 0.0050497173524213755 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37146228338370496, "Y": 0.16788696195433328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.406496564566301, "Y": 0.15156305204064338 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44103033905325845, "Y": 0.13395267488189772 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46990206166806425, "Y": 0.11283092923708206 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49820987192566185, "Y": 0.0933413613640384 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5268048865276319, "Y": 0.0750778042677162 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5544316698307336, "Y": 0.059303740262610746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5788701921163963, "Y": 0.04212873549002538 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5959402621420208, "Y": 0.024110995529660662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6084726987787488, "Y": 0.0050497173524213755 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37146228338370496, "Y": 0.16788696195433328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.406496564566301, "Y": 0.15156305204064338 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44103033905325845, "Y": 0.13395267488189772 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46990206166806425, "Y": 0.11283092923708206 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49820987192566185, "Y": 0.0933413613640384 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5268048865276319, "Y": 0.0750778042677162 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5544316698307336, "Y": 0.059303740262610746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5788701921163963, "Y": 0.04212873549002538 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5959402621420208, "Y": 0.024110995529660662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6084726987787488, "Y": 0.0050497173524213755 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37146228338370496, "Y": 0.16788696195433328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.406496564566301, "Y": 0.15156305204064338 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44103033905325845, "Y": 0.13395267488189772 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46990206166806425, "Y": 0.11283092923708206 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49820987192566185, "Y": 0.0933413613640384 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5268048865276319, "Y": 0.0750778042677162 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5544316698307336, "Y": 0.059303740262610746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5788701921163963, "Y": 0.04212873549002538 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5959402621420208, "Y": 0.024110995529660662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6084726987787488, "Y": 0.0050497173524213755 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37146228338370496, "Y": 0.16788696195433328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.406496564566301, "Y": 0.15156305204064338 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44103033905325845, "Y": 0.13395267488189772 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46990206166806425, "Y": 0.11283092923708206 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49820987192566185, "Y": 0.0933413613640384 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5268048865276319, "Y": 0.0750778042677162 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5544316698307336, "Y": 0.059303740262610746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5788701921163963, "Y": 0.04212873549002538 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5959402621420208, "Y": 0.024110995529660662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6084726987787488, "Y": 0.0050497173524213755 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37146228338370496, "Y": 0.16788696195433328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.406496564566301, "Y": 0.15156305204064338 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44103033905325845, "Y": 0.13395267488189772 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46990206166806425, "Y": 0.11283092923708206 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49820987192566185, "Y": 0.0933413613640384 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5268048865276319, "Y": 0.0750778042677162 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5544316698307336, "Y": 0.059303740262610746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5788701921163963, "Y": 0.04212873549002538 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5959402621420208, "Y": 0.024110995529660662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6084726987787488, "Y": 0.0050497173524213755 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37146228338370496, "Y": 0.16788696195433328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.406496564566301, "Y": 0.15156305204064338 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44103033905325845, "Y": 0.13395267488189772 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46990206166806425, "Y": 0.11283092923708206 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49820987192566185, "Y": 0.0933413613640384 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5268048865276319, "Y": 0.0750778042677162 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5544316698307336, "Y": 0.059303740262610746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5788701921163963, "Y": 0.04212873549002538 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5959402621420208, "Y": 0.024110995529660662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6084726987787488, "Y": 0.0050497173524213755 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37146228338370496, "Y": 0.16788696195433328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.406496564566301, "Y": 0.15156305204064338 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44103033905325845, "Y": 0.13395267488189772 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46990206166806425, "Y": 0.11283092923708206 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49820987192566185, "Y": 0.0933413613640384 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5268048865276319, "Y": 0.0750778042677162 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5544316698307336, "Y": 0.059303740262610746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5788701921163963, "Y": 0.04212873549002538 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5959402621420208, "Y": 0.024110995529660662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6084726987787488, "Y": 0.0050497173524213755 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37146228338370496, "Y": 0.16788696195433328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.406496564566301, "Y": 0.15156305204064338 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44103033905325845, "Y": 0.13395267488189772 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46990206166806425, "Y": 0.11283092923708206 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49820987192566185, "Y": 0.0933413613640384 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5268048865276319, "Y": 0.0750778042677162 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5544316698307336, "Y": 0.059303740262610746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5788701921163963, "Y": 0.04212873549002538 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5959402621420208, "Y": 0.024110995529660662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6084726987787488, "Y": 0.0050497173524213755 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5277928547571716, "Y": 0.05553341802170174 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5610967800448166, "Y": 0.03791930691287462 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5986788873352781, "Y": 0.022777584667947572 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6409631290945261, "Y": 0.006010353504989106 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5277928547571716, "Y": 0.05553341802170174 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5610967800448166, "Y": 0.03791930691287462 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5986788873352781, "Y": 0.022777584667947572 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6409631290945261, "Y": 0.006010353504989106 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5277928547571716, "Y": 0.05553341802170174 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5610967800448166, "Y": 0.03791930691287462 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5986788873352781, "Y": 0.022777584667947572 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6409631290945261, "Y": 0.006010353504989106 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5235082685829185, "Y": 0.05417094978355112 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5490028869859905, "Y": 0.03787162608691825 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5795076067569219, "Y": 0.0213045474798786 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6075415011028229, "Y": 0.007238353972601173 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5235082685829185, "Y": 0.05417094978355112 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5490028869859905, "Y": 0.03787162608691825 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5795076067569219, "Y": 0.0213045474798786 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6075415011028229, "Y": 0.007238353972601173 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5235082685829185, "Y": 0.05417094978355112 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5490028869859905, "Y": 0.03787162608691825 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5795076067569219, "Y": 0.0213045474798786 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6075415011028229, "Y": 0.007238353972601173 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5235082685829185, "Y": 0.05417094978355112 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5490028869859905, "Y": 0.03787162608691825 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5795076067569219, "Y": 0.0213045474798786 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6117994345812957, "Y": 0.004113810983075804 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5235082685829185, "Y": 0.05417094978355112 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5490028869859905, "Y": 0.03787162608691825 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5795076067569219, "Y": 0.0213045474798786 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6117994345812957, "Y": 0.004113810983075804 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5258020682052938, "Y": 0.05324839893869771 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5499372369086811, "Y": 0.035664557968704665 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5671766939846303, "Y": 0.01836525144056505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5858177757000866, "Y": 0.002163084957957407 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5258020682052938, "Y": 0.05324839893869771 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5499372369086811, "Y": 0.035664557968704665 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5671766939846303, "Y": 0.01836525144056505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5858177757000866, "Y": 0.002163084957957407 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5258020682052938, "Y": 0.05324839893869771 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5499372369086811, "Y": 0.035664557968704665 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5671766939846303, "Y": 0.01836525144056505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5858177757000866, "Y": 0.002163084957957407 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.418304824620685, "Y": 0.1247345104579328 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44415411784533915, "Y": 0.10742985237090857 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4714725260015922, "Y": 0.09066442050647268 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4997711212413936, "Y": 0.07232118703726038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5258020682052938, "Y": 0.05324839893869771 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5499372369086811, "Y": 0.035664557968704665 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5671766939846303, "Y": 0.01836525144056505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5858177757000866, "Y": 0.002163084957957407 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.510543803641569, "Y": 0.07439237612780701 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5376709920967517, "Y": 0.06054410013658778 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609537384195166, "Y": 0.042238001798521004 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5829971207678224, "Y": 0.025350577828863786 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5991409434202528, "Y": 0.009513163384471817 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6001866497200343, "Y": 0.01874923811805947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6238782318487894, "Y": 0.0024913035193562597 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6001866497200343, "Y": 0.01874923811805947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6238782318487894, "Y": 0.0024913035193562597 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6001866497200343, "Y": 0.01874923811805947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6238782318487894, "Y": 0.0024913035193562597 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6001866497200343, "Y": 0.01874923811805947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6238782318487894, "Y": 0.0024913035193562597 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6001866497200343, "Y": 0.01874923811805947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6238782318487894, "Y": 0.0024913035193562597 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6001866497200343, "Y": 0.01874923811805947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6238782318487894, "Y": 0.0024913035193562597 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6082064185056434, "Y": 0.01937177146703478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6473883259106232, "Y": 0.003986036761488827 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6082064185056434, "Y": 0.01937177146703478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6473883259106232, "Y": 0.003986036761488827 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6082064185056434, "Y": 0.01937177146703478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6473883259106232, "Y": 0.003986036761488827 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6082064185056434, "Y": 0.01937177146703478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6473883259106232, "Y": 0.003986036761488827 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6082064185056434, "Y": 0.01937177146703478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6473883259106232, "Y": 0.003986036761488827 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6082064185056434, "Y": 0.01937177146703478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6492517832954655, "Y": 0.0048949932936631566 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.33464603871916826, "Y": 0.18583574218298587 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36359669099640607, "Y": 0.1653097592726341 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3920805717870425, "Y": 0.1441522273290616 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41803790766612176, "Y": 0.12845256076352055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44780397778571973, "Y": 0.10736495208197308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4809764939266371, "Y": 0.09020954496650124 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5093542072990245, "Y": 0.07315535239775298 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5415842459214427, "Y": 0.057809987131930925 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5724465907125911, "Y": 0.03884289353433087 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6082064185056434, "Y": 0.01937177146703478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6492517832954655, "Y": 0.0048949932936631566 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3293591389097297, "Y": 0.18344673624041274 }, { "Outlier": "\"inferred: SMC\"", "X": -0.35247994933481797, "Y": 0.15873754415980354 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37008711605074174, "Y": 0.13602816168861173 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38240567796362623, "Y": 0.11276789376583789 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3930867613704837, "Y": 0.09382074636949778 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40365081309577455, "Y": 0.07763276152062928 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41498549669967033, "Y": 0.06178897644151429 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4308093564917972, "Y": 0.04503603250538034 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44833172406843447, "Y": 0.030666998473786254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46942913505901224, "Y": 0.016279967568723908 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4912621058841393, "Y": 0.002186730037256057 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3293591389097297, "Y": 0.18344673624041274 }, { "Outlier": "\"inferred: SMC\"", "X": -0.35247994933481797, "Y": 0.15873754415980354 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37008711605074174, "Y": 0.13602816168861173 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38240567796362623, "Y": 0.11276789376583789 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3930867613704837, "Y": 0.09382074636949778 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40365081309577455, "Y": 0.07763276152062928 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41498549669967033, "Y": 0.06178897644151429 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4308093564917972, "Y": 0.04503603250538034 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44833172406843447, "Y": 0.030666998473786254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46942913505901224, "Y": 0.016279967568723908 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4912621058841393, "Y": 0.002186730037256057 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3293591389097297, "Y": 0.18344673624041274 }, { "Outlier": "\"inferred: SMC\"", "X": -0.35247994933481797, "Y": 0.15873754415980354 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37008711605074174, "Y": 0.13602816168861173 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38240567796362623, "Y": 0.11276789376583789 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3930867613704837, "Y": 0.09382074636949778 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40365081309577455, "Y": 0.07763276152062928 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41498549669967033, "Y": 0.06178897644151429 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4308093564917972, "Y": 0.04503603250538034 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44833172406843447, "Y": 0.030666998473786254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46942913505901224, "Y": 0.016279967568723908 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4912621058841393, "Y": 0.002186730037256057 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24818384827981851, "Y": 0.25981957629693375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.27907728430321327, "Y": 0.23340706107468834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3048932533577885, "Y": 0.20990989266838433 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3293591389097297, "Y": 0.18344673624041274 }, { "Outlier": "\"inferred: SMC\"", "X": -0.35247994933481797, "Y": 0.15873754415980354 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37008711605074174, "Y": 0.13602816168861173 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38240567796362623, "Y": 0.11276789376583789 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3930867613704837, "Y": 0.09382074636949778 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40365081309577455, "Y": 0.07763276152062928 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41498549669967033, "Y": 0.06178897644151429 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4308093564917972, "Y": 0.04503603250538034 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44833172406843447, "Y": 0.030666998473786254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46002490954717734, "Y": 0.019427797263168622 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47564668383188824, "Y": 0.006994707719981971 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4864228329855015, "Y": 0.0884033917422711 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5227204354397034, "Y": 0.07295579265949186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5610130295377457, "Y": 0.05727810706739815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6020298355848356, "Y": 0.03770742564471537 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6373126020330481, "Y": 0.02113013047405573 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6727948461237295, "Y": 0.0030681659851870165 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4864228329855015, "Y": 0.0884033917422711 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5227204354397034, "Y": 0.07295579265949186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5610130295377457, "Y": 0.05727810706739815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6020298355848356, "Y": 0.03770742564471537 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6373126020330481, "Y": 0.02113013047405573 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6727948461237295, "Y": 0.0030681659851870165 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5915407613758468, "Y": 0.02350182530887189 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6174522779247366, "Y": 0.006649060454341656 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5915407613758468, "Y": 0.02350182530887189 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6174522779247366, "Y": 0.006649060454341656 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5937286629360872, "Y": 0.02327867868417936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6203490212074908, "Y": 0.0017833418009145924 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5937286629360872, "Y": 0.02327867868417936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6203490212074908, "Y": 0.0017833418009145924 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5937286629360872, "Y": 0.02327867868417936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6203490212074908, "Y": 0.0017833418009145924 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5937286629360872, "Y": 0.02327867868417936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6203490212074908, "Y": 0.0017833418009145924 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5937286629360872, "Y": 0.02327867868417936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6203490212074908, "Y": 0.0017833418009145924 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5937286629360872, "Y": 0.02327867868417936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6203490212074908, "Y": 0.0017833418009145924 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5937286629360872, "Y": 0.02327867868417936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6203490212074908, "Y": 0.0017833418009145924 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5937286629360872, "Y": 0.02327867868417936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6203490212074908, "Y": 0.0017833418009145924 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5633484128967676, "Y": 0.04104945024256901 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5937286629360872, "Y": 0.02327867868417936 }, { "Outlier": "\"inferred: SMC\"", "X": -0.622996122483022, "Y": 0.010954541289445702 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.56580306284154, "Y": 0.038549400727790784 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5966462694771552, "Y": 0.019157783993890173 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6264354724162736, "Y": 0.0014514179560782998 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.56580306284154, "Y": 0.038549400727790784 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5966462694771552, "Y": 0.019157783993890173 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6264354724162736, "Y": 0.0014514179560782998 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.56580306284154, "Y": 0.038549400727790784 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5966462694771552, "Y": 0.019157783993890173 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6264354724162736, "Y": 0.0014514179560782998 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.56580306284154, "Y": 0.038549400727790784 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5966462694771552, "Y": 0.019157783993890173 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6264354724162736, "Y": 0.0014514179560782998 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.316791753728334, "Y": 0.211416681662516 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34299372706277254, "Y": 0.18438739665768075 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36888656783710616, "Y": 0.16454854283838455 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39708716757104184, "Y": 0.14254296670263522 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4237561963765269, "Y": 0.12308080977069805 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45094798986020806, "Y": 0.1050743880359111 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4787262459990884, "Y": 0.09069648974665899 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067841150752334, "Y": 0.0761035733266365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5359464647177236, "Y": 0.05698914672564042 }, { "Outlier": "\"inferred: SMC\"", "X": -0.56580306284154, "Y": 0.038549400727790784 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5966462694771552, "Y": 0.019157783993890173 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6264354724162736, "Y": 0.0014514179560782998 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.32090451932580033, "Y": 0.21504016063456188 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3582946318994006, "Y": 0.19129224719193172 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39442706329205796, "Y": 0.17223562305774093 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4276512192742732, "Y": 0.153686847639388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4569933254503359, "Y": 0.13588955631417038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4905397058700153, "Y": 0.11476891802687318 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5206773934577983, "Y": 0.09525240545256695 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5488819091524848, "Y": 0.0815354254108916 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5757963154128435, "Y": 0.06421690493525276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6056071191548458, "Y": 0.04562338372534841 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6321207379814785, "Y": 0.025922375148819846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6601016016047784, "Y": 0.007055312282303536 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.32090451932580033, "Y": 0.21504016063456188 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3582946318994006, "Y": 0.19129224719193172 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39442706329205796, "Y": 0.17223562305774093 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4276512192742732, "Y": 0.153686847639388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4569933254503359, "Y": 0.13588955631417038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4905397058700153, "Y": 0.11476891802687318 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5206773934577983, "Y": 0.09525240545256695 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5488819091524848, "Y": 0.0815354254108916 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5757963154128435, "Y": 0.06421690493525276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6056071191548458, "Y": 0.04562338372534841 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6321207379814785, "Y": 0.025922375148819846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6601016016047784, "Y": 0.007055312282303536 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2172941645078076, "Y": 0.2876197243053505 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2551502948489095, "Y": 0.259149555800849 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2878549546621706, "Y": 0.23589388960439478 }, { "Outlier": "\"inferred: SMC\"", "X": -0.32090451932580033, "Y": 0.21504016063456188 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3582946318994006, "Y": 0.19129224719193172 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39442706329205796, "Y": 0.17223562305774093 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4276512192742732, "Y": 0.153686847639388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4569933254503359, "Y": 0.13588955631417038 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4905397058700153, "Y": 0.11476891802687318 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5206773934577983, "Y": 0.09525240545256695 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5488819091524848, "Y": 0.0815354254108916 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5757963154128435, "Y": 0.06421690493525276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6056071191548458, "Y": 0.04562338372534841 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6321207379814785, "Y": 0.025922375148819846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6601016016047784, "Y": 0.007055312282303536 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5947990783235746, "Y": 0.03861236488339499 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6260591931026698, "Y": 0.021419994164212364 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6626276040142571, "Y": 0.006082371086596023 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5947990783235746, "Y": 0.03861236488339499 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6260591931026698, "Y": 0.021419994164212364 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6626276040142571, "Y": 0.006082371086596023 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5947990783235746, "Y": 0.03861236488339499 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6260591931026698, "Y": 0.021419994164212364 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6626276040142571, "Y": 0.006082371086596023 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5963252842546677, "Y": 0.03855695898885321 }, { "Outlier": "\"inferred: SMC\"", "X": -0.628616487850507, "Y": 0.01919348839431484 }, { "Outlier": "\"inferred: SMC\"", "X": -0.663645648049156, "Y": 0.001040740449111413 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5963252842546677, "Y": 0.03855695898885321 }, { "Outlier": "\"inferred: SMC\"", "X": -0.628616487850507, "Y": 0.01919348839431484 }, { "Outlier": "\"inferred: SMC\"", "X": -0.663645648049156, "Y": 0.001040740449111413 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5963252842546677, "Y": 0.03855695898885321 }, { "Outlier": "\"inferred: SMC\"", "X": -0.628616487850507, "Y": 0.01919348839431484 }, { "Outlier": "\"inferred: SMC\"", "X": -0.663645648049156, "Y": 0.001040740449111413 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5963252842546677, "Y": 0.03855695898885321 }, { "Outlier": "\"inferred: SMC\"", "X": -0.628616487850507, "Y": 0.01919348839431484 }, { "Outlier": "\"inferred: SMC\"", "X": -0.663645648049156, "Y": 0.001040740449111413 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5963252842546677, "Y": 0.03855695898885321 }, { "Outlier": "\"inferred: SMC\"", "X": -0.628616487850507, "Y": 0.01919348839431484 }, { "Outlier": "\"inferred: SMC\"", "X": -0.663645648049156, "Y": 0.001040740449111413 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5963252842546677, "Y": 0.03855695898885321 }, { "Outlier": "\"inferred: SMC\"", "X": -0.628616487850507, "Y": 0.01919348839431484 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6590931276417477, "Y": 0.004568267006669983 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5963252842546677, "Y": 0.03855695898885321 }, { "Outlier": "\"inferred: SMC\"", "X": -0.628616487850507, "Y": 0.01919348839431484 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6590931276417477, "Y": 0.004568267006669983 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44507747140895276, "Y": 0.13165575968899776 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47586863595852386, "Y": 0.11695152377508879 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5067728781448166, "Y": 0.09939207754506565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5367931987098093, "Y": 0.07729903551624528 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5655596785333227, "Y": 0.05755559850475021 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5963252842546677, "Y": 0.03855695898885321 }, { "Outlier": "\"inferred: SMC\"", "X": -0.628616487850507, "Y": 0.01919348839431484 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6590931276417477, "Y": 0.004568267006669983 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6885262971416679, "Y": 0.004801673874014544 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6885262971416679, "Y": 0.004801673874014544 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6885262971416679, "Y": 0.004801673874014544 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6885262971416679, "Y": 0.004801673874014544 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6885262971416679, "Y": 0.004801673874014544 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6885262971416679, "Y": 0.004801673874014544 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6885262971416679, "Y": 0.004801673874014544 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6865217295968901, "Y": 0.005897318607085478 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6865217295968901, "Y": 0.005897318607085478 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6471926133673755, "Y": 0.021877300612510794 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6865217295968901, "Y": 0.005897318607085478 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009275479073758, "Y": 0.09417905128200302 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5332162585842555, "Y": 0.07660485970140102 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5669446414046941, "Y": 0.05872091709082222 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6062165506279763, "Y": 0.03948069496351521 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6459119500981602, "Y": 0.021149249550155166 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6895746898997234, "Y": 0.005666529100881178 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6271692697721637, "Y": 0.0015852375155021546 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6271692697721637, "Y": 0.0015852375155021546 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6271692697721637, "Y": 0.0015852375155021546 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6297535969032434, "Y": 0.009865244950396028 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6297535969032434, "Y": 0.009865244950396028 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6297535969032434, "Y": 0.009865244950396028 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6297535969032434, "Y": 0.009865244950396028 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6265656449162522, "Y": 0.00579889247232937 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6265656449162522, "Y": 0.00579889247232937 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6265656449162522, "Y": 0.00579889247232937 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6265656449162522, "Y": 0.00579889247232937 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5337806003354174, "Y": 0.07837732391954734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5612209495235924, "Y": 0.06017694254778223 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5878322370350959, "Y": 0.03904349844969769 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6076874106020332, "Y": 0.023170073261030447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6265656449162522, "Y": 0.00579889247232937 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5342797506718355, "Y": 0.0766418497861995 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5685144270555744, "Y": 0.06149793288578658 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6012506808919086, "Y": 0.04627109525506609 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6357277604664702, "Y": 0.030308807707984994 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6703995687369659, "Y": 0.016959805788405162 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5342797506718355, "Y": 0.0766418497861995 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5685144270555744, "Y": 0.06149793288578658 }, { "Outlier": "\"inferred: SMC\"", "X": -0.602832006942389, "Y": 0.04151620934904375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6338129772298717, "Y": 0.025196991312764615 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6629435638035519, "Y": 0.011272669114022497 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5342797506718355, "Y": 0.0766418497861995 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5685144270555744, "Y": 0.06149793288578658 }, { "Outlier": "\"inferred: SMC\"", "X": -0.602832006942389, "Y": 0.04151620934904375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6338129772298717, "Y": 0.025196991312764615 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6629435638035519, "Y": 0.011272669114022497 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5342797506718355, "Y": 0.0766418497861995 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5685144270555744, "Y": 0.06149793288578658 }, { "Outlier": "\"inferred: SMC\"", "X": -0.602832006942389, "Y": 0.04151620934904375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6338129772298717, "Y": 0.025196991312764615 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6629435638035519, "Y": 0.011272669114022497 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34314833639629005, "Y": 0.1858854513352741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3760897877558494, "Y": 0.16651277407500978 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40925175675206993, "Y": 0.1490808173994662 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43936179673711145, "Y": 0.12803136729313846 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47108137441040415, "Y": 0.11220855714379221 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5022815971698137, "Y": 0.09341616163371447 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5342797506718355, "Y": 0.0766418497861995 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5685144270555744, "Y": 0.06149793288578658 }, { "Outlier": "\"inferred: SMC\"", "X": -0.602832006942389, "Y": 0.04151620934904375 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6338129772298717, "Y": 0.025196991312764615 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6629435638035519, "Y": 0.011272669114022497 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5428429655612316, "Y": 0.07618012659151654 }, { "Outlier": "\"inferred: SMC\"", "X": -0.570731458581952, "Y": 0.055923879500868905 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5942699462639898, "Y": 0.03861264106385154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6168788952064779, "Y": 0.021385912135437286 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6416209040003239, "Y": 0.007370847613644524 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5428429655612316, "Y": 0.07618012659151654 }, { "Outlier": "\"inferred: SMC\"", "X": -0.570731458581952, "Y": 0.055923879500868905 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5942699462639898, "Y": 0.03861264106385154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6168788952064779, "Y": 0.021385912135437286 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6416209040003239, "Y": 0.007370847613644524 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5428429655612316, "Y": 0.07618012659151654 }, { "Outlier": "\"inferred: SMC\"", "X": -0.570731458581952, "Y": 0.055923879500868905 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5942699462639898, "Y": 0.03861264106385154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6168788952064779, "Y": 0.021385912135437286 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6416209040003239, "Y": 0.007370847613644524 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5428429655612316, "Y": 0.07618012659151654 }, { "Outlier": "\"inferred: SMC\"", "X": -0.570731458581952, "Y": 0.055923879500868905 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5942699462639898, "Y": 0.03861264106385154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6168788952064779, "Y": 0.021385912135437286 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6416209040003239, "Y": 0.007370847613644524 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6241349546499799, "Y": 0.038176764689027705 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6633273576677234, "Y": 0.02241240343566652 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7069282985188999, "Y": 0.00865209210683719 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6938532433850554, "Y": 0.010871486442345271 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6938532433850554, "Y": 0.010871486442345271 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6938532433850554, "Y": 0.010871486442345271 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6938532433850554, "Y": 0.010871486442345271 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6938532433850554, "Y": 0.010871486442345271 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6852216663618013, "Y": 0.003955146350262749 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6852216663618013, "Y": 0.003955146350262749 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6852216663618013, "Y": 0.003955146350262749 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6852216663618013, "Y": 0.003955146350262749 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47294524222147616, "Y": 0.11618104863376423 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5068066934784046, "Y": 0.09770113149698681 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5436733468243418, "Y": 0.07706202057035039 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5835526093764752, "Y": 0.05678556957751198 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6220981589125236, "Y": 0.03729891891765083 }, { "Outlier": "\"inferred: SMC\"", "X": -0.656181308034505, "Y": 0.020675885220531258 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6852216663618013, "Y": 0.003955146350262749 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.476151183390649, "Y": 0.11321114136223365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5153314981249475, "Y": 0.09715646588211191 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5538817122471563, "Y": 0.08010077673122463 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5978936801002322, "Y": 0.06105526143880218 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6467943841292383, "Y": 0.045721605122369544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7002333759121572, "Y": 0.02933783970983177 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7545409559575499, "Y": 0.015026509906687135 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.476151183390649, "Y": 0.11321114136223365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5153314981249475, "Y": 0.09715646588211191 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5538817122471563, "Y": 0.08010077673122463 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5978936801002322, "Y": 0.06105526143880218 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6467943841292383, "Y": 0.045721605122369544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7002333759121572, "Y": 0.02933783970983177 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7545409559575499, "Y": 0.015026509906687135 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2792028284649037, "Y": 0.23129325601047276 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31067044314662895, "Y": 0.20671471457917712 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3419467973814976, "Y": 0.1820015907506381 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3713804139568171, "Y": 0.16387822435958885 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40218193859824725, "Y": 0.14802015377733915 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43785322848478614, "Y": 0.13216418371542726 }, { "Outlier": "\"inferred: SMC\"", "X": -0.476151183390649, "Y": 0.11321114136223365 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5153314981249475, "Y": 0.09715646588211191 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5538817122471563, "Y": 0.08010077673122463 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5978936801002322, "Y": 0.06105526143880218 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6467943841292383, "Y": 0.045721605122369544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7002333759121572, "Y": 0.02933783970983177 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7545409559575499, "Y": 0.015026509906687135 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5194763303336528, "Y": 0.017097562926434016 }, { "Outlier": "\"inferred: SMC\"", "X": -0.54121189327279, "Y": 0.0017151581055048406 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5194763303336528, "Y": 0.017097562926434016 }, { "Outlier": "\"inferred: SMC\"", "X": -0.54121189327279, "Y": 0.0017151581055048406 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5194763303336528, "Y": 0.017097562926434016 }, { "Outlier": "\"inferred: SMC\"", "X": -0.54121189327279, "Y": 0.0017151581055048406 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5194763303336528, "Y": 0.017097562926434016 }, { "Outlier": "\"inferred: SMC\"", "X": -0.54121189327279, "Y": 0.0017151581055048406 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5371293002447183, "Y": 0.0006284409238416777 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5371293002447183, "Y": 0.0006284409238416777 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5371293002447183, "Y": 0.0006284409238416777 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5371293002447183, "Y": 0.0006284409238416777 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5371293002447183, "Y": 0.0006284409238416777 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5371293002447183, "Y": 0.0006284409238416777 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5373099601982263, "Y": 0.003009552303436719 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5373099601982263, "Y": 0.003009552303436719 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5373099601982263, "Y": 0.003009552303436719 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5394968597247236, "Y": 0.0017290815776572054 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39527251322488854, "Y": 0.11773796062857746 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4190178144978661, "Y": 0.10244285269823544 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43938812941378375, "Y": 0.08709368594846295 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4614002454415714, "Y": 0.06813248171592413 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4819985312263175, "Y": 0.048667803768636 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5009562963052225, "Y": 0.03242844509604144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5209918842493634, "Y": 0.01704883201691912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5394968597247236, "Y": 0.0017290815776572054 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39255235704970204, "Y": 0.11885713911515158 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4165548074960653, "Y": 0.10012401660646739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43860652309292497, "Y": 0.08373322746420633 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4584304660814772, "Y": 0.0670386548254166 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4781069424974416, "Y": 0.04925746861639335 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5000863360827517, "Y": 0.034862502335241116 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5179401576662823, "Y": 0.0187401653369316 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5387734050433184, "Y": 0.0001721262217898832 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2998988460116584, "Y": 0.2001706445609565 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3226667075287177, "Y": 0.17688660530645267 }, { "Outlier": "\"inferred: SMC\"", "X": -0.34627376278915456, "Y": 0.1556572102087902 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3691691168761392, "Y": 0.1383536366179508 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39255235704970204, "Y": 0.11885713911515158 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4165548074960653, "Y": 0.10012401660646739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43860652309292497, "Y": 0.08373322746420633 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4584304660814772, "Y": 0.0670386548254166 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4781069424974416, "Y": 0.04925746861639335 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5000863360827517, "Y": 0.034862502335241116 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5179401576662823, "Y": 0.0187401653369316 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5387734050433184, "Y": 0.0001721262217898832 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44290912716013786, "Y": 0.014830889886285533 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45688536465237234, "Y": 0.004972459439000463 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44290912716013786, "Y": 0.014830889886285533 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45688536465237234, "Y": 0.004972459439000463 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44290912716013786, "Y": 0.014830889886285533 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45688536465237234, "Y": 0.004972459439000463 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44290912716013786, "Y": 0.014830889886285533 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45688536465237234, "Y": 0.004972459439000463 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44290912716013786, "Y": 0.014830889886285533 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45688536465237234, "Y": 0.004972459439000463 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44290912716013786, "Y": 0.014830889886285533 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45688536465237234, "Y": 0.004972459439000463 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44290912716013786, "Y": 0.014830889886285533 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45688536465237234, "Y": 0.004972459439000463 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.44290912716013786, "Y": 0.014830889886285533 }, { "Outlier": "\"inferred: SMC\"", "X": -0.45688536465237234, "Y": 0.004972459439000463 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4428669458278141, "Y": 0.011127313331630515 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46029093028383206, "Y": 0.00043301488999437227 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.24784520708305846, "Y": 0.25618837319980126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.274785990313542, "Y": 0.22731348972380414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3014433759410433, "Y": 0.2018309918003907 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3267021865145535, "Y": 0.17634166401071308 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3428860173228706, "Y": 0.15515881281904498 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3577224922700596, "Y": 0.13249517286182802 }, { "Outlier": "\"inferred: SMC\"", "X": -0.37254554564898534, "Y": 0.1120957105880586 }, { "Outlier": "\"inferred: SMC\"", "X": -0.384716039542933, "Y": 0.09248033722769283 }, { "Outlier": "\"inferred: SMC\"", "X": -0.39637263471680767, "Y": 0.07720785227164881 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40625278670593457, "Y": 0.060356925648269055 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4191801766937926, "Y": 0.04577491558643439 }, { "Outlier": "\"inferred: SMC\"", "X": -0.430963776903801, "Y": 0.02864993838754987 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4428669458278141, "Y": 0.011127313331630515 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46029093028383206, "Y": 0.00043301488999437227 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6982710026880471, "Y": 0.026818231485974904 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7417008274819702, "Y": 0.0034970780008706064 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6982710026880471, "Y": 0.026818231485974904 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7417008274819702, "Y": 0.0034970780008706064 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6982710026880471, "Y": 0.026818231485974904 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7417008274819702, "Y": 0.0034970780008706064 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6982710026880471, "Y": 0.026818231485974904 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7417008274819702, "Y": 0.0034970780008706064 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6982710026880471, "Y": 0.026818231485974904 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7417008274819702, "Y": 0.0034970780008706064 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6982710026880471, "Y": 0.026818231485974904 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7417008274819702, "Y": 0.0034970780008706064 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6887874241714173, "Y": 0.023591821478897834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7210839397589422, "Y": 0.005520746600371489 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6887874241714173, "Y": 0.023591821478897834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7210839397589422, "Y": 0.005520746600371489 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6887874241714173, "Y": 0.023591821478897834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7210839397589422, "Y": 0.005520746600371489 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6887874241714173, "Y": 0.023591821478897834 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7210839397589422, "Y": 0.005520746600371489 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7361595495302435, "Y": 0.0037575930738195026 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7361595495302435, "Y": 0.0037575930738195026 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7361595495302435, "Y": 0.0037575930738195026 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7361595495302435, "Y": 0.0037575930738195026 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7361595495302435, "Y": 0.0037575930738195026 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7361595495302435, "Y": 0.0037575930738195026 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7361595495302435, "Y": 0.0037575930738195026 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7333448337808445, "Y": 0.006967699727372403 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7333448337808445, "Y": 0.006967699727372403 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7333448337808445, "Y": 0.006967699727372403 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28604713275182275, "Y": 0.2370240911764912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3234113043396357, "Y": 0.21473896068229345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.36004916499749484, "Y": 0.19308319487910808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.396387489516233, "Y": 0.17631333678545638 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4309246754539237, "Y": 0.16033646676681154 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4665747749954334, "Y": 0.14626315402252738 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5075227355985902, "Y": 0.12910653915020032 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5448800769633616, "Y": 0.10726854636275815 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5808653262896497, "Y": 0.08449838452347357 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6170106651128199, "Y": 0.06649805685084739 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6554601343951676, "Y": 0.04551892273729057 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6956486618833144, "Y": 0.02502516421295808 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7333448337808445, "Y": 0.006967699727372403 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5702660437595304, "Y": 0.08157170202677755 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6064287845870358, "Y": 0.06391686916007347 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6403558844097974, "Y": 0.04511090676027414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6791069170320125, "Y": 0.02364673331820956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7214472789708484, "Y": 0.0011167267298162198 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5702660437595304, "Y": 0.08157170202677755 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6064287845870358, "Y": 0.06391686916007347 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6403558844097974, "Y": 0.04511090676027414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6791069170320125, "Y": 0.02364673331820956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7214472789708484, "Y": 0.0011167267298162198 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5702660437595304, "Y": 0.08157170202677755 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6064287845870358, "Y": 0.06391686916007347 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6403558844097974, "Y": 0.04511090676027414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6791069170320125, "Y": 0.02364673331820956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7214472789708484, "Y": 0.0011167267298162198 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5702660437595304, "Y": 0.08157170202677755 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6064287845870358, "Y": 0.06391686916007347 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6403558844097974, "Y": 0.04511090676027414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6791069170320125, "Y": 0.02364673331820956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7214472789708484, "Y": 0.0011167267298162198 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5702660437595304, "Y": 0.08157170202677755 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6064287845870358, "Y": 0.06391686916007347 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6403558844097974, "Y": 0.04511090676027414 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6791069170320125, "Y": 0.02364673331820956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.7214472789708484, "Y": 0.0011167267298162198 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.685121257463999, "Y": 0.0035077130384635474 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.685121257463999, "Y": 0.0035077130384635474 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.685121257463999, "Y": 0.0035077130384635474 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.685121257463999, "Y": 0.0035077130384635474 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.685121257463999, "Y": 0.0035077130384635474 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6863205629512481, "Y": 0.005165502724970121 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6863205629512481, "Y": 0.005165502724970121 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6863205629512481, "Y": 0.005165502724970121 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6863205629512481, "Y": 0.005165502724970121 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.690695025115444, "Y": 0.0023579360924105286 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.690695025115444, "Y": 0.0023579360924105286 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6053027595345777, "Y": 0.06321349120342193 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6336672440490879, "Y": 0.046269816175405126 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6603830318184294, "Y": 0.025726396340543342 }, { "Outlier": "\"inferred: SMC\"", "X": -0.690695025115444, "Y": 0.0023579360924105286 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6568164231360494, "Y": 0.02741607288708269 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6848415801861084, "Y": 0.007372619787926273 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6568164231360494, "Y": 0.02741607288708269 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6848415801861084, "Y": 0.007372619787926273 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6568164231360494, "Y": 0.02741607288708269 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6848415801861084, "Y": 0.007372619787926273 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6568164231360494, "Y": 0.02741607288708269 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6848415801861084, "Y": 0.007372619787926273 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6568164231360494, "Y": 0.02741607288708269 }, { "Outlier": "\"inferred: SMC\"", "X": -0.681043550460873, "Y": 0.014069719699924923 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6547590361208603, "Y": 0.026370798262019598 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6772568148482645, "Y": 0.008179268678566179 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6547590361208603, "Y": 0.026370798262019598 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6772568148482645, "Y": 0.008179268678566179 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6547590361208603, "Y": 0.026370798262019598 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6772568148482645, "Y": 0.008179268678566179 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6547590361208603, "Y": 0.026370798262019598 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6772568148482645, "Y": 0.008179268678566179 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.2857447358062829, "Y": 0.23487566804894558 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3225955447557907, "Y": 0.21506239154706405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3612373237593007, "Y": 0.19531861403579637 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3965231451619146, "Y": 0.1794114915716545 }, { "Outlier": "\"inferred: SMC\"", "X": -0.43044478100195704, "Y": 0.1608260209432863 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4652724707928147, "Y": 0.14050491233515047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4994711542299102, "Y": 0.11815671627034956 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5344525995238006, "Y": 0.0980627582594388 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5708803142128502, "Y": 0.07961095708327097 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6035383921759153, "Y": 0.06061469006676752 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6309219559983303, "Y": 0.043692434219421025 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6547590361208603, "Y": 0.026370798262019598 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6772568148482645, "Y": 0.008179268678566179 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5862125197674879, "Y": 0.06178057292013678 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181896423666559, "Y": 0.041236245274847254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.648062503653634, "Y": 0.01973664096781251 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6755142096756601, "Y": 0.0014442975240974917 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5862125197674879, "Y": 0.06178057292013678 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181896423666559, "Y": 0.041236245274847254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.648062503653634, "Y": 0.01973664096781251 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6755142096756601, "Y": 0.0014442975240974917 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5862125197674879, "Y": 0.06178057292013678 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181896423666559, "Y": 0.041236245274847254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.648062503653634, "Y": 0.01973664096781251 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6755142096756601, "Y": 0.0014442975240974917 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5862125197674879, "Y": 0.06178057292013678 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181896423666559, "Y": 0.041236245274847254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.648062503653634, "Y": 0.01973664096781251 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6755142096756601, "Y": 0.0014442975240974917 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5862125197674879, "Y": 0.06178057292013678 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181896423666559, "Y": 0.041236245274847254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6545119385865543, "Y": 0.01904915810483982 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6936469683298314, "Y": 0.0010696601969186863 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5862125197674879, "Y": 0.06178057292013678 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181896423666559, "Y": 0.041236245274847254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6545119385865543, "Y": 0.01904915810483982 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6936469683298314, "Y": 0.0010696601969186863 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5862125197674879, "Y": 0.06178057292013678 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181896423666559, "Y": 0.041236245274847254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6545119385865543, "Y": 0.01904915810483982 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6936469683298314, "Y": 0.0010696601969186863 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5862125197674879, "Y": 0.06178057292013678 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181896423666559, "Y": 0.041236245274847254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6545119385865543, "Y": 0.01904915810483982 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6936469683298314, "Y": 0.0010696601969186863 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5862125197674879, "Y": 0.06178057292013678 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181896423666559, "Y": 0.041236245274847254 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6545119385865543, "Y": 0.01904915810483982 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6936469683298314, "Y": 0.0010696601969186863 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5877050572596199, "Y": 0.06218939842479358 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6262336260207828, "Y": 0.04785154793890249 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6631281341503751, "Y": 0.027310287322521724 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6962618209629883, "Y": 0.005283848519034972 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5877050572596199, "Y": 0.06218939842479358 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6262336260207828, "Y": 0.04785154793890249 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6631281341503751, "Y": 0.027310287322521724 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6962618209629883, "Y": 0.005283848519034972 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38156640560502736, "Y": 0.1735921790984511 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41074776942340757, "Y": 0.15333287011718144 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4418377158382337, "Y": 0.13627433065616407 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47666117343521264, "Y": 0.11839403159752551 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5141588940896763, "Y": 0.09780291858801503 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5509783146076754, "Y": 0.08038237273133282 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5877050572596199, "Y": 0.06218939842479358 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6262336260207828, "Y": 0.04785154793890249 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6631281341503751, "Y": 0.027310287322521724 }, { "Outlier": "\"inferred: SMC\"", "X": -0.702729493133634, "Y": 0.008471373018400074 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4528739532496861, "Y": 0.10983389112587244 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47673371599372877, "Y": 0.0906694800948818 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5025065772732288, "Y": 0.07214529494671035 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5313728586780839, "Y": 0.05309923010390435 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5566456778813921, "Y": 0.03739639994782584 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5806667706910098, "Y": 0.023133773464972546 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6094140718249482, "Y": 0.00788367303243808 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6948239572273156, "Y": 0.008539698760850242 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6948239572273156, "Y": 0.008539698760850242 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6975307039508771, "Y": 0.004821307382239775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6975307039508771, "Y": 0.004821307382239775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6975307039508771, "Y": 0.004821307382239775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6975307039508771, "Y": 0.004821307382239775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6975307039508771, "Y": 0.004821307382239775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6975307039508771, "Y": 0.004821307382239775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6975307039508771, "Y": 0.004821307382239775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42978460467961566, "Y": 0.12933958441345061 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4567009311701835, "Y": 0.10904001661872884 }, { "Outlier": "\"inferred: SMC\"", "X": -0.488745935479984, "Y": 0.09097627258326996 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5226995926137651, "Y": 0.075277949820104 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5609775917545126, "Y": 0.05916375787805751 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6073914226312319, "Y": 0.041929981804032734 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6500033602375986, "Y": 0.025475005181643345 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6975307039508771, "Y": 0.004821307382239775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42970792281028836, "Y": 0.1289040094482714 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4551449050852421, "Y": 0.10816191676732405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.48231370941269813, "Y": 0.09038155453018343 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5098450084383118, "Y": 0.07290321278949374 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5360454825937537, "Y": 0.05469415581528559 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5559314769527912, "Y": 0.03758895958161651 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5746989272782822, "Y": 0.021160304120237002 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5962120773994956, "Y": 0.007806573176749161 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42970792281028836, "Y": 0.1289040094482714 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4551449050852421, "Y": 0.10816191676732405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.48231370941269813, "Y": 0.09038155453018343 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5098450084383118, "Y": 0.07290321278949374 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5360454825937537, "Y": 0.05469415581528559 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5559314769527912, "Y": 0.03758895958161651 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5746989272782822, "Y": 0.021160304120237002 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5962120773994956, "Y": 0.007806573176749161 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42970792281028836, "Y": 0.1289040094482714 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4551449050852421, "Y": 0.10816191676732405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.48231370941269813, "Y": 0.09038155453018343 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5098450084383118, "Y": 0.07290321278949374 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5360454825937537, "Y": 0.05469415581528559 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5559314769527912, "Y": 0.03758895958161651 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5746989272782822, "Y": 0.021160304120237002 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5962120773994956, "Y": 0.007806573176749161 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3782143382970573, "Y": 0.1681582901929449 }, { "Outlier": "\"inferred: SMC\"", "X": -0.40585800052882914, "Y": 0.1477519496774359 }, { "Outlier": "\"inferred: SMC\"", "X": -0.42970792281028836, "Y": 0.1289040094482714 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4551449050852421, "Y": 0.10816191676732405 }, { "Outlier": "\"inferred: SMC\"", "X": -0.48231370941269813, "Y": 0.09038155453018343 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5098450084383118, "Y": 0.07290321278949374 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5360454825937537, "Y": 0.05469415581528559 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5559314769527912, "Y": 0.03758895958161651 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5746989272782822, "Y": 0.021160304120237002 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5962120773994956, "Y": 0.007806573176749161 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38282795152784793, "Y": 0.1698215723870893 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41193858893908264, "Y": 0.15108997020485557 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4407968938716116, "Y": 0.134433703079847 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47146516125427496, "Y": 0.11619398714032034 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5049158063103905, "Y": 0.09757718727280426 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5382439826842788, "Y": 0.07628177145656294 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5690563375663207, "Y": 0.058195345062633595 }, { "Outlier": "\"inferred: SMC\"", "X": -0.604561949062135, "Y": 0.03841307443326778 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6412332238252006, "Y": 0.017855708648847647 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6738692278344038, "Y": 0.00047825105033353493 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38282795152784793, "Y": 0.1698215723870893 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41193858893908264, "Y": 0.15108997020485557 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4407968938716116, "Y": 0.134433703079847 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47146516125427496, "Y": 0.11619398714032034 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5049158063103905, "Y": 0.09757718727280426 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5382439826842788, "Y": 0.07628177145656294 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5690563375663207, "Y": 0.058195345062633595 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6002146128697411, "Y": 0.04100047206541155 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6363577981721181, "Y": 0.026855953264606984 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6770789346611286, "Y": 0.015065741348282693 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38282795152784793, "Y": 0.1698215723870893 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41193858893908264, "Y": 0.15108997020485557 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4407968938716116, "Y": 0.134433703079847 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47146516125427496, "Y": 0.11619398714032034 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5049158063103905, "Y": 0.09757718727280426 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5382439826842788, "Y": 0.07628177145656294 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5690563375663207, "Y": 0.058195345062633595 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6002146128697411, "Y": 0.04100047206541155 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6314536830286597, "Y": 0.023253523572415592 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6622300617658394, "Y": 0.002513257505690168 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38282795152784793, "Y": 0.1698215723870893 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41193858893908264, "Y": 0.15108997020485557 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4407968938716116, "Y": 0.134433703079847 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47146516125427496, "Y": 0.11619398714032034 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5049158063103905, "Y": 0.09757718727280426 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5382439826842788, "Y": 0.07628177145656294 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5690563375663207, "Y": 0.058195345062633595 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6002146128697411, "Y": 0.04100047206541155 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6314536830286597, "Y": 0.023253523572415592 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6622300617658394, "Y": 0.002513257505690168 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.38282795152784793, "Y": 0.1698215723870893 }, { "Outlier": "\"inferred: SMC\"", "X": -0.41193858893908264, "Y": 0.15108997020485557 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4407968938716116, "Y": 0.134433703079847 }, { "Outlier": "\"inferred: SMC\"", "X": -0.47146516125427496, "Y": 0.11619398714032034 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5049158063103905, "Y": 0.09757718727280426 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5382439826842788, "Y": 0.07628177145656294 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5690563375663207, "Y": 0.058195345062633595 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6002146128697411, "Y": 0.04100047206541155 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6314536830286597, "Y": 0.023253523572415592 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6622300617658394, "Y": 0.002513257505690168 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5489693920552101, "Y": 0.036311726415789336 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5767422385845212, "Y": 0.018518092338331727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6071754471405173, "Y": 0.0006580918434104348 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5489693920552101, "Y": 0.036311726415789336 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5767422385845212, "Y": 0.018518092338331727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6071754471405173, "Y": 0.0006580918434104348 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5489693920552101, "Y": 0.036311726415789336 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5767422385845212, "Y": 0.018518092338331727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6071754471405173, "Y": 0.0006580918434104348 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5489693920552101, "Y": 0.036311726415789336 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5767422385845212, "Y": 0.018518092338331727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6071754471405173, "Y": 0.0006580918434104348 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5489693920552101, "Y": 0.036311726415789336 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5767422385845212, "Y": 0.018518092338331727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6071754471405173, "Y": 0.0006580918434104348 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5517432734346004, "Y": 0.03629582486875252 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5815243246158537, "Y": 0.022428465550001855 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6117193959258399, "Y": 0.006837506973981472 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5517432734346004, "Y": 0.03629582486875252 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5815243246158537, "Y": 0.022428465550001855 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6117193959258399, "Y": 0.006837506973981472 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5517432734346004, "Y": 0.03629582486875252 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5815243246158537, "Y": 0.022428465550001855 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6117193959258399, "Y": 0.006837506973981472 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5517432734346004, "Y": 0.03629582486875252 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5815243246158537, "Y": 0.022428465550001855 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6117193959258399, "Y": 0.006837506973981472 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5881802519775663, "Y": 0.023096965551105984 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181808125656785, "Y": 0.0040711102216721 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5881802519775663, "Y": 0.023096965551105984 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181808125656785, "Y": 0.0040711102216721 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5881802519775663, "Y": 0.023096965551105984 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6181808125656785, "Y": 0.0040711102216721 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6291920146277327, "Y": 0.007344262886394795 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6291920146277327, "Y": 0.007344262886394795 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6291920146277327, "Y": 0.007344262886394795 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6291920146277327, "Y": 0.007344262886394795 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6205951111366921, "Y": 0.004388063310678893 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6205951111366921, "Y": 0.004388063310678893 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6205951111366921, "Y": 0.004388063310678893 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6205951111366921, "Y": 0.004388063310678893 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6205951111366921, "Y": 0.004388063310678893 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6205951111366921, "Y": 0.004388063310678893 }, { "Outlier": "\"inferred: SMC\"", "X": 0.010257539405063914, "Y": 0.9287357058770148 }, { "Outlier": "\"inferred: SMC\"", "X": 0.015374881739881472, "Y": 0.913031356948891 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01702494589338526, "Y": 0.8958209258372534 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01840256376164657, "Y": 0.8749172752945775 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01817745608694796, "Y": 0.8583583757868019 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017670993430082285, "Y": 0.8447613513312131 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016544567221656564, "Y": 0.8234327939171653 }, { "Outlier": "\"inferred: SMC\"", "X": 0.017748212460411642, "Y": 0.8066149754818942 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02098624123979211, "Y": 0.7859505841421761 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02644716805431388, "Y": 0.7660098680505538 }, { "Outlier": "\"inferred: SMC\"", "X": 0.025479960956679468, "Y": 0.7466404153202828 }, { "Outlier": "\"inferred: SMC\"", "X": 0.02303563641976475, "Y": 0.7312149819178336 }, { "Outlier": "\"inferred: SMC\"", "X": 0.016216098751713377, "Y": 0.7128933533489029 }, { "Outlier": "\"inferred: SMC\"", "X": 0.01235694754958596, "Y": 0.6882287351984667 }, { "Outlier": "\"inferred: SMC\"", "X": 0.006763928456459557, "Y": 0.6625817738819002 }, { "Outlier": "\"inferred: SMC\"", "X": 0.0019668047857608504, "Y": 0.6370582145685727 }, { "Outlier": "\"inferred: SMC\"", "X": -0.005937056124430612, "Y": 0.6065879770514311 }, { "Outlier": "\"inferred: SMC\"", "X": -0.01912921545239656, "Y": 0.5750469394506992 }, { "Outlier": "\"inferred: SMC\"", "X": -0.03259520085509389, "Y": 0.5407671382927453 }, { "Outlier": "\"inferred: SMC\"", "X": -0.05094237680498244, "Y": 0.5015892159460326 }, { "Outlier": "\"inferred: SMC\"", "X": -0.07209798297935455, "Y": 0.45839774945219186 }, { "Outlier": "\"inferred: SMC\"", "X": -0.09531195753995325, "Y": 0.4147654752394703 }, { "Outlier": "\"inferred: SMC\"", "X": -0.12071723292987079, "Y": 0.37270831689522954 }, { "Outlier": "\"inferred: SMC\"", "X": -0.1513174723551429, "Y": 0.34112733521655947 }, { "Outlier": "\"inferred: SMC\"", "X": -0.18588407299531767, "Y": 0.314447626960074 }, { "Outlier": "\"inferred: SMC\"", "X": -0.21998249049949375, "Y": 0.2856631857445848 }, { "Outlier": "\"inferred: SMC\"", "X": -0.25252478140599094, "Y": 0.2600838242330022 }, { "Outlier": "\"inferred: SMC\"", "X": -0.28484686462029984, "Y": 0.23340314414559948 }, { "Outlier": "\"inferred: SMC\"", "X": -0.31815184783861084, "Y": 0.21014286128804882 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3504821407986397, "Y": 0.18937284952443742 }, { "Outlier": "\"inferred: SMC\"", "X": -0.3775526490581649, "Y": 0.1694845991351741 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4013004900222111, "Y": 0.14545598553937017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.4258939455980504, "Y": 0.12752035891218327 }, { "Outlier": "\"inferred: SMC\"", "X": -0.446146711823753, "Y": 0.10834077336401017 }, { "Outlier": "\"inferred: SMC\"", "X": -0.46665494241961936, "Y": 0.08732110497878912 }, { "Outlier": "\"inferred: SMC\"", "X": -0.49449430889822127, "Y": 0.07025627214070655 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5237265487457912, "Y": 0.053799476478384047 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5564083061500829, "Y": 0.03858759135731976 }, { "Outlier": "\"inferred: SMC\"", "X": -0.5903190852663619, "Y": 0.02250634762532965 }, { "Outlier": "\"inferred: SMC\"", "X": -0.6205951111366921, "Y": 0.004388063310678893 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = (\\(x,d) -> zip x (Prelude.repeat $ T.pack \"inferred: SMC\")) =<< sSMC\n", "\n", "plot $ zip positions (Prelude.repeat $ T.pack \"prior\") <> take 10000 a\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sRMSMC <- sampleIOfixed $ runPopulationT $ rmsmcBasic\n", " MCMCConfig {numMCMCSteps = 50, numBurnIn = 0, proposal = SingleSiteMH}\n", " SMCConfig {numSteps = numPoints, numParticles = 10, resampler = resampleSystematic} \n", " $ P.toListM $ model observations" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"prior\"", "X": 0.004040813897471044, "Y": 0.9449190340942227 }, { "Outlier": "\"prior\"", "X": 0.012150145221787412, "Y": 0.9274840162884911 }, { "Outlier": "\"prior\"", "X": 0.016860379454206022, "Y": 0.9084312248689684 }, { "Outlier": "\"prior\"", "X": 0.01641581595718638, "Y": 0.8887040051034267 }, { "Outlier": "\"prior\"", "X": 0.015828991403087755, "Y": 0.8674914294672441 }, { "Outlier": "\"prior\"", "X": 0.013970393980576864, "Y": 0.8491059059946388 }, { "Outlier": "\"prior\"", "X": 0.013001848439189199, "Y": 0.8269556146407302 }, { "Outlier": "\"prior\"", "X": 0.01410869793872522, "Y": 0.8038747409690314 }, { "Outlier": "\"prior\"", "X": 0.01891146561873628, "Y": 0.7845426338677167 }, { "Outlier": "\"prior\"", "X": 0.018379957973462722, "Y": 0.7690718944391366 }, { "Outlier": "\"prior\"", "X": 0.019385135042090187, "Y": 0.7533513585553672 }, { "Outlier": "\"prior\"", "X": 0.021669742734900348, "Y": 0.7352018223991866 }, { "Outlier": "\"prior\"", "X": 0.021156808398970484, "Y": 0.7192462544141534 }, { "Outlier": "\"prior\"", "X": 0.01993478817383033, "Y": 0.7047315558870912 }, { "Outlier": "\"prior\"", "X": 0.012857449682167671, "Y": 0.6860649039570736 }, { "Outlier": "\"prior\"", "X": 0.0036585006460101623, "Y": 0.6652639227003978 }, { "Outlier": "\"prior\"", "X": -0.006704504890859797, "Y": 0.6407731335881577 }, { "Outlier": "\"prior\"", "X": -0.01875148718315546, "Y": 0.6119187708433766 }, { "Outlier": "\"prior\"", "X": -0.03144164055357166, "Y": 0.5811819303837852 }, { "Outlier": "\"prior\"", "X": -0.05337633763916294, "Y": 0.5490664469820896 }, { "Outlier": "\"prior\"", "X": -0.07909088956959608, "Y": 0.5147636649156484 }, { "Outlier": "\"prior\"", "X": -0.10852176412035312, "Y": 0.4771049794440802 }, { "Outlier": "\"prior\"", "X": -0.1415041339372596, "Y": 0.4358158404651159 }, { "Outlier": "\"prior\"", "X": -0.17802868429398142, "Y": 0.3982038738682732 }, { "Outlier": "\"prior\"", "X": -0.21934710951238376, "Y": 0.3670200926908218 }, { "Outlier": "\"prior\"", "X": -0.26185119674488966, "Y": 0.3397953088971217 }, { "Outlier": "\"prior\"", "X": -0.3048405327299023, "Y": 0.3115994429918755 }, { "Outlier": "\"prior\"", "X": -0.3390203782102853, "Y": 0.2796626489022578 }, { "Outlier": "\"prior\"", "X": -0.3763687877889552, "Y": 0.25260454272789823 }, { "Outlier": "\"prior\"", "X": -0.4139616671134319, "Y": 0.22373706411803673 }, { "Outlier": "\"prior\"", "X": -0.4536834468207761, "Y": 0.20157554577872092 }, { "Outlier": "\"prior\"", "X": -0.49218501672059023, "Y": 0.1788270187745313 }, { "Outlier": "\"prior\"", "X": -0.5348912174560139, "Y": 0.1577231534193479 }, { "Outlier": "\"prior\"", "X": -0.5830061761886975, "Y": 0.1372253105135145 }, { "Outlier": "\"prior\"", "X": -0.6318383342252785, "Y": 0.11904963434534747 }, { "Outlier": "\"prior\"", "X": -0.684053191400329, "Y": 0.09799287602519839 }, { "Outlier": "\"prior\"", "X": -0.7356802946475065, "Y": 0.07233980458833347 }, { "Outlier": "\"prior\"", "X": -0.7811584193497797, "Y": 0.04963154818166052 }, { "Outlier": "\"prior\"", "X": -0.8289870413375418, "Y": 0.026265300163843604 }, { "Outlier": "\"prior\"", "X": -0.8814219602100856, "Y": 0.0033672219692231498 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9467274097909931 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9435472970911881 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.9394485502705697 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.936721606646251 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9346229075164824 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.9267714299246226 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9169967796038087 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9062276765688088 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02730757135572791, "Y": 0.8925575772247655 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027829366700922935, "Y": 0.8730234043625444 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02554265602614982, "Y": 0.8533462490277748 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.026558886333323508, "Y": 0.8282144476945056 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028682203652753403, "Y": 0.8009355034550913 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028799803056951748, "Y": 0.7712901966819744 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02445263550073632, "Y": 0.7417673628129733 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013314444247533929, "Y": 0.7053208537289923 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.004083245717426246, "Y": 0.6696806573230097 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.019158169877899145, "Y": 0.6349986344372018 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.036960310690805256, "Y": 0.6000666768403518 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.059586848859735995, "Y": 0.5619649140933167 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.08308217602610972, "Y": 0.5205112809185841 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.11234924187526288, "Y": 0.47958323069046094 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14697041449342838, "Y": 0.44006703115257023 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.1813128555878352, "Y": 0.4040391131089684 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.22041937520295213, "Y": 0.3701126625100612 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.2625104990211642, "Y": 0.3405094174447242 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.30314619862637304, "Y": 0.31110513366839554 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.34074549829404593, "Y": 0.2835629444636712 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.37500688705405216, "Y": 0.25418547331478214 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.41213544214135023, "Y": 0.2235086902748926 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4473691337423051, "Y": 0.19861365649752036 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.48520152357181007, "Y": 0.1767311422549989 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5204931838329226, "Y": 0.15624517757883935 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.557164057796764, "Y": 0.1340274260229287 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5957461822970941, "Y": 0.11349416261367107 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6300704933177195, "Y": 0.09498835318727497 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6668629394068296, "Y": 0.07426423253518108 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6995413651893544, "Y": 0.053175213056351714 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7310712572464292, "Y": 0.030648100417335865 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.765367004688498, "Y": 0.01014859535350203 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9467274097909931 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9435472970911881 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.9394485502705697 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.936721606646251 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9346229075164824 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.9267714299246226 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9169967796038087 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9062276765688088 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02730757135572791, "Y": 0.8925575772247655 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027829366700922935, "Y": 0.8730234043625444 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02554265602614982, "Y": 0.8533462490277748 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.026695831399355243, "Y": 0.8282144476945056 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028956093784816873, "Y": 0.8009355034550913 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.029210638255046953, "Y": 0.7712901966819744 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02500041576486326, "Y": 0.7417673628129733 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013999169577692604, "Y": 0.7053208537289923 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.0032615753212358357, "Y": 0.6696806573230097 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.018199554415677, "Y": 0.6349986344372018 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.035864750162551376, "Y": 0.6000666768403518 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.05835434326545038, "Y": 0.5619649140933167 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.08171272536579237, "Y": 0.5205112809185841 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.11084284614891379, "Y": 0.47958323069046094 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14532707370104755, "Y": 0.44006703115257023 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.17953256972942264, "Y": 0.4040391131089684 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.21850214427850784, "Y": 0.3701126625100612 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.26045632303068816, "Y": 0.3405094174447242 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3009550775698653, "Y": 0.31110513366839554 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.33841743217150644, "Y": 0.2835629444636712 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.37254187586548093, "Y": 0.25418547331478214 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4095334858867472, "Y": 0.2235086902748926 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4415557145926518, "Y": 0.19861365649752036 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.47617664152710654, "Y": 0.1767311422549989 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5123310712421772, "Y": 0.15624517757883935 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5498647146599768, "Y": 0.1340274260229287 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5893096086142648, "Y": 0.11349416261367107 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6287103315896534, "Y": 0.09498835318727497 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6705791896335266, "Y": 0.07426423253518108 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7122961578346972, "Y": 0.053175213056351714 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7528645923104179, "Y": 0.030648100417335865 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7961988821711328, "Y": 0.007728701760656348 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9467274097909931 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9435472970911881 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.9394485502705697 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.936721606646251 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9346229075164824 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.9267714299246226 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9169967796038087 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9062276765688088 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02730757135572791, "Y": 0.8925575772247655 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027829366700922935, "Y": 0.8730234043625444 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02554265602614982, "Y": 0.8533462490277748 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.026695831399355243, "Y": 0.8282144476945056 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028956093784816873, "Y": 0.8009355034550913 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.029381177840480673, "Y": 0.7712901966819744 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.0253414949357307, "Y": 0.7417673628129733 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.01451078833399376, "Y": 0.7053208537289923 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.002579416979500964, "Y": 0.6696806573230097 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.01734685648850841, "Y": 0.6349986344372018 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.03484151264994907, "Y": 0.6001399712730898 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.057160566167414356, "Y": 0.5621115029587929 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.08034840868232263, "Y": 0.5206305308216507 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.10930798988001032, "Y": 0.47967514163111785 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14362167784671037, "Y": 0.44013160313081756 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.17765663428965173, "Y": 0.40392207495040333 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.21645566925330323, "Y": 0.3696338324815025 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.25917259124496944, "Y": 0.33966879554617185 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.300205638911785, "Y": 0.3099027198998496 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.33909784371030016, "Y": 0.2819987388251316 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3751152591754063, "Y": 0.25329561587825766 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4125495823633271, "Y": 0.2232931810403832 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4464612743908316, "Y": 0.19907249546502606 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.48003296544324797, "Y": 0.1775016695486978 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5141333712302704, "Y": 0.15732739319873149 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5463204442774224, "Y": 0.13269033167737498 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5831563313299571, "Y": 0.1102503960789711 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6196911304334671, "Y": 0.09061347846972317 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6586768647106479, "Y": 0.06698735081299483 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6989259294800346, "Y": 0.04560832102023001 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7348706567397809, "Y": 0.025049728624555425 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7695444713456311, "Y": 0.007264473587541998 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9467274097909931 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9435472970911881 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.9394485502705697 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.936721606646251 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9346229075164824 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.9267714299246226 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9169967796038087 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9062276765688088 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02730757135572791, "Y": 0.8925575772247655 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027829366700922935, "Y": 0.8730234043625444 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02554265602614982, "Y": 0.8533462490277748 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.026695831399355243, "Y": 0.8282144476945056 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028956093784816873, "Y": 0.8009355034550913 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.029381177840480673, "Y": 0.7712901966819744 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.0253414949357307, "Y": 0.7417673628129733 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.01451078833399376, "Y": 0.7053208537289923 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.002579416979500964, "Y": 0.6696806573230097 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.01734685648850841, "Y": 0.6349986344372018 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.03484151264994907, "Y": 0.6000666768403518 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.057160566167414356, "Y": 0.5619649140933167 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.08034840868232263, "Y": 0.5204106475234365 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.10930798988001032, "Y": 0.4793819639001656 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14362167784671037, "Y": 0.4397651309671272 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.17765663428965173, "Y": 0.4034823083539749 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.21645566925330323, "Y": 0.369120771452336 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.25823930842004983, "Y": 0.3390824400842673 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.2983390732619457, "Y": 0.30924307000520695 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3362979952355412, "Y": 0.2812657944977509 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.37138212787572766, "Y": 0.2524893771181389 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4078831682387289, "Y": 0.22241364784752637 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.44086157744131377, "Y": 0.19811966783943116 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.47349998566881046, "Y": 0.17647554749036481 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5066671086309131, "Y": 0.15622797670766042 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5417468521070992, "Y": 0.13151762075356585 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5776838115990157, "Y": 0.10900439072242388 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6123208243633929, "Y": 0.08917487731289964 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6458628119496247, "Y": 0.06535615385589497 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6806681300280625, "Y": 0.043784528262853825 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7139736288783997, "Y": 0.02303334006690291 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7493344552256145, "Y": 0.0019189563796947404 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9467274097909931 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9435472970911881 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.9394485502705697 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.936721606646251 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9346229075164824 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.9267714299246226 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9169967796038087 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9062276765688088 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02730757135572791, "Y": 0.8925575772247655 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027829366700922935, "Y": 0.8730234043625444 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02554265602614982, "Y": 0.8533462490277748 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.026695831399355243, "Y": 0.8282144476945056 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028956093784816873, "Y": 0.8009355034550913 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.029381177840480673, "Y": 0.7712901966819744 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.0253414949357307, "Y": 0.7417673628129733 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.01451078833399376, "Y": 0.7053208537289923 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.002579416979500964, "Y": 0.6696806573230097 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.01734685648850841, "Y": 0.6349986344372018 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.03484151264994907, "Y": 0.6000666768403518 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.057160566167414356, "Y": 0.5619649140933167 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.08034840868232263, "Y": 0.5204106475234365 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.10930798988001032, "Y": 0.4793819639001656 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14362167784671037, "Y": 0.4397651309671272 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.17765663428965173, "Y": 0.4034823083539749 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.21645566925330323, "Y": 0.369120771452336 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.25823930842004983, "Y": 0.3390824400842673 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.2983390732619457, "Y": 0.30924307000520695 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3362979952355412, "Y": 0.2812657944977509 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.37138212787572766, "Y": 0.2524893771181389 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4078831682387289, "Y": 0.22241364784752637 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.44086157744131377, "Y": 0.19811966783943116 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.47349998566881046, "Y": 0.17647554749036481 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5066671086309131, "Y": 0.15622797670766042 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5417468521070992, "Y": 0.13151762075356585 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.581475409588668, "Y": 0.10900439072242388 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.620902879121212, "Y": 0.08917487731289964 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6595613643117261, "Y": 0.06535615385589497 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7017144773193058, "Y": 0.043784528262853825 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7454691108218506, "Y": 0.024307077617078482 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7879528316704993, "Y": 0.007602964329964303 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9467274097909931 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9435472970911881 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.9394485502705697 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.936721606646251 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9346229075164824 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.9267714299246226 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9169967796038087 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9062276765688088 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02730757135572791, "Y": 0.8925575772247655 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027829366700922935, "Y": 0.8730234043625444 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02554265602614982, "Y": 0.8533462490277748 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.026695831399355243, "Y": 0.8282144476945056 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028956093784816873, "Y": 0.8009355034550913 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.029381177840480673, "Y": 0.7712901966819744 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.0253414949357307, "Y": 0.7417673628129733 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.01451078833399376, "Y": 0.7053208537289923 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.002579416979500964, "Y": 0.6696806573230097 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.01734685648850841, "Y": 0.6349986344372018 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.03484151264994907, "Y": 0.6000666768403518 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.057160566167414356, "Y": 0.5619649140933167 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.08034840868232263, "Y": 0.5204106475234365 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.10930798988001032, "Y": 0.4793819639001656 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14362167784671037, "Y": 0.4397651309671272 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.17765663428965173, "Y": 0.4034823083539749 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.21645566925330323, "Y": 0.369120771452336 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.25823930842004983, "Y": 0.3390824400842673 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.2983390732619457, "Y": 0.30924307000520695 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3362979952355412, "Y": 0.2812657944977509 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.37138212787572766, "Y": 0.2524893771181389 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4078831682387289, "Y": 0.22241364784752637 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.44086157744131377, "Y": 0.19811966783943116 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.47349998566881046, "Y": 0.17647554749036481 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5066671086309131, "Y": 0.15622797670766042 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5401137909618443, "Y": 0.13151762075356585 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5782092872981581, "Y": 0.10900439072242388 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6170948426633659, "Y": 0.08917487731289964 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6590270224484086, "Y": 0.06535615385589497 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7022225327256573, "Y": 0.043784528262853825 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7411137054932654, "Y": 0.023183783084886167 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7827725337012837, "Y": 0.004589249932374272 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9453479854092705 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9421678727094654 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.938069125888847 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.9353831185539387 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9332695323410425 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.925403167666055 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9158334402964013 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9052692602125616 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027253470185046123, "Y": 0.8918040838196785 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027721164359559358, "Y": 0.8724748339086177 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025380352514104455, "Y": 0.8530026015250082 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02647942671662809, "Y": 0.8280757231428991 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028685587931407935, "Y": 0.801001701854645 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028886031230956226, "Y": 0.7715613180326883 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.024556257667299963, "Y": 0.7419704889840638 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013435460406656736, "Y": 0.7054559847204596 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.003944835565744275, "Y": 0.6697638993916774 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.01900236573365801, "Y": 0.6350299875830699 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.036787112554004964, "Y": 0.6000461410634202 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.05939625673037654, "Y": 0.5618924893935856 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.0828741899041911, "Y": 0.5202863339009057 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.11212386176078509, "Y": 0.4790079877771239 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14672764038639144, "Y": 0.4391414923435746 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.181497099837004, "Y": 0.40260900722991133 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.22029008065491024, "Y": 0.3679523064448994 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.2626524309966449, "Y": 0.33761881119345766 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3024473349139499, "Y": 0.30735709141416484 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3414314795054989, "Y": 0.27895746620647627 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.37697298258267903, "Y": 0.2507108269377487 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4157389037420603, "Y": 0.22675393092941498 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4541264304284575, "Y": 0.20145170753150235 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4964873196168898, "Y": 0.17988754561893905 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.535945660057487, "Y": 0.15887277374519315 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5773686144882101, "Y": 0.13638570866897337 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6176176868566647, "Y": 0.11285862993784762 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6591466683586606, "Y": 0.09346473659093016 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6986526505595524, "Y": 0.07053000363965028 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7348081995928503, "Y": 0.05024020505045808 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7690050130800572, "Y": 0.0359804581206518 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.8019557002845479, "Y": 0.020699618292371484 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9453479854092705 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9421678727094654 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.938069125888847 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.9353831185539387 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9332695323410425 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.925403167666055 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9158334402964013 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9052692602125616 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027253470185046123, "Y": 0.8918040838196785 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027721164359559358, "Y": 0.8724748339086177 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025380352514104455, "Y": 0.8530026015250082 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02647942671662809, "Y": 0.8280757231428991 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028685587931407935, "Y": 0.801001701854645 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028886031230956226, "Y": 0.7715613180326883 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.024556257667299963, "Y": 0.7419704889840638 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013435460406656736, "Y": 0.7054559847204596 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.003944835565744275, "Y": 0.6697638993916774 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.01900236573365801, "Y": 0.6350299875830699 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.036787112554004964, "Y": 0.6000461410634202 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.05939625673037654, "Y": 0.5618924893935856 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.0828741899041911, "Y": 0.5202863339009057 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.11212386176078509, "Y": 0.4790079877771239 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14672764038639144, "Y": 0.439067652397251 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.181497099837004, "Y": 0.40246132733726414 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.22029008065491024, "Y": 0.3677307866059286 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.2626524309966449, "Y": 0.33732345140816333 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3024473349139499, "Y": 0.3069878916825469 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3414314795054989, "Y": 0.27851442652853475 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.37697298258267903, "Y": 0.2501939473134836 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4116255875517516, "Y": 0.2261632113588263 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.44589979804784013, "Y": 0.20078714801459008 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4841473710459637, "Y": 0.17914914615570318 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5207360327452443, "Y": 0.15806053433563372 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5581846033588332, "Y": 0.13549962931309034 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5944592919101538, "Y": 0.1117826903145483 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.627995466239501, "Y": 0.09219893670021453 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6563777957263693, "Y": 0.06919752220619217 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6874332403527487, "Y": 0.04981964787347274 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7225863881185116, "Y": 0.033352482279871754 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7614175293384061, "Y": 0.015864223787796724 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9453479854092705 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9421678727094654 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.938069125888847 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.9353831185539387 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9332695323410425 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.925403167666055 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9158334402964013 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9052692602125616 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027253470185046123, "Y": 0.8918040838196785 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027721164359559358, "Y": 0.8724748339086177 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025380352514104455, "Y": 0.8530026015250082 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02647942671662809, "Y": 0.8280757231428991 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028685587931407935, "Y": 0.801001701854645 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028886031230956226, "Y": 0.7715613180326883 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.024556257667299963, "Y": 0.7419704889840638 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013435460406656736, "Y": 0.7054559847204596 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.003944835565744275, "Y": 0.6697638993916774 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.01900236573365801, "Y": 0.6350299875830699 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.036787112554004964, "Y": 0.6000461410634202 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.05939625673037654, "Y": 0.5618924893935856 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.0828741899041911, "Y": 0.5202863339009057 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.11212386176078509, "Y": 0.4790079877771239 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14672764038639144, "Y": 0.439067652397251 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.181497099837004, "Y": 0.40246132733726414 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.22029008065491024, "Y": 0.3677307866059286 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.2626524309966449, "Y": 0.33732345140816333 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3024473349139499, "Y": 0.3069878916825469 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3414314795054989, "Y": 0.27851442652853475 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.37697298258267903, "Y": 0.2501235385962399 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4116255875517516, "Y": 0.22602239392433898 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.44589979804784013, "Y": 0.20057592186285914 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4841473710459637, "Y": 0.17886751128672862 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5207360327452443, "Y": 0.1571962040076392 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5581846033588332, "Y": 0.13405260352607587 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5944592919101538, "Y": 0.10975296906851387 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.627995466239501, "Y": 0.08958651999516015 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6563777957263693, "Y": 0.06600241004211783 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6814974730168428, "Y": 0.04604184025037844 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7092979458768159, "Y": 0.02779000519706246 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7407764121909206, "Y": 0.008517077245272436 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.009410967595545772, "Y": 0.9453479854092705 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.010296375679498098, "Y": 0.9421678727094654 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013319936905183356, "Y": 0.938069125888847 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02000789756456543, "Y": 0.9353831185539387 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02389206252136242, "Y": 0.9332695323410425 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02189748247339938, "Y": 0.925403167666055 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025824833951295978, "Y": 0.9158334402964013 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02665853998923309, "Y": 0.9052692602125616 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027253470185046123, "Y": 0.8918040838196785 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.027721164359559358, "Y": 0.8724748339086177 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.025380352514104455, "Y": 0.8530026015250082 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.02647942671662809, "Y": 0.8280757231428991 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028685587931407935, "Y": 0.801001701854645 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.028886031230956226, "Y": 0.7715613180326883 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.024556257667299963, "Y": 0.7419704889840638 }, { "Outlier": "\"inferred: RMSMC\"", "X": 0.013435460406656736, "Y": 0.7054559847204596 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.003944835565744275, "Y": 0.6697638993916774 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.01900236573365801, "Y": 0.6350299875830699 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.036787112554004964, "Y": 0.6000461410634202 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.05939625673037654, "Y": 0.5618924893935856 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.0828741899041911, "Y": 0.5202863339009057 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.11212386176078509, "Y": 0.4790079877771239 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.14672764038639144, "Y": 0.439067652397251 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.181497099837004, "Y": 0.40246132733726414 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.22029008065491024, "Y": 0.3677307866059286 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.2626524309966449, "Y": 0.33732345140816333 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3024473349139499, "Y": 0.3069878916825469 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.3414314795054989, "Y": 0.27851442652853475 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.37697298258267903, "Y": 0.2501939473134836 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4109810964891817, "Y": 0.2261632113588263 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.44461081592270035, "Y": 0.20078714801459008 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.4822138978582541, "Y": 0.17914914615570318 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5199044134217659, "Y": 0.15806053433563372 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.554823027174373, "Y": 0.13549962931309034 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.5885677588647118, "Y": 0.1117826903145483 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6195739763330772, "Y": 0.09219893670021453 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6514124667588541, "Y": 0.06919752220619217 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.6815954439468591, "Y": 0.04981964787347274 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7158761242742477, "Y": 0.03215050827917672 }, { "Outlier": "\"inferred: RMSMC\"", "X": -0.7538347980557679, "Y": 0.013460275786406652 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a = (\\(x,d) -> zip x (Prelude.repeat $ T.pack \"inferred: RMSMC\")) =<< sRMSMC\n", "\n", "plot $ zip positions (Prelude.repeat $ T.pack \"prior\") <> take 10000 a\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The good results are if the inferred distribution is close to prior, as observed samples are only modified by a small noise from prior.\n", "\n", "And it works! Look how few particles it needed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Particle Marginal Metropolis Hastings (PMMH)\n", "\n", "PMMH is fundamentally an MCMC technique. It performs a random walk through parameter space, but to estimate the likelihood at each step, it uses an unbiased estimate from a population of samples. We'll reuse the regression with outliers example from `tutorials/MCMC.ipynb`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"True\"", "X": -10, "Y": 30.14316922289284 }, { "Outlier": "\"True\"", "X": -9.9, "Y": -0.26284958638081857 }, { "Outlier": "\"False\"", "X": -9.8, "Y": 18.535731623658606 }, { "Outlier": "\"True\"", "X": -9.700000000000001, "Y": -68.99193947945726 }, { "Outlier": "\"True\"", "X": -9.600000000000001, "Y": -8.043397861176732 }, { "Outlier": "\"False\"", "X": -9.500000000000002, "Y": 32.14739624905583 }, { "Outlier": "\"True\"", "X": -9.400000000000002, "Y": -23.81048273352878 }, { "Outlier": "\"False\"", "X": -9.300000000000002, "Y": 17.786702036490702 }, { "Outlier": "\"True\"", "X": -9.200000000000003, "Y": 23.375038991852755 }, { "Outlier": "\"False\"", "X": -9.100000000000003, "Y": 25.793440850750983 }, { "Outlier": "\"True\"", "X": -9.000000000000004, "Y": 24.42143345836998 }, { "Outlier": "\"False\"", "X": -8.900000000000004, "Y": 21.51934491681228 }, { "Outlier": "\"False\"", "X": -8.800000000000004, "Y": 31.353977381067146 }, { "Outlier": "\"True\"", "X": -8.700000000000005, "Y": 18.278561094651828 }, { "Outlier": "\"False\"", "X": -8.600000000000005, "Y": 33.00501809401699 }, { "Outlier": "\"True\"", "X": -8.500000000000005, "Y": 6.437669090883507 }, { "Outlier": "\"True\"", "X": -8.400000000000006, "Y": -37.03228429479993 }, { "Outlier": "\"True\"", "X": -8.300000000000006, "Y": 13.11298520672979 }, { "Outlier": "\"False\"", "X": -8.200000000000006, "Y": 21.21403593146131 }, { "Outlier": "\"True\"", "X": -8.100000000000007, "Y": 11.824928463235018 }, { "Outlier": "\"False\"", "X": -8.000000000000007, "Y": 21.72725799674079 }, { "Outlier": "\"True\"", "X": -7.9000000000000075, "Y": 29.057062960826705 }, { "Outlier": "\"False\"", "X": -7.800000000000008, "Y": 20.649998496823716 }, { "Outlier": "\"True\"", "X": -7.700000000000008, "Y": 8.660181503529154 }, { "Outlier": "\"False\"", "X": -7.6000000000000085, "Y": 15.408422855489516 }, { "Outlier": "\"True\"", "X": -7.500000000000009, "Y": 0.3644817992929184 }, { "Outlier": "\"True\"", "X": -7.400000000000009, "Y": -21.224596735432097 }, { "Outlier": "\"True\"", "X": -7.30000000000001, "Y": -5.08262280552811 }, { "Outlier": "\"True\"", "X": -7.20000000000001, "Y": -22.40271677868227 }, { "Outlier": "\"False\"", "X": -7.10000000000001, "Y": 30.244036005466885 }, { "Outlier": "\"True\"", "X": -7.000000000000011, "Y": 40.6554142943223 }, { "Outlier": "\"True\"", "X": -6.900000000000011, "Y": 0.7924834976117429 }, { "Outlier": "\"True\"", "X": -6.800000000000011, "Y": -3.0689825793534142 }, { "Outlier": "\"True\"", "X": -6.700000000000012, "Y": 2.0777625946734073 }, { "Outlier": "\"False\"", "X": -6.600000000000012, "Y": 12.550388887411348 }, { "Outlier": "\"True\"", "X": -6.500000000000012, "Y": 4.46724475614298 }, { "Outlier": "\"True\"", "X": -6.400000000000013, "Y": -1.5461624315036717 }, { "Outlier": "\"True\"", "X": -6.300000000000013, "Y": 24.71362894119821 }, { "Outlier": "\"True\"", "X": -6.2000000000000135, "Y": 42.412216707046596 }, { "Outlier": "\"False\"", "X": -6.100000000000014, "Y": 18.557621556888908 }, { "Outlier": "\"False\"", "X": -6.000000000000014, "Y": 9.641681498947396 }, { "Outlier": "\"False\"", "X": -5.900000000000015, "Y": 14.163347001302109 }, { "Outlier": "\"False\"", "X": -5.800000000000015, "Y": 20.84295190897201 }, { "Outlier": "\"True\"", "X": -5.700000000000015, "Y": 4.130260844964003 }, { "Outlier": "\"False\"", "X": -5.600000000000016, "Y": 10.475034728856496 }, { "Outlier": "\"True\"", "X": -5.500000000000016, "Y": 19.404328857643684 }, { "Outlier": "\"False\"", "X": -5.400000000000016, "Y": 6.909511750502322 }, { "Outlier": "\"False\"", "X": -5.300000000000017, "Y": 24.44453706966264 }, { "Outlier": "\"True\"", "X": -5.200000000000017, "Y": -14.86585011237548 }, { "Outlier": "\"True\"", "X": -5.100000000000017, "Y": -27.7443807834266 }, { "Outlier": "\"False\"", "X": -5.000000000000018, "Y": 18.14228368221277 }, { "Outlier": "\"False\"", "X": -4.900000000000018, "Y": 26.622225089502074 }, { "Outlier": "\"False\"", "X": -4.8000000000000185, "Y": 7.975229527386173 }, { "Outlier": "\"True\"", "X": -4.700000000000019, "Y": -2.236758643252085 }, { "Outlier": "\"True\"", "X": -4.600000000000019, "Y": 25.095015724308034 }, { "Outlier": "\"True\"", "X": -4.5000000000000195, "Y": 17.18991809253406 }, { "Outlier": "\"True\"", "X": -4.40000000000002, "Y": -24.744360068281004 }, { "Outlier": "\"True\"", "X": -4.30000000000002, "Y": -7.931801457689884 }, { "Outlier": "\"False\"", "X": -4.200000000000021, "Y": 8.09920420163646 }, { "Outlier": "\"False\"", "X": -4.100000000000021, "Y": 14.383701713795746 }, { "Outlier": "\"False\"", "X": -4.000000000000021, "Y": 7.20943981411068 }, { "Outlier": "\"True\"", "X": -3.9000000000000217, "Y": -7.806116027860427 }, { "Outlier": "\"False\"", "X": -3.800000000000022, "Y": 16.98021084694896 }, { "Outlier": "\"False\"", "X": -3.7000000000000224, "Y": 14.379488244709558 }, { "Outlier": "\"False\"", "X": -3.6000000000000227, "Y": 10.476203691783232 }, { "Outlier": "\"False\"", "X": -3.500000000000023, "Y": 18.54439155366095 }, { "Outlier": "\"False\"", "X": -3.4000000000000234, "Y": 17.443166242574943 }, { "Outlier": "\"False\"", "X": -3.300000000000024, "Y": 28.868081826988078 }, { "Outlier": "\"True\"", "X": -3.200000000000024, "Y": -0.36221408177898484 }, { "Outlier": "\"False\"", "X": -3.1000000000000245, "Y": 18.75847005425409 }, { "Outlier": "\"True\"", "X": -3.000000000000025, "Y": -6.3470305289160835 }, { "Outlier": "\"True\"", "X": -2.9000000000000252, "Y": 10.805876165134304 }, { "Outlier": "\"True\"", "X": -2.8000000000000256, "Y": 43.073138049536766 }, { "Outlier": "\"False\"", "X": -2.700000000000026, "Y": 21.39560142763903 }, { "Outlier": "\"True\"", "X": -2.6000000000000263, "Y": 20.17816530884579 }, { "Outlier": "\"False\"", "X": -2.5000000000000266, "Y": 10.593481420961691 }, { "Outlier": "\"True\"", "X": -2.400000000000027, "Y": 24.67140741709269 }, { "Outlier": "\"True\"", "X": -2.3000000000000274, "Y": 3.7143759689601357 }, { "Outlier": "\"False\"", "X": -2.2000000000000277, "Y": 4.205664689951466 }, { "Outlier": "\"False\"", "X": -2.100000000000028, "Y": 5.696299360450189 }, { "Outlier": "\"True\"", "X": -2.0000000000000284, "Y": 14.405714990070868 }, { "Outlier": "\"False\"", "X": -1.9000000000000288, "Y": 13.570785875314327 }, { "Outlier": "\"True\"", "X": -1.8000000000000291, "Y": 25.768223310681986 }, { "Outlier": "\"True\"", "X": -1.7000000000000295, "Y": -37.59090989382119 }, { "Outlier": "\"True\"", "X": -1.6000000000000298, "Y": 23.64144553913516 }, { "Outlier": "\"True\"", "X": -1.5000000000000302, "Y": -8.576305123038889 }, { "Outlier": "\"False\"", "X": -1.4000000000000306, "Y": -10.36508656039852 }, { "Outlier": "\"False\"", "X": -1.300000000000031, "Y": 5.5494955124482335 }, { "Outlier": "\"True\"", "X": -1.2000000000000313, "Y": -4.881619552621354 }, { "Outlier": "\"False\"", "X": -1.1000000000000316, "Y": 5.697048084463994 }, { "Outlier": "\"False\"", "X": -1.000000000000032, "Y": 3.6802215741202695 }, { "Outlier": "\"True\"", "X": -0.9000000000000323, "Y": -31.588506775217645 }, { "Outlier": "\"True\"", "X": -0.8000000000000327, "Y": -4.276114964288603 }, { "Outlier": "\"False\"", "X": -0.700000000000033, "Y": 4.706472715938526 }, { "Outlier": "\"True\"", "X": -0.6000000000000334, "Y": 24.61942267029316 }, { "Outlier": "\"True\"", "X": -0.5000000000000338, "Y": -17.830933767956402 }, { "Outlier": "\"False\"", "X": -0.4000000000000341, "Y": -5.40950732388878 }, { "Outlier": "\"False\"", "X": -0.30000000000003446, "Y": 3.45113720087071 }, { "Outlier": "\"False\"", "X": -0.20000000000003482, "Y": -0.6945661730385604 }, { "Outlier": "\"True\"", "X": -0.10000000000003517, "Y": -11.881720947448017 }, { "Outlier": "\"True\"", "X": -3.552713678800501e-14, "Y": 15.275164463456829 }, { "Outlier": "\"False\"", "X": 0.09999999999996412, "Y": 3.130005271532676 }, { "Outlier": "\"False\"", "X": 0.19999999999996376, "Y": 7.217065710866146 }, { "Outlier": "\"True\"", "X": 0.2999999999999634, "Y": 17.70645751790601 }, { "Outlier": "\"False\"", "X": 0.39999999999996305, "Y": 13.888934006536498 }, { "Outlier": "\"True\"", "X": 0.4999999999999627, "Y": 21.884620845377007 }, { "Outlier": "\"True\"", "X": 0.5999999999999623, "Y": 9.545267231818897 }, { "Outlier": "\"False\"", "X": 0.699999999999962, "Y": 3.703565536648379 }, { "Outlier": "\"False\"", "X": 0.7999999999999616, "Y": 11.858526678970597 }, { "Outlier": "\"False\"", "X": 0.8999999999999613, "Y": -2.745279389040704 }, { "Outlier": "\"False\"", "X": 0.9999999999999609, "Y": 8.1754196030146 }, { "Outlier": "\"False\"", "X": 1.0999999999999606, "Y": -3.0892286454167217 }, { "Outlier": "\"True\"", "X": 1.1999999999999602, "Y": -1.6888315159113967 }, { "Outlier": "\"True\"", "X": 1.2999999999999599, "Y": -26.34560770532998 }, { "Outlier": "\"True\"", "X": 1.3999999999999595, "Y": 7.322340575226373 }, { "Outlier": "\"True\"", "X": 1.4999999999999591, "Y": 1.9729072168157065 }, { "Outlier": "\"False\"", "X": 1.5999999999999588, "Y": 4.3653523448307645 }, { "Outlier": "\"True\"", "X": 1.6999999999999584, "Y": 13.227480313783765 }, { "Outlier": "\"True\"", "X": 1.799999999999958, "Y": 12.226379085662112 }, { "Outlier": "\"False\"", "X": 1.8999999999999577, "Y": 7.283550315220926 }, { "Outlier": "\"False\"", "X": 1.9999999999999574, "Y": -8.322354413220943 }, { "Outlier": "\"False\"", "X": 2.099999999999957, "Y": -6.278804583310366 }, { "Outlier": "\"True\"", "X": 2.1999999999999567, "Y": 0.41427397930309406 }, { "Outlier": "\"False\"", "X": 2.2999999999999563, "Y": -6.593316457144327 }, { "Outlier": "\"False\"", "X": 2.399999999999956, "Y": 10.882276064502353 }, { "Outlier": "\"True\"", "X": 2.4999999999999556, "Y": 4.7901246139126625 }, { "Outlier": "\"False\"", "X": 2.5999999999999552, "Y": -14.966747660332494 }, { "Outlier": "\"False\"", "X": 2.699999999999955, "Y": -0.20721316523803424 }, { "Outlier": "\"False\"", "X": 2.7999999999999545, "Y": -12.56576752329331 }, { "Outlier": "\"True\"", "X": 2.899999999999954, "Y": -28.61214419700161 }, { "Outlier": "\"True\"", "X": 2.999999999999954, "Y": 8.248524918035386 }, { "Outlier": "\"True\"", "X": 3.0999999999999535, "Y": -3.622542781911439 }, { "Outlier": "\"True\"", "X": 3.199999999999953, "Y": 4.494708619120168 }, { "Outlier": "\"True\"", "X": 3.2999999999999527, "Y": 3.860145108336754 }, { "Outlier": "\"False\"", "X": 3.3999999999999524, "Y": -1.2940361660477206 }, { "Outlier": "\"False\"", "X": 3.499999999999952, "Y": -13.140490329409484 }, { "Outlier": "\"True\"", "X": 3.5999999999999517, "Y": -35.32932042223501 }, { "Outlier": "\"True\"", "X": 3.6999999999999513, "Y": 11.866841248128683 }, { "Outlier": "\"False\"", "X": 3.799999999999951, "Y": -10.99204883957837 }, { "Outlier": "\"True\"", "X": 3.8999999999999506, "Y": 9.661220540277904 }, { "Outlier": "\"False\"", "X": 3.9999999999999503, "Y": -11.548148095443524 }, { "Outlier": "\"False\"", "X": 4.09999999999995, "Y": -24.714424629770342 }, { "Outlier": "\"False\"", "X": 4.1999999999999496, "Y": -17.618320611698103 }, { "Outlier": "\"True\"", "X": 4.299999999999949, "Y": 21.41545900080303 }, { "Outlier": "\"True\"", "X": 4.399999999999949, "Y": 41.510023167064915 }, { "Outlier": "\"False\"", "X": 4.4999999999999485, "Y": -10.860842878493102 }, { "Outlier": "\"True\"", "X": 4.599999999999948, "Y": -17.678231065336824 }, { "Outlier": "\"True\"", "X": 4.699999999999948, "Y": -18.253167364985977 }, { "Outlier": "\"False\"", "X": 4.799999999999947, "Y": -9.378753061533567 }, { "Outlier": "\"True\"", "X": 4.899999999999947, "Y": -4.690038562027151 }, { "Outlier": "\"False\"", "X": 4.999999999999947, "Y": -0.9358718826342507 }, { "Outlier": "\"True\"", "X": 5.099999999999946, "Y": 8.693733167616339 }, { "Outlier": "\"False\"", "X": 5.199999999999946, "Y": -11.56589951591252 }, { "Outlier": "\"True\"", "X": 5.299999999999946, "Y": 1.6720822524528287 }, { "Outlier": "\"False\"", "X": 5.399999999999945, "Y": -14.993796469246613 }, { "Outlier": "\"False\"", "X": 5.499999999999945, "Y": -12.054460619537675 }, { "Outlier": "\"True\"", "X": 5.599999999999945, "Y": 34.62535634020814 }, { "Outlier": "\"True\"", "X": 5.699999999999944, "Y": 39.28048699796905 }, { "Outlier": "\"True\"", "X": 5.799999999999944, "Y": -18.522573806115407 }, { "Outlier": "\"True\"", "X": 5.8999999999999435, "Y": 2.8254226810055205 }, { "Outlier": "\"True\"", "X": 5.999999999999943, "Y": -7.598929518634544 }, { "Outlier": "\"False\"", "X": 6.099999999999945, "Y": -18.100980640560156 }, { "Outlier": "\"False\"", "X": 6.1999999999999424, "Y": -25.830275802284074 }, { "Outlier": "\"True\"", "X": 6.29999999999994, "Y": 8.34770023404953 }, { "Outlier": "\"True\"", "X": 6.399999999999942, "Y": 2.6439493039811115 }, { "Outlier": "\"False\"", "X": 6.499999999999943, "Y": -22.37132300541551 }, { "Outlier": "\"True\"", "X": 6.599999999999941, "Y": -15.051462287577962 }, { "Outlier": "\"True\"", "X": 6.699999999999939, "Y": 4.3207686658048425 }, { "Outlier": "\"True\"", "X": 6.79999999999994, "Y": -8.60953644756132 }, { "Outlier": "\"False\"", "X": 6.899999999999942, "Y": -3.1913795194640606 }, { "Outlier": "\"False\"", "X": 6.99999999999994, "Y": -19.490448624441953 }, { "Outlier": "\"False\"", "X": 7.0999999999999375, "Y": -21.056095484822293 }, { "Outlier": "\"True\"", "X": 7.199999999999939, "Y": -0.40330975452223106 }, { "Outlier": "\"True\"", "X": 7.29999999999994, "Y": -48.28017130034681 }, { "Outlier": "\"False\"", "X": 7.399999999999938, "Y": -19.761363998873605 }, { "Outlier": "\"True\"", "X": 7.499999999999936, "Y": -27.496292304878267 }, { "Outlier": "\"False\"", "X": 7.5999999999999375, "Y": -17.935428564824285 }, { "Outlier": "\"False\"", "X": 7.699999999999939, "Y": -23.81440736389056 }, { "Outlier": "\"True\"", "X": 7.799999999999937, "Y": 6.610475555129508 }, { "Outlier": "\"False\"", "X": 7.899999999999935, "Y": -33.59846198877505 }, { "Outlier": "\"True\"", "X": 7.999999999999936, "Y": 10.913894309977062 }, { "Outlier": "\"True\"", "X": 8.099999999999937, "Y": 22.467791024883127 }, { "Outlier": "\"True\"", "X": 8.199999999999935, "Y": 12.316122841847735 }, { "Outlier": "\"False\"", "X": 8.299999999999933, "Y": -16.009405184781222 }, { "Outlier": "\"False\"", "X": 8.399999999999935, "Y": -34.4150409769924 }, { "Outlier": "\"True\"", "X": 8.499999999999936, "Y": -25.738038864954476 }, { "Outlier": "\"False\"", "X": 8.599999999999934, "Y": -14.077826822779741 }, { "Outlier": "\"True\"", "X": 8.699999999999932, "Y": -9.172720756633 }, { "Outlier": "\"False\"", "X": 8.799999999999933, "Y": -18.18037822337765 }, { "Outlier": "\"False\"", "X": 8.899999999999935, "Y": -30.72733727472214 }, { "Outlier": "\"False\"", "X": 8.999999999999932, "Y": -22.567123894072513 }, { "Outlier": "\"True\"", "X": 9.09999999999993, "Y": 31.120217815149882 }, { "Outlier": "\"False\"", "X": 9.199999999999932, "Y": -23.4759662318214 }, { "Outlier": "\"False\"", "X": 9.299999999999933, "Y": -19.285316135497098 }, { "Outlier": "\"True\"", "X": 9.399999999999931, "Y": 1.3453810777338207 }, { "Outlier": "\"True\"", "X": 9.499999999999929, "Y": 30.106740007028108 }, { "Outlier": "\"True\"", "X": 9.59999999999993, "Y": -8.245762291377288 }, { "Outlier": "\"False\"", "X": 9.699999999999932, "Y": -31.37003628215042 }, { "Outlier": "\"False\"", "X": 9.79999999999993, "Y": -27.560708350871 }, { "Outlier": "\"True\"", "X": 9.899999999999928, "Y": 7.612568009425738 }, { "Outlier": "\"True\"", "X": 9.999999999999929, "Y": -13.529703374498748 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "paramPrior = do\n", " slope <- normal 0 2\n", " intercept <- normal 0 2\n", " noise <- gamma 7 7\n", " prob_outlier <- uniform 0 0.5 \n", " return (slope, intercept, noise, prob_outlier)\n", "\n", "forward (slope, intercept, noise, probOutlier) x = do\n", " isOutlier <- bernoulli probOutlier\n", " let meanParams = if isOutlier\n", " then (0, 20)\n", " else (x*slope + intercept, sqrt noise)\n", " return (meanParams, isOutlier)\n", "\n", "regressionWithOutliersData :: (MonadDistribution m, Traversable t) => t Double -> m (t ((Double, Double), Bool))\n", "regressionWithOutliersData xs = do\n", " params <- paramPrior\n", "\n", " forM xs \\x -> do\n", " ((mu, std), isOutlier) <- forward params x\n", " y <- normal mu std\n", " return ((x, y), isOutlier)\n", " \n", "range = [-10,-9.9..10] :: [Double]\n", "samples <- sampleIOfixed $ regressionWithOutliersData range\n", "plot (fmap (second (T.pack . show)) samples)\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "regressionWithOutliers xs ys params = do\n", " \n", " outliers <- forM (zip xs ys) \\(x, y) -> do\n", " ((mu, std), isOutlier) <- forward params x\n", " factor $ normalPdf mu std y\n", " return isOutlier\n", " return (params, outliers)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mhRuns <- sampleIOfixed $ pmmh \n", " MCMCConfig {numMCMCSteps = 20, numBurnIn = 10, proposal = SingleSiteMH}\n", " SMCConfig {numSteps = 200, numParticles = 100, resampler = resampleSystematic}\n", " paramPrior\n", " (regressionWithOutliers range (snd . fst <$> samples))\n", " " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "m = (\\((_,a), b) -> (a,b)) <$> head mhRuns" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "outlierProb s = (\\(x, y) -> ln (exp (x / (x+y))) )\n", " <$> foldr\n", " (\\(lb, d) li -> [ if b then (num1+d, num2) else (num1,num2+d) | (b,(num1, num2)) <- zip lb li])\n", " (Prelude.repeat (0 :: Log Double,0 :: Log Double)) \n", " s" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": 0, "X": -10, "Y": 30.14316922289284 }, { "Outlier": 1, "X": -9.9, "Y": -0.26284958638081857 }, { "Outlier": 0.5437623578186656, "X": -9.8, "Y": 18.535731623658606 }, { "Outlier": 1, "X": -9.700000000000001, "Y": -68.99193947945726 }, { "Outlier": 1, "X": -9.600000000000001, "Y": -8.043397861176732 }, { "Outlier": 0, "X": -9.500000000000002, "Y": 32.14739624905583 }, { "Outlier": 1, "X": -9.400000000000002, "Y": -23.81048273352878 }, { "Outlier": 1, "X": -9.300000000000002, "Y": 17.786702036490702 }, { "Outlier": 0.09018386763657092, "X": -9.200000000000003, "Y": 23.375038991852755 }, { "Outlier": 0, "X": -9.100000000000003, "Y": 25.793440850750983 }, { "Outlier": 0, "X": -9.000000000000004, "Y": 24.42143345836998 }, { "Outlier": 0.10477368795464265, "X": -8.900000000000004, "Y": 21.51934491681228 }, { "Outlier": 0, "X": -8.800000000000004, "Y": 31.353977381067146 }, { "Outlier": 0.651195197772548, "X": -8.700000000000005, "Y": 18.278561094651828 }, { "Outlier": 0, "X": -8.600000000000005, "Y": 33.00501809401699 }, { "Outlier": 1, "X": -8.500000000000005, "Y": 6.437669090883507 }, { "Outlier": 1, "X": -8.400000000000006, "Y": -37.03228429479993 }, { "Outlier": 0.5437623578186656, "X": -8.300000000000006, "Y": 13.11298520672979 }, { "Outlier": 0.10477368795464265, "X": -8.200000000000006, "Y": 21.21403593146131 }, { "Outlier": 0.4389886698640229, "X": -8.100000000000007, "Y": 11.824928463235018 }, { "Outlier": 0, "X": -8.000000000000007, "Y": 21.72725799674079 }, { "Outlier": 0.34880480222745197, "X": -7.9000000000000075, "Y": 29.057062960826705 }, { "Outlier": 0.09018386763657092, "X": -7.800000000000008, "Y": 20.649998496823716 }, { "Outlier": 0.5464215098179054, "X": -7.700000000000008, "Y": 8.660181503529154 }, { "Outlier": 0.19495755559121355, "X": -7.6000000000000085, "Y": 15.408422855489516 }, { "Outlier": 1, "X": -7.500000000000009, "Y": 0.3644817992929184 }, { "Outlier": 1, "X": -7.400000000000009, "Y": -21.224596735432097 }, { "Outlier": 0.5437623578186656, "X": -7.30000000000001, "Y": -5.08262280552811 }, { "Outlier": 1, "X": -7.20000000000001, "Y": -22.40271677868227 }, { "Outlier": 0, "X": -7.10000000000001, "Y": 30.244036005466885 }, { "Outlier": 0, "X": -7.000000000000011, "Y": 40.6554142943223 }, { "Outlier": 0.8282496477499643, "X": -6.900000000000011, "Y": 0.7924834976117429 }, { "Outlier": 1, "X": -6.800000000000011, "Y": -3.0689825793534142 }, { "Outlier": 0.8229455500225836, "X": -6.700000000000012, "Y": 2.0777625946734073 }, { "Outlier": 0.5610113301359771, "X": -6.600000000000012, "Y": 12.550388887411348 }, { "Outlier": 0.718171862067941, "X": -6.500000000000012, "Y": 4.46724475614298 }, { "Outlier": 1, "X": -6.400000000000013, "Y": -1.5461624315036717 }, { "Outlier": 0.17705444997741634, "X": -6.300000000000013, "Y": 24.71362894119821 }, { "Outlier": 0.4562376421813345, "X": -6.2000000000000135, "Y": 42.412216707046596 }, { "Outlier": 0.4562376421813345, "X": -6.100000000000014, "Y": 18.557621556888908 }, { "Outlier": 0.7380657801133934, "X": -6.000000000000014, "Y": 9.641681498947396 }, { "Outlier": 0.5610113301359771, "X": -5.900000000000015, "Y": 14.163347001302109 }, { "Outlier": 0.17705444997741634, "X": -5.800000000000015, "Y": 20.84295190897201 }, { "Outlier": 0.4389886698640229, "X": -5.700000000000015, "Y": 4.130260844964003 }, { "Outlier": 0.7327616823860127, "X": -5.600000000000016, "Y": 10.475034728856496 }, { "Outlier": 0, "X": -5.500000000000016, "Y": 19.404328857643684 }, { "Outlier": 0.34880480222745197, "X": -5.400000000000016, "Y": 6.909511750502322 }, { "Outlier": 0, "X": -5.300000000000017, "Y": 24.44453706966264 }, { "Outlier": 1, "X": -5.200000000000017, "Y": -14.86585011237548 }, { "Outlier": 1, "X": -5.100000000000017, "Y": -27.7443807834266 }, { "Outlier": 0.28182813793205896, "X": -5.000000000000018, "Y": 18.14228368221277 }, { "Outlier": 0.17175035225003563, "X": -4.900000000000018, "Y": 26.622225089502074 }, { "Outlier": 0.19495755559121355, "X": -4.8000000000000185, "Y": 7.975229527386173 }, { "Outlier": 0.7327616823860127, "X": -4.700000000000019, "Y": -2.236758643252085 }, { "Outlier": 0.09018386763657092, "X": -4.600000000000019, "Y": 25.095015724308034 }, { "Outlier": 0, "X": -4.5000000000000195, "Y": 17.18991809253406 }, { "Outlier": 1, "X": -4.40000000000002, "Y": -24.744360068281004 }, { "Outlier": 1, "X": -4.30000000000002, "Y": -7.931801457689884 }, { "Outlier": 0.2672383176139873, "X": -4.200000000000021, "Y": 8.09920420163646 }, { "Outlier": 0.30039958311344106, "X": -4.100000000000021, "Y": 14.383701713795746 }, { "Outlier": 0.43236209927257174, "X": -4.000000000000021, "Y": 7.20943981411068 }, { "Outlier": 0.6996004168865589, "X": -3.9000000000000217, "Y": -7.806116027860427 }, { "Outlier": 0.2765240402046783, "X": -3.800000000000022, "Y": 16.98021084694896 }, { "Outlier": 0.2672383176139873, "X": -3.7000000000000224, "Y": 14.379488244709558 }, { "Outlier": 0.30039958311344106, "X": -3.6000000000000227, "Y": 10.476203691783232 }, { "Outlier": 0.5822277210455, "X": -3.500000000000023, "Y": 18.54439155366095 }, { "Outlier": 0, "X": -3.4000000000000234, "Y": 17.443166242574943 }, { "Outlier": 0.718171862067941, "X": -3.300000000000024, "Y": 28.868081826988078 }, { "Outlier": 0.6279879944313701, "X": -3.200000000000024, "Y": -0.36221408177898484 }, { "Outlier": 0.5822277210455, "X": -3.1000000000000245, "Y": 18.75847005425409 }, { "Outlier": 0.5278500646365234, "X": -3.000000000000025, "Y": -6.3470305289160835 }, { "Outlier": 0.3110077785682023, "X": -2.9000000000000252, "Y": 10.805876165134304 }, { "Outlier": 0.9098161323634291, "X": -2.8000000000000256, "Y": 43.073138049536766 }, { "Outlier": 0.4376661969999524, "X": -2.700000000000026, "Y": 21.39560142763903 }, { "Outlier": 0.526527591772453, "X": -2.6000000000000263, "Y": 20.17816530884579 }, { "Outlier": 0.17705444997741634, "X": -2.5000000000000266, "Y": 10.593481420961691 }, { "Outlier": 0.4250671891135359, "X": -2.400000000000027, "Y": 24.67140741709269 }, { "Outlier": 0.5988083537952268, "X": -2.3000000000000274, "Y": 3.7143759689601357 }, { "Outlier": 0.383288540590976, "X": -2.2000000000000277, "Y": 4.205664689951466 }, { "Outlier": 0.5464215098179054, "X": -2.100000000000028, "Y": 5.696299360450189 }, { "Outlier": 0.22612800865901214, "X": -2.0000000000000284, "Y": 14.405714990070868 }, { "Outlier": 0.7393882529774638, "X": -1.9000000000000288, "Y": 13.570785875314327 }, { "Outlier": 0.4376661969999524, "X": -1.8000000000000291, "Y": 25.768223310681986 }, { "Outlier": 1, "X": -1.7000000000000295, "Y": -37.59090989382119 }, { "Outlier": 0.12466760600009497, "X": -1.6000000000000298, "Y": 23.64144553913516 }, { "Outlier": 0.7884618116590596, "X": -1.5000000000000302, "Y": -8.576305123038889 }, { "Outlier": 1, "X": -1.4000000000000306, "Y": -10.36508656039852 }, { "Outlier": 0.30039958311344106, "X": -1.300000000000031, "Y": 5.5494955124482335 }, { "Outlier": 0.47745403309085727, "X": -1.2000000000000313, "Y": -4.881619552621354 }, { "Outlier": 0.6651166785230349, "X": -1.1000000000000316, "Y": 5.697048084463994 }, { "Outlier": 0.48806222854561865, "X": -1.000000000000032, "Y": 3.6802215741202695 }, { "Outlier": 1, "X": -0.9000000000000323, "Y": -31.588506775217645 }, { "Outlier": 0.5676379007274283, "X": -0.8000000000000327, "Y": -4.276114964288603 }, { "Outlier": 0.5119377714543814, "X": -0.700000000000033, "Y": 4.706472715938526 }, { "Outlier": 0.40318245863642843, "X": -0.6000000000000334, "Y": 24.61942267029316 }, { "Outlier": 0.8952263120453574, "X": -0.5000000000000338, "Y": -17.830933767956402 }, { "Outlier": 0.4051732710680835, "X": -0.4000000000000341, "Y": -5.40950732388878 }, { "Outlier": 0.5769236233181192, "X": -0.30000000000003446, "Y": 3.45113720087071 }, { "Outlier": 0.5437623578186656, "X": -0.20000000000003482, "Y": -0.6945661730385604 }, { "Outlier": 0.875332393999905, "X": -0.10000000000003517, "Y": -11.881720947448017 }, { "Outlier": 0.8050424444087865, "X": -3.552713678800501e-14, "Y": 15.275164463456829 }, { "Outlier": 0.3720120055686299, "X": 0.09999999999996412, "Y": 3.130005271532676 }, { "Outlier": 0.2115381883409404, "X": 0.19999999999996376, "Y": 7.217065710866146 }, { "Outlier": 0.2115381883409404, "X": 0.2999999999999634, "Y": 17.70645751790601 }, { "Outlier": 0.3328925090453099, "X": 0.39999999999996305, "Y": 13.888934006536498 }, { "Outlier": 0.9098161323634291, "X": 0.4999999999999627, "Y": 21.884620845377007 }, { "Outlier": 0.47745403309085727, "X": 0.5999999999999623, "Y": 9.545267231818897 }, { "Outlier": 0.22612800865901214, "X": 0.699999999999962, "Y": 3.703565536648379 }, { "Outlier": 0.7068953270455948, "X": 0.7999999999999616, "Y": 11.858526678970597 }, { "Outlier": 0.3348833214769651, "X": 0.8999999999999613, "Y": -2.745279389040704 }, { "Outlier": 0.8050424444087865, "X": 0.9999999999999609, "Y": 8.1754196030146 }, { "Outlier": 0.29840877068178584, "X": 1.0999999999999606, "Y": -3.0892286454167217 }, { "Outlier": 0.09018386763657092, "X": 1.1999999999999602, "Y": -1.6888315159113967 }, { "Outlier": 0.7234759597953216, "X": 1.2999999999999599, "Y": -26.34560770532998 }, { "Outlier": 0.5822277210455, "X": 1.3999999999999595, "Y": 7.322340575226373 }, { "Outlier": 0.8952263120453574, "X": 1.4999999999999591, "Y": 1.9729072168157065 }, { "Outlier": 0.10477368795464265, "X": 1.5999999999999588, "Y": 4.3653523448307645 }, { "Outlier": 0.5046428612953455, "X": 1.6999999999999584, "Y": 13.227480313783765 }, { "Outlier": 0.875332393999905, "X": 1.799999999999958, "Y": 12.226379085662112 }, { "Outlier": 0.43236209927257174, "X": 1.8999999999999577, "Y": 7.283550315220926 }, { "Outlier": 0.12135432070436945, "X": 1.9999999999999574, "Y": -8.322354413220943 }, { "Outlier": 0.3348833214769651, "X": 2.099999999999957, "Y": -6.278804583310366 }, { "Outlier": 0.12466760600009497, "X": 2.1999999999999567, "Y": 0.41427397930309406 }, { "Outlier": 0, "X": 2.2999999999999563, "Y": -6.593316457144327 }, { "Outlier": 0.7884618116590596, "X": 2.399999999999956, "Y": 10.882276064502353 }, { "Outlier": 0.19495755559121355, "X": 2.4999999999999556, "Y": 4.7901246139126625 }, { "Outlier": 0.4396570094316077, "X": 2.5999999999999552, "Y": -14.966747660332494 }, { "Outlier": 0.4250671891135359, "X": 2.699999999999955, "Y": -0.20721316523803424 }, { "Outlier": 0.30039958311344106, "X": 2.7999999999999545, "Y": -12.56576752329331 }, { "Outlier": 0.3720120055686299, "X": 2.899999999999954, "Y": -28.61214419700161 }, { "Outlier": 0.5623338030000476, "X": 2.999999999999954, "Y": 8.248524918035386 }, { "Outlier": 0.2294412939547376, "X": 3.0999999999999535, "Y": -3.622542781911439 }, { "Outlier": 0.30172205597751134, "X": 3.199999999999953, "Y": 4.494708619120168 }, { "Outlier": 0.17705444997741634, "X": 3.2999999999999527, "Y": 3.860145108336754 }, { "Outlier": 0.12135432070436945, "X": 3.3999999999999524, "Y": -1.2940361660477206 }, { "Outlier": 0.10477368795464265, "X": 3.499999999999952, "Y": -13.140490329409484 }, { "Outlier": 0.5769236233181192, "X": 3.5999999999999517, "Y": -35.32932042223501 }, { "Outlier": 0.6996004168865589, "X": 3.6999999999999513, "Y": 11.866841248128683 }, { "Outlier": 0.3110077785682023, "X": 3.799999999999951, "Y": -10.99204883957837 }, { "Outlier": 0.48806222854561865, "X": 3.8999999999999506, "Y": 9.661220540277904 }, { "Outlier": 0.12466760600009497, "X": 3.9999999999999503, "Y": -11.548148095443524 }, { "Outlier": 0.12135432070436945, "X": 4.09999999999995, "Y": -24.714424629770342 }, { "Outlier": 0.30039958311344106, "X": 4.1999999999999496, "Y": -17.618320611698103 }, { "Outlier": 1, "X": 4.299999999999949, "Y": 21.41545900080303 }, { "Outlier": 1, "X": 4.399999999999949, "Y": 41.510023167064915 }, { "Outlier": 0.09018386763657092, "X": 4.4999999999999485, "Y": -10.860842878493102 }, { "Outlier": 0.17705444997741634, "X": 4.599999999999948, "Y": -17.678231065336824 }, { "Outlier": 0, "X": 4.699999999999948, "Y": -18.253167364985977 }, { "Outlier": 0.3003995831134411, "X": 4.799999999999947, "Y": -9.378753061533567 }, { "Outlier": 0.7738719913409878, "X": 4.899999999999947, "Y": -4.690038562027151 }, { "Outlier": 0.1047736879546426, "X": 4.999999999999947, "Y": -0.9358718826342507 }, { "Outlier": 1, "X": 5.099999999999946, "Y": 8.693733167616339 }, { "Outlier": 0.36670790784124924, "X": 5.199999999999946, "Y": -11.56589951591252 }, { "Outlier": 0.7393882529774638, "X": 5.299999999999946, "Y": 1.6720822524528287 }, { "Outlier": 0.31631187629558294, "X": 5.399999999999945, "Y": -14.993796469246613 }, { "Outlier": 0.03448373836352408, "X": 5.499999999999945, "Y": -12.054460619537675 }, { "Outlier": 1, "X": 5.599999999999945, "Y": 34.62535634020814 }, { "Outlier": 1, "X": 5.699999999999944, "Y": 39.28048699796905 }, { "Outlier": 0.039787836090904784, "X": 5.799999999999944, "Y": -18.522573806115407 }, { "Outlier": 0.7194943349320113, "X": 5.8999999999999435, "Y": 2.8254226810055205 }, { "Outlier": 0.17042787938596518, "X": 5.999999999999943, "Y": -7.598929518634544 }, { "Outlier": 0.33090169661365465, "X": 6.099999999999945, "Y": -18.100980640560156 }, { "Outlier": 0.22082391093163142, "X": 6.1999999999999424, "Y": -25.830275802284074 }, { "Outlier": 1, "X": 6.29999999999994, "Y": 8.34770023404953 }, { "Outlier": 0.9098161323634291, "X": 6.399999999999942, "Y": 2.6439493039811115 }, { "Outlier": 0.2553076492951555, "X": 6.499999999999943, "Y": -22.37132300541551 }, { "Outlier": 0.1392574263181667, "X": 6.599999999999941, "Y": -15.051462287577962 }, { "Outlier": 1, "X": 6.699999999999939, "Y": 4.3207686658048425 }, { "Outlier": 0.4867397556815484, "X": 6.79999999999994, "Y": -8.60953644756132 }, { "Outlier": 0.965516261636476, "X": 6.899999999999942, "Y": -3.1913795194640606 }, { "Outlier": 0.28580976279536935, "X": 6.99999999999994, "Y": -19.490448624441953 }, { "Outlier": 0.1047736879546426, "X": 7.0999999999999375, "Y": -21.056095484822293 }, { "Outlier": 0.9854101796819283, "X": 7.199999999999939, "Y": -0.40330975452223106 }, { "Outlier": 0.28182813793205896, "X": 7.29999999999994, "Y": -48.28017130034681 }, { "Outlier": 0, "X": 7.399999999999938, "Y": -19.761363998873605 }, { "Outlier": 0.12466760600009497, "X": 7.499999999999936, "Y": -27.496292304878267 }, { "Outlier": 0.26591584474991686, "X": 7.5999999999999375, "Y": -17.935428564824285 }, { "Outlier": 0.33222416947772515, "X": 7.699999999999939, "Y": -23.81440736389056 }, { "Outlier": 0.8952263120453574, "X": 7.799999999999937, "Y": 6.610475555129508 }, { "Outlier": 0, "X": 7.899999999999935, "Y": -33.59846198877505 }, { "Outlier": 1, "X": 7.999999999999936, "Y": 10.913894309977062 }, { "Outlier": 1, "X": 8.099999999999937, "Y": 22.467791024883127 }, { "Outlier": 1, "X": 8.199999999999935, "Y": 12.316122841847735 }, { "Outlier": 0.3129985909998576, "X": 8.299999999999933, "Y": -16.009405184781222 }, { "Outlier": 0.1956258951587983, "X": 8.399999999999935, "Y": -34.4150409769924 }, { "Outlier": 0.13726661388651148, "X": 8.499999999999936, "Y": -25.738038864954476 }, { "Outlier": 0.6598125807956543, "X": 8.599999999999934, "Y": -14.077826822779741 }, { "Outlier": 0.26591584474991686, "X": 8.699999999999932, "Y": -9.172720756633 }, { "Outlier": 0.40517327106808354, "X": 8.799999999999933, "Y": -18.18037822337765 }, { "Outlier": 0.14655233647720253, "X": 8.899999999999935, "Y": -30.72733727472214 }, { "Outlier": 0.19363508272714317, "X": 8.999999999999932, "Y": -22.567123894072513 }, { "Outlier": 1, "X": 9.09999999999993, "Y": 31.120217815149882 }, { "Outlier": 0.059681754136357165, "X": 9.199999999999932, "Y": -23.4759662318214 }, { "Outlier": 0.2732107549089527, "X": 9.299999999999933, "Y": -19.285316135497098 }, { "Outlier": 1, "X": 9.399999999999931, "Y": 1.3453810777338207 }, { "Outlier": 1, "X": 9.499999999999929, "Y": 30.106740007028108 }, { "Outlier": 0.7811669015000237, "X": 9.59999999999993, "Y": -8.245762291377288 }, { "Outlier": 0.16047381722768955, "X": 9.699999999999932, "Y": -31.37003628215042 }, { "Outlier": 0.2785148526363334, "X": 9.79999999999993, "Y": -27.560708350871 }, { "Outlier": 1, "X": 9.899999999999928, "Y": 7.612568009425738 }, { "Outlier": 0.6425494022072435, "X": 9.999999999999929, "Y": -13.529703374498748 } ] }, "encoding": { "color": { "field": "Outlier", "scale": { "scheme": "viridis" }, "type": "quantitative" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot $ take 1000 (zip (fst <$> samples) (outlierProb m))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the above plot shows, this works nicely: the `slope`, `intercept`, `noise` and `prob_outlier` variables are inferred by a random walk through the space, while the score to determine whether to accept a new proposed step in this walk is determined by a particle filter which guesses which points are outliers after each observation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# $SMC^2$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The culmination of monad-bayes is $SMC^2$, which combines all the previous piece together into one inference algorithm.\n", "\n", "It is $RMSMC$, but with the $MCMC$ walk performed using $PMMH$." ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: notebooks/tutorials/Bayesian.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "1323d9af-17b2-4e43-ba81-b5b3979b3528", "metadata": {}, "outputs": [], "source": [ ":e TupleSections\n", ":e BlockArguments \n", "\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Enumerator\n", "import Control.Monad\n", "import Control.Arrow\n", "import Data.Bifunctor (bimap)\n", "import Numeric.Log\n", "import qualified Data.Text as T\n", "\n", "\n", ":l ../plotting.hs" ] }, { "cell_type": "markdown", "id": "bb6b1d04-808f-4700-9e5b-b880f64432bc", "metadata": {}, "source": [ "# Bayesian Models\n", "\n", "Monad-Bayes exports a `Bayesian` datatype to make using Bayesian models easier. The idea is that you specify your model as a prior, generative model and a likelihood. \n", "\n", "```\n", "data Bayesian m z o = Bayesian\n", " { prior :: m z, -- prior over latent variable Z; p(z)\n", " generative :: z -> m o, -- distribution over observations given Z=z\n", " likelihood :: z -> o -> Log Double -- p(o|z)\n", " }\n", "```\n", "\n", "Likelihood is the PDF of the generative model and is a function from the problem domain to positive real numbers. Generative model allows for drawing samples from the distribution.\n", "Likelihood and generative model are separated because you can't always get the density from a distribution definition.\n", "\n", "\n", "Once you've defined Bayesian model, a number of standard operations, like obtaining the posterior or the posterior predictive, are automated for you.\n", "\n", "Here's a simple example:" ] }, { "cell_type": "code", "execution_count": 2, "id": "5df6eb5a-8545-43fa-83d7-bd25f74804cc", "metadata": {}, "outputs": [], "source": [ "model :: MonadDistribution m => Bayesian m Bool Double\n", "model = Bayesian {\n", " prior = bernoulli 0.5,\n", " generative = \\x -> normal (if x then 2 else (-2)) 1,\n", " likelihood = \\x y -> normalPdf (if x then 2 else (-2)) 1 y\n", " }" ] }, { "cell_type": "markdown", "id": "a31299a8-2294-4182-823d-d85bd2136945", "metadata": {}, "source": [ "## Prior" ] }, { "cell_type": "code", "execution_count": 3, "id": "07c4584c-8351-4e0b-814b-d3746b6b6962", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": "True", "Y": 0.50094 }, { "X": "False", "Y": 0.49906 } ] }, "encoding": { "x": { "field": "X", "type": "nominal" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcQAAAHOCAYAAAAGxHl5AAAAAXNSR0IArs4c6QAAIABJREFUeF7tnX9Mned59y8wjqnj/BjUlrPY01ZEsRbaLS6GJiNz6Ji9IcKWvMEZBoVlaRZCkzlrotEGha1bRMBL5tqpTYNjiaU0ShuzhsSUlpF5zqCpmiCYUm31EvcPt2hJyhrkrMVKMLx6nve1Zeof58vdw/G58OeRqha47udc9+e+vv3yPRyfkzE7OztrXBCAAAQgAIGLnEAGhniRTwDbhwAEIACBmACGyCBAAAIQgAAEMERmAAIQgAAEIPD/CJAQmQQIQAACEIAAhsgMQAACEIAABEiIzAAEIAABCEDgFIEFeco0+pccTU1NNjo6atPT09bW1mYlJSVzsN9555125MgRy8rKir//xS9+0aJ1tbW1tmrVqvh769evt+3bt3NcEIAABCAAgQUnsCCGODg4aF1dXdbd3W3j4+NWVVVlIyMjczZzww03WE9Pzynzi3740ksvxd/bs2fPgm+cB4AABCAAAQicTmBBDLGlpcUKCgritBddRUVFNjAwYDk5Oace+2Mf+5g99dRT9sMf/jBOjx/5yEdiMzx06JBt3LjRli9fbps2bbIlS5ZwYhCAAAQgAIEFJ7AghtjY2GgVFRVWWVkZb6C8vNw6OjosPz//1IYis4uMMi8vz3bs2GFPPvmk/eQnP7Hdu3fb1q1bbWxsLH5K9cCBA/GaoaEhGx4engNk5cqVduONNy44JB4AAhCAAAQWF4HoT3SR/yx4QmxubrbCwkKrqamJH6u4uNj6+/stNzf3rET3799vBw8ejM3w9Cu6R/T9yPjOdrW3t8d/q+SCAAQgAAEIzIdA9Oxk9MzkghtiX1+f9fb2Wmdnp01MTMQJMUp8U1NT8YtoMjMz7aGHHrLW1tb4KdEoIb733nuxcWZkZNjmzZvt+PHjtm7dOjt8+LAtW7YMQ5zPSVMLAQhAAALnJZAyQ5yZmbH6+nqbnJyMDTEyvrKyMmtoaLDS0lKrq6uzRx991F588UVbvXp1bIbPPfecHTt2zLZs2WJr1661o0ePWvTUa3Sfc10kRCYeAhCAAARCCKTMEE82FyXC7OzsOPWd7frggw/s/ffft0svvXTOjyNjXLFiRZwkz3dhiCFjwBoIQAACEEi5IS40cgxxoQlzfwhAAAKLkwCGuDjPlV1BAAIQgMA8CWCI8wRGOQQgAAEILE4CGOLiPFd2BQEIQAAC8ySAIc4TGOUQgAAEILA4CWCIi/Nc2RUEIAABCMyTAIY4T2CUQwACEIDA4iSAIS7Oc2VXEIAABCAwTwIY4jyBUQ4BCEAAAouTAIa4OM+VXUEAAhCAwDwJYIjzBEY5BCAAAQgsTgIY4uI8V3YFAQhAAALzJIAhzhMY5RCAAAQgsDgJYIiL81zZFQQgAAEIzJMAhjhPYJRDAAIQgMDiJIAhLs5zZVcQgAAEIDBPAhjiPIFRDgEIQAACi5MAhrg4z5VdQQACEIDAPAlgiPMERjkEIAABCCxOAhji4jxXdgUBCEAAAvMkgCHOExjlEIAABCCwOAlgiIvzXNkVBCAAAQjMkwCGOE9glEMAAhCAwOIkgCEuznNlVxCAAAQgME8CGOI8gVEOAQhAAAKLkwCGuDjPlV1BAAIQgMA8CWCI8wRGOQQgAAEILE4CGOLiPFd2BQEIQAAC8ySQMkOcnZ21pqYmGx0dtenpaWtra7OSkpI57d5555125MgRy8rKir//xS9+0a655pqE606/SXt7e1zPBQEIQAACEJgPgZQZ4uDgoHV1dVl3d7eNj49bVVWVjYyMzOn1hhtusJ6eHlu1atWp7yvrMMT5HDm1EIAABCBwNgIpM8SWlhYrKCiw2trauI+ioiIbGBiwnJycU3197GMfs6eeesqipqL0+JGPfMSUdRgiww2B5BL4zvd/nNwbcjcIXEAC1xeukR49ZYbY2NhoFRUVVllZGTdWXl5uHR0dlp+ff6rRTZs2xUaZl5dnO3bssCeffNK++tWvnnPd0NCQDQ8Pn7HR6upqafMUQQACZyewbe+roIHAoiGw864N0l6iP+1F/nP6lTEbfTfJV3NzsxUWFlpNTU185+LiYuvv77fc3NyzPtL+/fvt4MGDduWVV85rnfo3xOq/7knyDrkdBC4cgee+8H+S+uA3ff5rSb0fN4PAhSTw4qO3SQ+fsoTY19dnvb291tnZaRMTE3FCHBsbs6mpqfhFNJmZmfbQQw9Za2urLVmyJE6I7733nn3iE58467pz7U41RAQvzQdFTgiogle3gz5UUtR5IKDqI2WGODMzY/X19TY5ORkbYmR8ZWVl1tDQYKWlpVZXV2ePPvqovfjii7Z69erYDJ977jm7/PLLz7oOQ/QwhvSYKgKq4NV+MESVFHUeCKj6SJkhnoQWJcLs7GzLyMg4K8cPPvjA3n//fbv00kvn/DzRupPFJEQP40mPySagCl59XAxRJUWdBwKqPlJuiAsND0NcaMLcPx0JqIJXe8cQVVLUeSCg6gND9HCa9AiBBARUwasgMUSVFHUeCKj6wBA9nCY9QgBDZAYgEEwAQ0yAjt+Ag2eLhWlIQBW82jr6UElR54GAqg8SoofTpEcIkBCZAQgEE8AQSYjBw8NCfwRUwas7IyGqpKjzQEDVBwnRw2nSIwRIiMwABIIJYIgkxODhYaE/Aqrg1Z2REFVS1HkgoOqDhOjhNOkRAiREZgACwQQwRBJi8PCw0B8BVfDqzkiIKinqPBBQ9UFC9HCa9AgBEiIzAIFgAhgiCTF4eFjoj4AqeHVnJESVFHUeCKj6ICF6OE16hAAJkRmAQDABDJGEGDw8LPRHQBW8ujMSokqKOg8EVH2QED2cJj1CgITIDEAgmACGSEIMHh4W+iOgCl7dGQlRJUWdBwKqPkiIHk6THiFAQmQGIBBMAEMkIQYPDwv9EVAFr+6MhKiSos4DAVUfJEQPp0mPECAhMgMQCCaAIZIQg4eHhf4IqIJXd0ZCVElR54GAqg8SoofTpEcIkBCZAQgEE8AQSYjBw8NCfwRUwas7IyGqpKjzQEDVBwnRw2nSIwRIiMwABIIJYIgkxODhYaE/Aqrg1Z2REFVS1HkgoOqDhOjhNOkRAiREZgACwQQwRBJi8PCw0B8BVfDqzkiIKinqPBBQ9UFC9HCa9AgBEiIzAIFgAhgiCTF4eFjoj4AqeHVnJESVFHUeCKj6SFlCnJ2dtaamJhsdHbXp6Wlra2uzkpKSs7J8+umnbf/+/fbCCy/Y66+/brW1tbZq1aq4dv369bZ9+/ZznkF7e3v8OIkuBJ+IED/3REAVvLon9KGSos4DAVUfKTPEwcFB6+rqsu7ubhsfH7eqqiobGRk5g+WPfvQju/vuu+2dd96x1157zV566SXr6emxPXv2SNwxRAkTRYuMgCp4ddsYokqKOg8EVH2kzBBbWlqsoKAgTnvRVVRUZAMDA5aTkzOH5y233GKPPPKI3X777bEhRmZ46NAh27hxoy1fvtw2bdpkS5YsISF6mEJ6TBkBVfBqQxiiSoo6DwRUfaTMEBsbG62iosIqKytjfuXl5dbR0WH5+fmneEZfR9cdd9xhpaWlsSE+//zztnv3btu6dauNjY3ZkSNH7MCBAxiihymkx5QRUAWvNoQhqqSo80BA1UfKDLG5udkKCwutpqYm5ldcXGz9/f2Wm5sbfx09VXrTTTfZjh077IMPPrD77rvPnn32Wbv22mvn8I7ucfDgQVu5cqUNDQ3Z8PDwGedRXV2d8Iy27X01YQ0FEPBCYOddG5LaKvpIKk5udoEJqPqIXuuSl5c3p9uM2ei7Sb76+vqst7fXOjs7bWJiIk6IUeKbmpqyrKwse+utt+KnR6MretHNrl277PHHH7fLLrvMMjIybPPmzXb8+HFbt26dHT582JYtW3bWDvkbYpIPjtu5IKD+BqxuhoSokqLOAwFVHylLiDMzM1ZfX2+Tk5OxIba2tlpZWZk1NDTET4/W1dWd4hoZ38mnTI8ePWpbtmyxtWvXWvS/o6deo/uc68IQPYwnPSabgCp49XExRJUUdR4IqPpImSGehBYlwuzs7Dj1zec6duyYrVixwjIzM8+7DEOcD1VqFwsBVfDqfjFElRR1Hgio+ki5IS40PAxxoQlz/3QkoApe7R1DVElR54GAqg8M0cNp0iMEEhBQBa+CxBBVUtR5IKDqA0P0cJr0CAEMkRmAQDABDDEBOn4DDp4tFqYhAVXwauvoQyVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKoPEqKH06RHCJAQmQEIBBPAEEmIwcPDQn8EVMGrOyMhqqSo80BA1QcJ0cNp0iMESIjMAASCCWCIJMTg4WGhPwKq4NWdkRBVUtR5IKDqg4To4TTpEQIkRGYAAsEEMEQSYvDwsNAfAVXw6s5IiCop6jwQUPVBQvRwmvQIARIiMwCBYAIYIgkxeHhY6I+AKnh1ZyRElRR1Hgio+iAhejhNeoQACZEZgEAwAQyRhBg8PCz0R0AVvLozEqJKijoPBFR9kBA9nCY9QoCEyAxAIJgAhkhCDB4eFvojoApe3RkJUSVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKoPEqKH06RHCJAQmQEIBBPAEEmIwcPDQn8EVMGrOyMhqqSo80BA1QcJ0cNp0iMESIjMAASCCWCIJMTg4WGhPwKq4NWdkRBVUtR5IKDqg4To4TTpEQIkRGYAAsEE0s4QZ2dnrampyUZHR216etra2tqspKTkrBt8+umnbf/+/fbCCy/YfNZFN2tvb48fJ9HFb8CJCPFzTwRUwat7Qh8qKeo8EFD1kbKEODg4aF1dXdbd3W3j4+NWVVVlIyMjZ7D80Y9+ZHfffbe988479tprr5m67uSNMEQP40mPySagCl59XAxRJUWdBwKqPlJmiC0tLVZQUGC1tbUxv6KiIhsYGLCcnJw5PG+55RZ75JFH7Pbbb48NUV2HIXoYS3pcKAKq4NXHxxBVUtR5IKDqI2WG2NjYaBUVFVZZWRnzKy8vt46ODsvPzz/FM/o6uu644w4rLS2NDfF864aGhmx4ePiM86iurk54Rtv2vpqwhgIIeCGw864NSW0VfSQVJze7wARUfUR/osvLy5vTbcZs9N0kX83NzVZYWGg1NTXxnYuLi62/v99yc3Pjr6OnSm+66SbbsWOHffDBB3bffffZs88+G/8t8XzrfrFNnjJN8sFxOxcE1N+A1c2QEFVS1HkgoOojZQmxr6/Pent7rbOz0yYmJuKEODY2ZlNTU5aVlWVvvfWW9fT0xGyjF93s2rXLHn/8cVu+fPlZ153rEDBED+NJj8kmoApefVwMUSVFnQcCqj5SZogzMzNWX19vk5OTsSG2trZaWVmZNTQ0xE+P1tXVneJ6/PjxU0+ZnmsdhuhhDOkxVQRUwav9YIgqKeo8EFD1kTJDPAktSoTZ2dmWkZExL47qOhLivLBSvEgIqIJXt4shqqSo80BA1UfKDXGh4WGIC02Y+6cjAVXwau8YokqKOg8EVH1giB5Okx4hkICAKngVJIaokqLOAwFVHxiih9OkRwhgiMwABIIJYIgJ0PEbcPBssTANCaiCV1tHHyop6jwQUPVBQvRwmvQIARIiMwCBYAIYIgkxeHhY6I+AKnh1ZyRElRR1Hgio+iAhejhNeoQACZEZgEAwAQyRhBg8PCz0R0AVvLozEqJKijoPBFR9kBA9nCY9QoCEyAxAIJgAhkhCDB4eFvojoApe3RkJUSVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKoPEqKH06RHCJAQmQEIBBPAEEmIwcPDQn8EVMGrOyMhqqSo80BA1QcJ0cNp0iMESIjMAASCCWCIJMTg4WGhPwKq4NWdkRBVUtR5IKDqg4To4TTpEQIkRGYAAsEEMEQSYvDwsNAfAVXw6s5IiCop6jwQUPVBQvRwmvQIARIiMwCBYAIYIgkxeHhY6I+AKnh1ZyRElRR1Hgio+iAhejhNeoQACZEZgEAwAQyRhBg8PCz0R0AVvLozEqJKijoPBFR9kBA9nCY9QoCEyAxAIJgAhkhCDB4eFvojoApe3RkJUSVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHylLiLOzs9bU1GSjo6M2PT1tbW1tVlJSModlf39//P1LLrnErrjiCvvKV75ib775ptXW1tqqVavi2vXr19v27dvPeQbt7e3x4yS6EHwiQvzcEwFV8Oqe0IdKijoPBFR9pMwQBwcHraury7q7u218fNyqqqpsZGRkDsvNmzfb1772Nbvyyivtnnvuseuvv95+9Vd/1Xp6emzPnj0SdwxRwkTRIiOgCl7dNoaokqLOAwFVHykzxJaWFisoKIjTXnQVFRXZwMCA5eTknMEzSpDV1dV277332uTkpB06dMg2btxoy5cvt02bNtmSJUtIiB6mkB5TRkAVvNoQhqiSos4DAVUfKTPExsZGq6iosMrKyphfeXm5dXR0WH5+/hyezzzzjDU3N9uNN95o+/btsxdeeMF2795tW7dutbGxMTty5IgdOHAgXjM0NGTDw8NnnEdkpomubXtfTVTCzyHghsDOuzYktVf0kVSc3OwCE1D1Ef1pLy8vb063GbPRd5N8RSZXWFhoNTU18Z2Li4st+pthbm7uGY8UPfyDDz5oV111Vfzfp1/RPQ4ePGgrV648a4c8ZZrkg+N2LgiovwGrmyEhqqSo80BA1UfKEmJfX5/19vZaZ2enTUxMxAkxSnxTU1OWlZVlkQlG6TFKf9GLap588kl744034rqMjAyL/r54/PhxW7dunR0+fNiWLVuGIXqYRHpMCQFV8GozGKJKijoPBFR9pMwQZ2ZmrL6+Pv6bYGSIra2tVlZWZg0NDVZaWmp1dXW2c+dOe/bZZ23NmjV29OjR+AU2mZmZtmXLFlu7dm38veip1+g+57pIiB7Gkx6TTUAVvPq4GKJKijoPBFR9pMwQT0KLEmF2dnac+s52nThxwn72s5/Z5ZdfPufHx44dsxUrVsQGeb4LQ/QwnvSYbAKq4NXHxRBVUtR5IKDqI+WGuNDwMMSFJsz905GAKni1dwxRJUWdBwKqPjBED6dJjxBIQEAVvAoSQ1RJUeeBgKoPDNHDadIjBDBEZgACwQQwxATo+A04eLZYmIYEVMGrraMPlRR1Hgio+iAhejhNeoQACZEZgEAwAQyRhBg8PCz0R0AVvLozEqJKijoPBFR9kBA9nCY9QoCEyAxAIJgAhkhCDB4eFvojoApe3RkJUSVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKoPEqKH06RHCJAQmQEIBBPAEEmIwcPDQn8EVMGrOyMhqqSo80BA1QcJ0cNp0iMESIjMAASCCWCIJMTg4WGhPwKq4NWdkRBVUtR5IKDqg4To4TTpEQIkRGYAAsEEMEQSYvDwsNAfAVXw6s5IiCop6jwQUPVBQvRwmvQIARIiMwCBYAIYIgkxeHhY6I+AKnh1ZyRElRR1Hgio+iAhejhNeoQACZEZgEAwAQyRhBg8PCz0R0AVvLozEqJKijoPBFR9kBA9nCY9QoCEyAxAIJgAhkhCDB4eFvojoApe3RkJUSVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKqPlCXE2dlZa2pqstHRUZuenra2tjYrKSmZw7K/vz/+/iWXXGJXXHGFfeUrX7Hs7OyE606/SXt7e1yf6ELwiQjxc08EVMGre0IfKinqPBBQ9ZEyQxwcHLSuri7r7u628fFxq6qqspGRkTksN2/ebF/72tfsyiuvtHvuuceuv/56u+qqqxKuwxA9jCQ9LiQBVfBqDxiiSoo6DwRUfaTMEFtaWqygoMBqa2tjfkVFRTYwMGA5OTln8IwSZHV1td1777126NAheV10IxKih/Gkx2QTUAWvPi6GqJKizgMBVR8pM8TGxkarqKiwysrKmF95ebl1dHRYfn7+HJ7PPPOMNTc324033mj79u2LTVFZd/ImGKKH8aTHZBNQBa8+LoaokqLOAwFVHykzxMjkCgsLraamJuZXXFxs0d8Mc3Nzz+AZ/b3xwQcfjJ8ufffdd8+5bmhoyIaHh89YH6XLRNe2va8mKuHnEHBDYOddG5LaK/pIKk5udoEJqPqIvCcvL29Otxmz0XeTfPX19Vlvb691dnbaxMREnBDHxsZsamrKsrKyLHrIKD0eOHAgflHNk08+aW+88YaVlZWddd252iMhJvnguJ0LAupvwOpmSIgqKeo8EFD1kbKEODMzY/X19TY5ORkbYmtra2x2DQ0NVlpaanV1dbZz50579tlnbc2aNXb06NH4BTa/9mu/dtZ1GKKHMaTHVBFQBa/2gyGqpKjzQEDVR8oM8SS0KBFG/5QiIyPjrBxPnDhhP/vZz+zyyy+f8/NE604WkxA9jCc9JpuAKnj1cTFElRR1Hgio+ki5IS40PAxxoQlz/3QkoApe7R1DVElR54GAqg8M0cNp0iMEEhBQBa+CxBBVUtR5IKDqA0P0cJr0CAEMkRmAQDABDDEBOn4DDp4tFqYhAVXwauvoQyVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKoPEqKH06RHCJAQmQEIBBPAEEmIwcPDQn8EVMGrOyMhqqSo80BA1QcJ0cNp0iMESIjMAASCCSyYIe7Zsyf+wN7f+73fC25uIRfy1m0LSZd7pysBVfBq/yRElRR1Hgio+ph3Qow++f7v/u7vbMuWLfYP//APdvXVV6cVDwwxrY6DZlJEQBW82g6GqJKizgMBVR/zNsTocwu7u7vtc5/7nB07dswig7zhhhtOMfnkJz95QflgiBcUPw9+gQioglfbwxBVUtR5IKDqY96GeHLz0Uc0bd68+YxPrF+AzxaeF28McV64KF4kBFTBq9vFEFVS1HkgoOojyBBHRkbsL//yL2Mz/PSnPz0nIUYf9HshLwzxQtLnsS8UAVXwan8YokqKOg8EVH3M2xC/9KUv2V/8xV/Y+vXrLXqBTXFxcVrxwBDT6jhoJkUEVMGr7WCIKinqPBBQ9TFvQ/zCF75gH/7wh+2ee+6xzMzMtGOBIabdkdBQCgiogldbwRBVUtR5IKDqY96GmO6bxxDT/YTobyEIqIJXHxtDVElR54GAqg8M0cNp0iMEEhBQBa+CxBBVUtR5IKDqA0P0cJr0CAEMkRmAQDABDDEBOn4DDp4tFqYhAVXwauvoQyVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKoPEqKH06RHCJAQmQEIBBPAEEmIwcPDQn8EVMGrOyMhqqSo80BA1QcJ0cNp0iMESIjMAASCCaSdIUYfC9XU1GSjo6M2PT1tbW1tVlJSMmeDL7/8sj388MO2bNkyu+yyy+LPXXzzzTettrbWVq1aFddGbyq+ffv2c4LhrduCZ4aFjgmogle3SEJUSVHngYCqj5QlxMHBQevq6opNbnx83Kqqqiz6GKnTr4qKCtu7d69dffXVtm3bNrv22mtt7dq11tPTE3+yhnJhiAolahYbAVXw6r4xRJUUdR4IqPpImSG2tLRYQUFBnPaiq6ioyAYGBiwnJ+esPOvr6+22226zqakpO3TokG3cuNGWL19umzZtsiVLlpAQPUwhPaaMgCp4tSEMUSVFnQcCqj5SZoiNjY0WJcDKysqYX3l5uXV0dFh+fv4ZPPft22ff/va37etf/7o9//zztnv3btu6dauNjY3ZkSNH7MCBA/GaoaGh+EOKf/Gqrq5OeEbb9r6asIYCCHghsPOuDUltFX0kFSc3u8AEVH1Ef9rLy8ub023GbPTdJF/Nzc1WWFhoNTU18Z2jDxbu7++33NzcOY/00EMP2bvvvmtPPPGEZWVlndFFdI+DBw/aypUrz9ohT5km+eC4nQsC6m/A6mZIiCop6jwQUPWRsoTY19dnvb291tnZaRMTE3FCjBJf9JRoZHxLly61HTt22Ntvvx2/4Obk9a1vfcsyMjJs8+bNdvz4cVu3bp0dPnw4fuHN2S4M0cN40mOyCaiCVx8XQ1RJUeeBgKqPlBnizMyMRX8XnJycjA2xtbXVysrKrKGhwUpLS62urs5WrFgRv5Dm5N8Ib7311vjFN1u2bIlfXHP06FGLnnqN7nOuC0P0MJ70mGwCquDVx8UQVVLUeSCg6iNlhngSWpQIs7Oz49Q3n+vYsWOxYWZmZp53GYY4H6rULhYCquDV/WKIKinqPBBQ9ZFyQ1xoeBjiQhPm/ulIQBW82juGqJKizgMBVR8YoofTpEcIJCCgCl4FiSGqpKjzQEDVB4bo4TTpEQIYIjMAgWACGGICdPwGHDxbLExDAqrg1dbRh0qKOg8EVH2QED2cJj1CgITIDEAgmACGSEIMHh4W+iOgCl7dGQlRJUWdBwKqPkiIHk6THiFAQmQGIBBMAEMkIQYPDwv9EVAFr+6MhKiSos4DAVUfJEQPp0mPECDyCCtqAAAc2ElEQVQhMgMQCCaAIZIQg4eHhf4IqIJXd0ZCVElR54GAqg8SoofTpEcIkBCZAQgEE8AQSYjBw8NCfwRUwas7IyGqpKjzQEDVBwnRw2nSIwRIiMwABIIJYIgkxODhYaE/Aqrg1Z2REFVS1HkgoOqDhOjhNOkRAiREZgACwQQwRBJi8PCw0B8BVfDqzkiIKinqPBBQ9UFC9HCa9AgBEiIzAIFgAhgiCTF4eFjoj4AqeHVnJESVFHUeCKj6ICF6OE16hAAJkRmAQDABDJGEGDw8LPRHQBW8ujMSokqKOg8EVH2QED2cJj1CgITIDEAgmACGSEIMHh4W+iOgCl7dGQlRJUWdBwKqPkiIHk6THiFAQmQGIBBMAEMkIQYPDwv9EVAFr+6MhKiSos4DAVUfJEQPp0mPECAhMgMQCCaAIZIQg4eHhf4IqIJXd0ZCVElR54GAqg8SoofTpEcIkBCZAQgEE8AQSYjBw8NCfwRUwas7IyGqpKjzQEDVR8oS4uzsrDU1Ndno6KhNT09bW1ublZSUzGH58ssv28MPP2zLli2zyy67zLq7uy07OzvhutNv0t7eHtcnuhB8IkL83BMBVfDqntCHSoo6DwRUfaTMEAcHB62rqys2ufHxcauqqrKRkZE5LCsqKmzv3r129dVX27Zt2+zaa6+1NWvWJFyHIXoYSXpcSAKq4NUeMESVFHUeCKj6SJkhtrS0WEFBgdXW1sb8ioqKbGBgwHJycs7Ks76+3m677Tb77ne/O691JEQP40mPySagCl59XAxRJUWdBwKqPlJmiI2NjRYlwMrKyphfeXm5dXR0WH5+/hk89+3bZ9/+9rft61//us1nXXQjDNHDeNJjsgmoglcfF0NUSVHngYCqj5QZYnNzsxUWFlpNTU3Mr7i42Pr7+y03N3cOz4ceesjeffdde+KJJywrK8vOt25oaMiGh4fPOI/q6uqEZ7Rt76sJayiAgBcCO+/akNRW0UdScXKzC0xA1Uf0Wpe8vLw53WbMRt9N8tXX12e9vb3W2dlpExMTcUIcGxuzqamp2PiWLl1qO3bssLfffjt+wc3J61zrztUeCTHJB8ftXBBQfwNWN0NCVElR54GAqo+UJcSZmRmL/i44OTkZG2Jra6uVlZVZQ0ODlZaWWl1dna1YsSJ+Ic2SJUtixrfeemv8lOnZ1mGIHsaQHlNFQBW82g+GqJKizgMBVR8pM8ST0KJEGP1TioyMjHlxVNeREOeFleJFQkAVvLpdDFElRZ0HAqo+Um6ICw0PQ1xowtw/HQmogld7xxBVUtR5IKDqA0P0cJr0CIEEBFTBqyAxRJUUdR4IqPrAED2cJj1CAENkBiAQTABDTICO34CDZ4uFaUhAFbzaOvpQSVHngYCqDxKih9OkRwiQEJkBCAQTwBBJiMHDw0J/BFTBqzsjIaqkqPNAQNUHCdHDadIjBEiIzAAEgglgiCTE4OFhoT8CquDVnZEQVVLUeSCg6oOE6OE06RECJERmAALBBDBEEmLw8LDQHwFV8OrOSIgqKeo8EFD1QUL0cJr0CAESIjMAgWACGCIJMXh4WOiPgCp4dWckRJUUdR4IqPogIXo4TXqEAAmRGYBAMAEMkYQYPDws9EdAFby6MxKiSoo6DwRUfZAQPZwmPUKAhMgMQCCYAIZIQgweHhb6I6AKXt0ZCVElRZ0HAqo+SIgeTpMeIUBCZAYgEEwAQyQhBg8PC/0RUAWv7oyEqJKizgMBVR8kRA+nSY8QICEyAxAIJoAhkhCDh4eF/giogld3RkJUSVHngYCqDxKih9OkRwiQEJkBCAQTwBBJiMHDw0J/BFTBqzsjIaqkqPNAQNUHCdHDadIjBEiIzAAEgglgiCTE4OFhoT8CquDVnZEQVVLUeSCg6oOE6OE06RECJERmAALBBDBEEmLw8LDQHwFV8OrOSIgqKeo8EFD1QUL0cJr0CAESIjMAgWACGCIJMXh4WOiPgCp4dWckRJUUdR4IqPpIWUKcnZ21pqYmGx0dtenpaWtra7OSkpI5LE+cOGGPPfaY9fT02Pe+9734Z6+//rrV1tbaqlWr4q/Xr19v27dvP+cZtLe3x4+T6ELwiQjxc08EVMGre0IfKinqPBBQ9ZEyQxwcHLSuri7r7u628fFxq6qqspGRkTksd+/ebatXr7Zt27bZj3/84/hnL730UmyQe/bskbhjiBImihYZAVXw6rYxRJUUdR4IqPpImSG2tLRYQUFBnPaiq6ioyAYGBiwnJ+cMnmvWrDlliJEZHjp0yDZu3GjLly+3TZs22ZIlS0iIHqaQHlNGQBW82hCGqJKizgMBVR8pM8TGxkarqKiwysrKmF95ebl1dHRYfn7+eQ3x+eeftyg5bt261cbGxuzIkSN24MABDNHDFNJjygioglcbwhBVUtR5IKDqI2WG2NzcbIWFhVZTUxPzKy4utv7+fsvNzT2vIf7iD6N7HDx40FauXGlDQ0M2PDx8xvrq6uqEZ7Rt76sJayiAgBcCO+/akNRW0UdScXKzC0xA1Uf0Wpe8vLw53WbMRt9N8tXX12e9vb3W2dlpExMTcUKMEt/U1JRlZWXZ0qVLTz3i6U+Zfutb37KMjAzbvHmzHT9+3NatW2eHDx+2ZcuWnbVD/oaY5IPjdi4IqL8Bq5shIaqkqPNAQNVHyhLizMyM1dfX2+TkZGyIra2tVlZWZg0NDVZaWmp1dXX2wAMPxC+0eeWVV+y6666zm2++Of7Pli1bbO3atXb06FGLnnqN7nOuC0P0MJ70mGwCquDVx8UQVVLUeSCg6iNlhngSWpQIs7Oz49Q3n+vYsWO2YsUKy8zMPO8yDHE+VKldLARUwav7xRBVUtR5IKDqI+WGuNDwMMSFJsz905GAKni1dwxRJUWdBwKqPjBED6dJjxBIQEAVvAoSQ1RJUeeBgKoPDNHDadIjBDBEZgACwQQwxATo+A04eLZYmIYEVMGrraMPlRR1Hgio+iAhejhNeoQACZEZgEAwAQyRhBg8PCz0R0AVvLozEqJKijoPBFR9kBA9nCY9QoCEyAxAIJgAhkhCDB4eFvojoApe3RkJUSVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKoPEqKH06RHCJAQmQEIBBPAEEmIwcPDQn8EVMGrOyMhqqSo80BA1QcJ0cNp0iMESIjMAASCCWCIJMTg4WGhPwKq4NWdkRBVUtR5IKDqg4To4TTpEQIkRGYAAsEEMEQSYvDwsNAfAVXw6s5IiCop6jwQUPVBQvRwmvQIARIiMwCBYAIYIgkxeHhY6I+AKnh1ZyRElRR1Hgio+iAhejhNeoQACZEZgEAwAQyRhBg8PCz0R0AVvLozEqJKijoPBFR9kBA9nCY9QoCEyAxAIJgAhkhCDB4eFvojoApe3RkJUSVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKoPEqKH06RHCJAQmQEIBBNIO0OcnZ21pqYmGx0dtenpaWtra7OSkpI5Gzxx4oQ99thj1tPTY9/73vfinynrTr9Je3t7/DiJLn4DTkSIn3sioApe3RP6UElR54GAqo+UJcTBwUHr6uqy7u5uGx8ft6qqKhsZGZnDcvfu3bZ69Wrbtm2b/fjHP45/pqzDED2MJD0uJAFV8GoPGKJKijoPBFR9pMwQW1parKCgwGpra2N+RUVFNjAwYDk5OWfwXLNmzSlDnM+66EYkRA/jSY/JJqAKXn1cDFElRZ0HAqo+UmaIjY2NVlFRYZWVlTG/8vJy6+josPz8/PMa4vnWDQ0N2fDw8Bnrq6urE57Rtr2vJqyhAAJeCOy8a0NSW0UfScXJzS4wAVUf0Z/o8vLy5nSbMRt9N8lXc3OzFRYWWk1NTXzn4uJi6+/vt9zc3PMa4nzWkRCTfGjczg0B9TdgdUMkRJUUdR4IqPpIWULs6+uz3t5e6+zstImJiTghjo2N2dTUlGVlZdnSpUtPcT39KdNzrTvXIfCUqYfxpMdkE1AFrz4uhqiSos4DAVUfKTPEmZkZq6+vt8nJydgQW1tbrayszBoaGqy0tNTq6ursgQceiF9o88orr9h1111nN998s913331nXYchehhDekwVAVXwaj8YokqKOg8EVH2kzBBPQosSYXZ2tmVkZMyLo7qOhDgvrBQvEgKq4NXtYogqKeo8EFD1kXJDXGh4GOJCE+b+6UhAFbzaO4aokqLOAwFVHxiih9OkRwgkIKAKXgWJIaqkqPNAQNUHhujhNOkRAhgiMwCBYAIYYgJ0/AYcPFssTEMCquDV1tGHSoo6DwRUfZAQPZwmPUKAhMgMQCCYAIZIQgweHhb6I6AKXt0ZCVElRZ0HAqo+SIgeTpMeIUBCZAYgEEwAQyQhBg8PC/0RUAWv7oyEqJKizgMBVR8kRA+nSY8QICEyAxAIJoAhkhCDh4eF/giogld3RkJUSVHngYCqDxKih9OkRwiQEJkBCAQTwBBJiMHDw0J/BFTBqzsjIaqkqPNAQNUHCdHDadIjBEiIzAAEgglgiCTE4OFhoT8CquDVnZEQVVLUeSCg6oOE6OE06RECJERmAALBBDBEEmLw8LDQHwFV8OrOSIgqKeo8EFD1QUL0cJr0CAESIjMAgWACGCIJMXh4WOiPgCp4dWckRJUUdR4IqPogIXo4TXqEAAmRGYBAMAEMkYQYPDws9EdAFby6MxKiSoo6DwRUfZAQPZwmPUKAhMgMQCCYAIZIQgweHhb6I6AKXt0ZCVElRZ0HAqo+SIgeTpMeIUBCZAYgEEwAQyQhBg8PC/0RUAWv7oyEqJKizgMBVR8kRA+nSY8QICEyAxAIJoAhkhCDh4eF/giogld3RkJUSVHngYCqDxKih9OkRwiQEJkBCAQTwBBJiMHDw0J/BFTBqzsjIaqkqPNAQNVHyhLi7OysNTU12ejoqE1PT1tbW5uVlJTMYfn666/b/fffb0uWLLH8/HzbtWuX/cd//IfV1tbaqlWr4tr169fb9u3bz3kG7e3t8eMkuhB8IkL83BMBVfDqntCHSoo6DwRUfaTMEAcHB62rq8u6u7ttfHzcqqqqbGRkZA7LG264wfbt22cf/ehH7TOf+Yx96lOfsiuvvNJ6enpsz549EncMUcJE0SIjoApe3TaGqJKizgMBVR8pM8SWlhYrKCiI0150FRUV2cDAgOXk5MRfR6nxmmuuscOHD8dfHzhwIP75xo0b7dChQ/F/L1++3DZt2hQnyHNdGKKH8aTHZBNQBa8+LoaokqLOAwFVHykzxMbGRquoqLDKysqYX3l5uXV0dMRPjUbXO++8E//8tddei78eGhqKf15dXW27d++2rVu32tjYmB05ciQ2SwzRwxjSY6oIqIJX+8EQVVLUeSCg6iNlhtjc3GyFhYVWU1MT8ysuLrb+/n7Lzc2Nv37//fft4x//uP3gBz+Iv/7mN78Z//yJJ56Ywzu6x8GDB23lypWxaQ4PD59xHpGJJrq27X01UQk/h4AbAjvv2pDUXtFHUnFyswtMQNVH9FqXvLy8Od1mzEbfTfLV19dnvb291tnZaRMTE3FCjBLf1NSUZWVl2dKlS+MX2ezfv9/Wrl1rDz74oG3YsMGuuOIKy8jIsM2bN9vx48dt3bp18dOqy5YtO2uHPGWa5IPjdi4IqL8Bq5shIaqkqPNAQNVHyhLizMyM1dfX2+TkZGyIra2tVlZWZg0NDVZaWmp1dXVx2vvsZz9rV111lX3oQx+yp59+2v77v//btmzZEpvk0aNHLXrqNbrPuS4M0cN40mOyCaiCVx8XQ1RJUeeBgKqPlBniSWhRIszOzo5T37muqCYyxNOvY8eO2YoVKywzM/O8/DFED+NJj8kmoApefVwMUSVFnQcCqj5SbogLDQ9DXGjC3D8dCaiCV3vHEFVS1HkgoOoDQ/RwmvQIgQQEVMGrIDFElRR1Hgio+sAQPZwmPUIAQ2QGIBBMAENMgI7fgINni4VpSEAVvNo6+lBJUeeBgKoPEqKH06RHCJAQmQEIBBPAEEmIwcPDQn8EVMGrOyMhqqSo80BA1QcJ0cNp0iMESIjMAASCCWCIJMTg4WGhPwKq4NWdkRBVUtR5IKDqg4To4TTpEQIkRGYAAsEEMEQSYvDwsNAfAVXw6s5IiCop6jwQUPVBQvRwmvQIARIiMwCBYAIYIgkxeHhY6I+AKnh1ZyRElRR1Hgio+iAhejhNeoQACZEZgEAwAQyRhBg8PCz0R0AVvLozEqJKijoPBFR9kBA9nCY9QoCEyAxAIJgAhkhCDB4eFvojoApe3RkJUSVFnQcCqj5IiB5Okx4hQEJkBiAQTABDJCEGDw8L/RFQBa/ujISokqLOAwFVHyRED6dJjxAgITIDEAgmgCGSEIOHh4X+CKiCV3dGQlRJUeeBgKoPEqKH06RHCJAQmQEIBBPAEEmIwcPDQn8EVMGrOyMhqqSo80BA1QcJ0cNp0iMESIjMAASCCWCIJMTg4WGhPwKq4NWdkRBVUtR5IKDqg4To4TTpEQIkRGYAAsEEMEQSYvDwsNAfAVXw6s5IiCop6jwQUPVBQvRwmvQIARIiMwCBYAIYIgkxeHhY6I+AKnh1ZyRElRR1Hgio+iAhejhNeoQACZEZgEAwgbQzxNnZWWtqarLR0VGbnp62trY2KykpmbPB119/3e6//35bsmSJ5efn265duywzMzPhutNv0t7eHtcnuvgNOBEhfu6JgCp4dU/oQyVFnQcCqj5SlhAHBwetq6vLuru7bXx83KqqqmxkZGQOyxtuuMH27dtnH/3oR+0zn/mMfepTn7Irrrgi4ToM0cNI0uNCElAFr/aAIaqkqPNAQNVHygyxpaXFCgoKrLa2NuZXVFRkAwMDlpOTE38dpcZrrrnGDh8+HH994MCB+OdXXnnledf94mGQED2MJz0mm4AqePVxMUSVFHUeCKj6SJkhNjY2WkVFhVVWVsb8ysvLraOjI35qNLreeeed+OevvfZa/PXQ0FD88yghnmtdVDM8PDznPJYvX24///nPPZwRPUIAAhCAQBoR+M3f/E276aab5nSUMRv9wS/JV3NzsxUWFlpNTU185+LiYuvv77fc3Nz46/fff98+/vGP2w9+8IP4629+85vxzy+//PLzrktym9wuyQTUxJ7kh+V2EHBBAH2k/zEtiCH29fVZb2+vdXZ22sTERJwQx8bGbGpqyrKysmzp0qXxi2z2799va9eutQcffNA2bNhgK1asOOu69MdIhxEBBM8cQODcBNBH+k/HghjizMyM1dfX2+TkZGyIra2tVlZWZg0NDVZaWmp1dXXx05+f/exn7aqrrrIPfehD9vTTT8evOD3buvTHSIcYIjMAgfMTwBDTf0IWxBBPbjtKhNnZ2ZaRkXFOElFNZIinX8q69Ed78XWI4C++M2fHOgH0obO6UJULaogXalM87oUhEL3wKXoGgAsCEDiTAPpI/6nAENP/jOgQAhCAAARSQABDTAHki+EhPvjgg/hFU9ErhbkgAIG5BNCHj4nAEH2cU1p3ee+999qLL75o1dXV8b/r+c53vmOf//zn07pnmoNAqgigj1SR/uUfB0P85Rle1Hd4+eWX7Z//+Z/tzjvvtC996Uv293//9/b7v//78b8tveSSSy5qNmweAujD1wxgiL7OK+26/cY3vmHR00HRmy9EhvjYY4/Z7bffbl/+8pcteichLghczATQh6/TxxB9nVfadRv9O9M/+IM/sD/8wz+M38A9egei6D1qo/8j4ILAxU4AffiaAAzR13mlZbfRJ5p89atftZ/85Ce2bt26+I0Xli1blpa90hQEUk0AfaSaePjjYYjh7Fj5/wlEafDmm2+OP/9y79699id/8if2u7/7u/CBwEVPIDLD6G/sp1/R20ffcccdFz2bdASAIabjqTjqKTLBRx55JH5f2uj9aKM3do8+7Plf/uVfzvsORY62SKsQCCbwxhtv2D/+4z+eWh99oEH08XfPP/988D1ZuHAEMMSFY3tR3Pm5556L9/lbv/Vb8T+16OnpsT//8z+P3+j7V37lVy4KBmwSAvMhcPfdd8cvOjvfW1rO537UJo8Ahpg8lhflnf793//d/vZv/zb+FJPo3yBGL7D54z/+Y4tebp6ZmXlRMmHTEDhJIPqgg+jj7k5ex44di/+8EH0g+qWXXgqoNCOAIabZgXhs55/+6Z/s3XfftT/7sz+LP/T57bffPvXh0B73Q88QSBaB6P1LP/3pT5+6XfRBBrfddpt97nOfS9ZDcJ8kEsAQkwjzYrrV//zP/9grr7xyzi1XVlZeTDjYKwTOSiBKhFFC/PCHPwwhBwQwRAeHlI4tRi8W2Llz5zlbi/6RPhcELnYC0StMo89+/Zu/+ZuLHYWL/WOILo4p/ZuM3q3mxIkTFv3N5Atf+EL8odDRBz5zQeBiJhD94njrrbdafn6+rV69+hQKfmFMz6nAENPzXNx09fOf/9w2bdpkP/zhD+O3cIv+lvhHf/RH8atNuSBwMRN466234leSRm92/4uvKI1eeMaVfgQwxPQ7E1cd9fb2WvSPj4uKimxsbCx+C7fo/UzP93Sqqw3SLAQCCdx44432r//6r4GrWXYhCGCIF4L6InrM6F1qfvrTn8bvTPPMM8/YX//1X9uf/umfWkdHh0WvqOOCwMVKAEP0d/IYor8zS6uO33vvPbv++uttcHAw/neIeXl5Fv3bxO9///v8O8S0OimaSTWB6H19z/Xq0uifY3ClHwEMMf3OxF1H0Qtpor+RTE5O2ne/+137xCc+YatWrXK3DxqGQDIJ/M7v/I7t27fvrLeMzJIr/QhgiOl3Jm46it6q7dFHH437jd7CLfo74m/8xm+46Z9GIbCQBHjKdCHpLsy9McSF4XpR3PV0wd9///3xp1x88pOfvCj2ziYhkIhAZ2dn/L6+XH4IYIh+zirtOsUQ0+5IaAgCEPglCGCIvwS8i30phnixTwD7h8DiIoAhLq7zTOluohcGnHz3jTfffDN+Ic3ll18e98C/v0rpUfBgEIBAEghgiEmAeLHe4r/+67/mfLTN6RwKCwsvVizsGwIQcEoAQ3R6cLQNAQhAAALJJYAhJpcnd4MABCAAAacEMESnB0fbEIAABCCQXAIYYnJ5cjcIQAACEHBKAEN0enC0DQEIQAACySWAISaXJ3eDwAUlEH3w7Ntvvx1/SHNmZqb97//+b/wJJNddd138QbVcEIDAuQlgiEwHBBYRgeHh4fijuJ544glrbGy06C31nnrqqfjTR3791399Ee2UrUAg+QQwxOQz5Y4QuKAEHnjggfhTFp5++mm75ZZb4g9sjoyRCwIQOD8BDJEJgcAiI3D8+HH77d/+bYveOGHDhg32yiuv8NmUi+yM2c7CEMAQF4Yrd4XABSPw/vvvx59JGT1NGn0m37/927/Fn1fJBQEIkBCZAQhcVAQefvhhe/zxx23Xrl1211132Ze//GW7++67LyoGbBYCIQRIiCHUWAOBNCUwNjZmxcXF9sgjj9hf/dVf2R133GHf+MY37D//8z/tqquuStOuaQsC6UEAQ0yPc6ALCPzSBKanp+O/Gc7Oztprr71mWVlZ9tOf/tSiTyXZuHGjPffcc7/0Y3ADCCxmAhjiYj5d9gYBCEAAAjIBDFFGRSEEIAABCCxmAhjiYj5d9gYBCEAAAjIBDFFGRSEEIAABCCxmAhjiYj5d9gYBCEAAAjIBDFFGRSEEIAABCCxmAhjiYj5d9gYBCEAAAjKB/wv0QMCkVGafGgAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler $ \n", " plot . fmap (first (T.pack . show)) . toEmpirical <$> \n", " replicateM 100000 (prior model)" ] }, { "cell_type": "markdown", "id": "1730423b-8190-481b-a215-a129ea9fdbc9", "metadata": {}, "source": [ "## Prior predictive\n", "\n", "Prior predictive model is an application of the generative distribution to the prior distribution.\n", "\n", "Similarly as in `Sampling.ipynb` example we need to introduce a constant weight by mapping samples to a pair (value, score) with `(,constWeight) <$> `." ] }, { "cell_type": "code", "execution_count": 4, "id": "c0de2347-77b2-4433-8bd0-e854791c23b8", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -5.990666102955002, "Y": 4 }, { "X": -5.871272770673965, "Y": 1 }, { "X": -5.751879438392927, "Y": 7 }, { "X": -5.63248610611189, "Y": 2 }, { "X": -5.513092773830853, "Y": 8 }, { "X": -5.393699441549815, "Y": 8 }, { "X": -5.274306109268778, "Y": 11 }, { "X": -5.154912776987741, "Y": 17 }, { "X": -5.035519444706703, "Y": 22 }, { "X": -4.916126112425666, "Y": 34 }, { "X": -4.796732780144629, "Y": 44 }, { "X": -4.6773394478635915, "Y": 70 }, { "X": -4.5579461155825545, "Y": 104 }, { "X": -4.4385527833015175, "Y": 131 }, { "X": -4.31915945102048, "Y": 169 }, { "X": -4.199766118739443, "Y": 203 }, { "X": -4.080372786458406, "Y": 318 }, { "X": -3.960979454177368, "Y": 349 }, { "X": -3.841586121896331, "Y": 443 }, { "X": -3.7221927896152938, "Y": 520 }, { "X": -3.6027994573342568, "Y": 662 }, { "X": -3.4834061250532193, "Y": 822 }, { "X": -3.364012792772182, "Y": 962 }, { "X": -3.244619460491145, "Y": 1103 }, { "X": -3.1252261282101075, "Y": 1185 }, { "X": -3.00583279592907, "Y": 1457 }, { "X": -2.886439463648033, "Y": 1551 }, { "X": -2.7670461313669956, "Y": 1827 }, { "X": -2.647652799085958, "Y": 1887 }, { "X": -2.528259466804921, "Y": 2082 }, { "X": -2.4088661345238838, "Y": 2209 }, { "X": -2.2894728022428468, "Y": 2328 }, { "X": -2.1700794699618093, "Y": 2355 }, { "X": -2.050686137680772, "Y": 2394 }, { "X": -1.9312928053997345, "Y": 2440 }, { "X": -1.8118994731186975, "Y": 2381 }, { "X": -1.6925061408376605, "Y": 2252 }, { "X": -1.5731128085566226, "Y": 2121 }, { "X": -1.4537194762755856, "Y": 2022 }, { "X": -1.3343261439945486, "Y": 1937 }, { "X": -1.2149328117135116, "Y": 1768 }, { "X": -1.0955394794324738, "Y": 1544 }, { "X": -0.9761461471514368, "Y": 1414 }, { "X": -0.8567528148703998, "Y": 1324 }, { "X": -0.7373594825893619, "Y": 1171 }, { "X": -0.6179661503083249, "Y": 999 }, { "X": -0.4985728180272879, "Y": 858 }, { "X": -0.37917948574625004, "Y": 782 }, { "X": -0.25978615346521305, "Y": 707 }, { "X": -0.14039282118417606, "Y": 669 }, { "X": -0.020999488903138186, "Y": 607 }, { "X": 0.0983938433778988, "Y": 640 }, { "X": 0.2177871756589358, "Y": 624 }, { "X": 0.3371805079399737, "Y": 739 }, { "X": 0.45657384022101066, "Y": 845 }, { "X": 0.5759671725020477, "Y": 947 }, { "X": 0.6953605047830855, "Y": 1102 }, { "X": 0.8147538370641225, "Y": 1243 }, { "X": 0.9341471693451595, "Y": 1433 }, { "X": 1.0535405016261965, "Y": 1539 }, { "X": 1.1729338339072344, "Y": 1692 }, { "X": 1.2923271661882714, "Y": 1830 }, { "X": 1.4117204984693084, "Y": 1934 }, { "X": 1.5311138307503462, "Y": 2166 }, { "X": 1.6505071630313832, "Y": 2309 }, { "X": 1.7699004953124202, "Y": 2269 }, { "X": 1.889293827593458, "Y": 2358 }, { "X": 2.008687159874495, "Y": 2471 }, { "X": 2.128080492155534, "Y": 2322 }, { "X": 2.247473824436571, "Y": 2363 }, { "X": 2.366867156717608, "Y": 2134 }, { "X": 2.486260488998645, "Y": 2122 }, { "X": 2.605653821279682, "Y": 1995 }, { "X": 2.725047153560719, "Y": 1837 }, { "X": 2.8444404858417576, "Y": 1745 }, { "X": 2.9638338181227946, "Y": 1526 }, { "X": 3.0832271504038316, "Y": 1270 }, { "X": 3.2026204826848685, "Y": 1117 }, { "X": 3.3220138149659055, "Y": 979 }, { "X": 3.4414071472469425, "Y": 858 }, { "X": 3.5608004795279795, "Y": 749 }, { "X": 3.6801938118090183, "Y": 568 }, { "X": 3.7995871440900553, "Y": 468 }, { "X": 3.9189804763710923, "Y": 341 }, { "X": 4.038373808652129, "Y": 298 }, { "X": 4.157767140933166, "Y": 242 }, { "X": 4.277160473214203, "Y": 163 }, { "X": 4.396553805495242, "Y": 139 }, { "X": 4.515947137776279, "Y": 92 }, { "X": 4.635340470057316, "Y": 76 }, { "X": 4.754733802338353, "Y": 58 }, { "X": 4.87412713461939, "Y": 28 }, { "X": 4.993520466900427, "Y": 26 }, { "X": 5.112913799181466, "Y": 18 }, { "X": 5.232307131462503, "Y": 14 }, { "X": 5.35170046374354, "Y": 10 }, { "X": 5.471093796024577, "Y": 7 }, { "X": 5.590487128305614, "Y": 5 }, { "X": 5.709880460586651, "Y": 3 }, { "X": 5.829273792867688, "Y": 0 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "constWeight = 1 :: Log Double\n", "\n", "sampler $ plot . histogramToList . histogram 100 <$> replicateM 100000 ((,constWeight) <$> priorPredictive model)" ] }, { "cell_type": "markdown", "id": "e43b4b99-dfa0-4fa7-86a8-37742e60cd25", "metadata": {}, "source": [ "## Posterior" ] }, { "cell_type": "markdown", "id": "7b9fd255-e24a-47a9-a7e4-330db726b31c", "metadata": {}, "source": [ "Posterior distribution is computed using `likelihood` and observations $os = \\{o_{1}, \\dots o_{n} \\}$.\n", "Factors are computed for posterior for `model`, and in the considered example yield following: \n", "- $ \\prod_{i=1}^{n} \\text{likelihood}(\\text{True}, o_{i}) $ for `True` \n", "- $ \\prod_{i=1}^{n} \\text{likelihood}(\\text{False}, o_{i}) $ for `False`.\n", "\n", "Note that factors do not have to sum up to 1. Empirical distribution is obtained by normalizing factors to 1.\n" ] }, { "cell_type": "markdown", "id": "5106188a-c959-455a-b155-e5a5e4a34cb0", "metadata": {}, "source": [ "### Observations\n", "\n", "In our example, we experiment how observed data change the a priori model given the likelihood function. Observations should be in the domain of the likelihood function." ] }, { "cell_type": "markdown", "id": "bab96b4a-eba8-445f-b612-d1acbaac902c", "metadata": {}, "source": [ "Taking symmetrical and evenly distributed points, we do not change the posterior distribution:" ] }, { "cell_type": "code", "execution_count": 5, "id": "e7de370d-616e-44b3-9fd3-ddea14e6d27e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(True,0.5005199999999994),(False,0.49948000000000126)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "observations1 = [-3..3]\n", "sampler $ toEmpiricalWeighted <$> replicateM 100000 (runWeightedT $ posterior model observations1)" ] }, { "cell_type": "markdown", "id": "616dd80b-6d54-4044-abf0-eef08f31e525", "metadata": {}, "source": [ "On the other hand, if the observations are unevenly distributed adjustments to posterior are well visible:" ] }, { "cell_type": "code", "execution_count": 6, "id": "cb53e65b-d75b-4c47-9ba4-14dbe4fecbea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(True,0.7669770523099223),(False,0.23302294769007775)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "observations2 = [-2.3, -2, 0, 0.3, 2, 2.3]\n", "sampler $ toEmpiricalWeighted <$> replicateM 100000 (runWeightedT $ posterior model observations2)" ] }, { "cell_type": "code", "execution_count": 7, "id": "064ba891-8d4c-4a2f-9aa3-530c64cce610", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": "True", "Y": 0.7858490286459617 }, { "X": "False", "Y": 0.21415097135403988 } ] }, "encoding": { "x": { "field": "X", "type": "nominal" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler $ \n", " plot . fmap (bimap (T.pack . show) (ln . exp)) . toEmpiricalWeighted <$> \n", " replicateM 1000 (runWeightedT $ posterior model observations2)" ] }, { "cell_type": "markdown", "id": "22d6ba01-5509-4268-8888-280b40d7cb5c", "metadata": {}, "source": [ "### Excercise\n", "\n", "Change the observations lists and see how the posterior is affected.\n", "Change standard deviation of `likelihood` and see how posterior changes.\n" ] }, { "cell_type": "markdown", "id": "dd0b08c8-e604-43be-9029-6708c61b4d1b", "metadata": {}, "source": [ "## Posterior predictive\n", "\n", "Analoguosly to prior predictive is generated by `generative` applied to `posterior`." ] }, { "cell_type": "code", "execution_count": 8, "id": "3021a681-2370-4157-b5c7-fc9bbe2f7849", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -6.5236444797796205, "Y": 1.2000820346917839e-12 }, { "X": -6.396991310288549, "Y": 0 }, { "X": -6.270338140797477, "Y": 0 }, { "X": -6.143684971306406, "Y": 0 }, { "X": -6.017031801815335, "Y": 0 }, { "X": -5.890378632324263, "Y": 1.2000820346917839e-12 }, { "X": -5.763725462833192, "Y": 3.6002461040753516e-12 }, { "X": -5.637072293342121, "Y": 6.000410173458919e-12 }, { "X": -5.510419123851049, "Y": 4.8003281387671354e-12 }, { "X": -5.383765954359978, "Y": 3.6002461040753516e-12 }, { "X": -5.257112784868907, "Y": 1.0800738312226055e-11 }, { "X": -5.130459615377835, "Y": 2.2801558659143882e-11 }, { "X": -5.003806445886764, "Y": 4.320295324890418e-11 }, { "X": -4.877153276395693, "Y": 3.6002461040753487e-11 }, { "X": -4.750500106904621, "Y": 5.6403855630513785e-11 }, { "X": -4.62384693741355, "Y": 9.720664481003459e-11 }, { "X": -4.497193767922479, "Y": 1.57210746544624e-10 }, { "X": -4.370540598431408, "Y": 1.6561132078746653e-10 }, { "X": -4.2438874289403365, "Y": 2.5681755542404247e-10 }, { "X": -4.117234259449265, "Y": 3.4442354395654305e-10 }, { "X": -3.9905810899581935, "Y": 4.0562772772582427e-10 }, { "X": -3.863927920467122, "Y": 4.932337162583237e-10 }, { "X": -3.7372747509760504, "Y": 7.296498770925958e-10 }, { "X": -3.6106215814849794, "Y": 8.052550452781753e-10 }, { "X": -3.483968411993908, "Y": 1.008068909141079e-09 }, { "X": -3.3573152425028363, "Y": 1.226483839454975e-09 }, { "X": -3.2306620730117652, "Y": 1.3980955704158935e-09 }, { "X": -3.1040089035206937, "Y": 1.6429123054930078e-09 }, { "X": -2.977355734029622, "Y": 1.911730681263957e-09 }, { "X": -2.850702564538551, "Y": 2.1001435607105596e-09 }, { "X": -2.7240493950474796, "Y": 2.3377598035795235e-09 }, { "X": -2.5973962255564085, "Y": 2.5477741596505775e-09 }, { "X": -2.470743056065337, "Y": 2.7217860546808794e-09 }, { "X": -2.344089886574266, "Y": 2.906598688023407e-09 }, { "X": -2.217436717083194, "Y": 2.8861972934336473e-09 }, { "X": -2.090783547592123, "Y": 3.0170062352150467e-09 }, { "X": -1.9641303781010517, "Y": 3.1310140285107617e-09 }, { "X": -1.8374772086099798, "Y": 3.0109582427108273e-09 }, { "X": -1.7108240391189087, "Y": 2.941353484698706e-09 }, { "X": -1.5841708696278376, "Y": 2.762157094961661e-09 }, { "X": -1.4575177001367665, "Y": 2.604946348417043e-09 }, { "X": -1.3308645306456945, "Y": 2.4479294391173596e-09 }, { "X": -1.2042113611546235, "Y": 2.2530844005985607e-09 }, { "X": -1.0775581916635524, "Y": 2.1027375620248778e-09 }, { "X": -0.9509050221724804, "Y": 1.875109649923317e-09 }, { "X": -0.8242518526814093, "Y": 1.659336303254499e-09 }, { "X": -0.6975986831903382, "Y": 1.5367851887236598e-09 }, { "X": -0.5709455136992663, "Y": 1.5119235581542422e-09 }, { "X": -0.4442923442081952, "Y": 1.410834681177463e-09 }, { "X": -0.3176391747171241, "Y": 1.4613878894705825e-09 }, { "X": -0.19098600522605214, "Y": 1.540457572611739e-09 }, { "X": -0.06433283573498105, "Y": 1.576898798395013e-09 }, { "X": 0.06232033375609003, "Y": 1.7621061220711967e-09 }, { "X": 0.188973503247162, "Y": 2.3371603014648102e-09 }, { "X": 0.3156266727382331, "Y": 2.69897702940749e-09 }, { "X": 0.44227984222930417, "Y": 3.2753740378280713e-09 }, { "X": 0.5689330117203761, "Y": 3.581694482346584e-09 }, { "X": 0.6955861812114472, "Y": 4.3484798012282575e-09 }, { "X": 0.8222393507025183, "Y": 5.130913768891697e-09 }, { "X": 0.9488925201935894, "Y": 5.596226553724365e-09 }, { "X": 1.0755456896846614, "Y": 6.241701406596872e-09 }, { "X": 1.2021988591757324, "Y": 7.435009210711222e-09 }, { "X": 1.3288520286668035, "Y": 8.030607531686927e-09 }, { "X": 1.4555051981578755, "Y": 8.58684105717161e-09 }, { "X": 1.5821583676489466, "Y": 9.42635204894e-09 }, { "X": 1.7088115371400177, "Y": 9.668585306468487e-09 }, { "X": 1.8354647066310887, "Y": 9.818792905971791e-09 }, { "X": 1.9621178761221598, "Y": 9.891712416103428e-09 }, { "X": 2.0887710456132327, "Y": 1.0178590128491209e-08 }, { "X": 2.2154242151043038, "Y": 9.75504230321895e-09 }, { "X": 2.342077384595375, "Y": 9.729935745151944e-09 }, { "X": 2.468730554086446, "Y": 9.267743875193853e-09 }, { "X": 2.595383723577517, "Y": 8.40711073803051e-09 }, { "X": 2.722036893068588, "Y": 7.434914046049696e-09 }, { "X": 2.848690062559661, "Y": 6.9089715733387646e-09 }, { "X": 2.975343232050732, "Y": 6.402951163988095e-09 }, { "X": 3.101996401541803, "Y": 5.566224502857067e-09 }, { "X": 3.228649571032874, "Y": 4.8251237458553e-09 }, { "X": 3.3553027405239453, "Y": 3.836989403186276e-09 }, { "X": 3.4819559100150164, "Y": 3.470423437357471e-09 }, { "X": 3.6086090795060874, "Y": 2.637681188898582e-09 }, { "X": 3.7352622489971603, "Y": 2.3826787778872478e-09 }, { "X": 3.8619154184882314, "Y": 1.8368142418161086e-09 }, { "X": 3.9885685879793025, "Y": 1.466263863315244e-09 }, { "X": 4.1152217574703736, "Y": 9.881343426689672e-10 }, { "X": 4.241874926961445, "Y": 7.610228203619858e-10 }, { "X": 4.368528096452516, "Y": 6.33521614856312e-10 }, { "X": 4.495181265943589, "Y": 4.821139333183243e-10 }, { "X": 4.62183443543466, "Y": 2.9484653773186803e-10 }, { "X": 4.748487604925731, "Y": 2.2312710963492739e-10 }, { "X": 4.875140774416802, "Y": 1.354700308497772e-10 }, { "X": 5.001793943907873, "Y": 9.164149145720218e-11 }, { "X": 5.128447113398944, "Y": 5.976619008078404e-11 }, { "X": 5.255100282890017, "Y": 4.7812952064627226e-11 }, { "X": 5.381753452381088, "Y": 2.3906476032313613e-11 }, { "X": 5.508406621872159, "Y": 2.3906476032313613e-11 }, { "X": 5.63505979136323, "Y": 1.1953238016156808e-11 }, { "X": 5.761712960854301, "Y": 3.187530137641815e-11 }, { "X": 5.888366130345372, "Y": 7.968825344104538e-12 }, { "X": 6.015019299836443, "Y": 0 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler $ plot . histogramToList . histogram 100 <$> replicateM 100000 (runWeightedT $ posteriorPredictive model observations2)" ] }, { "cell_type": "markdown", "id": "d3c062c3-e5f8-4a72-b631-0635a6730b25", "metadata": {}, "source": [ "# Regression\n", "\n", "The classical example of linear regression:" ] }, { "cell_type": "code", "execution_count": 9, "id": "080d34a0-3324-4bf2-bb25-72b06db1f342", "metadata": {}, "outputs": [], "source": [ "range :: [Double]\n", "range = [-10,-9.9..10] \n", "\n", "\n", "regression :: MonadDistribution m => Bayesian m (Double, Double, Double) [Double]\n", "regression = Bayesian {\n", " prior = do \n", " slope <- normal 0 2\n", " intercept <- normal 0 2\n", " noise <- gamma 4 4\n", " return (slope, intercept, noise),\n", " \n", " generative = \\(slope, intercept, noise) -> do\n", " forM range \\x -> normal (x*slope + intercept) (sqrt noise),\n", " \n", " likelihood = \\(slope, intercept, noise) ys -> Prelude.sum [normalPdf (x*slope + intercept) (sqrt noise) y | (x,y) <- zip range ys] \n", " \n", " }\n" ] }, { "cell_type": "markdown", "id": "da26e20a-6112-408e-90c3-9af694e9bfb6", "metadata": {}, "source": [ "## Prior predictive\n", "\n", "`prior` distribution has three independent parameters. `generative` combines them into a new distribution." ] }, { "cell_type": "code", "execution_count": 10, "id": "0621fc17-5c64-409b-9d08-d52b067b8dcd", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"\\\"N/A\\\"\"", "X": -10, "Y": -24.696066745861014 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.9, "Y": -21.381259313040246 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.8, "Y": -32.73005432786171 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.700000000000001, "Y": -22.657276541487107 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.600000000000001, "Y": -30.19731332469662 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.500000000000002, "Y": -26.94191144606625 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.400000000000002, "Y": -29.621782489998168 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.300000000000002, "Y": -20.473278582744076 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.200000000000003, "Y": -20.75628633100779 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.100000000000003, "Y": -26.689333441022768 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.000000000000004, "Y": -18.2370766625895 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.900000000000004, "Y": -20.800840383026074 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.800000000000004, "Y": -22.59667318866466 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.700000000000005, "Y": -20.657903522473575 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.600000000000005, "Y": -27.590236656692873 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.500000000000005, "Y": -20.060901971997946 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.400000000000006, "Y": -20.363465003267542 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.300000000000006, "Y": -17.976596844148816 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.200000000000006, "Y": -25.936642514956873 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.100000000000007, "Y": -20.84529549153013 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.000000000000007, "Y": -22.554423352626788 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.9000000000000075, "Y": -21.21360523641598 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.800000000000008, "Y": -14.3652122284511 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.700000000000008, "Y": -22.764237189775677 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.6000000000000085, "Y": -24.104606694350913 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.500000000000009, "Y": -16.19150562230182 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.400000000000009, "Y": -17.189187562495917 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.30000000000001, "Y": -13.36509411590376 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.20000000000001, "Y": -11.749890163374976 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.10000000000001, "Y": -7.8401043738294725 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.000000000000011, "Y": -11.80097376182295 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.900000000000011, "Y": -16.54381413023416 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.800000000000011, "Y": -9.447631219614676 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.700000000000012, "Y": -17.972692486597523 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.600000000000012, "Y": -7.215547942625641 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.500000000000012, "Y": -12.342877144768519 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.400000000000013, "Y": -14.417874954220588 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.300000000000013, "Y": -15.062932481636377 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.2000000000000135, "Y": -14.008502402456015 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.100000000000014, "Y": -21.609612064222155 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.000000000000014, "Y": -3.382078735201878 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.900000000000015, "Y": -13.116977497216707 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.800000000000015, "Y": -17.478757632067765 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.700000000000015, "Y": -16.27212080976474 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.600000000000016, "Y": -10.03953346938355 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.500000000000016, "Y": -9.12915901717275 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.400000000000016, "Y": -11.202335781461699 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.300000000000017, "Y": -12.770601823882581 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.200000000000017, "Y": -16.533469530786192 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.100000000000017, "Y": -20.400193136182107 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.000000000000018, "Y": -9.472177577814106 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.900000000000018, "Y": -18.19037863545404 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.8000000000000185, "Y": -11.436494344462007 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.700000000000019, "Y": -8.767450710978649 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.600000000000019, "Y": -2.26356720097605 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.5000000000000195, "Y": -12.186952681793706 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.40000000000002, "Y": -9.830370794113218 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.30000000000002, "Y": -0.9954506297705308 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.200000000000021, "Y": -11.964060798738288 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.100000000000021, "Y": -1.5320531197877703 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.000000000000021, "Y": -10.91854471730393 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.9000000000000217, "Y": -12.18235042465571 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.800000000000022, "Y": -6.377524604385539 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.7000000000000224, "Y": -10.706161682039268 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.6000000000000227, "Y": -8.20315270083335 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.500000000000023, "Y": -7.2747642197247435 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.4000000000000234, "Y": -14.1891288953255 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.300000000000024, "Y": -14.733617972023097 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.200000000000024, "Y": -4.517513125835107 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.1000000000000245, "Y": -8.233892536840202 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.000000000000025, "Y": -0.08440505904882123 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.9000000000000252, "Y": -9.383294890693119 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.8000000000000256, "Y": -10.443429838168191 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.700000000000026, "Y": 0.9517159555199584 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.6000000000000263, "Y": -0.03889378059487658 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.5000000000000266, "Y": -9.590716305496027 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.400000000000027, "Y": -5.24144084714031 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.3000000000000274, "Y": -3.416300048010222 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.2000000000000277, "Y": -5.921966234116257 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.100000000000028, "Y": -1.986713963352214 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.0000000000000284, "Y": -7.1783973322317465 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.9000000000000288, "Y": -4.421293274590913 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.8000000000000291, "Y": -1.3198522416368355 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.7000000000000295, "Y": -5.13330862095895 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.6000000000000298, "Y": -8.304718415284588 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.5000000000000302, "Y": 2.4322583648929665 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.4000000000000306, "Y": -0.9075133454733146 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.300000000000031, "Y": -3.4210367564944795 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.2000000000000313, "Y": -6.0185764726709134 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.1000000000000316, "Y": -2.653576919771158 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.000000000000032, "Y": -3.971966134615479 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.9000000000000323, "Y": 1.3931793752460457 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.8000000000000327, "Y": -1.6720322703456505 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.700000000000033, "Y": 5.464366619345535 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.6000000000000334, "Y": -1.9240571018418877 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.5000000000000338, "Y": -8.93628410392278 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.4000000000000341, "Y": -5.785500318757535 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.30000000000003446, "Y": -6.348760671441806 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.20000000000003482, "Y": 3.534102095700111 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.10000000000003517, "Y": 2.0474856290031456 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.552713678800501e-14, "Y": 5.8790228189787035 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.09999999999996412, "Y": -2.2907430777160593 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.19999999999996376, "Y": -5.668596585592506 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.2999999999999634, "Y": -0.15608673045361787 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.39999999999996305, "Y": 2.535293077355314 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.4999999999999627, "Y": 4.969692427138009 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.5999999999999623, "Y": -1.448339308781924 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.699999999999962, "Y": -0.31843594750021853 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.7999999999999616, "Y": 1.230794222807322 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.8999999999999613, "Y": 2.775401943795419 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.9999999999999609, "Y": 2.9746625486363563 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.0999999999999606, "Y": 4.7517860788276165 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.1999999999999602, "Y": -7.938683953155905 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.2999999999999599, "Y": 0.9392067152273382 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.3999999999999595, "Y": -1.9112693986647988 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.4999999999999591, "Y": 7.995758513407569 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.5999999999999588, "Y": 2.9420539907849754 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.6999999999999584, "Y": -0.5963301351835262 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.799999999999958, "Y": 0.7722775033874285 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.8999999999999577, "Y": 2.160919953802293 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.9999999999999574, "Y": 6.658270824728535 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.099999999999957, "Y": 2.4788217207966485 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.1999999999999567, "Y": 0.44219554899480995 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.2999999999999563, "Y": 2.1636696172205445 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.399999999999956, "Y": 11.088227243765683 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.4999999999999556, "Y": -1.6594851528379229 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.5999999999999552, "Y": 3.664345812260441 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.699999999999955, "Y": 1.6410871024421039 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.7999999999999545, "Y": 6.745395209778488 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.899999999999954, "Y": 2.081611836962877 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.999999999999954, "Y": 4.716006524790677 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.0999999999999535, "Y": 7.817608997573727 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.199999999999953, "Y": 6.9502956424929065 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.2999999999999527, "Y": 14.183825887851718 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.3999999999999524, "Y": 6.723951106137135 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.499999999999952, "Y": 4.598697838940964 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.5999999999999517, "Y": 4.184679977051012 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.6999999999999513, "Y": 4.834379380539673 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.799999999999951, "Y": 6.934437085966483 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.8999999999999506, "Y": 11.95622948079815 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.9999999999999503, "Y": 8.924504221156031 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.09999999999995, "Y": 7.318822344359841 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.1999999999999496, "Y": 8.980768639999756 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.299999999999949, "Y": 1.754130997966758 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.399999999999949, "Y": 14.077113306709183 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.4999999999999485, "Y": 5.229970296582034 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.599999999999948, "Y": 15.827885593655012 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.699999999999948, "Y": 12.973975802909859 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.799999999999947, "Y": 6.700514010279003 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.899999999999947, "Y": 16.76667139509806 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.999999999999947, "Y": 8.435014802876584 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.099999999999946, "Y": 10.07976788408538 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.199999999999946, "Y": 17.885265342077933 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.299999999999946, "Y": 11.349302586110902 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.399999999999945, "Y": 11.114806895058242 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.499999999999945, "Y": 8.667313872580895 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.599999999999945, "Y": 18.349935446393147 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.699999999999944, "Y": 10.657730791650907 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.799999999999944, "Y": 8.820069761153336 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.8999999999999435, "Y": 8.800472286449592 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.999999999999943, "Y": 7.37552442281961 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.099999999999945, "Y": 16.531044018723648 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.1999999999999424, "Y": 12.844810815162809 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.29999999999994, "Y": 16.98437916984567 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.399999999999942, "Y": 16.00921557272676 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.499999999999943, "Y": 17.04351123412836 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.599999999999941, "Y": 10.099819885019928 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.699999999999939, "Y": 13.311530286628537 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.79999999999994, "Y": 13.98160192264979 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.899999999999942, "Y": 18.482932402259515 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.99999999999994, "Y": 15.576864220558743 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.0999999999999375, "Y": 16.129895721765994 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.199999999999939, "Y": 14.764662452334697 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.29999999999994, "Y": 10.884328321195783 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.399999999999938, "Y": 14.830012347745212 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.499999999999936, "Y": 17.121410098573666 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.5999999999999375, "Y": 15.156932707079505 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.699999999999939, "Y": 16.22971683198343 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.799999999999937, "Y": 18.46429831454368 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.899999999999935, "Y": 13.684335970515946 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.999999999999936, "Y": 17.45623113177597 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.099999999999937, "Y": 18.052853709956302 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.199999999999935, "Y": 20.9522265576577 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.299999999999933, "Y": 17.792706420018572 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.399999999999935, "Y": 12.693025163426196 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.499999999999936, "Y": 18.608483628616618 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.599999999999934, "Y": 22.533013844212288 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.699999999999932, "Y": 19.063530901564054 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.799999999999933, "Y": 14.10848168296044 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.899999999999935, "Y": 17.273137735808355 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.999999999999932, "Y": 18.494979089256088 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.09999999999993, "Y": 19.79060185819667 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.199999999999932, "Y": 20.264105066531886 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.299999999999933, "Y": 22.546130321808807 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.399999999999931, "Y": 16.72624887015287 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.499999999999929, "Y": 25.425511145903716 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.59999999999993, "Y": 19.27111904716608 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.699999999999932, "Y": 16.306940960065084 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.79999999999993, "Y": 20.457019593367335 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.899999999999928, "Y": 20.01341122289898 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.999999999999929, "Y": 25.114459749239604 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ys <- sampler $ priorPredictive regression\n", "\n", "plot (fmap (second (T.pack . show)) (zip (zip range ys) (Prelude.repeat \"N/A\")))" ] }, { "cell_type": "markdown", "id": "a9c0dd92-a771-4e98-a33e-327c788b4599", "metadata": {}, "source": [ "## Posterior predictive\n", "\n", "`posterior` distribution is computed base on the observed samples. Let's see what happens if the model sees samples of the same value only.\n", "\n", "To explore the space of possible parameters we use Monte Carlo Markov Chain inference algorithm." ] }, { "cell_type": "code", "execution_count": 11, "id": "693d83bf-14c9-4e1b-870e-a15b8f8edc1d", "metadata": {}, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import Control.Monad.Bayes.Inference.MCMC\n", "\n", "observations = [replicate 100 1]\n", "\n", "ys <- sampleIOfixed $ mcmc MCMCConfig {numMCMCSteps = 5000, numBurnIn = 300, proposal = SingleSiteMH} \n", " $ posteriorPredictive regression observations\n" ] }, { "cell_type": "markdown", "id": "22fa414e-49ef-4f72-b684-23ed0c24a05e", "metadata": {}, "source": [ "`ys` is a **list of lists** containing values generated for arguments from `range`. Let's see how an example of generated samples look like:" ] }, { "cell_type": "code", "execution_count": 12, "id": "a72a7dc0-209c-449c-9ffb-617328a7ffc5", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"\\\"N/A\\\"\"", "X": -10, "Y": -5.17625056645207 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.9, "Y": -6.74198785888861 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.8, "Y": -4.46996514840461 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.700000000000001, "Y": -0.9145123000823716 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.600000000000001, "Y": -6.6376245868525885 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.500000000000002, "Y": -1.1877157127876954 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.400000000000002, "Y": -7.083563289259863 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.300000000000002, "Y": -7.59464378610835 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.200000000000003, "Y": -8.8143641113504 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.100000000000003, "Y": -7.189303776465281 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.000000000000004, "Y": -6.457479489578328 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.900000000000004, "Y": -13.743197427077128 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.800000000000004, "Y": 0.2498447171475906 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.700000000000005, "Y": -9.221283755583197 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.600000000000005, "Y": -1.7419194473527906 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.500000000000005, "Y": -2.7199972063866724 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.400000000000006, "Y": 0.7705292360894669 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.300000000000006, "Y": -4.6060190698938 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.200000000000006, "Y": -4.581555676499136 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.100000000000007, "Y": -5.722579265373266 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.000000000000007, "Y": -8.2120363115473 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.9000000000000075, "Y": -3.1004974386468875 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.800000000000008, "Y": -1.3487799605604054 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.700000000000008, "Y": -3.6654498865503182 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.6000000000000085, "Y": -4.9904538923435515 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.500000000000009, "Y": -6.854763496260949 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.400000000000009, "Y": -4.323421532043624 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.30000000000001, "Y": -2.03483996899133 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.20000000000001, "Y": -3.17169458392855 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.10000000000001, "Y": -6.922057391997367 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.000000000000011, "Y": -10.032064082188356 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.900000000000011, "Y": -4.046272839718915 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.800000000000011, "Y": 2.7473076505967553 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.700000000000012, "Y": -7.852385118535792 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.600000000000012, "Y": 0.14172754917420116 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.500000000000012, "Y": -4.32198723067437 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.400000000000013, "Y": -4.953825058020628 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.300000000000013, "Y": -2.7877595733046876 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.2000000000000135, "Y": -10.027152586262392 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.100000000000014, "Y": -4.477758517334158 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.000000000000014, "Y": -4.9159878914108965 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.900000000000015, "Y": -7.1469770912052635 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.800000000000015, "Y": 2.112997024380019 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.700000000000015, "Y": -1.8571837510706082 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.600000000000016, "Y": -0.22827753672871287 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.500000000000016, "Y": -3.626386684194655 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.400000000000016, "Y": 0.6090869856742809 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.300000000000017, "Y": -2.7227553911429068 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.200000000000017, "Y": -4.0874670462028 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.100000000000017, "Y": -6.247248288824204 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.000000000000018, "Y": 2.236653807265935 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.900000000000018, "Y": 2.9092838062669824 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.8000000000000185, "Y": 1.2021304587300752 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.700000000000019, "Y": 2.5975153753961857 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.600000000000019, "Y": 2.461936898176616 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.5000000000000195, "Y": -0.7845340844908039 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.40000000000002, "Y": -0.7670671298779603 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.30000000000002, "Y": -0.15192989948132352 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.200000000000021, "Y": 1.7064879395086088 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.100000000000021, "Y": -1.6996791293795637 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.000000000000021, "Y": 0.3554764109974138 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.9000000000000217, "Y": 1.358804127746921 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.800000000000022, "Y": -0.035656060154032276 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.7000000000000224, "Y": 5.535628846793199 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.6000000000000227, "Y": -1.2378477221745832 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.500000000000023, "Y": 0.8660235246768853 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.4000000000000234, "Y": -0.6788861027206263 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.300000000000024, "Y": -0.05257396604366652 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.200000000000024, "Y": -0.4120268364764729 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.1000000000000245, "Y": 1.2506447833544743 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.000000000000025, "Y": -0.2815836842423026 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.9000000000000252, "Y": -0.7076114080210056 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.8000000000000256, "Y": -1.6873575170045618 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.700000000000026, "Y": 2.224069228264313 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.6000000000000263, "Y": -1.9416158618364738 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.5000000000000266, "Y": -0.023422730951444537 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.400000000000027, "Y": -0.09214285162821245 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.3000000000000274, "Y": -0.538716149627507 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.2000000000000277, "Y": 3.2479158300787687 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.100000000000028, "Y": 9.180241759206082 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.0000000000000284, "Y": 5.00300068732531 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.9000000000000288, "Y": -1.0236500714956085 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.8000000000000291, "Y": 0.8534822715156205 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.7000000000000295, "Y": 2.9331000883958755 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.6000000000000298, "Y": -3.4994950826063462 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.5000000000000302, "Y": -6.324060883026326 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.4000000000000306, "Y": -3.9121683283550994 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.300000000000031, "Y": 1.8253885072217224 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.2000000000000313, "Y": -0.8524676805201556 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.1000000000000316, "Y": 3.4974242753111646 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.000000000000032, "Y": 11.40614255244902 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.9000000000000323, "Y": -4.656479782548988 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.8000000000000327, "Y": 6.292338705191222 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.700000000000033, "Y": 1.9865704673786286 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.6000000000000334, "Y": -0.7567695194357884 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.5000000000000338, "Y": -0.14908749626789985 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.4000000000000341, "Y": -0.8032689320157922 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.30000000000003446, "Y": -0.04581563857222459 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.20000000000003482, "Y": 3.9037999665042955 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.10000000000003517, "Y": 6.954410784799855 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.552713678800501e-14, "Y": 3.1290086055648576 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.09999999999996412, "Y": -3.2902600000697304 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.19999999999996376, "Y": 4.2142647787930185 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.2999999999999634, "Y": -1.963960848110677 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.39999999999996305, "Y": 3.4763117868643394 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.4999999999999627, "Y": 4.059504625211241 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.5999999999999623, "Y": -0.44764800660599935 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.699999999999962, "Y": 4.782648767426403 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.7999999999999616, "Y": 6.681911362779827 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.8999999999999613, "Y": 5.279049396925319 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.9999999999999609, "Y": 3.3213434528719357 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.0999999999999606, "Y": 1.9921686871831845 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.1999999999999602, "Y": 5.539169790758686 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.2999999999999599, "Y": 6.028876475541786 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.3999999999999595, "Y": 8.290037634450846 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.4999999999999591, "Y": 6.732232238157904 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.5999999999999588, "Y": 6.366514078646105 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.6999999999999584, "Y": 3.3111810467676595 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.799999999999958, "Y": 6.626677031782874 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.8999999999999577, "Y": 0.5225570638884749 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.9999999999999574, "Y": 7.270967385312421 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.099999999999957, "Y": 5.299908805015013 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.1999999999999567, "Y": 4.527777175927255 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.2999999999999563, "Y": 0.007799090015922161 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.399999999999956, "Y": 4.800813117419075 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.4999999999999556, "Y": 2.4473647327899632 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.5999999999999552, "Y": 7.02514440331092 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.699999999999955, "Y": 11.4047060781164 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.7999999999999545, "Y": 3.9759479492539893 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.899999999999954, "Y": 10.619939356516722 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.999999999999954, "Y": 0.8326485717768604 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.0999999999999535, "Y": 4.389378696040785 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.199999999999953, "Y": 11.608895400913633 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.2999999999999527, "Y": 0.8204624747684086 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.3999999999999524, "Y": 6.598297407357292 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.499999999999952, "Y": 4.747817248223881 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.5999999999999517, "Y": 9.072575719322346 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.6999999999999513, "Y": 9.577247559272129 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.799999999999951, "Y": 6.722104451236058 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.8999999999999506, "Y": 8.741040621583716 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.9999999999999503, "Y": 7.32485173582382 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.09999999999995, "Y": 9.244400583636214 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.1999999999999496, "Y": 11.906301064957326 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.299999999999949, "Y": 9.363329258428571 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.399999999999949, "Y": 8.966407025153561 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.4999999999999485, "Y": 2.5251367682053543 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.599999999999948, "Y": 7.169731951914051 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.699999999999948, "Y": 6.287824035082818 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.799999999999947, "Y": 4.560974852036324 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.899999999999947, "Y": 1.6202806580452247 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.999999999999947, "Y": 15.157703108721682 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.099999999999946, "Y": 6.537262710032024 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.199999999999946, "Y": 5.028946607403666 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.299999999999946, "Y": 13.87566718271566 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.399999999999945, "Y": 18.06905909274711 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.499999999999945, "Y": 4.286513087332971 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.599999999999945, "Y": 3.847412015791491 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.699999999999944, "Y": 9.85551453312075 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.799999999999944, "Y": 8.473275165706236 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.8999999999999435, "Y": 12.265452527287497 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.999999999999943, "Y": 11.525190450414279 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.099999999999945, "Y": 16.13291984478301 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.1999999999999424, "Y": 9.89259319386118 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.29999999999994, "Y": 9.379616950741632 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.399999999999942, "Y": 16.92620218414739 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.499999999999943, "Y": 12.974295234944249 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.599999999999941, "Y": 8.212353736992618 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.699999999999939, "Y": 9.422963783231628 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.79999999999994, "Y": 11.085897157743593 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.899999999999942, "Y": 4.144920281466468 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.99999999999994, "Y": 10.192109718231327 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.0999999999999375, "Y": 6.405515503864371 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.199999999999939, "Y": 8.795674422411013 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.29999999999994, "Y": 8.474547691225151 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.399999999999938, "Y": 9.733116273962844 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.499999999999936, "Y": 7.9346056603251025 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.5999999999999375, "Y": 4.262583016163337 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.699999999999939, "Y": 14.16220629504127 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.799999999999937, "Y": 8.499430686907544 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.899999999999935, "Y": 8.040945595498638 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.999999999999936, "Y": 10.26934577508566 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.099999999999937, "Y": 7.476732510658916 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.199999999999935, "Y": 13.25979119613538 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.299999999999933, "Y": 7.72167768418044 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.399999999999935, "Y": 17.676635606102522 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.499999999999936, "Y": 14.999377871971395 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.599999999999934, "Y": 4.346728328525292 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.699999999999932, "Y": 6.008369118820892 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.799999999999933, "Y": 20.75013082269247 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.899999999999935, "Y": 16.608071055134527 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.999999999999932, "Y": 13.774647248099143 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.09999999999993, "Y": 13.229869207551848 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.199999999999932, "Y": 15.295264674478819 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.299999999999933, "Y": 15.789827880287193 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.399999999999931, "Y": 14.634278015358738 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.499999999999929, "Y": 8.35853398606148 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.59999999999993, "Y": 13.0317043147681 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.699999999999932, "Y": 8.949309677231202 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.79999999999993, "Y": 8.574310183314578 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.899999999999928, "Y": 8.86781223853769 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.999999999999929, "Y": 12.19571432039153 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot (fmap (second (T.pack . show)) (zip (zip range $ head ys) (Prelude.repeat \"N/A\")))" ] }, { "cell_type": "markdown", "id": "fde6d7a2-01bb-4d66-bd34-1ba72bba575a", "metadata": {}, "source": [ "Given the observations, we expect the model have average value of `slope` to be 0 and `intercept` 1." ] }, { "cell_type": "code", "execution_count": 13, "id": "f1fc13c3-3062-4395-b3fd-6b82916b64f9", "metadata": {}, "outputs": [], "source": [ "avg l = Prelude.sum l / fromIntegral (length l)\n", "\n", "avX = avg range\n", "avSqr = Prelude.sum $ map (\\a -> (a-avX)**2) range" ] }, { "cell_type": "code", "execution_count": 14, "id": "90c589fb-3882-4f18-a5ae-37a474acbd8d", "metadata": {}, "outputs": [], "source": [ "slopeValue l = covXY / avSqr\n", " where\n", " avY = avg l\n", " covXY = Prelude.sum $ (\\(x,y) -> (x - avX) * (y - avY)) <$> zip range l\n", " \n", "interceptValue l = avg l - slopeValue l * avX" ] }, { "cell_type": "code", "execution_count": 15, "id": "087733c4-feca-476c-8958-2fecbcd4ad52", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Average slope for 100 observations: 0.6482984353867459\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "\"Average intercept for 100 observations: 1.2220350681014394\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nObservations = length $ head observations\n", "\n", "print $ \"Average slope for \" <> show nObservations <> \" observations: \" <> (show . avg $ slopeValue <$> ys)\n", "\n", "print $ \"Average intercept for \" <> show nObservations <> \" observations: \" <> (show . avg $ interceptValue <$> ys)" ] }, { "cell_type": "markdown", "id": "5a2d7a24-ef39-49b3-b4dc-2de622908604", "metadata": {}, "source": [ "Slope and intercept average values differ a bit from the expected 0 and 1. Let's how they behave given more observations of the same kind." ] }, { "cell_type": "code", "execution_count": 16, "id": "31b3536b-a6bf-44f7-bd58-798a6d0f69cd", "metadata": {}, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "observations1 = [replicate 200 1]\n", "\n", "ys1 <- sampleIOfixed $ mcmc MCMCConfig {numMCMCSteps = 5000, numBurnIn = 300, proposal = SingleSiteMH} \n", " $ posteriorPredictive regression observations1\n" ] }, { "cell_type": "code", "execution_count": 17, "id": "6a5aacc2-0ccb-4446-8bd1-678475278983", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Average slope for 200 observations: 0.20039201335018098\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "\"Average intercept for 200 observations: 1.1534663432408423\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nObservations = length $ head observations1\n", "\n", "print $ \"Average slope for \" <> show nObservations <> \" observations: \" <> (show . avg $ slopeValue <$> ys1)\n", "print $ \"Average intercept for \" <> show nObservations <> \" observations: \" <> (show . avg $ interceptValue <$> ys1)" ] }, { "cell_type": "markdown", "id": "fbf34420-34d7-40c6-9377-8df70afd0255", "metadata": {}, "source": [ "### Excercise\n", "\n", "To better understand how the Bayesian model works here, you may do following:\n", "1. play with prior: change it to be constant, randomize `noise` only\n", "2. play with observations: empirically check the limit where new observations change posterior insignificantly given the inference parameters\n", "3. change inference paramers." ] }, { "cell_type": "code", "execution_count": null, "id": "271502db-ff8f-4196-8f06-f733963a0fc6", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/tutorials/Introduction.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ ":e ImportQualifiedPost\n", ":e FlexibleContexts\n", ":e BlockArguments\n", ":e TupleSections\n", ":e FlexibleContexts\n", ":e OverloadedStrings\n", ":e LambdaCase\n", "\n", "import Control.Arrow (first)\n", "import Data.Text (pack)\n", "import Control.Monad\n", "import Numeric.Log\n", "\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Enumerator\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad.Bayes.Integrator\n", "\n", ":l ../plotting.hs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Monad-Bayes\n", "\n", "This serves as an interactive alternative to [the user guide](https://monad-bayes.netlify.app/probprog.html). This isn't intended as a tutorial to Haskell, but if you're familiar with probabilistic programming, the general flow of the code should look familiar.\n", "\n", "To get a sense of how probabilistic programming with monad-bayes works, consider the following:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model :: MonadMeasure m => Double -> m Double\n", "model observation = do\n", " mean <- uniformD [-1, 0, 1]\n", " factor (normalPdf mean 1 observation)\n", " return mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The idea of monad-bayes, and probabilistic programming languages in general is to define distributions as programs. `model` corresponds to the distribution that you would express mathematically as:\n", "\n", "$$ P(m | o) = \\frac{P(m)P(o|m)}{P(o)} = \\frac{1/3 * \\mathbb{N}(o; m, 1)}{\\sum_{m' \\in \\{-1,0,1\\}} 1/3 * \\mathbb{N}(o; m', 1) } $$\n", "\n", "As a program, you can think of `model` as doing the following:\n", "\n", "- first draw from the prior over possible values of `mean` (that's the line `mean <- uniformD [-1, 0, 1]`)\n", "- then score a draw higher according to the likelihood placed on `observation` (the argument to `model`) by a normal with $\\mu$=`mean`\n", "\n", "- then return the `mean`\n", "\n", "To orient you on the relationship between the mathematical view of a distribution and the programming one, here are some notes:\n", "\n", "- a distribution over values of type `a` has type `MonadMeasure m => m a`\n", "- a joint distribution over values of types `a` and `b` is a distribution over a tuple: `MonadMeasure m => m (a, b)`\n", "- a conditional distribution over values of type `a` conditioned on values of type `b` is a function into a distribution: \n", " `MonadMeasure m => b -> m a`\n", "\n", "\n", "\n", "For example, if the value observed is $0.3$, then we can calculate the distribution over the mean:\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(0.0,0.44090549839518783),(1.0,0.36098289073731515),(-1.0,0.198111610867497)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "inferredDistribution = enumerator $ model 0.3\n", "inferredDistribution" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": "0.0", "Y": 0.44090549839518783 }, { "X": "1.0", "Y": 0.36098289073731515 }, { "X": "-1.0", "Y": 0.198111610867497 } ] }, "encoding": { "x": { "field": "X", "type": "nominal" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot (first (pack . show) <$> inferredDistribution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To produce this plot, we performed *inference*, to obtain the exact form of the distribution represented by `model`. Because the only random variable in `model` had a support that was small and discrete (the set $\\{-1, 0, 1\\}$), performing this inference exactly was straightforward.\n", "\n", "`enumerate` is an exact inference method offered by monad-bayes targeted as discrete distributions.\n", "\n", "You are encouraged to change `model` in a number of ways and observe how the results change:\n", "- try changing the prior (currently `uniformD [-1, 0, 1]`)\n", "- try changing the score (currently `factor (normalPdf mean 1 observation)`)\n", "- try changing the types of the observation and latent variable (i.e. `mean`)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Distributions as programs\n", "\n", "If you are familiar with Haskell, then it should be clear that the class of distributions you can express in this way is very broad, since we have monadic control flow. For example, you could build:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "lengthDist :: MonadMeasure m => Double -> m Int\n", "lengthDist observation = do\n", " means <- replicateM 3 (model observation)\n", " return (length $ filter (>=1) means)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exact inference for continuous distributions\n", "\n", "`enumerate` only works for discrete random variables (like categorical or bernoulli distributions).\n", "\n", "For continuous distributions, there is almost a (near) exact inference algorithm, which performs numerical integration. It is defined in `Control.Monad.Bayes.Integrator`, which also provides `histogram`:\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given an observation, this is the probability on how many (out of 3) independent draws from the posterior of the model conditioned on the observation will be greater or equal to 1. Consider the hassle of defining this with an equation, and you'll see why probabilistic programming is appealing as a way of accelerating modelling and inference." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(1,0.4228030765220977),(2,0.3090938161626379),(0,0.19278121201287118),(3,7.532189530239404e-2)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "enumerator $ lengthDist 0.5" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -4.3, "Y": 1.130592323995006e-05 }, { "X": -4.1000000000000005, "Y": 2.6962655231994744e-05 }, { "X": -3.9000000000000004, "Y": 6.453120955372455e-05 }, { "X": -3.7, "Y": 0.00011893668061056686 }, { "X": -3.5, "Y": 0.00025201490031562045 }, { "X": -3.3000000000000003, "Y": 0.0004717412175174998 }, { "X": -3.1, "Y": 0.0009386806724130974 }, { "X": -2.9000000000000004, "Y": 0.0016579159951963904 }, { "X": -2.7, "Y": 0.0027804948691254813 }, { "X": -2.5, "Y": 0.004448368834902848 }, { "X": -2.3000000000000003, "Y": 0.00681568286985589 }, { "X": -2.1, "Y": 0.011045176260450752 }, { "X": -1.9000000000000001, "Y": 0.01607186189903378 }, { "X": -1.7000000000000002, "Y": 0.022441046003227313 }, { "X": -1.5, "Y": 0.03011723227393387 }, { "X": -1.3, "Y": 0.03888527634021224 }, { "X": -1.1, "Y": 0.04831693653557609 }, { "X": -0.9, "Y": 0.05777073830312829 }, { "X": -0.7000000000000001, "Y": 0.06644067264567828 }, { "X": -0.5, "Y": 0.07345895150629281 }, { "X": -0.30000000000000004, "Y": 0.07803999760903894 }, { "X": -0.1, "Y": 0.07963311467629174 }, { "X": 0.1, "Y": 0.07803999760903894 }, { "X": 0.30000000000000004, "Y": 0.07345895150629281 }, { "X": 0.5, "Y": 0.06644067264567828 }, { "X": 0.7000000000000001, "Y": 0.05777073830312829 }, { "X": 0.9, "Y": 0.04831693653557609 }, { "X": 1.1, "Y": 0.03888527634021224 }, { "X": 1.3, "Y": 0.03011723227393387 }, { "X": 1.5, "Y": 0.022441046003227313 }, { "X": 1.7000000000000002, "Y": 0.01607186189903378 }, { "X": 1.9000000000000001, "Y": 0.011045176260450752 }, { "X": 2.1, "Y": 0.00681568286985589 }, { "X": 2.3000000000000003, "Y": 0.004448368834902848 }, { "X": 2.5, "Y": 0.0027804948691254813 }, { "X": 2.7, "Y": 0.0016579159951963904 }, { "X": 2.9000000000000004, "Y": 0.0009386806724130974 }, { "X": 3.1, "Y": 0.0004717412175174998 }, { "X": 3.3000000000000003, "Y": 0.00025201490031562045 }, { "X": 3.5, "Y": 0.00011893668061056686 }, { "X": 3.7, "Y": 6.453120955372455e-05 }, { "X": 3.9000000000000004, "Y": 2.6962655231994744e-05 }, { "X": 4.1000000000000005, "Y": 1.130592323995006e-05 }, { "X": 4.3, "Y": 5.5621114130316836e-06 }, { "X": 4.5, "Y": 2.05165127372887e-06 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import Data.List\n", "import Data.Ord\n", "import qualified Data.Text as T\n", "\n", "plot $ Control.Monad.Bayes.Integrator.histogram 45 0.2 (normal 0 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This will work for any distribution in principle, but in practice it is unfeasibly slow for complex distributions. That said, quite a few interesting distributions can be expressed, like this mixture of Gaussians:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -24.8, "Y": 5.3095963671804324e-06 }, { "X": -24.6, "Y": 6.165627647681546e-06 }, { "X": -24.400000000000002, "Y": 7.142426872944732e-06 }, { "X": -24.200000000000003, "Y": 8.254377982243924e-06 }, { "X": -24, "Y": 9.517217568292708e-06 }, { "X": -23.8, "Y": 1.0948119678973923e-05 }, { "X": -23.6, "Y": 1.951916511397483e-05 }, { "X": -23.400000000000002, "Y": 1.53873326738432e-05 }, { "X": -23.200000000000003, "Y": 1.756447251953682e-05 }, { "X": -23, "Y": 2.0007057866674773e-05 }, { "X": -22.8, "Y": 2.2741709649609795e-05 }, { "X": -22.6, "Y": 2.5797043317974205e-05 }, { "X": -22.400000000000002, "Y": 2.920375038464111e-05 }, { "X": -22.200000000000003, "Y": 5.1043211362140733e-05 }, { "X": -22, "Y": 3.947883961364423e-05 }, { "X": -21.8, "Y": 4.438883568868708e-05 }, { "X": -21.6, "Y": 4.981604384789415e-05 }, { "X": -21.400000000000002, "Y": 5.5803886480783915e-05 }, { "X": -21.200000000000003, "Y": 9.627310649706154e-05 }, { "X": -21, "Y": 7.353406698112101e-05 }, { "X": -20.8, "Y": 8.186283113755633e-05 }, { "X": -20.6, "Y": 9.097940195119327e-05 }, { "X": -20.400000000000002, "Y": 0.00015542877950402179 }, { "X": -20.200000000000003, "Y": 0.0001176030725961721 }, { "X": -20, "Y": 0.00012994936307383746 }, { "X": -19.8, "Y": 0.00022043035575747914 }, { "X": -19.6, "Y": 0.0001656444511166623 }, { "X": -19.400000000000002, "Y": 0.00018204250426452149 }, { "X": -19.200000000000003, "Y": 0.00030672846672537724 }, { "X": -19, "Y": 0.00022900418687446427 }, { "X": -18.8, "Y": 0.00025038660506084855 }, { "X": -18.6, "Y": 0.0004192169646104488 }, { "X": -18.400000000000002, "Y": 0.00031107530922898813 }, { "X": -18.2, "Y": 0.0005185837672412176 }, { "X": -18, "Y": 0.0003832048398221772 }, { "X": -17.8, "Y": 0.00041558110144749964 }, { "X": -17.6, "Y": 0.0006888181760479613 }, { "X": -17.400000000000002, "Y": 0.0005061612922303652 }, { "X": -17.2, "Y": 0.000835656696506249 }, { "X": -17, "Y": 0.0006117199264780828 }, { "X": -16.8, "Y": 0.001006103121920774 }, { "X": -16.6, "Y": 0.0007337779924051356 }, { "X": -16.400000000000002, "Y": 0.0012024350560064876 }, { "X": -16.2, "Y": 0.0008738440118643176 }, { "X": -16, "Y": 0.0014268870141138828 }, { "X": -15.8, "Y": 0.0010333804232739619 }, { "X": -15.600000000000001, "Y": 0.0016815964699223697 }, { "X": -15.4, "Y": 0.0012137652729090045 }, { "X": -15.200000000000001, "Y": 0.0019685429326754145 }, { "X": -15, "Y": 0.0014162493419906026 }, { "X": -14.8, "Y": 0.0022894803727306276 }, { "X": -14.600000000000001, "Y": 0.0016419090158662855 }, { "X": -14.4, "Y": 0.002645863614316109 }, { "X": -14.200000000000001, "Y": 0.0018915954247418377 }, { "X": -14, "Y": 0.0030387696830687394 }, { "X": -13.8, "Y": 0.003292322586302597 }, { "X": -13.600000000000001, "Y": 0.002342373597746243 }, { "X": -13.4, "Y": 0.00374472560044742 }, { "X": -13.200000000000001, "Y": 0.0026563505776088787 }, { "X": -13, "Y": 0.004234089126490092 }, { "X": -12.8, "Y": 0.004545075472352339 }, { "X": -12.600000000000001, "Y": 0.003209076685363121 }, { "X": -12.4, "Y": 0.0050912499414268765 }, { "X": -12.200000000000001, "Y": 0.0035843756991705495 }, { "X": -12, "Y": 0.005670273981989368 }, { "X": -11.8, "Y": 0.006032222334389711 }, { "X": -11.600000000000001, "Y": 0.004227504911733166 }, { "X": -11.4, "Y": 0.006657082448059182 }, { "X": -11.200000000000001, "Y": 0.004652199290268652 }, { "X": -11, "Y": 0.007304984998232411 }, { "X": -10.8, "Y": 0.007702575045606567 }, { "X": -10.600000000000001, "Y": 0.005358481300238634 }, { "X": -10.4, "Y": 0.008375725553296841 }, { "X": -10.200000000000001, "Y": 0.005810273443431885 }, { "X": -10, "Y": 0.009056025256592605 }, { "X": -9.8, "Y": 0.009464504364960952 }, { "X": -9.600000000000001, "Y": 0.0065357322362273405 }, { "X": -9.4, "Y": 0.010140142190877145 }, { "X": -9.200000000000001, "Y": 0.006982288056300124 }, { "X": -9, "Y": 0.010801803976785741 }, { "X": -8.8, "Y": 0.011188334576243604 }, { "X": -8.6, "Y": 0.007668627394691908 }, { "X": -8.4, "Y": 0.011808672869082982 }, { "X": -8.200000000000001, "Y": 0.008070364799286928 }, { "X": -8, "Y": 0.012391059176428562 }, { "X": -7.800000000000001, "Y": 0.012718425893634153 }, { "X": -7.6000000000000005, "Y": 0.008651553198501605 }, { "X": -7.4, "Y": 0.013221056792861181 }, { "X": -7.2, "Y": 0.008967019281621344 }, { "X": -7, "Y": 0.013662649695284454 }, { "X": -6.800000000000001, "Y": 0.013895057367178457 }, { "X": -6.6000000000000005, "Y": 0.009379571751890135 }, { "X": -6.4, "Y": 0.014223355365981644 }, { "X": -6.2, "Y": 0.009572616707840676 }, { "X": -6, "Y": 0.014472770164764372 }, { "X": -5.800000000000001, "Y": 0.014582570750033136 }, { "X": -5.6000000000000005, "Y": 0.009767512178953679 }, { "X": -5.4, "Y": 0.014696779267426735 }, { "X": -5.2, "Y": 0.009814530455681262 }, { "X": -5, "Y": 0.014723268976915527 }, { "X": -4.800000000000001, "Y": 0.014696779267426735 }, { "X": -4.6000000000000005, "Y": 0.009767512178953679 }, { "X": -4.4, "Y": 0.014582570750033136 }, { "X": -4.2, "Y": 0.014472770164764372 }, { "X": -4, "Y": 0.009572616707840676 }, { "X": -3.8000000000000003, "Y": 0.014223355365981644 }, { "X": -3.6, "Y": 0.009379571751890135 }, { "X": -3.4000000000000004, "Y": 0.013895057367178457 }, { "X": -3.2, "Y": 0.013662649695284454 }, { "X": -3, "Y": 0.008967019281621344 }, { "X": -2.8000000000000003, "Y": 0.013221056792861181 }, { "X": -2.6, "Y": 0.008651553198501605 }, { "X": -2.4000000000000004, "Y": 0.012718425893634153 }, { "X": -2.2, "Y": 0.012391059176428562 }, { "X": -2, "Y": 0.008070364799286928 }, { "X": -1.8, "Y": 0.011808672869082982 }, { "X": -1.6, "Y": 0.007668627394691908 }, { "X": -1.4000000000000001, "Y": 0.011188334576243604 }, { "X": -1.2000000000000002, "Y": 0.010801803976785741 }, { "X": -1, "Y": 0.006982288056300124 }, { "X": -0.8, "Y": 0.010140142190877145 }, { "X": -0.6000000000000001, "Y": 0.0065357322362273405 }, { "X": -0.4, "Y": 0.009464504364960952 }, { "X": -0.2, "Y": 0.009056025256592605 }, { "X": 0, "Y": 0.005810273443431885 }, { "X": 0.2, "Y": 0.008375725553296841 }, { "X": 0.4, "Y": 0.005358481300238634 }, { "X": 0.6000000000000001, "Y": 0.007702575045606567 }, { "X": 0.8, "Y": 0.007304984998232411 }, { "X": 1, "Y": 0.004652199290268652 }, { "X": 1.2000000000000002, "Y": 0.006657082448059182 }, { "X": 1.4000000000000001, "Y": 0.004227504911733166 }, { "X": 1.6, "Y": 0.006032222334389711 }, { "X": 1.8, "Y": 0.005670273981989368 }, { "X": 2, "Y": 0.0035843756991705495 }, { "X": 2.2, "Y": 0.0050912499414268765 }, { "X": 2.4000000000000004, "Y": 0.0032090766853715828 }, { "X": 2.6, "Y": 0.004545075472404506 }, { "X": 2.8000000000000003, "Y": 0.004234089126669467 }, { "X": 3, "Y": 0.002656350578426903 }, { "X": 3.2, "Y": 0.0037447256036590893 }, { "X": 3.4000000000000004, "Y": 0.0023423736094486908 }, { "X": 3.6, "Y": 0.0032923226260335938 }, { "X": 3.8000000000000003, "Y": 0.0030387698092297524 }, { "X": 4, "Y": 0.0018915958944567674 }, { "X": 4.2, "Y": 0.0026458651255221624 }, { "X": 4.4, "Y": 0.0016419135353878634 }, { "X": 4.6000000000000005, "Y": 0.0022894929933032497 }, { "X": 4.800000000000001, "Y": 0.0014162890889872167 }, { "X": 5, "Y": 0.0019686515763010747 }, { "X": 5.2, "Y": 0.0012140419610368388 }, { "X": 5.4, "Y": 0.001682256247895249 }, { "X": 5.6000000000000005, "Y": 0.0010348602959120942 }, { "X": 5.800000000000001, "Y": 0.0014305303394102473 }, { "X": 6, "Y": 0.0008817023236705112 }, { "X": 6.2, "Y": 0.001220669132895143 }, { "X": 6.4, "Y": 0.0007709761855503958 }, { "X": 6.6000000000000005, "Y": 0.0010775432999527918 }, { "X": 6.800000000000001, "Y": 0.0007415719645205989 }, { "X": 7, "Y": 0.0010868554817407183 }, { "X": 7.2, "Y": 0.0009382280004843571 }, { "X": 7.4, "Y": 0.0013961737025615243 }, { "X": 7.6000000000000005, "Y": 0.001522551128616199 }, { "X": 7.800000000000001, "Y": 0.0022142478680458356 }, { "X": 8, "Y": 0.0029898582096525832 }, { "X": 8.200000000000001, "Y": 0.0040797108205224576 }, { "X": 8.4, "Y": 0.005585183695811193 }, { "X": 8.6, "Y": 0.007060207346670868 }, { "X": 8.8, "Y": 0.008867014272076047 }, { "X": 9, "Y": 0.01085140149296492 }, { "X": 9.200000000000001, "Y": 0.012566493408287312 }, { "X": 9.4, "Y": 0.014152923036878371 }, { "X": 9.600000000000001, "Y": 0.01540374307780132 }, { "X": 9.8, "Y": 0.015963490067288637 }, { "X": 10, "Y": 0.017178328407119536 }, { "X": 10.200000000000001, "Y": 0.015338741501547862 }, { "X": 10.4, "Y": 0.014078257987712901 }, { "X": 10.600000000000001, "Y": 0.012466313735160347 }, { "X": 10.8, "Y": 0.010618207093220663 }, { "X": 11, "Y": 0.008734283191698645 }, { "X": 11.200000000000001, "Y": 0.006865624628090803 }, { "X": 11.4, "Y": 0.0052157827750486385 }, { "X": 11.600000000000001, "Y": 0.0038130243469821565 }, { "X": 11.8, "Y": 0.00251075328202501 }, { "X": 12, "Y": 0.0018820862395857992 }, { "X": 12.200000000000001, "Y": 0.0011361737775533406 }, { "X": 12.4, "Y": 0.0007331525698315374 }, { "X": 12.600000000000001, "Y": 0.0004548084179036016 }, { "X": 12.8, "Y": 0.0002712058431011442 }, { "X": 13, "Y": 0.00014741651056205293 }, { "X": 13.200000000000001, "Y": 8.682751070586094e-05 }, { "X": 13.4, "Y": 5.671735825923499e-05 }, { "X": 13.600000000000001, "Y": 2.918219656762929e-05 }, { "X": 13.8, "Y": 1.7375529374486354e-05 }, { "X": 14, "Y": 1.1897703278608336e-05 }, { "X": 14.200000000000001, "Y": 8.622299511077096e-06 }, { "X": 14.4, "Y": 6.825405620560989e-06 }, { "X": 14.600000000000001, "Y": 5.586284495014563e-06 }, { "X": 14.8, "Y": 4.669861980384239e-06 }, { "X": 15, "Y": 3.948313554694825e-06 }, { "X": 15.200000000000001, "Y": 3.3534425460400294e-06 }, { "X": 15.4, "Y": 2.8527159283821076e-06 }, { "X": 15.600000000000001, "Y": 2.4233690209484857e-06 }, { "X": 15.8, "Y": 2.054328147189825e-06 }, { "X": 16, "Y": 1.7371920198927467e-06 }, { "X": 16.2, "Y": 1.46513697892469e-06 }, { "X": 16.400000000000002, "Y": 6.430045608227638e-07 }, { "X": 16.6, "Y": 1.1289058668345064e-06 }, { "X": 16.8, "Y": 9.45432750205755e-07 }, { "X": 17, "Y": 7.894740185842172e-07 }, { "X": 17.2, "Y": 6.572915930046536e-07 }, { "X": 17.400000000000002, "Y": 5.455944919957147e-07 }, { "X": 17.6, "Y": 4.5149403031157523e-07 }, { "X": 17.8, "Y": 1.9525799762733373e-07 }, { "X": 18, "Y": 3.378964915674886e-07 }, { "X": 18.2, "Y": 2.774244639758001e-07 }, { "X": 18.400000000000002, "Y": 2.270377464987253e-07 }, { "X": 18.6, "Y": 1.8519131251684698e-07 }, { "X": 18.8, "Y": 7.920249677766002e-08 }, { "X": 19, "Y": 1.3557216891866329e-07 }, { "X": 19.200000000000003, "Y": 1.0965115492621583e-07 }, { "X": 19.400000000000002, "Y": 8.837751973876313e-08 }, { "X": 19.6, "Y": 7.097930240632512e-08 }, { "X": 19.8, "Y": 2.999435665118401e-08 }, { "X": 20, "Y": 5.074307876946539e-08 }, { "X": 20.200000000000003, "Y": 4.03840509672416e-08 }, { "X": 20.400000000000002, "Y": 1.6945424661950835e-08 }, { "X": 20.6, "Y": 2.8470764678943016e-08 }, { "X": 20.8, "Y": 2.244466737896972e-08 }, { "X": 21, "Y": 9.348942461918773e-09 }, { "X": 21.200000000000003, "Y": 1.5595477957054267e-08 }, { "X": 21.400000000000002, "Y": 1.2173775578461567e-08 }, { "X": 21.6, "Y": 5.032087529533858e-09 }, { "X": 21.8, "Y": 8.332006961460162e-09 }, { "X": 22, "Y": 6.437450105521275e-09 }, { "X": 22.200000000000003, "Y": 2.6398048870269847e-09 }, { "X": 22.400000000000002, "Y": 4.337201860213885e-09 }, { "X": 22.6, "Y": 1.7697821375641629e-09 }, { "X": 22.8, "Y": 2.8938473215601114e-09 }, { "X": 23, "Y": 2.197451537277538e-09 }, { "X": 23.200000000000003, "Y": 8.890601143487822e-10 }, { "X": 23.400000000000002, "Y": 1.4418071583810853e-09 }, { "X": 23.6, "Y": 5.802653553133105e-10 }, { "X": 23.8, "Y": 9.362400492222958e-10 }, { "X": 24, "Y": 3.747638369657277e-10 }, { "X": 24.200000000000003, "Y": 6.015208610865836e-10 }, { "X": 24.400000000000002, "Y": 2.3944960617131523e-10 }, { "X": 24.6, "Y": 3.822842156214353e-10 }, { "X": 24.8, "Y": 2.8084119923931763e-10 }, { "X": 25, "Y": 1.1073367866069837e-10 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model2 = do\n", " p <- bernoulli 0.2\n", " if p then normal 10 1 else normal (-5) 5\n", "\n", "plot $ Control.Monad.Bayes.Integrator.histogram 250 0.2 model2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or this normal distribution restricted to the positive reals (i.e. $p(x) \\propto N(x;0,4)\\cdot I[x>0]$)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -24.5, "Y": 0 }, { "X": -24, "Y": 0 }, { "X": -23.5, "Y": 0 }, { "X": -23, "Y": 0 }, { "X": -22.5, "Y": 0 }, { "X": -22, "Y": 0 }, { "X": -21.5, "Y": 0 }, { "X": -21, "Y": 0 }, { "X": -20.5, "Y": 0 }, { "X": -20, "Y": 0 }, { "X": -19.5, "Y": 0 }, { "X": -19, "Y": 0 }, { "X": -18.5, "Y": 0 }, { "X": -18, "Y": 0 }, { "X": -17.5, "Y": 0 }, { "X": -17, "Y": 0 }, { "X": -16.5, "Y": 0 }, { "X": -16, "Y": 0 }, { "X": -15.5, "Y": 0 }, { "X": -15, "Y": 0 }, { "X": -14.5, "Y": 0 }, { "X": -14, "Y": 0 }, { "X": -13.5, "Y": 0 }, { "X": -13, "Y": 0 }, { "X": -12.5, "Y": 0 }, { "X": -12, "Y": 0 }, { "X": -11.5, "Y": 0 }, { "X": -11, "Y": 0 }, { "X": -10.5, "Y": 0 }, { "X": -10, "Y": 0 }, { "X": -9.5, "Y": 0 }, { "X": -9, "Y": 0 }, { "X": -8.5, "Y": 0 }, { "X": -8, "Y": 0 }, { "X": -7.5, "Y": 0 }, { "X": -7, "Y": 0 }, { "X": -6.5, "Y": 0 }, { "X": -6, "Y": 0 }, { "X": -5.5, "Y": 0 }, { "X": -5, "Y": 0 }, { "X": -4.5, "Y": 0 }, { "X": -4, "Y": 0 }, { "X": -3.5, "Y": 0 }, { "X": -3, "Y": 0 }, { "X": -2.5, "Y": 0 }, { "X": -2, "Y": 0 }, { "X": -1.5, "Y": 0 }, { "X": -1, "Y": 0 }, { "X": -0.5, "Y": 0 }, { "X": 0, "Y": 0.09847923259850619 }, { "X": 0.5, "Y": 0.09688712321071996 }, { "X": 1, "Y": 0.0938735457320932 }, { "X": 1.5, "Y": 0.08957959553646837 }, { "X": 2, "Y": 0.08419940071719337 }, { "X": 2.5, "Y": 0.07796444369864483 }, { "X": 3, "Y": 0.07112607677780432 }, { "X": 3.5, "Y": 0.07141720800224513 }, { "X": 4, "Y": 0.055733279426873776 }, { "X": 4.5, "Y": 0.04857122789700993 }, { "X": 5, "Y": 0.04171858464689489 }, { "X": 5.5, "Y": 0.035313819914731925 }, { "X": 6, "Y": 0.029455693707741988 }, { "X": 6.5, "Y": 0.02420521543207655 }, { "X": 7, "Y": 0.01958961252353236 }, { "X": 7.5, "Y": 0.015607537144417724 }, { "X": 8, "Y": 0.012234833688048747 }, { "X": 8.5, "Y": 0.009430329724680782 }, { "X": 9, "Y": 0.0071412649856702265 }, { "X": 9.5, "Y": 0.0047307925595266595 }, { "X": 10, "Y": 0.004029192470264292 }, { "X": 10.5, "Y": 0.00257568309521291 }, { "X": 11, "Y": 0.002113460657256411 }, { "X": 11.5, "Y": 0.0012980250065589738 }, { "X": 12, "Y": 0.0009156464715116446 }, { "X": 12.5, "Y": 0.0006330594154629887 }, { "X": 13, "Y": 0.00042845707476134315 }, { "X": 13.5, "Y": 0.00028350200739059056 }, { "X": 14, "Y": 0.0001831453366031207 }, { "X": 14.5, "Y": 0.00010205277856315487 }, { "X": 15, "Y": 6.730544243028251e-05 }, { "X": 15.5, "Y": 4.899427881498245e-05 }, { "X": 16, "Y": 2.5399813650379086e-05 }, { "X": 16.5, "Y": 1.5627846530421348e-05 }, { "X": 17, "Y": 9.384658289544393e-06 }, { "X": 17.5, "Y": 5.493599502781822e-06 }, { "X": 18, "Y": 3.130827261430701e-06 }, { "X": 18.5, "Y": 1.5138117814231015e-06 }, { "X": 19, "Y": 1.0370383982854682e-06 }, { "X": 19.5, "Y": 4.7630049019556284e-07 }, { "X": 20, "Y": 3.0976739338026207e-07 }, { "X": 20.5, "Y": 1.344913840194566e-07 }, { "X": 21, "Y": 7.28402989686893e-08 }, { "X": 21.5, "Y": 3.846088400234724e-08 }, { "X": 22, "Y": 1.977832080262644e-08 }, { "X": 22.5, "Y": 9.89503402994438e-09 }, { "X": 23, "Y": 4.810830756000415e-09 }, { "X": 23.5, "Y": 2.2703683583527174e-09 }, { "X": 24, "Y": 8.918881098938926e-10 }, { "X": 24.5, "Y": 5.430293326052203e-10 }, { "X": 25, "Y": 2.0204098332974636e-10 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model3 :: MonadMeasure m => m Double\n", "model3 = do\n", "\n", " x <- normal 0 4\n", " condition (x > 0)\n", " return x\n", "\n", "plot $ Control.Monad.Bayes.Integrator.histogram 100 0.5 model3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or this unusual distribution with $p(x) \\propto N(x;0,4)*e^{cos(x)}$:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -19.900000000000002, "Y": 0.001156262886841532 }, { "X": -19.8, "Y": 0.001354012903533711 }, { "X": -19.700000000000003, "Y": 0 }, { "X": -19.6, "Y": 0.001559897607835023 }, { "X": -19.5, "Y": 0.0017632040272647213 }, { "X": -19.400000000000002, "Y": 0 }, { "X": -19.3, "Y": 0.0019510745222390512 }, { "X": -19.200000000000003, "Y": 0 }, { "X": -19.1, "Y": 0.002109936666272357 }, { "X": -19, "Y": 0.002227374182915131 }, { "X": -18.900000000000002, "Y": 0 }, { "X": -18.8, "Y": 0.002294056566030615 }, { "X": -18.7, "Y": 0.002305263703786686 }, { "X": -18.6, "Y": 0 }, { "X": -18.5, "Y": 0.00226161392888563 }, { "X": -18.400000000000002, "Y": 0.0021688166333168066 }, { "X": -18.3, "Y": 0 }, { "X": -18.2, "Y": 0.0020365454489922863 }, { "X": -18.1, "Y": 0 }, { "X": -18, "Y": 0.0018767582502326862 }, { "X": -17.900000000000002, "Y": 0.0017018942662864512 }, { "X": -17.8, "Y": 0 }, { "X": -17.7, "Y": 0.0015233365030816496 }, { "X": -17.6, "Y": 0.001350381261261749 }, { "X": -17.5, "Y": 0 }, { "X": -17.400000000000002, "Y": 0.0011897784691130644 }, { "X": -17.3, "Y": 0.001045761930007302 }, { "X": -17.2, "Y": 0 }, { "X": -17.1, "Y": 0.0009204108174068761 }, { "X": -17, "Y": 0.0008141722022182503 }, { "X": -16.900000000000002, "Y": 0 }, { "X": -16.8, "Y": 0.0007264072729664774 }, { "X": -16.7, "Y": 0 }, { "X": -16.6, "Y": 0.000655874541151163 }, { "X": -16.5, "Y": 0.0006011113365011507 }, { "X": -16.400000000000002, "Y": 0 }, { "X": -16.3, "Y": 0.000560709935132182 }, { "X": -16.2, "Y": 0.0005335047831996477 }, { "X": -16.1, "Y": 0 }, { "X": -16, "Y": 0.0005186952975732348 }, { "X": -15.9, "Y": 0.0005159289268464203 }, { "X": -15.8, "Y": 0 }, { "X": -15.700000000000001, "Y": 0.0005253652835879647 }, { "X": -15.600000000000001, "Y": 0.0005477366276902922 }, { "X": -15.5, "Y": 0 }, { "X": -15.4, "Y": 0.0005844138495995048 }, { "X": -15.3, "Y": 0.0006374803669493952 }, { "X": -15.200000000000001, "Y": 0 }, { "X": -15.100000000000001, "Y": 0.0007098083145886206 }, { "X": -15, "Y": 0 }, { "X": -14.9, "Y": 0.0008051210901373436 }, { "X": -14.8, "Y": 0.0009280129383938439 }, { "X": -14.700000000000001, "Y": 0 }, { "X": -14.600000000000001, "Y": 0.0010838799877914424 }, { "X": -14.5, "Y": 0.001278700157673131 }, { "X": -14.4, "Y": 0 }, { "X": -14.3, "Y": 0.0015185870291939054 }, { "X": -14.200000000000001, "Y": 0.0018090445807295702 }, { "X": -14.100000000000001, "Y": 0 }, { "X": -14, "Y": 0.002153878515478712 }, { "X": -13.9, "Y": 0.0025537884202021543 }, { "X": -13.8, "Y": 0 }, { "X": -13.700000000000001, "Y": 0.00300477802702303 }, { "X": -13.600000000000001, "Y": 0.0034966649448083887 }, { "X": -13.5, "Y": 0 }, { "X": -13.4, "Y": 0.004012106116885054 }, { "X": -13.3, "Y": 0.00452661566441389 }, { "X": -13.200000000000001, "Y": 0 }, { "X": -13.100000000000001, "Y": 0.005009967305289213 }, { "X": -13, "Y": 0 }, { "X": -12.9, "Y": 0.005429106369633845 }, { "X": -12.8, "Y": 0.005752281429944365 }, { "X": -12.700000000000001, "Y": 0 }, { "X": -12.600000000000001, "Y": 0.005953665306063443 }, { "X": -12.5, "Y": 0.006017446312588966 }, { "X": -12.4, "Y": 0 }, { "X": -12.3, "Y": 0.0059403842942642344 }, { "X": -12.200000000000001, "Y": 0.005732181546606902 }, { "X": -12.100000000000001, "Y": 0 }, { "X": -12, "Y": 0.005413605491718834 }, { "X": -11.9, "Y": 0.005012900245328253 }, { "X": -11.8, "Y": 0 }, { "X": -11.700000000000001, "Y": 0.004561417551090564 }, { "X": -11.600000000000001, "Y": 0.004089464998123666 }, { "X": -11.5, "Y": 0 }, { "X": -11.4, "Y": 0.0036231368673916957 }, { "X": -11.3, "Y": 0.0031824963794949788 }, { "X": -11.200000000000001, "Y": 0 }, { "X": -11.100000000000001, "Y": 0.0027810828451118367 }, { "X": -11, "Y": 0.00242644316013039 }, { "X": -10.9, "Y": 0 }, { "X": -10.8, "Y": 0.0021212756920568556 }, { "X": -10.700000000000001, "Y": 0 }, { "X": -10.600000000000001, "Y": 0.0018648000123744752 }, { "X": -10.5, "Y": 0.001654069421817784 }, { "X": -10.4, "Y": 0 }, { "X": -10.3, "Y": 0.0014850666765360016 }, { "X": -10.200000000000001, "Y": 0.0013535280645435369 }, { "X": -10.100000000000001, "Y": 0 }, { "X": -10, "Y": 0.001255511006135172 }, { "X": -9.9, "Y": 0.0011877562727703832 }, { "X": -9.8, "Y": 0 }, { "X": -9.700000000000001, "Y": 0.0011479064738874533 }, { "X": -9.600000000000001, "Y": 0.0011346378543831766 }, { "X": -9.5, "Y": 0 }, { "X": -9.4, "Y": 0.0011477507553396948 }, { "X": -9.3, "Y": 0.0011882501735408163 }, { "X": -9.200000000000001, "Y": 0 }, { "X": -9.1, "Y": 0.0012584336498282212 }, { "X": -9, "Y": 0.0013619890589598798 }, { "X": -8.9, "Y": 0 }, { "X": -8.8, "Y": 0.0015040883772681306 }, { "X": -8.700000000000001, "Y": 0.001691443428590112 }, { "X": -8.6, "Y": 0 }, { "X": -8.5, "Y": 0.0019322648854741787 }, { "X": -8.4, "Y": 0 }, { "X": -8.3, "Y": 0.0022360374444147144 }, { "X": -8.200000000000001, "Y": 0.0026129970656184405 }, { "X": -8.1, "Y": 0 }, { "X": -8, "Y": 0.0030731813545692966 }, { "X": -7.9, "Y": 0.003624939290830121 }, { "X": -7.800000000000001, "Y": 0 }, { "X": -7.7, "Y": 0.004272853956866564 }, { "X": -7.6000000000000005, "Y": 0.005015170968237787 }, { "X": -7.5, "Y": 0 }, { "X": -7.4, "Y": 0.005841037743875676 }, { "X": -7.300000000000001, "Y": 0.006728110927228744 }, { "X": -7.2, "Y": 0 }, { "X": -7.1000000000000005, "Y": 0.007641300256573755 }, { "X": -7, "Y": 0.008533467933319789 }, { "X": -6.9, "Y": 0 }, { "X": -6.800000000000001, "Y": 0.009348677639421031 }, { "X": -6.7, "Y": 0.010028044267644583 }, { "X": -6.6000000000000005, "Y": 0 }, { "X": -6.5, "Y": 0.010517469503292456 }, { "X": -6.4, "Y": 0.010775804195815733 }, { "X": -6.300000000000001, "Y": 0 }, { "X": -6.2, "Y": 0.010781572042988952 }, { "X": -6.1000000000000005, "Y": 0 }, { "X": -6, "Y": 0.010536558117554922 }, { "X": -5.9, "Y": 0.010065325178841338 }, { "X": -5.800000000000001, "Y": 0 }, { "X": -5.7, "Y": 0.009410820878581305 }, { "X": -5.6000000000000005, "Y": 0.00862726945097449 }, { "X": -5.5, "Y": 0 }, { "X": -5.4, "Y": 0.007772128772908578 }, { "X": -5.300000000000001, "Y": 0.006898875367178023 }, { "X": -5.2, "Y": 0 }, { "X": -5.1000000000000005, "Y": 0.006051855421235253 }, { "X": -5, "Y": 0.005263683779545624 }, { "X": -4.9, "Y": 0 }, { "X": -4.800000000000001, "Y": 0.004554981264913124 }, { "X": -4.7, "Y": 0.003935805483336922 }, { "X": -4.6000000000000005, "Y": 0 }, { "X": -4.5, "Y": 0.0034079960551787673 }, { "X": -4.4, "Y": 0.0029677530449326124 }, { "X": -4.3, "Y": 0 }, { "X": -4.2, "Y": 0.002607982242270454 }, { "X": -4.1000000000000005, "Y": 0 }, { "X": -4, "Y": 0.0023201700810518175 }, { "X": -3.9000000000000004, "Y": 0.00209573221926412 }, { "X": -3.8000000000000003, "Y": 0 }, { "X": -3.7, "Y": 0.0019268924639906835 }, { "X": -3.6, "Y": 0.0018071998276895217 }, { "X": -3.5, "Y": 0 }, { "X": -3.4000000000000004, "Y": 0.0017317998237324002 }, { "X": -3.3000000000000003, "Y": 0.0016975608408589552 }, { "X": -3.2, "Y": 0 }, { "X": -3.1, "Y": 0.0017031317296692908 }, { "X": -3, "Y": 0.001748980593888839 }, { "X": -2.9000000000000004, "Y": 0 }, { "X": -2.8000000000000003, "Y": 0.0018374401513802643 }, { "X": -2.7, "Y": 0.0019727615305498544 }, { "X": -2.6, "Y": 0 }, { "X": -2.5, "Y": 0.002161153921626808 }, { "X": -2.4000000000000004, "Y": 0.0024107598103611616 }, { "X": -2.3000000000000003, "Y": 0 }, { "X": -2.2, "Y": 0.0027314836107976705 }, { "X": -2.1, "Y": 0 }, { "X": -2, "Y": 0.003134557664074391 }, { "X": -1.9000000000000001, "Y": 0.0036317016381808157 }, { "X": -1.8, "Y": 0 }, { "X": -1.7000000000000002, "Y": 0.004233724850104972 }, { "X": -1.6, "Y": 0.004948459210805355 }, { "X": -1.5, "Y": 0 }, { "X": -1.4000000000000001, "Y": 0.005778019902262646 }, { "X": -1.3, "Y": 0.0067155894966732235 }, { "X": -1.2000000000000002, "Y": 0 }, { "X": -1.1, "Y": 0.007742199105428649 }, { "X": -1, "Y": 0.008824278705883445 }, { "X": -0.9, "Y": 0 }, { "X": -0.8, "Y": 0.009912952514267758 }, { "X": -0.7000000000000001, "Y": 0.010946014246054729 }, { "X": -0.6000000000000001, "Y": 0 }, { "X": -0.5, "Y": 0.011853110223874704 }, { "X": -0.4, "Y": 0.01256388160990049 }, { "X": -0.30000000000000004, "Y": 0 }, { "X": -0.2, "Y": 0.013017849545896217 }, { "X": -0.1, "Y": 0 }, { "X": 0, "Y": 0.01317401305631529 }, { "X": 0.1, "Y": 0.013017849545896217 }, { "X": 0.2, "Y": 0 }, { "X": 0.30000000000000004, "Y": 0.01256388160990049 }, { "X": 0.4, "Y": 0.01185311022387471 }, { "X": 0.5, "Y": 0 }, { "X": 0.6000000000000001, "Y": 0.010946014246054729 }, { "X": 0.7000000000000001, "Y": 0.009912952514267758 }, { "X": 0.8, "Y": 0 }, { "X": 0.9, "Y": 0.008824278705883445 }, { "X": 1, "Y": 0.007742199105428649 }, { "X": 1.1, "Y": 0 }, { "X": 1.2000000000000002, "Y": 0.0067155894966732235 }, { "X": 1.3, "Y": 0.005778019902262656 }, { "X": 1.4000000000000001, "Y": 0 }, { "X": 1.5, "Y": 0.004948459210805355 }, { "X": 1.6, "Y": 0.004233724850104972 }, { "X": 1.7000000000000002, "Y": 0 }, { "X": 1.8, "Y": 0.0036317016381808157 }, { "X": 1.9000000000000001, "Y": 0.003134557664074391 }, { "X": 2, "Y": 0 }, { "X": 2.1, "Y": 0.0027314836107976675 }, { "X": 2.2, "Y": 0 }, { "X": 2.3000000000000003, "Y": 0.002410759810361158 }, { "X": 2.4000000000000004, "Y": 0.0021611539216268064 }, { "X": 2.5, "Y": 0 }, { "X": 2.6, "Y": 0.001972761530549856 }, { "X": 2.7, "Y": 0.0018374401513802643 }, { "X": 2.8000000000000003, "Y": 0 }, { "X": 2.9000000000000004, "Y": 0.0017489805938888383 }, { "X": 3, "Y": 0.0017031317296692908 }, { "X": 3.1, "Y": 0 }, { "X": 3.2, "Y": 0.0016975608408589552 }, { "X": 3.3000000000000003, "Y": 0.0017317998237324002 }, { "X": 3.4000000000000004, "Y": 0 }, { "X": 3.5, "Y": 0.0018071998276895217 }, { "X": 3.6, "Y": 0.0019268924639906835 }, { "X": 3.7, "Y": 0 }, { "X": 3.8000000000000003, "Y": 0.00209573221926412 }, { "X": 3.9000000000000004, "Y": 0.0023201700810518175 }, { "X": 4, "Y": 0 }, { "X": 4.1000000000000005, "Y": 0.002607982242270454 }, { "X": 4.2, "Y": 0 }, { "X": 4.3, "Y": 0.0029677530449326124 }, { "X": 4.4, "Y": 0.0034079960551787673 }, { "X": 4.5, "Y": 0 }, { "X": 4.6000000000000005, "Y": 0.003935805483336922 }, { "X": 4.7, "Y": 0.004554981264913124 }, { "X": 4.800000000000001, "Y": 0 }, { "X": 4.9, "Y": 0.005263683779545624 }, { "X": 5, "Y": 0.006051855421235253 }, { "X": 5.1000000000000005, "Y": 0 }, { "X": 5.2, "Y": 0.006898875367178023 }, { "X": 5.300000000000001, "Y": 0.007772128772908578 }, { "X": 5.4, "Y": 0 }, { "X": 5.5, "Y": 0.00862726945097449 }, { "X": 5.6000000000000005, "Y": 0.009410820878581295 }, { "X": 5.7, "Y": 0 }, { "X": 5.800000000000001, "Y": 0.010065325178841338 }, { "X": 5.9, "Y": 0.010536558117554922 }, { "X": 6, "Y": 0 }, { "X": 6.1000000000000005, "Y": 0.010781572042988952 }, { "X": 6.2, "Y": 0 }, { "X": 6.300000000000001, "Y": 0.010775804195815733 }, { "X": 6.4, "Y": 0.01051746950329246 }, { "X": 6.5, "Y": 0 }, { "X": 6.6000000000000005, "Y": 0.010028044267644583 }, { "X": 6.7, "Y": 0.009348677639421028 }, { "X": 6.800000000000001, "Y": 0 }, { "X": 6.9, "Y": 0.008533467933319789 }, { "X": 7, "Y": 0.007641300256573755 }, { "X": 7.1000000000000005, "Y": 0 }, { "X": 7.2, "Y": 0.006728110927228744 }, { "X": 7.300000000000001, "Y": 0.005841037743875676 }, { "X": 7.4, "Y": 0 }, { "X": 7.5, "Y": 0.005015170968237779 }, { "X": 7.6000000000000005, "Y": 0.004272853956866571 }, { "X": 7.7, "Y": 0 }, { "X": 7.800000000000001, "Y": 0.003624939290830121 }, { "X": 7.9, "Y": 0.0030731813545692966 }, { "X": 8, "Y": 0 }, { "X": 8.1, "Y": 0.0026129970656184453 }, { "X": 8.200000000000001, "Y": 0.0022360374444147144 }, { "X": 8.3, "Y": 0 }, { "X": 8.4, "Y": 0.0019322648854741787 }, { "X": 8.5, "Y": 0 }, { "X": 8.6, "Y": 0.0016914434285901097 }, { "X": 8.700000000000001, "Y": 0.0015040883772681288 }, { "X": 8.8, "Y": 0 }, { "X": 8.9, "Y": 0.0013619890589598787 }, { "X": 9, "Y": 0.0012584336498282212 }, { "X": 9.1, "Y": 0 }, { "X": 9.200000000000001, "Y": 0.0011882501735408163 }, { "X": 9.3, "Y": 0.0011477507553396948 }, { "X": 9.4, "Y": 0 }, { "X": 9.5, "Y": 0.0011346378543831764 }, { "X": 9.600000000000001, "Y": 0.0011479064738874538 }, { "X": 9.700000000000001, "Y": 0 }, { "X": 9.8, "Y": 0.0011877562727703832 }, { "X": 9.9, "Y": 0.0012555110061351709 }, { "X": 10, "Y": 0 }, { "X": 10.100000000000001, "Y": 0.0013535280645435369 }, { "X": 10.200000000000001, "Y": 0.0014850666765359973 }, { "X": 10.3, "Y": 0 }, { "X": 10.4, "Y": 0.0016540694218177886 }, { "X": 10.5, "Y": 0.0018648000123744752 }, { "X": 10.600000000000001, "Y": 0 }, { "X": 10.700000000000001, "Y": 0.0021212756920568556 }, { "X": 10.8, "Y": 0 }, { "X": 10.9, "Y": 0.00242644316013039 }, { "X": 11, "Y": 0.0027810828451118323 }, { "X": 11.100000000000001, "Y": 0 }, { "X": 11.200000000000001, "Y": 0.0031824963794949788 }, { "X": 11.3, "Y": 0.0036231368673916835 }, { "X": 11.4, "Y": 0 }, { "X": 11.5, "Y": 0.00408946499812368 }, { "X": 11.600000000000001, "Y": 0.004561417551090551 }, { "X": 11.700000000000001, "Y": 0 }, { "X": 11.8, "Y": 0.005012900245328253 }, { "X": 11.9, "Y": 0.005413605491718834 }, { "X": 12, "Y": 0 }, { "X": 12.100000000000001, "Y": 0.005732181546606894 }, { "X": 12.200000000000001, "Y": 0.0059403842942642344 }, { "X": 12.3, "Y": 0 }, { "X": 12.4, "Y": 0.006017446312588966 }, { "X": 12.5, "Y": 0.005953665306063443 }, { "X": 12.600000000000001, "Y": 0 }, { "X": 12.700000000000001, "Y": 0.005752281429944362 }, { "X": 12.8, "Y": 0.005429106369633851 }, { "X": 12.9, "Y": 0 }, { "X": 13, "Y": 0.005009967305289222 }, { "X": 13.100000000000001, "Y": 0 }, { "X": 13.200000000000001, "Y": 0.00452661566441389 }, { "X": 13.3, "Y": 0.004012106116885044 }, { "X": 13.4, "Y": 0 }, { "X": 13.5, "Y": 0.003496664944808404 }, { "X": 13.600000000000001, "Y": 0.00300477802702303 }, { "X": 13.700000000000001, "Y": 0 }, { "X": 13.8, "Y": 0.0025537884202021543 }, { "X": 13.9, "Y": 0.002153878515478723 }, { "X": 14, "Y": 0 }, { "X": 14.100000000000001, "Y": 0.0018090445807295765 }, { "X": 14.200000000000001, "Y": 0.0015185870291939 }, { "X": 14.3, "Y": 0 }, { "X": 14.4, "Y": 0.001278700157673131 }, { "X": 14.5, "Y": 0.0010838799877914424 }, { "X": 14.600000000000001, "Y": 0 }, { "X": 14.700000000000001, "Y": 0.0009280129383938467 }, { "X": 14.8, "Y": 0.0008051210901373395 }, { "X": 14.9, "Y": 0 }, { "X": 15, "Y": 0.0007098083145886206 }, { "X": 15.100000000000001, "Y": 0 }, { "X": 15.200000000000001, "Y": 0.0006374803669493941 }, { "X": 15.3, "Y": 0.0005844138495995058 }, { "X": 15.4, "Y": 0 }, { "X": 15.5, "Y": 0.0005477366276902926 }, { "X": 15.600000000000001, "Y": 0.0005253652835879645 }, { "X": 15.700000000000001, "Y": 0 }, { "X": 15.8, "Y": 0.0005159289268464206 }, { "X": 15.9, "Y": 0.0005186952975732348 }, { "X": 16, "Y": 0 }, { "X": 16.1, "Y": 0.0005335047831996493 }, { "X": 16.2, "Y": 0.0005607099351321832 }, { "X": 16.3, "Y": 0 }, { "X": 16.400000000000002, "Y": 0.0006011113365011507 }, { "X": 16.5, "Y": 0.000655874541151163 }, { "X": 16.6, "Y": 0 }, { "X": 16.7, "Y": 0.0007264072729664819 }, { "X": 16.8, "Y": 0 }, { "X": 16.900000000000002, "Y": 0.0008141722022182556 }, { "X": 17, "Y": 0.0009204108174068761 }, { "X": 17.1, "Y": 0 }, { "X": 17.2, "Y": 0.001045761930007302 }, { "X": 17.3, "Y": 0.0011897784691130644 }, { "X": 17.400000000000002, "Y": 0 }, { "X": 17.5, "Y": 0.001350381261261749 }, { "X": 17.6, "Y": 0.0015233365030816344 }, { "X": 17.7, "Y": 0 }, { "X": 17.8, "Y": 0.0017018942662864512 }, { "X": 17.900000000000002, "Y": 0.0018767582502326862 }, { "X": 18, "Y": 0 }, { "X": 18.1, "Y": 0.002036545448992291 }, { "X": 18.2, "Y": 0 }, { "X": 18.3, "Y": 0.0021688166333168066 }, { "X": 18.400000000000002, "Y": 0.002261613928885639 }, { "X": 18.5, "Y": 0 }, { "X": 18.6, "Y": 0.0023052637037866875 }, { "X": 18.7, "Y": 0.0022940565660306165 }, { "X": 18.8, "Y": 0 }, { "X": 18.900000000000002, "Y": 0.002227374182915131 }, { "X": 19, "Y": 0.002109936666272357 }, { "X": 19.1, "Y": 0 }, { "X": 19.200000000000003, "Y": 0.0019510745222390512 }, { "X": 19.3, "Y": 0 }, { "X": 19.400000000000002, "Y": 0.0017632040272647213 }, { "X": 19.5, "Y": 0.001559897607835034 }, { "X": 19.6, "Y": 0 }, { "X": 19.700000000000003, "Y": 0.0013540129035337185 }, { "X": 19.8, "Y": 0.0011562628868415214 }, { "X": 19.900000000000002, "Y": 0 }, { "X": 20, "Y": 0.0009744269454590605 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "model4 :: MonadMeasure m => m Double\n", "model4 = do\n", "\n", " x <- normal 0 10\n", " factor $ Exp (cos x )\n", " return x\n", "\n", "plot $ Control.Monad.Bayes.Integrator.histogram 400 0.1 model4\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also plot CDFs, as in:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -3.4000000000000004, "Y": 6.950317017405424e-05 }, { "X": -3.3000000000000003, "Y": 0.00010164148887630666 }, { "X": -3.2, "Y": 0.00014714278717500919 }, { "X": -3.1, "Y": 0.0002116359331091854 }, { "X": -3, "Y": 0.0003008830961431438 }, { "X": -2.9000000000000004, "Y": 0.0004245565559178024 }, { "X": -2.8000000000000003, "Y": 0.000592958914929261 }, { "X": -2.7, "Y": 0.0008210921898807828 }, { "X": -2.6, "Y": 0.0011295218764828085 }, { "X": -2.5, "Y": 0.0015370833432355548 }, { "X": -2.4000000000000004, "Y": 0.0020767346995768524 }, { "X": -2.3000000000000003, "Y": 0.0027750550507635594 }, { "X": -2.2, "Y": 0.003682003095772265 }, { "X": -2.1, "Y": 0.004836377479561093 }, { "X": -2, "Y": 0.0063037376968154545 }, { "X": -1.9000000000000001, "Y": 0.00815563985387498 }, { "X": -1.8, "Y": 0.010445470950775741 }, { "X": -1.7000000000000002, "Y": 0.013287292043947026 }, { "X": -1.6, "Y": 0.016729364345415422 }, { "X": -1.5, "Y": 0.020920698541232886 }, { "X": -1.4000000000000001, "Y": 0.025949070122595248 }, { "X": -1.3, "Y": 0.0319278845185567 }, { "X": -1.2000000000000002, "Y": 0.038977618089017146 }, { "X": -1.1, "Y": 0.04717377520148923 }, { "X": -1, "Y": 0.05669628899414307 }, { "X": -0.9, "Y": 0.06763582816867231 }, { "X": -0.8, "Y": 0.07996363197165399 }, { "X": -0.7000000000000001, "Y": 0.09399792755350353 }, { "X": -0.6000000000000001, "Y": 0.10961956296236852 }, { "X": -0.5, "Y": 0.1270642721005723 }, { "X": -0.4, "Y": 0.14594054174594856 }, { "X": -0.30000000000000004, "Y": 0.1667455754322485 }, { "X": -0.2, "Y": 0.18898965850078664 }, { "X": -0.1, "Y": 0.21300741023510444 }, { "X": 0, "Y": 0.23837738025618643 }, { "X": 0.1, "Y": 0.26529176788222536 }, { "X": 0.2, "Y": 0.29324552125305514 }, { "X": 0.30000000000000004, "Y": 0.32243763790607055 }, { "X": 0.4, "Y": 0.3522145494355974 }, { "X": 0.5, "Y": 0.38302771430139804 }, { "X": 0.6000000000000001, "Y": 0.41380020335761064 }, { "X": 0.7000000000000001, "Y": 0.4453312940716577 }, { "X": 0.8, "Y": 0.47627118275475033 }, { "X": 0.9, "Y": 0.5076247482728029 }, { "X": 1, "Y": 0.5378594530580645 }, { "X": 1.1, "Y": 0.5681276391528831 }, { "X": 1.2000000000000002, "Y": 0.597067457157867 }, { "X": 1.3, "Y": 0.6256058513648695 }, { "X": 1.4000000000000001, "Y": 0.6525978918543351 }, { "X": 1.5, "Y": 0.6788978994261632 }, { "X": 1.6, "Y": 0.7035287765151896 }, { "X": 1.7000000000000002, "Y": 0.7270983751452831 }, { "X": 1.8, "Y": 0.7491904916216832 }, { "X": 1.9000000000000001, "Y": 0.7700751600787092 }, { "X": 2, "Y": 0.7895590499347593 }, { "X": 2.1, "Y": 0.8076546744364452 }, { "X": 2.2, "Y": 0.8244722667497988 }, { "X": 2.3000000000000003, "Y": 0.8399754533832623 }, { "X": 2.4000000000000004, "Y": 0.854374010814152 }, { "X": 2.5, "Y": 0.8675029404443491 }, { "X": 2.6, "Y": 0.8796500039536462 }, { "X": 2.7, "Y": 0.8906621206366212 }, { "X": 2.8000000000000003, "Y": 0.9008027011551201 }, { "X": 2.9000000000000004, "Y": 0.9099942009284938 }, { "X": 3, "Y": 0.918454890673649 }, { "X": 3.1, "Y": 0.9260988435620305 }, { "X": 3.2, "Y": 0.9330649684929808 }, { "X": 3.3000000000000003, "Y": 0.939388711836513 }, { "X": 3.4000000000000004, "Y": 0.9451029311503909 }, { "X": 3.5, "Y": 0.9503124607308405 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb8AAAG/CAYAAADIE9lyAAAAAXNSR0IArs4c6QAAIABJREFUeF7tnXtwXdV1/5eQBQjkJDxMkLF/iRGSlQiUFpCVtDgPmxZQGYaiGhVohqbBjcFJUYSSeEihpUCaprR/yFA1bt0wKv7xA5QhThUJFPFwLEEAGUzkhwQGY8APZMLDwsjIsvWbfVLZurq6umefu/e+55z7OTPMmHvXXvvs79p7fbT2edy8sbGxMeFAARRAARRAgRxSIA/45VC0GSoKoAAKoICnAPBjIqAACqAACuScAsAv50LOgFEABVAABYAfcwAFUAAFUCDnFAB+ORdyBowCKIACKAD8mAMogAIogAI5p4AV+B06dEjuuusu+elPfyrPPvtskqjq6Yrvfe978sILL8jo6Kj88Ic/lOrq6pwTnwGjAAqgAApkRwEr8Lvnnnvk9NNPlxtvvFHefPPNpJF1dXXJvffeK/fdd5/s3LlTLrvsMtmwYUN2FKBXFEABFECBnFPACvzGVZwzZ86U8Lv11ltl/vz5cs0113im559/vnR2dsrJJ5+ccwFgwCiAAiiAAu4VyAr8brjhBqmpqZFLL73UG/GFF14ozc3NUlpaKt3d3dLT05OgxKxZs+TLX/6ye3XoEQVQAAVQINIKqMtsJSUlSWPICvy+//3vy9lnny1XXXWVd0ILFiyQjo4OOeWUU6YU+Z/+6Z+8a4RRP1599VU588wzoz4MYRzhCiHxIB42FIj7vHIKv6GhISkqKpL29nZZu3atrFq1St5++22v8tu4cWPK+AE/G1M7uM+4L4rgymSnJfHIju6peiUe0YiHFfjddNNN3g0sTz/9tHzhC1+QP/3TP/VufikvLxd1s8vs2bPl2muvlffee8+D3w9+8AP5yle+AvzCNWdSng2LO1yBIh7Ew4YCcZ9XVuDnNxDDw8Ny/PHHS15e3rRNqPz8KurGLu6Lwo2K5nohHua0NOGJeJhQ0ZyPVPHIKvz8Dg/4+VXKjR2L243OfnshHn6VcmNHPNzo7LcX4OdXKYt2LAqL4gZwTTwCiGaxCfGwKG4A13GPB5VfgEkRtEncJ1NQXbLVjnhkS/mp+yUexMOGAlR+NlTV9Mni1hTMsjnxsCywpnvioSmYZfO4x4PKz/IEmug+7pPJoZRGuiIeRmQ05oR4GJPSiKO4xwP4GZkm/pzEfTL5UyE8VsQjPLFQZ0I8iIcNBdj2tKGqpk8Wt6Zgls2Jh2WBNd0TD03BLJvHPR5UfpYnENueDgXW7Crui1tTjqybE4+shyDhBOIeD+DncL7FfTI5lNJIV8TDiIzGnBAPY1IacRT3eAA/I9PEn5O4TyZ/KoTHiniEJxZc8wtXLHIhHsDP4Zwj2ToU20dXxMOHSA5NiIdDsX10Ffd4AD8fk8CUSdwnkymdXPkhHq6U9tcP8fCnkyuruMcD+LmaSdzK7VBpf13FfXH7UyE8VsQjPLFg2zMkseDF1iEJxP+eBkmKeNhQgHllQ9XgPuMeDyq/4HNDu2XcJ5O2IFluQDyyHIBJ3ROP3IjHW+/un3KgnzzpRCsC8JC7FVn1nLK49fSybU08bCus55946Oll29pWPP5v1ya5/7HNCad/1eIKufrCs60MCfhZkVXPqa3JpHcWmVszjsw1NOmBeJhUM3NfxOOohgdGRpMEffCJLfLQk1uBn5+pxjU/Pyq5s2Fxu9PaT0/Ew49K7myIx1Gtp6ryzp43SzZt3wv8/ExJ4OdHJXc2LG53WvvpiXj4UcmdDfEAfsZmG/AzJqURRyxuIzIac0I8jElpxFGuxkNVeX2vJlZ0ImNJVR6Vn8Y0A34aYjkwzdXF7UDaQF0Qj0CyWWuUq/Hwu8UJ/DSmHvDTEMuBaa4ubgfSBuqCeASSzVqjXIjH1h1vJ+n3y95X5Ze92xM+nwp0wE9j6gE/DbEcmObC4nYgo7EuiIcxKY04yoV4ZFLlAT+NaQb8NMRyYJoLi9uBjMa6IB7GpDTiKBfiAfyMTJX0ToBfeo1cWuTC4napZ6Z9EY9MFTTbPm7xWNs9kCTQ8y/tkedf3hNoi5PKT2O+AT8NsRyYxm1xO5DMahfEw6q82s7jFg/TVR7w05hSwE9DLAemcVvcDiSz2gXxsCqvtvO4xQP4aU8Bcw2AnzktTXiK2+I2oUk2fRCPbKqf3HeU46Hu2Bw/9u7dK7NmzZJntuyUZ7buMrbFSeWnMV+Bn4ZYDkyjvLgnysM4HEwWjS6Ih4ZYlkxdVHnATyN4wE9DLAemJCkHImt0QTw0xHJgGuV4AD8HE0SnC+Cno5Z92ygvbio/+/MjaA/Mq6DKmWsH/MxpacQT8DMiozEnJCljUhpxRDyMyGjMSVTioUD3/x5P/F29ik8n/+KC37e0ZGIXm9/z6+vrk/r6esnPz5fS0lJpamry/j1+jI6Oyl//9V/LG2+8IYcOHfK+P/vs1D9kCPyMrUsjjqKyuNMNlnGkU8jt98TDrd7ZqvJifc1v4cKFsnr1aikrK5Ply5fLokWLpLa29khkW1pa5LnnnpOVK1dKf3+/LFu2TJ588smUkQd+bhdFut5IUukUcvs98XCrd7reohIP4Dc2NpYumDrfq6quoqJCBgZ+91aAtrY26ezs9Kq78eP222+X008/XZYuXep9pP69c+fOhOpwYp/ATycC9m2jsrjTKcE40ink9nviYU/vpza9meR8/W9el+6+NxI+z2TrMpO2sdj2HBwclJqaGunt7fVE7e7ulubmZlmzZs0RkZ944gn5+7//e++zdevWyde//nXZvXu3nHTSSZ59T09PUqCWLFlib2bgGQVQAAVirEDHhp3yyPOJz+qdVTxTtu0eShh1tj67+NzZcsl5Z1iJgKrvSkpKknznjRmu/EZGRqSystLbzlRHe3u7dHR0eFucE48HHnjAqwi/+MUvyj/+4z/K1q1bJS8vb8rBU/lZmROBnfIXemDprDQkHlZkDew0jPEI0xZnrK/5VVdXS2trq8ydO1caGxulqqpK6urqZGhoSIqKirwbXV566SW58MILRd0cs2LFCvnFL36RcrIBv8Dr0ErDMC7uIANlHEFUs9eGeNjTFvidab/yUz2obcuGhgYpLi6WwsJCUTe4FBQUSHl5uXR1dcnMmTPl4osv9r7/7W9/K6tWrZL58+cDP3tz36hnkpRROTN2RjwyltCog2zHY+O2t5LG8/jzr8kTL7yW8Hkm1+hMt43FNb+J6g4PD3vwS3Xs27dPPvaxj6WdeFR+aSVyapDtxW1qsIzDlJJm/BAPMzqGvcqL9banmRAe9QL8TCuamT+SVGb6mW5NPEwrmpm/bMcD+CXGL1U8jN/wktm0mbo18LOhanCf2V7cwc/c36Iw5d+VH+LhSml//WQ7HsDP3zoHfv7msxGrbC8KI4MQEcZhSkkzfoiHGR1Necl2PIAf8DM1l435yfaiMDUQxmFKSTN+iIcZHU15cRkPBbonN+5IOPVTPlYom7bvTfjM9A0qpv3F7oYXU5OJbU9TSprx43Jxmznjqb0wDpvq6vsmHvqaRbHK44YXjTgDPw2xHJiSpByIrNEF8dAQy4Gpy3gAv/QB5YaX9BpZt3C5KGwOhnHYVFffN/HQ18xmC5fxAH7pIwn80mtk3cLlorA5GMZhU11938RDXzObLVzGA/iljyTwS6+RdQuXi8LmYBiHTXX1fRMPfc1strAVj7iAjmt+GrOPa34aYjkwtbW4HZx6QheMw7Xi0/dHPKbXB/gFm69UfsF0M9qKxW1UzoydEY+MJTTqgHgAP6MT6n+dAT8bqmr6ZHFrCmbZnHhYFljTPfEAfppTxpc58PMlk10jFrddfXW9Ew9dxezaEw/gZ2OGAT8bqmr6ZHFrCmbZnHhYFljTPfE4Kpi6vjf5LS3qd8ej9uYWv2+C4Q0vKRYLN7xoZhHL5iQpywJruicemoJZNjcRjzjf3MLdnhoTEPhpiOXA1MTidnCaabtgHGklcmpAPBIrv/sf25ygv98qKop2VH5Ufk6TTdDOSFJBlbPTjnjY0TWoVxPxoPILqn5yO675mdMysCcTiyJw5wYbMg6DYhpwRTwMiGjQhYl4AD9zAQF+5rQM7MnEogjcucGGjMOgmAZcEQ8DIhp0YSIewM9cQICfOS0DezKxKAJ3brAh4zAopgFXxMOAiAZd6MZDge6BJ7YknMFnP3VqbO/s5IYXjcnGDS8aYjkw1V3cDk4pUBeMI5Bs1hrlajxyrcoDfhpLCPhpiOXANFeTlANpA3VBPALJZq2RbjyAnwh3e6aYjsDP2joN5Fh3cQfqxEEjxuFAZI0ucjUewA/4pVwmwE8jgzgwzdUk5UDaQF0Qj0CyWWukGw/gB/yAn7XlaNax7uI227s5b4zDnJYmPOVqPIAf8AN+JjKIAx+5mqQcSBuoC+IRSDZrjaaLhwLdhpf2JPR97IxjcurOTm540Zh6bHtqiOXAlGTrQGSNLoiHhlgOTNPBL5deW+b3VWvc8JJiYgI/BytWowuSrYZYDkyJhwORNboAfkfFAn4aE2cqU+CXoYCGm5NsDQuaoTvikaGAhpsDP+BnbEoBP2NSGnFEsjUiozEnxMOYlEYcAT/gZ2QiKSfAz5iURhyRbI3IaMwJ8TAmpRFHwA/4GZlIwM+YjMYckWyNSWnEEfEwIqMxJ+Px2D88kuSzdd1WaV3Xn/C53+ticbaLzQ0vfX19Ul9fL/n5+VJaWipNTU3ev8ePkZERWbp0qRw4cED27dsn3/jGN+Tyyy9POfmo/IytSyOOSLZGZDTmhHgYk9KIo/F48PyeiF9gxwZ+CxculNWrV0tZWZksX75cFi1aJLW1tUcm1oMPPijr1q2Te+65R4aGhqSyslK2b98O/IwsPftOSLb2NdbpgXjoqGXfFvjl6Lbn6OioVFRUyMDAgKdAW1ubdHZ2etXf+PHMM8/IbbfdJj/72c+kv79fvvnNb8qvfvUr4Gd/XRrpgWRrREZjToiHMSmNOAJ+OQq/wcFBqampkd7eXk+B7u5uaW5uljVr1hxR5ODBg3LllVfK+++/L3v27JE77rhDrrjiiiP2PT09SZNwyZIlRiYmTlAABVDAhQIdG3bKI8/vSujqrOKZsm33EJ9N0uDic2fLJeedYSUsY2NjUlJSkuQ7b0x9Y/BQ1/PUNqaq6NTR3t4uHR0dsnLlyiO9qH+/9957csstt4iyP+ecc2Tjxo1SWFg45Zlwzc9ggAy4otIwIKJBF8TDoJgGXFH55Wjlp4ZdXV0tra2tMnfuXGlsbJSqqiqpq6vzru8VFRXJnXfeKSeeeKJ8+9vflnEqv/jiizJz5kzgZ2Dx2XZBsrWtsJ5/4qGnl21r4JfD8FPblg0NDVJcXOxVcy0tLVJQUCDl5eXS1dXl/fuqq66SefPmye7du+WCCy6Qm2++OeWcpPKzvVz1/JNs9fSybU08bCus5x/45TD8xoc+PDyccitT2ahKUMFxxowZ084u4Ke3+Gxbk2xtK6znn3jo6WXDWj3W8PTmNz3X6lLOscceK0WFx+b8rzXk3KMOpicX8DOtaGb+SLaZ6We6NfEwrai+P57p8/9MHz9ppDG/gJ+GWA5MSbYORNbognhoiGXJFPgBPytTC/hZkTWwU5JtYOmsNCQeVmTVcgr8gJ/WhPFrDPz8KuXGjmTrRme/vRAPv0rZswN+wM/K7AJ+VmQN7JRkG1g6Kw2JhxVZtZwCP+CnNWH8GgM/v0q5sSPZutHZby/Ew69S9uyAH/CzMruAnxVZAzsl2QaWzkpD4mFF1pROR0YPJX33wONb5MEntiR87vc2f+xEYvOrDqanIvAzrWhm/ki2melnujXxMK3o9P6o8jKr8njUQWO+Aj8NsRyYkmwdiKzRBfHQEMuAKfADfgamkT8XwM+fTq6sSLaulPbXD/Hwp5MpK+AH/EzNpbR+gF9aiZwakGydyp22M+KRViKjBsAP+BmdUNM5A37OpPbVEcnWl0zOjIiHM6m9joAf8HM244CfM6l9dUSy9SWTMyPi4Uxq4Pe/Upu+Q5W7PVPMYeDndnGn641km04ht98TD7d6U/lR+TmbccDPmdS+OiLZ+pLJmRHxcCY1lR+Vn9vJBvzc6p2uN5JtOoXcfk887Omtqrwdb+1L6GDf/gP8Tt+8WUY1YNuTbU97q9igZ5KtQTENuCIeBkRM4YItTvNbnDzkrjFfqfw0xHJgSrJ1ILJGF8RDQyxNU+AH/DSnjFlz4GdWz0y9kWwzVdBse+JhVs+J3oAf8LM3u3x4Bn4+RHJoQrJ1KLaProiHD5ECmgA/4Bdw6phpBvzM6GjKC8nWlJJm/BAPMzpO5QX4AT97s8uHZ+DnQySHJiRbh2L76Ip4+BApoAnwA34Bp46ZZsDPjI6mvJBsTSlpxg/xMKMjlZ8b0HG3p8Z8BX4aYjkwJdk6EFmjC+KhIZamKZWfGyDynF+KiQn8NFesZXOSrWWBNd0TD03BUpgr0HX3vZHw7cdPPM7ow9ym34kZF3/AD/iZWcWWvZBsLQus6Z54aAo2Dfzuf2xzwrdxgUvYxwH8gJ+ZVWzZC8nWssCa7omHpmDAz1MgTEAEfsDPzCq27IVka1lgTffEQ1Mw4Af8RCRvbGxszMzUseeFa372tA3imWQbRDV7bYiHGW25uSV71SCVH5WfmVVs2QvJ1rLAmu6Jh6ZgVH5UflR+ZhaNXy8kKb9KubEjHm509ttLtuNB5Ufl53euOrNj29OZ1L46ynaS8nWSPowYhw+RHJpkOx7AD/hlPN37+vqkvr5e8vPzpbS0VJqamrx/jx9r1qyRn/zkJ0f+/4033pANGzZIUVHRlH0Dv4xDYtRBtpOUqcEwDlNKmvGT7XgAP+CX8UxeuHChrF69WsrKymT58uWyaNEiqa2tndJvb2+v3H333XLvvfem7Bf4ZRwSow6ynaRMDYZxmFLSjB+X8Wj/9bakk36uf5f0DuxO+DxMjwPE+VxiccPL6OioVFRUyMDAgDeJ2trapLOz06v+Jh/qRtPFixfLAw88ILNmzQJ+ZnKIdS8uk5TNwTAOm+rq+3YZD6q87FV5sX235+DgoNTU1Iiq6NTR3d0tzc3NorY6Jx9r166VZ599Vu68884jXyn7np6eJNslS5boryZaoAAKoMAUCnRs2CmPPL8r4ZuzimfKtt1DfJYFDS4+d7Zcct4ZVuaqKrJKSkqSfBt/zm9kZEQqKyulv7/f66y9vV06Ojpk5cqVSZ3/0R/9kbflOX/+/GkHzbanlTkR2KnLv9ADn6SPhozDh0gOTVzGg8qPys84/NRaqa6ultbWVpk7d640NjZKVVWV1NXVydDQkHdTS15enuzfv18+97nPybZtyXvvk9cb8HOYgXx05TJJ+TidwCaMI7B0Vhq6jAfwA35W4Ke2LRsaGqS4uFgKCwulpaVFCgoKpLy8XLq6umTOnDmyefNmWbZsmaxfvz7tQgJ+aSVyauAySdkcGOOwqa6+b5fxAH7Azwr8xqf98PCwB79MD+CXqYJm27tMUmbPPNEb47Cprr5vl/EAfsDPKvz0p//ULYCfKSXN+HGZpMyc8dReGIdNdfV9u4wH8AN+wE9/jQZu4XJxBz5JHw0Zhw+RHJoQD32xgR/wA3766yZwC5JUYOmsNCQeVmQN7NRlPIAf8AN+gZeqfkOXi1v/7Py3YBz+tXJhSTymV1mB7sOPRhOMXtn5jmzavjfhszi/QSXsY4vFG15sLHau+dlQNbhPkm1w7Wy0JB7p4Xf/Y5sBXYhhD/xSzGHgZyNlBvdJsg2unY2WxAP4TVQg7FVebF9vZmNxAz8bqgb3SbINrp2NlsQD+AG/1HMg1frgmp+NbJTCJ0nKodg+uiIePkRyaGIrHtzcEq6bW6j8NBYVlZ+GWA5MbSUpB6ee0AXjcK349P3ZigfwA35nnnlm0uSj8nO4/m0tbodD8LpiHK4Vzw40XI/S1rwCfsAP+LlezZP6s7W4XQ+LcbhWHPhlojjwA37AL5MVZKAt0DAgokEXxMOgmAZc2YoH8AN+wM/AAs3Eha3Fnck5BWnLOIKoZq8N8ZheW+AH/ICfvfzjyzNJypdMzoyIhzOpfXVkIh4fHTyU1NeDT2wR9d/EI4rPwsX5nHnIPcUS4W5PX7nDmZGJJOXsZKfpiHGEIQpHz8FEPKjywl/l8aiDxroDfhpiOTA1kaQcnGbaLhhHWomcGpiIB/ADfpMnLQ+5O13GU3dmYnGHYBg86hCGIEw4B+bVUTGAH/ADfiFLUOp0SFLhCgrxiF88gB/wA37hWtfe2ZBswxUU4hG/eAA/4Af8wrWugR/xsKYAEGfbc+LkiuJdodztmSI9cMOLtbwZyDHJNpBs1hoRD+AH/FIvL254sZZ6/DsmSfnXyoUl8XChsv8+TMSDbU+2Pdn29L/mnFmaWNzOTnaajhhHGKJw9ByIB5UflR+VX7iy0qSzIUmFKzzEI37xoPKj8qPyC9e69s6GZBuuoBCPaMdj0/a9SQPo2rBdHtuwPeHzKN4AkmvnzA0vKdYiN7xEO0mF6+zZLoxLPKjyolnl8XozjRUI/DTEcmBKxeRAZI0ucjUewA/4+Vkm3O3pRyXLNrmapCzLGtg98QgsnZWGuvEAfsDPz0QEfn5Usmyju7gtn05g94wjsHRWGuZqPIAf8POzoICfH5Us2+RqkrIsa2D3xCOwdFYa6sYD+AE/PxMR+PlRybKN7uK2fDqB3TOOwNJZaZir8QB+wM/PggJ+flSybJOrScqyrIHdE4/A0llpqBsP4Af8/ExEp/Dr6+uT+vp6yc/Pl9LSUmlqavL+PfFYs2aN/Nd//ZcMDQ1JQ0OD/Pmf/3nKcXC3p58Qu7PRTVLuzkyvJ8ahp5dta914AD/g52dOOoXfwoULZfXq1VJWVibLly+XRYsWSW1t7ZHzfOutt+SKK66QJ598Uj788EO57777PLtUB/DzE2J3NrpJyt2Z6fXEOPT0sm2tGw/gB/z8zEln8BsdHZWKigoZGBjwzqutrU06Ozu96m/8ULDbsWOHfOYzn5EPPvhAlixZIoWFhcDPTyRDYKObpEJwylOeAuMIV2R04wH8gJ+fGewMfoODg1JTUyO9vb3eeXV3d0tzc7Oobc7x46677pLW1lZZsWKF98qvn//8514VOG7f09OTNCYFSA4UQAEUGFegY8NOeeT5XQmCnFU8U7btHuKziGlw8bmz5ZLzzrAyucfGxqSkpCTJd96Y+sbgMTIyIpWVldLf3+95bW9vl46ODlm5cuWRXn784x/Lzp075R/+4R+8zz73uc/J448/LqeccsqUZ8K2p8EAGXCl+xe6gS6tuGAcVmQN7HS6eKgq76lNbyb4nnnCsTL5/Z659k7MuIw3Nu/2rK6u9iq7uXPnSmNjo1RVVUldXZ13c0tRUZG8+OKLctttt8nDDz8sBw8e9K4Nqm3SY489FvgFTh3uGgINd1r76SkX4sEWZ3y2OGP9bk+1banu4CwuLvau5bW0tEhBQYGUl5dLV1eXzJkzR7773e/K1q1b5Z133pGvfvWrsmzZspTrnMrPTwp0Z5MLydadmpn3lAvxAH7AL+hKcXbNb+IJDg8PT3sjy0cffeQ9AjFjxoxpxwX8gobdTrtcSLZ2lLPjNRfiAfyAX9DVkxX4BT3Zye2AnyklzfjJhWRrRik3XnIhHsAP+AVdTcAvqHIG2+VCkjIol3VXxMO6xFodpLvh5f7HNif4i8vNHoxDJDY3vGjNeB/GVH4+RHJoAjQciu2jq1yIB5UflZ+PpTClCZVfUOUMtsuFJGVQLuuuiId1ibU6oPI7KleuVYNUfimWCpWfVg6xbgw0rEus1UEuxIPKj8pPa1FMMKbyC6qcwXa5kKQMymXdFfGwLrFWB1R+VH5aE8anMfDzKZRNM5KtTXX1fRMPfc1stgB+wM/G/AJ+NlTV9Emy1RTMsjnxsCywpvvxePS9OpjU8rEN2+Wx519L+DzXrovFebxc8+Oan2a6yI450MiO7ql6jVs8uL4X7+t7sX69menUwA0vphXNzF/ckm1mamS/ddziAfyAn8lVxbanSTUD+opbkgooQ2iaEY/QhMI7kfF4AD/gZ3JmAj+Tagb0RbINKJylZsTDkrAB3QK/o8LF+foe254aC4RtTw2xHJgCDQcia3QRt3hQ+VH5aUz/tKZUfmklsm8QtyRlXzG7PRAPu/rqeqfyo/LTnTN+7IGfH5Us25BsLQus6Z54aApm2Rz4AT8bUwz42VBV0yfJVlMwy+bEw7LAmu6BH/DTnDK+zIGfL5nsGpFs7eqr65146Cpm1x74AT8bMwz42VBV0yfJVlMwy+bEw7LAmu6BH/DTnDK+zIGfL5nsGpFs7eqr65146Cpm1x74AT8bMwz42VBV0yfJVlMwy+bEw7LAPtzvenvoiNUbb7whc+fOlf956mVpe/rlhNa59txbro2Xd3umWCw85+cjizg0ARoOxfbRVZTjwTN9ufdMHw+5+1jU4ybAT0MsB6ZRTrYT5WEcDiZLmi6AH/BTU4TKj8ov+9nIxxkADR8iOTSJcjyAH/ADftMkCyo/h5nUR1dRTrZUfj4C7NAE+AE/4Af8HKaczLoCfpnpZ7p1lOMB/IAf8AN+pnOiNX9RTrZUftamRSDHwA/4AT/gFyh5ZKMR8MuG6qn7jHI8gB/wA37AL1wZdZqziXKypfIL1zQDfsAP+AG/cGUl4Ec8HCgA/IAf8AN+DlKNmS6o/MzoaMpLlOMB/IAf8AN+pnKhdT9RTrZse1qfHlodAD/gB/yAn1bSyKYx8Mum+sl9RyUewx+NJp38Q09ukYee3Jrwea6915LxhvANL//2b/8m8+fPl8WLF2ut9r6+Pqmvr5f8/HwpLS2VpqYm79/jh/r+mmuukdNOO8376Nxzz5Uf/ehHKfvgIXct+a0bRyXZphOCcaRTyOz3VHlUeWpGReLdnrfeeqvcfvvtcuWVV8q//uu/yhlnnOFrNSxcuFBWr14tZWVlsnz5clm0aJHU1tYeafvYY4/JT3/ZQ6PaAAAgAElEQVT6U1Fw9XMAPz8qubMBGu609tNTVOIB/IBfZOA3NjYm9913n6xYsUL27dsnCoYKbOPH5z//+aS1OTo6KhUVFTIwMOB919bWJp2dnV71N34o8K1bt06+9KUvyQknnCB//Md/nFAZTnYK/PykQHc2UUm26RRhHOkUMvs98AN+kYHf+NTfv3+/XHTRRdLT05OwGhQcJx+Dg4NSU1Mjvb293lfd3d3S3Nwsa9asOWL6s5/9TO655x65+uqrZePGjfLKK694kBy3n9yP+nzJkiVmVyLeUAAFnCrQsWGnPPL8roQ+zyqeKdt2H/1NP/Uln+WeBhefO1suOc/fzqLupFWcKikpSWqWNzYVwSaYbdiwQb797W974LvuuusSKr+/+Iu/SHI4MjIilZWV0t/f733X3t4uHR0dsnLlypTnfPbZZ8sTTzwhs2bNmtKGyk833HbtqZjs6qvrPSrxoPKj8otM5Xf33XfL3/zN33g3pKjrcwsWLPC1Lqurq6W1tdX7VebGxkapqqqSuro6GRoakqKiInn00UclLy/PqyYPHDgg5eXl3jbpcccdB/x8KZxdo6gk23QqMY50Cpn9HvgBv8jA77bbbpNTTz1Vrr/+ejnmmGN8rwRVJTY0NEhxcbEUFhZKS0uLFBQUeJDr6uqSw4cPezfRKDi+/vrrcsMNN8i1116b0j+Vn2/pnRgCDScy++4kKvEAfsAvMvDzvfpSGA4PD3vwS3Wom2hUJZgOrMAv00iYbR+VZJtu1IwjnUJmvwd+wC9n4Gdq6QA/U0qa8QM0zOhoyktU4gH8gB/w01z1wE9TMMvmUUm26WRgHOkUMvs98AN+wE9zTQE/TcEsmwMNywJruo9KPIAf8AN+mosb+GkKZtk8Ksk2nQyMI51CZr8HfsAP+GmuKeCnKZhlc6BhWWBN92GMhwLd5KPv1UHZtH1vwse81Bkgqglx1eIKufrCszVnvj/zVOsj7UPu/tzbtQJ+dvXV9R7GZKs7BmXPOIKo5q8NVR5QS1XlReLF1v6muX0r4GdfY50egIaOWvZtwxgP4Af8gJ+BtQ/8DIho0EUYk22Q4TGOIKr5awP8gB/w87dWprUCfgZENOgCaBgU04CrMMYD+AE/4GdgcQM/AyIadBHGZBtkeIwjiGr+2gA/4Af8/K0VKj8DOrlyATRcKe2vnzDGA/gBP+Dnb/0CPwM6uXIRxmQbZOyMI4hq/toAP+AH/PytFeBnQCdXLoCGK6X99RPGeAA/4Af8/K1f4GdAJ1cuwphsg4ydcQRRzV8b4Af8gJ+/tQL8DOjkygXQcKW0v37CGA/gB/yAn7/1C/wM6OTKRRiTbZCxM44gqvlrA/yAH/Dzt1aAnwGdXLkAGq6U9tdPtuOx550Pkk705z0vyf889XLC57zHEyCmAiLv9kyx1nnOz18SdGWV7WRrapyMw4ySVHlATafK492eGusO+GmI5cAUaDgQWaOLbMcD+AE/4KexYHVMgZ+OWvZts51sTY2QcZhREvgBP+BnZi0leQF+loQN6BZoBBTOUrNsxwP4AT/gZ2lxAz9LwgZ0m+1kG/C0k5oxDjNKAj/gB/zMrCUqP0s6mnILNEwpacZPtuMB/IAf8DOzloGfJR1Nuc12smUciQpkOx7AD/gBP1NZaZIftj0tCRvQbbaTbcDTZtvTlHCT/AA/4Af8LC0u4GdJ2IBugV9A4Sw1y3Y8gB/wA36WFjfwsyRsQLfZTrYBT5vKz5RwVH6yafveBBV4e01mfwDwhpcUixP4WcpaAd0Cv4DCWWqW7XhQ+WWW+AGnCPADfpbSo1m32U62pkbDOMwoCfyAH9ueZtZSkhcqP0vCBnQLNAIKZ6mZy3go0B06PJYwki2v7WUbcN4sNMhAAyo/Kj9L6dGsW5fJ1uyZJ3pjHPrqUuVR5WVa5fFia411R+WnIZYDU6DhQGSNLlzGA/gBP+A3zeLs6+uT+vp6yc/Pl9LSUmlqavL+Pfk4cOCALF68WG6//XZZtGhRSo/ATyMTOjB1mWxtDodx6KsL/IAf8Jtm3SxcuFBWr14tZWVlsnz5cg9stbW1SS1uuukmefrpp+Xmm2+WSy+9FPjp56KstAAaWZE9Zacu4wH8gB/wS7EUR0dHpaKiQgYGBjyLtrY26ezs9Kq/icf69evlgQcekKKiIrnggguAX7jy6bRn4zLZ2pSFceirC/yAH/BLsW4GBwelpqZGent7PYvu7m5pbm6WNWvWHGmxf/9+ufzyy+Xhhx+WO+64IwF+yr6npyfJ+5IlS/RXKi1QAAWMKtCxYac88vyuBJ9nFc+UbbuH+AwNAs+Di8+dLZecd4bRuTrubGxsTEpKSpJ8542pbwweIyMjUllZKf39/Z7X9vZ26ejokJUrVx7p5cYbb5RTTz1V1PboqlWr5JxzzpFly5bJSSedNOWZcM3PYIAMuKJiMiCiQRcu40HlR+VH5TfN4q2urpbW1laZO3euNDY2SlVVldTV1cnQ0JC3zam+27lzp+dBbYueeeaZcsstt3j2Ux3Az2CmNODKZbI1cLopXTAOfXWBH/ADftOsG7Vt2dDQIMXFxVJYWCgtLS1SUFAg5eXl0tXVJXPmzDnSesWKFVzz089BWW0BNLIqf1LnLuMB/IAf8POx/oeHhz34ZXpQ+WWqoNn2LpOt2TNP9MY49NUFfsAP+Omvm8AtgF9g6aw0BBpWZA3s1GU8gB/wA36Bl6p+Q+Cnr5nNFi6TLeNIr4CteLz0xm+TOn/0uVel87lXEz7nVwkAYqZA5N2eKdY58EufAF1a2Eq2Lseg+mIc0ytOlQfUMoWa3z+MgB/wc53/A/UHNALJZq2RrXgAP+AH/KwtW3+Oqfz86eTKylaydXX+4/0wDiq/iQr4rVKwM/9HAZUflZ/r/B+oP6ARSDZrjWzFg8rPfJIHnFNrCvyAn7UEadKxrWRr8hz9+GIcVH5UfnsTJkG24Az8gJ+fnJ11G6CR9RAknICteFD5UflxzS/La51rflkOwKTubSVb16NkHFR+VH5Ufq7zjlZ/wE9LLuvGQMO6xFod2IoHlR+VH5Wf1lI0bwz8zGuaiUdbyTaTcwrSlnFQ+VH5UfkFyR3O2gA/Z1L76gho+JLJmZGteFD5UflR+TlbxlN3BPyyHACu+YUrAI7iAfyAH/DL8tIHflkOgKNk63qUtiqmuIwD+AE/4Od6NU/qD/hlOQDAL1wBsBAPBbpf9m5P8Hz6ySfKpu3huCaUrefP6NfNHwA855cixQC/cOVeKqb4xYMqz02SB6a84UUrewA/LbmsGwM/6xJrdWAiHsAP+Lna4pzqDwAqPyo/raSXLWMTyTZb5z6xX8ZxVA3gB/yAXxiyEtf8QhiFo6cENMIVHhPxAH7AD/iFa117Z8O2Z7iCYiLZhmFEjIPKb+I85Hpc9v4AYNuTbc8wMCHtOQCNtBI5NTARDyq/7CV+oCsC/ICf06QZtDMTyTZo3ybbMQ4qPyq/cDzKAvyAn8ncbs0X0LAmbSDHJuJB5UflxzW/QMvPbiOu+dnVV9e7iWSr26cNe8ZB5UflR+VnI7cY8wn8jElpxBHQMCKjMScm4kHlR+VH5WdsSZpzBPzMaWnCk4lka+I8MvXBOKj8qPyo/DLNI1bbAz+r8mo7BxraklltoBsPVeU99OTWhHMq/z+n8B7PebPQIEsacMNLihQB/KzmTm3nuslWuwNHDXJ1HGxxssWZzS1OXm+mkeCAn4ZYDkxzFRoOpA3UhW48gB/wA34ieWNjY2OBVpzDRsDPodg+utJNtj5cZsUkV8cB/IAf8AN+TpNuriZbpyJrdJar8QB+wA/4AT+NVJm5aa4m28yVs+MhV+MB/IAf8LMEv76+Pqmvr5f8/HwpLS2VpqYm79/jx4EDB+S6666TwcFB+eCDD2TFihVy2WWXpcxwbHvaSf5BveYqNILqZbudbjyAH/ADfpbgt3DhQlm9erWUlZXJ8uXLZdGiRVJbW3skBzz88MMyMDDgQW/Pnj1ywQUXyLZt24Cf7SxpyL9usjXUrXE3uToO4Af8gJ8F+I2OjkpFRYUHN3W0tbVJZ2enV/1NdWzYsMGrEtevXw/8jKd3Ow5zFRp21Mzcq248gB/wA34W4Ke2MmtqaqS3t9db1d3d3dLc3Cxr1qxJWuVf/vKXRS3cBx98UD7/+c8fse/p6UmyXbJkSeZZAg8ogALSsWGnPPL8rgQlziqeKdt2D/EZGmRlHlx87my55LwzrKxO9UBDSUlJkm/jjzqMjIxIZWWl9Pf3e521t7dLR0eHrFy5csqB7dixQxYvXiybN2+W4447bkobrvlZmROBnepWGoE7stwwV8dB5UflR+VnofJTolZXV0tra6vMnTtXGhsbpaqqSurq6mRoaEiKiopk1apVcvzxx8u1114rhw4dknnz5nnwmzlzJvCznPBNuM9VaJjQzoaP6eLxXH9ihaf6X7dxh6x78fWEU+EHVQFiNoEYm9ebqW3LhoYGKS4ulsLCQmlpaZGCggIpLy+Xrq4u787PK6+8Uj75yU/Krl275JJLLpFbbrklZV6g8rORMoP7BH7BtbPRcrp4UOUBtWxCze8fVbGB3/gCHx4e9uCX6lCVoPp+xowZ0+YE4GcjZQb3CfyCa2ejJfA7qqrfZItduP4oiB38TC104GdKSTN+gJ8ZHU15AX7Ab+JciiLYgV+KbAD8TKVJM36AnxkdTXkBfsAP+KVeTanWh/G7PU0t6Il+gJ8NVYP7BH7BtbPREvgBP+AH/GzkFmM+gYYxKY04yoV4cMNLuK5tRXFL0sU5s+3JtqeRpG7bSS5Aw7aGJv1T+VH5UflR+ZnMKcZ9AQ3jkmbkMBfiQeVH5acWiYvqLZM+qPyo/DJK5q4a5wI0XGlpoh8qPyo/Kj8qPxO5xJoPoGFN2kCOcyEeVH7hr3oyqZji0pbKj8ovUBJ33SgXoOFa00z6G4/H3vc+THKztntA1va8lPB5XBIm44gP2IEf8MskBzprC/ycSe2ro/F4UOXFBwa5BnbgB/x8JbtsGwG/bEcgsX/gxzU/rvlxzS9cWWnS2QCNcIUnbvGg8qPyUyssilUjlR+VX7jokOJs4gaNSIg+zUlS+VH5UflR+YU6jwGNcIUnbvGg8otm1RPFSs30OVP5UfmFiw5UfpGKB/ADfmx7Ji9ZXmwdgjQWt0ojBJJmdApxiwfwA37AD/hllBRtNY5bsrWlkyu/cYsH8AN+wA/4ucqfWv3ELdlqDT6ExnGLB/ADfsAP+IUw1YrELdmGUmSNk4pbPIAf8AN+wE8jBbozjVuydaecnZ6iHI91G3ccEWVwcFBOO+00eWrTm/LU5jcTxDJ9Vx7+AKwNwHK3Z4ocxy+520n+Qb1GGRoTxxzlcVDlASEbEMrWHzfAD/gF5ZHTdlGGBvADGnGCRrZgZbpf4Af8nEIsaGfAL6hy5tpR+QHxOEEc+AE/c9nRoifgZ1Fcn66BH/ADfv4WCw+5+9PJqhXQsCqvtvMoxwP4AT/g52/JAz9/Olm1inKyjcu1sriMA/gBP+DnL10DP386WbUCflbl1XYe5XgAP+AH/PwteeDnTyerVlFOtnGpmOIyDuAH/ICfv3QN/PzpZNUK+FmVV9t5lOMB/IAf8PO35IGfP52sWkU52calYoriOF5+852kefnos6/Io8+9mvC56Wev8AdgXQGWRx1SoIc3vFhlsrZzIK4tWUYNqPKAkCsIZesPHuAH/DJKkq4aAz9XSv+uH+AH/IBf8DXndNuzr69P6uvrJT8/X0pLS6Wpqcn79/gxOjoq119/vWzfvl2Gh4flO9/5jlx++eUpR0flFzzwNloCPxuqpvYJ/IAf8Au+5pzCb+HChbJ69WopKyuT5cuXy6JFi6S2tvbI2T/66KPS1dUl//zP/yxvv/22LFiwwPu5n1QH8AseeBstgZ8NVYHfuALZ2nqj3+z9kRGLbU9V1VVUVMjAwIA3l9va2qSzs9Or/qY6VPVXV1cnzz77LPBzm1MD9wb8AksXqCGVX/aSMkB0o30s4Kd+W6ympkZ6e3u9hd7d3S3Nzc2yZs2apIW/d+9eWbJkiajKrrq6+oh9T09Pkq2y40CBXFSgY8NOeeT5XQlDP6t4pmzbPcRnaBCLeXDxubPlkvPOsLK8x8bGpKSkJMl33pj6xuAxMjIilZWV0t/f73ltb2+Xjo4OWblyZUIvL730kvzlX/6lVxGef/75054B254GA2TAFZWfARE1XFD5uak+qPKyp3MsKj+1plUV19raKnPnzpXGxkapqqrytjaHhoakqKhI9u3bJxdddJHcf//9Mm/evLRpAPillcipAfBzKjd3e0r2kjJAdKN9bOCnti0bGhqkuLhYCgsLpaWlRQoKCqS8vNy70UVBT1V8E0tRdW1QgXGqA/i5TbbpegN+6RQy+z2Vn5sEDOiyp3Ns4De+9NVjDAp+mR7AL1MFzbYHfmb1nOhNge7dDw4kdPDm4D7ZtH1vwmck6uwlarQ3r33s4GcqRQA/U0qa8QP8zOg4lReqPPOJFViFX1PglyKnAD97yTaIZ+AXRDV/bYBf+BM1MDUfI+AH/PxlyCxbAT97AQB+5hMrsAq/psAP+NnLqgY9Az+DYk5yBfzCn6iBqfkYAT/gZy+rGvQM/AyKCfy4mWferJzXAPgBP3tZ1aBn4GdQTOCX84mfSlIE+AE/e1nVoGfgZ1BM4Af8qPyAX6qUwt2e9pJtEM/AL4hq/tpwzc/89SQqq/BrSuVH5ecvQ2bZCviZCYAC3Su73k1w9uGBg1RCVEKSa8AGfsDPTFa17AX4mRGYKi/8FUmuQShb4wV+wM9MVrXsBfiZERj4AT81k7IFnDD1C/yAn5msatkL8DMjMPAj8QO/360l4Af8zGRVy16AnxmBgR/wA37Ab9pswt2eZpKtKS/Az4ySwA/4AT/gB/zM5FMnXoCfGZmBH/ADfsAP+JnJp068AD8zMgM/4Af8gB/wM5NPnXgBfvoyd23YntTo11t2yjNbdiZ8HqY78DgX4OwKztzwwg0v+lk1Cy2An77oVHmAxBVIovhHC/ADfvpZNQstgJ++6MAP+AG/362bqeAM/ICfflbNQgvgpy868AN+wA/4aWcOHnXQlsxqA+CnLy/wA37AD/hpZw7gpy2Z1QbAT19e4Af8gB/w084cwE9bMqsNgJ++vMAP+AE/4KedOYCftmRWGwA/fXmBH/ADfsBPO3MAP23JrDYAftPLq0D39OY3E4yKCo/ld/r4nT5+wYG7PfVyM/DT08u2NfBLD7/7H9ucYBTFZ684Z6pVV9UqjzqkyCnAzzbO9PwDP+A3UQEgCSQzhSTwA356FMqSNfADfsBvL5X9dnMaAD/glyWc6XUL/IAf8DOX+Kmc+THblBmFbU89ONm2Bn7AD/gBv01UfrZTrQjws6+xTg/AD/gBP+AH/HSyZkBb4BdQOEvNgN9RYdVjDb/6zesJSp9UdDyPNfBYA481pHisIdYvtu7r65P6+nrJz8+X0tJSaWpq8v498di6datcf/31snTpUrnmmmumTdPAzxLFAroFfonw47GGWcAe2GcE+9jc8LJw4UJZvXq1lJWVyfLly2XRokVSW1t7JGOMjIzI1772NfnkJz/p2Sxbtgz4BQRRNpoBP+DHtifbnmx7Tsq+o6OjUlFRIQMDA943bW1t0tnZ6VV/k4877rhDTj31VOCXDYJl0CfwA37AD/gBv0lJdHBwUGpqaqS3t9f7pru7W5qbm2XNmjW+4Kfse3p6kmyXLFmSQbqmKQrYUaBjw0555PldCc7PKp4p23YP8RkaMA98roWLz50tl5x3hpVFOjY2JiUlJUm+88bUNwYPtaVZWVkp/f39ntf29nbp6OiQlStX+oLfVKfCNT+DATLgisqPyo/Kj8qPym+KZFpdXS2tra0yd+5caWxslKqqKqmrq5OhoSEpKiqSvLw8rxXbngZIlAUXuQo/dWfnux8cSFD8zcF93OzBzR4Z3ezBQ+4xeshdbVs2NDRIcXGxFBYWSktLixQUFEh5ebl0dXXJu+++K9/61rdkx44d3uezZ8+WtWvXysc//vEpUzmVXxYIN02XuQw/7uzkzk5gZf5dprG523M8bw4PD3vwy/QAfpkqaLY98DuqJ4nQfCJE09zTNHbwM5VygZ8pJc34AX7Aj2t+XPPjmp+ZfDqtF+DnQGSNLoAf8AN+wA/4aSTNoKbAL6hydtoBP+AH/IAf8LOTXxO8Aj8HImt0kQvwU3d2PvjElgRVPvOpU7mzkzs7ubNT452dfq/fcs0vRQIGfhpkcmCaK/Djzk7u7PSbvLHL7CYd4Af8HKAr8y6AH9uebHuy7cm2Z+a5NK0HKr+0Ejk1AH7AD/gBP+DnIO0CPwcia3QRN/h9+NHBpNE/9ORWaX1ya8LnbG1ltrWFfuinFlSsf89PI4/6MgV+vmRyZhQ3+KmbW7i+x/U94Jw9OHPNj2t+zgCWSUfAj21Ptj3Z9mTbM5Ms6rMtlZ9PoRyZAT/gB/yAH/BzkHCBnwORNboAfsAP+AE/4KeRNIOaAr+gytlpF2X4qet7O9563xNm//79cuKJJ8q+/R/x8DoPr/PwuoWH1/1eR+WaH9f87NDKsNeow4+bW7i5xW9Sxs7NTTDAD/gZxpQdd8CPbU+2Pdn2ZNvTTn5N8Mq2pwORNboAfsAP+AE/4KeRNIOaAr+gytlpFxX4qet7j7/wWoIIsz5+Atf3uL7H9b0sXt/jIXeNvAz8NMRyYBol+HF9j+t7XLdzc90uE5255sc1PwfoyrwL4Me2J9uebHuy7Zl5Lk3rgcovrURODcIIP7XF+f7+jxJ0eP2t99niZIuTLc6QbXGy7amRroGfhlgOTMMKP7Y42eLMZOuNttnbHmXbk21PB+jKvAvgx7Yn255se7LtmXkuTeuByi+tRE4Nsg0/tcW5cdtbCWPOPyaPLU62ONnijMAWJ9ueGuka+GmI5cA0DPBji5MtTrYps7dNaVp7tj3Z9nSArsy7cAm/rt7tSSf86y075ZmtOxM+N70Y8RefxEoswx9L4Af8MieTAw8u4ccPzYY/cQEXYqTSTibzAPgBPwfoyrwLW/B7/PnEt7GoM31685uiKj1usOAGC5M3WGSSqGmbGei45qeRg7nmpyGWA1Nb8KPKM59USNRommlV5mIOUflR+TlAV+ZdmICfAt323e8lnMwHwyPcsckdmxltn7lI1PRh/g8K4Af8MieTAw+68FNbl5OP9b95Xdb/5g22M7ezncl2JnfuAj/g5wBdmXcxHfxe25NYzane2n+9TTqeeQXQAToqeyr7KSt74Af8MieTAw/j8Htx0oPmqmt108rknxFim8j8NhGaomkUruX5naexgV9fX5/U19dLfn6+lJaWSlNTk/fv8WNsbEy+973vyQsvvCCjo6Pywx/+UKqrq1OmbW54cUC0NF2oa3SHDo95Vu+995584hOfkC2v7eUvef6S5xpdhrf5+wVEnO1iA7+FCxfK6tWrpaysTJYvXy6LFi2S2traI+m1q6tL7r33Xrnvvvtk586dctlll8mGDRuAn2XGvf3+h0k9tD39spx4fEHC56qie2VX4vblp0//OKADdIAO0Hm5wjSIYwE/VclVVFTIwMCAJ1JbW5t0dnZ61d/4ceutt8r8+fPlmmuu8T46//zzPZuTTz55yvTvsvI7fHhMflffHD0OHTos+fnHJHw2cvCQ5OfnJXz20cghmTHJ7sOPDspxBb+rel977TX59Kc/LUPDI3J8wYyEtu8OHZDjj038bPC9/VI46bM973yQ1Meed/fLyMHRBH/vDB2QN97al/BZ4XEzpHdgd8Jnpicx/swnBjRFUxvACdO8igX8BgcHpaamRnp7e70k293dLc3NzbJmzZojSfeGG27wbC699FLvswsvvNCzUVukyr6npychQRcUFMjBgwenBKPpD8cmk09E1G5fXiLn5PBYnhwz+TNRnyU6OKTsJp3k6OFj5Ji8wwmfHhzLl4JjEj8bOZwvM/IOJQJ2bIYcO+mzkcMz5LhjEuE3KsfIsXmJnylHBZP6Na0f/lAABVAgTArMmjVL/uqv/irplPLG1AU4g8fIyIhUVlZKf3+/57W9vV06Ojpk5cqVR3r5/ve/L2effbZcddVV3mcLFizwbE455ZQpz8Rl5WdQiiRXjMOmuvq+iYe+ZjZbEA+b6ur7jns8jMNPSaxuXmltbZW5c+dKY2OjVFVVSV1dnQwNDUlRUZEHxLVr18qqVavk7bff9iq/jRs3poxO3IOgPy2z24J4ZFf/yb0TD+JhQ4G4zysr8FPblg0NDVJcXCyFhYXS0tIiauuyvLxc1M0us2fPlmuvvda7a1DB7wc/+IF85StfAX42ZrAFn3FfFBYks+qSeFiVV9s58dCWzGqDVPGwAr/xkQwPD3vwS3Wo748//njJm3xBbVIDJpPVuaHtnHhoS2a1AfGwKq+2c+KhLZnVBlmBn6kRqZtgLrjgAlPusuaHcWRN+ik7Jh7Ew4YCzCsbqgb3mSoeViu/4KdLSxRAARRAARSwpwDws6ctnlEABVAABUKqAPALaWA4LRRAARRAAXsKxAZ+6sH5OXPmyM0332xPLQOeX3nlFVHnqm7y+eijj+THP/6x9xq4qB27du2Sr3/966IeE1XjUC8pUHfzRvFQbyFS75r9+c9/LiUlJZEZgrpbeunSpfL+++97N47953/+p5x22mmROf+JJxrVGEwcQ1zWRBxy1IEDB2Tx4sVy++23e6/XnOqIBfxU0lJ39HzhC1+Qu+66K9SLf8WKFXLRRRd5j3Y88MAD3mvd1HtQo3bcdttt3osK1DtbH374Ye+5TfW+1qgdTz31lBeDdevWRQ7gf/u3f+u9Lu+6666Thx56SH71q18lvEwiKrGIcgwmahyXNRGHHHXTTTfJ008/7RVD428Sm7weIg+/3/72t3L11VfLN7/5TQCQEFsAAAR5SURBVC+BhR1+EwPwL//yL96zjuqvkygfP/nJT2Tr1q3yox/9KLLDUC9auPvuuyNVvX7xi1+U+++/X8444wz54IMP5A/+4A/kN7/5DTEIgQJxWBNKxijmqPXr13uFhXqhinpKIPLwu/zyyxOmtHqAXm21ffWrX/XeIrN7927vAfqwwE/9Vb5p06aEc1YVnnqF25YtWzxgH3PMMfLLX/4y5WvdQrCGZbpxqPN77rnn5MYbb/ReYJ7qxeRRGEcU4ffZz35Wnn32WW+Rq+NTn/qU7NixIwxyBzqHKMZgqoFGZU1MF6Qo5aiJ49i/f78oVqjdqDvuuCMe8FN7uBMPBY5f/OIX3s8ifetb3/KSgPpZJPXbgPPmzQu0+Ew2Uu84PXw48UXV6rrMxENtFf7Hf/yHB46wHtONQ52/emerqj7Uy2PDfKSLRxQT7x/+4R96rxFUfwh++OGH3msF1W9pRvWIYgwmax2lNeFnnkQhR00ch/pD/NRTTxX1s3rq9ZnnnHOOLFu2TE466aSk4UZ621Pt6T7zzDPeoNSLtF966SW58847vWt/YT3U9Rn16jf1V7v62Sf1s07jv4AR1nOe6ryU7n/3d3/n/YU13Vt8ojKmKCbe73znO/L7v//73i6C+gNK/TGodkOiekQxBhO1jsuaiHKOUte+1W/EqkOtiTPPPFNuueUW7z3Tk49Iw2/iYB555JFQbXumSkDquqTaplV3Fb788sve9T71805RO/7kT/5EXn/99SNbtmeddZZ3t2HUjv/+7//2bjh68cUXvZ/UUr9Fqa7XROHYs2eP98son/jEJ7xrx2os6o7nqB1RjsFEreOyJuKSo9SNO7G45he1BZ3ufNXt6R/72MfSvtc0nR++RwG15XnCCScgBAoYVSDuOSo2lZ/RqOMMBVAABVAg1goAv1iHl8GhAAqgAApMpQDwY16gAAqgAArknALAL+dCzoBRAAVQAAWAH3MABVAABVAg5xQAfjkXcgaMAiiAAigA/JgDKBBBBdR7SN966y1RL1NWbztS7/ZULx1QL3j4sz/7swiOiFNGAbcKAD+3etMbChhRoKenR9SLrdXr5dRPZNXX13svGVDvk1W/9MCBAigwvQLAjxmCAhFVQP1si3o7TUtLi1xxxRXeS90VBDlQAAXSKwD80muEBQqEUgH1svff+73f895pW1VV5f1+mdoC5UABFEivAPBLrxEWKBBKBdQvVZx33nneVqf6hQf1O2Z5eXmhPFdOCgXCpgDwC1tEOB8U8KmAelu9+rHRpqYmWbp0qfz7v/+7fOMb3/DZGjMUyG0FgF9ux5/RR1SBjRs3yoIFC7wf7Pzud78rX/va17yfl9q6dav3+34cKIAC0ysA/JghKBAxBUZHR71rfGNjY95vQc6YMUPeeecdKS8vly996UuiftOMAwVQAPgxB1AABVAABVAgQQEqPyYECqAACqBAzikA/HIu5AwYBVAABVAA+DEHUAAFUAAFck4B4JdzIWfAKIACKIACwI85gAIogAIokHMK/H8eXyfYQ9db4QAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot $ plotCdf 70 0.1 0 (normal 0 1 + gamma 1 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Expressing distributions with code\n", "\n", "If you are familiar with Haskell, then it should be clear that the class of distributions you can express by writing probabilistic programs is very broad, since `m` is a monad. For example, you can use `fmap` to apply a function to the support of the distribution, like:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "mapped :: MonadMeasure m => m Bool\n", "mapped = fmap (> 0) (model 0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And you can use the standard set of monadic and applicative combinators, like `when`, `filterM` or `replicateM`:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "lengthDist :: MonadMeasure m => Double -> m Int\n", "lengthDist observation = do\n", " means <- replicateM 3 (model observation)\n", " return (length $ filter (>=1) means)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given an observation, this is the probability on how many (out of 3) independent draws from the posterior of the model conditioned on the observation will be greater or equal to 1. Consider the hassle of defining this with an equation, and you'll see why probabilistic programming is appealing as a way of accelerating modelling and inference." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(1,0.4228030765220977),(2,0.3090938161626379),(0,0.19278121201287118),(3,7.532189530239404e-2)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "enumerate $ lengthDist 0.5" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": "2", "Y": 0.4197137519723297 }, { "X": "1", "Y": 0.31507728008413355 }, { "X": "3", "Y": 0.18636658869600853 }, { "X": "0", "Y": 0.07884237924752768 } ] }, "encoding": { "x": { "field": "X", "type": "nominal" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot $ fmap (first (pack . show)) $ enumerate $ lengthDist 0.99" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": "1", "Y": 0.43338893548101054 }, { "X": "0", "Y": 0.38211546660576284 }, { "X": "2", "Y": 0.16384748050615444 }, { "X": "3", "Y": 0.020648117407072838 } ] }, "encoding": { "x": { "field": "X", "type": "nominal" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot $ fmap (first (pack . show)) $ enumerator $ lengthDist 0.001" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exact sampling is pretty limited. For models with continuous random variables, or large discrete ones, it is a no-go. \n", "\n", "The broader goal is to be able to define your distribution of interest, like `model`, and then apply different inference technique, usually approximate, to it. This is what monad-bayes (and other probabilistic programming languages) enable. See the following tutorials for details." ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: notebooks/tutorials/Lazy.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "043dfea4-1d1a-42c7-8dbe-404e1c3442e4", "metadata": {}, "outputs": [], "source": [ "import qualified Control.Monad.Bayes.Sampler.Lazy as Lazy\n", "import qualified Control.Monad.Bayes.Inference.Lazy.MH as Lazy\n", "import qualified Control.Monad.Bayes.Inference.Lazy.WIS as Lazy\n", "import qualified Control.Monad.Bayes.Sampler.Strict as Strict\n", "\n", "import Control.Monad.Bayes.Class\n", "import qualified Data.Text as T\n", "import Numeric.Log\n", "import Control.Arrow (first, second)\n", "import Statistics.Distribution\n", "import qualified Statistics.Distribution.Poisson as S\n", "import Control.Monad.Bayes.Enumerator\n", "\n", ":l ../plotting.hs" ] }, { "cell_type": "markdown", "id": "6cc366b0-9a2d-41b5-9a18-6f2dd022d383", "metadata": {}, "source": [ "# Lazy Sampling\n", "\n", "The sampler defined in `Control.Monad.Bayes.Sampler.Strict` fares poorly with infinite data structures. For example, `finiteList` defined below is a distribution over a small finite set, but because it is defined as the marginal of an infinite list, running it with a strict sampling monad will not terminate." ] }, { "cell_type": "code", "execution_count": 2, "id": "54b733d5-7914-4fd0-888c-21fa7992f38a", "metadata": {}, "outputs": [], "source": [ "finiteList :: MonadDistribution m => m [Double]\n", "finiteList = do\n", " infiniteList <- sequence $ repeat random\n", " return (take 3 infiniteList)" ] }, { "cell_type": "code", "execution_count": 3, "id": "df4ce909-5303-4c57-a6c9-ae0813873789", "metadata": {}, "outputs": [], "source": [ "-- Code below will not terminate:\n", "-- Strict.sampler finiteList" ] }, { "cell_type": "markdown", "id": "e5728ea8-fbba-42be-93ec-6df139dcbdcd", "metadata": {}, "source": [ "One solution is to use a streaming library, such as `pipes`, and indeed this proves useful in many situations. But another is to define a lazy sampler, which is the approach taken by [LazyPPL](https://lazyppl.bitbucket.io/). " ] }, { "cell_type": "code", "execution_count": 4, "id": "b38c604a-3dc1-4fc3-b4c2-429a32528cb8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.8503508027827793,0.21861861054454101,0.975152268854938]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Lazy.sampler finiteList" ] }, { "cell_type": "markdown", "id": "daee5898-3b3d-4876-ad21-14ae16f2fe7e", "metadata": {}, "source": [ "`Control.Monad.Bayes.Sampler.Lazy` is simply a port of LazyPPL, with some refactoring. In particular, what LazyPPL calls the `Prob` monad, we now call `SamplerT`, and `Meas` is built modularly as `WeightedT SamplerT`. \n", "\n", "LazyPPL also comes with a number of inference algorithms that make special use of the sampler's laziness, including weighted importance sampling and Metropolis-Hastings. Monad-Bayes has these also, as shown below:" ] }, { "cell_type": "code", "execution_count": 5, "id": "bbefc916-65fd-4a99-a5e0-7273c4f3d29b", "metadata": {}, "outputs": [], "source": [ "model :: MonadMeasure m => m Bool\n", "model = do\n", " -- Prior belief: it is Sunday with prob. 1/7\n", " sunday <- bernoulli (1/7)\n", " -- I know the rates of buses on the different days:\n", " let rate = if sunday then 3 else 10 \n", " -- observe 4 buses\n", " factor $ Exp $ logProbability (S.poisson rate) 4\n", " -- score $ poissonPdf rate 4\n", " return sunday" ] }, { "cell_type": "code", "execution_count": 6, "id": "de863302-a371-49f0-81a6-f8e45ab740bb", "metadata": {}, "outputs": [], "source": [ "xws <- Lazy.lwis 10000 model" ] }, { "cell_type": "code", "execution_count": 7, "id": "634c7c3e-2731-43a5-af60-14bb168a3863", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": "True", "Y": 0.59 }, { "X": "False", "Y": 0.41 } ] }, "encoding": { "x": { "field": "X", "type": "nominal" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot $ first (T.pack . show) <$> toEmpirical (take 10000 xws)" ] }, { "cell_type": "code", "execution_count": 8, "id": "d500e37c-0926-4956-ad03-f58b7d8efce6", "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": "(True,0.16803135574154085)", "Y": 0.6333 }, { "X": "(False,1.891663740103538e-2)", "Y": 0.3667 } ] }, "encoding": { "x": { "field": "X", "type": "nominal" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "xws <- Lazy.mh 0.5 model\n", "\n", "plot $ first (T.pack . show) <$> toEmpirical (take 10000 xws)" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: notebooks/tutorials/MCMC.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ ":e ImportQualifiedPost\n", ":e FlexibleContexts\n", ":e BlockArguments\n", ":e TupleSections\n", ":e FlexibleContexts\n", ":e OverloadedStrings\n", ":e LambdaCase\n", "\n", "import qualified Data.Text as T\n", "import Control.Arrow (first,second)\n", "import Control.Monad\n", "import Graphics.Vega.VegaLite hiding (runDensityT)\n", "import qualified Graphics.Vega.VegaLite as VL\n", "import IHaskell.Display.Hvega (vlShow)\n", "\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Enumerator\n", "\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad.Bayes.Traced.Static\n", "import Control.Monad.Bayes.Inference.MCMC\n", "\n", ":l ../plotting.hs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# MCMC\n", "\n", "Following [wikipedia](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo):\n", "\n", "> Markov chain Monte Carlo (MCMC) is a powerful class of methods to sample from probability distributions known only up to an (unknown) normalization constant.\n", "\n", "Markov Chain is a stochastic process where new state depends only on the previous state (Markov property for discrete-time process). Transition between states is governed by transition kernell $T$. In the limit of long simulation, probability distribution of steps of the Markov process converges to the stationary distribution $\\pi_{\\star}$. Reasonable assumptions of existence of the stationary distribution, non-periodicy of MC and availibility of all states must be held.\n", "\n", "If we want to **sample** from a complicated distribution $\\pi$, we can construct a Markov process which stationary distribution $\\pi_{\\star} = \\pi$. The question is how to construct transition kernell to achieve $\\pi$ as a stationary distribution.\n", "\n", "## Metropolis-Hastings\n", "MH is a classical and most widely use algorithm with many flavours. Here we present a general outline.\n", "\n", "The algoritm is used to generate next point of the Markov Chain, namely $x_{i+1}$, in the space state.\n", "MH algorithms consist of proposal and acceptance/rejection steps:\n", "1. **Proposal**: draw next candidate\\\n", " We construct transition kernel:\n", "\n", " $$ \n", " T(x_{i+1}|x_{i}) = q(x_{i+1}|x_{i})\\times p_{acc}(x_{i+1}|x_{i})\n", " $$\n", " where $q$ is proposal distribution for the next state of the chain and $p_{acc}$ is probability of sample being accepted.\n", "\n", " Proposal distribution $q$ should be simple to draw from, for example uniform distribution around current point $\\mathcal{U}(x_i - \\delta, x_{i}+\\delta)$, where $\\delta$ is a small positive number. \n", " \n", "2. **Acceptance/rejection** assess if the candidate might have been drawn from sampled distribution $\\pi$\\\n", " Having symmetric proposal distribution and adding constraint of microscopic reversibility on the transition kernell:\n", "\n", " $$ \n", " \\pi(x_{i})T(x_{i+1}|x_{i}) = \\pi(x_{i+1})T(x_{i}|x_{i+1})\n", " $$\n", "\n", " we have acceptance probability:\n", " $$\n", " p_{acc}(x_{i+1}|x_{i}) = min\\left\\{1, \\frac{\\pi_{x+1}}{\\pi_{x}}\\right\\}\n", " $$\n", "\n", "For details on MCMC see the series of blog posts starting with https://www.tweag.io/blog/2019-10-25-mcmc-intro1/ or an online book https://bookdown.org/rdpeng/advstatcomp/markov-chain-monte-carlo.html.\n", "\n", "\n", "\n", "## `monad-bayes` implementation\n", "There are several versions of Metropolis-Hastings algorith for MCMC defined in monad-bayes. The standard version is found in `Control.Monad.Bayes.Inference.MCMC` - Traced MH.\n", "Implementatio of a single step of of MH algorithm is `mhTransWithBool`.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear regression\n", "\n", "We'll start with the example of a simple regression. Function `regressionData` defines a distribution of a linear regression values given a list of arguments." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "paramPriorRegression = do\n", " slope <- normal 0 2\n", " intercept <- normal 0 2\n", " noise <- gamma 4 4\n", " return (slope, intercept, noise)\n", "\n", "\n", "regressionData :: (MonadDistribution m, Traversable t) => t Double -> m (t (Double, Double))\n", "regressionData xs = do\n", " (slope, intercept, noise) <- paramPriorRegression\n", " forM xs \\x -> do\n", " y <- normal (x*slope + intercept) (sqrt noise)\n", " return (x, y)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "range = [-10,-9.9..10] :: [Double]\n", "regressionSamples <- sampleIOfixed $ regressionData range" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"N/A\"", "X": -10, "Y": 24.57864936391078 }, { "Outlier": "\"N/A\"", "X": -9.9, "Y": 38.294498409280365 }, { "Outlier": "\"N/A\"", "X": -9.8, "Y": 26.301690856119443 }, { "Outlier": "\"N/A\"", "X": -9.700000000000001, "Y": 28.558284447764866 }, { "Outlier": "\"N/A\"", "X": -9.600000000000001, "Y": 23.195769246319948 }, { "Outlier": "\"N/A\"", "X": -9.500000000000002, "Y": 29.319038347154173 }, { "Outlier": "\"N/A\"", "X": -9.400000000000002, "Y": 29.786080440510776 }, { "Outlier": "\"N/A\"", "X": -9.300000000000002, "Y": 27.56660652064134 }, { "Outlier": "\"N/A\"", "X": -9.200000000000003, "Y": 27.577242127315213 }, { "Outlier": "\"N/A\"", "X": -9.100000000000003, "Y": 33.153907631508574 }, { "Outlier": "\"N/A\"", "X": -9.000000000000004, "Y": 30.083599819996856 }, { "Outlier": "\"N/A\"", "X": -8.900000000000004, "Y": 23.119721320813433 }, { "Outlier": "\"N/A\"", "X": -8.800000000000004, "Y": 31.191041902991987 }, { "Outlier": "\"N/A\"", "X": -8.700000000000005, "Y": 26.589308682033725 }, { "Outlier": "\"N/A\"", "X": -8.600000000000005, "Y": 32.921926427807016 }, { "Outlier": "\"N/A\"", "X": -8.500000000000005, "Y": 32.70229229815947 }, { "Outlier": "\"N/A\"", "X": -8.400000000000006, "Y": 19.394506109909656 }, { "Outlier": "\"N/A\"", "X": -8.300000000000006, "Y": 32.27291522848526 }, { "Outlier": "\"N/A\"", "X": -8.200000000000006, "Y": 28.64721000519075 }, { "Outlier": "\"N/A\"", "X": -8.100000000000007, "Y": 25.79253712162261 }, { "Outlier": "\"N/A\"", "X": -8.000000000000007, "Y": 27.68797532534977 }, { "Outlier": "\"N/A\"", "X": -7.9000000000000075, "Y": 22.079901350454247 }, { "Outlier": "\"N/A\"", "X": -7.800000000000008, "Y": 21.25658020864288 }, { "Outlier": "\"N/A\"", "X": -7.700000000000008, "Y": 28.112113874853225 }, { "Outlier": "\"N/A\"", "X": -7.6000000000000085, "Y": 23.657829423215986 }, { "Outlier": "\"N/A\"", "X": -7.500000000000009, "Y": 26.44252808237886 }, { "Outlier": "\"N/A\"", "X": -7.400000000000009, "Y": 15.694603973012033 }, { "Outlier": "\"N/A\"", "X": -7.30000000000001, "Y": 17.839513408053318 }, { "Outlier": "\"N/A\"", "X": -7.20000000000001, "Y": 20.452854225610146 }, { "Outlier": "\"N/A\"", "X": -7.10000000000001, "Y": 20.139288022425166 }, { "Outlier": "\"N/A\"", "X": -7.000000000000011, "Y": 21.23396373514547 }, { "Outlier": "\"N/A\"", "X": -6.900000000000011, "Y": 17.320206263082085 }, { "Outlier": "\"N/A\"", "X": -6.800000000000011, "Y": 19.89843133273544 }, { "Outlier": "\"N/A\"", "X": -6.700000000000012, "Y": 15.764273491799775 }, { "Outlier": "\"N/A\"", "X": -6.600000000000012, "Y": 20.798807821690723 }, { "Outlier": "\"N/A\"", "X": -6.500000000000012, "Y": 18.728021703264186 }, { "Outlier": "\"N/A\"", "X": -6.400000000000013, "Y": 7.885719830482385 }, { "Outlier": "\"N/A\"", "X": -6.300000000000013, "Y": 18.85912922874157 }, { "Outlier": "\"N/A\"", "X": -6.2000000000000135, "Y": 15.123788006831436 }, { "Outlier": "\"N/A\"", "X": -6.100000000000014, "Y": 17.10995200455398 }, { "Outlier": "\"N/A\"", "X": -6.000000000000014, "Y": 14.624114456535843 }, { "Outlier": "\"N/A\"", "X": -5.900000000000015, "Y": 14.845108837556172 }, { "Outlier": "\"N/A\"", "X": -5.800000000000015, "Y": 14.269557147602468 }, { "Outlier": "\"N/A\"", "X": -5.700000000000015, "Y": 13.861095746942324 }, { "Outlier": "\"N/A\"", "X": -5.600000000000016, "Y": 13.692211160545364 }, { "Outlier": "\"N/A\"", "X": -5.500000000000016, "Y": 23.739370754539276 }, { "Outlier": "\"N/A\"", "X": -5.400000000000016, "Y": 11.308333171220132 }, { "Outlier": "\"N/A\"", "X": -5.300000000000017, "Y": 27.08980446608318 }, { "Outlier": "\"N/A\"", "X": -5.200000000000017, "Y": 15.88679842771568 }, { "Outlier": "\"N/A\"", "X": -5.100000000000017, "Y": 22.961734902847223 }, { "Outlier": "\"N/A\"", "X": -5.000000000000018, "Y": 16.299525923838925 }, { "Outlier": "\"N/A\"", "X": -4.900000000000018, "Y": 15.308171860934474 }, { "Outlier": "\"N/A\"", "X": -4.8000000000000185, "Y": 29.02455599307806 }, { "Outlier": "\"N/A\"", "X": -4.700000000000019, "Y": 10.749377104496931 }, { "Outlier": "\"N/A\"", "X": -4.600000000000019, "Y": 11.27400546222972 }, { "Outlier": "\"N/A\"", "X": -4.5000000000000195, "Y": 22.50722014898807 }, { "Outlier": "\"N/A\"", "X": -4.40000000000002, "Y": 14.872396898873859 }, { "Outlier": "\"N/A\"", "X": -4.30000000000002, "Y": 12.33515917932203 }, { "Outlier": "\"N/A\"", "X": -4.200000000000021, "Y": 11.582055626013798 }, { "Outlier": "\"N/A\"", "X": -4.100000000000021, "Y": 23.943604970811563 }, { "Outlier": "\"N/A\"", "X": -4.000000000000021, "Y": 15.789328270738938 }, { "Outlier": "\"N/A\"", "X": -3.9000000000000217, "Y": 12.904946954760849 }, { "Outlier": "\"N/A\"", "X": -3.800000000000022, "Y": 7.426966312329666 }, { "Outlier": "\"N/A\"", "X": -3.7000000000000224, "Y": 15.51136598116963 }, { "Outlier": "\"N/A\"", "X": -3.6000000000000227, "Y": 5.11123343053363 }, { "Outlier": "\"N/A\"", "X": -3.500000000000023, "Y": 13.432712523791547 }, { "Outlier": "\"N/A\"", "X": -3.4000000000000234, "Y": 11.241708914656906 }, { "Outlier": "\"N/A\"", "X": -3.300000000000024, "Y": 15.300584697969027 }, { "Outlier": "\"N/A\"", "X": -3.200000000000024, "Y": 6.832401443016748 }, { "Outlier": "\"N/A\"", "X": -3.1000000000000245, "Y": 7.255676920155957 }, { "Outlier": "\"N/A\"", "X": -3.000000000000025, "Y": 11.929815035881218 }, { "Outlier": "\"N/A\"", "X": -2.9000000000000252, "Y": 4.207732498665361 }, { "Outlier": "\"N/A\"", "X": -2.8000000000000256, "Y": 19.30618112216324 }, { "Outlier": "\"N/A\"", "X": -2.700000000000026, "Y": 14.42052477335049 }, { "Outlier": "\"N/A\"", "X": -2.6000000000000263, "Y": 6.5390713397756315 }, { "Outlier": "\"N/A\"", "X": -2.5000000000000266, "Y": 8.668423810434627 }, { "Outlier": "\"N/A\"", "X": -2.400000000000027, "Y": 2.7167038170804414 }, { "Outlier": "\"N/A\"", "X": -2.3000000000000274, "Y": 9.69108166357378 }, { "Outlier": "\"N/A\"", "X": -2.2000000000000277, "Y": 11.965742234290293 }, { "Outlier": "\"N/A\"", "X": -2.100000000000028, "Y": 9.419874051581413 }, { "Outlier": "\"N/A\"", "X": -2.0000000000000284, "Y": 3.2081644713774073 }, { "Outlier": "\"N/A\"", "X": -1.9000000000000288, "Y": 1.4087628728947221 }, { "Outlier": "\"N/A\"", "X": -1.8000000000000291, "Y": 10.760742900504018 }, { "Outlier": "\"N/A\"", "X": -1.7000000000000295, "Y": 12.93271063217119 }, { "Outlier": "\"N/A\"", "X": -1.6000000000000298, "Y": 3.45947791097803 }, { "Outlier": "\"N/A\"", "X": -1.5000000000000302, "Y": 9.959864604913854 }, { "Outlier": "\"N/A\"", "X": -1.4000000000000306, "Y": 0.757296063587316 }, { "Outlier": "\"N/A\"", "X": -1.300000000000031, "Y": -3.180315182028453 }, { "Outlier": "\"N/A\"", "X": -1.2000000000000313, "Y": 4.437373365223296 }, { "Outlier": "\"N/A\"", "X": -1.1000000000000316, "Y": 1.658197062714779 }, { "Outlier": "\"N/A\"", "X": -1.000000000000032, "Y": 8.864952001575826 }, { "Outlier": "\"N/A\"", "X": -0.9000000000000323, "Y": 1.2698523563653445 }, { "Outlier": "\"N/A\"", "X": -0.8000000000000327, "Y": 0.5973684788411564 }, { "Outlier": "\"N/A\"", "X": -0.700000000000033, "Y": -4.555365575462204 }, { "Outlier": "\"N/A\"", "X": -0.6000000000000334, "Y": 6.2469449541721325 }, { "Outlier": "\"N/A\"", "X": -0.5000000000000338, "Y": 5.767062936848807 }, { "Outlier": "\"N/A\"", "X": -0.4000000000000341, "Y": 10.173383557699086 }, { "Outlier": "\"N/A\"", "X": -0.30000000000003446, "Y": -4.548849292729719 }, { "Outlier": "\"N/A\"", "X": -0.20000000000003482, "Y": 1.425004325853201 }, { "Outlier": "\"N/A\"", "X": -0.10000000000003517, "Y": 3.8745173498347683 }, { "Outlier": "\"N/A\"", "X": -3.552713678800501e-14, "Y": -0.15546257425564614 }, { "Outlier": "\"N/A\"", "X": 0.09999999999996412, "Y": 4.956019987386602 }, { "Outlier": "\"N/A\"", "X": 0.19999999999996376, "Y": 4.962872610201469 }, { "Outlier": "\"N/A\"", "X": 0.2999999999999634, "Y": 4.637451686696391 }, { "Outlier": "\"N/A\"", "X": 0.39999999999996305, "Y": 0.9640110776827278 }, { "Outlier": "\"N/A\"", "X": 0.4999999999999627, "Y": 9.02600284216226 }, { "Outlier": "\"N/A\"", "X": 0.5999999999999623, "Y": 7.6481555413632805 }, { "Outlier": "\"N/A\"", "X": 0.699999999999962, "Y": -1.2776766744840604 }, { "Outlier": "\"N/A\"", "X": 0.7999999999999616, "Y": -1.011569749593121 }, { "Outlier": "\"N/A\"", "X": 0.8999999999999613, "Y": -7.172733709218706 }, { "Outlier": "\"N/A\"", "X": 0.9999999999999609, "Y": 9.030710040554313 }, { "Outlier": "\"N/A\"", "X": 1.0999999999999606, "Y": -0.2873863465351798 }, { "Outlier": "\"N/A\"", "X": 1.1999999999999602, "Y": -0.0761996977400099 }, { "Outlier": "\"N/A\"", "X": 1.2999999999999599, "Y": 2.958613875892526 }, { "Outlier": "\"N/A\"", "X": 1.3999999999999595, "Y": -2.0380371379167297 }, { "Outlier": "\"N/A\"", "X": 1.4999999999999591, "Y": -4.208793003410444 }, { "Outlier": "\"N/A\"", "X": 1.5999999999999588, "Y": 3.090366258779815 }, { "Outlier": "\"N/A\"", "X": 1.6999999999999584, "Y": -11.074287645445791 }, { "Outlier": "\"N/A\"", "X": 1.799999999999958, "Y": -4.227175584211664 }, { "Outlier": "\"N/A\"", "X": 1.8999999999999577, "Y": -6.025452600463005 }, { "Outlier": "\"N/A\"", "X": 1.9999999999999574, "Y": -3.6913770293879176 }, { "Outlier": "\"N/A\"", "X": 2.099999999999957, "Y": -7.62696530095745 }, { "Outlier": "\"N/A\"", "X": 2.1999999999999567, "Y": -1.0070407313158323 }, { "Outlier": "\"N/A\"", "X": 2.2999999999999563, "Y": -0.21160285598304895 }, { "Outlier": "\"N/A\"", "X": 2.399999999999956, "Y": -6.938935480005792 }, { "Outlier": "\"N/A\"", "X": 2.4999999999999556, "Y": 0.6807052586522895 }, { "Outlier": "\"N/A\"", "X": 2.5999999999999552, "Y": -6.413300609057294 }, { "Outlier": "\"N/A\"", "X": 2.699999999999955, "Y": -6.536288487970634 }, { "Outlier": "\"N/A\"", "X": 2.7999999999999545, "Y": -14.394025296298128 }, { "Outlier": "\"N/A\"", "X": 2.899999999999954, "Y": -8.28023513622189 }, { "Outlier": "\"N/A\"", "X": 2.999999999999954, "Y": -3.4124362461292495 }, { "Outlier": "\"N/A\"", "X": 3.0999999999999535, "Y": -5.3562809471748025 }, { "Outlier": "\"N/A\"", "X": 3.199999999999953, "Y": -7.526017621519014 }, { "Outlier": "\"N/A\"", "X": 3.2999999999999527, "Y": -14.00346965318624 }, { "Outlier": "\"N/A\"", "X": 3.3999999999999524, "Y": -5.942935566955495 }, { "Outlier": "\"N/A\"", "X": 3.499999999999952, "Y": -7.9805889243735315 }, { "Outlier": "\"N/A\"", "X": 3.5999999999999517, "Y": -6.232785013070575 }, { "Outlier": "\"N/A\"", "X": 3.6999999999999513, "Y": -2.6255801288971146 }, { "Outlier": "\"N/A\"", "X": 3.799999999999951, "Y": -16.150956461350795 }, { "Outlier": "\"N/A\"", "X": 3.8999999999999506, "Y": -9.852019711110866 }, { "Outlier": "\"N/A\"", "X": 3.9999999999999503, "Y": -10.820545824863508 }, { "Outlier": "\"N/A\"", "X": 4.09999999999995, "Y": -9.641144263319418 }, { "Outlier": "\"N/A\"", "X": 4.1999999999999496, "Y": -16.824573051695413 }, { "Outlier": "\"N/A\"", "X": 4.299999999999949, "Y": -7.584368869801537 }, { "Outlier": "\"N/A\"", "X": 4.399999999999949, "Y": -15.921597577697376 }, { "Outlier": "\"N/A\"", "X": 4.4999999999999485, "Y": -10.564144416396802 }, { "Outlier": "\"N/A\"", "X": 4.599999999999948, "Y": -8.719322820788562 }, { "Outlier": "\"N/A\"", "X": 4.699999999999948, "Y": -13.706770144040625 }, { "Outlier": "\"N/A\"", "X": 4.799999999999947, "Y": -14.71638534254468 }, { "Outlier": "\"N/A\"", "X": 4.899999999999947, "Y": -8.11320601142597 }, { "Outlier": "\"N/A\"", "X": 4.999999999999947, "Y": -9.079761935368413 }, { "Outlier": "\"N/A\"", "X": 5.099999999999946, "Y": -7.741729875490136 }, { "Outlier": "\"N/A\"", "X": 5.199999999999946, "Y": -7.709427648269321 }, { "Outlier": "\"N/A\"", "X": 5.299999999999946, "Y": -10.164452904568247 }, { "Outlier": "\"N/A\"", "X": 5.399999999999945, "Y": -17.037300164945133 }, { "Outlier": "\"N/A\"", "X": 5.499999999999945, "Y": -9.530200659868438 }, { "Outlier": "\"N/A\"", "X": 5.599999999999945, "Y": -6.805904336249347 }, { "Outlier": "\"N/A\"", "X": 5.699999999999944, "Y": -18.518345122128725 }, { "Outlier": "\"N/A\"", "X": 5.799999999999944, "Y": -16.66401480644169 }, { "Outlier": "\"N/A\"", "X": 5.8999999999999435, "Y": -13.035777619073194 }, { "Outlier": "\"N/A\"", "X": 5.999999999999943, "Y": -10.453747100452984 }, { "Outlier": "\"N/A\"", "X": 6.099999999999945, "Y": -10.966412014185101 }, { "Outlier": "\"N/A\"", "X": 6.1999999999999424, "Y": -9.074186841328956 }, { "Outlier": "\"N/A\"", "X": 6.29999999999994, "Y": -10.953103638117929 }, { "Outlier": "\"N/A\"", "X": 6.399999999999942, "Y": -8.6614528465355 }, { "Outlier": "\"N/A\"", "X": 6.499999999999943, "Y": -11.767577514991192 }, { "Outlier": "\"N/A\"", "X": 6.599999999999941, "Y": -15.749273101756014 }, { "Outlier": "\"N/A\"", "X": 6.699999999999939, "Y": -20.535868312369907 }, { "Outlier": "\"N/A\"", "X": 6.79999999999994, "Y": -18.238578928251155 }, { "Outlier": "\"N/A\"", "X": 6.899999999999942, "Y": -22.60243906276875 }, { "Outlier": "\"N/A\"", "X": 6.99999999999994, "Y": -21.86297579565081 }, { "Outlier": "\"N/A\"", "X": 7.0999999999999375, "Y": -17.08344647050758 }, { "Outlier": "\"N/A\"", "X": 7.199999999999939, "Y": -19.369295599963984 }, { "Outlier": "\"N/A\"", "X": 7.29999999999994, "Y": -18.678956845823837 }, { "Outlier": "\"N/A\"", "X": 7.399999999999938, "Y": -15.61678580989824 }, { "Outlier": "\"N/A\"", "X": 7.499999999999936, "Y": -26.27818766241228 }, { "Outlier": "\"N/A\"", "X": 7.5999999999999375, "Y": -19.420291255188108 }, { "Outlier": "\"N/A\"", "X": 7.699999999999939, "Y": -17.74467794634894 }, { "Outlier": "\"N/A\"", "X": 7.799999999999937, "Y": -16.946636703176477 }, { "Outlier": "\"N/A\"", "X": 7.899999999999935, "Y": -26.71150723727269 }, { "Outlier": "\"N/A\"", "X": 7.999999999999936, "Y": -24.113700090735545 }, { "Outlier": "\"N/A\"", "X": 8.099999999999937, "Y": -18.385630502824597 }, { "Outlier": "\"N/A\"", "X": 8.199999999999935, "Y": -16.16025577987224 }, { "Outlier": "\"N/A\"", "X": 8.299999999999933, "Y": -22.192371003190164 }, { "Outlier": "\"N/A\"", "X": 8.399999999999935, "Y": -21.633943313778616 }, { "Outlier": "\"N/A\"", "X": 8.499999999999936, "Y": -33.4081634879886 }, { "Outlier": "\"N/A\"", "X": 8.599999999999934, "Y": -13.971218302650211 }, { "Outlier": "\"N/A\"", "X": 8.699999999999932, "Y": -30.517273136049173 }, { "Outlier": "\"N/A\"", "X": 8.799999999999933, "Y": -18.20026092214388 }, { "Outlier": "\"N/A\"", "X": 8.899999999999935, "Y": -29.619423062052164 }, { "Outlier": "\"N/A\"", "X": 8.999999999999932, "Y": -20.757216839068352 }, { "Outlier": "\"N/A\"", "X": 9.09999999999993, "Y": -28.846849347810682 }, { "Outlier": "\"N/A\"", "X": 9.199999999999932, "Y": -28.75698430739407 }, { "Outlier": "\"N/A\"", "X": 9.299999999999933, "Y": -26.74093489476592 }, { "Outlier": "\"N/A\"", "X": 9.399999999999931, "Y": -28.4891432150645 }, { "Outlier": "\"N/A\"", "X": 9.499999999999929, "Y": -23.961935678746933 }, { "Outlier": "\"N/A\"", "X": 9.59999999999993, "Y": -31.757248852429687 }, { "Outlier": "\"N/A\"", "X": 9.699999999999932, "Y": -23.795671636576557 }, { "Outlier": "\"N/A\"", "X": 9.79999999999993, "Y": -25.698267857158072 }, { "Outlier": "\"N/A\"", "X": 9.899999999999928, "Y": -32.23329810811726 }, { "Outlier": "\"N/A\"", "X": 9.999999999999929, "Y": -27.44784410052699 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot (zip regressionSamples (Prelude.repeat $ T.pack \"N/A\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function`regression` computes paramteres of the model, namely `(slope, intercept, noise)` and their score." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "regression :: (MonadMeasure m) => [Double] -> [Double] -> m (Double, Double, Double)\n", "regression xs ys = do\n", " params@(slope, intercept, noise) <- paramPriorRegression\n", " forM_ (zip xs ys) \\(x, y) -> factor $ normalPdf (slope * x + intercept) (sqrt noise) y\n", " return (slope, intercept, noise)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mcmcConfig = MCMCConfig {numMCMCSteps = 1000, numBurnIn = 0, proposal = SingleSiteMH}\n", "mhRunsRegression <- sampleIOfixed . unweighted . mcmc mcmcConfig $ uncurry regression (unzip regressionSamples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MCMC solver puts new points at the beginning of the list. Last (in the list, so first from the simulation start) `numBurnIn` points are removed as distributions from which they were drawn are not considered convergent to the stationary distribution $\\pi$.\n", "\n", "Let's se how the intercept and slope evolve during the simulations:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Slope\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": 1, "X": 1001, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 1000, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 999, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 998, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 997, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 996, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 995, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 994, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 993, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 992, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 991, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 990, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 989, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 988, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 987, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 986, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 985, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 984, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 983, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 982, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 981, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 980, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 979, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 978, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 977, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 976, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 975, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 974, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 973, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 972, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 971, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 970, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 969, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 968, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 967, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 966, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 965, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 964, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 963, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 962, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 961, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 960, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 959, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 958, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 957, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 956, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 955, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 954, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 953, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 952, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 951, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 950, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 949, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 948, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 947, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 946, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 945, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 944, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 943, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 942, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 941, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 940, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 939, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 938, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 937, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 936, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 935, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 934, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 933, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 932, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 931, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 930, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 929, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 928, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 927, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 926, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 925, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 924, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 923, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 922, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 921, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 920, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 919, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 918, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 917, "Y": -2.823624445842976 }, { "Outlier": 1, "X": 916, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 915, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 914, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 913, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 912, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 911, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 910, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 909, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 908, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 907, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 906, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 905, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 904, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 903, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 902, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 901, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 900, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 899, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 898, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 897, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 896, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 895, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 894, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 893, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 892, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 891, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 890, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 889, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 888, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 887, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 886, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 885, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 884, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 883, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 882, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 881, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 880, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 879, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 878, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 877, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 876, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 875, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 874, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 873, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 872, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 871, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 870, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 869, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 868, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 867, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 866, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 865, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 864, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 863, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 862, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 861, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 860, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 859, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 858, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 857, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 856, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 855, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 854, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 853, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 852, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 851, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 850, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 849, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 848, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 847, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 846, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 845, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 844, "Y": -2.8121213968098955 }, { "Outlier": 1, "X": 843, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 842, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 841, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 840, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 839, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 838, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 837, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 836, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 835, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 834, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 833, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 832, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 831, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 830, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 829, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 828, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 827, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 826, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 825, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 824, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 823, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 822, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 821, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 820, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 819, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 818, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 817, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 816, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 815, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 814, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 813, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 812, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 811, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 810, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 809, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 808, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 807, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 806, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 805, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 804, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 803, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 802, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 801, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 800, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 799, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 798, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 797, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 796, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 795, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 794, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 793, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 792, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 791, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 790, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 789, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 788, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 787, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 786, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 785, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 784, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 783, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 782, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 781, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 780, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 779, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 778, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 777, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 776, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 775, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 774, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 773, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 772, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 771, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 770, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 769, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 768, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 767, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 766, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 765, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 764, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 763, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 762, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 761, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 760, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 759, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 758, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 757, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 756, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 755, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 754, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 753, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 752, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 751, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 750, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 749, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 748, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 747, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 746, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 745, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 744, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 743, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 742, "Y": -2.789610545041284 }, { "Outlier": 1, "X": 741, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 740, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 739, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 738, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 737, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 736, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 735, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 734, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 733, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 732, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 731, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 730, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 729, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 728, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 727, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 726, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 725, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 724, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 723, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 722, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 721, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 720, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 719, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 718, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 717, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 716, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 715, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 714, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 713, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 712, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 711, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 710, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 709, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 708, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 707, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 706, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 705, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 704, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 703, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 702, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 701, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 700, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 699, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 698, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 697, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 696, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 695, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 694, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 693, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 692, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 691, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 690, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 689, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 688, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 687, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 686, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 685, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 684, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 683, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 682, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 681, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 680, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 679, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 678, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 677, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 676, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 675, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 674, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 673, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 672, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 671, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 670, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 669, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 668, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 667, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 666, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 665, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 664, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 663, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 662, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 661, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 660, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 659, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 658, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 657, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 656, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 655, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 654, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 653, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 652, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 651, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 650, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 649, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 648, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 647, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 646, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 645, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 644, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 643, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 642, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 641, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 640, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 639, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 638, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 637, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 636, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 635, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 634, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 633, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 632, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 631, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 630, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 629, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 628, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 627, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 626, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 625, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 624, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 623, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 622, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 621, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 620, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 619, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 618, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 617, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 616, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 615, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 614, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 613, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 612, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 611, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 610, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 609, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 608, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 607, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 606, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 605, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 604, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 603, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 602, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 601, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 600, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 599, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 598, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 597, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 596, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 595, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 594, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 593, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 592, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 591, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 590, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 589, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 588, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 587, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 586, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 585, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 584, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 583, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 582, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 581, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 580, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 579, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 578, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 577, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 576, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 575, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 574, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 573, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 572, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 571, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 570, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 569, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 568, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 567, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 566, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 565, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 564, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 563, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 562, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 561, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 560, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 559, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 558, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 557, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 556, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 555, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 554, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 553, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 552, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 551, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 550, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 549, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 548, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 547, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 546, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 545, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 544, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 543, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 542, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 541, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 540, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 539, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 538, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 537, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 536, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 535, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 534, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 533, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 532, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 531, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 530, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 529, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 528, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 527, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 526, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 525, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 524, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 523, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 522, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 521, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 520, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 519, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 518, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 517, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 516, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 515, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 514, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 513, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 512, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 511, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 510, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 509, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 508, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 507, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 506, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 505, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 504, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 503, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 502, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 501, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 500, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 499, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 498, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 497, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 496, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 495, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 494, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 493, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 492, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 491, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 490, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 489, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 488, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 487, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 486, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 485, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 484, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 483, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 482, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 481, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 480, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 479, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 478, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 477, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 476, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 475, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 474, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 473, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 472, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 471, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 470, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 469, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 468, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 467, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 466, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 465, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 464, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 463, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 462, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 461, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 460, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 459, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 458, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 457, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 456, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 455, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 454, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 453, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 452, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 451, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 450, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 449, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 448, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 447, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 446, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 445, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 444, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 443, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 442, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 441, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 440, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 439, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 438, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 437, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 436, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 435, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 434, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 433, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 432, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 431, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 430, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 429, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 428, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 427, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 426, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 425, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 424, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 423, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 422, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 421, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 420, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 419, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 418, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 417, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 416, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 415, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 414, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 413, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 412, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 411, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 410, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 409, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 408, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 407, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 406, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 405, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 404, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 403, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 402, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 401, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 400, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 399, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 398, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 397, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 396, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 395, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 394, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 393, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 392, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 391, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 390, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 389, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 388, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 387, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 386, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 385, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 384, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 383, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 382, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 381, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 380, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 379, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 378, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 377, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 376, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 375, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 374, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 373, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 372, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 371, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 370, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 369, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 368, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 367, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 366, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 365, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 364, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 363, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 362, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 361, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 360, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 359, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 358, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 357, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 356, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 355, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 354, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 353, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 352, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 351, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 350, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 349, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 348, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 347, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 346, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 345, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 344, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 343, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 342, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 341, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 340, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 339, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 338, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 337, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 336, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 335, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 334, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 333, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 332, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 331, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 330, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 329, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 328, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 327, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 326, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 325, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 324, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 323, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 322, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 321, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 320, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 319, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 318, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 317, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 316, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 315, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 314, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 313, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 312, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 311, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 310, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 309, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 308, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 307, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 306, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 305, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 304, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 303, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 302, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 301, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 300, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 299, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 298, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 297, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 296, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 295, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 294, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 293, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 292, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 291, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 290, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 289, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 288, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 287, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 286, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 285, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 284, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 283, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 282, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 281, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 280, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 279, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 278, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 277, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 276, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 275, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 274, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 273, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 272, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 271, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 270, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 269, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 268, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 267, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 266, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 265, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 264, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 263, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 262, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 261, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 260, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 259, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 258, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 257, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 256, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 255, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 254, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 253, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 252, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 251, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 250, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 249, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 248, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 247, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 246, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 245, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 244, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 243, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 242, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 241, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 240, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 239, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 238, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 237, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 236, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 235, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 234, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 233, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 232, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 231, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 230, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 229, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 228, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 227, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 226, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 225, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 224, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 223, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 222, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 221, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 220, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 219, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 218, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 217, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 216, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 215, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 214, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 213, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 212, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 211, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 210, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 209, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 208, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 207, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 206, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 205, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 204, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 203, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 202, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 201, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 200, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 199, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 198, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 197, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 196, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 195, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 194, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 193, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 192, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 191, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 190, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 189, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 188, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 187, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 186, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 185, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 184, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 183, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 182, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 181, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 180, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 179, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 178, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 177, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 176, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 175, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 174, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 173, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 172, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 171, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 170, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 169, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 168, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 167, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 166, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 165, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 164, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 163, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 162, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 161, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 160, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 159, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 158, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 157, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 156, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 155, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 154, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 153, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 152, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 151, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 150, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 149, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 148, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 147, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 146, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 145, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 144, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 143, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 142, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 141, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 140, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 139, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 138, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 137, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 136, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 135, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 134, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 133, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 132, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 131, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 130, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 129, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 128, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 127, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 126, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 125, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 124, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 123, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 122, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 121, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 120, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 119, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 118, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 117, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 116, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 115, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 114, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 113, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 112, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 111, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 110, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 109, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 108, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 107, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 106, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 105, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 104, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 103, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 102, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 101, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 100, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 99, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 98, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 97, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 96, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 95, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 94, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 93, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 92, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 91, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 90, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 89, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 88, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 87, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 86, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 85, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 84, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 83, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 82, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 81, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 80, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 79, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 78, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 77, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 76, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 75, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 74, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 73, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 72, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 71, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 70, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 69, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 68, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 67, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 66, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 65, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 64, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 63, "Y": -2.779155643098428 }, { "Outlier": 1, "X": 62, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 61, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 60, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 59, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 58, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 57, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 56, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 55, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 54, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 53, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 52, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 51, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 50, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 49, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 48, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 47, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 46, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 45, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 44, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 43, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 42, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 41, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 40, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 39, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 38, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 37, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 36, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 35, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 34, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 33, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 32, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 31, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 30, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 29, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 28, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 27, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 26, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 25, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 24, "Y": -2.8108182040295433 }, { "Outlier": 1, "X": 23, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 22, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 21, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 20, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 19, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 18, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 17, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 16, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 15, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 14, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 13, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 12, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 11, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 10, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 9, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 8, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 7, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 6, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 5, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 4, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 3, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 2, "Y": -3.351193305487453 }, { "Outlier": 1, "X": 1, "Y": -3.351193305487453 } ] }, "encoding": { "color": {}, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "line", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print \"Slope\"\n", "l = length mhRunsRegression\n", "xs = reverse $ fromIntegral <$> [1..l] ::[Double]\n", "plot (xs, (\\(x,_,_) -> x) <$> mhRunsRegression) \n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"Intercept\"" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": 1, "X": 1001, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 1000, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 999, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 998, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 997, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 996, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 995, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 994, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 993, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 992, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 991, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 990, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 989, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 988, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 987, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 986, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 985, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 984, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 983, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 982, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 981, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 980, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 979, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 978, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 977, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 976, "Y": 1.7617903982284406 }, { "Outlier": 1, "X": 975, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 974, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 973, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 972, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 971, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 970, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 969, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 968, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 967, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 966, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 965, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 964, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 963, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 962, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 961, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 960, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 959, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 958, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 957, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 956, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 955, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 954, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 953, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 952, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 951, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 950, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 949, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 948, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 947, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 946, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 945, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 944, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 943, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 942, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 941, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 940, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 939, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 938, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 937, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 936, "Y": 1.8287906177483493 }, { "Outlier": 1, "X": 935, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 934, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 933, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 932, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 931, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 930, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 929, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 928, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 927, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 926, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 925, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 924, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 923, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 922, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 921, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 920, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 919, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 918, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 917, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 916, "Y": 1.7001514943530478 }, { "Outlier": 1, "X": 915, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 914, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 913, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 912, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 911, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 910, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 909, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 908, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 907, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 906, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 905, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 904, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 903, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 902, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 901, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 900, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 899, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 898, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 897, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 896, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 895, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 894, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 893, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 892, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 891, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 890, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 889, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 888, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 887, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 886, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 885, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 884, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 883, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 882, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 881, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 880, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 879, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 878, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 877, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 876, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 875, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 874, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 873, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 872, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 871, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 870, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 869, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 868, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 867, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 866, "Y": 2.017287381040768 }, { "Outlier": 1, "X": 865, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 864, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 863, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 862, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 861, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 860, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 859, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 858, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 857, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 856, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 855, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 854, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 853, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 852, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 851, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 850, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 849, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 848, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 847, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 846, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 845, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 844, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 843, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 842, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 841, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 840, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 839, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 838, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 837, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 836, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 835, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 834, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 833, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 832, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 831, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 830, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 829, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 828, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 827, "Y": 1.6254911344857144 }, { "Outlier": 1, "X": 826, "Y": 1.4191618565767918 }, { "Outlier": 1, "X": 825, "Y": 1.4191618565767918 }, { "Outlier": 1, "X": 824, "Y": 1.4191618565767918 }, { "Outlier": 1, "X": 823, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 822, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 821, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 820, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 819, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 818, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 817, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 816, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 815, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 814, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 813, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 812, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 811, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 810, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 809, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 808, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 807, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 806, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 805, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 804, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 803, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 802, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 801, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 800, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 799, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 798, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 797, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 796, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 795, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 794, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 793, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 792, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 791, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 790, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 789, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 788, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 787, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 786, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 785, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 784, "Y": 2.47421581651972 }, { "Outlier": 1, "X": 783, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 782, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 781, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 780, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 779, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 778, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 777, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 776, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 775, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 774, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 773, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 772, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 771, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 770, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 769, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 768, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 767, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 766, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 765, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 764, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 763, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 762, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 761, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 760, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 759, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 758, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 757, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 756, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 755, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 754, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 753, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 752, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 751, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 750, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 749, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 748, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 747, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 746, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 745, "Y": 1.4676519131057528 }, { "Outlier": 1, "X": 744, "Y": 2.048241495785841 }, { "Outlier": 1, "X": 743, "Y": 2.048241495785841 }, { "Outlier": 1, "X": 742, "Y": 2.048241495785841 }, { "Outlier": 1, "X": 741, "Y": 2.048241495785841 }, { "Outlier": 1, "X": 740, "Y": 2.048241495785841 }, { "Outlier": 1, "X": 739, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 738, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 737, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 736, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 735, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 734, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 733, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 732, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 731, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 730, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 729, "Y": 1.5017180402196402 }, { "Outlier": 1, "X": 728, "Y": 1.891843416488586 }, { "Outlier": 1, "X": 727, "Y": 1.891843416488586 }, { "Outlier": 1, "X": 726, "Y": 1.891843416488586 }, { "Outlier": 1, "X": 725, "Y": 1.891843416488586 }, { "Outlier": 1, "X": 724, "Y": 1.891843416488586 }, { "Outlier": 1, "X": 723, "Y": 1.891843416488586 }, { "Outlier": 1, "X": 722, "Y": 1.891843416488586 }, { "Outlier": 1, "X": 721, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 720, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 719, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 718, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 717, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 716, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 715, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 714, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 713, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 712, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 711, "Y": 1.7497018785898255 }, { "Outlier": 1, "X": 710, "Y": 2.2946001483092227 }, { "Outlier": 1, "X": 709, "Y": 2.2946001483092227 }, { "Outlier": 1, "X": 708, "Y": 2.2946001483092227 }, { "Outlier": 1, "X": 707, "Y": 2.2946001483092227 }, { "Outlier": 1, "X": 706, "Y": 2.2946001483092227 }, { "Outlier": 1, "X": 705, "Y": 2.2946001483092227 }, { "Outlier": 1, "X": 704, "Y": 1.658811912435686 }, { "Outlier": 1, "X": 703, "Y": 1.658811912435686 }, { "Outlier": 1, "X": 702, "Y": 1.658811912435686 }, { "Outlier": 1, "X": 701, "Y": 1.658811912435686 }, { "Outlier": 1, "X": 700, "Y": 1.658811912435686 }, { "Outlier": 1, "X": 699, "Y": 1.658811912435686 }, { "Outlier": 1, "X": 698, "Y": 1.658811912435686 }, { "Outlier": 1, "X": 697, "Y": 1.658811912435686 }, { "Outlier": 1, "X": 696, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 695, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 694, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 693, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 692, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 691, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 690, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 689, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 688, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 687, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 686, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 685, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 684, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 683, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 682, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 681, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 680, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 679, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 678, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 677, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 676, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 675, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 674, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 673, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 672, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 671, "Y": 1.7652097877311717 }, { "Outlier": 1, "X": 670, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 669, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 668, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 667, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 666, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 665, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 664, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 663, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 662, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 661, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 660, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 659, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 658, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 657, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 656, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 655, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 654, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 653, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 652, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 651, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 650, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 649, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 648, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 647, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 646, "Y": 1.8469835673638269 }, { "Outlier": 1, "X": 645, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 644, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 643, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 642, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 641, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 640, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 639, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 638, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 637, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 636, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 635, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 634, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 633, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 632, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 631, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 630, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 629, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 628, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 627, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 626, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 625, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 624, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 623, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 622, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 621, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 620, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 619, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 618, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 617, "Y": 2.087905156221181 }, { "Outlier": 1, "X": 616, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 615, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 614, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 613, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 612, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 611, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 610, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 609, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 608, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 607, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 606, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 605, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 604, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 603, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 602, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 601, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 600, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 599, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 598, "Y": 2.0388835361098283 }, { "Outlier": 1, "X": 597, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 596, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 595, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 594, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 593, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 592, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 591, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 590, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 589, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 588, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 587, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 586, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 585, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 584, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 583, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 582, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 581, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 580, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 579, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 578, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 577, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 576, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 575, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 574, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 573, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 572, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 571, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 570, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 569, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 568, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 567, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 566, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 565, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 564, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 563, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 562, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 561, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 560, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 559, "Y": 1.7160317487364638 }, { "Outlier": 1, "X": 558, "Y": 1.5493630413376596 }, { "Outlier": 1, "X": 557, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 556, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 555, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 554, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 553, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 552, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 551, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 550, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 549, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 548, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 547, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 546, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 545, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 544, "Y": 1.8671317303862804 }, { "Outlier": 1, "X": 543, "Y": 1.3142115128659677 }, { "Outlier": 1, "X": 542, "Y": 1.3142115128659677 }, { "Outlier": 1, "X": 541, "Y": 1.3142115128659677 }, { "Outlier": 1, "X": 540, "Y": 1.3142115128659677 }, { "Outlier": 1, "X": 539, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 538, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 537, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 536, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 535, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 534, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 533, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 532, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 531, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 530, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 529, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 528, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 527, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 526, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 525, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 524, "Y": 1.7552043632871244 }, { "Outlier": 1, "X": 523, "Y": 1.9093972904713568 }, { "Outlier": 1, "X": 522, "Y": 1.9093972904713568 }, { "Outlier": 1, "X": 521, "Y": 1.9093972904713568 }, { "Outlier": 1, "X": 520, "Y": 1.9093972904713568 }, { "Outlier": 1, "X": 519, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 518, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 517, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 516, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 515, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 514, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 513, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 512, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 511, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 510, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 509, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 508, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 507, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 506, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 505, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 504, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 503, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 502, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 501, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 500, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 499, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 498, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 497, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 496, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 495, "Y": 2.0013081290944768 }, { "Outlier": 1, "X": 494, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 493, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 492, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 491, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 490, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 489, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 488, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 487, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 486, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 485, "Y": 1.3485580071926517 }, { "Outlier": 1, "X": 484, "Y": 1.3267836505401627 }, { "Outlier": 1, "X": 483, "Y": 1.3267836505401627 }, { "Outlier": 1, "X": 482, "Y": 1.3267836505401627 }, { "Outlier": 1, "X": 481, "Y": 1.3267836505401627 }, { "Outlier": 1, "X": 480, "Y": 1.2269337425750118 }, { "Outlier": 1, "X": 479, "Y": 1.2269337425750118 }, { "Outlier": 1, "X": 478, "Y": 1.2269337425750118 }, { "Outlier": 1, "X": 477, "Y": 1.794878565178167 }, { "Outlier": 1, "X": 476, "Y": 1.794878565178167 }, { "Outlier": 1, "X": 475, "Y": 1.794878565178167 }, { "Outlier": 1, "X": 474, "Y": 1.794878565178167 }, { "Outlier": 1, "X": 473, "Y": 2.0665018567601443 }, { "Outlier": 1, "X": 472, "Y": 2.0665018567601443 }, { "Outlier": 1, "X": 471, "Y": 2.0665018567601443 }, { "Outlier": 1, "X": 470, "Y": 2.0665018567601443 }, { "Outlier": 1, "X": 469, "Y": 2.0665018567601443 }, { "Outlier": 1, "X": 468, "Y": 2.0665018567601443 }, { "Outlier": 1, "X": 467, "Y": 2.0665018567601443 }, { "Outlier": 1, "X": 466, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 465, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 464, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 463, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 462, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 461, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 460, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 459, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 458, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 457, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 456, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 455, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 454, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 453, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 452, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 451, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 450, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 449, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 448, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 447, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 446, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 445, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 444, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 443, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 442, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 441, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 440, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 439, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 438, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 437, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 436, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 435, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 434, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 433, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 432, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 431, "Y": 1.6433515625028146 }, { "Outlier": 1, "X": 430, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 429, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 428, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 427, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 426, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 425, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 424, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 423, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 422, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 421, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 420, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 419, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 418, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 417, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 416, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 415, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 414, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 413, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 412, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 411, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 410, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 409, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 408, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 407, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 406, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 405, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 404, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 403, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 402, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 401, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 400, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 399, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 398, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 397, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 396, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 395, "Y": 2.2296557820519336 }, { "Outlier": 1, "X": 394, "Y": 1.8147052322565385 }, { "Outlier": 1, "X": 393, "Y": 1.8147052322565385 }, { "Outlier": 1, "X": 392, "Y": 1.8147052322565385 }, { "Outlier": 1, "X": 391, "Y": 1.8147052322565385 }, { "Outlier": 1, "X": 390, "Y": 1.30462588195755 }, { "Outlier": 1, "X": 389, "Y": 1.30462588195755 }, { "Outlier": 1, "X": 388, "Y": 1.30462588195755 }, { "Outlier": 1, "X": 387, "Y": 1.30462588195755 }, { "Outlier": 1, "X": 386, "Y": 1.30462588195755 }, { "Outlier": 1, "X": 385, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 384, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 383, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 382, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 381, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 380, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 379, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 378, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 377, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 376, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 375, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 374, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 373, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 372, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 371, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 370, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 369, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 368, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 367, "Y": 1.6560496747938094 }, { "Outlier": 1, "X": 366, "Y": 1.6078459291993832 }, { "Outlier": 1, "X": 365, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 364, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 363, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 362, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 361, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 360, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 359, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 358, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 357, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 356, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 355, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 354, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 353, "Y": 1.7776754548531566 }, { "Outlier": 1, "X": 352, "Y": 1.52594575733393 }, { "Outlier": 1, "X": 351, "Y": 1.52594575733393 }, { "Outlier": 1, "X": 350, "Y": 2.489442576493648 }, { "Outlier": 1, "X": 349, "Y": 2.489442576493648 }, { "Outlier": 1, "X": 348, "Y": 2.489442576493648 }, { "Outlier": 1, "X": 347, "Y": 2.489442576493648 }, { "Outlier": 1, "X": 346, "Y": 2.489442576493648 }, { "Outlier": 1, "X": 345, "Y": 2.489442576493648 }, { "Outlier": 1, "X": 344, "Y": 2.489442576493648 }, { "Outlier": 1, "X": 343, "Y": 2.489442576493648 }, { "Outlier": 1, "X": 342, "Y": 2.489442576493648 }, { "Outlier": 1, "X": 341, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 340, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 339, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 338, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 337, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 336, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 335, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 334, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 333, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 332, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 331, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 330, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 329, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 328, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 327, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 326, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 325, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 324, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 323, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 322, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 321, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 320, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 319, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 318, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 317, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 316, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 315, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 314, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 313, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 312, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 311, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 310, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 309, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 308, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 307, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 306, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 305, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 304, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 303, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 302, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 301, "Y": 1.8828997947595156 }, { "Outlier": 1, "X": 300, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 299, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 298, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 297, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 296, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 295, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 294, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 293, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 292, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 291, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 290, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 289, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 288, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 287, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 286, "Y": 1.3422258844770831 }, { "Outlier": 1, "X": 285, "Y": 2.344535984885695 }, { "Outlier": 1, "X": 284, "Y": 2.344535984885695 }, { "Outlier": 1, "X": 283, "Y": 2.344535984885695 }, { "Outlier": 1, "X": 282, "Y": 2.344535984885695 }, { "Outlier": 1, "X": 281, "Y": 2.344535984885695 }, { "Outlier": 1, "X": 280, "Y": 2.344535984885695 }, { "Outlier": 1, "X": 279, "Y": 2.344535984885695 }, { "Outlier": 1, "X": 278, "Y": 2.344535984885695 }, { "Outlier": 1, "X": 277, "Y": 1.296164254389404 }, { "Outlier": 1, "X": 276, "Y": 1.296164254389404 }, { "Outlier": 1, "X": 275, "Y": 1.296164254389404 }, { "Outlier": 1, "X": 274, "Y": 1.296164254389404 }, { "Outlier": 1, "X": 273, "Y": 1.296164254389404 }, { "Outlier": 1, "X": 272, "Y": 1.296164254389404 }, { "Outlier": 1, "X": 271, "Y": 1.296164254389404 }, { "Outlier": 1, "X": 270, "Y": 1.296164254389404 }, { "Outlier": 1, "X": 269, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 268, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 267, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 266, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 265, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 264, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 263, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 262, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 261, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 260, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 259, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 258, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 257, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 256, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 255, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 254, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 253, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 252, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 251, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 250, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 249, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 248, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 247, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 246, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 245, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 244, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 243, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 242, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 241, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 240, "Y": 1.858996607673116 }, { "Outlier": 1, "X": 239, "Y": 2.2333897375156417 }, { "Outlier": 1, "X": 238, "Y": 1.246649510892441 }, { "Outlier": 1, "X": 237, "Y": 1.246649510892441 }, { "Outlier": 1, "X": 236, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 235, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 234, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 233, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 232, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 231, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 230, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 229, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 228, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 227, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 226, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 225, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 224, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 223, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 222, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 221, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 220, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 219, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 218, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 217, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 216, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 215, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 214, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 213, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 212, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 211, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 210, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 209, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 208, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 207, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 206, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 205, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 204, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 203, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 202, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 201, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 200, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 199, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 198, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 197, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 196, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 195, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 194, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 193, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 192, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 191, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 190, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 189, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 188, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 187, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 186, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 185, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 184, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 183, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 182, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 181, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 180, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 179, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 178, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 177, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 176, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 175, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 174, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 173, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 172, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 171, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 170, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 169, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 168, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 167, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 166, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 165, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 164, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 163, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 162, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 161, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 160, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 159, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 158, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 157, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 156, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 155, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 154, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 153, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 152, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 151, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 150, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 149, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 148, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 147, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 146, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 145, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 144, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 143, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 142, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 141, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 140, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 139, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 138, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 137, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 136, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 135, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 134, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 133, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 132, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 131, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 130, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 129, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 128, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 127, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 126, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 125, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 124, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 123, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 122, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 121, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 120, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 119, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 118, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 117, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 116, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 115, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 114, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 113, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 112, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 111, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 110, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 109, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 108, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 107, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 106, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 105, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 104, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 103, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 102, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 101, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 100, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 99, "Y": 1.4201427917391793 }, { "Outlier": 1, "X": 98, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 97, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 96, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 95, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 94, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 93, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 92, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 91, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 90, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 89, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 88, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 87, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 86, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 85, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 84, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 83, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 82, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 81, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 80, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 79, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 78, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 77, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 76, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 75, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 74, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 73, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 72, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 71, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 70, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 69, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 68, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 67, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 66, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 65, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 64, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 63, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 62, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 61, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 60, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 59, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 58, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 57, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 56, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 55, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 54, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 53, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 52, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 51, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 50, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 49, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 48, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 47, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 46, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 45, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 44, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 43, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 42, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 41, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 40, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 39, "Y": 1.7142197811374518 }, { "Outlier": 1, "X": 38, "Y": 1.858600127811928 }, { "Outlier": 1, "X": 37, "Y": 1.858600127811928 }, { "Outlier": 1, "X": 36, "Y": 1.858600127811928 }, { "Outlier": 1, "X": 35, "Y": 1.858600127811928 }, { "Outlier": 1, "X": 34, "Y": 1.858600127811928 }, { "Outlier": 1, "X": 33, "Y": 1.858600127811928 }, { "Outlier": 1, "X": 32, "Y": 1.858600127811928 }, { "Outlier": 1, "X": 31, "Y": 1.858600127811928 }, { "Outlier": 1, "X": 30, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 29, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 28, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 27, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 26, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 25, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 24, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 23, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 22, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 21, "Y": 1.4431168371690295 }, { "Outlier": 1, "X": 20, "Y": 1.5802552971615031 }, { "Outlier": 1, "X": 19, "Y": 1.5802552971615031 }, { "Outlier": 1, "X": 18, "Y": 1.3145215619778121 }, { "Outlier": 1, "X": 17, "Y": 2.2234710547460788 }, { "Outlier": 1, "X": 16, "Y": 2.2234710547460788 }, { "Outlier": 1, "X": 15, "Y": 2.2234710547460788 }, { "Outlier": 1, "X": 14, "Y": 2.2234710547460788 }, { "Outlier": 1, "X": 13, "Y": 2.2234710547460788 }, { "Outlier": 1, "X": 12, "Y": 2.2234710547460788 }, { "Outlier": 1, "X": 11, "Y": 2.2234710547460788 }, { "Outlier": 1, "X": 10, "Y": 2.2234710547460788 }, { "Outlier": 1, "X": 9, "Y": 2.529685152521397 }, { "Outlier": 1, "X": 8, "Y": 2.529685152521397 }, { "Outlier": 1, "X": 7, "Y": 2.529685152521397 }, { "Outlier": 1, "X": 6, "Y": 2.529685152521397 }, { "Outlier": 1, "X": 5, "Y": 2.529685152521397 }, { "Outlier": 1, "X": 4, "Y": 2.529685152521397 }, { "Outlier": 1, "X": 3, "Y": 2.529685152521397 }, { "Outlier": 1, "X": 2, "Y": 2.529685152521397 }, { "Outlier": 1, "X": 1, "Y": 2.529685152521397 } ] }, "encoding": { "color": {}, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "line", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print \"Intercept\"\n", "l = length mhRunsRegression\n", "xs = reverse $ fromIntegral <$> [1..l] ::[Double]\n", "plot (xs, (\\(_,x,_) -> x) <$> mhRunsRegression) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a sample from the MCMC walk. Since this is an easy inference problem, it wasn't hard to generate good samples.\n", "\n", "We can also view the posterior predictive, as follows:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"\\\"N/A\\\"\"", "X": -10, "Y": 34.30944810257017 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.9, "Y": 27.24085149708539 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.8, "Y": 38.505594739738974 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.700000000000001, "Y": 24.670936444125363 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.600000000000001, "Y": 21.818485342895272 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.500000000000002, "Y": 37.53521801611909 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.400000000000002, "Y": 24.81836033904785 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.300000000000002, "Y": 26.1468516568216 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.200000000000003, "Y": 46.21426014304069 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.100000000000003, "Y": 23.366630457628382 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -9.000000000000004, "Y": 17.26103440266769 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.900000000000004, "Y": 36.996524465235666 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.800000000000004, "Y": 40.521391215856795 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.700000000000005, "Y": 31.30239997365902 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.600000000000005, "Y": 23.959440654739563 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.500000000000005, "Y": 27.74437564349818 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.400000000000006, "Y": 18.965794903862403 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.300000000000006, "Y": 21.058539751786654 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.200000000000006, "Y": 37.814198368799225 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.100000000000007, "Y": 31.215922756108043 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -8.000000000000007, "Y": 24.90491777484204 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.9000000000000075, "Y": 37.0399759154341 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.800000000000008, "Y": 23.986058255244796 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.700000000000008, "Y": 20.63702948447236 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.6000000000000085, "Y": 17.834350368718596 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.500000000000009, "Y": 30.889289474711006 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.400000000000009, "Y": 25.55914094504301 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.30000000000001, "Y": 38.17677226102148 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.20000000000001, "Y": 23.968277250518753 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.10000000000001, "Y": 28.822417229203452 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -7.000000000000011, "Y": 13.757426417546313 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.900000000000011, "Y": 30.75120251034535 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.800000000000011, "Y": 26.871153315875464 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.700000000000012, "Y": 12.37861745653536 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.600000000000012, "Y": 33.117194179468406 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.500000000000012, "Y": 9.516020271152541 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.400000000000013, "Y": 17.630803136855665 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.300000000000013, "Y": 36.21084742871229 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.2000000000000135, "Y": 14.251021837486869 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.100000000000014, "Y": 20.062215187158685 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -6.000000000000014, "Y": 18.083116343489216 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.900000000000015, "Y": 15.28740704322962 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.800000000000015, "Y": 17.070638420024245 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.700000000000015, "Y": 13.511563137515212 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.600000000000016, "Y": 13.26658019005712 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.500000000000016, "Y": 23.608852195580432 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.400000000000016, "Y": 24.35149718263962 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.300000000000017, "Y": 21.703168581725432 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.200000000000017, "Y": 17.1774381779952 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.100000000000017, "Y": 14.187270317166158 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -5.000000000000018, "Y": 19.76926076295428 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.900000000000018, "Y": 13.718309147013098 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.8000000000000185, "Y": 20.042551878916267 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.700000000000019, "Y": 15.388665012999017 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.600000000000019, "Y": 16.719625210279844 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.5000000000000195, "Y": 12.684268185800466 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.40000000000002, "Y": 20.972001504282858 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.30000000000002, "Y": 26.949964928714074 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.200000000000021, "Y": 11.60909265617898 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.100000000000021, "Y": 13.175012961884832 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -4.000000000000021, "Y": 12.141710305404924 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.9000000000000217, "Y": 31.50777579637058 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.800000000000022, "Y": 10.122434264175412 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.7000000000000224, "Y": 14.394935513223766 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.6000000000000227, "Y": 17.8050754658001 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.500000000000023, "Y": 13.128129937914753 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.4000000000000234, "Y": 10.447725487357562 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.300000000000024, "Y": 2.3216247096763745 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.200000000000024, "Y": -2.3935463334637976 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.1000000000000245, "Y": 19.175108843091987 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.000000000000025, "Y": 17.66358075057135 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.9000000000000252, "Y": 1.1571654749816656 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.8000000000000256, "Y": 16.326339805358813 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.700000000000026, "Y": 9.01821962277669 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.6000000000000263, "Y": 12.504975994167143 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.5000000000000266, "Y": 14.12775355293637 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.400000000000027, "Y": 8.286012562357495 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.3000000000000274, "Y": 6.290269992341997 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.2000000000000277, "Y": 9.138818479389405 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.100000000000028, "Y": 16.526642178010842 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -2.0000000000000284, "Y": 10.581307935901794 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.9000000000000288, "Y": 14.932795437535695 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.8000000000000291, "Y": 4.71114648257332 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.7000000000000295, "Y": 7.416810754656249 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.6000000000000298, "Y": 7.5050755000615075 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.5000000000000302, "Y": 12.042999790643961 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.4000000000000306, "Y": 12.465550257656759 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.300000000000031, "Y": 6.2763293540898575 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.2000000000000313, "Y": 14.799162604951395 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.1000000000000316, "Y": 8.228323269351655 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -1.000000000000032, "Y": 7.924289350621528 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.9000000000000323, "Y": -3.997254163811429 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.8000000000000327, "Y": -1.8524902509807584 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.700000000000033, "Y": 3.426322847759426 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.6000000000000334, "Y": -3.106050261897967 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.5000000000000338, "Y": -4.781485585909262 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.4000000000000341, "Y": 16.84527197689767 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.30000000000003446, "Y": 7.372377811427535 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.20000000000003482, "Y": -2.8116008367068375 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -0.10000000000003517, "Y": 4.007360365629696 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": -3.552713678800501e-14, "Y": 3.540259043365712 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.09999999999996412, "Y": 0.15385328307732515 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.19999999999996376, "Y": 2.9727983467794257 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.2999999999999634, "Y": -0.3615413178577145 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.39999999999996305, "Y": -3.6587266575239252 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.4999999999999627, "Y": -3.4334709451498187 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.5999999999999623, "Y": -2.236111483844569 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.699999999999962, "Y": 8.592489656527107 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.7999999999999616, "Y": 3.818271678583908 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.8999999999999613, "Y": -2.394229774870067 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 0.9999999999999609, "Y": -10.141157923513603 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.0999999999999606, "Y": 0.7542522253411339 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.1999999999999602, "Y": -0.038618187230164525 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.2999999999999599, "Y": -4.726387546002522 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.3999999999999595, "Y": 1.3121290948912852 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.4999999999999591, "Y": -3.478432861044001 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.5999999999999588, "Y": 0.9176670311917343 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.6999999999999584, "Y": -11.732814125277214 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.799999999999958, "Y": -3.91662715975036 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.8999999999999577, "Y": -7.6658409698277366 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 1.9999999999999574, "Y": -3.0064736806234347 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.099999999999957, "Y": -7.633426766279966 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.1999999999999567, "Y": -1.8353625186801565 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.2999999999999563, "Y": -12.962838281711724 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.399999999999956, "Y": -6.874329709778972 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.4999999999999556, "Y": -6.865995937830962 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.5999999999999552, "Y": -7.282490114073971 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.699999999999955, "Y": -10.940058951874157 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.7999999999999545, "Y": -2.8634238348035304 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.899999999999954, "Y": -14.5410594919809 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 2.999999999999954, "Y": -6.903380049422234 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.0999999999999535, "Y": -0.020859891889068116 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.199999999999953, "Y": -17.26560271809112 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.2999999999999527, "Y": -7.852826303734025 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.3999999999999524, "Y": -7.180158292066604 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.499999999999952, "Y": 1.9056213946149931 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.5999999999999517, "Y": -20.565516571186564 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.6999999999999513, "Y": -9.40184128156832 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.799999999999951, "Y": -6.134616264543523 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.8999999999999506, "Y": -1.344224483494365 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 3.9999999999999503, "Y": -15.08419036718375 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.09999999999995, "Y": -5.634321124484247 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.1999999999999496, "Y": -6.399374422093152 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.299999999999949, "Y": -12.635575801687034 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.399999999999949, "Y": -17.97391945574281 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.4999999999999485, "Y": -6.065455879541104 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.599999999999948, "Y": -4.832412280989314 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.699999999999948, "Y": -23.24206254329853 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.799999999999947, "Y": -9.357452307456455 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.899999999999947, "Y": -21.82506319969983 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 4.999999999999947, "Y": -11.843968928601074 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.099999999999946, "Y": -17.08269653889447 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.199999999999946, "Y": -10.738847657227115 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.299999999999946, "Y": -13.587572774398549 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.399999999999945, "Y": -4.339898860449246 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.499999999999945, "Y": -11.825243839048284 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.599999999999945, "Y": -19.100335230738146 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.699999999999944, "Y": -13.909073281212025 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.799999999999944, "Y": -29.523186095150173 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.8999999999999435, "Y": -19.940887518753886 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 5.999999999999943, "Y": -24.137053483958532 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.099999999999945, "Y": -16.177412372433064 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.1999999999999424, "Y": -11.621861497627325 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.29999999999994, "Y": -14.597257000044214 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.399999999999942, "Y": -16.49469138575554 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.499999999999943, "Y": -17.89162526935495 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.599999999999941, "Y": -19.953612663648663 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.699999999999939, "Y": -26.736893263134277 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.79999999999994, "Y": -28.95533737137772 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.899999999999942, "Y": -11.224080374306551 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 6.99999999999994, "Y": -9.879582955928694 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.0999999999999375, "Y": -12.271446635835595 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.199999999999939, "Y": -18.111073939488563 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.29999999999994, "Y": -12.938979400261417 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.399999999999938, "Y": -18.42237381905723 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.499999999999936, "Y": -19.824818093181428 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.5999999999999375, "Y": -18.734945476464684 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.699999999999939, "Y": -30.514553276196345 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.799999999999937, "Y": -18.72577871443827 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.899999999999935, "Y": -22.826908504247566 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 7.999999999999936, "Y": -17.13314378191 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.099999999999937, "Y": -23.706409239116628 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.199999999999935, "Y": -22.3317613559741 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.299999999999933, "Y": -31.81928076819391 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.399999999999935, "Y": -26.620202580420756 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.499999999999936, "Y": -17.747629012694816 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.599999999999934, "Y": -28.82125577870797 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.699999999999932, "Y": -23.810830550294764 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.799999999999933, "Y": -23.161876110781726 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.899999999999935, "Y": -20.966647381889807 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 8.999999999999932, "Y": -25.161472279354456 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.09999999999993, "Y": -32.99039763218319 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.199999999999932, "Y": -29.31726942102719 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.299999999999933, "Y": -21.378083562836963 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.399999999999931, "Y": -23.73444157889014 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.499999999999929, "Y": -20.21159541976074 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.59999999999993, "Y": -25.082521718832254 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.699999999999932, "Y": -22.019587638620745 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.79999999999993, "Y": -29.517670888640144 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.899999999999928, "Y": -33.19577101992945 }, { "Outlier": "\"\\\"N/A\\\"\"", "X": 9.999999999999929, "Y": -35.382569991486605 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "posteriorPredictive :: MonadMeasure m => [Double] -> [Double] -> m [Double]\n", "posteriorPredictive xs ys = do\n", " (slope, intercept, noise) <- regression xs ys\n", " forM xs \\x -> do\n", " let y' = x * slope + intercept\n", " normal y' (sqrt noise)\n", "\n", "\n", "predictive <- head <$> (sampleIOfixed . unweighted . mcmc mcmcConfig $ uncurry posteriorPredictive (unzip regressionSamples))\n", "plot (fmap (second (T.pack . show)) (zip (zip range predictive) (Prelude.repeat \"N/A\")))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear regression with outliers\n", "\n", "Inspired by the tutorials on probabilistic programming language Gen (https://www.gen.dev/tutorials/iterative-inference/tutorial), we'll make the inference problem harder by using the example of a regression with outliers. The idea is that each datapoint $(x,y)$ has $y$ either linearly dependent on $x$, or randomly sampler (an outlier). So the goal of inference is to *jointly* work out what the linear relationship is and which points flout it." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "paramPrior = do\n", " slope <- normal 0 2\n", " intercept <- normal 0 2\n", " noise <- gamma 4 4\n", " prob_outlier <- uniform 0 0.5 \n", " return (slope, intercept, noise, prob_outlier)\n", "\n", "forward (slope, intercept, noise, probOutlier) x = do\n", " isOutlier <- bernoulli probOutlier\n", " let meanParams = if isOutlier\n", " then (0, 20)\n", " else (x*slope + intercept, sqrt noise)\n", " return (meanParams, isOutlier)\n", "\n", "regressionWithOutliersData :: (MonadDistribution m, Traversable t) => t Double -> m (t ((Double, Double), Bool))\n", "regressionWithOutliersData xs = do\n", " params <- paramPrior\n", "\n", " forM xs \\x -> do\n", " ((mu, std), isOutlier) <- forward params x\n", " y <- normal mu std\n", " return ((x, y), isOutlier)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is our model. It describes a process for getting $y$ from $x$. Specifically, you start by drawing values for the slope $s$, bias $b$ and noise $n$. Then for each input $x$, you flip a coin. If it lands one way, you draw a $y$ value from a normal with mean $x*slope + bias$ and std $n$, and otherwise you draw from a centered normal with large variance.\n", "\n", "Given a list of $x$ values, this gives a distribution over lists of $y$ values, from which we can sample:\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"True\"", "X": -10, "Y": 30.14316922289284 }, { "Outlier": "\"True\"", "X": -9.9, "Y": -0.26284958638081857 }, { "Outlier": "\"False\"", "X": -9.8, "Y": 23.786459587439385 }, { "Outlier": "\"True\"", "X": -9.700000000000001, "Y": -68.99193947945726 }, { "Outlier": "\"True\"", "X": -9.600000000000001, "Y": -8.043397861176732 }, { "Outlier": "\"False\"", "X": -9.500000000000002, "Y": 31.37356835946967 }, { "Outlier": "\"True\"", "X": -9.400000000000002, "Y": -23.81048273352878 }, { "Outlier": "\"False\"", "X": -9.300000000000002, "Y": 22.735033981928762 }, { "Outlier": "\"True\"", "X": -9.200000000000003, "Y": 23.375038991852755 }, { "Outlier": "\"False\"", "X": -9.100000000000003, "Y": 27.169089429019078 }, { "Outlier": "\"True\"", "X": -9.000000000000004, "Y": 24.42143345836998 }, { "Outlier": "\"False\"", "X": -8.900000000000004, "Y": 24.425641978269297 }, { "Outlier": "\"False\"", "X": -8.800000000000004, "Y": 30.050736689827513 }, { "Outlier": "\"True\"", "X": -8.700000000000005, "Y": 18.278561094651828 }, { "Outlier": "\"False\"", "X": -8.600000000000005, "Y": 30.770220409890687 }, { "Outlier": "\"True\"", "X": -8.500000000000005, "Y": 6.437669090883507 }, { "Outlier": "\"True\"", "X": -8.400000000000006, "Y": -37.03228429479993 }, { "Outlier": "\"True\"", "X": -8.300000000000006, "Y": 13.11298520672979 }, { "Outlier": "\"False\"", "X": -8.200000000000006, "Y": 23.38808489858207 }, { "Outlier": "\"True\"", "X": -8.100000000000007, "Y": 11.824928463235018 }, { "Outlier": "\"False\"", "X": -8.000000000000007, "Y": 23.442573379452057 }, { "Outlier": "\"True\"", "X": -7.9000000000000075, "Y": 29.057062960826705 }, { "Outlier": "\"False\"", "X": -7.800000000000008, "Y": 22.567508920540522 }, { "Outlier": "\"True\"", "X": -7.700000000000008, "Y": 8.660181503529154 }, { "Outlier": "\"False\"", "X": -7.6000000000000085, "Y": 19.25862039466749 }, { "Outlier": "\"True\"", "X": -7.500000000000009, "Y": 0.3644817992929184 }, { "Outlier": "\"True\"", "X": -7.400000000000009, "Y": -21.224596735432097 }, { "Outlier": "\"True\"", "X": -7.30000000000001, "Y": -5.08262280552811 }, { "Outlier": "\"True\"", "X": -7.20000000000001, "Y": -22.40271677868227 }, { "Outlier": "\"False\"", "X": -7.10000000000001, "Y": 27.315599934341897 }, { "Outlier": "\"True\"", "X": -7.000000000000011, "Y": 40.6554142943223 }, { "Outlier": "\"True\"", "X": -6.900000000000011, "Y": 0.7924834976117429 }, { "Outlier": "\"True\"", "X": -6.800000000000011, "Y": -3.0689825793534142 }, { "Outlier": "\"True\"", "X": -6.700000000000012, "Y": 2.0777625946734073 }, { "Outlier": "\"False\"", "X": -6.600000000000012, "Y": 16.360935345131573 }, { "Outlier": "\"True\"", "X": -6.500000000000012, "Y": 4.46724475614298 }, { "Outlier": "\"True\"", "X": -6.400000000000013, "Y": -1.5461624315036717 }, { "Outlier": "\"True\"", "X": -6.300000000000013, "Y": 24.71362894119821 }, { "Outlier": "\"True\"", "X": -6.2000000000000135, "Y": 42.412216707046596 }, { "Outlier": "\"False\"", "X": -6.100000000000014, "Y": 19.25819017706044 }, { "Outlier": "\"False\"", "X": -6.000000000000014, "Y": 13.924560064643464 }, { "Outlier": "\"False\"", "X": -5.900000000000015, "Y": 16.44450463396404 }, { "Outlier": "\"False\"", "X": -5.800000000000015, "Y": 20.22565144666506 }, { "Outlier": "\"True\"", "X": -5.700000000000015, "Y": 4.130260844964003 }, { "Outlier": "\"False\"", "X": -5.600000000000016, "Y": 13.920685536449982 }, { "Outlier": "\"True\"", "X": -5.500000000000016, "Y": 19.404328857643684 }, { "Outlier": "\"False\"", "X": -5.400000000000016, "Y": 11.591361762658316 }, { "Outlier": "\"False\"", "X": -5.300000000000017, "Y": 21.716931658828592 }, { "Outlier": "\"True\"", "X": -5.200000000000017, "Y": -14.86585011237548 }, { "Outlier": "\"True\"", "X": -5.100000000000017, "Y": -27.7443807834266 }, { "Outlier": "\"False\"", "X": -5.000000000000018, "Y": 17.665401268686168 }, { "Outlier": "\"False\"", "X": -4.900000000000018, "Y": 22.498750208363987 }, { "Outlier": "\"False\"", "X": -4.8000000000000185, "Y": 11.477829247049517 }, { "Outlier": "\"True\"", "X": -4.700000000000019, "Y": -2.236758643252085 }, { "Outlier": "\"True\"", "X": -4.600000000000019, "Y": 25.095015724308034 }, { "Outlier": "\"True\"", "X": -4.5000000000000195, "Y": 17.18991809253406 }, { "Outlier": "\"True\"", "X": -4.40000000000002, "Y": -24.744360068281004 }, { "Outlier": "\"True\"", "X": -4.30000000000002, "Y": -7.931801457689884 }, { "Outlier": "\"False\"", "X": -4.200000000000021, "Y": 10.813897350066041 }, { "Outlier": "\"False\"", "X": -4.100000000000021, "Y": 14.36412464187282 }, { "Outlier": "\"False\"", "X": -4.000000000000021, "Y": 10.048413936752135 }, { "Outlier": "\"True\"", "X": -3.9000000000000217, "Y": -7.806116027860427 }, { "Outlier": "\"False\"", "X": -3.800000000000022, "Y": 15.513453565319507 }, { "Outlier": "\"False\"", "X": -3.7000000000000224, "Y": 13.87073636869106 }, { "Outlier": "\"False\"", "X": -3.6000000000000227, "Y": 11.46674012488362 }, { "Outlier": "\"False\"", "X": -3.500000000000023, "Y": 16.059440743337102 }, { "Outlier": "\"False\"", "X": -3.4000000000000234, "Y": 15.293100956976737 }, { "Outlier": "\"False\"", "X": -3.300000000000024, "Y": 21.84763262761431 }, { "Outlier": "\"True\"", "X": -3.200000000000024, "Y": -0.36221408177898484 }, { "Outlier": "\"False\"", "X": -3.1000000000000245, "Y": 15.693632661329104 }, { "Outlier": "\"True\"", "X": -3.000000000000025, "Y": -6.3470305289160835 }, { "Outlier": "\"True\"", "X": -2.9000000000000252, "Y": 10.805876165134304 }, { "Outlier": "\"True\"", "X": -2.8000000000000256, "Y": 43.073138049536766 }, { "Outlier": "\"False\"", "X": -2.700000000000026, "Y": 16.743971720184916 }, { "Outlier": "\"True\"", "X": -2.6000000000000263, "Y": 20.17816530884579 }, { "Outlier": "\"False\"", "X": -2.5000000000000266, "Y": 10.185237062991554 }, { "Outlier": "\"True\"", "X": -2.400000000000027, "Y": 24.67140741709269 }, { "Outlier": "\"True\"", "X": -2.3000000000000274, "Y": 3.7143759689601357 }, { "Outlier": "\"False\"", "X": -2.2000000000000277, "Y": 6.083699392485441 }, { "Outlier": "\"False\"", "X": -2.100000000000028, "Y": 6.832165636012283 }, { "Outlier": "\"True\"", "X": -2.0000000000000284, "Y": 14.405714990070868 }, { "Outlier": "\"False\"", "X": -1.9000000000000288, "Y": 11.188926560244449 }, { "Outlier": "\"True\"", "X": -1.8000000000000291, "Y": 25.768223310681986 }, { "Outlier": "\"True\"", "X": -1.7000000000000295, "Y": -37.59090989382119 }, { "Outlier": "\"True\"", "X": -1.6000000000000298, "Y": 23.64144553913516 }, { "Outlier": "\"True\"", "X": -1.5000000000000302, "Y": -8.576305123038889 }, { "Outlier": "\"False\"", "X": -1.4000000000000306, "Y": -3.413990873751257 }, { "Outlier": "\"False\"", "X": -1.300000000000031, "Y": 5.764515053668303 }, { "Outlier": "\"True\"", "X": -1.2000000000000313, "Y": -4.881619552621354 }, { "Outlier": "\"False\"", "X": -1.1000000000000316, "Y": 5.6052889213735035 }, { "Outlier": "\"False\"", "X": -1.000000000000032, "Y": 4.303828323139834 }, { "Outlier": "\"True\"", "X": -0.9000000000000323, "Y": -31.588506775217645 }, { "Outlier": "\"True\"", "X": -0.8000000000000327, "Y": -4.276114964288603 }, { "Outlier": "\"False\"", "X": -0.700000000000033, "Y": 4.535423923547052 }, { "Outlier": "\"True\"", "X": -0.6000000000000334, "Y": 24.61942267029316 }, { "Outlier": "\"True\"", "X": -0.5000000000000338, "Y": -17.830933767956402 }, { "Outlier": "\"False\"", "X": -0.4000000000000341, "Y": -1.7450293908309056 }, { "Outlier": "\"False\"", "X": -0.30000000000003446, "Y": 3.3108205257662364 }, { "Outlier": "\"False\"", "X": -0.20000000000003482, "Y": 0.7651432133855662 }, { "Outlier": "\"True\"", "X": -0.10000000000003517, "Y": -11.881720947448017 }, { "Outlier": "\"True\"", "X": -3.552713678800501e-14, "Y": 15.275164463456829 }, { "Outlier": "\"False\"", "X": 0.09999999999996412, "Y": 2.6322100577194067 }, { "Outlier": "\"False\"", "X": 0.19999999999996376, "Y": 4.898150793977379 }, { "Outlier": "\"True\"", "X": 0.2999999999999634, "Y": 17.70645751790601 }, { "Outlier": "\"False\"", "X": 0.39999999999996305, "Y": 8.552044532855996 }, { "Outlier": "\"True\"", "X": 0.4999999999999627, "Y": 21.884620845377007 }, { "Outlier": "\"True\"", "X": 0.5999999999999623, "Y": 9.545267231818897 }, { "Outlier": "\"False\"", "X": 0.699999999999962, "Y": 2.2310373255180265 }, { "Outlier": "\"False\"", "X": 0.7999999999999616, "Y": 6.8744523688958274 }, { "Outlier": "\"False\"", "X": 0.8999999999999613, "Y": -1.783436689012548 }, { "Outlier": "\"False\"", "X": 0.9999999999999609, "Y": 4.47640686579331 }, { "Outlier": "\"False\"", "X": 1.0999999999999606, "Y": -2.229919825404161 }, { "Outlier": "\"True\"", "X": 1.1999999999999602, "Y": -1.6888315159113967 }, { "Outlier": "\"True\"", "X": 1.2999999999999599, "Y": -26.34560770532998 }, { "Outlier": "\"True\"", "X": 1.3999999999999595, "Y": 7.322340575226373 }, { "Outlier": "\"True\"", "X": 1.4999999999999591, "Y": 1.9729072168157065 }, { "Outlier": "\"False\"", "X": 1.5999999999999588, "Y": 1.513234082565413 }, { "Outlier": "\"True\"", "X": 1.6999999999999584, "Y": 13.227480313783765 }, { "Outlier": "\"True\"", "X": 1.799999999999958, "Y": 12.226379085662112 }, { "Outlier": "\"False\"", "X": 1.8999999999999577, "Y": 2.850573235675812 }, { "Outlier": "\"False\"", "X": 1.9999999999999574, "Y": -6.392989857152452 }, { "Outlier": "\"False\"", "X": 2.099999999999957, "Y": -5.3213737430909305 }, { "Outlier": "\"True\"", "X": 2.1999999999999567, "Y": 0.41427397930309406 }, { "Outlier": "\"False\"", "X": 2.2999999999999563, "Y": -5.750652275493272 }, { "Outlier": "\"False\"", "X": 2.399999999999956, "Y": 4.340182272052314 }, { "Outlier": "\"True\"", "X": 2.4999999999999556, "Y": 4.7901246139126625 }, { "Outlier": "\"False\"", "X": 2.5999999999999552, "Y": -11.012677316789915 }, { "Outlier": "\"False\"", "X": 2.699999999999955, "Y": -2.5092360333006916 }, { "Outlier": "\"False\"", "X": 2.7999999999999545, "Y": -9.854893391572105 }, { "Outlier": "\"True\"", "X": 2.899999999999954, "Y": -28.61214419700161 }, { "Outlier": "\"True\"", "X": 2.999999999999954, "Y": 8.248524918035386 }, { "Outlier": "\"True\"", "X": 3.0999999999999535, "Y": -3.622542781911439 }, { "Outlier": "\"True\"", "X": 3.199999999999953, "Y": 4.494708619120168 }, { "Outlier": "\"True\"", "X": 3.2999999999999527, "Y": 3.860145108336754 }, { "Outlier": "\"False\"", "X": 3.3999999999999524, "Y": -4.0035470084411795 }, { "Outlier": "\"False\"", "X": 3.499999999999952, "Y": -11.04990869937726 }, { "Outlier": "\"True\"", "X": 3.5999999999999517, "Y": -35.32932042223501 }, { "Outlier": "\"True\"", "X": 3.6999999999999513, "Y": 11.866841248128683 }, { "Outlier": "\"False\"", "X": 3.799999999999951, "Y": -10.162451780099051 }, { "Outlier": "\"True\"", "X": 3.8999999999999506, "Y": 9.661220540277904 }, { "Outlier": "\"False\"", "X": 3.9999999999999503, "Y": -10.73292544867606 }, { "Outlier": "\"False\"", "X": 4.09999999999995, "Y": -18.550653995894045 }, { "Outlier": "\"False\"", "X": 4.1999999999999496, "Y": -14.52608532847757 }, { "Outlier": "\"True\"", "X": 4.299999999999949, "Y": 21.41545900080303 }, { "Outlier": "\"True\"", "X": 4.399999999999949, "Y": 41.510023167064915 }, { "Outlier": "\"False\"", "X": 4.4999999999999485, "Y": -10.944888800178278 }, { "Outlier": "\"True\"", "X": 4.599999999999948, "Y": -17.678231065336824 }, { "Outlier": "\"True\"", "X": 4.699999999999948, "Y": -18.253167364985977 }, { "Outlier": "\"False\"", "X": 4.799999999999947, "Y": -10.446879435653443 }, { "Outlier": "\"True\"", "X": 4.899999999999947, "Y": -4.690038562027151 }, { "Outlier": "\"False\"", "X": 4.999999999999947, "Y": -5.757921683446889 }, { "Outlier": "\"True\"", "X": 5.099999999999946, "Y": 8.693733167616339 }, { "Outlier": "\"False\"", "X": 5.199999999999946, "Y": -12.216077387002828 }, { "Outlier": "\"True\"", "X": 5.299999999999946, "Y": 1.6720822524528287 }, { "Outlier": "\"False\"", "X": 5.399999999999945, "Y": -14.464965977547303 }, { "Outlier": "\"False\"", "X": 5.499999999999945, "Y": -12.869809983276069 }, { "Outlier": "\"True\"", "X": 5.599999999999945, "Y": 34.62535634020814 }, { "Outlier": "\"True\"", "X": 5.699999999999944, "Y": 39.28048699796905 }, { "Outlier": "\"True\"", "X": 5.799999999999944, "Y": -18.522573806115407 }, { "Outlier": "\"True\"", "X": 5.8999999999999435, "Y": 2.8254226810055205 }, { "Outlier": "\"True\"", "X": 5.999999999999943, "Y": -7.598929518634544 }, { "Outlier": "\"False\"", "X": 6.099999999999945, "Y": -17.140071943688035 }, { "Outlier": "\"False\"", "X": 6.1999999999999424, "Y": -21.78017043908215 }, { "Outlier": "\"True\"", "X": 6.29999999999994, "Y": 8.34770023404953 }, { "Outlier": "\"True\"", "X": 6.399999999999942, "Y": 2.6439493039811115 }, { "Outlier": "\"False\"", "X": 6.499999999999943, "Y": -20.126788491353174 }, { "Outlier": "\"True\"", "X": 6.599999999999941, "Y": -15.051462287577962 }, { "Outlier": "\"True\"", "X": 6.699999999999939, "Y": 4.3207686658048425 }, { "Outlier": "\"True\"", "X": 6.79999999999994, "Y": -8.60953644756132 }, { "Outlier": "\"False\"", "X": 6.899999999999942, "Y": -9.408044612231928 }, { "Outlier": "\"False\"", "X": 6.99999999999994, "Y": -19.056725876851928 }, { "Outlier": "\"False\"", "X": 7.0999999999999375, "Y": -20.094495666483716 }, { "Outlier": "\"True\"", "X": 7.199999999999939, "Y": -0.40330975452223106 }, { "Outlier": "\"True\"", "X": 7.29999999999994, "Y": -48.28017130034681 }, { "Outlier": "\"False\"", "X": 7.399999999999938, "Y": -19.705987406023656 }, { "Outlier": "\"True\"", "X": 7.499999999999936, "Y": -27.496292304878267 }, { "Outlier": "\"False\"", "X": 7.5999999999999375, "Y": -18.884286907276728 }, { "Outlier": "\"False\"", "X": 7.699999999999939, "Y": -22.442972670784375 }, { "Outlier": "\"True\"", "X": 7.799999999999937, "Y": 6.610475555129508 }, { "Outlier": "\"False\"", "X": 7.899999999999935, "Y": -28.406701583023757 }, { "Outlier": "\"True\"", "X": 7.999999999999936, "Y": 10.913894309977062 }, { "Outlier": "\"True\"", "X": 8.099999999999937, "Y": 22.467791024883127 }, { "Outlier": "\"True\"", "X": 8.199999999999935, "Y": 12.316122841847735 }, { "Outlier": "\"False\"", "X": 8.299999999999933, "Y": -18.61774741315339 }, { "Outlier": "\"False\"", "X": 8.399999999999935, "Y": -29.49760626562651 }, { "Outlier": "\"True\"", "X": 8.499999999999936, "Y": -25.738038864954476 }, { "Outlier": "\"False\"", "X": 8.599999999999934, "Y": -17.85703560203325 }, { "Outlier": "\"True\"", "X": 8.699999999999932, "Y": -9.172720756633 }, { "Outlier": "\"False\"", "X": 8.799999999999933, "Y": -20.500224282886077 }, { "Outlier": "\"False\"", "X": 8.899999999999935, "Y": -27.955994289017063 }, { "Outlier": "\"False\"", "X": 8.999999999999932, "Y": -23.309509588194302 }, { "Outlier": "\"True\"", "X": 9.09999999999993, "Y": 31.120217815149882 }, { "Outlier": "\"False\"", "X": 9.199999999999932, "Y": -24.086143061403874 }, { "Outlier": "\"False\"", "X": 9.299999999999933, "Y": -21.7596596113579 }, { "Outlier": "\"True\"", "X": 9.399999999999931, "Y": 1.3453810777338207 }, { "Outlier": "\"True\"", "X": 9.499999999999929, "Y": 30.106740007028108 }, { "Outlier": "\"True\"", "X": 9.59999999999993, "Y": -8.245762291377288 }, { "Outlier": "\"False\"", "X": 9.699999999999932, "Y": -29.313469546701562 }, { "Outlier": "\"False\"", "X": 9.79999999999993, "Y": -27.209848874368774 }, { "Outlier": "\"True\"", "X": 9.899999999999928, "Y": 7.612568009425738 }, { "Outlier": "\"True\"", "X": 9.999999999999929, "Y": -13.529703374498748 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "range = [-10,-9.9..10] :: [Double]\n", "samples <- sampleIOfixed $ regressionWithOutliersData range\n", "plot (fmap (second (T.pack . show)) samples)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "regressionWithOutliers :: (MonadMeasure m) =>\n", " [Double] -> [Double] -> m ((Double, Double, Double, Double), [Bool])\n", "regressionWithOutliers xs ys = do\n", " params <- paramPrior\n", " \n", " outliers <- forM (zip xs ys) \\(x, y) -> do\n", " ((mu, std), isOutlier) <- forward params x\n", " factor $ normalPdf mu std y\n", " return isOutlier\n", " return (params, outliers)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "mhRuns <- sampleIOfixed . unweighted . mcmc mcmcConfig $ regressionWithOutliers range (snd . fst <$> samples)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "outlierProb s = (\\(x, y) -> x / (x+y) ) <$> \n", " foldr \n", " (\\(_,lb) li -> [ if b then (num1+1, num2) else (num1,num2+1) | (b,(num1, num2)) <- zip lb li]) \n", " (Prelude.repeat (0,0)) s\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": 0.7472527472527473, "X": -10, "Y": 30.14316922289284 }, { "Outlier": 0.6463536463536463, "X": -9.9, "Y": -0.26284958638081857 }, { "Outlier": 0.8171828171828172, "X": -9.8, "Y": 23.786459587439385 }, { "Outlier": 1, "X": -9.700000000000001, "Y": -68.99193947945726 }, { "Outlier": 0.7112887112887113, "X": -9.600000000000001, "Y": -8.043397861176732 }, { "Outlier": 0, "X": -9.500000000000002, "Y": 31.37356835946967 }, { "Outlier": 0.058941058941058944, "X": -9.400000000000002, "Y": -23.81048273352878 }, { "Outlier": 0.34665334665334663, "X": -9.300000000000002, "Y": 22.735033981928762 }, { "Outlier": 0.12687312687312688, "X": -9.200000000000003, "Y": 23.375038991852755 }, { "Outlier": 0, "X": -9.100000000000003, "Y": 27.169089429019078 }, { "Outlier": 0, "X": -9.000000000000004, "Y": 24.42143345836998 }, { "Outlier": 0, "X": -8.900000000000004, "Y": 24.425641978269297 }, { "Outlier": 0.4355644355644356, "X": -8.800000000000004, "Y": 30.050736689827513 }, { "Outlier": 0.41858141858141856, "X": -8.700000000000005, "Y": 18.278561094651828 }, { "Outlier": 0, "X": -8.600000000000005, "Y": 30.770220409890687 }, { "Outlier": 1, "X": -8.500000000000005, "Y": 6.437669090883507 }, { "Outlier": 0.5364635364635365, "X": -8.400000000000006, "Y": -37.03228429479993 }, { "Outlier": 0.8561438561438561, "X": -8.300000000000006, "Y": 13.11298520672979 }, { "Outlier": 0.04595404595404595, "X": -8.200000000000006, "Y": 23.38808489858207 }, { "Outlier": 0, "X": -8.100000000000007, "Y": 11.824928463235018 }, { "Outlier": 0, "X": -8.000000000000007, "Y": 23.442573379452057 }, { "Outlier": 0, "X": -7.9000000000000075, "Y": 29.057062960826705 }, { "Outlier": 0.0969030969030969, "X": -7.800000000000008, "Y": 22.567508920540522 }, { "Outlier": 0.7472527472527473, "X": -7.700000000000008, "Y": 8.660181503529154 }, { "Outlier": 0, "X": -7.6000000000000085, "Y": 19.25862039466749 }, { "Outlier": 1, "X": -7.500000000000009, "Y": 0.3644817992929184 }, { "Outlier": 1, "X": -7.400000000000009, "Y": -21.224596735432097 }, { "Outlier": 0.45854145854145856, "X": -7.30000000000001, "Y": -5.08262280552811 }, { "Outlier": 0.44755244755244755, "X": -7.20000000000001, "Y": -22.40271677868227 }, { "Outlier": 0.19480519480519481, "X": -7.10000000000001, "Y": 27.315599934341897 }, { "Outlier": 0, "X": -7.000000000000011, "Y": 40.6554142943223 }, { "Outlier": 0, "X": -6.900000000000011, "Y": 0.7924834976117429 }, { "Outlier": 0.37462537462537465, "X": -6.800000000000011, "Y": -3.0689825793534142 }, { "Outlier": 0, "X": -6.700000000000012, "Y": 2.0777625946734073 }, { "Outlier": 0.6943056943056943, "X": -6.600000000000012, "Y": 16.360935345131573 }, { "Outlier": 0.24075924075924077, "X": -6.500000000000012, "Y": 4.46724475614298 }, { "Outlier": 1, "X": -6.400000000000013, "Y": -1.5461624315036717 }, { "Outlier": 0.24075924075924077, "X": -6.300000000000013, "Y": 24.71362894119821 }, { "Outlier": 0.5194805194805194, "X": -6.2000000000000135, "Y": 42.412216707046596 }, { "Outlier": 0.4955044955044955, "X": -6.100000000000014, "Y": 19.25819017706044 }, { "Outlier": 0.2597402597402597, "X": -6.000000000000014, "Y": 13.924560064643464 }, { "Outlier": 0.16383616383616384, "X": -5.900000000000015, "Y": 16.44450463396404 }, { "Outlier": 0, "X": -5.800000000000015, "Y": 20.22565144666506 }, { "Outlier": 0.17782217782217782, "X": -5.700000000000015, "Y": 4.130260844964003 }, { "Outlier": 0, "X": -5.600000000000016, "Y": 13.920685536449982 }, { "Outlier": 0, "X": -5.500000000000016, "Y": 19.404328857643684 }, { "Outlier": 0.03496503496503497, "X": -5.400000000000016, "Y": 11.591361762658316 }, { "Outlier": 0.06493506493506493, "X": -5.300000000000017, "Y": 21.716931658828592 }, { "Outlier": 0, "X": -5.200000000000017, "Y": -14.86585011237548 }, { "Outlier": 0.995004995004995, "X": -5.100000000000017, "Y": -27.7443807834266 }, { "Outlier": 0, "X": -5.000000000000018, "Y": 17.665401268686168 }, { "Outlier": 0.09290709290709291, "X": -4.900000000000018, "Y": 22.498750208363987 }, { "Outlier": 0, "X": -4.8000000000000185, "Y": 11.477829247049517 }, { "Outlier": 0, "X": -4.700000000000019, "Y": -2.236758643252085 }, { "Outlier": 0.2057942057942058, "X": -4.600000000000019, "Y": 25.095015724308034 }, { "Outlier": 0.18081918081918083, "X": -4.5000000000000195, "Y": 17.18991809253406 }, { "Outlier": 1, "X": -4.40000000000002, "Y": -24.744360068281004 }, { "Outlier": 0, "X": -4.30000000000002, "Y": -7.931801457689884 }, { "Outlier": 0.7212787212787213, "X": -4.200000000000021, "Y": 10.813897350066041 }, { "Outlier": 0.14985014985014986, "X": -4.100000000000021, "Y": 14.36412464187282 }, { "Outlier": 0, "X": -4.000000000000021, "Y": 10.048413936752135 }, { "Outlier": 0.7472527472527473, "X": -3.9000000000000217, "Y": -7.806116027860427 }, { "Outlier": 0, "X": -3.800000000000022, "Y": 15.513453565319507 }, { "Outlier": 0, "X": -3.7000000000000224, "Y": 13.87073636869106 }, { "Outlier": 0.2867132867132867, "X": -3.6000000000000227, "Y": 11.46674012488362 }, { "Outlier": 0.03696303696303696, "X": -3.500000000000023, "Y": 16.059440743337102 }, { "Outlier": 0.15184815184815184, "X": -3.4000000000000234, "Y": 15.293100956976737 }, { "Outlier": 0.2977022977022977, "X": -3.300000000000024, "Y": 21.84763262761431 }, { "Outlier": 0, "X": -3.200000000000024, "Y": -0.36221408177898484 }, { "Outlier": 0, "X": -3.1000000000000245, "Y": 15.693632661329104 }, { "Outlier": 1, "X": -3.000000000000025, "Y": -6.3470305289160835 }, { "Outlier": 0.1968031968031968, "X": -2.9000000000000252, "Y": 10.805876165134304 }, { "Outlier": 0, "X": -2.8000000000000256, "Y": 43.073138049536766 }, { "Outlier": 0.14985014985014986, "X": -2.700000000000026, "Y": 16.743971720184916 }, { "Outlier": 0.17482517482517482, "X": -2.6000000000000263, "Y": 20.17816530884579 }, { "Outlier": 0, "X": -2.5000000000000266, "Y": 10.185237062991554 }, { "Outlier": 0.3336663336663337, "X": -2.400000000000027, "Y": 24.67140741709269 }, { "Outlier": 0, "X": -2.3000000000000274, "Y": 3.7143759689601357 }, { "Outlier": 0, "X": -2.2000000000000277, "Y": 6.083699392485441 }, { "Outlier": 0.17082917082917082, "X": -2.100000000000028, "Y": 6.832165636012283 }, { "Outlier": 0.2987012987012987, "X": -2.0000000000000284, "Y": 14.405714990070868 }, { "Outlier": 0.16283716283716285, "X": -1.9000000000000288, "Y": 11.188926560244449 }, { "Outlier": 1, "X": -1.8000000000000291, "Y": 25.768223310681986 }, { "Outlier": 0.6873126873126874, "X": -1.7000000000000295, "Y": -37.59090989382119 }, { "Outlier": 0, "X": -1.6000000000000298, "Y": 23.64144553913516 }, { "Outlier": 0, "X": -1.5000000000000302, "Y": -8.576305123038889 }, { "Outlier": 0.34765234765234765, "X": -1.4000000000000306, "Y": -3.413990873751257 }, { "Outlier": 0.23476523476523475, "X": -1.300000000000031, "Y": 5.764515053668303 }, { "Outlier": 0, "X": -1.2000000000000313, "Y": -4.881619552621354 }, { "Outlier": 0, "X": -1.1000000000000316, "Y": 5.6052889213735035 }, { "Outlier": 0.5154845154845155, "X": -1.000000000000032, "Y": 4.303828323139834 }, { "Outlier": 0.20679320679320679, "X": -0.9000000000000323, "Y": -31.588506775217645 }, { "Outlier": 0, "X": -0.8000000000000327, "Y": -4.276114964288603 }, { "Outlier": 0.15584415584415584, "X": -0.700000000000033, "Y": 4.535423923547052 }, { "Outlier": 0.39760239760239763, "X": -0.6000000000000334, "Y": 24.61942267029316 }, { "Outlier": 0.5614385614385614, "X": -0.5000000000000338, "Y": -17.830933767956402 }, { "Outlier": 0.1798201798201798, "X": -0.4000000000000341, "Y": -1.7450293908309056 }, { "Outlier": 0.11388611388611389, "X": -0.30000000000003446, "Y": 3.3108205257662364 }, { "Outlier": 0.6753246753246753, "X": -0.20000000000003482, "Y": 0.7651432133855662 }, { "Outlier": 0, "X": -0.10000000000003517, "Y": -11.881720947448017 }, { "Outlier": 0.6413586413586414, "X": -3.552713678800501e-14, "Y": 15.275164463456829 }, { "Outlier": 0, "X": 0.09999999999996412, "Y": 2.6322100577194067 }, { "Outlier": 0, "X": 0.19999999999996376, "Y": 4.898150793977379 }, { "Outlier": 0.7932067932067932, "X": 0.2999999999999634, "Y": 17.70645751790601 }, { "Outlier": 0.22077922077922077, "X": 0.39999999999996305, "Y": 8.552044532855996 }, { "Outlier": 0.9300699300699301, "X": 0.4999999999999627, "Y": 21.884620845377007 }, { "Outlier": 0, "X": 0.5999999999999623, "Y": 9.545267231818897 }, { "Outlier": 0, "X": 0.699999999999962, "Y": 2.2310373255180265 }, { "Outlier": 0, "X": 0.7999999999999616, "Y": 6.8744523688958274 }, { "Outlier": 0, "X": 0.8999999999999613, "Y": -1.783436689012548 }, { "Outlier": 0.5304695304695305, "X": 0.9999999999999609, "Y": 4.47640686579331 }, { "Outlier": 0, "X": 1.0999999999999606, "Y": -2.229919825404161 }, { "Outlier": 0.04995004995004995, "X": 1.1999999999999602, "Y": -1.6888315159113967 }, { "Outlier": 0, "X": 1.2999999999999599, "Y": -26.34560770532998 }, { "Outlier": 0.07192807192807193, "X": 1.3999999999999595, "Y": 7.322340575226373 }, { "Outlier": 0.6753246753246753, "X": 1.4999999999999591, "Y": 1.9729072168157065 }, { "Outlier": 0, "X": 1.5999999999999588, "Y": 1.513234082565413 }, { "Outlier": 0.20279720279720279, "X": 1.6999999999999584, "Y": 13.227480313783765 }, { "Outlier": 1, "X": 1.799999999999958, "Y": 12.226379085662112 }, { "Outlier": 0, "X": 1.8999999999999577, "Y": 2.850573235675812 }, { "Outlier": 0.11288711288711288, "X": 1.9999999999999574, "Y": -6.392989857152452 }, { "Outlier": 0, "X": 2.099999999999957, "Y": -5.3213737430909305 }, { "Outlier": 0, "X": 2.1999999999999567, "Y": 0.41427397930309406 }, { "Outlier": 0.13586413586413587, "X": 2.2999999999999563, "Y": -5.750652275493272 }, { "Outlier": 0, "X": 2.399999999999956, "Y": 4.340182272052314 }, { "Outlier": 0.5794205794205795, "X": 2.4999999999999556, "Y": 4.7901246139126625 }, { "Outlier": 0, "X": 2.5999999999999552, "Y": -11.012677316789915 }, { "Outlier": 0.13686313686313686, "X": 2.699999999999955, "Y": -2.5092360333006916 }, { "Outlier": 0.38461538461538464, "X": 2.7999999999999545, "Y": -9.854893391572105 }, { "Outlier": 1, "X": 2.899999999999954, "Y": -28.61214419700161 }, { "Outlier": 0.10589410589410589, "X": 2.999999999999954, "Y": 8.248524918035386 }, { "Outlier": 0, "X": 3.0999999999999535, "Y": -3.622542781911439 }, { "Outlier": 0.5264735264735265, "X": 3.199999999999953, "Y": 4.494708619120168 }, { "Outlier": 0, "X": 3.2999999999999527, "Y": 3.860145108336754 }, { "Outlier": 0.07292707292707293, "X": 3.3999999999999524, "Y": -4.0035470084411795 }, { "Outlier": 0, "X": 3.499999999999952, "Y": -11.04990869937726 }, { "Outlier": 0, "X": 3.5999999999999517, "Y": -35.32932042223501 }, { "Outlier": 0.4405594405594406, "X": 3.6999999999999513, "Y": 11.866841248128683 }, { "Outlier": 0, "X": 3.799999999999951, "Y": -10.162451780099051 }, { "Outlier": 0.981018981018981, "X": 3.8999999999999506, "Y": 9.661220540277904 }, { "Outlier": 0.45354645354645357, "X": 3.9999999999999503, "Y": -10.73292544867606 }, { "Outlier": 0.03896103896103896, "X": 4.09999999999995, "Y": -18.550653995894045 }, { "Outlier": 0.04795204795204795, "X": 4.1999999999999496, "Y": -14.52608532847757 }, { "Outlier": 0.6403596403596403, "X": 4.299999999999949, "Y": 21.41545900080303 }, { "Outlier": 0, "X": 4.399999999999949, "Y": 41.510023167064915 }, { "Outlier": 0, "X": 4.4999999999999485, "Y": -10.944888800178278 }, { "Outlier": 0.1088911088911089, "X": 4.599999999999948, "Y": -17.678231065336824 }, { "Outlier": 0, "X": 4.699999999999948, "Y": -18.253167364985977 }, { "Outlier": 0, "X": 4.799999999999947, "Y": -10.446879435653443 }, { "Outlier": 0, "X": 4.899999999999947, "Y": -4.690038562027151 }, { "Outlier": 0.11688311688311688, "X": 4.999999999999947, "Y": -5.757921683446889 }, { "Outlier": 1, "X": 5.099999999999946, "Y": 8.693733167616339 }, { "Outlier": 0.13286713286713286, "X": 5.199999999999946, "Y": -12.216077387002828 }, { "Outlier": 0.8861138861138861, "X": 5.299999999999946, "Y": 1.6720822524528287 }, { "Outlier": 0, "X": 5.399999999999945, "Y": -14.464965977547303 }, { "Outlier": 0.1878121878121878, "X": 5.499999999999945, "Y": -12.869809983276069 }, { "Outlier": 0.7472527472527473, "X": 5.599999999999945, "Y": 34.62535634020814 }, { "Outlier": 0.18081918081918083, "X": 5.699999999999944, "Y": 39.28048699796905 }, { "Outlier": 0, "X": 5.799999999999944, "Y": -18.522573806115407 }, { "Outlier": 0.04395604395604396, "X": 5.8999999999999435, "Y": 2.8254226810055205 }, { "Outlier": 0.08191808191808192, "X": 5.999999999999943, "Y": -7.598929518634544 }, { "Outlier": 0, "X": 6.099999999999945, "Y": -17.140071943688035 }, { "Outlier": 0, "X": 6.1999999999999424, "Y": -21.78017043908215 }, { "Outlier": 0.13986013986013987, "X": 6.29999999999994, "Y": 8.34770023404953 }, { "Outlier": 1, "X": 6.399999999999942, "Y": 2.6439493039811115 }, { "Outlier": 0, "X": 6.499999999999943, "Y": -20.126788491353174 }, { "Outlier": 0.1108891108891109, "X": 6.599999999999941, "Y": -15.051462287577962 }, { "Outlier": 0.3386613386613387, "X": 6.699999999999939, "Y": 4.3207686658048425 }, { "Outlier": 0.6513486513486514, "X": 6.79999999999994, "Y": -8.60953644756132 }, { "Outlier": 0, "X": 6.899999999999942, "Y": -9.408044612231928 }, { "Outlier": 0, "X": 6.99999999999994, "Y": -19.056725876851928 }, { "Outlier": 0.06493506493506493, "X": 7.0999999999999375, "Y": -20.094495666483716 }, { "Outlier": 0.24075924075924077, "X": 7.199999999999939, "Y": -0.40330975452223106 }, { "Outlier": 0, "X": 7.29999999999994, "Y": -48.28017130034681 }, { "Outlier": 0, "X": 7.399999999999938, "Y": -19.705987406023656 }, { "Outlier": 0.38461538461538464, "X": 7.499999999999936, "Y": -27.496292304878267 }, { "Outlier": 0.25774225774225773, "X": 7.5999999999999375, "Y": -18.884286907276728 }, { "Outlier": 0.06493506493506493, "X": 7.699999999999939, "Y": -22.442972670784375 }, { "Outlier": 1, "X": 7.799999999999937, "Y": 6.610475555129508 }, { "Outlier": 0, "X": 7.899999999999935, "Y": -28.406701583023757 }, { "Outlier": 0.5174825174825175, "X": 7.999999999999936, "Y": 10.913894309977062 }, { "Outlier": 0.15884115884115885, "X": 8.099999999999937, "Y": 22.467791024883127 }, { "Outlier": 0.26073926073926074, "X": 8.199999999999935, "Y": 12.316122841847735 }, { "Outlier": 0, "X": 8.299999999999933, "Y": -18.61774741315339 }, { "Outlier": 0, "X": 8.399999999999935, "Y": -29.49760626562651 }, { "Outlier": 0.030969030969030968, "X": 8.499999999999936, "Y": -25.738038864954476 }, { "Outlier": 0.08691308691308691, "X": 8.599999999999934, "Y": -17.85703560203325 }, { "Outlier": 0.4325674325674326, "X": 8.699999999999932, "Y": -9.172720756633 }, { "Outlier": 0.3596403596403596, "X": 8.799999999999933, "Y": -20.500224282886077 }, { "Outlier": 0, "X": 8.899999999999935, "Y": -27.955994289017063 }, { "Outlier": 0, "X": 8.999999999999932, "Y": -23.309509588194302 }, { "Outlier": 0.3966033966033966, "X": 9.09999999999993, "Y": 31.120217815149882 }, { "Outlier": 0, "X": 9.199999999999932, "Y": -24.086143061403874 }, { "Outlier": 0, "X": 9.299999999999933, "Y": -21.7596596113579 }, { "Outlier": 0.3856143856143856, "X": 9.399999999999931, "Y": 1.3453810777338207 }, { "Outlier": 0.2817182817182817, "X": 9.499999999999929, "Y": 30.106740007028108 }, { "Outlier": 0.7592407592407593, "X": 9.59999999999993, "Y": -8.245762291377288 }, { "Outlier": 0, "X": 9.699999999999932, "Y": -29.313469546701562 }, { "Outlier": 0.23276723276723277, "X": 9.79999999999993, "Y": -27.209848874368774 }, { "Outlier": 0.6093906093906094, "X": 9.899999999999928, "Y": 7.612568009425738 }, { "Outlier": 0.9590409590409591, "X": 9.999999999999929, "Y": -13.529703374498748 } ] }, "encoding": { "color": { "field": "Outlier", "scale": { "scheme": "viridis" }, "type": "quantitative" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot $ take 5000 (zip (fst <$> samples) (outlierProb mhRuns))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Running MCMC gives us a list of samples. The results make sense: points that are very near the line are probably not outliers and ones very far are. \n", "\n", "It would be nice to make our approach more sample efficient though. The `SingleSiteMH` proposal is very naive. Two ways to improve on this would be to use customizable proposals, as in `Gen`, or to use Hamiltonian Monte Carlo as in `Stan`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: notebooks/tutorials/SMC.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ ":e ImportQualifiedPost\n", ":e FlexibleContexts\n", ":e BlockArguments\n", ":e TupleSections\n", ":e FlexibleContexts\n", ":e OverloadedStrings\n", ":e LambdaCase\n", ":e PackageImports\n", "\n", "\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Enumerator\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Sampler.Strict\n", "import Control.Monad.Bayes.Density.Free\n", "import Control.Monad.Bayes.Population\n", "import Control.Monad.Bayes.Sequential.Coroutine\n", "import Control.Monad.Bayes.Inference.SMC\n", "\n", "import qualified Graphics.Vega.VegaLite as VL\n", "import IHaskell.Display.Hvega (vlShow)\n", "import Control.Applicative\n", "import qualified Data.Text as T\n", "import Pipes (Producer, (>->))\n", "import qualified Pipes as P\n", "import Pipes.Prelude (unfoldr)\n", "import qualified Pipes.Prelude as P\n", "import Data.Ord\n", "import Data.List\n", "import Control.Monad\n", "import Control.Arrow (first)\n", "import Numeric.Log\n", "import Data.Vector qualified as V\n", "\n", "import \"matrix\" Data.Matrix hiding ((!))\n", "import Data.Vector.Generic as VG (Vector, map, mapM, null, sum, (!))\n", "\n", ":l ../plotting.hs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sequential Inference\n", "\n", "This tutorial discusses particle filters, and sequential Monte Carlo more generally.\n", "\n", "These techniques are relevant when performing inference on models where there is a large series of factor statements, some of which can be performed earlier than others. This situation often arises in time series models where the factor statements are the result of incoming observations, but the technique works for *any* probabilistic program.\n", "\n", "As a motivating example, consider the following program:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ex :: MonadMeasure m => Int -> m [Bool]\n", "ex n = replicateM n do\n", " x <- bernoulli 0.5\n", " condition x\n", " return x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What distribution does this represent? It is the distribution over all lists of Booleans of length $n$, which for e.g. $n=4$, places all the weight on the sequence `[True,True,True,True]`. Function `condition` give a default score of 1 if x else 0 and effectively filters out all `False` values.\n", "\n", "Function `normalForm` computes all possible combinations of discrete variable values, here a sequence of $n$ booleans, filters out ones with weight 0 and aggregates weights of equal values. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[([True,True,True,True],6.25e-2)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "normalForm $ ex 4" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[([True,True],0.25),([True,False],0.0),([False,True],0.0),([False,False],0.0)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "explicit $ ex 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**This is inefficient**\n", "\n", "However, the naive approach to exactly inferring this distribution will not work. Why? Because it first constructs all $2^n$ possible solutions, and then throws away all but one. This has complexity exponential in $n$ and for e.g. $n=100$, it is hopeless. \n", "\n", "Now if we look at the structure of the program, it's clear that this is unnecessary. Each time a `condition` statement is made, we should throw away all possibilities with $0$ probability mass. If we do this, the size of the set of possible solutions never explodes.\n", "\n", "We can perform this *sequential enumeration* with monad-bayes, as follows:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[([True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True],1.0)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "enumerate $ sis removeZeros 100 $ ex 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`sis` (Sequential Importance Sampling) is an inference method which performs a step of inference at each `factor` statement in the program. In the present case, we have used it in conjunction with exact inference, but the idea generalizes naturally to approximate inference methods.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random walk example\n", "\n", "To motivate sis with approximate inferece methods, let's examine a problem for which exact inference is no longer feasible, a non-linear state space model. Here we will make use of the [pipes](https://hackage.haskell.org/package/pipes-4.3.16/docs/Pipes-Prelude.html) library, which offers an elegant way to represent distributions over discrete random walks as infinite streams. See the `examples/Streaming` notebook for more info.\n", "\n", "A `Producer` represents a stream of data, and a `Pipe` maps one stream to another. Both are parametrized by a monad `m`, which in our case will be `MonadDistribution m => m`. \n", "\n", "For example, a 2D random walk with a Gaussian `transition` kernel can be written as follows:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "-- how to move from one latent state to the next \n", "-- for multivariate normal distribution\n", "transition :: MonadDistribution m => V.Vector Double -> m (V.Vector Double)\n", "transition vector = mvNormal (fmap (+0.5) vector) (identity (V.length vector))\n", "\n", "\n", "-- For 2D case: a Markovian random walk starting at (0,0),\n", "-- with `transition` as the kernel\n", "-- a Producer is an infinite stream of values\n", "prior :: MonadDistribution m => Producer (V.Vector Double) m r\n", "prior = P.unfoldr (\\s -> Right . (s,) <$> transition s) (V.fromList [0,0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's straightforward to take the first `n` steps of this infinite stream, and convert it into a distribution over finite list, from which we can then sample:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"Latent\"", "X": 0, "Y": 0 }, { "Outlier": "\"Latent\"", "X": -0.976725852798596, "Y": 1.6137193880196077 }, { "Outlier": "\"Latent\"", "X": 0.20408138974710444, "Y": 1.7919034094222825 }, { "Outlier": "\"Latent\"", "X": 0.4831200523092556, "Y": 1.907099116274424 }, { "Outlier": "\"Latent\"", "X": -0.09176882753590254, "Y": 1.8955141804642897 }, { "Outlier": "\"Latent\"", "X": -1.2218589977848304, "Y": 2.1822412112642686 }, { "Outlier": "\"Latent\"", "X": -0.7668458940561108, "Y": 2.212530442551486 }, { "Outlier": "\"Latent\"", "X": -0.6690157871149474, "Y": 3.6065228326525913 }, { "Outlier": "\"Latent\"", "X": 0.11244333089173442, "Y": 2.9159986959761524 }, { "Outlier": "\"Latent\"", "X": 1.2687408682854557, "Y": 3.1217227285265796 }, { "Outlier": "\"Latent\"", "X": 2.9374928178780935, "Y": 4.807186806397708 }, { "Outlier": "\"Latent\"", "X": 1.7506468484445976, "Y": 6.528258479316207 }, { "Outlier": "\"Latent\"", "X": 2.736589222593742, "Y": 6.949265904332787 }, { "Outlier": "\"Latent\"", "X": 3.641180710652565, "Y": 6.681148574533908 }, { "Outlier": "\"Latent\"", "X": 3.256520244565894, "Y": 7.874942228007868 }, { "Outlier": "\"Latent\"", "X": 3.532287597906861, "Y": 8.830585157823975 }, { "Outlier": "\"Latent\"", "X": 2.1806733831668645, "Y": 8.017622208601368 }, { "Outlier": "\"Latent\"", "X": 2.009761220323491, "Y": 7.84268801368802 }, { "Outlier": "\"Latent\"", "X": 2.141654233585815, "Y": 7.1758683184915455 }, { "Outlier": "\"Latent\"", "X": 2.109134026193262, "Y": 6.295985176823156 }, { "Outlier": "\"Latent\"", "X": 2.405745472992563, "Y": 6.200693454492153 }, { "Outlier": "\"Latent\"", "X": -0.01763593390225182, "Y": 6.26472827677569 }, { "Outlier": "\"Latent\"", "X": -0.7129309873545127, "Y": 6.073043706411563 }, { "Outlier": "\"Latent\"", "X": -1.3881353873773867, "Y": 5.511813869639958 }, { "Outlier": "\"Latent\"", "X": -2.0112168014112006, "Y": 4.863763667368105 }, { "Outlier": "\"Latent\"", "X": -2.6313526403453142, "Y": 6.526587675025231 }, { "Outlier": "\"Latent\"", "X": -3.647308978418825, "Y": 9.059358389741346 }, { "Outlier": "\"Latent\"", "X": -3.522248228389822, "Y": 10.81130328807151 }, { "Outlier": "\"Latent\"", "X": -3.1756973573574925, "Y": 11.004220161318386 }, { "Outlier": "\"Latent\"", "X": 0.11010814466808982, "Y": 10.321234798745154 }, { "Outlier": "\"Latent\"", "X": -0.39187980161433855, "Y": 12.364011379280585 }, { "Outlier": "\"Latent\"", "X": 0.03081207681047786, "Y": 12.291837576109366 }, { "Outlier": "\"Latent\"", "X": -0.14240533533610533, "Y": 14.912448411461696 }, { "Outlier": "\"Latent\"", "X": 0.7434588861709679, "Y": 15.226819972736129 }, { "Outlier": "\"Latent\"", "X": -0.08616212989934202, "Y": 16.246907651854926 }, { "Outlier": "\"Latent\"", "X": -1.2965615882985957, "Y": 16.938548777168933 }, { "Outlier": "\"Latent\"", "X": -1.0233599219588343, "Y": 18.172882376939775 }, { "Outlier": "\"Latent\"", "X": -1.593063313624189, "Y": 17.761804204307506 }, { "Outlier": "\"Latent\"", "X": -0.9071992020452398, "Y": 16.80832237549457 }, { "Outlier": "\"Latent\"", "X": 1.537278546495295, "Y": 18.239554653824978 }, { "Outlier": "\"Latent\"", "X": 1.293986040876521, "Y": 18.531479436575104 }, { "Outlier": "\"Latent\"", "X": 0.33734706739165166, "Y": 19.17952773388102 }, { "Outlier": "\"Latent\"", "X": 1.5526873356861643, "Y": 19.89809726940538 }, { "Outlier": "\"Latent\"", "X": 0.9653041361138308, "Y": 18.978715147896484 }, { "Outlier": "\"Latent\"", "X": 2.1754309252424315, "Y": 20.733465934111884 }, { "Outlier": "\"Latent\"", "X": 1.9042944402780204, "Y": 21.96238995944239 }, { "Outlier": "\"Latent\"", "X": 1.1670764368639701, "Y": 20.42119386858742 }, { "Outlier": "\"Latent\"", "X": 1.3725692433059047, "Y": 20.078416515233386 }, { "Outlier": "\"Latent\"", "X": 2.685773076792982, "Y": 19.780304063644362 }, { "Outlier": "\"Latent\"", "X": 2.304412655375019, "Y": 18.3267445924497 }, { "Outlier": "\"Latent\"", "X": 3.3005017878405236, "Y": 19.282099858122923 }, { "Outlier": "\"Latent\"", "X": 5.293682460243824, "Y": 18.09075163407331 }, { "Outlier": "\"Latent\"", "X": 5.48617010881213, "Y": 18.88912742921528 }, { "Outlier": "\"Latent\"", "X": 5.460178753240708, "Y": 20.05661588255413 }, { "Outlier": "\"Latent\"", "X": 6.694373042710004, "Y": 21.28417138017432 }, { "Outlier": "\"Latent\"", "X": 7.176262338621055, "Y": 23.610823147215058 }, { "Outlier": "\"Latent\"", "X": 9.263967259279326, "Y": 23.793471620769253 }, { "Outlier": "\"Latent\"", "X": 9.570548244239397, "Y": 22.80525709268169 }, { "Outlier": "\"Latent\"", "X": 12.224205146716235, "Y": 23.467270848547056 }, { "Outlier": "\"Latent\"", "X": 12.998028549367675, "Y": 24.976179113989346 }, { "Outlier": "\"Latent\"", "X": 13.469190861482701, "Y": 25.033371366639106 }, { "Outlier": "\"Latent\"", "X": 15.202761232337336, "Y": 23.705490410204654 }, { "Outlier": "\"Latent\"", "X": 15.45147544674246, "Y": 23.62245393809943 }, { "Outlier": "\"Latent\"", "X": 15.948845956272837, "Y": 23.316292902745392 }, { "Outlier": "\"Latent\"", "X": 17.16913170577638, "Y": 24.777353392198744 }, { "Outlier": "\"Latent\"", "X": 17.210423055711384, "Y": 26.565764557732845 }, { "Outlier": "\"Latent\"", "X": 17.498127213076074, "Y": 26.891514119480085 }, { "Outlier": "\"Latent\"", "X": 16.154587096331557, "Y": 26.96269886332814 }, { "Outlier": "\"Latent\"", "X": 17.365461967568603, "Y": 27.809691065821152 }, { "Outlier": "\"Latent\"", "X": 17.798709082450195, "Y": 26.878326604248347 }, { "Outlier": "\"Latent\"", "X": 18.711785330404158, "Y": 27.40681326568193 }, { "Outlier": "\"Latent\"", "X": 19.69126789860091, "Y": 29.24772788318292 }, { "Outlier": "\"Latent\"", "X": 18.611842559840028, "Y": 29.623895863932383 }, { "Outlier": "\"Latent\"", "X": 18.839415485737106, "Y": 30.176998016059404 }, { "Outlier": "\"Latent\"", "X": 17.872071800282136, "Y": 31.31449032366575 }, { "Outlier": "\"Latent\"", "X": 17.234433457755895, "Y": 31.92462627820767 }, { "Outlier": "\"Latent\"", "X": 18.316980583807805, "Y": 31.971459033847758 }, { "Outlier": "\"Latent\"", "X": 18.20614845743506, "Y": 33.38337742068083 }, { "Outlier": "\"Latent\"", "X": 19.469906680607902, "Y": 35.00768028137768 }, { "Outlier": "\"Latent\"", "X": 21.166533013229724, "Y": 36.2275881565787 }, { "Outlier": "\"Latent\"", "X": 20.934551476362326, "Y": 37.71788456932134 }, { "Outlier": "\"Latent\"", "X": 23.091390514827275, "Y": 37.35456321648551 }, { "Outlier": "\"Latent\"", "X": 23.202579247316468, "Y": 38.33182657807645 }, { "Outlier": "\"Latent\"", "X": 24.814982095085746, "Y": 39.896259204044675 }, { "Outlier": "\"Latent\"", "X": 26.862289553534435, "Y": 41.594015899000084 }, { "Outlier": "\"Latent\"", "X": 29.13108856747485, "Y": 43.24237558248829 }, { "Outlier": "\"Latent\"", "X": 29.96573917639689, "Y": 43.08564704913662 }, { "Outlier": "\"Latent\"", "X": 30.38129760060132, "Y": 42.60313904655006 }, { "Outlier": "\"Latent\"", "X": 30.127208744625026, "Y": 43.46925607531138 }, { "Outlier": "\"Latent\"", "X": 30.55395052376439, "Y": 44.113573468167885 }, { "Outlier": "\"Latent\"", "X": 31.939391547518845, "Y": 43.2108558967726 }, { "Outlier": "\"Latent\"", "X": 32.61564963693414, "Y": 44.322174851055706 }, { "Outlier": "\"Latent\"", "X": 33.968317987343596, "Y": 43.58458105865007 }, { "Outlier": "\"Latent\"", "X": 33.869346145845, "Y": 44.815191784044025 }, { "Outlier": "\"Latent\"", "X": 35.656369696990275, "Y": 45.33590548300918 }, { "Outlier": "\"Latent\"", "X": 36.36554153078791, "Y": 43.511280010066635 }, { "Outlier": "\"Latent\"", "X": 38.89653663422507, "Y": 42.455163627123405 }, { "Outlier": "\"Latent\"", "X": 40.62441756480252, "Y": 41.727621228442274 }, { "Outlier": "\"Latent\"", "X": 41.91827352178923, "Y": 41.3009998512681 }, { "Outlier": "\"Latent\"", "X": 41.57668178338979, "Y": 41.469615373408814 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "-- convert the stream to a list, taking only the first 100 steps\n", "toList :: Monad m => P.Producer a m () -> m [a]\n", "toList prod = P.toListM (prod >-> P.take 100)\n", "\n", "randomWalkPrior <- sampleIOfixed $ toList prior\n", "\n", "plot \n", " (zip \n", " (fmap (\\v -> (v V.! 0, v V.! 1)) $ randomWalkPrior) \n", " (replicate 100 (T.pack \"Latent\")))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also produce a stream of observations from `prior`, each a noisy perturbation of the datapoint in question, by using a `Pipe`:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "observationModel = (`mvNormal` fromLists [[20,0],[0,20]])\n", "\n", "observations :: MonadDistribution m => P.Pipe (V.Vector Double) (V.Vector Double) m ()\n", "observations = P.mapM observationModel" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"Prior\"", "X": 0, "Y": 0 }, { "Outlier": "\"Prior\"", "X": -0.976725852798596, "Y": 1.6137193880196077 }, { "Outlier": "\"Prior\"", "X": 0.20408138974710444, "Y": 1.7919034094222825 }, { "Outlier": "\"Prior\"", "X": 0.4831200523092556, "Y": 1.907099116274424 }, { "Outlier": "\"Prior\"", "X": -0.09176882753590254, "Y": 1.8955141804642897 }, { "Outlier": "\"Prior\"", "X": -1.2218589977848304, "Y": 2.1822412112642686 }, { "Outlier": "\"Prior\"", "X": -0.7668458940561108, "Y": 2.212530442551486 }, { "Outlier": "\"Prior\"", "X": -0.6690157871149474, "Y": 3.6065228326525913 }, { "Outlier": "\"Prior\"", "X": 0.11244333089173442, "Y": 2.9159986959761524 }, { "Outlier": "\"Prior\"", "X": 1.2687408682854557, "Y": 3.1217227285265796 }, { "Outlier": "\"Prior\"", "X": 2.9374928178780935, "Y": 4.807186806397708 }, { "Outlier": "\"Prior\"", "X": 1.7506468484445976, "Y": 6.528258479316207 }, { "Outlier": "\"Prior\"", "X": 2.736589222593742, "Y": 6.949265904332787 }, { "Outlier": "\"Prior\"", "X": 3.641180710652565, "Y": 6.681148574533908 }, { "Outlier": "\"Prior\"", "X": 3.256520244565894, "Y": 7.874942228007868 }, { "Outlier": "\"Prior\"", "X": 3.532287597906861, "Y": 8.830585157823975 }, { "Outlier": "\"Prior\"", "X": 2.1806733831668645, "Y": 8.017622208601368 }, { "Outlier": "\"Prior\"", "X": 2.009761220323491, "Y": 7.84268801368802 }, { "Outlier": "\"Prior\"", "X": 2.141654233585815, "Y": 7.1758683184915455 }, { "Outlier": "\"Prior\"", "X": 2.109134026193262, "Y": 6.295985176823156 }, { "Outlier": "\"Prior\"", "X": 2.405745472992563, "Y": 6.200693454492153 }, { "Outlier": "\"Prior\"", "X": -0.01763593390225182, "Y": 6.26472827677569 }, { "Outlier": "\"Prior\"", "X": -0.7129309873545127, "Y": 6.073043706411563 }, { "Outlier": "\"Prior\"", "X": -1.3881353873773867, "Y": 5.511813869639958 }, { "Outlier": "\"Prior\"", "X": -2.0112168014112006, "Y": 4.863763667368105 }, { "Outlier": "\"Prior\"", "X": -2.6313526403453142, "Y": 6.526587675025231 }, { "Outlier": "\"Prior\"", "X": -3.647308978418825, "Y": 9.059358389741346 }, { "Outlier": "\"Prior\"", "X": -3.522248228389822, "Y": 10.81130328807151 }, { "Outlier": "\"Prior\"", "X": -3.1756973573574925, "Y": 11.004220161318386 }, { "Outlier": "\"Prior\"", "X": 0.11010814466808982, "Y": 10.321234798745154 }, { "Outlier": "\"Prior\"", "X": -0.39187980161433855, "Y": 12.364011379280585 }, { "Outlier": "\"Prior\"", "X": 0.03081207681047786, "Y": 12.291837576109366 }, { "Outlier": "\"Prior\"", "X": -0.14240533533610533, "Y": 14.912448411461696 }, { "Outlier": "\"Prior\"", "X": 0.7434588861709679, "Y": 15.226819972736129 }, { "Outlier": "\"Prior\"", "X": -0.08616212989934202, "Y": 16.246907651854926 }, { "Outlier": "\"Prior\"", "X": -1.2965615882985957, "Y": 16.938548777168933 }, { "Outlier": "\"Prior\"", "X": -1.0233599219588343, "Y": 18.172882376939775 }, { "Outlier": "\"Prior\"", "X": -1.593063313624189, "Y": 17.761804204307506 }, { "Outlier": "\"Prior\"", "X": -0.9071992020452398, "Y": 16.80832237549457 }, { "Outlier": "\"Prior\"", "X": 1.537278546495295, "Y": 18.239554653824978 }, { "Outlier": "\"Prior\"", "X": 1.293986040876521, "Y": 18.531479436575104 }, { "Outlier": "\"Prior\"", "X": 0.33734706739165166, "Y": 19.17952773388102 }, { "Outlier": "\"Prior\"", "X": 1.5526873356861643, "Y": 19.89809726940538 }, { "Outlier": "\"Prior\"", "X": 0.9653041361138308, "Y": 18.978715147896484 }, { "Outlier": "\"Prior\"", "X": 2.1754309252424315, "Y": 20.733465934111884 }, { "Outlier": "\"Prior\"", "X": 1.9042944402780204, "Y": 21.96238995944239 }, { "Outlier": "\"Prior\"", "X": 1.1670764368639701, "Y": 20.42119386858742 }, { "Outlier": "\"Prior\"", "X": 1.3725692433059047, "Y": 20.078416515233386 }, { "Outlier": "\"Prior\"", "X": 2.685773076792982, "Y": 19.780304063644362 }, { "Outlier": "\"Prior\"", "X": 2.304412655375019, "Y": 18.3267445924497 }, { "Outlier": "\"Prior\"", "X": 3.3005017878405236, "Y": 19.282099858122923 }, { "Outlier": "\"Prior\"", "X": 5.293682460243824, "Y": 18.09075163407331 }, { "Outlier": "\"Prior\"", "X": 5.48617010881213, "Y": 18.88912742921528 }, { "Outlier": "\"Prior\"", "X": 5.460178753240708, "Y": 20.05661588255413 }, { "Outlier": "\"Prior\"", "X": 6.694373042710004, "Y": 21.28417138017432 }, { "Outlier": "\"Prior\"", "X": 7.176262338621055, "Y": 23.610823147215058 }, { "Outlier": "\"Prior\"", "X": 9.263967259279326, "Y": 23.793471620769253 }, { "Outlier": "\"Prior\"", "X": 9.570548244239397, "Y": 22.80525709268169 }, { "Outlier": "\"Prior\"", "X": 12.224205146716235, "Y": 23.467270848547056 }, { "Outlier": "\"Prior\"", "X": 12.998028549367675, "Y": 24.976179113989346 }, { "Outlier": "\"Prior\"", "X": 13.469190861482701, "Y": 25.033371366639106 }, { "Outlier": "\"Prior\"", "X": 15.202761232337336, "Y": 23.705490410204654 }, { "Outlier": "\"Prior\"", "X": 15.45147544674246, "Y": 23.62245393809943 }, { "Outlier": "\"Prior\"", "X": 15.948845956272837, "Y": 23.316292902745392 }, { "Outlier": "\"Prior\"", "X": 17.16913170577638, "Y": 24.777353392198744 }, { "Outlier": "\"Prior\"", "X": 17.210423055711384, "Y": 26.565764557732845 }, { "Outlier": "\"Prior\"", "X": 17.498127213076074, "Y": 26.891514119480085 }, { "Outlier": "\"Prior\"", "X": 16.154587096331557, "Y": 26.96269886332814 }, { "Outlier": "\"Prior\"", "X": 17.365461967568603, "Y": 27.809691065821152 }, { "Outlier": "\"Prior\"", "X": 17.798709082450195, "Y": 26.878326604248347 }, { "Outlier": "\"Prior\"", "X": 18.711785330404158, "Y": 27.40681326568193 }, { "Outlier": "\"Prior\"", "X": 19.69126789860091, "Y": 29.24772788318292 }, { "Outlier": "\"Prior\"", "X": 18.611842559840028, "Y": 29.623895863932383 }, { "Outlier": "\"Prior\"", "X": 18.839415485737106, "Y": 30.176998016059404 }, { "Outlier": "\"Prior\"", "X": 17.872071800282136, "Y": 31.31449032366575 }, { "Outlier": "\"Prior\"", "X": 17.234433457755895, "Y": 31.92462627820767 }, { "Outlier": "\"Prior\"", "X": 18.316980583807805, "Y": 31.971459033847758 }, { "Outlier": "\"Prior\"", "X": 18.20614845743506, "Y": 33.38337742068083 }, { "Outlier": "\"Prior\"", "X": 19.469906680607902, "Y": 35.00768028137768 }, { "Outlier": "\"Prior\"", "X": 21.166533013229724, "Y": 36.2275881565787 }, { "Outlier": "\"Prior\"", "X": 20.934551476362326, "Y": 37.71788456932134 }, { "Outlier": "\"Prior\"", "X": 23.091390514827275, "Y": 37.35456321648551 }, { "Outlier": "\"Prior\"", "X": 23.202579247316468, "Y": 38.33182657807645 }, { "Outlier": "\"Prior\"", "X": 24.814982095085746, "Y": 39.896259204044675 }, { "Outlier": "\"Prior\"", "X": 26.862289553534435, "Y": 41.594015899000084 }, { "Outlier": "\"Prior\"", "X": 29.13108856747485, "Y": 43.24237558248829 }, { "Outlier": "\"Prior\"", "X": 29.96573917639689, "Y": 43.08564704913662 }, { "Outlier": "\"Prior\"", "X": 30.38129760060132, "Y": 42.60313904655006 }, { "Outlier": "\"Prior\"", "X": 30.127208744625026, "Y": 43.46925607531138 }, { "Outlier": "\"Prior\"", "X": 30.55395052376439, "Y": 44.113573468167885 }, { "Outlier": "\"Prior\"", "X": 31.939391547518845, "Y": 43.2108558967726 }, { "Outlier": "\"Prior\"", "X": 32.61564963693414, "Y": 44.322174851055706 }, { "Outlier": "\"Prior\"", "X": 33.968317987343596, "Y": 43.58458105865007 }, { "Outlier": "\"Prior\"", "X": 33.869346145845, "Y": 44.815191784044025 }, { "Outlier": "\"Prior\"", "X": 35.656369696990275, "Y": 45.33590548300918 }, { "Outlier": "\"Prior\"", "X": 36.36554153078791, "Y": 43.511280010066635 }, { "Outlier": "\"Prior\"", "X": 38.89653663422507, "Y": 42.455163627123405 }, { "Outlier": "\"Prior\"", "X": 40.62441756480252, "Y": 41.727621228442274 }, { "Outlier": "\"Prior\"", "X": 41.91827352178923, "Y": 41.3009998512681 }, { "Outlier": "\"Prior\"", "X": 41.57668178338979, "Y": 41.469615373408814 }, { "Outlier": "\"Observed\"", "X": -6.604118781978018, "Y": 4.980704518942615 }, { "Outlier": "\"Observed\"", "X": 2.0679366950141507, "Y": 0.17451457924113445 }, { "Outlier": "\"Observed\"", "X": -0.784087752073494, "Y": 0.07100629439754713 }, { "Outlier": "\"Observed\"", "X": -4.323929154875499, "Y": -0.38077826919822977 }, { "Outlier": "\"Observed\"", "X": -7.381753687797518, "Y": 0.9417284666753574 }, { "Outlier": "\"Observed\"", "X": -1.42304651410346, "Y": 0.0816307940533405 }, { "Outlier": "\"Observed\"", "X": -2.5654043328228697, "Y": 6.210585953818649 }, { "Outlier": "\"Observed\"", "X": 0.5897076543852038, "Y": -1.7176629642729448 }, { "Outlier": "\"Observed\"", "X": 3.047495145047877, "Y": 1.5999565612526332 }, { "Outlier": "\"Observed\"", "X": 6.495558484534547, "Y": 8.423279254534474 }, { "Outlier": "\"Observed\"", "X": -4.606311692571564, "Y": 10.267985338488014 }, { "Outlier": "\"Observed\"", "X": 3.9238472119348433, "Y": 6.174992944554853 }, { "Outlier": "\"Observed\"", "X": 4.545977363428388, "Y": 3.5141407760809513 }, { "Outlier": "\"Observed\"", "X": -0.31514116770032263, "Y": 9.78388811758532 }, { "Outlier": "\"Observed\"", "X": 2.253721363157317, "Y": 9.91263935707983 }, { "Outlier": "\"Observed\"", "X": -4.748382906620191, "Y": 2.958836345023265 }, { "Outlier": "\"Observed\"", "X": -0.8197370229315197, "Y": 4.999224728270691 }, { "Outlier": "\"Observed\"", "X": 0.3635367296473775, "Y": 2.6245117017982134 }, { "Outlier": "\"Observed\"", "X": -0.23984853265825468, "Y": 1.0048433069385636 }, { "Outlier": "\"Observed\"", "X": 1.1995527645890707, "Y": 3.6337596616730496 }, { "Outlier": "\"Observed\"", "X": -10.668013626958995, "Y": 4.250997908098581 }, { "Outlier": "\"Observed\"", "X": -5.363157919279252, "Y": 3.1714208401318422 }, { "Outlier": "\"Observed\"", "X": -5.968604839170517, "Y": 1.327079596866934 }, { "Outlier": "\"Observed\"", "X": -6.410708159469776, "Y": 0.3775772819154639 }, { "Outlier": "\"Observed\"", "X": -7.0206165611920675, "Y": 10.064070721348244 }, { "Outlier": "\"Observed\"", "X": -9.41091548605336, "Y": 15.617414676577361 }, { "Outlier": "\"Observed\"", "X": -5.324088279154697, "Y": 14.65822618324197 }, { "Outlier": "\"Observed\"", "X": -4.20849359530951, "Y": 9.437985795745169 }, { "Outlier": "\"Observed\"", "X": 9.282803591886768, "Y": 5.713748787116422 }, { "Outlier": "\"Observed\"", "X": -4.370918176377746, "Y": 17.220741415088963 }, { "Outlier": "\"Observed\"", "X": -0.7376122317241958, "Y": 9.805172341609724 }, { "Outlier": "\"Observed\"", "X": -2.979907717582027, "Y": 21.775497539450214 }, { "Outlier": "\"Observed\"", "X": 1.5832319234135985, "Y": 14.082292796366653 }, { "Outlier": "\"Observed\"", "X": -5.2027870663201385, "Y": 17.552722782275595 }, { "Outlier": "\"Observed\"", "X": -7.735301045218452, "Y": 17.10395281882828 }, { "Outlier": "\"Observed\"", "X": -2.3108345707945404, "Y": 20.222588471668395 }, { "Outlier": "\"Observed\"", "X": -5.807218921010465, "Y": 14.098416923295684 }, { "Outlier": "\"Observed\"", "X": -0.7618537374879171, "Y": 11.261635857734632 }, { "Outlier": "\"Observed\"", "X": 7.788769650899518, "Y": 20.972919729872174 }, { "Outlier": "\"Observed\"", "X": -1.7868265929641516, "Y": 17.309013993417466 }, { "Outlier": "\"Observed\"", "X": -5.220301485898842, "Y": 19.193571550033354 }, { "Outlier": "\"Observed\"", "X": 3.5364460012905883, "Y": 20.15700041236707 }, { "Outlier": "\"Observed\"", "X": -3.310238167983752, "Y": 13.550427449921857 }, { "Outlier": "\"Observed\"", "X": 4.141087682384249, "Y": 24.590131253494373 }, { "Outlier": "\"Observed\"", "X": -1.2731962753789046, "Y": 23.993313276255467 }, { "Outlier": "\"Observed\"", "X": -3.6287126769627465, "Y": 12.833883530325284 }, { "Outlier": "\"Observed\"", "X": -0.1499997724530746, "Y": 16.652178964593464 }, { "Outlier": "\"Observed\"", "X": 5.009327345786954, "Y": 16.509149124349253 }, { "Outlier": "\"Observed\"", "X": -1.2557905531438727, "Y": 11.043720512284752 }, { "Outlier": "\"Observed\"", "X": 4.522990701558552, "Y": 20.363155248365302 }, { "Outlier": "\"Observed\"", "X": 9.978208760205773, "Y": 11.718160652925963 }, { "Outlier": "\"Observed\"", "X": 3.9184454167996794, "Y": 19.425128755629302 }, { "Outlier": "\"Observed\"", "X": 3.1338652555422057, "Y": 21.874226540939016 }, { "Outlier": "\"Observed\"", "X": 8.743595433131716, "Y": 23.310342982718993 }, { "Outlier": "\"Observed\"", "X": 6.613379511783457, "Y": 29.453206424820713 }, { "Outlier": "\"Observed\"", "X": 14.276694600226666, "Y": 22.191583975422777 }, { "Outlier": "\"Observed\"", "X": 8.398971127738657, "Y": 17.137973920956128 }, { "Outlier": "\"Observed\"", "X": 19.20199471253909, "Y": 23.52980463549172 }, { "Outlier": "\"Observed\"", "X": 13.448780631034062, "Y": 27.97924577772778 }, { "Outlier": "\"Observed\"", "X": 12.869062488518232, "Y": 22.99588266591196 }, { "Outlier": "\"Observed\"", "X": 18.985885270003877, "Y": 16.858839219909573 }, { "Outlier": "\"Observed\"", "X": 14.078977035598177, "Y": 21.098072040226764 }, { "Outlier": "\"Observed\"", "X": 15.439716007869933, "Y": 20.01719218637296 }, { "Outlier": "\"Observed\"", "X": 19.170061754501454, "Y": 27.614286072559228 }, { "Outlier": "\"Observed\"", "X": 15.117724258951394, "Y": 30.539303290406707 }, { "Outlier": "\"Observed\"", "X": 16.26100718476508, "Y": 25.78649290764824 }, { "Outlier": "\"Observed\"", "X": 9.253565172498794, "Y": 24.97379399439062 }, { "Outlier": "\"Observed\"", "X": 19.33371616749645, "Y": 28.514495168201634 }, { "Outlier": "\"Observed\"", "X": 17.066933989930615, "Y": 21.408434592512798 }, { "Outlier": "\"Observed\"", "X": 19.646042223081434, "Y": 27.00572282708338 }, { "Outlier": "\"Observed\"", "X": 20.856096563432388, "Y": 33.40356573919262 }, { "Outlier": "\"Observed\"", "X": 12.627863052890977, "Y": 28.69393425751238 }, { "Outlier": "\"Observed\"", "X": 17.39351164662902, "Y": 29.86137590774749 }, { "Outlier": "\"Observed\"", "X": 12.277255031672343, "Y": 33.02795028594141 }, { "Outlier": "\"Observed\"", "X": 12.784398464884404, "Y": 31.80703328591086 }, { "Outlier": "\"Observed\"", "X": 19.83966340565432, "Y": 29.898000751077628 }, { "Outlier": "\"Observed\"", "X": 15.5852562689874, "Y": 36.04968203962917 }, { "Outlier": "\"Observed\"", "X": 21.621779068212916, "Y": 38.41141266831211 }, { "Outlier": "\"Observed\"", "X": 24.821382327425248, "Y": 38.2272061743515 }, { "Outlier": "\"Observed\"", "X": 17.89301206380918, "Y": 40.65632835011219 }, { "Outlier": "\"Observed\"", "X": 28.344160911928352, "Y": 33.85699410658533 }, { "Outlier": "\"Observed\"", "X": 21.35257366568323, "Y": 39.48894985586034 }, { "Outlier": "\"Observed\"", "X": 28.177396019269384, "Y": 43.09211399634357 }, { "Outlier": "\"Observed\"", "X": 31.73475141345314, "Y": 45.252789984896225 }, { "Outlier": "\"Observed\"", "X": 34.772599220945175, "Y": 46.72963652879966 }, { "Outlier": "\"Observed\"", "X": 30.62769158799761, "Y": 40.30539629581213 }, { "Outlier": "\"Observed\"", "X": 29.588104969184702, "Y": 38.691737684694466 }, { "Outlier": "\"Observed\"", "X": 27.00890971452523, "Y": 44.24046417461117 }, { "Outlier": "\"Observed\"", "X": 29.79958802111487, "Y": 44.11466307683677 }, { "Outlier": "\"Observed\"", "X": 34.51376316212833, "Y": 37.84042978242134 }, { "Outlier": "\"Observed\"", "X": 32.727641686552495, "Y": 45.94475737219482 }, { "Outlier": "\"Observed\"", "X": 36.428898424490455, "Y": 38.7874871543542 }, { "Outlier": "\"Observed\"", "X": 31.289634478945427, "Y": 46.8519715527927 }, { "Outlier": "\"Observed\"", "X": 39.625090443853026, "Y": 44.90782626194713 }, { "Outlier": "\"Observed\"", "X": 36.59181457568985, "Y": 34.939864323554914 }, { "Outlier": "\"Observed\"", "X": 45.448427757297324, "Y": 36.55211598374231 }, { "Outlier": "\"Observed\"", "X": 44.387787092318824, "Y": 36.96542712969508 }, { "Outlier": "\"Observed\"", "X": 44.174649333133374, "Y": 37.583644450910434 }, { "Outlier": "\"Observed\"", "X": 38.154560849062484, "Y": 39.81900341290482 }, { "Outlier": "\"Observed\"", "X": 38.660476984766554, "Y": 43.314041616491615 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "randomWalkObserved <- sampleIOfixed $ toList $ P.each randomWalkPrior >-> observations\n", "\n", "\n", "plot \n", " (zip \n", " (fmap (\\v -> (v V.! 0, v V.! 1)) $ randomWalkPrior <> randomWalkObserved) \n", " (replicate 100 (T.pack \"Prior\") <> replicate 100 (T.pack \"Observed\")))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conditioning\n", "\n", "But most interestingly, we can update our prior, based on a stream of incoming observations. `conditioning` below takes two `Producer`s, the stream corresponding to the prior (see `prior` above) and the stream corresponding to the observations (`P.each observedVectors`, where `P.each` lifts a list to a stream), and a likelihood, and returns a posterior, also expressed as a stream. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "-- take the original random walk as a prior and condition on the observations\n", "-- to obtain a posterior random walk\n", "conditioning :: MonadMeasure m => \n", " P.Producer (V.Vector Double) m () -> \n", " P.Producer (V.Vector Double) m () -> \n", " ((V.Vector Double, V.Vector Double) -> m ()) ->\n", " P.Producer (V.Vector Double) m ()\n", "conditioning prior observations observationModel = \n", " P.zip prior observations\n", " >-> P.chain observationModel\n", " >-> P.map fst \n", "\n", "\n", "posterior :: MonadMeasure m => m [V.Vector Double]\n", "posterior = toList $ conditioning \n", " prior\n", " (P.each randomWalkObserved)\n", " (\\(v, v') -> do\n", " prediction <- observationModel v\n", " let (x, y, x', y') = (prediction V.! 0, prediction V.! 1, v' V.! 0, v' V.! 1)\n", " factor $ normalPdf x 2 x' * normalPdf y 2 y' )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sequential Monte Carlo" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then use an inference method of our liking, but in this tutorial, we would like to see how SMC behaves.\n", "\n", "Following [Scibor et al](https://dl.acm.org/doi/pdf/10.1145/3236778):\n", "> Within the context of SMC, recall\n", "that a sample in a population is called a particle. The algorithm starts by initialising a population\n", "of size n, then repeatedly runs the program to the next `score`, resamples the population, runs to\n", "the next score and so on." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "particles <- sampleIOfixed $ runPopulationT $ \n", " smc SMCConfig {\n", " numSteps = 100, \n", " numParticles = 1000, \n", " resampler = resampleMultinomial} \n", " posterior" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Distribution is generated over whole paths. We may compare realizations of prior and inferred (here 5 paths) with the observed path." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "Outlier": "\"Prior\"", "X": 0, "Y": 0 }, { "Outlier": "\"Prior\"", "X": -0.976725852798596, "Y": 1.6137193880196077 }, { "Outlier": "\"Prior\"", "X": 0.20408138974710444, "Y": 1.7919034094222825 }, { "Outlier": "\"Prior\"", "X": 0.4831200523092556, "Y": 1.907099116274424 }, { "Outlier": "\"Prior\"", "X": -0.09176882753590254, "Y": 1.8955141804642897 }, { "Outlier": "\"Prior\"", "X": -1.2218589977848304, "Y": 2.1822412112642686 }, { "Outlier": "\"Prior\"", "X": -0.7668458940561108, "Y": 2.212530442551486 }, { "Outlier": "\"Prior\"", "X": -0.6690157871149474, "Y": 3.6065228326525913 }, { "Outlier": "\"Prior\"", "X": 0.11244333089173442, "Y": 2.9159986959761524 }, { "Outlier": "\"Prior\"", "X": 1.2687408682854557, "Y": 3.1217227285265796 }, { "Outlier": "\"Prior\"", "X": 2.9374928178780935, "Y": 4.807186806397708 }, { "Outlier": "\"Prior\"", "X": 1.7506468484445976, "Y": 6.528258479316207 }, { "Outlier": "\"Prior\"", "X": 2.736589222593742, "Y": 6.949265904332787 }, { "Outlier": "\"Prior\"", "X": 3.641180710652565, "Y": 6.681148574533908 }, { "Outlier": "\"Prior\"", "X": 3.256520244565894, "Y": 7.874942228007868 }, { "Outlier": "\"Prior\"", "X": 3.532287597906861, "Y": 8.830585157823975 }, { "Outlier": "\"Prior\"", "X": 2.1806733831668645, "Y": 8.017622208601368 }, { "Outlier": "\"Prior\"", "X": 2.009761220323491, "Y": 7.84268801368802 }, { "Outlier": "\"Prior\"", "X": 2.141654233585815, "Y": 7.1758683184915455 }, { "Outlier": "\"Prior\"", "X": 2.109134026193262, "Y": 6.295985176823156 }, { "Outlier": "\"Prior\"", "X": 2.405745472992563, "Y": 6.200693454492153 }, { "Outlier": "\"Prior\"", "X": -0.01763593390225182, "Y": 6.26472827677569 }, { "Outlier": "\"Prior\"", "X": -0.7129309873545127, "Y": 6.073043706411563 }, { "Outlier": "\"Prior\"", "X": -1.3881353873773867, "Y": 5.511813869639958 }, { "Outlier": "\"Prior\"", "X": -2.0112168014112006, "Y": 4.863763667368105 }, { "Outlier": "\"Prior\"", "X": -2.6313526403453142, "Y": 6.526587675025231 }, { "Outlier": "\"Prior\"", "X": -3.647308978418825, "Y": 9.059358389741346 }, { "Outlier": "\"Prior\"", "X": -3.522248228389822, "Y": 10.81130328807151 }, { "Outlier": "\"Prior\"", "X": -3.1756973573574925, "Y": 11.004220161318386 }, { "Outlier": "\"Prior\"", "X": 0.11010814466808982, "Y": 10.321234798745154 }, { "Outlier": "\"Prior\"", "X": -0.39187980161433855, "Y": 12.364011379280585 }, { "Outlier": "\"Prior\"", "X": 0.03081207681047786, "Y": 12.291837576109366 }, { "Outlier": "\"Prior\"", "X": -0.14240533533610533, "Y": 14.912448411461696 }, { "Outlier": "\"Prior\"", "X": 0.7434588861709679, "Y": 15.226819972736129 }, { "Outlier": "\"Prior\"", "X": -0.08616212989934202, "Y": 16.246907651854926 }, { "Outlier": "\"Prior\"", "X": -1.2965615882985957, "Y": 16.938548777168933 }, { "Outlier": "\"Prior\"", "X": -1.0233599219588343, "Y": 18.172882376939775 }, { "Outlier": "\"Prior\"", "X": -1.593063313624189, "Y": 17.761804204307506 }, { "Outlier": "\"Prior\"", "X": -0.9071992020452398, "Y": 16.80832237549457 }, { "Outlier": "\"Prior\"", "X": 1.537278546495295, "Y": 18.239554653824978 }, { "Outlier": "\"Prior\"", "X": 1.293986040876521, "Y": 18.531479436575104 }, { "Outlier": "\"Prior\"", "X": 0.33734706739165166, "Y": 19.17952773388102 }, { "Outlier": "\"Prior\"", "X": 1.5526873356861643, "Y": 19.89809726940538 }, { "Outlier": "\"Prior\"", "X": 0.9653041361138308, "Y": 18.978715147896484 }, { "Outlier": "\"Prior\"", "X": 2.1754309252424315, "Y": 20.733465934111884 }, { "Outlier": "\"Prior\"", "X": 1.9042944402780204, "Y": 21.96238995944239 }, { "Outlier": "\"Prior\"", "X": 1.1670764368639701, "Y": 20.42119386858742 }, { "Outlier": "\"Prior\"", "X": 1.3725692433059047, "Y": 20.078416515233386 }, { "Outlier": "\"Prior\"", "X": 2.685773076792982, "Y": 19.780304063644362 }, { "Outlier": "\"Prior\"", "X": 2.304412655375019, "Y": 18.3267445924497 }, { "Outlier": "\"Prior\"", "X": 3.3005017878405236, "Y": 19.282099858122923 }, { "Outlier": "\"Prior\"", "X": 5.293682460243824, "Y": 18.09075163407331 }, { "Outlier": "\"Prior\"", "X": 5.48617010881213, "Y": 18.88912742921528 }, { "Outlier": "\"Prior\"", "X": 5.460178753240708, "Y": 20.05661588255413 }, { "Outlier": "\"Prior\"", "X": 6.694373042710004, "Y": 21.28417138017432 }, { "Outlier": "\"Prior\"", "X": 7.176262338621055, "Y": 23.610823147215058 }, { "Outlier": "\"Prior\"", "X": 9.263967259279326, "Y": 23.793471620769253 }, { "Outlier": "\"Prior\"", "X": 9.570548244239397, "Y": 22.80525709268169 }, { "Outlier": "\"Prior\"", "X": 12.224205146716235, "Y": 23.467270848547056 }, { "Outlier": "\"Prior\"", "X": 12.998028549367675, "Y": 24.976179113989346 }, { "Outlier": "\"Prior\"", "X": 13.469190861482701, "Y": 25.033371366639106 }, { "Outlier": "\"Prior\"", "X": 15.202761232337336, "Y": 23.705490410204654 }, { "Outlier": "\"Prior\"", "X": 15.45147544674246, "Y": 23.62245393809943 }, { "Outlier": "\"Prior\"", "X": 15.948845956272837, "Y": 23.316292902745392 }, { "Outlier": "\"Prior\"", "X": 17.16913170577638, "Y": 24.777353392198744 }, { "Outlier": "\"Prior\"", "X": 17.210423055711384, "Y": 26.565764557732845 }, { "Outlier": "\"Prior\"", "X": 17.498127213076074, "Y": 26.891514119480085 }, { "Outlier": "\"Prior\"", "X": 16.154587096331557, "Y": 26.96269886332814 }, { "Outlier": "\"Prior\"", "X": 17.365461967568603, "Y": 27.809691065821152 }, { "Outlier": "\"Prior\"", "X": 17.798709082450195, "Y": 26.878326604248347 }, { "Outlier": "\"Prior\"", "X": 18.711785330404158, "Y": 27.40681326568193 }, { "Outlier": "\"Prior\"", "X": 19.69126789860091, "Y": 29.24772788318292 }, { "Outlier": "\"Prior\"", "X": 18.611842559840028, "Y": 29.623895863932383 }, { "Outlier": "\"Prior\"", "X": 18.839415485737106, "Y": 30.176998016059404 }, { "Outlier": "\"Prior\"", "X": 17.872071800282136, "Y": 31.31449032366575 }, { "Outlier": "\"Prior\"", "X": 17.234433457755895, "Y": 31.92462627820767 }, { "Outlier": "\"Prior\"", "X": 18.316980583807805, "Y": 31.971459033847758 }, { "Outlier": "\"Prior\"", "X": 18.20614845743506, "Y": 33.38337742068083 }, { "Outlier": "\"Prior\"", "X": 19.469906680607902, "Y": 35.00768028137768 }, { "Outlier": "\"Prior\"", "X": 21.166533013229724, "Y": 36.2275881565787 }, { "Outlier": "\"Prior\"", "X": 20.934551476362326, "Y": 37.71788456932134 }, { "Outlier": "\"Prior\"", "X": 23.091390514827275, "Y": 37.35456321648551 }, { "Outlier": "\"Prior\"", "X": 23.202579247316468, "Y": 38.33182657807645 }, { "Outlier": "\"Prior\"", "X": 24.814982095085746, "Y": 39.896259204044675 }, { "Outlier": "\"Prior\"", "X": 26.862289553534435, "Y": 41.594015899000084 }, { "Outlier": "\"Prior\"", "X": 29.13108856747485, "Y": 43.24237558248829 }, { "Outlier": "\"Prior\"", "X": 29.96573917639689, "Y": 43.08564704913662 }, { "Outlier": "\"Prior\"", "X": 30.38129760060132, "Y": 42.60313904655006 }, { "Outlier": "\"Prior\"", "X": 30.127208744625026, "Y": 43.46925607531138 }, { "Outlier": "\"Prior\"", "X": 30.55395052376439, "Y": 44.113573468167885 }, { "Outlier": "\"Prior\"", "X": 31.939391547518845, "Y": 43.2108558967726 }, { "Outlier": "\"Prior\"", "X": 32.61564963693414, "Y": 44.322174851055706 }, { "Outlier": "\"Prior\"", "X": 33.968317987343596, "Y": 43.58458105865007 }, { "Outlier": "\"Prior\"", "X": 33.869346145845, "Y": 44.815191784044025 }, { "Outlier": "\"Prior\"", "X": 35.656369696990275, "Y": 45.33590548300918 }, { "Outlier": "\"Prior\"", "X": 36.36554153078791, "Y": 43.511280010066635 }, { "Outlier": "\"Prior\"", "X": 38.89653663422507, "Y": 42.455163627123405 }, { "Outlier": "\"Prior\"", "X": 40.62441756480252, "Y": 41.727621228442274 }, { "Outlier": "\"Prior\"", "X": 41.91827352178923, "Y": 41.3009998512681 }, { "Outlier": "\"Prior\"", "X": 41.57668178338979, "Y": 41.469615373408814 }, { "Outlier": "\"Observed\"", "X": -6.604118781978018, "Y": 4.980704518942615 }, { "Outlier": "\"Observed\"", "X": 2.0679366950141507, "Y": 0.17451457924113445 }, { "Outlier": "\"Observed\"", "X": -0.784087752073494, "Y": 0.07100629439754713 }, { "Outlier": "\"Observed\"", "X": -4.323929154875499, "Y": -0.38077826919822977 }, { "Outlier": "\"Observed\"", "X": -7.381753687797518, "Y": 0.9417284666753574 }, { "Outlier": "\"Observed\"", "X": -1.42304651410346, "Y": 0.0816307940533405 }, { "Outlier": "\"Observed\"", "X": -2.5654043328228697, "Y": 6.210585953818649 }, { "Outlier": "\"Observed\"", "X": 0.5897076543852038, "Y": -1.7176629642729448 }, { "Outlier": "\"Observed\"", "X": 3.047495145047877, "Y": 1.5999565612526332 }, { "Outlier": "\"Observed\"", "X": 6.495558484534547, "Y": 8.423279254534474 }, { "Outlier": "\"Observed\"", "X": -4.606311692571564, "Y": 10.267985338488014 }, { "Outlier": "\"Observed\"", "X": 3.9238472119348433, "Y": 6.174992944554853 }, { "Outlier": "\"Observed\"", "X": 4.545977363428388, "Y": 3.5141407760809513 }, { "Outlier": "\"Observed\"", "X": -0.31514116770032263, "Y": 9.78388811758532 }, { "Outlier": "\"Observed\"", "X": 2.253721363157317, "Y": 9.91263935707983 }, { "Outlier": "\"Observed\"", "X": -4.748382906620191, "Y": 2.958836345023265 }, { "Outlier": "\"Observed\"", "X": -0.8197370229315197, "Y": 4.999224728270691 }, { "Outlier": "\"Observed\"", "X": 0.3635367296473775, "Y": 2.6245117017982134 }, { "Outlier": "\"Observed\"", "X": -0.23984853265825468, "Y": 1.0048433069385636 }, { "Outlier": "\"Observed\"", "X": 1.1995527645890707, "Y": 3.6337596616730496 }, { "Outlier": "\"Observed\"", "X": -10.668013626958995, "Y": 4.250997908098581 }, { "Outlier": "\"Observed\"", "X": -5.363157919279252, "Y": 3.1714208401318422 }, { "Outlier": "\"Observed\"", "X": -5.968604839170517, "Y": 1.327079596866934 }, { "Outlier": "\"Observed\"", "X": -6.410708159469776, "Y": 0.3775772819154639 }, { "Outlier": "\"Observed\"", "X": -7.0206165611920675, "Y": 10.064070721348244 }, { "Outlier": "\"Observed\"", "X": -9.41091548605336, "Y": 15.617414676577361 }, { "Outlier": "\"Observed\"", "X": -5.324088279154697, "Y": 14.65822618324197 }, { "Outlier": "\"Observed\"", "X": -4.20849359530951, "Y": 9.437985795745169 }, { "Outlier": "\"Observed\"", "X": 9.282803591886768, "Y": 5.713748787116422 }, { "Outlier": "\"Observed\"", "X": -4.370918176377746, "Y": 17.220741415088963 }, { "Outlier": "\"Observed\"", "X": -0.7376122317241958, "Y": 9.805172341609724 }, { "Outlier": "\"Observed\"", "X": -2.979907717582027, "Y": 21.775497539450214 }, { "Outlier": "\"Observed\"", "X": 1.5832319234135985, "Y": 14.082292796366653 }, { "Outlier": "\"Observed\"", "X": -5.2027870663201385, "Y": 17.552722782275595 }, { "Outlier": "\"Observed\"", "X": -7.735301045218452, "Y": 17.10395281882828 }, { "Outlier": "\"Observed\"", "X": -2.3108345707945404, "Y": 20.222588471668395 }, { "Outlier": "\"Observed\"", "X": -5.807218921010465, "Y": 14.098416923295684 }, { "Outlier": "\"Observed\"", "X": -0.7618537374879171, "Y": 11.261635857734632 }, { "Outlier": "\"Observed\"", "X": 7.788769650899518, "Y": 20.972919729872174 }, { "Outlier": "\"Observed\"", "X": -1.7868265929641516, "Y": 17.309013993417466 }, { "Outlier": "\"Observed\"", "X": -5.220301485898842, "Y": 19.193571550033354 }, { "Outlier": "\"Observed\"", "X": 3.5364460012905883, "Y": 20.15700041236707 }, { "Outlier": "\"Observed\"", "X": -3.310238167983752, "Y": 13.550427449921857 }, { "Outlier": "\"Observed\"", "X": 4.141087682384249, "Y": 24.590131253494373 }, { "Outlier": "\"Observed\"", "X": -1.2731962753789046, "Y": 23.993313276255467 }, { "Outlier": "\"Observed\"", "X": -3.6287126769627465, "Y": 12.833883530325284 }, { "Outlier": "\"Observed\"", "X": -0.1499997724530746, "Y": 16.652178964593464 }, { "Outlier": "\"Observed\"", "X": 5.009327345786954, "Y": 16.509149124349253 }, { "Outlier": "\"Observed\"", "X": -1.2557905531438727, "Y": 11.043720512284752 }, { "Outlier": "\"Observed\"", "X": 4.522990701558552, "Y": 20.363155248365302 }, { "Outlier": "\"Observed\"", "X": 9.978208760205773, "Y": 11.718160652925963 }, { "Outlier": "\"Observed\"", "X": 3.9184454167996794, "Y": 19.425128755629302 }, { "Outlier": "\"Observed\"", "X": 3.1338652555422057, "Y": 21.874226540939016 }, { "Outlier": "\"Observed\"", "X": 8.743595433131716, "Y": 23.310342982718993 }, { "Outlier": "\"Observed\"", "X": 6.613379511783457, "Y": 29.453206424820713 }, { "Outlier": "\"Observed\"", "X": 14.276694600226666, "Y": 22.191583975422777 }, { "Outlier": "\"Observed\"", "X": 8.398971127738657, "Y": 17.137973920956128 }, { "Outlier": "\"Observed\"", "X": 19.20199471253909, "Y": 23.52980463549172 }, { "Outlier": "\"Observed\"", "X": 13.448780631034062, "Y": 27.97924577772778 }, { "Outlier": "\"Observed\"", "X": 12.869062488518232, "Y": 22.99588266591196 }, { "Outlier": "\"Observed\"", "X": 18.985885270003877, "Y": 16.858839219909573 }, { "Outlier": "\"Observed\"", "X": 14.078977035598177, "Y": 21.098072040226764 }, { "Outlier": "\"Observed\"", "X": 15.439716007869933, "Y": 20.01719218637296 }, { "Outlier": "\"Observed\"", "X": 19.170061754501454, "Y": 27.614286072559228 }, { "Outlier": "\"Observed\"", "X": 15.117724258951394, "Y": 30.539303290406707 }, { "Outlier": "\"Observed\"", "X": 16.26100718476508, "Y": 25.78649290764824 }, { "Outlier": "\"Observed\"", "X": 9.253565172498794, "Y": 24.97379399439062 }, { "Outlier": "\"Observed\"", "X": 19.33371616749645, "Y": 28.514495168201634 }, { "Outlier": "\"Observed\"", "X": 17.066933989930615, "Y": 21.408434592512798 }, { "Outlier": "\"Observed\"", "X": 19.646042223081434, "Y": 27.00572282708338 }, { "Outlier": "\"Observed\"", "X": 20.856096563432388, "Y": 33.40356573919262 }, { "Outlier": "\"Observed\"", "X": 12.627863052890977, "Y": 28.69393425751238 }, { "Outlier": "\"Observed\"", "X": 17.39351164662902, "Y": 29.86137590774749 }, { "Outlier": "\"Observed\"", "X": 12.277255031672343, "Y": 33.02795028594141 }, { "Outlier": "\"Observed\"", "X": 12.784398464884404, "Y": 31.80703328591086 }, { "Outlier": "\"Observed\"", "X": 19.83966340565432, "Y": 29.898000751077628 }, { "Outlier": "\"Observed\"", "X": 15.5852562689874, "Y": 36.04968203962917 }, { "Outlier": "\"Observed\"", "X": 21.621779068212916, "Y": 38.41141266831211 }, { "Outlier": "\"Observed\"", "X": 24.821382327425248, "Y": 38.2272061743515 }, { "Outlier": "\"Observed\"", "X": 17.89301206380918, "Y": 40.65632835011219 }, { "Outlier": "\"Observed\"", "X": 28.344160911928352, "Y": 33.85699410658533 }, { "Outlier": "\"Observed\"", "X": 21.35257366568323, "Y": 39.48894985586034 }, { "Outlier": "\"Observed\"", "X": 28.177396019269384, "Y": 43.09211399634357 }, { "Outlier": "\"Observed\"", "X": 31.73475141345314, "Y": 45.252789984896225 }, { "Outlier": "\"Observed\"", "X": 34.772599220945175, "Y": 46.72963652879966 }, { "Outlier": "\"Observed\"", "X": 30.62769158799761, "Y": 40.30539629581213 }, { "Outlier": "\"Observed\"", "X": 29.588104969184702, "Y": 38.691737684694466 }, { "Outlier": "\"Observed\"", "X": 27.00890971452523, "Y": 44.24046417461117 }, { "Outlier": "\"Observed\"", "X": 29.79958802111487, "Y": 44.11466307683677 }, { "Outlier": "\"Observed\"", "X": 34.51376316212833, "Y": 37.84042978242134 }, { "Outlier": "\"Observed\"", "X": 32.727641686552495, "Y": 45.94475737219482 }, { "Outlier": "\"Observed\"", "X": 36.428898424490455, "Y": 38.7874871543542 }, { "Outlier": "\"Observed\"", "X": 31.289634478945427, "Y": 46.8519715527927 }, { "Outlier": "\"Observed\"", "X": 39.625090443853026, "Y": 44.90782626194713 }, { "Outlier": "\"Observed\"", "X": 36.59181457568985, "Y": 34.939864323554914 }, { "Outlier": "\"Observed\"", "X": 45.448427757297324, "Y": 36.55211598374231 }, { "Outlier": "\"Observed\"", "X": 44.387787092318824, "Y": 36.96542712969508 }, { "Outlier": "\"Observed\"", "X": 44.174649333133374, "Y": 37.583644450910434 }, { "Outlier": "\"Observed\"", "X": 38.154560849062484, "Y": 39.81900341290482 }, { "Outlier": "\"Observed\"", "X": 38.660476984766554, "Y": 43.314041616491615 }, { "Outlier": "\"Inferred\"", "X": 0, "Y": 0 }, { "Outlier": "\"Inferred\"", "X": -1.4959834833661376, "Y": 1.3545122401020775 }, { "Outlier": "\"Inferred\"", "X": -0.46692867593012344, "Y": 1.2568136970723294 }, { "Outlier": "\"Inferred\"", "X": -1.866384280379635, "Y": -0.3790080315332305 }, { "Outlier": "\"Inferred\"", "X": -2.0216593626524055, "Y": 1.1571646856516027 }, { "Outlier": "\"Inferred\"", "X": -0.8117064980987132, "Y": 1.4925861552150301 }, { "Outlier": "\"Inferred\"", "X": -1.722727721700016, "Y": 3.4863260958686197 }, { "Outlier": "\"Inferred\"", "X": -0.37547781732650076, "Y": 2.5409261013823894 }, { "Outlier": "\"Inferred\"", "X": -0.2817038765883308, "Y": 4.159156266975698 }, { "Outlier": "\"Inferred\"", "X": 0.1667041295923471, "Y": 5.130870216791928 }, { "Outlier": "\"Inferred\"", "X": 0.3063259837784219, "Y": 6.0494478042882385 }, { "Outlier": "\"Inferred\"", "X": 0.9886298127833987, "Y": 6.241715204795343 }, { "Outlier": "\"Inferred\"", "X": 0.38496827432593106, "Y": 5.080720182682791 }, { "Outlier": "\"Inferred\"", "X": -0.0462180841414912, "Y": 5.942526622696139 }, { "Outlier": "\"Inferred\"", "X": -0.2251744493459894, "Y": 6.5612141945954665 }, { "Outlier": "\"Inferred\"", "X": -0.814146491805117, "Y": 6.9248379196231085 }, { "Outlier": "\"Inferred\"", "X": -0.7561087684322328, "Y": 6.62553822183286 }, { "Outlier": "\"Inferred\"", "X": 0.43914666664058133, "Y": 5.968357481439281 }, { "Outlier": "\"Inferred\"", "X": -0.12399654710156549, "Y": 6.12794282548626 }, { "Outlier": "\"Inferred\"", "X": -0.42208439701240297, "Y": 6.0998614517598835 }, { "Outlier": "\"Inferred\"", "X": -1.0537808269584867, "Y": 6.1962556328145055 }, { "Outlier": "\"Inferred\"", "X": -1.6736031774198217, "Y": 6.750007721251283 }, { "Outlier": "\"Inferred\"", "X": -1.7833668854997602, "Y": 7.681301119927426 }, { "Outlier": "\"Inferred\"", "X": -2.0611405381121775, "Y": 8.167752558749864 }, { "Outlier": "\"Inferred\"", "X": -2.2383582402513835, "Y": 9.359656490408558 }, { "Outlier": "\"Inferred\"", "X": -2.735982039185112, "Y": 10.804738228768336 }, { "Outlier": "\"Inferred\"", "X": -2.1205816100690718, "Y": 11.348794078362255 }, { "Outlier": "\"Inferred\"", "X": -1.0551680452461532, "Y": 11.034241068055147 }, { "Outlier": "\"Inferred\"", "X": -1.3524152565281415, "Y": 11.214208812917592 }, { "Outlier": "\"Inferred\"", "X": 0.38392332366332016, "Y": 12.52097689413072 }, { "Outlier": "\"Inferred\"", "X": -0.2673468302718842, "Y": 14.07716588278852 }, { "Outlier": "\"Inferred\"", "X": -1.4399510387629983, "Y": 15.953255031302792 }, { "Outlier": "\"Inferred\"", "X": -1.8232951752429258, "Y": 16.74054808359849 }, { "Outlier": "\"Inferred\"", "X": -2.1597296904624903, "Y": 14.680518334121674 }, { "Outlier": "\"Inferred\"", "X": -0.96472904813252, "Y": 15.13940147107535 }, { "Outlier": "\"Inferred\"", "X": -0.4250433824874331, "Y": 16.12393699144529 }, { "Outlier": "\"Inferred\"", "X": -0.4703277914016233, "Y": 14.578669365055553 }, { "Outlier": "\"Inferred\"", "X": 0.6564802222940759, "Y": 15.118393817361142 }, { "Outlier": "\"Inferred\"", "X": 1.2934887231854395, "Y": 15.847674814809938 }, { "Outlier": "\"Inferred\"", "X": 1.4154694944684096, "Y": 16.308982707174362 }, { "Outlier": "\"Inferred\"", "X": 2.2290237130424866, "Y": 16.07176229204904 }, { "Outlier": "\"Inferred\"", "X": 3.113541805788172, "Y": 16.39959240449963 }, { "Outlier": "\"Inferred\"", "X": 2.727374183344879, "Y": 16.539569187571324 }, { "Outlier": "\"Inferred\"", "X": 3.115556283090038, "Y": 18.66480697731124 }, { "Outlier": "\"Inferred\"", "X": 2.747380851426324, "Y": 18.083826913156145 }, { "Outlier": "\"Inferred\"", "X": 4.017539334489981, "Y": 17.34819318803999 }, { "Outlier": "\"Inferred\"", "X": 3.2106775079187786, "Y": 18.72699096758819 }, { "Outlier": "\"Inferred\"", "X": 4.064433069759858, "Y": 18.930938861615648 }, { "Outlier": "\"Inferred\"", "X": 4.886199754552055, "Y": 18.822292641259693 }, { "Outlier": "\"Inferred\"", "X": 5.693570082393493, "Y": 18.352523954726017 }, { "Outlier": "\"Inferred\"", "X": 4.764219023719597, "Y": 19.071723695171766 }, { "Outlier": "\"Inferred\"", "X": 5.109648787640866, "Y": 20.161076174063425 }, { "Outlier": "\"Inferred\"", "X": 6.684585131049911, "Y": 21.81265734233153 }, { "Outlier": "\"Inferred\"", "X": 8.07453949257289, "Y": 22.80841644425841 }, { "Outlier": "\"Inferred\"", "X": 7.412231069790365, "Y": 23.795701704568753 }, { "Outlier": "\"Inferred\"", "X": 8.875225307277638, "Y": 24.134979184688905 }, { "Outlier": "\"Inferred\"", "X": 9.786720807812639, "Y": 24.798570393123562 }, { "Outlier": "\"Inferred\"", "X": 10.839675266822008, "Y": 24.11060847582407 }, { "Outlier": "\"Inferred\"", "X": 11.951885850540787, "Y": 23.54370832545031 }, { "Outlier": "\"Inferred\"", "X": 12.362900938006847, "Y": 24.116558302935186 }, { "Outlier": "\"Inferred\"", "X": 13.403537769403211, "Y": 25.84854640696169 }, { "Outlier": "\"Inferred\"", "X": 13.087572364941128, "Y": 26.388585481883272 }, { "Outlier": "\"Inferred\"", "X": 15.123642440895985, "Y": 26.737432791961233 }, { "Outlier": "\"Inferred\"", "X": 16.27403404057166, "Y": 27.717366941234275 }, { "Outlier": "\"Inferred\"", "X": 17.54855755489548, "Y": 27.76578014438041 }, { "Outlier": "\"Inferred\"", "X": 17.02936531710395, "Y": 26.575680729713927 }, { "Outlier": "\"Inferred\"", "X": 17.846636670428634, "Y": 28.658403537125473 }, { "Outlier": "\"Inferred\"", "X": 17.75069874240183, "Y": 26.464358097759444 }, { "Outlier": "\"Inferred\"", "X": 18.642413159085244, "Y": 29.585749042045443 }, { "Outlier": "\"Inferred\"", "X": 18.114534945239527, "Y": 29.96056288469767 }, { "Outlier": "\"Inferred\"", "X": 17.89063941029048, "Y": 30.40967128533151 }, { "Outlier": "\"Inferred\"", "X": 19.177413425943513, "Y": 29.790316975358717 }, { "Outlier": "\"Inferred\"", "X": 19.421545582612655, "Y": 29.849565902325587 }, { "Outlier": "\"Inferred\"", "X": 20.428695873575105, "Y": 30.063955131069637 }, { "Outlier": "\"Inferred\"", "X": 20.271184767571977, "Y": 33.586006703235064 }, { "Outlier": "\"Inferred\"", "X": 21.23012653905654, "Y": 34.19202362990785 }, { "Outlier": "\"Inferred\"", "X": 22.383839376835358, "Y": 36.099918931011395 }, { "Outlier": "\"Inferred\"", "X": 24.849517743655664, "Y": 37.18137630871274 }, { "Outlier": "\"Inferred\"", "X": 23.018234864790475, "Y": 37.3878341474434 }, { "Outlier": "\"Inferred\"", "X": 25.368665864726108, "Y": 38.69565105530858 }, { "Outlier": "\"Inferred\"", "X": 26.194861372477984, "Y": 40.551406609263104 }, { "Outlier": "\"Inferred\"", "X": 27.776889871741783, "Y": 40.59409860728754 }, { "Outlier": "\"Inferred\"", "X": 27.309304890413063, "Y": 40.72390802396453 }, { "Outlier": "\"Inferred\"", "X": 30.788805704227972, "Y": 41.58481755616034 }, { "Outlier": "\"Inferred\"", "X": 32.244979307217676, "Y": 42.071660279644924 }, { "Outlier": "\"Inferred\"", "X": 30.32538655373424, "Y": 41.84495076984768 }, { "Outlier": "\"Inferred\"", "X": 30.893989422839287, "Y": 41.51912265716761 }, { "Outlier": "\"Inferred\"", "X": 31.430885488062984, "Y": 42.08569337266744 }, { "Outlier": "\"Inferred\"", "X": 32.793607076579164, "Y": 41.84192456391744 }, { "Outlier": "\"Inferred\"", "X": 34.7225274242704, "Y": 41.03590208891771 }, { "Outlier": "\"Inferred\"", "X": 35.117703749646374, "Y": 42.82520646262462 }, { "Outlier": "\"Inferred\"", "X": 35.668789570173224, "Y": 42.2452814758927 }, { "Outlier": "\"Inferred\"", "X": 37.080852175953844, "Y": 42.54964434552788 }, { "Outlier": "\"Inferred\"", "X": 36.70290290306099, "Y": 42.681481194621675 }, { "Outlier": "\"Inferred\"", "X": 38.1939543748931, "Y": 41.28917454255082 }, { "Outlier": "\"Inferred\"", "X": 38.599813330117975, "Y": 41.0425825970788 }, { "Outlier": "\"Inferred\"", "X": 38.43535967863466, "Y": 42.189364919518475 }, { "Outlier": "\"Inferred\"", "X": 39.571556813508664, "Y": 41.6516274116565 }, { "Outlier": "\"Inferred\"", "X": 41.86921905756014, "Y": 42.912380505387745 }, { "Outlier": "\"Inferred\"", "X": 40.56176494375606, "Y": 42.15780010331323 }, { "Outlier": "\"Inferred\"", "X": 0, "Y": 0 }, { "Outlier": "\"Inferred\"", "X": -1.4959834833661376, "Y": 1.3545122401020775 }, { "Outlier": "\"Inferred\"", "X": -0.46692867593012344, "Y": 1.2568136970723294 }, { "Outlier": "\"Inferred\"", "X": -1.866384280379635, "Y": -0.3790080315332305 }, { "Outlier": "\"Inferred\"", "X": -2.0216593626524055, "Y": 1.1571646856516027 }, { "Outlier": "\"Inferred\"", "X": -0.8117064980987132, "Y": 1.4925861552150301 }, { "Outlier": "\"Inferred\"", "X": -1.722727721700016, "Y": 3.4863260958686197 }, { "Outlier": "\"Inferred\"", "X": -0.37547781732650076, "Y": 2.5409261013823894 }, { "Outlier": "\"Inferred\"", "X": -0.2817038765883308, "Y": 4.159156266975698 }, { "Outlier": "\"Inferred\"", "X": 0.1667041295923471, "Y": 5.130870216791928 }, { "Outlier": "\"Inferred\"", "X": 0.3063259837784219, "Y": 6.0494478042882385 }, { "Outlier": "\"Inferred\"", "X": 0.9886298127833987, "Y": 6.241715204795343 }, { "Outlier": "\"Inferred\"", "X": 0.38496827432593106, "Y": 5.080720182682791 }, { "Outlier": "\"Inferred\"", "X": -0.0462180841414912, "Y": 5.942526622696139 }, { "Outlier": "\"Inferred\"", "X": -0.2251744493459894, "Y": 6.5612141945954665 }, { "Outlier": "\"Inferred\"", "X": -0.814146491805117, "Y": 6.9248379196231085 }, { "Outlier": "\"Inferred\"", "X": -0.7561087684322328, "Y": 6.62553822183286 }, { "Outlier": "\"Inferred\"", "X": 0.43914666664058133, "Y": 5.968357481439281 }, { "Outlier": "\"Inferred\"", "X": -0.12399654710156549, "Y": 6.12794282548626 }, { "Outlier": "\"Inferred\"", "X": -0.42208439701240297, "Y": 6.0998614517598835 }, { "Outlier": "\"Inferred\"", "X": -1.0537808269584867, "Y": 6.1962556328145055 }, { "Outlier": "\"Inferred\"", "X": -1.6736031774198217, "Y": 6.750007721251283 }, { "Outlier": "\"Inferred\"", "X": -1.7833668854997602, "Y": 7.681301119927426 }, { "Outlier": "\"Inferred\"", "X": -2.0611405381121775, "Y": 8.167752558749864 }, { "Outlier": "\"Inferred\"", "X": -2.2383582402513835, "Y": 9.359656490408558 }, { "Outlier": "\"Inferred\"", "X": -2.735982039185112, "Y": 10.804738228768336 }, { "Outlier": "\"Inferred\"", "X": -2.1205816100690718, "Y": 11.348794078362255 }, { "Outlier": "\"Inferred\"", "X": -1.0551680452461532, "Y": 11.034241068055147 }, { "Outlier": "\"Inferred\"", "X": -1.3524152565281415, "Y": 11.214208812917592 }, { "Outlier": "\"Inferred\"", "X": 0.38392332366332016, "Y": 12.52097689413072 }, { "Outlier": "\"Inferred\"", "X": -0.2673468302718842, "Y": 14.07716588278852 }, { "Outlier": "\"Inferred\"", "X": -1.4399510387629983, "Y": 15.953255031302792 }, { "Outlier": "\"Inferred\"", "X": -1.8232951752429258, "Y": 16.74054808359849 }, { "Outlier": "\"Inferred\"", "X": -2.1597296904624903, "Y": 14.680518334121674 }, { "Outlier": "\"Inferred\"", "X": -0.96472904813252, "Y": 15.13940147107535 }, { "Outlier": "\"Inferred\"", "X": -0.4250433824874331, "Y": 16.12393699144529 }, { "Outlier": "\"Inferred\"", "X": -0.4703277914016233, "Y": 14.578669365055553 }, { "Outlier": "\"Inferred\"", "X": 0.6564802222940759, "Y": 15.118393817361142 }, { "Outlier": "\"Inferred\"", "X": 1.2934887231854395, "Y": 15.847674814809938 }, { "Outlier": "\"Inferred\"", "X": 1.4154694944684096, "Y": 16.308982707174362 }, { "Outlier": "\"Inferred\"", "X": 2.2290237130424866, "Y": 16.07176229204904 }, { "Outlier": "\"Inferred\"", "X": 3.113541805788172, "Y": 16.39959240449963 }, { "Outlier": "\"Inferred\"", "X": 2.727374183344879, "Y": 16.539569187571324 }, { "Outlier": "\"Inferred\"", "X": 3.115556283090038, "Y": 18.66480697731124 }, { "Outlier": "\"Inferred\"", "X": 2.747380851426324, "Y": 18.083826913156145 }, { "Outlier": "\"Inferred\"", "X": 4.017539334489981, "Y": 17.34819318803999 }, { "Outlier": "\"Inferred\"", "X": 3.2106775079187786, "Y": 18.72699096758819 }, { "Outlier": "\"Inferred\"", "X": 4.064433069759858, "Y": 18.930938861615648 }, { "Outlier": "\"Inferred\"", "X": 4.886199754552055, "Y": 18.822292641259693 }, { "Outlier": "\"Inferred\"", "X": 5.693570082393493, "Y": 18.352523954726017 }, { "Outlier": "\"Inferred\"", "X": 4.764219023719597, "Y": 19.071723695171766 }, { "Outlier": "\"Inferred\"", "X": 5.109648787640866, "Y": 20.161076174063425 }, { "Outlier": "\"Inferred\"", "X": 6.684585131049911, "Y": 21.81265734233153 }, { "Outlier": "\"Inferred\"", "X": 8.07453949257289, "Y": 22.80841644425841 }, { "Outlier": "\"Inferred\"", "X": 7.412231069790365, "Y": 23.795701704568753 }, { "Outlier": "\"Inferred\"", "X": 8.875225307277638, "Y": 24.134979184688905 }, { "Outlier": "\"Inferred\"", "X": 9.786720807812639, "Y": 24.798570393123562 }, { "Outlier": "\"Inferred\"", "X": 10.839675266822008, "Y": 24.11060847582407 }, { "Outlier": "\"Inferred\"", "X": 11.951885850540787, "Y": 23.54370832545031 }, { "Outlier": "\"Inferred\"", "X": 12.362900938006847, "Y": 24.116558302935186 }, { "Outlier": "\"Inferred\"", "X": 13.403537769403211, "Y": 25.84854640696169 }, { "Outlier": "\"Inferred\"", "X": 13.087572364941128, "Y": 26.388585481883272 }, { "Outlier": "\"Inferred\"", "X": 15.123642440895985, "Y": 26.737432791961233 }, { "Outlier": "\"Inferred\"", "X": 16.27403404057166, "Y": 27.717366941234275 }, { "Outlier": "\"Inferred\"", "X": 17.54855755489548, "Y": 27.76578014438041 }, { "Outlier": "\"Inferred\"", "X": 17.02936531710395, "Y": 26.575680729713927 }, { "Outlier": "\"Inferred\"", "X": 17.846636670428634, "Y": 28.658403537125473 }, { "Outlier": "\"Inferred\"", "X": 17.75069874240183, "Y": 26.464358097759444 }, { "Outlier": "\"Inferred\"", "X": 18.642413159085244, "Y": 29.585749042045443 }, { "Outlier": "\"Inferred\"", "X": 18.114534945239527, "Y": 29.96056288469767 }, { "Outlier": "\"Inferred\"", "X": 17.89063941029048, "Y": 30.40967128533151 }, { "Outlier": "\"Inferred\"", "X": 19.177413425943513, "Y": 29.790316975358717 }, { "Outlier": "\"Inferred\"", "X": 19.421545582612655, "Y": 29.849565902325587 }, { "Outlier": "\"Inferred\"", "X": 20.428695873575105, "Y": 30.063955131069637 }, { "Outlier": "\"Inferred\"", "X": 20.271184767571977, "Y": 33.586006703235064 }, { "Outlier": "\"Inferred\"", "X": 21.23012653905654, "Y": 34.19202362990785 }, { "Outlier": "\"Inferred\"", "X": 22.383839376835358, "Y": 36.099918931011395 }, { "Outlier": "\"Inferred\"", "X": 24.849517743655664, "Y": 37.18137630871274 }, { "Outlier": "\"Inferred\"", "X": 23.018234864790475, "Y": 37.3878341474434 }, { "Outlier": "\"Inferred\"", "X": 25.368665864726108, "Y": 38.69565105530858 }, { "Outlier": "\"Inferred\"", "X": 26.194861372477984, "Y": 40.551406609263104 }, { "Outlier": "\"Inferred\"", "X": 27.776889871741783, "Y": 40.59409860728754 }, { "Outlier": "\"Inferred\"", "X": 27.309304890413063, "Y": 40.72390802396453 }, { "Outlier": "\"Inferred\"", "X": 30.788805704227972, "Y": 41.58481755616034 }, { "Outlier": "\"Inferred\"", "X": 32.244979307217676, "Y": 42.071660279644924 }, { "Outlier": "\"Inferred\"", "X": 30.32538655373424, "Y": 41.84495076984768 }, { "Outlier": "\"Inferred\"", "X": 31.38541255625376, "Y": 42.555891150999805 }, { "Outlier": "\"Inferred\"", "X": 32.126310224968, "Y": 42.5289072319434 }, { "Outlier": "\"Inferred\"", "X": 32.7961367160771, "Y": 43.6302990958513 }, { "Outlier": "\"Inferred\"", "X": 33.52062185359709, "Y": 41.838957567052745 }, { "Outlier": "\"Inferred\"", "X": 34.00499099918828, "Y": 41.4238466550717 }, { "Outlier": "\"Inferred\"", "X": 34.97176205418849, "Y": 41.582768517165796 }, { "Outlier": "\"Inferred\"", "X": 37.11733194220762, "Y": 40.388388912652786 }, { "Outlier": "\"Inferred\"", "X": 39.30399129001337, "Y": 41.02016527304278 }, { "Outlier": "\"Inferred\"", "X": 39.846536186239405, "Y": 41.76965920514953 }, { "Outlier": "\"Inferred\"", "X": 43.07081880036374, "Y": 41.728010760953346 }, { "Outlier": "\"Inferred\"", "X": 44.01200563791459, "Y": 40.49836682902172 }, { "Outlier": "\"Inferred\"", "X": 44.8445525967339, "Y": 39.042447260935184 }, { "Outlier": "\"Inferred\"", "X": 46.316377810937674, "Y": 37.8485091389301 }, { "Outlier": "\"Inferred\"", "X": 47.09604547880665, "Y": 37.378853477272365 }, { "Outlier": "\"Inferred\"", "X": 0, "Y": 0 }, { "Outlier": "\"Inferred\"", "X": -1.4959834833661376, "Y": 1.3545122401020775 }, { "Outlier": "\"Inferred\"", "X": -0.46692867593012344, "Y": 1.2568136970723294 }, { "Outlier": "\"Inferred\"", "X": -1.866384280379635, "Y": -0.3790080315332305 }, { "Outlier": "\"Inferred\"", "X": -2.0216593626524055, "Y": 1.1571646856516027 }, { "Outlier": "\"Inferred\"", "X": -0.8117064980987132, "Y": 1.4925861552150301 }, { "Outlier": "\"Inferred\"", "X": -1.722727721700016, "Y": 3.4863260958686197 }, { "Outlier": "\"Inferred\"", "X": -0.37547781732650076, "Y": 2.5409261013823894 }, { "Outlier": "\"Inferred\"", "X": -0.2817038765883308, "Y": 4.159156266975698 }, { "Outlier": "\"Inferred\"", "X": 0.1667041295923471, "Y": 5.130870216791928 }, { "Outlier": "\"Inferred\"", "X": 0.3063259837784219, "Y": 6.0494478042882385 }, { "Outlier": "\"Inferred\"", "X": 0.9886298127833987, "Y": 6.241715204795343 }, { "Outlier": "\"Inferred\"", "X": 0.38496827432593106, "Y": 5.080720182682791 }, { "Outlier": "\"Inferred\"", "X": -0.0462180841414912, "Y": 5.942526622696139 }, { "Outlier": "\"Inferred\"", "X": -0.2251744493459894, "Y": 6.5612141945954665 }, { "Outlier": "\"Inferred\"", "X": -0.814146491805117, "Y": 6.9248379196231085 }, { "Outlier": "\"Inferred\"", "X": -0.7561087684322328, "Y": 6.62553822183286 }, { "Outlier": "\"Inferred\"", "X": 0.43914666664058133, "Y": 5.968357481439281 }, { "Outlier": "\"Inferred\"", "X": -0.12399654710156549, "Y": 6.12794282548626 }, { "Outlier": "\"Inferred\"", "X": -0.42208439701240297, "Y": 6.0998614517598835 }, { "Outlier": "\"Inferred\"", "X": -1.0537808269584867, "Y": 6.1962556328145055 }, { "Outlier": "\"Inferred\"", "X": -1.6736031774198217, "Y": 6.750007721251283 }, { "Outlier": "\"Inferred\"", "X": -1.7833668854997602, "Y": 7.681301119927426 }, { "Outlier": "\"Inferred\"", "X": -2.0611405381121775, "Y": 8.167752558749864 }, { "Outlier": "\"Inferred\"", "X": -2.2383582402513835, "Y": 9.359656490408558 }, { "Outlier": "\"Inferred\"", "X": -2.735982039185112, "Y": 10.804738228768336 }, { "Outlier": "\"Inferred\"", "X": -2.1205816100690718, "Y": 11.348794078362255 }, { "Outlier": "\"Inferred\"", "X": -1.0551680452461532, "Y": 11.034241068055147 }, { "Outlier": "\"Inferred\"", "X": -1.3524152565281415, "Y": 11.214208812917592 }, { "Outlier": "\"Inferred\"", "X": 0.38392332366332016, "Y": 12.52097689413072 }, { "Outlier": "\"Inferred\"", "X": -0.2673468302718842, "Y": 14.07716588278852 }, { "Outlier": "\"Inferred\"", "X": -1.4399510387629983, "Y": 15.953255031302792 }, { "Outlier": "\"Inferred\"", "X": -1.8232951752429258, "Y": 16.74054808359849 }, { "Outlier": "\"Inferred\"", "X": -2.1597296904624903, "Y": 14.680518334121674 }, { "Outlier": "\"Inferred\"", "X": -0.96472904813252, "Y": 15.13940147107535 }, { "Outlier": "\"Inferred\"", "X": -0.4250433824874331, "Y": 16.12393699144529 }, { "Outlier": "\"Inferred\"", "X": -0.4703277914016233, "Y": 14.578669365055553 }, { "Outlier": "\"Inferred\"", "X": 0.6564802222940759, "Y": 15.118393817361142 }, { "Outlier": "\"Inferred\"", "X": 1.2934887231854395, "Y": 15.847674814809938 }, { "Outlier": "\"Inferred\"", "X": 1.4154694944684096, "Y": 16.308982707174362 }, { "Outlier": "\"Inferred\"", "X": 2.2290237130424866, "Y": 16.07176229204904 }, { "Outlier": "\"Inferred\"", "X": 3.113541805788172, "Y": 16.39959240449963 }, { "Outlier": "\"Inferred\"", "X": 2.727374183344879, "Y": 16.539569187571324 }, { "Outlier": "\"Inferred\"", "X": 3.115556283090038, "Y": 18.66480697731124 }, { "Outlier": "\"Inferred\"", "X": 2.747380851426324, "Y": 18.083826913156145 }, { "Outlier": "\"Inferred\"", "X": 4.017539334489981, "Y": 17.34819318803999 }, { "Outlier": "\"Inferred\"", "X": 3.2106775079187786, "Y": 18.72699096758819 }, { "Outlier": "\"Inferred\"", "X": 4.064433069759858, "Y": 18.930938861615648 }, { "Outlier": "\"Inferred\"", "X": 4.886199754552055, "Y": 18.822292641259693 }, { "Outlier": "\"Inferred\"", "X": 5.693570082393493, "Y": 18.352523954726017 }, { "Outlier": "\"Inferred\"", "X": 4.764219023719597, "Y": 19.071723695171766 }, { "Outlier": "\"Inferred\"", "X": 5.109648787640866, "Y": 20.161076174063425 }, { "Outlier": "\"Inferred\"", "X": 6.684585131049911, "Y": 21.81265734233153 }, { "Outlier": "\"Inferred\"", "X": 8.07453949257289, "Y": 22.80841644425841 }, { "Outlier": "\"Inferred\"", "X": 7.412231069790365, "Y": 23.795701704568753 }, { "Outlier": "\"Inferred\"", "X": 8.875225307277638, "Y": 24.134979184688905 }, { "Outlier": "\"Inferred\"", "X": 9.786720807812639, "Y": 24.798570393123562 }, { "Outlier": "\"Inferred\"", "X": 10.839675266822008, "Y": 24.11060847582407 }, { "Outlier": "\"Inferred\"", "X": 11.951885850540787, "Y": 23.54370832545031 }, { "Outlier": "\"Inferred\"", "X": 12.362900938006847, "Y": 24.116558302935186 }, { "Outlier": "\"Inferred\"", "X": 13.403537769403211, "Y": 25.84854640696169 }, { "Outlier": "\"Inferred\"", "X": 13.087572364941128, "Y": 26.388585481883272 }, { "Outlier": "\"Inferred\"", "X": 15.123642440895985, "Y": 26.737432791961233 }, { "Outlier": "\"Inferred\"", "X": 16.27403404057166, "Y": 27.717366941234275 }, { "Outlier": "\"Inferred\"", "X": 17.54855755489548, "Y": 27.76578014438041 }, { "Outlier": "\"Inferred\"", "X": 17.02936531710395, "Y": 26.575680729713927 }, { "Outlier": "\"Inferred\"", "X": 17.846636670428634, "Y": 28.658403537125473 }, { "Outlier": "\"Inferred\"", "X": 17.75069874240183, "Y": 26.464358097759444 }, { "Outlier": "\"Inferred\"", "X": 18.642413159085244, "Y": 29.585749042045443 }, { "Outlier": "\"Inferred\"", "X": 18.114534945239527, "Y": 29.96056288469767 }, { "Outlier": "\"Inferred\"", "X": 17.89063941029048, "Y": 30.40967128533151 }, { "Outlier": "\"Inferred\"", "X": 19.177413425943513, "Y": 29.790316975358717 }, { "Outlier": "\"Inferred\"", "X": 19.421545582612655, "Y": 29.849565902325587 }, { "Outlier": "\"Inferred\"", "X": 20.428695873575105, "Y": 30.063955131069637 }, { "Outlier": "\"Inferred\"", "X": 20.271184767571977, "Y": 33.586006703235064 }, { "Outlier": "\"Inferred\"", "X": 21.23012653905654, "Y": 34.19202362990785 }, { "Outlier": "\"Inferred\"", "X": 22.383839376835358, "Y": 36.099918931011395 }, { "Outlier": "\"Inferred\"", "X": 24.849517743655664, "Y": 37.18137630871274 }, { "Outlier": "\"Inferred\"", "X": 23.018234864790475, "Y": 37.3878341474434 }, { "Outlier": "\"Inferred\"", "X": 25.368665864726108, "Y": 38.69565105530858 }, { "Outlier": "\"Inferred\"", "X": 26.194861372477984, "Y": 40.551406609263104 }, { "Outlier": "\"Inferred\"", "X": 27.776889871741783, "Y": 40.59409860728754 }, { "Outlier": "\"Inferred\"", "X": 27.309304890413063, "Y": 40.72390802396453 }, { "Outlier": "\"Inferred\"", "X": 30.788805704227972, "Y": 41.58481755616034 }, { "Outlier": "\"Inferred\"", "X": 32.244979307217676, "Y": 42.071660279644924 }, { "Outlier": "\"Inferred\"", "X": 30.32538655373424, "Y": 41.84495076984768 }, { "Outlier": "\"Inferred\"", "X": 31.38541255625376, "Y": 42.555891150999805 }, { "Outlier": "\"Inferred\"", "X": 32.126310224968, "Y": 42.5289072319434 }, { "Outlier": "\"Inferred\"", "X": 32.7961367160771, "Y": 43.6302990958513 }, { "Outlier": "\"Inferred\"", "X": 33.52062185359709, "Y": 41.838957567052745 }, { "Outlier": "\"Inferred\"", "X": 34.00499099918828, "Y": 41.4238466550717 }, { "Outlier": "\"Inferred\"", "X": 34.97176205418849, "Y": 41.582768517165796 }, { "Outlier": "\"Inferred\"", "X": 37.11733194220762, "Y": 40.388388912652786 }, { "Outlier": "\"Inferred\"", "X": 39.30399129001337, "Y": 41.02016527304278 }, { "Outlier": "\"Inferred\"", "X": 39.846536186239405, "Y": 41.76965920514953 }, { "Outlier": "\"Inferred\"", "X": 41.41289090018148, "Y": 40.40980685025504 }, { "Outlier": "\"Inferred\"", "X": 41.341374038004815, "Y": 38.877159633685075 }, { "Outlier": "\"Inferred\"", "X": 42.151979000825584, "Y": 39.606699247527324 }, { "Outlier": "\"Inferred\"", "X": 42.68088796888283, "Y": 39.14346319779684 }, { "Outlier": "\"Inferred\"", "X": 43.925889140270286, "Y": 40.27833386283229 }, { "Outlier": "\"Inferred\"", "X": 0, "Y": 0 }, { "Outlier": "\"Inferred\"", "X": -1.4959834833661376, "Y": 1.3545122401020775 }, { "Outlier": "\"Inferred\"", "X": -0.46692867593012344, "Y": 1.2568136970723294 }, { "Outlier": "\"Inferred\"", "X": -1.866384280379635, "Y": -0.3790080315332305 }, { "Outlier": "\"Inferred\"", "X": -2.0216593626524055, "Y": 1.1571646856516027 }, { "Outlier": "\"Inferred\"", "X": -0.8117064980987132, "Y": 1.4925861552150301 }, { "Outlier": "\"Inferred\"", "X": -1.722727721700016, "Y": 3.4863260958686197 }, { "Outlier": "\"Inferred\"", "X": -0.37547781732650076, "Y": 2.5409261013823894 }, { "Outlier": "\"Inferred\"", "X": -0.2817038765883308, "Y": 4.159156266975698 }, { "Outlier": "\"Inferred\"", "X": 0.1667041295923471, "Y": 5.130870216791928 }, { "Outlier": "\"Inferred\"", "X": 0.3063259837784219, "Y": 6.0494478042882385 }, { "Outlier": "\"Inferred\"", "X": 0.9886298127833987, "Y": 6.241715204795343 }, { "Outlier": "\"Inferred\"", "X": 0.38496827432593106, "Y": 5.080720182682791 }, { "Outlier": "\"Inferred\"", "X": -0.0462180841414912, "Y": 5.942526622696139 }, { "Outlier": "\"Inferred\"", "X": -0.2251744493459894, "Y": 6.5612141945954665 }, { "Outlier": "\"Inferred\"", "X": -0.814146491805117, "Y": 6.9248379196231085 }, { "Outlier": "\"Inferred\"", "X": -0.7561087684322328, "Y": 6.62553822183286 }, { "Outlier": "\"Inferred\"", "X": 0.43914666664058133, "Y": 5.968357481439281 }, { "Outlier": "\"Inferred\"", "X": -0.12399654710156549, "Y": 6.12794282548626 }, { "Outlier": "\"Inferred\"", "X": -0.42208439701240297, "Y": 6.0998614517598835 }, { "Outlier": "\"Inferred\"", "X": -1.0537808269584867, "Y": 6.1962556328145055 }, { "Outlier": "\"Inferred\"", "X": -1.6736031774198217, "Y": 6.750007721251283 }, { "Outlier": "\"Inferred\"", "X": -1.7833668854997602, "Y": 7.681301119927426 }, { "Outlier": "\"Inferred\"", "X": -2.0611405381121775, "Y": 8.167752558749864 }, { "Outlier": "\"Inferred\"", "X": -2.2383582402513835, "Y": 9.359656490408558 }, { "Outlier": "\"Inferred\"", "X": -2.735982039185112, "Y": 10.804738228768336 }, { "Outlier": "\"Inferred\"", "X": -2.1205816100690718, "Y": 11.348794078362255 }, { "Outlier": "\"Inferred\"", "X": -1.0551680452461532, "Y": 11.034241068055147 }, { "Outlier": "\"Inferred\"", "X": -1.3524152565281415, "Y": 11.214208812917592 }, { "Outlier": "\"Inferred\"", "X": 0.38392332366332016, "Y": 12.52097689413072 }, { "Outlier": "\"Inferred\"", "X": -0.2673468302718842, "Y": 14.07716588278852 }, { "Outlier": "\"Inferred\"", "X": 0.024963119400584516, "Y": 13.93326448589509 }, { "Outlier": "\"Inferred\"", "X": 0.01668690681705276, "Y": 14.83684373655884 }, { "Outlier": "\"Inferred\"", "X": 2.17911547602422, "Y": 15.084074410502849 }, { "Outlier": "\"Inferred\"", "X": 1.9108705892057958, "Y": 13.887471102911695 }, { "Outlier": "\"Inferred\"", "X": 2.6784610809277485, "Y": 14.659133933032681 }, { "Outlier": "\"Inferred\"", "X": 2.4018452726915647, "Y": 15.608512892237467 }, { "Outlier": "\"Inferred\"", "X": 1.2912375493838337, "Y": 15.997567490220362 }, { "Outlier": "\"Inferred\"", "X": 1.1050679766955862, "Y": 15.752401275473284 }, { "Outlier": "\"Inferred\"", "X": 0.19917206731955894, "Y": 15.377820080513631 }, { "Outlier": "\"Inferred\"", "X": -0.9741543649500806, "Y": 13.614632058863588 }, { "Outlier": "\"Inferred\"", "X": -0.4990785318155075, "Y": 13.854551762050791 }, { "Outlier": "\"Inferred\"", "X": 0.2200996415571056, "Y": 15.738329159979111 }, { "Outlier": "\"Inferred\"", "X": 0.7352628450603202, "Y": 15.3303445791598 }, { "Outlier": "\"Inferred\"", "X": 1.094012160536499, "Y": 16.38408247206461 }, { "Outlier": "\"Inferred\"", "X": 1.5673766000647635, "Y": 16.97018692648492 }, { "Outlier": "\"Inferred\"", "X": 2.0469033151093625, "Y": 16.616370658076875 }, { "Outlier": "\"Inferred\"", "X": 2.3963907566016593, "Y": 18.314095401143472 }, { "Outlier": "\"Inferred\"", "X": 4.166301538985277, "Y": 19.69339124841146 }, { "Outlier": "\"Inferred\"", "X": 5.312289793343187, "Y": 20.895979091356306 }, { "Outlier": "\"Inferred\"", "X": 7.225390520072812, "Y": 20.05514728382199 }, { "Outlier": "\"Inferred\"", "X": 8.274950352304558, "Y": 21.748224816446445 }, { "Outlier": "\"Inferred\"", "X": 10.077144277369639, "Y": 22.20028425004137 }, { "Outlier": "\"Inferred\"", "X": 11.648130476884795, "Y": 22.14028530286502 }, { "Outlier": "\"Inferred\"", "X": 12.150662300936114, "Y": 22.530497385437407 }, { "Outlier": "\"Inferred\"", "X": 12.386758213852861, "Y": 21.407269138905697 }, { "Outlier": "\"Inferred\"", "X": 12.750381749456752, "Y": 21.999311217832176 }, { "Outlier": "\"Inferred\"", "X": 14.02714510943885, "Y": 23.182960884690576 }, { "Outlier": "\"Inferred\"", "X": 13.27298822179917, "Y": 23.531086350970302 }, { "Outlier": "\"Inferred\"", "X": 13.988745103331425, "Y": 24.324866195918368 }, { "Outlier": "\"Inferred\"", "X": 14.97399897741783, "Y": 23.871020803936624 }, { "Outlier": "\"Inferred\"", "X": 15.137869876167708, "Y": 24.144965731455162 }, { "Outlier": "\"Inferred\"", "X": 15.565176995801204, "Y": 25.12036070475441 }, { "Outlier": "\"Inferred\"", "X": 17.600256218729754, "Y": 24.678147688007876 }, { "Outlier": "\"Inferred\"", "X": 17.237482795901055, "Y": 26.542536752281652 }, { "Outlier": "\"Inferred\"", "X": 17.015954839425017, "Y": 28.450372658629945 }, { "Outlier": "\"Inferred\"", "X": 17.32787978081421, "Y": 26.75434260574275 }, { "Outlier": "\"Inferred\"", "X": 18.229555681882005, "Y": 27.282709577031078 }, { "Outlier": "\"Inferred\"", "X": 18.10563921397335, "Y": 27.752039881712587 }, { "Outlier": "\"Inferred\"", "X": 17.123756040356, "Y": 29.56098407271677 }, { "Outlier": "\"Inferred\"", "X": 15.460570214108486, "Y": 30.819617268587045 }, { "Outlier": "\"Inferred\"", "X": 16.315799863680635, "Y": 32.82542887662225 }, { "Outlier": "\"Inferred\"", "X": 16.901473532111954, "Y": 34.409088099712484 }, { "Outlier": "\"Inferred\"", "X": 17.42628716729539, "Y": 34.29954984262977 }, { "Outlier": "\"Inferred\"", "X": 18.4645589421396, "Y": 35.705103134817925 }, { "Outlier": "\"Inferred\"", "X": 19.854679777751464, "Y": 34.847546859528244 }, { "Outlier": "\"Inferred\"", "X": 21.061631904164262, "Y": 35.128817736081885 }, { "Outlier": "\"Inferred\"", "X": 21.742962935562577, "Y": 34.7043175359872 }, { "Outlier": "\"Inferred\"", "X": 22.665780613197352, "Y": 34.79885327766628 }, { "Outlier": "\"Inferred\"", "X": 22.948111911315912, "Y": 35.993516839684936 }, { "Outlier": "\"Inferred\"", "X": 23.387651560179787, "Y": 36.74307978279117 }, { "Outlier": "\"Inferred\"", "X": 23.653409918697086, "Y": 38.22971702186132 }, { "Outlier": "\"Inferred\"", "X": 26.070568899608688, "Y": 38.94807029034141 }, { "Outlier": "\"Inferred\"", "X": 27.878580768011403, "Y": 38.44185309899433 }, { "Outlier": "\"Inferred\"", "X": 29.191471700552917, "Y": 38.697472562659584 }, { "Outlier": "\"Inferred\"", "X": 29.348002508807642, "Y": 38.99777212620182 }, { "Outlier": "\"Inferred\"", "X": 30.66538491817076, "Y": 39.44463741855792 }, { "Outlier": "\"Inferred\"", "X": 31.838701070645463, "Y": 40.6532118676781 }, { "Outlier": "\"Inferred\"", "X": 32.54565337408032, "Y": 41.106613014512725 }, { "Outlier": "\"Inferred\"", "X": 35.80016973266396, "Y": 39.93602216733599 }, { "Outlier": "\"Inferred\"", "X": 37.024230065322435, "Y": 39.611212346613435 }, { "Outlier": "\"Inferred\"", "X": 37.22509072925038, "Y": 39.32867188584064 }, { "Outlier": "\"Inferred\"", "X": 38.60571810176744, "Y": 39.82400773293471 }, { "Outlier": "\"Inferred\"", "X": 38.01245930720338, "Y": 41.786165891712976 }, { "Outlier": "\"Inferred\"", "X": 40.33399764927648, "Y": 42.008472387625936 }, { "Outlier": "\"Inferred\"", "X": 39.79674808754906, "Y": 41.90460204710545 }, { "Outlier": "\"Inferred\"", "X": 41.21137926317559, "Y": 42.596290047005986 }, { "Outlier": "\"Inferred\"", "X": 42.18496312014487, "Y": 43.08261415850917 }, { "Outlier": "\"Inferred\"", "X": 43.63661001006544, "Y": 43.36339026973433 }, { "Outlier": "\"Inferred\"", "X": 44.29745808099413, "Y": 44.78221762225468 }, { "Outlier": "\"Inferred\"", "X": 0, "Y": 0 }, { "Outlier": "\"Inferred\"", "X": -1.4959834833661376, "Y": 1.3545122401020775 }, { "Outlier": "\"Inferred\"", "X": -0.46692867593012344, "Y": 1.2568136970723294 }, { "Outlier": "\"Inferred\"", "X": -1.866384280379635, "Y": -0.3790080315332305 }, { "Outlier": "\"Inferred\"", "X": -2.0216593626524055, "Y": 1.1571646856516027 }, { "Outlier": "\"Inferred\"", "X": -0.8117064980987132, "Y": 1.4925861552150301 }, { "Outlier": "\"Inferred\"", "X": -1.722727721700016, "Y": 3.4863260958686197 }, { "Outlier": "\"Inferred\"", "X": -0.37547781732650076, "Y": 2.5409261013823894 }, { "Outlier": "\"Inferred\"", "X": -0.2817038765883308, "Y": 4.159156266975698 }, { "Outlier": "\"Inferred\"", "X": 0.1667041295923471, "Y": 5.130870216791928 }, { "Outlier": "\"Inferred\"", "X": 0.3063259837784219, "Y": 6.0494478042882385 }, { "Outlier": "\"Inferred\"", "X": 0.9886298127833987, "Y": 6.241715204795343 }, { "Outlier": "\"Inferred\"", "X": 0.38496827432593106, "Y": 5.080720182682791 }, { "Outlier": "\"Inferred\"", "X": -0.0462180841414912, "Y": 5.942526622696139 }, { "Outlier": "\"Inferred\"", "X": -0.2251744493459894, "Y": 6.5612141945954665 }, { "Outlier": "\"Inferred\"", "X": -0.814146491805117, "Y": 6.9248379196231085 }, { "Outlier": "\"Inferred\"", "X": -0.7561087684322328, "Y": 6.62553822183286 }, { "Outlier": "\"Inferred\"", "X": 0.43914666664058133, "Y": 5.968357481439281 }, { "Outlier": "\"Inferred\"", "X": -0.12399654710156549, "Y": 6.12794282548626 }, { "Outlier": "\"Inferred\"", "X": -0.42208439701240297, "Y": 6.0998614517598835 }, { "Outlier": "\"Inferred\"", "X": -1.0537808269584867, "Y": 6.1962556328145055 }, { "Outlier": "\"Inferred\"", "X": -1.6736031774198217, "Y": 6.750007721251283 }, { "Outlier": "\"Inferred\"", "X": -1.7833668854997602, "Y": 7.681301119927426 }, { "Outlier": "\"Inferred\"", "X": -2.0611405381121775, "Y": 8.167752558749864 }, { "Outlier": "\"Inferred\"", "X": -2.2383582402513835, "Y": 9.359656490408558 }, { "Outlier": "\"Inferred\"", "X": -2.735982039185112, "Y": 10.804738228768336 }, { "Outlier": "\"Inferred\"", "X": -2.1205816100690718, "Y": 11.348794078362255 }, { "Outlier": "\"Inferred\"", "X": -1.0551680452461532, "Y": 11.034241068055147 }, { "Outlier": "\"Inferred\"", "X": -1.3524152565281415, "Y": 11.214208812917592 }, { "Outlier": "\"Inferred\"", "X": 0.38392332366332016, "Y": 12.52097689413072 }, { "Outlier": "\"Inferred\"", "X": -0.2673468302718842, "Y": 14.07716588278852 }, { "Outlier": "\"Inferred\"", "X": 0.024963119400584516, "Y": 13.93326448589509 }, { "Outlier": "\"Inferred\"", "X": 0.01668690681705276, "Y": 14.83684373655884 }, { "Outlier": "\"Inferred\"", "X": 2.17911547602422, "Y": 15.084074410502849 }, { "Outlier": "\"Inferred\"", "X": 1.9108705892057958, "Y": 13.887471102911695 }, { "Outlier": "\"Inferred\"", "X": 2.6784610809277485, "Y": 14.659133933032681 }, { "Outlier": "\"Inferred\"", "X": 2.4018452726915647, "Y": 15.608512892237467 }, { "Outlier": "\"Inferred\"", "X": 1.2912375493838337, "Y": 15.997567490220362 }, { "Outlier": "\"Inferred\"", "X": 1.1050679766955862, "Y": 15.752401275473284 }, { "Outlier": "\"Inferred\"", "X": 0.19917206731955894, "Y": 15.377820080513631 }, { "Outlier": "\"Inferred\"", "X": -0.9741543649500806, "Y": 13.614632058863588 }, { "Outlier": "\"Inferred\"", "X": -0.4990785318155075, "Y": 13.854551762050791 }, { "Outlier": "\"Inferred\"", "X": 0.2200996415571056, "Y": 15.738329159979111 }, { "Outlier": "\"Inferred\"", "X": 0.7352628450603202, "Y": 15.3303445791598 }, { "Outlier": "\"Inferred\"", "X": 1.094012160536499, "Y": 16.38408247206461 }, { "Outlier": "\"Inferred\"", "X": 1.5673766000647635, "Y": 16.97018692648492 }, { "Outlier": "\"Inferred\"", "X": 2.0469033151093625, "Y": 16.616370658076875 }, { "Outlier": "\"Inferred\"", "X": 2.3963907566016593, "Y": 18.314095401143472 }, { "Outlier": "\"Inferred\"", "X": 4.166301538985277, "Y": 19.69339124841146 }, { "Outlier": "\"Inferred\"", "X": 5.312289793343187, "Y": 20.895979091356306 }, { "Outlier": "\"Inferred\"", "X": 7.225390520072812, "Y": 20.05514728382199 }, { "Outlier": "\"Inferred\"", "X": 8.274950352304558, "Y": 21.748224816446445 }, { "Outlier": "\"Inferred\"", "X": 10.077144277369639, "Y": 22.20028425004137 }, { "Outlier": "\"Inferred\"", "X": 11.648130476884795, "Y": 22.14028530286502 }, { "Outlier": "\"Inferred\"", "X": 12.150662300936114, "Y": 22.530497385437407 }, { "Outlier": "\"Inferred\"", "X": 12.386758213852861, "Y": 21.407269138905697 }, { "Outlier": "\"Inferred\"", "X": 12.750381749456752, "Y": 21.999311217832176 }, { "Outlier": "\"Inferred\"", "X": 14.02714510943885, "Y": 23.182960884690576 }, { "Outlier": "\"Inferred\"", "X": 13.27298822179917, "Y": 23.531086350970302 }, { "Outlier": "\"Inferred\"", "X": 13.988745103331425, "Y": 24.324866195918368 }, { "Outlier": "\"Inferred\"", "X": 14.97399897741783, "Y": 23.871020803936624 }, { "Outlier": "\"Inferred\"", "X": 15.137869876167708, "Y": 24.144965731455162 }, { "Outlier": "\"Inferred\"", "X": 15.565176995801204, "Y": 25.12036070475441 }, { "Outlier": "\"Inferred\"", "X": 17.600256218729754, "Y": 24.678147688007876 }, { "Outlier": "\"Inferred\"", "X": 17.237482795901055, "Y": 26.542536752281652 }, { "Outlier": "\"Inferred\"", "X": 17.015954839425017, "Y": 28.450372658629945 }, { "Outlier": "\"Inferred\"", "X": 17.32787978081421, "Y": 26.75434260574275 }, { "Outlier": "\"Inferred\"", "X": 18.229555681882005, "Y": 27.282709577031078 }, { "Outlier": "\"Inferred\"", "X": 18.10563921397335, "Y": 27.752039881712587 }, { "Outlier": "\"Inferred\"", "X": 17.123756040356, "Y": 29.56098407271677 }, { "Outlier": "\"Inferred\"", "X": 15.460570214108486, "Y": 30.819617268587045 }, { "Outlier": "\"Inferred\"", "X": 16.315799863680635, "Y": 32.82542887662225 }, { "Outlier": "\"Inferred\"", "X": 16.901473532111954, "Y": 34.409088099712484 }, { "Outlier": "\"Inferred\"", "X": 17.42628716729539, "Y": 34.29954984262977 }, { "Outlier": "\"Inferred\"", "X": 18.4645589421396, "Y": 35.705103134817925 }, { "Outlier": "\"Inferred\"", "X": 19.854679777751464, "Y": 34.847546859528244 }, { "Outlier": "\"Inferred\"", "X": 21.061631904164262, "Y": 35.128817736081885 }, { "Outlier": "\"Inferred\"", "X": 22.265669317998004, "Y": 36.25282068300822 }, { "Outlier": "\"Inferred\"", "X": 22.35839233579931, "Y": 36.79521681982773 }, { "Outlier": "\"Inferred\"", "X": 23.530034470726942, "Y": 37.81481503722004 }, { "Outlier": "\"Inferred\"", "X": 24.79990792503274, "Y": 38.164587201161694 }, { "Outlier": "\"Inferred\"", "X": 25.800458739095653, "Y": 39.85911422057112 }, { "Outlier": "\"Inferred\"", "X": 24.796759275791104, "Y": 41.70901349837008 }, { "Outlier": "\"Inferred\"", "X": 27.333671371269205, "Y": 41.84934201926996 }, { "Outlier": "\"Inferred\"", "X": 28.69925643311348, "Y": 43.467359851059534 }, { "Outlier": "\"Inferred\"", "X": 28.916638028062533, "Y": 45.45708168972819 }, { "Outlier": "\"Inferred\"", "X": 29.973577906836102, "Y": 46.41633625757215 }, { "Outlier": "\"Inferred\"", "X": 32.05606284604073, "Y": 45.28473893333061 }, { "Outlier": "\"Inferred\"", "X": 32.88988156762814, "Y": 44.8538155889555 }, { "Outlier": "\"Inferred\"", "X": 31.19806934467899, "Y": 43.66015178077464 }, { "Outlier": "\"Inferred\"", "X": 32.81704478887686, "Y": 44.28486876827402 }, { "Outlier": "\"Inferred\"", "X": 32.52189150648387, "Y": 43.67878912795227 }, { "Outlier": "\"Inferred\"", "X": 33.63905056412499, "Y": 43.38602829802608 }, { "Outlier": "\"Inferred\"", "X": 34.2299330139971, "Y": 44.002045551626516 }, { "Outlier": "\"Inferred\"", "X": 34.5801094897995, "Y": 43.55850184725563 }, { "Outlier": "\"Inferred\"", "X": 35.61480024416932, "Y": 41.49485106639062 }, { "Outlier": "\"Inferred\"", "X": 37.1027658095741, "Y": 41.92963421495869 }, { "Outlier": "\"Inferred\"", "X": 41.14763144259741, "Y": 42.5867872280412 }, { "Outlier": "\"Inferred\"", "X": 40.25689869478788, "Y": 43.699882941801995 }, { "Outlier": "\"Inferred\"", "X": 41.73373040718483, "Y": 43.61522453002227 } ] }, "encoding": { "color": { "field": "Outlier" }, "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "circle", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "inferredVectors = fst =<< particles\n", "\n", "plot \n", " (zip \n", " (fmap (\\v -> (v V.! 0, v V.! 1)) $ randomWalkPrior <> randomWalkObserved <> inferredVectors) \n", " (replicate 100 (T.pack \"Prior\") <> replicate 100 (T.pack \"Observed\") <> replicate 500 (T.pack \"Inferred\")))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see here that `SMC` has done a pretty good job.\n", "\n", "The intuition of how SMC does well is that it is searching the space of solutions with some backtracking (because it has a whole population of guesses to rely on), and moreover, this search results in unbiased sampling from the distribution of interest. So if it makes a series of guesses the leads it astray, it can throw away this possiblity at a later stage. " ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: notebooks/tutorials/Sampling.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ ":e ImportQualifiedPost\n", ":e FlexibleContexts\n", ":e BlockArguments\n", ":e TupleSections\n", ":e FlexibleContexts\n", ":e OverloadedStrings\n", ":e LambdaCase\n", "\n", "import Control.Monad.Bayes.Class\n", "import Control.Monad.Bayes.Enumerator\n", "import Control.Monad.Bayes.Weighted\n", "import Control.Monad.Bayes.Sampler.Strict\n", "\n", "import Control.Monad\n", "import Data.List\n", "import Data.Ord\n", "import Control.Arrow (first)\n", "import Data.Text (pack, Text)\n", "import Numeric.Log\n", "import Control.Arrow (second)\n", "\n", ":l ../plotting.hs" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "# Sampling\n", "\n", "Before discussing inference, we should understand how to sample from models. This notebook explains how to do that." ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Discrete distribution\n", "We'll start with a very simple model, namely:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "model :: MonadDistribution m => m Bool\n", "model = bernoulli 0.7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To take a sample, do:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or with a fixed seed:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampleIOfixed model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To take multiple samples, you could rerun `sampler` many times, but it's somewhat more in the probabilistic programming spirit to instead define a distribution over multiple draws from `model` and then just sample once, as follows:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[False,True,True,True,True,True,False,False,True,True]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "multipleDraws :: MonadDistribution m => m [Bool]\n", "multipleDraws = replicateM 10 model\n", "\n", "draws <- sampler multipleDraws\n", "\n", "draws" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We convert a list of samples to an empirical distribution, where `toEmpirical` function returns list of distribution values with share of their occurence in the :" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(True,0.7),(False,0.3)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "emp = toEmpirical draws :: [(Bool, Double)]\n", "\n", "emp" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": "True", "Y": 0.7 }, { "X": "False", "Y": 0.3 } ] }, "encoding": { "x": { "field": "X", "type": "nominal" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot $ first (pack . show) <$> emp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In fact, we could lean even further into the spirit of probabilistic programming, and transform `model` into a distribution over plots, and sample from that:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": "True", "Y": 0.9 }, { "X": "False", "Y": 0.1 } ] }, "encoding": { "x": { "field": "X", "type": "nominal" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb4AAAHOCAYAAADnr2woAAAAAXNSR0IArs4c6QAAIABJREFUeF7tnX9UX2Udxz+MoaJox5w/MDmnyYFRTOyYDKuhxvzJMVsSommpKTrFGiLJsjNzOjPSOmegYluUcaSOis0VwZHIX4Msx07zkGdgNo95loo/GyoL0W/n3oLj3Nh9+H7v5T4/XvevyZ77PJ/P6/3c8/L58t33m5ZIJBLCBQEIQAACEHCEQBricyRp2oQABCAAAZ8A4mMjQAACEICAUwQQn1Nx0ywEIAABCCA+9gAEIAABCDhFAPE5FTfNQgACEIAA4mMPQAACEICAUwQiEd/g4KDU1tZKenq65OXlSVNTk//nyWtiYkIuv/xyefHFF+X999/3/37hwoVOgadZCEAAAhCIh0Ak4istLZXW1lbJz8+XmpoaKSsrk4qKiqkO29raZNOmTdLc3CxDQ0OybNkyeeyxx+IhwKoQgAAEIOAUgdDF553mCgsLZXh42AfZ2dkpPT09/qlu8rr55pvliCOOkOrqav9H3p+3b9++y6nQqRRoFgIQgAAEZo1A6OIbGRmR8vJyGRgY8Jvo6+uTlpYWaW9vn2rq0UcflRtvvNH/2eOPPy6XXnqpvPTSS3LwwQf74/v7+3cBcOihh8rJJ588a1BYCAI2Eli+bpONbdGTowTWVBcrde59OFlubu4uY0MX3/j4uBQVFfkvYXpXV1eXdHd3+y9rfvi67777/JPgiSeeKLfeeqts3bpV0tLS9thIY2OjNDQ0KDXJIAhAYM8EvvTd+0ADAWsI/O7WKqVetm3bJkcffXS04vNmLykpkY6ODsnJyZH6+nopLi6WqqoqGR0dlaysLP9NLc8++6yccsop4r0RZsWKFfL73/9+2iYQn1K+DILAXgkgPjaITQS0E5/3UmVdXZ1kZ2dLZmameG9mycjIkIKCAunt7ZUDDzxQzjjjDP/vX3/9dVm7dq0sWLAA8dm0K+lFOwKIT7tIKCgFAtqJb7KXsbExX3zTXTt27JCDDjoosHVOfIGIGACBQAKILxARAwwioK34wmKI+MIiyTwuE0B8LqdvX++Iz75M6QgCoRNAfKEjZcIYCSC+GOGzNARMIYD4TEmKOlUIID4VSoyBgOMEEJ/jG8Cy9hGfZYHSDgSiIID4oqDKnHERQHxxkWddCBhEAPEZFBalBhJAfIGIGAABCCA+9oBNBBCfTWnSCwQiIoD4IgLLtLEQQHyxYGdRCJhFAPGZlRfV7p0A4mOHQAACgQQQXyAiBhhEAPEZFBalQiAuAogvLvKsGwUBxBcFVeaEgGUEEJ9lgTreDuJzfAPQPgRUCCA+FUqMMYUA4jMlKeqEQIwEEF+M8Fk6dAKIL3SkTAgB+wggPvsydbkjxOdy+vQOAUUCiE8RFMOMIID4jIiJIiEQLwHEFy9/Vg+XAOILlyezQcBKAojPylidbQrxORs9jUNAnQDiU2fFSP0JID79M6JCCMROAPHFHgEFhEgA8YUIk6kgYCsBxGdrsm72hfjczJ2uITAjAohvRrgYrDkBxKd5QJQHAR0IID4dUqCGsAggvrBIMg8ELCaA+CwO18HWEJ+DodMyBGZKAPHNlBjjdSaA+HROh9ogoAkBxKdJEJQRCgHEFwpGJoGA3QQQn935utYd4nMtcfqFQBIEEF8S0LhFWwKIT9toKAwC+hBAfPpkQSWpE0B8qTNkBghYTwDxWR+xUw0iPqfiplkIJEcA8SXHjbv0JID49MyFqiCgFQHEp1UcFJMiAcSXIkBuh4ALBBCfCym70yPicydrOoVA0gQQX9LouFFDAohPw1AoCQK6EUB8uiVCPakQQHyp0ONeCDhCAPE5ErQjbWonvsHBQamtrZX09HTJy8uTpqYm/8+T1/j4uFRXV8vOnTtlx44dcsUVV8jSpUunjauxsVEaGhociZM2IRANAcQXDVdmjYeAduIrLS2V1tZWyc/Pl5qaGikrK5OKioopOvfff788/vjjcuedd8ro6KgUFRXJ888/j/ji2T+s6ggBxOdI0I60qZX4JiYmpLCwUIaHh338nZ2d0tPT45/6Jq+//OUvsmrVKnnooYdkaGhIrr76anniiScQnyMbljbjIYD44uHOqtEQ0Ep8IyMjUl5eLgMDA363fX190tLSIu3t7VPdv/fee3LuuefKv//9b3n55Zdl9erVcs4550yN7+/v341UZWVlNPSYFQKOEFi+bpMjndKmCwTWVBcrtZlIJCQ3N3eXsWkJ76chXt7v77yXLr2TnHd1dXVJd3e3NDc3T63i/fmtt96SlStXijf+mGOOkS1btkhmZuYeK+F3fCEGxFTOEuDE52z0Vjau1YnPI1xSUiIdHR2Sk5Mj9fX1UlxcLFVVVf7v87KysuSWW26RAw44QK655hqZtPHTTz8tBx54IOKzcovSlA4EEJ8OKVBDWAS0E5/3UmVdXZ1kZ2f7p7i2tjbJyMiQgoIC6e3t9f98/vnny/z58+Wll16SxYsXy/XXXz8tD058YW0V5nGZAOJzOX37etdOfJOIx8bGpn350hvjnQA9Mc6dO3evqSA++zYtHc0+AcQ3+8xZMToC2oovrJYRX1gkmcdlAojP5fTt6x3x2ZcpHUEgdAKIL3SkTBgjAcQXI3yWhoApBBCfKUlRpwoBxKdCiTEQcJwA4nN8A1jWPuKzLFDagUAUBBBfFFSZMy4CiC8u8qwLAYMIID6DwqLUQAKILxARAyAAAcTHHrCJAOKzKU16gUBEBBBfRGCZNhYCiC8W7CwKAbMIID6z8qLavRNAfOwQCEAgkADiC0TEAIMIID6DwqJUCMRFAPHFRZ51oyCA+KKgypwQsIwA4rMsUMfbQXyObwDah4AKAcSnQokxphBAfKYkRZ0QiJEA4osRPkuHTgDxhY6UCSFgHwHEZ1+mLneE+FxOn94hoEgA8SmCYpgRBBCfETFRJATiJYD44uXP6uESQHzh8mQ2CFhJAPFZGauzTSE+Z6OncQioE0B86qwYqT8BxKd/RlQIgdgJIL7YI6CAEAkgvhBhMhUEbCWA+GxN1s2+EJ+budM1BGZEAPHNCBeDNSeA+DQPiPIgoAMBxKdDCtQQFgHEFxZJ5oGAxQQQn8XhOtga4nMwdFqGwEwJIL6ZEmO8zgQQn87pUBsENCGA+DQJgjJCIYD4QsHIJBCwmwDisztf17pDfK4lTr8QSIIA4ksCGrdoSwDxaRsNhUFAHwKIT58sqCR1AogvdYbMAAHrCSA+6yN2qkHE51TcNAuB5AggvuS4cZeeBBCfnrlQFQS0IoD4tIqDYlIkgPhSBMjtEHCBAOJzIWV3ekR87mRNpxBImgDiSxodN2pIAPFpGAolQUA3AohPt0SoJxUCiC8VetwLAUcIID5HgnakTe3ENzg4KLW1tZKeni55eXnS1NTk/3nyam9vl1/84hdT//3iiy/K5s2bJSsra4+RNTY2SkNDgyNx0iYEoiGA+KLhyqzxENBOfKWlpdLa2ir5+flSU1MjZWVlUlFRsUc6AwMDcscdd8g999wzLT3EF8/GYlW7CCA+u/J0vRutxDcxMSGFhYUyPDzs59LZ2Sk9PT3+qe+jVyKRkCVLlsh9990nhx56KOJzfSfTf6QEEF+keJl8lgloJb6RkREpLy8X7yTnXX19fdLS0iLey5sfvTZs2CBPPfWU3HLLLXtFxolvlncUy1lJAPFZGauzTWklvvHxcSkqKpKhoSE/kK6uLunu7pbm5ubdAjr11FP9lzkXLFgw9XeeKPv7+3cbW1lZ6WzANA6BMAgsX7cpjGmYAwJaEFhTXaxUh/fKYm5u7i5j0xLeT0O+SkpKpKOjQ3JycqS+vl6Ki4ulqqpKRkdH/TewpKWlyTvvvCPHHnusPPfcc4Grc+ILRMQACAQS4MQXiIgBBhHQ6sTncfNObHV1dZKdnS2ZmZnS1tYmGRkZUlBQIL29vXLUUUfJM888I8uWLZONGzcGokZ8gYgYAIFAAogvEBEDDCKgnfgm2Y2NjfniS/VCfKkS5H4IiCA+doFNBLQVX1iQEV9YJJnHZQKIz+X07esd8dmXKR1BIHQCiC90pEwYIwHEFyN8loaAKQQQnylJUacKAcSnQokxEHCcAOJzfANY1j7isyxQ2oFAFAQQXxRUmTMuAogvLvKsCwGDCCA+g8Ki1EACiC8QEQMgAAHExx6wiQDisylNeoFARAQQX0RgmTYWAogvFuwsCgGzCCA+s/Ki2r0TQHzsEAhAIJAA4gtExACDCCA+g8KiVAjERQDxxUWedaMggPiioMqcELCMAOKzLFDH20F8jm8A2oeACgHEp0KJMaYQQHymJEWdEIiRAOKLET5Lh04A8YWOlAkhYB8BxGdfpi53hPhcTp/eIaBIAPEpgmKYEQQQnxExUSQE4iWA+OLlz+rhEkB84fJkNghYSQDxWRmrs00hPmejp3EIqBNAfOqsGKk/AcSnf0ZUCIHYCSC+2COggBAJIL4QYTIVBGwlgPhsTdbNvhCfm7nTNQRmRADxzQgXgzUngPg0D4jyIKADAcSnQwrUEBYBxBcWSeaBgMUEEJ/F4TrYGuJzMHRahsBMCSC+mRJjvM4EEJ/O6VAbBDQhgPg0CYIyQiGA+ELByCQQsJsA4rM7X9e6Q3yuJU6/EEiCAOJLAhq3aEsA8WkbDYVBQB8CiE+fLKgkdQKIL3WGzAAB6wkgPusjdqpBxOdU3DQLgeQIIL7kuHGXngQQn565UBUEtCKA+LSKg2JSJID4UgTI7RBwgQDicyFld3pEfO5kTacQSJoA4ksaHTdqSADxaRgKJUFANwKIT7dEqCcVAogvFXrcCwFHCCA+R4J2pE3E50jQtAmBVAggvlToca9uBLQT3+DgoNTW1kp6errk5eVJU1OT/+cPX+3t7fLzn/9cRkdHpa6uTs4777xpuTY2NkpDQ4Nu3KkHAkYRQHxGxUWxAQS0E19paam0trZKfn6+1NTUSFlZmVRUVEy18corr8g555wjjz32mLz77rty7733+uOmuxAfzwAEUieA+FJnyAz6ENBKfBMTE1JYWCjDw8M+oc7OTunp6fFPfZOXJ7oXXnhBPvWpT8nbb78tlZWVkpmZifj02VNUYiEBxGdhqA63pJX4RkZGpLy8XAYGBvxI+vr6pKWlRbyXNiev22+/XTo6OmTFihWybds2+e1vf+uf/ibH9/f37xanJ0cuCEAgeQLL121K/mbuhIBmBNZUFytVlEgkJDc3d5exaQnvpyFe4+PjUlRUJENDQ/6sXV1d0t3dLc3NzVOr/PSnP5Xt27fLTTfd5P/s2GOPlUceeUQOOeSQPVbCS50hBsRUzhLgxOds9FY2rtWJzyNcUlLin+hycnKkvr5eiouLpaqqyn8jS1ZWljz99NOyatUqWb9+vbz33nv+7wK9l0b32WcfxGflFqUpHQggPh1SoIawCGgnPu+lSu+dmtnZ2f7v7tra2iQjI0MKCgqkt7dXjjrqKLnuuutk69at8sYbb8jXv/51WbZs2bQ8OPGFtVWYx2UCiM/l9O3rXTvxTSIeGxvb65tW/vOf//j/zGHu3Ll7TQXx2bdp6Wj2CSC+2WfOitER0FZ8YbWM+MIiyTwuE0B8LqdvX++Iz75M6QgCoRNAfKEjZcIYCSC+GOGzNARMIYD4TEmKOlUIID4VSoyBgOMEEJ/jG8Cy9hGfZYHSDgSiIID4oqDKnHERQHxxkWddCBhEAPEZFBalBhJAfIGIGAABCCA+9oBNBBCfTWnSCwQiIoD4IgLLtLEQQHyxYGdRCJhFAPGZlRfV7p0A4mOHQAACgQQQXyAiBhhEAPEZFBalQiAuAogvLvKsGwUBxBcFVeaEgGUEEJ9lgTreDuJzfAPQPgRUCCA+FUqMMYUA4jMlKeqEQIwEEF+M8Fk6dAKIL3SkTAgB+wggPvsydbkjxOdy+vQOAUUCiE8RFMOMIID4jIiJIiEQLwHEFy9/Vg+XAOILlyezQcBKAojPylidbQrxORs9jUNAnQDiU2fFSP0JID79M6JCCMROAPHFHgEFhEgA8YUIk6kgYCsBxGdrsm72hfjczJ2uITAjAohvRrgYrDkBxKd5QJQHAR0IID4dUqCGsAggvrBIMg8ELCaA+CwO18HWEJ+DodMyBGZKAPHNlBjjdSaA+HROh9ogoAkBxKdJEJQRCgHEFwpGJoGA3QQQn935utYd4nMtcfqFQBIEEF8S0LhFWwKIT9toKAwC+hBAfPpkQSWpE0B8qTNkBghYTwDxWR+xUw0iPqfiplkIJEcA8SXHjbv0JID49MyFqiCgFQHEp1UcFJMiAcSXIkBuh4ALBBCfCym70yPicydrOoVA0gQQX9LouFFDAohPw1AoCQK6EUB8uiVCPakQQHyp0ONeCDhCAPE5ErQjbWonvsHBQamtrZX09HTJy8uTpqYm/8+Tl/f3F1xwgRx22GH+j4477jj50Y9+NG1cjY2N0tDQ4EictAmBaAggvmi4Mms8BLQTX2lpqbS2tkp+fr7U1NRIWVmZVFRUTNH54x//KA8++KDcddddSsQQnxImBkFgrwQQHxvEJgJaiW9iYkIKCwtleHjYZ9zZ2Sk9PT3+qW/y8qT3+OOPy0knnST777+/nHbaabucCD8aDuKzabvSS1wEEF9c5Fk3CgJaiW9kZETKy8tlYGDA77Wvr09aWlqkvb19qveHHnpI7rzzTvna174mW7ZskX/84x++IKe7EF8U24Y5XSOA+FxL3O5+tRLf+Pi4FBUVydDQkE+9q6tLuru7pbm5edoUFi5cKI8++qgceuihvij7+/t3G1tZWWl3inQHgYgJLF+3KeIVmB4Cs0dgTXWx0mKJREJyc3N3GZuW8H4a8lVSUiIdHR2Sk5Mj9fX1UlxcLFVVVTI6OipZWVny8MMPS1pampx++umyc+dOKSgo8F8a3XffffdYCSe+kANiOicJcOJzMnZrm9bqxOdR9k5sdXV1kp2dLZmZmdLW1iYZGRm+4Hp7e+WDDz6Qc8891xfjP//5T7nqqqvkoosu4qVOa7cojelAAPHpkAI1hEVAO/FNNjY2NuaLb7prx44d/glwzpw5e2XBiS+srcI8LhNAfC6nb1/v2oovLNSILyySzOMyAcTncvr29Y747MuUjiAQOgHEFzpSJoyRAOKLET5LQ8AUAojPlKSoU4UA4lOhxBgIOE4A8Tm+ASxrH/FZFijtQCAKAogvCqrMGRcBxBcXedaFgEEEEJ9BYVFqIAHEF4iIARCAAOJjD9hEAPHZlCa9QCAiAogvIrBMGwsBxBcLdhaFgFkEEJ9ZeVHt3gkgPnYIBCAQSADxBSJigEEEEJ9BYVEqBOIigPjiIs+6URBAfFFQZU4IWEYA8VkWqOPtID7HNwDtQ0CFAOJTocQYUwggPlOSok4IxEgA8cUIn6VDJ4D4QkfKhBCwjwDisy9TlzuKTHx33XWXLFiwQJYsWRIrX76WKFb8LG4JAcRnSZC04ROITHw33HCD3Hzzzf63pf/kJz+RT3ziE7EgR3yxYGdRywggPssCdbydyMSXSCTk3nvvlRUrVoj3bemeCEtLS6dwn3DCCbOCHvHNCmYWsZwA4rM8YMfai0x8kxzfeecdOf3006W/v38XtJ4YZ+NCfLNBmTVsJ4D4bE/Yrf4iFd/mzZvlmmuu8aV32WWX7XLiu/DCC2eFNOKbFcwsYjkBxGd5wI61F5n47rjjDvn2t78txx13nHhvdFm0aFEsaBFfLNhZ1DICiM+yQB1vJzLxrVq1SubNmydXXnmlzJkzJzbMiC829CxsEQHEZ1GYtBLduzp1YYv4dEmCOkwmgPhMTo/aP0ogshOfLqgRny5JUIfJBBCfyelRO+JjD0AAAjMmgPhmjIwbNCbAiU/jcCgNAroQQHy6JEEdYRBAfGFQZA4IWE4A8VkesGPtIT7HAqddCCRDAPElQ417dCWA+HRNhrogoBEBxKdRGJSSMgHElzJCJoCA/QQQn/0Zu9Qh4nMpbXqFQJIEEF+S4LhNSwKIT8tYKAoCehFAfHrlQTWpEUB8qfHjbgg4QQDxORGzM00iPmeiplEIJE8A8SXPjjv1I4D49MuEiiCgHQHEp10kFJQCAcSXAjxuhYArBBCfK0m70SficyNnuoRASgQQX0r4uFkzAtqJb3BwUGprayU9PV3y8vKkqanJ//NHr507d8qSJUvk5ptvlrKysmmx8u0Mmu04yjGSAOIzMjaKnoaAduIrLS2V1tZWyc/Pl5qaGl9qFRUVu5V/7bXXypNPPinXX3+9nHXWWYiPLQ6BCAkgvgjhMvWsE9BKfBMTE1JYWCjDw8M+iM7OTunp6fFPfR++Nm7cKPfdd59kZWXJ4sWLEd+sbxsWdI0A4nMtcbv71Up8IyMjUl5eLgMDAz71vr4+aWlpkfb29qkU3nnnHVm6dKmsX79eVq9evYv4vPH9/f27JVZZWWl3inQHgYgJLF+3KeIVmB4Cs0dgTXWx0mKJREJyc3N3GZuW8H4a4jU+Pi5FRUUyNDTkz9rV1SXd3d3S3Nw8tcry5ctl3rx54r0kunbtWjnmmGNk2bJlcvDBB++xEn7HF2JATOUsAU58zkZvZeNanfg8wiUlJdLR0SE5OTlSX18vxcXFUlVVJaOjo/5Lm97fbd++3Q/Deyn06KOPlpUrV/rj93QhPiv3LU3NMgHEN8vAWS5SAtqJz3upsq6uTrKzsyUzM1Pa2tokIyNDCgoKpLe3V4466qgpICtWrOB3fJFuDyaHwP8IID52gk0EtBPfJNyxsTFffKlenPhSJcj9EEB87AG7CGgrvrAwI76wSDKPywQ48bmcvn29Iz77MqUjCIROAPGFjpQJYySA+GKEz9IQMIUA4jMlKepUIYD4VCgxBgKOE0B8jm8Ay9pHfJYFSjsQiIIA4ouCKnPGRQDxxUWedSFgEAHEZ1BYlBpIAPEFImIABCCA+NgDNhFAfDalSS8QiIgA4osILNPGQgDxxYKdRSFgFgHEZ1ZeVLt3AoiPHQIBCAQSQHyBiBhgEAHEZ1BYlAqBuAggvrjIs24UBBBfFFSZEwKWEUB8lgXqeDuIz/ENQPsQUCGA+FQoMcYUAojPlKSoEwIxEkB8McJn6dAJIL7QkTIhBOwjgPjsy9TljhCfy+nTOwQUCSA+RVAMM4IA4jMiJoqEQLwEEF+8/Fk9XAKIL1yezAYBKwkgPitjdbYpxOds9DQOAXUCiE+dFSP1J4D49M+ICiEQOwHEF3sEFBAiAcQXIkymgoCtBBCfrcm62RficzN3uobAjAggvhnhYrDmBBCf5gFRHgR0IID4dEiBGsIigPjCIsk8ELCYAOKzOFwHW0N8DoZOyxCYKQHEN1NijNeZAOLTOR1qg4AmBBCfJkFQRigEEF8oGJkEAnYTQHx25+tad4jPtcTpFwJJEEB8SUDjFm0JID5to6EwCOhDAPHpkwWVpE4A8aXOkBkgYD0BxGd9xE41iPiciptmIZAcAcSXHDfu0pMA4tMzF6qCgFYEEJ9WcVBMigQQX4oAuR0CLhBAfC6k7E6PiM+drOkUAkkTQHxJo+NGDQkgPg1DoSQI6EYA8emWCPWkQgDxpUKPeyHgCAHE50jQjrSpnfgGBweltrZW0tPTJS8vT5qamvw/T147d+6Uyy67TEZGRuTtt9+WFStWyNlnnz1tXI2NjdLQ0OBInLQJgWgIIL5ouDJrPAS0E19paam0trZKfn6+1NTUSFlZmVRUVEzRWb9+vQwPD/vCe/nll2Xx4sXy3HPPIb549g+rOkIA8TkStCNtaiW+iYkJKSws9MXmXZ2dndLT0+Of+vZ0bd682T8dbty4EfE5smFpMx4CiC8e7qwaDQGtxOe9fFleXi4DAwN+t319fdLS0iLt7e27dX/yySfLtm3b5P7775cTTjgB8UWzP5gVAj4BxMdGsImAVuIbHx+XoqIiGRoa8hl3dXVJd3e3NDc375H5Cy+8IEuWLJFnnnlG9t13X1+U/f39u42trKy0KTN6gcCsE1i+btOsr8mCEIiKwJrqYqWpE4mE5Obm7jI2LeH9NOSrpKREOjo6JCcnR+rr66W4uFiqqqpkdHRUsrKyZO3atbLffvvJRRddJO+//77Mnz/fF9+BBx64x0p4c0vIATGdkwQ48TkZu7VNa3Xi8yh7J7a6ujrJzs6WzMxMaWtrk4yMDCkoKJDe3l7/HZ7nnnuuHH744fKvf/1LzjzzTFm5ciUvdVq7RWlMBwKIT4cUqCEsAtqJb7KxsbExX3zTXd4J0Pv7uXPn7pUFJ76wtgrzuEwA8bmcvn29ayu+sFAjvrBIMo/LBBCfy+nb1zvisy9TOoJA6AQQX+hImTBGAogvRvgsDQFTCCA+U5KiThUCiE+FEmMg4DgBxOf4BrCsfcRnWaC0A4EoCCC+KKgyZ1wEEF9c5FkXAgYRQHwGhUWpgQQQXyAiBkAAAoiPPWATAcRnU5r0AoGICCC+iMAybSwEEF8s2FkUAmYRQHxm5UW1eyeA+NghEIBAIAHEF4iIAQYRQHwGhUWpEIiLAOKLizzrRkEA8UVBlTkhYBkBxGdZoI63g/gc3wC0DwEVAohPhRJjTCGA+ExJijohECMBxBcjfJYOnQDiCx0pE0LAPgKIz75MXe4I8bmcPr1DQJEA4lMExTAjCCA+I2KiSAjESwDxxcuf1cMlgPjC5clsELCSAOKzMlZnm0J8zkZP4xBQJ4D41FkxUn8CiE//jKgQArETQHyxR0ABIRJAfCHCZCoI2EoA8dmarJt9IT43c6drCMyIAOKbES4Ga04A8WkeEOVBQAcCiE+HFKghLAKILyySzAMBiwkgPovDdbA1xOdg6LQMgZkSQHwzJcZ4nQkgPp3ToTYIaEIA8WkSBGWEQgDxhYKRSSBgNwHEZ3e+rnWH+FxLnH4hkAQBxJcENG7RlgDi0zYaCoOAPgQQnz5ZUEnqBBBf6gyZAQLWE0B81kfsVIOIz6m4aRYCyRFAfMlx4y49CSA+PXOhKghoRQC7gXy3AAASPklEQVTxaRUHxaRIAPGlCJDbIeACAcTnQsru9Ij43MmaTiGQNAHElzQ6btSQAOLTMBRKgoBuBBCfbolQTyoEEF8q9LgXAo4QQHyOBO1Im4jPkaBpEwKpEEB8qdDjXt0IaCe+wcFBqa2tlfT0dMnLy5Ompib/z5PXxMSEXHnllfL888/L2NiYfOc735GlS5dOy7WxsVEaGhp04049EDCKAOIzKi6KDSCgnfhKS0ultbVV8vPzpaamRsrKyqSiomKqjYcfflh6e3vltttuk9dee00WLVok27ZtQ3xsdQhESADxRQiXqWedgFbi805zhYWFMjw87IPo7OyUnp4e/9S3p8s79VVVVclTTz2F+GZ967CgSwQQn0tp29+rVuIbGRmR8vJyGRgY8Mn39fVJS0uLtLe375bEq6++KpWVleK9lFlSUjI1vr+/f7ex3jguCEAgeQLL121K/mbuhIBmBNZUFytVlEgkJDc3d5exaQnvpyFe4+PjUlRUJENDQ/6sXV1d0t3dLc3Nzbus8uyzz8rFF1/snwSPP/74vVbA7/hCDIipnCXAic/Z6K1sXKsTn0fYO711dHRITk6O1NfXS3Fxsf9y5ujoqGRlZcmOHTvk9NNPl1//+tcyf/78wFAQXyAiBkAgkADiC0TEAIMIaCc+76XKuro6yc7OlszMTGlra5OMjAwpKCjw39TiCc876X34+On9LtCT4p4uxGfQbqRUbQkgPm2jobAkCGgnvskevH+q4Ikv1QvxpUqQ+yEggvjYBTYR0FZ8YUFGfGGRZB6XCSA+l9O3r3fEZ1+mdASB0AkgvtCRMmGMBBBfjPBZGgKmEEB8piRFnSoEEJ8KJcZAwHECiM/xDWBZ+4jPskBpBwJREEB8UVBlzrgIIL64yLMuBAwigPgMCotSAwkgvkBEDIAABBAfe8AmAojPpjTpBQIREUB8EYFl2lgIIL5YsLMoBMwigPjMyotq904A8bFDIACBQAKILxARAwwigPgMCotSIRAXAcQXF3nWjYIA4ouCKnNCwDICiM+yQB1vB/E5vgFoHwIqBBCfCiXGmEIA8ZmSFHVCIEYCiC9G+CwdOgHEFzpSJoSAfQQQn32ZutwR4nM5fXqHgCIBxKcIimFGEEB8RsREkRCIlwDii5c/q4dLAPGFy5PZIGAlAcRnZazONoX4nI2exiGgTgDxqbNipP4EEJ/+GVEhBGIngPhij4ACQiSA+EKEyVQQsJUA4rM1WTf7Qnxu5k7XEJgRAcQ3I1wM1pwA4tM8IMqDgA4EEJ8OKVBDWAQQX1gkmQcCFhNAfBaH62BriM/B0GkZAjMlgPhmSozxOhNAfDqnQ20Q0IQA4tMkCMoIhQDiCwUjk0DAbgKIz+58XesO8bmWOP1CIAkCiC8JaNyiLQHEp200FAYBfQggPn2yoJLUCSC+1BkyAwSsJ4D4rI/YqQYRn1Nx0ywEkiOA+JLjxl16EkB8euZCVRDQigDi0yoOikmRAOJLESC3Q8AFAojPhZTd6RHxuZM1nUIgaQKIL2l03KghAcSnYSiUBAHdCCA+3RKhnlQIIL5U6HEvBBwhgPgcCdqRNrUT3+DgoNTW1kp6errk5eVJU1OT/+cPX1u3bpUrr7xSqqur5YILLthrVI2NjdLQ0OBInLQJgWgIIL5ouDJrPAS0E19paam0trZKfn6+1NTUSFlZmVRUVEzRGR8fl0suuUQOP/xwf8yyZcsQXzx7h1UdIoD4HArbgVa1Et/ExIQUFhbK8PCwj76zs1N6enr8U99Hr9WrV8u8efMQnwOblBbjJ4D44s+ACsIjoJX4RkZGpLy8XAYGBvwO+/r6pKWlRdrb2yMX30Mb/ydbLgjYQGBp6YJQ20B8oeJkspgJaCU+72XMoqIiGRoa8rF0dXVJd3e3NDc3K4nPE2V/f/9uYysrKwMxL1+3KXAMAyBgCoE11cWhlsrzESpOJouZgOrzkUgkJDc3d5dq0xLeT0O+SkpKpKOjQ3JycqS+vl6Ki4ulqqpKRkdHJSsrS9LS0vwVw36pk/+jDTlIpouVgOr/0aoWyfOhSopxJhBQfT62bdsmRx99dPTi805sdXV1kp2dLZmZmdLW1iYZGRlSUFAgvb298uabb8q3vvUteeGFF/yfH3nkkbJhwwb52Mc+tkfequ/q5ME2YbtSoyoB1QdbdT6eD1VSjDOBgOrzMWvim4Q2Njbmiy/VC/GlSpD7TSSg+mCr9ob4VEkxzgQCqs/HrIsvLHiILyySzGMSAdUHW7UnxKdKinEmEFB9PhCfCWlSIwT+T0D1wVYFhvhUSTHOBAKqzwfiMyFNaoQA4mMPQCCQAOL7PyL+jzZwrzDAIAKqD7ZqSzwfqqQYZwIB1eeDE58JaVIjBDjxsQcgEEgA8XHiC9wkDDCPgOqDrdoZJz5VUowzgYDq88GJz4Q0qRECnPjYAxAIJID4OPEFbhIGmEdA9cFW7YwTnyopxplAQPX54MRnQprUCAFOfOwBCAQSQHyc+AI3CQPMI6D6YKt2xolPlRTjTCCg+nxw4jMhTWqEACc+9gAEAgkgPk58gZuEAeYRUH2wVTvjxKdKinEmEFB9PjjxmZAmNUKAEx97AAKBBBAfJ77ATcIA8wioPtiqnXHiUyXFOBMIqD4fnPhMSJMaIcCJjz0AgUACiI8TX+AmYYB5BFQfbNXOOPGpkmKcCQRUnw9OfCakSY0Q4MTHHoBAIAHEx4kvcJMwwDwCqg+2amec+FRJMc4EAqrPByc+E9KkRghw4mMPQCCQAOLjxBe4SRhgHgHVB1u1M058qqQYZwIB1eeDE58JaVIjBDjxsQcgEEgA8XHiC9wkDDCPgOqDrdoZJz5VUowzgYDq88GJz4Q0qRECnPjYAxAIJID4OPEFbhIGmEdA9cFW7YwTnyopxplAQPX54MRnQprUCAFOfOwBCAQSQHyc+AI3CQPMI6D6YKt2xolPlRTjTCCg+nxw4jMhTWqEACc+9gAEAgkgPk58gZuEAeYRUH2wVTvjxKdKinEmEFB9PjjxmZAmNUKAEx97AAKBBBAfJ77ATcIA8wioPtiqnXHiUyXFOBMIqD4fnPhMSJMaIcCJjz0AgUACiI8TX+AmYYB5BFQfbNXOOPGpkmKcCQRUnw9OfCakSY0Q4MTHHoBAIAHEx4kvcJMwwDwCqg+2amec+FRJMc4EAqrPByc+E9KkRghw4mMPQCCQAOLjxBe4SRhgHgHVB1u1M058qqQYZwIB1eeDE58JaVIjBDjxsQcgEEhAO/ENDg5KbW2tpKenS15enjQ1Nfl/nrwSiYQ0NDTIX//6V5mYmJAf/vCHUlJSMm2jjY2N/vigi/+jDSLE35tEQPXBVu2J50OVFONMIKD6fMzaia+0tFRaW1slPz9fampqpKysTCoqKqZY9vb2yj333CP33nuvbN++Xc4++2zZvHkz4jNht1HjrBFQfbBVC0J8qqQYZwIB1edjVsTnneAKCwtleHjYZ9fZ2Sk9PT3+qW/yuuGGG2TBggVywQUX+D86/vjj/TEf//jH98ibE58J25Aawyag+mCrrov4VEkxzgQCqs/HrIhvZGREysvLZWBgwGfX19cnLS0t0t7ePsXyqquu8secddZZ/s9OOeUUf4z3sqg3vr+/fxfu+++/v7z77rsmZEGNEIAABCCgEYFPf/rT8qUvfWmXitIS3i/cQrzGx8elqKhIhoaG/Fm7urqku7tbmpubp1b53ve+JwsXLpTzzz/f/9miRYv8MYccckiIlTBVVARUT+BRrc+8ENCZAM+Hzun8r7bQxedN6r1RpaOjQ3JycqS+vl6Ki4ulqqpKRkdHJSsry5fhhg0bZO3atfLaa6/5J74tW7boT4sKfQI82GwECExPgOdD/90Rifi8lyrr6uokOztbMjMzpa2tTTIyMqSgoEC8N7YceeSRctFFF8lbb73li+8HP/iBfPGLX9SfFhUiPvYABAIIID79t0gk4ptse2xszBffdJf39/vtt5+kpaXpT4oKpwjwYLMZIMCJz+Q9EKn4TAZD7dMT8N6AtHjxYhBBAAJ7IMDzof+2QHz6Z0SFEIAABCAQIgHEFyJMF6Z67733xHuJ+qCDDnKhXXqEwIwI8HzMCFdsgxFfbOjNW/jqq6+W3/3ud1JZWen/u5g//elP8t3vfte8RqgYAhEQ4PmIAGpEUyK+iMDaNu0TTzwhf/jDH+TSSy+VO+64Q2677TY59dRT/X+ass8++9jWLv1AYEYEeD5mhCv2wYgv9gjMKGD9+vXivYzjfdiAJ77bb79dvvGNb8jdd98t3ifrcEHAZQI8H2alj/jMyiu2ar1/b3nGGWfImWee6X+guPfpPN7nsXoPPBcEXCfA82HWDkB8ZuUVa7XeN2l4n7n66quv+h9GcOGFF8q+++4ba00sDgFdCPB86JJEcB2IL5gRI/5PwDvdfeUrX/G/R3HdunVy3nnnyYknnggfCDhPwJOe9zvwD1/exyBfcsklzrPREQDi0zEVDWvyZLd69Wr/M1i9z171Pmjc+6qpRx55hE/e0TAvSppdAn//+9/ll7/85dSi3of0e1/R9tBDD81uIaymRADxKWFi0AMPPOBDOPbYY/1/wvDggw/K5Zdf7n9g9cEHHwwgCEDgIwSuuOIK/81ffCSjflsD8emXiZYVPf3003LTTTfJ3Llz/X/D573RZenSpeK9jXvOnDla1kxREJgtAh988IF4X8k2ee3YscP/tYD3BdsHHHDAbJXBOooEEJ8iKIaJ/OY3v5E333xTvvnNb/pfNPzKK69MfZkwfCDgMgHv8zkvu+yyKQTeh/N7X8W2YsUKl7Fo2zvi0zYaPQp7/fXX5cknn5y2mLPOOkuPQqkCAjES8E543olv3rx5MVbB0qoEEJ8qKUfHeb+0X7NmzbTde/+YnQsCrhPw3tHpfQ/pjTfe6DoKI/pHfEbEpE+R3qe3vP/+++L9TmPVqlX+lwinp6frUyCVQCAGAt7/IH71q1+VvLw8OeKII6Yq4H8MYwhDYUnEpwCJISLvvvuunHbaabJt2zb/o8u83/V9+ctf9t/dyQUBlwm8/PLL/js3vQ9t/+g7OL03gHHpRwDx6ZeJlhVt2LBBvH+ke/zxx8uWLVv8jy7zPq9zby+DatkIRUEgZAInn3yyPPbYYyHPynRREkB8UdK1aG7vU1veeOMN/5NafvWrX8n3v/99ufjii6WlpUW8d7BxQcBVAojPvOQRn3mZxVLx6OiofP7zn5fe3l7/3/Hl5uaK92/7/va3v/Hv+GJJhEV1IeB9bu107+b0/pkDl34EEJ9+mWhbkfeGFu93GG+99Zb8+c9/ls9+9rNy2GGHaVsvhUFgNgh84QtfkNbW1j0u5UmRSz8CiE+/TLSryPuIsltvvdWvy/voMu/3fPPnz9euTgqCQBwEeKkzDuqprYn4UuPnxN0ffrBra2v9b2U44YQTnOidJiEQRGDt2rX+59ZymUMA8ZmTVWyVIr7Y0LMwBCAQAQHEFwFU26ZEfLYlSj8QcJsA4nM7f6XuvV/QT34axXPPPee/oeWggw7y7+XfLykhZBAEIKARAcSnURi6lvLss8/u8pUrH65z4cKFupZNXRCAAAT2SADxsTEgAAEIQMApAojPqbhpFgIQgAAEEB97AAIQgAAEnCKA+JyKm2YhAAEIQADxsQcgAAEIQMApAojPqbhp1hYC3hecvvLKK/6XAc+ZM0fefvtt/xszPve5z/lfiMoFAQhMTwDxsTsgYCCB/v5+/yuimpub5aqrrhLvo+R+9rOf+d+W8clPftLAjigZArNHAPHNHmtWgkCoBK699lr/WwHa2trknHPO8b8Y2BMgFwQgsHcCiI8dAgFDCezcuVM+85nPiPcBA8XFxfLkk0/y3YiGZknZs0sA8c0ub1aDQGgExsfH/e9E9F7e9L4TbuPGjf73JXJBAAKc+NgDELCSwMqVK+XHP/6xNDU1SXV1tdx9991yxRVXWNkrTUEgTAKc+MKkyVwQmCUCW7ZskUWLFsnq1avluuuuk0suuUTWr18vW7dulezs7FmqgmUgYCYBxGdmblTtMIGJiQn/d3qJREIGBgZk7ty58sYbb4j3LRonnXSSPPDAAw7ToXUIBBNAfMGMGAEBCEAAAhYRQHwWhUkrEIAABCAQTADxBTNiBAQgAAEIWEQA8VkUJq1AAAIQgEAwAcQXzIgREIAABCBgEQHEZ1GYtAIBCEAAAsEEEF8wI0ZAAAIQgIBFBP4L/wkASpFjhnAAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "distributionOverPlots :: MonadDistribution m => m VegaLiteLab -- the type of plots\n", "distributionOverPlots = plot . fmap (first (pack . show)) . toEmpirical <$> replicateM 10 model\n", "\n", "sampler distributionOverPlots\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(True,0.7),(False,0.3)]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "toEmpirical <$> replicateM 10 model :: forall {f :: * -> *} {b}. (Fractional b, Ord b, MonadDistribution f) => f [(Bool, b)]" ], "text/plain": [ "toEmpirical <$> replicateM 10 model :: forall {f :: * -> *} {b}. (Fractional b, Ord b, MonadDistribution f) => f [(Bool, b)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler $ toEmpirical <$> replicateM 10 model\n", ":t toEmpirical <$> replicateM 10 model" ] }, { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "## Continuous distributions\n", "\n", "Consider normal distribution:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "model2 :: MonadDistribution m => m Double\n", "model2 = normal 0 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sampling is no different to before:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-4.521959900261888e-2" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler model2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And as before, to obtain multiple draws:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[-0.6216690402229845,-0.47608570111519477,0.4670018245275328,-0.731326261945339,1.8293339635456327,-0.31691850879028954,-0.1522034252473077,-0.4871801689979811,0.4312750347264313,0.6641920998609923]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "multipleDraws2 :: MonadDistribution m => m [Double]\n", "multipleDraws2 = replicateM 10 model2\n", "\n", "draws2 <- sampler multipleDraws2\n", "\n", "draws2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'd like to view a histogram of samples, which in the limit of many samples should tend to the PDF of a normal distribution.\n", "\n", "Again, we could generate a histogram from a drawn list of samples:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -4.306541355732829, "Y": 2 }, { "X": -4.217784111853617, "Y": 0 }, { "X": -4.129026867974405, "Y": 2 }, { "X": -4.040269624095192, "Y": 1 }, { "X": -3.9515123802159797, "Y": 1 }, { "X": -3.8627551363367676, "Y": 3 }, { "X": -3.773997892457555, "Y": 1 }, { "X": -3.6852406485783424, "Y": 5 }, { "X": -3.5964834046991303, "Y": 4 }, { "X": -3.5077261608199177, "Y": 5 }, { "X": -3.418968916940705, "Y": 6 }, { "X": -3.330211673061493, "Y": 14 }, { "X": -3.2414544291822804, "Y": 19 }, { "X": -3.152697185303068, "Y": 25 }, { "X": -3.0639399414238557, "Y": 33 }, { "X": -2.975182697544643, "Y": 40 }, { "X": -2.8864254536654306, "Y": 46 }, { "X": -2.7976682097862184, "Y": 77 }, { "X": -2.7089109659070063, "Y": 101 }, { "X": -2.6201537220277933, "Y": 123 }, { "X": -2.531396478148581, "Y": 158 }, { "X": -2.442639234269369, "Y": 182 }, { "X": -2.353881990390156, "Y": 194 }, { "X": -2.2651247465109443, "Y": 277 }, { "X": -2.1763675026317317, "Y": 357 }, { "X": -2.087610258752519, "Y": 390 }, { "X": -1.998853014873307, "Y": 491 }, { "X": -1.9100957709940944, "Y": 577 }, { "X": -1.8213385271148819, "Y": 705 }, { "X": -1.7325812832356697, "Y": 776 }, { "X": -1.6438240393564572, "Y": 920 }, { "X": -1.555066795477245, "Y": 1079 }, { "X": -1.4663095515980324, "Y": 1244 }, { "X": -1.3775523077188199, "Y": 1382 }, { "X": -1.2887950638396077, "Y": 1574 }, { "X": -1.2000378199603952, "Y": 1733 }, { "X": -1.111280576081183, "Y": 1915 }, { "X": -1.0225233322019704, "Y": 2145 }, { "X": -0.9337660883227579, "Y": 2254 }, { "X": -0.8450088444435457, "Y": 2408 }, { "X": -0.7562516005643332, "Y": 2597 }, { "X": -0.6674943566851206, "Y": 2891 }, { "X": -0.5787371128059084, "Y": 2999 }, { "X": -0.48997986892669587, "Y": 3226 }, { "X": -0.4012226250474833, "Y": 3222 }, { "X": -0.3124653811682707, "Y": 3378 }, { "X": -0.22370813728905858, "Y": 3471 }, { "X": -0.13495089340984556, "Y": 3423 }, { "X": -0.04619364953063343, "Y": 3501 }, { "X": 0.042563594348578704, "Y": 3515 }, { "X": 0.13132083822779173, "Y": 3579 }, { "X": 0.22007808210700386, "Y": 3360 }, { "X": 0.308835325986216, "Y": 3356 }, { "X": 0.397592569865429, "Y": 3313 }, { "X": 0.48634981374464115, "Y": 3107 }, { "X": 0.5751070576238533, "Y": 3060 }, { "X": 0.6638643015030663, "Y": 2801 }, { "X": 0.7526215453822784, "Y": 2607 }, { "X": 0.8413787892614906, "Y": 2564 }, { "X": 0.9301360331407027, "Y": 2189 }, { "X": 1.0188932770199157, "Y": 2099 }, { "X": 1.1076505208991279, "Y": 1908 }, { "X": 1.19640776477834, "Y": 1762 }, { "X": 1.285165008657553, "Y": 1609 }, { "X": 1.3739222525367651, "Y": 1419 }, { "X": 1.4626794964159773, "Y": 1220 }, { "X": 1.5514367402951903, "Y": 1053 }, { "X": 1.6401939841744024, "Y": 900 }, { "X": 1.7289512280536146, "Y": 790 }, { "X": 1.8177084719328276, "Y": 704 }, { "X": 1.9064657158120397, "Y": 536 }, { "X": 1.9952229596912519, "Y": 487 }, { "X": 2.083980203570464, "Y": 426 }, { "X": 2.172737447449677, "Y": 311 }, { "X": 2.261494691328889, "Y": 282 }, { "X": 2.3502519352081013, "Y": 218 }, { "X": 2.4390091790873143, "Y": 169 }, { "X": 2.5277664229665264, "Y": 161 }, { "X": 2.6165236668457386, "Y": 125 }, { "X": 2.7052809107249516, "Y": 88 }, { "X": 2.7940381546041637, "Y": 82 }, { "X": 2.882795398483376, "Y": 52 }, { "X": 2.971552642362589, "Y": 46 }, { "X": 3.060309886241801, "Y": 28 }, { "X": 3.149067130121013, "Y": 22 }, { "X": 3.237824374000226, "Y": 18 }, { "X": 3.3265816178794383, "Y": 17 }, { "X": 3.4153388617586504, "Y": 11 }, { "X": 3.5040961056378634, "Y": 7 }, { "X": 3.5928533495170756, "Y": 7 }, { "X": 3.681610593396287, "Y": 5 }, { "X": 3.7703678372755, "Y": 3 }, { "X": 3.859125081154711, "Y": 0 }, { "X": 3.947882325033924, "Y": 2 }, { "X": 4.036639568913137, "Y": 3 }, { "X": 4.125396812792348, "Y": 0 }, { "X": 4.214154056671561, "Y": 0 }, { "X": 4.302911300550774, "Y": 0 }, { "X": 4.391668544429986, "Y": 1 }, { "X": 4.480425788309199, "Y": 0 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "constWeight = 1 :: Log Double\n", "\n", "samples <- sampler $ replicateM 100000 ((,constWeight) <$> model2)\n", "plot . histogramToList . histogram 100 $ samples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note, that the type of histogram is:\n", "```haskell\n", "histogram :: Int -> [(Double, Log Double)] -> Histogram\n", "```\n", "because it is used in more general context for weighted samples (see next chapter).\\\n", "That is why we need to introduce a constant weight for our simple example by mapping samples to a pair (value, score) with `(,constWeight) <$> `." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0.40485488235651435,1.1348389537359198,-1.092972060021726]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler $ replicateM 3 model2\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "but it's nicer to apply a `histogram` function to multiple draws to define a distribution over histograms from which we'll sample.\\\n", "In that approach a distribution is composed and transformed first and the sampling is clearly split from the distribution definition." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -4.118259020372659, "Y": 1 }, { "X": -4.031956574996215, "Y": 0 }, { "X": -3.9456541296197702, "Y": 1 }, { "X": -3.8593516842433253, "Y": 4 }, { "X": -3.773049238866881, "Y": 3 }, { "X": -3.6867467934904363, "Y": 4 }, { "X": -3.6004443481139914, "Y": 8 }, { "X": -3.514141902737547, "Y": 10 }, { "X": -3.4278394573611024, "Y": 6 }, { "X": -3.3415370119846575, "Y": 12 }, { "X": -3.255234566608213, "Y": 18 }, { "X": -3.1689321212317685, "Y": 24 }, { "X": -3.0826296758553235, "Y": 33 }, { "X": -2.9963272304788795, "Y": 37 }, { "X": -2.9100247851024346, "Y": 52 }, { "X": -2.82372233972599, "Y": 78 }, { "X": -2.7374198943495456, "Y": 83 }, { "X": -2.6511174489731006, "Y": 123 }, { "X": -2.564815003596656, "Y": 150 }, { "X": -2.4785125582202117, "Y": 168 }, { "X": -2.3922101128437667, "Y": 183 }, { "X": -2.3059076674673222, "Y": 237 }, { "X": -2.2196052220908777, "Y": 292 }, { "X": -2.133302776714433, "Y": 372 }, { "X": -2.047000331337988, "Y": 409 }, { "X": -1.9606978859615434, "Y": 529 }, { "X": -1.8743954405850989, "Y": 608 }, { "X": -1.7880929952086544, "Y": 669 }, { "X": -1.7017905498322095, "Y": 800 }, { "X": -1.615488104455765, "Y": 945 }, { "X": -1.5291856590793205, "Y": 1048 }, { "X": -1.4428832137028755, "Y": 1211 }, { "X": -1.356580768326431, "Y": 1314 }, { "X": -1.2702783229499865, "Y": 1558 }, { "X": -1.1839758775735416, "Y": 1742 }, { "X": -1.0976734321970971, "Y": 1866 }, { "X": -1.0113709868206526, "Y": 2032 }, { "X": -0.9250685414442077, "Y": 2263 }, { "X": -0.8387660960677632, "Y": 2402 }, { "X": -0.7524636506913187, "Y": 2570 }, { "X": -0.6661612053148742, "Y": 2739 }, { "X": -0.5798587599384293, "Y": 2982 }, { "X": -0.4935563145619848, "Y": 3109 }, { "X": -0.4072538691855403, "Y": 3251 }, { "X": -0.32095142380909536, "Y": 3365 }, { "X": -0.23464897843265087, "Y": 3330 }, { "X": -0.14834653305620638, "Y": 3397 }, { "X": -0.06204408767976144, "Y": 3386 }, { "X": 0.024258357696683497, "Y": 3396 }, { "X": 0.11056080307312754, "Y": 3450 }, { "X": 0.19686324844957248, "Y": 3272 }, { "X": 0.28316569382601653, "Y": 3240 }, { "X": 0.36946813920246147, "Y": 3233 }, { "X": 0.4557705845789064, "Y": 3054 }, { "X": 0.5420730299553504, "Y": 3001 }, { "X": 0.6283754753317954, "Y": 2743 }, { "X": 0.7146779207082403, "Y": 2671 }, { "X": 0.8009803660846844, "Y": 2507 }, { "X": 0.8872828114611293, "Y": 2346 }, { "X": 0.9735852568375742, "Y": 2081 }, { "X": 1.0598877022140183, "Y": 2007 }, { "X": 1.1461901475904632, "Y": 1781 }, { "X": 1.2324925929669082, "Y": 1612 }, { "X": 1.3187950383433522, "Y": 1502 }, { "X": 1.4050974837197971, "Y": 1285 }, { "X": 1.491399929096242, "Y": 1133 }, { "X": 1.5777023744726861, "Y": 990 }, { "X": 1.664004819849131, "Y": 843 }, { "X": 1.750307265225576, "Y": 766 }, { "X": 1.83660971060202, "Y": 655 }, { "X": 1.922912155978465, "Y": 536 }, { "X": 2.00921460135491, "Y": 463 }, { "X": 2.095517046731354, "Y": 361 }, { "X": 2.181819492107799, "Y": 336 }, { "X": 2.268121937484244, "Y": 275 }, { "X": 2.354424382860688, "Y": 209 }, { "X": 2.440726828237133, "Y": 159 }, { "X": 2.5270292736135778, "Y": 143 }, { "X": 2.613331718990022, "Y": 127 }, { "X": 2.6996341643664667, "Y": 90 }, { "X": 2.785936609742911, "Y": 83 }, { "X": 2.8722390551193557, "Y": 59 }, { "X": 2.9585415004958007, "Y": 40 }, { "X": 3.0448439458722447, "Y": 33 }, { "X": 3.1311463912486897, "Y": 24 }, { "X": 3.2174488366251346, "Y": 11 }, { "X": 3.3037512820015786, "Y": 15 }, { "X": 3.3900537273780236, "Y": 11 }, { "X": 3.4763561727544685, "Y": 9 }, { "X": 3.5626586181309126, "Y": 6 }, { "X": 3.6489610635073575, "Y": 4 }, { "X": 3.7352635088838024, "Y": 3 }, { "X": 3.8215659542602465, "Y": 5 }, { "X": 3.9078683996366905, "Y": 1 }, { "X": 3.9941708450131355, "Y": 1 }, { "X": 4.08047329038958, "Y": 2 }, { "X": 4.166775735766025, "Y": 0 }, { "X": 4.2530781811424685, "Y": 1 }, { "X": 4.339380626518913, "Y": 0 }, { "X": 4.425683071895358, "Y": 0 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler $ plot . histogramToList . histogram 100 <$> replicateM 100000 ((,constWeight) <$> model2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Mixture model:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "model3 = do\n", " p <- bernoulli 0.7\n", " if p then normal 0 1 else normal 3 1" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -4.062200110756421, "Y": 2 }, { "X": -4.006272440638702, "Y": 0 }, { "X": -3.950344770520982, "Y": 0 }, { "X": -3.8944171004032624, "Y": 0 }, { "X": -3.8384894302855432, "Y": 1 }, { "X": -3.7825617601678236, "Y": 1 }, { "X": -3.726634090050104, "Y": 1 }, { "X": -3.670706419932385, "Y": 5 }, { "X": -3.6147787498146653, "Y": 3 }, { "X": -3.5588510796969457, "Y": 4 }, { "X": -3.5029234095792265, "Y": 4 }, { "X": -3.446995739461507, "Y": 5 }, { "X": -3.3910680693437874, "Y": 4 }, { "X": -3.335140399226068, "Y": 8 }, { "X": -3.2792127291083486, "Y": 6 }, { "X": -3.223285058990629, "Y": 13 }, { "X": -3.1673573888729094, "Y": 17 }, { "X": -3.11142971875519, "Y": 14 }, { "X": -3.0555020486374707, "Y": 16 }, { "X": -2.999574378519751, "Y": 22 }, { "X": -2.9436467084020315, "Y": 20 }, { "X": -2.8877190382843123, "Y": 32 }, { "X": -2.8317913681665923, "Y": 28 }, { "X": -2.775863698048873, "Y": 35 }, { "X": -2.719936027931154, "Y": 42 }, { "X": -2.664008357813434, "Y": 30 }, { "X": -2.6080806876957148, "Y": 48 }, { "X": -2.552153017577995, "Y": 63 }, { "X": -2.4962253474602756, "Y": 80 }, { "X": -2.4402976773425564, "Y": 73 }, { "X": -2.384370007224837, "Y": 87 }, { "X": -2.3284423371071172, "Y": 105 }, { "X": -2.272514666989398, "Y": 129 }, { "X": -2.2165869968716785, "Y": 123 }, { "X": -2.160659326753959, "Y": 141 }, { "X": -2.1047316566362397, "Y": 167 }, { "X": -2.04880398651852, "Y": 199 }, { "X": -1.9928763164008005, "Y": 208 }, { "X": -1.9369486462830814, "Y": 238 }, { "X": -1.8810209761653618, "Y": 262 }, { "X": -1.8250933060476422, "Y": 288 }, { "X": -1.769165635929923, "Y": 348 }, { "X": -1.7132379658122034, "Y": 383 }, { "X": -1.6573102956944838, "Y": 441 }, { "X": -1.6013826255767643, "Y": 441 }, { "X": -1.545454955459045, "Y": 461 }, { "X": -1.4895272853413255, "Y": 551 }, { "X": -1.433599615223606, "Y": 542 }, { "X": -1.3776719451058868, "Y": 634 }, { "X": -1.3217442749881672, "Y": 691 }, { "X": -1.2658166048704476, "Y": 666 }, { "X": -1.209888934752728, "Y": 769 }, { "X": -1.1539612646350088, "Y": 800 }, { "X": -1.0980335945172892, "Y": 829 }, { "X": -1.0421059243995696, "Y": 919 }, { "X": -0.98617825428185, "Y": 947 }, { "X": -0.9302505841641309, "Y": 1010 }, { "X": -0.8743229140464113, "Y": 1043 }, { "X": -0.8183952439286917, "Y": 1082 }, { "X": -0.7624675738109725, "Y": 1143 }, { "X": -0.7065399036932529, "Y": 1257 }, { "X": -0.6506122335755333, "Y": 1272 }, { "X": -0.5946845634578137, "Y": 1324 }, { "X": -0.5387568933400946, "Y": 1315 }, { "X": -0.482829223222375, "Y": 1409 }, { "X": -0.4269015531046554, "Y": 1414 }, { "X": -0.37097388298693623, "Y": 1502 }, { "X": -0.31504621286921664, "Y": 1500 }, { "X": -0.25911854275149704, "Y": 1487 }, { "X": -0.20319087263377744, "Y": 1563 }, { "X": -0.1472632025160583, "Y": 1623 }, { "X": -0.09133553239833869, "Y": 1546 }, { "X": -0.03540786228061865, "Y": 1657 }, { "X": 0.0205198078371005, "Y": 1476 }, { "X": 0.07644747795482054, "Y": 1553 }, { "X": 0.1323751480725397, "Y": 1535 }, { "X": 0.18830281819025885, "Y": 1582 }, { "X": 0.2442304883079789, "Y": 1494 }, { "X": 0.30015815842569804, "Y": 1521 }, { "X": 0.3560858285434172, "Y": 1489 }, { "X": 0.41201349866113723, "Y": 1480 }, { "X": 0.4679411687788564, "Y": 1433 }, { "X": 0.5238688388965755, "Y": 1416 }, { "X": 0.5797965090142956, "Y": 1332 }, { "X": 0.6357241791320147, "Y": 1364 }, { "X": 0.6916518492497348, "Y": 1268 }, { "X": 0.7475795193674539, "Y": 1265 }, { "X": 0.8035071894851731, "Y": 1144 }, { "X": 0.8594348596028931, "Y": 1172 }, { "X": 0.9153625297206123, "Y": 1106 }, { "X": 0.9712901998383314, "Y": 1101 }, { "X": 1.0272178699560515, "Y": 1048 }, { "X": 1.0831455400737706, "Y": 941 }, { "X": 1.1390732101914898, "Y": 937 }, { "X": 1.1950008803092098, "Y": 936 }, { "X": 1.250928550426929, "Y": 848 }, { "X": 1.3068562205446481, "Y": 824 }, { "X": 1.3627838906623682, "Y": 795 }, { "X": 1.4187115607800873, "Y": 766 }, { "X": 1.4746392308978074, "Y": 736 }, { "X": 1.5305669010155265, "Y": 735 }, { "X": 1.5864945711332457, "Y": 654 }, { "X": 1.6424222412509657, "Y": 626 }, { "X": 1.6983499113686849, "Y": 651 }, { "X": 1.754277581486404, "Y": 616 }, { "X": 1.810205251604124, "Y": 633 }, { "X": 1.8661329217218432, "Y": 597 }, { "X": 1.9220605918395623, "Y": 622 }, { "X": 1.9779882619572824, "Y": 629 }, { "X": 2.0339159320750015, "Y": 643 }, { "X": 2.0898436021927216, "Y": 650 }, { "X": 2.1457712723104407, "Y": 640 }, { "X": 2.20169894242816, "Y": 608 }, { "X": 2.25762661254588, "Y": 632 }, { "X": 2.313554282663599, "Y": 611 }, { "X": 2.3694819527813182, "Y": 639 }, { "X": 2.4254096228990383, "Y": 601 }, { "X": 2.4813372930167574, "Y": 631 }, { "X": 2.5372649631344766, "Y": 680 }, { "X": 2.5931926332521966, "Y": 666 }, { "X": 2.6491203033699158, "Y": 691 }, { "X": 2.705047973487635, "Y": 687 }, { "X": 2.760975643605355, "Y": 686 }, { "X": 2.816903313723074, "Y": 652 }, { "X": 2.872830983840794, "Y": 699 }, { "X": 2.9287586539585133, "Y": 678 }, { "X": 2.9846863240762325, "Y": 686 }, { "X": 3.0406139941939525, "Y": 706 }, { "X": 3.0965416643116717, "Y": 684 }, { "X": 3.152469334429391, "Y": 664 }, { "X": 3.208397004547111, "Y": 656 }, { "X": 3.26432467466483, "Y": 637 }, { "X": 3.320252344782549, "Y": 639 }, { "X": 3.376180014900269, "Y": 641 }, { "X": 3.4321076850179884, "Y": 629 }, { "X": 3.4880353551357084, "Y": 592 }, { "X": 3.5439630252534275, "Y": 578 }, { "X": 3.5998906953711467, "Y": 568 }, { "X": 3.6558183654888667, "Y": 492 }, { "X": 3.711746035606586, "Y": 526 }, { "X": 3.767673705724305, "Y": 465 }, { "X": 3.823601375842025, "Y": 479 }, { "X": 3.8795290459597442, "Y": 505 }, { "X": 3.9354567160774625, "Y": 403 }, { "X": 3.9913843861951825, "Y": 406 }, { "X": 4.047312056312903, "Y": 365 }, { "X": 4.103239726430621, "Y": 379 }, { "X": 4.159167396548341, "Y": 353 }, { "X": 4.215095066666061, "Y": 306 }, { "X": 4.271022736783779, "Y": 274 }, { "X": 4.326950406901499, "Y": 270 }, { "X": 4.382878077019219, "Y": 252 }, { "X": 4.4388057471369375, "Y": 216 }, { "X": 4.494733417254658, "Y": 208 }, { "X": 4.550661087372378, "Y": 213 }, { "X": 4.606588757490096, "Y": 198 }, { "X": 4.662516427607816, "Y": 165 }, { "X": 4.718444097725536, "Y": 162 }, { "X": 4.774371767843254, "Y": 123 }, { "X": 4.830299437960974, "Y": 133 }, { "X": 4.886227108078694, "Y": 105 }, { "X": 4.942154778196413, "Y": 89 }, { "X": 4.998082448314133, "Y": 94 }, { "X": 5.054010118431853, "Y": 88 }, { "X": 5.109937788549571, "Y": 69 }, { "X": 5.165865458667291, "Y": 71 }, { "X": 5.221793128785011, "Y": 58 }, { "X": 5.277720798902731, "Y": 37 }, { "X": 5.333648469020449, "Y": 41 }, { "X": 5.389576139138169, "Y": 28 }, { "X": 5.445503809255889, "Y": 22 }, { "X": 5.501431479373608, "Y": 20 }, { "X": 5.557359149491328, "Y": 22 }, { "X": 5.613286819609048, "Y": 25 }, { "X": 5.669214489726766, "Y": 26 }, { "X": 5.725142159844486, "Y": 13 }, { "X": 5.781069829962206, "Y": 11 }, { "X": 5.836997500079924, "Y": 16 }, { "X": 5.892925170197644, "Y": 8 }, { "X": 5.948852840315364, "Y": 10 }, { "X": 6.004780510433083, "Y": 11 }, { "X": 6.060708180550803, "Y": 6 }, { "X": 6.116635850668523, "Y": 10 }, { "X": 6.172563520786241, "Y": 6 }, { "X": 6.228491190903961, "Y": 4 }, { "X": 6.284418861021681, "Y": 4 }, { "X": 6.340346531139399, "Y": 3 }, { "X": 6.396274201257119, "Y": 1 }, { "X": 6.4522018713748395, "Y": 3 }, { "X": 6.508129541492558, "Y": 3 }, { "X": 6.564057211610278, "Y": 3 }, { "X": 6.619984881727998, "Y": 1 }, { "X": 6.675912551845716, "Y": 0 }, { "X": 6.731840221963436, "Y": 2 }, { "X": 6.787767892081156, "Y": 1 }, { "X": 6.843695562198876, "Y": 2 }, { "X": 6.8996232323165945, "Y": 0 }, { "X": 6.9555509024343145, "Y": 0 }, { "X": 7.0114785725520345, "Y": 1 }, { "X": 7.067406242669753, "Y": 0 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler $ \n", " plot . histogramToList . histogram 200\n", " <$> replicateM 100000 (runWeightedT model3)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Weighted Sampling\n", "\n", "For models with factor statements, we cannot just sample. For example, consider a distribution which is conditioned to positive values only:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "model4 = do\n", " p <- bernoulli 0.7\n", " out <- if p then normal 0 1 else normal 3 1\n", " condition (out > 0)\n", " return out\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`condition` is a special case of `factor` statement for binary factors." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we use `runWeightedT` to convert `model4` into a distribution over pairs of numbers and their weights. We then sample from that. To view " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -4.265869358608886, "Y": 0 }, { "X": -4.208461706073316, "Y": 0 }, { "X": -4.151054053537747, "Y": 0 }, { "X": -4.093646401002177, "Y": 0 }, { "X": -4.036238748466607, "Y": 0 }, { "X": -3.978831095931037, "Y": 0 }, { "X": -3.9214234433954673, "Y": 0 }, { "X": -3.8640157908598973, "Y": 0 }, { "X": -3.8066081383243278, "Y": 0 }, { "X": -3.7492004857887578, "Y": 0 }, { "X": -3.6917928332531877, "Y": 0 }, { "X": -3.634385180717618, "Y": 0 }, { "X": -3.576977528182048, "Y": 0 }, { "X": -3.5195698756464786, "Y": 0 }, { "X": -3.4621622231109086, "Y": 0 }, { "X": -3.4047545705753386, "Y": 0 }, { "X": -3.347346918039769, "Y": 0 }, { "X": -3.2899392655041995, "Y": 0 }, { "X": -3.2325316129686295, "Y": 0 }, { "X": -3.1751239604330594, "Y": 0 }, { "X": -3.1177163078974894, "Y": 0 }, { "X": -3.06030865536192, "Y": 0 }, { "X": -3.0029010028263503, "Y": 0 }, { "X": -2.9454933502907803, "Y": 0 }, { "X": -2.8880856977552103, "Y": 0 }, { "X": -2.8306780452196403, "Y": 0 }, { "X": -2.7732703926840707, "Y": 0 }, { "X": -2.715862740148501, "Y": 0 }, { "X": -2.658455087612931, "Y": 0 }, { "X": -2.601047435077361, "Y": 0 }, { "X": -2.543639782541791, "Y": 0 }, { "X": -2.4862321300062216, "Y": 0 }, { "X": -2.428824477470652, "Y": 0 }, { "X": -2.371416824935082, "Y": 0 }, { "X": -2.314009172399512, "Y": 0 }, { "X": -2.2566015198639424, "Y": 0 }, { "X": -2.1991938673283724, "Y": 0 }, { "X": -2.141786214792803, "Y": 0 }, { "X": -2.084378562257233, "Y": 0 }, { "X": -2.026970909721663, "Y": 0 }, { "X": -1.9695632571860933, "Y": 0 }, { "X": -1.9121556046505233, "Y": 0 }, { "X": -1.8547479521149537, "Y": 0 }, { "X": -1.7973402995793837, "Y": 0 }, { "X": -1.7399326470438141, "Y": 0 }, { "X": -1.682524994508244, "Y": 0 }, { "X": -1.625117341972674, "Y": 0 }, { "X": -1.5677096894371045, "Y": 0 }, { "X": -1.5103020369015345, "Y": 0 }, { "X": -1.452894384365965, "Y": 0 }, { "X": -1.395486731830395, "Y": 0 }, { "X": -1.3380790792948254, "Y": 0 }, { "X": -1.2806714267592554, "Y": 0 }, { "X": -1.2232637742236854, "Y": 0 }, { "X": -1.1658561216881158, "Y": 0 }, { "X": -1.1084484691525458, "Y": 0 }, { "X": -1.0510408166169762, "Y": 0 }, { "X": -0.9936331640814062, "Y": 0 }, { "X": -0.9362255115458362, "Y": 0 }, { "X": -0.8788178590102667, "Y": 0 }, { "X": -0.8214102064746966, "Y": 0 }, { "X": -0.7640025539391271, "Y": 0 }, { "X": -0.7065949014035571, "Y": 0 }, { "X": -0.6491872488679875, "Y": 0 }, { "X": -0.5917795963324175, "Y": 0 }, { "X": -0.5343719437968475, "Y": 0 }, { "X": -0.47696429126127793, "Y": 0 }, { "X": -0.4195566387257079, "Y": 0 }, { "X": -0.36214898619013836, "Y": 0 }, { "X": -0.30474133365456835, "Y": 0 }, { "X": -0.24733368111899878, "Y": 0 }, { "X": -0.18992602858342877, "Y": 0 }, { "X": -0.13251837604785877, "Y": 0 }, { "X": -0.07511072351228876, "Y": 0 }, { "X": -0.017703070976719637, "Y": 292 }, { "X": 0.03970458155885037, "Y": 1629 }, { "X": 0.09711223409442038, "Y": 1602 }, { "X": 0.1545198866299904, "Y": 1645 }, { "X": 0.2119275391655604, "Y": 1542 }, { "X": 0.2693351917011295, "Y": 1527 }, { "X": 0.3267428442366995, "Y": 1558 }, { "X": 0.38415049677226953, "Y": 1504 }, { "X": 0.44155814930783954, "Y": 1544 }, { "X": 0.49896580184340866, "Y": 1403 }, { "X": 0.5563734543789787, "Y": 1439 }, { "X": 0.6137811069145487, "Y": 1349 }, { "X": 0.6711887594501187, "Y": 1366 }, { "X": 0.7285964119856887, "Y": 1290 }, { "X": 0.7860040645212578, "Y": 1225 }, { "X": 0.8434117170568278, "Y": 1171 }, { "X": 0.9008193695923978, "Y": 1131 }, { "X": 0.9582270221279678, "Y": 1056 }, { "X": 1.0156346746635379, "Y": 969 }, { "X": 1.073042327199107, "Y": 1048 }, { "X": 1.130449979734677, "Y": 956 }, { "X": 1.187857632270247, "Y": 950 }, { "X": 1.245265284805817, "Y": 849 }, { "X": 1.302672937341387, "Y": 842 }, { "X": 1.3600805898769561, "Y": 802 }, { "X": 1.4174882424125261, "Y": 799 }, { "X": 1.4748958949480961, "Y": 708 }, { "X": 1.5323035474836662, "Y": 730 }, { "X": 1.5897112000192353, "Y": 725 }, { "X": 1.6471188525548053, "Y": 674 }, { "X": 1.7045265050903753, "Y": 668 }, { "X": 1.7619341576259453, "Y": 614 }, { "X": 1.8193418101615153, "Y": 638 }, { "X": 1.8767494626970844, "Y": 638 }, { "X": 1.9341571152326544, "Y": 622 }, { "X": 1.9915647677682244, "Y": 664 }, { "X": 2.0489724203037945, "Y": 668 }, { "X": 2.1063800728393645, "Y": 619 }, { "X": 2.1637877253749336, "Y": 636 }, { "X": 2.2211953779105036, "Y": 641 }, { "X": 2.2786030304460736, "Y": 640 }, { "X": 2.3360106829816436, "Y": 674 }, { "X": 2.3934183355172136, "Y": 658 }, { "X": 2.4508259880527827, "Y": 678 }, { "X": 2.5082336405883527, "Y": 634 }, { "X": 2.5656412931239228, "Y": 688 }, { "X": 2.6230489456594928, "Y": 653 }, { "X": 2.680456598195062, "Y": 724 }, { "X": 2.737864250730632, "Y": 677 }, { "X": 2.795271903266202, "Y": 721 }, { "X": 2.852679555801772, "Y": 662 }, { "X": 2.910087208337342, "Y": 685 }, { "X": 2.967494860872911, "Y": 722 }, { "X": 3.024902513408481, "Y": 696 }, { "X": 3.082310165944051, "Y": 681 }, { "X": 3.139717818479621, "Y": 678 }, { "X": 3.197125471015191, "Y": 678 }, { "X": 3.25453312355076, "Y": 683 }, { "X": 3.31194077608633, "Y": 700 }, { "X": 3.3693484286219, "Y": 638 }, { "X": 3.42675608115747, "Y": 647 }, { "X": 3.48416373369304, "Y": 567 }, { "X": 3.5415713862286093, "Y": 641 }, { "X": 3.5989790387641794, "Y": 548 }, { "X": 3.6563866912997494, "Y": 566 }, { "X": 3.7137943438353185, "Y": 539 }, { "X": 3.7712019963708885, "Y": 486 }, { "X": 3.8286096489064585, "Y": 498 }, { "X": 3.8860173014420285, "Y": 452 }, { "X": 3.9434249539775985, "Y": 423 }, { "X": 4.0008326065131685, "Y": 396 }, { "X": 4.0582402590487385, "Y": 400 }, { "X": 4.1156479115843085, "Y": 368 }, { "X": 4.173055564119877, "Y": 331 }, { "X": 4.230463216655447, "Y": 309 }, { "X": 4.287870869191017, "Y": 314 }, { "X": 4.345278521726587, "Y": 275 }, { "X": 4.402686174262157, "Y": 261 }, { "X": 4.460093826797727, "Y": 229 }, { "X": 4.517501479333297, "Y": 216 }, { "X": 4.574909131868867, "Y": 164 }, { "X": 4.632316784404437, "Y": 185 }, { "X": 4.689724436940007, "Y": 159 }, { "X": 4.747132089475575, "Y": 141 }, { "X": 4.804539742011145, "Y": 120 }, { "X": 4.861947394546715, "Y": 139 }, { "X": 4.919355047082285, "Y": 105 }, { "X": 4.976762699617855, "Y": 109 }, { "X": 5.034170352153425, "Y": 77 }, { "X": 5.091578004688995, "Y": 78 }, { "X": 5.148985657224565, "Y": 69 }, { "X": 5.206393309760135, "Y": 74 }, { "X": 5.263800962295703, "Y": 60 }, { "X": 5.321208614831273, "Y": 43 }, { "X": 5.378616267366843, "Y": 45 }, { "X": 5.436023919902413, "Y": 40 }, { "X": 5.493431572437983, "Y": 34 }, { "X": 5.550839224973553, "Y": 24 }, { "X": 5.608246877509123, "Y": 24 }, { "X": 5.665654530044693, "Y": 18 }, { "X": 5.7230621825802634, "Y": 11 }, { "X": 5.7804698351158335, "Y": 17 }, { "X": 5.837877487651402, "Y": 13 }, { "X": 5.895285140186972, "Y": 12 }, { "X": 5.952692792722542, "Y": 6 }, { "X": 6.010100445258112, "Y": 12 }, { "X": 6.067508097793682, "Y": 3 }, { "X": 6.124915750329252, "Y": 2 }, { "X": 6.182323402864822, "Y": 2 }, { "X": 6.239731055400392, "Y": 7 }, { "X": 6.297138707935962, "Y": 1 }, { "X": 6.35454636047153, "Y": 3 }, { "X": 6.4119540130071, "Y": 3 }, { "X": 6.46936166554267, "Y": 2 }, { "X": 6.52676931807824, "Y": 2 }, { "X": 6.58417697061381, "Y": 1 }, { "X": 6.64158462314938, "Y": 3 }, { "X": 6.69899227568495, "Y": 2 }, { "X": 6.75639992822052, "Y": 1 }, { "X": 6.81380758075609, "Y": 1 }, { "X": 6.87121523329166, "Y": 0 }, { "X": 6.928622885827228, "Y": 0 }, { "X": 6.986030538362798, "Y": 1 }, { "X": 7.043438190898368, "Y": 0 }, { "X": 7.100845843433938, "Y": 0 }, { "X": 7.158253495969508, "Y": 0 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler $ \n", " plot . histogramToList . histogram 200\n", " <$> replicateM 100000 (runWeightedT model4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could ignore the `condition` statement using `unweighted`, as in:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -4.424718428568858, "Y": 1 }, { "X": -4.368596691690609, "Y": 0 }, { "X": -4.312474954812361, "Y": 0 }, { "X": -4.256353217934112, "Y": 0 }, { "X": -4.200231481055864, "Y": 0 }, { "X": -4.144109744177615, "Y": 0 }, { "X": -4.087988007299367, "Y": 0 }, { "X": -4.0318662704211174, "Y": 0 }, { "X": -3.975744533542869, "Y": 0 }, { "X": -3.919622796664621, "Y": 0 }, { "X": -3.863501059786372, "Y": 0 }, { "X": -3.8073793229081234, "Y": 2 }, { "X": -3.751257586029875, "Y": 1 }, { "X": -3.6951358491516264, "Y": 1 }, { "X": -3.6390141122733777, "Y": 5 }, { "X": -3.5828923753951294, "Y": 1 }, { "X": -3.5267706385168807, "Y": 2 }, { "X": -3.470648901638632, "Y": 3 }, { "X": -3.4145271647603836, "Y": 3 }, { "X": -3.358405427882135, "Y": 5 }, { "X": -3.302283691003886, "Y": 5 }, { "X": -3.246161954125638, "Y": 9 }, { "X": -3.190040217247389, "Y": 12 }, { "X": -3.1339184803691404, "Y": 14 }, { "X": -3.077796743490892, "Y": 12 }, { "X": -3.0216750066126434, "Y": 20 }, { "X": -2.9655532697343947, "Y": 18 }, { "X": -2.9094315328561464, "Y": 18 }, { "X": -2.8533097959778977, "Y": 29 }, { "X": -2.797188059099649, "Y": 23 }, { "X": -2.7410663222214007, "Y": 45 }, { "X": -2.684944585343152, "Y": 48 }, { "X": -2.628822848464903, "Y": 53 }, { "X": -2.572701111586655, "Y": 61 }, { "X": -2.516579374708406, "Y": 56 }, { "X": -2.4604576378301575, "Y": 84 }, { "X": -2.404335900951909, "Y": 88 }, { "X": -2.348214164073661, "Y": 104 }, { "X": -2.292092427195412, "Y": 126 }, { "X": -2.2359706903171634, "Y": 149 }, { "X": -2.179848953438915, "Y": 138 }, { "X": -2.1237272165606664, "Y": 176 }, { "X": -2.0676054796824177, "Y": 203 }, { "X": -2.0114837428041694, "Y": 207 }, { "X": -1.9553620059259207, "Y": 231 }, { "X": -1.899240269047672, "Y": 255 }, { "X": -1.8431185321694237, "Y": 298 }, { "X": -1.786996795291175, "Y": 331 }, { "X": -1.7308750584129262, "Y": 370 }, { "X": -1.674753321534678, "Y": 430 }, { "X": -1.6186315846564292, "Y": 375 }, { "X": -1.562509847778181, "Y": 466 }, { "X": -1.5063881108999322, "Y": 517 }, { "X": -1.4502663740216835, "Y": 533 }, { "X": -1.3941446371434352, "Y": 559 }, { "X": -1.3380229002651864, "Y": 679 }, { "X": -1.2819011633869377, "Y": 687 }, { "X": -1.2257794265086894, "Y": 694 }, { "X": -1.1696576896304407, "Y": 745 }, { "X": -1.113535952752192, "Y": 813 }, { "X": -1.0574142158739437, "Y": 920 }, { "X": -1.001292478995695, "Y": 932 }, { "X": -0.9451707421174462, "Y": 970 }, { "X": -0.8890490052391979, "Y": 1060 }, { "X": -0.8329272683609492, "Y": 1003 }, { "X": -0.7768055314827005, "Y": 1133 }, { "X": -0.7206837946044522, "Y": 1218 }, { "X": -0.6645620577262035, "Y": 1213 }, { "X": -0.6084403208479547, "Y": 1327 }, { "X": -0.5523185839697065, "Y": 1375 }, { "X": -0.4961968470914577, "Y": 1327 }, { "X": -0.44007511021320855, "Y": 1472 }, { "X": -0.38395337333496027, "Y": 1518 }, { "X": -0.327831636456712, "Y": 1462 }, { "X": -0.2717098995784637, "Y": 1542 }, { "X": -0.21558816270021453, "Y": 1557 }, { "X": -0.15946642582196624, "Y": 1561 }, { "X": -0.10334468894371795, "Y": 1557 }, { "X": -0.04722295206546878, "Y": 1613 }, { "X": 0.008898784812779503, "Y": 1568 }, { "X": 0.06502052169102779, "Y": 1580 }, { "X": 0.12114225856927696, "Y": 1498 }, { "X": 0.17726399544752525, "Y": 1588 }, { "X": 0.23338573232577353, "Y": 1543 }, { "X": 0.2895074692040227, "Y": 1470 }, { "X": 0.345629206082271, "Y": 1542 }, { "X": 0.4017509429605193, "Y": 1480 }, { "X": 0.45787267983876845, "Y": 1393 }, { "X": 0.5139944167170167, "Y": 1339 }, { "X": 0.570116153595265, "Y": 1401 }, { "X": 0.6262378904735142, "Y": 1242 }, { "X": 0.6823596273517625, "Y": 1390 }, { "X": 0.7384813642300108, "Y": 1198 }, { "X": 0.7946031011082599, "Y": 1241 }, { "X": 0.8507248379865082, "Y": 1247 }, { "X": 0.9068465748647565, "Y": 1120 }, { "X": 0.9629683117430057, "Y": 1076 }, { "X": 1.019090048621254, "Y": 1042 }, { "X": 1.0752117854995022, "Y": 932 }, { "X": 1.1313335223777505, "Y": 957 }, { "X": 1.1874552592559997, "Y": 944 }, { "X": 1.243576996134248, "Y": 888 }, { "X": 1.2996987330124963, "Y": 874 }, { "X": 1.3558204698907454, "Y": 800 }, { "X": 1.4119422067689937, "Y": 766 }, { "X": 1.468063943647242, "Y": 740 }, { "X": 1.5241856805254912, "Y": 740 }, { "X": 1.5803074174037395, "Y": 693 }, { "X": 1.6364291542819878, "Y": 688 }, { "X": 1.692550891160237, "Y": 670 }, { "X": 1.7486726280384852, "Y": 640 }, { "X": 1.8047943649167335, "Y": 660 }, { "X": 1.8609161017949827, "Y": 635 }, { "X": 1.917037838673231, "Y": 641 }, { "X": 1.9731595755514793, "Y": 624 }, { "X": 2.0292813124297284, "Y": 610 }, { "X": 2.0854030493079767, "Y": 622 }, { "X": 2.141524786186225, "Y": 623 }, { "X": 2.197646523064474, "Y": 619 }, { "X": 2.2537682599427225, "Y": 640 }, { "X": 2.3098899968209707, "Y": 593 }, { "X": 2.36601173369922, "Y": 601 }, { "X": 2.422133470577468, "Y": 649 }, { "X": 2.4782552074557165, "Y": 662 }, { "X": 2.5343769443339657, "Y": 655 }, { "X": 2.590498681212214, "Y": 702 }, { "X": 2.6466204180904622, "Y": 715 }, { "X": 2.7027421549687114, "Y": 681 }, { "X": 2.7588638918469597, "Y": 714 }, { "X": 2.814985628725208, "Y": 677 }, { "X": 2.871107365603457, "Y": 706 }, { "X": 2.9272291024817054, "Y": 686 }, { "X": 2.9833508393599537, "Y": 665 }, { "X": 3.039472576238203, "Y": 678 }, { "X": 3.095594313116451, "Y": 647 }, { "X": 3.1517160499946995, "Y": 681 }, { "X": 3.2078377868729486, "Y": 642 }, { "X": 3.263959523751197, "Y": 657 }, { "X": 3.320081260629445, "Y": 613 }, { "X": 3.3762029975076944, "Y": 628 }, { "X": 3.4323247343859427, "Y": 645 }, { "X": 3.488446471264191, "Y": 625 }, { "X": 3.54456820814244, "Y": 594 }, { "X": 3.6006899450206884, "Y": 604 }, { "X": 3.6568116818989385, "Y": 530 }, { "X": 3.7129334187771867, "Y": 506 }, { "X": 3.769055155655435, "Y": 478 }, { "X": 3.8251768925336833, "Y": 474 }, { "X": 3.8812986294119316, "Y": 440 }, { "X": 3.93742036629018, "Y": 402 }, { "X": 3.99354210316843, "Y": 414 }, { "X": 4.049663840046678, "Y": 368 }, { "X": 4.1057855769249265, "Y": 362 }, { "X": 4.161907313803175, "Y": 349 }, { "X": 4.218029050681423, "Y": 294 }, { "X": 4.274150787559671, "Y": 300 }, { "X": 4.330272524437921, "Y": 275 }, { "X": 4.38639426131617, "Y": 252 }, { "X": 4.442515998194418, "Y": 251 }, { "X": 4.498637735072666, "Y": 237 }, { "X": 4.554759471950915, "Y": 211 }, { "X": 4.610881208829163, "Y": 182 }, { "X": 4.667002945707413, "Y": 168 }, { "X": 4.723124682585661, "Y": 145 }, { "X": 4.7792464194639095, "Y": 134 }, { "X": 4.835368156342158, "Y": 133 }, { "X": 4.891489893220406, "Y": 99 }, { "X": 4.947611630098654, "Y": 110 }, { "X": 5.003733366976904, "Y": 89 }, { "X": 5.059855103855153, "Y": 73 }, { "X": 5.115976840733401, "Y": 70 }, { "X": 5.172098577611649, "Y": 61 }, { "X": 5.2282203144898975, "Y": 48 }, { "X": 5.284342051368146, "Y": 60 }, { "X": 5.340463788246396, "Y": 41 }, { "X": 5.396585525124644, "Y": 39 }, { "X": 5.4527072620028925, "Y": 39 }, { "X": 5.508828998881141, "Y": 35 }, { "X": 5.564950735759389, "Y": 27 }, { "X": 5.621072472637637, "Y": 19 }, { "X": 5.677194209515887, "Y": 25 }, { "X": 5.733315946394136, "Y": 19 }, { "X": 5.789437683272384, "Y": 20 }, { "X": 5.845559420150632, "Y": 14 }, { "X": 5.9016811570288805, "Y": 9 }, { "X": 5.957802893907129, "Y": 6 }, { "X": 6.013924630785379, "Y": 7 }, { "X": 6.070046367663627, "Y": 6 }, { "X": 6.126168104541875, "Y": 3 }, { "X": 6.182289841420124, "Y": 6 }, { "X": 6.238411578298372, "Y": 1 }, { "X": 6.29453331517662, "Y": 3 }, { "X": 6.35065505205487, "Y": 5 }, { "X": 6.406776788933119, "Y": 1 }, { "X": 6.462898525811367, "Y": 5 }, { "X": 6.519020262689615, "Y": 0 }, { "X": 6.5751419995678635, "Y": 2 }, { "X": 6.631263736446112, "Y": 0 }, { "X": 6.68738547332436, "Y": 2 }, { "X": 6.74350721020261, "Y": 1 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sampler . unweighted $ plot . histogramToList . histogram 200 <$> replicateM 100000 (runWeightedT $ unweighted model4)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But obviously, we'd like to take samples from the posterior. We can do so as follows:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(4.22455827914325,1.0),(0.906234848690838,1.0),(2.6860029976528135,1.0),(-0.17404924439421926,0.0),(-1.1044111213397145,0.0),(-0.5604681812378783,0.0),(-0.1850630831580971,0.0),(3.354382098966137,1.0),(2.641529507690727,1.0),(0.7142981214125107,1.0)]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draws4 <- sampler $ replicateM 10 $ runWeightedT model4\n", "draws4" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "application/vnd.vegalite.v4+json": { "$schema": "https://vega.github.io/schema/vega-lite/v4.json", "data": { "values": [ { "X": -22.842799802410326, "Y": 0.5651872960063646 }, { "X": -22.618977555338, "Y": 0 }, { "X": -22.39515530826567, "Y": 0 }, { "X": -22.171333061193344, "Y": 0 }, { "X": -21.947510814121017, "Y": 0 }, { "X": -21.723688567048686, "Y": 0 }, { "X": -21.49986631997636, "Y": 0 }, { "X": -21.276044072904032, "Y": 0.44956216495009277 }, { "X": -21.052221825831705, "Y": 0 }, { "X": -20.828399578759377, "Y": 0 }, { "X": -20.60457733168705, "Y": 0 }, { "X": -20.380755084614723, "Y": 0 }, { "X": -20.156932837542396, "Y": 0 }, { "X": -19.93311059047007, "Y": 0 }, { "X": -19.70928834339774, "Y": 2.0687075203995375 }, { "X": -19.48546609632541, "Y": 4.339858032002964 }, { "X": -19.261643849253083, "Y": 0 }, { "X": -19.037821602180756, "Y": 0 }, { "X": -18.81399935510843, "Y": 8.123148756505628 }, { "X": -18.5901771080361, "Y": 7.82393823497889 }, { "X": -18.366354860963774, "Y": 4.750111874678893 }, { "X": -18.142532613891447, "Y": 4.322394361536562 }, { "X": -17.91871036681912, "Y": 14.558395289295394 }, { "X": -17.694888119746793, "Y": 2.793615142217626 }, { "X": -17.471065872674465, "Y": 2.5293823056503517 }, { "X": -17.247243625602138, "Y": 3.917679517980818 }, { "X": -17.023421378529807, "Y": 4.700100876648693 }, { "X": -16.79959913145748, "Y": 3.111510893131353 }, { "X": -16.575776884385153, "Y": 3.7208314513304197 }, { "X": -16.351954637312826, "Y": 5.899405014149403 }, { "X": -16.1281323902405, "Y": 2.8030587284845763 }, { "X": -15.904310143168171, "Y": 4.4849334652679875 }, { "X": -15.680487896095844, "Y": 3.6916894041523993 }, { "X": -15.456665649023517, "Y": 6.16174027489882 }, { "X": -15.232843401951188, "Y": 9.12472870011728 }, { "X": -15.00902115487886, "Y": 6.068944530998471 }, { "X": -14.785198907806533, "Y": 13.621247367763253 }, { "X": -14.561376660734204, "Y": 14.820722997393482 }, { "X": -14.337554413661877, "Y": 24.270452897658643 }, { "X": -14.11373216658955, "Y": 29.906150722818847 }, { "X": -13.889909919517223, "Y": 54.65893457599302 }, { "X": -13.666087672444895, "Y": 74.35004726562961 }, { "X": -13.442265425372566, "Y": 97.50029363161545 }, { "X": -13.21844317830024, "Y": 133.2435347634858 }, { "X": -12.994620931227912, "Y": 153.67966743690567 }, { "X": -12.770798684155585, "Y": 146.1827696205524 }, { "X": -12.546976437083257, "Y": 252.15076905023312 }, { "X": -12.323154190010928, "Y": 223.9218805775959 }, { "X": -12.099331942938601, "Y": 201.7504372793475 }, { "X": -11.875509695866274, "Y": 242.87041129443276 }, { "X": -11.651687448793947, "Y": 233.26776877306673 }, { "X": -11.42786520172162, "Y": 213.62637221569526 }, { "X": -11.20404295464929, "Y": 199.2272986148573 }, { "X": -10.980220707576963, "Y": 181.5139443350445 }, { "X": -10.756398460504636, "Y": 161.67880499843506 }, { "X": -10.532576213432309, "Y": 120.96154559492571 }, { "X": -10.308753966359982, "Y": 120.2768278269716 }, { "X": -10.084931719287653, "Y": 110.65477434269435 }, { "X": -9.861109472215325, "Y": 105.14968794000318 }, { "X": -9.637287225142998, "Y": 105.32719766435925 }, { "X": -9.413464978070671, "Y": 101.40255815754203 }, { "X": -9.189642730998344, "Y": 125.1977999599531 }, { "X": -8.965820483926015, "Y": 149.3173714894611 }, { "X": -8.741998236853687, "Y": 175.30457064935766 }, { "X": -8.51817598978136, "Y": 219.06088557122433 }, { "X": -8.294353742709033, "Y": 287.1911932581536 }, { "X": -8.070531495636706, "Y": 418.44333043573374 }, { "X": -7.846709248564377, "Y": 537.5326407260524 }, { "X": -7.62288700149205, "Y": 731.2557797637198 }, { "X": -7.399064754419722, "Y": 957.9513790260437 }, { "X": -7.175242507347395, "Y": 1189.0720818409345 }, { "X": -6.951420260275068, "Y": 1477.9954678948889 }, { "X": -6.727598013202741, "Y": 1772.693177059477 }, { "X": -6.503775766130413, "Y": 2166.7207798931513 }, { "X": -6.279953519058083, "Y": 2137.567572013035 }, { "X": -6.056131271985755, "Y": 2268.101636620837 }, { "X": -5.832309024913428, "Y": 2203.9294468416347 }, { "X": -5.608486777841101, "Y": 1957.904143590563 }, { "X": -5.384664530768774, "Y": 1741.9374720073022 }, { "X": -5.1608422836964465, "Y": 1676.8221548815127 }, { "X": -4.937020036624119, "Y": 1371.9753119020352 }, { "X": -4.713197789551792, "Y": 1150.4812551114735 }, { "X": -4.489375542479465, "Y": 1000.4558768529924 }, { "X": -4.265553295407138, "Y": 781.0420827272407 }, { "X": -4.041731048334807, "Y": 656.425198095446 }, { "X": -3.8179088012624796, "Y": 612.0626584390457 }, { "X": -3.5940865541901523, "Y": 575.3175532743659 }, { "X": -3.370264307117825, "Y": 550.9977986135556 }, { "X": -3.146442060045498, "Y": 550.0229468532232 }, { "X": -2.9226198129731706, "Y": 567.371914038189 }, { "X": -2.6987975659008434, "Y": 618.4035360573534 }, { "X": -2.474975318828516, "Y": 710.1690524342496 }, { "X": -2.251153071756189, "Y": 843.7111069292761 }, { "X": -2.0273308246838617, "Y": 1069.3291273657564 }, { "X": -1.803508577611531, "Y": 1334.1799971484531 }, { "X": -1.5796863305392037, "Y": 1632.091652938732 }, { "X": -1.3558640834668765, "Y": 2088.180952417543 }, { "X": -1.1320418363945493, "Y": 2611.9334318513947 }, { "X": -0.908219589322222, "Y": 3260.4838066853945 }, { "X": -0.6843973422498948, "Y": 3799.7843546170266 }, { "X": -0.46057509517756756, "Y": 4290.4941356038735 }, { "X": -0.23675284810524033, "Y": 4752.75132377283 }, { "X": -0.012930601032913103, "Y": 4773.632933935296 }, { "X": 0.21089164603941413, "Y": 4802.474435476092 }, { "X": 0.4347138931117449, "Y": 4489.091512776677 }, { "X": 0.6585361401840721, "Y": 4072.283855938352 }, { "X": 0.8823583872563994, "Y": 3260.177115952519 }, { "X": 1.1061806343287266, "Y": 2779.035079621072 }, { "X": 1.3300028814010538, "Y": 2176.991236301665 }, { "X": 1.553825128473381, "Y": 1814.7844291946233 }, { "X": 1.7776473755457083, "Y": 1356.4062797960905 }, { "X": 2.0014696226180355, "Y": 1099.9199931943251 }, { "X": 2.2252918696903627, "Y": 871.8518467630896 }, { "X": 2.44911411676269, "Y": 722.647020990324 }, { "X": 2.6729363638350208, "Y": 618.8397879243232 }, { "X": 2.896758610907348, "Y": 575.9344154646544 }, { "X": 3.120580857979675, "Y": 545.3298210412927 }, { "X": 3.3444031050520024, "Y": 546.9202605021705 }, { "X": 3.5682253521243297, "Y": 578.464107940347 }, { "X": 3.792047599196657, "Y": 588.7622731534417 }, { "X": 4.015869846268984, "Y": 698.3629534754714 }, { "X": 4.239692093341311, "Y": 768.2105721241956 }, { "X": 4.463514340413639, "Y": 954.3706485592971 }, { "X": 4.687336587485966, "Y": 1103.6977020723532 }, { "X": 4.911158834558297, "Y": 1399.939813693509 }, { "X": 5.134981081630624, "Y": 1613.9747339674761 }, { "X": 5.358803328702951, "Y": 1805.982971939544 }, { "X": 5.582625575775278, "Y": 2136.595376725489 }, { "X": 5.8064478228476055, "Y": 2219.846610918295 }, { "X": 6.030270069919933, "Y": 2198.247299165407 }, { "X": 6.25409231699226, "Y": 2207.205843536388 }, { "X": 6.477914564064587, "Y": 1932.4079782945 }, { "X": 6.701736811136914, "Y": 1837.8596051007985 }, { "X": 6.925559058209245, "Y": 1509.9541410112358 }, { "X": 7.1493813052815725, "Y": 1264.5913007337067 }, { "X": 7.3732035523539, "Y": 955.3791020084158 }, { "X": 7.597025799426227, "Y": 738.9068387956111 }, { "X": 7.820848046498554, "Y": 522.4334059272857 }, { "X": 8.044670293570881, "Y": 385.61301476042195 }, { "X": 8.268492540643209, "Y": 296.6211210432971 }, { "X": 8.492314787715536, "Y": 269.8331604679822 }, { "X": 8.716137034787863, "Y": 169.918034982686 }, { "X": 8.93995928186019, "Y": 148.06606176262508 }, { "X": 9.163781528932521, "Y": 110.31798286636354 }, { "X": 9.387603776004848, "Y": 113.6431873536555 }, { "X": 9.611426023077176, "Y": 100.92640387889041 }, { "X": 9.835248270149503, "Y": 109.45722767832254 }, { "X": 10.05907051722183, "Y": 97.45361962728197 }, { "X": 10.282892764294164, "Y": 111.66698878495313 }, { "X": 10.506715011366492, "Y": 119.87081429690616 }, { "X": 10.730537258438819, "Y": 146.30913569441122 }, { "X": 10.954359505511146, "Y": 151.51013799914887 }, { "X": 11.178181752583473, "Y": 197.77596501990487 }, { "X": 11.4020039996558, "Y": 193.78412092295815 }, { "X": 11.625826246728128, "Y": 237.18895731727562 }, { "X": 11.849648493800455, "Y": 260.46813837982273 }, { "X": 12.073470740872782, "Y": 187.67569619232503 }, { "X": 12.29729298794511, "Y": 222.4272067542196 }, { "X": 12.521115235017437, "Y": 205.97451188803967 }, { "X": 12.744937482089764, "Y": 200.5860698690712 }, { "X": 12.968759729162091, "Y": 145.9769251167198 }, { "X": 13.192581976234418, "Y": 103.52405527038997 }, { "X": 13.416404223306746, "Y": 108.05326531274945 }, { "X": 13.640226470379073, "Y": 64.7013127028666 }, { "X": 13.8640487174514, "Y": 43.657503740975066 }, { "X": 14.087870964523727, "Y": 30.690622608579325 }, { "X": 14.311693211596054, "Y": 32.99220168412673 }, { "X": 14.535515458668382, "Y": 17.77364360530488 }, { "X": 14.759337705740716, "Y": 13.194314156356667 }, { "X": 14.983159952813043, "Y": 8.07512664814914 }, { "X": 15.20698219988537, "Y": 7.149438276443698 }, { "X": 15.430804446957698, "Y": 8.426814402370677 }, { "X": 15.654626694030025, "Y": 6.645675323843104 }, { "X": 15.878448941102352, "Y": 5.614160212905122 }, { "X": 16.10227118817468, "Y": 2.378011712614433 }, { "X": 16.326093435247007, "Y": 3.471594782806771 }, { "X": 16.549915682319334, "Y": 2.6179325969870044 }, { "X": 16.77373792939166, "Y": 6.7469142437904885 }, { "X": 16.99756017646399, "Y": 3.7791641744264495 }, { "X": 17.221382423536316, "Y": 3.9525780642898423 }, { "X": 17.445204670608643, "Y": 2.373881125937457 }, { "X": 17.66902691768097, "Y": 0 }, { "X": 17.892849164753297, "Y": 3.4534677683567336 }, { "X": 18.116671411825624, "Y": 4.107939892728238 }, { "X": 18.34049365889795, "Y": 9.416950016355077 }, { "X": 18.56431590597028, "Y": 5.202114883682357 }, { "X": 18.788138153042606, "Y": 8.13062146105124 }, { "X": 19.011960400114933, "Y": 0 }, { "X": 19.235782647187268, "Y": 2.48356260163596 }, { "X": 19.459604894259595, "Y": 4.778556709905142 }, { "X": 19.683427141331922, "Y": 2.067113298630091 }, { "X": 19.90724938840425, "Y": 3.290158732154683 }, { "X": 20.131071635476577, "Y": 0 }, { "X": 20.354893882548904, "Y": 1.0597666013075362 }, { "X": 20.57871612962123, "Y": 0.7790652984532493 }, { "X": 20.80253837669356, "Y": 0.6468429751884207 }, { "X": 21.026360623765886, "Y": 0 }, { "X": 21.250182870838213, "Y": 0 }, { "X": 21.47400511791054, "Y": 0 }, { "X": 21.697827364982867, "Y": 0 } ] }, "encoding": { "x": { "field": "X", "type": "quantitative" }, "y": { "field": "Y", "type": "quantitative" } }, "height": 400, "mark": "bar", "width": 400 }, "image/png": "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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "model5 :: MonadMeasure m => m Double\n", "model5 = do\n", " x <- normal 0 5\n", " (factor . Exp) (cos x)\n", " return x\n", "\n", "sampler $ \n", " plot . histogramToList . histogram 200\n", " <$> replicateM 100000 (runWeightedT model5)" ] } ], "metadata": { "kernelspec": { "display_name": "Haskell - nixpkgs", "language": "haskell", "name": "ihaskell_nixpkgs" }, "language_info": { "codemirror_mode": "ihaskell", "file_extension": ".hs", "mimetype": "text/x-haskell", "name": "haskell", "pygments_lexer": "Haskell", "version": "9.0.2" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: plots.py ================================================ import matplotlib.pyplot as plt import pandas as pd import re benchmark_pattern = "(?P(MonadBayes|Anglican|WebPPL))_(?P(LR|HMM|LDA))(?P[0-9]+)_(?P(SMC(?P[0-9]+$)|MH(?P[0-9]+$)|RMSMC(?P[0-9]+-[0-9]+$)))" benchmark_reg = re.compile(benchmark_pattern) rmsmc_pattern = "(?P[0-9]+)-(?P[0-9]+)" rmsmc_reg = re.compile(rmsmc_pattern) def unpack_name(benchmark_name): m = benchmark_reg.match(benchmark_name) if m is None: return None def lookup(property_name): return m.expand("\g<" + property_name + ">") system = lookup("system") model = lookup("model") length = int(lookup("length")) alg = lookup("alg") if alg[:3] == "SMC": alg_name = "SMC" particles = int(lookup("smcparam")) steps = 0 elif alg[:2] == "MH": alg_name = "MH" particles = 0 steps = int(lookup("mhparam")) elif alg[:5] == "RMSMC": alg_name = "RMSMC" t = rmsmc_reg.match(lookup("rmsmcparam")) particles = int(t.expand("\g")) steps = int(t.expand("\g")) else: raise ValueError("Unrecognized algorithm: " + alg) return system, model, length, alg_name, particles, steps def unpack_names(series): x = list(filter(lambda y: y is not None, [unpack_name(name) for name in series])) systems = [y[0] for y in x] models = [y[1] for y in x] lengths = [y[2] for y in x] algs = [y[3] for y in x] particless = [y[4] for y in x] stepss = [y[5] for y in x] return pd.DataFrame( { "system": systems, "model": models, "length": lengths, "alg": algs, "particles": particless, "steps": stepss, } ) def style(system): if system == "MonadBayes": return "ro" elif system == "Anglican": return "bs" else: return "gX" models = ["LR", "HMM", "LDA"] algs = ["MH", "SMC", "RMSMC"] systems = ["MonadBayes", "Anglican", "WebPPL"] # plot execution time vs. dataset size benchmarks = pd.read_csv("speed-length.csv") results = unpack_names(benchmarks["Name"]) results["time"] = benchmarks["Mean"] results["timeLB"] = benchmarks["MeanLB"] results["timeUB"] = benchmarks["MeanUB"] mhsteps = 100 smcsize = 100 rmsize, rmsteps = 10, 1 fig, subplots = plt.subplots(nrows=len(models), ncols=len(algs), figsize=(12, 8)) lines = [] for i in range(len(models)): model = models[i] for j in range(len(algs)): alg = algs[j] subplot = subplots[i, j] data = results.loc[(results["model"] == model) & (results["alg"] == alg)] if alg == "MH": data = data.loc[data["steps"] == mhsteps] elif alg == "SMC": data = data.loc[data["particles"] == smcsize] else: data = data.loc[(data["steps"] == rmsteps) & (data["particles"] == rmsize)] for system in systems: t = data.loc[data["system"] == system] xs = t["length"] ys = t["time"] if model == "LDA": # LDA has 5 documents xs = xs * 5 (line,) = subplot.plot(xs, ys, style(system), label=system) lines.append((line, system)) if i == len(models) - 1: subplot.set_xlabel("Dataset size") if j == 0: subplot.set_ylabel("Execution time [s]") pad = 5 algnames = [ "MH" + str(mhsteps), "SMC" + str(smcsize), "RMSMC" + str(rmsize) + "-" + str(rmsteps), ] for ax, col in zip(subplots[0], algnames): ax.annotate( col, xy=(0.5, 1), xytext=(0, pad), xycoords="axes fraction", textcoords="offset points", size="large", ha="center", va="baseline", ) for ax, row in zip(subplots[:, 0], models): ax.annotate( row, xy=(0, 0.5), xytext=(-ax.yaxis.labelpad - pad, 0), xycoords=ax.yaxis.label, textcoords="offset points", size="large", ha="right", va="center", ) a, b = zip(*lines[:3]) b = ("Ours", b[1], b[2]) plt.figlegend(a, b, "upper right") plt.savefig("length.pdf") # plot execution time vs. # samples benchmarks = pd.read_csv("speed-samples.csv") results = unpack_names(benchmarks["Name"]) results["time"] = benchmarks["Mean"] results["timeLB"] = benchmarks["MeanLB"] results["timeUB"] = benchmarks["MeanUB"] lrlength = 50 hmmlength = 20 ldalength = 10 rmparticles = 10 fig, subplots = plt.subplots(nrows=len(models), ncols=len(algs), figsize=(12, 8)) lines = [] for i in range(len(models)): model = models[i] for j in range(len(algs)): alg = algs[j] subplot = subplots[i, j] data = results.loc[(results["model"] == model) & (results["alg"] == alg)] if model == "LR": data = data.loc[data["length"] == lrlength] elif model == "HMM": data = data.loc[data["length"] == hmmlength] else: data = data.loc[data["length"] == ldalength] for system in systems: t = data.loc[data["system"] == system] if alg == "MH": xs = t["steps"] if i == len(models) - 1: subplot.set_xlabel("Number of steps") elif alg == "SMC": xs = t["particles"] if i == len(models) - 1: subplot.set_xlabel("Number of particles") else: t = t.loc[t["particles"] == rmparticles] xs = t["steps"] if i == len(models) - 1: subplot.set_xlabel("Number of rejuvenation steps") ys = t["time"] (line,) = subplot.plot(xs, ys, style(system), label=system) lines.append((line, system)) if j == 0: subplot.set_ylabel("Execution time [s]") pad = 5 algnames = ["MH", "SMC", "RMSMC" + str(rmsize)] for ax, col in zip(subplots[0], algnames): ax.annotate( col, xy=(0.5, 1), xytext=(0, pad), xycoords="axes fraction", textcoords="offset points", size="large", ha="center", va="baseline", ) modelnames = ["LR" + str(lrlength), "HMM" + str(hmmlength), "LDA" + str(ldalength * 5)] for ax, row in zip(subplots[:, 0], modelnames): ax.annotate( row, xy=(0, 0.5), xytext=(-ax.yaxis.labelpad - pad, 0), xycoords=ax.yaxis.label, textcoords="offset points", size="large", ha="right", va="center", ) a, b = zip(*lines[:3]) b = ("Ours", b[1], b[2]) plt.figlegend(a, b, "upper right") plt.savefig("samples.pdf") ================================================ FILE: profile.sh ================================================ #!/usr/bin/env sh # Generates profiling information for selected models and inference algorithms. stack build --profile for model in LR100 HMM100 LDA50 do for alg in SMC MH RMSMC do echo "Profiling $alg on $model" stack exec --profile -- example -a $alg -m $model +RTS -p >/dev/null file=$model-$alg mv example.prof $file.prof sed 's/no location info/no_location_info/' $file.prof | awk '$8 !~ /0\.0/' >$file-small.prof done done ================================================ FILE: regenerate_notebooks.sh ================================================ #!/usr/bin/env sh # Regenerates the notebook html files in `docs/docs/notebooks/` nix --print-build-logs develop .#default-jupyter --command jupyter-nbconvert --to html notebooks/examples/*.ipynb --output-dir docs/docs/notebooks/ ================================================ FILE: shell.nix ================================================ ( import ( let lock = builtins.fromJSON (builtins.readFile ./flake.lock); in fetchTarball { url = "https://github.com/edolstra/flake-compat/archive/${lock.nodes.flake-compat.locked.rev}.tar.gz"; sha256 = lock.nodes.flake-compat.locked.narHash; } ) { src = ./.; } ).shellNix ================================================ FILE: src/Control/Applicative/List.hs ================================================ {-# LANGUAGE StandaloneDeriving #-} module Control.Applicative.List where -- base import Control.Applicative import Data.Functor.Compose -- * Applicative ListT -- | _Applicative_ transformer adding a list/nondeterminism/choice effect. -- It is not a valid monad transformer, but it is a valid 'Applicative'. newtype ListT m a = ListT {getListT :: Compose m [] a} deriving newtype (Functor, Applicative, Alternative) listT :: m [a] -> ListT m a listT = ListT . Compose lift :: (Functor m) => m a -> ListT m a lift = ListT . Compose . fmap pure runListT :: ListT m a -> m [a] runListT = getCompose . getListT ================================================ FILE: src/Control/Monad/Bayes/Class.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} {-# LANGUAGE RankNTypes #-} {-# LANGUAGE RecordWildCards #-} {-# OPTIONS_GHC -Wno-deprecations #-} -- | -- Module : Control.Monad.Bayes.Class -- Description : Types for probabilistic modelling -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- This module defines 'MonadMeasure', which can be used to represent any probabilistic program, -- such as the following: -- -- @ -- import Control.Monad (when) -- import Control.Monad.Bayes.Class -- -- model :: MonadMeasure m => m Bool -- model = do -- rain <- bernoulli 0.3 -- sprinkler <- -- bernoulli $ -- if rain -- then 0.1 -- else 0.4 -- let wetProb = -- case (rain, sprinkler) of -- (True, True) -> 0.98 -- (True, False) -> 0.80 -- (False, True) -> 0.90 -- (False, False) -> 0.00 -- score wetProb -- return rain -- @ module Control.Monad.Bayes.Class ( MonadDistribution, random, uniform, normal, gamma, beta, bernoulli, categorical, logCategorical, uniformD, geometric, poisson, dirichlet, MonadFactor, score, factor, condition, MonadMeasure, discrete, normalPdf, Bayesian (..), poissonPdf, posterior, priorPredictive, posteriorPredictive, independent, mvNormal, Histogram, histogram, histogramToList, Distribution, Measure, Kernel, Log (ln, Exp), MonadUniformRange, uniformR, ) where import Control.Arrow (Arrow (second)) import Control.Monad (replicateM, when) import Control.Monad.Cont (ContT) import Control.Monad.Except (ExceptT) import Control.Monad.Identity (IdentityT) import Control.Monad.Reader (ReaderT) import Control.Monad.State (StateT) import Control.Monad.Trans.Class (lift) import Control.Monad.Writer (WriterT) import Data.Histogram qualified as H import Data.Histogram.Fill qualified as H import Data.Matrix ( Matrix, cholDecomp, colVector, getCol, multStd, ) import Data.Vector qualified as V import Data.Vector.Generic as VG (Vector, map, mapM, null, sum, (!)) import Numeric.Log (Log (..)) import Statistics.Distribution ( ContDistr (logDensity, quantile), DiscreteDistr (logProbability, probability), ) import Statistics.Distribution.Beta (betaDistr) import Statistics.Distribution.Gamma (gammaDistr) import Statistics.Distribution.Geometric (geometric0) import Statistics.Distribution.Normal (normalDistr) import Statistics.Distribution.Poisson qualified as Poisson import Statistics.Distribution.Uniform (uniformDistr) import System.Random (UniformRange) -- | Monads that can draw random variables. class (Monad m) => MonadDistribution m where -- | Draw from a uniform distribution. random :: -- | \(\sim \mathcal{U}(0, 1)\) m Double -- | Draw from a uniform distribution. -- -- See also 'MonadUniformRange.uniformR' for types other than 'Double'. uniform :: -- | lower bound a Double -> -- | upper bound b Double -> -- | \(\sim \mathcal{U}(a, b)\). m Double uniform a b = draw (uniformDistr a b) -- | Draw from a normal distribution. normal :: -- | mean μ Double -> -- | standard deviation σ Double -> -- | \(\sim \mathcal{N}(\mu, \sigma^2)\) m Double normal m s = draw (normalDistr m s) -- | Draw from a gamma distribution. gamma :: -- | shape k Double -> -- | scale θ Double -> -- | \(\sim \Gamma(k, \theta)\) m Double gamma shape scale = draw (gammaDistr shape scale) -- | Draw from a beta distribution. beta :: -- | shape α Double -> -- | shape β Double -> -- | \(\sim \mathrm{Beta}(\alpha, \beta)\) m Double beta a b = draw (betaDistr a b) -- | Draw from a Bernoulli distribution. bernoulli :: -- | probability p Double -> -- | \(\sim \mathrm{B}(1, p)\) m Bool bernoulli p = fmap (< p) random -- | Draw from a categorical distribution. categorical :: (Vector v Double) => -- | event probabilities v Double -> -- | outcome category m Int categorical ps = if VG.null ps then error "empty input list" else fromPMF (ps !) -- | Draw from a categorical distribution in the log domain. logCategorical :: (Vector v (Log Double), Vector v Double) => -- | event probabilities v (Log Double) -> -- | outcome category m Int logCategorical = categorical . VG.map (exp . ln) -- | Draw from a discrete uniform distribution. uniformD :: -- | observable outcomes @xs@ [a] -> -- | \(\sim \mathcal{U}\{\mathrm{xs}\}\) m a uniformD xs = do let n = Prelude.length xs i <- categorical $ V.replicate n (1 / fromIntegral n) return (xs !! i) -- | Draw from a geometric distribution. geometric :: -- | success rate p Double -> -- | \(\sim\) number of failed Bernoulli trials with success probability p before first success m Int geometric = discrete . geometric0 -- | Draw from a Poisson distribution. poisson :: -- | parameter λ Double -> -- | \(\sim \mathrm{Pois}(\lambda)\) m Int poisson = discrete . Poisson.poisson -- | Draw from a Dirichlet distribution. dirichlet :: (Vector v Double) => -- | concentration parameters @as@ v Double -> -- | \(\sim \mathrm{Dir}(\mathrm{as})\) m (v Double) dirichlet as = do xs <- VG.mapM (`gamma` 1) as let s = VG.sum xs let ys = VG.map (/ s) xs return ys -- | Draw from a continuous distribution using the inverse cumulative density -- function. draw :: (ContDistr d, MonadDistribution m) => d -> m Double draw d = fmap (quantile d) random -- | Draw from a discrete distribution using a sequence of draws from -- Bernoulli. fromPMF :: (MonadDistribution m) => (Int -> Double) -> m Int fromPMF p = f 0 1 where f i r = do when (r < 0) $ error "fromPMF: total PMF above 1" let q = p i when (q < 0 || q > 1) $ error "fromPMF: invalid probability value" b <- bernoulli (q / r) if b then pure i else f (i + 1) (r - q) -- | Draw from a discrete distributions using the probability mass function. discrete :: (DiscreteDistr d, MonadDistribution m) => d -> m Int discrete = fromPMF . probability -- | Monads that can score different execution paths. class (Monad m) => MonadFactor m where -- | Record a likelihood. score :: -- | likelihood of the execution path Log Double -> m () -- | Synonym for 'score'. factor :: (MonadFactor m) => -- | likelihood of the execution path Log Double -> m () factor = score -- | synonym for pretty type signatures, but note that (A -> Distribution B) won't work as intended: for that, use Kernel -- Also note that the use of RankNTypes means performance may take a hit: really the main point of these signatures is didactic type Distribution a = forall m. (MonadDistribution m) => m a type Measure a = forall m. (MonadMeasure m) => m a type Kernel a b = forall m. (MonadMeasure m) => a -> m b -- | Hard conditioning. condition :: (MonadFactor m) => Bool -> m () condition b = score $ if b then 1 else 0 independent :: (Applicative m) => Int -> m a -> m [a] independent = replicateM -- | Monads that support both sampling and scoring. class (MonadDistribution m, MonadFactor m) => MonadMeasure m -- | Probability density function of the normal distribution. normalPdf :: -- | mean μ Double -> -- | standard deviation σ Double -> -- | sample x Double -> -- | relative likelihood of observing sample x in \(\mathcal{N}(\mu, \sigma^2)\) Log Double normalPdf mu sigma x = Exp $ logDensity (normalDistr mu sigma) x poissonPdf :: Double -> Integer -> Log Double poissonPdf rate n = Exp $ logProbability (Poisson.poisson rate) (fromIntegral n) -- | multivariate normal mvNormal :: (MonadDistribution m) => V.Vector Double -> Matrix Double -> m (V.Vector Double) mvNormal mu bigSigma = do let n = length mu ss <- replicateM n (normal 0 1) let bigL = cholDecomp bigSigma let ts = (colVector mu) + bigL `multStd` (colVector $ V.fromList ss) return $ getCol 1 ts -- | a useful datatype for expressing bayesian models data Bayesian m z o = Bayesian { prior :: m z, -- prior over latent variable Z; p(z) generative :: z -> m o, -- distribution over observations given Z=z; p(o|Z=z) likelihood :: z -> o -> Log Double -- p(o|z) } -- | p(z|o) posterior :: (MonadMeasure m, Foldable f, Functor f) => Bayesian m z o -> f o -> m z posterior Bayesian {..} os = do z <- prior factor $ product $ fmap (likelihood z) os return z priorPredictive :: (Monad m) => Bayesian m a b -> m b priorPredictive bm = prior bm >>= generative bm posteriorPredictive :: (MonadMeasure m, Foldable f, Functor f) => Bayesian m a b -> f b -> m b posteriorPredictive bm os = posterior bm os >>= generative bm -- helper funcs -------------------- type Histogram = H.Histogram H.BinD Double histogram :: Int -> [(Double, Log Double)] -> Histogram histogram n v = H.fillBuilder buildr $ fmap (second (ln . exp)) v where v1 = fmap fst v mi = Prelude.minimum v1 ma = Prelude.maximum v1 bins = H.binD mi n ma buildr = H.mkWeighted bins histogramToList :: Histogram -> [(Double, Double)] histogramToList = H.asList -- | Monads that can efficiently draw uniform variables of -- 'UniformRange' types. class (Monad m) => MonadUniformRange m where -- | Draw from a uniform distribution for some 'UniformRange' type. uniformR :: (UniformRange a) => -- | lower bound l (inclusive for discrete types) a -> -- | upper bound u (inclusive for discrete types) a -> -- | \(\sim \mathcal{U}(l, u)\). m a ---------------------------------------------------------------------------- -- Instances that lift probabilistic effects to standard tranformers. instance (MonadDistribution m) => MonadDistribution (IdentityT m) where random = lift random bernoulli = lift . bernoulli instance (MonadFactor m) => MonadFactor (IdentityT m) where score = lift . score instance (MonadMeasure m) => MonadMeasure (IdentityT m) instance (MonadUniformRange m) => MonadUniformRange (IdentityT m) where uniformR l u = lift $ uniformR l u instance (MonadDistribution m) => MonadDistribution (ExceptT e m) where random = lift random uniformD = lift . uniformD instance (MonadFactor m) => MonadFactor (ExceptT e m) where score = lift . score instance (MonadMeasure m) => MonadMeasure (ExceptT e m) instance (MonadDistribution m) => MonadDistribution (ReaderT r m) where random = lift random bernoulli = lift . bernoulli instance (MonadUniformRange m) => MonadUniformRange (ExceptT e m) where uniformR l u = lift $ uniformR l u instance (MonadFactor m) => MonadFactor (ReaderT r m) where score = lift . score instance (MonadMeasure m) => MonadMeasure (ReaderT r m) instance (MonadUniformRange m) => MonadUniformRange (ReaderT r m) where uniformR l u = lift $ uniformR l u instance (Monoid w, MonadDistribution m) => MonadDistribution (WriterT w m) where random = lift random bernoulli = lift . bernoulli categorical = lift . categorical instance (Monoid w, MonadFactor m) => MonadFactor (WriterT w m) where score = lift . score instance (Monoid w, MonadMeasure m) => MonadMeasure (WriterT w m) instance (Monoid w, MonadUniformRange m) => MonadUniformRange (WriterT w m) where uniformR l u = lift $ uniformR l u instance (MonadDistribution m) => MonadDistribution (StateT s m) where random = lift random bernoulli = lift . bernoulli categorical = lift . categorical uniformD = lift . uniformD instance (MonadFactor m) => MonadFactor (StateT s m) where score = lift . score instance (MonadMeasure m) => MonadMeasure (StateT s m) instance (MonadUniformRange m) => MonadUniformRange (StateT s m) where uniformR l u = lift $ uniformR l u instance (MonadDistribution m) => MonadDistribution (ContT r m) where random = lift random instance (MonadFactor m) => MonadFactor (ContT r m) where score = lift . score instance (MonadMeasure m) => MonadMeasure (ContT r m) instance (MonadUniformRange m) => MonadUniformRange (ContT r m) where uniformR l u = lift $ uniformR l u ================================================ FILE: src/Control/Monad/Bayes/Density/Free.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE MultiParamTypeClasses #-} {-# LANGUAGE RankNTypes #-} -- | -- Module : Control.Monad.Bayes.Density.Free -- Description : Free monad transformer over random sampling -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- 'DensityT' is a free monad transformer over random sampling. module Control.Monad.Bayes.Density.Free ( DensityT (..), hoist, interpret, withRandomness, runDensityT, traced, ) where import Control.Monad.Bayes.Class (MonadDistribution (random)) import Control.Monad.RWS import Control.Monad.State (evalStateT) import Control.Monad.Trans.Free.Church (FT, MonadFree (..), hoistFT, iterT, iterTM, liftF) import Control.Monad.Writer (WriterT (..)) import Data.Functor.Identity (Identity, runIdentity) -- | Random sampling functor. newtype SamF a = Random (Double -> a) deriving (Functor) -- | Free monad transformer over random sampling. -- -- Uses the Church-encoded version of the free monad for efficiency. newtype DensityT m a = DensityT {getDensityT :: FT SamF m a} deriving newtype (Functor, Applicative, Monad, MonadTrans) instance MonadFree SamF (DensityT m) where wrap = DensityT . wrap . fmap getDensityT instance (Monad m) => MonadDistribution (DensityT m) where random = DensityT $ liftF (Random id) -- | Hoist 'DensityT' through a monad transform. hoist :: (Monad m, Monad n) => (forall x. m x -> n x) -> DensityT m a -> DensityT n a hoist f (DensityT m) = DensityT (hoistFT f m) -- | Execute random sampling in the transformed monad. interpret :: (MonadDistribution m) => DensityT m a -> m a interpret (DensityT m) = iterT f m where f (Random k) = random >>= k -- | Execute computation with supplied values for random choices. withRandomness :: (Monad m) => [Double] -> DensityT m a -> m a withRandomness randomness (DensityT m) = evalStateT (iterTM f m) randomness where f (Random k) = do xs <- get case xs of [] -> error "DensityT: the list of randomness was too short" y : ys -> put ys >> k y -- | Execute computation with supplied values for a subset of random choices. -- Return the output value and a record of all random choices used, whether -- taken as input or drawn using the transformed monad. runDensityT :: (MonadDistribution m) => [Double] -> DensityT m a -> m (a, [Double]) runDensityT randomness (DensityT m) = runWriterT $ evalStateT (iterTM f $ hoistFT lift m) randomness where f (Random k) = do -- This block runs in StateT [Double] (WriterT [Double]) m. -- StateT propagates consumed randomness while WriterT records -- randomness used, whether old or new. xs <- get x <- case xs of [] -> random y : ys -> put ys >> return y tell [x] k x -- | Like 'density', but use an arbitrary sampling monad. traced :: (MonadDistribution m) => [Double] -> DensityT Identity a -> m (a, [Double]) traced randomness m = runDensityT randomness $ hoist (return . runIdentity) m ================================================ FILE: src/Control/Monad/Bayes/Density/State.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE FlexibleInstances #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE MultiParamTypeClasses #-} -- | -- Slower than Control.Monad.Bayes.Density.Free, so not used by default, -- but more elementary to understand. Just uses standard -- monad transformer techniques. module Control.Monad.Bayes.Density.State where import Control.Monad.Bayes.Class (MonadDistribution (random)) import Control.Monad.State (MonadState (get, put), StateT, evalStateT) import Control.Monad.Writer newtype DensityT m a = DensityT {getDensityT :: WriterT [Double] (StateT [Double] m) a} deriving newtype (Functor, Applicative, Monad) instance MonadTrans DensityT where lift = DensityT . lift . lift instance (Monad m) => MonadState [Double] (DensityT m) where get = DensityT $ lift $ get put = DensityT . lift . put instance (Monad m) => MonadWriter [Double] (DensityT m) where tell = DensityT . tell listen = DensityT . listen . getDensityT pass = DensityT . pass . getDensityT instance (MonadDistribution m) => MonadDistribution (DensityT m) where random = do trace <- get x <- case trace of [] -> random r : xs -> put xs >> pure r tell [x] pure x runDensityT :: (Monad m) => DensityT m b -> [Double] -> m (b, [Double]) runDensityT (DensityT m) = evalStateT (runWriterT m) ================================================ FILE: src/Control/Monad/Bayes/Enumerator.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE ImportQualifiedPost #-} -- | -- Module : Control.Monad.Bayes.Enumerator -- Description : Exhaustive enumeration of discrete random variables -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC module Control.Monad.Bayes.Enumerator ( Enumerator, logExplicit, explicit, evidence, mass, compact, enumerator, enumerate, expectation, normalForm, toEmpirical, toEmpiricalWeighted, normalizeWeights, enumerateToDistribution, removeZeros, fromList, ) where import Control.Applicative (Alternative) import Control.Arrow (second) import Control.Monad (MonadPlus) import Control.Monad.Bayes.Class ( MonadDistribution (bernoulli, categorical, logCategorical, random), MonadFactor (..), MonadMeasure, ) import Control.Monad.Writer (WriterT (..)) import Data.AEq (AEq, (===), (~==)) import Data.List (sortOn) import Data.Map qualified as Map import Data.Maybe (fromMaybe) import Data.Monoid (Product (..)) import Data.Ord (Down (Down)) import Data.Vector qualified as VV import Data.Vector.Generic qualified as V import Numeric.Log as Log (Log (..), sum) -- | An exact inference transformer that integrates -- discrete random variables by enumerating all execution paths. newtype Enumerator a = Enumerator (WriterT (Product (Log Double)) [] a) deriving newtype (Functor, Applicative, Monad, Alternative, MonadPlus) instance MonadDistribution Enumerator where random = error "Infinitely supported random variables not supported in Enumerator" bernoulli p = fromList [(True, (Exp . log) p), (False, (Exp . log) (1 - p))] categorical v = fromList $ zip [0 ..] $ map (Exp . log) (V.toList v) instance MonadFactor Enumerator where score w = fromList [((), w)] instance MonadMeasure Enumerator -- | Construct Enumerator from a list of values and associated weights. fromList :: [(a, Log Double)] -> Enumerator a fromList = Enumerator . WriterT . map (second Product) -- | Returns the posterior as a list of weight-value pairs without any post-processing, -- such as normalization or aggregation logExplicit :: Enumerator a -> [(a, Log Double)] logExplicit (Enumerator m) = map (second getProduct) $ runWriterT m -- | Same as `toList`, only weights are converted from log-domain. explicit :: Enumerator a -> [(a, Double)] explicit = map (second (exp . ln)) . logExplicit -- | Returns the model evidence, that is sum of all weights. evidence :: Enumerator a -> Log Double evidence = Log.sum . map snd . logExplicit -- | Normalized probability mass of a specific value. mass :: (Ord a) => Enumerator a -> a -> Double mass d = f where f a = fromMaybe 0 $ lookup a m m = enumerator d -- | Aggregate weights of equal values. -- The resulting list is sorted ascendingly according to values. compact :: (Num r, Ord a, Ord r) => [(a, r)] -> [(a, r)] compact = sortOn (Down . snd) . Map.toAscList . Map.fromListWith (+) -- | Aggregate and normalize of weights. -- The resulting list is sorted ascendingly according to values. -- -- > enumerator = compact . explicit enumerator, enumerate :: (Ord a) => Enumerator a -> [(a, Double)] enumerator d = filter ((/= 0) . snd) $ compact (zip xs ws) where (xs, ws) = second (map (exp . ln) . normalize) $ unzip (logExplicit d) -- | deprecated synonym enumerate = enumerator -- | Expectation of a given function computed using normalized weights. expectation :: (a -> Double) -> Enumerator a -> Double expectation f = Prelude.sum . map (\(x, w) -> f x * (exp . ln) w) . normalizeWeights . logExplicit normalize :: (Fractional b) => [b] -> [b] normalize xs = map (/ z) xs where z = Prelude.sum xs -- | Divide all weights by their sum. normalizeWeights :: (Fractional b) => [(a, b)] -> [(a, b)] normalizeWeights ls = zip xs ps where (xs, ws) = unzip ls ps = normalize ws -- | 'compact' followed by removing values with zero weight. normalForm :: (Ord a) => Enumerator a -> [(a, Double)] normalForm = filter ((/= 0) . snd) . compact . explicit toEmpirical :: (Fractional b, Ord a, Ord b) => [a] -> [(a, b)] toEmpirical ls = normalizeWeights $ compact (zip ls (repeat 1)) toEmpiricalWeighted :: (Fractional b, Ord a, Ord b) => [(a, b)] -> [(a, b)] toEmpiricalWeighted = normalizeWeights . compact enumerateToDistribution :: (MonadDistribution n) => Enumerator a -> n a enumerateToDistribution model = do let samples = logExplicit model let (support, logprobs) = unzip samples i <- logCategorical $ VV.fromList logprobs return $ support !! i removeZeros :: Enumerator a -> Enumerator a removeZeros (Enumerator (WriterT a)) = Enumerator $ WriterT $ filter ((\(Product x) -> x /= 0) . snd) a instance (Ord a) => Eq (Enumerator a) where p == q = normalForm p == normalForm q instance (Ord a) => AEq (Enumerator a) where p === q = xs == ys && ps === qs where (xs, ps) = unzip (normalForm p) (ys, qs) = unzip (normalForm q) p ~== q = xs == ys && ps ~== qs where (xs, ps) = unzip $ filter (not . (~== 0) . snd) $ normalForm p (ys, qs) = unzip $ filter (not . (~== 0) . snd) $ normalForm q ================================================ FILE: src/Control/Monad/Bayes/Inference/Lazy/MH.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} {-# LANGUAGE RankNTypes #-} {-# LANGUAGE ScopedTypeVariables #-} {-# OPTIONS_GHC -Wno-name-shadowing #-} module Control.Monad.Bayes.Inference.Lazy.MH where import Control.Monad.Bayes.Class (Log (ln)) import Control.Monad.Bayes.Sampler.Lazy ( Sampler, Tree (..), Trees (..), randomTree, runSampler, ) import Control.Monad.Bayes.Weighted (WeightedT, runWeightedT) import Control.Monad.Extra (iterateM) import Control.Monad.State.Lazy (MonadState (get, put), runState) import System.Random (RandomGen (split), getStdGen, newStdGen) import System.Random qualified as R mh :: forall a. Double -> WeightedT Sampler a -> IO [(a, Log Double)] mh p m = do -- Top level: produce a stream of samples. -- Split the random number generator in two -- One part is used as the first seed for the simulation, -- and one part is used for the randomness in the MH algorithm. g <- newStdGen >> getStdGen let (g1, g2) = split g let t = randomTree g1 let (x, w) = runSampler (runWeightedT m) t -- Now run step over and over to get a stream of (tree,result,weight)s. let (samples, _) = runState (iterateM step (t, x, w)) g2 -- The stream of seeds is used to produce a stream of result/weight pairs. return $ map (\(_, x, w) -> (x, w)) samples where -- where {- NB There are three kinds of randomness in the step function. 1. The start tree 't', which is the source of randomness for simulating the program m to start with. This is sort-of the point in the "state space". 2. The randomness needed to propose a new tree ('g1') 3. The randomness needed to decide whether to accept or reject that ('g2') The tree t is an argument and result, but we use a state monad ('get'/'put') to deal with the other randomness '(g,g1,g2)' -} -- step :: RandomGen g => (Tree, a, Log Double) -> State g (Tree, a, Log Double) step (t, x, w) = do -- Randomly change some sites g <- get let (g1, g2) = split g let t' = mutateTree p g1 t -- Rerun the model with the new tree, to get a new -- weight w'. let (x', w') = runSampler (runWeightedT m) t' -- MH acceptance ratio. This is the probability of either -- returning the new seed or the old one. let ratio = w' / w let (r, g2') = R.random g2 put g2' if r < min 1 (exp $ ln ratio) then return (t', x', w') else return (t, x, w) -- Replace the labels of a tree randomly, with probability p mutateTree :: forall g. (RandomGen g) => Double -> g -> Tree -> Tree mutateTree p g (Tree a ts) = let (a', g') = (R.random g :: (Double, g)) (a'', g'') = R.random g' in Tree { currentUniform = if a' < p then a'' else a, lazyUniforms = mutateTrees p g'' ts } mutateTrees :: (RandomGen g) => Double -> g -> Trees -> Trees mutateTrees p g (Trees t ts) = let (g1, g2) = split g in Trees { headTree = mutateTree p g1 t, tailTrees = mutateTrees p g2 ts } ================================================ FILE: src/Control/Monad/Bayes/Inference/Lazy/WIS.hs ================================================ module Control.Monad.Bayes.Inference.Lazy.WIS where import Control.Monad (guard) import Control.Monad.Bayes.Sampler.Lazy (SamplerT, weightedSamples) import Control.Monad.Bayes.Weighted (WeightedT) import Data.Maybe (mapMaybe) import Numeric.Log (Log (Exp)) import System.Random (Random (randoms), getStdGen, newStdGen) -- | Weighted Importance Sampling -- | Likelihood weighted importance sampling first draws n weighted samples, -- and then samples a stream of results from that regarded as an empirical distribution lwis :: Int -> WeightedT (SamplerT IO) a -> IO [a] lwis n m = do xws <- weightedSamples m let xws' = take n $ accumulate xws 0 let max' = snd $ last xws' _ <- newStdGen rs <- randoms <$> getStdGen return $ take 1 =<< fmap (\r -> mapMaybe (\(a, p) -> guard (p >= Exp (log r) * max') >> Just a) xws') rs where accumulate :: (Num t) => [(a, t)] -> t -> [(a, t)] accumulate ((x, w) : xws) a = (x, w + a) : (x, w + a) : accumulate xws (w + a) accumulate [] _ = [] ================================================ FILE: src/Control/Monad/Bayes/Inference/MCMC.hs ================================================ {-# LANGUAGE RankNTypes #-} {-# LANGUAGE RecordWildCards #-} -- | -- Module : Control.Monad.Bayes.Inference.MCMC -- Description : Markov Chain Monte Carlo (MCMC) -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : tweag.io -- Stability : experimental -- Portability : GHC module Control.Monad.Bayes.Inference.MCMC where import Control.Monad.Bayes.Class (MonadDistribution) import Control.Monad.Bayes.Traced.Basic qualified as Basic import Control.Monad.Bayes.Traced.Common ( MHResult (MHResult, trace), Trace (probDensity), burnIn, mhTransWithBool, ) import Control.Monad.Bayes.Traced.Dynamic qualified as Dynamic import Control.Monad.Bayes.Traced.Static qualified as Static import Control.Monad.Bayes.Weighted (WeightedT, unweighted) import Pipes ((>->)) import Pipes qualified as P import Pipes.Prelude qualified as P data Proposal = SingleSiteMH data MCMCConfig = MCMCConfig {proposal :: Proposal, numMCMCSteps :: Int, numBurnIn :: Int} defaultMCMCConfig :: MCMCConfig defaultMCMCConfig = MCMCConfig {proposal = SingleSiteMH, numMCMCSteps = 1, numBurnIn = 0} mcmc :: (MonadDistribution m) => MCMCConfig -> Static.TracedT (WeightedT m) a -> m [a] mcmc (MCMCConfig {..}) m = burnIn numBurnIn $ unweighted $ Static.mh numMCMCSteps m mcmcBasic :: (MonadDistribution m) => MCMCConfig -> Basic.TracedT (WeightedT m) a -> m [a] mcmcBasic (MCMCConfig {..}) m = burnIn numBurnIn $ unweighted $ Basic.mh numMCMCSteps m mcmcDynamic :: (MonadDistribution m) => MCMCConfig -> Dynamic.TracedT (WeightedT m) a -> m [a] mcmcDynamic (MCMCConfig {..}) m = burnIn numBurnIn $ unweighted $ Dynamic.mh numMCMCSteps m -- -- | draw iid samples until you get one that has non-zero likelihood independentSamples :: (Monad m) => Static.TracedT m a -> P.Producer (MHResult a) m (Trace a) independentSamples (Static.TracedT _w d) = P.repeatM d >-> P.takeWhile' ((== 0) . probDensity) >-> P.map (MHResult False) -- | convert a probabilistic program into a producer of samples mcmcP :: (MonadDistribution m) => MCMCConfig -> Static.TracedT m a -> P.Producer (MHResult a) m () mcmcP MCMCConfig {..} m@(Static.TracedT w _) = do initialValue <- independentSamples m >-> P.drain ( P.unfoldr (fmap (Right . (\k -> (k, trace k))) . mhTransWithBool w) initialValue >-> P.drop numBurnIn ) ================================================ FILE: src/Control/Monad/Bayes/Inference/PMMH.hs ================================================ {-# LANGUAGE RankNTypes #-} -- | -- Module : Control.Monad.Bayes.Inference.PMMH -- Description : Particle Marginal Metropolis-Hastings (PMMH) -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- Particle Marginal Metropolis-Hastings (PMMH) sampling. -- -- Christophe Andrieu, Arnaud Doucet, and Roman Holenstein. 2010. Particle Markov chain Monte Carlo Methods. /Journal of the Royal Statistical Society/ 72 (2010), 269-342. module Control.Monad.Bayes.Inference.PMMH ( pmmh, pmmhBayesianModel, ) where import Control.Monad.Bayes.Class (Bayesian (generative), MonadDistribution, MonadMeasure, prior) import Control.Monad.Bayes.Inference.MCMC (MCMCConfig, mcmc) import Control.Monad.Bayes.Inference.SMC (SMCConfig (), smc) import Control.Monad.Bayes.Population as Pop ( PopulationT, hoist, pushEvidence, runPopulationT, ) import Control.Monad.Bayes.Sequential.Coroutine (SequentialT) import Control.Monad.Bayes.Traced.Static (TracedT) import Control.Monad.Bayes.Weighted import Control.Monad.Trans (lift) import Numeric.Log (Log) -- | Particle Marginal Metropolis-Hastings sampling. pmmh :: (MonadDistribution m) => MCMCConfig -> SMCConfig (WeightedT m) -> TracedT (WeightedT m) a1 -> (a1 -> SequentialT (PopulationT (WeightedT m)) a2) -> m [[(a2, Log Double)]] pmmh mcmcConf smcConf param model = mcmc mcmcConf ( param >>= runPopulationT . pushEvidence . Pop.hoist lift . smc smcConf . model ) -- | Particle Marginal Metropolis-Hastings sampling from a Bayesian model pmmhBayesianModel :: (MonadMeasure m) => MCMCConfig -> SMCConfig (WeightedT m) -> (forall m'. (MonadMeasure m') => Bayesian m' a1 a2) -> m [[(a2, Log Double)]] pmmhBayesianModel mcmcConf smcConf bm = pmmh mcmcConf smcConf (prior bm) (generative bm) ================================================ FILE: src/Control/Monad/Bayes/Inference/RMSMC.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} {-# LANGUAGE RecordWildCards #-} -- | -- Module : Control.Monad.Bayes.Inference.RMSMC -- Description : Resample-Move Sequential Monte Carlo (RM-SMC) -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- Resample-move Sequential Monte Carlo (RM-SMC) sampling. -- -- Walter Gilks and Carlo Berzuini. 2001. Following a moving target - Monte Carlo inference for dynamic Bayesian models. /Journal of the Royal Statistical Society/ 63 (2001), 127-146. module Control.Monad.Bayes.Inference.RMSMC ( rmsmc, rmsmcDynamic, rmsmcBasic, ) where import Control.Monad.Bayes.Class (MonadDistribution) import Control.Monad.Bayes.Inference.MCMC (MCMCConfig (..)) import Control.Monad.Bayes.Inference.SMC import Control.Monad.Bayes.Population ( PopulationT, spawn, withParticles, ) import Control.Monad.Bayes.Sequential.Coroutine as Seq import Control.Monad.Bayes.Sequential.Coroutine qualified as S import Control.Monad.Bayes.Traced.Basic qualified as TrBas import Control.Monad.Bayes.Traced.Dynamic qualified as TrDyn import Control.Monad.Bayes.Traced.Static as Tr ( TracedT, marginal, mhStep, ) import Control.Monad.Bayes.Traced.Static qualified as TrStat import Data.Monoid (Endo (..)) -- | Resample-move Sequential Monte Carlo. rmsmc :: (MonadDistribution m) => MCMCConfig -> SMCConfig m -> -- | model SequentialT (TracedT (PopulationT m)) a -> PopulationT m a rmsmc (MCMCConfig {..}) (SMCConfig {..}) = marginal . S.sequentially (composeCopies numMCMCSteps mhStep . TrStat.hoist resampler) numSteps . S.hoistFirst (TrStat.hoist (spawn numParticles >>)) -- | Resample-move Sequential Monte Carlo with a more efficient -- tracing representation. rmsmcBasic :: (MonadDistribution m) => MCMCConfig -> SMCConfig m -> -- | model SequentialT (TrBas.TracedT (PopulationT m)) a -> PopulationT m a rmsmcBasic (MCMCConfig {..}) (SMCConfig {..}) = TrBas.marginal . S.sequentially (composeCopies numMCMCSteps TrBas.mhStep . TrBas.hoist resampler) numSteps . S.hoistFirst (TrBas.hoist (withParticles numParticles)) -- | A variant of resample-move Sequential Monte Carlo -- where only random variables since last resampling are considered -- for rejuvenation. rmsmcDynamic :: (MonadDistribution m) => MCMCConfig -> SMCConfig m -> -- | model SequentialT (TrDyn.TracedT (PopulationT m)) a -> PopulationT m a rmsmcDynamic (MCMCConfig {..}) (SMCConfig {..}) = TrDyn.marginal . S.sequentially (TrDyn.freeze . composeCopies numMCMCSteps TrDyn.mhStep . TrDyn.hoist resampler) numSteps . S.hoistFirst (TrDyn.hoist (withParticles numParticles)) -- | Apply a function a given number of times. composeCopies :: Int -> (a -> a) -> (a -> a) composeCopies k = withEndo (mconcat . replicate k) withEndo :: (Endo a -> Endo b) -> (a -> a) -> b -> b withEndo f = appEndo . f . Endo ================================================ FILE: src/Control/Monad/Bayes/Inference/SMC.hs ================================================ {-# LANGUAGE RankNTypes #-} {-# LANGUAGE RecordWildCards #-} -- | -- Module : Control.Monad.Bayes.Inference.SMC -- Description : Sequential Monte Carlo (SMC) -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- Sequential Monte Carlo (SMC) sampling. -- -- Arnaud Doucet and Adam M. Johansen. 2011. A tutorial on particle filtering and smoothing: fifteen years later. In /The Oxford Handbook of Nonlinear Filtering/, Dan Crisan and Boris Rozovskii (Eds.). Oxford University Press, Chapter 8. module Control.Monad.Bayes.Inference.SMC ( smc, smcPush, SMCConfig (..), ) where import Control.Monad.Bayes.Class (MonadDistribution, MonadMeasure) import Control.Monad.Bayes.Population ( PopulationT, pushEvidence, withParticles, ) import Control.Monad.Bayes.Sequential.Coroutine as Coroutine data SMCConfig m = SMCConfig { resampler :: forall x. PopulationT m x -> PopulationT m x, numSteps :: Int, numParticles :: Int } -- | Sequential importance resampling. -- Basically an SMC template that takes a custom resampler. smc :: (MonadDistribution m) => SMCConfig m -> Coroutine.SequentialT (PopulationT m) a -> PopulationT m a smc SMCConfig {..} = Coroutine.sequentially resampler numSteps . Coroutine.hoistFirst (withParticles numParticles) -- | Sequential Monte Carlo with multinomial resampling at each timestep. -- Weights are normalized at each timestep and the total weight is pushed -- as a score into the transformed monad. smcPush :: (MonadMeasure m) => SMCConfig m -> Coroutine.SequentialT (PopulationT m) a -> PopulationT m a smcPush config = smc config {resampler = (pushEvidence . resampler config)} ================================================ FILE: src/Control/Monad/Bayes/Inference/SMC2.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} -- | -- Module : Control.Monad.Bayes.Inference.SMC2 -- Description : Sequential Monte Carlo squared (SMC²) -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- Sequential Monte Carlo squared (SMC²) sampling. -- -- Nicolas Chopin, Pierre E. Jacob, and Omiros Papaspiliopoulos. 2013. SMC²: an efficient algorithm for sequential analysis of state space models. /Journal of the Royal Statistical Society Series B: Statistical Methodology/ 75 (2013), 397-426. Issue 3. module Control.Monad.Bayes.Inference.SMC2 ( smc2, SMC2, ) where import Control.Monad.Bayes.Class ( MonadDistribution (random), MonadFactor (..), MonadMeasure, MonadUniformRange (uniformR), ) import Control.Monad.Bayes.Inference.MCMC import Control.Monad.Bayes.Inference.RMSMC (rmsmc) import Control.Monad.Bayes.Inference.SMC (SMCConfig (SMCConfig, numParticles, numSteps, resampler), smcPush) import Control.Monad.Bayes.Population as Pop (PopulationT, resampleMultinomial, runPopulationT) import Control.Monad.Bayes.Sequential.Coroutine (SequentialT) import Control.Monad.Bayes.Traced import Control.Monad.Trans (MonadTrans (..)) import Numeric.Log (Log) -- | Helper monad transformer for preprocessing the model for 'smc2'. newtype SMC2 m a = SMC2 (SequentialT (TracedT (PopulationT m)) a) deriving newtype (Functor, Applicative, Monad) setup :: SMC2 m a -> SequentialT (TracedT (PopulationT m)) a setup (SMC2 m) = m instance MonadTrans SMC2 where lift = SMC2 . lift . lift . lift instance (MonadDistribution m) => MonadDistribution (SMC2 m) where random = lift random instance (MonadUniformRange m) => MonadUniformRange (SMC2 m) where uniformR l u = lift $ uniformR l u instance (Monad m) => MonadFactor (SMC2 m) where score = SMC2 . score instance (MonadDistribution m) => MonadMeasure (SMC2 m) -- | Sequential Monte Carlo squared. smc2 :: (MonadDistribution m) => -- | number of time steps Int -> -- | number of inner particles Int -> -- | number of outer particles Int -> -- | number of MH transitions Int -> -- | model parameters SequentialT (TracedT (PopulationT m)) b -> -- | model (b -> SequentialT (PopulationT (SMC2 m)) a) -> PopulationT m [(a, Log Double)] smc2 k n p t param m = rmsmc MCMCConfig {numMCMCSteps = t, proposal = SingleSiteMH, numBurnIn = 0} SMCConfig {numParticles = p, numSteps = k, resampler = resampleMultinomial} (param >>= setup . runPopulationT . smcPush (SMCConfig {numSteps = k, numParticles = n, resampler = resampleMultinomial}) . m) ================================================ FILE: src/Control/Monad/Bayes/Inference/TUI.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE ImportQualifiedPost #-} {-# OPTIONS_GHC -Wno-type-defaults #-} module Control.Monad.Bayes.Inference.TUI where import Brick import Brick qualified as B import Brick.BChan qualified as B import Brick.Widgets.Border import Brick.Widgets.Border.Style import Brick.Widgets.Center import Brick.Widgets.ProgressBar qualified as B import Control.Arrow (Arrow (..)) import Control.Concurrent (forkIO) import Control.Foldl qualified as Fold import Control.Monad (void) import Control.Monad.Bayes.Enumerator (toEmpirical) import Control.Monad.Bayes.Inference.MCMC import Control.Monad.Bayes.Sampler.Strict (SamplerIO, sampleIO) import Control.Monad.Bayes.Traced (TracedT) import Control.Monad.Bayes.Traced.Common hiding (burnIn) import Control.Monad.Bayes.Weighted import Data.Maybe (listToMaybe) import Data.Scientific (FPFormat (Exponent), formatScientific, fromFloatDigits) import Data.Text qualified as T import Data.Text.Lazy qualified as TL import Data.Text.Lazy.IO qualified as TL import GHC.Float (double2Float) import Graphics.Vty import Graphics.Vty qualified as V import Graphics.Vty.Platform.Unix qualified as V import Numeric.Log (Log (ln)) import Pipes (runEffect, (>->)) import Pipes qualified as P import Pipes.Prelude qualified as P import Text.Pretty.Simple (pShow, pShowNoColor) data MCMCData a = MCMCData { numSteps :: Int, numSuccesses :: Int, samples :: [a], lk :: [Double], totalSteps :: Int } deriving stock (Show) -- | Brick is a terminal user interface (TUI) -- which we use to display inference algorithms in progress -- | draw the brick app drawUI :: ([a] -> Widget n) -> MCMCData a -> [Widget n] drawUI handleSamples state = [ui] where completionBar = updateAttrMap ( B.mapAttrNames [ (doneAttr, B.progressCompleteAttr), (toDoAttr, B.progressIncompleteAttr) ] ) $ toBar $ fromIntegral $ numSteps state likelihoodBar = updateAttrMap ( B.mapAttrNames [ (doneAttr, B.progressCompleteAttr), (toDoAttr, B.progressIncompleteAttr) ] ) $ B.progressBar (Just $ "Mean likelihood for last 1000 samples: " <> take 10 (maybe "(error)" show (listToMaybe $ lk state <> [0]))) (double2Float (Fold.fold Fold.mean $ take 1000 $ lk state) / double2Float (maximum $ 0 : lk state)) displayStep c = Just $ "Step " <> show c numFailures = numSteps state - numSuccesses state toBar v = B.progressBar (displayStep v) (v / fromIntegral (totalSteps state)) displaySuccessesAndFailures = withBorderStyle unicode $ borderWithLabel (str "Successes and failures") $ center (str (show $ numSuccesses state)) <+> vBorder <+> center (str (show numFailures)) warning = if numSteps state > 1000 && (fromIntegral (numSuccesses state) / fromIntegral (numSteps state)) < 0.1 then withAttr (attrName "highlight") $ str "Warning: acceptance rate is rather low.\nThis probably means that your proposal isn't good." else str "" ui = (str "Progress: " <+> completionBar) <=> (str "Likelihood: " <+> likelihoodBar) <=> str "\n" <=> displaySuccessesAndFailures <=> warning <=> handleSamples (samples state) noVisual :: b -> Widget n noVisual = const emptyWidget showEmpirical :: (Show a, Ord a) => [a] -> Widget n showEmpirical = txt . T.pack . TL.unpack . pShow . (fmap (second (formatScientific Exponent (Just 3) . fromFloatDigits))) . toEmpirical showVal :: (Show a) => [a] -> Widget n showVal = txt . T.pack . (\case [] -> ""; a -> maybe "(error)" show $ listToMaybe a) -- | handler for events received by the TUI appEvent :: B.BrickEvent n s -> B.EventM n s () appEvent (B.VtyEvent (V.EvKey (V.KChar 'q') [])) = B.halt appEvent (B.VtyEvent _) = pure () appEvent (B.AppEvent d) = put d appEvent _ = error "unknown event" doneAttr, toDoAttr :: B.AttrName doneAttr = B.attrName "theBase" <> B.attrName "done" toDoAttr = B.attrName "theBase" <> B.attrName "remaining" theMap :: B.AttrMap theMap = B.attrMap V.defAttr [ (B.attrName "theBase", bg V.brightBlack), (doneAttr, V.black `on` V.white), (toDoAttr, V.white `on` V.black), (attrName "highlight", fg yellow) ] tui :: (Show a) => Int -> TracedT (WeightedT SamplerIO) a -> ([a] -> Widget ()) -> IO () tui burnIn distribution visualizer = void do eventChan <- B.newBChan 10 initialVty <- buildVty _ <- forkIO $ run (mcmcP MCMCConfig {numBurnIn = burnIn, proposal = SingleSiteMH, numMCMCSteps = -1} distribution) eventChan n samples <- B.customMain initialVty buildVty (Just eventChan) ( ( B.App { B.appDraw = drawUI visualizer, B.appChooseCursor = B.showFirstCursor, B.appHandleEvent = appEvent, B.appStartEvent = return (), B.appAttrMap = const theMap } ) ) (initialState n) TL.writeFile "data/tui_output.txt" (pShowNoColor samples) return samples where buildVty = V.mkVty V.defaultConfig n = 100000 initialState n = MCMCData {numSteps = 0, samples = [], lk = [], numSuccesses = 0, totalSteps = n} run prod chan i = runEffect $ P.hoist (sampleIO . unweighted) prod >-> P.scan ( \mcmcdata@(MCMCData ns nsc smples lk _) a -> mcmcdata { numSteps = ns + 1, numSuccesses = nsc + if success a then 1 else 0, samples = output (trace a) : smples, lk = exp (ln (probDensity (trace a))) : lk } ) (initialState i) id >-> P.take i >-> P.mapM_ (B.writeBChan chan) ================================================ FILE: src/Control/Monad/Bayes/Integrator.hs ================================================ {-# LANGUAGE ApplicativeDo #-} {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE FlexibleContexts #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE ImportQualifiedPost #-} {-# OPTIONS_GHC -Wno-type-defaults #-} {-# OPTIONS_GHC -Wno-unused-top-binds #-} -- | -- This is adapted from https://jtobin.io/giry-monad-implementation -- but brought into the monad-bayes framework (i.e. Integrator is an instance of MonadMeasure) -- It's largely for debugging other inference methods and didactic use, -- because brute force integration of measures is -- only practical for small programs module Control.Monad.Bayes.Integrator ( probability, variance, expectation, cdf, empirical, enumeratorWith, histogram, plotCdf, volume, normalize, Integrator, momentGeneratingFunction, cumulantGeneratingFunction, integrator, runIntegrator, ) where import Control.Applicative (Applicative (..)) import Control.Foldl (Fold) import Control.Foldl qualified as Foldl import Control.Monad.Bayes.Class (MonadDistribution (bernoulli, random, uniformD)) import Control.Monad.Bayes.Weighted (WeightedT, runWeightedT) import Control.Monad.Cont ( Cont, ContT (ContT), cont, runCont, ) import Data.Foldable (Foldable (..)) import Data.Set (Set, elems) import Numeric.Integration.TanhSinh (Result (result), trap) import Numeric.Log (Log (ln)) import Statistics.Distribution qualified as Statistics import Statistics.Distribution.Uniform qualified as Statistics -- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md -- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore import Prelude hiding (Applicative (..), Foldable (..)) newtype Integrator a = Integrator {getIntegrator :: Cont Double a} deriving newtype (Functor, Applicative, Monad) runIntegrator :: (a -> Double) -> Integrator a -> Double runIntegrator f (Integrator a) = runCont a f integrator :: ((a -> Double) -> Double) -> Integrator a integrator = Integrator . cont instance MonadDistribution Integrator where random = fromDensityFunction $ Statistics.density $ Statistics.uniformDistr 0 1 bernoulli p = Integrator $ cont (\f -> p * f True + (1 - p) * f False) uniformD ls = fromMassFunction (const (1 / fromIntegral (length ls))) ls fromDensityFunction :: (Double -> Double) -> Integrator Double fromDensityFunction d = Integrator $ cont $ \f -> integralWithQuadrature (\x -> f x * d x) where integralWithQuadrature = result . last . (\z -> trap z 0 1) fromMassFunction :: (Foldable f) => (a -> Double) -> f a -> Integrator a fromMassFunction f support = Integrator $ cont \g -> foldl' (\acc x -> acc + f x * g x) 0 support empirical :: (Foldable f) => f a -> Integrator a empirical = Integrator . cont . flip weightedAverage where weightedAverage :: (Foldable f, Fractional r) => (a -> r) -> f a -> r weightedAverage f = Foldl.fold (weightedAverageFold f) weightedAverageFold :: (Fractional r) => (a -> r) -> Fold a r weightedAverageFold f = Foldl.premap f averageFold averageFold :: (Fractional a) => Fold a a averageFold = (/) <$> Foldl.sum <*> Foldl.genericLength expectation :: Integrator Double -> Double expectation = runIntegrator id variance :: Integrator Double -> Double variance nu = runIntegrator (^ 2) nu - expectation nu ^ 2 momentGeneratingFunction :: Integrator Double -> Double -> Double momentGeneratingFunction nu t = runIntegrator (\x -> exp (t * x)) nu cumulantGeneratingFunction :: Integrator Double -> Double -> Double cumulantGeneratingFunction nu = log . momentGeneratingFunction nu normalize :: WeightedT Integrator a -> Integrator a normalize m = let m' = runWeightedT m z = runIntegrator (ln . exp . snd) m' in do (x, d) <- runWeightedT m Integrator $ cont $ \f -> (f () * (ln $ exp d)) / z return x cdf :: Integrator Double -> Double -> Double cdf nu x = runIntegrator (negativeInfinity `to` x) nu where negativeInfinity :: Double negativeInfinity = negate (1 / 0) to :: (Num a, Ord a) => a -> a -> a -> a to a b k | k >= a && k <= b = 1 | otherwise = 0 volume :: Integrator Double -> Double volume = runIntegrator (const 1) containing :: (Num a, Eq b) => [b] -> b -> a containing xs x | x `elem` xs = 1 | otherwise = 0 instance (Num a) => Num (Integrator a) where (+) = liftA2 (+) (-) = liftA2 (-) (*) = liftA2 (*) abs = fmap abs signum = fmap signum fromInteger = pure . fromInteger probability :: (Ord a) => (a, a) -> Integrator a -> Double probability (lower, upper) = runIntegrator (\x -> if x < upper && x >= lower then 1 else 0) enumeratorWith :: (Ord a) => Set a -> Integrator a -> [(a, Double)] enumeratorWith ls meas = [ ( val, runIntegrator (\x -> if x == val then 1 else 0) meas ) | val <- elems ls ] histogram :: (Enum a, Ord a, Fractional a) => Int -> a -> WeightedT Integrator a -> [(a, Double)] histogram nBins binSize model = do x <- take nBins [1 ..] let transform k = (k - (fromIntegral nBins / 2)) * binSize return ( (fst) (transform x, transform (x + 1)), probability (transform x, transform (x + 1)) $ normalize model ) plotCdf :: Int -> Double -> Double -> Integrator Double -> [(Double, Double)] plotCdf nBins binSize middlePoint model = do x <- take nBins [1 ..] let transform k = (k - (fromIntegral nBins / 2)) * binSize + middlePoint return (transform x, cdf model (transform x)) ================================================ FILE: src/Control/Monad/Bayes/Population.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE ImportQualifiedPost #-} {-# LANGUAGE NamedFieldPuns #-} {-# LANGUAGE RankNTypes #-} {-# OPTIONS_GHC -Wno-deprecations #-} -- | -- Module : Control.Monad.Bayes.Population -- Description : Representation of distributions using multiple samples -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- 'PopulationT' turns a single sample into a collection of weighted samples. module Control.Monad.Bayes.Population ( PopulationT (..), runPopulationT, explicitPopulation, fromWeightedList, spawn, multinomial, resampleMultinomial, systematic, resampleSystematic, stratified, resampleStratified, extractEvidence, pushEvidence, proper, evidence, hoist, collapse, popAvg, withParticles, ) where import Control.Applicative (Alternative) import Control.Arrow (second) import Control.Monad (forM, replicateM) import Control.Monad.Bayes.Class ( MonadDistribution (..), MonadFactor (..), MonadMeasure, MonadUniformRange (..), factor, ) import Control.Monad.Bayes.Weighted ( WeightedT, applyWeight, extractWeight, runWeightedT, weightedT, ) import Control.Monad.IO.Class import Control.Monad.Trans.Class import Data.Functor.Compose import Data.List (unfoldr) import Data.List qualified import Data.Maybe (catMaybes) import Data.Vector ((!)) import Data.Vector qualified as V import Numeric.Log (Log, ln, sum) import Numeric.Log qualified as Log import Prelude hiding (all, sum) -- | The old-fashioned, broken list transformer, adding a list/nondeterminism/choice effect. -- It is not a valid monad transformer, but it is a valid 'Applicative'. newtype ListT m a = ListT {getListT :: Compose m [] a} deriving newtype (Functor, Applicative, Alternative) listT :: m [a] -> ListT m a listT = ListT . Compose runListT :: ListT m a -> m [a] runListT = getCompose . getListT -- | This monad instance is _unlawful_, -- it is only by accident and careful construction that it can be used here. instance (Monad m) => Monad (ListT m) where ma >>= f = ListT $ Compose $ do as <- runListT ma fmap concat $ forM as $ runListT . f instance MonadTrans ListT where lift = ListT . Compose . fmap pure instance (MonadIO m) => MonadIO (ListT m) where liftIO = lift . liftIO instance (MonadDistribution m) => MonadDistribution (ListT m) where random = lift random bernoulli = lift . bernoulli categorical = lift . categorical instance (MonadUniformRange m) => MonadUniformRange (ListT m) where uniformR l u = lift $ uniformR l u instance (MonadFactor m) => MonadFactor (ListT m) where score = lift . score instance (MonadMeasure m) => MonadMeasure (ListT m) -- | A collection of weighted samples, or particles. newtype PopulationT m a = PopulationT {getPopulationT :: WeightedT (ListT m) a} deriving newtype (Functor, Applicative, Monad, MonadIO, MonadDistribution, MonadFactor, MonadMeasure, MonadUniformRange) instance MonadTrans PopulationT where lift = PopulationT . lift . lift -- | Explicit representation of the weighted sample with weights in the log -- domain. runPopulationT :: PopulationT m a -> m [(a, Log Double)] runPopulationT = runListT . runWeightedT . getPopulationT -- | Explicit representation of the weighted sample. explicitPopulation :: (Functor m) => PopulationT m a -> m [(a, Double)] explicitPopulation = fmap (map (second (exp . ln))) . runPopulationT -- | Initialize 'PopulationT' with a concrete weighted sample. fromWeightedList :: (Monad m) => m [(a, Log Double)] -> PopulationT m a fromWeightedList = PopulationT . weightedT . listT -- | Increase the sample size by a given factor. -- The weights are adjusted such that their sum is preserved. -- It is therefore safe to use 'spawn' in arbitrary places in the program -- without introducing bias. spawn :: (Monad m) => Int -> PopulationT m () spawn n = fromWeightedList $ pure $ replicate n ((), 1 / fromIntegral n) withParticles :: (Monad m) => Int -> PopulationT m a -> PopulationT m a withParticles n = (spawn n >>) resampleGeneric :: (MonadDistribution m) => -- | resampler (V.Vector Double -> m [Int]) -> PopulationT m a -> PopulationT m a resampleGeneric resampler m = fromWeightedList $ do pop <- runPopulationT m let (xs, ps) = unzip pop let n = length xs let z = Log.sum ps if z > 0 then do let weights = V.fromList (map (exp . ln . (/ z)) ps) ancestors <- resampler weights let xvec = V.fromList xs let offsprings = map (xvec V.!) ancestors return $ map (,z / fromIntegral n) offsprings else -- if all weights are zero do not resample return pop -- | Systematic sampler. -- Sample \(n\) values from \((0,1]\) as follows -- \[ -- \begin{aligned} -- u^{(1)} &\sim U\left(0, \frac{1}{n}\right] \\ -- u^{(i)} &=u^{(1)}+\frac{i-1}{n}, \quad i=2,3, \ldots, n -- \end{aligned} -- \] -- and then pick integers \(m\) according to -- \[ -- Q^{(m-1)} V.Vector Double -> [Int] systematic u ps = f 0 (u / fromIntegral n) 0 0 [] where prob i = ps V.! i n = length ps inc = 1 / fromIntegral n f i _ _ _ acc | i == n = acc f i v j q acc = if v < q then f (i + 1) (v + inc) j q (j - 1 : acc) else f i v (j + 1) (q + prob j) acc -- | Resample the population using the underlying monad and a systematic resampling scheme. -- The total weight is preserved. resampleSystematic :: (MonadDistribution m) => PopulationT m a -> PopulationT m a resampleSystematic = resampleGeneric (\ps -> (`systematic` ps) <$> random) -- | Stratified sampler. -- -- Sample \(n\) values from \((0,1]\) as follows -- \[ -- u^{(i)} \sim U\left(\frac{i-1}{n}, \frac{i}{n}\right], \quad i=1,2, \ldots, n -- \] -- and then pick integers \(m\) according to -- \[ -- Q^{(m-1)} V.Vector Double -> m [Int] stratified weights = do let bigN = V.length weights dithers <- V.replicateM bigN (uniform 0.0 1.0) let positions = V.map (/ fromIntegral bigN) $ V.zipWith (+) dithers (V.map fromIntegral $ V.fromList [0 .. bigN - 1]) cumulativeSum = V.scanl (+) 0.0 weights coalg (i, j) | i < bigN = if (positions ! i) < (cumulativeSum ! j) then Just (Just j, (i + 1, j)) else Just (Nothing, (i, j + 1)) | otherwise = Nothing return $ map (\i -> i - 1) $ catMaybes $ unfoldr coalg (0, 0) -- | Resample the population using the underlying monad and a stratified resampling scheme. -- The total weight is preserved. resampleStratified :: (MonadDistribution m) => PopulationT m a -> PopulationT m a resampleStratified = resampleGeneric stratified -- | Multinomial sampler. Sample from \(0, \ldots, n - 1\) \(n\) -- times drawn at random according to the weights where \(n\) is the -- length of vector of weights. multinomial :: (MonadDistribution m) => V.Vector Double -> m [Int] multinomial ps = replicateM (V.length ps) (categorical ps) -- | Resample the population using the underlying monad and a multinomial resampling scheme. -- The total weight is preserved. resampleMultinomial :: (MonadDistribution m) => PopulationT m a -> PopulationT m a resampleMultinomial = resampleGeneric multinomial -- | Separate the sum of weights into the 'WeightedT' transformer. -- Weights are normalized after this operation. extractEvidence :: (Monad m) => PopulationT m a -> PopulationT (WeightedT m) a extractEvidence m = fromWeightedList $ do pop <- lift $ runPopulationT m let (xs, ps) = unzip pop let z = sum ps let ws = map (if z > 0 then (/ z) else const (1 / fromIntegral (length ps))) ps factor z return $ zip xs ws -- | Push the evidence estimator as a score to the transformed monad. -- Weights are normalized after this operation. pushEvidence :: (MonadFactor m) => PopulationT m a -> PopulationT m a pushEvidence = hoist applyWeight . extractEvidence -- | A properly weighted single sample, that is one picked at random according -- to the weights, with the sum of all weights. proper :: (MonadDistribution m) => PopulationT m a -> WeightedT m a proper m = do pop <- runPopulationT $ extractEvidence m let (xs, ps) = unzip pop index <- logCategorical $ V.fromList ps let x = xs !! index return x -- | Model evidence estimator, also known as pseudo-marginal likelihood. evidence :: (Monad m) => PopulationT m a -> m (Log Double) evidence = extractWeight . runPopulationT . extractEvidence -- | Picks one point from the population and uses model evidence as a 'score' -- in the transformed monad. -- This way a single sample can be selected from a population without -- introducing bias. collapse :: (MonadMeasure m) => PopulationT m a -> m a collapse = applyWeight . proper -- | PopulationT average of a function, computed using unnormalized weights. popAvg :: (Monad m) => (a -> Double) -> PopulationT m a -> m Double popAvg f p = do xs <- explicitPopulation p let ys = map (\(x, w) -> f x * w) xs let t = Data.List.sum ys return t -- | Applies a transformation to the inner monad. hoist :: (Monad n) => (forall x. m x -> n x) -> PopulationT m a -> PopulationT n a hoist f = fromWeightedList . f . runPopulationT ================================================ FILE: src/Control/Monad/Bayes/Sampler/Lazy.hs ================================================ {-# LANGUAGE BangPatterns #-} {-# LANGUAGE DeriveFunctor #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE RankNTypes #-} {-# LANGUAGE ScopedTypeVariables #-} -- | This is a port of the implementation of LazyPPL: https://lazyppl.bitbucket.io/ module Control.Monad.Bayes.Sampler.Lazy where import Control.Monad (ap) import Control.Monad.Bayes.Class (MonadDistribution (random)) import Control.Monad.Bayes.Weighted (WeightedT, runWeightedT) import Control.Monad.IO.Class import Control.Monad.Identity (Identity (runIdentity)) import Control.Monad.Trans import Numeric.Log (Log (..)) import System.Random ( RandomGen (split), getStdGen, newStdGen, ) import System.Random qualified as R -- | A 'Tree' is a lazy, infinitely wide and infinitely deep tree, labelled by Doubles. -- -- Our source of randomness will be a Tree, populated by uniform [0,1] choices for each label. -- Often people just use a list or stream instead of a tree. -- But a tree allows us to be lazy about how far we are going all the time. data Tree = Tree { currentUniform :: Double, lazyUniforms :: Trees } -- | An infinite stream of 'Tree's. data Trees = Trees { headTree :: Tree, tailTrees :: Trees } -- | A probability distribution over @a@ is a function 'Tree -> a'. -- The idea is that it uses up bits of the tree as it runs. type Sampler = SamplerT Identity runSampler :: Sampler a -> Tree -> a runSampler = (runIdentity .) . runSamplerT newtype SamplerT m a = SamplerT {runSamplerT :: Tree -> m a} deriving (Functor) -- | Split a tree in two (bijectively). splitTree :: Tree -> (Tree, Tree) splitTree (Tree r (Trees t ts)) = (t, Tree r ts) -- | Generate a tree with uniform random labels. -- -- Preliminary for the simulation methods. This uses 'split' to split a random seed. randomTree :: (RandomGen g) => g -> Tree randomTree g = let (a, g') = R.random g in Tree a (randomTrees g') randomTrees :: (RandomGen g) => g -> Trees randomTrees g = let (g1, g2) = split g in Trees (randomTree g1) (randomTrees g2) instance (Monad m) => Applicative (SamplerT m) where pure = lift . pure (<*>) = ap -- | Sequencing is done by splitting the tree -- and using different bits for different computations. instance (Monad m) => Monad (SamplerT m) where return = pure (SamplerT m) >>= f = SamplerT \g -> do let (g1, g2) = splitTree g a <- m g1 let SamplerT m' = f a m' g2 instance MonadTrans SamplerT where lift = SamplerT . const instance (MonadIO m) => MonadIO (SamplerT m) where liftIO = lift . liftIO -- | Sampling gets the label at the head of the tree and discards the rest. instance (Monad m) => MonadDistribution (SamplerT m) where random = SamplerT \(Tree r _) -> pure r -- | Runs a 'SamplerT' by creating a new 'StdGen'. runSamplerTIO :: (MonadIO m) => SamplerT m a -> m a runSamplerTIO m = liftIO newStdGen *> (runSamplerT m =<< randomTree <$> liftIO getStdGen) -- | Draw a stream of independent samples. independent :: (Monad m) => m a -> m [a] independent = sequence . repeat -- | Runs a probability measure and gets out a stream of @(result,weight)@ pairs weightedSamples :: (MonadIO m) => WeightedT (SamplerT m) a -> m [(a, Log Double)] weightedSamples = runSamplerTIO . sequence . repeat . runWeightedT ================================================ FILE: src/Control/Monad/Bayes/Sampler/Strict.hs ================================================ {-# LANGUAGE ApplicativeDo #-} {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE ImportQualifiedPost #-} {-# LANGUAGE UnboxedTuples #-} {-# LANGUAGE UndecidableInstances #-} -- | -- Module : Control.Monad.Bayes.Sampler -- Description : Pseudo-random sampling monads -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- 'SamplerIO' and 'SamplerST' are instances of 'MonadDistribution'. Apply a 'MonadFactor' -- transformer to obtain a 'MonadMeasure' that can execute probabilistic models. module Control.Monad.Bayes.Sampler.Strict ( SamplerT (..), SamplerIO, SamplerST, sampleIO, sampleIOfixed, sampleWith, sampleSTfixed, sampleMean, sampler, ) where import Control.Foldl qualified as F hiding (random) import Control.Monad.Bayes.Class ( MonadDistribution ( bernoulli, beta, categorical, gamma, geometric, normal, random, uniform ), MonadUniformRange ( uniformR ), ) import Control.Monad.Primitive (PrimMonad) import Control.Monad.Reader (MonadIO, ReaderT (..)) import Control.Monad.ST (ST) import Control.Monad.Trans (MonadTrans) import Numeric.Log (Log (ln)) import System.Random.MWC.Distributions qualified as MWC import System.Random.Stateful (IOGenM (..), STGenM, StatefulGen, StdGen, initStdGen, mkStdGen, newIOGenM, newSTGenM, uniformDouble01M, uniformRM) -- | The sampling interpretation of a probabilistic program -- Here m is typically IO or ST newtype SamplerT g m a = SamplerT {runSamplerT :: ReaderT g m a} deriving (Functor, Applicative, Monad, MonadIO, MonadTrans, PrimMonad) -- | convenient type synonym to show specializations of SamplerT -- to particular pairs of monad and RNG type SamplerIO = SamplerT (IOGenM StdGen) IO -- | convenient type synonym to show specializations of SamplerT -- to particular pairs of monad and RNG type SamplerST s = SamplerT (STGenM StdGen s) (ST s) instance (StatefulGen g m) => MonadDistribution (SamplerT g m) where random = SamplerT (ReaderT uniformDouble01M) uniform a b = SamplerT (ReaderT $ uniformRM (a, b)) normal m s = SamplerT (ReaderT (MWC.normal m s)) gamma shape scale = SamplerT (ReaderT $ MWC.gamma shape scale) beta a b = SamplerT (ReaderT $ MWC.beta a b) bernoulli p = SamplerT (ReaderT $ MWC.bernoulli p) categorical ps = SamplerT (ReaderT $ MWC.categorical ps) geometric p = SamplerT (ReaderT $ MWC.geometric0 p) instance (StatefulGen g m) => MonadUniformRange (SamplerT g m) where uniformR l u = SamplerT (ReaderT $ uniformRM (l, u)) -- | Sample with a random number generator of your choice e.g. the one -- from `System.Random`. -- -- >>> import Control.Monad.Bayes.Class -- >>> import System.Random.Stateful hiding (random) -- >>> newIOGenM (mkStdGen 1729) >>= sampleWith random -- 4.690861245089605e-2 sampleWith :: SamplerT g m a -> g -> m a sampleWith (SamplerT m) = runReaderT m -- | initialize random seed using system entropy, and sample sampleIO, sampler :: SamplerIO a -> IO a sampleIO x = initStdGen >>= newIOGenM >>= sampleWith x sampler = sampleIO -- | Run the sampler with a fixed random seed sampleIOfixed :: SamplerIO a -> IO a sampleIOfixed x = newIOGenM (mkStdGen 1729) >>= sampleWith x -- | Run the sampler with a fixed random seed sampleSTfixed :: SamplerST s b -> ST s b sampleSTfixed x = newSTGenM (mkStdGen 1729) >>= sampleWith x sampleMean :: [(Double, Log Double)] -> Double sampleMean samples = let z = F.premap (ln . exp . snd) F.sum w = (F.premap (\(x, y) -> x * ln (exp y)) F.sum) s = (/) <$> w <*> z in F.fold s samples ================================================ FILE: src/Control/Monad/Bayes/Sequential/Coroutine.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE RankNTypes #-} -- | -- Module : Control.Monad.Bayes.Sequential -- Description : Suspendable probabilistic computation -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- 'SequentialT' represents a computation that can be suspended. module Control.Monad.Bayes.Sequential.Coroutine ( SequentialT, suspend, finish, advance, finished, hoistFirst, hoist, sequentially, sis, ) where import Control.Monad.Bayes.Class ( MonadDistribution (bernoulli, categorical, random), MonadFactor (..), MonadMeasure, ) import Control.Monad.Coroutine ( Coroutine (..), bounce, mapMonad, pogoStick, ) import Control.Monad.Coroutine.SuspensionFunctors ( Await (..), await, ) import Control.Monad.Trans (MonadIO, MonadTrans (..)) import Data.Either (isRight) -- | Represents a computation that can be suspended at certain points. -- The intermediate monadic effects can be extracted, which is particularly -- useful for implementation of Sequential Monte Carlo related methods. -- All the probabilistic effects are lifted from the transformed monad, but -- also `suspend` is inserted after each `factor`. newtype SequentialT m a = SequentialT {runSequentialT :: Coroutine (Await ()) m a} deriving newtype (Functor, Applicative, Monad, MonadTrans, MonadIO) extract :: Await () a -> a extract (Await f) = f () instance (MonadDistribution m) => MonadDistribution (SequentialT m) where random = lift random bernoulli = lift . bernoulli categorical = lift . categorical -- | Execution is 'suspend'ed after each 'score'. instance (MonadFactor m) => MonadFactor (SequentialT m) where score w = lift (score w) >> suspend instance (MonadMeasure m) => MonadMeasure (SequentialT m) -- | A point where the computation is paused. suspend :: (Monad m) => SequentialT m () suspend = SequentialT await -- | Remove the remaining suspension points. finish :: (Monad m) => SequentialT m a -> m a finish = pogoStick extract . runSequentialT -- | Execute to the next suspension point. -- If the computation is finished, do nothing. -- -- > finish = finish . advance advance :: (Monad m) => SequentialT m a -> SequentialT m a advance = SequentialT . bounce extract . runSequentialT -- | Return True if no more suspension points remain. finished :: (Monad m) => SequentialT m a -> m Bool finished = fmap isRight . resume . runSequentialT -- | Transform the inner monad. -- This operation only applies to computation up to the first suspension. hoistFirst :: (forall x. m x -> m x) -> SequentialT m a -> SequentialT m a hoistFirst f = SequentialT . Coroutine . f . resume . runSequentialT -- | Transform the inner monad. -- The transformation is applied recursively through all the suspension points. hoist :: (Monad m, Monad n) => (forall x. m x -> n x) -> SequentialT m a -> SequentialT n a hoist f = SequentialT . mapMonad f . runSequentialT -- | Apply a function a given number of times. composeCopies :: Int -> (a -> a) -> (a -> a) composeCopies k f = foldr (.) id (replicate k f) -- | Sequential importance sampling. -- Applies a given transformation after each time step. sequentially, sis :: (Monad m) => -- | transformation (forall x. m x -> m x) -> -- | number of time steps Int -> SequentialT m a -> m a sequentially f k = finish . composeCopies k (advance . hoistFirst f) -- | synonym sis = sequentially ================================================ FILE: src/Control/Monad/Bayes/Traced/Basic.hs ================================================ {-# LANGUAGE RankNTypes #-} -- | -- Module : Control.Monad.Bayes.Traced.Basic -- Description : Distributions on full execution traces of full programs -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC module Control.Monad.Bayes.Traced.Basic ( TracedT, hoist, marginal, mhStep, mh, ) where import Control.Applicative (Applicative (..)) import Control.Monad.Bayes.Class ( MonadDistribution (random), MonadFactor (..), MonadMeasure, ) import Control.Monad.Bayes.Density.Free (DensityT) import Control.Monad.Bayes.Traced.Common ( Trace (..), bind, mhTrans', scored, singleton, ) import Control.Monad.Bayes.Weighted (WeightedT) import Data.Functor.Identity (Identity) import Data.List.NonEmpty as NE (NonEmpty ((:|)), toList) -- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md -- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore import Prelude hiding (Applicative (..)) -- | Tracing monad that records random choices made in the program. data TracedT m a = TracedT { -- | Run the program with a modified trace. model :: WeightedT (DensityT Identity) a, -- | Record trace and output. traceDist :: m (Trace a) } instance (Monad m) => Functor (TracedT m) where fmap f (TracedT m d) = TracedT (fmap f m) (fmap (fmap f) d) instance (Monad m) => Applicative (TracedT m) where pure x = TracedT (pure x) (pure (pure x)) (TracedT mf df) <*> (TracedT mx dx) = TracedT (mf <*> mx) (liftA2 (<*>) df dx) instance (Monad m) => Monad (TracedT m) where (TracedT mx dx) >>= f = TracedT my dy where my = mx >>= model . f dy = dx `bind` (traceDist . f) instance (MonadDistribution m) => MonadDistribution (TracedT m) where random = TracedT random (fmap singleton random) instance (MonadFactor m) => MonadFactor (TracedT m) where score w = TracedT (score w) (score w >> pure (scored w)) instance (MonadMeasure m) => MonadMeasure (TracedT m) hoist :: (forall x. m x -> m x) -> TracedT m a -> TracedT m a hoist f (TracedT m d) = TracedT m (f d) -- | Discard the trace and supporting infrastructure. marginal :: (Monad m) => TracedT m a -> m a marginal (TracedT _ d) = fmap output d -- | A single step of the Trace Metropolis-Hastings algorithm. mhStep :: (MonadDistribution m) => TracedT m a -> TracedT m a mhStep (TracedT m d) = TracedT m d' where d' = d >>= mhTrans' m -- | Full run of the Trace Metropolis-Hastings algorithm with a specified -- number of steps. mh :: (MonadDistribution m) => Int -> TracedT m a -> m [a] mh n (TracedT m d) = fmap (map output . NE.toList) (f n) where f k | k <= 0 = fmap (:| []) d | otherwise = do (x :| xs) <- f (k - 1) y <- mhTrans' m x return (y :| x : xs) ================================================ FILE: src/Control/Monad/Bayes/Traced/Common.hs ================================================ -- | -- Module : Control.Monad.Bayes.Traced.Common -- Description : Numeric code for Trace MCMC -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC module Control.Monad.Bayes.Traced.Common ( Trace (..), singleton, scored, bind, mhTrans, mhTransWithBool, mhTransFree, mhTrans', burnIn, MHResult (..), ) where import Control.Monad.Bayes.Class ( MonadDistribution (bernoulli, random), discrete, ) import Control.Monad.Bayes.Density.Free qualified as Free import Control.Monad.Bayes.Density.State qualified as State import Control.Monad.Bayes.Weighted as WeightedT ( WeightedT, hoist, runWeightedT, ) import Control.Monad.Writer (WriterT (WriterT, runWriterT)) import Data.Functor.Identity (Identity (runIdentity)) import Numeric.Log (Log, ln) import Statistics.Distribution.DiscreteUniform (discreteUniformAB) data MHResult a = MHResult { success :: Bool, trace :: Trace a } -- | Collection of random variables sampler during the program's execution. data Trace a = Trace { -- | Sequence of random variables sampler during the program's execution. variables :: [Double], -- output :: a, -- | The probability of observing this particular sequence. probDensity :: Log Double } instance Functor Trace where fmap f t = t {output = f (output t)} instance Applicative Trace where pure x = Trace {variables = [], output = x, probDensity = 1} tf <*> tx = Trace { variables = variables tf ++ variables tx, output = output tf (output tx), probDensity = probDensity tf * probDensity tx } instance Monad Trace where t >>= f = let t' = f (output t) in t' {variables = variables t ++ variables t', probDensity = probDensity t * probDensity t'} singleton :: Double -> Trace Double singleton u = Trace {variables = [u], output = u, probDensity = 1} scored :: Log Double -> Trace () scored w = Trace {variables = [], output = (), probDensity = w} bind :: (Monad m) => m (Trace a) -> (a -> m (Trace b)) -> m (Trace b) bind dx f = do t1 <- dx t2 <- f (output t1) return $ t2 {variables = variables t1 ++ variables t2, probDensity = probDensity t1 * probDensity t2} -- | A single Metropolis-corrected transition of single-site Trace MCMC. mhTrans :: (MonadDistribution m) => (WeightedT (State.DensityT m)) a -> Trace a -> m (Trace a) mhTrans m t@Trace {variables = us, probDensity = p} = do let n = length us us' <- do i <- discrete $ discreteUniformAB 0 (n - 1) u' <- random case splitAt i us of (xs, _ : ys) -> return $ xs ++ (u' : ys) _ -> error "impossible" ((b, q), vs) <- State.runDensityT (runWeightedT m) us' let ratio = (exp . ln) $ min 1 (q * fromIntegral n / (p * fromIntegral (length vs))) accept <- bernoulli ratio return $ if accept then Trace vs b q else t mhTransFree :: (MonadDistribution m) => WeightedT (Free.DensityT m) a -> Trace a -> m (Trace a) mhTransFree m t = trace <$> mhTransWithBool m t -- | A single Metropolis-corrected transition of single-site Trace MCMC. mhTransWithBool :: (MonadDistribution m) => WeightedT (Free.DensityT m) a -> Trace a -> m (MHResult a) mhTransWithBool m t@Trace {variables = us, probDensity = p} = do let n = length us us' <- do i <- discrete $ discreteUniformAB 0 (n - 1) u' <- random case splitAt i us of (xs, _ : ys) -> return $ xs ++ (u' : ys) _ -> error "impossible" ((b, q), vs) <- runWriterT $ runWeightedT $ WeightedT.hoist (WriterT . Free.runDensityT us') m let ratio = (exp . ln) $ min 1 (q * fromIntegral n / (p * fromIntegral (length vs))) accept <- bernoulli ratio return if accept then MHResult True (Trace vs b q) else MHResult False t -- | A variant of 'mhTrans' with an external sampling monad. mhTrans' :: (MonadDistribution m) => WeightedT (Free.DensityT Identity) a -> Trace a -> m (Trace a) mhTrans' m = mhTransFree (WeightedT.hoist (Free.hoist (return . runIdentity)) m) -- | burn in an MCMC chain for n steps (which amounts to dropping samples of the end of the list) burnIn :: (Functor m) => Int -> m [a] -> m [a] burnIn n = fmap dropEnd where dropEnd ls = let len = length ls in take (len - n) ls ================================================ FILE: src/Control/Monad/Bayes/Traced/Dynamic.hs ================================================ {-# LANGUAGE RankNTypes #-} -- | -- Module : Control.Monad.Bayes.Traced.Dynamic -- Description : Distributions on execution traces that can be dynamically frozen -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC module Control.Monad.Bayes.Traced.Dynamic ( TracedT, hoist, marginal, freeze, mhStep, mh, ) where import Control.Monad (join) import Control.Monad.Bayes.Class ( MonadDistribution (random), MonadFactor (..), MonadMeasure, ) import Control.Monad.Bayes.Density.Free (DensityT) import Control.Monad.Bayes.Traced.Common ( Trace (..), bind, mhTransFree, scored, singleton, ) import Control.Monad.Bayes.Weighted (WeightedT) import Control.Monad.Trans (MonadTrans (..)) import Data.List.NonEmpty as NE (NonEmpty ((:|)), toList) -- | A tracing monad where only a subset of random choices are traced and this -- subset can be adjusted dynamically. newtype TracedT m a = TracedT {runTraced :: m (WeightedT (DensityT m) a, Trace a)} pushM :: (Monad m) => m (WeightedT (DensityT m) a) -> WeightedT (DensityT m) a pushM = join . lift . lift instance (Monad m) => Functor (TracedT m) where fmap f (TracedT c) = TracedT $ do (m, t) <- c let m' = fmap f m let t' = fmap f t return (m', t') instance (Monad m) => Applicative (TracedT m) where pure x = TracedT $ pure (pure x, pure x) (TracedT cf) <*> (TracedT cx) = TracedT $ do (mf, tf) <- cf (mx, tx) <- cx return (mf <*> mx, tf <*> tx) instance (Monad m) => Monad (TracedT m) where (TracedT cx) >>= f = TracedT $ do (mx, tx) <- cx let m = mx >>= pushM . fmap fst . runTraced . f t <- return tx `bind` (fmap snd . runTraced . f) return (m, t) instance MonadTrans TracedT where lift m = TracedT $ fmap ((,) (lift $ lift m) . pure) m instance (MonadDistribution m) => MonadDistribution (TracedT m) where random = TracedT $ fmap ((,) random . singleton) random instance (MonadFactor m) => MonadFactor (TracedT m) where score w = TracedT $ fmap (score w,) (score w >> pure (scored w)) instance (MonadMeasure m) => MonadMeasure (TracedT m) hoist :: (forall x. m x -> m x) -> TracedT m a -> TracedT m a hoist f (TracedT c) = TracedT (f c) -- | Discard the trace and supporting infrastructure. marginal :: (Monad m) => TracedT m a -> m a marginal (TracedT c) = fmap (output . snd) c -- | Freeze all traced random choices to their current values and stop tracing -- them. freeze :: (Monad m) => TracedT m a -> TracedT m a freeze (TracedT c) = TracedT $ do (_, t) <- c let x = output t return (return x, pure x) -- | A single step of the Trace Metropolis-Hastings algorithm. mhStep :: (MonadDistribution m) => TracedT m a -> TracedT m a mhStep (TracedT c) = TracedT $ do (m, t) <- c t' <- mhTransFree m t return (m, t') -- | Full run of the Trace Metropolis-Hastings algorithm with a specified -- number of steps. mh :: (MonadDistribution m) => Int -> TracedT m a -> m [a] mh n (TracedT c) = do (m, t) <- c let f k | k <= 0 = return (t :| []) | otherwise = do (x :| xs) <- f (k - 1) y <- mhTransFree m x return (y :| x : xs) fmap (map output . NE.toList) (f n) ================================================ FILE: src/Control/Monad/Bayes/Traced/Static.hs ================================================ {-# LANGUAGE RankNTypes #-} {-# LANGUAGE RecordWildCards #-} -- | -- Module : Control.Monad.Bayes.Traced.Static -- Description : Distributions on execution traces of full programs -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC module Control.Monad.Bayes.Traced.Static ( TracedT (..), hoist, marginal, mhStep, mh, ) where import Control.Applicative (Applicative (..)) import Control.Monad.Bayes.Class ( MonadDistribution (random), MonadFactor (..), MonadMeasure, ) import Control.Monad.Bayes.Density.Free (DensityT) import Control.Monad.Bayes.Traced.Common ( Trace (..), bind, mhTransFree, scored, singleton, ) import Control.Monad.Bayes.Weighted (WeightedT) import Control.Monad.Trans (MonadTrans (..)) import Data.List.NonEmpty as NE (NonEmpty ((:|)), toList) -- Prelude exports liftA2 from GHC 9.6 on, see https://github.com/haskell/core-libraries-committee/blob/main/guides/export-lifta2-prelude.md -- import Control.Applicative further up can be removed once we don't support GHC <= 9.4 anymore import Prelude hiding (Applicative (..)) -- | A tracing monad where only a subset of random choices are traced. -- -- The random choices that are not to be traced should be lifted from the -- transformed monad. data TracedT m a = TracedT { model :: WeightedT (DensityT m) a, traceDist :: m (Trace a) } instance (Monad m) => Functor (TracedT m) where fmap f (TracedT m d) = TracedT (fmap f m) (fmap (fmap f) d) instance (Monad m) => Applicative (TracedT m) where pure x = TracedT (pure x) (pure (pure x)) (TracedT mf df) <*> (TracedT mx dx) = TracedT (mf <*> mx) (liftA2 (<*>) df dx) instance (Monad m) => Monad (TracedT m) where (TracedT mx dx) >>= f = TracedT my dy where my = mx >>= model . f dy = dx `bind` (traceDist . f) instance MonadTrans TracedT where lift m = TracedT (lift $ lift m) (fmap pure m) instance (MonadDistribution m) => MonadDistribution (TracedT m) where random = TracedT random (fmap singleton random) instance (MonadFactor m) => MonadFactor (TracedT m) where score w = TracedT (score w) (score w >> pure (scored w)) instance (MonadMeasure m) => MonadMeasure (TracedT m) hoist :: (forall x. m x -> m x) -> TracedT m a -> TracedT m a hoist f (TracedT m d) = TracedT m (f d) -- | Discard the trace and supporting infrastructure. marginal :: (Monad m) => TracedT m a -> m a marginal (TracedT _ d) = fmap output d -- | A single step of the Trace Metropolis-Hastings algorithm. mhStep :: (MonadDistribution m) => TracedT m a -> TracedT m a mhStep (TracedT m d) = TracedT m d' where d' = d >>= mhTransFree m -- $setup -- >>> import Control.Monad.Bayes.Class -- >>> import Control.Monad.Bayes.Sampler.Strict -- >>> import Control.Monad.Bayes.Weighted -- | Full run of the Trace Metropolis-Hastings algorithm with a specified -- number of steps. Newest samples are at the head of the list. -- -- For example: -- -- * I have forgotten what day it is. -- * There are ten buses per hour in the week and three buses per hour at the weekend. -- * I observe four buses in a given hour. -- * What is the probability that it is the weekend? -- -- >>> :{ -- let -- bus = do x <- bernoulli (2/7) -- let rate = if x then 3 else 10 -- factor $ poissonPdf rate 4 -- return x -- mhRunBusSingleObs = do -- let nSamples = 2 -- sampleIOfixed $ unweighted $ mh nSamples bus -- in mhRunBusSingleObs -- :} -- [True,True,True] -- -- Of course, it will need to be run more than twice to get a reasonable estimate. mh :: (MonadDistribution m) => Int -> TracedT m a -> m [a] mh n (TracedT m d) = fmap (map output . NE.toList) (f n) where f k | k <= 0 = fmap (:| []) d | otherwise = do (x :| xs) <- f (k - 1) y <- mhTransFree m x return (y :| x : xs) ================================================ FILE: src/Control/Monad/Bayes/Traced.hs ================================================ -- | -- Module : Control.Monad.Bayes.Traced -- Description : Distributions on execution traces -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC module Control.Monad.Bayes.Traced ( module Control.Monad.Bayes.Traced.Static, ) where import Control.Monad.Bayes.Traced.Static ================================================ FILE: src/Control/Monad/Bayes/Weighted.hs ================================================ {-# LANGUAGE DerivingStrategies #-} {-# LANGUAGE GeneralizedNewtypeDeriving #-} {-# LANGUAGE RankNTypes #-} -- | -- Module : Control.Monad.Bayes.Weighted -- Description : Probability monad accumulating the likelihood -- Copyright : (c) Adam Scibior, 2015-2020 -- License : MIT -- Maintainer : leonhard.markert@tweag.io -- Stability : experimental -- Portability : GHC -- -- 'WeightedT' is an instance of 'MonadFactor'. Apply a 'MonadDistribution' transformer to -- obtain a 'MonadMeasure' that can execute probabilistic models. module Control.Monad.Bayes.Weighted ( WeightedT, weightedT, extractWeight, unweighted, applyWeight, hoist, runWeightedT, ) where import Control.Monad.Bayes.Class ( MonadDistribution, MonadFactor (..), MonadMeasure, MonadUniformRange, factor, ) import Control.Monad.State (MonadIO, MonadTrans, StateT (..), lift, mapStateT, modify) import Numeric.Log (Log) -- | Execute the program using the prior distribution, while accumulating likelihood. newtype WeightedT m a = WeightedT (StateT (Log Double) m a) -- StateT is more efficient than WriterT deriving newtype (Functor, Applicative, Monad, MonadIO, MonadTrans, MonadDistribution, MonadUniformRange) instance (Monad m) => MonadFactor (WeightedT m) where score w = WeightedT (modify (* w)) instance (MonadDistribution m) => MonadMeasure (WeightedT m) -- | Obtain an explicit value of the likelihood for a given value. runWeightedT :: WeightedT m a -> m (a, Log Double) runWeightedT (WeightedT m) = runStateT m 1 -- | Compute the sample and discard the weight. -- -- This operation introduces bias. unweighted :: (Functor m) => WeightedT m a -> m a unweighted = fmap fst . runWeightedT -- | Compute the weight and discard the sample. extractWeight :: (Functor m) => WeightedT m a -> m (Log Double) extractWeight = fmap snd . runWeightedT -- | Embed a random variable with explicitly given likelihood. -- -- > runWeightedT . weightedT = id weightedT :: (Monad m) => m (a, Log Double) -> WeightedT m a weightedT m = WeightedT $ do (x, w) <- lift m modify (* w) return x -- | Use the weight as a factor in the transformed monad. applyWeight :: (MonadFactor m) => WeightedT m a -> m a applyWeight m = do (x, w) <- runWeightedT m factor w return x -- | Apply a transformation to the transformed monad. hoist :: (forall x. m x -> n x) -> WeightedT m a -> WeightedT n a hoist t (WeightedT m) = WeightedT $ mapStateT t m ================================================ FILE: src/Math/Integrators/StormerVerlet.hs ================================================ module Math.Integrators.StormerVerlet ( integrateV, stormerVerlet2H, Integrator, ) where import Control.Lens import Control.Monad.Primitive import Data.Vector (Vector, (!)) import Data.Vector qualified as V import Data.Vector.Mutable import Linear (V2 (..)) -- | Integrator function -- - \Phi [h] |-> y_0 -> y_1 type Integrator a = -- | Step Double -> -- | Initial value a -> -- | Next value a -- | Störmer-Verlet integration scheme for systems of the form -- \(\mathbb{H}(p,q) = T(p) + V(q)\) stormerVerlet2H :: (Applicative f, Num (f a), Fractional a) => -- | Step size a -> -- | \(\frac{\partial H}{\partial q}\) (f a -> f a) -> -- | \(\frac{\partial H}{\partial p}\) (f a -> f a) -> -- | Current \((p, q)\) as a 2-dimensional vector V2 (f a) -> -- | New \((p, q)\) as a 2-dimensional vector V2 (f a) stormerVerlet2H hh nablaQ nablaP prev = V2 qNew pNew where h2 = hh / 2 hhs = pure hh hh2s = pure h2 qsPrev = prev ^. _1 psPrev = prev ^. _2 pp2 = psPrev - hh2s * nablaQ qsPrev qNew = qsPrev + hhs * nablaP pp2 pNew = pp2 - hh2s * nablaQ qNew -- | -- Integrate ODE equation using fixed steps set by a vector, and returns a vector -- of solutions corrensdonded to times that was requested. -- It takes Vector of time points as a parameter and returns a vector of results integrateV :: (PrimMonad m) => -- | Internal integrator Integrator a -> -- | initial value a -> -- | vector of time points Vector Double -> -- | vector of solution m (Vector a) integrateV integrator initial times = do out <- new (V.length times) write out 0 initial compute initial 1 out V.unsafeFreeze out where compute y i out | i == V.length times = return () | otherwise = do let h = (times ! i) - (times ! (i - 1)) y' = integrator h y write out i y' compute y' (i + 1) out ================================================ FILE: test/Spec.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} import Data.AEq (AEq ((~==))) import Test.Hspec (context, describe, hspec, it, shouldBe) import Test.Hspec.QuickCheck (prop) import Test.QuickCheck (ioProperty, property, (==>)) import TestAdvanced qualified import TestBenchmarks qualified import TestDistribution qualified import TestEnumerator qualified import TestInference qualified import TestIntegrator qualified import TestPipes (hmms) import TestPipes qualified import TestPopulation qualified import TestSSMFixtures qualified import TestSampler qualified import TestSequential qualified import TestStormerVerlet qualified import TestWeighted qualified main :: IO () main = hspec do describe "Stormer Verlet" $ it "conserves energy" $ do p1 <- TestStormerVerlet.passed1 p1 `shouldBe` True describe "Distribution" $ it "gives correct mean, variance and covariance" $ do p1 <- TestDistribution.passed1 p1 `shouldBe` True p2 <- TestDistribution.passed2 p2 `shouldBe` True p3 <- TestDistribution.passed3 p3 `shouldBe` True describe "Weighted" $ it "accumulates likelihood correctly" $ do passed <- TestWeighted.passed passed `shouldBe` True describe "Enumerator" do it "sorts samples and aggregates weights" $ TestEnumerator.passed2 `shouldBe` True it "gives correct answer for the sprinkler model" $ TestEnumerator.passed3 `shouldBe` True it "computes expectation correctly" $ TestEnumerator.passed4 `shouldBe` True describe "Integrator Expectation" do prop "expectation numerically" $ \mean var -> var > 0 ==> property $ TestIntegrator.normalExpectation mean (sqrt var) ~== mean describe "Integrator Variance" do prop "variance numerically" $ \mean var -> -- Because of rounding issues, require the variance to be a bit bigger than 0 -- See https://github.com/tweag/monad-bayes/issues/275 var > 0.1 ==> property $ TestIntegrator.normalVariance mean (sqrt var) ~== var describe "SamplerT mean and variance" do it "gets right mean and variance" $ TestSampler.testMeanAndVariance `shouldBe` True describe "Integrator Volume" do prop "volume sums to 1" $ property $ \case [] -> True ls -> (TestIntegrator.volumeIsOne ls) describe "Integrator" do it "" $ all (== True) [ TestIntegrator.passed1, TestIntegrator.passed2, TestIntegrator.passed3, TestIntegrator.passed4, TestIntegrator.passed5, TestIntegrator.passed6, TestIntegrator.passed7, TestIntegrator.passed8, TestIntegrator.passed9, TestIntegrator.passed10, TestIntegrator.passed11, TestIntegrator.passed12, TestIntegrator.passed13, TestIntegrator.passed14 ] `shouldBe` True describe "Population" do context "controlling population" do it "preserves the population when not explicitly altered" do popSize <- TestPopulation.popSize popSize `shouldBe` 5 it "multiplies the number of samples when spawn invoked twice" do manySize <- TestPopulation.manySize manySize `shouldBe` 15 it "correctly computes population average" $ TestPopulation.popAvgCheck `shouldBe` True context "distribution-preserving transformations" do it "collapse preserves the distribution" do TestPopulation.transCheck1 `shouldBe` True TestPopulation.transCheck2 `shouldBe` True it "resample preserves the distribution" do TestPopulation.resampleCheck 1 `shouldBe` True TestPopulation.resampleCheck 2 `shouldBe` True describe "Sequential" do it "stops at every factor" do TestSequential.checkTwoSync 0 `shouldBe` True TestSequential.checkTwoSync 1 `shouldBe` True TestSequential.checkTwoSync 2 `shouldBe` True it "preserves the distribution" $ TestSequential.checkPreserve `shouldBe` True it "produces correct intermediate weights" do TestSequential.checkSync 0 `shouldBe` True TestSequential.checkSync 1 `shouldBe` True TestSequential.checkSync 2 `shouldBe` True describe "SMC" do it "terminates" $ seq TestInference.checkTerminateSMC () `shouldBe` () it "preserves the distribution on the sprinkler model" $ TestInference.checkPreserveSMC `shouldBe` True prop "number of particles is equal to its second parameter" $ \observations particles -> observations >= 0 && particles >= 1 ==> ioProperty do checkParticles <- TestInference.checkParticles observations particles return $ checkParticles == particles describe "SMC with systematic resampling" $ prop "number of particles is equal to its second parameter" $ \observations particles -> observations >= 0 && particles >= 1 ==> ioProperty do checkParticles <- TestInference.checkParticlesSystematic observations particles return $ checkParticles == particles describe "Equivalent Expectations" do prop "Gamma Normal" $ ioProperty . TestInference.testGammaNormal prop "Normal Normal" $ \n -> ioProperty (TestInference.testNormalNormal [max (-3) $ min 3 n]) prop "Beta Bernoulli" $ ioProperty . TestInference.testBetaBernoulli describe "Pipes: Urn" do it "Distributions are equivalent" do TestPipes.urns 10 `shouldBe` True describe "Pipes: HMM" do prop "pipe model is equivalent to standard model" $ \num -> property $ hmms $ take 5 num describe "SMC with stratified resampling" $ prop "number of particles is equal to its second parameter" $ \observations particles -> observations >= 0 && particles >= 1 ==> ioProperty do checkParticles <- TestInference.checkParticlesStratified observations particles return $ checkParticles == particles describe "Expectation from all inference methods" $ it "gives correct answer for the sprinkler model" do passed1 <- TestAdvanced.passed1 passed1 `shouldBe` True passed2 <- TestAdvanced.passed2 passed2 `shouldBe` True passed3 <- TestAdvanced.passed3 passed3 `shouldBe` True passed4 <- TestAdvanced.passed4 passed4 `shouldBe` True passed5 <- TestAdvanced.passed5 passed5 `shouldBe` True passed6 <- TestAdvanced.passed6 passed6 `shouldBe` True passed7 <- TestAdvanced.passed7 passed7 `shouldBe` True TestBenchmarks.test TestSSMFixtures.test ================================================ FILE: test/TestAdvanced.hs ================================================ module TestAdvanced where import Control.Arrow import Control.Monad (join) import Control.Monad.Bayes.Class import Control.Monad.Bayes.Enumerator import Control.Monad.Bayes.Inference.MCMC import Control.Monad.Bayes.Inference.PMMH import Control.Monad.Bayes.Inference.RMSMC import Control.Monad.Bayes.Inference.SMC import Control.Monad.Bayes.Inference.SMC2 import Control.Monad.Bayes.Population import Control.Monad.Bayes.Sampler.Strict mcmcConfig :: MCMCConfig mcmcConfig = MCMCConfig {numMCMCSteps = 0, numBurnIn = 0, proposal = SingleSiteMH} smcConfig :: (MonadDistribution m) => SMCConfig m smcConfig = SMCConfig {numSteps = 0, numParticles = 1000, resampler = resampleMultinomial} passed1, passed2, passed3, passed4, passed5, passed6, passed7 :: IO Bool passed1 = do sample <- sampleIOfixed $ mcmc MCMCConfig {numMCMCSteps = 10000, numBurnIn = 5000, proposal = SingleSiteMH} random return $ abs (0.5 - (expectation id $ fromList $ toEmpirical sample)) < 0.01 passed2 = do sample <- sampleIOfixed $ runPopulationT $ smc (SMCConfig {numSteps = 0, numParticles = 10000, resampler = resampleMultinomial}) random return $ close 0.5 sample passed3 = do sample <- sampleIOfixed $ runPopulationT $ rmsmcDynamic mcmcConfig smcConfig random return $ close 0.5 sample passed4 = do sample <- sampleIOfixed $ runPopulationT $ rmsmcBasic mcmcConfig smcConfig random return $ close 0.5 sample passed5 = do sample <- sampleIOfixed $ runPopulationT $ rmsmc mcmcConfig smcConfig random return $ close 0.5 sample passed6 = do sample <- fmap join $ sampleIOfixed $ pmmh mcmcConfig {numMCMCSteps = 100} smcConfig {numSteps = 0, numParticles = 100} random (normal 0) return $ close 0.0 sample close :: Double -> [(Double, Log Double)] -> Bool passed7 = do sample <- fmap join $ sampleIOfixed $ fmap (fmap (\(x, y) -> fmap (second (* y)) x)) $ runPopulationT $ smc2 0 100 100 100 random (normal 0) return $ close 0.0 sample close n sample = abs (n - (expectation id $ fromList $ toEmpiricalWeighted sample)) < 0.01 ================================================ FILE: test/TestBenchmarks.hs ================================================ module TestBenchmarks where import Control.Monad (forM_) import Data.Maybe (fromJust) import Helper import Paths_monad_bayes (getDataDir) import System.IO (readFile') import System.IO.Error (catchIOError, isDoesNotExistError) import Test.Hspec fixtureToFilename :: Model -> Alg -> String fixtureToFilename model alg = "/test/fixtures/" ++ fromJust (serializeModel model) ++ "-" ++ show alg ++ ".txt" models :: [Model] models = [LR 10, HMM 10, LDA (5, 10)] algs :: [Alg] algs = [minBound .. maxBound] test :: SpecWith () test = describe "Benchmarks" $ forM_ models $ \model -> forM_ algs $ testFixture model testFixture :: Model -> Alg -> SpecWith () testFixture model alg = do dataDir <- runIO getDataDir let filename = dataDir <> fixtureToFilename model alg it ("should agree with the fixture " ++ filename) $ do fixture <- catchIOError (readFile' filename) $ \e -> if isDoesNotExistError e then return "" else ioError e sampled <- runAlgFixed model alg -- Reset in case of fixture update or creation writeFile filename sampled fixture `shouldBe` sampled ================================================ FILE: test/TestDistribution.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} {-# LANGUAGE Trustworthy #-} module TestDistribution ( passed1, passed2, passed3, ) where import Control.Monad (replicateM) import Control.Monad.Bayes.Class (mvNormal) import Control.Monad.Bayes.Sampler.Strict import Data.Matrix (fromList) import Data.Vector qualified as V -- Test the sampled covariance is approximately the same as the -- specified covariance. passed1 :: IO Bool passed1 = sampleIOfixed $ do let mu = (V.fromList [0.0, 0.0]) sigma11 = 2.0 sigma12 = 1.0 bigSigma = (fromList 2 2 [sigma11, sigma12, sigma12, sigma11]) nSamples = 200000 nSamples' = fromIntegral nSamples ss <- replicateM nSamples $ (mvNormal mu bigSigma) let xbar = (/ nSamples') $ sum $ fmap (V.! 0) ss ybar = (/ nSamples') $ sum $ fmap (V.! 1) ss let term1 = (/ nSamples') $ sum $ zipWith (*) (fmap (V.! 0) ss) (fmap (V.! 1) ss) let term2 = xbar * ybar return $ abs (sigma12 - (term1 - term2)) < 2e-2 -- Test the sampled means are approximately the same as the specified -- means. passed2 :: IO Bool passed2 = sampleIOfixed $ do let mu = (V.fromList [0.0, 0.0]) sigma11 = 2.0 sigma12 = 1.0 bigSigma = (fromList 2 2 [sigma11, sigma12, sigma12, sigma11]) nSamples = 100000 nSamples' = fromIntegral nSamples ss <- replicateM nSamples $ (mvNormal mu bigSigma) let xbar = (/ nSamples') $ sum $ fmap (V.! 0) ss ybar = (/ nSamples') $ sum $ fmap (V.! 1) ss return $ abs xbar < 1e-2 && abs ybar < 1e-2 -- Test the sampled variances are approximately the same as the -- specified variances. passed3 :: IO Bool passed3 = sampleIOfixed $ do let mu = (V.fromList [0.0, 0.0]) sigma11 = 2.0 sigma12 = 1.0 bigSigma = (fromList 2 2 [sigma11, sigma12, sigma12, sigma11]) nSamples = 200000 nSamples' = fromIntegral nSamples ss <- replicateM nSamples $ (mvNormal mu bigSigma) let xbar = (/ nSamples') $ sum $ fmap (V.! 0) ss ybar = (/ nSamples') $ sum $ fmap (V.! 1) ss let xbar2 = (/ nSamples') $ sum $ fmap (\x -> x * x) $ fmap (V.! 0) ss ybar2 = (/ nSamples') $ sum $ fmap (\x -> x * x) $ fmap (V.! 1) ss let xvar = xbar2 - xbar * xbar let yvar = ybar2 - ybar * ybar return $ abs (xvar - sigma11) < 1e-2 && abs (yvar - sigma11) < 2e-2 ================================================ FILE: test/TestEnumerator.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} module TestEnumerator (passed1, passed2, passed3, passed4) where import Control.Monad.Bayes.Class ( MonadDistribution (categorical, uniformD), ) import Control.Monad.Bayes.Enumerator ( enumerator, evidence, expectation, ) import Data.AEq (AEq ((~==))) import Data.Vector qualified as V import Numeric.Log (Log (ln)) import Sprinkler (hard, soft) unnorm :: (MonadDistribution m) => m Int unnorm = categorical $ V.fromList [0.5, 0.8] passed1 :: Bool passed1 = (exp . ln) (evidence unnorm) ~== 1 agg :: (MonadDistribution m) => m Int agg = do x <- uniformD [0, 1] y <- uniformD [2, 1] return (x + y) passed2 :: Bool passed2 = enumerator agg ~== [(2, 0.5), (1, 0.25), (3, 0.25)] passed3 :: Bool passed3 = enumerator Sprinkler.hard ~== enumerator Sprinkler.soft passed4 :: Bool passed4 = expectation (^ (2 :: Int)) (fmap (fromIntegral . (+ 1)) $ categorical $ V.fromList [0.5, 0.5]) ~== 2.5 ================================================ FILE: test/TestInference.hs ================================================ {-# LANGUAGE ImportQualifiedPost #-} {-# LANGUAGE Rank2Types #-} {-# LANGUAGE TypeFamilies #-} {-# OPTIONS_GHC -Wno-missing-export-lists #-} module TestInference where import ConjugatePriors ( betaBernoulli', betaBernoulliAnalytic, gammaNormal', gammaNormalAnalytic, normalNormal', normalNormalAnalytic, ) import Control.Monad (replicateM) import Control.Monad.Bayes.Class (MonadMeasure, posterior) import Control.Monad.Bayes.Enumerator (enumerator) import Control.Monad.Bayes.Inference.SMC import Control.Monad.Bayes.Integrator (normalize) import Control.Monad.Bayes.Integrator qualified as Integrator import Control.Monad.Bayes.Population import Control.Monad.Bayes.Sampler.Strict (SamplerT, sampleIOfixed) import Control.Monad.Bayes.Sampler.Strict qualified as Sampler import Control.Monad.Bayes.Weighted (WeightedT) import Control.Monad.Bayes.Weighted qualified as WeightedT import Data.AEq (AEq ((~==))) import Numeric.Log (Log) import Sprinkler (soft) import System.Random.Stateful (IOGenM, StdGen) sprinkler :: (MonadMeasure m) => m Bool sprinkler = Sprinkler.soft -- | Count the number of particles produced by SMC checkParticles :: Int -> Int -> IO Int checkParticles observations particles = sampleIOfixed (fmap length (runPopulationT $ smc SMCConfig {numSteps = observations, numParticles = particles, resampler = resampleMultinomial} Sprinkler.soft)) checkParticlesSystematic :: Int -> Int -> IO Int checkParticlesSystematic observations particles = sampleIOfixed (fmap length (runPopulationT $ smc SMCConfig {numSteps = observations, numParticles = particles, resampler = resampleSystematic} Sprinkler.soft)) checkParticlesStratified :: Int -> Int -> IO Int checkParticlesStratified observations particles = sampleIOfixed (fmap length (runPopulationT $ smc SMCConfig {numSteps = observations, numParticles = particles, resampler = resampleStratified} Sprinkler.soft)) checkTerminateSMC :: IO [(Bool, Log Double)] checkTerminateSMC = sampleIOfixed (runPopulationT $ smc SMCConfig {numSteps = 2, numParticles = 5, resampler = resampleMultinomial} sprinkler) checkPreserveSMC :: Bool checkPreserveSMC = (enumerator . collapse . smc SMCConfig {numSteps = 2, numParticles = 2, resampler = resampleMultinomial}) sprinkler ~== enumerator sprinkler expectationNearNumeric :: WeightedT Integrator.Integrator Double -> WeightedT Integrator.Integrator Double -> Double expectationNearNumeric x y = let e1 = Integrator.expectation $ normalize x e2 = Integrator.expectation $ normalize y in (abs (e1 - e2)) expectationNearSampling :: WeightedT (SamplerT (IOGenM StdGen) IO) Double -> WeightedT (SamplerT (IOGenM StdGen) IO) Double -> IO Double expectationNearSampling x y = do e1 <- sampleIOfixed $ fmap Sampler.sampleMean $ replicateM 10 $ WeightedT.runWeightedT x e2 <- sampleIOfixed $ fmap Sampler.sampleMean $ replicateM 10 $ WeightedT.runWeightedT y return (abs (e1 - e2)) testNormalNormal :: [Double] -> IO Bool testNormalNormal n = do let e = expectationNearNumeric (posterior (normalNormal' 1 (1, 10)) n) (normalNormalAnalytic 1 (1, 10) n) return (e < 1e-0) testGammaNormal :: [Double] -> IO Bool testGammaNormal n = do let e = expectationNearNumeric (posterior (gammaNormal' (1, 1)) n) (gammaNormalAnalytic (1, 1) n) return (e < 1e-1) testBetaBernoulli :: [Bool] -> IO Bool testBetaBernoulli bs = do let e = expectationNearNumeric (posterior (betaBernoulli' (1, 1)) bs) (betaBernoulliAnalytic (1, 1) bs) return (e < 1e-1) ================================================ FILE: test/TestIntegrator.hs ================================================ {-# LANGUAGE BlockArguments #-} module TestIntegrator where import Control.Monad (replicateM) import Control.Monad.Bayes.Class ( MonadDistribution (bernoulli, gamma, normal, random, uniformD), MonadFactor (score), MonadMeasure, condition, factor, normalPdf, ) import Control.Monad.Bayes.Integrator import Control.Monad.Bayes.Sampler.Strict import Control.Monad.Bayes.Weighted (runWeightedT) import Control.Monad.ST (runST) import Data.AEq (AEq ((~==))) import Data.List (sortOn) import Data.Set (fromList) import Numeric.Log (Log (Exp, ln)) import Sprinkler (hard, soft) import Statistics.Distribution (Distribution (cumulative)) import Statistics.Distribution.Normal (normalDistr) normalExpectation :: Double -> Double -> Double normalExpectation mean std = expectation (normal mean std) normalVariance :: Double -> Double -> Double normalVariance mean std = variance (normal mean std) volumeIsOne :: [Double] -> Bool volumeIsOne = (~== 1.0) . volume . uniformD agg :: (MonadDistribution m) => m Int agg = do x <- uniformD [0, 1] y <- uniformD [2, 1] return (x + y) within :: (Ord a, Num a) => a -> a -> a -> Bool within n x y = abs (x - y) < n passed1, passed2, passed3, passed4, passed5, passed6, passed7, passed8, passed9, passed10, passed11, passed12, passed13, passed14 :: Bool -- enumerator from Integrator works passed1 = sortOn fst (enumeratorWith (fromList [3, 1, 2]) agg) ~== sortOn fst [(2, 0.5), (1, 0.25), (3, 0.25)] -- hard and soft sprinkers are equivalent under enumerator from Integrator passed2 = enumeratorWith (fromList [True, False]) (normalize (Sprinkler.hard)) ~== enumeratorWith (fromList [True, False]) (normalize (Sprinkler.soft)) -- expectation is as expected passed3 = expectation (fmap ((** 2) . (+ 1)) $ uniformD [0, 1]) == 2.5 -- distribution is normalized passed4 = volume (uniformD [1, 2]) ~== 1.0 -- enumerator is as expected passed5 = sortOn fst (enumeratorWith (fromList [0, 1 :: Int]) (empirical [0 :: Int, 1, 1, 1])) == sortOn fst [(1, 0.75), (0, 0.25)] -- normalization works right for enumerator, when there is conditioning passed6 = sortOn fst [(2, 0.5), (3, 0.5), (1, 0.0)] == sortOn fst ( enumeratorWith (fromList [1, 2, 3]) $ normalize $ do x <- uniformD [1 :: Int, 2, 3] condition (x > 1) return x ) -- soft factor statements work with enumerator and normalization passed7 = sortOn fst [(True, 0.75), (False, 0.25)] ~== sortOn fst ( enumeratorWith (fromList [True, False]) $ normalize do x <- bernoulli 0.5 factor $ if x then 0.3 else 0.1 return x ) -- volume of weight remains 1 passed8 = 1 == ( volume $ fmap (ln . exp . snd) $ runWeightedT do x <- bernoulli 0.5 factor $ if x then 0.2 else 0.1 return x ) -- normal probability in positive region is half passed9 = probability (1, 1000) (normal 1 10) - 0.5 < 0.05 -- cdf as expected passed10 = cdf (normal 5 5) 5 - 0.5 < 0.05 -- cdf as expected passed11 = (within 0.001) ( cdf ( do x <- normal 0 1 return x ) 3 ) (cumulative (normalDistr 0 1) 3) -- volume as expected passed12 = volume ( do x <- gamma 2 3 return x ) ~== 1 -- normalization preserves volume passed13 = (volume . normalize) ( do x <- gamma 2 3 factor (normalPdf 0 1 x) return x ) ~== 1 -- sampler and integrator agree on a non-trivial model passed14 = let sample = runST $ sampleSTfixed $ fmap sampleMean $ replicateM 10000 $ runWeightedT $ model1 quadrature = expectation $ normalize $ model1 in abs (sample - quadrature) < 0.01 model1 :: (MonadMeasure m) => m Double model1 = do x <- random y <- random score (Exp $ log (f x + y)) return x where f x = cos (x ** 4) + x ** 3 ================================================ FILE: test/TestPipes.hs ================================================ {-# OPTIONS_GHC -Wno-monomorphism-restriction #-} module TestPipes where import BetaBin (urn, urnP) import Control.Monad.Bayes.Class () import Control.Monad.Bayes.Enumerator (enumerator) import Data.AEq (AEq ((~==))) import Data.List (sort) import HMM (hmm, hmmPosterior) import Pipes.Prelude (toListM) urns :: Int -> Bool urns n = enumerator (urn n) ~== enumerator (urnP n) hmms :: [Double] -> Bool hmms observations = let hmmWithoutPipe = hmm observations hmmWithPipe = reverse . init <$> toListM (hmmPosterior observations) in -- Sort enumerator again although it is already sorted, see https://github.com/tweag/monad-bayes/issues/283 sort (enumerator hmmWithPipe) ~== sort (enumerator hmmWithoutPipe) ================================================ FILE: test/TestPopulation.hs ================================================ module TestPopulation (weightedSampleSize, popSize, manySize, sprinkler, sprinklerExact, transCheck1, transCheck2, resampleCheck, popAvgCheck) where import Control.Monad.Bayes.Class (MonadDistribution, MonadMeasure) import Control.Monad.Bayes.Enumerator (enumerator, expectation) import Control.Monad.Bayes.Population as Population ( PopulationT, collapse, popAvg, pushEvidence, resampleMultinomial, runPopulationT, spawn, ) import Control.Monad.Bayes.Sampler.Strict (sampleIOfixed) import Data.AEq (AEq ((~==))) import Sprinkler (soft) weightedSampleSize :: (MonadDistribution m) => PopulationT m a -> m Int weightedSampleSize = fmap length . runPopulationT popSize :: IO Int popSize = sampleIOfixed (weightedSampleSize $ spawn 5 >> sprinkler) manySize :: IO Int manySize = sampleIOfixed (weightedSampleSize $ spawn 5 >> sprinkler >> spawn 3) sprinkler :: (MonadMeasure m) => m Bool sprinkler = Sprinkler.soft sprinklerExact :: [(Bool, Double)] sprinklerExact = enumerator Sprinkler.soft transCheck1 :: Bool transCheck1 = enumerator (collapse sprinkler) ~== sprinklerExact transCheck2 :: Bool transCheck2 = enumerator (collapse (spawn 2 >> sprinkler)) ~== sprinklerExact resampleCheck :: Int -> Bool resampleCheck n = (enumerator . collapse . resampleMultinomial) (spawn n >> sprinkler) ~== sprinklerExact popAvgCheck :: Bool popAvgCheck = expectation f Sprinkler.soft ~== expectation id (popAvg f $ pushEvidence Sprinkler.soft) where f True = 10 f False = 4 ================================================ FILE: test/TestSSMFixtures.hs ================================================ module TestSSMFixtures where import Control.Monad.Bayes.Sampler.Strict (sampleIOfixed) import NonlinearSSM import NonlinearSSM.Algorithms import Paths_monad_bayes (getDataDir) import System.IO (readFile') import System.IO.Error (catchIOError, isDoesNotExistError) import Test.Hspec fixtureToFilename :: Alg -> FilePath fixtureToFilename alg = "/test/fixtures/SSM-" ++ show alg ++ ".txt" testFixture :: Alg -> SpecWith () testFixture alg = do dataDir <- runIO getDataDir let filename = dataDir <> fixtureToFilename alg it ("should agree with the fixture " ++ filename) $ do ys <- sampleIOfixed $ generateData t fixture <- catchIOError (readFile' filename) $ \e -> if isDoesNotExistError e then return "" else ioError e sampled <- sampleIOfixed $ runAlgFixed (map fst ys) alg -- Reset in case of fixture update or creation writeFile filename sampled fixture `shouldBe` sampled test :: SpecWith () test = describe "TestSSMFixtures" $ mapM_ testFixture algs ================================================ FILE: test/TestSampler.hs ================================================ module TestSampler where import Control.Foldl qualified as Fold import Control.Monad (replicateM) import Control.Monad.Bayes.Class (MonadDistribution (normal)) import Control.Monad.Bayes.Sampler.Strict (sampleSTfixed) import Control.Monad.ST (runST) testMeanAndVariance :: Bool testMeanAndVariance = isDiff where m = runST (sampleSTfixed (foldWith Fold.mean (normal 2 4))) v = runST (sampleSTfixed (foldWith Fold.variance (normal 2 4))) foldWith f = fmap (Fold.fold f) . replicateM 100000 isDiff = abs (2 - m) < 0.01 && abs (16 - v) < 0.1 ================================================ FILE: test/TestSequential.hs ================================================ module TestSequential (twoSync, finishedTwoSync, checkTwoSync, checkPreserve, pFinished, isFinished, checkSync) where import Control.Monad.Bayes.Class ( MonadDistribution (uniformD), MonadMeasure, factor, ) import Control.Monad.Bayes.Enumerator as Dist (enumerator, mass) import Control.Monad.Bayes.Sequential.Coroutine (advance, finish, finished) import Data.AEq (AEq ((~==))) import Sprinkler (soft) twoSync :: (MonadMeasure m) => m Int twoSync = do x <- uniformD [0, 1] factor (fromIntegral x) y <- uniformD [0, 1] factor (fromIntegral y) return (x + y) finishedTwoSync :: (MonadMeasure m) => Int -> m Bool finishedTwoSync n = finished (run n twoSync) where run 0 d = d run k d = run (k - 1) (advance d) checkTwoSync :: Int -> Bool checkTwoSync 0 = mass (finishedTwoSync 0) False ~== 1 checkTwoSync 1 = mass (finishedTwoSync 1) False ~== 1 checkTwoSync 2 = mass (finishedTwoSync 2) True ~== 1 checkTwoSync _ = error "Unexpected argument" sprinkler :: (MonadMeasure m) => m Bool sprinkler = Sprinkler.soft checkPreserve :: Bool checkPreserve = enumerator (finish sprinkler) ~== enumerator sprinkler pFinished :: Int -> Double pFinished 0 = 0.8267716535433071 pFinished 1 = 0.9988062077198566 pFinished 2 = 1 pFinished _ = error "Unexpected argument" isFinished :: (MonadMeasure m) => Int -> m Bool isFinished n = finished (run n sprinkler) where run 0 d = d run k d = run (k - 1) (advance d) checkSync :: Int -> Bool checkSync n = mass (isFinished n) True ~== pFinished n ================================================ FILE: test/TestStormerVerlet.hs ================================================ module TestStormerVerlet ( passed1, ) where import Control.Lens import Control.Monad.ST import Data.Maybe (fromJust) import Data.Vector qualified as V import Linear qualified as L import Linear.V import Math.Integrators.StormerVerlet import Statistics.Function (square) gConst :: Double gConst = 6.67384e-11 nStepsTwoPlanets :: Int nStepsTwoPlanets = 44 stepTwoPlanets :: Double stepTwoPlanets = 24 * 60 * 60 * 100 sunMass, jupiterMass :: Double sunMass = 1.9889e30 jupiterMass = 1.8986e27 jupiterPerihelion :: Double jupiterPerihelion = 7.405736e11 jupiterV :: [Double] jupiterV = [-1.0965244901087316e02, -1.3710001990210707e04, 0.0] jupiterQ :: [Double] jupiterQ = [negate jupiterPerihelion, 0.0, 0.0] sunV :: [Double] sunV = [0.0, 0.0, 0.0] sunQ :: [Double] sunQ = [0.0, 0.0, 0.0] tm :: V.Vector Double tm = V.enumFromStepN 0 stepTwoPlanets nStepsTwoPlanets keplerP :: L.V2 (L.V3 Double) -> L.V2 (L.V3 Double) keplerP (L.V2 p1 p2) = L.V2 dHdP1 dHdP2 where dHdP1 = p1 / pure jupiterMass dHdP2 = p2 / pure sunMass keplerQ :: L.V2 (L.V3 Double) -> L.V2 (L.V3 Double) keplerQ (L.V2 q1 q2) = L.V2 dHdQ1 dHdQ2 where r = q2 L.^-^ q1 ri = r `L.dot` r rr = ri * (sqrt ri) q1' = pure gConst * r / pure rr q2' = negate q1' dHdQ1 = q1' * pure sunMass * pure jupiterMass dHdQ2 = q2' * pure sunMass * pure jupiterMass listToV3 :: [a] -> L.V3 a listToV3 [x, y, z] = fromV . fromJust . fromVector . V.fromList $ [x, y, z] listToV3 xs = error $ "Only supply 3 elements not: " ++ show (length xs) initPQ2s :: L.V2 (L.V2 (L.V3 Double)) initPQ2s = L.V2 (L.V2 (listToV3 jupiterQ) (listToV3 sunQ)) (L.V2 (pure jupiterMass * listToV3 jupiterV) (pure sunMass * listToV3 sunV)) result2 :: V.Vector (L.V2 (L.V2 (L.V3 Double))) result2 = runST $ integrateV (\h -> stormerVerlet2H (pure h) keplerQ keplerP) initPQ2s tm energy :: (L.V2 (L.V2 (L.V3 Double))) -> Double energy x = keJ + keS + peJ + peS where qs = x ^. _1 ps = x ^. _2 qJ = qs ^. _1 qS = qs ^. _2 pJ = ps ^. _1 pS = ps ^. _2 keJ = (* 0.5) $ (/ jupiterMass) $ sum $ fmap square pJ keS = (* 0.5) $ (/ sunMass) $ sum $ fmap square pS r = qJ L.^-^ qS ri = r `L.dot` r peJ = 0.5 * gConst * sunMass * jupiterMass / (sqrt ri) peS = 0.5 * gConst * sunMass * jupiterMass / (sqrt ri) energies :: V.Vector Double energies = fmap energy result2 diffs :: V.Vector Double diffs = V.zipWith (\x y -> abs (x - y) / x) energies (V.tail energies) passed1 :: IO Bool passed1 = return $ V.all (< 1.0e-3) diffs ================================================ FILE: test/TestWeighted.hs ================================================ {-# LANGUAGE TypeFamilies #-} module TestWeighted (check, passed, result, model) where import Control.Monad (unless, when) import Control.Monad.Bayes.Class ( MonadDistribution (normal, uniformD), MonadMeasure, factor, ) import Control.Monad.Bayes.Sampler.Strict (sampleIOfixed) import Control.Monad.Bayes.Weighted (runWeightedT) import Data.AEq (AEq ((~==))) import Data.Bifunctor (second) import Numeric.Log (Log (Exp, ln)) model :: (MonadMeasure m) => m (Int, Double) model = do n <- uniformD [0, 1, 2] unless (n == 0) (factor 0.5) x <- if n == 0 then return 1 else normal 0 1 when (n == 2) (factor $ (Exp . log) (x * x)) return (n, x) result :: (MonadDistribution m) => m ((Int, Double), Double) result = second (exp . ln) <$> runWeightedT model passed :: IO Bool passed = fmap check (sampleIOfixed result) check :: ((Int, Double), Double) -> Bool check ((0, 1), 1) = True check ((1, _), y) = y ~== 0.5 check ((2, x), y) = y ~== 0.5 * x * x check _ = False ================================================ FILE: test/fixtures/HMM10-MH.txt ================================================ ["[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,2,1,1,1,1,1,1,0]","[1,1,1,2,1,2,1,1,2,0]","[1,1,1,2,1,2,1,1,2,0]","[1,1,1,2,1,2,1,1,2,0]","[1,1,1,2,1,2,1,1,2,0]","[1,1,1,2,1,2,1,1,2,0]","[1,1,1,2,1,2,1,1,2,0]","[1,1,1,2,1,2,1,1,2,0]","[1,1,1,2,1,2,1,1,2,0]","[1,1,1,2,1,2,1,1,2,0]","[1,1,1,2,1,2,1,1,2,0]","[1,2,1,1,1,2,1,1,2,0]","[1,2,1,1,1,2,1,1,2,0]","[1,2,1,1,1,2,1,1,2,0]","[1,2,1,1,1,2,1,1,2,0]","[1,2,1,1,1,2,1,1,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[1,2,1,1,1,1,1,2,2,0]","[2,2,1,1,1,1,1,2,2,0]","[2,2,1,1,1,2,1,1,2,0]","[2,2,1,1,1,2,1,1,2,0]","[2,2,1,1,1,2,1,1,2,0]","[2,2,1,1,1,2,1,1,2,0]","[2,2,1,1,1,2,1,1,2,0]","[2,1,2,1,1,2,1,1,2,0]","[2,1,2,1,1,2,1,1,2,0]","[2,1,2,1,1,2,1,1,2,0]","[2,1,2,1,1,2,1,1,2,0]","[1,1,2,1,1,2,1,1,2,0]","[1,2,1,1,1,2,1,1,2,0]","[1,2,1,1,1,2,1,1,2,0]","[1,1,1,1,1,2,1,1,2,0]","[1,1,1,1,1,2,1,1,2,0]","[1,1,1,1,1,2,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[1,1,1,1,1,0,1,1,2,0]","[0,1,1,1,1,0,1,1,2,0]","[0,1,1,1,1,0,1,1,2,0]","[0,1,1,1,1,0,1,1,2,0]","[0,1,1,1,1,0,1,1,2,0]","[0,1,1,1,1,0,1,1,2,0]","[0,1,1,1,1,0,1,1,2,0]","[0,1,1,1,1,0,1,1,2,0]","[0,1,1,1,1,0,1,1,2,0]"] ================================================ FILE: test/fixtures/HMM10-RMSMC.txt ================================================ [("[2,1,1,1,0,1,1,2,1,1]",2.4438034074800498e-8),("[1,1,1,1,1,2,1,1,1,0]",2.4438034074800498e-8),("[1,1,2,2,0,2,1,1,1,2]",2.4438034074800498e-8),("[1,1,2,2,0,2,1,1,1,2]",2.4438034074800498e-8),("[1,1,1,2,0,2,1,1,1,2]",2.4438034074800498e-8),("[1,1,2,2,0,2,1,1,1,2]",2.4438034074800498e-8),("[1,1,2,1,2,1,2,1,1,2]",2.4438034074800498e-8),("[1,1,2,1,2,1,2,1,1,2]",2.4438034074800498e-8),("[1,1,2,1,2,1,2,1,1,2]",2.4438034074800498e-8),("[1,2,1,1,2,2,0,1,1,1]",2.4438034074800498e-8)] ================================================ FILE: test/fixtures/HMM10-SMC.txt ================================================ [("[1,1,0,1,1,2,1,1,1,2]",2.964810681340389e-9),("[1,1,0,1,1,2,1,1,1,2]",2.964810681340389e-9),("[1,1,0,1,1,2,1,1,1,2]",2.964810681340389e-9),("[1,2,0,2,1,2,1,1,1,0]",2.964810681340389e-9),("[1,2,0,2,1,2,1,1,1,0]",2.964810681340389e-9),("[1,1,1,1,0,1,1,1,1,1]",2.964810681340389e-9),("[1,1,1,1,0,1,1,1,1,1]",2.964810681340389e-9),("[1,1,1,1,0,1,1,1,1,1]",2.964810681340389e-9),("[1,1,1,1,1,0,2,0,1,2]",2.964810681340389e-9),("[1,1,1,1,1,0,2,0,1,2]",2.964810681340389e-9),("[1,1,1,1,1,1,2,0,1,1]",2.964810681340389e-9),("[1,1,2,1,1,1,2,0,1,1]",2.964810681340389e-9),("[1,1,2,1,1,1,2,0,1,1]",2.964810681340389e-9),("[1,1,1,1,1,1,1,0,1,1]",2.964810681340389e-9),("[1,1,1,1,1,1,1,0,1,1]",2.964810681340389e-9),("[1,1,1,1,1,1,1,0,1,1]",2.964810681340389e-9),("[1,1,1,1,2,1,2,1,1,2]",2.964810681340389e-9),("[1,1,1,1,2,1,2,1,1,2]",2.964810681340389e-9),("[1,1,1,1,2,1,2,1,1,2]",2.964810681340389e-9),("[1,1,1,1,1,2,2,1,1,0]",2.964810681340389e-9),("[1,1,1,1,1,2,2,1,1,0]",2.964810681340389e-9),("[1,1,1,1,1,2,2,1,1,0]",2.964810681340389e-9),("[1,1,1,1,1,2,2,1,1,2]",2.964810681340389e-9),("[1,1,1,1,1,2,2,1,1,2]",2.964810681340389e-9),("[1,1,1,1,1,2,2,1,1,2]",2.964810681340389e-9),("[1,2,0,1,1,1,2,1,2,0]",2.964810681340389e-9),("[1,2,0,1,1,1,2,1,2,0]",2.964810681340389e-9),("[1,2,1,1,0,2,2,1,2,0]",2.964810681340389e-9),("[1,2,1,1,0,2,2,1,2,0]",2.964810681340389e-9),("[1,1,1,1,0,2,2,1,2,0]",2.964810681340389e-9),("[1,2,2,1,1,1,1,1,2,0]",2.964810681340389e-9),("[1,2,2,1,1,1,1,1,2,0]",2.964810681340389e-9),("[1,1,1,1,1,1,2,1,1,0]",2.964810681340389e-9),("[1,1,1,2,1,0,0,1,1,0]",2.964810681340389e-9),("[1,1,1,2,1,0,0,1,1,0]",2.964810681340389e-9),("[1,1,1,2,1,0,0,1,1,0]",2.964810681340389e-9),("[1,1,1,1,2,0,1,1,1,0]",2.964810681340389e-9),("[1,1,1,1,2,0,1,1,1,0]",2.964810681340389e-9),("[1,1,1,1,1,2,0,1,2,2]",2.964810681340389e-9),("[1,1,1,1,1,2,0,1,2,2]",2.964810681340389e-9),("[1,1,1,1,1,2,0,1,1,1]",2.964810681340389e-9),("[2,1,1,1,1,0,1,1,1,1]",2.964810681340389e-9),("[1,1,1,1,1,0,1,1,1,1]",2.964810681340389e-9),("[1,1,1,1,1,2,2,0,1,2]",2.964810681340389e-9),("[1,1,1,1,1,2,2,0,1,2]",2.964810681340389e-9),("[1,1,1,1,1,2,2,0,1,2]",2.964810681340389e-9),("[1,1,1,1,1,2,2,0,1,2]",2.964810681340389e-9),("[1,1,1,1,2,1,2,0,1,2]",2.964810681340389e-9),("[2,1,1,1,2,1,2,0,1,2]",2.964810681340389e-9),("[1,1,1,2,0,2,1,2,1,0]",2.964810681340389e-9),("[1,1,1,2,0,1,1,1,1,2]",2.964810681340389e-9),("[1,1,1,2,0,1,1,1,1,2]",2.964810681340389e-9),("[1,1,1,1,0,0,1,1,1,1]",2.964810681340389e-9),("[1,1,1,1,0,0,1,1,1,1]",2.964810681340389e-9),("[1,1,1,1,0,0,1,1,1,1]",2.964810681340389e-9),("[1,1,1,1,1,1,1,2,1,0]",2.964810681340389e-9),("[1,2,0,1,1,1,1,2,1,0]",2.964810681340389e-9),("[1,2,0,1,1,1,1,2,1,0]",2.964810681340389e-9),("[1,1,1,1,2,1,2,1,1,1]",2.964810681340389e-9),("[1,1,2,1,1,1,1,1,2,2]",2.964810681340389e-9),("[1,1,2,1,1,1,1,1,2,2]",2.964810681340389e-9),("[1,1,2,1,1,1,1,2,1,1]",2.964810681340389e-9),("[1,1,2,1,1,1,1,2,1,1]",2.964810681340389e-9),("[1,2,1,1,2,1,1,1,2,0]",2.964810681340389e-9),("[1,2,1,1,2,1,1,1,2,0]",2.964810681340389e-9),("[1,2,1,1,2,1,1,1,2,0]",2.964810681340389e-9),("[1,1,1,1,2,1,1,1,2,0]",2.964810681340389e-9),("[1,1,0,1,0,1,0,1,1,2]",2.964810681340389e-9),("[1,1,0,1,0,1,0,1,1,2]",2.964810681340389e-9),("[1,1,0,1,0,1,0,1,1,2]",2.964810681340389e-9),("[0,1,1,1,1,0,1,1,2,0]",2.964810681340389e-9),("[1,1,1,1,1,0,1,1,2,0]",2.964810681340389e-9),("[1,1,2,1,2,2,0,1,1,1]",2.964810681340389e-9),("[1,1,1,1,0,1,1,1,1,0]",2.964810681340389e-9),("[1,1,1,1,0,1,1,1,1,0]",2.964810681340389e-9),("[1,1,1,1,2,1,0,0,1,1]",2.964810681340389e-9),("[1,1,1,1,2,1,0,0,1,1]",2.964810681340389e-9),("[1,1,2,1,2,2,1,2,1,2]",2.964810681340389e-9),("[1,1,2,1,2,2,1,2,1,2]",2.964810681340389e-9),("[1,1,1,1,2,2,1,2,1,2]",2.964810681340389e-9),("[1,1,1,1,2,2,1,2,1,2]",2.964810681340389e-9),("[1,1,1,1,1,1,2,2,1,2]",2.964810681340389e-9),("[1,1,1,1,1,1,2,2,1,2]",2.964810681340389e-9),("[1,1,1,1,1,1,2,2,1,2]",2.964810681340389e-9),("[1,1,1,1,1,2,0,1,1,2]",2.964810681340389e-9),("[1,1,1,2,2,2,0,1,1,2]",2.964810681340389e-9),("[1,1,1,2,2,2,0,1,1,2]",2.964810681340389e-9),("[1,1,1,2,2,2,0,1,1,2]",2.964810681340389e-9),("[1,1,1,1,2,2,0,1,1,2]",2.964810681340389e-9),("[1,1,1,1,2,2,0,1,1,2]",2.964810681340389e-9),("[1,1,1,1,1,1,2,1,2,0]",2.964810681340389e-9),("[1,1,1,1,1,1,2,1,2,0]",2.964810681340389e-9),("[1,1,1,1,1,1,2,1,2,0]",2.964810681340389e-9),("[1,1,1,1,1,2,1,2,1,0]",2.964810681340389e-9),("[1,1,1,1,1,2,1,2,1,0]",2.964810681340389e-9),("[1,2,1,1,1,2,1,2,1,0]",2.964810681340389e-9),("[2,1,1,1,2,1,1,2,1,0]",2.964810681340389e-9),("[1,1,1,1,1,1,0,2,1,0]",2.964810681340389e-9),("[1,1,1,1,1,1,0,2,1,0]",2.964810681340389e-9),("[2,2,2,1,1,1,1,1,2,1]",2.964810681340389e-9)] ================================================ FILE: test/fixtures/LDA10-MH.txt ================================================ ["(fromList [(\"topic1\",[(\"bear\",1.4089582472383573e-2),(\"wolf\",7.763677263154158e-2),(\"python\",0.694917073788123),(\"prolog\",0.21335657110795198)]),(\"topic2\",[(\"bear\",0.28727305543035053),(\"wolf\",0.35728080053205036),(\"python\",0.26593974632470524),(\"prolog\",8.950639771289397e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.4089582472383573e-2),(\"wolf\",7.763677263154158e-2),(\"python\",0.694917073788123),(\"prolog\",0.21335657110795198)]),(\"topic2\",[(\"bear\",0.28727305543035053),(\"wolf\",0.35728080053205036),(\"python\",0.26593974632470524),(\"prolog\",8.950639771289397e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.4089582472383573e-2),(\"wolf\",7.763677263154158e-2),(\"python\",0.694917073788123),(\"prolog\",0.21335657110795198)]),(\"topic2\",[(\"bear\",0.28727305543035053),(\"wolf\",0.35728080053205036),(\"python\",0.26593974632470524),(\"prolog\",8.950639771289397e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.4089582472383573e-2),(\"wolf\",7.763677263154158e-2),(\"python\",0.694917073788123),(\"prolog\",0.21335657110795198)]),(\"topic2\",[(\"bear\",0.28727305543035053),(\"wolf\",0.35728080053205036),(\"python\",0.26593974632470524),(\"prolog\",8.950639771289397e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",1.4089582472383573e-2),(\"wolf\",7.763677263154158e-2),(\"python\",0.694917073788123),(\"prolog\",0.21335657110795198)]),(\"topic2\",[(\"bear\",0.28727305543035053),(\"wolf\",0.35728080053205036),(\"python\",0.26593974632470524),(\"prolog\",8.950639771289397e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",1.4089582472383573e-2),(\"wolf\",7.763677263154158e-2),(\"python\",0.694917073788123),(\"prolog\",0.21335657110795198)]),(\"topic2\",[(\"bear\",0.28727305543035053),(\"wolf\",0.35728080053205036),(\"python\",0.26593974632470524),(\"prolog\",8.950639771289397e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",1.4089582472383573e-2),(\"wolf\",7.763677263154158e-2),(\"python\",0.694917073788123),(\"prolog\",0.21335657110795198)]),(\"topic2\",[(\"bear\",0.28727305543035053),(\"wolf\",0.35728080053205036),(\"python\",0.26593974632470524),(\"prolog\",8.950639771289397e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",1.4089582472383573e-2),(\"wolf\",7.763677263154158e-2),(\"python\",0.694917073788123),(\"prolog\",0.21335657110795198)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",1.8296104002428423e-2),(\"wolf\",0.10081565364081875),(\"python\",0.6038329057458999),(\"prolog\",0.2770553366108529)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",1.8296104002428423e-2),(\"wolf\",0.10081565364081875),(\"python\",0.6038329057458999),(\"prolog\",0.2770553366108529)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",1.8296104002428423e-2),(\"wolf\",0.10081565364081875),(\"python\",0.6038329057458999),(\"prolog\",0.2770553366108529)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",1.8296104002428423e-2),(\"wolf\",0.10081565364081875),(\"python\",0.6038329057458999),(\"prolog\",0.2770553366108529)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",1.908222771066056e-2),(\"wolf\",6.2180645190366916e-2),(\"python\",0.629777629439783),(\"prolog\",0.28895949765918966)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",6.917861644240221e-2),(\"topic2\",0.9308213835575978)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35658871266764597),(\"topic2\",0.6434112873323541)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.23848662278652338),(\"topic2\",0.7615133772134766)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.174895228495006),(\"topic2\",0.8251047715049941)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.174895228495006),(\"topic2\",0.8251047715049941)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.174895228495006),(\"topic2\",0.8251047715049941)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.174895228495006),(\"topic2\",0.8251047715049941)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",4.693396178537035e-2),(\"topic2\",0.9530660382146297)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.174895228495006),(\"topic2\",0.8251047715049941)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.174895228495006),(\"topic2\",0.8251047715049941)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.174895228495006),(\"topic2\",0.8251047715049941)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.174895228495006),(\"topic2\",0.8251047715049941)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.174895228495006),(\"topic2\",0.8251047715049941)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.7441949161510276),(\"wolf\",0.12823178053541068),(\"python\",9.54485299393893e-2),(\"prolog\",3.2124773374172376e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",2.141341558601777e-2),(\"wolf\",6.977696823752828e-2),(\"python\",0.7067146619593686),(\"prolog\",0.20209495421708534)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",5.2449539316739855e-3),(\"topic2\",0.9947550460683261)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",8.794233725304898e-3),(\"topic2\",0.9912057662746951)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8676891481331068),(\"topic2\",0.13231085186689318)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2346165122377453),(\"topic2\",0.7653834877622547)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.952217831915492),(\"topic2\",4.778216808450798e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8996063324850888),(\"topic2\",0.10039366751491115)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2346165122377453),(\"topic2\",0.7653834877622547)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.952217831915492),(\"topic2\",4.778216808450798e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8996063324850888),(\"topic2\",0.10039366751491115)])])","(fromList [(\"topic1\",[(\"bear\",1.1018154566299667e-2),(\"wolf\",0.5213588834570013),(\"python\",0.3636361209382676),(\"prolog\",0.10398684103843148)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2346165122377453),(\"topic2\",0.7653834877622547)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.952217831915492),(\"topic2\",4.778216808450798e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8996063324850888),(\"topic2\",0.10039366751491115)])])","(fromList [(\"topic1\",[(\"bear\",6.964362003984619e-3),(\"wolf\",0.3295408479286923),(\"python\",0.5977667227866645),(\"prolog\",6.572806728065864e-2)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2346165122377453),(\"topic2\",0.7653834877622547)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.952217831915492),(\"topic2\",4.778216808450798e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8996063324850888),(\"topic2\",0.10039366751491115)])])","(fromList [(\"topic1\",[(\"bear\",6.964362003984619e-3),(\"wolf\",0.3295408479286923),(\"python\",0.5977667227866645),(\"prolog\",6.572806728065864e-2)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2346165122377453),(\"topic2\",0.7653834877622547)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.952217831915492),(\"topic2\",4.778216808450798e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8996063324850888),(\"topic2\",0.10039366751491115)])])","(fromList [(\"topic1\",[(\"bear\",6.964362003984619e-3),(\"wolf\",0.3295408479286923),(\"python\",0.5977667227866645),(\"prolog\",6.572806728065864e-2)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2346165122377453),(\"topic2\",0.7653834877622547)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.952217831915492),(\"topic2\",4.778216808450798e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8996063324850888),(\"topic2\",0.10039366751491115)])])","(fromList [(\"topic1\",[(\"bear\",6.964362003984619e-3),(\"wolf\",0.3295408479286923),(\"python\",0.5977667227866645),(\"prolog\",6.572806728065864e-2)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2346165122377453),(\"topic2\",0.7653834877622547)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.952217831915492),(\"topic2\",4.778216808450798e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8996063324850888),(\"topic2\",0.10039366751491115)])])","(fromList [(\"topic1\",[(\"bear\",6.964362003984619e-3),(\"wolf\",0.3295408479286923),(\"python\",0.5977667227866645),(\"prolog\",6.572806728065864e-2)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2346165122377453),(\"topic2\",0.7653834877622547)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.952217831915492),(\"topic2\",4.778216808450798e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8996063324850888),(\"topic2\",0.10039366751491115)])])","(fromList [(\"topic1\",[(\"bear\",6.964362003984619e-3),(\"wolf\",0.3295408479286923),(\"python\",0.5977667227866645),(\"prolog\",6.572806728065864e-2)]),(\"topic2\",[(\"bear\",0.67206655938002),(\"wolf\",0.11580338655540852),(\"python\",0.18311885626694854),(\"prolog\",2.9011197797622974e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",3.801790281431827e-2),(\"topic2\",0.9619820971856817)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",6.842555675627451e-2),(\"topic2\",0.9315744432437254)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2346165122377453),(\"topic2\",0.7653834877622547)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.952217831915492),(\"topic2\",4.778216808450798e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.961812709661957),(\"topic2\",3.818729033804298e-2)])])"] ================================================ FILE: test/fixtures/LDA10-RMSMC.txt ================================================ [("(fromList [(\"topic1\",[(\"bear\",0.2781038843406247),(\"wolf\",0.24113305845206004),(\"python\",9.91752372000113e-2),(\"prolog\",0.38158782000730385)]),(\"topic2\",[(\"bear\",0.17566639841583537),(\"wolf\",0.4102949762997866),(\"python\",4.180482337961342e-2),(\"prolog\",0.3722338019047647)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.9493543715937027),(\"topic2\",5.0645628406297175e-2)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.21960032583483474),(\"topic2\",0.7803996741651653)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.15452401070056435),(\"topic2\",0.8454759892994357)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32518687685958453),(\"topic2\",0.6748131231404155)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.48687311065156597),(\"topic2\",0.5131268893484341)])])",8.797732649220901e-36),("(fromList [(\"topic1\",[(\"bear\",0.2781038843406247),(\"wolf\",0.24113305845206004),(\"python\",9.91752372000113e-2),(\"prolog\",0.38158782000730385)]),(\"topic2\",[(\"bear\",0.17566639841583537),(\"wolf\",0.4102949762997866),(\"python\",4.180482337961342e-2),(\"prolog\",0.3722338019047647)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.8667701657488383),(\"topic2\",0.13322983425116178)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.21960032583483474),(\"topic2\",0.7803996741651653)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.15452401070056435),(\"topic2\",0.8454759892994357)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32518687685958453),(\"topic2\",0.6748131231404155)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.48687311065156597),(\"topic2\",0.5131268893484341)])])",8.797732649220901e-36),("(fromList [(\"topic1\",[(\"bear\",0.16315227606498015),(\"wolf\",0.1414629911201733),(\"python\",0.4715226263920085),(\"prolog\",0.2238621064228381)]),(\"topic2\",[(\"bear\",0.17566639841583537),(\"wolf\",0.4102949762997866),(\"python\",4.180482337961342e-2),(\"prolog\",0.3722338019047647)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.9493543715937027),(\"topic2\",5.0645628406297175e-2)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.21960032583483474),(\"topic2\",0.7803996741651653)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.15452401070056435),(\"topic2\",0.8454759892994357)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32518687685958453),(\"topic2\",0.6748131231404155)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.48687311065156597),(\"topic2\",0.5131268893484341)])])",8.797732649220901e-36),("(fromList [(\"topic1\",[(\"bear\",0.16315227606498015),(\"wolf\",0.1414629911201733),(\"python\",0.4715226263920085),(\"prolog\",0.2238621064228381)]),(\"topic2\",[(\"bear\",0.27478658473370615),(\"wolf\",0.6418049000124093),(\"python\",6.539329516329537e-2),(\"prolog\",1.8015220090589205e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.9493543715937027),(\"topic2\",5.0645628406297175e-2)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.21960032583483474),(\"topic2\",0.7803996741651653)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.15452401070056435),(\"topic2\",0.8454759892994357)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32518687685958453),(\"topic2\",0.6748131231404155)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.48687311065156597),(\"topic2\",0.5131268893484341)])])",8.797732649220901e-36),("(fromList [(\"topic1\",[(\"bear\",0.16315227606498015),(\"wolf\",0.1414629911201733),(\"python\",0.4715226263920085),(\"prolog\",0.2238621064228381)]),(\"topic2\",[(\"bear\",0.27478658473370615),(\"wolf\",0.6418049000124093),(\"python\",6.539329516329537e-2),(\"prolog\",1.8015220090589205e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.5510622572104782),(\"topic2\",0.44893774278952187)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.11510795718182044),(\"topic2\",0.8848920428181795)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.4686001824195545),(\"topic2\",0.5313998175804455)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6717889660960497),(\"topic2\",0.32821103390395034)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.2670680718669392),(\"topic2\",0.7329319281330607)])])",8.797732649220901e-36),("(fromList [(\"topic1\",[(\"bear\",0.16315227606498015),(\"wolf\",0.1414629911201733),(\"python\",0.4715226263920085),(\"prolog\",0.2238621064228381)]),(\"topic2\",[(\"bear\",0.27478658473370615),(\"wolf\",0.6418049000124093),(\"python\",6.539329516329537e-2),(\"prolog\",1.8015220090589205e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.9493543715937027),(\"topic2\",5.0645628406297175e-2)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.21960032583483474),(\"topic2\",0.7803996741651653)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.15452401070056435),(\"topic2\",0.8454759892994357)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32518687685958453),(\"topic2\",0.6748131231404155)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9645145666946825),(\"topic2\",3.548543330531747e-2)])])",8.797732649220901e-36),("(fromList [(\"topic1\",[(\"bear\",0.16315227606498015),(\"wolf\",0.1414629911201733),(\"python\",0.4715226263920085),(\"prolog\",0.2238621064228381)]),(\"topic2\",[(\"bear\",0.27478658473370615),(\"wolf\",0.6418049000124093),(\"python\",6.539329516329537e-2),(\"prolog\",1.8015220090589205e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.9493543715937027),(\"topic2\",5.0645628406297175e-2)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.21960032583483474),(\"topic2\",0.7803996741651653)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.15452401070056435),(\"topic2\",0.8454759892994357)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32518687685958453),(\"topic2\",0.6748131231404155)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7650916587070631),(\"topic2\",0.2349083412929369)])])",8.797732649220901e-36),("(fromList [(\"topic1\",[(\"bear\",0.16315227606498015),(\"wolf\",0.1414629911201733),(\"python\",0.4715226263920085),(\"prolog\",0.2238621064228381)]),(\"topic2\",[(\"bear\",0.27478658473370615),(\"wolf\",0.6418049000124093),(\"python\",6.539329516329537e-2),(\"prolog\",1.8015220090589205e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.9493543715937027),(\"topic2\",5.0645628406297175e-2)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.21960032583483474),(\"topic2\",0.7803996741651653)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.15452401070056435),(\"topic2\",0.8454759892994357)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32518687685958453),(\"topic2\",0.6748131231404155)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7650916587070631),(\"topic2\",0.2349083412929369)])])",8.797732649220901e-36),("(fromList [(\"topic1\",[(\"bear\",0.16315227606498015),(\"wolf\",0.1414629911201733),(\"python\",0.4715226263920085),(\"prolog\",0.2238621064228381)]),(\"topic2\",[(\"bear\",0.27478658473370615),(\"wolf\",0.6418049000124093),(\"python\",6.539329516329537e-2),(\"prolog\",1.8015220090589205e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.9493543715937027),(\"topic2\",5.0645628406297175e-2)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.21960032583483474),(\"topic2\",0.7803996741651653)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.15452401070056435),(\"topic2\",0.8454759892994357)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32518687685958453),(\"topic2\",0.6748131231404155)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.52776415174419),(\"topic2\",0.4722358482558099)])])",8.797732649220901e-36),("(fromList [(\"topic1\",[(\"bear\",0.16315227606498015),(\"wolf\",0.1414629911201733),(\"python\",0.4715226263920085),(\"prolog\",0.2238621064228381)]),(\"topic2\",[(\"bear\",0.27478658473370615),(\"wolf\",0.6418049000124093),(\"python\",6.539329516329537e-2),(\"prolog\",1.8015220090589205e-2)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.9493543715937027),(\"topic2\",5.0645628406297175e-2)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.21960032583483474),(\"topic2\",0.7803996741651653)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.15452401070056435),(\"topic2\",0.8454759892994357)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32518687685958453),(\"topic2\",0.6748131231404155)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.52776415174419),(\"topic2\",0.4722358482558099)])])",8.797732649220901e-36)] ================================================ FILE: test/fixtures/LDA10-SMC.txt ================================================ [("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.2634772352592057),(\"topic2\",0.7365227647407943)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.20068470549098844),(\"topic2\",0.7993152945090116)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.41454269168514973),(\"topic2\",0.5854573083148503)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8355127529929074),(\"topic2\",0.16448724700709264)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.2634772352592057),(\"topic2\",0.7365227647407943)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.20068470549098844),(\"topic2\",0.7993152945090116)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.41454269168514973),(\"topic2\",0.5854573083148503)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8355127529929074),(\"topic2\",0.16448724700709264)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.2634772352592057),(\"topic2\",0.7365227647407943)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.20068470549098844),(\"topic2\",0.7993152945090116)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.41454269168514973),(\"topic2\",0.5854573083148503)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8355127529929074),(\"topic2\",0.16448724700709264)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.2634772352592057),(\"topic2\",0.7365227647407943)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.20068470549098844),(\"topic2\",0.7993152945090116)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.41454269168514973),(\"topic2\",0.5854573083148503)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8355127529929074),(\"topic2\",0.16448724700709264)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.697550756779644),(\"topic2\",0.30244924322035605)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.965171190101216),(\"topic2\",3.482880989878406e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7060492271797333),(\"topic2\",0.2939507728202666)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.697550756779644),(\"topic2\",0.30244924322035605)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.965171190101216),(\"topic2\",3.482880989878406e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7060492271797333),(\"topic2\",0.2939507728202666)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.697550756779644),(\"topic2\",0.30244924322035605)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.965171190101216),(\"topic2\",3.482880989878406e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7060492271797333),(\"topic2\",0.2939507728202666)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.697550756779644),(\"topic2\",0.30244924322035605)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.965171190101216),(\"topic2\",3.482880989878406e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7060492271797333),(\"topic2\",0.2939507728202666)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.697550756779644),(\"topic2\",0.30244924322035605)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.965171190101216),(\"topic2\",3.482880989878406e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7060492271797333),(\"topic2\",0.2939507728202666)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.697550756779644),(\"topic2\",0.30244924322035605)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.965171190101216),(\"topic2\",3.482880989878406e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7060492271797333),(\"topic2\",0.2939507728202666)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.697550756779644),(\"topic2\",0.30244924322035605)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.965171190101216),(\"topic2\",3.482880989878406e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7060492271797333),(\"topic2\",0.2939507728202666)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.697550756779644),(\"topic2\",0.30244924322035605)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.965171190101216),(\"topic2\",3.482880989878406e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7060492271797333),(\"topic2\",0.2939507728202666)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",2.844753859121769e-2),(\"topic2\",0.9715524614087824)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.37965567186815763),(\"topic2\",0.6203443281318423)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.3540083496835881),(\"topic2\",0.6459916503164119)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",2.844753859121769e-2),(\"topic2\",0.9715524614087824)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.37965567186815763),(\"topic2\",0.6203443281318423)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.3540083496835881),(\"topic2\",0.6459916503164119)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",2.844753859121769e-2),(\"topic2\",0.9715524614087824)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.37965567186815763),(\"topic2\",0.6203443281318423)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.3540083496835881),(\"topic2\",0.6459916503164119)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2652913724654582),(\"topic2\",0.7347086275345417)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.47764843231101173),(\"topic2\",0.5223515676889883)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.6925579943073733),(\"topic2\",0.3074420056926267)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2652913724654582),(\"topic2\",0.7347086275345417)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.47764843231101173),(\"topic2\",0.5223515676889883)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.6925579943073733),(\"topic2\",0.3074420056926267)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2652913724654582),(\"topic2\",0.7347086275345417)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.47764843231101173),(\"topic2\",0.5223515676889883)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.6925579943073733),(\"topic2\",0.3074420056926267)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2652913724654582),(\"topic2\",0.7347086275345417)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.47764843231101173),(\"topic2\",0.5223515676889883)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.2292417221473923),(\"topic2\",0.7707582778526078)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2652913724654582),(\"topic2\",0.7347086275345417)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.47764843231101173),(\"topic2\",0.5223515676889883)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9646060460810639),(\"topic2\",3.5393953918936e-2)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.2652913724654582),(\"topic2\",0.7347086275345417)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.47764843231101173),(\"topic2\",0.5223515676889883)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9646060460810639),(\"topic2\",3.5393953918936e-2)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6791784514707125),(\"topic2\",0.32082154852928735)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.3870992946622541),(\"topic2\",0.6129007053377459)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6791784514707125),(\"topic2\",0.32082154852928735)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8509047569221565),(\"topic2\",0.14909524307784347)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6791784514707125),(\"topic2\",0.32082154852928735)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8509047569221565),(\"topic2\",0.14909524307784347)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6791784514707125),(\"topic2\",0.32082154852928735)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8509047569221565),(\"topic2\",0.14909524307784347)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6791784514707125),(\"topic2\",0.32082154852928735)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8509047569221565),(\"topic2\",0.14909524307784347)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6791784514707125),(\"topic2\",0.32082154852928735)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8509047569221565),(\"topic2\",0.14909524307784347)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.18895435275112443),(\"topic2\",0.8110456472488755)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.10735220295140778),(\"topic2\",0.8926477970485922)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8642486394294454),(\"topic2\",0.13575136057055465)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8642486394294454),(\"topic2\",0.13575136057055465)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8642486394294454),(\"topic2\",0.13575136057055465)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8642486394294454),(\"topic2\",0.13575136057055465)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9930888003417866),(\"topic2\",6.911199658213415e-3)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9930888003417866),(\"topic2\",6.911199658213415e-3)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9930888003417866),(\"topic2\",6.911199658213415e-3)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9930888003417866),(\"topic2\",6.911199658213415e-3)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9039948939737742),(\"topic2\",9.600510602622578e-2)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35729424423731515),(\"topic2\",0.6427057557626848)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35729424423731515),(\"topic2\",0.6427057557626848)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35729424423731515),(\"topic2\",0.6427057557626848)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35729424423731515),(\"topic2\",0.6427057557626848)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35729424423731515),(\"topic2\",0.6427057557626848)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1285103528481879),(\"topic2\",0.8714896471518121)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.4512829757824692),(\"topic2\",0.5487170242175309)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.35729424423731515),(\"topic2\",0.6427057557626848)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.5808491374569021),(\"topic2\",0.4191508625430979)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9446461959958755),(\"topic2\",5.535380400412449e-2)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.5808491374569021),(\"topic2\",0.4191508625430979)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9446461959958755),(\"topic2\",5.535380400412449e-2)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.5808491374569021),(\"topic2\",0.4191508625430979)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8641637601451785),(\"topic2\",0.1358362398548215)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.5808491374569021),(\"topic2\",0.4191508625430979)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8641637601451785),(\"topic2\",0.1358362398548215)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.5808491374569021),(\"topic2\",0.4191508625430979)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8641637601451785),(\"topic2\",0.1358362398548215)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9172894215885682),(\"topic2\",8.271057841143187e-2)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9172894215885682),(\"topic2\",8.271057841143187e-2)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7744969569641502),(\"topic2\",0.2255030430358497)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7744969569641502),(\"topic2\",0.2255030430358497)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7744969569641502),(\"topic2\",0.2255030430358497)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.6080285452173136),(\"topic2\",0.3919714547826863)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.6080285452173136),(\"topic2\",0.3919714547826863)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.6080285452173136),(\"topic2\",0.3919714547826863)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.6080285452173136),(\"topic2\",0.3919714547826863)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.323491635727712),(\"topic2\",0.676508364272288)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.6080285452173136),(\"topic2\",0.3919714547826863)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5245975070005406),(\"topic2\",0.47540249299945947)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5245975070005406),(\"topic2\",0.47540249299945947)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5245975070005406),(\"topic2\",0.47540249299945947)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5078739049652533),(\"topic2\",0.4921260950347467)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5078739049652533),(\"topic2\",0.4921260950347467)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5078739049652533),(\"topic2\",0.4921260950347467)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5078739049652533),(\"topic2\",0.4921260950347467)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5078739049652533),(\"topic2\",0.4921260950347467)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5078739049652533),(\"topic2\",0.4921260950347467)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",9.298735878604396e-2),(\"topic2\",0.907012641213956)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3839328816817768),(\"topic2\",0.6160671183182231)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5078739049652533),(\"topic2\",0.4921260950347467)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11200527806845688),(\"topic2\",0.8879947219315432)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.9445201216105829),(\"topic2\",5.5479878389417044e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5388215809473412),(\"topic2\",0.4611784190526587)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11200527806845688),(\"topic2\",0.8879947219315432)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.9445201216105829),(\"topic2\",5.5479878389417044e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.5388215809473412),(\"topic2\",0.4611784190526587)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11200527806845688),(\"topic2\",0.8879947219315432)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.9445201216105829),(\"topic2\",5.5479878389417044e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.33854346795237344),(\"topic2\",0.6614565320476266)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11200527806845688),(\"topic2\",0.8879947219315432)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.9445201216105829),(\"topic2\",5.5479878389417044e-2)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.33854346795237344),(\"topic2\",0.6614565320476266)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11200527806845688),(\"topic2\",0.8879947219315432)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3143392883693011),(\"topic2\",0.685660711630699)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7848587293144608),(\"topic2\",0.2151412706855392)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.47240464061393594),(\"topic2\",0.5275953593860641)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11200527806845688),(\"topic2\",0.8879947219315432)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.3143392883693011),(\"topic2\",0.685660711630699)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7848587293144608),(\"topic2\",0.2151412706855392)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1763598910823267),(\"topic2\",0.8236401089176733)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.7823253440734026),(\"topic2\",0.2176746559265975)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.9937415482106086),(\"topic2\",6.2584517893913665e-3)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1763598910823267),(\"topic2\",0.8236401089176733)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.7823253440734026),(\"topic2\",0.2176746559265975)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8008272802252768),(\"topic2\",0.19917271977472314)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1763598910823267),(\"topic2\",0.8236401089176733)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.7823253440734026),(\"topic2\",0.2176746559265975)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8008272802252768),(\"topic2\",0.19917271977472314)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.1763598910823267),(\"topic2\",0.8236401089176733)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.7823253440734026),(\"topic2\",0.2176746559265975)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8008272802252768),(\"topic2\",0.19917271977472314)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",6.015301862198376e-2),(\"topic2\",0.9398469813780163)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6133192521509675),(\"topic2\",0.3866807478490325)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7874320225653269),(\"topic2\",0.21256797743467318)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",6.015301862198376e-2),(\"topic2\",0.9398469813780163)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6133192521509675),(\"topic2\",0.3866807478490325)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7874320225653269),(\"topic2\",0.21256797743467318)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",6.015301862198376e-2),(\"topic2\",0.9398469813780163)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6133192521509675),(\"topic2\",0.3866807478490325)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8058502581083504),(\"topic2\",0.19414974189164957)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",6.015301862198376e-2),(\"topic2\",0.9398469813780163)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6133192521509675),(\"topic2\",0.3866807478490325)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8058502581083504),(\"topic2\",0.19414974189164957)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",6.015301862198376e-2),(\"topic2\",0.9398469813780163)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6133192521509675),(\"topic2\",0.3866807478490325)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8058502581083504),(\"topic2\",0.19414974189164957)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",6.015301862198376e-2),(\"topic2\",0.9398469813780163)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.6133192521509675),(\"topic2\",0.3866807478490325)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8058502581083504),(\"topic2\",0.19414974189164957)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.707057945542015),(\"topic2\",0.29294205445798505)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7950291577206223),(\"topic2\",0.20497084227937762)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.5034586205941756),(\"topic2\",0.49654137940582455)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",8.936715899518691e-2),(\"topic2\",0.9106328410048131)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.5034586205941756),(\"topic2\",0.49654137940582455)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",8.936715899518691e-2),(\"topic2\",0.9106328410048131)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32075379988749575),(\"topic2\",0.6792462001125041)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8836485209177353),(\"topic2\",0.11635147908226473)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32075379988749575),(\"topic2\",0.6792462001125041)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8836485209177353),(\"topic2\",0.11635147908226473)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32075379988749575),(\"topic2\",0.6792462001125041)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8836485209177353),(\"topic2\",0.11635147908226473)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32075379988749575),(\"topic2\",0.6792462001125041)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8836485209177353),(\"topic2\",0.11635147908226473)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32075379988749575),(\"topic2\",0.6792462001125041)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8836485209177353),(\"topic2\",0.11635147908226473)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32075379988749575),(\"topic2\",0.6792462001125041)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.8836485209177353),(\"topic2\",0.11635147908226473)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.5126775929912853),(\"wolf\",9.583969035940434e-2),(\"python\",0.11063230119114982),(\"prolog\",0.28085041545816053)]),(\"topic2\",[(\"bear\",0.1948611183111976),(\"wolf\",0.5036273719863957),(\"python\",0.14809948543424897),(\"prolog\",0.15341202426815767)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6530116082745558),(\"topic2\",0.34698839172544416)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.6507409151431025),(\"topic2\",0.34925908485689744)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.11049756649934124),(\"topic2\",0.8895024335006588)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.32075379988749575),(\"topic2\",0.6792462001125041)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",0.7404063685886478),(\"topic2\",0.25959363141135217)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.22414695309814184),(\"wolf\",0.36397967833281775),(\"python\",3.543477605736618e-2),(\"prolog\",0.37643859251167416)]),(\"topic2\",[(\"bear\",0.41133940400109575),(\"wolf\",0.13799674144155313),(\"python\",0.26930098135747826),(\"prolog\",0.18136287319987282)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6967249670088582),(\"topic2\",0.3032750329911418)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.4608178279094073),(\"topic2\",0.5391821720905928)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.856205109713736),(\"topic2\",0.14379489028626394)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.25991281762756324),(\"topic2\",0.7400871823724368)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",3.242694194982843e-2),(\"topic2\",0.9675730580501716)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.22414695309814184),(\"wolf\",0.36397967833281775),(\"python\",3.543477605736618e-2),(\"prolog\",0.37643859251167416)]),(\"topic2\",[(\"bear\",0.41133940400109575),(\"wolf\",0.13799674144155313),(\"python\",0.26930098135747826),(\"prolog\",0.18136287319987282)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6967249670088582),(\"topic2\",0.3032750329911418)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.4608178279094073),(\"topic2\",0.5391821720905928)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.856205109713736),(\"topic2\",0.14379489028626394)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.25991281762756324),(\"topic2\",0.7400871823724368)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",3.242694194982843e-2),(\"topic2\",0.9675730580501716)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.22414695309814184),(\"wolf\",0.36397967833281775),(\"python\",3.543477605736618e-2),(\"prolog\",0.37643859251167416)]),(\"topic2\",[(\"bear\",0.41133940400109575),(\"wolf\",0.13799674144155313),(\"python\",0.26930098135747826),(\"prolog\",0.18136287319987282)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6967249670088582),(\"topic2\",0.3032750329911418)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.4608178279094073),(\"topic2\",0.5391821720905928)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.856205109713736),(\"topic2\",0.14379489028626394)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.25991281762756324),(\"topic2\",0.7400871823724368)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",3.242694194982843e-2),(\"topic2\",0.9675730580501716)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.22414695309814184),(\"wolf\",0.36397967833281775),(\"python\",3.543477605736618e-2),(\"prolog\",0.37643859251167416)]),(\"topic2\",[(\"bear\",0.41133940400109575),(\"wolf\",0.13799674144155313),(\"python\",0.26930098135747826),(\"prolog\",0.18136287319987282)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6967249670088582),(\"topic2\",0.3032750329911418)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.4608178279094073),(\"topic2\",0.5391821720905928)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.856205109713736),(\"topic2\",0.14379489028626394)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.25991281762756324),(\"topic2\",0.7400871823724368)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",3.242694194982843e-2),(\"topic2\",0.9675730580501716)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.22414695309814184),(\"wolf\",0.36397967833281775),(\"python\",3.543477605736618e-2),(\"prolog\",0.37643859251167416)]),(\"topic2\",[(\"bear\",0.41133940400109575),(\"wolf\",0.13799674144155313),(\"python\",0.26930098135747826),(\"prolog\",0.18136287319987282)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6967249670088582),(\"topic2\",0.3032750329911418)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.4608178279094073),(\"topic2\",0.5391821720905928)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.856205109713736),(\"topic2\",0.14379489028626394)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.25991281762756324),(\"topic2\",0.7400871823724368)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",3.242694194982843e-2),(\"topic2\",0.9675730580501716)])])",6.62470063222773e-33),("(fromList [(\"topic1\",[(\"bear\",0.22414695309814184),(\"wolf\",0.36397967833281775),(\"python\",3.543477605736618e-2),(\"prolog\",0.37643859251167416)]),(\"topic2\",[(\"bear\",0.41133940400109575),(\"wolf\",0.13799674144155313),(\"python\",0.26930098135747826),(\"prolog\",0.18136287319987282)])],[(\"bear prolog prolog wolf prolog bear wolf bear bear prolog\",[(\"topic1\",0.6967249670088582),(\"topic2\",0.3032750329911418)]),(\"wolf prolog bear wolf wolf python bear bear wolf bear\",[(\"topic1\",0.4608178279094073),(\"topic2\",0.5391821720905928)]),(\"wolf prolog wolf wolf wolf wolf prolog python python bear\",[(\"topic1\",0.856205109713736),(\"topic2\",0.14379489028626394)]),(\"bear prolog prolog bear wolf bear python wolf prolog wolf\",[(\"topic1\",0.25991281762756324),(\"topic2\",0.7400871823724368)]),(\"bear bear prolog bear python bear wolf python prolog prolog\",[(\"topic1\",3.242694194982843e-2),(\"topic2\",0.9675730580501716)])])",6.62470063222773e-33)] ================================================ FILE: test/fixtures/LR10-MH.txt ================================================ ["2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","2.950544941215132e-6","4.417248310074391e-5","4.417248310074391e-5","4.417248310074391e-5","4.417248310074391e-5","4.417248310074391e-5","4.417248310074391e-5","4.417248310074391e-5","4.417248310074391e-5","3.542845305464785e-4","3.542845305464785e-4","3.542845305464785e-4","3.542845305464785e-4","3.542845305464785e-4","3.542845305464785e-4","3.542845305464785e-4","3.542845305464785e-4","3.542845305464785e-4","3.542845305464785e-4","1.283996726025695e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","5.8758436338617424e-6","0.21036341524185248","0.21036341524185248","0.21036341524185248","0.11759542598406439","0.11759542598406439","0.11759542598406439","0.11759542598406439","0.11759542598406439","0.11759542598406439","4.7312512236572256e-2","4.7312512236572256e-2","4.7312512236572256e-2","4.7312512236572256e-2","0.4947233726255189","0.4947233726255189","0.38932500660802716","0.38932500660802716","0.38932500660802716","0.38932500660802716","0.7207054705150296","0.6553804082004575","0.6553804082004575","0.6553804082004575","0.6553804082004575","0.8884089778243283","0.8884089778243283","0.8884089778243283","7.972233473120037e-3","7.972233473120037e-3","7.972233473120037e-3","7.972233473120037e-3","7.972233473120037e-3","7.972233473120037e-3","7.972233473120037e-3","7.972233473120037e-3","7.972233473120037e-3","0.9255022758580312","0.6388734801405944","2.2127051202286738e-5","2.2127051202286738e-5","5.340807350374084e-6","5.340807350374084e-6","5.340807350374084e-6","5.340807350374084e-6","5.340807350374084e-6","5.340807350374084e-6","5.340807350374084e-6","5.340807350374084e-6","5.340807350374084e-6","5.181558495552509e-6","5.181558495552509e-6","5.1274144757719514e-6","5.1274144757719514e-6","5.1274144757719514e-6","5.1274144757719514e-6","5.1274144757719514e-6","5.1274144757719514e-6"] ================================================ FILE: test/fixtures/LR10-RMSMC.txt ================================================ [("5.333997036871341e-6",6.253483531321334e-5),("4.7394126422869915e-6",6.253483531321334e-5),("7.798412010176118e-6",6.253483531321334e-5),("3.6537314189667804e-3",6.253483531321334e-5),("2.8719951155583437e-3",6.253483531321334e-5),("8.106430574751586e-3",6.253483531321334e-5),("0.8083297132912659",6.253483531321334e-5),("0.3178833271596258",6.253483531321334e-5),("0.3487591395231385",6.253483531321334e-5),("2.027192522287899e-2",6.253483531321334e-5)] ================================================ FILE: test/fixtures/LR10-SMC.txt ================================================ [("2.324831917751426e-5",8.615785653338886e-6),("4.0753397495054896e-8",8.615785653338886e-6),("7.274125904873279e-9",8.615785653338886e-6),("1.3743372716534289e-5",8.615785653338886e-6),("4.254983717992536e-6",8.615785653338886e-6),("1.2272127506266697e-6",8.615785653338886e-6),("1.214022726807993e-7",8.615785653338886e-6),("8.847449654106116e-8",8.615785653338886e-6),("1.6000818494452112e-3",8.615785653338886e-6),("2.0279043319035277e-3",8.615785653338886e-6),("3.5018249880037834e-5",8.615785653338886e-6),("0.16426780827561477",8.615785653338886e-6),("0.10773403426050324",8.615785653338886e-6),("0.3304703458482792",8.615785653338886e-6),("0.10913515995295375",8.615785653338886e-6),("3.8113707417415554e-7",8.615785653338886e-6),("1.1707029254682367e-4",8.615785653338886e-6),("2.047893831024706e-3",8.615785653338886e-6),("1.8725448138433734e-3",8.615785653338886e-6),("6.053601828693627e-2",8.615785653338886e-6),("0.1757594676547173",8.615785653338886e-6),("1.3511280746237259e-3",8.615785653338886e-6),("0.23723482733424164",8.615785653338886e-6),("0.16151469556914314",8.615785653338886e-6),("7.191080175022858e-3",8.615785653338886e-6),("1.6108325498387603e-7",8.615785653338886e-6),("1.6659054333722745e-7",8.615785653338886e-6),("4.28535218704211e-4",8.615785653338886e-6),("4.305836021545625e-4",8.615785653338886e-6),("4.7576965500979653e-4",8.615785653338886e-6),("4.4539756971784895e-4",8.615785653338886e-6),("3.96355147105004e-4",8.615785653338886e-6),("2.91792488057767e-3",8.615785653338886e-6),("0.1387157529569227",8.615785653338886e-6),("0.12679393576798442",8.615785653338886e-6),("0.23010548225976238",8.615785653338886e-6),("0.25223594655800285",8.615785653338886e-6),("6.97387528630698e-5",8.615785653338886e-6),("9.333831289852731e-5",8.615785653338886e-6),("8.375626668711192e-5",8.615785653338886e-6),("1.5213683176568195e-2",8.615785653338886e-6),("3.5712314627571923e-4",8.615785653338886e-6),("7.636217014509781e-4",8.615785653338886e-6),("2.0872090258618442e-4",8.615785653338886e-6),("5.196909883379355e-4",8.615785653338886e-6),("5.701798151154428e-4",8.615785653338886e-6),("5.093049208576314e-4",8.615785653338886e-6),("1.4278233878183234e-2",8.615785653338886e-6),("0.409063870280768",8.615785653338886e-6),("2.413899012911671e-2",8.615785653338886e-6),("4.9820455672153804e-2",8.615785653338886e-6),("2.3987373289798696e-3",8.615785653338886e-6),("2.3730997104438635e-3",8.615785653338886e-6),("1.5421674679073712e-2",8.615785653338886e-6),("6.127942201533583e-4",8.615785653338886e-6),("0.29100637723657274",8.615785653338886e-6),("6.466851911961999e-4",8.615785653338886e-6),("6.899429519546635e-4",8.615785653338886e-6),("6.796536628553891e-4",8.615785653338886e-6),("1.5687561944396283e-2",8.615785653338886e-6),("1.0114194783259966e-2",8.615785653338886e-6),("8.650936462496627e-4",8.615785653338886e-6),("8.659503706938266e-4",8.615785653338886e-6),("0.9866480964309671",8.615785653338886e-6),("0.9844641052458803",8.615785653338886e-6),("0.9877580766923669",8.615785653338886e-6),("0.3570814146622049",8.615785653338886e-6),("0.3333232242467457",8.615785653338886e-6),("0.28788395991944565",8.615785653338886e-6),("0.6235083846866266",8.615785653338886e-6),("2.9830083243998794e-2",8.615785653338886e-6),("0.6715098168079819",8.615785653338886e-6),("0.5092770566793119",8.615785653338886e-6),("0.14931145218944078",8.615785653338886e-6),("0.9476390701984468",8.615785653338886e-6),("0.19844654340805706",8.615785653338886e-6),("0.8692525556246279",8.615785653338886e-6),("0.2857288027412788",8.615785653338886e-6),("0.9128866108374706",8.615785653338886e-6),("0.5972931052418653",8.615785653338886e-6),("3.185136062093685e-2",8.615785653338886e-6),("0.7815143883956358",8.615785653338886e-6),("9.045285261453962e-4",8.615785653338886e-6),("1.0570963598552236e-4",8.615785653338886e-6),("0.9988704519081596",8.615785653338886e-6),("0.9983306238100842",8.615785653338886e-6),("0.999817180959221",8.615785653338886e-6),("0.999632323087958",8.615785653338886e-6),("0.9978593357863148",8.615785653338886e-6),("0.9932458970319142",8.615785653338886e-6),("0.9997633989820984",8.615785653338886e-6),("0.25474879877870016",8.615785653338886e-6),("0.12254684366442331",8.615785653338886e-6),("6.952221863096741e-2",8.615785653338886e-6),("0.38421927032809844",8.615785653338886e-6),("0.13255536658985864",8.615785653338886e-6),("0.1506084845751688",8.615785653338886e-6),("0.3502412842110343",8.615785653338886e-6),("1.28565002377979e-2",8.615785653338886e-6),("0.3782237084074611",8.615785653338886e-6)] ================================================ FILE: test/fixtures/SSM-PMMH.txt ================================================ [[([74405.69500410178,143777.3515026691,195290.64675632896,483878.28639985673,600603.4104497777],1.0),([74405.69500410178,143777.3515026691,195290.64675632896,483878.28639985673,600603.4104497777],1.0),([74405.69500410178,143777.3515026691,195290.64675632896,483878.28639985673,600603.4104497777],1.0)],[([157620.04097610444,26661.523636563594,321204.4219216401,421274.0528523404,487363.3134055787],1.0),([157620.04097610444,26661.523636563594,321204.4219216401,421274.0528523404,487363.3134055787],1.0),([157620.04097610444,26661.523636563594,321204.4219216401,421274.0528523404,487363.3134055787],1.0)],[([-1.2600621067470811e67,-1.3171618074660135e67,3.55155213532486e66,-9.486041679240111e66,-1.4476178361450074e67],1.0),([-1.2600621067470811e67,-1.3171618074660135e67,3.55155213532486e66,-9.486041679240111e66,-1.4476178361450074e67],1.0),([-1.2600621067470811e67,-1.3171618074660135e67,3.55155213532486e66,-9.486041679240111e66,-1.4476178361450074e67],1.0)]] ================================================ FILE: test/fixtures/SSM-RMSMC.txt ================================================ [([-2.660097878548362e12,4.2406657192899927e11,5.124397279021509e11,-1.5388049692223555e12,5.100591467413694e11],1.7934178371940385e-141),([85360.89249769927,82937.71043376798,236528.18865034508,184344.32920611685,257818.8194711436],1.7934178371940385e-141),([111456.98861817457,175718.5311026478,-123025.40752936345,-216315.66615773254,-216313.3788230968],1.7934178371940385e-141),([19634.90868499715,3982.228157420627,71064.25154723959,-61863.47069790381,-124170.21806752698],1.7934178371940385e-141),([-1.5475119433556266e13,7.505784271326075e13,1.8087989772377637e13,2.3121907140178836e13,6.957846697896459e13],1.7934178371940385e-141),([3.3114425601587117e13,-7.174858488587559e12,-5.3895766067097984e13,-5.574198391310134e13,-7.085806871739197e13],1.7934178371940385e-141),([1.7797637411011798e9,2.257078068208236e9,1.7822875201019692e9,-3.1059378475534713e8,-2.022387835329388e8],1.7934178371940385e-141),([-362.4859209624216,597.209862141266,354.2417236032639,699.0287190356412,-719.9079388224256],1.7934178371940385e-141),([-6962602.171312882,4072501.6094401204,-1935864.3227236385,6378383.05623946,2107530.9972696295],1.7934178371940385e-141),([535.0932822165735,229.4681249747279,-318.4034398226934,-425.6731795563692,-736.158075205745],1.7934178371940385e-141)] ================================================ FILE: test/fixtures/SSM-RMSMCBasic.txt ================================================ [([-2.660097878548362e12,4.2406657192899927e11,5.124397279021509e11,-1.5388049692223555e12,5.100591467413694e11],1.7934178371940385e-141),([85360.89249769927,82937.71043376798,236528.18865034508,184344.32920611685,257818.8194711436],1.7934178371940385e-141),([111456.98861817457,175718.5311026478,-123025.40752936345,-216315.66615773254,-216313.3788230968],1.7934178371940385e-141),([19634.90868499715,3982.228157420627,71064.25154723959,-61863.47069790381,-124170.21806752698],1.7934178371940385e-141),([-1.5475119433556266e13,7.505784271326075e13,1.8087989772377637e13,2.3121907140178836e13,6.957846697896459e13],1.7934178371940385e-141),([3.3114425601587117e13,-7.174858488587559e12,-5.3895766067097984e13,-5.574198391310134e13,-7.085806871739197e13],1.7934178371940385e-141),([1.7797637411011798e9,2.257078068208236e9,1.7822875201019692e9,-3.1059378475534713e8,-2.022387835329388e8],1.7934178371940385e-141),([-362.4859209624216,597.209862141266,354.2417236032639,699.0287190356412,-719.9079388224256],1.7934178371940385e-141),([-6962602.171312882,4072501.6094401204,-1935864.3227236385,6378383.05623946,2107530.9972696295],1.7934178371940385e-141),([535.0932822165735,229.4681249747279,-318.4034398226934,-425.6731795563692,-736.158075205745],1.7934178371940385e-141)] ================================================ FILE: test/fixtures/SSM-RMSMCDynamic.txt ================================================ [([61234.923743603955,79039.83817954235,354024.81636628765,225755.73057039993,-78843.37322818518],0.0),([61234.923743603955,-205024.66324964678,-438520.7645656072,-526045.6062936985,17959.08713788638],0.0),([61234.923743603955,130707.33129683959,260276.7204227042,538891.1815485102,432537.1717560617],0.0),([61234.923743603955,425968.16738967673,72802.89417099475,97062.29318414515,-90904.59187690681],0.0),([61234.923743603955,-80888.00179367141,122235.67304475381,48742.27626015559,-149682.32933231423],0.0),([61234.923743603955,43.833902800088254,-417728.0201965655,49565.634594935604,-303943.3354524304],0.0),([61234.923743603955,350501.69936972257,118986.06426751378,99950.78931739656,-60488.53431816819],0.0),([61234.923743603955,-117376.5868812376,116017.94360094423,378976.39475725644,74865.6296219704],0.0),([61234.923743603955,156368.9791422615,-586653.2615030725,-238480.82081038723,51581.15175237715],0.0),([61234.923743603955,-150776.59937461224,-30862.03908288705,200382.13919586508,-107135.36343350058],0.0)] ================================================ FILE: test/fixtures/SSM-SMC.txt ================================================ [([-1.6946443595984358e8,-2.0398900541476977e8,5.988104418627801e8,5.186441087015647e7,-1.1107580460544899e9],3.747925572660412e-147),([-1.6946443595984358e8,1.762322765772586e8,1.3143034131110222e9,2.917359439754021e7,-4.678360689283452e8],3.747925572660412e-147),([-1.6946443595984358e8,-4.978125179866476e8,-6.568379081060445e8,-1.0039010467494124e9,-4.5194462919398534e8],3.747925572660412e-147),([-1.6946443595984358e8,-2.0398900541476977e8,6.65407483202134e8,-1.3610874802534976e9,1.7804869696064534e9],3.747925572660412e-147),([-1.6946443595984358e8,-4.978125179866476e8,-6.568379081060445e8,-1.0039010467494124e9,-4.5194462919398534e8],3.747925572660412e-147),([-1.6946443595984358e8,-7.848111477226721e8,-1.536250656089418e9,-1.2593852525318892e9,9.33478070563457e8],3.747925572660412e-147),([-1.6946443595984358e8,1.762322765772586e8,1.3143034131110222e9,2.917359439754021e7,-4.678360689283452e8],3.747925572660412e-147),([-1.6946443595984358e8,-4.978125179866476e8,-6.568379081060445e8,7.201669253635451e8,-6.528627637915363e8],3.747925572660412e-147),([-1.6946443595984358e8,-4.978125179866476e8,-6.568379081060445e8,7.201669253635451e8,-6.528627637915363e8],3.747925572660412e-147),([-1.6946443595984358e8,-7.848111477226721e8,-1.536250656089418e9,-1.2593852525318892e9,9.33478070563457e8],3.747925572660412e-147)] ================================================ FILE: test/fixtures/SSM-SMC2.txt ================================================ [([([-9090.483553160731,-18364.240866577857,38447.317849110055,-3829.950678281628,-18689.938602553048],0.3333333333333341),([-9090.483553160731,-18364.240866577857,38447.317849110055,-3829.950678281628,-18689.938602553048],0.3333333333333341),([-9090.483553160731,-18364.240866577857,38447.317849110055,25131.836867847727,47603.03068211828],0.3333333333333341)],9.474658864966518e-180),([([-2.2418721864335723e9,2.4219211687208967e9,5.4463793547824545e9,7.074651672385337e8,2.595090695345872e8],0.3333333333333341),([-2.2418721864335723e9,2.4219211687208967e9,5.4463793547824545e9,7.074651672385337e8,2.595090695345872e8],0.3333333333333341),([-2.2418721864335723e9,2.4219211687208967e9,5.4463793547824545e9,7.074651672385337e8,2.595090695345872e8],0.3333333333333341)],9.474658864966518e-180)]