[
  {
    "path": ".github/FUNDING.yml",
    "content": "# These are supported funding model platforms\n\ngithub: ctgk # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]\n"
  },
  {
    "path": ".github/workflows/run-notebooks.yaml",
    "content": "# This workflow will install Python dependencies, run full checks defined in `.pre-commit-config.yaml` with a variety of Python versions\n# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions\n\nname: Jupyter Notebooks\n\non:\n  push:\n    branches: [ main, develop ]\n  pull_request:\n    branches: [ main, develop ]\n  schedule:\n    # run every month on 6th day\n    - cron: '0 0 6 * *'\n\njobs:\n  jupyter-notebooks:\n\n    runs-on: ubuntu-latest\n    strategy:\n      matrix:\n        python-version: ['3.9', '3.10', '3.11', '3.12']\n\n    steps:\n    - uses: actions/checkout@v2\n    - name: Set up Python ${{ matrix.python-version }}\n      uses: actions/setup-python@v2\n      with:\n        python-version: ${{ matrix.python-version }}\n    - name: Upgrade pip\n      run: python -m pip install --upgrade pip\n    - name: Install packages\n      run: pip install .[develop]\n    - name: Run Jupyter notebooks\n      run: for note in `ls notebooks/*.ipynb`; do jupyter nbconvert --to html --execute $note; if [ $? != 0 ]; then echo $note; exit 1; fi; done\n"
  },
  {
    "path": ".gitignore",
    "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# IPython Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n\n# VSCode project settings\n.vscode/\n"
  },
  {
    "path": ".pre-commit-config.yaml",
    "content": "# See https://pre-commit.com for more information\n# See https://pre-commit.com/hooks.html for more hooks\nrepos:\n-   repo: https://github.com/pre-commit/pre-commit-hooks\n    rev: v3.2.0\n    hooks:\n    -   id: trailing-whitespace\n    -   id: end-of-file-fixer\n    -   id: check-yaml\n    -   id: check-added-large-files\n-   repo: local\n    hooks:\n    -   id: assert_ascii\n        language: system\n        name: Check file encoding\n        entry: bash -c 'for file in \"$@\"; do file --mime-encoding $file | grep -q \"ascii\\|binary\"; if [ $? != 0 ]; then echo $file; exit 1; fi; done' --\n        types: [text]\n    -   id: flake8\n        name: Check Python format\n        entry: flake8 --count --show-source --statistics\n        language: system\n        types: [python]\n    -   id: unittest\n        name: Run Python unittests\n        language: system\n        entry: python setup.py test\n        pass_filenames: false\n"
  },
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2018 ctgk\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "# PRML\nPython codes implementing algorithms described in Bishop's book \"Pattern Recognition and Machine Learning\"\n\n## Required Packages\n- python 3\n- numpy\n- scipy\n- jupyter (optional: to run jupyter notebooks)\n- matplotlib (optional: to plot results in the notebooks)\n- sklearn (optional: to fetch data)\n\n## Notebooks\n\nThe notebooks in this repository can be viewed with nbviewer or other tools, or you can use [Amazon SageMaker Studio Lab](https://studiolab.sagemaker.aws/), a free computing environment on AWS (prior [registration with an email address](https://studiolab.sagemaker.aws/requestAccount) is required. Please refer to [this document](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lab-onboard.html) for usage).\n\nFrom the table below, you can open the notebooks for each chapter in each of these environments.\n\n|nbviewer|Amazon SageMaker Studio Lab|\n|:-------|:--------------------------:|\n|[ch1. Introduction](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch01_Introduction.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch01_Introduction.ipynb)|\n|[ch2. Probability Distributions](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch02_Probability_Distributions.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch02_Probability_Distributions.ipynb)|\n|[ch3. Linear Models for Regression](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch03_Linear_Models_for_Regression.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch03_Linear_Models_for_Regression.ipynb)|\n|[ch4. Linear Models for Classification](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch04_Linear_Models_for_Classfication.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch04_Linear_Models_for_Classfication.ipynb)|\n|[ch5. Neural Networks](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch05_Neural_Networks.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch05_Neural_Networks.ipynb)|\n|[ch6. Kernel Methods](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch06_Kernel_Methods.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch06_Kernel_Methods.ipynb)|\n|[ch7. Sparse Kernel Machines](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch07_Sparse_Kernel_Machines.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch07_Sparse_Kernel_Machines.ipynb)|\n|[ch8. Graphical Models](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch08_Graphical_Models.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch08_Graphical_Models.ipynb)|\n|[ch9. Mixture Models and EM](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch09_Mixture_Models_and_EM.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch09_Mixture_Models_and_EM.ipynb)|\n|[ch10. Approximate Inference](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch10_Approximate_Inference.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch10_Approximate_Inference.ipynb)|\n|[ch11. Sampling Methods](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch11_Sampling_Methods.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch11_Sampling_Methods.ipynb)|\n|[ch12. Continuous Latent Variables](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch12_Continuous_Latent_Variables.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch12_Continuous_Latent_Variables.ipynb)|\n|[ch13. Sequential Data](https://nbviewer.jupyter.org/github/ctgk/PRML/blob/main/notebooks/ch13_Sequential_Data.ipynb)|[![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/ctgk/PRML/blob/main/notebooks/ch13_Sequential_Data.ipynb)|\n\nIf you use the SageMaker Studio Lab, open a terminal and execute the following commands to install the required libraries.\n\n```bash\nconda env create -f environment.yaml  # might be optional\nconda activate prml\npython setup.py install\n```\n"
  },
  {
    "path": "environment.yaml",
    "content": "# run: conda env create -n environment.yaml\n#      conda activate prml\n#      python setup.py install\nname: prml\ndependencies:\n  - numpy\n  - pandas\n  - scipy\n  - scikit-learn\n  - matplotlib\n  - ipykernel\n  - ffmpeg\n"
  },
  {
    "path": "notebooks/ch01_Introduction.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1. Introduction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.preprocess import PolynomialFeature\\n\",\n    \"from prml.linear import (\\n\",\n    \"    LinearRegression,\\n\",\n    \"    RidgeRegression,\\n\",\n    \"    BayesianRegression\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1.1. Example: Polynomial Curve Fitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data(func, sample_size, std):\\n\",\n    \"    x = np.linspace(0, 1, sample_size)\\n\",\n    \"    t = func(x) + np.random.normal(scale=std, size=x.shape)\\n\",\n    \"    return x, t\\n\",\n    \"\\n\",\n    \"def func(x):\\n\",\n    \"    return np.sin(2 * np.pi * x)\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data(func, 10, 0.25)\\n\",\n    \"x_test = np.linspace(0, 1, 100)\\n\",\n    \"y_test = func(x_test)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", s=50, label=\\\"training data\\\")\\n\",\n    \"plt.plot(x_test, y_test, c=\\\"g\\\", label=\\\"$\\\\sin(2\\\\pi x)$\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i, degree in enumerate([0, 1, 3, 9]):\\n\",\n    \"    plt.subplot(2, 2, i + 1)\\n\",\n    \"    feature = PolynomialFeature(degree)\\n\",\n    \"    X_train = feature.transform(x_train)\\n\",\n    \"    X_test = feature.transform(x_test)\\n\",\n    \"\\n\",\n    \"    model = LinearRegression()\\n\",\n    \"    model.fit(X_train, y_train)\\n\",\n    \"    y = model.predict(X_test)\\n\",\n    \"\\n\",\n    \"    plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", s=50, label=\\\"training data\\\")\\n\",\n    \"    plt.plot(x_test, y_test, c=\\\"g\\\", label=\\\"$\\\\sin(2\\\\pi x)$\\\")\\n\",\n    \"    plt.plot(x_test, y, c=\\\"r\\\", label=\\\"fitting\\\")\\n\",\n    \"    plt.ylim(-1.5, 1.5)\\n\",\n    \"    plt.annotate(\\\"M={}\\\".format(degree), xy=(-0.15, 1))\\n\",\n    \"plt.legend(bbox_to_anchor=(1.05, 0.64), loc=2, borderaxespad=0.)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def rmse(a, b):\\n\",\n    \"    return np.sqrt(np.mean(np.square(a - b)))\\n\",\n    \"\\n\",\n    \"training_errors = []\\n\",\n    \"test_errors = []\\n\",\n    \"\\n\",\n    \"for i in range(10):\\n\",\n    \"    feature = PolynomialFeature(i)\\n\",\n    \"    X_train = feature.transform(x_train)\\n\",\n    \"    X_test = feature.transform(x_test)\\n\",\n    \"\\n\",\n    \"    model = LinearRegression()\\n\",\n    \"    model.fit(X_train, y_train)\\n\",\n    \"    y = model.predict(X_test)\\n\",\n    \"    training_errors.append(rmse(model.predict(X_train), y_train))\\n\",\n    \"    test_errors.append(rmse(model.predict(X_test), y_test + np.random.normal(scale=0.25, size=len(y_test))))\\n\",\n    \"\\n\",\n    \"plt.plot(training_errors, 'o-', mfc=\\\"none\\\", mec=\\\"b\\\", ms=10, c=\\\"b\\\", label=\\\"Training\\\")\\n\",\n    \"plt.plot(test_errors, 'o-', mfc=\\\"none\\\", mec=\\\"r\\\", ms=10, c=\\\"r\\\", label=\\\"Test\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.xlabel(\\\"degree\\\")\\n\",\n    \"plt.ylabel(\\\"RMSE\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Regularization\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"feature = PolynomialFeature(9)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X_test = feature.transform(x_test)\\n\",\n    \"\\n\",\n    \"model = RidgeRegression(alpha=1e-3)\\n\",\n    \"model.fit(X_train, y_train)\\n\",\n    \"y = model.predict(X_test)\\n\",\n    \"\\n\",\n    \"y = model.predict(X_test)\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", s=50, label=\\\"training data\\\")\\n\",\n    \"plt.plot(x_test, y_test, c=\\\"g\\\", label=\\\"$\\\\sin(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x_test, y, c=\\\"r\\\", label=\\\"fitting\\\")\\n\",\n    \"plt.ylim(-1.5, 1.5)\\n\",\n    \"plt.legend()\\n\",\n    \"plt.annotate(\\\"M=9\\\", xy=(-0.15, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 1.2.6 Bayesian curve fitting\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"model = BayesianRegression(alpha=2e-3, beta=2)\\n\",\n    \"model.fit(X_train, y_train)\\n\",\n    \"\\n\",\n    \"y, y_err = model.predict(X_test, return_std=True)\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", s=50, label=\\\"training data\\\")\\n\",\n    \"plt.plot(x_test, y_test, c=\\\"g\\\", label=\\\"$\\\\sin(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x_test, y, c=\\\"r\\\", label=\\\"mean\\\")\\n\",\n    \"plt.fill_between(x_test, y - y_err, y + y_err, color=\\\"pink\\\", label=\\\"std.\\\", alpha=0.5)\\n\",\n    \"plt.xlim(-0.1, 1.1)\\n\",\n    \"plt.ylim(-1.5, 1.5)\\n\",\n    \"plt.annotate(\\\"M=9\\\", xy=(0.8, 1))\\n\",\n    \"plt.legend(bbox_to_anchor=(1.05, 1.), loc=2, borderaxespad=0.)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch02_Probability_Distributions.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 2. Probability Distributions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.rv import (\\n\",\n    \"    Bernoulli,\\n\",\n    \"    Beta,\\n\",\n    \"    Categorical,\\n\",\n    \"    Dirichlet,\\n\",\n    \"    Gamma,\\n\",\n    \"    Gaussian,\\n\",\n    \"    MultivariateGaussian,\\n\",\n    \"    MultivariateGaussianMixture,\\n\",\n    \"    StudentsT,\\n\",\n    \"    Uniform\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2.1 Binary Variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Bernoulli(\\n\",\n      \"    mu=0.75\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = Bernoulli()\\n\",\n    \"model.fit(np.array([0., 1., 1., 1.]))\\n\",\n    \"print(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.1.1 The beta distributions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 1, 100)\\n\",\n    \"for i, [a, b] in enumerate([[0.1, 0.1], [1, 1], [2, 3], [8, 4]]):\\n\",\n    \"    plt.subplot(2, 2, i + 1)\\n\",\n    \"    beta = Beta(a, b)\\n\",\n    \"    plt.xlim(0, 1)\\n\",\n    \"    plt.ylim(0, 3)\\n\",\n    \"    plt.plot(x, beta.pdf(x))\\n\",\n    \"    plt.annotate(\\\"a={}\\\".format(a), (0.1, 2.5))\\n\",\n    \"    plt.annotate(\\\"b={}\\\".format(b), (0.1, 2.1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"beta = Beta(2, 2)\\n\",\n    \"plt.subplot(2, 1, 1)\\n\",\n    \"plt.xlim(0, 1)\\n\",\n    \"plt.ylim(0, 2)\\n\",\n    \"plt.plot(x, beta.pdf(x))\\n\",\n    \"plt.annotate(\\\"prior\\\", (0.1, 1.5))\\n\",\n    \"\\n\",\n    \"model = Bernoulli(mu=beta)\\n\",\n    \"model.fit(np.array([1]))\\n\",\n    \"plt.subplot(2, 1, 2)\\n\",\n    \"plt.xlim(0, 1)\\n\",\n    \"plt.ylim(0, 2)\\n\",\n    \"plt.plot(x, model.mu.pdf(x))\\n\",\n    \"plt.annotate(\\\"posterior\\\", (0.1, 1.5))\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Maximum likelihood estimation\\n\",\n      \"10000 out of 10000 is 1\\n\",\n      \"Bayesian estimation\\n\",\n      \"6649 out of 10000 is 1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Maximum likelihood estimation\\\")\\n\",\n    \"model = Bernoulli()\\n\",\n    \"model.fit(np.array([1]))\\n\",\n    \"print(\\\"{} out of 10000 is 1\\\".format(model.draw(10000).sum()))\\n\",\n    \"\\n\",\n    \"print(\\\"Bayesian estimation\\\")\\n\",\n    \"model = Bernoulli(mu=Beta(1, 1))\\n\",\n    \"model.fit(np.array([1]))\\n\",\n    \"print(\\\"{} out of 10000 is 1\\\".format(model.draw(10000).sum()))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2.2 Multinomial Variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Categorical(\\n\",\n      \"    mu=[0.5  0.25 0.25]\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model = Categorical()\\n\",\n    \"model.fit(np.array([[0, 1, 0], [1, 0, 0], [1, 0, 0], [0, 0, 1]]))\\n\",\n    \"print(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.2.1 The Dirichlet distribution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Categorical(\\n\",\n      \"    mu=Dirichlet(\\n\",\n      \"        alpha=[1. 1. 1.]\\n\",\n      \"    )\\n\",\n      \")\\n\",\n      \"Categorical(\\n\",\n      \"    mu=Dirichlet(\\n\",\n      \"        alpha=[3. 2. 1.]\\n\",\n      \"    )\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"mu = Dirichlet(alpha=np.ones(3))\\n\",\n    \"model = Categorical(mu=mu)\\n\",\n    \"print(model)\\n\",\n    \"\\n\",\n    \"model.fit(np.array([[1., 0., 0.], [1., 0., 0.], [0., 1., 0.]]))\\n\",\n    \"print(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2.3 The Gaussian Distribution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x360 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"uniform = Uniform(low=0, high=1)\\n\",\n    \"plt.figure(figsize=(10, 5))\\n\",\n    \"\\n\",\n    \"plt.subplot(1, 3, 1)\\n\",\n    \"plt.xlim(0, 1)\\n\",\n    \"plt.ylim(0, 5)\\n\",\n    \"plt.annotate(\\\"N=1\\\", (0.1, 4.5))\\n\",\n    \"plt.hist(uniform.draw(100000), bins=20, density=True)\\n\",\n    \"\\n\",\n    \"plt.subplot(1, 3, 2)\\n\",\n    \"plt.xlim(0, 1)\\n\",\n    \"plt.ylim(0, 5)\\n\",\n    \"plt.annotate(\\\"N=2\\\", (0.1, 4.5))\\n\",\n    \"plt.hist(0.5 * (uniform.draw(100000) + uniform.draw(100000)), bins=20, density=True)\\n\",\n    \"\\n\",\n    \"plt.subplot(1, 3, 3)\\n\",\n    \"plt.xlim(0, 1)\\n\",\n    \"plt.ylim(0, 5)\\n\",\n    \"sample = 0\\n\",\n    \"for _ in range(10):\\n\",\n    \"    sample = sample + uniform.draw(100000)\\n\",\n    \"plt.annotate(\\\"N=10\\\", (0.1, 4.5))\\n\",\n    \"plt.hist(sample * 0.1, bins=20, density=True)\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.3.4 Maximum Likelihood for the Gaussian\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MultivariateGaussian(\\n\",\n      \"    mu=[0.91852581 1.17919155]\\n\",\n      \"    cov=[[4.29224408 0.1551223 ]\\n\",\n      \" [0.1551223  3.58170912]]\\n\",\n      \")\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X = np.random.normal(loc=1., scale=2., size=(100, 2))\\n\",\n    \"gaussian = MultivariateGaussian()\\n\",\n    \"gaussian.fit(X)\\n\",\n    \"print(gaussian)\\n\",\n    \"\\n\",\n    \"x, y = np.meshgrid(\\n\",\n    \"    np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))\\n\",\n    \"p = gaussian.pdf(\\n\",\n    \"    np.array([x, y]).reshape(2, -1).T).reshape(100, 100)\\n\",\n    \"plt.scatter(X[:, 0], X[:, 1], facecolor=\\\"none\\\", edgecolor=\\\"steelblue\\\")\\n\",\n    \"plt.contour(x, y, p)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 2.3.6 Bayesian inference for the Gaussian\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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aNGZncJUlya4rJZC0YFuec5C68vdFER58e5/dUMrlLkuTWjAUFYDQ6ZaXMKZrYWGuhMg8mk7skSW7NmStlTjmV3BWLxWnXtDeZ3CVJcmun17g7M7nHx6Ho9ZiOH3faNe1NJndJktyaITsblb8/XhERTrvmqTcSTx6akcldkiS3ZsjKsi6DFMJp1zyd3D14UlUmd0mS3JohO9upk6kA3q1aIbRa2XOXJElyBMVgwJif77RlkKcIlQpN2xiZ3CVJkhzBkJcHFotTJ1NP0cTFnZ7M9UQyuUuS5LZOL4N08rAMWMfdjbm5KGaz069tDzK5S5LktlyxDPIUdWwsitFovYnKA8nkLkmS2zJkZ+MVHIxXSIjTr+3pK2ZkcpckyW25YqXMKZrYuNoYslxy/caSyV2SJLdlyMpy+kqZU7xbRCL8/GTPXZIkyZ4sNTWYjh1D7YLxdgAhRG2NmSyXXL+xZHKXJMktGXJyAdC6aFgGPLs6pEzukiS5pVMrZVzVc4fa5ZB5+ShGo8tiaCiZ3CVJckunesynJjZdQRMbC2Yzxvx8l8XQUDK5S5LklgxZWXhHRuIV4O+yGE5N5nri0IxM7pIkuSXrSpk4l8Zw6vqeWIZAJndJktySK5dBnuIVGooqMFD23CVJkuzBXFaGuaTE5T3308shPXCtu0zukiS5ndOTqS5O7uC5yyFlcpckye24VXKPi8N49CgWvd7VodhEJndJktyOITMLVCrUMTGuDgVNfDwoCsacHFeHYhOZ3CVJcjuGrCzUbdqg0mhcHcrpTw/6zEzXBmIjmdwlSXI77rAM8pTTa909bFJVJndJktyKoihuldy9AgLwjozE4GE9d29XByBJUvOhKArbjm9jZc5K8iryyKvM43jVcaIDo0mJSKFLRBf6qjtgqa52m+QOnrmfqkzukiQ5nEWxsCZ3Df/b9z/2FO7Bx8uHmKAYYgJj6NGyB9nl2SzNXMqiw4vokqfiacDYJsLVYZ+miY+nYtkyV4dhE5ncJUlyqLyKPB5e8zBpxWlEBUTxnz7/4cqkK/Hx9jnjOItiIassi7XvPwNs5/aDzzCuRT43dboJL5WXa4KvpYmLw1xaiunkSbxDQ10aS33J5C5JksNsyN/Ao+sexaJYeHHAi1wWfxneqrOnHZVQkRCSgL/3RZSodxPbrgezt89m54mdvDrwVfzUfk6O/m+a+DigtpiZhyR3OaEqSZLdKYrCvL3zuHvF3bTwa8FXY75ibOLYcyb2ugxZ2WhiY3ln+Hs80fsJ1uat5eYlN3Oi+oQTIj+7vwuIec6KGZncJUmyu9k7ZvPWjrcYETuCBaMX0Daobb3PrbtS5oaONzD30rlklWdxw683cOTkEQdFfH6a6Gjw9vaoSdV6J3chRIwQYpUQYr8QIk0I8aAjA5MkyTN9su8TPtn3CRM6TOC1Qa/ZNJyimM0Yc3LO2FpvUPQgPhv9GRbFwp3L7+RY1TEHRH1+Qq1GEx3tUcshbem5m4AZiqJ0AvoC9wohOjkmLEmSPNEPf/3ArO2zGBU3iif7PIkQwqbzjQUFKEbjv5ZBJocl8/6w96kyVnHvinupNFTaMer60cTHN82eu6IoBYqi7Kj9vgI4AEQ5KjBJkjzLqpxVzNw4k/5t+vPSgJdQCdtHfQ2ZWcDZC4Z1COvArMGzSC9NZ8aaGRgtzt3XVBMXhyE7G8Vicep1G6pBY+5CiDigG7D5H89PFUJsE0JsKywsbHx0kiR5hJzyHJ748wk6hXVi9pDZqL3UDWrnVM9Yc45NsftH9efZfs+y4egGXtz0YkPDbRBNfByKXo+poMCp120om5O7ECIA+A54SFGU8rqvKYryoaIoPRVF6RkZGWmvGCVJcmMGs4GH1zyMl/Bi1pBZjVqyaMjKQuXvj1fEuW9gGtduHHd0uYPv/vqOxRmLG3wtW/1dQCzLaddsDJuSuxBCjTWxf6EoyveOCUmSJE/y5rY3OVBygBcufoHWAa0b1ZYhKwtNbOwFx+rvSb2Hbi268cKmF8ityG3UNevL0/ZTtWW1jADmAQcURZnluJAkSfIUy7OXs/DgQqZ0msIlbS9pdHv6jAw0iYkXPM5b5c0rA19BhYrH1z3ulPF378hIVP7+HrNixpae+8XAFOBSIcSu2sdlDopLkiQ3d7zqOM9seIYuEV2Y1n1ao9szV1ZhKihAm5hQr+PbBLTh6X5Ps6dwDx/s/qDR178QIYRHFRCrd/kBRVH+BGxb1yRJUpOkKAovbn4Ro9nIKwNfafAEal2GzAyAevXcTxkdP5r1+ev5aM9HDIwaSGqL1EbHcT6a+Hhqdu506DXsRd6hKkmSzZZlL2NV7iruTb3XprtPz0efng6A1obkDvBEnydo6d+SmRtmYjQ7dnjm9H6qOp1Dr2MPMrlLkmSTUl0pL21+iU7hnZjcabLd2jWkp4NajcbGfVP91f481ecp0svS+STtE7vFczaa+DhQFAwesJ+qrAopSVL9KQqvb5hJub6UD6PH4r3mdVB5gTYIfIIhOBqie4La1+am9ekZaGLbItS2D/EMiRnC8Njh/Hf3fxkZN5LYoLOvk2+s0ytmMjLxad/eIdewF5ncJUk6P7MJcjbCwV/ZeORXfg6CO0rL6PDH/539eJUaonpA3ABIvQHC6zfMYkhPR5uc3OAwH+/9OBuPbuT5jc/z0YiPbC59UB/ahAQQAn36EWCk3du3J5ncJUk6O6MOdnwG69+C8nyMXlpebhtDW7Uvd46ZBWGJEBwDigL6CtCXQdERyP4TsjfAn7Nh3RuQNBx6T4WkYaA6+0iwRa/HkJtL0JiGL8Br4deCh7o/xAubX+CXjF8Ymzi2wW2di8rXF3V0NPojrqlOaQuZ3CVJOpPJAFs/gvVvQ+VxiOkLI1/kS3MRmTvn8M7AWWhjBp95jnc4+IdDWAK0H2F9ruIYbP8Utn0MC8dD64vgsjcgpve/LmnIygaLxaaVMmczvsN4fs74mVnbZnFpzKUEaAIa1d7ZaJOSMHhAcpcTqpIk/S1/O3w4BJY+CRHt4abFcOsSihIG8v6+jxkQNYBB0YPq11ZgKxjyODy0D8b9FyoLYd5w+OFuqDh+xqGGdGuytHWlzD+phIonej9Bsa6Yj/Z+1Ki2zkWblIQ+KxvF6NzCZbaSyV2SJDDWwLKn4X/DoKYErv8Kbl4M8QNBCObunIvOrOPRXo/aPpbtrYGLJsJ9W2HAdNj7LbzbCw78XRdGn54BKtVZq0HaqnNEZ8YmjuXz/Z+TW27/0gTadklgNGLIdu9dmWRyl6Tm7mS2tUe9YQ50mwL3bIIOo0+/vK9oHz/89QOTO04mPji+4dfRBsCwZ63thyXA15Pg98fBZECfno46OhqVj8+F26mHB7s/iLfKmze2vWGX9urSJiUBuP24u0zuktScZay2DsOU5sAN38LYOeAbcvplRVF4ZcsrhPmEcWfXO+1zzYgkuHUp9LkbNr8PH4/A8NfBRg/J1NXCrwVTu05lZe5KNhVsslu7AJqEBFCp0P8lk7skSe5o43vw+TgIaAl3rPp7IrSOP7L/YHfhbh7o/oB9Jye9tTD6FZiwAKUwHX1mJtpWgfZrH5jSaQpRAVG8uuVVTBaT3dpV+figjnH/FTMyuUtSc6Mo8MczsPQJSB4Dt/9x1rXoRrORt3a8RVJIElcmXumYWDpegWHkx2ARaLK/tn6SsBOtl5bpPaZzpPQIP6f/bLd2AbRJ7WRylyTJjVjM8MuD1mWOvW6H8Z+B9uw95m8Of0NuRS7TekzDS+XlsJAMZdY0pI2OhAXXwJ5v7db28NjhdI3syrs736XGVGO3drVJSdYt9wwGu7VpbzK5S1JzYTLAolthx3wY+LB1zfk5bioqN5Tzwe4P6NOqDwOjBjo0LP0Ra8Ewzf0/WtfUf38H7P7KLm0LIZjeYzonak6wYP8Cu7QJtZOqJhN6Ny7/K5O7JDUHZhN8dyvs/xFGvABDn4bzLGn8eO/HlOpLmd5zukNu46/LkJGOd6tWeEVGwaRvrcsvf7gLdn9tl/Z7tOzBkJghzNs3jxJdiV3a1Lazrphx55uZZHKXpKbOYoYf74YDv8DIl6H//ec9/FjVMRYcWMDlCZfTKbyTw8PTH0m31mwB0PjB9V9b69L8aL8EP637NGpMNXy450O7tKeJj7eumJHJXZIkl1AUWDwN9n4DQ5+Bfvdc8JT3dr2HRbFwf7fzvwnYJTyLBX1mJpqkOhO6Gj+44WuIvdia4Pc3fjI0ISSBcUnj+PrQ13a5sUml1aJp29atl0PK5C5JTZWiwNKn/h5jHzjjgqekl6bzU/pPTOgwgTYBbRweoqmgAKW6Gm3CP1braPytCT6qB3x3O2T92ehr3Zt6L2qVmrm75ja6LbAOzcieuyRJzrfxXdj0LvS5Cy79T71OmbtzLr7evkztOtXBwVnpDh4EwCe5w79f1PjDDd9AaBx8eT0c29uoa0X6RTK542R+z/ydgyUHG9UWgCYpCUNODhY3XTEjk7skNUX7voNlT0Gnq6zj7PWYFN1duJsVOSu4OeVmQn1CnRAk6PYfACHQnmvjC78wmPK9dbnmgmvgZFajrndz55sJ0gTx9o63G9UO1K6YMZsxZGY2ui1HkMldkpqarD+tq03a9rNWYzzHcse6FEXhre1vEeYTxo2dbnRCkFa6gwfRxMej8vM790HB0TD5ezDp4YvxUFPa4OsFaYK4vcvt/Jn/J1uPbW1wO2C9kQlw23F3mdwlqSkpPAxf3QCh8TBxIajrV4hr/dH1bDu+jTu73omf+jyJ1s50B/bj07HjhQ9skQwTv4CSTPjmRmjERtjXJ19PC98WvL3jbRRFaXA7mvg48PJCf+SvBrfhSDK5S1JTUV0CC68DL411vbhfWL1OsygW3t7xNlEBUYxvP97BQf7NdPIkpqMF+HSs59Z6cQOshc0y18Cv060Txg3g4+3D3al3s7twN6tzVzeoDQCVRoMmNlb23CVJciCTAb6eAuX5MOELCK3/BtFLs5ZysOQg93W7D7WX7ZtTN5T+0CEAtPXpuZ+SeoN15c+Oz6wlihvoqqSriAuK4+0db2O2mBvcjk/HjugO7G/w+Y4kk7skeTpFgd8etu5dOvYdaNun3qcaLUbm7pxL+9D2XBbf8P1LG0K3/wAAPp1svFHqkqcgZRz88SwcXtqga3urvLm/2/2kl6WzOGPxhU84B5+UFExHCzCV2OfOV3uSe6hKTYrZopBbUk1OSTW5J6vJLamhpEpPWY2RshojNUbL6WMFEOjjTZCvmmBfNS0CtbQN8yMmzI+4cH8iA7Wu+0FssfmD2rXsM+CiCTad+v3h78mtyOXdoe+iEs7t6+kOHMC7VSu8Q21cmaNSwZXvQUkGLLoN7lgBkWdZSnkBw2OHkxKewru73mVU/Ci0Xrb/ffukpACgS0sjYKBja/DYSiZ3yaMdL9exMb2YXbml7MsvY39BOdWGvz9ma7xUhPlrCK5N4MG+ak4tCrQoCpV6E/mlNZRVGympNpwxjBsZqKVzmyA6RwXTMy6MXnGh+Gnc7L9MxmrrjUodxsAl9VvLfkq1sZoP9nxA9xbdHV4c7Gz0Bw/gk1zP8fZ/0vhZJ4w/vAS+nAh3rARf294khBA81OMh7lh2B98c+oYpnabYHIZPivVTh27fPpncJakxDCYLmzKKWX7gOOuPFJFeWAWAn8aLlDZBXNczhk6tg4gN96NtuB8tA31QqepX+EpvMpN/soackmoyCqtIO1rOvvwy1hwuxKKA2kuQGhPCgKRIRqS0JLlVoMOLap1XSSZ8e7N1I+ur67fksa6FBxdSVFPEm4PfdPrPYdHp0GdkEjh8eMMbCY6GCQtg/uXw7S0waRF42ZbS+rbuS9/Wffloz0eMSxpn84YkXgEBaOLiqElLs+k8Z5DJXXJ7RrOF1YcKWbznKCsPnqBCZ8JX7UXfhDAm9Iqhf2IEHVsH4VXPJH4uWm8vEiIDSIgMYEidT/nVBhPbsk6yMaOYDUeKeGvFYWYvP0zbMD9GprTkytQoUtoEOTdB6ivhq0mgWKxLBM9Rk/1cSnWlfLz3YwZFD6J7y+4OCvLc9IcPg9ls22Tq2bTtA2Nmwc/3wfJnYeSLNjfxUPeHmPjrRObvn8+9qffafB7fAloAACAASURBVL5PSgrV27fbfJ6jyeQuua19+WUs2p7Hz7uPUlJlIMxfw+jOrRiZ0oqLkyLwUTtuA4m6/DTeDGofyaD2kQAUVuhZceA4S9OOMX9DNh+ty6RDy0Cu7h7F1d2jHT9Wryjw0z1QeMDaWz3LLkoX8tHej6g0VvJg9wcdEOCF6Q7Ulh1obHIH6D4Fju2Bje9A61ToattyzpSIFEbEjmB+2nwmdJhAhG+ETef7dO5M+a+/Yiouxjs83KZzHUkmd8mt6E1mft97jPkbs9iZU4rGS8XwTi25unsUg9pHovZy/QKvyEAtE3u3ZWLvtpRWG1i8p4DvduTx8u8HeWPZIUZ3bs2N/WLpERvqmN78n7Nh/08w/P8gaajNpx+tPMqXB79kbOJY2oee47Z/B9Md2I8qKAh1VJR9Ghz5EhzbBz/fb51cbd3VptMf6P4AK3NW8sHuD/hPX9vmLk6Pu6elETBokE3nOpJM7pJbKK028NnGbD7bmEVRpYH4CH+evrwT13SPIsRP4+rwzinET8PkvrFM7hvLkROVfLE5m0XbrJ82UtoEMXVQAmO6tMbbXm9Kfy2HFf8HKVdD/wca1MQ7O99BJVTc1+0++8TUALoD1slUu735eanhuvnw38HW4aqpq8G//r3o2KBYrml/DYsOL2JSx0nEB8fX+1yfTp1ACGr27XOr5O76bpDUrBWU1fB/v+yn/ysrmfXHYTpHBfPZrb1ZMX0wtw2Id+vE/k9JLQJ49ooUNj05lBfHdabGaObBr3ZxyZur+WxjFjpjw2+WAaxL/767FVqmwJXv1KsY2D8dKjnE4ozF3NDxBlr5t2pcPA2kmM3oDx22z5BMXQEtrBOslcdh0S3W3adscNdFd6H10jJnh203R52aVNWludfNTDK5Sy5xrEzHMz/tY/Brq5m/MYuRKa1Y8tBAPr2lN4PaR9Z7hYs78td6M6lPLMunDea/U3oQEaDlmZ/SGPTaKuZvaGCSN1TBV5MBYU1gGv8GxTZ7x2wCNYHc1vm2Bp1vD4asLBSdDm19yw7YIroHXD7LWqJgxUybTo3wjeCWzrewPGc5u07ssulcn5QUdPv22XSOo9U7uQshPhZCnBBCuNdPIHmUoko9z/2SxqDXV7Fwcw7X9Ihi9cNDmD0hleRWQa4Oz65UKsHIlFZ8f3d/Ft7Rh7gIf579OY0hr69mwaZsjGbLhRuB2gnU+6wTqNfOg7D6DxnUtalgE+vz13NHlzsI1gY3qA17OH1nakcHbeHXbTL0uh02zLWWPrbBjZ1uJMI3glnbZ9lUVMyncwqm48cxFRbaGq3D2NJz/xQY5aA4pCauSm/i7eV/Mfi1VXy2MZurUtuw6uEhvHx1V2LCnFeF0BWEEPRPjODrqX1ZeHsfokN9+c+P+xgxey2/7S24cBLZMAfSvrduk5c0rEExmC1mXt/6OlEBUVzf8foGtWEvurQ0hEaDNqFhb1L1MvJliOlrfVM8Vv/+qJ/aj3tS72HniZ2szFlZ7/N8a+9Udaf17vVO7oqirAXcr4CC5NbMFoWFm3MY/PoqZi8/zKD2kSybNojXrr2oySf1fxJC0D8pgm/v6se8m3qi9hLc88UOrnpvA9uyzvFfK30lLJ9p3XTj4ocafO0fj/zI4ZOHeajHQw26zd6eqrdvx7drV4TagUXKvDXWCVZtEHw9yVoxs57GJY0jITiBWdtnYTDXb5clbUfrpKrOE5N7fQghpgohtgkhthW60ccTyTXWHylizJx1PPnDXuIj/Pn+nv68P7kHiZG23QXY1AghGNqxJb8/OIjXru3K8TId136wkfsW7iDvZPXfB57MgkW3QmQyXPlugyZQAaqMVczdOZfUyFRGxo60zw/RQJaqKnT79+Pbs4fjLxbYyjo/UX7U+nus5wSrt8qbR3o9Qk5FDl8e/LJe53gF+KOJj3erSVW7JndFUT5UFKWnoig9IyMj7dm05EFyiqu547NtTPrfZir1Jt6b1J1v7uxH97bO2brNU3ipBNf1jGHlw4N5cGg7lh84zqVvrmHWskPUVJb/fQfqhAWgbfgb4ry98yjWFfNor0ddWy4BqNm9G8xm/Hr0dM4FY3rBZW9AxiqbJlgHRA1gYNRAPtj9ASW6+vX63W1SVa6WkeymxmBm1h+HGTZ7DeuPFPHoqA4snz6Yy7q0dnlScWd+Gm+mDW/PyhlDGN25FXNW/sXGWdehHN+Pcs3HDboD9ZSjlUeZnzafMQlj6BLZxY5RN0z1tu2gUuHbLdV5F+1x098TrHu+rfdpD/d6GJ1Jx7s7363X8b6dUzCdOIHx+PGGRmpXMrlLjaYoCsvSjjFs1hrmrPiLUSmtWDljCPcMSXJaiYCmoE2IL29P7Ma6fju41LKRl4wTmbImkPTCyga3OXv7bFRCxUPdGz5eb0/V27fjk5yMV4CTh+ZGvgxt+1tr0Byt3zLHhOAEJiZPZNFfizhUcuiCx/vWfhqp3rKlUaHaiy1LIb8ENgIdhBB5QgjXLZSV3EZuSTW3z9/G1M+3E6D15qupfZlzfTdaBddv707pHw7+RszON7F0Hk/MmEfZnVfKqLfW8sbSQ9QYbFsfv6VgC0uylnBL51tcdsNSXYrBQM3u3c4Zb/8nbw1c9xn4RVj3mK2oX+/6rovuIlATyOtbX7/gqiafjsmogoKo2rzZHhE3mi2rZa5XFKW1oihqRVGiFUWZ58jAJPdmMFl4Z+VfDJu1hk0ZxTx1WUcWPzCAvgnuUzjJ4xzfD99PhdapqK6cy43941k5YwhXXNSGd1YdYfjsNaw8WL+kZLQYeXHzi0QHRHNr51sdHHj96PbvR9HpnDfe/k8BkXD9Qqg5aV1BY9Rd8JRgbTD3pd7H5mObWZp9/l2fhJcXfr16Ub3Jw5K7JJ2yOaOYy+as441lh7k0uQXLZwzmjkEJblHUy2NVFsKXE6x3nk5cCGpfwFqkbNZ1qXw1tS8+ai9u/XQbdy/YzrGy8yemL/Z/QUZZBo/3fhwfb/f4FHWqLK5fD+eXGD6t9UUw7gPI2wq/PFivTbbHtx9Px7COvLblNSoN5x8i8+/TB2NeHsb8fHtF3GDyf6NUbyerDDy6aDcTPtxEjcHMJzf34v3JPWgd7Ovq0DybSQ9fT4bKE9aeZfC/KyX2TQjntwcG8sjIDqw8eIJhs9bw6fpMzJZ/J6fjVcd5b/d7DIkewuCYwc74Ceqlett2NHFxeEfYVlLX7jpdad2Hdc9XsP6tCx7upfLimX7PUFRTxLu7zj+56tfHun9t1WbXj7vL5C5dkKIofL8jj6Gz1vDdjnzuHJzAH9MHcUlyC1eH5vkUBX5+AHI3wVXvQ9S5x6M13iruvSSJZdMG0a1tCDN/2c/V760n7WjZGce9ue1NLIqFx3o/5ujo602xWKjescM14+1nM+gRa2XN5TMh7ccLHt45ojPXdbiOhQcXcrDk4DmP07ZLwis0lOrNm+wYbMPI5C6dV1ZRFZPnbWb6N7uJDfdj8f0DeGJ0R/fbS9RTrXvD2oMc8iR0vrpep8SG+/PZrb15e2Iq+aU1jH1nPS/+up9qg4n1+ev5Pet3but8G9GB0Q4Ovv70fx3BUlbmuvH2fxICrnoPonvDD3dC3rYLnnJ/t/sJ0YbwwqYXsChnrwskVCr8+vShavMWm2rTOIJM7tJZnZowHfHWWvbklvH8VZ357q7+dGzdtIp7udTur2DlC9B1Agx+1KZThRBcmRrFiulDuK5nNB+ty2TY7GU8sfYZEoITuK2Ley1mq95uTZ4uHW//J7UvXP+l9U7WLyda7wg+j2BtMDN6zmB34W6+PXTu9fL+fXpjOnYMY06OnQO2jUzu0r9szy7h8rnWCdNhHa0TplP6xnp0GV63k7EafroX4gbC2IbVZgcI9lPz8tVd+faufphCFlOiLySochKl1fWsOOkkNdu24x0ZiTomxtWhnMk/wrpVodkIX1xnXUlzHlckXEG/1v2YtX0W+ZVnnzT9e9zdtatmZHKXTiurNvLE93u55v2NVOpMzLupJ+9N6kHLIPdYbdFkHE+Dr6dAeDtraQHvxm9IInwyqPFZR5fAMWw+GMjQN9fw+aZsLGeZcHU2RVGo3rYN35493PNO5Yh21k3GT2bCwolgqD7noUIIZvafCcCzG54969CLJj4e78hIly+JlMldQlEUftqVz9BZq/lmWy53DIznj+mDGdqxpatDa3pOZsOCa61LHicvAt+QRjepM+mYuXEmUQFRzBv7NEsfGkTX6GCe/nEf13ywgQMF5XYIvBHx7duH6cQJAga5z8qdf4kbAFd/BLmbL1hkrE1AG2b0nMHmgs0s+mvRv14XQljH3be4dtxdJvdmLqOwksnzNvPgV7uICvXj5/su5qkxnfDXyglTu6s4Dp9fBcYqmPwdBNtnwnPOzjlkl2czs/9M/NR+xEf4s+C2PsyecBHZxdVcPvdPXvx1P1V627ads5eKP5aDlxeBlwxxyfXrLeUqGPMGHP4dFp9/Dfz49uPp06oPb2x9g6OVR//1ul+f3piLijBkZDgy4vOSyb2Z0hmtRb5GvbWOPXllPH9lCt/f3Z+UNq7boadJqymFBVdDxTHrGG/LFLs0uz5/PZ/v/5yJHSbSt3Xf088LIRjXLZqVMwb/PeE6aw1L9tVjcxA7q1i+HL9evfAKafynFIfrdTsMfgx2LoA/nj5nghdC8NzFz6Gg8Mz6ZzBbziwN4d/X+ndRtWGjw0M+F5ncm6GVB48zYvZa5qz4i9FdWrFixmCm9IvDS06YOoahChZOgMJD1jH2mN52aba4ppin/nyKpJAkZvSccdZjQvw0vHx1V767ux/BvmruWrCDWz7dSlZRlV1iuBB9RgaGjAwChzVsBymXGPLE31UkV798zsOiAqJ4vPfjbD62mY/3fXzGa+roaDSJiVQsW+boaM9JJvdmJLfEWmf91k+3ofYSLLy9D29P7EaLQDlh6jCGKvhiPORtgWs+gqShdmlWURSeXv80FYYKXh306gVLDPSIDWPx/QP4z5iObMs6yYjZa611420sRmarij+WAxA4zD4/t1MIAaNfh25TYM2rsPaNcx46Lmkco+NG8+6ud9l5YmedJgRBl42mets2l5UAlsm9GagxmJn9x2GGzbLWWX98dDK/PziI/kkuvg28qTNUWZfX5Wy0TtaljLNb0wsPLmRd/jqm95xO+9D29TrH20vF7QMTWDljMJd1acWclUcYNmsNv9dnH9cGqli+HJ8uXVC3cn1VSpuoVHDF29Z7EFY+b+3Fn4UQgmf6PUNr/9Y8tvYxyvR/3y0cNPoyUBQqlixxVtRnkMm9CVMUhd/2FjBs1hreXvEXI1JasXz6YO4anIjGW/7VO9SpoZicDdbE3uVauzW968Qu3tj2BoOiB3FD8g02n98iyIe3Jnbjq6l9CfTx5u4vdjDpf5s5dKzCbjECGI8dQ7d3r2cNydSl8oIr37O+KS/7zzl78AGaAN4Y/AaFNYU8s/6Z02+U2oR4tJ06Uvbrb86M+jT5P7yJ2pNXyoT/buKeL3YQ5Kvm66l9mXt9N9qEyCJfDldTCguugez1MO5Duyb2E9UnmLZ6Gq38WvHSgJcatW68b0I4i+8fwPNXdWZ/QTmXzVnHf37cS3Gl3i6xVixfAUDgcA9N7gBe3nD1/6DLddYe/PLnzjrJmhKRwrTu01iZu5L/7f3f6eeDx4xBt2cPhtxcZ0YNyOTe5BSU1TD9m12MfWc9GUWVvDiuM4vvH0AfWWfdOSqOw6djrLVKrv0Yuo63W9MGs4Fpq6ZRZaxizqVzCNY2fmWTt5eKKX1jWTVjCFP6xvLlllyGvL6a/65JR2ds3Hh8xfLlaBIS0CYkNDpOl/LyhnH/hR43w5+zYMnjYPn3HcBTOk1hTMIY5uycw4ps6xtb0KhRAJT/9rszIwZkcm8yymqMvPL7QYa8vprFewq4e0giqx4ewqQ+sXIVjLOUZMLHI6xfJ31j1zF2RVF4YdML7Cnaw0sDXqJdaDu7tQ0Q6q9h5tgUlj40iF7xYbz8+0GGvrmG77bnnbWs8IWYTp6keutWzx2S+SeVCi5/C/reA5s/gB+mWks11yGE4Ln+z9ElogtP/PkEh0oOoY6KwrdbN8p/c/7QjEzuHk5nNPO/dRkMfn0V/12bzmVdWrNi+mAeG5VMoI/a1eE1H3nbYd4I0JXBTT9D4qV2bf7jfR/zw5EfmNp1KsNiHZcwk1oE8PHNvVh4ex/C/DXM+HY3Y+asY9XBEzZNupb/+huYzQSOGOGwWJ1OCBj5Elz6NOz9Fj6/+l+1aLReWt6+5G0CNYHcv/J+imqKCLrsMvSHDqE/csSp4crk7qEMJgufb8pm8OureOHXA3SJCmbx/QOYPSGVmDA/V4fXvOz7Dj69zFpl8JYlEG3fsrbfHf6Ot3a8xej40dybeq9d2z6X/kkR/HTvxbw9MZUqg4lbPt3K+A82sjG9+ILnKmYzJZ99hm9qKr6d7XOzltsQAgY9bB2Hz90M80ZaS0rUEekXyZxL53BSd5J7lt+DaujFoFI5fWhGOGoJVM+ePZVt2y5cI1myjcFk4Yedebyz6gi5JTX0iA1lxoj29E+UyxqdTlFgzWuw+iVo2896g5K/ff8elmcvZ8aaGfRr3Y+5l85F7eX8T2MGk4VvtuUyd+VfHC/Xc3FSOA8ObU/v+LCzHl+xfDl5991P1FuzT485N0mZ66x7saq8rfMrCUPOeHld3joeWPUAXSK68NxXYM4vIHHJ7wjv85f2EEJsVxSl0T0Emdw9hN5k5ttteby/Op380hq6RAUzfUR7hrSPdM9Ke01dzUn48V449CtcdL11TbS31q6X2FSwiXuW30PH8I58NPwj/NSu/USmM5pZsCmbD9akU1RpoG9CGA8MbUe/hPAz/g1mT56C8ehREpctvWAi83hFR6wJvugwDJsJ/R84o3zzsqxlPLL2EW44kcjY/x2gzWuvEjx27HmblMm9mSjXGflycw4fr8/keLmebm1DeGBoO5nUXSl/B3x7M5Tnw/Dnoe/dDa7Hfi5r89YyffV0YgJj+HTUp3ZZGWMvNQYzC7fk8N816Zyo0JMaE8JdgxMZ3qklhrQ0ssaPp8VjjxF+y82uDtU59BXW2vz7f7Luz3rFnDOqff545Eee+fM/vPu5L601ESQuXozw8jpnczK5N3FHS2uYvzGLhZtyqNCbuDgpnLsGJzIgKUImdVexWGDLh9aCUgEt4dpPIKaX3S/ze+bvPLnuSdqHteeDYR8Q6hNq92vYg85o5tttuXy4LoPckhoSIvx5Pu0bwvdsod3qVXgFBro6ROdRFNgwx7oOPqiNdelk3MWnX/764Ncs//T/mP6jhdBXn6fVlee+90Em9yZIURR25Jzk4/VZLNl3DEVRGN2lNXcOSqBrtAdU1GvKTmZbe2dZ66D9KOtm1n5nH3NujG8Pf8vzG5+ne8vuvHPpOwRoAux+DXszmS0sSTvGl4u38vjnT7K0/SBMdz3Ijf1im99Nc7lb4fs7rFv2DZhmLUJWuxnL7+m/4n3TI6i9NHT8dQktA85ekkEm9yakUm/ip135LNycQ9rRcoJ8vJnYuy1T+sbKlS+uZrHAjk9h2dOAgFEvWQtK2fnTk8li4s1tb7LgwAIGRg1k1pBZFywG5m6OvfIKJZ99zid3v8l3R80IIRia3IJJfWMZmBTRfLZp1Fdab3Ta+Tm06GRdH9/WuvXe1s9nE/Dih3xyfQS3PzDvrHWBZHL3cNZeeimLtufx8658qgxmOrYOYlKftlzdPQo/TROfiPIE+Tvgt0cgf5t1JcTYdyDE/nuAluhKeHjNw2w9tpXJHSczved01CrPukdBt38/meOvI/jKK2nz0ovkllTzxeYcvt2WS3GVgZgwXyb0jGFc92iimktv/tAS+HUGlOdBj1tg2LMomiAOjBpBnvEET92m5en+zzA28cwJVpncPVRuSTU/7z7Kd9vzyCiqwlftxZiurZnUpy2pMSFyPN0dVBVZ64hsnw/+kTDieWt1QAf83ew6sYtH1z5Kia6EZ/s9yxWJV9j9Go6mGAxkXjcBU3ERib/8csamHAaThaVpx1i4OYeNGcUIAf0Tw7mmezQjUloR0NR3/NJXWmvCb3oPfENh8GOU5kVQ8NR/WDohgXkJOVzb/loe7/04Wi/raiuZ3D3I8XIdv+0t4JfdR9mRUwpA7/gwru0RzWVdWjf9f+CeoqYUNr4DG98Dk866Cmbwo+Bj/5UqOpOOuTvn8vn+z2kT0IZZQ2bRKbyT3a/jDIXvvUfRnLlEv/sOgUPPXbc9t6Sa73fks2hHLrklNWi9VQzt2IIrurZhSIcW+GrOvYLE4xXsgaVPQtY6lNB4cjfEUH0wlz+fG8vbZT+QGJzIzP4zSW2RKpO7u8ssqmJp2jGWph1jZ21C79g6iCsuas0VXdvIsXR3Ul0CW+fBxrnW8gEpV8MlT0KEfeu3nLL9+HZmbphJVnkWEzpMYFqPafir/R1yLUfTHTpM5rXXEjRiBFFvnntTi7osFoWduSf5eddRft1bQFGlAR+1ikHtIhmZ0oqhHVsQ4qdxcOQuoCjw1x/wxzOYcg+Rsaw1XmERFLz/NM/tfo3jVce5Pvl6nuz7pEzu7qTGYGZLVgmrDp5g9aETZBVXA9AlKpiRKS0Z1bkVSS2a0dIwT1CcDpveh11fgLHaugrmkqegdVeHXC67PJu3tr/F8pzltPFvw/9d/H/0ad3HIddyBktNjfWGpYICEn5djHeo7Us2TWYLmzNLWJp2jGVpxzlWrkMloFvbUC7pEMmQDi3o1DqoaU3GWsywdxFVC18j56cqgpO9CH70TuYoJXx55Hv23rxXJndX0pvM7M0rY2N6MevTi9iRXYrBbEHrraJ/YjhDOrRgWKeWzWfyyFMYdXBwsXUlQ8Ya8FJba3X3uxdaOmZYpKCygE/SPuHbQ9+i8dJwS+dbuLHTjS6/47QxLFVV5N51N9XbtxM95227VH+0WBT25JexsraDtCfPuqtRqJ+afonh9E+MoG9COImR/k1jbspi4cQzD1C8aAWte58kpINgV/Iwul27QCZ3Zyqq1LMrp5SduSfZmnWS3bml6E3Wms4dWwdxcWI4F7eLoF9COD7qJjx26InMRshcA/t/hv0/WodeQtpC6mRrje7Alg657L6ifXyW9hnLsq2bJF/T7hruTr2bCF/PrgNkrqggd+qd1OzZQ5tXXyX48jEOuU5hhZ61hwtZn17EhiPFHCvXARDmr6FnbCi94sJIbRtC5zbBHjter5hM5NxxB9UbNxE5KpHwiO2onj4mk7sjKIrC8XI9+wvK2H+0nLSj5ezJKyO/tAYAL5UgpU0QveLCah+hhAfYt6aIZAeVhZCxGo4sh8NLQFcKmgDoMBq6TYa4QdYa3XZWWF3I75m/82vmr+wv3k+AOoBr21/LDck30Dqgtd2v52ymkhJy77wL3cGDRL35BkFOKumrKAqZRVVsySxha9ZJtmaVkFNiHfr0Ugk6tAwkpU2Q9REVTHKrQI8peW0xGCh48inKFy8meOwYol5/Uyb3xjCaLeSdrCGzqJKMwirSC6v463gFh49XUK4znT4uLtyPlKhgUqNDSG0bQkqbILkG3R2V5VlLsOZusW5vd2yv9XnfUOtYesex1hrravveGKQoChllGfyZ/yfr8tax9fhWLIqFTuGdGJs4lquSrvLYydK6FJOJk199TeGcOSg1NUS9/TaBl17i0pgKK/Tszi1ld14pu3JL2X+0nOIqw+nX2wT70K5lIO1aBJAQGUBCpD8JEf5EBmrdblhHURSK3nmXonffpdOhgzK5n0+l3sTxch3Hy3Tkl9ZQUKbjaGkNuSerySmp5mip7owdZkL81LRvEUi7lgG0bxlIx9ZBdGztOe/+zYahGoqPWKvwHU+zJvFje6HymPV1tR9E9bDedJR4KbS+yLrRsb0ubzZw+ORhdhfuZnfhbnae2MmxKuu1k0KSuCTmEi5PvJyEYA/fWq6WYjJRtX49J96chf7wYfz69aXVU0+hTUpydWj/cupTd9rRMg4dr+DwsQoOH68kvbDy9BAqgK/ai5gwX9qG+REd6kebEB9aB/vSJsSHlkE+RAZq0Xq7Zpin7OefCbnySucndyHEKOBtwAv4n6Ior5zrWHsld0VR0BktVOiNlNcYKasxUV5jpLTGQEmVkdJqAyVVBooq9RRXWr8WVuipMvx7/8eIAA0xYX60rfNIiAwgIcKfUP8muPTK05hNUFMCFceg8jhUFEBZPpTmQFmudfu68ry/j1d5Q2RHaNXFmsTb9oGWna2TpI1gNBsprCkkvzKfvIo88ivzySjL4EjpEXLKczAr1n9bLXxbcFGLi+jXph8D2gxoEsMuYJ0srdm7l/IlS6hYugzzyZN4t2lNy8ceJ3DEcLfr9V6IxaJwtKyGzKIqMouqyCm2dvBySqrJO1lDpd70r3NC/dREBmqJCNASHqAl3F9DmL+GUD81of4agn3Vpx+BPmoCfbxRe9lnmM/p69yFEF7AYWA4kAdsBa5XFGX/2Y5vl3KR8sYXv2E0KxhMFgwmMwazBb3RgsFsQWc0ozNa0JvMVBvM6IzWr9aHiSq9mSqDiUqdCdN59nBUCQjx0xDuryEiQEtYgIaWgT60DNLSIkhLy0AfokJ9aRXs47J3Y7enKLU7uiugWGofCijmv/9sMVsfSu1Xi9H61Wy0fm8+9dCDyWC9CcikA2NN7ddqMFRZH/oK60NXZn3UnITqYuu4+L8ICGqDEhyNEhKLOTwJJTwBc1gClrB4zF7eWCwWTIoJs8WMSTFhspgwmA0YLUYMZgN6sx69WY/OpKPGVEOVser0o9xQTpm+jDJDGcU1xRTXFHNSf+bWaSqhIiYwhsTgRBJDEukQ1oGLIi+ilf/ZCz+5C+XU36vJhGI0ohiNWPQGLNVVWKqrsVRVYS4pwXSiEFNhIYbcXPQHqxHl2QAAB5pJREFUDmDIyQFFQfj6EnjJEAJHjyZg0CBU2qY5t1SuM1JQav1kf6JCx/FyPcfKdRRV6Cmu7TiWVBqoOMubQF1abxWBPt74abzx03jhr7V+9VV74Vv71UfthVatQuvthdZbhdZbhcZbhcbL+lXtpWJsapTTk3s/YKaiKCNr//wEgKIoL5/t+E5+vsqCpLjGxidJDiUQCABh/U7U+apCIITq72Nc4Jz/O0/9v637VVH+TugWi/VRX97eqFu3xic5GW3HZHw6dcK/d29Ufp67XNPeDCYLpTUGTlYZKdcZKas2UlZjpEJnpEJnolJvolxnosZgospgpkpvosZopsZgPv1VZzSjM1kwmM79d5P96uV2Se62zAxGAbl1/pwHnHEHhhBiKjC19o/6HnsP7mtceE4RARS5Ooh6kHHal4zzn9L2wfI/Gnq2J/w+PSFGgA72aMSuyz4URfkQ+BBACLHNHu8+jibjtC8Zp33JOO3HE2IEa5z2aMeWGYB8oG690+ja5yRJkiQ3Y0ty3wq0E0LECyE0wETgZ8eEJUmSJDVGvYdlFEUxCSHuA5ZiXQr5saIoaec55cPGBuckMk77knHal4zTfjwhRrBTnA67iUmSJElyHfsX15AkSZJcTiZ3SZKkJqhRyV0IMV4IkSaEsAghzrnESAgxSghxSAhxRAjxeJ3n44UQm2uf/7p2otbuhBBhQog/hBB/1X79164CQohLhBC76jx0Qoiral/7VAiRWee1VFfFWXucuU4sP9d53p1+n6lCiI21/z72CCEm1HnNYb/Pc/1bq/O6tvZ3c6T2dxVX57Unap8/JIQYaa+YGhjndCHE/trf3QohRGyd18769++iOG8WQhTWief2Oq/dVPtv5C8hxE0ujnN2nRgPCyFK67zmlN+nEOJjIcQJIcRZ7/8RVnNqf4Y9QojudV6z/Xep1N7V1pAH0BHrgvvVQM9zHOMFpAMJgAbYDXSqfe0bYGLt9x8AdzcmnvPE+RrweO33jwOvXuD4MKAE8Kv986fAtY6IrSFxApXneN5tfp//3975hMZVxHH889NiexBtWkVaD5pApQiClVKKgtUq9c8hqVgkQrHWHmwr3gSRXEQQvXlRUBDEP9CKkWJES2lMihejeFCilrZpvLTGBqr2IsQKXw/zezpudve9ZPe9XZb5wLKzszPzvvud9+bNmzc7D7gF2ODh9cAcsLpMP5vta1Gag8CbHh4GPvTwrZ5+JdDv5VxZkn9FdN4b7X8HMp3N6r9DOp8EXq+Tdw0w6+99Hu7rlM6a9M8SJoRU7efdwB3ADw2+fxg4ChiwFfi6FS9b6rlLOinpVE6yLcCMpFlJfwGHgSEzM2A7MOrp3gV2tqKnCUNeftHt7AKOSvqzJD2NWKrOf+k2PyWdlnTGw78A88D1JenJqLuv1aSJtY8C97l3Q8BhSQuSfgZmvLyO6JQ0Ge1/U4T/lVRNET8b8QBwXNJvkn4HjgMPdonOx4FDJWlpiKQvCZ3GRgwB7ykwBaw2s3Us08sqxtzrLVtwI7AW+EPS3zXxZXCDpDkP/wrkPXpnmMWV/7JfKr1mZmWtoFRU5yoz+9bMprKhI7rYTzPbQuhRnY2iy/Cz0b5WN417dYngXZG87WKp29pH6NFl1Kv/Miiq81Gvy1Ezy/7o2JV++vBWPzARRVflZx6NfseyvMyd525m40C95e9GJH2Sl78qmumMP0iSmTWc/+lnytsI8/kzXiA0YlcR5qA+D7zUQZ03STpvZgPAhJlNExqpttFmP98H9kjKVktqm5+9jpntBjYD26LoRfUv6Wz9EkrnU+CQpAUze5pwVbS9Q1qKMAyMSorXBO8mP9tGbuMuqdUn3zZatuAi4bJjhfegWlrOoJlOM7tgZuskzXljM9+kqMeAI5IuR2VnvdQFM3sHeK6TOiWd9/dZMzsBbAI+psv8NLNrgM8IHYGpqOy2+VlDkSUysjTnzGwFcC1hX6xyeY1C2zKz+wkn022SFrL4BvVfRmOUq1PSxejj24T7MVnee2rynmi7wv+2VbTuhoFn4ogK/cyj0e9YlpdVDMvUXbZA4U7BJGF8G2APUNaVwJiXX2Q7i8bjvAHLxrV3AmWtdpmr08z6smEMM7sOuAv4qdv89Lo+QhhDHK35riw/iyyREWvfBUy4d2PAsIXZNP3ABuCbNulask4z2wS8BQxKmo/i69Z/B3XGTygZBE56+Biww/X2ATv4/9VwpTpd60bCDcmvorgq/cxjDHjCZ81sBS55R2h5XrZ49/cRwvjPAnABOObx64HPa+4CnyacDUei+AHCATQDfASsbEVPE51rgS+AM8A4sMbjNxOeKJWlu5lwlryiJv8EME1ohD4Aru6UTuBO1/K9v+/rRj+B3cBl4LvodXvZftbb1whDPoMeXuXezLhXA1HeEc93CnioDO+WoHPcj6nMu7G8+u+QzleAH13PJLAxyvuU+zwD7O2kTv/8IvBqTb7K/CR0Guf8uDhHuJeyH9jv3xvwhv+GaaIZiMvxMi0/kEgkEj1I+odqIpFI9CCpcU8kEokeJDXuiUQi0YOkxj2RSCR6kNS4JxKJRA+SGvdEIpHoQVLjnkgkEj3IPywzKIxDWqsCAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mu = Gaussian(0, 0.1)\\n\",\n    \"model = Gaussian(mu, 0.1)\\n\",\n    \"\\n\",\n    \"x = np.linspace(-1, 1, 100)\\n\",\n    \"plt.plot(x, model.mu.pdf(x), label=\\\"N=0\\\")\\n\",\n    \"\\n\",\n    \"model.fit(np.random.normal(loc=0.8, scale=0.1, size=1))\\n\",\n    \"plt.plot(x, model.mu.pdf(x), label=\\\"N=1\\\")\\n\",\n    \"\\n\",\n    \"model.fit(np.random.normal(loc=0.8, scale=0.1, size=1))\\n\",\n    \"plt.plot(x, model.mu.pdf(x), label=\\\"N=2\\\")\\n\",\n    \"\\n\",\n    \"model.fit(np.random.normal(loc=0.8, scale=0.1, size=8))\\n\",\n    \"plt.plot(x, model.mu.pdf(x), label=\\\"N=10\\\")\\n\",\n    \"\\n\",\n    \"plt.xlim(-1, 1)\\n\",\n    \"plt.ylim(0, 5)\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(0, 2, 100)\\n\",\n    \"for i, [a, b] in enumerate([[0.1, 0.1], [1, 1], [2, 3], [4, 6]]):\\n\",\n    \"    plt.subplot(2, 2, i + 1)\\n\",\n    \"    gamma = Gamma(a, b)\\n\",\n    \"    plt.xlim(0, 2)\\n\",\n    \"    plt.ylim(0, 2)\\n\",\n    \"    plt.plot(x, gamma.pdf(x))\\n\",\n    \"    plt.annotate(\\\"a={}\\\".format(a), (1, 1.6))\\n\",\n    \"    plt.annotate(\\\"b={}\\\".format(b), (1, 1.3))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Gaussian(\\n\",\n      \"    mu=0\\n\",\n      \"    tau=Gamma(\\n\",\n      \"        a=1\\n\",\n      \"        b=1\\n\",\n      \"    )\\n\",\n      \"    var=None\\n\",\n      \")\\n\",\n      \"Gaussian(\\n\",\n      \"    mu=0\\n\",\n      \"    tau=Gamma(\\n\",\n      \"        a=51.0\\n\",\n      \"        b=94.73272357871433\\n\",\n      \"    )\\n\",\n      \"    var=None\\n\",\n      \")\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"tau = Gamma(a=1, b=1)\\n\",\n    \"model = Gaussian(mu=0, tau=tau)\\n\",\n    \"print(model)\\n\",\n    \"\\n\",\n    \"model.fit(np.random.normal(scale=1.414, size=100))\\n\",\n    \"print(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### 2.3.7 Student's t-distribution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Gaussian(\\n\",\n      \"    mu=2.2695020512383577\\n\",\n      \"    var=46.95748684941486\\n\",\n      \"    tau=0.02129585859666723\\n\",\n      \")\\n\",\n      \"StudentsT(\\n\",\n      \"    mu=-0.1946342109447806\\n\",\n      \"    tau=1.4665461746068318\\n\",\n      \"    dof=0.9028354354811657\\n\",\n      \")\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"X = np.random.normal(size=20)\\n\",\n    \"X = np.concatenate([X, np.random.normal(loc=20., size=3)])\\n\",\n    \"plt.hist(X.ravel(), bins=50, density=1., label=\\\"samples\\\")\\n\",\n    \"\\n\",\n    \"students_t = StudentsT()\\n\",\n    \"gaussian = Gaussian()\\n\",\n    \"\\n\",\n    \"gaussian.fit(X)\\n\",\n    \"students_t.fit(X)\\n\",\n    \"\\n\",\n    \"print(gaussian)\\n\",\n    \"print(students_t)\\n\",\n    \"\\n\",\n    \"x = np.linspace(-5, 25, 1000)\\n\",\n    \"plt.plot(x, students_t.pdf(x), label=\\\"student's t\\\", linewidth=2)\\n\",\n    \"plt.plot(x, gaussian.pdf(x), label=\\\"gaussian\\\", linewidth=2)\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### 2.3.9 Mixture of Gaussians\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"MultivariateGaussianMixture(\\n\",\n      \"    mu=[[ 5.06087392 -5.07813706]\\n\",\n      \" [-4.9724865  -5.09928156]\\n\",\n      \" [-0.05584106  4.99523381]]\\n\",\n      \"    cov=[[[ 1.11567912 -0.01717074]\\n\",\n      \"  [-0.01717074  1.04457722]]\\n\",\n      \"\\n\",\n      \" [[ 0.89251355 -0.0138146 ]\\n\",\n      \"  [-0.0138146   1.07986159]]\\n\",\n      \"\\n\",\n      \" [[ 0.81797404  0.03778106]\\n\",\n      \"  [ 0.03778106  0.93690783]]]\\n\",\n      \"    coef=[0.33333333 0.33333333 0.33333333]\\n\",\n      \")\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x1 = np.random.normal(size=(100, 2))\\n\",\n    \"x1 += np.array([-5, -5])\\n\",\n    \"x2 = np.random.normal(size=(100, 2))\\n\",\n    \"x2 += np.array([5, -5])\\n\",\n    \"x3 = np.random.normal(size=(100, 2))\\n\",\n    \"x3 += np.array([0, 5])\\n\",\n    \"X = np.vstack((x1, x2, x3))\\n\",\n    \"\\n\",\n    \"model = MultivariateGaussianMixture(n_components=3)\\n\",\n    \"model.fit(X)\\n\",\n    \"print(model)\\n\",\n    \"\\n\",\n    \"x_test, y_test = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))\\n\",\n    \"X_test = np.array([x_test, y_test]).reshape(2, -1).transpose()\\n\",\n    \"probs = model.pdf(X_test)\\n\",\n    \"Probs = probs.reshape(100, 100)\\n\",\n    \"plt.scatter(X[:, 0], X[:, 1])\\n\",\n    \"plt.contour(x_test, y_test, Probs)\\n\",\n    \"plt.xlim(-10, 10)\\n\",\n    \"plt.ylim(-10, 10)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch03_Linear_Models_for_Regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3. Linear Models for Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from scipy.stats import multivariate_normal\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.preprocess import GaussianFeature, PolynomialFeature, SigmoidalFeature\\n\",\n    \"from prml.linear import (\\n\",\n    \"    BayesianRegression,\\n\",\n    \"    EmpiricalBayesRegression,\\n\",\n    \"    LinearRegression,\\n\",\n    \"    RidgeRegression\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_toy_data(func, sample_size, std, domain=[0, 1]):\\n\",\n    \"    x = np.linspace(domain[0], domain[1], sample_size)\\n\",\n    \"    np.random.shuffle(x)\\n\",\n    \"    t = func(x) + np.random.normal(scale=std, size=x.shape)\\n\",\n    \"    return x, t\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3.1 Linear Basis Function Models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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srmpk7piv3TOresaZtO96Fz//kT1qPuBMyZ/svqANAlBN+ASsnzjaOeijbCaXboXKf/6ZL7aGjE0RaE1iTwJoAIfFgiQZLDJgjwRQBpnAw2sAQBjoLqE7+fV/dWs2+2n3sq93HgboDHGo4RElzyVEX8yHaEGItscSaYok2RRNpjCTSGInNYMOmtxFmCCNUF0qoLhSjxnjS73tFUfCUlODctw/n3r04c/bjysvDW1Fx1Hrq8HC0sbFoYmN/vDmjiYxAHR6OxmZDHRaGympFHRqKpNefNK4sK9SXt1Bd3Ex1YTM1JXbqK1pwNB9d+8pk1WGxGbDY9JjD9JhCdJhCdRhDtBjM2rYbNlr0Rg3q49TW/G8+r4fqwgKq8vOoKsijquAwdaUluFpbflxHklRYIiIIiYgiJDwCS3g4JqsNkzUMU6gVY0goBosFvdmC3mRGrTl5fRXZ7cNTasddZsdT1oKn3I632oHi+Vm1ZbWEOkyPOlSP2qrzzy1aVBYtarMWlVmLyqhBZdQgGTRIp/C963Q6KS0tpby8nIqKCiorK6mtrUWWf4qr0WgICwsjNDSUkJAQQkJCsFgsmM1mTCYTJpMJo9GIwWBAp9OhOoVj2e2uobFxF832bOz2HOz2/TgcJcBPcdVqCwZDPAZDHHpdDDp9FDpdFDptOFpdOFqtDa3GilZrRaU6+bF8uk5UTnRWAulC4HbgAvwdZr+gKMqQtk60twNHRkvYAQw+0idSezr6hX/rywtZX2FgmjaHiAtCuTvz7tPehiAIQsC01MCSa6FoA/emD6fr7kxuNL3ExyEXcOU9H3Rok/+DFwbHLT9Ots2AXxgUboQls8DngSsWQPqp12LdVrGNu3+4G6fXyeMjH2d8yvhTfu1XBV9x37r7iDBG8PL4l+ka1vWUX9vw6VIqHnoItc1GwnPPYhp0agMTKV4v9e+/T+WT/8TQuzdJr72KJiLi1F6rKGxedpjtKwuJSLAwaU5vwuNO7aLX7fSyZVk+u78rpkv/SCbN6Y1Ge2oX/LLPx4qXnuHAhjV0GTCYSbfeicV2as0O7XW1fLvgNQ5t3ciA86cy7vqbkU7hBBBAdvmoWbgXd34T5mFxWKekotKfWuVud3kL9R8fxFNqx3phF0JGJZ7S6wCampp49913qa+vZ+zYsQwbNgy1+tQ+q7y8PD777DNaWlq47LLL6N371GtT2O0H2LnremTZRY8e/yA6avIpnbQqikJFxaccOPgQkqSjX7/XsIWdc8pxv69tYs6+AsI0at7ok8qg0FM7pmRF4fnCSp7Kr6CrSc9bfbuQbjr1psHvbSrkwWX76B4Twuu/G0yi7dSSqS6vj4eXZ/PB5iJGdYvkhZkD2006HcPnha//DptehrQxcMVC/4V2AIlywk8kkE5RSw3kr4GCtVCwDmoO/rQsLAWie0F0T4jMgIiuYOviTxSd4QVtRUsFm8o3sbViK9sqtlHWUgaASlKRGppKelg66WHppFpTSQ5JJjEkEav+zGqwKoqCOz+flg0bad26ldZt2/DV1gIgabXouqWjT09H3zUdXVoXdMnJ6BITUZnPLLmryArVxc2U7K+nLLeB8kMNuJ3+5lwanYqIBAvh8WZssWZssSasUUZCIgynXEa2R/b5KDuYQ2HWLkr3Z1OeewCv2wWA3mQmKrULEQnJhCckEZ6QSFh0LCGRUaeUFDrh+/X4cOU34TxYj6uwCU+pHdqaBassWrTxFrTRJjRRRrRRRtThRtShulNKCp2Iw+EgPz+fw4cPU1RURFVV1Y/LrFYrsbGxREVFERERQWRkJDabDbPZfMbJGZerirq69dTVr6exYTsOZ1HbEgmjMYUQS0/M5nRMpjRMplSMxhQ0mtAziqsoCg5ZwXSaN+OOCHoCSZKkRfhrE0UClfhHVtMCKIrymuR/9y/h7yC7FbhBUZRtba+dDfxf26b+oSjKWyeL19Ev/Cc+/ozXtml5ylzJP1Je4MNpH5JhE1WABUH4BVTug0UzwV7FhrF3seiT90iIn85f7S/xcfJcLp/9RIc2+1u7MDiT8uNEgnJh0FAEi66Cqmy4+DXof+VJX7KpfBNzv5lLoiWR58c+T1pY+zU12rOvZh+3f3c7Tq+TF8e9SGbsyX+9DUs/o/xvf8N87nDin34aTfjp9+HU/N13lN51N5roaJLfeB1dysmrQm9edphtKwroOSKO0TMzOnRyu+f7EtYuOUh8tzAuuK0feuOJT1Rl2cfKl55l//ofGDnzdwy5+IrTr0WkKPzw3gK2f76U3mMmMOmWP6A6SSfbsstLzVv7cBc1ET6jO6YBp9/sVPHJ1C0+gCOrBuuUVELOO3l/BQ0NDbz99tu0tLRw9dVXk5qaetpxW1tbWbx4McXFxVxxxRX06tXr5HEbt7N79xzUKiMDBizEYjn986eWlsPsyboFl6uKgQPfwRra/6SvWVJex90HiuhuNvB+v67E6k+/6d26+mZu2VeITiWxbFA3ktqpuXSEoig8+eUBXvshj3E9onnhqoFYTjExeNS+by3igc/2kRZlZsktw7EaT7LvPi98dB3s/xyG3gqT/gHqwPc2IcoJP5FAOoG6fMhZDgdWQNEmQPHXFEo5F5KGQsJgiB94Rs26j+dA3QG+KfqG1cWr2V+3HwCb3kZmbCYDogbQJ7IPPcJ7BLQViSLLOHbupPmrr2hevRpPof/iXhMfh/mcczAOHIShbx8M3br5m5YFiM8nU5xdx+Gd1RTuraW1yQ2ALdZEXHoYcelWopNDCYs1oQpgB/0el5O87VvI3byBwj07cbW2IEkqorukkdC9F/HdexHbNZ3QqJiA1mqRWz049tXSmlWD63AjeGXQSOgSQ9CnhqJLDkWXGII6NHCfMUBjYyPZ2dlkZ2dTUlKCoijodDqSkpJ+nOLj4zEaA9fnJEBLyyGqqlZSVb0Kuz0HAK02nLCwTKyhA7FaBxES0gu1OnDHsqwo7Gpq5fPqRlbUNDDaFsI/u3esH6ROqYHUmTr6hf/Rpi38+bNqHg3N4/24b4iODWPh5IWoTqOTMEEQhDO2fwV8ehPoLDhnLOSybY8xZ4GefZO6cH/dfNaNfoWR467p0KZ/axcGwRK0CwOX3Z/4K9oIsz7x1xBoR05tDjesuoE4cxwLJy88ozuj5fZybvnmFmpaa3jvgvdOmIhq/uYbSu78I+ahQ0l87VVUZ3DC69i1i+Jb56IymUj96MMT1kTasaqQjUvz6HluHGNn9TijO4UHt1Tw7cIcIpNDuOSugWh0x0/myLKPVa/8i+y13zPq6usZctHlHY6pKAobP17Exo8/oPu5o7nwD/e0WxNJdnqpWbAXd0kz4TN7YOoX1fG4PoW6Dw/g2F1N6PkphI5tv2Pf+vp63nrrLdxuN7NmzSIx8dRrLf03l8vFu+++S1lZGTNmzKBHjx7trtvYtJsdO65Gr49l4IC3MRo7HtfpqmD79pl4vU0MGvQBIZb2466obmD23gJG2Sws6NOFkA40QTsix+7g4p2HiNBq+M+gdKJ07Sdz5q89zGNf5HDN0GQent4bTQfv4AKsy63hhoVbGJhs453ZQzC0l1RVFPjibtj2Jkx+AobN7XDMkxHlhJ9IIP0XVzPsWwq7PvCXcQAxfaHHBZA+EeIHgDpwAywcUeOo4YvDX7AsbxkH6w+iklQMiBrAmKQxjEgYQXpYelCu1dxFRTQsXUrTsuV4SkuRdDpMw4ZiGTMGy+jR6M7g+/VEqoubyVlXRu72Kpx2DzqDmqReEaT2jSC5dwSmACdQwF++lWRnkfXdVxzaugmPy4k5zEaXgefQZeBgUvoOQG8K/Aiwik/Bub+Olq0VOHPrwaegDjdg7BmOIcOGPs2KdIa1qI7H4/Gwb98+tm/fTnGxv3/9mJgYunfvTteuXUlMTDzlWrunF7eRioqllJV9iL3lACBhtQ4iMnI84eEjCLH0QgrCsVzqdLO4vI5FFbWUOD1oJYmRNgtXxoZzcUzHarCKBFKbnYVVXPLqVu6O3o0tfgBPuu7loeEPcVnGZUHYS0EQhP+iKLDuOfj2Ef+J2MwPeDHvE75e9W+uXX8+h8e1cm/jB1Rfv46o1L4dCiEuDPyCemHgaIAFk6GpFGavgphja28UNxdz7Ypr0aq1vDflPWLMMWcctsxexlVfXIVRY+SDCz8g3HBsraKWTZspvukm9L16krJgwRlXqwdwZGVROOtaDH36kPzWguMmpPauKeWHDw7QLTOaCbN7B+Ru6eGd1az8dxYZQ2OYcH2v494J/W7hv9m5cjkjrryWYZeevEbYqdj82UesW/Q2515xDcMvv+qY5YqsUPtONs6D9URc3aND/Rcds02fQv3HB2ndWYXtyu6YBx5bm8ntdvPmm2/S2NjIddddR1zcmXX6Df7+H959913Ky8v53e9+d9zaTC5XFVu3Xoyk0pKZ+Ql63Zm/X4ejmO07ZiLLHjIHf4jJdGzcvc2tTNtxiJ4WA58OSMdwBkmcI7Y2tjBj1yG6mgx8OjCd0OMkpL7aV8Et721nSp9YXrpqUECO5WW7y7hj0U4m947l5WsGHb9PpHXPwTcPwYg/wsSHzzjmiYhywk8kkNo0FMOmV2HH2+C2Q0Q3GHA19LkUbKlBC3ug7gDvZL/DivwVeGUv/SL7Ma3rNCalTjpu+RYIiqLQumUrdW+/jf3770GSMA8fjvWi6YSMHx+QMvN4ZJ/M4V017Pm+mPJDjai1Krr0iyRjSAzJvSNQa4JTmcHjcpK95nt2rfqcmuJC9GYzGcNG0nPEeST07H3SmrYdJbd6sG8qp2VzBb5GF+pQHcYBUZj6RaFNsAS8v54jGhsb2bhxI7t27cLpdBIREUH//v3p1asXkZFnXna1x96SS1HhG1RWfY4suwgN7U9szEVER09Grz/z87/2bGmw81JRFV/XNqEAo20WLo8N5/yIUKzaM6u9eqJy4jczClsgdInyV7OsUntJLokms08mz25/ljFJY4gwnlr/DoIgCB3iccLyO2DPEuhzGVz0ModbylmwdwEP5SZQHx5DiGcvAJHxp97PjfALMIbBNR/B/Anw/uUw51sI/eli3u62M/ebuXgVLwsmLAhI8ggg3hLPi+NeZPaq2dz53Z3MP38+erX+x+We0lJK7rgDXWoKSa+9FrATYWPfvsTPe5zSu+6m4qGHifvHY0ed/FXkN7J28UFS+kYw/oZeAatqnzYwiiHTurBleT5RSSEMmHB0zZz9G9awc+VyBl1wUcCSRwBDLrqcutJiNnz8ATFp6aQNOrqvnubVxTj31xF2UdeAJI8AJLWE7fIMvA1OGj7NRRdnRhv70+9PURSWL19OZWUl11xzTUCSRwAGg4FZs2bxxhtv8PHHH3PrrbdisVh+XC7LLvZk3YbH20Rm5scBSR4BGI1JDBzwLtt3zCBr7x/IHPwx6p8dy9VuD9dl5WPTqlnYp0tAkkcA51jNLOjThd9l5XNbdiHv9u1y1LGcVdLInYt30S8xjGdnDAjYsTy9fzzVzS4e/Tybx1fk8MDU/0o6Z33sTx71uQzGPxiQmIJwUvUF8P08yPrI/7jPZTDkJkg854z7LzqR/XX7eX7H86wrXYdRY+TybpdzVc+rSLOefhPv09GycSNVzz6HMysLtc1G5NxbCbtyJtqY4I16q8j+QSK2fJ5PQ2UrIREGRlyeTs9z2x9YIhC8bjd7vl3Fls8+pKWhnujUrpx/6510HzEarU5/8g10kNzqoXldKfb1ZSguH/puYYRNT8PQIwJJHbxjqqGhgXXr1rFz504URaFnz55kZmaSmpoatGQVQHNzNgUFr1BV/SVqtZG4uMtJiJ9JSMjJm4WfiTV1zTxbUMGmxhbCtWruTInhqrhwUozB+93+3FmVQLIatZg0bioUcBQ0c+/V/8dV387g6W1PM2/UvF969wRB+F/VXAGLr4HSbTDufhh1DwrwyKZHiHIbiM+WaOmvYPY0UEMokToxQuSvXliSP4m0YDIsvRmu/c+Po8g8vvlxSppLePP8NzvU59GJ9Ivqx+MjH+fuH+5m3uZ5PHTuQ4C/4+vSP/8FfD4SX3kFjS2wne6GXnABrkN51LzyCvqMbkRcfz0ArlYPX83fhzlMz8QbeqEO0IX+EZlTUqktsfnuqlUAACAASURBVLPhk0OEx5tJ7uW/2VNfXsrXr79IXEYPRl9zQ0BjSpLEhJt+T01RIStefJpr5j2HLdY/Wpkzt56mrwsxDojCPCwwSZwf46olIq7uSeULO6h9L4fo2wegMvhP0zZv3kxWVhZjx46lW7fTHwXwRIxGIzNmzGD+/Pl8+umnzJo1C5VKhaIoHDjwEE1NO+nT56UTNjXrCLM5jV49n2L3njkcyptH94yHAPDICrOzCqjz+Fg2KJ3oDvR5dCJjI0J5MD2e+3NLWVBaw42J/uaHja0ebnpnG+FmHW/8bnD7Tc066MaRXSiqbeHNdfmM6hbJmO5tF621ebDsD5A8HC5+9ZRGoxKEM+Ko948uuuV1kNT+/raGzfWXa0FUai/lxZ0v8sXhL7Dqrdwx8A5mdJ9xxh1fn4wzO5uqp5+mZcNGNHFxxD78MNaLpqMynHqH+h1RnF3H+k8OUVtqJzzezOSb+9BlQFRA+zP6b4qikLNuNWsXvY29tobEXn248M6/kNizT1ATKYpPxr6hjKZvilBcPox9Iwkdn3zUjZBgcLlcrFmzho0b/U0uBw0axMiRIwkLC2zfXMfGrSbv8NOUl3+MWm0hNfU2khKvR6cLTs25Iw61Onkwt4xv65qI12t5ND2Bq+PDMQehOd6JnFUJJEmSiA/1UukxEqaRUQrMzO4zm9f3vM5F6RcxLG7YL72LgiD8rynbBYuv9jd7uvI96DkNgM9yl7K9cjsvVo2jKlSFNsxBrKeOSnUkwatkKwRUXD84/x/w+R9hy79h2Fy+zP+S5YeXM7f/XAbHDA5K2Empk7ih9gbe2vsW45PHMypxFDWvvoZjxw7in3oKXVJwLgIib/89zoMHqH7mWSwjRqBLT+f79/bTUu/iknsGBeVuqqSSGHddTxqqWvl6QTZXPzgUjV5h+XNPoFJrmHrnvWc8GszxaHV6pt/9f7z3tz+y7JnHuebx56BFpm7xfjTRJmyXdgvKybg6REfEVT2pnr+H+o8PEn5NT0pKSvjqq6/o3r07o0aNCnhMgNjYWKZMmcLy5ctZs2YNY8aMobJyGWXlH5Kachsx0VOCEjcycixJSbMpLl6AzTac6KjzeaGwkq1NLfy7dwp9Q4KTTL8xIZLva5t5JK+M4WEWelmMPLR8HzV2F0tvG0F0SHAuLP92QU82Hq7lno/2sOqPo4gwqmHprf5+ZS57EzSdc/dYOEspCuz9BFb82Z9EGnANjLsPQuODGtYn+3gv5z1e2vkSCgo39rmR2X1nE6oLDWpc2eGg+sWXqFu4ELXVSszf/krYzJmo9MH9O2ttcrPuo1xyt1YSGmVk4o296DY45oxHEDuZurISvpn/CsX79hDbtRuT5/6R5D79g5o4AnDlN1L/2SG8la0YutsIndwF3SmOvtpRiqKwd+9evvrqK5qbmxkwYABjx47Fag1uMlJRfBQXL+Rw/gvIsovk5JtITbkNrTa4x7LDJ/NkfjnzS6oxqlT8vWs8NyZGov+FbjicVQkkgESbngMV4YSH15G3s5qbbrmJL/O/5JGNj/Dp9E8xaIKbjRYE4SyybyksnesfzvbGVRDr79eo1lHLM9ufYXDEQBIW7mFt2lUQVczgpmoqDF1+4Z0WTsvg6+HASvjmISri+/HIpkfoF9mPm/vdHNSwtw+4nbUla3lww4MsTnqQ2ldfxXrRdKzTpgYtpqRSEffwwxyeOo2yv/0fLbc/Q96OaoZf0pXYtOCdtOkMGibe2JsP/7GVtUsOolGvp7own0v++iChkR3vvPpkrNExTLn9LpY+8TCbP/2QjJb+KB6ZiFk9UbXTqXcg6NOsWCd3oXFFPk3byvnP5v9gsVi45JJLUAXxZHHQoEEUFBSwevVqkpNtlJQ+SmjoQNLS/hi0mADpXf9MQ8NWcnL+SrHUg+cK67k0xsZF0YGtRfdzkiTxr55JjNt6gFv3FXKPMZSlO0u5c3w3+iYG71g2aNU8P3MgF720nns/yeKNLquRSrb4k0fWhKDFFQSaK+GLu/wj/CVkwrR//XhOEkyH6g/x4IYH2VOzhzGJY7hv2H3EmmODHrdl02bK778fT0kJYTNmEH3P3ahDg3uRD/4BINYsPojH7eOcC1MZNDmlQ6ORng5Fltm6/FM2fPgeGp2eCXNuo+/484PWv9GPcb0yjSvzsa8vQx2mJ+LaXhh6hQc9YdXa2sry5cvJyckhLi6OGTNmkBSkG2c/53AUsy/7bhobtxMRMYaMbvdjMgX/nH1nUyt35BSS2+piVlwE96bFnnAQiM5w1iWQUiKtrMuXMUQXU5wdieTR8Pfhf2fOV3N4bfdr/HFwcE+UBEE4C8gy/PAk/PAEJA3z1zyy/HSh++TWJ2n1tHKfZyKNDfOx+5qptWpIaKqlOPS8X3DHhdMmSTD9ReRXhnHfN7fj1WuZN2oeGlVwi1edWsc/Rv6DGz67msJ7/oQtMZGYB/4e1JgAmogIYv/+AHl/fYQtHx4kqWcEAye2P2pYoETEW8i8IJVNSzfibv6cgZOnkTbwnJO/8AylDTyHnqPGUv51FimRyVinpaGNCn4TU8vIBBx7a/hhxbfUKDVcc801GILc3EKSJKZOnUpRURG79/wVq9VOz57zkKTgXoSoVDr69H6eDVsu5o7sA4Rp4nmsW/CTKVE6LS/2TGbm1lz+srKQ3vGh3D4uPehxe8aF8pfJ3Vm6YiVKwTyk3pdC346PHigIJ5X3PXw8G9wtMPERGH47BDm5ALAsbxmPbHwEo8bIk6OeZEqXKUFPLiiyTO2//031Cy+iS04m+Z23MQ8ZEtSYAF63j7Uf5pK9roy4rlbGXtsDW5CbbwG0NjWy8uVnKdi1nW5DzmX8jXMxhwUv+X6Ep6qVukX78ZS3YDk3ntDJqUG9sXJEfn4+S5cuxW63M2HCBM4999yg3lg5orx8KQcOPgRAr17PEBtzUdCPZVlR+FdhJc8UVBCj0/Jh/66MDg8JasxTddYlkNKiY/HKdmo0VWhkhYLd1QwdNpSL0y9m4b6FTOkyhe7h3X/p3RQE4bfK3QKfzYXs/8CAWTD12aOaJawtWcvK/JXc1v82dM98TW3X8wiRGsmVJPSSB0uU6ED7NyckhuXDr2NL/ic8aM0kOTT4CRWAXhG9ePhQPyy1Wyn72yWkW4J/sgoQOmUKBctrkD1ezh1hCHq1/CMGTExk44ffIanNnDN9ZqfEBBh9xfVUFm2jiXrihgZvNJWfk1QS3jE2di05TPewlID3e9QevV7P2LFh1NYdRKW6FIu5c+KaTCmsCXuMvPpo/pVQQ/gZjh5zqsaEh5Ke76DY5eX2qT3QBrgPr/bMHp7ExB/eoNobgnbMPILba4Zw1lIU2PAifPMgRGbAjHcgKvjXOB6fhye3PsmSA0s4J/Yc/jn6n0Qag98439fQQOm999LywxpCp00j7uGHUJmCn/BvqGrly9f3UltiZ9DkFIZO64KqE75Lyg7msPzZeTjszYy/8Tb6Twx+gg6gNauG+g8PIGlVRFzXC2PP4A9EpSgKmzZtYtWqVYSHhzNnzhzi44Pb9BJAlr3kHnqckpK3CbOeQ69eT2M0JgY9bovXxx9yilhR08ilMTbmdUs441HVAums66kvNcpfTJdoW4iz6ji0oxqAezLvwaq38tCGh/DJvl9yFwVB+K1qLPF3qpyzHCY9Bhe9dFTyqNXTymObHiPNmsYs3Shat26lOnEIVq0KyesBICa+cy7YhMCxu+08V76afpKJS3d/Dk1lnRLXXVBAyue72D3QyqPOT3B6nZ0Styi7ljI5gS5Va7A/8yiKonRK3KxvVuBxVqA1jmXris75jAHca2vRqY1sLF3K7q9XdkpMWZZZseFrdFodmZXJOA81dEpcj6eJZvu/8XjiWL8uhPr6+k6Jm9fq5K2GREapd5NU8QA+X2unxF2XW0NJQSPaDCv/bmjotGNZtfNtUrz5POK5jqfWVnVKTOEs43XBJ3Pg6wegx1SY802nJI8aXY3M+WoOSw4s4YbeN/D6xNc7JXnkLikh/8orad2wkdgH/078P5/slORRZX4Tnzy5HXu9kwt/34/hF3ftlORR7taNfPTIfWh0eq5+7BkGTLog+LW7FIXmtaXUfZCDNs5MzJ2DOiV55PP5WLFiBatWraJnz57ccsstnZI88nqb2ZN1MyUlb5OUdAODBr3fKcmjYqeb6Ttz+bKmkUfTE3i5Z/KvKnkEZ2ECKTnc/2VSJnlIjTVRlF2Ly+HFqrfy1yF/ZW/tXt7Pef8X3ktBEH5zirfC62P9w+JetQTO/cMxw+C+tOslylrKeHD4gzR/sBhHWCL1zXXojDbMThcAUYnBb0IhBNZru1+jzlnH30Y+ikr2+YfjDjJFUah4/HFUOh3p9z9KRUsF72S/E/S4Po/MmsUHsUYbybx6II6dO2letSrocZvrali35D26DBjMwCnjyFlfTnVRc9DjuvIbad1WScioRGy9klm3+B1aGoKfVNm5cyclJSVMvmAylohQGj47hOKRgx63oPAV3O4a+vZ9ClCzYsWKTkmqPJZXjk4l8XjP3jhdZeQXvBz0mF6fzKOfZ5McbuK+8Rlsamzh08pOSJi11sF3/4DUUcQNm8HircXsKemcBKFwlnC3+gfv2PsxjHvAX/NIH/ymLzWOGmavmk1WTRZPnfcUd2XeFfTm3ADOgwcpvOpqfA2NJL/9NrarruqUmjiF+2r57Lkd6IxqLv9LJql9O2cIlF2rvmD5M/OITEnlqseeJjo1sKO9Ho8iKzQuP0zjF4cx9oog6qa+qK3B7/Tf7XazePFitm7dyrnnnssVV1yBPsidoAO43DVs334ldXXr6N79UTK63R/05twAB1qcXLj9IMVON+/3S+OmpKhOOZZP11mXQEoIMyKhUK6osUoyslehYE8NAJNTJzM6cTQv7XqJ4ubiX3hPBUH4zdi9GBZeCDoT3Pg1ZEw6dpXq3byX/R4zMmbQT51M07LlNI2cic9biBKqwua0A6CPTOnsvRfOwOHGw7yf8z6XdLuEPmmT4NzbYc8SKN4S1Lj2776jZc1aIm+/nczeE5mQPIH5WfOpag1ubYadXxfSWOXgvJndibj8UvQZGVQ9/Qyy2x3UuGveewvF52Pc7Lmcc2EXDGYt6z/JDWpyQ5EVGj4/jNqqJ3RCCmNvuAWfx83Gjz8IWkzwnzB///33JCUl0X/gAGwXpeOtcWDfVB7UuA5HCcXFbxMXewkJ8cMZO3Ysubm5HDx4MKhxN9TbWVnTyB3JMXSLyiQu9lKKit6kpeVQUOMu2VbMgcpm/jalB79LiqJ/iJGH88po9ga5FvoPT4KzASY/wR0TM4gw63lo2T5kuXNqPwn/41zN8P4VcOhbmP4SjL7nmJtZwVBmL+O6lddR3FzMy+NfZnLq5KDHBHDs2kXhtb8DIOXddzANGtgpcXO3VbLi5T1Yo01c+ufBhMUEv7YTwOalH/LtglfpMnAwMx54HFNocEcdA39ZWP9JLvYNZVhGJhB+TU+kIHcMDuDxeFi0aBGHDh1i6tSpTJo0qVP6O3K5a9i5cxatjiIG9H+LxISrgx4TYH+Lg8t2+su95YMyGBsR/E7fO+qsSyDpNCqiLApVTiu+5lIsYTrydvhPuCVJ4oFhD6CSVDy88eFOq8osCMJvlCzD1w/C0lsgaQjc9D1E9zhmNbfPzYPrHyTGHMOfBv+J+kWLUdxuKkzdMWlqcIZWE+OupxkTGIJ/MiAEhqIoPLnlSYwaI3cMvMP/5Mi7wBILK+/1Hx/BiOt2UznvCfTd0gmfdQ0Adw2+C6/s5YUdLwQlJviHJt6+qoiuA6NI6hWOpFYTfe9f8JSUUP/ue0GLW5mfx/71PzB46iWExcSiN2o4Z2oqpQcaKNxbG7S4jr01eErthE5KQaVTY4uNp9+Eyez5dhV1ZSVBi7tx40bsdjsTJ05EkiQMGTb03cJo/r4I2ekNWty8w88gSRJpaXcBMHToUMLDw/n222+Rg3Qsy4rCQ4dKSdBruTnJP9BAevq9qFR68vKeDkpMgCanh2e+OsiQLuFM7hOLWpKYl5FItdvLvworgxaXqv2w5Q0YfAPE9iHUoOXeyd3ZUdTA0p2lwYsrnB1cdnj3EijaCJfNh0HXdkrYypZKrv/yeupd9bw+8XWGxw/vlLjOnByK5tyEOsxKyqIPMGRkdErcgqwavl6QTUxaKJfcPQhzJ9TEAdixchnrFr9Dz5FjuOie+9EGeWAF8J/nNCzLo3V7JSHjkwmbmtYp/R56PB4WL15Mfn4+F198MZmZmUGPCeBuSx45HCUM6P8m4eEjOiVujt3BZTvzUEvw6cB0upt/3aPCn3UJJIAkm55qRwQufSk9MmwU7avD7fCflMWaY7lr8F1sLt/M0kNLf+E9FQThV8vVDEuugfX/8l8MXLsUTMfvCvWNrDfIa8zjgWEPYJI11H/wAdKYC6mpaMQqa6gMayTBV02NNrqT34RwJjaVb2JD2QbmDphLhLGtHwC9BSY+DGU7IOvDoMRt+OQTPCUlRP/5z0ha/1CuSaFJzOo1i//k/Yd9tfuCEnfHl4X4PDLDLv6po3fLiBGYzxtNzWuv4Q1SfznrF7+DwWwhc9olPz7Xe1QC1mgjGz7NQ/YFPrmh+GSaVhWgiTFhGvjT3+Xwy65Co9OzblFwmgva7XbWr19Pjx49SE7+qTN26+QuyK1emn8ITuKqqWkPlZXLSE6ajcEQB4BarWbcuHFUVVWRlZUVlLgfV9azx+7g/9LiMLb1G6LTRZKSPIfqmq9pbNodlLgvfXeI+lY3f5/a68fmAYNCzVwWY2NBSTWVLk9Q4vLVff7viLH3/fjUZYMS6Z8UxtNfHcAV7NpPwv8unxc+uh5Kd/ibrHXSyH5N7iZu/eZWmtxNzJ80nwHRAzolrru4mKKbbkZlsZCycCG6xOD3TQNQltvAl6/vJTLRwtTf90dv7Jy+afau/obvF75O+jnDmHzbn1Cpg18DSFEUGlfm07KpHMvoREIndM4AIV6vlw8//JC8vDymT59O//79OyWux9PEjp3X4nAUM6D/fGy2oZ0St9Dh4vJdeWgliU8HppNu+nUnj+AsTSClRoZR7YjEZS4j0abD55UpyKr5cfnlGZeTGZPJ01ufDnpzAEEQfoPqC+DNSXBwFUx5CqY+B2rtcVc9UHeA+XvmMzVtKqMTR9O4bBm++nrqB1+M7C0iwpBAhQUS5GpaTMEfuloIDEVReHnXy8SYYriy+5VHL+w7A2L7weon/Cf1ASQ7ndS8+hrGQYMwjxp11LKb+95MuCGc57c/H9CYAM11TrLWlNBjWOwxVfVj/vxn5NZWal5+JeBxS7L3kr9rO0MuvgKD2fLj82qNiuGXdKW+vIWcDYFv2tWytRJvrRPr5NSj7raarGGcM/1ScrdsoPRATsDjrlmzBo/Hw4QJE456Xpdgwdg/Cvu6UnxNgW0uqCgKuYeeQKsNJyXllqOW9erVi9jYWL7//nu83sAeyw6fzLzD5QwIMXFJzNHDTicl3YBWG87hvGcDGhOgvNHBwvUFXDYokT4JR9f4vDs1Frei8EIwaiEVbYJD38Cou8H8U8ezKpXEnyd1p7zRyZKtovsEoQMUBb74Exz62j/ya8+pnRLW5XNx53d3UtBUwHNjnqNXRK9OieutqaHoxjng8ZD85ny0cXGdEre6uJkvXt5NSLiBaX/oj66TkkeHtm3mq9deIKXfQC68895OSR4B2NeWYl9Tinl4HNYpqZ3SF4+iKKxcuZLc3FwuvPBCBg0aFPSY4B9tbe/eP9Damk//fq9jsw3rlLiNHi+z9hzGpyh8PLArXX8DySM4axNINhpdVuzhleibPZjD9ORu+ylRpJJUPHTuQ7hlN49u7LwRZgRB+A0oWA9vjIOmUpj1CQy9ud3+Bbyyl79v+Duh+lD+cs5fUGSZurffQd+rJ4WVOvSGMnSWKCo0NhKkGqSwpE5+M0JHrS9bz+7q3dzc72Z0at3RC1UqGPM3qM/394cUQA1LluCtqiLqzjuPOZmz6Czc0PsGNpZvZFfVroDG3baiAIDMC1OPWaZPTyfs0ktpWLIET2XgLrwVRWHtorex2MIZMPnYC6K0AVHEdbWy5fN8vJ7A1dyQ3T6avi1ElxqKocextQozL7wEc5iNNe+/FdDzg9raWrZt28bgwYOJjDy2M1brxBQUn0LTt4UBiwlQV7eGhobNdOlyBxrN0R3tqlQqxo8fT0NDAzt27Aho3PfLayl3eXigaxyq/zqWNRoLqSm3Ule/jvr6TQGN+9rqPGRF4c7xx4542cWkZ2ZsOO+W1VLqDHC/XqvngTkKzplzzKIR6REMSQ3n5e8P4QzgsSycJdY8BTvegVH3wODrOyWkoijcv+5+tlVu47ERj3VaszXZ7aZ47m14q6tJ+vdr6Lt2PfmLAsBhd7PilT3ojBqm3zkAY4ju5C8KgNqSYla8+DQxaV256O770GiPf7My0JwH62lcmY+xbyRh07p2WkfOW7duZfv27YwcOZJzzjmnU2IC5B56jLr6dfTo/minNVvzyAo37ysk3+Fifp/U30TNoyPOygRScoT/7mmpvgl3YRPdBkdRtLcWp/2nKsspoSn8YeAfWF2ymi/yv/ildlUQhF+T7W/DO9PBGA5zvoOuY0+4+sJ9C8muzea+ofdhM9hoWbsWd14e6stmU1PcDO5SnDoVdreBUMlBSGSXTnojwplQFIWXd75MvDmeS9IvOf5K3af4ayGteSpgtZDk1lZqXn8D0/BhmIcOOe46M7rPINwQzmu7XwtITICGylZyNpTTe1QCoRHG464TccvNKLJM7ZtvBizu4R1bKTuYw7DLrkKrO7aPCUmSGDI9jdZGN/sDWAvJvqEMudmDdUqX4540aw0Ghl12FWUHsinKClwTq7Vr16JSqTjvvPOOu1wTacQ8NJaWrRV4ax0BiakoCvkFL6HXx5EQf+Vx10lPTyclJYUffvgBd4A6S3fJMi8XVTHMamaE7fijQyUkXINeF0Pe4WcClqirbHKyaGsxlw9OJCn8+J3e/ik1FiCwfSEVboTDq2HEnaAzH7NYkiT+NDGDyiYXH2wuClxc4X/fgZXw/T+g30wYd3+nhX03+12+LPiSPw76IxemXdhpcSvnzcOZlUX8P5/EOKBzmsvJssJX8/fhaPZwwdx+hIR3zoW+q7WF/zz9GFq9nul339cpfR4BeGsd1C7ajzbGhO3yjE7p8wjg8OHDrFy5koyMDMaNG9cpMQFKSt6jpORdkpNuJD7+ik6Le39uCT/UN/NU9yRGtlMO/lqdlQmkIycNJW43vlYH6d1tyLJC3s6jm6vN6jmL/lH9mbd5HjWOmuNtShCEs4HPCyv/CsvvgC6jYc43EJl+wpccqj/EK7teYVLKJCal+kdlq13wFpqYGMqN3UGpx+wxUmcoQ9Pi/yq2xgV/KFbhzK0pWcPe2r3c0v8WtO00XUSSAl4Lqe799/HV1hJ1xx3trmPSmriu93WsL1vPnuo9AYm79Yt81BqJwZPbHyFQl5iIdfp0GpZ8iLfmzMtLRVHY+PEHhMXE0WfsxHbXS8gIIzbNyvZVhfgC0BeS7PZhX1uKPsOGPqX9EVD6jJ2I2RbO5s8C089VQ0MDe/bsYfDgwYSEtH8iGTo2GSSJ5jWB6QupoWEzjY07SEm5BZXq+HfUJUli/PjxtLS0sG3btoDEXVxeR7nLw11tyZrjUasNpHa5ncbGHdTWrg5I3FdX5yHLCr8f2/73d6JBx6z4CBaV11LgcAUkLj884a99lDm73VWGd41geFoEr6zOw+EWtZCEU9BYAp/N9d+smPZ8p4y2BrCnes//s/fe4XGU5/7+PVu0Wq20q96LJVmyiqvcGzYG4wI2vRgwSSghJ+GcAN/kJCchvZDw44Qk54ST0GKaaQYMmObg3otkW7K6LFldu2rbtXXm98faYHBT2Z04sPd1cYlr5p35jGBX877P+zyfhyfKn2BJ1hLunnj+z3Swsbz3HuZXXiX+nrvRLz3/eyHYHHy3mY66QS5bU0hStjwLfUkU+fAvf8Bi6mHVgz8kJuHsrNRQILr99L1QgyBAwtoSFBp5yuXMZjNvvPEGCQkJ3HDDDbJ0WwOwWI7Q0PhLEhIuZ/z4H8iiCbDROMjzXf18OyuZNWkJF7/gEuOrGUCKCwSQeodiceu60bl9xKZE0XDw87tNSoWSX87/JS6fi1/u+2W4lC1MmK8iQ2ZYfzMc+D+Y8224/Q3Qxl7wEp/o4yd7fkK0Opofzf5R4DaVlTgPHCBu7V00VvSij+8lMTILp/4kOkdgkaJPDQeQLnVOex9lRmeyKn/VhQdPWAFpU2DnY+Afmymv6HQy8Myz6BZdRtS0C7cpvm3CbcRqYoOShWTpHaLxkJGJizIv2mkm8f5vInm99P/972PWbas6hrG5iZnX3ohSdX6fCUEQmL4iB/uAm4YDPWPWdZYbER1e9IsvXE6qUquZcc31tFdX0tUwdi+kvXv3AjBv3rwLjlPqI9BNT8Fx2BgUL6STJ58kIiKR9LQL77pmZ2czbtw49u3bN2YvJI8o8udWI9P1USyMi77g2PS0m4mMzOBk69g/yyari1cOtnFDWcZ5s49O892cFFSCwB9PBiEL6dPsowfPmX10Jg8tLaTP7ual/cEtUwzzJcTvgw33BN4tN68DtTzZKRa3he/t+B4puhR+Of+XspU2uRsb6f7pz9DOmE7yQw/JognQcqyX8o9aKVmQTsn8dNl0D733FicOH2DR2nvILJkom675vRP4TE7i1xShOk/GcbARRZG3334bv9/PmjVriJQr08pnp7rmYTSaVCaWPoEgyBMsaxty858N7czQR/GjPHn8u4LNVzKAlBgdQVSEAqMzCU9iB56TNgpnpdDVaMY24Prc2DxDHg9Me4Bt7dv4oOWDf9IThwkT5p9CXxM8cyW07IJVf4blj4Lyof7MtgAAIABJREFU4qaJz1c/z/H+4/xozo8+7c7V//QzKPR63HNWYO1zgdRKkn4cLn0/ya5A9yohVp4OF2FGz96uvdQO1PLNyd9ErbiIF8GnWUgnoXJsmSrmN9/Cb7GQ+K1vXXTs6SykXZ27ON53fEy6xz5pQ1AITL3i4v5cEePGob/6agZfeXXMHdkOvrsBXVw8JZddcdGxORMTSMyKpvyjVkRx9Bs9kl/EtqODiBw9Ebnnzz46zeQrlxMZHcOBjW+MWhMCndcqKiqYMmUKBoPhouNjFmWCKGHbM7a27xbLEQYG95CdfS9K5cUn7AsWLMBms425I9uGnkE63V4eGpd60cWnQqEmO+tuLJbDmC3lY9L9285mfBfJPjpNikbNmrQE3jQO0jPWjmzDyD46zazceBaMT+TpXc3hjmxhLsz230L7frjmj5Agjw+QJEk8sucReod6eXzR4xg0F/97FQxEj4fOhx9GodOR8Yc/IFxgUyGYDNk8bH2xjqTsGBbeerZnWqjobTvJntdeomD2PKYtv8hGVRAZqunHedhIzKJMIgviLn5BkNi3bx+tra2sWLGChAT5snEaGn/N0FAHJSX/fZb/X6jwiRIP1LYhSvCXkhxUMpUHBpuvZABJEATGJ0XT7UjDm9YT8EGamQJA4+Gzd5vuKrmLyYmT+e2B39Lr7JX7ccOECfPP4MQ2eGYJDA3AXe/A9K8N67LGwUb+cvQvLM1ZyrKcZQC4m1uwffIJcWvWcOK4DYXKx2BHI361Fqs+kky3EbcQEVhkhLmkWVe9jmRtMtfkDbPLTeFySJkEe/8n0ClnFEg+HwPr1qEtK7to9tFp1hStQR+h5+nKp0elCYHJc+3ebibMTkUXe+Hso9Mkfut+pKEhBtY9P2pdY3MTbVVHKVuxeliGoYIgMGPlOCymIU6Uj75zqvNYL36zm5jFmcPaVY+I1FK2YjXN5QfpbW0Zte6+ffvw+/3Mnz88405Vghbt5CQc+7sRh0afDXTy5JOoVLFkpN8+rPH5+fmkpqaye/duRHF05YI+UeLPbUYmx2i5In54E/b09FtQqWJpax39Z3nQ4eHlA61cNzWDnIQLZwGd5v6sJPySxDMdY5j3dVcGso/mfgciLpz19KnuojxMNjfvHu0avW6YLzdtB2DXH2DanTBZPs+WTc2b2N6+nYfKHmJionxZMf1//SvuxibSf/Nr1MnJsunufLUBj8vHFV8rRqWWJzvF7/Px0ZNPoNHpuPLe78iW4eV3eBl8qxF1qg79lecvVw82PT09bN26laKiIqbK5GkFYDJ9THf3G4zLuZ+4WPnMuv/YauSgxcFjE7LI0Q5vXnUp8pUMIAGMT46hx5mBO6YNX98QMVoVyeP0NB46O4CkVCj59YJf4/a7+fm+n4dL2cKE+TIjSXDgKXjpRtBnwH1bYdzwFnZe0cuPd/+YmIgYHpnzyKcv/oG/P4egVhN7+x00lRtJTDOjV8TRJ9jo1caS4zdi1aTJ5l8QZnTUDdSxv3s/txfffn7voy8iCIHFY28tnNgyKl3b5s14OztJuPsbw75Gp9Zx64Rb2da+jXbr6FqDV23vwOcVmbp0+Jlxmvx8Yq66isFXXkF0Okele/CdDWiidExZunLY1+RNSSIuTcfhD0+O6h0tiRK27R2oUqLO2XntfExbvgp1pHbUWUhDQ0McOnSIkpKSc3ZeOx8xizKR3H7s+0YXZLDZ6+jr30p21jdQqYYXUBEEgfnz59Pf3099ff2odD/ss3ByyMN3c1KGvTBSKqPIzLyT3r5PcDhOjEp3/cE2XF6R+xcNv0x4nFbDyiQDL3T1YR9tNtC+v4BaN6LOWAvGJ1KUGsMzu1rC880wZ+PzwHvfDcxPlv9eNtkB1wCPHXqMKUlTuKP4Dtl0XXV19D31NIZrVxN9ngYDoeBEhYmmchMzV+aSkHHhUttgcvCdNzC1nODKe79NlF6eDC8A8ztNiEM+4m6dgKCSJzzg8/l46623iIyMZNWqVbIFyzyePmrrfkRMzERyc8/vKRlsauxDPNHaw00pcdyQIl+GVyj4ygaQ8pOjGRjSMSC2IyHhabVSOCuFvnY7A12Os8bnGnJ5sOxBdnbsZGPTxn/CE4cJEybk+L2w6SH48PtQcBXcsxnixg378qcrn6Z2oJafzv0p8ZGBRajXaMKy8R0MN95AT5+CIZsXhaKVlJhc+tW9tPkyyRW68cWG/Y8udV6ofgGtSstNhTeN7MKJN0J0amAxOUIkSaL/2eeIGDeO6BF2Jbmt6DaUCiUv1708Yl2v20/V9k7GTU4kPm14AYbTxH/9a4hWK+aNI39XDnZ30nBgD1OWrkATNbyMDQBBITBtaTYDXQ466kZePueqG8BncqJfnDWiSWxkdDRTr1pJw77dWEwj92A6fPgwHo+HBQsWjOi6iPRoIifEYd/TiTgKw+X29r+jUGjJzFw7outKSkqIjY1l9+7dowpuPN3RS05kBMsTR7Ywysq8C4Uigra2Z0as6fGJPL/3JAsLEilMGVmZwrezk7H6RF7q6h+xLtYuOL4BytaCdviLBUEQuG9hHvVGGzsbww1cwnyBvX8ObEhc/Tho5AtsPHboMexeOz+f+3OUCnmycSSfj+4fP4LSYCD5hz+URRNgyO5hxyv1JGXHMG2ZfNYCppPN7H/zVYrmL6Jwtjyt5AGcVX0MVfahvzKHiBG+78fC3r17MZlMrF69Gp1OPt2mpsfw+x2Ulvz3eZtHBBtRkvhBfQcGlZJfFmTIohlKghJAEgRhuSAI9YIgNAmCcNY3XBCEJwRBOHrqnwZBEMxnnPOfce7dYDzPcBifHPij22GLwqcbxN1sYfz0ZAQBGg6eexJ4e/HtzEiZwe8P/Z4uezi1OEyYLxWOfnjxeij/Oyx4CG5bD5rhLzaq+6t5qvIpVuWt4orsz3xbBl54HsnvJ+Huu6k/2IM6UknvySqyE0txGtrosKUzTjCiSi4MxW8VJkj0OHr4sOVDbiy4ceS+D6oImP1NOLEVjNUjutR54CCu6mriv/ENhBF2JUmOSmbFuBW81fgWVo91RNfW7u3C5fBSdtXIJ8/aqVOJnDyZwRdeRBphqdPhTW+jVKkoW3ntiHULZiajjVFTuXXkGVe2XZ0oYzVoJ4+8jHTa8lUgwNHNI/NJ9Pv9HDp0iNzcXNLSRm6kGbMoC9Hhw3l0ZGV7Hk8/RuO7pKVdj1o9ss+yUqlk/vz5dHZ20to6MqPnI1YnBy0O7s1MQjnCneaIiATS0m6mu2cjbvfIft9NlV2YbG7uXTjyIH2ZXsccg46nO3rxjtRf6+BTIIkw++K+ZV9k1ZR0UvQant7ZPOJrw3yJ6T8BOx6DkusCTRpkYlfHLt5vfp/7Jt3H+LiLe4gFi/6//x1XdTWpP3kEVZx8GRt7NjThdvpYclcxSqU8uRaSJPHJs08SGR3Dkm/cL4smBDqPWjY1o07TEXNZpmy6ZrOZnTt3UlxczIQJE2TUPUx3z5tkZ9+DTiffZ/nVngEOWR38JD+deLU8Hl6hZMzfCiFgWf4XYAVQAqwRBKHkzDGSJD0kSdJUSZKmAv8DvHXG6aHT5yRJWj3W5xku+UmBAFK3IwV/fh+uE2Z0Bg1ZJfHUH+g5pxGnQlDwq/m/+tRETpTG3jI4TJgwlwCmWnj6cmg/CNc/BVf+HEawWHf5XPxo149IiEzgB7M+awPqGxzE/Mqr6Jcvh8Q0mit6ySjw4jQPohFjQNdNhM2PRvCizywO/u8VJmisr12PiMidJXeO7gbTvwHqKNj35Igu63/uWZQJCRiuG3lABWBtyVqGfEO81fDWxQefQhQljm1pJzVPT9r4C3ccPBeCIBB/1114Tp7EsWvXsK9z2e3U7NpG8YLF6GJHvlhQqZWULszg5PF+zKbhl895uh14WixEz01HUI48hT4mIZGC2fOp2voxXpfr4hecoq6uDqvVyuzZs0esCRCRq0edpsOxt2tE2UCdna8gih6yMofn6/ZFpk6dilar5eDBgyO67umOXqKVCm5LG36J4JnkZN+DJPno6Hhh2NdIksQzu1ooSI7msoLRtcH+dnYynW4v75hGkNnmtsPh56DoGojPHbFmhErB1+flsrupj+ouy4ivD/MlRJJg04OgioQV8pWuuXwufrX/V+QZ8rh30r2y6XqNJvqe/D+ir7yCmGXLZNM1tlip39/D1CuzSMyUL8Orbs8OuhvqWLDmLrQxF2/iECxsOzrwW9zErs4f1ftvtGzevBmAZTL+vxVFH/UNP0ejSSN33Hdk0x3w+vj1iS5mG3Tcmjq6999osLnG2ADiAgQjrDoLaJIkqVmSJA/wKnChme4a4JUg6I6JnIQoVAqBbkcq3rRufEYnfpuHorlp2AfddJ4nBT4zJpMfzvohh3oO8WLNizI/dZgwYYJOw8fwzFLwDsHX34cpt474Fn+q+BPNlmZ+teBXn8tOGXzxRUSnk4Rv3U9ThQmfVyQiop1YTSr9og1iPGTZA7vpmnAG0iWLw+tgQ8MGluYsJSN6lKnHUfEw9Q6oeh1sw2sN7m5pwbFzF3G3r0GhGZ3ZYnFCMTNTZ/Jy3cv4xOEZLrdV92PtczF5ycU7r50P/bKrUKWkMPD88Bf71Ts+wed2j6nrzMRFGSgUAlXbOoZ9jWNfF4Jage5UM43RULZ8FW6Hg5pd24Z9zYEDB4iNjaWwcHTffUEQiJ6XjrfHiadleEEGUfTQ0fky8fELR737qlarmTZtGrW1tVgsw9Ptdnt41zTI7WkJxKhGV/6i1WaTmLiEzq7XEEX3sK7Z3zxATbeVuxfkjtpf48oEPQVRGp7pGEE52dH14LLA3AdGpQlw+6xsoiKUPLtr9AbtYb5E1L4HLTvhyp9CTKpssi/Xvky3o5sfz/4xEUp5yn0Aev/8J/D5SPnBD2TzxpEkiT0bGtHGqJm+fJwsmgBel4ud69eRnJvPxEVXyqbrG3Rh29GBdkoSmlz5/Jaam5upqalhwYIFxMaOfJNqtHR2vozdXkthwSMolcMvkR8rvznRhcXn53eFmShk/CyvffYg33vjWEjuH4wAUgZwZr54x6ljZyEIQg6QC2w943CkIAiHBUHYLwjCdUF4nmGhVioYl6jD5MrFHdUGgLvZTO6URDRRKmr3dZ/32uvGX8flWZfz54o/0zjYKNcjhwkTJphIEuz5E6y/NbBD/M1tkDXyTgz7u/fzUu1L3F50O/PS53163G+zMfDiS8QsXUpkYSF1+7qJTYnC2FLJ+Mzp9ApWzDFa8pynFroJ8qXShhkZ7ze/j81r487iUWYfnWbOvwV8tg4/O6zh5ldfA5WKuFtuGZPs2uK19Dh6+KT1k2GNr9reSZQhgrxpo+8KKKjVxN1xB469e3E1NFx0vCSKHP34fTKKSkgeN3o/MJ1Bw/jpydTu68YzjA5lotOL84iJqKnJKKKGaYx+DtInFJOcm8+Rj94bVjZQd3c3bW1tzJo1C8UISxPPJGpqEoooFfa9wyurN5k+xOMxkZX19VFrAsycORNJkigvLx/W+HWd/YgS3JM5uiyg02RmrsXrHcBk+mhY45/d3UK8LoLrp43ec0IhCHwtI5GjNidHrMPIbBNFOPB/kDEDsmaNWtcQpebm6Zlsquym3z68gFmYLyl+H2z5BSROgLKvyyY76BrkmapnWJS5iFlpo/8sjxRXXR2Wt94m7s47icga/UbGSGkqN9F9wsLs1XlEaOUrMzr03pvY+/u4/Gv3jbhUfSxYPmhBEMCwcuRZkqPF7/fzwQcfEBsbO+zOo8HA4+nnRPMfiI9fSFKSfFlPNfYh1ncPcG9mEsXRWtl0P642crTdzKxxocl4kttE+zZggyRJZzo+5kiSNAO4HfijIAj557pQEIRvngo0He7tHUNL1TMYnxRNtyMVh78BIVKJu8mCSq2kYEYKzUd7cZ9n8ikIAj+b+zOiI6L5r13/hcfvCcrzhAkTRiZ8btj4bfjHT6HkWrj7YzCMvPbb4rbwyO5HAib70x/83LnBl19GtNlI+Nb9mE1Oupss5E2NwniikfTofPq0Nvoi48nzduBSREG0fK1pwwwfSZJ4rf41iuKLmJI0ZWw3S8iHgqVQ/nwgkHQBxKEhzG+/jf6qpaiSRh/IAViUtYjsmGzW162/6FhLr5O2mn5KF6SP2fsh9uabECIjGXzx4tm6LcfKMRu7mbrsmjFpAkxekoXX5ad27/k3gk7jOGxE8oro5qWPSVMQBMpWrKa/o4224xff8Ttw4MCnmTxj0lUriZqZylBNPz7zhcvnJEmivX0dUVG5JMRfNibduLg4CgsLKS8vx+e7cKBuyC/yYlcfyxMNY25bHB83j6ioXDo6Lv6Zah9wsqXOyO2zsokcYwvuW1LjiVIqeL5zGFlILTtgoBlm3z/mzpp3zMnB4xd5o3z4GXVhvoQceQH6mwLl9Ur5AhtPVT6F0+fkoekPyaYpSRKmxx5DqdeT+C35vIB8Xj/73j5BQkY0xfPH9j4YCda+Xg69+xaFcxeSWTxRNl3XCTNDVX3ELM5CZZCvnXxFRQV9fX0sX74ctXr0mzYjpbX1b/j9TgoLHpEtow3gd83dxKgUPJQz+gznkeLzizy+uZ78JB03lIXGsDsYAaRO4MzwcOapY+fiNr5QviZJUuepn83AduCcsylJkp6SJGmGJEkzksY4mT5NfrKObpsOu7MLVb4a14mAt3fRvDT8XpGmw+cvM0jQJvCLeb+gfrCe/z36v0F5njBhwsiA3QTPr4Jj62Hxf8HN6yBi5KmskiTxmwO/oX+on0cXPIpW9dnOguhwMLDueaIXLUJbWkrdvm4EAdTqDhQo0TgjsUSe5IQ7lzy6ccTkjnmhESY0HOs9RsNgA7dMuCU4k44Zd4O9B+o/vOAw6wcfIFqtxK1ZM2ZJhaDg5sKbOWI6QtNg0wXHHt/RiUIQKF049kmHKi4Ow6pVWN7bhN9mu+DYox9tQhcXT8GsuWPWTRmnJzVPT+X2DqQLGB9LooR9XxcRufqgdJ6ZMHchWr2BIx+9d8FxDoeDqqoqpkyZglY79h3J6DlpIIFj/4UDZlbrMay2SjIzv4YgjH36N2vWLBwOBzU1NRcc916vmQGvn7vHmH0EIAgKMjLuwGI9gs12YUP6Vw62IQC3zx57FyW9SslNKXFsNA0y6L1IZtvhZ0EbD8Vjt/UsTIlhdm48Lx9oPac3Z5ivAB4HbP8dZM2R1Ti73drOq/Wvcv3468mPPefefkhw7NqFY+8+Er/zbZQG+cqqKrd2YOt3Mf/m8SgU8s3H9r7+MkgSi+74hmyakiRh3dyK0hBBzGXydQTzer3s2rWLzMxMWY2z3W4jHZ0vkZZ6nazG2YctDjb3W/lOVgqxMhpnv1XRSZPJzveXTUAVIhP4YNz1EFAgCEKuIAgRBIJEZ3VTEwShCIgD9p1xLE4QBM2pf08E5gMXnokEkfHJ0YiSgMmZiH9cL/4BF74BF8k5McSl6ai7QBkbwOKsxdxUeBPrjq/jYPfIzCTDhAnzT6C7Ep66PPDz5nWw+IejDtxsat7Ehy0f8q0p36I0sfRz5wZffRW/2Uzit/8NUZSo399DVkk8nXUVZCYXM+i3o44y0u7IJFfoQYoPl69dqrxW/xo6tY6rc68Ozg0LrgJ9ZsBg9zxIksTgy+vRFBSgnTEjKLLXjr8WtULNGw1vnHeM1xPI2smbloQuNjg7krG33orkcmF59/xNVgd7umg5Ws7kK5ajVAVnR3LS4kysvUN0NJzf+NhVN4B/0E30GLOPTqOKiGDKlcs5UX4Qi+n8G1AVFRX4/X5mzQpOSYgqLpLI4gQcB3uQvP7zjuvsegWlUkda6vVB0c3LyyMhIeGiZtovdvYzPkrD/NjgGNKmpd6IQqGlo+Ol847x+EReP9zBkqJk0mODUzbwtYxEXKLEa90D5x9k7Ya6D2DanaCODIrunXNyaB8YYkdjcLLvw/yLse9JsBth6S9l3Wj605E/oVao+c5U+cyGJVHE9Ph/o87JJu6222TT9bh8VGxuJWdiAllF8pkcm3u6qdm1lclLV6BPki8L3d1kxtNqJebyLIQxZmeOhIqKCqxWK0uWLJE1C6jl5JNIkp/c3P+QTVOSJH7b3E2iWsW9Qdg8GS4ur58nPmlgSlYsy0pD55U25gCSJEk+4AHgY6AWeF2SpGpBEH4pCMKZ2y+3Aa9KnzcHKAYOC4JwDNgG/E6SJPkCSEmBFt3djhQ8CYH0YHeTGUEQKJqbSk+zlcEexwXv8f0Z3ydHn8N/7f4vLO5wp4wwYS5Zat6F55YBEtz9EZSOfhHVYevgNwd+Q1ly2VldSUSHg/5nn0M3bx7aKVPorB/EPuhm/PQ4Th6toDB7FkaFGZ1uEJstmgyhj6i0sIH2pciAa4CPT37M6vzVRKmDZLioUML0r0PztkBL5nPgqqzEVVND3O1rgjbJiouMY2nOUt478R5DvqFzjmk8ZMTt9DFpcfB2JLUTS4ksKcH82uvn9QY6tvl9FEolk69cHjTdvGlJaHQqanad3xvIcaAbpT4CbUnwJneTlgS8FY5v/8c5z4uiSEVFBTk5OSQnB2/BED0vDdHpw3m8/5znfT4bRuMmUlJWoVIFJ5CjUCiYOXMmHR0ddHaeO/G81j7EIauDO9MSgvZZVqv1pKZeS4/xHbxe8znH/KPGSJ/dzR2zc4KiCVAarWWWQcfzXX2I5/O5OvIiSP7AdzxILCtNJTFaw8v7W4N2zzD/IjgHAl6NRddA9ui6NY6GxsFGPj75MWtL1pIUFZyqj+Fg37oVd0MDSQ88gBAhn2H38R2duB0+Zl4tnxcQwIGNr6NUqpi5+kbZNCVJwvpJG0qDBt0M+czYT2cf5eTkkJsr33/noaEOurpeIz39FrRa+fy0dg7a2Wu28+C4FHSjbBwxGl7a30q3xcUPlk8IaZAuKHlNkiR9IElSoSRJ+ZIk/ebUsZ9KkvTuGWN+LknSD79w3V5JkiZJkjTl1M/hOYsGibykQMq6yZWPQ6pHEfNZGduE2akICuGiWUhR6ih+d9nvGBga4Bf7fjGiVrphwoSRAUmCHY/B62shuQTu2wrpU0d9O5/o44e7foiAwKMLH0Wp+PyLYeDl9fgHBkj6j38HoHZvN5ooFYit+Lwe4hWpmKIdSHGQYTOhECSi0orG9CuGCQ0bmzbiFb3cUjg2E+uzKFsLghLK/37O04PrX0ERFYV+1dhLYM7klgm3YPPa+Kjl3AbEx3d0Ep+uI218cLuixN56K+6GBlzHzvYG8nm9VO/YyviZc4mOC97Or0qtpGh2Gs1He3Faz/Yp9JnduBoGiZqREtTWxfqkZHKnlHF82z8Q/WdnA508eZLBwUGmT58eNE0ATV4syoRIHAd7znm+p+cdRNFFRvrIu0xeiKlTp6JSqaioqDjn+Re7+okQBG4OcuvizMy1iKKb7u43z3l+/cFWMmK1XFYY3MXvNzISaRnysHPwHCWZfh+Ur4P8JQG/syARoVJw28wsttaZ6Bgchol3mC8PB58Cjw0u/7Gsss8efxatSstdJXfJpilJEn1//RvqrCz0K+Qr1fN6/Bz9pI2sknhScvWy6VpMPdTs3MqkK5cF9d13MdyNZ2QfqeSzQj506BB2u53LL79c5uyj/0EQBMblfFs2TUmSeLS5mwyNmrXpCbLpDnn8PLn9BAsLEpmXH9qsJ7lNtC8pdBoV6YZIet0FWG3H0OTH4j5hRpIkdAYNORMTqN3bjd8nXvA+pQmlPDDtAf7R+g82Nm2U6enDhAlzUTxO2HA3bPsNTL4Vvv7+mNvfPl35NMd6j/GTOT8hPfrzpS9+u52BZ59Ft+gytFOnMmT3cOKIicLZqZw4vI8YQxJCv4hR6mdApyPfeaqBZRAXG2GCgyiJvFH/BtNTpjM+LsglhjGpUHQ1HHkZvJ83PvZbLFg//BD9tatRRo/dl+dMypLLyDPknbOMrbfdRm+bjdKFGUGf3OmvvhpFVBSDr71+1rmmQ/tw2W1MuiL4XVFKFqQj+iXqzuEN5DwcCLSEYgd20hXLsA/003L07A5lFRUVREZGUlxcHFRNQSGgm5GKp8WCt/fzQQZJkujseoWY6FJiYiYFVTcyMpLS0lKqqqrweD4fqHP6RTYYB7gmOZaEiOD6P8REF6HXT6Or+42zNu5a+hzsaepnzawslEH2MlmZZCBRreKFznNkejVuBmtnwOcsyKw55eP0ysG2oN87zCWKxwEH/gaFKyClRDbZdls7H7V8xC2Ft2DQyOdB5NizF9fx4yTcdy+CSj6/mJpdXQzZvMxYOU42TYADb7+OIAj/hOyj1lPZR/KZOns8Hnbv3k1eXh7jxo2TTdfpbKW7+y0yMu4kMjJNNt1dg3aO2pw8PC4VjYxd9TaUtzPg8PDvSwpCrvWVDiAB5CdH0+1IxulsQZWnQLR78RkDk6/ShekM2by0HLt4142vl36dWamzePTgo7RYWkL92GHChLkY1i5YtxKq3w50Lrn+b2P2pCg3lvPXyr9yTd41rMxbedb5gRdewG+xkPRAIPuobl8Pok9iwpxEmo8cZmLJYuziEH51J51CKnmuU2UfCWEPpEuNA90H6LB3BD/76DQz74GhAah553OHLZs2IXk8xN18c9AlBUHglgm3UNVXRW1/7efO1e7pRqlSUDgr+JNKZbQO/TXXYP3wQ/xW6+fOVW3djD4pmZyJY+xwdw4C2VQGanZ1fS7IIIkSjsNGNONjUcUHx6fmTPLKZhFliKVyy+czvRwOB7W1tUyZMiUk3Wd001NAEegsdyZWWyV2ex3pGbeFZOe3rKwMj8dDdfXnTa3fNQ1i9Ykh24FNT78Zh6MRq/Xo546/crANlULglhnBL1fQKBTcnBrH5n4LvZ4vdFI8/BzEpAUW/EEmI1bL5ROSef1wBz7/hTc1w3xJqHgx8I5YIF8HNIB1x9ehEBTcVSpf9hFA31//D1VKCoaf2HVrAAAgAElEQVTrrpNN0+f1U7G5lYzCWNKDnHl7ISwmI9U7tjDpimXExMvnj+NuNONpsxGzRN7so4qKCpxOJ4sXL5ZNE6C94+8IgpKc7HsvPjiIPNlmIjlCxU2pcbJp+vwiT+9qYVp2LDPHhV73Kx9AGp8cTbs5AlEScCcH6stdTYEytuzSBKLjNVTvOl9Tuc9QKpT8dsFv0Sg1/OfO/8TjPztlPkyYMDLRUR4wy+5rhNvWByZgY1w4WdwWfrjrh2RGZ/LInEfOOu+3Whn4+zqilyxBO2kikiRRs7uLtHwDtt5GvK4hMg2FGNUWYmL6aPbkkid24YhIBE3MmJ4tTPDZ2LSRmIgYrsi5IjQC4y6DuNyAZ8oZWN58C01xMZElodlxvibvGjRKDRsaNnx6zOf103Cwh7ypiUTqQtNWN/bWW06ZaX/Wocxi6qGt6igTFy9FCNEuXemCdCy9Q3Q2fOaV424cxG92o5sZGv8HpUrFxMVX0lJxGNvAZxtQlZWV+P1+ysrKQqOrjyCyKAFnuRHpjCBDV+erKBRaUlNWhUQ3OzubhIQEjhw58rnjL3b1UxClYY4huJl0p0lJvhqlMoqurs8y29w+P28cbmdpSQrJ+uAHBwFuS0vAJ8GbPWcYtFs6oemTgHl2iNqs3zIzi16bm51hM+0vP34v7P0fyJ4nq/dRr7OXjU0buXb8tSRHyWfq7Dx8mKHD5STcczcKGb2Pavd047R4ZM8+OrzpLQRBYNa1wd8ouhC2XR0o9BGBzQaZEEWR/fv3k5WVRXb22DtiDhev10xX1wZSU1ah0cj3+9bYh9g+aOPezCRZs48+qu6hbcDJ/Zfly1IiGA4gJUcz5IVBVxx2qRZVohbXqa4tCoVAyfx0OuoGMZsuXneeokvh1/N/Td1AHU+UPxHqRw8TJsy5qNoQyDxSRcA9/4CiszOFRookSfxs78/oG+rjsUWPoVOfvSAaWLcO0WYj6d8fAKCrwYzZ6KRkYTqNB/YQqYsmYlBNr96JIXaAk8588hTdeGLzxvx8YYKL1WNlS9sWVuauRKMMTjeys1AoYOodcHIXDASyVl21tbhqaoi94YbQaAIGjYGlOUv5sOVDXL5A+VzL0T7cTh/F84PTjexcaEtLiSwtxfzGZyVHx7d/AoJA6eIQBemA/LJkNFEqas7YCHIc6kGhU6EtCZ03waQly5AkkeptnwCBvyHl5eVkZmaSkhK6yaxuZgqi3YurNtApzOezYTRtIjVlFSpVaALVgiBQVlZGW1sbvb2B4EatfYhyq5M704Nnnv1FVKpokpNXYjS9j98fmKNtrTUx6PRy68zQmaVO0EUyXR/F+u6BzzLbjr0CSIHvdIhYUpRMgi6C1w91hEwjzCVC1QawdsCCB2WVfbHmRXySj7tLg1+GeSH6/vYUyvh4YkOQeXs+JFHi6JZ2UnL1ZEyQL1PE5bBTvX0LRfMXE5MgX/aR1+jA3Wgmem6arNlH9fX1mM1m5syZI5smQGfnK4jiEFnZ98iq+2SbiSilgrtk9D6SJIm/7WgmL1HH0hJ5gmVf+QBSflKgG8mgvwyr5QiRE+JwN5sRPQHzy5L56QgKgZrd5+/kciaLshZxR/EdvFT7Etvbt4fqscOECfNFRBG2/ArevAfSy+C+bUHzDXi9/nW2tG3hwbIHKU0oPeu8r7+f/nXPE7NsGZGnvE2qd3WiiVKROzmOE+UHKJq6EJ9pCKNgJirOSZ89llyhG3VS6GuVw4yMj1o+wu13c31BcNqdn5epawDh1OITzG+9jaBWY1h1TUhlrxt/HTavjW3t2wCo2dNFTEIkmSGeRBtuvAF3fT3u2lpE0c/x7Z8wbvI09Imh2+lWRSgDHmRHe3E5vPjtHoZqBogqSwnpJDo2NY3siZOp2rYZSRRpb2+nr68vZNlHp4ksjEepj8BxKODxZDR9gN/vJD3I5tlfZMqUKSgUik+zkF7rGUAlwI0poTWHTU+7Gb/fgdH0AQAbyjtI0WtYWBDazlG3pyXQ4HRxxOoMNGo4+jLkLID40HUXUisVXD8tgy11Rvrt7pDphPknI4qBzmvJJVBwlWyydo+d1xteZ1nOMrL08nWrcje34Ni1i7g770Ch1cqm21rdj7V3iClXZMlq6nx82z/wul1MWx7a9/wXse/pApUC3Sz5vIAA9u3bh8FgoKhIvmYxouihveMF4uMWEBMtn26Xy8NG0yB3pMUTq5bPx2vfiX6qOi3cd1le0H3/zsdXPoA0PjkQQOr3TsRiPYamMA58Eu5mCwC6WA3jJiVQt+/iZtqneXj6wxTFF/HInkfocZy7I0qYMGGCiNse6LK263Eouwvuegd0wdnZqRuo47FDjzE/Yz5rS9aec0zf//0Vye0m6bvfBWDI5uHEkV4mzEmlu6Eat8NBXuo03HgZGOqhUx1DrM1MgmALd2C7BHm78W0K4gooiQ+xcakhE/IWw9FXEF0urO++S8zSK1HGhtaLYWbqTNJ0abzT9A7WviE66gYpnpeGEOKJh2HlSgS1GvPGjbQeO4K9v49JS0K/QCqem4bok2g8ZMRZYQJRCln52plMXLIMa6+J9poqjhw5QkREBKWlZwegg4mgFIiakYKrYRCfOdClLCoqH70++B5TZxIdHU1hYSHHjh3D5fXypnGQpQkGEoNsnv1FDIbpREXl0dX1Biabi+0NvdxQlhnySfTq5Fi0CgWv9AxA+wEYaIZpocs+Os3NM7Lw+iU2Hh3epmaYf0Gat0FvLcz/7phL70fCpuZNOLyO885zQsXgK6+AWk3cLSHyGzwPVds60BkiyJsW2mDzmYiin6MfbyKjqISUPPm8L/0OL44KE7ppyShDVKZ+Ljo7O2lra2POnDkolfK1sjca38PjMZEtc/bR0x29SMB9mfJ9pgD+urOZxGgN10/LkE3zKx9AStBFEK+LoMuRhc9nQUwbRFArcNUNfDqm9LIMhmxemo8Or+48QhnB44sex+v38v0d38crei9+UZgwYUaHuQ2eWwb1H8Dy38GqPwfK14KAw+vgezu+R6wmlt8u+C0K4ew/mZ6ODgZfe43YG25AkxfYfa7d143olyhdmEHDwb2oI7VE22PoMwwRE9NPC/nk2gLddBSJYQPtS4nGwUaO9x/n+vHXy7MrOfUOsLRhf/Uv+C0WDDeEviOLQlCwOn81e7v2cnhHIwhQNDf0u5LK2FiilyzB+t4mqrZ8hDZGT/6M0Pt7JGZFE5+uo25fN45yIxHZMaiTo0KuO37mHCK0URzfsYXq6mpKSkrQaEJUEnkGuukpIMFARQUWSzlpqTfI8lkuKyvD4XCw/ng9vR4ft8hgICoIAulpN2GxHOb1A1X4RYkbyzJDrhujUrIq2cDbxkG8FS+CWgfFq0OuOyE1himZBt443H5W97kwXxIOPwdRCVAa4gzYM5AkidfqX6MkoYSJiRNl0xUdDixvv43+qqtQJcpXzmU2OmmrGaD0sgyUSvmWws0Vh7GYjJStCP3fijNxHOwBn0h0CMvUz8X+/fuJiIhg2rRpsmlKkkRb27PodIXExy+UTdfh8/NSVz+rkmLJ1ob+PX+alj4HOxt6uWtuDpFq+YJ0X/kAkiAIlKbrOTEQyESyOirRjI/FVfdZbXt2cTz6xEiqtg+/7jxHn8PP5v6Mo71H+d8j/xuSZw8T5itP2/6AWba5He54A+b8W9B27CRJ4hf7fkG7rZ3fX/Z74iPPXYrR++c/IygUJD7wHQBEUaJ6Zydp4w0YkiJo3L+Hgulz8LRY6YsdQm/o44SUT4GzPXCDxHAJ26XExqaNqAQVV+ddLY9g8TWgMWB+801UaWno5srjE3Bt/rVIUiDYmVUcT0wIupGdC8P11+GyWjhRfojiBYtRqkK/GyoIAkVz0nB32PAZnUSVyeMRoI7QUDhnAdXHq/F4PEyZEtosoNOoErREjNPT0/M2IJCaeq0suvn5+URHR/N6zwDxaiVXJOhl0U1NvQFQsqG8jWnZsZ9mloeaNWkJiB5HoNNn6fWgkUf3phlZ1PXYON5pvfjgMP9aWLug/sOAGbtKvkVoubGcJnMTt00ITafG82F57z1Eu524O0KfvXcmVds7UCgFShfKl7EBcOTDd4lOSGT8zLmyaUp+Efu+LjTjY1GnhqahwbmwWq1UV1dTVlZGZKQ88wsAs/kgdkc92Vl3y/pZ3mgyY/OL3CNz9tErB9tQKgRuC6Hv37n4ygeQAErS9TT1epAEAxbrUSKL4vGb3fhOGWcLCoGJizLpbrLQ22Yb9n1X5q3kpsKbeO74c+zq2BWqxw8T5qvJkZdh3TUQaYD7tsD4K4N6+zcb3+TDlg/5ztTvMCN1xjnHuOrrsb63ifi1d6I+ZYzbWtWHtc/F5MuzaDlagcthp3j8AiSvSKevj6REO82eIsaJXYiCEmJzgvrcYUaPV/SyqXkTi7MWnzdgGHTUWrxZK3E0DmC4ZjmCTGneWfosFimXg13NhNmhL+c6TfSCBfRkpCKKfkouWyKbbuHsFLIiFEgCRE2Wb6e7dNESXDo9URoNOTnyfde10xIZNGwnNmo2kZHyeF4olUpyJ02mMkLH6oQYImTqQKPRJDHIak4ORnJTmXwLwjkGHV+37EHtdchSvnaa1VPS0agUvH64XTbNMDJR/jxIfpj+DVllX6t/jZiIGJbnLpdNU5IkBl9ej6a4GO20qbLpelw+avd1M356MlF6+Tq+9bW30nb8GFOvuhqFjOVcQ8f7EK0eohfIGyyrqKhAFEVmzZolq25X12uoVDGkpMjrMfViVz8TdJHM0Ic+u/k0bp+fDeUdLC0OXdfR8xEOIAET0w14/RIWaT5W61EiJwQWDq66z1q0Fs9LQxWhoHIEWUgAP5j5AwrjCvnR7h/Rbe8O6nOHCfOVRPTDxz+Gd74N4+YHgkdBzuKpG6jj0QOPMjdtLvdOuve840x/+AOKmBgS7rvv02OV2zqIjtOQNzWRut3b0cbo0Xvi8ar9dA/0gM5Mr1XPeKELT3RW0Mrtwoyddls7KoWK68ZfJ6uu1ZQGCMQWyWe6CDDdsgSPwoU9Q753k6BS0ZOZQrTLQ7xBvs43UdFqsrUqTBIQKd9/Z316Fn6dnijPEAoZW/p6ck7i0/YTO7BINk2Ajqx8RIWSKWZ5W83vNy5GrfCwIKfz4oODhCAIfL13M83aDDqTp8uma9CqWT4xlW7LkGyaYWTA74OK5yH/ipCasX+RvqE+Pmn9hOvGX4dWJZ+J9dDhw7gbG4m/43ZZM0Xq9/fgdfmZdHnoS13P5OjH76NSR8ji+3cmjkNGlHEaIgvle9+KosiRI0fIy8sjPl6mzTjA67Vg6v2QlJRrUSrl+yxX2ZwctTlZG8Kuo+fio+M9DDg83D47WzbN04QDSEBpeiDNusc1Gbu9DiFGQp2qw1X/mQ9SpE5N0Zw0Gg8aGbJ5hn3vSFUkf1j8B7yil+/t+B5ef9gPKUyYUeOywPpbYd//wqz74Y43QRvcl6LVY+Xh7Q8TGxnL7y773Tl9jwDse/bg2LGTxPu/idJgAKC/y05H3SATF2Xg87g4UX6QwjkLcTeY6U31oNHYaCUZweqlWGhFlT4pqM/+r4ogCMsFQagXBKFJEIQfnuN8tiAI2wRBOCIIQqUgCCtD8Rx5hjw237iZBRkLQnH782LZeYTIFAURpk9k0/R5/HibtLQlVvNe2zuy6Zp7uul1WMkYtGF7/wPZdF2NZtSixEm7j84zNodCTdXx4yAIDDXVYO2TL6hiHHgHhahFc6QQaZgNQILBR04/KS4H9upjsmm6fX7+0aBmekotDvO7sulibie75wBvpCzjbZNZPl3g8Zun8MzXZsqq+c/kUnlHhJSGD8HWDTPlNf59s+FNfJKPWwrlNbEeeHk9CoMB/dUylYsTyHqq3tVJck4MqbkG2XS9Hjd1e3ZQMHseUXr5dH0DLtxNZnTTU0LeJONMWlpasFgssnofAfT0bEQUPWSEuOvoF3mpq59IhcCNKfIF6QBePtBGdnwUC8bLl1V9mnAACRiXoEMXoaTVloUk+bDZjhNZFIf7pBXR5ft03KTFmfh9ItW7R9b9Ikefw6/m/4rKvkoeP/x4sB8/TJivBv0n4JmlgQ4l1zwBKx8DZXAzCSRJ4ie7f0K3vZv/XvTf5y1jkvx+TL9/DHVGBnF33vnp8aptHSjVCkoWpNN0aD8+j5uiyQvwD7jo0pqJjRukhXx0Fis5ChOq9MlBff5/RQRBUAJ/AVYAJcAaQRC+2P7sEeB1SZKmAbcBT4bqeZQKJUqFfOnlrvoG3HX1GC6fBa17AqbwMtBS2YfX5Uc/UWJz62Y8/uFvjIyFml3bQBDITUzDsnGjLJoAzgojQpSKQbWSuv3yZFxJksSxY8dITUlG4XFRu3u7LLp+vxNT70ckRi8FmxJXgzwBs0aHiyM2J8t1Kjo7Oujv75dFd3t9L5YhHytKFJhMH+H3y5SZc3wDAA15q3jTKF9QEkAto/HvP5tL7R0RMg49C/oMKFgmm6Rf9PNGwxvMTZvLOMM42XR9g4PYtmwh9rprUWjlyxTpa7fT3+mgWGYz6aZD+3E7HUy8fKmsuo5yIwgQNUMe37/TVFRUoNVqKSqSr8uwJEl0db1GTMwkYmJC3EH3DBx+P28aB7kmKZY4tXzZzU0mGwdbBlgzKxuFjMHB03x13kAXQKEQKE7T09Qf+CNmOV3GJkq4Gj+bFMSn68gqjuP4jk78/pHt6C3NWcrakrWsr1vPRy0fBfX5w4T50tOyE565AhwmWPs2zLg7JDLPVz/P1vatPDzjYaYmn78m3/L227gbGkj+fw+jONVVyeXwUr+/h8JZKWijI6jdvR19Ugp6V2BHos3aQ3q6ixahkIn2ZhRIkCJft5NLmFlAkyRJzZIkeYBXgS+6/krAaUdeA/Cl6WFtfe9dUKnQr/1u4EDVBll0Gw4a0RkiuGLOHGweG7s6Q+/TJ0kStbu2kV06ibRrr8VVU4O7uTnkuuKQj6GafqKmJJE/I5nmI714ztgcChU9PT2YTCamz5hJ+oQSanZskaVzVm/vP/D7HWQU3IpCp8ZZYQy5JsBbxkEUwH2lgZLiY8fkyUJ652gnCboIVpRdht9vp69viyy6VL4BmbNYkD+ZWoeLGnu4pCxEfPnfEQMtgc2xsq8FfWPsQhzoOYDRaeTGwtB3/zwT6/sfgNeL4Xr5Os1BoGmEUqVg/PRkWXWrt3+CPimFrBL5ss4lUcJZbkQzPhZVrHz+OA6Hg7q6OiZPnoxaHfomGaex2iqxO+pJT5c3k+4dkxm7X2RteoKsuusPtKNWCtw8Q95SzNOEA0inmJhhoL5nCE1kDmbzISKy9QhaFa6agc+Nm3x5Fg6zm+YjI09Ff2j6Q0xLnsZP9/6UpsGmYD16mDBfbg49Cy9eD9EpcN9WyL0sJDIHuw/yx4o/sjRnKXcW33necaLDgelPf0I7ZQoxK1Z8erx2Tzc+r8jkyzNxmAdprTpK8YJFuOsGGEqCAfMA0dE9NPsmUORtCVyUGg4gARnAmW6wHaeOncnPgTsFQegAPgD+XZ5HCy2SKGJ5bxPRCxagyp8KWbOh8nUIcZBhyOah7Xg/hbNSmZc5j/jIeN5vfj+kmgBdDXWYjd0UL1wS+O4oFFg3bQq5rrOqF3wSurIUJsxKxecVOVnZF3LdyspKFAoFpaWllF62hIGuDkwtJ0Ku22N8l0hNOnHxs4iamsRQ7QCiM7Tl85Ik8ZZxkAVx0YxPiCcvL4/KysqQB8xsLi+f1Jq4ZnIaSfFz0GhS6e6RIbOt5ziYqmHyLaxOikUlwIYeebOQvkJ8+d8Rla8Hfk69XVbZ9068R0xEDIuzFsuqa3nnHTRFRUTKmKHi94o0HOwhd2oikToZAxt9JlqrjlK6aAmCjD547hNm/GY3uhnyNcmAwHvP7/fLXr7W1fkqCoWW1JRVsuq+1NVPQZSGWQb5Otx5fCJvHengqtJUEqPl69Z4JuEA0ilK0vU4PH6GFIswmw+CQkRbHM9Q7cDn/ANyJiZgSNJy9JP2EU+M1Ao1jy96HJ1ax4PbH8TmGX5HtzBhvnL4vfD+/4P3Hw6YSt7zD4jPC4lUj6OH7+/8/qflphcywet/9jn8vX0k/+AHn47z+0Uqt7WTXhBLYmYM9ft2I4kiE8oW4G6xYEp2oVK5sUt9mMw6ioVWfBF6MMjbdvNfmDXAOkmSMoGVwIuCcLY5lSAI3xQE4bAgCId7e+U18R0NzoOH8BmNGFafmvBMuhl6a8F4PKS6jYdNiKLEhDmpqBQqlo1bxo72HSF/J9Xu2ooqQkPh7Hmok5PRzZmNZdP7IQ8yOI/0okrSos6MJi3fQHSchoZDoc3KEUWR48ePU1BQQFRUFAVz5qNQqqjdsyOkuh5PPwMDu0hJWYUgKIialgx+Cefx0AbMjlidtLo83HDKA2LKlCmYzWba2kJbkvnR8R48PpFrp2UgCApSU65lYGAnHk+IA4RVr4NCBaU3kBChYkm8nreMg/hlyDALc06G9Y6AS/A9IUlQ+RqMWwix8s0JnF4nW9q2sGzcMjRK+Rah7hMncFVVYbjui0lkoeXk8T7cDh9Fc+XpSnmamh1bQZIoXXSFrLqOw0YErQptiXyZMZIkceTIEdLT00lNlS9w5fM5MJo2kZJyDSpVjGy6jQ4XFVYnd6TJa569o6EXs9PLTWX/nOwjCAeQPuVTI213GT6fFbu9Du3ERCSXD3ez5dNxgkJg6tJsTCetdDeN3DQxOSqZxxc9Tqetkx/v/jGiJJ+5ZZgw/zI4B+ClG+HQMzDvP2DNKxCpv/h1o8Dj9/Dw9odx+908cfkT6NTn30XwdnbS/+yzxKxYTlTZZ7srTYeM2AfdTLsq0AmhZudWknJy0Zq1IEEn/aSkWDlJLoLNS7GiDZJLQcYXziVMJ3DmrDnz1LEzuQd4HUCSpH1AJHCWa6AkSU9JkjRDkqQZSUlJIXrc4GF5910UOh3RS061sy+9IbAoPb0bHSIaDvaQkBlNQkY0AFfnXY1H9LClLXSlP36fj/r9e8ifMZsIbaDNrf7qa/C2teGqrAydrsWN56SFqClJCIKAoBAomJlCe/UAQ/bQ+T61trZis9mYNClQsqCNjmHc1DLq9+5EEkP33jeZPkKS/KSkrgZAnRGNKlHL0NHQLpTfNA6iUQisTIoFoKioCJVKRVVVVUh13znaRU5CFNOyArqpqdchSX6MxhBmtokiVL0Z2NjQBRZnN6XG0+PxsnfQHjrdry5Be0ecOn9pvSc6y2HgBEyWt/Tmk7ZPGPINsTp/tay6lo0bQanEcI28bdbr9vWgM0SQVSxfVzBJkqjesYWs0skYkuULqIhOL0PVfURNTUJQy7fU7+zsxGQyUVZWJpsmQG/vZvx+J+lpN8mqe7ps+3qZzbM3HukkXhfBggL5zbNPEw4gnaIgOQa1UqDVGjBWGxzcT2RBHEKEkqEv7NwVzUlFG6OmYvPodtamp0znezO/x7b2bTxT9cyYnz1MmC8VvQ0Bv6O2fXDd/8FVv4IQmho/evBRqvqq+M3835BnuHCGk/Gx/w8EgZTvf//TY5IkUbG5jfh0HTkTE+htbcHY3MjExVcydLwfRZyGk91tZGTaaRGKUFrcFCvawwban3EIKBAEIVcQhAgCBqhfbKXUBlwBIAhCMYHFwSWwdTx6RJcL28cfE3PVVSgiT/kT6BJg/JUBH6QQBRksvUMYW6wUzvzMVHNy4mQyozNDWsbWWnUEl81K0fzP2srHXLUUISICy6bQ6Tore0EC7ZTPFoqFs1IQRYkTFaH7CFVVVaFWqyksLPz0WPH8RdgH+umoqw6Zbo/xXXS6AqJ1E4BAq3ntlCTcLRb+f/bOOzqO+zzXz2xvWNRdEAQIgOiNvYKkJFLsRZRrYsdxiyL3JseWbMdFke3YceJ2b6zrxE6PW+zYquy994ZeCRBEXSzK9j73jyFYAaLtDmlxn3N4xLNTvlme1e7M9/ve9w07/DGpGYqIvNo3xLpUM2aV9F2t1WopLi6mtraWcDgck7p9Dh8nWvp5et7Mm6u/JlMRJlNpbBtI106A4/odD/zrU80kKBX8TmYz7UeEt/ZvxJXfgFILZfJO5LzW8hpZpizmW8b2e4w2YjjM8GuvS7LtNPkefj2OAO3VdoqXz5DVcLizroah3m4qVq+TrSaA5/IN2bbM8rWqqiqUSiUVFfLaM/T2vY5Ol0li4iLZat4u207XyiiJ9AXZV9fLU3MzHmiYQryBdAONSkFRegINfSEMhtkMDp1GUCvQlSTjrbEjRm6NJas0SuauyaK9yo69c2qrTX9W8mdszdvKP178R45cPxKttxEnzh83Tfuk5pHfCR98I+Z+AP/T8D/8rvF3PFPxDGtz7j9e7D51Cufu3aR99COoZ95K8LhWM8BAl5sFG7IRBIGqg3tQqlQUL34Mf/MQrjwlHo8Hg6GDa+pFFLo6MeCL+x/dQBTFEPApYDdQh5SkUyMIwkuCIIwsjf4V8KwgCJeBXwEfEuVwI44hriNHiLjdJD511yrsnHeDswvaj8WkbvN5SbpVeFsDSRAEtuRt4UzPGWye2DxzNRw/gtZoJHferZVJZUICptWrcezciRiKjam157INdaYJtcVw87XUTBPJGUYaz/TEpGYoFKK2tpaSkhI0Gs3N1/MXLUOl1VJ/LDYyNq+3k+Hhc8xI337HOL1hngVE8MTI9+nYkJP+YOieCOOKigo8Hg+tMTJKf/1KNxERts+/0w4n3bqNYcdFvN7rManLld+AxgTFt5Li9UoFWyxJ7LAN4Y/hhNmjyFv6NyIchOr/heLNoJMv3r3H3cOZ7jM8lf+UrNIb96lTkmz77W+TrSZIU7diRKR4ubzytZojB9Do9RQuWyFrXc9lG6p0A5obU8ZyEIlEqKmpobCwEJ1OPtNuSbZ97IZsW77P8oW7ZNtysau6B/8N2faDJN5Auo2KmYnUdF39gsEAACAASURBVDlITFzG0NAZRDGMviKNiDtIoG34zn0fz0KlUXBp79SmkARB4MXKFylJKeGFIy/QOhz7JJo4cR5aRBFOvgy/fDck5cCzByF7WUxLXui9wHdOf4dVmav49IL7+22KoRC93/426qwsUv7izgS4i3vbMSZpKVycTigYpO7oIQqWVEJHECIiPYZhVCofoXA7Nf4cirw3THTjCWw3EUVxhyiKRaIo5oui+O0br31dFMXXbvy9VhTFlaIozhNFcb4oinse7BVPH8eOnShTUzEsXXrnhuIt0sNp1W9jUrfpbC8Z+YkkpNx5g7c1bysRMcLOqzujXjMY8NN09hSFS1eiuiuVxbxtK+H+ftynTke/br+X4HWX1EC5DUEQKFqSTnfzMM4BX9TrtrS04PP5bsrXRlDrdBQsXk7j6eOEQ9E3te7tk6Zu0u8yEVVbDahnGqVV6Rjwv72DmFUKnky5U2ZcWFiIVqulujo2nl6vXuqkItNMgfXOh6T0dKkp29sXg8m2UABqX4WSbaAx3LHpbdYknOEIB+1xf8to85b9jWjeDx47zHuPrGXfbH0TEZGn8uQ1HB5+5VUUZjOmNWtkrdtwugdrTgIpGfIZHYdDQZrOHKdg8XLUWvkaKqFhP4E2B4a58soz29vbcblc9/zuxZqbsm2ZzbN/f5dsWy5evdR5h2z7QRFvIN1GeaaZAXeAkHo5oZATp7MGXXEKqBR4q+137KszqSlbOZPGM724Bqd2A6pT6fjxmh+jUWr47IHPxk214zyahALw2qdh95ehZCs8szvmRpI97h6eO/QcWQlZ/N3jf4dyHInc4C9/hb+pmfQvvYBCe8tssrfNQWfDEPPWzkKpUtB89iQ+l5OKJzfgre5Hmajhan8H2dleupnJ8DCUKtqJCEqwlsb0PcZ5eIm43bgOHcK8cSOC6q7IZo1BWo2ue11anY4i9i4X9k43BYvT79mWl5hHaUppTBpIVy+cJejzUrLy3gRF0xNPoEhIwPH661Gv671sA+FO+doIIxNYTTEw066qqkKv15Ofn3/PtpKVT+BzOWm7fDHqdXt7XyPRvAC9/t7vT8M8K8EOJyF7dKPmveEIO2zDbLMkobtrnF6lUlFaWkp9fT3BYHQ/y239bq5cH+bpefeuwur1WZjNC2IjY2s5AL5hqLg39nxVcgIpaiWv9MVlbHEmyJVfgz5F8tOSCVEUeb3ldeZb5jPLLJ9pd8Trxbl/P+ZNm+64j4o1Q70e+jtcd0zdykF71SX8bjdFlY/JWtd7RVooGO13L5ZUVVWh0WgoLCyUtW5v7+t3yLblYES2vf422bYc9Az7ONFi5+n5mbJOW41GvIF0GyNG2p0e6UM4OHQahVaJrigZb3X/HTI2gHlrZyECl/Z33H2qCZNhyuD7T3yf687rvHDkBcKR2HgFxInzUOLuh/98Gi7+Fzz+PLz7P0ET2xUib8jLZw9+Fn/Yz4/X/Biz5v7m3MG+Pmz/5/9gXLEC09o7b/Iu7m5Ho1dRvkqStFUf3IvZYmVWYTm+pkGEYjPt7e3MnOmiQZiHwhGkVLhGODkf1PqYvcc4DzfOg4cQfT7MWzaPvkP5O8A7CK2Holq3+VwfggD5C0e/sdw0exPV9mo6nFP/TRuN+hNHMCQmMav83pVJhVZLwvr1OPfvJ+KPnkePKIp4LvehyTWjSrz3YSXRoid9tjnqaWyBQICGhgbKy8tRKu+9scydtwCdKYH6KKexuVyNuFz1N82z70Y/T/IbifYU0n67A3c4wtuto4/xV1RU4Pf7aWpqimrdN650AbB17uiSlPT0rbhctbjdLVGtS83vQZcEeavv2aRWCGyzJLG734E7Rr5Pcd5C+BzQsFNqRqo04+8fJZqHmmkZbmFr3lbZagK4jhxF9HjG/t2LEc0X+gDIX2iVtW7DiaNoDUZy58kbZ++50i/JttPku8cMhULU1dVRXFx8h2w71vh8XQwNn5VdvnZ0UJJtyy1fe/1yF6IIb5s/c/ydY0y8gXQbpRlmBAHqe8FgyGdw8BQA+jlphB0BAtfvnBAyp+kpWppOzeFOPI6pp7ksnrGYLy/7Mkc7j/KjCz+a1nuIE+ePht4a+Nka6LoA7/pXePKvQRHbryRRFPna8a9RZ6/ju499l7yk+5tmA/T93fcQAwFmfP1rd/xA2TtdtFy0MWd1Jhq9iuG+XtqrLlH+xDr8jcMQEulOdBKJRNDq2riqWYHeHaJCeS1uoP2I49ixA1V6OvqxkkoK1oI2Eap/H7WaoijSdK6XzOJkjKM0VAA25m4EYE9b9NQffo+H1gtnKa58DMUYk37mzZuJuFy4j0XP9ynY7SbU58Uwb+yHhsLF6divuxjscUetbkNDA8FgcMwxfqVKTdHylbScO03QFz35nCTXUmC1bhl1uypJhybXHPUG0qt9Q6SpVVQmje61MXv2bIxGY9RlbG9c6WZxTjIzk0Z/SEq3bgGE6E4hBb1Q/yaUPjXmA//T1iS8kQj77I7o1Y3z1qRxF4R8MEfe5Ki97XsREFiXI6+xs3P3bpTJyRgWL5a1bsuFPmbkme+RbceSUDBI89lTFCypRKmSz2A5ZPcS7HBimCtvOldrayter1d+8+wb3+/pVnkT/UZk22tTY5MOPRavXOpkXlYieRb5vK3GIipPa4IgbBIEoUEQhGZBEL40yvYPCYJgEwTh0o0/f3nbtg8KgtB0488Ho3E9U8WgUVGcnsCFa4MkJy9jaOgckUgIfWkKKAW8oxhQLt6cSzgU4dK+qXkhjfAnxX/Ce0vey7/X/DuvNL8yrXPFifPQU/8m/MsGSaLz4Z2jygFiwU+v/JTdbbt5btFzrJ61etz9XceP43jzTVI/8hE0ubl3bDu3sw21Vsn8tdkAVB/aB0DFmnV4q/tRmNS0DF4jMVFBINBGTSSPtKFBMuhHiBtoP7KEh4dxHT2KefNmhLEapiotlG6D+jcgFJ2pHNs1J8N93vuO8WeaMpmbNpfdbbujUhOg5dwpwsHgqPK1EYzLl6FMSsKxc1fU6nov20AhoJ8z9o10/kIrCNB8vi9qdaurq0lISGDWrLGlISUrHifo99F68VxUaoqiSF/fDpKTl6HVjP1+DfMshHo9BKPUMHOHw+yzD7PNmoRqjGQjpVJJWVkZjY2N+KM0Ydbc56S+x8m2MaaPALTadJKTltHb9wZR81Ju2gsBF1S8Y8xdlieZSNeoeLV3KDo147x1qX0VEjIga+n4+0aRve17WZS+iDS9fE2GiM+H89AhEtavv1e2HUNG5GtyTx+1Xb5AwOuheIW88jVPlfScqp8jr3yturoanU43qmw7lvT0vo7ZPB+DIUe2mt5whJ39kmxbG+NF79tpt7up6XKwbe6Dnz6CKDSQBEFQAj8BNgNlwHsFQSgbZdff3DC2my+K4s9vHJsCfANYBiwFviEIgrzzYHexODeZi9eGMJuXEw67cLpqUOhU6IqS8Vyx3SNjS0o3ULgknarDnXhdU59CAnh+yfMsz1jO35z8Gy70XpjWueLEeSgRRTj6ffj1+yCtSDLLzhxjCiPK7Gnbw8uXXmZ7/nY+VP6hcfeP+P30vPQSmpwcUp/9yzu2DXS5aT7fx5w1WehMasKhENUHdpM7dwEmYwreOjuaihSampooLBLpJ40ej5oc14iBtrwmg3EeHpz79kMwiHnr6JMiNyl/B/gd0LwvKnWbzvWhUArkzb//jeXG3I3UDdTR7miPSt36E0cwW6xkFJaMuY+gVpOwfj2uAweIRGEqRxRFPFX9aAuSUBrHXv01JWvJyE+MWgPJ5/PR3NxMeXk5ivvcWGaWlmNITKLx5NGo1HW5G/B4WsecPhpBPycNBPBcic4U0t5+B96IyPZxTEQrKioIhULU19dHpe7rl7sRBNg85/6JSunp2/B4WnG5aqNSl5rfgyENcsduhioFge3WJPYPOHCE4jK2OGMQcEsG2iXbYj55fTutQ600DzXLPn3kOnpDvrZpo6x1H5R8rfHkUXSmBLIr5sla13vZhiY7AZWM01bBYJD6+nrKyspQydgcdLul7/aR0AS5ODLoxB2OsN0qr4n17hopNXZTxQxZ645FNL61lgLNoii2iqIYAH4NPD3BYzcCe0VRHBBFcRDYC2yKwjVNmSW5Kbj8IfoC0oTAiIzNsNBKxBHA33LvqtKizbmEAmEu7Zueb4RKoeIfnvgHMk2ZfO7g5+hwRNeHIk6cB0rQC79/Fva/JE0cfXgHmOWJVK3ur+avj/018y3z+UblNyaklbb/888Itl9jxje+fo/h47mdbag0Suavk6YMms+exDU4wPyN2/BW9UNIxJ4RwO/3k5baT5NiPoqhAKWKGw/l8QmkRxbHjh2oZ81CN96od94TkrlqFGRsoijSfK6X7LIUdPdpqABsyN0AwK6r058G8rlctF+5RNHyVeP+P2fespmIx4PryJFp1w12uggP+DDcZ/pohIJF6Qx0uRnomv5UTmNjI+FwmPLy8vvup1AoKVq+ktaL5wj4pm9q3dd7Q75muf/DmdKkQZuXiLeqPypTOa/bhrBqVCxLur9v3axZs0hISKC2dvqNHFEUeeNKF0tzU0g33/8hyWrdhCCooiNjC7ihcTeUbQfl/R+S3mZNxh8R2dU/fN/94jzCNO2FkFf6PMnI3va9AKzLllu+tgdlUtK9qaMxpvm8/PK1YMBP87nTFC6tRCljQyXY5yHY7UYvc/paU1MTgUBAdvmazSZJ7a0WedsGO2zDJKqUrBhDth0rdlX3UJFpZlaKYfydZSAaDaRM4PZOx/Ubr93NOwVBuCIIwu8EQRiZ7Z7osbKxODcFgMudIiZTCXa7ZHSpL0lF0CnxXLh3pTIlw0jhIitVB6/jc00vaSRRm8hP1v6ECBE+eeCTDPvjNyBx3gI4e+Dft0rR5E9+Dd75c9lMpLtd3Xxq/6dI1afy4yel1MPx8Dc10f/P/4x52zaMK1bcsW2wx03zuV7mPJGJ3iSd6+KuN0hMn8HsBYtwX+xFlaanxX4NlUpJOHyFFs1j6IaDVCivIRrSwCRvGkich4PQwADuU6ck+dp4TUylWvJaadgJAc+06vZedeAa9FOwaPxV2BnGGSy0LmRX2/QbSM3nThEJhyhevmrcfQ1LlqBMScGxc/opcJ6qfkm+Vp467r75Cy2SjG2U3/bJUlNTg9lsJjNz/NuY4uWPEQr4ab1wdlo1RVGkt28HKcmVaDTjv1/9XAshm5dgz/Q+U65QmP12B09ZklCO81lWKBSUlZXR3NyMb5oTZg29TlpsbrbNG3+MX61OJjm5kt6+ndNvmDXshKBnQnLrhWYDs3QaXumNp7HFGYO618CQCtkrxt83iuxt38t8y3zSjfLdg0T8flwHD5Kwfp3s8jX7dRcFi+S932q7eJ6gzyt/+toN+dpEFk6iSX19PXq9npwc+WRkALb+PZgT5qLTybMQDVL62p7+YdanmtHIODnYM+zjwrUhNpU/HNNHIJ+J9utAriiKc5GmjP5jsicQBOEjgiCcEwThnM0WXRPI28lM0pORqONs2wCpqWsYHj5PMOhAUCswzLXgreknErh3LHnRllyCgfC0vZAAcsw5/Gj1j+hwdvBXh/6KYJSjnOPEkZWui/DPa6CvHv70F/D4F0CmtARXwMUn9n+CQDjAy2tfJkWXMu4xYjhM11e/itJkIv0rX75n+7mdbSjVCuavk7yP+tpa6ayvYf76LUSGggSuOtDPt9DY2EhhYQI+/3XqxEKMjhDL1C0ImYtke/9xHi6c+/ZBOIx58wRXzCreCUE3NE3P1Lr5Qh8KlUDuBGN9N+RukJJ6hqaXYNV48ihmSzrp+ePH+goqFQkbN+A6dJiIZ+rNDVEU8Vb1oytMQmEY37zUmKglszCJ5nO902oyTFS+NsLMklKMySk0npyecbjLVYvX2zaufG0EfXkqCLeinqfKHrsDX0Sc8Bh/eXk54XCYhoaGadV943I3CgE2T3CMP926BZ+vA6dzmibeNX8A0wzIrhx3V0EQ2GZJ5Oigi6FgaHp147z1CPqkabaSreNOs0WTdkc7DYMNrM9ZL1tNAPexY0TcbhI2yjspMiJNHit1NFY0nj6OPsFMdrm8YSneWjua7ASUY4RkxIJwOExjYyNFRUWjpo7GCp+/B4fjMhbLBtlqApwadjEYCrPZkihr3T21D5d8DaLTQOoEbneLzLrx2k1EUbSLojjinvhzYNFEj73tHP8siuJiURQXWyyx/TJYnJvCubZBUlOeQBRDDAxIPgWGBVbEQARvjf2eY1JnmihcZOXygY5pJbLdvIYZi/mbFX/D6Z7TvHTqpeiZQMaJIyfVv4d/3QwKJTyzWzIGlolgJMgXDn+BtuE2frDmBxNKXAMY/O//xnf5Culf+QqqlDsbTvZOF41nepnzRBYG863pI5VGS8WaDXguSzcs7myBoaEhsmYNMoyZNq8G1UA/s8IdMEveEe44Dw/OXbtR52SjLRnbD+gOcleB0SI9vE4RURRpudBHdmkKWv3EHlY25GxAQJiWmbbP5aK96jJFy1dOOF7XvGkzoteL6/DUI+5H5Gv3M8++m4JFVgZ7PNOSsTU0NBAOhykrG80C8l4UCiWFS1dwdZoytt6+HQiCcsI30kqTBm1+0rRlbK/2DZKhVbMk8f7ytRGysrKmLWMbka9V5qeSZprYQ5LFsh5BUNLXN43JNr9TkhyVPS39lk2Ap6xJBEWR3f3xNLY4d9F6UDJjL52o20d0GJGvyd1AcuzejTIxEeMyee99Wi/ZmJFnxpQsn3wtHApx9eI58hYtRSFjQyU07CfY6UJXNv4UajRpb2/H5/NRMtF7mijRb5O8IeVuIO2wDaNXCKxOSZC17q7qHvItRgqs8ta9H9FoIJ0FCgVBmC0IggZ4D/Da7TsIgnD7fNl2oO7G33cDGwRBSL5hnr3hxmsPlCW5yfQ4fLjEUlSqJPrtBwHQ5JhRJmnxXOgd9bil2/OIhETO7WiLynVsz9/Ox+d9nFeaX+Gnl38alXPGiSMLkQgc/Fv43YchY55klj1DPuNoURT55slvcrzrOF+r/BrLM5ZP6LhARwd9P/oxpieewLxt6z3bT73aikanYuEmaVTX63JSf+wQpY+tRms04rnQhybXTHNPGwBqdR3XtOsQhgPMp1E6SfbEriXOW4vQ4CDu06cxb9w04YYKCqUkY2vaM2UZW2+bA9eAn/wJyNdGsBgsLJ6xmD1tU598mox8bQTD4kUoLWk4dkz9Yd9T1Q9KAf0kbqTzFlgRppnGVlNTQ2JiIllZWRM+prhyFaFggNbzZ6ZUUxRF+np3kJy8Ao1m/OnKEfRz0gj1ewl2T61h5giFOWh38pQlCcUEP8vRkLHVdjtos3vYOmfiKTSSjG0FfdORsTXuhrAfyt824UMWJBjI1Kp53RZPY4tzF3WvgzYRZo9txh4L9rXvY07aHDJM8kl+xGAQ18FDmNauRVDLF2fvGvRhu+Zk9gSnbqNFZ30Nfo+b/MXLZK3rq5UGGybzuxcN6uvrUalUsqev2Wx7MRjyMBrlqxsRRXb2D7M6xYxRxubggDvA6asDD9X0EUShgSSKYgj4FFLjpw74H1EUawRBeEkQhBF3uM8IglAjCMJl4DPAh24cOwB8E6kJdRZ46cZrD5TFOdKN2Pn2YVJTH8duP4wohhEUAoYFVvzNQ4RHmTJKshooXTWTmqOdDNumb4wJ8PF5H+fp/Kd5+fLL/KFp6qvQceLIRsANv/0gHP47mP8++OBrYJL3R/ynV37KH5r/wEfnfpR3FI4duXw7YiRC99e+jqBQMOPFe422u5uHaLvSz8KN2TeNiKsP7CEUDLBg4zaCnS5CNi+GBVZqa2vJzk7D5bpEi3YV2uEAixSNiIISZsqTOhfn4WJEvpawcZIrZmVPS94rU0xjazkvpa/Nnjs5X4T1OetpGW6Zsoyt8dSxCcvXRhCUSswbNuI6coSIe/LNDVEU8V6xoSuYmHxtBINZw8yiZJrP902pyeD1emlubqasrGzizUEgs7gMY3IKDVOUsTmd1Xh910ifoHxtBH15KihueWZMlj39wwREkacmmUIzImNrbGycUt2dVT0oFQIbyyfnaWK1bsbru4bTVTOlutS+IvnWzZr4Q6EgCGyzJnF4wMlwXMYWZ4RwEOrfhOLNoBrfjzFa9Lh7qLHXsDZ7rWw1ATznzxNxOklY+6SsdduuSN9tuZP83ZsuzedOoVJryJ2zQNa63lo7qjQ9Kos83qIg/d7W19eTn5+PRiPfZzkYHGZw6JTs00eXnB66/UHZ5Wv76noJR0Q2lcvX+J0IUfFAEkVxhyiKRaIo5oui+O0br31dFMXXbvz9y6IolouiOE8UxTWiKNbfduy/iqJYcOPPv0XjeqZL8YwEErQqzrYNkJa6hmBwAIfjCiClsSGC59LoK5VLtuSiUAiceaM1KtciCALfWPENKjMqeenkS5zoPBGV88aJExOGOuBfN0L9G7Dh2/D0T0Alnx4b4JXmV3j50stsz9/OJ+d/csLHDf7il3hOncL6wvOoM+78ohZFkZOvtKA3a5i7RlLdhkMhLu56g6yyCiw5syWDfaWAeyb09PRQVBRCFMNcCGaT5AzzmLYFIWMuaB6OBIU48uLcvUdKX5ugxOkmOasks9XaVyddUxRFmi/0MassBe0kGioAa7PXIiDclD1MhlvpaxOXr42QsHEDot+P6+jkI+6DnS7Cg370cybfsC5YZGWod2oytoaGBiKRyLjpa3cjKBQULV/J1Uvn8E/B96nPtuuGfG1ykhQpjW3qMrY3bENkaNUsMk/uu2xExlZTM/lGjiiK7KjqZnleCqkTlK+NYEmbhozN75Lka6XbJyxfG2G7RZKx7bHHZWxxbtB+AnxD0mSpjBzukGTBa2atkbWu6+BBBI0GY+X43mHR5OoVO4kWPckz5LvfEkWRlnNnyJ4zD7VOPtlcxBfC3zqMrixl0r+306GnpweHwyG/fM1+EFEMYUmTV4q50zaMSoD1qWZZ6+6u7iEzSU9Fprx1x0M+C/E/IpQKgQU5yZxvHyQ19XFAcVPGprYY0GQn4D7bM+qNlzFJy9wns2g800v/dVdUrketUPOD1T8gPymf5w49R03/FFfR4sSJJR1n4GdPwmA7vPc3sOJTsptFH7l+hBdPvEhlRiUvrnhxwj+m/qtX6fv+9zE+/hhJ7373Pdvbq+10Nw+zZEsuaq30EFF//DBOu40l29+JGAzjvtiHvjyVmqY6BEHAlHCVAWU+LV4IDbgojTTBrLh87VEkPDQkpa9t2jj5GzylCkq2QeMuyXx1EozI1woWTly+NoLVYGWBdcGUGkhTka+NYFi0CGVqKs49k5fPea6MyNcmLucaIW++lMbWMoU0ttraWhITEyeUvnY3RctXEQ4Gab04uTQ2URTp69tJclIlanXypOtOVcbmCoU5OOBkqyVxwvK1EaYjY2vsddHa72ZzxeRXYTWaFJKTKunr2zH5hlnzXgj5pEnASbLQfEPG1heXscW5QeNuUGohb7WsZQ9fP0yWKYvZibNlqymKIs6DhzAsX4bCIF8jJ+ALcb1hgNy5abI2VPo72nHYeuWXrzUMQlh8IPI1QRAoKiqSta7NthetJh2zWT6TclEU2WEbZkWSiWS1fMb3nkCIo839bChPl/WzPBHiDaQxWJKTTEOvE0/QSGLiQuz9h25uMy7LIGTz4m8dHvXYBRty0OpVnPxDc9Sux6Qx8fK6l0nWJfOJ/Z+g3dEetXPHiTNtLv0K/n0raIzwzF4okne0FOCy7TJfOPwFilOK+eGaH6JWTGziQgyF6PrSlxC0WjK++a17vqQj4QinXmnBnKajbJXkvSFGIpx97X9Jy85l9vzFeK70I3pDGJbOoLq6mtzcbByOEzQZ3oHgCpEXaEUj+uMG2o8ozv37IRSaegpN2dOS6WrLgUkddlO+Nm9qY/zrc9bTONhI23DbpI6binxtBEGpJGHdOpyHDhOZRJNBFEW81f2Tlq+NYDBrmFmQRMvFyaWT+Xw+WlpaKC0tndINXmZRKcakZJpOH5/UcS53A15vO1br1D5TN9PYJilj22d34I+IbLNMTr42wlRlbDuquhEE2DjFGGOrdTNe7zVcrkmaeNe+CoY0yJl83LqUxpbEoQEnjtC96b1xHkGadkvhCFqTbCU9QQ+nu0+zetZqWR9CA62tBK9dI2GNvFNPHXUDRELipGXb06Xl7CkA8hfJ20Dy1tpRGNVosuWdUKmvryc7OxujcWJBCtEgHPZhtx8mzbIOQZCvhdHi9dPi9bMpTV752olmO4FQhLUlk5Nty0G8gTQGi3KTEUW4cG2QtNQ1OF01+PxSjJ5hbhqCToX7dPeox+qMahZvyeVazQDt1fcmtk0Vq8HKT9f9FFEU+ejej2LzTC+GN06caRMJw96vwysfk8yhnz0AVnnHWQFah1r55P5PkqZP4+W1L2NUT/wHzf7zf8F3+QozvvY11On3TmrUHu/G3umm8u0FKFXSV2brxbPYr19jyfZ3IggC7tPdqNL0DOg9DAwMUFqqJxgc4DJzSXOFWaS48bA0CQ+NOG8dHLt2o87MRFc+SfnaCLMfB32y5MUyQaT0NRuzSicvXxthXc46APZdm7j/kt/jpv3KJQqXrZjyw4p54wZEjwf3sYl7AwW73FL6WsXUHxryF1oY6HIz2DPxqZympqZJpa/djaBQULCkkquXzhOcRMNMkmMpJi1fG0Fp0qCdnYi3enINpDdsQ1g1qgmnr93NVNPYdlZ3szQ3BUvC1CTRFssGBEFJ72RkbAGPNDFS+tSk5WsjbLMmERBF9vSPvuAY5xHC3gL2ZiiSN87+TM8ZApEAj2fJa9rtOigpN0yrV8tat+1KP1qDihkF8j7st5w/TUZBMcakyU+EThUxHMHXMICuNAVBIV9zcGBggN7eXtnla4ODJ4lEvLLL1/bfi/NowwAAIABJREFUkCGvk1m+dqChD6NGydLZk5+qjjXxBtIYzJ+VhEohcPrqAGlpUvd8ZApJUCsxLrLirbETdt5rpg0wZ3UWiVY9x3/XRDgcidp15Sbm8vK6lxnwDfCxfR9j2B+/KYnzgPA54Nd/Bsd/DIufgT//PRjk/5LrdnXz0X0fRSWo+Kd1/0SqfuJjvN6qKmz/+I8kbN6Eeeu9RrR+T5DTr7UyszCJ/IW3fFXOvPq/mC1WiisfI9DlInDNiXFZBlVVVSiVShITrxFAyxmPkRRXmBWaFsTELEicvMQlzh834eFh3CdPkjAV+doISjWUbIWGnRDyT+gQ2zUnzgHfHZ/byTLDOIO5lrmTSmNrPX+GSDhE0bLJT2yMYFiyBGVSEo7dE6/rre4HBdOKMc6bLzWQWy5MfHGmtrYWk8k0qfS1uylavpKQ38/Vy+cnfExf3y6Skpag0Uy9Yaafk0bI5iXYO7GGmSccYb/dyea0RJRT/CwrFApKSkpobm4mEBj9/ulumvucNPa62DJn6iaiGk0KSUnLsNl2TVzG1rxPMrCfRPra3SwyG8jQqnkjnsYWp/FGyLTME9qHrx/GoDKwOH2xrHWdBw+hLS29x1MylkQiIm1VdrLLU1Eq5XvEdQ3Y6Wlpkl2+5m8dRvSFZZevjUyQFhcXy1rXbj+MQqEnOVneaf79dgdFBh3Zevk8XUVR5GB9H6sK09CoHr52zcN3RQ8JBo2KhdnJHGm0YTQWodNlYbPtvrnduCwDwiLu872jHq9UKVj5rkIGezxUH+6M6rVVpFXwozU/4urwVT65/5N4glOLd44TZ8oMXIV/2SCZi275B9j2A+khV2bsXjsf2fsR3AE3P13/U2aZZ0342LDLTecXvoDKYiHjxdH9ks7taMPnDrLq3YU3t3fW19LVUMuirW9HqboxiahSoF+QRk1NDQUF+QwM7qfT+DTecAR7l5MlyiaE+PTRI4nz4EEIhTBvmOZDQ9nbwO+A1kMT2r3log1BITB77vQSEDfkbKBuoI4OZ8eE9m88fQJTcgoZBVO/sRTUakzr1uI6eJDIBJsM3up+tLMTURqn/j1kStYyI89My8WJ+SAFAgGampooLS1FoZj67VRWaQW6BDNNpycWkuFyN+HxNGO1bp5yTbghYwO8E5yUPjjgwBuJTDp97W7KysoIhUI0NTVNaP+dVdL093RjjK2WTXg8V3G7J1aX2ldBnyIZ2U8RhSCwJS2RQwNO3HEZ26NN4y6wlEByrmwlRVHkSMcRVmauRC3jPVpocBDvxYskrFktW02A3qsOfK7glGXbU6X1guRhJ7v/Ud0AqBRoC6b3nTxZmpqaSE1NJSVFvkVjURSx2w+TkrIChUK+Ro47FObkkFv26aP6Hifdwz6eLJm8h6UcxBtI9+GJYgu13Q5sTj/p1q0MDB4nEBgAQG01oM1LxH26GzEy+mpW7pxUZpUmc/aNq/hcwahe24qZK/je49+jqr+K5w49RyA8sZvsOHGmTdsxySzb2Q3v/z0sffaBXIYj4OBj+z5Gj7uHn6z7CSUpkxul7f3Wtwh2XCfz77+HMvHeUeehXg9XDl6ndEUGluyEm6+fefW36BLMzFmznog/hOeiDcPcNDpsXTidTkpKzXg8zdRo1qJ1hjD7ekkK2eLytUcU5959qGbMQDdnzvRONPsJ0CZOKI1NFEVaL9rILEpCZ5reQ8NNGVv7+DK2oM9H2+ULFCxdgTCNhgqAecMGIi4X7hPjN1WCvW5CNu+05Gsj5C+00t/hYtjmHXff5uZmQqHQlOVrIyiUSgoWL6f1whlCE2iY2fp2AWCdZoyx0qxFk2OesIztjb4hUtRKlidOz78lOzsbg8FAXV3dhPbfUd3D4pxk0s3TSzaS5H4CfbctBo5J0Cc98JdslYzsp8FWSxK+iMj+Aee0zhPnjxifQ0pgK5R3+qhuoI4+b5/s8jX30aMQiTwA+ZoNhUIgu1zeiZyWC2dItKaTmpUta11f4yC6/EQUmqlJbKdCIBCgra2NgoIC2WoCeDxX8fqukZq6Wta6RwadBEWRtakJ4+8cRQ7USwtZa4rjDaQ/OlYXSyu3hxttpKdvQxTD9Nl23dxuXJ5BeNCPv2lw1OMFQWDluwoJeEOcfq016te3LmcdL1a+yImuE7xw5AVCkVDUa8SJcwfn/g3+82kpVvzZA7IniYzgCXr41P5P0TzUzA/X/JAF1gWTOn74zTcZfuUV0j72MQyL7x3rFkWR479rQqlWsGx73s3Xuxrrab1wlsVb34Zap8NzyYYYCGNcLsnXNBoNRkMtgqDkjH8Gs1wRFivj/kePKhG3G/exYySsWzfthgoqDRRvgoYdEL7/gsRAl5uhXg/5C6Y3fQSQacqkPLV8QmlsVy+dIxTwT0u+NoJx+XIUCQk4JyBj81bbQQB9+fQbSHnzpX+ziUwh1dbWYjAYyM6e/kND0fKVBLxe2qsujrtvn203iYmL0Gqnb6ypr0gl2O0mZL9/w8wXjrDH7mBLmiTvnw5KpZKSkhIaGxsJBu//WW7rd1PX7Zj29BGAVmslMXEhttvu48ak9ZBkXD+F9LW7WZZkJFWt4s24jO3RpfUgRIKy+x8dvn4YAYHHMh+Tta7z4EGUljR0FRWy1m2rspNRmIRWL19SVigYpKP6CrnzF8tqUh6yewn1e9EWyee5BNDW1kY4HKawcPIhGdPBbj8EQGrKE7LW3W93YlIqWDrNhZPJcrC+j4pMM9ZpLpzEingD6T6UZZixJmg51GjDZCrFYMijr/fNm9v1ZakoTGpcJ7rGPEdqpok5q7OoPtpJb5sj6tf49sK38/yS59l3bR9fPf5VwpH4iHScGBAOwY7n4Y3PSZMQf7kPUvMfyKX4Qj4+c+AzXLZd5ruPfZdVmZOTF/ivXqXn699Av2ABaZ/4+Kj7XL3cT1uVnSVbZmNMvDUqe+K3v0BvTmTB5qcQRRHXiS7UGUZEq4bq6mrKykrp79+Jz7yJFm8Isc/LxoQ2UBshXd4bqTgPHtfRo4h+PwkbomT4WLodvIPQfv/ErtZLNhBg9vzpN5BAWqyo6q+ix91z3/0aT59Ab04ks7R82jUFjYaEJ9fgPHAAcZwmg7e6H022GaVZM+265jQ91pyEcX2QgsEgjY2NlJSUoFROf/U3u2IuWoNxXBmbx9OGy1U3bfnaCCNNt/GmkI4MOnGHI2y1RMeYtrS0lEAgQGvr/RfXdtdER742gtWyCZerHo+n7f471r0OWrP0ezdNlILA5rRE9tkd+KLoiRnnj4jG3aBLlH0h6UjHEeZY5kzKG3K6iKEQ7mPHMT3++PQXTiaBa9DHQJebHJmnj7oaagn6fcyev1DWur5GaXhBVyyv92hzczMqlYqcnBxZ69rthzAaC9Hr5fMSFUWR/QMOnkhJQC2jSfmgO8CFa4M8+ZBOH0G8gXRfBEHgiSILRxtthCMi6dZtDA6dxu+XViYFlQJT5Ux8DYME75Pasmx7HgazhkO/qCcSg5uH95e9n88s+Axvtr7JS6deIiLGb1DiRBHvIPziXXDmn2D5J+DP/gf08uqtRwiEAzx36DnO9JzhWyu/xcbcjZM6PuLz0fm55xDUajJ/8H0E1b2rVAFfiKO/aSQ108TctbeMca/XVtN+5SJLt78TjU6Pr2GQUK8H06pMqqurCQQClJeb8Pqu0aB7CgJhunrdVApVkL1s2jKIOH98OPfsQZmSgmHRouicMP9JUBukh9v70HLBRkZ+4h3Nz+mwLluSse2/tn/MfUKBAK0XzlKwZDmKKSZW3U3Chg1EhofxnD07dl27l2C3OyrytRHyFljoa3PgHBg7Fa21tZVAIDBt+doISpWa/EVLaT53inBo7IbZiBej1TK5776xUKXoUGea8Izjg/SmbZhElZKVydFZhZ09ezZarXbcNLad1T3MzUokK9kQlbqWG/9utvvJ2MIhaHhTmhZRTb8pCbDFkog7HOHIYFzG9sgRiUDTHihYJ+t9QL+3n2p7NY9nyitf8165QsTpxPSYvHWv1UoWI9nl8jZUrl46j0KpYlb5XFnr+hoHUaboUKXKO6HS1NTE7NmzUatl9NQKuRkcOktqqrzTR7VuH93+oOz+R0eabEREWPOQ+h9BvIE0LquLrTh8IS51DJGevhUQ6evbcXO7cXkGglqB88j1Mc+h0at47E+K6O9wURVlQ+0Rnp37LB+Z+xF+3/R7/vb03048ZSROnPvR3ww/Xyf5Hm3/R9j0nQfWCAlGgnzx8Bc51nmMr1V+jafyn5r0OXq+9S38DQ3M/PvvjZkMcvaNq7gG/ax+X/HNFA9RFDn2m//CmJzCvA1SWpvryHWUZg2GeRbOnz9Peno6ImcQBDUnA9nMcIbJFPpI8bZDgbyRo3EePBG/H9ehwySsXYsQhQkVADQG6SGk7g3poWQUhvo82DtdN6VY0SA3MZeCpIL7NpDaqy4S9HkpWjp9+doIxpUrEfR6HHvHls+NTM7o50Rv1Tl/gXTTdvXy2FNItbW16HQ6cnNzo1a3cNlK/G43HTVVY+7TZ9tDQsIcdLqZUaurn5NGsMNJaGj0hlkwIsXQr081o4nSRIFKpaK4uJiGhgbC4dEnp7uHvVzqGGJjeXSmjwD0+kwSEiru74PUfkxaOCnbHrW6q5JNmFUK3rTFk3MfObovgdsGhdFp+k6Uk10nASY9pT1d3MeOgUKBsXK5rHWv1QxgTNSQMtMoa922yxfIKi1Do9PLVlMMRfC3DKErTpZVNme32xkcHJTd/2hw8CSiGHgA8jVJOfRkirwNpAP1faQYNczNejCL9RMh3kAah1UFaSgEyQfJaCzAZCqlt/eNm9uVRjXGJTPwXLYRHh47Xjl/oYXs8hROv9aKe2hiMcyT5VPzP8WHyj/Ebxp+w3fOfCfeRIozPVoOwM+flG6kP/gaLHz/A7uUYCTI84ef50DHAb609Eu8u+jdkz7H0B9eYfh3/0vqxz6K6bHR/QD6r7u4fOA6ZY/NZEbeLalGe9UlOutrWPa2d6PW6ghcd+JvHca0KpOu3m66u7tZuHABfX07UCZv5PiwF8twmC26aukEBeum9L7j/PHiPn6CiMdDwnTT1+6mdDu4euD66FM5rRelpkdeFPyPbmdt9lrO955nwDcw6vam0yfQGo3MqojeKqxCp8P0+OM49+1DHKNh5q22o84yoUqK3ipsUrqBlJnGm/+WdxMOh2lsbKSoqAjVKFOMUyVn3gLUWh3NZ0+Out3n68bhuBS16aMRRqa3vDWjTyGdGnIxGApHTb42QllZGT6fj6tXr466fU+NlHIbLfnaCFbLJhyOy/h8Y9gP1L0OKj3kr41aTY1CwYbURHb3DxMcI3glzluUlgPSf/OflLXsya6TJGmTKE0tlbWu69hx9HPnjhpOEisi4QjX6weYVZ4qa0PFOdBP/7U2cudFacp4gvjbhhEDEXQy+x+NJGc+CP8jpdJIUtK9nqWxZL/dwVyTnnStfNNW4YjIkUYbq4ssKGWUzU2WeANpHBINahZmJ3OoQbqRTLduY9hxEa/31sSRaVUmREScx8f2QhIEgcffU0QkLHLk140xae4IgsDnF32eD5R9gF/V/4rvnvluvIkUZ/KIIpz+J/jvd4E5SzLLzoneVMFkCUaCvHDkBfZd28cLS17gfaXvm/Q5vNU19Lz4IoZly7B8+tOj7hOJiBz6RT06o4rKt93ydxIjEY796j9JSLUwZ61kgOk8ch1Bq8S4dAbnz59HrVaTOzuM39/DFe3ThCMiti4nTxlrISkb0uT9sY3z4HHu3YsiIQHjsqXRPXHRRlBqoO61UTe3XLRhzUnAnBrd1dB1OeuIiBEOdRy6Z1s4FKLl/BnyFy5FqYrujVbC+vWEbf14L12+Z1to2E+gwxkV8+y7yZtvoatpCK/z3lS09vZ2vF4vpaXRfTBTa7TMnr+I5rOnRm2Y2fqlSSxLlBtI6jQ9qnSDZEY+Cm/2D6NXKHgiyquw+fn5qNVq6uvrR92+q7qHQquJfEt0zUutVul7fNQppEhEmvArXCdN/EWRrZZEhkJhTg65onreOA85LQdhxlwwRbepfz9EUeRE1wkqMypRCPI96oUGB/FVVWFcJe/UU1+7E78nRHaZvPK1tssXAMidL28DydcwCEoBbb68EyrNzc2kpKSQkiLfv7Moitjth0lJXoFCER1J8UQYCoY4O+zmSZnlazVdwwx6gjxRLN/3xVSIN5AmwOpiC1Wdw9ic/hsyNu6YQlKl6NDPScN9upuIb+wktESLgaXbZtN6yUbzufETXqaCIAh8YfEXeH/Z+/ll/S/jTaQ4kyMUkIyydz4vPag+sxuScx/Y5Yw0j/a27+X5Jc/z52V/PulzhOx2rn/60yhTU8j84Q/GlBNd2nuN3qsOVv1JITrjrYfguuOH6W1tYuWf/jkqtZqQ3Yu3qh/jsgwChKiqqqKiooLBwd0oFDr2ezPJCQh43B5KvRcl+ZqMK2JxHjxiMIjzwAESnlyDoInyDY/OLKUf1r0uNXtvwzXoo6/NEfXpI4Di5GIyTZnsa993z7brddX4XE4KopC+djem1U8gqNU4R5Gx+W5MzOgrom+amjffgijC1Sv3mkvX1dWhUqnIz49+kEDB0krcQ4N0NTXcs81m24PBUIDRmDfKkdNDX55KoG2YsOvOhllEFNlpG+LJ1AQMyujeMqrVagoLC6mvrydyV8NswB3g9FV71KePAAyG2RiNhdhso0gjO89JE36l009fu5vVKWb0CgVvxNPYHh38Tug4Lfv0UeNgI3afncqZlbLW9Zw8CaKIadVKWeteq7EjCDCrROYG0qULmJJTSJslr6G0r3EQ7exEFJooyeMnQDAYpK2tTfbpI7e7CZ+/S3b/o2ODLiLAmpQEWesebZLuOVYWRH9hLJrEG0gTYPUNF/QjjTb0+lkkJi6mq/u3iLeZVSc8noXoD+M+ff+UmvnrZmHNNXPk1414HPeubEYDQRD44uIv8oGyD/DL+l/yzVPfjBtrxxkftx3+6+1w/t9h1XPwp78ArbxfnLcTCAf4/KHPs7d9L19c/EXeXzZ5CZ0YDNL52c8RHhgg6//+X1RjrJoMdLs58/pV8uZbKFx8Kxo76Pdx9Ff/QXpeIWWPrQHAeawTFAIJK2dSVVVFMBhkwYJyentfQ0h5G6eHvcx2iyxWNKAKe6Ew7n/0qOE5e5bI8DCmdTGSLpY+BUPt0HPljpdbL0k3HtH0PxpBEATWZa/jVPcpnIE7jYCbz55EpdGSO3dB1OsqTSYMlctx7t17z2KIt6YflVWP2hLdSRGAtFkmElJ1UqLdbUQiEerr6yksLEQT7eYgkLdwCQqliqYzd6axBQIDDA2dwWqJsiTyBvqKNBDBV3enRPGCw0NvIMSWtNjIUUpKSnC5XFy/fqeP5L7aXiIiUfU/uh2LZSNDQ2cJBO6auqp9FRRqKIr+v7NeqWBtagK7+oeJxBf2Hg3ajkMkKHsD6USX9P2xYqa80+OuY8dRJCaimzNH1rrXagew5prRmeSTGkXCYdqrLpI7f5GssrnQkJ9Qrwddsbzytba2NkKhkOwNpIFBKXVW7gbSkUEnJqWChWZ5PbWONtkozTCTZopOCEqsiDeQJkBZhhlrgpY9tVJzKDPzvXi9bQwO3vIp0GQloC1MwnnkOhH/6IaQAAqlgrUfLCXoD3P4lw0xmw4amUR6puIZftv4W75x4huEI2NfV5xHnN5a+NkayVflHT+DdS+CjPGrd+ML+fjswc9yqOMQX1n2FT5Q/oEpnaf3O9/Fc+4cGd/6Jvry0aPFI+EI+/+jDrVWyRN/VnzHjcC51/+Ay97P6g88g6BQEBry4z7Tg2GBFSFBzZkzZ5gxYwZK1UVCISeXdO9ABAauO3l3Yp0kNcod3W8pzlsX5759CDodpliN8RdvAUFxTxpb66U+kmcYSJ4RmxuedTnrCEaCHL1+9OZrYiRC85mTzJ6/CLU2NmkwCevXE7x+HX/DramcsDuI/+pwTORrIP2G5i2w0FE3QMB7a7K4s7MTp9MZdfnaCFqDkZw582g+e/KO+4P+/gOIYhiLNTZGvOoMI8pk7U1T8hF22IZRC0LMUmiKiopQKBTU1dXd8fqumh6ykvWUz4xNXakRF6G//zZjeFGU/p/KWy1FrseALZYk+gIhzjs8MTl/nIeMlgOSn1a2vIbSJ7pOUJBUQLoxffydo4QoiriPHcNYWRm94IgJ4HMF6WtzyC5f625uxO92y+5/5GuUmvxy+x+1traiVCrJyZF32mpg4Bh6fW5UgyMmwpFBJyuSTKhl9CFy+0Ocbx/k8cKHe/oI4g2kCaFQCGyZk8HBBhsOXxCrZTMqVRKdXb++Yz/z+hwi7iCuE2N7IQGkZBhZ+pQkZWs61xuz6xYEgc8u/CyfmPcJXml+hS8f+zLByNjxwHEeURp2wb9sgJAPPrwD5v7JA70cT9DDp/Z/iuOdx3mx8kXeW/LeKZ1n4L/+m8Ff/pKUv/gLEp8aO7Ht0r4O+tocPP6eIgzmWxMFzoF+zrz2O4qWrSSrtEJ67eA1AMxrs2lubsZms1FZWUlX168wGArY40igWFTS2O1kteIyZFeCNrr+HXEebsRIBOe+/ZgeW4VCH6NUFmMa5KyUvFpu4HMF6Woajsn00QhzLXOx6C3su3ZLxtbT0oRrcICCpbGTSiQ8+SQoFDj33JIc+eoGICJJr2JF/nwLkZBI+23eQHV1dSgUipiuwhYsrWS4twdb+y1zaZttNzpdJgmm0Rvh00UQBPTlafiah25K8UVRZEf/EKuSTSSqY5O+qdPpyMvLo76+/mbDzOkLcqypn43lM2K2sm8ylaHTZdFn23Prxd5qabKvdPIJnxNlXaoZtSCwIy5jezRoOQC5q0Al3zSBN+TlQu8F2aeP/E1NhPr6MK6Ut25H/QCiCNkx/C0YjbbL5xEEBTlz5sta1980hNKsQWWN/uTt/WhtbSU7Oxu1WsYpr0iAoaEzpKTI66nV7vXT5g3wuMzytTNXBwiGRVbFG0hvHbbPn0kgFGF3dQ9KpZaZGe/EZtuD339rvF2bbUZXmoLz8HUi3rG9kECSsqXPNnP4l4047N6YXbcgCHx8/sf53MLPsfPqTp47+By+0OhRvXEeMUQRjv8YfvUeSM2DZw9ClrwJB3cz7B/m2T3Pcq73HN9e9W3eWfTOKZ3HeegQvd/5Dqa1a7H+1efH3K+v3cHp11rJX2ChYLH1jm3HfvWfiOEwj73vwwCE7F7cZ3sxLp2BKlnH8ePHMZvN5OQocDgug/UDXHB6yBwMkSn0k+xujcvXHkF8VVWE+vpIiJV8bYSSbWCrg/5mQPLqESNiTPyPRlAICtbMWsOxzmM3f0eazpxAoVSSt2BJzOqqUlMxLFqEc++th31vTT/KJC3qzNg1aGfkJWIwa2i5kcYmiiL19fXMnj0bfayag0DB4uUgCDfT2EIhF/aBY1gsG2MqldBXpEJYxNcgrXDXuX20eQNsiXL62t2UlpYyODhIb6+0oHawwUYgHImJ/9EIgiBgsWxgYOA4odANSWbdG9JkX/GWmNU1q5SsSjaxs3847k/5VmfoGtibZJevne89TyASkL2B5D4mSY1iNnk7BtdqB9AaVFhz5H3Yb79ykRkFhehM8i0SihERf8sQ2sJkWWVzLpeL3t5e8vKi7793P4aHLxEOe0hNkddT68ig9JvwRLL8/kdalYIlufJO002FeANpgiyYlcSsFD2vXZami2bOfA+iGKK7+3d37Gden4PoC+E8en2009xEoVSw/i/KEEWRff9aSyQcW4+iZ+Y8w1eXfZUj14/w8X0fxxWIp4A80gR98MrHYe/Xofxt8OFdkJj5QC/J5rHxoV0fom6gju+v/j5P5U9tFdhXX0/X5/8KXUkJmX//vTFHqQO+EHv+pQZ9gobV7yu548e4o+YKtUcOsGjb20lKlx5iHPuvgULAvGYWHR0dtLe3U1lZSU/Pb1AotJxmFYgi168O8UGr9FBPQbyB9Kjh3LcPVCpMq1fHtlCJFOhAvSRja71kw5SsxZId2xuetdlr8Ya8nOySJFZNZ04wq3xuzG+iE9avw9/UTKCtjYg/hK9pEH2MI5sFhcDseWm019gJBcL09fUxMDAQM/naCIbEJLJKymk6IzWQ7PZDiGIAS4z8j0bQZJtRmNQ309jetA0hAJti5H80QnFxMcBNGdvumh7STBoWZsdWomGxbEAUA9jth6UX6t+AWctjnpa1xZJImzdAnTu+mPeWpuWg9N8H4H+kUWhYlC6vtMp97Bia/HzUGRmy1RRFket1A2SVJKOIssn//fB73PQ0N8k+fRTsdhPxhNAWyJu+1traCiB7A2lg8BigIClJXgno4QEnM7VqCgzy+hAdbbKxdHYKOrV8EtCpEm8gTRBBEHhq7kxOtNjpd/kxGvNITlpOZ9ev7zDT1sw0oZ+ThutYF2H3/eViiRYDT7y3mO6WYc7tbI/1W+BPS/6U7zz2HS72XeSZPc9g944e2RvnLY6zF/7jKbj8K1j9FXjXv0U9rniydDg6+OCuD9Lp6uTldS+zNnvtlM4T7Oqi46MfQ5GQQNb/+38oDGO/r2P/08Swzcv6D5fdYbwYCgTY+7OfkJg+g+XvfI903j4Pnot9mCozUJq1HD9+HJ1Ox9y5xfT0vobVspXf2TxUiCqu9XvYor0MibPAUjyl9xHnjxNRFHH+f/beO76t+mz/fx9tW/K2vOJtJ14Zzt5kbwKEPQqFMp4HytNSaCmFlm/LD1qgg1IKlAJllE0oScggm5Bpx9leifeKh2zLsiVr6/z+UBwSsjyk4wb8fr3yAuQj3UfC0fmc+3Nf17V5C9pJk5CH+Pemm9AEiM2FkrU4bC7qittJzdX7fVdyYsxEgpRBbK3dSltdDR1NjQz3o3yth6B53u+Eri1bvBHGLtFv/kdnkpqrx2V3U1dqPN3gyMyNYwV8AAAgAElEQVTM9Hvd9IlTaa2txth0khbDJpTKcEJDxvm1piATCMiJwHa8HdHpYYPBxKQQLXqVfyULOp2OxMRESkpKsDndfFXawoLsaOR+9p4IDRmHUhnhlbG1V3klbFlX+rUmeBtyAl5/qSG+w1Rsg6A4ydcBexr2MD56PBqFfzzpzofHbqf7wAHJ5WumFitmo514idPX6ooLEUUPiSPHSFrXXu6VvmrSpG8gaTQaYiVsDgK0t+8hOHgMSqV/vPDOh1sU2WU0c0VYkKRTXk0mG2UtZmZeBvI1GGog9YmrcuNwe0TWH2sEvGbaNls97e07zzoueEESotNN1/a6S75mxuQYRkyOpmBdFSfL/a+JX5a6jBfnvEhlRyV3bLiDuq5Ln+MQ3yEaj8Drc6HpGNzwDsz+5aBHzBe3FfODDT+g09HJ6wtfZ0ps/3YaXEYjtffci6e7m4R/voYyOuqCx5YVNFOyp5Hxi5MY9q0ki7xVn2JsbGD+3Q+gVHl3Hzo31yAoZQTNiqe1tZXS0lImTZpER8dG3G4LdcG3UN5tJ77dRYTMwrDWPZB99aB/tkNIi6OiAkd1NUEL/Cxf6yHrSmgooLagHLfL41f/ox6UciVXJFzBjvodHM/fDYJA2gT/7w4qhw1Dk51N1+YtWIvakGmVqJL9v6gclhGGKkBB5WEDJSUlJCQkoJNAspA+0fuZluXvpK1tB/rI+QiC/3clA3IiER0eTpQYKLbYWOLn6aMesrKyaGlpYePhKiwONwv9lL52JoIgR6+fT1vbV3iKV3kfzPR/A0mvUjIpRMuG1iEfpO8sHjdU7YC0OZKuA5otzVSYKpga5/+m/plYDx1CtNvRTpW2bn2pV24bL3EiWW3hYRQqNbEj/DuN+m1s5UYU0YHIg32fAHohRFGksrKS1NRUZBKG6zidnXR2HiE8XNqm5NEuKx0uN7Mk9j/aWeaVys9I9/86zhcMNZD6QGZMMBnRQaw57JWx6fULUSojqK1766zjlFGBBI6Pxrz3JE7DpZM2Zt2cQVBkAJveKKK70+GXcz+rXsIsXl/4OiaHidvX305pe6nfaw7xX0DxavjXYkCEuzd6pWuDzL7Gfdz15V2o5WreXfIuY/T9283xdHdT97//i7O+noRXXkaTceEdP2OThe3vlRKdEszEK1PO+llbQx35qz4lc/osksd4d/ttFR1Yj7USdEU8cp2K3bt3I5fLmThxAnX176DTZfJpRxhhChnHy9v5SUwhgsc56GbkQ0hP12avybNubv8m6PpMplfmWbnnOBqdkth0aW725yXOo8PewbE924kbnokuTJrd36AF87EeK8JW0oYmKxxBgnQUuUJG0sgIyo/W0dzc7Hf5Wg8hUdFEJadRV7EWt9vsd/laD+rUEASNnLW13jS2JX72P+qh53P9fH8VOrWCaWnSGOJG6RfhdltwF30MMaMgTJqEoSWRIRSZbdRY7ZLUG0JiGg+D1QipcyQtm9+UD9Dvjbj+Ytm3D+RyAif6zwvvfNSXGtGFqQmJ8p8n3fmoPXaEYZnZKCQ0lBadHhzVnWgklq+1tbXR2dkpuXyto2Mf4CE8TFpPra/bvf5HM8KkDcDZVd5KpE5NZoy0jav+MtRA6iNX5cZRUGOkocOKTKYiMeEu2tt30tlVeNZxIYuSERQyTGsrL/maqgAFi+8bic3iZNMbhX73QwLIjcrl3cXvopQrufPLO9ndsNvvNYcYJEQRdjwPn9wB0Tles+xYacduz8cXFV9w/5b7GRY0jPeWvkdqSP8uTqLDQf1DD2E7Vkjcn/900QWMw+Ziw2uFKJQyFt07EvkZmnmPx83mf76EUqNm9h33eF/bLWL6ohJ5qPr09NHhw4cZP348NlseFksZmtj7+bLNxFyZhkaTjWXCLojMgJjR/Xo/Q1y+dG3eQsCYMRedfvMp+gzcYZnU1KhIGR0pmQfE9LjphNsDsTQ0+TV97dsEzZ+PPDID0eEhYKR0Y96puXo6PU2ANPK1HtInTcGtKkYmC5RsF1ZQyNBkhrPZZSNHqyEpQBoPiNDQUKKiY8hvsDInMwq1QhoPiLCwqWjcASgaS043ZKWgpzE3JGP7jlL1tfefqbMkLZvflE+IOoSMcGllc9179xEwciRyiQ2l608Yic+U2FDa2E5bfa308rXaTkSnB/UgyNdAev+jtvbdyOWBhIRI6zO1w9jFSF2A36XbZyKKIrvLW5mRHoFMgo0xX+CT1aYgCIsFQTguCEK5IAiPnefnDwuCUCwIwlFBELYKgpB0xs/cgiAcPvVnjS/Ox58sHx0HcHoKKT7+B8jlOmpqXjvrOHmQiuD5idiOG7GeGrG8GPqEIObclkHDiQ72fl7h+xM/D6mhqby35D3idfH8eOuP+ezEZ5LUHUJCHN2w8kew/RkYfTP8cC0ERQ/qKYmiyKtHXuXxXY8zPmo8by9+m6jA/t1wiy4XDY/8HMvXO4n57f8jeMGFTatFUWTbu6V0NFlYeHcOQeFn+wMUfPE5DaXFzPnhfWhDvePQlv2NOJsshCxLQVDK2bZtGwqFgpkzZ1Jd8yoaTQIbnWPxiKBotJKqbCOy7QCMvmFIvvY9w9nQgK24WDr5GoAg0BBxKw63mtRs6XZgA5WBzOj23qCkTZgsWV1VejrqETMRPU5JPSASc8JxBLShU4cSHi6d10b6hEkEJ3WhcGUik0ln5mnOCuVIsIwFEkaPAyhiRtDtlnNFqjRTTwAymYpkWwICIGb6L33t2yQFqBmpC2BD61AD6TtJ1degzwKdRJsJeNc4eY15TIqZhEyQbj7AbTZjLSwkcKq0U0+t9WbsFpf0/keFRwAkN9C2l3eAzDslKiWVlZWEhoYSFiatTLC9fRehoZOQyaST61ncbvabLFwhcfraiWYzrWYH09IvD/8j8EEDSfCK8l8GlgDZwC2CIGR/67BDwARRFEcDK4Hnz/iZVRTF3FN/rhro+fibxIhAJiWH82F+LW6PiEIRREL87bS0bMBiOXvaSDc1DoU+ANPaSkTXpaeKMqbEMmrWMA5vqaOsoNlfb+EsorXRvLPkHabETeG3e3/LiwdfxCP6fwJqCAnoPAlvLYGiz2H+72DFP0Apnani+XC6nfxm92945fArXJV2Fa/Of5VgVf98TES3m5OP/YquzZuJfvxXhN14ccnY4c11VBxsYcqKtHMWHC3Vlez++D2GT55G9hXexBRPt5POTTWoU0MIGBlJQ0MDxcXFTJs2DafzGJ2dRxiWeB/vNxq5IiiQrceaeDjmqPcFR93Qr/c0xOVL19ZtgHdKRkoqu8ejEKzEC/skrRvXqMKoc9ColNDHRQS5fiSupqN4urskK+tw2XAqTSi6wyWNXlcEt6MMdGOskFaesSNUjigIzG68eBCIr6lyBiPDQ7SnVdK6kS0WujUyOtRWSesujgxhv8mCwSHt5zyEn3E5oGYvpFwhadn6rnoaLY1Mipkkad3u/fvB7UY7RdoGUt0g+R/VHDuCRheEPjnl0gf7EHt5B6qEYGQahWQ13W43VVVVpKamSjrlZbOdxGqtJjxcWvnafpMFpygyU2L52p4K7zVPKum2L/BFi3oSUC6KYqUoig7gI+DqMw8QRXG7KIo9ZkD7gHgf1B00fjgtmdr2braXtgCQkHAnMpmamtqzp5AEhYzQ5Wm4Wq107Wro1WtPv2E4sWkhbH2nhOaqTp+f+/nQKrW8NPclrh9xPW8ce4NHvnqEbuelvZuG+C+m/gD8cw60lcMtH8GMhwZ9IsZoM3Lv5ntZXbGaB8Y8wNPTn0Yp79+IqOjx0Pjkk3SuXYv+4YcJv+OOix5fdbSVPZ+XkzZOz9gFiWf9zOVwsOHvfyYgKIj59/z49EXStKkGj9VF6FVpCILA1q1bCQwMZOrUqVTXvIpKFcURxQKaHS5S2t2Y7U7mOXdAwmQIS+7X+xri8qVryxbUw9NRJSdLVlP0iFSVQ5K2BEXZF5LV7e40Ya9ppi7GxtbarZLVddR2gqjC1XAQ844dktU9fvw4AIIxlNY6s2R1W1s3gyinao8Re7dFsrobOjqJd0LisQ7JGmaiKLKzqpMktZXaihOS1ATAZkLVUEyrPgBD62bp6gJL9SGIwMZWadZ6Q0hEQwG4rJI3kPKa8gCYFCtxA2lfHoJaTcDYsZLWbSg1EhYTiDZUuklJURSpLTxCQs4oZDLpotY9VheO+i7UEvsfNTY2YrfbJZevtbfvASA8TFoD7d1GMwoBJoVqJa27t6KNxPBA4sMGNxG7L/iigTQMODPKq/7UYxfibmDDGf+tEQShQBCEfYIgDL6rby9YmBNNbIiGd/ZWA6BSRRIXdxNNTauw2U6edaxmRBianAg6t9T2ylBbrpCx5H9HoQ1Rse7Vo3S2SbMjppQpeXLKk/xiwi/YVreNO7+8kyZLkyS1h/AxRz/1Th4p1HD3ZshYPNhnREVHBbesu4VjhmM8N/M57s+9v9+7GaLbTeOvHsf02X+I/PGPibzv3oseb6jtYtObRUQlBjHvzuxz6u766F1a62pY9L8/JTDYOxpsrzRh2deIblocyhgtFRUVVFZWMnPmTOz2UozGPSQk3M2/TnYwTK0g/1gzV8caCeg4MTR99D3EZTTSXVCATuLpo+bqTro7HaQOl0H5Vq9kVQIqDuQhiiK6nGRJG0jWojaQC+BupGvzFsnqlpaWEhIcgsKtpfKwQZKaoijSYtiEVpOLyyZSeXC/JHU7XW52Gc0sCgzE02HHeVKaxlVxYyf1RiszUoKprKzEZrNJUpeyzQgeJ7aUSRgMmySdMMvSakjSqFhvGEpj+05R9TUgQPJ0ScvmN+YTFRBFSrC0kzGWffsIGDcWmVq6Ro7b5eFkeYfk8rWOppN0tRlIHCmxfK2yA0QklW7DN/5HKSnS/k4ZO/aiVIaj1Q6XtO7uDjPjgrVo5dI1B90ekX2VbUxNvXymj0BiE21BEH4ATAD+eMbDSaIoTgBuBf4qCELaBZ5736lGU4HBIM0C7kIo5TJ+MCWJnWWtlDV7x+iTEr2mu9U1r55zfNg16QhKGcaVZYieSy9OAoJULHtgDG6nh3UvH8Vudfn2DVwAQRC4I+cOXpr7ErVdtdy89mYOtRySpPYQPsDjga1PwX/ugfgJcO82iP62mlR6ttdu57b1t2F323lr8VssTe2/z4TocnHyF49iWr0a/U9/gv7/Hrzo8WajnXUvH0ETqGDpA6NRqs6+KJTl7+HAulWMWbiMlLETAPA43LR/dgJ5uIbgRcm43W42bdpESEgIEydOpKr67ygUIdRorybPZGGRqKbSYOHBiIMgU0DOtf1+f0Ncnpi3bQePR3r52iEDMrlA0sxc74535XZJ6pbn7yVYH8XU3IVUmiqpMlX5vaYoiliL21CnhaKbMwPzrl14JGgy2Gw2KisrycrOIi49TLIGktlcis1WR3zSCrShYZTn75Wk7ra2TpyiyJVpUSCAtUgaOdmmomZkAlw/ZQQej4eysjJJ6lK6FrRRaDNuwmarx2yWLpVWEAQW60PYZTTT5XJLVncIP1P1tTeoJEA6aZUoiuQ15TEpdpKkUiNXWxv248fRTpZWvtZcZcLl8BCfKb18DSBplLQG2rbyDgSlDFWitN481dXVREVFodVKN5EjiiJG4z7CQicjSOjl1eVyc6Srm+mh0srXSho76bS5mJb+/WsgNQAJZ/x3/KnHzkIQhPnAE8BVoiiezi0VRbHh1D8rga+A885AiqL4T1EUJ4iiOEGv1/vgtAfGzRMTUClkp6eQNJo4hsXdwsmTH2O2nL3wkQepCL0qDUdNJ+bdJ899sfMQHqdl8X0j6Wjq5svXjuF2SudLdEX8Fby35D20Si0/2vgjPj3xqWS1h+gndjN8cjvs/DOM+yHcvgq0g2vG5hE9vHr4VX6y/SckByfz4bIPGa3vfzKZx+Gg4WcP07l+PVG/+DmR999/0ePt3U7WvnwEh93Nsh+PQRty9u6YsbGBL1/5KzFpw0+nrgF0bqzG3WYj7LrhyFRy8vPzaW5uZtGiRXR25tHWtp3ExP/hhdoOYtVKGo4bGaaF9IZVMGIxaC+vi8AQA6dr61YUcbFosqVr2IqiSOVhA8MywlBnzARNCJSs9Xtdh7WbmmOHSZ84lflJ3obZttptfq/rau7G3WYjICeCoPnzEa1WLHv2+L1ueXk5brebrKwsUnP1tJ+00NHs/0kvg2ETIKDXzydtwmSqDh/A5XD4ve76VhORSgWTYkJQJQdjK27ze02AjUVNTEgKZ9SIZLRaLaWlEjRynDYo2wyZS9HrFwDCqc9dOpZGhuAQRba2DcnYvhM4uqEuX3L5WnlHOe22dun9j/LzAdBKbKBdV2pEEGDYCGkncuoKjxAUoSc0Jk7SuvZKE6rkYASFdA0Vl8tFXV0dyRLK8gGs1hrs9ibCwqT9ndrXYcYtwvRB8j/6Pk4g7QeGC4KQIgiCCrgZOCtNTRCEscBreJtHLWc8HiYIgvrUv0cC04FiH5yT34nQqbl6TBz/OdiAyeo1QExJ+T/k8kDKy5895/jAXD2arHA6N1XjbO2dLC0hK5zZP8ikvtTI5n8V4enF9JKvSA9L54NlHzA5djJP7X2K3+75LXa3/dJPHEJ6OmrhzYVwfD0sfg6WvwgK6VILzkeno5OHtj/EK0e8ZtlvL36bGG1Mv1/PbTZTd9//nDLMfpyIu+++6PFOh5t1rxzF2Ghh8X0jiYw/+4LgtNlY8+ffI1MoWP7wr1AovV5M9moT5j0n0U6NRZMWislkYtu2bQwfPpzMzBGUlf8ejSaeWt0N7DNZuCU4mB0nDDyVdBTB2g5THuj3e/y+cqkUz1PH3HgqybNIEIQPpD7Hi+Hp7sayezdB8+ZLuvPb3mjBZLCSmqsHudLbvDyxAdz+nVitOnwQt9NJ+sQpxGhjyI7IZlud/xtI1sJWECAgOwLtxInIgoIkkbGVlpYSGBhIQkICKbnepnzlEf9PIRlaNxMSMg61Ws/wiVNx2m3UHDvs15p2j4etbZ0sigxGLggE5ETibOrG1cs1S3+pbeumtKmLhTnRyGQyMjMzKSsrw+n0s7l01Q5wmCFzOSpVJCEh4yX3QZoQoiVCqRhKY7sEl811oi4PPE5ImSVp2fwmbyNncqx0qZgAlr37kOl0aHJyJK3bcNyIPjEIdaC0Uet1xcdIyBkl6bXebXbgau5GnSpts6yhoQGn0ym9fM3oDQSRuoG0u8OMWiYwIVh6/6M0vZao4MENOeorA24giaLoAh4ENgIlwCeiKBYJgvCUIAg9qWp/BHTAp4IgHBYEoafBlAUUCIJwBNgOPCuK4mXRQAKvmXa3w80n+70WUCpVBMnJP6at7Sva2naedawgCIStSAe5DOPHx3uVygaQNS2W6denU3HIwFfvl0qqzw9Rh/Dy3Je5e+TdfFb2GXdsuIP6rnrJ6g/RC2r2es2yTfVw20qY8r+DbpZ9vP04N6+9mZ31O/nlxF/y9PSn0Sj6/8Xoam2l9o4f0l1QQNwfnyf8jtsverzb5eHL1wpprDCx4Ec5JGaf3dUXRZFN/3yJ1vpalj34CMGR3phdj81F+ycnkIeqCVnsvWBu2LABURRZunQpjU2fYTaXkpb6KC/UthGjUtJVYUIlF5ll/BTixkKStIZ/lzu9SfEUBGE48CtguiiKOcBDkp/oRTDv3IVot0suX6s6bAABUsacmjTMvBKsRqj171RO+f69BAQFMyzT+79pXuI8jhqO0tLdcolnDgxrURuqxGDkQSoElQrd7NmYt29HdPmvYeZyuThx4gSZmZnIZDKCIwLQJwZ5P3s/YrXWYTaXoNcvBCBh5GhUAYGU7/evjG2n0YzF7WGJ3nujEnDqu9Pq5ymkTcVev8VFOd5NhszMTBwOB1VVfpZGlq4FVRCkzAQgSr8Qs7kEq7XuEk/0HXJBYHFkMFvbOrF7hhJwz8dldZ2o+torZU+U9uY3rzGPeF08cTppJ2MsefsInDgRQSFdMpjT4aa5qpNhEqevtdXXYu3qJCF7lKR17VXe5rI6NUTSutXV1QAkJSVJWtfYkYdKpScw8LyONn5jt9HM+GAtGrl0U15Ot4f8qnamXkbpaz345FMSRXG9KIojRFFME0XxmVOPPSmK4ppT/z5fFMVoURRzT/256tTje0RRHCWK4phT/3zTF+cjFSOHhTAtLYLXvq7EYvcuYhPi7yBAk0hZ+TN4PGcvbOXBasKuG46jrgvTl9W9rpM7P5EJS5Mp2d3I7k/LJW0iyWVyHhr/EH+b8zfqOuu4ae1N7KiTLv1miItw8N/wznIICIV7t0L6vME+Iz4v+9zrd+Sy86/F/+IH2T8Y0E6NvbKK6ltvw15ZScIrLxOyfPlFj/e4PWx5q5jaojZm35pB+vioc47Zu/JDSnfvYPqNPyA5dzxwSnP96QncHXbCb85EppZTWlpKaWkps2fPJihISWXlXwgJGUe56gr2mSz8MCKUT/bX8cvUWhTGCpj64KA37y5DLpniCdwLvCyKohHgzCnW/wa6tm5BHhpK4PhxktatPNxKTErwN9LM9Hmg0EDpOr/VdLucVB0qIG3C5NMJNHMT5gLwVd1XfqvrarfhbLQQkPPNIito3jzcHR10Hzjot7pVVVU4HA4yMzNPP5YyJpKmyk4sJv9N5BoM3ikYfeQCAOQKJanjJlJRkIfH4z+vnC8NJrRy2ekIY0W4BmWs1mte7kc2FjWRFRtMQrg3gSYlJQWVSuVfGZvHDaXrYcRCb+AEnJKxIbmMbYk+FLPbwy6jdAl/lxmXz3Wi6msYNh7U0slg3B43BU0Fkk8fOZuacNbUEjhZWtlcU6UJj1tk2AhpG0h1RUcBSMiRuIFUaUJQyVDFSyutqq6uJjo6msBA6ZLBzvY/km49bXS6KDRbJfc/OlpvwuJwMy1tcC1H+oOkJtrfRX6+KINWs523dnt3ymQyNenpv8RiKeNk4yfnHB84KhLdtDjMuxr6ZE45aXkKo+fEc2RbHbs+LZO0iQQwJ3EOH1/5MXG6OB7c9iB/2v8nnG4/j5cPcX48btj4BKx5EJJnwD1bIFLapIJvY3FaeHzn4zy550ly9bl8svwTxkYNLNLVkpdP9S234DGbSXr7LXRXXNxTwO32sOnNIsoPtDDtunRyZp4bBln89Tb2rvyAnFnzmbzixtOPm/ecxFrURsjiZNRJwVgsFtatW4der2fKlClU1/wDh6OVtPQn+ENVIzEqJScL2/CIIrd51kBwPGR/ez07RC/oTYrnCGCEIAi7T6V1Dn6s4ClEpxPzVzvQzZkj6Q5sV7sNQ20XKWPO8ANUaSFtrreB5KfrQ13hUezdFtInTj39WFpoGknBSX5NY+uZgAk4Y5pQN3MGgkpF1xb/ydhKS0tRqVRnjfCn5no/86oj/jOXNhg2odNmEBj4zc5v+sSpWLs6OVla4peablHky1YT8yKCUcu+WRoG5ETgqO3E3eUf/yVDl52CGiOLcqJPP6ZQKBgxYgSlpaV4/DWVU5cH3a3eyb1TBAQkotNlnm7gScWMUB1auYwNhiEZ2wW4PK4TNhOcPCi5/1GpsZQuZ5f0/kf7vcmQ2knS1j15ogNBJhCbJu1ETl3xMYL1UYRE9d+OoT/YK0yokkMQJJyM6fE/klq+1t1dhcPRMij+RyIwQ2L/o32V3rXNlMvM/wiGGkgDZlxiGAuyo3ltRyVGi3eBpdcvIix0CuXlz2GznWuaHbI0BWW8jvZPT+Bq712KjCAIzLhxOGPmJXB0Wz1ff3SiV4luviQhOIH3lr7HTRk38U7xO9y+4XbqOqUb9R4C7wLlg5tg799h8v96ZWsSJn2cj5K2Em5aexPrqtbxwJgHeG3Ba0QEDOzLsOOz/1B7990o9JEkf/IxAbkXj0x1uzxser2IioMGpl+fztgFieccU1d0lI3/+BuJI0ez4L4fn97dcNR1YVpfhSYrHN3MYYiiyJo1a7BYLFx77bVYrSeorX2dmJgVbLAkcKCzm3vDQ/nPwXp+PsqKpmGvVzool06L/z1DAQwHZgO3AK8LgnBeMwCp0zot+fl4OjsJWiBx+topCVVPM+M0mVeCqQ4aj/ilbtn+vSjVGpJGffP3URAE5ibOJb8xn06Hf4yArUVtKKIDUUQGnH5MptWinTaNrq1b/LKh4vF4KC0tJT09HaXym7/b4XFagvUBfpOxORytdJgOnJav9ZCSOw65UkmZn2RsB0wWWp0ulkaefVOmyYkE0X8ytq0lzYgiLMw++6YsMzOT7u5u6ur8tMYoXQdyFaSf/XdXH7mQDlMBDoc06XMAGrmMeRHBfNlqwi3x5uB3iMG/TtTuA9EDyTN995q9oKCpAIAJMRMkrdudn48sOBh1RoakdRtOeP2PVAHSbdqIHg/1xYWSy9fcZgeulm7J5Wv19fW4XC7JDbSNHYPnfxQgkzE2WLppK/AaaGfGBBGuHVzf2v4w1EDyAT9fmIHZ4eIfOyoA74I6K+sPgIeS0sfPWdwKChkRt2YB0PZuMR577zwcBEHw3hwvTKRwRwPb3y/F45ZWM6+Wq/n1lF/zwuwXqO2q5Ya1N7C6fLXkE1HfS9oq4I353qjuK/8KS54DuXQX0G/jET28Xfg2t62/DavTyhsL3+D+3PuRn5K29AfR6aTp/3uaxieeQDtpIskffIAqPv6iz3E63Gx47RiVhw3MvGk4ufPPbR41VZSx6o9PExoTy/KHH0eu8N4QujvttL1XjDxIRfgNIxAEgYKCAo4fP878+fOJjo6guOQXKJVh6JN/xdOVJ5kcoqXkUDNqhYwfelZ5PTTG3dHv9/w9pzcpnvXAGlEUnaIoVgEn8N4onIPUaZ1dW7YgBASgnSat91XVYQPhcVpCo7+12BmxGASZ19vFx4geDxUFeaTkjkehOnuxMzdhLi7Rxc76nRd4dv9xmx04qk1nydd6CFowH9fJRmzFvrdOrK+vx2KxkJWVddEQiEwAACAASURBVNbjgiCQmqun/rgRu9X3/kutrdsAz2k5VQ+qgECSRuVSvn+fX66361tNKAWBeRHBZz2ujAlEHq7xWxrbxqImEsIDyIo9O546PT0duVzuHxmbKELJF5A6GzRnv19v407E0Oq/ibrzsTQyhFaniwKTRdK6lwmXx3Wieqe3KZkg7UROQVMBScFJRAWeK9n3J5b8fAInTECQ93/N11dO+x9JnL7W438UL7X/UeX3zP/IuBe1OoaAgGRJ6+4ympkUokUlk64tYne5OVBjvCz9j2CogeQTMmKCWDF2GG/vqabJ5J0oCghIJC3tUdrbd55XyqYI1xBxaxbOFgtt75ciunu3IBQEgakr0piwzOuJtOG1QpwO/3kiXIj5SfNZuXwlmeGZ/Hr3r3lkxyN02DokP4/vDZU74PW5YGmFO1bDhLsG9XSaLE3cu+le/nzgz8wcNpOVV61kYszEAb2my2Cg5q67ML7/PuF33UXCP/+JPDj4os+xmh2sfuEQNYVtzLo1g9FzEs45pqW6ks+e+Q0aXRDXPf4UGq13RNVjd9P6dhEeq5uIO7KRBSppaWlh48aNpKWlMWXKFKqqX8ZsLiUz42n+VNeNyeXmnqAQ1h5t5NdjutCcWOOdPtJIe3H/DnHJFE9gFd5d5Z60zhFApZQneT5Ejwfzlq3oZsxAppEuPcNqdnCyrOMb8+wz0UZA0nQo8X0DqbH8OJYOI+kTz90ZHK0fTWRApF9kbLaSdhAhIOfc96ubMwdkMr/I2EpLS5HJZAwffu49aOqYSDxukZpC30+ptBg2odEMQ6fLPudn6ROn0mloxlDjW3NpURTZYDAxI0xHkOLsm0FBEAjIicBW3oHH5tuGmdnuYnd5G4uyY87xu9BoNKSmplJSUuL7hllzIXTUnCVf60Gny0SjSZDcB2luRDBKQRhKYzs/l8d1onq31/9IGXDpY32E2+PmQPMBJkRLO33kbG72+h9NHNi6r68Mmv9R8TEA6Q20K00IKjmqYdJKq6qqqoiNjSUgQLrfZa//UR5hoVMk9T9qdbgotdiYLrF87Wi9CZvTc1nK12CogeQzfjZ/BB5R5PmN3+yWxQ+7jbDQKZSV/f68UjbNiDBCr0nHfsJIx5rem2MLgsDk5alccfMIqo+1svqFQ9jM0vsRxenieHPhmzw07iG2123n2jXXDhls+4P9b8C/V0BQLNy7zet7NEiIosiq8lVcu/paClsLeWraU/x1zl8J0wzsYm7Jy6fquuuxFRYR96c/Ef3LRy/pKdPZauU/fzxIa52ZxfeNZOQV53oetdXXsvKZ36DQaLjxyWcIjvTuNooekfYPS3E2Wgi/NRNVnA6bzcYnn3yCWq3mmmuuwWwppqbmVWJiVlCvnsa/T7Zxd1wkH2yvJCxAzo1tr4IuBqb/V4WCXVb0MsVzI9AmCEIx3rTOX4ii6F9X315gO3YMl8EguXyt+mgbonge+VoPmVeCocQ7sehDyvL3IpPLSRl37g2DTJAxJ2EOuxp2YXP1TpbdW6zFbchD1Sjjzo3WVYSHEzhuHGYfN5BEUaS0tJSUlBQ052kOxqSGEBCsovKQbxtILpcZo3E3ev3C8y6g0yZMRhBklOX7VsZWarFRY3OwVH/+RnhATgS4RWzH231ad3tpCw63h4U55/cUyczMpKOjg+bmZp/W9TZYBchYes6PBEFAr19Ae/seXC7pTK2DFXJmhOnYYDANTXR/i8viOmHr9EqHJV6fnTCeoMvZNQjyNa//UeAkaRtIg+Z/VNTjfxR96YN9iL2iA3VKsKT+R06nk/r6esnlaxZLGU5n26D4HwFMk9hAO++U/9Gk5HBJ6/qKoQaSj0gID+S+K1L5z8EGvjruDX8QBBlZWc8CHgqLfobHc64JpW5SLLpZ8VjymujaUd+nmqNmx7P4vpG01plZ+VwB7Y3Sjz7LZXLuHnU3Hyz9gFBNKA9ue5Andj3hNy+M7xVuJ6x7xPtn+AK4exOES2todyYt3S08uO1BfrP7NwwPG87K5StZMXzFgHYKRLcbw99fpvauu5AFBpL88UeEXLnsks9rLO9g5fMHsHY5uOqnuaSNPXd0u6W6kk+eehyZTMYNv37mtPGhKIp0rKnAVtpO6FVpBGSG43a7+fTTT2lvb+f6668nIACKin6GShlJQuoTPFJaS5RKQUqbi72Vbbw0qhLFyQMw7zeSpq18F+lFiqcoiuLDoihmn0rr/Ghwz9hL15YtoFCgmzVL0rqVhw3owtToE4POf0DmqZviki98VlMURcr37yUhZ/TpCb5vMy9xHlaXlbzGPJ/V9djd2MqMBOREXPB7JmjBfOxl5ThqanxWt6Wlhfb29nPkaz0IMoGUMZHUFLXhcvpuArit/Ws8Hsfp9LVvExgcwrDMbCp87IO03mBCABZFnP+mTJUYjEyn9Hka26biZiK0KsYnnX8DIuOUt4rPZWyla70x67rzN2H1+oWIooO2Nmk3xJbqQ6ixOSix+LYJ+13gv/46UZcHots7ASoh+5u8jRypJ5C68/ORBQWhOSOhUgoGy/+orqSQhOzRktUEcHc5cBmsg+J/5Ha7JW8gdXTkAxAWJm2a4N5T/kdjgqT1P8qraiczJoiwy9D/CIYaSD7l/+YOJz1Kx+P/OUaXzTsRFBCQQGbGM5hMBZSV/f68zwtZlEzAGD2dX1bTtfPbsu6LkzY2iqt/NhaH3c3K5wqoOiqd8eOZZEVk8dGyj7h31L2sq1zHNauuYUuN/9JxvvN0t8N713qnj6b/FG7+4ByvBqnwiB4+O/EZ16y+hvzGfB6d+ChvLX6LhOBz5WJ9wdnQQO2dd9H6978Tsnw5KZ+tRNMLM8biXSdZ9cIhVGo51/58PHHDz9XC1xcX8vFvH0OuUHLDk78nPM47nSSKIqZ1VVj2NaK7Ih7d1DgANm7cSEVFBcuWLSM5OYmi4p9jtdaQk/MXfl9rodhi44m4aP785XFmJWuZXv0yxIyGMbcO6DMY4vJEFEW6Nm9BO2kS8hDpFndOu5u6knZScvUXbtyGJkLsGJ/6ILXV19LR1HhW+tq3mRQziSBlkE9lbLYTRnCJaLIvPOKtm+edAPOljK2nYZFxke+j1DF6XHY39SVGn9U1GDahVIYTGnrhm8H0iVMw1FbT0dTos7obWk1MDNESpT5/EIAgEwjIjsBWakR0+cZ30e5ys720hQXZ0chl5/9d1ul0JCYmUlLiw+S59iqvhO088rUeQkPGoVSGSy5jWxwZggBDaWyXI9W7QKaQ3v+ouYCEoARitNImg3Xv30/g+PHfC/+j1vpabF2dJOQMlv+RtO+3uroaQRCk9z/qyEOtjkGjGdi9RV/Z22FmYkggygtch/yB0+2hoNrI5JTLc/oIhhpIPkWjlPP89aNp6rTx7IZvdsxiYq4iMeFu6hv+zcmTK895niATCL9xBAEjIzCtq6Rrd9+aSLFpIdzw2ARCowJZ/+pR9q+rwiNxQhuASq7iJ+N+wvvL3idcE87PvvoZP932U5otPh4//65jOO71O6rdB9e8CgueggEYUw+EKlMVP9r4I36797dkhGXw6fJPuT37dmRC/786RFGk47PPqLzqamzFxcT+4Q/EPfcsMu25EpUzcTs97PjwONvfK2VYRhjXPzaB8PPIWioO5PHZ759EFxbOzU89T8SwhNN1TRuqMO9qQDctjpAlyQDk5eWRn5/P1KlTGT9+PFXVL9PauoXh6Y+z25nJ2w2t/E98JBu+qsLtEXkxaRdCZz0s/gNIaLg3xH8PjooKHNXVksvXaovacDs9F5av9ZC5HOr3Q6dvmgzlpyRT6RMuvDOolCuZGT+Tr+q+wuXxjVeOragVWaACdfKFm3Sq+GGos7Po2uzbBlJ8fDxBQReY8gLiM8JQauSnE/EGisfjoLV1O5GR8xCEC3/f9zTxyn00hVRjtVNotrIk8uKNUE1OBKLDja3cN16HeyraMNtdLLqAfK2HzMxMmpubMRp91KjraaxmXbiBJAhy9JHzaW37Co/H7pu6vUCvUjIxRDvkg3Q5UnPK/0h18XWML/GIHg40Hxiw/2RfcTa34KiuJnCStM2yQfM/KvL6H8VnjZS0rr2yA0EtRxkn7ZR7TU0NMTEx55Vv+wtRFOnoyCc0dJKk/kdGp4sSi42pEsvXjtabsDrdTL5M/Y9gqIHkc8YlhnH3jBTez6tlT/k300BpaY8SFjaN4yd+g6nz3IhlQS4j/JZMNNkRmL6opGtX35pIQeEarv35ODImxZD/RRVf/O0wFpN0C58zyYnI4cMrP+ShcQ+x++Rurl59Ne8WvYvTI71P02VH2WZv0prDAneug9zBmXCxuqy8dOglrltzHSeMJ/jt1N/y5qI3SQ5JHtDrOpuaqL//ARqf+DWa7GxSVq8mdMU1l3xeR3M3K58voHBHA7kLErnyx6PRaM/eLRdFkf1rPmPVH58mMjGJm3733DeeRz3No68b0E6NJWR5KoIgcPDgQTZs2EBGRgYLFizA0LqVqqq/EhOzAjHiFh4+XsvYoECyTSLbjxt4bpqH0P1/hexrBtWLaojBpWfaRTd3rqR1K48YUGsVxKVfYuqp5+b4+Hqf1C3bv5fY9Ax04Rdf7MxLnIfRbuRQy6EB1xRdHqyl7WiyIhDkF19QBs2fj/XwYZwtLQOuazQaaWxsvKB8rQe5UkbyyAiqjrb6JA3VaNyL220mSr/woseFREWjT06lbP++AdcE+PJUs+JC/kc9aNJCEdRybD6SsW0qakKnVjAt/eK/U5mnJDI+m0IqXQfRoyAs+aKH6fULcbvNGI2++Zx7y+LIEArNVmqsg7N+G6If2M1w8pDk8rUyYxmdjk7p5Wv7e/yPpG0gDZb/UX3xIPkfVZlQJwdf8vrnS1wuF/X19ZJPH3V3V+FwtBIWKu3vVF6HBREkbyDlVZ3yPxqaQBriTB5ekEFqpJaffHSYRpMVAJlMwcicF1Gpojhy5B7MlrJznifIZUTcmokmJwLT2ko61lci9mGSSKGSM+/OLObcnklThYmPn86nxseeBb1FKVNy96i7+fyqzxkbNZY/FvyRm9bexIHmA4NyPv/1iCLs+Tt8cCOEJXnNsiUehfaehsi22m2sWL2Cfx79JwuTF7LmmjVcN+K6gU0dud20v/c+lUuXYdm3j6jHfkniO2+jij/X+Prb53M8r4lPfr+frnYbSx8YzfTr0pF9y1DQ6bCz4eW/8PX7bzFi8nRufPIPBAZ7Fxmiy4Px4+Onm0ehV6UhCAKHDh1izZo1pKenc/3112MyFVBY+BOCgkYSn/Y77imqRhThF5Hh/G5NETMSA1he/hvQ6uHKF/r9WQxx+dO1eQuaMaNRRku3oHS7PNQcayNlVOQ5v//noM+E8DSfyNg6DS20VFWQPunC8rUeZgybgUqmYlvttgHXtVeaEG1ur4HzJQia750EM28beN0e+VpmL7w9UnL12MxOGisGPjHSYtiEXK4lLOzSN6HDJ07l5IkSLB0Dn8rZYDCRrdWQFKC+6HGCQoYmMxxrcVuf1iXnw+0R2VzczOwMPWrFxadrw8PDiY6O9o0PkrnFO9l7kemjHsLCpiGXa2kxbBx43T7Q08j7cmgK6fKhLg88Lkj+Hvkf6XRosgbB/yhBJ63/kShSX1Ioefqa2+zA1WJFlSJts6yhoQGXyzVo/kehodL7H2lkAmODJfY/qmxneJSOSN3Fr7v/zQw1kPxAgErOa7ePx+Z0c9+7B7A6vCabKlU4Y3PfRhDkHDp0B93d1ec8V1DIiLgtC+2UWMxfN9D+USmis/e7m4IgkD09jht+NZGAIBVrXzrC9n+X4LD6Nn63tyQEJ/DKvFf465y/YnaYufPLO3nkq0eo7+qbYfh3GpcdVj8Im57w+jL8aCOESqsBBm+ax32b7+On239KgCKAfy36F8/OfJbIgPPEhfcB67FjVN96K81PP03A2LGkrv2CiDvvRLiE/MtisrPhH8fY8lYxkQk6bnpiEimjzz0XY2MDH/+/X1KyczvTb/wBVz70S5SnRm89Vhet/yqk+7CB4EVJp5tHhw8fZvXq1aSmpnLTTTfRbS3myNF70GiGkT3qDe4raaLIYuW55Dh+89FRgjQKXo9aidBWAde+BoGX767BEAPD2diIrajodNNCKhpOGLF3u0i5lHwNQBC8N8lVX4N1YJKjHqnU8F40kAKVgUyLm8bW2q0DTpKyFrUiqGRohl9arqAePhxlUqJPZGylpaVERUUREXHpxlXSyAjkCtmAZWyi6Ka1dQsREbOQyy+9oEyfNBVEkfIBTiEZHE7yTBaWXGL6qIeAnAg8FieO6oGFZBysNdJqdlxSvtZDVlYWtbW1mM0DTEU7vh4QL+p/1INcriYiYhYGwxZE0XdG6ZciOUBNllYz5IN0OVGzGwQ5JEh781vQXMAw3TBidbGS1h0M/yOXw01zdafk8rX2hjqsXZ0My8qRtK69yvsdq5a4gVRzKowiMTFR0rodHfmoVJEEBkobFLS3w8y4YC1qCe0oXG4PBdXtTE69vO8jhhpIfmJ4dBB/vSmXwpMmHv3s6OnFdGBgCmNz30UUnRw6dDtW67lSNUEmEHp1GiFLkrEebcXw+lFcHX0bZw6P03LDryYwblEiJXsa+fCpvEGbRhIEgXmJ81h9zWoeyH2AnQ07uXrV1bxw4IWhtDazAd69Gg6/B7N+CTe8I6mGHsDQbeB3e3/HDV/cQHFbMY9NeoxPln8yYF29q7WVk48/QfUNN+JsOEnc88+R8MbrqOLjL/q8nqmjD5/Ko7aonWnXpnPNw+MICtecc1zRjq38+7GHMDU3cfXPf82U624+rZ92NltoeeUw9ppOwm7KIHiO94K4Y8cOVq1aRWpqKrfccgs2WzmHD9+JShnBmNx3eLSymx3GLp5NH8ZHG8poNdv5eEYzAYXvw8yHIeWKAX0uQ1ze9DQppG4gVR5uRaGSkZjdy0VH5nLvrnjZwIyAy/bvJSI+kbDYi08L9jA3cS6NlkZK2vsvORI9ItbiNjQZ4QjKSy9TBEEgaP58LHl5uDv7f02xWCzU1tZeUr7Wg0qjICErjMrDhgE1zEymQzgcregvIV/rITIhidDo2AH7IG1q7UQElup7Z9KqyQgDhYC1aGBhHRsLm1DJZczO6EUzlG+mwY4fPz6gupSs9UrXont3MxilX4TT2YbJNHBJZl9Yog8hz2TB4BiS/V8WVO+GuLGgvrBnmq/xiB4Kmgsk9z9yGQw4qqoInCRt3eaqTjwu8byhKf6kvqQQgIQsaSeQHFUmBKUM1TBppVXV1dVERUURGCjdRI4oihg78iT3P+p0uSk0W5kaKu09V9HJTiwON5NTLl//IxhqIPmV+dnR/GJRBl8cOckLW76RrOl0Ixib+w4udxcHDt5Il/nc0WxBEAialUD4bZk4m7pp+dtBbyJNH1Ao5Uxdkc51j05AqZaz9qUjbHjtGF3tgxMRG6AI4P4x9/PFNV+wOGUxbxW+xZLPlvBW4VvYXN/D2NqmQnh9jlc7f/2/YM7jkpoydzm6+NvBv7Hs82WsKlvFLZm3sP7a9dyWdRtK2fnTeHqDx2LB8PLLVCxajOmLLwj/0Y9I+3IDIVdddcmLQ9tJM6v/eogtbxUTGhXITb+eyNiFici+lY7Q3Wli3d/+yJevvEB0Shq3P/8S6ROnnP655WAzLX8/jMfqIvJHI9GOjcLlcrFq1Sq2b9/O6NGjufXWW+kyF3Dw0K3I5VrG5L7LM7Vu/tNs5LHkGI7uPUl+VTuvz3GT/PUjMGwCzP5Vvz+XIb4bdG3Zgnp4OuoU6XbKRI9I1WEDSTkRKFS93PUdNh50MVCypt91uztNNJQU92r6qIfZCbORCbIBpbE56rrwdDl7JV/rIWj+fHC5MO/4ut91jx8/jiiKvZKv9ZCSq8fcbqe1rv/TMQbDJgRBRWTE7F4dLwgC6ZOmUlt4FHu3pd911xtMJGpUZGt7Z5YqUyvQpIdhLWrrd8NMFEU2FjcxPT2CIE3vrjPR0dGEhYUNzAfJ1glVO7zTR728SYmImIUgqCRPY1umD0XE2+Ab4r8cRzc0HJBcvlbRUYHJbmJ89HhJ63Yf8NpQBE6UtoF0srwDBIi9lP+fj6krLkQXFk5ItLQpd/YqE6qkYASFdPcEbreburo6yf2PbLY67PYmQiX3PzLjYfD8j4YmkIa4KPfPSuOG8fH8bWsZf950/PSiKygoh3FjPwTgwIGbaGs7/6I3cJSeqP/LRR6sovWtQkwbqvokaQOITgnmpicmMeWaVGoL2/jg/+1j/7oqnHbpxrLPOh9tNM/MeIZPln/CKP0o/nLgLyz7fBkflX6Ew+0YlHOSnJK18OZC74TAXRtg5HWSlbY4Lbxx7A2W/GcJrx97ndnxs1l9zWoem/QYIer+X5w9djvt771P+cJFtL70d7TTppK6ejXRj/4Cue7iX9A2i5NdK8v45On9tNaZmXVrBtf+YjxhMWfvDIiiSPHX23jr4fspy9vDtBtv44Ynnzltlu2xuWhfeQLjJydQxuuI/sk4NGmhmEwm3n33XY4cOcLs2bNZsWIFBsMaDh++C7U6mlG5H/JotYc3G1q5Ny6Suv1NfHqgnqemCFyx/wEIjoNbPgJ5/xtrQ1z+uNrb6S4oQCfx9FFTVSfdnQ5Sx/ZuYgPwNqMzl0H5VnBa+1W3oiAPUfSQPmlar58TpgljfPR4ttb0v4FkLWwFuYAms/cLrIAxY1Do9acNzvtDSUkJoaGhxMT0/mYhZXQkgkC/ZWyiKNJi2ER4+FQUit5PMKRPnIrH7aLy4P5+1e1yudlp7GKJPqRPu74BORG4O+w4T/avcVXS2EVdu7XX8jXwNsyysrKorKzEZuvnZlPZJnA7IGt5r5+iUAQRHj6dFsPGAUsy+0K2VkOiRsU6g28S74bwI/X7weOEJGlDNQqaC4BB8D/aX4AQGIiml1OavuJkWQeR8TrUgdKtwURRpKGkkPjsUZJOxni6nTibLKiTgyWrCdDY2IjT6ZTc/8h4yv9IagPtvR0WlILAuGBpJ5D2VbaTGqklKki6lDt/MNRA8jOCIPDsdaO5eWICL20r59kNpWc0kbKYMH4lAQEJHDl6D3V175x3kaLUB6J/IBfthBi6dtTT/NJB7LV925mSK2WMX5zMrb+bQtLICPK/qOK93+yl8OsG3D5IkOkPmeGZ/GP+P3hz4ZsM0w3jmbxnWPqfpXxU+tF3dyJJFOHrP8HHt4E+A+7dDsPGSVK6y9HFG8feYPFni3nx4IuMjhzNx1d+zPOznicxuP96Z4/VSvu771KxYCHNTz+NOjWV5I8+JP6ll1CnXnxKw+lwc+DLav79670c2VpH5tQYbntqCiOvGHbO1JGhpoqVT/+aDS//hbDYOG5/7kWmXncLMpl3IsNa2k7zXw7QfaCZoNkJ6O8ZjTxYRUlJCa+++ipNTU1cd911XHHFDCor/0JxyS8IDZ1AxuiP+J8yB581G/llUgymgy18drCBJ2cEcHv5z0Cphds/B10fbt6H+E5i3r4dPJ5BkK8ZkMkFkkb10Y8sazk4u6Gif+bS5fv3EqyPIio5tU/Pm5c4jwpTBVWmqj7XFEURa1Eb6rRQZJrem6UKMhm6eXMx79yJpx9NBpvNRmVlJZmZmX1rqASpiE0P7XcDyWwuxWarQx/ZO/laD3HDM9CGhlGe3z8Z29a2ThyiyNLIvm0aaLLCQaDfMrYvi5qQCd4J7b6QmZmJx+OhrOzcAJJeUfIF6KIhvm83KVH6hdhs9ZjNPkqB6wWCILBUH8Iuo5lO1+Bs9A3RS2r2gCCDRGn9jw40HyBGG8MwXe+kxb6ie/9+AnNzEZTSNXLcLg9NFSbi0qWVr3U0N2I2thMvtf9RdSeIoE6VdtqquroaQPIJpA5jPkplGFrtcEnr7u0wMzY4kMBLhZL4ELdHZP93wP8IQDor++8xcpnA71eMQqWQ8drXlXR0O/nd1TlolHI0mljGj/uIoqKfcaLsKdqNu8nK/AMq1dmj+zKVnLDrhhMwKhLjZ2UYXj2CdkoswfOTkGt7/0UeFK5h8f+MorHCxN7Py9nxwXEObaph3KIkMqfEIu+F34SvmRQ7iXdi3mFf4z5eOfwKz+Q9w6tHXuX27Nu5MeNGglXSduH9htPqNcsuXAmjboCrXgJlgN/Ltlpbea/4PT4+/jFmp5mZw2Zy/5j7GaUfmKbb1d6O8cMPMX7wIe62NgInTiTu+ecInDz5kjdgDpuLoq9PcnhLLd2dDpJHRzLl6lQizqP3Nhvb2fPJexRu34I6MJB5P7qfMQuWnDbhdnXYMX1ZhfWwAUV0IFG3Z6NKCMJqtbJ582YOHjxIbGws119/PYGB3Rw8dAsm00HiYm9EFv841x1r4Hi3jWdSYtm7o5YtJS08P13kxhMPgtsOd33pTcYb4ntP16bNKOPi0GRnS1ZTFEUqDxuIzwhD3df0meQZoAn13jxnLuvTUx3WbmqOHmLMwmV93n2dlziPZ/OfZWvtVu4ZdU+fnuts6sbdbiNo1sW90s5H0IIFdHz0MZY9ewiaO7dPzy0vL8ftdvfa/+hMUnP17Pq0jI7mbkKj++Yd4ZVHCUTq+9aUFGQy0iZMpmTnVzgddpSqvqW5rG81oVcpmBDSt91XuU6FKjkYa1EbIQuT+/Rc8PofTUgO73P6THx8PDqdjpKSEkaN6uO1y2mFss0w+sY+y8QjI+cBMgyGTQQFSff3fmlkCP+oM7C1rZMV0dIaBw/RB2p2Q8wo0Eh3sy+KIgeaDzA59tJrLV/i7ujAfuIEwUsWS1YTwFDbhcvpGTT/o/iskZLWtVebQC6gSpDOUwu8BtoRERHoLqEY8DXGjnxCQyciDCDpua9YXG6Omrt5MFG6JF2A0qZOumyuy97/CIYaSJIhkwn87qocgjVK/r69nOLGTl65bRwJ4YEoFDpGj36N+vp3Ka94jrz8ZWRll/HfCQAAIABJREFU/p7IyHMXwJoRYUQ/PA7Tl9VY9jXSfchA8LwEdFPj+qSVjU0LYcUj46g51sb+dVV89f5x9q+rJnd+AlnTYiUdEwXvjtvUuKlMiZ1CQXMBbx57kxcPvsjrR19nxfAV3Jp564CmZAadzkb46FY4eRDmPQkzHu61D0N/Od5+nPdK3mN95Xqc/z97Zx7f1l2m++/RvkteJDved8exk2ZPmjRJ06RJmnSB0pZSYAqU/ZZhGQaGyzADDFzobMAwcIdl4LbQUlrKpNnabM3SZnc2x0u875sk25IlWbvO/UNxadpstqWTLvp+PufjxJb0epGOzu/5vc/zxsJsKNrAJ2o+wZyMmV0EB5qaGHvmD7i3bUMMBtGvWU3mJz95Q574ifEQ9Yf7qTvQS9AXIW92Ghs/VXPFCwPPqJPabX+mbv9uYtEoCzffy/L7H0Zz6c0tFoziOdSL53A/IGJcV4BpbT7I41PW9uzZg9/vZ+XKldx+++04ndu4UP9dAOZU/YgD4kq+eaYTrVzg+zYbv32+kf4xP/9v+TC3130TtOnw6HawSTuqNsXbk6jXh+/oUdIe+ZCkF+0j/T7GHX4WbpjG+U+uhMrN0LwTouEpWTA7ztYSjUSmlH80SbY+m5qMGl7peWXKAlKgwQkCaOdM/QJLv3QpMpMJz569UxaQmpqa0Ov15OdPfQJmyYK4gNRxzsHCjVMTmx2O3VjMi1Grpj7tsnzpCur2vUx33TnKFt94B0QgGmPfyDgPZKUhn8ZzWVudiXtHB2GnH2XmjW+CdDp9NA97+Ie7p/4eJJPJmD17NufPnyccDqOcSgdE+wEI+6ZkX5tEpcrAYlmC3bGbkpIvTfn+02WxWY9NpWCXw50SkN6uREJxC9viT0hatsfTg9PvlD7/6MwZ4CbkH7XGrZxSC0j9TQ1ojSbSc6WdihzscKPKNyIopZtyF4vF6OnpoaZGWrEsEBggEOglP/9RSevWjk8QFWH5FDdQZsrJzlEAlhanOpBSTAFBEPjqxkpuybfwlefOcfdPX+MH98/lrppsBEFGfv7HSEu7lfqGL3G+7lNkZKylovybbxlrKFMrSLuvDMPyWbh2duLe2Yn3yADGNXnoF2fd8ElHEASK5mVSODeDvqYxal/q4sif2jixvZPKZdnMXZN7xY6QZCIIAkuyl7AkewlNI0081fgUf2z+I880PcOqvFU8VPEQt+Xehlwm3Yl1xvSfiYtHgXF4+JkpdwJMhVA0xP6e/TzX/By1w7VoFVreX/5+PjrnoxSapt9FE/X68OzezdhzfyRwvg5BrcZ87z2kP/oo6rKya95XFEWGu8a5cLCPtlo7sahI0bxMFm0qJPsKLbrO3m7OvrydhoP7iMVizFl1B8vv/yCW7Pio2lgggvfYIN7X+on5wmjnWzFvLEKRpqGjo4NXXnmFvr4+8vLy2LJlC3rDCOfrHsHtPoPZvAhz6b/wzd4Yu5y93GbRs84n5wfPnCdDK+PQspPknv1x3Fb48B/AKO3uRIq3L75XDyOGwxjvvFPSuh3nHCBA8S3TtFBW3Q3nn4Gu16B07Q3fre3kMbQmMzmV08u6WFe4jp+c+QlDviGy9Teed+OvH0FVaEJuVE25pqBUYly7Fs+BA4jh8A3bLMLhMK2trcydOxfZNAYZGNM12AqNtJ+dmoA0MdGJ19dMefnfT7kmQH71XNR6PW0nj05JQDo85mEiGmOzdXpdE9rqDNw7Ogg0OFGuufHF1e6GIQA21kwvkHb27NnU1tbS0dFBZWXljd/x4o54h0jRqmnVtVrvpLX1e0xMdKHTFU3rMaaKTBDYlGnmT8NjBKIxNBLaLFLcIANnIRKAwhvPiEsEp4fjQdaSC0inahFUKjRT7QCcIQOtLtKydWin8Z4wE3ob68mrqpE2/ygYITzgxXi7tKLV0NAQwWBQevuaK57lZbFIK0oed3mRC7DkJghIeWlacizJd58km5SAdBO4c04WO7+wis8/c5rPP32G2yutfPueaooy9RgMlSxd8iK9fU/R2flTjp+4i7y8j1BY8CnU6ssXs8osPdZP1BBoHWN8Xw+uF9sZ39+DYWUO+sXZN3wBLggC+XPSyZ+TjqPHQ92BXi4eHaThcD+2QiNVK2ZRtjgLzRSscomgKqOKH6z6AV9Z9BX+2PxH/tTyJx7ve5xsfTb3l93PvWX3Su7/njL1L8DWz4PeBo/tgezkqPvtrnZebHuRrW1bGQuOkWvI5SuLvsL95fdPOxhbDIfxHT+Oe9t2PHv3IgYCqEpLyfrf38B8333Izdd+XJ87SPOJIS4eG2Js0IdSI6d6dS5z1+S+JRw7Eg7TceYk5/fspKe+DrlSSfWa9Sx93wOYbfHFRmQ0gO/kIN7jg4iBKJrKNIzrClDlG+nq6uLQi4fo6urCaDRy7733UlGRRk/vj2lsegGlMp2iiif4c2gFPzvvQAA+ZTRTf3SAJ3pdPFLg5rvC/0Vxtk5Se2GKdw6evXuRp6ejXbBA0rod5xzMKjWjM03z4rn0DlDq4ja2GxSQIqEQHWdrmb1i1esZY1NlfcF6fnLmJ+zv2c+Hqz58Y3WdfsJDPsxbppa59EaMG+7E/eKLTJw6hX7FjS3sOjo6CIVC07KvTVKywMrxrR14RgMY028sHHNyutdU848mkSuUlC5cSnvtCaKRCHLFjV3S7XS4MSvkrJjm9BlFmgZlroGJ+hGMUxCQXq4fYm6umdxpXjwXFRWh0Whoamq6cQEpGobmXVCxCRTTew3ZrBtpbf0eDsduCgs/M63HmA6brWaeGhjh8JiHDVPMqkohAd1H4h8Lpt6lORNOD58mXZNOsUm6SaAAE7W1aOfNQ6aemv10JsRiIoNtLsqXSLuZN+60M+4YZtGW+yStG+r2QAzUxdK+3ru7u4GbkH/kOolcbsBokDaU/ZjLy1yDDoNCumYEURQ52TnKmsp3R55qSkC6SRRk6Nj6+ZU8eaybH+1tYcOPD/PxlUU8dlsxNqOGwoJPkp11H+0d/0Zf31P09T3NrFn3U1jwybd0JGnK01CXWQh1jjN+oIfx3d2M7+tBW52Bfkk26hILgvzGFHRrgZF1j85hxf1llxb/gxz6QwuvPtdKflU6pQttFN+SKamYZNVZeXzB43xm3mc40HuAP7X8iZ+f/zk/P/9zFmUt4p6Se1hfuH5GE8QSTiwGB38Ah/85fnHx0O8SHsLsmHCwp3sP29q30TjSiFyQsyZvDQ9WPsiKnBXIpuEnjgUC+I4dw7N3H579+4m53chMJszvuw/zvfehXTD/mrsxPneQjrMO2s/YGWh1IYqQXWLi9g9XUr44C9UbMlzEWIyB1maaXj1A89HDBHxejJlWVj3yMWrW3onOZEaMxPA3OPGdHCLQMgaAtiYzvjtjVVFXV8fJHSex2+0YDAY2bdpEZaWW/v7fcOLkLmQyBRm5n+ZV9cN8oXuc4ZCdO7U60nonePpMI6U6P3vmHKa88/cIugx46CmYI+0FQ4q3P7FQCO+hw5g234Ugl+6Cw+2YYKTPy8oHrt3ld02UWii/M96FsflfbygDpvvCOcIBP+XLpj+aushcRJmlbEoC0mQws7Zm+vkA+pUrEbRaxvfuvWEBqampCbVaPaPpM6ULbBzf2kHneQfz1t6YqGJ37MFonItWO/2NkLJlK2h89QB9jfUUzpt/3duHYyJ7nG7uzDChmka31STamkzGd3cRcQVRWK6/oBx0+znX6+JvN06hc+hNKBQKKioqaG5uJhqNIr+R12L3EfCPTcu+NolGk4PRWIPdsUdSAWmFxYBJIWOnw50SkN6OdB8F62zQT91+OhNOD59mUdYiafOPvD4CjY1kfPpTktUEGOnzEgpEyamQOv+oAbgJ+UedbpCBqkDa7Nfu7m4sFgvm62wMJ5ox1ykslsUIgnTXVYFojLOeCT6eK+3rtt3hY8QXYtm7wL4GKQHppqKQy3jstmLunjeLH750kV8d7uC3R7p4aHEen1hZTInVypyqH1Jc9Hm6e37FwMCfGBh4FotlGTmzHsRm24RcHt/JEwQBdYkZa8lcwo4JfCeG8J0exl/nRGZQoq3JRDs3E3WRCeEGWqG1RhXz1xdwy7p8HD0eWmvttJ+2013fhCATyC4xUViTQWFNBhk5BgRZ8t/IlHIlG4o2sKFoAwPeAXZ27GRb+za+fezbfO/491iWs4wNhRtYnbeaTK20J4bLCPngfz4T3/Gf/xG4+99BkZgdm0HvIAd6D7Cnew9nhs8gIlKVXsXXl3ydTcWbpvxzi6JIqKsL37Fj+A4dxnfiBGIggMxgwLjuDowbNqBftQqZ6so7t7FoDHuPh576EbrrR7B3ewBIy9axaHMRFUuyLus2CgcD9DXW0376BG21J/CNjaJQqSlbspzq1XdQMHc+QgwC7W5Gd7fgb3AiBqLIjCqMdxSgWWilZ7SfQyf2cPHiRUKhEFlZWdxzzzoyrV0MD/+AM2frkcv1RGd9kUOyTTxrn2A8MspCFNT0hjl6sQ2b4OZ3+a+yYux/EDoDMP8RuPOfQPfuOLGnSCy+I0eI+XyS29faz8Yne5XMn6H4XHUvNL4Yz+u4gWlBrSeOotbpKaiZN6Oy6wrW8asLv2I0MEq65vqvLX/9CMpcA4q06Y+3lWk0GFavxrNvH9nf+tbrYftXIxqN0tzcTGVlJYob7OC5EpYsHek5ejrO3piAFAgMMj5+jtKSr067JkDRvAUo1GpaTx65IQHpuMvLWCTKlmna1ybR1mQwvruLQIMTw8rrC2B7GoYB2Fg9PfvaJHPmzKGuro6uri5KS0uvf4em7aDQQum6GdW1WTfR3vGvBAKDaDSzZvRYN4pKJmNDhpk9TjfhmIhSguusFDdILAo9x2Heg5KWHfQO0u/t56NzPippXf/ZsxCNor9Z+UcST2Dra6pHrdOTWSBtR06w040q14hMLW1nTHd3NxUVFZLVBAiFnExMtDFr1v2S1j3nmSAYE7l1mh240+Uv+Ufv/ABtSAlIbwuyTBp+9MH5fHFdOb843M5zp/r4/fEeFhRYuH9hHptrspld+U8UF32BwcEXGBh8jsamr3Kx+VtkZKzCmnknGRm3o1LFL9CVVh2Wu0swbywi0DzKRJ2DidPD+I4PIqjlaMosqCvSUBebUVi119zFEAQBW6EJW6GJFfeXYu/y0FnnoLt+hONbOzi+tQONXklOhYWcMgvZJWYy8w3IpxDoPR1yDDl8at6n+OTcT9I42sierj3s7trNPx79RwQE5lrnsiZvDStyVlCVXiVdZpKrF/7wIbA3wMb/A8s/P6Ow7HA0TJ2zjqMDRznUe4jmsWYAyixlfG7+59hYuJESy43bPcRYjGBbG/6z5/CfOY3vxEkiQ/FcCmV+PpYHHsCwZg26ZUuvKBpFQlHsPR6GOtwMtLgYaHMRDkRBgOxiE8vuLab4FivpOXoEQSAcDNDbeIGB5ia6L5xjoLmRaCSCUq2heMFiypYsp3j+EmTjIsFON6NPNRFod0MkhqCWo63JJFahoy/qoLX9JO2/aicQCKDRaKipyaWoyEM0dpKxsZ8w5g7j1q6kNfM/2B8o5sJQCEXQTdW4SKjPS4vdzUbVBXbZTlAxfhTBEYOaB2D134JV2jfOFO8sPHv2IjMa0S9fLmndjrMOrAVGTFMIKr4i5XeCTAlN264rIEUjEdpPn6Bk0VLkipl1mq4vXM8v6n7Bod5DvL/8/de8bcQdJNTrwbSxaEY1IT6NzbN7N/5z59EtvLblsLu7G7/fPyP72iQl862cfqkLvyd03bwOh3MvAFbrxhnVVKo1lMxfTOvJY9zxic9e13K40+lGK5OxJn1mO9xKqw5Flo6J+pEbEpB2NwxRZjNQZpvZRXtpaSlKpZKmpqbrC0ixGFzcCWXrQDW16XhvxmaLC0gOxx5JA1+3WOM5SMdcXlanSzuVKcU1GLoAIQ8UTr9LczrUDsczYxZnLZa07kRtLSgUaOdfX6ROJP0tY5gyNRhmsKkwHfqaGsidPWfaFu7pIIajhHo9N3Q+TSQOhwO/338T7GvxLK+0m5B/BLBU8vyjEaxGNUUZM3sveruQEAFJEIRNwE8AOfBrURR/+Kavq4GngEXACPBBURS7Ln3tG8BjQBT4a1EUdyfie3onUpSp5wf3z+PL6yvYeq6fF073862t9fzDi/XMyzWzptLGitKHuGXhY4QmzjBsfwmnY+/rOQoGfSWWtGVYzIsxGmvQagvinUc1mcSCUYJtLgLNowSaR/E3jAAg0ytQFZhQ5RpQ5hlRzdIjM6muKCoJgkBWsYmsYhPL7yvFOxakr3mU/uYx+ptddFzaLZcrZGTmG8jMN5KZZyAzz0Bati4pk90EQaA6o5rqjGq+tPBLXBy9yMG+gxzqPcRPz/6Un579KSaViaXZS1mYtZCFtoVUpleikCVBO+05AX/8MESC8Mhz8UXbFAlFQzSMNHBm+Aynh09TO1yLP+JHJsiYb53PVxZ9hTX5aygxX180EkMhgp1dBFtaCDQ2EmhoINDYSMwbP3nK09PRLV2Kfvly9MuXoSwsvOzv7veEGBvy4ezz4uz14uzzMtLnJRYTgfiOe8XSbHIrLOTNTkMmizDS10Nf42HO7OxguLMNR3cnsWgUAGthMQs23UthyTwy9XlEhwOEWryM7DuPGIjfhnQlvmolY+YgQ0E7Pb21jDaMAjEyMsLUzJWTkTGKKDYz4e+i0WmlU7WSbv0TnA4X0uuJIesLkeUZo2AkiGykj1vk9bzP0MgSw3nUkXEIW2HZZ2HRxyFzBtagFO8JxHAY7yuvYFh7O8JVOvGSgXcswHDnOMvum34e0OtozFBye7wbY8P3rilq9zXWE/B6KF8282DYyrRKcg257O3ee10BKVA/c/vaJIbb1yAolXj27r2ugNTU1IRSqbyxbpbrULLASu2uLjrrnMxZmXPN2zrsu9Hry9HrZ/73LVu2gpYTRxhsaSZ39tUnnMVEkZccLu7IMKJLQCiztjoDz4Feot4QcsPVXxujvhAnOkf57JqZ/6xKpZLy8nKamprYvHnztUPP+2vBMxjvwJshOl0xen05dsduSQWk29NN6OQydjhcKQHp7UT30fjHm5B/ZFQZKbNIe+0yUVuLpnoOMp10i18xJjLY5qZonrQdGz7XGGMDfcxdK23HcajXA1ERdbH09jW4OflHMpkGo1Fam+Bxl48qvYY0pXQ9NKIocqJzlKXF6ZJaT5PJjH97Qty4+DPgTqAPOCUIwjZRFBvfcLPHgDFRFMsEQXgYeAL4oCAIc4CHgWogB9gnCEKFKIrRmX5f72RsJg2fXl3Kp1aV0Dg4zr5GO4da7PznK638x/5W5DKBiiwjNTn3U2b7KHmWEczy8wjhYwwMPE9f31MAKBRGDIYqdLoS9LpSdFnFaApzMNxTDW45oU43wU43oV4PgYujENcFENRylFk6FJlaFBlaFOka5Glq5GY1cqMK4VJ3kSFNzezls5i9PN7O7R0LMNQxznCnG3u3h9ZTwzQc7n/959KaVKRl6TBZtZgzta/vKhjS1Ogt6hl3LQmCQFVGFVUZVXzuls/h9Ds5OXiSY4PHODl4kn09++Lfh0JLZVol1ZnVVKVXUZZWRom5BK1iBrv8556B7V8EUy58bCdYr5/z4A15aXe30zbWRuNIIw0jDbSMtRCOhQEoMhVxb+m9LJ+1nCXZS66Y8RQLBgkPDMSP/n5C3d2EuroJdXUR6u6GSCT+u1GpUFdWYrp7C9r589EtWIAwKxefK4R3LMjwUIDx+i7GHX7cDj+u4QkCvvDrdTQGJRk5WuasNKBPi6BS+/F7enHbT3HupUFe+XUf3rF4e6ZMkGMxZDErr5zqlatIN85CKzMhjoWJtEwQafDRI9Thlfnxm0Q81jDjCj9joXFc40Oo+sZRO32YTH5KSsPMmethFD9DooXjZDHgL6FPuIueQCbjHhC8EQxeP+XeMyzydVMm9DNP3sViRQfpmrhQijI7voiouhvK1k9pnHmK9zYTp04RdbsxbZhe0PF06TgXF+RLFyQoO23OvbDtCzB4HnKuvovcevIoCrWaonkzDwsXBIH1Bet5+uLTeEIejKqrL4D9DSMobDqU1pkvUOQGA7oVt+LZuxfb1/72qhdssViMpqYmysrKUCVAHMzMM2DK1NBx1nFNASkUGmXMdZKios/NuCZAyYIlyBUKWk8evaaAdGZ8guFQhM0JytPR1mTieaUXf+MIhqVXt3XtbRwiGhPZVJ0Y61dVVRWNjY309vZee9HT+GK8865iZl1ek9ism+js+hmhkBOVShqbvFYuY126iZecbn5QkYf8XbL4eMfTfQTSisAsbbfI6eHTLLQtlHQScSwQIFBXR9pHpbXNjQ75CPjC5JRLa1/rv3iz8o/GQQB1ofQCksFgID1d2ggHl+sUZvMCZDLpNuYiMZGT4z4+mC3tz9o35mfQHXjX5B9BYjqQlgJtoih2AAiC8CxwH/BGAek+4NuX/v0n4D+F+BXdfcCzoigGgU5BENouPd6xBHxf73gEQaA6x0x1jpkvri/HNRHibI+Ls70uzvaMcbDFwfOn+y7dOhdBeACb8cNYDSJm9QRGxQha+TAqBtHK9qNVBNAoAmgUQXQqJXqNCUOOCV2JCa3CiDqsQelXI/MqYUwODjlihwxZVI0QUyHElMiiSuRaLTKdFoVOg1yvRa5VItMqkWnlZKsV5JSZEeakg1KG3x/BPRZkfCyAaySA2xlgoN5J63iYGK9rVgCo9Qp0RhU6kwqNXonGoEStV6LWKlBpFai1CpRqOUq1HIVajkIpQ6GKf5QrZcgVMuQKAUEmIAgCmdpMNpdsZnPJZgCGfEOcs5/jnOMcjSON/Ln1z/gj/vjvGoEcQw75xnzyjHnkGfKw6WxYdVYyNZmY1WZMahNq+ZuyjGJR2PdtOPofULwaHnwSdOkEIgHcQTfukBvnhBO73459wk6/t59eTy894z0MTwyDKCKPQQZGqg1lrM64h2pdKRXqfIwRBdGRcWKdDsLuZxkeGyU8MkZkbIyQc4yQc5SIZ4KoXEVMpiQmUxJV6xGyc6HgNsSFDxJLtxHVmQjKNQQnggS8ASaO+/HvO0k4EEQUwyCGEcQwAhE0mhhqtYhBFcVsiSATI8RCQaK+IL4LYYINKuRyFXKZCrlcjUqtJ11Thi17DkKuQEyMEhEDhGQBgvIgrb5G/KGzBFURgpoQgeIwQSFCRCkQlssJyRWEBDlhuYoIMoJZKsLhXEIRFWJYAT0y5OEohvAE5pAHc8TLLZFm1kVPkiGMky2MkiOMYBPiPnkuvQ/FLMXI8u+A3MXxEbvZc2dkJUzx3mV8zx4ErRb9SmmtCu1nHKTn6N8ysXDaVG4B4UtxG9tVBCQxFqPt1DFK5i9GqU6MZWB94XqebHySQ32HuLvk7iveJuoNEex0Y7zB8OkbwXTnnQwe+hbBpiY0c64sqvT19eH1ehNiX4P4e3bJfCt1B/oI+iOotVe+xHI69wExrNbEiJJqnY6CufNpPXmUNR997KqC2Q6HC6UgcGeCBCTlLD3ydA3++msLSC/VD5GXpqUmNzELo4qKCuRyOU1NTVcXkEQRGrfFJw9qE7MAtdo20dn1UxyOveTmfighj3kjbLGa2e5wUev2sUzi3I4UV0AU4x1IlXdJWtbpd9I13nXdbs5E46+rQwyH0S2R1jY32OYGkFxA6rvYgEKtxlY8867UqRDscqPM0iNLglvjakzmHxW+yYGQbCIRDx5vI8XFfy1ZTYALXj8T0RjLLVLb1ybzjyQWkEQxaWufRAhIuUDvG/7fB7w5ZOH124iiGBEEwQ1kXPr88Tfd920+l/3mYdGpWDvbxtrZttc/55oI0Wr30jc8wphjAM/IAEHPCLFxF6LfjSzkQUMQLSIaBNQoUAsxlERQ4UDJICJRQsSIClHkxJATQyCGDBGZICJDRJg8BCAgIgREGI0/L4XLZKA4b366Gi8d+QC6+HGl+wEwfum4AiIQunRMh8pLx5XpvHS8Fe+l4+pkQ2cL/PPl7cxKYNal49qMAicvHRC4dLwFQ/xQ5scf+8rU/+Wf7kvHG9FcOm4IEbSA+fK/6Rv/dsKlmwkRERARACEmIkREhCDIiCEQ/yi79PySIaIgipwoKmFqDYcxQUZAayGiSQdjDprM5ZBeAGnF8SyjjHJk6tRFdoqZI0ajePbtx7B6NTLtDHOIpsDEeIjBNheL7ipK3IPqM6Dotvii+o5vXfGiYqDlIj7XGGUJsK9NMs86D5vWxt6uvVcVkAJN8Q5YbU3iujoM69bBP36b8d17riogNTU1IZPJEhoeWrrQxrl9vXTVOalcduXAaLvjZTSafIyG6oTVLV+6gs6ztdg728kqeau9RRRFdjhcrEk3YkrQ6GJBENDWZOA9MkDMH0F2BcHM7Q9zpM3Jx1YUJWyBolarKS0tpampiY0bN175cQfPgbsH1nwtITUhHhOg1RZgd+yWVEBan2FCLRPY6XCnBKS3A45m8I/GN6ck5MzwGeAm5R8JArqFCyWtO9DqQm9WzTwDcIr0NTWQUz4b+QyGKkwVMRoj1D2OblGWZDUBXC4XHo/nJtjXagERy03KP1pulj5A26xVUmGT2Ib8u/fHJ0Xe9cPr33aKvGNCtAVB+DTwaYCCgoKb/N3cJLyOeDizowWcLTDWhcXdx5LxfpYEr6K4XPoLT8g0BAQVAZmKoKAkjJIwCkKCgigywigIoyAoCsQEGTFRQBQhRtxW9hdp4FLXkAii8Mb/T17A/UVYEN8gOVxNLBLfIjWlSATT+q1e8U5/+csJ/OVvLhD/28X/K8Ab/y3ED0GQgSBDmPy3TIEgUyDI5MjlKmQyBXKFHIVCjVKpRq1UoVZrUap1qNRaNDo9WoMJtdaAoDbFc1w0ZmQaMzoJ27dTvHfxnztH1OnEuEHaLITO8w5EEUoXJsi+Nsmce2Hn34DjIthyyyJMAAAgAElEQVTe2nXTevIIcoWCkgWJu6iTCTLWF67nhdYXmAhPoFO+1aLmr3ciT9egnJW4XUFFWhq6pUvw7N6N9UtffIvIIIoijY2NlJaWotEkLqA1q8iE3qKm/Yz9igJSODzO6OhR8vM/ltAd39LFyxB+JaPlxJErCkh1Xj99gTB/UzSzKWhvRludifdwP/6mEfQL37r42d80TDgqctfcxE4uq6qqoqWlhcHBQXJyrmAXbNwGghxmb0lYTUEQsFk30dP7G8JhN0qlNCOvDQo5a9KM7HS4+E5ZzrsmQ+MdixiFOffFBXkJOT18Gq1CS1VGYjombxR/bS3qykrkEo54F0WRgVYXOeUWSZ/vAZ8XR3cnKx54RLKaAOEBH2IohrpYut8x3Mz8o1MIghKzSdpQ9uNuLyVaNVlqaWMsTnaNsqQoHZmUkzRDE9D1Ksy6JSkPnwgBqZ9LjSWXyLv0uSvdpk8QBAVgJh6mfSP3BUAUxV8CvwRYvHjxVVpX3mWMtEPbfug5Fg+DdPW8/iVRZSRgKcSuy6PDvIBmmYUWwYBDmcaYwoRbYUCtNWM2pJGmNWJCRDXhRfCMEx4dITQ6QszrQRUNo4xEUMYiGDUazCYTRqMRg8GAXq9Hp9Oh0+lQq9WEfR68Tgfe4QHGh4dwDfTiHh6KTzqZRBDQW9IwpKWjt6ShM1vQmsxoDUY0RiMavQG1To9ap0el1aLUaFFpNCjU6sumHcSCQYIXLxK42EywtTV+tLcTdTov+xXJzGaUs2ahyLKhtNmQZ2aiSM9Anp6GIi0NmcmM3GxCZjAg1+sTE4Lb+So891cgxuChp6BkzcwfM0WKFG87PHv2IKhUGNbcLmndjrMOTFYtGbkJ3iWbfQ/s/Go8G+ZNApIoirSePEbhvAWoExyUur5wPc9cfIbD/YfZVLTpsq/FJsIE2lwYVuYmfKFg2riRoW9/h2BrK5o3dRkNDAzgdru5/fbbE1pTkAmULrDS8OoAoUAElebyyyyncz+iGMZmS6z9RWcyk189j5bjr3Hbw3/1lt/lDrsLhQCbEmRfm0SVb0RuUuGvv7KA9FL9ENkmDfPzEmtDqaysRBAEGhsb3yogiWLcqll0G+gSaxmw2jbR3fNLnM79ko6f3mK1sGdknHMePwtM744pPu9Ysqrj134Sc3r4NLdYb0Epk9DiFA4zcfYclg98QLKaAOPOAD5XUHL72kBzE4gieVWJ6w69EYKdcavAzRCQtFotVmuCN6uug8t1EpNpLnK5dN1lMVHkhMvHZqu0v2P7eIBOp48PLU2cRf+G6DsFsUjSJkUmQkA6BZQLglBMXPx5GHizdLsNeJR4ttEDwCuiKIqCIGwDnhEE4d+Jh2iXM+nhea8y3AjnnobmXTDaEf+cOR9yFzGx6DFqtaXsIoutExpc0bhwk6VSsMCko0qv5XaDlnKdGqPfS197O12dF+np6WFiYgIAhUJBVlYWthwbmZlVZGZmkp6ejtlsvixENODzMtDcRH9zI83NTQx1tBIJBgGQyeWkzcrFVlDE7OW3Yc7KxpI1C7M1C31a+pTbPkVRJNzTw8TZs/jPnMVfV0ewre31AGiZToe6vBzDmtWoiopQFRaiKixEmZuL3CBxO3ftb2HXVyG9BD70LGRI65FOkSKFNIiiyPjevehXrkRukM4vH/CF6bs4xi3r8hO/82rMgoLl8e6M2//usi8Nt7cy7rBzaxJ2XhfaFpKuSWdf9763CEj+plGIiujmJj6U2LhuHUPf+S6e3XveIiA1NjYik8morLz+wIOpUrownoPUXT9C+eLLRRW742XU6lmYjPMSXrdi2Ur2/fpnOHu6sBYWv/75SfvaSosx4ZNnBJmAtiYT78lBYsEIMvVfHt8bjHCoxcEjSwsSvvOq0+koLi6msbGRdevWXf5asTfBSBssT0xI+RsxGeehVmdjd7wsqYC0MdOEQoCdDldKQHoP4g66aRlr4fPzPy9p3UBjI6Lfj26xtLa5gdZ4ruUsqfOPmuqRyRVklyf+feFaBDvdKDK1yI3SBUpDXEAqKCi49jTLBBON+hn3XKCg4JOS1QRo9gVwRaIsk9q+1hXPP1pWLO00wfikSAEK3pwqlBhmfCVxKdPocWA3IAd+I4pigyAI3wVqRVHcBvw38LtLIdmjxEUmLt3uOeKB2xHgf70nJ7BFQnD+D3D6tzBwFmQKKFkLyz5HqHQd+8UMnh8aY+/IOGGPSKZSwUariTXpRpaY9eRdasUbGBig/uwJ9jY3Mzoaf8KmpaVRUVFBQUEBeXl5ZGRkIJe/1f4jxmIMtDTRee403XVnGWprRRRjyORybEUlzF27AVtxKVnFpaTn5s/YGxweHsZ35Ci+48eYOH6CiN0OgMxoRDtvHoY1a9BUz0EzZw7KnBwECU9uVyQagd3/G07+Ij7F64HfxC1VKVKkeFcSuHCByMAgxse/IGndzvNOYjGR0kW26994Osy5D17+u3iH6xsE8JYTR5DJ5ZQtXp7wknKZnHUF69jRsYNAJIBG8RfLmL/eidyiRpmX+Is6hdWKbtEiPHt2Y/3C469/ftK+VlxcjC4JY6mzSy1oTSrazzguE5AiEQ+jo4fJzf1wUmwZZUuWs/+//y8tJ45cJiA1+QJ0+kN8viA5zyntvEy8RwcIXBxFd8tfahy4aCcUiXFXTWJtc5NUV1ezfft2hoeHyc5+Q42mbYAQ77hLMIIgYLPdRX//00QiHhQKaTItLEoFt1mM7HC4+GbJrJSN7T3GOfs5REQWZS2StO5EbS0AusXS1h1oc6HRK0lP1BCJG6SvqZ7ssgqUKvX1b5wgxJhIsGscXQIzAG+E8fFxRkdHWSyxOOh2n0UUI1jM0tY9din/6NabEKCtV8mpzpF2uh7dR+IDhJK0Vk3IVpQoiruAXW/63D+84d8B4MGr3Pf7wPcT8X2844gE4ezv4bUfgbsXsmpg4w9g3kN41Gk8NTDCL5vtDIe6yFQq+ERuJvdlWZhv1CG7dPHg9Xo5fOIY58+fZ3R0FJlMRklJCcuXL6esrOyaYxljsSi9DRdoOf4a7bUn8LnGEAQZ2WXlLLv/IQqq58VPpAmYxiOKIoH6Bjz79+E9dJhgUxMA8vR09MuXoVu6DN2ihahKS2++WPRm/GPw/Meg4yDc+jjc+V1IZfCkSPGuZvzl3aBUYlx3h6R128/aMaZrsBUmaWFadU9cQGrcCqv+Boifn1tOHKFg7nw0SerqXF+4nudbnufIwBHWFawDIBaIEGgZw3Br8nJdjBs3Mvz97xPs6EBdUgLA0NAQY2Nj3HZbcjJMZDKB0vlWLh4fJByKolTF3y+czgPEYiFs1k3XeYTpobekkVdVTcvxI6x86COvf36Hw4WMxNvXJlEVmJAZVfjrnJcJSC/XD5FpULG4KDmTZ2bPns2OHTtobGy8XEBqfBEKbo133CUBm3UTvb2/xek8QHb2vUmpcSXutln4anMvDV4/NcZUF9J7idPDp1HKlMzNnCtp3YlTtaiKi1FkSituDLS6mFVmRpAwMyYcDDDc0cbiu6WdchexTyD6I6iKpBUYenrisSg3I/8IZFgsUgtIPnLVSvI10nZ5negYZVFROgq5hGvbSChuYVv08aSVeMeEaL/r6DgE2/8axrogbync82MoXYcvFuNnPXZ+3dfAeCTGqjQD/1Jp5Y50E4o3nEgHBgY4fvw4DQ0NRKNRioqKuO2226iqqkJ7nWlBowP9NBzcS+NrB/GOOFGqNRTPX0TZ0lspXrAYjT4xC4hJ0Wh81y48u3cTHhgAuRztgvlY/+YrGFavRl1R8fbeSXO2wh8ehrFuuPc/YeFHb/Z3lCJFiiQjiiKe3bvR37pc0uDQ4ESY3sZR5q3NS9550ZwHuYvii+xLApK9sx338BDL3vdQcmoCS7KXYFKZ2Nu993UBKXDJvqZNgn1tEuOGOxn+/vfx7NmD+rOfBeL2NUEQmD17dtLqli60Un+4n56GEUoXxEUVu2M3KpUNszl504zKl6/kld/8FyN9PWTkxQeO7LC7WW4xYFUlJzslbmPLwHdqmFgwikwtJxCOcqDZzvsW5CJP0iJQr9dTVFREQ0MDa9eujb9mnK1gb4RNiZ86M4nZvBC1Kgu7fZekAtJdmWa+3tLLdoc7JSC9xzg9fJq5mXMv695MNmI0ysTp05g2JUfwvho+V5Bxh5+5a6Qdyj3Y2kwsGiWvqkbSujcz/0ilUl0uvkvAmOsERmOVZN2bEL+mO+72sibNKOmac8wXonnYw73zrzDoIZkMnIVIIKmTIlMCktT4XbD3W3DmqXiOzkdegNJ1iMCLdhffbR9gIBhmi9XMFwqymP8mr/vg4CAHDx6kubkZlUrFokWLWLp0KZnX2R0QYzE6ztZybvcOus6fQZDJKLplIWs+8glKFy9LaLtmeHgY94vbcL/4IqH2dlAqMaxYQebjj2O8Yy1yi7Se5mnTth+e/zjIFfDodii89WZ/RylSpJCAQH0D4f5+Mj8vbd5EV52TWFSkdGGS7GuTzHlf/H1otBPSi2k5cQRBJqNsSeLta5MoZUruKLiDfd37CEaDqOVqJi44kZtUqPKTdyGpzMpCO38+47v3kPnZz75uXysqKkKvT14re065BY1eSfsZB6ULbESjE4yMHCRn1oPxqZRJonzJrbzy21/QcuIIt+YV0OIL0DIR4GO5yV2MaWsy8R0bJNA8im6elYPNdiZCUTbXJHb62puZM2cOO3fuxG63k5WVBQ1b41+oSp6wIwgyrLaNDAw8SyTiRaGQJlMjQ6VgpcXAdruLvyvOfntvvqVIGBPhCRpHGvl4TfK6Ca5EsLWVmMdzU+xrgOQB2n1N9QiCjJxKaafcBTvcyM0q5GnS2eYgLiDl5+dfMdYkWcRiQcbHz5GbK+2Uuw5/EEcownKLtPlHpy7lHy0tTk4X7lXpPhL/mBKQ3iUMN8AzD8N4H6z8Itz+DVBqGQiE+OumHl5zeZlr0PJfcwpZ+qYn+fj4OHv27KG+vh6NRsPatWtZtmzZdccPx6JRml47yImtzzM20IchLZ0VD32Yees2obekJexHE2MxfEeOMvbHZ/EeOAjRKNpFi8j+zncwbdoo6S7+jBFFOPEL2P0NsFbBh/4AadK2eKZIkeLm4dn9MigUktvX2s44MKSpySpOcit79SUBqXEr4sov0XL8NQpqbkFrTG7djUUb2dq2laP9R1mTtYpAyyiGpbOSblMwbtyI/YknCHV349JqGRkZYfny5IllADK5jJL5mbTW2omEo4yMHSIWC2CzJXc335CeQW5lFa3Hj3DrBz7EdrsLAdhsTe5iTF1sRqZX4q93optnZUfdIOl6FctLknvhXFVVxc6dO2lsbIwLSI1bIX85mJMrmNlsm+nrewrnyAGysxKftXQ17rFZ+NvmPhp9AaoN0k0wSnHzOO84T0SMSJ9/dGoy/0j6AG2lWk5mEnLxrkVfUwPWwmLUOukyckRRJNjpRlNmkVQQ9vl82O12amqk7bYaH79ALBYkzbJU0rrHXT4Alt+E/COVQsa8PInXwN1HIbMS9Mnr7k4JSFLRshv+9AlQG+GxvZAXPyG/7HDz5Ys9BEWRJyry+EhOBvI3nESi0SgnTpzg4MGDxGIxVq9eza233npdm1osFqXx8AGOv/AH3PZhrIXFbPni1yhfumLGAdiX1fH7cW/dyuj/e5JQdzfy9HQyPvFxLA8+iKqgIGF1JCMSik9ZO/MkVG6B+38JaoknvaVIkeKmIYoi4y/vRn/rrZJ2S4b8EXoaR5i7Oon2tUksBXEbW8P/4Mi7D9fQIEvuSf6Y5mWzlmFSmdjTvYdlYzUQSa59bRLTxg3Yn3iC8Zd301BRDsSFh2RTushG45FBehpG8Sl2oVRmYLEsSXrdimUrOfDkrxgd6Ge7w8Mys55sdXJHf0/a2CbO2vF5Q7xyMW5fS3bug8FgoLCwkMbGRtbOzYPh+qTa1yaxmBeiUlmx21+WVEDalGnm68197LC7UgLSe4TTw6eRCTLm2+ZLWneithZFziyUSe5efDMDrS6yS83IJMyMiYTDDLZcZN6dd0lWEyDi9BPzhlGVSCswTOYfFRUVSVo3nn8E5psQoJ2pVFCqlbbL62TXKAvyLagVEubmxqLQcxzmPpDUMikBSQpO/BJe/no8Df1Dz4Iph6go8p22AX7Z54h3HVUXUqq7vJvI6XTywgsvMDg4SHl5OXfdddc1Q7En6a47x6Hf/zeO7k6ySspZ+7FPU7JwaUIXJVGXi9GnfsfY008TdbvR1NSQ8y//gnHjBmQqaQPKEoZvBJ77aLz177avwB3fgrdboHeKFCmSSqChkXBfH5mf+6ykdTvrnMQiSZy+9mYu2dhaD+5EEGSULU2+RVcpU7KuYB17uvfw5eiHkRmVqAqTHxyqzMlBe8stuF9+mYZwiMLCQgxJCgt/I7mVaWj0StrO9KAsOcCsWR9AEJJ/IVl+SUDaf+IEFy0lfL9cmgWgtiYT34kh9h3uYiIUZcvc5NrXJqmurmbXrl14T/4eAyTVvjaJIMixWTcxMPg80egEcrk0mURWlZIVFgPbHS6+lrKxvSc4PXyaqvQq9EppO2MmamvRr0yeBeZK+L0hRgd8lC9JTgD+1RhqbyESDpE3572Tf6RQKMjJkTabZ8x1Ar2+HJVKWkvXMZeX5Ra9pOdLbzBCfb+bx9eWSVYTgKELEPJA4cqklkmtjpNN7W/hpb+Firvg4y+BKYdANManG7r4ZZ+DT+ZlsmNR+WXikSiKnD17ll/84he4XC4eeughHnnkkeuKR+NOB1v/5Xv86ft/T3Bigi1f/Bof/j//TumiZQl70UTGxrD/6Me0rVuP8+c/R7t4MYVP/56i55/DfM/d71zxyN4Ev1oLfbVw/69h/T+mxKMUKd6D/MW+tk7Suu1n7OgtarKTbV+bZM59iCI0H3uVvDk16EzSXMBuKNpANBgh0DyKtiZTsik7xrs2YR8YwOl0Sta2L5fLKFlgxeE4QCzmJ8u2WZK6xoxMcirn8KJ9DAG4O8n2tUnUJRZkegU7zw+QoVexTKLch9e7yRq2Qv6ypNvXJrHZ7iIWC+AcOShJvUnusVlomwhy0ReQtG4K6QlFQ9Q56iS3r4U6O4mOjEhuXxtsjQsquVLnHzXWA5A3u1rSuqEONzKDEkWmtN2E3d3d5OXloUigI+V6xGIR3O4zWCzLJKsJ0BsI0R8Mc6vE+Uenu8eIibC0OEPSunQfjX9Mcm5vqgMpmTRshR1fhrI74aEnQa7EHY7w6IVOjrt9/FNZLp/Kt152l3A4zPbt26mrq6OoqIj7778fk+naCwoxFuPc3l28+syTiLEYqx75GAvvuhdFAsWc2MQEo089xciv/5uYz4dx40YyP/dZNJWVCatx02h+CV74JKj08PFdr9sLU6RI8d7idfva8uXS29caRqlelSPd2OK0QhzmxYxdnGDRA6ukqUncxna7fymyiIBunvX6d0gQpo0b6d2xEwFp7GuTlC2y4YqeRC5kSDq2uPLW2/jehJrFWgVZSbavTSLIBahK53BtG/cvyZdsbLHRaGRujgbDQBeskq5z0GJZjEqViX14l2TiIMBmq5lvtPSxze6iKmVje1dzwXmBUCwkff7RybjVSLck+ZbbN9LfOoZCKcMm8Uj7vqZ6MvMLk54D+EYm84/UJWZJO2MCgQBDQ0OsXr1aspoAXm8j0aiPNAls3G/kuMsLILmAdLJzBIVMYGGhxIOjuo+ApTA+cTeJpASkZNH+SlyUyF8GDz0FciXjkSgfONdOsy/Af80p5H1Zl4dY+3w+nn32WXp7e1m7di2rVq1Cdp0umHGHnV3/+W/0X2ygcN4C1n/yf2HJStxIRjEaxfXCCzh++lOiDieGdeuwfvGv0VRUJKzGTUMU4chPYN+3YdYt8PAzku1cpkiR4u1HsKWVcG8vGZ/+lKR1O+ucRCMxyqSyr12iOToHgQ7KK6Q77yllSu4NrWNE4SYzT7o8AkV2Nn3l5WR5PJLY1ybJKlVi6LtA2HWnJPa117llKSPNQ3zA0QdIZ8s4ZZETANYbpQ0rXWYYBMBhXYFUsqQgyLFaNzE4+CciER8KhTQ/s1Wl5FaLgZedbr5eIo1NMMXN4dTQKQSEmxCgfQq5NROVxBk5A60uskrMyBXSOQCikQgDzU1U3y5t13F0LEjUHZLcvtbT04MoihQWSjscaMx1EkCSHMA3ctzlxayQM1t/7aFTieZk5yg1uWZ0KgmlllgMeo5B+cakl0oJSMlgrAueexQyK+CRZ0GlIxCN8bELnVz0+Xlqbgl3ZFyucjudTp5++mk8Hg8PPvgg1dXXb6NsPvYae3/5U0QxxqbPf5k5q+9IqIo9ceYMQ9/7HsHGJrQLF2L7yX+gW7ggYY9/UwkHYPsXoe5ZqL4f7vsZqKTJMEiRIsXbE01lBSXbt6HITpwIfyO01Q5jSFOTLWGQpiiKNHd5KNC70PXuh8J5ktSNBSIUOWy8aHqFkUEddxRIM+lueHiYcbWKirrzBDs6UZcUS1J3bOwQMnmIvrq5hLdEUaqkEZH2B0UEUcR6bC/ck9zJb29kz/A4aYJA9bC09qpZYyfoIYe27hHuKJWubpZtC/39v8fp3E92dvKzlyb5YUUemVIuTFLcFGqHaylPK8eslva9YeLUKfRLlkjbGeML4+zzsmSLNOfmSeyd7YSDAfKq5kpaN9hx8/KPZDIZeXnJ7VB5My7XKbTaQtRqafOtjrl8LDPrkUn5XA5HOd/r5mMriySrCYDjIkyMQFFy848glYGUeKIReOHS7vWH/gDaNKKiyONN3Rx1efnJ7IK3iEfDw8P85je/IRgM8uijj15XPIpGwuz79c/Y8eMfkpaTy0d/+B9Ur1mXuJyj0VEGvv51uh/5MNHRMXL//d8ofPr37x7xyDMMT94dF4/WfhMe+E1KPEqRIgUA6vJy5EajZPUCvjA9jaOULbJJZ18jftHsdjqpLNBBw/9IVtffNIoQhdOZzezu2i1Z3YaGBgRBILevP55zJRHD9l3IZZl4B4vpqR+RpKYoimyzu5grBgm1N+Ps7Zakrj8U5ZWLdtbZTISbXcSCUUnq4mhB7mhiKH0ZDQ0NiKIoTV3iNja1Koth+07JagKU6zWkKVMC0ruZcDTMeft5FmdJG6sQ7ukhYrdLbl8bbHeDeBPyj5ou5R9VSZt/FOx0I9MpUNikXX90d3eTm5uLSsLMWlGM4XKdIk3i/KPhYJgOf5DlEtvXzvSMEYrGWF4ibVg43UfiH4tuS3qplICUaA49AX0n4e4fQVq8PfAfWvvZ4XDz7dIcPpB9+ZPJbrfz5JNPIpfLeeyxx8jPz7/mw0+4XTz/T9/k/N6XWHLvB3j4O/+MJTsxLcyiKOLaupWOzVtw79xFxmc+Q+munZg2b373TPoYPB8Pyx5ugId+B2u+Bu+Wny1FihTvODrPO4hFRcoWS7srd/HoYWRyOWVrNsfPi842Ser6zzuQm9UUzq7gYO9BApHkd6qIokhDQwPFxcWkVVcz/pI0AlIk4mVk5CCzZm1Ga1TTdtouSd2LvgCtE0EeKMgBQaD52GuS1H3loh1/OMo9S/IhEiPQJI1gRsOfAQH1wocZGRlhaGhImrqAIMiwZW1mZOQwkYhHsrop3v00jDQQiAZYnC2tgDRx6ubkHw20upApBLKkGiRxib6metJy8tBb0q5/4wQS7HSjKjJLunEUCoUYGBiQ3L7m9bUQibglt68du0n5R8c7RpEJsLhIYgGp61Uw5cUzkJJMSkBKJF1H4NV/hVsegbkPALB1eIz/7nfy6Twrny24PN/C4XDw5JNPIpPJePTRR8nIuHZSu72rg99/48sMd7Sz5YtfY/WHP448QQn64eFhej/1aQb/7huoiooo/vML2L78JWS6d1FnTuOL8JtNgACf2A1zpGs3T5EiRYor0VZrx5SpwVYoXdeTKIq0HH+NwnkL0C76ICBcWoQnl5g/QqB1DO28TDaVbGIiMsHhvsNJrzs4OMjo6CjV1dWYNm0i2NJCsC35gpnT+QqxWJCsrC2ULrDRdcFJWIKunBftLmTA+wtnkV9VQ/OxVyXpytl2vh+bUc3K5fnITCom6pxJr4koQv0LUHQbZQtWIQgCDQ0Nya/7BrJsWxDFEA7HXknrpnh3UztcC3Bz8o/S01GVSugFBQZaxsgqMqGQyOYLEItF6WtqIL9Kupw4gIg7SHQ0ILl9rbe3l1gsJrmA5Bo7AYDFslTSukddXgxyGXMlHjZwomOE6hwzJo00AyyA+Hth99G4fU2CxoiUgJQown74n89CWhFs/mcAuv1B/ra5l8UmHd8qzbns5uPj4zz11FMAPProo2RmZl7z4Xvqz/PsP34dEZGHv/MEs1ckJj1fFEXc27fTcc+9TJw+Tdbf/z2Fzzz97gjJnkQU4eAT8Nxfwf9n773D4zjPc/17tmEXi94LSXQQIAmABewiJUqiRIkiZYmyLMuSS6y4JCflHJf4XPEv8Ymd2E5O7OQ4seMkji076o2SKFkixSJ2ggABEAAJondg0Xax2F5mfn8sKbGAJMruB0qa+7pwgVzMzrOQljvzvd/7PG/6MvjKQcgUk/ehoqKicj3ckz56m60UVqYL7fIcbL2AfWSYxes3QVwW5GyAhpdDn5URxN00BkGF6PJUVqevJtmYzDtdke8GampqQqPRUFpaStx920Cjwf722xHXtVjeJCoqg/j4lRRWphHwyXSeHYmopqIo7B62sikxllSDnsUbNmEd6GOkuzOiunaPn4MXRthenolOpyG6LAVPyziyJxBRXSyNMNoCyx7GbDaTn59PY2OjUBtbXNxyjMZsLMN7hGmqfPypHqqmIL6AJKPYLgbn6dNEV1YKvSb5PAFGeh1kF4vtAhrp7sLndgm3r/k6L+YfCcw9hJB9TZKkm7pdwo3VdgqjcQEmk9jcpRM2B2vjY9AJ7P6ozfAAACAASURBVPLy+IPU9tpYmye4+2i0BZwjQuxroBaQwseJf4GJHtjx/yAqFp8s89WmbjSSxC+W5qK/7M3r8/l47rnn8Hq9PPnkk6Sm3nhmSOup47z6w78mPjWNz/3gH0nPLwzLSw5OTjLwjW8w8K1vE5WfT/5rr5L0xOeQbjL57SOFzwUvfwkO/R1UfBa+uAdixE46UlFRUZmK9toRFFmhqFLw9LUTR9DqdBSuXhd6YNnDMHoBhs9FVNd1dgRtYhT6BTFoNVruyb2Hw32HcfgcEdOUZZnGxkby8/OJjo5Gl5pK9Jo1TLz1VkSLDH6/jbHxI6SnbUeSNGQWJmCON9B6OrI2trpJN11uHw+mh3JEitZuRNJouHA8sp1e+5os+AIyOypCm2Wm8lQIKLjPRdjG1vgKSFoofRCAZcuWYbPZGBgYiKzuZUiSRFra/YyPH8PvtwrTVfn4EpAD1A7XCrev+fr6CQwMzkv+kSIrZBUKzj86dzH/aIn4AG0pSos+U+y0yq6uLrKysjAaxU0kC+UfVQnPPxr2+ml1edmQKNa+VttjwxeQWZd/Y1dR2Ok6EvqeE/kAbVALSOFhcgiO/BRKHoC8TQD8sGOQukkXPylZyELjh0Flsizz2muvMTg4yK5du8i4ybSfxoP7ePOnPyItv5BHv/cjYpLC84Z0NzTQ+fAu7O/uJfXP/5ycZ/5b+LjOiDPRD7++D5p2w9a/gU/9AnTixkarqKio3Ii2GgsJ6dEkZ4u7wZHlIC0nj5K7fBVR0RdvXksfDC3CG1+JmG7Q4cPbZiW6IvWDne378+7HG/RysPdgxHT7+vqYmJigrOzDBULc9vvxd/fgaYyc1Wl45F0UxU96+g4ANBqJwsp0eprG8Dj9EdPdbbFikCS2p4R2tqPj4skpX0Hz8cMRLZi9UT/AgkQTKxaGFoCGRbFoE6Jw10ew4+qSfa1gC5hD90YlJSVoNBoaGxsjpzsFIRtbgOGRvUJ1VT6enB87jyvgEh6g/UH+0RrB+UctNjQaiYwCsR05fecbiE9LJzb5xi6QcOPtmCAqT3z+UV9fH7mC13pOZyt+v5XERLEFpBMTl/KPxBbpTnWOIUmwWnQHUtcxiM2EpHwhcmoBKRwc+D4EfaEiBVBnd/FvvSN8PiuZ7alXVtMPHTrE+fPnueeee1i8ePENT9v0/n7e/eX/I6d8OZ/+yx9gipl7RoaiKIz/9rd0Pf45lGCAnN/9jpSvfRVJK85zLIS+6lBY9lg7fPZ52Phnali2iorKLYNzwstAi43CyjShVoH+8004xsco2Xj7hw/GpEL+7aHFeISKDO6GUZAhevmH3VblqeVkmjP5fefvI6IJ0NDQgE6no6Sk5IPH4rZuBb0+ojY2i+VNTKZcYmM/zNYoWp2OHFToqItMUUVWFF4ftnFncizxl03oKtmwGfvIMAMtzRHRHXf6ONo2yo6KrA/ey5IkEb08FU+rlaDDFxFd+mvA1gPLHvngIZPJRFFREY2NjciyHBndKYiNXYbJlIPF8qYwTZWPL5fyj+YjQFsbH09UUZFQ3YFWK6k5seijxK1FFFmm71wjC5eKjbQI2r0ERt3C7Wt9fX3Isiy8gGS1ngQgQXAH0nGrA7NWQ3mM2Czfkx1jLMmMI94kOv/oWMi+Juh+Ui0gzZXBs1D7DKz9KiQXICsK32npI9Wg47tX5R61t7dz+PBhVqxYwfr162942ubjh3n3F//MomUVPPjN76IPQ7uh7HYz8K1vY/m7HxKzaRP5r75K9MoVcz7vLcfZF+HX94POCE/tg8Xb5vsVqaio3ABJkrZJknRBkqQ2SZK+c4PjdkmSpEiSJPauOgK0VQ+jKFC8Wuz0tfPH3kcfZaRg1VVhlst2gbULBs5ERNdVN4IuPRp9xoe7gRpJw7bcbZwYOIHNYwu7ZjAYpKmpieLiYqKiPuw+1SYkELNxI/bf/x4lAkUGr3cYq/UkGek7rigOpuXEEp9qovW0JeyaACdtToZ8fj6VdmWOSOHq9ej0BpqPvR8R3d83DhKUFXaUX3nPY6pIA/li8TASNL4C2igouf+Kh8vKypicnKS7uzsyulMgSRLp6TuwWk/i9YqZtvdJ4pN2jai2VJMbl0uKSWxnjKu6GlNlpdAoC58nwHDXpPj8o54uPE4HC+fBvgbi8486OzuRJIlFixYJ1Z2v/KPjNgdr483i8496bKzNE2xfG2sDh0WYfQ3UAtLc2ftdMCXC5m8B8MzgGHWTLv66IIs43YeVdKfTyWuvvUZqair33XffDXecW0+f4O2f/V+yS5bwqW99F53BcN1jp4uvr4+uxz+H/a23SP3zP2PBv/wMbYJYr3HEkWV473vw6h/CgtXwhwchrXS+X5WKisoNkCRJC/wrcB+wBPisJElLpjguFvgz4JTYVxgZWqqGSF0US2KGuPbqgN9P68ljFK5Zjz7qqk2JkgdAo4eG8NvYAlYPvm77Fd1Hl9iWt42AEmBfT/gnWHV2duJyua6wr10ibvt2AkNDuGtqwq5rGX4LUD6wr11CkiSKVqfTf8GKc8Ibdt3dw1ZMGg1bU64cgx0VHU3+ytW0nDyKHAz/FLg36gYoSDVTmnlll7Q+IxpdejSuSNjY5CA0vQZFW8F45UKsuLgYvV5PQ0ND+HVvQEb6TkC5+P9fJVx80q4RQTnIGcsZ4dPX/IOD+Ht6iF4ttvY21D6BLCtkLxa7JultOgsgvAPJ2zGBZNSizxKbzXMp/+jyzZRIM1/5RyO+UP7R+gSx/43re214AzLr8kXb146GvgsK0Aa1gDQ3+mqg833Y9A0wJTDmC/B37YOsTzDzcPqHlXRFUdi9ezdut5tdu3ZhuEFBqP/Ced76578nI7+Ih/7ir669yZ8FrjO1dH36Ufz9/Sz85b+R8rWvfbyCsgG8k/DCE3D0p7DqS/Dkax9kIqioqNzSrAHaFEXpUBTFBzwPPDjFcd8Hfgx4RL64SGCzuBjunqR4jdjuo676M3icDkovt69dwpQARfeEujrk8BYZLhUQoiuuHRhRmlRKblxuRGxsDQ0NREVFUVh47eCJ2Du3IBmNTETAxmax7CEmZglm87VjsItWp6Mo0FYT3i4Vv6ywZ8TGvSlxmKewpJdsvB3XhI2exvqw6g5NeKjqGmdnRfY1G2OXbGy+LjsBa5j/2XYdhcnBUOfcVRgMBkpLSzl37hyBQISnwF2G2VxAbOxShoZeF6b5CeETdY1otjbj8DuE29ecp0J1N/NasYv9/hYrGq1EZoHYAlJP01kSMjLF5x+12+Yl/6i/v/+Tk39kcwKwUXAB6VTnOJIEa4TnHx2FmHRIDs+QrenwMasiCObEv0BUHKz6AgB/1zHAZDDID4sXXHEjVVVVRWtrK/fcc88NQ7NtQ4O8/g/fJzY5hU/9xV9hMM3dtzmx5y16vvhFNHGx5L7wPDGbN8/5nLcc1m741b3Q8g7c9/fwwE9BN/euLRUVFSFkA72X/b3v4mMfIEnSSmChoigfi639ltMWkKBwldgCUvPRQ5hi41hUtnzqA8o/DY6hD6d5hAl33QiGRbHokq7dEJEkifvy7qN6qJoh51DYNP1+P83NzZSWlqLXX5tFoDGbidlyB5PvvIviD1+otdvdg91eR0b6A1P+PCnTTPKCmLDb2A5bJxn3B3kofWobSN6KSgymaJqPhXca2xv1/SgK7KjInPLn0eWhomHYu5DOvgiGWFh835Q/Lisrw+Px0NbWFl7dm5CevpPJyQZcrk6huh9zPlHXiKrBKgDWZohddLtOVYXyj26Szxpu+i7YSM+NE5p/JMtB+s83CbevBSa8BMY8ROUL7rbq7Z2f/CNbqCgpPP/IFso/KosVn39UkhFHQrTANeil/KOcjUKzftUC0myx9cC510PFo6hYWp0enhsc58vZqZSYTR8cZrVa2bdvH0VFRaxZs+a6p3NP2nn1R99DAR7+zveIjpubN1ZRFEb/7d8Y+OY3MZaXkfv880Tl5c3pnLck3cdDYdn2Pnji5VAWlRqWraLysUGSJA3wE+Ab0zj2K5IkVUuSVD0yEsHpT3NAURRaT1vILk4gJlFcK7nP7aK9pori9ZvQ6nRTH1S8LbQoP/ti2HT9Fif+IeeU9rVLbM/fjoIS1i6k1tZWvF7vlPa1S8Tv2EHQasVx7FjYdIcuhiinpU1dQIJQ7pWl087EiDtsuq9YrCTqtGxJmnrYhs5goGjNBlqrjhPwhS/UenftABULE8hPnXqnV5dswrAoNrzT2PxuOP8GLNkJetOUh+Tn52MymYTb2NLTHwCkD94HKpFnJteIi8ff0teJqqEq8uLzSI2+tmMzUiiKgvPUSaLXrBGbf+QOMNIzSfZiwflHXZ14Xc55sa+B+Pyjrq6u+ck/ss5P/tEJm4M18Wb0Aru8vIEgZ3qsrBXdfTTWFurGzRPbIKIWkGbLqV+GChVrvwbAP3VbiNJo+JOcD3eUFUXhrbfeQqPR8MADD1w390gOBnnzJz/EPmLhwW/+JYmZ2VMeN10UWcby/R8w8k//TNzOHSz6r/9Clyj2w1kIZ34LT+8MZVA9dQAK7pzvV6SiojJz+oGFl/19wcXHLhELLAMOSZLUBawD3pgqJFVRlH9XFKVSUZTK1FRxN98zYaRnEpvFRfGa63ejRoK26lMEfN6p7WuX0JtgyYNw7o3QIj0MuOpGQAJT2fVtAjlxOZSnlLOnY09YNCFkXzObzTfccY257Ta08fHY3wjPYl9RFIaGdpMQvxqT6frX8aKLwektVeHpuHIEgvx+xMaOtAQMN1j8ldx2Oz63i47a02HRvTA0yblBOw8tz7rhcaaKVPyDTvwWZ1h0aXkHvHYo+/R1D9FqtSxdupQLFy7g9YY/b+p6GKMySExYy9DQ6ygRmmj4CSRs1wi4ta8TftlPjaWGNRnX33COiG5fH4GBQaIF29cG2mwoskJ2sXj7GiA+QLvdhmTSoc8UO1r+k5Z/dMHpYYNg+1ptjw2PX2ZDgeD4lM6LwzE+SgUkSZKSJEnaJ0lS68Xv11QpJElaLknSCUmSmiRJOitJ0mcu+9lvJEnqlCSp7uLXdfrqbzE8E1DzNCx9COIX0Oby8JrFypeyU0gxfLize+7cOdra2tiyZQvx8devNh957ml6zzVwz1f/lAUlS+f00mSfj4FvfhPrs8+S9KUvkfWjH6EJQwj3LUUwAO/8b3jjTyBvEzz1HqSI832qqKiEldNAkSRJeZIkGYDHgDcu/VBRlAlFUVIURclVFCUXOAnsVBSlen5e7txoqbKg0UnkLxe7cGk+eoi41DSyiktufGD5p8E3GVqkzxFFVnDVDRNVmIA29sbXoe3522mxttBibZmzrtvtpqWlhbKyMrRT5AFdQjIYiL3/Pib37yfocMxZN2Rd6iAj41M3PC42yUh2cQIXTg2Fpcjw9ugEblnhkevY1y6xaFk55oREzh0+OGdNgN11/Wg1Eg9U3LiAFF2eChK4asPU7XH2JYjJuOkNc1lZGYFAgObm5vDoTpP0jJ243V1MTortfvoY84m5RjSNNuEOuIUXkFwf5B+J1e2/YEWjk8gQPdL+XAOJmdnEJIld7Hs7Jj5h+UfjwvOPjttC13LR+UfH28fQSLA2X3QB6TDELYCkfKGyc+1A+g6wX1GUImD/xb9fjQv4vKIoS4FtwD9JknR5qflbiqIsv/hVN8fXI4YzvwvdYK//YwD+qSvUffT1RR8uCDweD++88w4ZGRk3tK61njpO9ZuvUnHPdpZsnlsHjex20/f1P8L+9u9J+9Y3Sf+Lb3/8wrLdNnj2UTj5c1j7dXj8pVAHkoqKykcSRVECwP8A3gXOAy8qitIkSdLfSJK0c35fXXiRZYXWags5S5Mxmq/N5YkUTpuVrrO1lGzYfPNrQu4miM0Mi43N120naPUSvfLmWU/b8rahlbS81TH3CJOmpiaCwSDl5Te3J8Tv3Ini9TK577056w4O7UaSDKSl3X/TY4vXZjAx7Ga4a3LOuq8MWVlkNLA6/sa72hqNlpKNt9NZW4170j4nTVlWeL22n01FKaTE3HhXWxtrIKooEVfdMIo8x4KZaxxa90LZI6C5cWbKwoULiY+P5+zZs3PTnCFpqduQJANDljdufrDKTfkkXSOqhkL5R6szVgvVdZ6qQpucjGGKgQORpL/FRkZePDqDwPyjYJC+840sXCo4/8jmITjuEW5fu5R/lCc4xsRqPQFAQsI6obpHrQ5itRrKBecfnWgfZVl2PPEmcfd2yDJ0HgltpgiOb5lrdeFB4OmLf34auGbbTVGUFkVRWi/+eQAYBm6tntGZIMsh+1rObZC1gjaXh1cvdh+lGj580xw8eJDJyUkeeOCB6+6Ajg/0884vfkpGYTF3fP6pub0sp5Per34N5/HjZP7g+yR/+ctzOt8tyVg7/OfdoXa9Hf8M9/0ItNfJ8lBRUfnIoCjK24qiFCuKUqAoyt9efOyvFEW5ZgWmKModH8WdZYC+5nFcEz7h9rXmY4dRZJklm++6+cEabWiyVeu+0GJ9DrjODCMZNJiW3nxHLsmYxMbsjbzV8RayIs9Jt76+ntTUVDIzpw52vhzT8uXoFy5k4o25Tc6SZT8Wy5ukpNyJXh930+MLVqah1Wm4cGpuNrYhr58j1kl2pSde1yZ/OUs234kcDHDh+NyC0qu6xhmY8PDQiulZ7s0r0wjavHg7J+aky7ndIPtvaF+7hEajoaKigo6ODuz2uRXMZoJeH09Kyh0MDb2BLIubAvdx5pNyjagarGJx4mISjeI2RhVFwXXqFOa1a6b1GRIuPE4/I72Twu1rls42fG73Jyb/qLOzE41Gw8KFC29+cBgZt57AZFp0Qzt3JDhqnWR9Qgw6gV1eLl+A2h4bGwrETvRjuAnc48LtazD3AlK6oiiDF/88BNxwm1GSpDWAAWi/7OG/vWht+6kkSeLMmbOl5zhM9MCqLwJTdx+Njo5SVVXFqlWrWLBg6uCwgN/Pnp/+EI1Oz47/+R10U0yJmS5Bh4Oer3wVV3U1WT/+EQmPPDLrc92ydByC/7gTXGPw+dc/+O+voqKi8lHhwskhoqJ15JWLvck4d/gA6flFJC+Y5g1k+WdCi/Sm12atqfhlXA0jmJaloJnm7vID+Q9gcVmosdTMWnd8fJze3l7Ky8untRiSJIn4HTtwnTyF3zL7yWjj40fx+8fJvIl97RJRJh255Sm0VlsIBmdfMNttsSIDuzKmt+BMzckjZWEO544cmLUmwOt1/UQbtGxdMr1JgsYlyUhRWlxnhueky9mXIGUxZFZM6/Dy8nIURREepp2Z8RB+/xjj4+GdaKjy8cUb9FI3Uie8+8jX2UVgeJjoNYLzj1ptoCA8QLu3KfRZID7/aAJNtA59htj8o46ODrKzs4XmH8lyAKv1JImJG4RpAvR5fHS6fWxKnHqYRKQ43WUlICvzkH90capq3iaxukyjgCRJ0nuSJDVO8fXg5ccpISP/dXuTJUnKBH4HfElRPthe/N9ACbAaSAL+4gbPvzWmJpx9EfRmKLmfQa+P14atfD4r+YruowMHDqDT6diyZct1T3Pshd8x0tPFtq//OXEp159OczOCDie9T/0h7ro6sv/x/xK/82PVzRui6j/gdw+HbBVfOQi5t833K1JRUVGZET53gI7aEQor09HqxVmLR3q6GO5qn5lFOqMMUkvh7Auz1nWfH0PxBIleMf3r2x0L7yBaFz2nMO1LdqXp2NcuEb9zBygK9j2zt88NDe1Gp0sgOfkGIeVXsXhdBh6Hn96m2Xd6vWyxsjw2msJo47SOlySJ0k1bGGy9gHWw/+ZPmAJvIMhbZwfZtjSDaMP0uoA1Bi2mZSm4G0eRfcFZ6WLtCm3ilX962u36KSkpZGdnU19fLzTUOjn5DvT6RAaHZl+EVflkcXbkLN6gl7WZYgs5rqqL+UfrxOr2t1jR6jVk5Am2dJ1rICl7IeYEsV1e3jYbUQUJQvOP3G43g4OD5OeLzceZnGwkGHSQlLheqO4Ra8gSflui4PyjtlH0WonKXMGRKp1HIKkA4sVOuYNpFJAURblbUZRlU3y9DlguFoYuFYim3FqSJCkOeAv4S0VRTl527kElhBf4NXDdsKBbYmpCwBtqny7ZDgYzzwyMIyvwpQUf7ib39/dz7tw51q9fT0zM1G/g3nMNVO95jfK7t1GwavaBdbLbTd8f/RHuhgayf/IT4u67b9bnuiUJ+mHP/4S3vwlFW+HLeyExd75flYqKisqMaa8dJuCXKVkn1r527vABNFotJRtn0OIsSVDxGPSeClmHZ4HrzDCaOANRBdO3J5h0Ju7OuZu9XXvxBDwz1lQUhbNnz5Kbm3vDwRVXY8jNxVhRzsQbs8usCQQmGRndR3r6djSa6Q+tWLQkCaNZz4VZTmNrdrppdLh5ZJrdR5cove0OkCTOHTk0K93954exewJ8apr2tUtEr0xD8QbxnB+blS71zwMSlD82o6dVVFQwPDzM0FB4pt5NB43GQHr6A4yO7sPvF2efU/noUjVUhUbSsCp9lVBd56lT6NLT0efkCNXtv2AjIz9e6IZKwO+n73wjOWViZzYFRt0EJ7wzuh6Gg66uLhRFmYf8o+MAJAouIB2zOkjW6ygxT29DJVwcbx9jxaLEaW+ohIVgALqPzYt9DeZuYXsD+MLFP38BuCZE4OLEhNeA3yqK8vJVP7tUfJII5Sc1zvH1RJbWfaEJbOWfwS8r/PfAGHckxZJr+rAtcP/+/ZhMJjZsmLptz+ty8c7Pf0pCega3Pzn7nCLZ56PvT/4U1+nTZP3oR8Tde8+sz3VL4hqH3z0E1f8FG/8cHnsWjDfPlVBRUVG5Fblwcoj4NBPpeeI+x2Q5yPmjh8hbUUl03Ax3ecs/A5IG6p+bsW7Q4cPTYiV6RdqMd1sfLHgQh9/BgZ6ZW6z6+voYHx+nomJ69qbLid+5E++FC3jOn5/xc4dH3kWWvdO2r11Cq9NQVJlGZ/0oXvfMs3JeHLSik+DBtJktSmKTU1i0tJzzRw/Oqivn5Zo+MuKMbCycmRUzKi8ebXzU7Gxssgx1z4ZulhNmluWxbNkyNBqN8DDtzIyHkWUfw8NvC9VV+WhSNVjFkqQlxBrE2W8UWcZ1qopowflHLruPsX4HC0rEdmwMtjYT8HpZJLiA5G2zAWAsFFtA6ujoQK/XXzdOJVKMW48TE1OCwSDO0qUoCkesk9yWGCP0vTzh8tM4MCHevjZYD177vNjXYO4FpB8BWyVJagXuvvh3JEmqlCTpPy8e8yiwGfiiJEl1F78u/ct9RpKkBqABSAF+MMfXE1kaXgRzKuTfwbujEwz5/Hwp+8MbqPb2djo6Oti8eTNG49TVz4NP/zuTo6Pc98ffwGA0zeplKIEAA9/4Bs6jR8n8wfeJ3/HArM5zyzLcDP+xJbT7/dAvYev/uem0FRUVFZVbFfuom/4WGyXrMoTe2PQ01OO0jrNk0/Xt1NclLhMK7oS650KL9xngqh8BWcE8A/vaJSozKskyZ/F6+8xDrevr69HpdJSWls74ufHbtyPp9dhem7nlaHDwVUymHOLiVsz4uYvXZRL0y7RVzyx/KSArvGwZ5+7kuCss9NNlyeY7mbAM0X/h3IyeN2z38H7LCA+vzEY7w+KgpJGIXpGGp9VKcNI3o+fScwJs3bD88Zk9D4iOjqa4uJizZ88SDM7SPjcLYmPLiI4uUG1sKjfF5XdxdvQsazJn70qYDd6WFoLj45jXie0U6WsO2XYXliYJ1e0+W4ek0bBwyTKhup42G9qEKLTJYjtjOjs7ycnJQacT1xkTDHqYmKgRnn/U5vJi8QWE5x+d7BxDURAfoN35fuh77kewgKQoypiiKHcpilJ00eo2fvHxakVRnrr45/9WFEWvKMryy77qLv7sTkVRyi5a4p5QFMUx918pQngm4MI7sPRh0Op4emCU7Cg9dyWHdpMVRWH//v3Ex8dTWVk55Sm6z9bRdOg9Vj+4i6ziklm9DEVRGPo/f8PkvvdI/8u/JGHXrln/SrckrfvgV1vB54Ivvh2yUaioqKh8hGm5aFEqXivevhZlNpO/cpaLkuWPg70Pug7P6GmuM8PoM82zCgvVSBp2Fu7kxMAJhpzTtxz5/X4aGxspLS297gbOjdAmJBBz113Y39yD4pt+ccPt7sFmO0VW5iOzKg6m5caSmGnm/PHBmx98GQfH7Qz7AnwmY3YLsKK1G9BHGWk6tH9Gz9td109QVti1anY72tEr00AGV90Mu5DqnwVDDJTumJVuRUUFTqeTjo6OWT1/NkiSRGbGw0xMVONydQvTVfnoUWOpISAHWJshNofIeSxkNTJvFLvY7222EhWtI3WR2MV+T0MdGYXFREWLC7JWZAVv+wRRhQlCN5Dsdjujo6PC7WsTEzXIso8kwQWkS/lHm+Yh/8ik17J8odjuMjoPQ9oSiJl9jvJcEGc8/ahz7g0IeqH8UVqdHo5YHXwhOwXtxQ+Dzs5OBgYG2Lx5M/opJqr5fV7e+9W/kpCRybpdsy+KjP7sZ9heeonkr32VpCefmPV5bjkUBY7/DJ75dCjn6CsHYaHYSRQqKioq4UZRFJpPDpFdnEBc8uy6TmeD1+WkteoEi9dvQmeYfi7PFSzeDlHxoS6kaeIbcODvdxBdOb3pXFOxM38nCsqMwrSbm5vxeDysWDHzLqBLJDz0KYJWK5Pvvz/t5wwMvgJoyMh4aFaakiRRuj4TS6ed8UHntJ/3wtA4yXoddyfPLoDWYDRRvP42Lpw4gt8zvbwpRVF4uaaPlYsSKEid3U26Pi0aw8JYnNWW6dvnfE5o2g1LPwWG2S38ioqKMJlM1NXVzer5syUjYycgMTS0W6iuykeLE4MnMGgMrExfKVTXefw4hoIC9Omz/7yeKYqi0Hd+nAWLE9EIDJT2OB0MtbcKzz/y9ztQPIF5sa8BwgO0x60nkCQdCQli13BHrQ4WGPUsMs7yH+qyFgAAIABJREFUfmeWHG8fY3VeEgadwJKK3wM9J+ct/wjUAtL0aXgREvMgexW/HRhFL0l8NvPDnb9jx44RExNz3ckvVa+9iG1okLuf+mP0htmNUhx/9llGf/4L4h/ZReqf/dmsznFLEvDC638Me78b2l38g3fmJVFeRUVFJdwMtk8wMexmseDw7AvHjxDweVm2ZevsT6I3wrKH4dzr4JleELCrxgJaiejls98VWxi3kFXpq9jdtnvaRYba2lri4+PJzc2dta5540Z0qalMvDa9xb6iyAwNvkpS0kaMxsxZ6xavTUfSSDSfmF4X0rg/wN5RO7vSE9HPYQG2bMtW/B43LaeOTev4hv4JWiwOHlk1swyiq4muTCdgceHvm2bT+fk94HNAxczta5fQ6XSUl5fT3NyMy+Wa9XlmitGYRVLiBgaHXuXDAcQqKldyYuAEK9NXYtSJszjJXi+u6mrM18lsjRQ2iwuH1csCwfa13nMNKIosvIDkuZh/JDpAu7Ozk+joaNIFFgchFKAdF1eOTieuEyioKBy3OdiUGCu0y2vY7qF12CE+/6jnBATcUHCXWN3LUAtI08E1Dl1HYdkuvIrCi0NWHkiN/yB3YHBwkPb2dtauXTtl99FYXw9Vr7/Ckk1bZv3BNXnoEJYf/C0xd9xB5ve+J/QfSERxjMDTO6HuGbj9O/Dpp2e9w6iioqJyq3H+2AD6KC0FK8W2GTce3EfygkVkFBTP7UTLHw/dqJy7eSaREpBx1Q5jWpKM1jzzXJ7LebDgQbrt3dSP1N/0WJvNRkdHB8uXL0ejmf1tjaTTEf/gThzvv09gdPSmx1utJ/B4B8jMnJuV3BwfRc6yZC6cHEIO3rzI8JrFik9ReCxzbguw7MVLSMzMovHQvmkd/3JNH1E6DdvLZ18sA4iuSEXSa3BWT9OiWPdMqDN50dxyWlasWEEwGBQfpp35CB5PH1brCaG6Kh8Nhl3DtNna2JAltpDjPnMGxevFvEFs/lHveSsAC0vFBmj3NNShjzKSWbRYqK63zYo+IxptrLjOGEVR6OjoIDc3d07XxJni99ux2xuE5x81TLqxBYLcliDWvna4NXSfsKlIcP5Rx0HQ6CF3o1jdy1ALSNOh4yAoMhTfy/vjk0wEgnw648ruI4PBMGX2kaIo7PuPf8VgMnH755+albynuZmB//UNokoWk/2Tf0QSGIYWUYYaQmHZg/XwyK9hy/8GgR90KioqKpHE5w7QVjNMUWUaBqO4z+3R3m4G2y5Qduc9c99sWLAakgtDi/ib4D4/huwKYJ6Dfe0S9+Teg0lnYnfbzbuB6utDRably+e+sxz/0EMQDDLx5s3tcwODL6PTxZGaMvcpqKXrM3HZffScG7/psS8MjVMWY2JJzNwskZIksfT2u+k714ht6MbdT95AkNfrBrh3aQbxprkVBzVGHaZlKbjqRpB9Nwm1tvWEsh4qPjvn+4OMjAyysrKora2d1fS52ZKaei86XTwDAy8K01T56HBy8CQA67PEFnKcx4+DTkf0arHB3X3N48SlGIlPjRaq291Qz4Ily9Dq5vb5NRMUfxBvt52oQrHFstHRUSYnJ4Xb12y2U4AsPP/o0HioQ3pzkthMrSOtI6TERFGaIXhKePsBWLRuXhsu1NX6dGjbD8YEyF7F7mEbSXrtBynvVquVpqYmKisrMZmuvZlrOXmM/uYmNn32CzMfowz4LcP0fu3raGJjWfiLX6CJFvuBGzHOvwm/ugfkIPzB70M2CRUVFZWPEW01wwR8MqUbs4TqNh7ch0aro3Q209euRpJgxZOhlumRlhse6qq2oI0zEFU095tls97M1pytvNP1Di7/9S1HsixTV1dHXl4eiYlz140qKMBYUc7Eq6/esMjg99sZGXmX9PQdaLWzs6VfTk5ZMsYYPc03CdM+73BzdtLNZ+bYfXSJJZvvRJI0NL3/3g2P29tkYcLt55FZhmdfTXRlOoo3iKdp7MYH1v536Pvyz4VFd8WKFVgsFgYHZxZaPhe02igyMnYyMroXv98mTFflo8HxgeMkGZMoTpxjt+gMcR47jml5BdoYcYtQOSjTf8Eq3L5mHx3BOtAn3L7m7bJDQCHqE5J/NDZ+BK3WTHz87LMIZ8Oh8UnKYkyzmkg6W2RZ4UjrKJuKUoRmeeEYDjVgFITh/m4OqAWkmyHL0PYeFNyJS5F4Z3SCB1ITPsgdOHHiBJIksW7dumueGvD5OPzMr0ldlMuyO2eeQyF7PPT98R8TtNtZ+G+/EBpyFzEUBQ7/A7zwBKSVhsKys8R+0KioqKiI4NyxARIzzaTnidudCgb8nDtykILKNbPatJiS5Y+DRgdnnr6+7oQXT4uV6FWhPJ9w8HDRwzj9Tt7teve6x/T09GC1WsPSfXSJhId34W1txXMDq5NleA+y7CUr85GwaGp1GhavyaDz7Chux/WnwD0zOIZBkngoLTw72rHJKeRUrKDx/f3I8vW7gZ4/3UN2gonbCsPTqh+VF482yXhjG5scDBWQCu+ChLnlLl1i2bJl6HQ6amtrw3K+6ZKV+Siy7GNo6OZWUJVPDrIic3LgJOsy16GRxC3JAlYrnvPnhecfWbom8XmCLCwRW0DqaQiF5y8SXUBqs4FGIiovTNfiadLe3k5iYmJYNlWmi6IojI0dJjFxPRqNOLveZCBItd3JHYK7j84N2hl3+ubBvnYo9L3gTrG6V6EWkG6GpQEcFijayntjdlxBmQfTQpVkj8dDbW0tZWVlxMVdu0A48/s3sI9YuP3zT6HRaGckqygKQ3/9PTyNjWT/w99jLC0Ny68zr/jd8MpTcOAHUPYofPEtiBUbLKuioqIigvEBJ5ZOO0s2ZgrNrOuoOY3bPjG38OyriUmDxfdB/XMQmLq44TwzDAqYV4Vvo2Nl2kry4vN4ufXl6x5TW1tLVFQUpWG8RsZt344UHY31pZeue8zAwAvEmBcTG1sWNt3SjZnIQYULJ6cuqriDMi8NWbk/NZ5kQ/gskcvu2IpjbJTus1NPKOsec3KsbYzPrF4Ytp1WSSNhXpWOt32CwPh1psC17Qd7P6z8Qlg0AUwmE6WlpZw9exa/3x+2896M2NglxMYuZWDwJaH2OZVbm1ZrK2OeMeH2NdeJE6AoxAguIPU1j4MECxaLtXR1N9QRHZ9AysIcobqeFiuGnFg0UTNbA86FQCBAZ2cnhYWFQu893O4uPJ5ekpM2CdMEOGZ1EFAQXkA63DoCwG2iC0jtB8CUBBkVYnWvQi0g3YzWi+GShXfz+rCVdIOOdRdDuhoaGvD7/axZc61/2Gmzcuq1F8hftWZWLZPW3/6WiddfJ+VP/gexd81fynrYsA/Cr++Hxpfhrr+Ch/8d9OJGWquoqKiI5NzxATQaieI1YovkjYf2EZOUTG5FmMdBr/wCuMbgwtvX/EiRFZzVQxjy4tGlhO9zXZIkdhXt4uzIWVqtrdf83O1209TURHl5OQZD+HY8tTFm4rffj/2ttwk6rp0UZrc3MDnZSFb2Y2G9QU/OjiEjP46mIwNTFhneGrExEQjyRFZ4J74Url6LKS6es++9M+XPXzjdi0aCT1eGdzpq9Ko0kMB5+jpdSGeeBnMqFG8Lq+7KlSvxer2cP38+rOe9GVmZj+JwnGdyslGorsqty4mBULD6+kyxBSTH8eNoYmMxLlsmVLf3/Dhpi2IxxgjMIZJlus/WklO+QmhBJWj34h90YlwsuNuqpwe/309hYaFQ3bGxwwAkCS4gHRy3Y9ZqWB0vNg/ocMsIpZlxpMWKm5yIooQKSAVb5j0zWC0g3Yy29yCzgkljMu+N2dmZloBWklAUherq6g9CGa/m+IvPEPD5uP2JL89Y0nniBJa//wdi7r6LlK9/PRy/xfzSXxMKyx65AJ95BjZ9I5SroaKiovIxJBiQaTk1RG5FCtFx4lq57aPDdNbWsPT2u2bc9XpTCu6EuAVT2ti87TaCYx5i1oa/WLazYCd6jZ5XWl+55mf19fUEAgFWrVoVdt2ERx9Fcbux77k2TLt/4Hk0GiMZ6Z8Ku+7STdnYLC4G267NyvnvgTHyTAY2hnnSjFanZ+ntd9FecwrH+JWZRP6gzEs1fdxZkkZmfHg3fXQJRozFiTirh1Cunj43OQQXfh+yT+rC+28oJyeHxMREampqwnrem5GevhONJoqBQTVMWyXE8YHjFMQXkG4WF1GhKArO48cxr1srdCiP1+VnqMMuPP9oqKMV96SdvOXhv07cCE9LaNqcsVhst1VbWxsajYbc3FyhumPjRzCZcoiOFtvldWh8ko0JMRgEFlSc3gA13VY2i+4+Gj4XckXNs30N1ALSjXHboLcKCu/mndEJvLLCpy7mDvT392OxWKisrLymoj0+0E/Dgb1UbL2fpKzsGUn6h4bo/1/fwJCXS9aPfoz0UZ9K1vByqPNIo4cv74XSB+b7FamoqKhElI66EdyTfpYIDs9uOLAXBYXyu8LbsQGARgsrnoD2g2DtvuJHzlODaMyhyVrhJtGYyF2L7uLN9jfxBr0fPH5pEyc7O5uMjPAXrozLlhFVUoLtxSttbIGAA4vlTdLTtqPXhz/bqmBVGgaTjqYjA1c83uL0cHLCyecykyOyi15+9zYUWabx0JVh2geahxmZ9PLY6kVh1wQwr81EnvTjOX/V9Lm6Z0EJworPh11To9GwatUquru7GRkZCfv5r4deH0da2n0MDb1BIOAUpqtya+INejkzfEa4fc3X0UFgYBDzRrEjwHvPW1FkhZxl4e2gvBmdtTUgSeSUi81b9bRY0cQa0GeK7Yxpa2sjJyeHqKi5D3eYLrLsxWo9SXLSZmGaAJ0uL90eH7cLtq+d6hzDH1TYXJwqVJf2g6Hv+fMboA1qAenGdBwK3cAUbmW3xcYCo56VcaEpaDU1Nej1epZN0f554uVn0Rr0rH3o0RnJKX4//f/zf6F4vSz4fz8TOhkh7MhyKOvolS+HQrK/chAyxLbKqqioqMwHje/3E5diZNEScTutwUCAhgN7yVu+irjUtMiIrHgi9P3SZCwgaPfhPjcWCs/WReaWYlfxLuw+O/u6933wWE9PD6Ojo1RWVkZEU5IkEh79NJ5z53A3Nn3wuMWyh2DQSXb2YxHR1Ru0LF6bQfuZETyODzN6nhkcQy9JYZu+djWJGVksWlZBw4F3rwjTfr6qh/S4KO5YHJkbZePiJLTxBhynLpuKJstw5reQcxukRMaGsXz5cjQaDdXV1RE5//XIzn6cYNCBxfKGUF2VW4/TQ6fxBr1syBKbQ+R4P2Q1itksdrHf3TRGVLSODIFDJQC66mrILCgO31CJaaAEFTytNozFiUJtc3a7neHhYeH2NZutGll2k5ws9j11cNwOwJYkse+pwy2jGPUaVuWI7S6j/QCkLIb4mTWnRAK1gHQj2vaBMZ7JzJUctk6yIzUBSZLweDw0NjZSVlaG0Xil93G0t5vm44dZsW0H5oSZvbGG//EnuGtryfzB94nKzwvnbyIWrwNefDI0bW3FE/D5N8AsuM1PRUVFZR4YH3Qy0Gpj6abssE0jmw4dZ6pwWsep2Hpf5EQSFkLh3aHFfTBU3HBWD4EM5jWZEZNdk7GGBTELeKXlQxtbdXU1UVFRLF26NGK68Tt2IBmN2C4L0+4feB6zuZi4uMjtZi/dlEUwINN8MlRU8QRlXhoa596UuIiOKS6/exv2kWG660MTyvptbg61jPBo5UJ02sjcLkpaCfPqDLytNgJj7tCDnYfA2gmrwheefTUxMTEsWbKE+vp6oWHa8XEriYkpob//OTVM+xPO4b7DGLVGVmesFqrrOHyYqKIi9FPEb0QKRVHoaRxjYWkSmgh9lkyFyz7BYHsLuYLta76+SRR3AKPgsPC2tjYA8flH40eQJD0JCWuF6r5vnWSR0UCeSVxUAIQCtNfmJWPUiwtHx+eC7mO3hH0N1ALS9VEUaDsA+Vs4MuHGryhsTQ5Vry9N75hq5/PES89iMBpZvePhGcnZ9+1j/De/IfHxx4m7//6w/Arzgq0H/mtbKGj13h/Czn8Je36BioqKyq1K0+F+NFqJkvWRK6hMRf2+3xObnEreish05HzA6qfAMQTNb4XCs6uGiCqIRx/G8Oyr0UgaHil+hGpLNW3WNlwuF+fOnQt7ePbVaGNjibvvPuxvvknQ4cQ+2cjkZAPZWeENz76a5OwY0vPiOHc0FKb99ugE4/7wh2dfTeHqdUTHJ1B/MUz72VPdSMBnVi+MqG706oxQmHbVxTDt07+C6GRY8mBEdSsrK/F4PDQ1Nd384DAhSRLZWY8z6WjCbq8Xpqtya6EoCkf6jrAmcw1GnbgQ3qDDgaumhpjbxXaKjPY6cNl9wu1r3WdrQVHIWyE4/+hCaNqcsTBBqG5bWxuxsbGkpUWoC/k6jI8fISGhEp1OnHPGJ8sctTq4IylWaJdX77iLjhGnePta52EIeKD4XrG610EtIF0PaxdMDkDeJg6MTRJ7WcJ7TU0NmZmZ14RnD3d10HLqGCvvfxBT7PTb6fwDAwz+5XcxLl1K2nf+Ipy/hVh6TsF/3Am2bnj8JVj/R2pYtoqKyicGvy9I88khClamCQ3Ptg0N0n22lrK77gl/ePbVFG2F+EVw+j/xtFgJ2ryY10a+WPZw0cMYNAaev/A89fX1BIPBiNnXLifxs48hu1xMvPE6/f3PhsKzM8Ifnn01SzdlYx1yMdBi49d9o+SZDGxOjGzOg1anZ+kdd9Nxpoqx4WGer+rlzpJ0FiRGR1RXFx+FsTQZZ40FZawntAG18vOgi2yGR05ODikpKcJtbBkZD6LVmunvf1aorsqtQ6e9kz5HH5uzxRZynMePg99PzO23C9XtbgyF8y9aKjj/qK4GU2wcGflFQnU9LVYMi+LQRIubNhcMBuno6KCwsFBoQcXrteBwNJMsePpa1YQTZ1Bmi+D8o/3nLQDcVSK2SEfLO2CIgRyx2WXXQy0gXY/eKgCUhWvZP25nc1Iseo2ExWLBYrGwYsW17evHX3qGKLOZVdunf3OpBIP0f+vbEAiQ/dOfoIngbmpEqXsWnn4AomLhqf1QdPd8vyIVFRUVobSetuBzB1i2WWx49tn97yBpNJRtuSfyYhotrP4D6DqC83Armhg9piWRXxQkGhPZlreNN9ve5FTVKRYsWEB6euQnF5nKyzEuW8boK79jaOh1MtJ3otdHPkujqDKNKLOOPSd6OW138sXsFDQCFgXld96Loij86tVDjDl9PLlezEQd89oMZIefwN5fhDrAK/8g4pqSJFFZWUlfXx+Dg4M3f0KY0OliyEjfiWV4D37/tdP2VD7+HOk7AsDmBWILSI7Dh9HExmJavlyobk/TGKmLYoVurCiyTFf9GXIrVgodSBR0+PD3OYRPX+vv78fj8Yi3r42FMrWSksUWJfeN2jFIUsQ3Vq5mf/Mw+almclME5hQrCrTuhYItt4yrRy0gXY/eUxAVx/noPAa9fu5KDnUUNTQ0IEnSNbkLI92dtFefYtX2T2E0T3/E7ugvf4m7poaMv/4rDIsiM+UkoshB2Ptd2P11WLQuVDxKLZ7vV6WioqIinKbD/SRmmskU2LYe8PloPLiPglVriUkStLu74kkC0gI8HV7MqzMiFp59NY8tfozYyVhsVhtr14rLWkj83OeYyGhHlj0sWPCkEE2dQcuSjVnsDroxaSQ+kyEmkD0hI5P8FZXsbnWRkxzNpkIx+YXGokS0iVq0rc9B8TZIEHM/VFFRgU6nm4cw7c8hy14Gh14Tqqtya3C47zCFCYVkxoizOiuKgvP9w5g3bkTSi+uM8Tj9DHVMCLevWTracNsnyBOcf+RpDRWFRecftba2IkkS+fn5QnVHR/djjMoixrxYmKaiKOwdm2BjYgxmnbgcIoc3wKmOcfHdR5ZGsPeHro23CGoB6Xr0noIFley3hkat3pkUh6IoNDY2UlBQgNl8ZeXx9BuvoI8ysvze6Y+pd52pZfRff07cjh3EPxhZr39E8Njhucfg+M9g9R/CE69CtLipQyoqKiq3CpZOO8PdkyzbnCW0fbz52Pu4J+2s2LZDmCbmFBzxfwLImFeIm35SllrGcvdy/Do/paWlwnRj77sH5x0KptFEYmOXCNNdsCGDhkUGbvPoSNDrhOnGrLmXAX0q96b50QgKgpc0Egn5TWhkK/78zwnRBDCZTJSXl1NfX4/L5RKmGxtbSnzcCvr6foeiyMJ0VeafSd8kZyxnhHcfeZubCYyMCLev9Z4bR1EQXkDqrKsBSSKnYqVQXU/zOJoYPfqs6TcThIMLFy6waNEiTKbI5RFeTTDoYWz8KCkpdwm972lzeel0+7gnRdxkPYCjrSP4gjJ3lUa++/kKWkK5hBRuFat7A9QC0lR47GBpgoVr2T9mZ1mMiYwoPX19fdhsNpYtu3Ic/cSwhebjhym/+15MMdNrpQs6nAx8+9voMzPJ+Ou/isRvEVnGO+BXW6FtP2z/R9j+f0ErbkdDRUVF5Vai/kAvBqNWaHi2oiiceedNUhbmsHBpmTBd2RfEaV2KSXMMXc9uYbpjY2PE2GNojWnlzMgZYbpWxymCyTLG1x34BVqd9vhcBHQSJadsBP3iigz7xkzolADpF/YL0wQwTrxCQMlkskdsXsmaNWsIBALU1tYK1V2w4PO43d2Mjb0vVFdlfjkxcIKAEhBvX3s/9D6L2XSbUN3upjGizDrScsWOWu+oPU1GQRHRceKKDEpQxnNhHGNJktAprFarleHhYUpKSoRphnRPIMtuUlLuEqq7d8wOwNZkse+p/eeHiTPqWJUjtruMlr2QtRJiBReuboBaQJqKvtOAgjNzFaftTu68GNDV0NCATqe75h9ozVu7kSQNq7Y/NG2J4R//GH9/P1l//2O0MWKr1HOm80goLHtyCJ58LTSVR0VFReUTitPmpb1mmNINWRiM4jpF+pubGOnqYMV9O4Tu/rlqh1G8EJN2Hk79e8ifL4Cqqio0Gg2jKaM8f+F5IZoAvX1PY9AmY6zXYH3hBSGaQUXh6f5RVuijSBjw0lZjEaI74fLzRv0Ad2RqsLU3MdTWIkSXwbNIfSfwZT+K6+woQYdPjC6QkZFBbm4uVVVVBINBYbppafcRZUint/c3wjRV5p/DfYeJM8RRkVohVNfx/mGMy5ahSxFjSwWQZYWepjEWLUkW1s0I4BgfY6ithcLKdcI0AbwdEyieoJBcwMu5cOECAIsXi7ORQci+ptWaSUxcI1R33+gES2OMLDCKywOSZYWDF4a5fXEaeq3A8olzNFSXuEWmr11CLSBNRW8VSBoOm0sIKnBXchzBYJCmpiaKi4sxGj8cuemyT9BwYC+lt91BbPL0PpQd77+P7aWXSP7yHxC9Umxr5Zyp/jX87lNgToM/PAD5YlthVVRUVG41Gg/3IysKZVsWCNWt/f2bGGNiKb3tDmGaiqLgOD6APsuM4fb7YeQ8tB+IuK7X66Wuro4lS5bwQOkD7O/ZT7+jP+K6Llcn4+NHyF70JLGbt2B78SVkrzfiuvvH7HR7fHy1KJ2E9GjOHor87wrw3OkePH6ZP9m5Fr3RRO07bwrR5eTPQW9Gv+1rEFBwnh4So3uRtWvXMjExQUuLoIIZoNHoyV7wOcatR3E4W4XpqswfsiJztP8oG7M2otOI22wIWK246+uJ2Sy262mofQL3pJ+8CnFFK4D2mlMAFK4WW0BynxtD0muIEpiDCKECUmpqKklJ4mJEFEVmdPQAyUmb0WgiOzHzcsb9AaomnNyTLNa+drZ/glGHT3z+Ues+QFELSB8Jek9C2lLenVSI12lZFWems7MTp9NJWdmVNoHad/YQ8HlZvXPXtE4dtNkY/O7/R1RRESl/+qeRePWRIRiAt78Ne/4c8rfAU/sguWC+X5WKiorKvBLwB2k60k9uWQrxqeKyB+yjw7SePkHZXfeijzLe/Alhwts+QcDiImZDNlLZrtBmwsmfR1y3vr4er9fL2rVrebzkcTRoePZ85Meg9/Y9jSTpyc56jKQnnyA4Po59z56I6/577wiZUXruT02g7I4FDHfZGeqYiKimPyjzm2NdbChIpiI/jaW338mFE0dw2qwR1WVyCBpehhWfQ78oi6jCBJwnB1GC4mx7xcXFxMfHc/LkSWGaANlZj6HRGOjrfVqorsr80DTaxJhnjE0LxI48dxw4ALJM7N1irUYd9SNodBI5S8V25LSdPklCRiZJ2QuFaSqKgufcOFFFiWgM4oKd3W43XV1dwruPJicb8fospKTcKVT3wJgdmfmwr1nQSHB7capQXVrfhZh0yBDbsXgz1ALS1chB6KtGWbiGg+N2bk+KRaeRaGxsJCoq6orxiH6fl7q9b1FQuZbkBdP7kBr6wd8SsFrJ+vGP0BhujVF8N8VthWd2QdUvYf3/gMdfAKPYyq+KiorKrUjraQvuST8Vd4rtPqrb+zYAy++5X6iu4/gAGrOO6IpU0EXBmq9A23swciFimrIsc+rUKTIzM1mwYAEZ5gy25m7l1dZXcfgcEdP1+20MDLxMevoDREWlEr1uHVElJYz/5jcoEbTtNU66OGpz8OXsFAwaDSXrM4iK1lH3Xk/ENAHebhhkyO7hqU15ACy/9wGCgQD1+96OqC6n/xPkAKz9GgAxG7IITvhwN41FVvcytFota9asobu7m6Ehcd1PBkMy6ekPMjj0Gn6/TZiuyvzwXs976CSd8PyjyX3voc/KIkrg8AFFUeisG2FhSRIGk7huK6/LSU/jWQpXrxdq7fYPOAlOeDEtETtMqK2tDUVR5sG+dgDQkJx8h1DdvWN2Ug06lsdFC9Xdf36YypwkEs0C1+4BbyhruOge0NxaJZs5vRpJkpIkSdonSVLrxe9TpkpJkhSUJKnu4tcblz2eJ0nSKUmS2iRJekGSpPmvqFiawOdgOH0lFl+ATYkxBAIBzp8/T2lpKfrLRl82H3sfz6SdVfdPb4La5IED2PfsIeXrX8PQHqGfAAAgAElEQVS4RNwklzkx0gL/cRd0H4cHfw73/i1oxFXWVVRUVG5VFEWh/kAfSVlmsgWO7PV7PDTsf5fC1euISxHXTu0fceE5P4Z5bSaS/uLtQ+UfgM4Y0S6klpYWxsbG2LBhwwcLgi8s+QIOv4PX2iI3Br2//zlk2c2iRaGcP0mSSPriF/C2tuE8eixiuv/WO0K0VsMTWaFde4NRx9JNWXTUjmAfdUdEU1EUfnW0k/xUM/8/e+cdHlWZ9v/PmZLMZCa990JN6BA60gQVRUXsvfuurmV339V1f+/2dXfd1V3dpmvva0FUlCYtgJQAoSVACOm910mZ/vz+GFBBQtrMI+6ez3XlIpl5nvM9wMmc59zPfX/v+SM911R4fCJpk6dy6PM1OGxWn+ji6IGcV2HUki+zmg2jw9CFG7B8Ue3TQN2ZTJ48Gb1eLz0LKTHxDtxuKzU1cvy1VL4dhBBsKt/EtNhpBPvL24R1dXbRtXMngYsXSw2oNFd30tFklV6+VnpoP26XU7r/Uc+xZlA8n18yKSgowGQyER8fL1W3sWkzIcFT8POT9/e1u91kNXewKDwIjcRrubqth2O1HSyQXb5Wsg1sHZAusctuPxlqOOtxYLMQYgSw+eTPZ6NHCDHx5NcVX3v9j8AzQojhQCtw9xDPZ+hUeupmdwd6Oq3NCDZTXl6OzWY7rW2wEIKD61cTkZhMQkbf3W9cFgt1v/o1/qNGEXHvvb45d29TtAleXuS5eG//DCbJa62roqKicr5TVdBKc1UnExYmSl2Y52VtwNppIXNp/xs3eIPOHdWgVTDPjPvqRVM4jL8eDr8HXb7JGNm1axfBwcFkfG3jZUzEGCZHTead/Hdwup1e13S77VRWvUlY6BwCzV81zgi+9FJ0kZG0vPaa1zUBam12Pmlo5abYMEL0X+3aj5vvucYOb670ie6+slZyq9q5e07qaWa3mZcvp8fSwbHtPvK5yn0fupthxgNfvqRoFMwXJOCotGAv7fCN7lkwGo1MmjSJ3NxcOjrk6QaaRxMaMoPKqjdxu+WZh6vIpaitiApLBRcmyS0j6/piO8LhIHDxIqm6JYeaQIHUCXJLfor2ZRMQHELsSLkZOdZjzfglB6E1y8uFcDqdFBYWMnLkSDQSM1Ss1ho6O49JL1/b3daFxeXmIsnla+vyPN1XLxkbI1WX/FXgHwRp8+Xq9oOhXm1XAqcKt98AlvV3ouJZbS8EPhzMfJ9RuRfM0WxyhRCu1zE8wJ+CggJ0Oh2pqalfDqspyKexrISJFy/t14NDw1NP42xqIvaJJ1DO99I1ISD7eXjnWghJ8phlJ8mN5KuoqKic7xz8vJyAID9GTpfXWtXtcrF/zSfEj84gbqS8cgRXp52u/fWYJkejDTzjHjbjAXBaPZkkXqaqqoqKigpmzpyJVnt69uttY26jurOaLRXeD27U13+G3d5AUtLp+1qKnx+ht9xC165dWAu8X7b3alUTbgH3Jpz+0GUO9WfEtGiO7arF2uXwuu7LX5QQGqBn+aTTSzET0scSM2wE+9d8gtvt5Q5lp9YaMeMg5fTW4qYpUWhMOizbq7yr2QczZ85ECMGePXuk6iYl34vNVkd9vSTTchXpbKrYhILCwiS5D92WjZvQhoVhnDRJqm7JoUZihwUTECQxoOJwUHpwH2mTp6GRWC3hbLXiqO2S3n3tVIKD7PK1xsaNAEREyA1KftbQRoBWw/wwyQGkI3WkxwaRGmGSJ+pywvG1HvNsnTyT8v4y1ABStBCi9uT3dUBvq2iDoig5iqJkK4pyKkgUDrQJIU5tHVYBcvPvzkZlNiROZ09HNzNCPBdKQUEBw4YNw+9rgZ+Dn6/GP8BExgUL+jxkV/Ye2j74gLA778A4bqzPTt0rOO3w2cOw/nEYdSnctd4TRFJRUVFR+ZLGCguV+a1MuDARnV7eQrUgewcdjQ39btzgLTp31YBLYL7gLLfpqNEwfJHHJ8/h3RKrXbt2YTAYmHSWh5/5CfNJDEzkjWNveLXUSQhBRcUrmEwjCQv7ptlt6PXXoRiNtLzuXePjLqeLN2uaWRIZTLLxmwvGiYsScdo8pu3epKypi4359dwyIxnjGeaviqKQeflyWmtrKN6/16u6nPgcGo97vBXP2IhT9FrMM+OwHm/BUd/lXd1zEBoaSkZGBjk5OVitPirbOwvhYfMwm0ZRXvESQsgzD1eRx+byzUyKmkSEUV5Jl9tup3PbNgIvvBBFK+8+1dHUQ3NVp/Tso6qjudh7eqR3X7Me82TfGiQHkPLz89HpdKSlpUnVrW9Yi8k0EpNJnq7TLVjb1Mbi8CCMWnnZVnXtVvaXt3Kp7Oyj8h3Q0wLpV/Q99lugz/8BRVE2KYpy5Cxfpxn/CM/qrbcVXLIQIhO4CXhWUZQBt+9SFOW+k0GonMbGxoFO7x/dLdBWQVv0RCqtdmYEm6mvr6e9vf206G5nSzOFe3YydsEi9IZzd79xW63U/vIX6JOTiHzwQd+ct7foaoa3lsGBN2Huo3DdW+Bv/rbPSkVFReW84+CGcvQGLWMuiOt7sJcQQrDv05WExSWQNmmqNF233UVXdi2G9HD0kb0YV875IXQ1wsG3vabb0tJCfn4+mZmZ+Pt/M6Ci1Wi5LeM2chtzyanP8aLuDjq7CkhKuvusGcbakBBCrrqK9tWrcdTXe0333boW2p0u7k88u89CREIgiemh5GZV4XJ4L8jwwvZi9FoNt85MPuv7I6bNIigympzPvOg3JQTs+AsEJ8HYswdDTTPjUPQaLF4OmPXF7Nmzsdls7N+/X5qmoigkJd9HV1chzc1bpemqyKGyo5KC1gLp5Wvdu3fj7ur6FsrXPM9paRPl+h8V5exB728gaZzcjlXdR5rRRQWgj5DXidXlcpGfn8+oUaNOS3DwNVZbHe3t+4mOktvAY3dbJy0OF5dHhkjV/fyop6nCknGxUnU59inoAzybc+chfQaQhBCLhBBjz/K1CqhXFCUW4OSfDb0co/rknyXAVmAS0AyEKIpyqsg/Aeh1lSCEeFEIkSmEyIyM9FFEu/4IAEdORlRnhJg4fvw44Gnxeorczetxu91MuOiyPg/Z/OJLOMoriP3Vr9AY5X2wDJj6o/DSfKjeD1e/Agt/dt45vquoqKicD3Q09VC0v4GxF8TjH6Dve4KXKM87RGNZCZlXLEeR+PncnVOPu9tJ4NxzJAknz4aEabDrb57Uay+QnZ2NoihMmzat1zHLhi8j3BDOy3kve0UToLz8X/j5RRET3btxZdhdd4LbTcur3inbs7vdPFfRwIxgE5nBvafJT1ycRHe7nYI93ukUVtdu5cP9VVyfmUhU4Nk3xDRaLVMuu5KagmNUF+R7RZfyXR7PyVkPgfbsv0Nak56AKdF0H2zA1SHPGyguLo7U1FSys7NxOr3vr9Ub0VGXYfCPo6z8BWmaKnLYVLEJgAuT5QaQLJs2oTGZCJghNyOn9HAT4fEmgnvbcPABbpeLwr27SJ04Bb2fvJIfV4cNe1k7AePlBsvKy8vp6upizJgxUnUbG9YDgijJAaTPGtswajQslOx/tDavlpHRZoZHSUyocLvh+GpP8MhPbre5/jLUFeinwO0nv78dWHXmAEVRQhVF8T/5fQQwGzh2MmMpC7jmXPOlUpcHwBZ9CoFaDRlmIwUFBSQkJGA2ey4cl9NJ7qb1pE6cQmjMuXeebSWlNL/0EkGXX45p5kyfn/6gOb4WXrnIU752x1oYd03fc1RUVFT+Szm0sQJFozDhwkSpuvs+XYkpNIz0OX2XTnsL4XJj+aIKv6RA/FPO0TlIUeCCH0FbBRxZOWRdi8XCgQMHmDBhAkFBvS8YDToDt2bcyq6aXRxtPjpk3ba2HFrbsklOuheNpveHEL+EBIKXLqX1gxU4W1qGrLuirpUam4MfpJzbTysxPYyo5ED2ry/D7Rp6FtJLX5TgFnDf3HOXIoxbcBGGwCD2fOylTmE7/gIBETDplnMOC7wgHtwCy075WUgWi4W8vDxpmhqNnqSku2lvz6GtXV72k4rv2VSxifSwdOLN8pw6hMuFZfMWzPPmoZGYodLVZqOmqI20SXI7VlUezaO7vY1Rs+dK1e3ObQIBxvFyy/WOHj2Kn58fI0aMkKpb37AOs2kUJtOAi4kGjUsI1ja2szgiiACJ5WuNFht7y1pYMlZy9lHlHuish4z+dXn/Nhjq/8KTwGJFUQqBRSd/RlGUTEVRTm0HpgM5iqIcxhMwelIIcezkez8BfqQoShEeT6RXhng+Q6PuCJii2GgzMDXYRGdHB7W1taeVr5UezKGrrZXxi5ac81BCCOp+8xsUg4Honzzm6zMfHELAF3+B926CiBFwXxYkTPm2z0pFRUXlvKW7w07+rlpGTY/BFCJvl7O2sICKvENMXnIFOr28rKfugw24Wm0ELuyHF96IiyEqA3Y849lBGwK7d+/G5XJxwQXf9CA6k+tHXU+gPpBX8oa+hCgrfw69Poz4+Bv6HBt+370Iq5WWN98ckqbTLfh7RT3jA43MCw0851hFUZiyJIWOJiuFOWdN+u43rV12/r2ngisnxJEYdu5dTr3BQOZlyyg9mEN9SdGQdKk97OnyOuP+PndXdeFGjOMj6dpdg8sH5uG9MWzYMGJiYtixYwfuIV7LAyEu7jp0uhDK1Syk/xjqu+rJbcxlUbLcUpSu7GxcLS0EXnyxVN3CnHoQMHKqvOYSAMd3bcfPaCR1UqZU3Z7cRvSxJvRR8jJFXC4Xx44dY9SoUeglrgc85Ws5REWd+xnY2+xu66TJ4ZRevrbhWB1CwKWyy9fyPwWtH4y4SK7uABhSAEkI0SyEuFAIMeJkqVvLyddzhBD3nPx+lxBinBBiwsk/X/na/BIhxDQhxHAhxLVCCNvQ/jpDpD4Pe9QYCrttzAwxc+LECYDTAkh5WRswhYSS1scHVMfqNXRnZxP1ox+ii5Cb1tgvHFb46D7Y/GsYuxzuXAdB8rw8VFRUVL6LHNxYgcvpZvLFZ/eL8RW7V76LITCIiRf3XTrtLYRL0JFViT7ejGFUaN8TNBqPF1JjPpxYP2jdrq4u9u3bx7hx4wgLC+tzvNnPzI3pN7KpfBMlbSWD1u2wHKG5eRtJiXei1fb9MOA/bBiBixfT+s6/cVksg9b9tLGNsh47P0iO7ldX19TxEYTFmdi/vhzhHrx5+Gu7yuhxuLh/fv92kidefBn+JhPZHw0xC2nHM+AXCFPv6dfwoIWJCIebzh3yspAURWHu3Lk0Nzdz5MgRabpabQCJiXfQ1LQZi2XoGXUq3z7ryzyfhRcly30Y7Fi9Bo3ZjHn+PKm6hfvqiUwKJCRaXkDF6XBQuHcnwzNnSC1fc7ZasVdYMEo2Cy8tLaWnp+dbKl9Devna6sZ2jBqFheHn3mDxNuvy6kiLNDEyWnL5Wv5nMGwhGOSW6w0E1eTmFC4HNBZQGeLxOpoRYqagoIDQ0FBOeS51trZQejCHjHkXojlHNwOXxUL9H/+IYdw4Qq67TsrpDwhLPbx+GeR94PE6uvoV0J/H/kwqKioq5wHdHXaObKtixLRoqYvjuuJCSg/mkHnZMvwM8j6ru3MbcTVbCVqY2K/ABgBjlkNIMnzxtCfLdRBkZ2fjcDj6lX10ilvSb8GgM/DKkcFnIZWVPYdOF0hCwq39nhPxvf/BbbHQ+s6/B6XpFoK/ltczymTgkohzlAh+DUWjkLkkhdbaLkoOD66pSKfNyes7S7l4TDQjovu3KPcPMDHpkiso2rebpoqyQenSVAjHVsHUu8HYv91kfbQJ49gIOnfV4O6Wl4U0evRooqKi2L59u9QspMSE29Hpgigt/bs0TRXfsbpkNWPDx5ISnCJN0221YtmwgcCLLkJzlgYEvqKtvpuGcgsjJGcflecewNbVJb18rSe3CYCAcXITBY4ePYq/vz/DhskrIwNP9zWzebT08rU1jW1cGB6ESWInweZOG7tLmlkyNqb/6x9vULEb2is9a6nzGDWAdIqmE+Cyc9CYhkGjkO6vo7S0lFGjRn154RzdthnhdjN2/uJzH+q553E1NxPzi19IbZvZL2oOwUsLoOEYXP+2p9uazF8MFRUVle8ohzZV4HK4yVySIlV398p3MZjMTLx4qTRN4RZYtlSgjzFhSB9Aa2KtDub+2NOQoXDDgHV7enrYu3cvGRkZDKRhRqghlGtGXsOakjVUdFQMWLez8wSNjZ+TkHAbOl3/dzkNGRmY5l5Ayxtv4O4aeLv59U3tFHRZeTgpCs0A7sXDpkQRHGUkZ20ZYhCButd3ltJhdfL9BcMHNG/ypVegNxjJ/viDAWsCsPVJ0Bk95tkDIHBBIsLmonNXzeB0B4FGo2HevHk0NTVx7Nixvid4Cb0+iMTEO2ls2ojF4iXTcpVvhaLWIo63HGfpMHmf3QCdW7fh7uoi+HK5uoU59aDAiEy5/kfHd27HYA4kedwkqbrduY3oEwPRhcvb2HE6nV92X5Navmatpb19P1GR8svXGu1OLo+SW7722eEaXG7BlRPl+ZYBkPs+6E2QLvd3d6CoAaRT1HlSlDfokpgcZKKuqhKXy/VldFcIwdGtG4kfPYawuN4vJltJCS1vvUXINVdjHDdWyqn3m6Mfw6uXAArc9Tmk995hRkVFRUXlK3osdvK2VTM8M5rQmN67ZHmb+tJiSvbvZfJlV+IfIC/rqSevCWdjD4ELE1E0A9xkmHAjhKZA1u8GnIW0Z88ebDYbc+cOfCf5rrF3odfoef7w8wOeW1r2D0/5UMIdA54bcf/9uFpbaRlgFpJbCJ4qrSPN6M+VUf0oEfwaGo3ClEuSaarspOzkLnh/ae9x8OL2EhalRzE+YWCLcqM5kIkXX0bB7i9oqaka0Fwa8j0G69PvA9PAduz94swYMsKx7KjBbZXXGS09PZ3IyEi2bdsmOQvpDnS6QErL1Cyk7zJrStegVbRcnCLXh6hjzWq0kREEnKODpbcRQnBibz1xw0Mwh569o6MvcFitFOVkM3L6bLQ6Xd8TvKXb1IOjulN697WSkhKsVitjx8p9xqxvWAPIL1/7oK6FQK2Gi8L7l6HrLT46WM3Y+CBG9jND1ys4rHD0E0/wyE/eOnMwqAGkU9TnIbR+rBdRTA82UVJSglarJTnZ43NRffworbU1jFvYew2zEIL63/0ejdFI5A9+IOvM+8bthqw/wIo7IHa8xyw7dvy3fVYqKioqKIpyiaIoBYqiFCmK8vhZ3v+RoijHFEXJVRRls6Iocs2HTnJoUwVOu4vMS1Ok6mavfO9k6ZC8gL9wCzq2VKCLMmIcO4jFsVYP837iMUsuWNvvad3d3ezevZvRo0cTExMzYNkIYwQ3jr6RNSVrKG4r7vc8i+UYDQ1rSEy4HT+/vj2XziRg0iTM8+bR/MorA/JC+rShjfwuK4+mxqAbaJAOGDk9huBII3s+LR2QF9IrOzzZRz9cPHLAmgCZly1D7+fPrhUDLNvb+qRnUTzr4UHpBi1MRFiddO6Um4U0d+5cGhsbyc+Xlw2k1weRmHAHjY2fY+k8Lk33fOW7cp/4Om7hZm3JWmbEzSDCKC/I4Gpvp3PrNoIvvVRqFURTZSdt9d3Sy9eKD+zFabMxapbk8rWT5cOyu6/l5uZiMBhISzt350xvIoSgtnYlQUETMZnk6Xa5XKxubOeKqBCMEruvFdZbyK1q56pJCdI0PcKfg60dxl8vV3cQqAGkU9QdoTt8JHaNjolBAZSUlJCYmIjfydaXR7I24mc0MnL67F4P0bllC107dxL50IPowgeQ8u9L7F3w4R2w7UmYcBPc/hmY5aaWqqioqJwNRVG0wD+BJUAGcKOiKBlnDDsIZAohxgMfAn+Se5ae7KPcrdWMyIwmLFberlBdcSFF+3Yz+dIrMJjkmTh2H2zAWd9N0KLkgWcfnWLcdRA2zLN50c/MjZ07d2Kz2ViwYMHgNIE7x96JUWfkuUPP9XtOSckz6HRBJCXdO2jdyEcext3eTstrr/drvNPtyT4abTJw5SBT87VaDdMuT6W5upOi/f3ryNbaZefVHaUsGRvDmLjB7egGBIcw+dIrKdi1nYayfpqW1x2BY594Oq8FDDxIB+CXEOjJQtpeJbUj25gxY4iIiGDr1q1ys5AS70SrNVNa+ldpmucj35X7xJkcajhETVcNl6XKa3wAYNm4EeFwELRUbpVB4b56NBqF4ZPll6+ZQsNIyJBnKC2EoPtAPX6pQeiC5XlM9fT0kJ+fz/jx49FJzLayWI7Q1XWC2NirpWkCrG1sp9vl5tqYwd0zBstHB6vRahSumCC5udTh98EcDWnz5eoOAjWAdIr6I1SFjAZguEZQV1f3ZXTXbu2hIHsHo2bNRW84e1qm226n/sk/4j9iOKE33ijttM9Je5WnZO3Yp3DRE7DsOdDJ+6BTUVFR6YNpQNHJjpx24D3gyq8PEEJkCSG6T/6YDUjeEoKcdWW47C6mXpYiVfeLd9/AGBjElMuukqYpnG46NpWjjzcPLvvoFFodzH8c6vM8LWn7wGKxsGfPHsaPH0909OB3sEMNodycfjMbyjdQ0FLQ5/j29oM0NW8hOele9PrBp8gbMjIIvPhiWt54A2dra5/jP6xvobjHxmOpMQPyPjqTEZnRhMeb2PNZCW5X38GNF7aX0GUffPbRKTIvvwp/k4md77/Vvwlb/wD+QTDz+0PSDb4oGWF3YdlWOaTjDASNRsPChQtpbGzk8OHD0nT1+mCSku6hsXED7e2HpOmeh3wn7hNnsrpkNUadkQuTLpSq2756DX7JyRjGyguouN2Cwpx6EseEYTDL8+Xpamul9OA+Rs+eh0YjL9vKXtaBs9mKKXPgmbJD4ciRI7hcLiZOnChVt7buIzQaP6Kj5AZDP6hrIdngx/RgeRt3brfgk4PVzBsZSWSgxGfm7haPb+S4a0HitTxY1AASeLqSdTWSF5BGtJ+O7mrPwuRUAKl4XzZOm42MC3rfFW19+x0clZVEPf44ikRTs16p3AcvLoCWUrjpA49hpWqWraKicn4RD3z9SbDq5Gu9cTewzqdndAYdTT0c2VZN+uw4qd5H5bmHqMg7xPSrrpfqfdS1tw5Xq43gi1MGn310irFXQ8QoyPo9uM7tW3Oq09X8+fOHpgncPuZ2AvWB/OPQP/ocW1zyZ/T6cBISbh+ybuRDD+Lu7qbllXN3grO73fy5rJ7xgUaW9LPzWm8oGoVpl6fR3tDD8ey6c45ttNh4Y1cZV0yIG7Kvg8FkZuoV11ByYB/Vx/swmK7eD8dXw4wHwDgwr6cz0ceYCJgUReeuWlzttiEdayCkp6cTFxdHVlYWDoe87KekxLvQ68MpKv7ToMzS/0M47+8TZ+JwOfi87HMWJC4gQC/v89tRU0P3nj0EXX651M5Rlcda6Gy1MXpGrDRNgGPbt+B2uRi3oHd7EV/QlVOP4q/FKLn72sGDB4mKiiI2Vt6/s9tto67uUyIiFg9pk2WgVFvt7Gjt5NqYMKnXcnZJM7XtVq6aJNk8++hH4HZ8J8rXQA0geajLA2CbXzITAj3lawaDgbg4T+pa/o6tBEZEEj/qzIxZD87WVpqefx7T3Aswz+69xE0ah9+D1y8DvwC4ZyOMlPvBqqKiouJtFEW5BcgEnjrHmPsURclRFCWnsXFw7c3PZM9nJSgahamXpXrleP1BCMEX775OYEQkExbL63jitrno2FKBf1ow/iO80PFEo4ULfw5NBXCw90yV1tZW9u/fz+TJkwkLG3qqerB/MLePuZ2tlVs5UH+g13EtLbtobd1NSsr96HRDDw76Dx9O0OVLaXn7HRz19b2Oe7ummUqrncdTY72yME6dEEFUShD7VpficvSehfTXzSewu9w8cuGIIWsCTL7kcgKCQ9jx3pu9BzeEgA0/h4CIIWcfnSJoUTIIQcfmgXfbGyyKorBo0SI6OjrIycmRpqvTmUhNfYi2tj00t2yTpvtd5du6T5xJVmUWHfYOlqbJ7aTU9uFKAEKuWiZV99jOGoyBelInyAuoCCHIy9pI3Mh0whMSpem6rU56chsJmBCJxk9epkhDQwM1NTVMmjRJakClqWkrTmcbcZLL11bWtyKAa2OGtukwYN0D1QT661icIdfLi8PvQ2Q6xIyTqztI1AASeFLsgY26ZCYEGikuLiY1NRWNRkN3RztluQcZPXseiubs/1xN/3wOd1cX0Y8+KvOsv4nbDRt/CR//DyRMhXu2QFT6t3tOKioqKr1TDXx95Zdw8rXTUBRlEfB/wBVCiF7TDoQQLwohMoUQmQNpAd8bTVUWTuytZ8LCRMyh8lKZT2TvpL6kiNnX3YLupA+fDDp3VuPudBB0SYr3Fqijl0LiDE8Wkq3zrEOysrK+NCv2Frdm3EqUMYqnc54+a3BDCDdFxX/E3z+W+LibvKYb+fDD4HLR+OzZfWs6nC6eLqtjVoiZBWHe6e6iKAozrkyjs9VGbtbZO6MVNVh4d28lN09PIi3SO35aeoOBGcuvpyr/CGWHewnUFayD8p2eckZDkFd0dWEGzNNj6cqpw9HU45Vj9oe0tDTS0tLYvn07VqtVmm583PUYDUkUFz+FEPI8mM4jzuv7xNlYcWIFsaZYZsXN8snxz4ZwOmlbuRLTBXPQx8vLnujusFN2uIlRM2LR6uQ9VtYU5NNaU3XO5ka+oCe3CeFwE5ApN8Bw8OBBNBoN48fLbYJUW7cSP78owsLmSNMUQvBBXQszgk0kG+WtvbpsTtYfqeXScbEY9BLLyBryoWovTLzxO1MtpAaQAOqOYAuMo00fSKpw0tHR8WX5WsHuLxBuN+lz5p91qq20lNb33iPk2mvxH+GdXb1BYbPA+zfDzmdhyh1w68dgOk+MvFVUVFTOzj5ghKIoqYqi+AE3AKcZ5iiKMgl4Ac9DQf+cgr3E7o9L8DfqmHxxkjRNl9PJzvffIjwhifQL5svTtdixbKvCkB6Gf5J3HvQBz2LooiegqwF2fdU11b8AACAASURBVLMleXV1Nbm5ucyYMYOgIO/pBugDeHDSg+Q15fF52effeL+u7hMsliMMH/YYWq33Fqh+CQmE3nYr7Z98gvUsXbv+Wl5Pq8PFr4bHeXUXOTE9jOSx4eSsLaXHYv/G+0+uO45Rr/Va9tEpxi+6hJDoWLa99Qpul+v0N11O2PRLCB/uWZd4kcAFiSg6De3rSr163L5YtGgRPT097Ny5U5qmRuNH2rAf0dl5nLr6vv3E/gM5r+8TZ1LRUUF2bTZXj7garUQvk87tX+Csryf0uuukaQIc312L2y3ImC23fC1vywb0BiMjZ8oLbAB05dShizLilyivvbvL5SI3N5eRI0diMskrpbfZm2hu3kpszDI8XvZy2N/RTVG3jeskm2evOlRDl93FdVMlW6jlvApaP5h4s1zdIaAGkADqj1AbMgoAU2Mt8JX/0fEd24hITCYyKeWsUxue/jMaf38iH3pQyqmeldYyeOUiOPE5LHkKlj4LOnm71ioqKiqDQQjhBB4EPgfygQ+EEEcVRfmNoihXnBz2FGAGViiKckhRFClPUJXHWqg42szkS5LxD5Dna3fo89W01lYz9+Y7pZqCdmwoRzjcBF/qg1K9xKmQsQx2/Q0sX/n0CCFYv349JpOJOXO8/xBwxbArGBk6kmcPPIvd9VVQxeXqprj4aYKCJhAd7f0Sk4j/+R+0wcHU//F035qKHhsvVTZyTUwo4wO974sya/lwHHY3+1afHlTZVdzEpvwGHlgwjHCzd3dztTo9c2+9i+aqCnI3rT/9zYNvQtMJWPRr0Hr3d0gb6Efg/ESsR5uxFrd59djnIi4ujrFjx7J7927a2uTpRkddRmDgGIqLn8blkpd1dT5wPt8nzsaHJz5Eq2i5aoS85gcAbR98gC4yEvO8edI0hRAc21FD7PBgqR6Btu5uCrK/YPTsufgZjNJ0HQ3d2CssmDJjpJaRFRYW0tXVJd88u3YlQriIiV0uVfe16iYCtZpBdygdDEII3txdRnpsEJOTJJbN2bs81jMZV4JJrqfWUFADSE47NBWSbxpGvL+e1tISgoODCQsLo72hjpoT+YzuJfuo+8ABOjdvJvzee9FFfEv/6eW74KWF0FENt3wI0+/7zqS/qaioqAgh1gohRgohhgkhfnfytV8IIT49+f0iIUS0EGLiya8rzn3EoeNyufnigxMERRqZsECet0J3exu7P3yXlIlTSJ2UKU3XXt1JV04d5llx6CN9ZPi66JfgckDW77586ejRo1RWVrJw4UIMvXQ4HQpajZb/zfxfqjur+Xf+v798vbziZWz2ekYM/38oiveXQdqgICIefJDu7Gw6t2798vXfl9SiVeCnqb7ZqQ+LMzHmgjiOfFFDS20X4Oko8/u1+cQFG7hrtm98vIZnziAxYxw7V7yDtfNkmaLNAll/gKSZMNo3nXsCL4hHG+JP++oShFuewfTixYsB2LBhgzRNRdEwYsTPsdlqKS9/UZru+cL5eJ84G3aXnU+KPmFB4gKiAuS1s3fU1tK5fTvBVy+X2sin5kQb7Y09jJkjt915we7tOG02+ebZ++pAoxAwSd7/LUBOTg5ms5kREitd3G4n1VVvExIyHbNJnm6j3cFnDW1cFxOGSSdvE+1ARSvH6yzcOiNZanCQvA/B1gGZd8vT9AJqAKm1DISLffp4xgcaKS0tJS0tDUVROL5zOwDps78ZzRdC0PDnv6CNjCDstlsln/RJDrwJb1wBxjCP39Gwhd/OeaioqKj8B3FkazWtdd3MuWY4Wr282+TO99/GYbMy/9Z7pC1ghBC0rS5GE6Aj6EIfluqFpcG0++DAW1B9AIfDwcaNG4mOjmbSpEk+k50VN4vZ8bN5MfdFmnqasNrqKC9/kaioSwkJ8V2QLvT66/BLTaXhqacRDgf727v4pKGN7yVGEWfwXYbwtKWp6P217FpZBMDKA1Ucqe7g0UtG+czTQVEU5t9+L9ZOC9kfvet5cdsfPWWLF/3OZ5tail5L8KWpOGq7PA92kggODmbOnDkcO3aMsrIyabqhIVOJirqM8ooX6On5hgWQynnApvJNtNpauXbktVJ121Z+BEIQcs01UnWP7azBz6gjbbK8gIoQgtxNnxORmEzM8JHSdN12F1376jGOCUcbKK/Ko6mpiaKiIjIzM9Fq5QVUmpu3YLXVkOiFDqUD4d81LdiF4I54uYkZb+0uJ9Bfx5UT5QZDyXkVojIgaYZc3SGiBpCaPYus3boYhuHGZrORkpICeLqvxY/OICjymx+Mndu20bN/P5EPPIBGYotlwOMrsP6n8OlDkDoX7tkEEcPlnoOKiorKfyA9Fjt7V5eSmBFGynh5C5iGshJyt3zOxIuXSu0o03OkGXtpB0GLU9AYdb4Vm/8TMEXC2h+ze9cu2tvbueSSS9D00qDCWzw29TF6XD08s/8Ziov+hBAuhg97zKeail5P1GOPYi8pofHNt/jpiSqi/XR8P8m3D1rGQD8yl6RQfqSZvJw6nlx3nMlJIVw5wbemulEpaYxbsJiD61fTdnQbZD8Pk26FhCk+1TWOi8AvJYiODeW4rU6fan2dWbNmERwczLp163C75Rlbjxj+OKBQVPykNE2V/rPixAoSzAnMiJP3MPilefbs2fglyPNu6e6wU3ygkVHTotFL7EZWXXCM+pJCJl58mdRMke4DDQirE/NsuQGGvXv3otVqycyUl5UMUFn1Jv7+sUREXChN0+kWvFHTxNxQMyNM3s9K7o2mThtr8+q4ekoCJn8fr4O+TvV+qD0EmXd956qH1ADSyQBSiTGRCEsLAElJSTRXVdBcVcGoWd/sCiNcLhr/8gz6pCTp0X562uDf10H2czD9frjpAzDKqxFVUVFR+U8me1UJTpuLOdeOkJoFlPX6ixjNgcy8+kYpmuDZUW1fU4I+JgDT1BjfCxqC4aLf0lpdyPZtWxk9ejSpqb4pq/o6acFp3J5xO/lVH1NXv4rk5PswGn0fpAtcsADzwoW8cugYuZ09/Hp4PGYJKfnjFyQQGhPAb1ceobXbzm+XjUWj8f21PPv6W9H5+WH/8AGEnxkW/crnmoqiEHL5MNzdDjo2lvtc7xR+fn4sXryY+vp6DhzopQOdDzAY4khJ/h4NDWtpbc2WpqvSN8VtxeTU53D1yKvR+KA0tjcsmzbhrK0l9IbrpWkC5G2twuVyM36hvA0PgANrVmEwB5IxV17VhXALOndWo48345fsxSYTfWC1Wjl06BBjx47FbPZO98z+0Nl5gtbW3STE34JGIy+gsqG5nRqbgzslZx99kFOJ3eXmlhnyGqYAnuwjvQnGy/3d9QZqAKm5iG5DGO36QAw1lQQFBRESEsKJPZ4OGyOmzvzGlI41a7CdOEHkIw9LrTWmuRheXgSl2+Hyv8GSJ0ErMVKqoqKi8h9Mc3Unx3bWMG5BAmGx8gxBj27bTFX+EebccBsGiYvEjk0VuNpshFw5HEUrKVg27jrWGpajcdtZMv+b91dfcc/YO7gh3E2724+ExHul6SqPPc7LS5YzraFamiGoVq8hanE8e902LooOZUxcsBRdU0goSxePJEpUUZdygzRDUL94M6bpsXTuqsFeZZGiCTBmzBiSk5PZtGkTnae8nySQlHQvBkMCJwqfOM2kXeXb5Y2jb2DQGlg+Qp7hsBCC5ldeRZ+chHnBAmm6DruLI9uqSRkXQUi0vCqMtvo6ivZlM37RJej95WWo2IracDb2YJ4TLzXr6eDBg9jtdqZPny5NE6Cq+i00Gj/i4uR29Hutuol4fz2Lw+XcswBcbsE72RXMTAtneJS8znp0NUHeShh/LRjkBSW9hRpAai6m2pREosGP5vIykpKSUBSFwj27iBuZjjks/LThwm6n8W9/xz8jnaAlS+SdZ3EWvLQAelrgtlUwRW5NqoqKisp/OqGxJhbcMpqpl6VI0+zuaGfbW68QNyqDcQvlGYLaazrp3FGFaWoM/qnyFmv5x49TaA1lAbsJznlWmm5j7TuEax2826ywovATabpPdrux+xt48J9P0/XFF1I03W7BPw5VEKTTMqrQSmtdlxRdbBZS6lbS7A5n1Zaqrwy1JRB8cQoas57Wj4sQLjlBFUVRWLp0KQ6Hg/Xr1/c9wUtotQbS058kffQf5Jq9qvRKQ3cDn5V8xrLhywgzyGs93pOTgzUvj/A770SR6I9TkF2HtcvBpMVys48OrvsURaMw8WLfGPP3hmVHNZpAPQHj5GXGuN1u9u7dS2JiInFx8srmHI4Oams/Jjr6Cvz85F3LBV1Wvmjt5Pb4CHQSMmZPse5ILdVtPdw2M1maJgB7XgCnFWY8IFfXS6gBpOYi8g3xZPjrsFgsJCUl0VZXS2N5KSOmz/rG8LaPP8FRVUXUI4+g+Ni3AQAhYM+L8PbVEBQP926BlNm+11VRUVH5L0OjUciYHYd/gLzM0m1vvoy9p4fF935fzj0FTzp+28dFaIx6gpekSNEEsNlsrFu3jpiYGKZNn+5J3y7f5XPdnp4Kysr+SWTkJYSHz+Ufh/5BXZfvTZd3tFr4sL6V7ydFMcxkpO63T+Du8X0L9nf2VnCoso2fXjqaQH8d294tkJOpsulXKJY6lMv+THeHhR3vveF7zZNojDpCLh+Go7qTzt010nQjIyO54IILOHLkCIWFhdJ0w0JnEhQ0Tpqeyrl5O/9t3MLNbWNuk6rb/OpraENDCb7ySmmawi04vLmSqORAYofLs9CwdXeRl7WRUbPmEhgmL5DjaOjGdqIV84w4FJ28x+bCwkJaW1ulZx/V1LyH291DQoLcBlF/L6/HqNFwc2x434O9hBCC57KKSYs0cdEYCWX8p7BZYO+Lng6lkaPk6XqR/+4AkrUDOuvI9Ysn1t4NePyPCvd6FrQjpp0eQHLb7TT9618YJ0zANPeb3khex+WA1T+EdY/CiIvg7g0QmuJ7XRUVFRUVn1OWe5BjX2Qx7cqriUiUt/vVtacWe6WFkKVpaCQGy7Zs2YLFYmHp0qVoF/4fhCTBqgfB4bugihCC48d/hqLoGDny5/xs+s9wCze/2v0rnwZVulwu/vd4JSlGPx5OiyPml7/EUVlJ41//5jNNgKrWbp5cm88FIyK4YVYyM5YNo7qgjfxdtT7VpWwn7HsZZtxP2PSrmLTkcg5vWk/NiXzf6n4N47gIDKNC6dhQjrPNJk13zpw5REREsGbNGux2uzRdlfMDi93CioIVXJR8EYmB8jJybMXFdGZlEXrTTWiMRmm6ZXlNtNV3M3FRktQMuLwtG3BYe5hyqbxgGUDnzmrQKZimywswCCHYsWMHQUFBpKenS9N1uXoor3iZsNA5BAWOlaZb3mPj44ZWbosPJ9xPnjXL1hONHKvt4HvzhqGVmPXE/jfA2gZzfihP08v8dweQWooBj4G2uaURf39/oqKiKNyzi6jUYQRHRZ82vG3FCpy1tUQ8/JDvPzS7W+Ctq2D/azD7B3DDO+AvsTZTRUVFRcVnOKxWNr/8HKGxcUy/Sp6BorPFSvv6MvyHh2CcGClNt7S0lD179jBt2jQSEhLA3wxX/N1zH876vc90q2vepaV1J8OHP47BP4aEwAR+MPkH7KzeyariVT7T/X1xLeVWO8+MTiJAq8E0YzohN1xPyxtv0H3goE80hRD89KM8AP6wfByKojBmThxxI0LYuaIQS4vVJ7rYu+HTBz0bXAt/BsDs624mMDyC9c89i8PmI90zUBSFkCuHgxC0flQozR9Ip9OxdOlS2trayMrKkqKpcv7w4YkP6XR0cufYO6Xqtrz+Ooq/P6E33yRV99CmSsxh/gybLO/+4bTb2b92FQnpY4lOk9d12tlmpSunHtOUaLRmP2m6JSUlVFZWMmfOHLQSSxOra97D4WgmJfVBaZoA/6hoQIvC/Ym+7VJ6Js9nFRMbbGDZRN92KT0Npx12/xNSLoAEuZ31vMl/dwCp+WQAKSABqspJTEykq7WF2qICRk4/vUzMbbXS/MKLGDOnYJr1zdI2r9KQ7/E7qtwLV70Ii38NGnkfICoqKioqvmXb26/Q1lDH4vseQucnZ2Eq3IKWFScACF0ur8uczWZj1apVhIWFsWjRoq/eSJsPk2+D3f/wtLP1Mj09lRQV/YGw0NnEx33V3e6G0TcwJXoKf9r3Jxq6G7yuu6u1k1eqm7gnIYKZIV+Zokf9+FH0sbHU/r//h9vq/aDK+/sq+aKwiccvTSch1GNsq2gUFt6WjltA1lv5vgmqbP09tJR4AoJ+HvN5P2MAl9z/A1prq9nx7pve1+wFXZiB4EtTsZ1opWuv78sUT5GSkkJmZia7d++mrKxMmq7Kt4vdZeftY28zI3YGGeEZ0nQd9fW0f7KK4KuWoQuT51NTXdBKTWEbEy9MQqOV9wiZt+VzOpubmHH1DdI0ASxZlQAELpDXnUsIwbZt2wgMDGTy5MnSdF0uGxXlLxESMp3QkKnSdGttdt6vbeGG2DBi/OVlRO8ra2FvWQv3zU3DT2JpInkfgKUG5vxAnqYP+C8PIBXhRqE+IAFHTRXJyclfla+d4X/U9v77OBsaiHzoYd8uuk9sgJcXe3b07lgDE757rf1UVFRUVHqn9GAOhzeuY8ply0jMkOdj0rmrBntpOyFL09CFyetgs2HDBtra2li2bBl+ZwbLLnoCzDHwyQNeLWUTws2x/J8AGtLTnzztvq1RNPxm1m9wuBz8ZvdvvBpU6XK5+OHxClKMfvw0Lfa097RmE7G/ewJ7WRmNz/7Va5oANW09/G5NPjPSwrh52ukPO8GRRmYvH0ZlfivHdnjZH6hij2c3dcqdkHp6aX/S2AlMvHgpB9Z9SuXRXO/qngPT9Fj8h4fQvqYEZ7PvPadOsXjxYkJDQ/n444+x+iBAqHL+sbJwJQ09DdKzj5qeex4BhN9zjzRNIQR7Pi3BFOLPmLkSTZ3tNvZ8soKE9LEkjZ0gTdfZejL7aGoMuhB/abqlpaVUVFQwZ84cdDp55Vy1tR9is9eTmvJ9aZoAz1c04kLw/SS52UfPZRURZvLjhqnygoO4XbDjWYgZB8MulKfrA4YUQFIUJUxRlI2KohSe/DP0LGMWKIpy6GtfVkVRlp1873VFUUq/9t7EoZzPgGkuoskYQ4yfPwon/Y/27CI8IYmwuIQvh7mtVppeepmA6dMxTZ/mm3MRAnb+Df59HYSlwn1ZkCgvAqyioqKi4nt6LB18/q+/EpGYzJzr5ZlUOhq6aV9fhiE9jIDM6L4neImioiL279/PrFmzSEo6y0LNEAxX/gMaj8OGn3tNt6rqTdra9jByxP9hMHzzYScpKIlHJj/CtqptfFDwgdd0f1VU82XpmukspQemmTO/LGXryt7jFU2XW/CD9w/hEoI/Xj0ezVm8HMZcEE/C6FB2flhEe6OXgirWdlh5j8fLavFvzjpk7k13EBITy/rn/4q9p9s7un2gaBRCrxkJGoWWFScQbjmlbP7+/ixfvpyOjg6pXdlUvh26Hd28cPgFMqMzmRk7U5quvbyctpUrCb32WvwSEvqe4CUqjrVQW9xO5qUp6PTyqiIOb1hLV2sLs667Warn0pfZR/Pldpr7NrKP3G475eX/IjhoEqGhPq6y+RqNdgdv1TSxPDqUZKO8IN3hyjayChq5c1YKRj+JFT6H34XmQrjgx/Ad76A51Aykx4HNQogRwOaTP5+GECJLCDFRCDERWAh0Axu+NuTRU+8LIQ4N8XwGRnMRhcZEIm3daLVaQgPNVB8/9s3sow9W4GpqIvJBH0VlnTbP7uvGn0PGFXDXegiWd1NQUVFRUfE9Qgg2vfwcPRYLSx78X3mla043Le8XoPHXSC1d6+zs5JNPPiEyMpIFCxb0PnD4hTDzQdj3EhSsG7KuxXKUwqI/Eh6+gNjYa3sdd1P6TcyOn81TOU9R2Dr0DlqfNrTxVk0z30+KOq107UyiH30Uv5QUah57DGdr65B1/5lVxN7SFn575ViSw01nHaNoFBbcOhpFo7Dh5SO4nO6hiQrhafLRUQ1XvwKGoLMO0xsMXPLAj7A0NbLplefl+RKF+BNyxTDsZR1YtlVJ0QRITExkzpw5HDp0iPx8eQbiKvJ5O/9tmq3NPDL5EamBjca//wNFpyP8e/8jTVMIwZ5VJQSGG0ifFdv3BC/hsFrZu+pDksZOkJqt62z5drKPysrKKC8vZ/bs2ej18sq5amo/xGqrISX1QanX8l/K6rELwSPJ8ja1hBD8bm0+EWY/7pyTKk0XR4/H7zF+CmTINYL3BUMNIF0JnOrT+gawrI/x1wDrhBBytqHOhRC4m4s4bojH1NJEXFwcVUdzEcLNsClftUx02+00v/IKAZmZBEz1QUZQZwO8vhQO/xvm/xSuef1LDwEVFRUVlf8ccjet50T2DmZdexNRKWnSdNvXluKo7iR0+Ui0gXKCVm63m48++gir1co111zT92L4wl940ro/eQA6Bt8xzOnsJO/Iw/jpQ8lI/9M5F8MaRcMTs5/ArDfz2PbHsDoHX3ZU0WPjxwUVTA4K4PHUcz9gaUwm4v/yZ1ytrdQ+/tMhBVVyylp4dtMJlk2MY/nkcxuBBoUbWXjraBrKLWR/UjxoTcCzk3pkJSz4aZ9GoPGj0pl5zY3kf5HFka0bh6Y7AAImRWEcH0HHxjJsZe3SdOfNm0dsbCyrVq2ira1Nmq6KPNqsbbx25DUWJC5gYpS84glrQQEda9YQduut6KPklfyUHmqiscLCtKWpaCX6xRz8fDU9He3Muu4WaZoAHVsqQIHABfKyj9xuNxs3bsRsNjNlyhRpuk6nhZKSZwkOziQ8bJ403aJuK2/WNHFLbDjDA+SV1G/Ob2BvaQuPLBqJ2V9eiSB7XvBsuCz+zXc++wiGHkCKFkKcWunVAX2FEG8A3j3jtd8pipKrKMoziqLIC/N2NqCxWSgJSERXW0VSUhIl+/diCgklOnXYl8PaP/oYZ309EQ/c7/1zqMuDlxZ6/rz2dZj/OGj+u22pVFRUVP4TqS8pIuv1F0iZMJlpV14jTbc7r5HOXTWY58RjHBMuTfeLL76gpKSEJUuWEB3dj91FnT9c/apnl+7j+8DlHLCmEIKCgl/Q01PBmDHP4ufXt7lshDGC38/5PUVtRTy176kBawI43IL7j5UjBDyfkYy+H+2ADenpRD32GJ3bttH65uBMptu7HTzy3iESQgP47bKx/do5HjY5irHz4jm0qZKyvKZB6dJUCGsfheTZMOdH/Zoyffl1JI0dz5ZXX6CpsnxwugNEURRCl49AF2qg+d/HcXXapejqdDquvfZahBCsWLECp3Pg17LK+c3LeS/T7ezm4UkPS9VtfPavaMxmwu+5W5qm2+Vmz2clhEQHMHKavEyR7o529q5aQcrEKcSPktfK3l5loXt/PeaZceiC5T2W5uXlUV1dzaJFi6RmH5WV/wuHo5mRI/5PavbRb4trMGo0/Dg1Rpqm0+XmD+vySYswccNUiaWJ3S2w4y8w4mJImSNP14f0Ga1QFGWToihHzvJ1Wv6V8Gyh9bqNpihKLDAO+PxrL/8UGA1MBcKAn5xj/n2KouQoipLT2NjY12n3TXMRAMXGREI624iNiaHs8AFSJ01FORnEEQ4HzS+9hHHCBAJmerm+Of8zeOUiEG5PydqYq7x7fBUVFRWV8wJrZyefPfMHjMEhLHnwf7+8x/gaZ1MPrR8W4pcYSPAlKVI0wZOGv3XrVsaNGzcwH4fIkXDZ01C6Hbac3VPnXNTWrqCufhVpqQ8TGtp/v8JZ8bO4Y8wdfHDiAz4t/nTAuk+U1LC/o5unRiUOyMch9JabMS9cSP3Tf6b74MEBabrcgkfeP0iDxcrfbpxEoKH/DxyzrxlOeIKZza/nY2kZYNaVtQPeu8kT8Fv+Yr87xGo0Wi596FH8jEY+e+ZJHJJMpjUGHWE3p+PudtDyfoE0P6SwsDCWLVtGdXU1GzZs6HuCyneGuq463j3+LpenXc7wUHkt5buys+nMyiL87rvRBgdL083bVk1LTRczlqVJ7by24903cFitzL9VXrBMCEHbZyVoTHqCFskzV7bZbGzatIm4uDjGjx8vTbenp4rKyleJiVlGUJA83Z2tFj5v6uDh5Ggi/eQFyz7IqaK4sYvHLhmNXuK1zI6/eO6di34pT9PH9PmvJ4RYJIQYe5avVUD9ycDQqQDRufrhXgd8LIRwfO3YtcKDDXgN6HXFJ4R4UQiRKYTIjIyM7O/fr3dOBpDqjQkE2G1oerqwdXeRNuWrMrX2Tz/DUV1N+P3f815UVgjY/hS8fwtEZcC9WyBOrne4ioqKioochNvN+uefxdLcxOU/+AkBQXIW/m67i+Z38kGjEHbTaBRJZQdtbW2sWLGCsLAwli5dOvB756RbIPMu2PlXOPpxv6e1tx/geMEvCQudTUrKAwM8a3h48sNMi5nGr3f9mqNNR/s9b0VdCy9UNnJXfATLor/RR+ScKIpC3O9/hz4mhqqHH8ZRX9/vuX/ZWMDWgkZ+dcUYJiaGDEhXp9dyyb1jcbncrPtXHg67q38T3W745H5oLvZkTQ/Qq9EUEsqlD/2Ylpoq1j//rDQ/JL84MyGXD8NW2EbH5gopmgDp6enMnDmTvXv3kpeXJ01Xxbe0WFsYHjqcByYO/HNmsLjtdup+/Rv0iYmE3XG7NN2udht7Py0hKSOMtIleePbqJ7VFBeRlbWTSkisIT5AXyOk51Ii9vIPgi1PQGOSVN+3cuROLxcKSJUvQSKxEKSr+I6BhWNqPpWm6heDXRTXE++u5N0HeNWWxOnhm0wkyk0O5eIy8TDqaijzlaxNvgugx8nR9zFCv0k+BU59ktwOrzjH2Rs4oX/ta8EnB4590ZIjn03+ai3Bo9Lh1QZhNJurz89DqdCSP8wRzhMtF84svYsjIwDzPSzWhjh5YeTdseQLGXQd3rIFAeal7KioqKipy2fnBOxTnZDPvlruIGyknDV+4Ba3vF+Co6yLshlHo4ytw/gAAIABJREFUQuX4C9jtdt577z2cTic33HAD/v6DTP+/5I+QMA0++T7UH+tzuNVaS27eAxgMMYwd+1cUZeBdVfQaPU/Ne4pwYziPZD1Cc09zn3MOdXTz44JKZoWY+fXwc/sP9YY2JISEf/4Dd1c3VQ89jNtm63PO2rxa/plVzI3TErl5evKgdEOiA7jo7jE0VlrY8mZ+/4I5O/4Cx1fDRb+F1LmD0k0eN5G5N9/JiewdZK98b1DHGAymaTEETI7CsrmC7lwvZLH3k0WLFpGYmMiqVauorq6WpqviOzLCM3jvsveIM8trZd/y6mvYS0uJ+fnP0Bjk+cXs+qgIp9PNBdePlFbeJNxutrz6L0zBIcy8+kYpmgBum4u2daXoE8wETJEXYGhra2PXrl2MGzeOxER5ZVVtbTk0NKwlOek+DAZ5xujv1DaT29nD/0uLxSgxC+ipzwto6rTxf5elyyvVEwJW/wD0RrjwPyf7CIYeQHoSWKwoSiGw6OTPKIqSqSjKy6cGKYqSAiQC286Y/46iKHlAHhABPDHE8+k3ormIMmM8ZouF+Ph4Sg/mkDhmPH4GIwCWjRuxl5cT/v/ZO+8wqcrrj3+m993Z3itsgaV3FZCOgooFRey9xCT+jMYSG2os0USNGnvDEgsqqBEEURCRvsDuspTtvc5O7+Xe3x+zGk1Ehd0ZNc7nee5zYefOe+6wlznve95zvufKKwfnQbN3wEsnwr73wg/R6c+CInpOIEaMGDFiRJf9mz5j+8q3GDlrHmNPPCVqdu2fNOOp7iN+YSGakh/WARoMBEFg5cqVdHd3s3jxYgaUKSxXwlmvgEoPby4F5+EX/KGQl8qqqwiF3Iwa+QwKxZFlAX2TRHUij858FKvPyvWfX48/dHjNnF5/gEv2NZKilPNsWf6P0j06HOriYjL/8gDeykq67lz2vcGc/R12blhRwbhcI8tOGdhuZv7IZI45dQh1u3rYvfYHdIkOftS/+XUmTBlY5sWEk05j+PRZbFnxOjXbNg9orB/LV3pIyrw4zG/X4G91RMWuTCZjyZIl6PV63njjDWy26Il5x4gc0dSK8be1YXrqKQzz5qGffnSB26Oho9ZCzfZuxs7NxZimjZrdfZ+vp6u+lunnXoxKGz27js9aEOx+jCcPQTKA7/MjQRRFVq9eDYSDzdEiFPJy4OAtqFWZ5OVdHjW7HV4/d9V1MNWo5/QjzNgdCLuazLy6rZkLj8lnbG707LL3dWj6IiycbYhi1lMUGFAASRTFPlEUZ4uiWNRf6mbu//kuURQv+8Z1TaIoZomiKPzH+2eJojiyvyTuPFEUnQO5nyMhaKqjRpODpq+HBIMBS2c7heMmfnVf9D33PMr8fAxzZg/cWHs5PDszLDx59j9h2h/+JxTYY8SIESPGd9N2sJp1zzxGTtkoZl96ddQWHO49PTg2tKKblI7+uOjtkG/cuJEDBw4wd+5cioqKBj5gXEbYXzq64Y0l4Hf91yWiGGL//htwOKopK3sEvb54wGaHJw3n7mPvpry7nNs234Yg/ne7e1cwxLmVDVgCQV4eUUCycuClDnFz55L8299iW7UK01NPfec1bRY3F720gzi1gqfOG49KfuSZVv/J2Hm5FE1MY9v7DdSVH0aFoHUnvHMJZI2Dkx8b8PxFIpEw9/LfklFcypp/PEJ3Q92AxvvRduVSks4fhkyvwPTKfkK2H872Ggz0ej1Lly7F7/fzxhtv4PsRWWYxYkB4PdL953tBJiPtT7dEzW4oIPD5GzUYEtWMPzE/anadFjObXnuJzJLhDJs2M2p2fS12HJva0I5PQ5UXFzW7FRUV1NTUMHv2bOKjqGvV2Ph33O4Ghg17AJksOkE6URS5saaNkCjyt9KcqM2JfMEQN79XRWa8hhvml0TFJhDe+Fp7K+QeA2MviJ7dKPHrbPklhJBZmmjUZJPosiPaLQBfB5DcW7fira4m8dJLkMgGOEGregdeWhDeUb10HZQuGOjdx4gRI0aMnzF97a188Nd7iUtJ5eQ/3IJMHh2RSG+tBfM7NagK4zGeMiRqE7QdO3awadMmxo4dyzGD2XAiewIsfgE69sA7l36rM5soihyquZue3jUMHXozKcmDsNnTz4LCBVw3/jrWNK3h4V0Pf+s1vyBw6b4mqp0eninLZ4Rh8Cbfydf8hvhFizA99jiWt9/+1mtWt5+LXtqJJxBi+SWTSIsbnAxmiUTCrPNLySiM55OXqmk7aP72BX314QCeIQOWvgXKwfm8cqWSRdffijY+nnfvvxNzR3TKu2R6JckXlSH6QvS+uA/BHfjhNw0CaWlpnHnmmXR3d/POO+8QCv1I3akYv2ps77+Pc+NGUn77WxTp0ZO82PZ+PeYOF9OXFqNQDjxQ/WMQRZG1T/+doN/P/Kt+HzX/JfhDWN6uQRavwnhyYVRsAthsNtasWUNubi6TJ0+Oot29NLc8T2bmEhITj4ua3fe6Lazvs3NzYcYRNZsYKE9uqKeux8mfTxuBXhU9XSvW/im88XXy3/8nO6z/732iH4OjC6ngp0WdQaLLjq3+IEnZucSnhr+c+55/HnlKCvGLFv3AQN+DIITTvd+9FLLGw+Ub/qfEs2LEiBEjxn9j6+nmnT/fhkQq5bSb7kSjN0TFrq/ZTt8r+1Gkakk6f3jURLOrqqpYvXo1JSUlRyea/UOULoQTH4SaNbD6hrCmANDY9ATt7a+Rm3s5ebmX/cAgR87FZRdzTuk5LN+/nFeqXwHC4p/XHWxlo8XBQyU5zEse3B1jiURCxp/vQTdtGl3L7sLx2WcAeAMhLn9lFy19bp67YAIl6YP7TMmVMhb8ZhTGVC2rn66it6W/vMvZA6+dEf7zee+CfnAFT3XGBM740z0AvHvf7TjMpkEd/3Ao0nUkXTCMoMmD6eVqBF90gjlFRUUsXLiQ2tpaVq5ciSD8d3ZbjBhf4W9upvvue9BOmEDihdHLYGg9aGbv+lbKpmeRPzI5anYrPllD095ypp93MYmZRybQPxDsHzcRNHlIWFwcNeFsURT58MMPEQSBU089NWrC2aGQj/0HbkKlSqVoaPQy2nr9AW6va2d8nJbLoiicva/dxpMb61g0JpOZJalRs0vVO1D1Nky7HlKimPUURX6dASRbKwBmRTJpOi1dB6u/zj7y7KvGtWUriRdegFSpPLrxfU54+/xwt7VxF8D5q0AXvS/hGDFixIgRfZwWM+/8+TYCPi+Lb72HhIyjE1Y+UgJdLkwvVyOLU5J8yQikmuhMguvq6li5ciV5eXksXrwY2UAzdg/HpMth6h+g/CX4+Bba2l6lsfFRMtJPZ+iQmyJiUiKRcOPEG5mbN5eHdj3E24dWcHttO+92W7i5IJ1zMpIiY1ehIPvRR1CXldF+3R8wb97CFa+Ws6vZwsNLRjOlMDJ21ToFJ/9uDCqtnA8f34u1sRmWnwzOblj6JiQNiYjdxMwszrjlLrxOB+/eewcehz0idv4T9dAEkpaW4m910PfafsRgdII5EyZMYM6cOezbt4+PPvooap3oYvyyEAMB2m/4I8jlZD704MCrIX4kXleAT18+gDFNy3GLh0bFJoC5o53PX32B/NHjGDNvYdTseuusOLd0oD82E/XQI+tmORDKy8upq6tj7ty5JCZGR6cQoK7+AdzuOoaV3odcHp3NrZAocnV1M+6QwMOluciilFlm9wb4zeu7SdaruPPkKCZw9NXDh9dCzmSYHr3udtHm1xlAsoYDSE5Rj1GrQQiFyB89DghnH0kNBoxnn32UY7fAiyfAodVwwgNhvQD5UQaiYsSIESPGLwKX1cK7996Oy2rh9JvvIiWvICp2A10uep+vQqKQknzpSGSG6Pib2tpa3nzzTVJTU1m6dCkKRYTL9GbfAZOvprXlJQ7VLCM5aRalpfdFtMxBJpXxwLQHmJo1jRsPNfJCu4krc1K4Ni+yYphSnY6cZ56GvAKueGk7m2p6+cvpozhpVGQ1rfQJKk75/RhU2BBeOgXR0gTnvA05kyJqN61wKItuuB1rdycr7rkVtz06QtOaEckknFGMr9ZK3+sHEAPRCSJNnTqVqVOnUl5eztq1a2NBpBj/Re/jT+CtqiLj7rtRZESnQ5Yoimx8/SAeu5+5lwyPWulawO9j9eMPIVcomH/VtVErXQvZfJjfOog8WUPcCflRsQnQ1tbGmjVrGDJkCBMmTIia3a6uD2hre4Wc7ItIShqk7uI/gocau9hsdXJ/cTYluug0jxJFkRtXVNJh9fDEOWNJ1EVpHR70wYqLQCqHM14AWXTkC34KfpUBJMHaAoDLK0fqcSFXqsgsGY6/tRXHunUknL0EmV5/5AO3bIPnZoWDSOeugClXx8SyY8SIEeN/HEefibeW3Yy1u4tTb7ydzOLSqNj1tzvpfbYSJBJSLhuJPDE6k7NDhw7x5ptvkpyczPnnn486Gm2lJRJayoqpKdKTbPIxsicNqSTymVYKqQJDzk14DfPQ2FczStgelQVOKC6eB066gV0pxVxbtZIF/paI2wRIMHhYknsfcZIOPnbcRp9qbFTs5o4Yxal/vB1LRztv33ULLqslKnZ1E9IwnjoE7wEzpleqEfzRKWebPXs2kydPZtu2bXz00UexcrYYX+P4bAN9zz1H/OIziDthftTs7l7bTP3uXiYvKiQ1SkLSoijyyTOP091Qx/zfXIc+MTIZlv9lNyBgeu0Aoi9E0nnDkEYpWOZ0Onn77bcxGAycccYZUStdczoPceDgn4iPn8DQoTdHxSbAJyYbjzZ3szQjkaURytr9Ll7e0sTH1V3ceEIJ4/Oil+HFutugqxJOexqMOdGz+xPwqwwgeczN9Mnj0XgDuNqayBk+ArlCgfmVV0EmI+G884980D2vw8sngSoOLv8UhkavHWOMGDFixPhpsHZ18uadN+GyWjjj1rvJHTE6KnZ9LXZ6n6tEopSRetUoFKnR6aRSXV3NW2+9RVpaGhdeeCE6nS7iNkVRpKn5GWrr7iUlZT4jtYuRfvl4OE38G8Lag01AEPnDoVZe6bBwZXYi83QtLNt6J8url0fMJoRT7y96cScbG6z8eV4hi+Qm2n5zDY716yNqN5xBPR+5rR7PSS/TxVhWPbyHnubolJXljx7HaTcvw9bbzVt33YKjLzqaSPopmSQsLsJXZ8X0UjWCL3LP1FdIJBJOOOEEpk6dyq5du1i1alVMWDsG3gMHaL/hBtRlZaTfemvU7Dbs7WXb+w0UTUhl7LzcqNnd+cG7HNi8keOWnM/QCdERkhZFEcuqOgKtDhLPKkGRHnkfBhAKhVixYgVut5slS5ag1UbHZweDDiqrrkYu1zNyxONIpdHJimn2+PjdgRbK9GruK4qeptXW+j7uW32AOcNSuXxa9ETR2fUi7HgWplwDJSdGz+5PxK8ygOSzNNOmTiPe48Td3kzeqHGE7Has775L/IITUaQdgdCWEAq36Xv/N5B/HFy2HpIHoYVxjBgxYsT4WdPdUMeby27C73Fz1h33kV0anTp7z/4+TM9XIdUpSLlqFPIkTVTsbt26lRUrVpCVlcUFF1yARhN5u6IY4lDNMurrHyQt9SRGlP0d6cJHYNoNsHs5rLgQAt5Bt+sKhriwqoE3Os1cl5fGsqE5PD7zMebnz+evu/7KgzsfRBAHP2uk2+7lrKe3srPJzKNLxnDerGHkvvwSqtIS2n5/LeZ//nPQbYYNV8ML88DVC+evxDBxIaf9YRxypZSVD++hqSo6wZzcEaM4409347L08c/brqenqSEqdnUT0klcUoK/2Ubvs1WE7L6I25RIJMyZM4dZs2ZRWVnJW2+9hc8Xebsxfp4EuntovepqZHFxZD/5D6RR+H4F6G118MlL+0nNi2PWBcOiVkLWsHsnX7yxnJJjpjH5tLOiYhPA+WUH7vJuDLNz0YyIjj6tKIqsXr2a5uZmTj75ZDKiVJYYCvmorLwKr7edkSOeQKWKjpB0rz/Akop6JMDzZQVoZNEJNxzotHPFK7vIT9LxtzPHRO1Z5tDH8NH1UDQP5t4dHZs/Mb/KAJLU1kabOo1MMYREEMgfPRbrihWIbjeJF1304wfy2uGNs2HrEzDpSjj3XdBGMVUuRowYMWL8JNTt3Maby25CKpOx5M77SSuMvOCoKIo4NrfT9+p+5KlaUq8ajdwY+fIxQRBYvXo1a9eupbS0NGpla6GQm8qqq7/utlZW9kh491Qigdm3h7uzHfwIXj0VnL2DZrfXH+C0vXVsNDt4qCSbmwozkEgkKGVKHpz+IOcOO5dX97/KTZtuwhscvOBVbbeD05/cQqvZzYsXTeTUsWERdnlCAnkvv4z++OPpvvseev72N8TBLHmq/wxe6t8xvXgN5B0LgDFNy+IbJ2BM1bD6qSqqv2gfPJvfQ3ZpGUuW/QWAN++8ica95VGxqx2TStIFZQR7PfT8Yy/+DmdU7E6fPp0FCxZQW1vLSy+9hM0WHQ2oGD8fQk4nbVdfjeBwkPP0UyhSo7PQt5s8rH6yErVWzoKrRyKPUilX28Fq/vXoX0jNK2T+1dHTPXKVd2P7VwPq4UnEzY5eptX69espLy9n6tSpjB4dnSxlQQiyr/r3WKzbGD7sQYzG6OgtOYIhzqlooNsX5NVRhRRoVVGx2271cNFLO9Cp5Cy/ZBLx2ijpD7WXwzsXQ/ooWPwSyKLTxOSn5tcXQBJFtI52OpSpJLpt6BOTSEhNx/zqa2inTEE9bNiPG8fcAM/PCU+8TnoEFjz4q3loYsSIEePXiiiK7PzgXd7/270k5+Rx7r0Pk5ybH3m7QQHr+/VfT35TrhgVFcFsj8fDG2+8wY4dOzjmmGM466yzUB5th9IjstvCrvIlmEwbKC5eRtHQm5FI/mPKMvlKWPwidOyFZ2dAx54B2y23uZi3q4Zal5eXRxZwfua3d6ilEik3TbyJ68Zfx9qmtVyw5gI6nB0DtrumqpNT//ElvqDAm1ccw/Tib7c6lmo0ZD/+GMYlS+h77nnarvktIfsAS8tEEbY8Dq+dAXFZcOk6SPt2Fp3OqOK068eRMyyBja8f4ou3aghFoWNZan4h59z7N4xp6az8y13sXvNBVMSmNaWJpFw1CkTofboSz/6+iNsEmDRpEkuXLsVsNvPcc8/R3h6dYF2Mn56Qw0HrpZfhrakh8+G/oS6Njoae3eRh1cN7CPhCLLxmFLr46Cz0O2oOsvKBZegTkzj9lmUoVNHR7nNX9mJ5pwZVkZGkpaVIpNEJWn3xxRd8+eWXTJgwgdmzZ0fFpigKHDz4J0ym9RQX3UF6+qKo2PWGBC6uauSAy8PzI/KZEB+d8kCT08eFL+7A7Q+x/JJJZBqjk71HdzW8fla40/o5b4PqKPSTf6H8+gJIbjPKkAeT1Ii/u5O8UWNxrF1HsKuLxIsu/HFjNH4RFst29cD5q2DCJZG95xgxYsSI8ZPjdTn58OH72fT6SxRPmcpZd96PzpgQcbtBi5eeZypxbetEPz2bpHOjI/rZ0dHBM888Q319PQsWLGD+/PlREf00mTawY+civN42Ro96lpzs79ElHHE6XPJxOCvpxROg4s2jsimKIsvbTZy6pw6FRMKH44qYlxz/nddKJBIuGXEJj896nFZHK2f/62y2d24/KrshQeTBjw9y9eu7KU438K/fTWVk9mHsyuWkL7uTtNtuw/nFFzSeeSbempqjsovfBe9dERb9LD0JLv0EjN+9I69Uy1nwm1GMmpVN5YY2Vj28B6cl8qVWhsRkzr7rLxSOm8iGl5/lX39/EL/HHXG7ykw9qb8dgzxFQ98r+7GuaUQMRT5oVlxczKWXXopMJuPFF19kx44dsQ5t/+OEHA5aLrsMT3U1WY88jGHGjKjYtfd5WPXIHvzeIIv+byzJ2dFp6d5VX8t799+JNs7ImXfcGxX/CeGyb/Obh1DmxZF0/nAkiugsf7ds2cKnn37KyJEjWbBgQVQyrQQhyMFDt9HZ9S4F+b8nJ+dHrm0HiDMY4rzKBjZbnTxSmsvspOgIsbdbPZz19FbaLG6eu2ACJenReZZp3w0vLwSZEs5bCYbIdmf9ufHrCyDZwp1MrMQjOCzkjRqL+eWXURYUoJ8+/Yffv/OFcLq8Pg0u/wwKpkX4hmPEiBEjxk9NV10Nr918LfXl2zn+/Es56dobUSgjv2PrOdBH92N7CPa4STx3GMYFBRHfORVFkZ07d/LCCy8gCAIXX3wxkyZFtpU7gCD4qav/KxWVl6FWZzNp4vskJ8/84TdmjoErNkL2RFh5ZTgw4v3xZUC2QJBrDrRwU00bUxP0rJ1QzAjDDwucHp9zPG8sfINEdSJXfHIFj+1+jIAQ+NF2W81ulj67jSc31rN0Ui5vXjGF9Pjv342XSCQknncuectfRnC7aVpyNpa33z6yQEPHHnhmOlStgFm3wVmv/ODOqUwmZdpZxcy7tAxTu5O379tBc3Xks3OUGi2Lrr+VaedcRO22L3ntT3+Iii6SLE5F6lWj0U1Ox/l5G73PVRG0RT5olpaWxhVXXEFhYSGrV69mxYoVeL2Dr/EV46cnaDbTcsmlePcfIPvvjxI3d25U7Jo7Xax6eA9+T5BTrh1DSm50FtzNlXtZcc+tqHR6zrzjXgyJ0dEfcm7vpO+1/Siy9CRfVBaVzRdBEFi3bh3r1q1j+PDhnHrqqVHZfAmFvOzb91s6Ot4iP+9qCgp+H3GbACZ/kDP21rHV5uTxYbmcmR4dOZeGXidnPrWFXoeP1y6dzJTCKHV6a9kGrywClQEuWQPJkZcw+Lkh+SXubkyYMEHctWvXUb03VP0+shUXcE3m7RRs3MzFv7mensuuIH3ZnSScffb3vDEAH98MO58Pi2Sd8Tyov3uXMEaMGDF+KiQSSbkoitEpdv8ZMxA/8U1CwQDbV65g+8q30SUkcNK1N5FZHPkSA8EbxPqvBty7ulFk6Eg6dxjy5MinZdtsNj744APq6+sZMmQIp59+elQ6rTmcB9m//waczgNkZpxFcfGdyGRHWNoQCsIXf4XP/wLx2XD6c5A75Xvf8oXZwbUHW+j2B7g+P51r89KQHeEusSvg4oEdD7CqbhVlSWXcP+1+CuILDnu9KIqs2tvOHauqEYG7TinjjPFH3qUm0N1Dx8034d66Df2MGWTcczfylJTDv0EIwZbH4LM/gy4VTn8GCn7Extl/YO5wsfb5fZg7XJRNy+TYM4aiVEe+hL+1upKPHnsIj8PBMWeczaRTz0Qqi/xi0L23B8t7tSCVYDx5CNpxqRHPJBAE4evshbi4OE455RSGDBkyaOPH/ESYwfITR4r3UA1tV19NsK+PrEcewTDrRwTKB4GW/X2sfa4amULKSdeMIjUvOlkilZ+u5dMXniQxK4fTbrqDuOTIazyJooh9XTOODa2oSxJIPGcYUlXkvy+CwSCrVq1i3759TJw4kRNPPDEqwaNAwEZl5ZVYbbsoLrqNnJyLIm4Twt3Wzq1soM3r59my/MNm7g42u5rMXPVaOaIIyy+ZxIisKK3Jq96B938L8VlwwQfh8/8o3+cnfnUBJPPnj5K44U4uT7mH49samOGV4PpyC0UbNyA9XEtFtxlWXASNn8Oxv4M5d4E0OkJzMWLEiHEkxBYGYQZjYdDdUMfapx6lt6WJYVNnMPPiK9HoI79b662xYHm3hpDdj+H4bOJm50U85V4QBCoqKvj4448RBIG5c+cyYcKEiE98BcFHc8vzNDY+gUIRR2npfaQkD1AnomU7vHd5uC39xMvCgtv/seFjDQS5v6GT5R19DNWqeHxYHmPjBtZW+ZPmT7hr6114g16uHHUlF5VdhEL2bSHPNoubZR/sZ/2BbibkJfDIkjHkJB69XVEQsLz2Oj1/+xtSjYbUm24i/tRF/x3g6KyAD68NZx8NXwQnPTqgph/BQIjtHzSyd30LcUlqZpxbSs6wyO86exx2Pn3xaQ5t2URaYRHzr76WlChokAVNHszv1OBvsqMuTcR42lDkUdCMaW1tZdWqVfT19TFu3DjmzZs3KAL2MT8R5qcIIDk++4yOG/6IVK8n+x9PoBk5MuI2RVGkamM7m1fUkpihY+E1ozAkRqERQjDI5jdfYdeH75E/ehwn/d/NqKLQvl7wBrG8W4unyoRuUjrGRUORyCJfPma1WnnnnXdoa2tjzpw5HHfccVEpW7PbK6na93t8vi6GD3+I9LSTI24TYJ3Jxu8OhCt7lo8sYIox8hpAoiiyfEsTf/7oAFkJGl64cCJDU6OgPRQKwvo7w42zco8NZ+7qv2fD5n+AWADpG7SuvI6EfW9wo+T3nJUWT9qjT5F4wQWk3XTjd7+h91C405qtDU7+O4w5ZwB3HiNGjBiRJbYwCDMQP+Fx2Nmy4nUq1q1BazQy57JrGDph8iDf4X8TNHuxftSAt7oPeaqGxDNLUOZEPmDV0dHB6tWraWtrIzc3l1NPPZXExMgHA/r6vuBQzTI8niZSUxdQUnwXSuUg2fU5wpk2258Jl5yfcD+UnYYIrOi2cFddB5ZAkMuyU7i5MAPtILUZ7nH38MCOB/ik+RMK4wu5bcptTEyfSCAk8OLmRh5dXwvAdXOLuHRqIbJBKkf01dXReetteCoq0EwYT/odd6AuLg53i/38L7DtSdAmh/8dRpwR1owaBDpqrXz2ygFsvR6GjEtl6plD0SdEfmF6aOtmPn3hSbwuJ2PmL+TYM89FrYvsIkIURJxbOrCvbQIJGGbmYpiWhUQe2SBrIBBg48aNbNmyBZ1Ox+zZsxk9evSAgrsxPxEmmgEkweul95FHMS9fjnrkSLKfeAJFWuQzcTxOPxtfP0TDnl7yRyYx99KyqGQMWrs6+ejxh+iqq2H03AXMuvjKqGQM+lrsmN88RMjqJX5+Pvrp2VEJ4hw8eJBVq1YhCAKLFi2irKzsh980QERRpK39NWpr70OpTGLkiMeIjx8XcbtBQeTBxk4ea+lhlF7DcyPyydNEPqDu8Aa44/1qVu41XCvuAAAgAElEQVRpZ86wVP521hjiNVHotmZrg1VXQ+MmmHQFzLsX5JFvJvJTEwsgfYOmF0/Ha2rg1e7jOdMQh+qtdxnyyTqU2d+RPl67PtyaT66CJa9DbuQXEDFixIgxEGILgzBH4ydCwSAVn6xh64rX8XncjJ57IseddT5qfWQXpoI3iGNTG45NbUgkEgyzcjFMzYp41pHNZuPzzz9n9+7d6HQ65syZM+CF6Y/B6TxEfcPDmEzr0WjyKSleRlJShPQE28vhw/+Drkq2DD2bewuvpNwnZXyclr8UZ/8oraOjYVPbJu7bfh9tjnZKlUvpbJ1AuyXA3OFpLDuljKwIdIkRBQHbe+/R89BfCbkcZJ5RRJxmLxKvBcZfDHPuBM3gi9YGAyH2ftLCrjXNSKQSxs7JYcycXJSayC5SPQ47X771GhXr16AxxHHsmecyctZcZPLILiiCZi/WfzXg3d+HLEmN8cQC1GVJEV+ktre3s3r1atrb28nMzOTEE08kJyfnqMaK+Ykw0QogefZV03HzTfjr6kk45xxSb/wj0kHIJPshWqr7+PSVA3idAaYsGsKYOTlR0dCr3riez15+FqlMytzLf0fJMVMjahPCnUodn7dh/7QFWZySxKWlqKJQoufxeFi/fj3l5eVkZGSwePFikpIir8Xj8bRzqOYO+vo2kpQ0g7Lhf0WhiLwoeaXDzfUHW6lyejg/M4l7hmahHqQNmO9j46Ee/vReFZ12L3+YU8w1M4cijXQnPVGEPa/C2ltBCMKCh2DseZG1+TMiFkD6Bm2PTOSQ1Miu6kxO3leLYew4cv7xxLcvEkXY9hSsuxVSy2DpPw/bnSRGjBgxfk7EFgZhjsZPmFqaeOXG35NTNpKZF15OcoRLYwRfCOeWdhyb2hE9QTSjU4hfUBDx0hin08nmzZvZuXMnoigyceJEZsyYgUYTWY0lt7uZxsbH6Op+H5lMR37eVeTmXoJUGtnPu9fq4C9Ve9gQjCPD18MfveWcPWE+0uzI7dSKosi6A+0s+9dOOs1KpKouZo2xc+fsxeTEHd2i/0cRChDavhxx3T3IseLqVePLO5+4y/6EPMJZZXaThy3v1VG/uxe1TsG4+XmMOD4LRYR1R7ob6tiw/FnaD+4nLiWNYxYvZfi0mRHPdvDWWLD+q55gjwdFpo64uXmoSxMjGkgSBIGqqirWr1+PXq/niiuuOCp7MT8RJtIBpJDVSu+TT2L55xvIk5LIuPde9FOPi5i9r7D3edjybvj/YmKmjjkXDyclCtmsPU0NfPri03Qc2k/28BGceM31xCVHvszHW2vB+kE9wV4PmlHJJJxWhDTCAWxRFKmqqmLt2rW43W6mTJnC7Nmzkcsja1cQgrS1Lae+4REAhhT+gZyci5BIIhvEcQVDPNzczdOtPSQp5NxXlM1JqcaI2gTocXh5YM1B3tvdTlGqngcXj2JsbhS69/UchLW3QP1nkDcVFj0OiYWRt/szIhZA+gaOP2fzkXEq7pp4jlm3idyXXkR3zDH/viDoh4/+EI44lp4Epz3zg91JYsSIEePnQmxhEOZo/URPUwMpeQURXQSG7H6cWztwbe9EcAdRlyYSNzcPZVZkfY3JZGLr1q1UVFQQCoUYPXo0xx9/PAkJkZ2M2ewVtDQ/R0/vWqRSBTnZF5KXdyUKReQmn6IostHs4ImWHr60OjHKZfwuK4FLWt5Cs+3v4S5thTNh6nVhIelB+n0HQwKr93Xx1MZ6DnTayTJquGJGJh2S91lR8xYhMcT8/PlcMuISShMHUYzd74K9/4QvHwt3m00bQWD0b+h5bw/2f32ERK3GePrpJF5y8XdnXA8iPc12tr/fQMt+MyqdnJHHZzNyRjbauMil/IuiSFPFbr5861W6G+qIS0lj/MJFjJg5F6U6coFRMSTi3tuD/dMWQmYviiw9hqlZaEYmR7S0zefz4XQ6jzrTIeYnwkQqgCT4/VjfXoHp8ccJORwYzzyT1D9chyw+skK/PneAvZ+2smddCxJg3Al5jJ2Xi1wR2WCq3dTLjlVvU7l+LWq9nmnnXsSI4+cgiXA2a6DLhf2TZjzV4WzAhFOGoC6JfPl1Y2Mjn332Ga2trWRlZXHSSSeRkZERUZuiKNDTs5qGxr/jdjeQnDSL4uJlaDSRFXH2CwKvdvTxSFM3pkCQczMSuX1IJkZFZANlDm+A5zY18NwXjQRCAlfPGMJvZw1FJY9wGaSjGzbeB7tfAaUeZt0e1lKMghD6z41YAOkrfA64P5snU5aQs6Gb4d4QhR9++O+FgrMX3j4fWrbC9Bthxi2/ygcmRowYv1xiC4MwP1V3ncMhiiL+JjuuHV24K3tBEFEPS8IwIxtVbuTS7EOhELW1tezevZuamhpkMhmjR4/m2GOPJTk5cm2UQyEPPT2rae94C5utHLncQFbWueRkX4hKFTndD0sgyIouM6929FHr9pGuVHBFTgrnZyZh+Gri6bXDrhdh6z/A1QMppeEJ4qgloD6630WnzcObO1p5c2cL3XYfQ1P1XHX8EBaNyUTRn97f6+7l1f2v8taht3AH3YxNHctZJWcxL28eStlRBld6a2DXC7D3DfDZIHsSTL8h3C22f27jq6+n74UXsX34IYRC6I8/HuOSs9BPm4Ykglk6nXVW9nzSQmOFCZlCStH4VIZPzSR9SHzEArSiKFK/azs7P3yPjkP7Uel0jJgxhxEz55GckxcRmwBiSMC9uwfHpjaCvR6kcUr0kzPQjk9Dboy8NsiREvMTYQbbTwguF5YVKzC/9DLB7m60U6aQdsvNqEtKBs3Gd+Fx+qn4tJWqDW34vSGGTkjl2NOHRlwo29bTzY73V7Bvw3pAZNScEznurPMiXvbt73Di2NCKp8qERCXDMD0bw/TsiJZ9i6JIY2MjmzZtoqmpCYPBwPHHH8+4ceMiWvYtCEF6TetoanoSp/MAOl0RQwpvIDl5dkQ3ulyhECu6LDzZ0kOL188xRh23FWYyPj6yXVn7nD5e29bC8q1NmF1+Fo7K4I/zSshPjnA3WEsTbH0ynEAS8sOES+H4G0EXuXnSz51YAKmfQNc+FE8fx18SLuLYV79gxPV/JGHp0vCLXfvgjaXhyeSif8DIxYN81zFixIgReWILgzA/lwBSsM+Du7IXd3kPQZMHiUqGdlwqhuOykCdHJjNCFEU6Ozuprq6moqICp9OJXq9n3LhxTJo0CX2EJveiGMJq3Ul3z0d0d39IMOhAqy0gK/McMjPPQi6PjF2fILDR7GBlt4WPTTa8gsi4OC0XZCZxWloCqsNN7gMe2Pce7HweOnaDXAOlC2HUWTBkFsi+X0vH6QuyrrqLDyo6+KLWhCCKTC9K4bwpecwuTT2sPoPNZ2Nl7UpW1KygxdFCvCqe+XnzWVC4gLGpY5H+UCmCywTVK6HyLWjbCVIFlJ0aDoLlTD5sNlWguxvL6//E+t57hEwm5BkZxJ+0kLiFC1GVlERsMWLpclHxWRs1O7oIeEMkZOgomZzG0PFpxKdELjuoo+Yg5avfp27HVoRQkIziUoZPm0XRpGPQGSOTdScKIt5aC87N7fhqrSABVVECurGpqIclIo2CePGPIeYnwgyWn/DV1mJ9511sq1YRstnQTp5M0hWXozv22IgGS7sb7VR/0U7drh6CQYEhY1MYf0I+KbmRK1cThBDNFXuoWL+GhvKdSKRSRs6ay6RFZxKXErnNATEo4NlnwrmtE3+THYlKhv64TAxTs5BqI6d75vP5qKysZMeOHfT29qLT6Zg2bRrjx49HoYigXb+Jzo53aGt/FZ+vC40mj8KCa0lLOwmJJHKB/0a3j9c7+3itow9rMMQYg5abCtKZkWiI6LNc1W7jjR2tvLe7DV9QYGZJCv83p5jROREskxNC4Q7ru1+B/e+DRAojz4RpN0Dy0MjZ/YUQCyD10175AVnvnc9dmss48+2NlHy+CZleBwc/gncvB5UhrHeUNT4Cdx0jRowYkSe2MAjzUwWQREEk0OnCe8iMp8pEoNMFgLIgDt2EdDQjk5EqB3/yFwqFaG1tpba2lgMHDmA2m5FIJBQVFTFu3DiKioqQRSDbJBTyYrFsxdS3kd7etfj9vUilGlJT5pGZuQSjcVJEJp32YIgNZjufmOx80mfHFgyRqJBxSmoC52cmUaY/wsBEeznseR2q3wOPJSw2XTQfSheEg0mq8IKs1+Fjw8EePjnQzRe1vXgDAllGDYvGZHL2xFxyk368KLcgCmzv3M7K2pVsbNuIJ+ghTZvGzJyZzMiZwcT0if/OTLI0waE1cGg1NH0JYgjSRoQnu2POPaJ2wmIggOOzDVjffQfXl1sgFEJZWIhh9mz0M2egGT06IplJfm+QuvIeDnzZSVeDDYDUPAMFo1PIG5lEcrY+Is+K225j/6bP2LfhE/raWpBIpOSUjWDIhGMoGDuehPTMQbcJYbFtV3k37l3dhGw+kEtQFyWgKUtGXZyALIIlfT9EzE+EGYifCHR24vjkE+wfrcZTUQEKBYZZs0i86EK0Y8cO8p3+G3Oni/rdPdTu6sHS6UKhklE8KY1Rs3JIzIhMloYoinTV13Bo62Zqtm7G0deLNt7IiJlzGT13QcR0jkRBxNdgxVNhwlNtQnAHkSWp0U/OQDchLWKBo0AgQF1dHVVVVdTU1BAMBsnIyGDy5MmUlZVFLHAUDDro7V1PV/f7WCxbEMUQCQnHkpNzEclJMyIWOOr2BVhtsvFOl5lyuxspsCAlniuyU5gYr4tY4KjR5OLjfV28t7uN2h4nSrmU08dmcenUAorSIhQEFUXo3AsHPoSKt8DeBup4GHcBTL4a4iNbEvhLIhZA6mfvx39lzLZ7uMdzHpdpU8m44w7Y/DB8eg9kjoGz/wlxkZlIxIgRI0Y0iC0MwkQrgCSKIqE+L75GG75GG94aC4IzAIAy14BmZDKakcnIjYNbSiAIAj09PTQ3N9PU1ERDQwM+nw+pVEp+fj5lZWUMGzYMrXZwu4wJQgCHoxqLdTtWyzYs1h0IghepVENS0nTSUheSnDwTmWxw7XpCAuV2F19anGyxOim3uwiKkKiQMScpjlNTE5iWYEAx0K4sQT/UrQ/vRtauxeb2sZthbNHOZEtwGPsdGkQgM17N3OFpnDImk3G5CQOeYLsDbja0bmBt01q2dW5D63UyJSByIlrGOG3EO3vDF6aUQsmJMGIxpI8Y2GcFghYLjrXrsH/8Me5duyAYRBYfj3byZLSTJ6GbPBllYeGga5nY+zzUlfdQX95DT7MDAJ1RRU5pApnFRrKKEzAkqQd14SKKIn2tzRzatplDWzdj6WgDICEjk9wRY8gePoKc4SMHPTtJFET8LXY8VSY8VSZCdj8AigwdqiIjqoJ4VHlxEc2i+E9ifiLM0fgJT9U+uu6+G29VFQCqoiLiTzuN+EWnII9A962AL0RHrZXWA2ZaqvuwdLlBAhmF8ZRMSadoYhrKCGS2eZ1OWvbtpalyD00Vu3GYepHK5OSPHsvw6bMYOnFKRDoehuw+vLVWvDUWfLUWBHcQiVKGZngi2nFpqIYaB72TnCiKmM1mGhoaqK2tpbGxkUAggFarpaysjNGjR5OVlTXogRRRDOF01mC2bMZk2oDNVo4oBlGrs0hPO4X09FPR6QY/E8YvCFQ4PGwyO1jXZ6PC4QFgmE7N4vRETks1kqke/AC32x+kvNnC5joT6/d3U98b3mCbkJfA6eOyWTgqg3hNBL4H3WZo/hIaNsKhj8NBI4k0vDk05hwoWQiKyHdF/KURCyD1s3n5VUxseofl1dO54M67UB98DKreDk/EFj0Bish2oIkRI0aMSBNbGISJRABJFEVCNj+BLheBNgf+dif+NgeCIxwwkurkqIYmoC4OHzLD4EzABEHAYrHQ09NDR0cH7e3tdHR04PV6AYiPj6ewsJCioiIKCwtRD1J7aEEI4HY34HQewuHYh82+F4ejGkEI29XpikhIOIbkpJkYjZORyQZH68UTEqhxe6l2eqiwu9ljd7Pf5SEoghQYZdAyLUHP3KQ4xsfrkA3SpN7pC3Kw087+TjtVbTb2tFio65/gKiVBxksOcZx0H7PklQzLMCLJmQjpoyBjVDiwIz/Kz+82Q1cVdFVCx16E1u1Iba3hfwupjB0qBds1arbqjcRljGFUyihKE0spTSwlLy4PuXRwFo8hhwPXl1/i3Pg5rh3bCXZ0AiCNi0MzciSa0aNQlZaiLi1FkZ09aEEll81HS3Ufzfv6aK+x4u0PwGrjlKTmx5FWEEdKjoGkLB06o2rQFnGWrg4a95TTtHcXbQf3E/CGF1HxqWmkDykmfWgxqflDSM7NQxs3OOLHotifoVhjwXvIjL/FASERJCBP1aLM0qPMMaDI1KNI00as5C3mJ8IcjZ8ItLfTdu3/YZg3D8PcOagKCgbtfoSQgLXHg6nNQVeDne4GG6ZWJ4IgIpNLyRgaT+GYFArHpKAbRG2tYCBAX1sLvU0NdNQepOPQAfraWgBQarTkjhjNkAmTGTpxCmrd4JUiC/4QgU4XgQ4n/hYHvmY7IXPYv0j1irAvHZaIpjQRySAKgXs8Hrq6uujs7KStrY2WlhacTicARqORoqIiSkpKKCgoGNTMXb/fhN2xD4c97E9ttl0Eg+EAul5XQlLyTJKTZhIfP27QuqqJoki7L0Clw02Fw0O5zUW53YVHEJEA4+O0zE2KZ15yHMOONHP3exAEkWazm8o2KxWtNva2WqhssxEURORSCVMKk5gzLJXZw9LISRzEDaegH/pqoX13uDS9bWdYpgYRFNpw84zShVA8/1etb/RjiFgASSKRnAksA4YBk0RR/M5vYYlEcgLwd0AGPC+K4gP9Py8A3gSSgHLgfFEU/T9k92gXBl8+tpBMdwOdFWUcO8scTlmfdTtMu37QOrDEiBEjxk/JL3FhcDgf8Y3XVcArwHigD1giimLT9415tH5CFEUEV4CQxUfQ4iXY5yFo8hI0eQh0uxC9of6bAnmyBmW2AWV+HKqCeOQpmqNe3AqCgNPpxGq1YrVaMZvN9PX10dfXR29vL4FAf5BKKiU1NZWsrCxycnLIz8/HaDx6jQBBCOLzdeP1tuLxtuFxN+FyN+J2N+B2NyKKX9lVYTCUERc3mvi4sRgTJqNSHv3kyycIdHgDtHj9tHh9NLr91Hu81Ll8NHl9hPqnJnqZlDEGLePitEyI1zHFqCduAF1Y3P4gHVYPrWYPLWY3TX0u6ntd1Pc4abd6vr4uQatgbG4CY3OMjM9LYFxeAuqADVq2hSekbTvDE9RAOMCERAaJBZBcAslFkJDff+SBIROCXrC1gbU5XI7WVw+mmvDh7P73DRoyIXsC5EwK6xlljqXXZ6W8u5yK3goqTZUc6DtAQOjPcpMqyYvPoyCugPz4fHIMOWTps8jWZ5OiTTnq4JIoigTa2nDv2IGnohJPZSW+mhoQBACkWi3KwkKUBQUoC/JR5uSiyM5CmZ2NLCnpqINLoiBi7nTRUWulu9FOd5Mda7f769dVWjkJ6TqM6VqMqRriU7TEJasxJKlR6xRH//8vFKKnsZ7WA/voqj1EV0Mt9t6er1/XGRNIys4hISOLhIws4lPTiUtJJS4ldUALajEQwt/qwNdox99ix9/mRHAFvn5dZlQhT9WiSNYgT1IjS9YgN6qQJagHVAr7S/MTkfAR8NOUOouCiNvhx9Hnxd7nwdbjwdLlxtrtxtzpIhQI/x+TK6Wk5sWRXhhPdkkCGUPjkQ/gdy4IIZxmM/bebmw93Vg6O7B0tGHuP4RQ2LepdDoyi0rJKC4lp2wUGUNLkA2gNb0YFAjZwv40ZPYRMHkI9roJ9rgJmr3Q/10v1StQ5cWF/WmhEUWGbkCZRoFAAJvNhsViwWKx0NfXh8lkore3F7vd/vV18fHx5OXlkZubS35+PklJSQMKUgeDTrzedjyeVjzeVtzuRlyuOtzuevx+09fXabVDMBonYDROIsE4CbX66CtgRFHEGgzR5vXT7PHT5PHR6PFxyOWlxu3FHux/piRQqtNwjFHHMUY9k+P1JCmP/ncrCCIml48Oq5fmPhfNfW6aTC5qehzU9Tjx9j/LKrmUssw4JhcmMaUwiQl5CehUAwiOCyFwdIGtFcyNYK4P+9Teg2CqhX7/iCo+XGGUPxXyp4UlauQ/XenwL41IBpCGAQLwDHDDdwWQJOGCzRpgLtAG7ASWiqK4XyKRvA28J4rimxKJ5GmgQhTFp37I7tF+4Vc9OBGXVE1RXR9JOVY4/RkYdvIRjxMjRowYP1d+gQuDw/qIb1zzG2CUKIpXSSSSs4HTRFFc8n3jHo2fCJo8dP99N2L/pOcrpAYl8mQNijQtinQtilQdikzdYbMDRFEkGAzi8/nwer34fD48Hg8ejwe3243b7cblcuFyuXA4HF8fgvBtu0ajkcTERFJSUkhLSyM1NZW0tLTD6i+IooggeAgGnQSDdoJBB4GAlUDQRiBgIeA34w/04ff34ff14PN14/P3EnbjYSQSGWp1DjptITrdUPT6UvT6UrTaQqTS77YriCLukIAjFMIWDGEPhLAGQ1gCISyBIH2BIL3+8NHtD9DpC9AXCH5rDKVEQqFWxRCtimKtmjK9hjK9hjyNEulhJvMhQcTlD+L0BrF7A9g9QaxuP1Z3AIvbj9nlp9fpo9cRPjptXmyewLfG0ChkDEnVMSRFz9AUPcMz4xiWEUdG/PeUT4UC4a6uXjv07IeOveGJq6UR7B3hjCKE737vV8hUoE+F+BxILIS0MsgcGw44qQyg0B22C2xACNBoa+SQ+RA1lhoabY002hppc7YhiP+2K5VISdYkk6ZNI0mTRLImmSR1EkaVEaPaiFFlJE4Zh0FpwKA0oJVr0cgPHwQVPB58dXV4Dx7Ed6gGf2MjvsaGrzOVvkahQJGSgjwtDXlyMrLkJORJyciMxn8f8XHIDAakBgNSnQ6J6vCZRT53gL52F33tTvranV8vtt32b+83yhVStEYVeqMKbZwSTZwSrUGBWq9EpZWj1itQaeSotHKUGjlKtRyZ/PCBLpfVQm9LE6aWJkwtzZg7WrF0duB1Or79cdUa9IlJGBIT0cYnoI03oo2LRxMXh1pvQKM3oNLpUWm1KLU6VBot0sNkNYSzHH0EOlwEut0EulwETR6CvR5Ef+hb1yoL4km9ctRh7//7+CX5iUj5CBh4AEkQRIK+EH5vEL8nfPa6AvjcQXzuAB5HAI8zgNfhx2Xz47L5cNl8CMFvrL0kYEhQY0zTkJilJzk7fCRm6JDKvvv5FEIh/F4Pfo8bv9uNz+3G63LidTrwuZy47Xbcditumw2X1YzT3IfLakH8ho+RSKUY09JJyMgiOTef1PxCUvIKSEjPPGwAWAwKCL4Qoi+E4A0iekMIniCCJ4DgDiK4A4ScAQRngJDdR8juDwdEv7nUlEnC/jRVG866y9SjyNIhi//u74Cv/Knf7/+WT/V6vbjd7q/9qtPp/JZP9Xg83xpHoVCQnJxMcnIyqampZGRkkJGRgU733bpRYX/qJRhyEQo6CPYfgaCdQMBCMGDDHzDj95v6j1683k5CIee3xpHLDWi1Q8P+VFeEwTACg2E4cvl3a/x85U9d/T7VHgwf1kAIS7Dfp/qD9AaC9PgC9PqDdPgCeP5j/pCkkFOiU1OsU1OiUzPaoGG4ToP6MM/UV/7U5fvKp37lVwNYXH7M7vDZ1O9Tux1eum0+/KFv202PU1OUpqc4zUBJmoERWfEUp+mRH8YuoSD4HeBzgt8JXht4rOC1hn2puw/cJnD2hDdbHN3g6AzrAH6FRBr2panD+o+ycOAocUism/oAiHgJm0Qi2cjhA0jHAMtEUZzf//db+l96AOgF0kVRDP7ndd/H0X7hd99bwC71cE4Um5Ce9yakjzziMWLEiBHj58wvaWEAh/cRoije/41r1vZfs1UikciBLiBF/B4HdjR+oqWlnn+9/xSiBJBKwvVSX89nRQRRBDE8sRQREQUREQFRBFEUwj8XhPB1X82YvzEf/ubUWCqTIJVKkEplyGQSpFJp/9+lSGUSZFIp8NX4/WPTfxZD4Z99/VoIQRQQxSDhpHRJ/x1L+u9C0v9nCUiUSCRKJFIlSFUgUYBUhUSiQpQoQKJEIFxVI4giITH8uYP9R0gInwMiBAWBoAgBQfjaUr/hf59FkCGilkpQSaSopaCRSFFJJagl/YdUgoJwFlZQEMPnUP8hCASDAgFBIBAUCASD4XMoRDAU+tquFBEpwrfOSqlInEqKQSXBoJRiVEuIU0qJU0mIV0KcCjRSAYkQDAeFQn4I+SDoC2cMBbwQ9ITPATf4XeEj5Pv+B0mhBW0iKA3hsjaZIizc+dW4Pjs4vx24+06U+vBYSm04oKRQhzvFKdThAJRcGT7LFCBTEpDK6BT9tIl+2gUv3YKX7pCH7pCHvpAXU8iDNeRF4PDzPikStFIlGqkCjUyJWqJALZWjkipQSuWopHKUEhlKiRyFVI5cIkMdAIM1gN7sQ2f2obb6Udt8qK0+FA4fCrsfhev7k8tFmQRBJUdQKRCVMkSlPHxWyBEVMv6/vXuNlaO87zj+/c/s7jnHPhjbgAg3E5O6TaiikojStFVCSWkuvACiktSRUJ2WKk1oK1WoUkBIFYoU9fImUtVKlCYpvUgkqSsUN4EgwEZRpUDDCxIgCDCgKiYkBFMjiH0uO/Pvi+eZ3TnHu3vmeG/jc34fNOzcdua/z+6Z3/qZ2V2aKd5I8EYKjQRPE9rpLEvJdhZsG0tsY4l5lnwry/lWln2Odj5H5oPPNhsZSdIOg2WdW0vyOJ1jtnLI80Wy9ltk7eNxWKDdXiDLFsjaS2TZEp5ng/dr4W/fkpQkSbq3lsRl3fFwFUbCTLKFLck8c+kZzNlW0rmU93/h5oH7GbD/0yYnxpURcGo58fA37+fJp79NOD4PqHvVRNE3YvEQbeF/kZduvLgJx43ObMeLDuLVO7YVN4CFj9rn+dwAAA7xSURBVEDF109CAkmCYVhq4bb8ESn3zj5CxsWd9Kqh7wMuZZ4V+Rkfo4EnYcDo5mjMynCTx915N+9yX5mnJ+0zPmaDNEmw1EisyNAwbjFrw5p5yG4Pt3geO909Zmg3V4vO+PC4y5laDIA1wJqYtfCkidGCpIUX86wJJOQe9py5085Dtq7IVXfaeXe8eE6KJsXB6D73TYNZS2gZMUPDSZhZjJkEWhjmTpbnoZMzD1maZWEI2ZqRdbK0TTsL2UmomAQnJWR7Gqe3NGHbjHFGy5hvJZzRgm2tML2tBfNNp0Ees3Qp5Gp7IXysLFssZeqJmKnH185TS2BuJ8yfC2ecG263XQBnXhiGnZeEziNdWTRyg3JiEr8negHwo9L0EeDXCB9bO+bu7dL8vl99bmafBj4NsGvXrnUX8coT/815y69zvDlLcvOhcOZPRESmrV9G9FwnnnB4g5Ahr5VXGjYnXvrJYW4++k/rvp9MQRKHtbTj8PPSPEsgaYaOlySNnTCtMN2I440ZaMzC7LZw29oaO3O2hiuEWvPhdm57+AWX2TNhy1nhjW6rwvc5uIczrcePhl99WzgWphfeCGdiF98MZ2OLN9nLx8Ob7vZCOCubLYUOqWwxnMHNlmhmy+zKl9lVdIj1+EdXBryZJBxLEo6lCW8m3eHnifFWknDcEhYS47gZJ8xYMmMhMY7F8WXCbdtgOc7LgPYOo72z91VEaZYyvwDzJ8KwdcHZsghbF2F2CeaWnLnFNjPLbWaWYXYZmm1oLjqtNjQzaMTbNIdGBjMZbMnjdJ++uNwaLDe3sNzcynJjK+3GFtqNWdqNLWTpDO3GLFk6Q5a0yNMWWdoiT5rkSZMsaZIlDXJrkScN3FLyJMWtNCRp+HdpM/TFNgiD+zL4Ap4v4H4CfBH3RfAl8CXCtzUsk/syeef5ahPeErfBc2AJ94zQ0ZiBvxbHwz94U9vJ+zm1DqTTzMgyAobPiReOPMafn7h73fcTqa2qeQqwGAdLY4Y24ngj5GrSiCc3WmG6MROG1jxsPSfkaXNu5cmRmflurnYydXs4ETNzpq4iqqE1O5DM7CHgbT0W3e7u3xh9Sb25+13AXRDOGKz3/mf/8hV856lb+YVd71bnkYjIBjRsTvzKu36V+0/cGc4MD3o3ZSvP9IazvVZaWD7VHOZZ3F4YS0qnoeNVBhhJhZ/o7VwUVaqhOOFbSC1ZcRK4s1+D1MKjS5NwtUlirPiImJUfWfmsthWPs7u/oo4k3idcNdHdl5X2YUlCauFMcBoHK7fV6rbrN16cNbe4g2KZpWG6GE/SVeNpeGNbzJ82s/BGee7Uv79qTXkeLvPP2+E7IzwjzTO2u7Pd4zLP41UHWbzSwLvzIHZk0F02aDxO556TeU7b253xPM/I41V8eTz773THcy9dzdf5j3jVRXEFRGdud9fFOOHqP7I8PNbMIc9J8pwZd2bcwzwccg/rda6wyFdcaREvKcSLfyn5STsr1VBcPWHdpsQgd9wt7sLCOLO4z61sus52rDzRez7lVZyZbdt6LpPBhs2Jq37nE9zz+E46l9YA5YPyiuNp6SBq5UwpH4NWHQJ7sV4jJx0+V21/dQyt2r712HF387ai/l7zO0fnuF+zoqaYdz0fonX3u6ows/Ka3YBJSu1sxTE/5grE/CzylPSkJrTyVov4KKqICzo5GR9XMSSdusLzmsS6G9ZdN00s5vLKba541J3c7K5jpfcNVmyvmJ+E/RlFrnbH0/gaS5IkZGvnvcQa2dlpc+tmZc/xIkeT7lBkqCUxR9Whs9mt2YHk7lcPuY+XgYtK0xfGeUeB7WbWiFchFfPHotls8YEbb1t7RRERmaR+GdFrnSPx4wlnEjJkpLafsZOPXvnJUW9WZDqSeFo5ndzPw0P3ZPZk9yobWG0yAmDPOy5lzzsuHcemRUROC5PoQvwesMfMdptZC9gLHIifSz4E3BDX2wdM7IomERGphZ4ZsWqdA4SMgJAZB9f6bgsREdkQlBEiIjUyVAeSmX3MzI4Avw58K36JHWZ2vpndB+GzyMCfAg8AzwBfd/en4yY+B9xiZocJn1X+8jD1iIjI6aVfRpjZ583s2rjal4GzYlbcAtw6nWpFRGSSlBEiIvUy1Jdou/u9wL095v8YuKY0fR9wX4/1XgSuGKYGERE5vfXKCHf/y9L4AvDxSdclIiLTp4wQEakPfQuWiIiIiIiIiIgMpA4kEREREREREREZSB1IIiIiIiIiIiIykDqQRERERERERERkIHUgiYiIiIiIiIjIQOpAEhERERERERGRgdSBJCIiIiIiIiIiA5m7T7uGdTOznwH/e4p3Pxt4bYTljIJqqq6Odamm6upY10ar6WJ3P2eUxZyOlBMTUceaoJ51qaZq6lgT1LMu5cSQlBMToZqqq2Ndqqm6OtY1lpw4LTuQhmFmj7v75dOuo0w1VVfHulRTdXWsSzXJanVsf9VUXR3rUk3V1LEmqGdddaxpM6lj+6umaupYE9SzLtVUXR3rGldN+gibiIiIiIiIiIgMpA4kEREREREREREZaDN2IN017QJ6UE3V1bEu1VRdHetSTbJaHdtfNVVXx7pUUzV1rAnqWVcda9pM6tj+qqmaOtYE9axLNVVXx7rGUtOm+w4kERERERERERFZn814BZKIiIiIiIiIiKzDhuxAMrOPm9nTZpabWd9vHjezj5jZs2Z22MxuLc3fbWaPxflfM7PWCGraaWYPmtnz8XZHj3WuMrMnSsOCmV0fl91tZi+Vll02iZriellpvwdK86fVTpeZ2Xfjc/wDM/u90rKRtlO/10hp+Ux87IdjW7y9tOy2OP9ZM/vwMHWss6ZbzOyHsW0eNrOLS8t6PpcTqOlTZvaz0r7/qLRsX3y+nzezfROs6Yulep4zs2OlZeNqp6+Y2atm9lSf5WZmfxdr/oGZvbe0bCzttBnVMSPidpUTI6ppUjlRx4yoWJdyolpNyolNSjkx2priesoJ5cSwNSknqEFOuPuGG4B3Ab8EPAJc3medFHgBuARoAd8HLo3Lvg7sjeN3Ap8dQU1/C9wax28F/maN9XcCrwNb4vTdwA0jbqdKNQFv9Zk/lXYCfhHYE8fPB14Bto+6nQa9Rkrr3AzcGcf3Al+L45fG9WeA3XE76YRquqr0uvlsUdOg53ICNX0K+Ps+r/MX4+2OOL5jEjWtWv/PgK+Ms53idj8AvBd4qs/ya4D7AQPeBzw2znbarAM1zIi4LeXEiGpiAjlR8dg30YxYR13KCeWEhsHPg3JixDX1+5sZR1tVqQnlhHJiBDWtWn9T5MSGvALJ3Z9x92fXWO0K4LC7v+juS8BXgevMzIAPAvvjev8CXD+Csq6L26q6zRuA+939+Aj2PaqaOqbZTu7+nLs/H8d/DLwKnDOCfa/W8zUyoN79wG/HtrkO+Kq7L7r7S8DhuL2x1+Tuh0qvm0eBC0ew36FqGuDDwIPu/rq7/x/wIPCRKdT0SeCeEex3IHf/DuGNXD/XAf/qwaPAdjM7j/G106ZU04wA5cTIappQTtQxIyrVpZw4pZqUE5uIcmKsNXUoJ06qVTmhnFjTtHNiQ3YgVXQB8KPS9JE47yzgmLu3V80f1rnu/koc/wlw7hrr7+XkF+AX4mVoXzSzmQnWNGtmj5vZoxYvgaUm7WRmVxB6hF8ozR5VO/V7jfRcJ7bFG4S2qXLfcdVUdhOhB7rQ67mcVE2/G5+X/WZ20TrvO66aiJfk7gYOlmaPo52q6Ff3uNpJ+pt0RoByYtQ1AWPNiTpmRNW6ypQTa2xXOSF9KCfWV5NyQjkxipqUE2sba040hiptiszsIeBtPRbd7u7fmHQ9MLim8oS7u5n1/fm72EP4buCB0uzbCAfAFuEn+T4HfH5CNV3s7i+b2SXAQTN7knBwOyUjbqd/A/a5ex5nn1I7bURmdiNwOXBlafZJz6W7v9B7CyP1X8A97r5oZn9MONPywQnst4q9wH53z0rzptVOMiJ1zAhQTlSlnJgM5URlyokNSDmhnIjbUU4MoJyobNPkxGnbgeTuVw+5iZeBi0rTF8Z5RwmXeTViL3Axf6iazOynZnaeu78SD1SvDtjUJ4B73X25tO2iF33RzP4Z+ItJ1eTuL8fbF83sEeA9wH8yxXYys23Atwgh/2hp26fUTn30e430WueImTWAMwmvoSr3HVdNmNnVhAC90t0Xi/l9nsthD2Rr1uTuR0uTXyJ8Nr2472+tuu8jQ9ZTqaaSvcCflGeMqZ2q6Ff3uNppw6pjRqxVl3JitDVNICfqmBFV61JOKCc2PeWEckI5MXjbygnlRC+b+SNs3wP2WPjm/xbhST/g7g4cInxmGGAfMIqzEAfitqps86TPT8aDX/FZ4euBnt+6PuqazGxHcdmmmZ0N/Cbww2m2U3y+7iV8tnP/qmWjbKeer5EB9d4AHIxtcwDYa+GXFXYDe4D/GaKWyjWZ2XuAfwSudfdXS/N7PpcTqum80uS1wDNx/AHgQ7G2HcCHWHmmbGw1xbreSfgSue+W5o2rnao4APy+Be8D3ohvYsbVTtLfpDMClBOjrGkSOVHHjKhUl3KiWk2xLuWE9KOcqFiTckI5MaKalBPVjDcnfAzfDD7tAfgY4TN9i8BPgQfi/POB+0rrXQM8R+gJvL00/xLCH+hh4D+AmRHUdBbwMPA88BCwM86/HPhSab23E3oHk1X3Pwg8STiA/TswP4magN+I+/1+vL1p2u0E3AgsA0+UhsvG0U69XiOES1ivjeOz8bEfjm1xSem+t8f7PQt8dISv77Vqeii+7ou2ObDWczmBmv4KeDru+xDwztJ9/zC232HgDyZVU5y+A/jrVfcbZzvdQ/iVj2XCMeom4DPAZ+JyA/4h1vwkpV9+GVc7bcaBGmZE3K5yYnQ1TSQn1jrOMIWMqFiXcqJCTXH6DpQTm25AOTHSmgb9zYyjrSrWpJxQTgxdU5y+g02UExY3JCIiIiIiIiIi0tNm/gibiIiIiIiIiIhUoA4kEREREREREREZSB1IIiIiIiIiIiIykDqQRERERERERERkIHUgiYiIiIiIiIjIQOpAEhERERERERGRgdSBJCIiIiIiIiIiA6kDSUREREREREREBvp/AtDGfTsMcMYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x360 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(-1, 1, 100)\\n\",\n    \"X_polynomial = PolynomialFeature(11).transform(x[:, None])\\n\",\n    \"X_gaussian = GaussianFeature(np.linspace(-1, 1, 11), 0.1).transform(x)\\n\",\n    \"X_sigmoidal = SigmoidalFeature(np.linspace(-1, 1, 11), 10).transform(x)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(20, 5))\\n\",\n    \"for i, X in enumerate([X_polynomial, X_gaussian, X_sigmoidal]):\\n\",\n    \"    plt.subplot(1, 3, i + 1)\\n\",\n    \"    for j in range(12):\\n\",\n    \"        plt.plot(x, X[:, j])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.1.1 Maximum likelihood and least squares\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def sinusoidal(x):\\n\",\n    \"    return np.sin(2 * np.pi * x)\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data(sinusoidal, 10, 0.25)\\n\",\n    \"x_test = np.linspace(0, 1, 100)\\n\",\n    \"y_test = sinusoidal(x_test)\\n\",\n    \"\\n\",\n    \"# Pick one of the three features below\\n\",\n    \"# feature = PolynomialFeature(8)\\n\",\n    \"feature = GaussianFeature(np.linspace(0, 1, 8), 0.1)\\n\",\n    \"# feature = SigmoidalFeature(np.linspace(0, 1, 8), 10)\\n\",\n    \"\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X_test = feature.transform(x_test)\\n\",\n    \"model = LinearRegression()\\n\",\n    \"model.fit(X_train, y_train)\\n\",\n    \"y, y_std = model.predict(X_test, return_std=True)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", s=50, label=\\\"training data\\\")\\n\",\n    \"plt.plot(x_test, y_test, label=\\\"$\\\\sin(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x_test, y, label=\\\"prediction\\\")\\n\",\n    \"plt.fill_between(\\n\",\n    \"    x_test, y - y_std, y + y_std,\\n\",\n    \"    color=\\\"orange\\\", alpha=0.5, label=\\\"std.\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.1.4 Regularized least squares\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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W15dSpU9y6dYv+/fuze/duzM3N+fLLL5k7dy7vv/8+Y8eOJSAggPr16zNkyJA8rz9p0iTat2/Pxo0byc7OJiUlhdmzZ3P+/HlOnz4NkFtyGWDBggUoisK5c+cICQmha9euhIWpFWlPnz5NUFAQxsbGuLu7M3HiRBwdHfO6bamQPXwt27kT3Nzg5ElwcoIjR8DVFQICHm13MymdN1edwtHGlO+HNc0/yWVnwtqRkBgLw9aoZYzLM+s66vBOxj31HUtGKs1dqjKjbwMCQ+OZ+3eoriOUyjhHR0f8/f0BGDlyZG4xtAcJ/MiRIwQHB+Pv74+3tzfLli0jOjqakJAQXFxccHV1RVEURuYzRzkgIIDXX38dUMsrW1kV/G76wIEDudfy8PDAyckpN+F36tQJKysrTExM8PLyIjo6uvh/AcUge/hadOcODB8OmzerNWse2LMHBg2CiAiwsFA3Bp+wKojk9EyWv9Icy4I2C9k1DWIOwYClUKdFiX8PpaKaJwz6BVYNhj/GweDljGxRh+C4RBYEXqZJbWu6NnDQdZRSYQrpiZeUx/dofvC1ubk5oBaE7NKlC6tXr36k3YPeeWl6vDZ/VlZWqcfwMNnD16LVq6Fr10eTPajz39u2hd9/V7/+cnsIx6JuM7t/YzwcCnjwenYdHP0RWr4BjcrfjIACuXaBbl9AyF8Q8CmKojCzbwMa1rLkvXVnuHL7nq4jlMqomJgYDh8+DMCqVato06bNI8dbtmzJwYMHuXRJXeeRmppKWFgYHh4eREVFcfnyZYAn/kN4oFOnTixcuBBQHwAnJiZiYWFBcnJynu3btm3LypUrAQgLCyMmJiZ3h6yyRiZ8LYqOhsaN8z7WuLF6/O/gG/x0IJJRrZzo17RW/he7cQG2TAInf+jySckErGstxoPvaDgwF4I3Y2ygz4LhPggBE1cHkZElq2tKT3J3d2fBggV4enpy586d3OGXB+zt7fn1118ZNmwYjRs3plWrVoSEhGBiYsLixYvp1asXPj4+VKuWd+XZ77//nsDAQBo1aoSvry/BwcHY2tri7+9Pw4YNmTJlyiPt33jjDTQaDY0aNWLIkCH8+uuvj/TsyxKlrC5v9/PzEydOnNB1GE/lp59g61bYuPHJYz17Qq+B6SyJ3UdtG1M2vN4aYwP9vC90PwUWtYOMVHhtH1hU4F2jsjLglx7qtovj9oBtPbadu8YbK0/xahsXpvX20nWE0kMe3gFKF6Kioujduzfnz5/XWQxlSV4/D0VRTgoh/PJqL3v4WjRkCBw6pCb9h/35J5w+I9ibofZa5w1tmn+yB9j+AdyJhIE/V+xkD2BgBIN+VXfV+v0lyEyjZ6MajGrlxE8HIgkMuanrCCWpwpAJX4ssLGDTJnj1VejRAz76CLp1gzffhFGzL3Ms6jaz+jagbkFlEy5sVMsLt5msbjBeGVg7Qv+f1GGsre8B8FEvTzwcLJiy/gy3Uu7rOECprHB2dpa9+2KQCV/LWrZUZ+O8+CKYmcGYMfDnvrv8fjGMPk1qMtC3dv4nJ8bClreglh90yH/VX3kUHQ2hoZDvJAXXztDuPfU/u7PrMDbQ57uh3iSlZTF1wzlZWVOStEAm/BJgaqpOz5w2DfoNyOaDP09T3cKYz/o1fGJKWS6NBjaOB002DFgC+gVM1SxHDh6EZs3UEs09ekDdurB0aT6N208FxxawdTLcicbDwZL3u7uz++IN1hy/UqpxS1JFJBN+CftyRwgR8al8NagJVqYFJPHjP0HUfug+G6rWLb0AS9C5c9CvH0yZopZojoiAP/6A2bPVDVmeoG8A/RerK4o3vgaabF72d8G/vi2fbAkm6lZqqX8PklSRyIRfgg5dvsUvB6MY1coJ//oFlEO4EwW7Z6r7wjYtjR1KSsecOfD++zB4MOjnPKP284OVK+GTT3hiC0YAbJyh19cQcxgOzEVPT+HrQU0w0Fd4f/1ZuVOWJBWDTPglJDk9kynrzuJiZ87UHgVMYxMCNk8CRQ/6fA/5DfmUQ3v2QF4VZJs3V5N9vqvMGw+BhgMg8L8QF0QNK1Om9/biWNRtlh2OKrmAJa2LjoZt2yAoSP1VL67Hq1Y+jZ49e3L37t0C20yfPp3du3c/0/UL8nBxtvzs2bOHQ4cOaf3eD5MJv4R8uSOEa4lpfD2oCaZGBUzBPLUMIvdC10/U2SoViKkpJCY++XpmJqSmqsfzpCjQ6xswt4c/34CsDAb61qajuz1f7giRQzvlQGKi+p+9nx/Mm6d+7usLwcHFu25BCb+wsgXbtm3D2tq6wDaffPIJnTt3fub4ikMm/HLqaEQCK47E8LK/C75OeZTJfCDpGuz6GFzage+Y0guwlAwaBPPnP/n66tXg5QU1ahRwsqkN9PkObgbDvq9QFIX/9m+Mob4e72+QQztl3bBhYGsLMTGwYweEh6vTk7t2hUI62QV6vEzxnj17aNu2LX379sXLS12k169fP3x9fWnQoAGLFy/OPdfZ2Zlbt24RFRWFp6cnY8eOpUGDBnTt2pW0tDQARo8enVvn3tnZmRkzZuDj40OjRo0ICQkBID4+ni5dutCgQQNeffVVnJycuHXryU18fvnlF9zc3GjevDkHDx7MfX3Lli20aNGCpk2b0rlzZ27cuEFUVBQ//vgj3377Ld7e3uzfvz/PdsUmhCiTH76+vqI8SsvIEh2+ChRtvwwQqfczC278+2ghPrEX4tal0gmulCUkCOHpKcTo0UIcOyZESIgQn3wihL29+nWRbBgnxEwbIa4GCSGEWHs8Rjh98Jf47VBkicUt5S84OLjQNqdPC1G7thCZefz6Dx0qxPffP/v9IyMjRYMGDXK/DgwMFGZmZiIiIiL3tYSEBCGEEPfu3RMNGjQQt27dEkII4eTkJOLj40VkZKTQ19cXQUHq79SgQYPE8uXLhRBCjBo1Sqxbty63/bx584QQQixYsEC88sorQggh3nzzTfHFF18IIYTYvn27AER8fPwjccbFxQlHR0dx8+ZNcf/+fdG6dWvx5ptvCiGEuH37ttBoNEIIIZYsWSImT54shBBixowZ4quvvsq9Rn7tHpbXzwM4IfLJq7JappZ9tzucyFuprHy1BWZGBfz1Xg6AC39Ah4/Kf8njfFStCgcOwPffw6hRkJ4Ozz0H+/aBh0cRL9L9vxARCJvehLGBDPKtzebTcczZEUoXLwccrExK9HuQnt6xY9ClCxjk8evfo4daQlybmjdvjouLS+7X8+bNY2NOfZMrV64QHh6Ora3tI+e4uLjg7e0NgK+v7yP17R/Wv3//3DYPNkw5cOBA7vW7d++OTR6bXRw9epQOHTpgb28PqKWbH5RMjo2NZciQIVy7do2MjIxHYn9YUds9DTmko0XnryayZH8EQ/wcC56Vk5kOW9+FqvXA/63SC1AHqlaFWbPUsduICLXeUJGTPagbtPf+Fm6ch0PzUBSFz19oSEa2hpmbL5RY3NKzs7GBq1fzPhYbq/5OaNODssigjoPv3r2bw4cPc+bMGZo2bUp6evoT5xS1bPGDdtosbTxx4kQmTJjAuXPnWLRoUZ7xPU27pyETvpZkawQfbTyHjZkRH/UspLjUwe/gdoQ6/dBQ9lAL5dELvJ6HvXMg4TJOtua81dmVHReus/PCdV1HJz2mZ084dQoer314+zYsWqSuQn9WBZUpBkhMTMTGxgYzMzNCQkI4cuTIs98sH/7+/vyeU+t8165d3Llz54k2LVq0YO/evSQkJJCZmcm6deseibFWLbVS7rJly3Jff/x7y69dcciEryUrj0ZzNjaRj3t7YmVWwAKr25Gwfy406A/1niu9AMu77l+CgbG6ClcIxrati4eDBTM2XSA5Xe6FW5aYmanv5Hr2VN/d7dkDixerZUeGDlWn5T6rgsoUgzrEkpWVhaenJ1OnTqVly5bPfrN8zJgxg127dtGwYUPWrVuHg4MDFhYWj7SpUaMGM2fOpFWrVvj7+z9S0XLmzJkMGjQIX19f7Oz+HQno06cPGzduzH1om1+7YslvcF/XH+Xpoe2NxDTRcPoOMWLJkdyHLPlaM0KIzxyESLxaOsFVJEcXCzHDUogza4UQQpyKvi2cp/4lZm4+r+PAKo+iPLR94MIFId54Q4h27YQYPFiInTtLMLBSlJ6eLjJznkgfOnRINGnSRGexyIe2OvDp1ovcz9bwaUG1cgAi98PFLdBxGljWLL0AKwq/V+DMGtjxIdTvTNM6VRnWvA7LDkUxyNcRr5oF7B4mlTovL1iwQNdRaF9MTAyDBw9Go9FgZGTEkiVLdB1SkckhnWLaHx7PljNxvNGhHi525vk31GSricqqDrQueMWdlA+9nNXIaXcg4FMA3u/mjrWZER9vOi/n5kulwtXVlaCgIM6cOcPx48dp1qyZrkMqMpnwiyEjS8OMTRdwsjVjfPtCplae+g1unFNX1Brmt8RUKpRDQ2jxGpz4BeKCsDYzYmoPD05G32H9qVhdR1cpCFmqukx4lp+DVhK+oijdFUUJVRTlkqIoTxRyVxRltKIo8YqinM75eFUb99W1Xw5GEnErlZl9GmBiWED5hPRECPgM6rQGr36lF2BF1WGqWnZh63ug0TDQpza+TjbM3h7C3XsZuo6uQjMxMSEhIUEmfR0TQpCQkICJydPN8iv2GL6iKPrAAqALEAscVxRlsxDi8aoZa4UQFWYs43piOvP+CaezZ3U6euS9GXKuA9/BvVvQfUOFKo6mMyZW6sbuf46H0yvR83mRT59vSO/5+5n7dxifPN9Q1xFWWLVr1yY2Npb4+Hhdh1LpmZiYULt2ARsq5UEbD22bA5eEEBEAiqKsAZ4HilkmqWz77/aLZGoE0wvbZDspDo4shEaDoaZ36QRXGTQZCid/hd0zwLM3XjVtGNnSiRVHohneog4eDvIBbkkwNDTUyopPSTe0MaRTC3h4O6LYnNceN0BRlLOKoqxXFCXPspCKooxTFOWEoignynIP4mhEAptOxzG+fT3q2JoV3DjwCxDZ8Ny00gmuslAU6PmV+gB3z2wAJndxw9LUkFmbg+WQgyTlobQe2m4BnIUQjYG/gTyXjQkhFgsh/IQQfg9qUJQ12RrBrC3B1LI25fXCHtTeDIHTK6HZWLBxKp0AK5MajcFnlLpbWHwY1mZGvNvFjcMRCWw/L1fgStLjtJHwrwIP99hr57yWSwiRIIS4n/PlT4CvFu6rE+tOXCH4WhJTe3gUXOce1F2sjKqom3NLJaPjf8DQDHb9B4Bhzevg4WDB51svkp6Z15ZaklR5aSPhHwdcFUVxURTFCBgKbH64gaIoD1c+7wtc1MJ9S11yeiZf7wrFz8mG3o0LKuYORB+GsO3Q5m21AJhUMqrYQ/v3IXwXhO/GQF+PmX0bcPVuGov3Reg6OkkqU4qd8IUQWcAEYCdqIv9dCHFBUZRPFEXpm9NskqIoFxRFOQNMAkYX97668L/AS9xKyWB6H6+CV9QKoS4MqlIdWrxeegFWVs1fUzd+3/kRZGfSsq4tPRo6sHDPZW4kFb/CoCRVFFoZwxdCbBNCuAkh6gkhPs95bboQYnPO5x8KIRoIIZoIIToKIUK0cd/SFJ2Qyi8HohjoW5vGtQveJo2IPRB9ENq+B0aFPNSVis/ACLp+BrdC1QVZwNQeHmRrBF/vDNVxcJJUdsiVtkX0320hGOgrTOnmXnBDIdRFVpa1wXdU6QQngXtPcG4Le2dDeiJOtuaM9ndm/alYzl/NY2NdSaqEZMIvguNRt9lx4Trj29ejumUhK9vCdsDVE+q4soFxwW0l7VEU6Pop3EtQF7oBb3asj42ZEZ9tldM0JQlkwi+URiP4bOtFqlsa82rbQhacaDQQ8Lk6nuw9vHQClP5Vs6m6wO3ID5AYi5WpIe90duVIxG12BWthA2hJKudkwi/EX+eucebKXd7r6l7wHrUAFzerBdI6fAj6BWyCIpWcTh//O6yGOk2zfrUqfLk9hMxsjY6DkyTdkgm/AOmZ2Xy5PQTPGpb09ymkZoVGo27BZ+sKDQeUToDSk6zrqNU0z6yBa2cw0NdjancPIm6lsuZYjK6jkySdkgm/AL8djuLq3TT+09MTfb1Cip6FboWbF9Sxe71CFmRJJavtu2BqrS58Azp5VqO5S1W+20nG2EMAACAASURBVB1Oyn3tbEQtSeWRTPj5uHsvg/8FXKK9mz1tXAvZT1II2PslVK2n7lUr6ZaptZr0LwdAxF4UReGjnp4kpGbw5abLxMSoPzJJqmxkws/Hwj2XSb6fxdQeHoU3Dt0O189BuymgL3eNLBOajVWnxu6eCUJw6ag1erE1+O1YBM3apePrC4GBug5SkkqXTPh5uHo3jV8ORdG/aW08axRSZvdB797GBRoNKp0ApcIZmkDHDyHuFMeWbeLtt+HLER4YGQtGfR3G9OkwZAgcParrQCWp9MiEn4e5u8IAmNzVrfDG4X/DtdNqgTTZuy9bmgxD2HtQ7dynLF2SxaAeZoxs6cT6k1do2DqZzz6DL77QdZCSVHpkwn9MyPUk/giKZXRrZ2pZF7L3rBCw7yt1Y/LGQ0onQKno9PRJaT4dZ4tLdLFbDsCEjvUxMzLgq52hDBoE//yj4xglqRTJhP+YOTtCsTA24I0OhdS6B7VeTuwx8J8k592XUYpHT45cbY6ydw5kpmFbxZixbeuy88INjoTfwVTuJy9VIjLhP+RY5G0CQm7yeof6WJsZFX7C/m/AvBo0HVnywUnPpIqFwp/J01FS4uD4UgBebeuCXRUjpm8IYeAgOV1Hqjxkws8hhGDOjhCqWxozurVz4SfEBanT/lq9AYaym1iWvfhxW/Zc6UjqzrmEX0gmIsyAGrddiVdu03lk2d1KU5K0TSb8HAEhNzkRfYdJnVwL38kKYP9cMLYCv1dKPjipWBo0gHpjP8ZcSWDzhwt54QVw1atDLSszfjoeikYje/lS5SATPuo+tXN2hOJsa8Zgvzz3V39UfChc3ALNx4JJIdM2pTLBsaUvePTm3VbzuXT2NnO/1mNKdzcuXkti67lrug5PkkqFTPjA5jNXCb2RzLtd3THUL8JfycF5YGACLeVuVuVKx//A/WQ4qJZP7tukJh4OFsz9O0wWVpMqhUqf8DOyNMz9OwyvGpb0alTIPrUASXFwdi34vAjmhZRckMqW6l7q4rhjSyDlJnp6Cu92dSfyVirrT8bqOjpJKnGVPuGvPXGFK7fTmNLdHb3CCqQBHP0RRDa0erPkg5O0r8NUyLoPB74FoLNnNXzqWPP97nDSM7N1HJwklaxKnfDTM7OZ/084fk42dHCzL8IJSeqeqV79wMa5xOOTSoBtPWgyTJ2imXgVRVGY0s2D60nprDgSrevoJKlEVeqE/9vhKG4m32dKN3cUpQi9+5O/wv0kdaGVVH61fx+ERl1HAbSqZ0tbVzsWBF6S5ZOlCq3SJvzk9EwW7rlMW1c7WtS1LfyErAx16zyXdupWelL5ZeOkPoM59RvcVTdFea+rO3fuZfLzgUgdBydJJafSJvylByK5cy+TKd3ci3bCuXWQfA383yrZwKTS0fY9UPTUSqdAE0drunpVZ8m+CO7ey9BxcJJUMiplwr+TmsHS/ZF0a1CdxrWtCz9BCDi8AKo1gHqdSj5AqeRZ1QK/MXB6NdyOANTqqCkZWSzaF6Hj4CSpZFTKhL9oXwQpGVlM7lLE3n1EoLp9Yas3oShj/VL50OYdtejdvq8B8HCwpE/jmvx6MIqbyek6Dk6StK/SJfybyen8eiiSvk1q4u5gUbSTDi9Qi6Q1GliywUmly8JBLY1xZjUkXAbg7c6uZGRrWLjnso6DkyTtq3QJf+Gey2RmC97q5Fq0E25ehEu7ofk4MDAu2eCk0tfmbdA3hr1zAKhrX4UBPrVYeSSGuLtpOg5OkrSrUiX8uLtprDwSwwCfWtS1r1K0k478AAam4PdyyQYn6UaVatD8VTj3O9wKB2BSJ1cEgv8FXtJxcJKkXZUq4f8v8BICwcTniti7T4mHM2vBexiYF2HqplQ+tX5LrY2UM2Onto0ZQ5o58vvxK1y5fU/HwUmS9lSahB+TcI/fj19hSDNHHKuaFe2kE0sh+z60fKNkg5N0q4q9Wvn03HqIV/czntDRFT09hXn/hOs4OEnSnkqT8OcFhKOnpzChYxF791n34fhP4NoV7Ip4jlR+tZ4EhmawTx3Ld7AyYWQLJzaciiUiPkXHwUmSdlSKhB95K5U/TsUysoUTDlYmRTvp/B+QGi9LIFcW5nYP9fJDAXi9Qz2MDfT5XvbypQqiUiT873eHYWygz+tF2Zgc1IVWR34Aew+o27Fkg5PKjge9/JwZO/YWxoxq7czmM3GE3UjWcXCSVHwVPuGH30hm05k4XmrthL1FEadVxhyG62ehxXi50KoyMbeFFuPg/Aa4GQLAa+3qYmaoz3e7w3QcnCQVX4VP+N/tDsfMUJ/X2hWxdw9q797UBhoPKbnApLKp1cScXr46Y8fG3IiX27iw7dx1guOSdBycJBVPhU74D/YrfbmNC1XNjYp20p1oCNkKPqPAqIizeaSK40Ev/8LG3F7+q23qYmFiwLeyly+VcxU64X/7dxgWJga82qZu0U86vgRQ1Ad4UuX0oJefM2PHysyQsW3r8nfwDc7FJuo4OEl6dhU24Z+LTWRX8A1ebVMXKzPDop2UkarWSPfsA1a1SzZAqezKHcv/I7eXP8bfGWszQ+b+Harj4CTp2Wkl4SuK0l1RlFBFUS4pijI1j+PGiqKszTl+VFEUZ23ctyDf7g7DytSQMW2e4lZnf4f0RGjxWonFJZUTub38rwCwMDFkXLu6BIbGcyrmjo6Dk6RnU+yEryiKPrAA6AF4AcMURfF6rNkrwB0hRH3gW+DL4t63IEExdwgIucm4dnWxNCli714IOLYYHBpBnVYlGZ5UHpjbqsN65zfkzssf1coZW3Mjvv1bjuVL5ZM2evjNgUtCiAghRAawBnj+sTbPA8tyPl8PdFKKtInss/l2dzhVzY0Y3dq56CdFHYCbwdD8NTkVU1K1nvjIvHxzYwPGt6/H/vBbHIu8rePgpIrq0KVbnL5yt0SurY2EXwu48tDXsTmv5dlGCJEFJAJPVCNTFGWcoignFEU5ER8f/0zBRN5KZX94POPb18Xc2KDoJx5bBKZVZc176V8PVt8+1Msf2VJdzyF7+VJJyMrW8J8/z/PhH+cQQmj9+mXqoa0QYrEQwk8I4Wdvb/9M13CxM2fX2+0Y2dKp6CfdvZIzFfMlMDR9pvtKFdRjvXxTI33e6FCPwxEJHLp8S8fBSRXNxqCrRN5K5Z3OrpTEIIg2Ev5VwPGhr2vnvJZnG0VRDAArIEEL986Ta3ULzIyeond/Yqn6Z7NXSiYgqfzKo5c/rHkdqluqvfyS6IVJlVNmtoZ5AeE0qmVFF6/qJXIPbST844CroiguiqIYAUOBzY+12QyMyvl8IBAgysq/lMx0OLkM3HuCdR1dRyOVRa0nqu/8cnr5Job6TOhYn+NRd9gfLnv5knasPxnLldtpTO7iViK9e9BCws8Zk58A7AQuAr8LIS4oivKJoih9c5otBWwVRbkETAaemLqpMxc2QtptudBKyt8jvXx17H5wM0dqWZsyV/byJS24n5XN/wIu4e1oTQf3ZxvOLgqtjOELIbYJIdyEEPWEEJ/nvDZdCLE55/N0IcQgIUR9IURzIUSENu6rFceXgJ0buLTXdSRSWdZ6ktrLz1l9a2ygz4Tn6nP6yl0CQ2/qODipvPv9+BWu3lV797GxCtHR6kxxbStTD21L3dVTcPUkNHtVTsWUCpZHvfyBvrVxrCp7+VLxpGdm87/AS7ha2/DWUDt8faFFC2jUCLZs0e69KnfCP/4TGJpDk6G6jkQqDx6rl2+or8ek51w5fzWJXcE3dBycVF6tPBrDjaT7nFvlzqyZCtevw7Vr8O23MG4c7NihvXtV3oR/77Y6JttkCJhY6ToaqTx4eCw/p8bOC01r4WJnzrd/h6HRyF6+9HTuZWSxcM8lzJJtmT3Zlr59QU9PHXDo0gUWLoSZM7V3v8qb8IOWQ1Y6NJMPa6WnkNvLV6uDGOjr8VYnV0KuJ7Pt/DUdByeVN78djuZWSgbRf7kxePCTx/v0gTNnIDVVO/ernAlfo4HjS8HJH6o/XvZHkgrwSL38iwD0aVIT12pV+PbvMLJlL18qopT7WSzae5l2bvbo3a5KSsqTbdLS1D8NnmJZUUEqZ8K//A/cjVYf1krS02o9CYzMc3v5+noK73Rx43J8KptOP77mUJLy9suBSO7cy2RyFzcGDYIFC55ss2QJ9OgBxkXcnbUwWvp/o5w5/hOYVwOP3rqORCqPzKqqJbT3z4V2wVDdi+4NHPCqYcn3/4TTp0lNDPUrZ19KKprEtEyW7I+gs2c1vB2tmTED/P3VHv3LL6s9+uXLYdEi2LNHe/etfL+Vd6IhbCf4jgKDIm57KEmPazUBjKrA3tkA6OkpTO7iRnTCPf44Favj4KSybun+CJLSs3inixsATk5w+DCkp0O3btCxI9y4AQcPgoeH9u5b+RL+yV/VR+C+o3UdiVSemVWFlq9D8Ca4fg6ATp7VaOJozbx/LnE/K1vHAUpl1e3UDJYeiKRXoxo0qPnvDEFHR5g3D6Kj4coV+PFHqFdPu/euXAk/6766haFbD7mFoVR8rd4AYyvYo/byFUXh3S5uXL2bxtrjVwo5WaqsFu29TFpmNu90cS31e1euhH9xC9y7Bc1e1nUkUkVgagOt3oSQvyAuCIC2rnY0d6nK/IBLpGXIXr70qJtJ6Sw7HEU/71rUr2ZR6vevXAn/+E9g4wJ1n9N1JFJF0XI8mFg/0st/r6s78cn3WX4kSrexSWXOD3suk5kteKtzAb17IdSp4yWg8iT8Gxcg5jD4jVGXskmSNphYQesJELYDYk8C0NylKu3c7Fm45zLJ6Zk6DlAqK67eTWPV0RgG+9XGydY870ZCwI4PYfPEEkn6lSfznfgF9I3Be6SuI5Eqmhbj1e0xAz/LfendLm7cuZfJLwejdBeXVKbM2x0OwMTn8undP0j2RxeCiWWJFHSsHAn/fgqcWQMN+qkrJSVJm4wtoM07cDkAog4C0MTRmq5e1VmyL4K79zJ0HKCka5fjU1h/KpaRLZ2oaZ3HNqpCwI6parJv+SZ0+0Im/Gd2fj1kJIOffFgrlZBmr0KV6hDwWW4h83e7upOSkcWPe8vO9g+Sbnz7dxjGBnq80TGPeZa5yf5HdX1Ht89LrFx7xU/4Qqh1c6o1AMcWuo5GqqiMzKDtexBzCCICAXB3sOD5JjX59VAkN5PSdRygpCvBcUn8dfYaL/u7YFflsRoJQsDumWqyb/kmdP2sRPfmqPgJ/+opuH5WfVgrNzmRSpLvKLCs/Ugv/50ubmRlC+YHXNJxcJKufLMrFEsTA8a2q/vkwX1fwcHvwO+VEu3ZP1DxE/6Jn9VNThoP0XUkUkVnYAzt31d3UQvdDoCTrTlDmjmy+lgMMQn3dBygVNpORN3mn5CbvNa+Hlamho8ePPQ/CPwcmgyHnl+XSoe0Yif8tDvqZhWNBqpPvSWppHkPh6r1IODT3Gl1kzq5YqCv8N3uMB0HJ5UmIQRzdoRib2HMGH/nRw8GrYBd/wGvftB3fqlNFa/YCf/MWshKg2av6DoSqbLQN4SOH8HNYHWyAFDd0oRRrZ3ZePoqodeTdRygVFr2hMVzLOo2kzq5Ymb0UGHii3+p8+zrPQf9l4B+6RUtrrgJXwh1OKeWL9RooutopMqkQX+o3kh9u56tLrx6vX09qhgb8NXOUB0HJ5UGjUbt3TvZmjG0meO/ByL3w/qXoaYPDF5e6hV7K27CjzkMt0LBd4yuI5EqGz096PQx3IlSi/UB1mZGjG9fj90Xb3Ai6rZu45NK3JazcVy8lsTkLm7/7o1w/RysGQ42zjBiHRhXKfW4Km7CP/GzWsmwYX9dRyJVRq5dwbEl7J0DGerD2jH+zlSzMGb29hCEkFshVlSZ2Rrm/h2GZw1L+jSuqb54NwZWDFT3UHjxD7W8tg5UzISfmqDWKW8yVN2KTpJKm6JAp+mQch2OLQLAzMiAtzq7ciL6Dv9cvKnjAKWSsvpYDNEJ93i/mzt6egrcu60m+8w0GLlBp6XZK2bCP70SsjPUufeSpCvO/mpP/8C36j96YLCfIy525szZGSI3PK+AUu5n8f3ucFrWrUoHd3vITFeHce5EwrBVUN1Lp/FVvISv0cDJX6BOK6jmqetopMqu0wxIT1KTPmCor8d7Xd0Ju5Eit0KsgJbsiyAhNYMPe3iiCAF/jlefJ76wCJzb6Dq8Cpjw70ZBeqKsmyOVDQ4N1aHFo4sgUU3wPRs50KS2FXP/DiM9U26SUlHcTE5nyf4IejWqQRNHa/hnFlzYCF0+LTPPEitewq9aFyZfVBc0SFJZ0PEjQEDgfwF1k5QPe3pyLTFdlk+uQOb9E05GloYp3dzVSSMPSia0nqjr0HJVvIQP6hL3Up7fKkn5sq4DzcfBmVVwIxiAlnVt6exZjR8CL3E7VZZPLu8i4lNYfewKw1vUwfnOYdj6nvr8psecMlXDq2ImfEkqa9q+C0YWamXEHB909yA1I4v5AeG6i0vSitnbQzAx0OPtxlmwbjRU84KBP5fqKtqikAlfkkqDWVVo9y6E74SIPQC4VrdgSLM6rDgSTXRCqm7jk57Z0YgEdgXf4F1/G6r++aI6FXz4GnVjnDJGJnxJKi3NXwOrOrBrGmjUh7XvdHbFUF+PL3eE6Dg46VloNIIvtl3E2VJhdPSHkBqvJnsdzrUviEz4klRaDE2g8wx1if3ZtQBUszThtXb12HbuOsdlyYVyZ8vZOM7E3mW53W/oxZ2E/ouhZlNdh5UvmfAlqTQ1HKAW9Pvn09ySC2PbueBgacJnfwWjkYuxyo30zGzm7Ajlc+u/cIzbDp1ngldfXYdVIJnwJak0KYq6jV1yHBz+H6CWXJjSzZ0zsYlsPhOn4wClolp6IBKfpH8Ykb5a3cTE/21dh1QomfAlqbQ5tQbPvurq2yQ1wb/QtBaNalnx5Y4Q0jLkYqyy7mZSOvsCdzDXeBHUaQ19vitT0y/zIxO+JOlC10/VB7c50zT19BSm9VIXYy3ZH6Hb2KRCLf5rH/OVr8CiBgxZrq79KQdkwpekUnb+PPy0wZmLVSeoD2+vHAegRV1bejR0YOGey1xLTNNxlFJ+zkdeo1/IFKz0MzAc+TuY2+k6pCIrVsJXFKWqoih/K4oSnvOnTT7tshVFOZ3zsbk49yzMlSuwbRucPKlueiVJZUVyMvTtC926wcGD8OHWyVxPdeD2qg9y97/9qKcnGiH47zY5TbMsEppsUta8gqdeDNkDfi53BRqL28OfCvwjhHAF/sn5Oi9pQgjvnI8SeYydkgIjRoC3N8ybB8OGQZMmcPp0SdxNkp7e+PFgawtRUfDLL/DntircbzOTqmknidmqTtN0rGrGa+3qsvlMnJymWQaFr/2QlvcPcsbzXcwa9NB1OE+tuAn/eWBZzufLAJ1VLBszBvT1ISYGduyA0FCYOhV69ICbcq8JSceuXoXt22H+fDA0/Pd1pz5DuKr4YXVsulrlFRjfoR41rEyYsemCrJlfhqSfWoNb6CJ2GnelycCPdB3OMyluwq8uhLiW8/l1oHo+7UwURTmhKMoRRVHy/U9BUZRxOe1OxMfHFzmI8HDYtw+WLAFz8wfXguHDoVcvWLq0yJeSpBIREqK+46zy+DamenrcavE1FnrxudU0zYwM+LCnJ8HXklh7/ErpBys9KfYEBlsmclTjgf2Q+ejrl8/Hn4VGrSjKbkVRzufx8fzD7YS6SWd+3REnIYQfMBz4TlGUenk1EkIsFkL4CSH87O3ti/xNHD8OHTqAcR4Pynv0gKNHi3wpSSoRDg5w+TJk5zHjMuh6U3YljIFji+H6eQD6NK5Bc+eqfLUzhDuymqZuJcaStWoYcRprtnrMwaeug64jemaFJnwhRGchRMM8PjYBNxRFqQGQ82eegydCiKs5f0YAewCtrj2uWlV9y5yX2Fj1uCTpUoMGUKMGLF786OuJiTBnDtDpYzCxgm3vgRAoisIn/RqQlJ7FnJ2hOolZAjJSEauHkpmWwiQ+YEKfFrqOqFiK+75kMzAq5/NRwKbHGyiKYqMoinHO53aAPxBczPs+4rnnICJCHdZ5WFISLFgAL76ozbtJ0rP57Tf44gsYMgSWL4cvv4SmTaFrV+jWr6q6ND/mMJxZA4CHgyWjWzuz5ngMp6/c1WnslZJGA3+Mg+sXeP3+BPp06UQ1CxNdR1UsxU34s4EuiqKEA51zvkZRFD9FUX7KaeMJnFAU5QwQCMwWQmg14RsZqbMeBg6EadMgMFAdt2/ZUp0C16GDNu8mSc/G3V2dg9+mjfoANyYGVqyA7x4s0mz6ItTyU6tp5mx6/nZnV+yrGDPtz3PyAW5pC/wMQv7ie/1RXK/WlpdaOek6omJTRBmdrO7n5ydOnDjxVOeEhak9+tOnoVo1GD0aevYsFyueJUl1/Twsagfew+D5BQBsPhPHpNVBfPp8A15s5azb+CqLM2th4ziC7J/nhSuD2fB6a3ydysfYsKIoJ3OemT6hbG3HUkxubvD997qOQpKKwaEhtJ4AB7+HxkPBpS19Gtdg7fEY5uwMpVsDB6pZlu9hhTIv+jBsnkBqjVYMjR7IsOZ1yk2yL0z5nFskSRVZ+6lg7QR/vQ2Z6SiKwqfPN+R+loZZW7Q6Gio97nYErBmOsK7D+My3qWJqygfdPXQdldbIhC9JZY2RGfT+FhIuwf5vAKhrX4VJz9Vn67lr7A6+oeMAK6i0u7BqCCDY7PUt+2Oz+U8vT6zNjHQdmdbIhC9JZVH9TtBoMByYC9fOAjCuXT3cqldh+qbzpNzP0nGAFUxWBvz+EtyOJKH3Uv6zL43W9Wx5oWktXUemVTLhS1JZ1eNLMK0Kf74B2ZkYGejx3/6NuZaUztdybr72CAFb34HIvYg+3/H+cUuyNBpm92+MUsFmfMiEL0lllVlVdWjnxjnYPxcAXycbRrZwYtnhKE5GP3txtb171WnMDRtC9+6wYUMlri67/xsIWgHt3meL3nP8E3KT97q6U8fWTNeRaZ1M+JJUlnn2hoYDYd8cdfNz4IMeHtS0MmXKurOkZz797lgLF8LIkdC5M6xaBaNGwcyZMHmylmMvD86th4BPodFgbjd/j5mbL9DE0Zox/i66jqxEyIQvSWVdz6/A1AY2vg5Z96libMCcgY2JuJXKN7uebmjn1i346CO1hz9+PDRurJYSP3AA1q+HU6dK6HsoiyL3w5+vg5M/J2v/j76zgrmTkklLvUbcT69YQzkPyIQvSWWdWVXoO18d2gn4DAD/+nYMb1GHnw5EPtXQzp9/qqvP69Z99HUrK3jlFVi9WpuBl2E3L8KaEQgbF/5zbiUvvJtArH4cbWxcOfCXJY0bQ3S0roPUPpnwJak8cO8BvmPg0HyI2AvAhw8N7RR14/OUFMivEK2dnborV4WXFAcrBoChKbuqbeCPPabYdD5PE0drfplSj61bYexY9T/AikYmfEkqL7p9Drb1YeN4uHcbCxPD3KGdL7ZdLNIl2rSBLVsgK49ZnZs2Qdu2Wo65rEm7AysGQnoSjFjHvN9qU2fAOdKzsvlmUBMMcurcv/OOWvcoooLtJy8TviSVF0bmMGAJpN5UV+EKgX99O15t48LyI9EEhhS+tZufH3h6qj3YuzkFONPS1Ie2sbHqzJ0KK+MerBoKCeEwdAXUaEwkVwhNvskH3T2oX+3f3WmMjKBePYiL02G8JUAmfEkqT2o2hec+huBNcFwtSPteN3c8HCyYsv4Mt1LuF3qJ339XK/+6uECzZuDoCMeOwe7deW8iVCFkZ8H6MXDlKPRfDHU7cDk+hXSvYByNbBnd2vmR5klJcPEiuLrqJNoSIxO+JJU3rSeBazfY+RFcPYWJoT7fDfUmKS2LqRvOUVgFXAsLWLbs3+qyQUGwbRvUqliLSv+l0cDmiRC2A3p9DQ1e4H5WNhNXBWFmosflld6Eh/87KycrC95+W90etXp+m7aWUzLhS1J5o6cHL/wIVarDulGQdgcPB0s+6OHB7os3WHYoqkiXsbeH5s3VHn6FJQTs+ADOrIIOH0KzVwGYsyOU4GtJfDe0CZ9PM6FlSxgwAF5/XR3KuX4dfvhBx7GXAJnwJak8MqsKg36FpGvq/HyNhpf9nensWY3Pt13kbKzcIQuAf2apewW3mgDtPwAgMOQmSw9EMrq1M529qjNmDERGQt++4OUFGzeq73gsLHQcewmQCV+SyqvafurMnbDtsOe/KIrC14OaUM3ChDdXnSIxLVPXEerWvq/hwLfqdNaun4GiEHvnHu/8fhoPBwum9vi37LG1tbrieOJE8PHRYcwlTCZ8SSrPmo+DpiPV0gvn/8DazIj5w5ty7W46H6w/W+h4foW1f65aMqHxEOg1FxSF9Mxs3lh5iuxswcKRvpgY6us6ylInE74klWeKoiY0x5ZqVc240/jUsWFqDw92XLjOj3sr2ETyotg/Vx3KaTQInv9BfeYBzNoSzNnYRL4Z3AQXO3MdB6kbMuFLUnlnYAxDVoCZLawZDolXeaWNC32a1GTOzhACQwufn19hPJzs+/0I+uourutOXGH1sRhe71CPrg0cdByk7siEL0kVQRV7GL5GXUG6ciBK+l3mDGiMp4Mlk1YHERGfousIS5YQap2hPJL9iajb/GfjeVrXs+XdLm46DlS3ZMKXpIrCoREMXalujbh6OKZKBotf8sVQX4+xv50gKb2CPsTVaGDHVNj3Ffi8BC8syk32V27f47XlJ6llY8oPI3xySydUVpX7u5ekiqZuezXhxRyGDa9S29KIBcN9iE64x+srTpKRpdF1hNqVnQmbJ8DRH6Hlm9BnHuipD2OT0zN5ZdlxMrM1LB3lV6H2pn1WMuFLUkXTsD90nw0hf8HGcbRytuLLAY05eCmBqRsq0Myd+8mweiicXqkuqur2ufoQG8jM1jBhVRCX41NZONKXuvZVCrlY5WCg6wAkSSoBLcdDVjrsngHAgBcWE3c3jW/+DqOWjSnvdnXXcYDFlHwdVg6CEDh+sQAAC9tJREFUGxegz/fgOzr3kEYjeG/dGfaGxTO7fyP869vpLs4yRiZ8Saqo2ryt/pmT9Cf0+5Grd9OYH3CJquZG5Xcbv2tn1dlI927DsDXg1jX3kBCCT/4KZtPpOKZ0c2do8zo6DLTskQlfkiqyh5K+kp7EpwN+5s69DGZtCcbYQJ/hLcpZQjy3HjZNULd8HLMNano/cnjeP5f+396dR0dVXwEc/94sDATCFnYCBiGhLKJAQKhYpGEXobKJHFpBDoiorVjRemjrdjxarVYWewRFUVsFFZEgcNi0oiwKiCyCSAIREqAEkNQQIITc/vGmNmAgg5O8Yebdzzk5Z+a9X+bdm0lu3vx+v/d7zFmbxdhuzZh4Q/MQBXn5soJvTKTrdi9UqQkfTCL2jYFMu2UudxQVM+X9bVSOjWJwh8RQR1i2s0Xw4WOwZqpzkdktb0C1euc0mb5qN39b+Q2DOzRmSv9WiETmfWmDYQXfGC/oONq/uuYYfHP6MHPYm4wpKub+d7YAXN5F//h+eG+cM/Oo4xjo9zTE/H/Gjary/MrdTF21m8HtG/PM0KuJirJiXxqbpWOMV7TsB7ctgsIT+F7txaup++jaPIH73t7CK5/uDXV0pdu5CF7sBoe2weCX4Kbnf1Ts/7p8F1NX7WZox0SeGXY10VbsL8gKvjFe0qQT3LEaGrTFt3AcrzWcz42tE3jsgx08t+Kby2fK5omj8N54mDcKajdzYm43/JwmZ84W88C7W3nho0xu7dyEp4e0s2JfBuvSMcZrqjeC0YthxZ+JWf93ZtT9hJZtJvHcqt0cOH6SJ25uiy8mRCtJqsL2+bD0AWeZiO4PwvX3n3NWD85FVRP/+QWf7D7Cb9OSmdQz2frsA2AF3xgvio6Fvk/ClTcgi37HPUcm0KX5KH696Zdk5uYzc1RH6lWv7G5MOV/A8j/Ct2ugcUcYOAPqt/5Rs6wjJ5jwj01kHM7n6aHtGJ4aybfsKl/WpWOMl6X0gYnrkWtG0jnndb6s9SCtD6Xzq+mr2fTtd+7EcCQD5o+Dl3pA7i5nueexK0ot9ku2HWTA9E85mHeKV8d0smJ/ieSy6bM7T2pqqm7cuDHUYRjjHVlrYMWfIGcTe6QJ0woHknT9SO7q1ZrYilh0LHujc0eqrxc7Szx3vQuuuxcqV/9R01NnzvLU0q+ZszaLa5rUZMbI9iTWiiv/mCKAiGxS1dRS91nBN8b8QBV2LOTsqseJPpZBrtZgVVx/rh18N82S2wb/+t8fgm3vwNZ5zsybyjWh8zjnzl3nzav/n3WZR3nova1kHS1gzHVJPNSvFZVirHPiQqzgG2MuTXEx7PmQf6+cRt1Dq4lCOVz5Smq0H4ivRXdo0A6qBrBGzak8OPAl7P0Y9q6GnE2gxdCoA1x9K1wzEnylL2x2JP80zy7fxVuf76dp7TietHVxAmIF3xjzkx3L2c3aD16jds4qOkd9TQz+JZbjGzlTJqvUgrjaEF0JCgvgTAHkH3bW5T/hv9uWRDsDsc17ODcoqZN8wePlnTzDy5/sYfanezldVMzYbs2Y1DOFKpW8dw/an6LCCr6IDAMeAVoBnVW11AotIn2BqUA08LKqPlXWa1vBN+bysi07j2fTP6cw+wtSfdn0q3OYFr7/EFt43FnI7GwhVKoKsXHOP4CEFs5X/TbQtAv44i/6+nty85m7YT/zNuwn7+QZBrRryKReKTS3pY0vycUKfrDTMrcDg4GZFzl4NPAC0AvIBjaISLqq7gjy2MYYF12VWIM5E3uxIasjMz/OZNrOw0RHCT9vnsBNnRqR1qoeCdV8l/SaWUdOsHp3Lku3HWLdnqPERAm929Tnrh4taNOoRgVl4l1BFXxV3QmUdcFDZyBDVff4284FBgFW8I0JQ52SatMpqTYZh79nweYcFm05yAPztwJwRUIc7ZvUpGWD6tSL91E33kdVXwyFRcUUni3meEEhmbknyMzNZ1t2HvuOFQCQlBDH5D4tGZaaSL14l+f/e4gbF141BvaXeJ4NXOvCcY0xFahFvXgm9/kZ9/duybacPNZlHmXzvuOszTzK+18euOD3RQk0rR1HywbxjO3WjO4pdUmqU9XFyL2rzIIvIiuBBqXsmqKqC8szGBEZD4wHaNo0zNbpNsajRIR2iTVpl1jzh235p4s48v1pcvNPk3+6CF9MFL6YaKpXjqFpQlzolm7wuDILvqr2DPIYOUDJy+ES/dtKO9YsYBY4g7ZBHtcYEyLVfDFU88XYmftlxo2rFzYAySLSTEQqASOAdBeOa4wJAwUFkJMDZ86EOpLIF1TBF5GbRSQb6AosFpFl/u2NRGQJgKoWAXcDy4CdwNuq+lVwYRtjwt2xY3D77dCwIaSmQuPG8PDDUFQU6sgiV1AFX1UXqGqiqvpUtb6q9vFvP6Cq/Uu0W6KqKaraXFWfCDZoY0x4KyyE3r2hShXIyICDB2HNGli3DiZMCHV0kcsWpDDGuG7BAqhaFWbMgLp1nW3Jyc72hQshMzO08UUqK/jGGNctXw4jRsD5l/BUrQoDBsDKlaGJK9JZwTfGuC42Fk6dKn3fyZPOflP+rOAbY1w3ZAjMnu305Zd04AAsW+ac5ZvyZwXfGOO6tDRISYEbb3QGa48dc/ru09Jg8mSoV/rS+CZIdk9bY4zroqJg3jyYPh3Gj3fO7Fu3hkcfheHDQx1d5LL18I0xJoJcbHlk69IxxhiPsIJvjDEeYQXfGGM8wgq+McZ4hBV8Y4zxiMt2lo6I5ALfBvESdYAj5RROuPBazl7LFyxnrwgm5ytUtW5pOy7bgh8sEdl4oalJkcprOXstX7CcvaKicrYuHWOM8Qgr+MYY4xGRXPBnhTqAEPBazl7LFyxnr6iQnCO2D98YY8y5IvkM3xhjTAlW8I0xxiPCuuCLSF8R2SUiGSLyh1L2+0Rknn//ZyKS5H6U5SuAnO8TkR0islVEVonIFaGIszyVlXOJdkNEREUk7KfwBZKziAz3v9dficibbsdY3gL43W4qIh+JyGb/73f/UMRZXkTkFRE5LCLbL7BfRGSa/+exVUQ6BH1QVQ3LLyAayASuBCoBW4DW57WZCLzofzwCmBfquF3IuQcQ5398pxdy9reLB1YD64HUUMftwvucDGwGavmf1wt13C7kPAu40/+4NZAV6riDzPkXQAdg+wX29weWAgJ0AT4L9pjhfIbfGchQ1T2qWgjMBQad12YQ8Jr/8btAmsj5t00OK2XmrKofqWqB/+l6INHlGMtbIO8zwOPAX4AL3Ck1rASS8zjgBVX9DkBVD7scY3kLJGcFqvsf1wAOuBhfuVPV1cCxizQZBLyujvVATRFpGMwxw7ngNwb2l3ie7d9WahtVLQLygARXoqsYgeRc0licM4RwVmbO/o+6TVR1sZuBVaBA3ucUIEVE1ojIehHp61p0FSOQnB8BRolINrAEuMed0ELmUv/ey2S3OIxQIjIKSAW6hzqWiiQiUcBzwOgQh+K2GJxunRtwPsWtFpGrVPV4SKOqWLcCc1T1WRHpCrwhIm1VtTjUgYWLcD7DzwGalHie6N9WahsRicH5GHjUlegqRiA5IyI9gSnAQFU97VJsFaWsnOOBtsC/RCQLp68zPcwHbgN5n7OBdFU9o6p7gW9w/gGEq0ByHgu8DaCq64DKOIuMRaqA/t4vRTgX/A1Asog0E5FKOIOy6ee1SQdu8z8eCnyo/tGQMFVmziLSHpiJU+zDvV8XyshZVfNUtY6qJqlqEs64xUBVDecbIgfyu/0+ztk9IlIHp4tnj5tBlrNAct4HpAGISCucgp/rapTuSgd+45+t0wXIU9WDwbxg2HbpqGqRiNwNLMMZ4X9FVb8SkceAjaqaDszG+diXgTM4MiJ0EQcvwJyfAaoB7/jHp/ep6sCQBR2kAHOOKAHmvAzoLSI7gLPAZFUN20+vAeb8e+AlEZmEM4A7OpxP4ETkLZx/2nX84xIPA7EAqvoizjhFfyADKADGBH3MMP55GWOMuQTh3KVjjDHmEljBN8YYj7CCb4wxHmEF3xhjPMIKvjHGeIQVfGOM8Qgr+MYY4xH/BZnlzWbsoiWfAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"model = RidgeRegression(alpha=1e-3)\\n\",\n    \"model.fit(X_train, y_train)\\n\",\n    \"y = model.predict(X_test)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", s=50, label=\\\"training data\\\")\\n\",\n    \"plt.plot(x_test, y_test, label=\\\"$\\\\sin(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x_test, y, label=\\\"prediction\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3.2 The Bias-Variance Decomposition\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 1440x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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3MNr4rbHjvrwdPxhhjjDHGGGNMOfItjzdxHOdjYASQ7Lpu64M87gCvA8OAHOA613WXlMexzTFY/H+we652/Or787FVEMV/B3OvhYAYaP8CLPmHdvbqP2b/2UauF7LjIX2VhkjnpUDSZAVMHl8Ij4WibFh4u4IcTwAEVoeQ+hBSD4LrQ2hDCIzRIOic7ZCzAwoztNW9xxeckltQTQhrXHb8gnTI3Qk5OyE3QbfifFUUbfkc3CJYeKuqmHp+AX6h0Pt7GN9Z6218E6x7TdVF3T9R6PNX1DhD85HmXgsLb9PPoOv7R660MsYYY/7ArsGMMcYYU57KJSQCRgJvAZ8d4vGzgKYlt27AuyX/NRVl80gFHr4hMGSBwpW/Ins7LL0P4r+FwJoQ2ggW3wWOn8KeSf00f6coU7OKijLVVvZHvmEQ3hz8wsCbrzY4b75ek7tLr9uPAxxlBY5PMLjFer/DKS0Q2vkzfBde8nkaQmQHSBitaqcWD8Ka56Fg79G10B1KYDUFaCv/Ayse1y5qfUZBYNW/9n7GGGNOVyOxazBjjDHGlJNyCYlc153hOE6DwzzlXOAz13VdYJ7jOJGO49R0XXdXeRzf/Ekps2He3/R1/9/AP+LoX5ubpMqfpKmQOAWyN5c9lp8GybPUqhVUHbK3loU7jo+CowMCIgci22lmj09gSZhToHAoZ7tmGrn7DJX2BKqKyPFVUFOYVvI2flC1O8T0VDi1/RfIjddjxQcZEu3xB79w8AlSu1dhRslaS4MnF/ISdCuVOl83gIQx8GNtaHgVVOkAkW0hss2fC9scD7R5DMKawrzrYEJ3BUfhzY/+PYwxxpzW7BrMVBivF3L2QH4GFOWrOruoQNd6/qEQGKFbQDj4lNfvpY0xxhxvJ+pv7NrA9n2+31Fyn12gnGhZW2HacMALsfdBtd5Hfo3rQtIU2PAu7PhJQY5vOASU7FTW4GpodCPMuwZytilnyU3UPKIaZ0CNQVClo8KjxClqJcvagEZieWHvMt0AcBQAucV6zPFRMOT4abcxb0l4BKrGqTIAAqLVtrZnAaTMLFu34wPhLaHGmQrCcndBylzIWKkgKn932TF9giGotv6bt1PVT76hUJQF4S0gaxt4s/c/LwV7YN3rZd/7V9EubrWHa4ZRQFTZY94iVQsVpAJuyRwiV8eu1gcGTYWZ58H47qpQqjHwKH6YxhhjzBHZNZj5y4q9LqkpCWRvWUzxziX4pW0iMCeBoNxEgvOS8HEPUh1+MEFVILpJya0xRDeFWu0hsn75bk5ijDHmmFW6WN9xnJuBmwHq1atXwas5xRRmKCAqzISQBtDu6SM/f9MnsPFdDW/2j4LY/4N6l8DGD2DTBwpD0uJgSj+9JrKdhlbXOQd8g8FbDImTYO41qr4pTNfzAmKgzrkQ0Urb0aevgM2fQNYmhUGgkKbu+dDwGg2zLs6FvGSFRGnLIHWJKpqSp+6zaEfhkROgSqL0lZCx6hDDph3wqwJugaqPcrM1A8kTALhlVVAZq/XZY86ApEl6ruOrUCg/peTzVIXgurBrPGz7EvBoy/vA6nqfrE0Hb7Mr5RsGIQ0Vsk05Ezq+ArH/OPzPxxhjjCkndv11essrLGZdYibrdmWQvG0NoTunUT99Mc29G6nl7CGm5Hk73Wi2udEkuPXY5XYgwY0iww2hAF8K8KMAX4rwIZRcagcVUje4iJqBhdTz3UvtogTCN0/DE/dV2YHDa0O9HlC/B9TvDTHNLTQyxpgKdqJCop1A3X2+r1Ny3wFc130feB+gc+fOtu1TefEWw+zLIWMN4EL3jw89T8f1wuZPIe4hhTLR3aHHZ1DvYrVpzb0Wtn6hqptdvykcAWhyK3R9V19nbIAtI2HLZxos7ROkkCegmo5de7iqaXZNgJVPaXhzUG2FI3XOVwi0fRTs+EVDpUMaQKMboPH1au/asxB2jlY1D0BYcw2p3rME8hL3/TBlu4f5BEFMH6h/OUR30sDu0nNQmAHxP8Dmj9SOF1BVIVX2Vh07eyvs/IXfq39wFBD5hEFxpr5PW1rSVuerFrmceN08/qqkqnMBRLYuufgpvXlVYZSxVj+bgt0K0pbcDWtehMY3Qr0LIaK1XTQZY4z5K47qGsyuv04veYXFLNmWxtzNe1i0KZGwnTPpyxL6epZTz6NfgO32q0VKRCcSolvjrd6OgLrtCYusSg0fD3V9PPh4HPx8HPKLvGTmFZKeW0RmXiF7cwrZnprDttQcftuTw7bUbJIyNBfScaBNjA+DYjLoHxJPbMEKArbOgpXfa2FRjaHVedDyPKjRxq59jDGmAjhuOW2/XdIPP/oQO2sMB+5EO2t0A95wXbfrkd6zc+fO7qJFi8plfae9xf+nQdWOBxpcBT0+Pfjzds+HRXdB6kKo2gM6vgZVS35UOYkwZZAqawBqDFXYs+T/1FbWb7RavlY+pYoaxwPVBihM2btcbWld3tZw6pS5et2e+QqZWj2sEOiPwVVxHmz/SeFN4qSSO0va1EABVuO/6bEdP5UMpy4Zal0a1jh+mnlUlKXKooCq0Ozv0PxOVQMB5KfC3jjYu0JrS5mh3c9+t88x9/OH+x2/klY5F2oNU7VUwlgFTK6ryqi2T0NEi0P/rNLXwYxztPtbqSodoPVjer9j2YXOGGMqGcdxFruu27mi13EyK+9rMLv+OjWlZhcwcXUiY1ckMm9TCu28azjPdw4jfBcQ7mZS5BNMft1eBLUYgqfpIIhqVG7HTssuIG7HXpZtL7vtzSnEcaBDnQjOrV/ImYGrqJkwEWfLTF1LRTWC1hdBp+sgona5rcUYY4wc6hqsXEIix3G+AvoDVYEk4AnAD8B13fdKtl99CxiKtl+93nXdI1592EVKOdn4ISy4CYJqKXQZsVbDn/dVkKYgacun2jq+/YvQ4Ar9BqcwC9a+AiufVitYZFvo8YW2pB/XAXCh63/1nF3jFcI0v1vPW3SHqpE6v6UwpyANlj2kVrWg2tDmcWh4nba7P5TCDFj7unYVK9pnLpB/lCp38lO0Hb23EPBA7XOg5pkaTL17LiRNU+BSGhjhqMUMwPEv+X6fnc88/gqu/MI1t6h0BpJ/tNYfGKOWtJz4Q6/ZE6jjucVQ5zxo8RAk/Azr3oTibGh0PbR5Ui1pB1OcBzMvUote3QsVsmVu0HDs1o/pPguLjDGnAAuJjs3xuAaz669TR0ZeIaPjdjF2xS7mbt5DtDeV20Knc4FnOhEFSbh+wTixw6HNxdBoAPge5nqsHHm9Lst3pjN1bTJT1yWzfIfGETSOCeGK1iFcEBJHlc2jYfM0Xe/EDoeuN0GDPlZdZIwx5eS4hkTHi12klIPUxTChp3bQSl8F3T5UWLPfc5YokMjZDi3ug1aPlGxFXwSbPoIVT0Bekp5b92Lo/Y2+nnMVbPsaoruoIiigKrS4H5rcBls/V8tUUG0NYq7SQW1jS+/T8Obmdysk8Qs99NqL82HdG7D6Ob3G8VGAE9lO7WYUlz3XEwhRHfm97WvfHc0Cq2sGUkGaWtHc4j8cyNF777uLWkh9qNYfqvfXaxfcrMoiT0BJtVLJMfFC83tg6/8gtyRMCqgOtQbDnsVlVVc4UKW9KpjSlmnOk+OBZndBq0cPvsNccQHMvQriv1P1UWhDWPlvtaZFtFSVV80zD33+jDHmJGAhUeVj118nN9d1Wb4jnf/N38avcbvILSxiRGQ8d4RMJjZ1GrjFOE0GQdvLIHYY+IdU9JJJycxn4uokfly6g4Vb03Ac6N4wmutawhnZY/BZ9jnkpkFMLPS8S2u3HdOMMeaYWEh0OspPhd86qcKmKEuVPWdMK6tAcV3Y9KHaywJjoPd32kYeYPcCVR/tXa4dwjLWQs0h0O8XbfO+4QNYeDPgAf9IaPkgNL1dYcuCWxQS1RquWUbFOZpjlDRF7WFd34Mq7Q6/9oTfYPHfVT0TUFUVPf5VFPT4BOvr3J0KjbxF/N7yFVhDlUQ1Bqm6aM8CtXqll4Q14S0UtmRuKHvvgBjNAwqpD01uAZ8QtZslTy/bAS2sBWRv0e5qoM8ZVFvnNagWnDEd1rwEq5/V4z5B0PcnhTlbPof170DuDj0WEAMNr9Usoh2j9Pqu/1Xr3h95i2DedQqhOr0OTe+A7d/D8sdVHdX4b9Dh5YOHTMYYcxKwkKjyseuvk1N+UTE/Ld3JZ3O3sSohg2A/h0cbbuD87G8I3rMKAiKgw1XQ9cZybSUrb/F7cvhx6U5GLd3Btj05xIQFcH2X6lwTsYTQpR9A4gqtv9+DqoDy+FT0ko0x5qRkIdHpxvXC9HMgcQLUGAKJ4+GsZQotQFu8L7xd7WU1zoSe/1NQVJgJyx9TBU9QTVW+rPwXhMcqCPELLWlfuxlwFXZ0eAkCq0J2PMw4X5U8bZ6E1v/UPJ5516l9qsNL0OTmw7dJZW3RrKIdPyuEyd9TUrnjaui1XzhkbeT3uUP+VdR6FdkGdk2EhNEKjnxDS6qPPBpWXfciqHteWXuX6yoEintELWnB9fTc7K0Q0xu6vKe5QTvHwNJ7FSgBZTOISo7vVwUK0xTWdPtQg7Mn9VVLGUDrJ6Dtk/o6eZYqg7K3lb1Xtb76PnuLZjZ1eg0CovY/J94imHUJ7PgRun8CjUrO54onNdw6sOahQyZjjKnkLCSqfOz66+SSU1DEl/Pj+WDmZpIy8mlZPZiH66+lZ8In+Oxep+3mu98GbS+FgMNUcFcyrusyY8NuPpq1hRnrUwjw9XB++1rcXW8TNRa/Akkr9Nn6PwStLgCPteEbY8yfYSHR6Wblf2D5PyH2Plj7ErR4ADo8r8fyUmDqELU9tX5cM248PgpEFt6m3cia3gZNb9F27D7BMHiuwpcFt6iSBQ/0+hrqX6z3TJoOsy5WoNPjC6g5GJY9COteV5tVr28gvNmh1+sthNXPa+6RxxeCG2jreoCQJhAYraogXK2j7oUawF3jDPWm7xyt1riEcfxeVeQJgOZ36fP5hR/8uK6r8GXZQwqCwppqhlJRtgZ3756nYKzloxDdWRU9Wz4Fb8H+g7HdQuj2sXZfy0uBsW3KWvTqnAe9v9c59haq4mjFk4CjaqeiLAhtooAssCp0eVcDrvdVnK/QL2mSzmW9i3T/noUw73q1Eja8VoPBfSu+bNwYY46WhUSVj11/nRwy8gr5dPZWPp69hbScQno0jOSJhmtovu5dnD0bIaYF9L0PWp1/0lfbbEjK5OPZWxm1ZAeFxV4u7FCLBxtspOqiVyB5NdTpCsNehFrtK3qpxhhz0rCQ6HSSOBmmDoZ6l0DmJs0aOnu95gzl7oLJg1Qx0/s7VZ8UZcPiu9V6FtESun6gdrDx3dXSdeYcbTU/+3LI2Ql4oedX0OAyhSzr31b1T1hj6PuzWrFmXaKKomZ/hw4vHLhr2b7S4hR0pC2FiLaQuU5hk18VBUwpMzRHyCcEWtyrm1+4qow2/lfHz01Q5VPD6zQU2lsAKx6H7aMgIBpaPqx2ON+gg6/BW6jPv+JJyNutoMpboGN2fU87laUuhtRFCo6SpkJR5oHv4xOkdjLfUIVObhHgQlhzGLoY/EoCnIx1MP8mSJmpKq3cRCjcC34RUJiuWUUdXtp/oHdRtsK9PQug7y9Qa6juL85XuLb6GbUU9v1ZQ8WNMeYkYCFR5WPXX5VbflExX8yL580pG9ibU8ig2Go82DyJZnHPQ+JyqN4a+j0AsWefctU1KZn5vDd9E5/P24bX63Jp59rcX2MxkbOfgezd0Pl6GPgYBEcd+c2MMeY0ZyHR6SInAca1L9lh7B+w8Fbo/ik0ukbtYJMHaXhzv9FQvZ+qiWZfrtCi5YPQ5ilVt8y5UkOp+42BtCUaXh1YQ6FR4xuh2wcKJxbdoQqe2mdDj88VpMy6CHDUGlXnnEOvtbgAVj0Dq/6jcAXKgpcqnbRdvTdflUwtH9SAZ48PpK+Bda9p1k9xLtQYrIqhmkMV7uxrz0KI+6fa7oJqQ9unFCQd7DdqRbmw5B7Y+J6+9wnW5yhtHSsV2kRVUdnxWuO+nABVARVlao5T1qZ9HvRAgyuh+d8hqhPgwvq3YOn92qmt5lDNKCrM1GNRXaHvD/vvgFawFyYP0M9r4ESI6VX2WMI4mH2ZKqj6/rj/Y8YYU0lZSFT52JJvQtEAACAASURBVPVX5eS6LqOX7+KF8WvZnppL7yZVeaK7h6ZxL8KG8RBRFwY9Aa0vPOXCoT9KTM/j7akb+XphPI7jcGePqtzmfoff4g8hMBzOeBI6Xms7oRljzGFYSHQ68Bargmj3PA2onn42hDSAwXPUyjRlkEKGAb9BdDfNHVr2gCptenwBNQbqfda+psqglg9r17KkKapKSlumUGb4ypIt2i+AlNkKb9r+S2HRwtsVoPT7FUIPMxQxbbl2R0tfoYCoOBfwVcjj8SsJixxofBN0eUv3Jc/S7l6JE9RyVvMsqH+pBk47vqqU8gtTlZFv6P6zj5Kmq6VszzyIaAXtn1d1UOnFw96VMOsytbhFtFLYVpimx3yCtb6Aqpr9s28r2Ip/q2IpuAHkbC15foja86q0gc0jVSXlH6UZSaUiWqo9rMFVakubfZkqj2Lv0byo0qDKNwz6/QTVB5S9Ni8FJvbS+505F8Kblj2WvhZmnKNKsS7vQeMbju7PjjHGVBALiSofu/6qfOK27+XxX1YRt30vsTXCeOyM2vTa9i4s+gj8w6DPPdDtVvALrOilnlA70nJ4afw6flqWQI3wQJ7p7cOATS/gbJsNjQbAuW9BRJ0jv5ExxpyGLCQ6Hax6DuIe1gDljHUaajx4vna+mjxQVTkDJii8mXO1hjzXGqGKn8Cqeo+k6QqTYvpAxnoFJZ3fUsi06mnoP1ZtXdPPhfxk6D5SQ6GX3g/rXlU1TK+vD73bluvC2lc1r8j1Al6FMhnrAbdsG/qAGFUTFexWxU7+bn6fNXS0AqtDcF21XgXXheD62h1t6/8ge7O2uG//AuyerfXjlrS1BasNr84Fqpxa8aTW6heuKqymd2i+U+nsn9KgKKorpC4oO37bp6HVI2oFW/G4dntLGAN4FBKlr1SQVf8KaFFy/jaPVAVQiwdh6X3awQyg1WOqgioNtTI3woQe4BepQKr05wf6jLMuhcSJet/2z9tv0owxlZaFRJWPXX9VHum5hbw0fh1fzN9GTGgADwxpzgW+s/FM/Cfk7IHON0D/RyAkuqKXWqEWb0vlyV9Ws2JnOp3rRfJG06XUWvCMKseHPgvtr7RrIWOM+QMLiU51u+fDxN5Q9wJo8y8Y10ZVKu2ehQndNc9m0GQNWZ5xLmRtho4va/ZN6f80c3bCbx1VlZOfpuCh36967LfOUP9y7RA252rtKtbvZ83amX2Zwo9mf9d7/rHlq1TOLpg2FPYu1/dhLTQUOnXhQZ7sURhVnKuKGZ8giOkLtYfpfsdPs49Kb26R2rSKMqEwQ3N9cndpHlN2POTE6xyU8g2BonygqOy+8FiFKvUvA9/gsvuztsL8GzSHKKS+diMLawa9v9HMJCgLiqr2UuhUKqaPgrkFNyqcanwLbPqv1tz1I1VSbXhHM5Ea/02BWdzDCqR6fatjrnxK4VXts6HvT2UVUilzFP5Fd4aBk8Bnn98eeotg8T/03k3vgM5vHH5XOWOMqSAWElU+dv1V8VzX5Ze4BP49eg2p2flc06MB93d0CZn4IGybBbU7wfBXbFDzPoq9Lt8t2s6L49exN7eQ+7oEcEvay3jiZ0PTIXDOGxBWo6KXaYwxlYaFRKeywgwY2x7wapv7OVdC8kw4Kw7mXK5Q5owZqoKZc6Vm1vT5XtuvlyougEn9NH/IWwDRXTUAOaCqQqbseM0iWv2sWtX6/qj3mTZMw5w7vwVNbz34+rzF2mp+zUtaoydQIca+AQ1ASCNo8zgEVtNA6h0/q6Ko5UPaae1Ydu1yXVUj7V2huUmbP9bMoH35R2uGUu0RUONMta79/novbHgXlj6gUM3xU4DV8RXtBOc4sPwJWPkviGwHe+MAH6BYn2HQVFh4C6QuUXXR8scBRwOooztqN7pN7wMeqHephnXnJqhlLKYnTOyryq2wpjBkEfiX7NYW/52GhNe7FHp9uX8Q5LpqJ1zzEjS5WbumWVBkjKlkLCSqfOz6q2Il7M3lwR+WM3PDbtrWieCZs5vTestHMOMl8A+BM56Ajted8nOH/qr03EKeGbOGbxZtp3F0EB+3XEr9pS9CQBhc+AE06l/RSzTGmErhUNdg9n+XU8HC2yFnG/T8n+YRJYzVtu/LHtBOWD2+0Byf6edo6PLQRfsHRKD2rz3zFBDVuxQGTYOgGrD2FYUq4c0UEDW4SvOOcGByf+1I1vv7gwdE3mJY9Tx8GwZrXuD3djH/qH0ubBwNe+7yDgyaAsnTYfoISJyiiqhzNkGLe459W3fH0XGzt2pYdsZatZV1/1wVO5HttIPb5pEw80L4PhqmDIF1b6rVzvFAsztgWBxU6aCdyAKiNbh71sWa9dTmSQVAe+MguB4KiKpDfoqGiTe4Wq/Z+IEGf+PCjLNVBdblLRixHhpcDls/VygV0VoVTOvf1nmI6a25RT/V0Y5wAPUuVstc/DcQ9+iBn7n9C1rTxvdh/t/0MzHGGGNMpeO6LqOW7GDIazNYvC2NJ89uyY8XhNF67Pkw7VloeS7cuUgtZhYQHVJEkB/PX9SWL/7WjXwv9JsZy5uN38cbGAmfnQfTnrPrIWOMOQyrJDrZbfkc5l6jXclaPQLj2qkqqO55qiBp9zykL1erU/3LNa9o31YqgB2/atgxQOsnoM0TChgy1sPYtgpTCvdCu2e0y1j2NphyhiqT+v4MNQbt/37p60u2n/8R3ALd5x+lVq69y2HbV2XPrdpLAVH8d7D2JVW/NLtDQ7P3nbNzrJKmK9BJX6Xvo7vDgLFqmyu1ewEsuRt2z4WAauAToHa10uc3vEoDvP2jFDStfBL8qiggCqkLvb+FqM4akL3mBQ3PLsoqmUU0DvBqztGusXq/pnfC7IsBR8FbtT46VvJMmHcDZG0sq0qqPgj6/KBqpfWvAx7Nkmp0jc7Zwts17LrH51rnvlxXFU4rntTuat1HHrol0BhjTjCrJKp87PrrxNudlc+jP65g/KokOtevwssXtqD+yndg1isQFAUjXoUWIyp6mSednIIiXhy/jpFzttK8isOXtb4jauMoVRNd8AGEVqvoJRpjTIWxdrNTUdYWGNtOc3EGTYVNH8DC26D+lbDtf1DjDMjeCZlrFMZEddTsG2+Bbo5H29jHfwd4oeYQbScfEKXwY9lDkLkWPEFqZap7nrafn3Km5vsMGAdVu2st3kLY/rO2s9+7rGyNjg90eAnCmsCCWzUIGgAPtH8W/KvCin9qflD9y3VfSP3yO0cFaWoR2/ShdkjzFkLrp6DNPw/eeuW6EP+tXpMTr7azKh1g128KuBxfnadG16qNbO41agvzC4PCbOj6LjS6QUOn176iz4kXOryqUKkwHcJidV6b3aXWvblXaW1DlkKVVlpHUY5a9Na9oZa/glQIb6HB4cnTYe61et+2T0PrR/W5pgxWNdiZsyCq04GfbdUzqjZqfCN0fd8GOBpjKgULiSofu/46sSavSeKB75eTmVfEvYObcWPzfHx+vAmSVkLbS2HocxAcVdHLPKkt2JLK/32zjKSMXN5vvYYBm1/ECYyES7+Aul0qennGGFMhLCQ61XiLYVIftR01uUWBxo6fSnYMO8jP1CdIM4R8/LV9vOOnoCg3Qc93fDUc+WCvBfCLUFiRs13BT9NbNUjaPxJ2TdQMoYI9eq4nUO8d2lDVLps/VhuX46sB00G1FQatfU0zkKK7QcdXIaZH+Z2f0rBn8T/U7uX46Rz0+hpqDTny64tyFfKsflYBTPO7ofa5sPNn2PYl5OzQAO36V2v49K5xOj/5uzX/p+PrauFb/4bez/GFAZNhwQ2QtUnnszAdun2s1yx7QBVbI9aqKqlU8oySqqIt+rkFREP/MfpMU4fqZ9b4Rs0uKkjVgHG8mlsUVP3AzxX3mHapa/OkKsaMMaaCWUhU+dj114lRWOzlhd/W8sHMLbSoGc5rl7SjefxXMOExzc85502IHVbRyzxlpOcW8uiPKxi9fBcX1UnnucLn8M1K1EDrdpdV9PKMMeaEs5DoVFCUq92ukqfC1q/2qcqhLHTwCdYuV46vBlp3eQ8aXLr/zlcAuYkwriPk7YLGN0O3/ypgKsiAFY/B+rcUKrV/Aby5Gvgc/72e4xui9rMD+EJgjN6z+gC1Uy35PwUqOECxKnOCasKWzxQWdXhBu4mV50Dl3ESYfxMkjIbgutq1LaKFWuPCGv+598pJUEXPlk8hsDq0+4+CoaSJmhW06zd9tsi22tLeN0Q/h+iumtW0+lkNvAa1tg1epJ3OkqfqZ4QDZ86ErV8qUPKP0myiwH22si1I18DxhDHgE6Jz1ecHhUZTzlDwVmOw7svcABN7qe1t4CSFgvtyXc0m2vwJdP0Amtx4LGfaGGOOmYVElY9dfx1/O9JyuPPLpSzbvperu9fn0X5RBI65CzZOgqaD4dy3rRXqOHBdlx+W7OTxn1cS7cnil2rvUyV5PvS6GwY9Dh6fil6iMcacMBYSnaxcL6TMVqgS/62Cn9Kqn/AWmucTWA3GdVBAk5uo1ifHB/qNgapdD3zP3F0wvruqj6K7w+A5aj0qyoX5N6pSBmDQdKjeFzI2wOR+Omb3z9SetvkTfh9E7RMMIQ1VIePN+8PBSnb4AqjaW2FTcTbE3qPh2n6h5Xu+dvysz1CUBRFtIXUB1DlXs3r23a3sz9qzUFVJu+eq/azT65ohlLlJs4A2faTWNk+AQhvHF/wjFBRt/qTkfAFhzWHIfFh6v9oDQfOPhi3XzKTtPyjYGrEOfIPKju8t1pynVc/ofHsLNFsopJ6CIm+BgqqBkyBxokKlprdDl7cP/CzeQph+roaZ9/1Ju7kZY8yhLH1Qf98dp78rLCSqfOz66/iasCqR+76Lw3XhuQvbMjxwBfx0KxRkw+CnocuN1hJ+nG3dnc0dXy5hXUIq39f/ifZJo6DZUM0pCgyv6OUZY8wJYbubnWzyUmD5k/BLE5jUV8Oe65wPfX7RTmNBNeDMGVC9H6z8t8KknB2abeMbBmfOPnhAlJcCkweqbcw/Gvr9oguR3ESYPKAkIPJAo78pIMraDFMGahh2zABteb/5Y8CrcKPT6wpC8hNVtdLhZQhpoGN5Avg9IALYPQuKMhVm4CrMyEspn/NVlA3zb4YZ50FgTc39SV2gIKrPqGMLiACiu+ic9vwf5CXpZzKhtwaHu16oNgDCY/W1WwzefH22Sf0ULAXW0PtkroOJvaH+FZqNBNrafuoQ6PmlZkflbNfcp30DXI+Pqph6fa1jOD4w92qFbgMm6FzvXQET+0D1gdDiPtjwDmz88MDP4vHTkO0qHWDWJdpdzRhjDqYwE9a8CKlLKnolxpz0ir0uL45fy82fL6Z+dAij7+jG8KR34cuLIawW3Dwdut5kAdEJ0KBqCD/c1pPzOzXgvG0XMTLyTtwNE+HjoZCRUNHLM8aYCmWVRJVN3m7t8rX+LQ0vrnEGNLwG6p6vVqYl98Hal6H/OKg1VP/An9Ad8KgNKbiOhliHNjjwvfNTFRClr1K1S//fNJ8ndal2N8tPVUVQXoJanoqzFTrk7wavF9x8vU9gTe2k1vhvqoZZcg+ENdMuXutf10yiomwFGW4R4GpAdYMrdOGze75mEXlLdj6LaAnV+pXdgmr8uXO2ZxHMuQIyN0Kj62DXJCjYDT0+1RbxxyI3SSFP2jLtMpYWB9lb9n+O46v5S36RausrSNXOcG5h2XMCYtSGVvqZS/mG6FyBgqb+o2F0rIKiZndA57cOXFPaMpg2QmGVW6TwKLp72YyisMaqKJp/o2YaDZ4LUR0OfJ+8ZJjQU+sauqh8B4YbY04NyTMVivf71SqJTiOn5fXXcZaRV8jdXy9jytpkLutSl6cGRBLw402wfR50ul7Dqf0Cj/xGply5rsvXC7fzxM+rGBa8hpd5GZ+gSLh6FMQ0r+jlGWPMcWXtZpVd/h5Y8zKsf1OhQf3LoPXjEBFb9pykqTB5EDS9TW1Erqv5M7vnA179I/+M6Qf/x35BuqpT0pYqSGhyE3T9L8T/oB26AqL1vnGPaI5R7XPht/YKEkqHWQfVhtb/VBCDo/aoTR9pzlBRriqFSmcj+Yapagg0WLv9cxpyXao4H1IXK8RIng4ps9QiBhDZRu9Z40yo1hd8gw9+zlxX1TJL7tG8oMY3w5rnFNb0+/ngO3wdSU4CJE/TmpJnQMbakgccVXBFtocq7dTq5wnQrmk7RpUM436+LAjzFmtd698sO3/RvVRJVLBb39e7WOcrYVzZfClPINQcrFlH3gLo9gk0vu7AdWZt1Z+FnHgFRS3uh6iuMPtihVbB9aDPdzD9HAVXQxer/e2PMtbD+K4Q2li7ou3b4maMMWtf02y58xM0T+44sJCo8jmtrr9OgI3JWdz82SLiU3N44pxWXBW1DufHW1Slffbr0Oaiil7iaW/5jr3c9sUSYrLW8nXIywQ6RXDFN1Cve0UvzRhjjhsLiSor14VtX8OiOzXTpt4l0OZxVdfsqyAdxrYFnwA4a6kqULaPgpkX6vGgWjB43v47Y5UqzFI70+752vHK4wdnLYd1r8GKJ6BqD83smdRHYUfH12DKILVMgQKHtv+GBpfrtXnJMPMCzUqqewHsmgzeHPAWqYXNm6+AKKgO9PoKqvU+8nnwFinASpqi3dJSZul9PP4KimoN12+xw5qUfKZMDaeO/0aPVemoXbuiOmvOTnCtozv/3iLYMx92joGEsaoWAvALh5jeqmyK6a1gyDfk4O+RMkfzilIXqaKn0+tlrX57lsDUwWU7v/mEQnEuasPzwKBJGvK95SuYe0XJc4JKnlOi+xfQ6MoDj5uToHlEmetLdjm7SetcdKfOW2AN6PCiqqzqnKu2wIOVsO8cA9PPhoZXa86RlbkbY0rNuRqSJiskOk4sJKp8TovrrxNkytok/vHVMvx9PbxzRXu6xX8I05+D6q3h4pFQtWlFL9GUSM0u4NbPF5OwbS2/RrxCZFEyzoUfQQub3WiMOTVZSFQZ5SbBwlu1dX10N+j2gapoDmbeDdph68zZULW7hg//WEfzbPwiYfhKCK594OuKC2D6CF3k1xyqIKT/WNj8qQKWhteoomjFU7D6OYUtaSWzJwJioNObUO+ist0e0papOiV/t9aRNLUs1KjaS4GLW6RKqO4jFWr9FUU5Cop2TdCaM9bo/rBmOm7iFMhNgLZPQfo62PYF1LsUun9y5GqYolzYNV5DohPGKJxzfBQG1RqmFr/Idn9uhwvXq+Hiyx6GvESd13bPKqzKT4WZF2lHsz/yCYZhcQq/do7Rzwr0WTLXKzgDzQ9q+y8FYvuGOHm7YcrgknDLq+MG1YHVz+jnEhCj8GfVfxT+xf7j4Otf8W8Nxu70BjS/6+g/tzHm1DamFYQ0gv6/HrdDWEhU+Zzy118nyMjZW3hq9Gpa1gzng0uaUmvy32HDeGh3BYx4BfysereyyS8q5tEfVzJ58WpGRb5Og/x1OGe/Dh2vqeilGWNMubOQqDLZt3qoKFtVOrH3HDqU2PGrZga1ekTzZwCWPgRrngc82gkrvMlBjuNVK9nW/0HLB2H189DwOti7XOFD+2ehxQOauTOxD7/vVgbQ9C7o/Pr+gcT2Ufqtsl+YKlVytuv+wBoaVr1nnr7v+CrE3n1s5+iPsjbDzrGw8X1IX6H7/MLVnpWfDK0eU2B0qCqYomyFMKXBUFG2tpuvfTbUHq7Wtn3b4f6qwkztQLb2FVVdtXpEP1vHVzuarXsNzYvf51wH1oSz1+m8bvwYFvxN97f5t9rTtn5RsqNdkcKi1o+pMsgpmTtfkA7Th6uiCVdDsT3+sGWkzk9IfQhtpEHhZ85UyPZHrhdmnK9zM3CyBqIbY05vRdnwXXjJ369PHrfDWEhU+Zyy118nSLHX5T9j1vDx7C2c2bI6bwwMIOiHqyF9u2YP2e5llZrrurw/YzOv/7aMz0PfplPhYhj6PHS/taKXZowx5cp2N6ssigs0UHjOFRDWVK1jLe8/dECUtxsW3KTKltZP6L60FbDmBX3dbeTBAyKAZQ8qIGr9OGz7Vq1kCWMhc4N2Nas5FOZcpblGeNF29ahdqssbZRcwrgsr/6PWtoCqWlPOTsBRZUtxviqI8EDPr8o/IAK1vGVtUkBUtRe0+qdaxfKT9fj6N2D+DZDwm6qsQOd652iYfSWMqg6zL9W8oQZXwcCJcEEi9Bip2UDlERCBgp72z8Lw1VBjMMQ9CqNbqFqs4yuq2nI84PiVvSZvF4zvpqCmyQ3Q9E7dv+Ix/RmJ6qyAKLqHQqiZF8C49pAwXs/zj9AQ8uiuqoja9qUqu2qepblG2Vsgd5d+/rMu0fyrP3I80OMzVTTNuhiyt5fP+TDGnLzSlunvpaiOFb0SY04auQXF3PbFYj6evYXrezXgvx3iCfp0MBTmwHVjbfeyk4DjONzSrzGvX92bG/L/j+mebvDbgzDr1YpemjHGnBBWSXQiFexV0JI0RdUgrR8Hj++hn++6+kf9zp9hyCKo0lZb1f/aHIoyNKvnjOkHf+3aVzU4uekd+n7DOwoQQupryPH27yFxEuAArtqS8lOg1aPQ7umy9ynKVai17cuy5wCEtYTQehqw7Bum3zj3/EJzi8pbXorOQ/I0aP4PqNoT5l0PAVHQ+zuFHtu/V6VTYYYqjIJqaxh0YQb4V1EQVP8yiOn759rIjlXiFFhyt7anr9YPOr2m9c68SIO63aKy50a0gmHL9fXEPrB7jr5u8SBsfE8DwXt8rvtWPAVZG/W5Or6qVsP8PTCpn0JAbwHUOU+DqXO2QXGeqpD2xqmlrs+PB79ITV+rQdYRLVV15PE78DnGmNPDujc0b+28HQdvZy4nVklU+Zxy118nSEpmPjd+upDlO9N5fHgs1xd8BTNehLrd4JLPIOxP7t5qKtyKHenc+Mkcnix+k7OYDf0ehP4PW9BnjDklWCVRRcvaqu3GU2ZC9081X+ZwARHAtq8UfrT5lwKigjRtYV+UoUqUPqMO/rqtXysgqnsh1L0INrwNuKpKcXw1B2nPAsCj4MgnVOFPo+vV+lYqNxEmD1BA5PjpOZ5AaHwLFKVrwHRoY4UdPUYen4AodTH81kmtbN1HajD27Es1u2nIArVO1R4OLR9RIBZYXcFQxhr9NyAGGt8KsfdqQPSJDIgAagyEoUugyzuQvlKfJf5bzYUKaYBCOvTf9FUwqpaCnX6/QkB1PbTmeahzgZ4z73rNThq+Qn8udvwCo2O1A5FfBAyYoCHmPsGqXgqpq13YAqppsHZYLOz4WaHTwUTEQrcPVRm2/Injf36MMZVX6mL9nRp0lBsBGHMa256aw0XvzWFdUiYfXhbL9TseV0DU4Wq4drQFRCepNnUi+O72frwYfA/feQfA9Odhwj/1i1xjjDlFWUh0IuxeABO6qeVnwARodBTD73J2wsI7tPNYi/vV0jXjvLIt2du/oG3r/yhpOsy7RlVG7V+A6cN0vydQwYnHX61rhRlQpb0qWby5msnT9b9lvxlJXQq/dYY9C/W9Wwi1z1MQtOm/aquq2k0tYN0+0nDk8rb1K5jYG3BgwHjY+YuGKze4Gs6YpvBjw7swvgeMbqbB25FtFMKdvwt6fKHqmbXPw+jmML67ZhoVZpT/Wg/H4wtNb4OzN0Czv8Omj2HaWdDoWlVFAeACHshP0qDYLZ+pJbC0BXDLJ1B9oH5eE3sr7GvzGAxfBTF9tEX1+C5QuFetdH5hCo12jdfPOy8ZwluoXS+4nkLEvSsPvt76l0Djv+l8Jk4+ASfIGFMppS6GqE72G3NjjmBtYgYXvjuHvTmFfH95HQbNuQbWjdX8oXPeBF//il6iOQb1ooP57vY+/K/avYwsGgJz37KgyBhzSrOQ6HhLngGT+4NPCAyeC9X7H/k1rqvdzLwFCjwcj1q+kmcALgTXP/guVRkbNK8mtDE0ulmzcEq3Uq8xCDq/rZa3jNXQ4eWSwdMetTn1+b6stSh+FEzornYtvBBUU7OPMtfB5k/U8hXaWO1QXd+HxteXx5na5/N7YdkjmtsU3VUtZQtvV2VM+xeg7vkw50r4sYbuL8qC9s/DedsVkDS6BoJqQMMrYeB4OHe7toIvyoQFt8ComqrISZl9Yv8H718FOr2qlrKq3WH5Y5qpFNW15AleVFnkVegT97AqzkolTYaQhvq5zLpY94U1hv5jtL19zk4Fe8nToX/JvKKAGO2qFt1ZIWFUZ8iJV2XY7MvVTngwnV6H8GYw92q1+xljTi9FOSV/Z3Sq6JUYU6kt2prKJe/NxeM4/HqeH61Hnwd7t8OV30H32yxkPUVEhwbw5c09mN7oXj4pDYomPWFBkTHmlGQh0fGUFgfTz9YcoMFz1cpzNDa8C4kToONLEN5U82e2fgG+oXq870HmyeSnaocrtxjwwLyrwC2A4DoaolxzsGbjeHzgzNmQvU3tYwFV1frkF67/0c2/CWZdqIAKD8TeD83vgYU3aSZO/3GQHa9dsLq8A01uKs8zpiqfGefB6mehyc3afW3qEB2z1nBVt8y8AFJmacDzWUsVurR84NAzM4JrQYv7YNhK/RwaXAHx36siZ0wrzd0o2Fu+n+NwIlroPPYbre9TF+jnAKiiqETyDFj9wj7VRmgIdWkr2YYPdZ/jQL0LYVicnjv/RljznGZEFaSpzXDPfAiPVctZlY4K1tJXase1g/ENgV5fa87RvOvtIsiY001aXMnQaguJjDmUqWuTueqj+VQNDWDMwCTq/XIpBIbDjZOgyRkVvTxTzoL9fXn/2i7EtXqIz4vOgNmv407+t10jGWNOORYSHS9ZmxVu+IWrVSqo+tG9LmM9LL0Pag6BJrfC5s9g5VMQ1UX/sK8xGKI67P+awlyY1BcyNyrIyVgNjj/4RcIZs2HlvzR8tOZQOGuJwqb1b6qaZNAkhStJ01Vhs6kkeAhvBUPmw96lsOx+qDVCs3U2fww7foROb6iFqjxlbtLcpoSx0OlNtchNHwHFOaoCSpyk89JvQ4u9uwAAIABJREFUDJy3Ezq9opa5o/0tneOogqfbB2pH6/aR2rIW/wN+rK1wJXVx+X6mw62l9nAFV+1fgKK8A5/jFoFfqCq2fMN1PkDnAwcW3bb/LmRBNfVnrd1/IP47fa4WD2qQdXQ3tSoG1oS9qyC8uX7+G97WXKODqdIeOrykQHDdG+V+CowxlVjp34VVbGczYw5mzPJd3PTZIprEhPBr+3lE/3Yb1O4IN06GmGYVvTxznPj5eHjl0g6s6/g4XxYNwJn1Mu7UZyt6WcYYU65sd7PjITdR28oX7IUzZ6ly5Gh4S+bNZK5XeJC5HqYOhqq9NbjZWwAXJGtXL9AW75tHKsQpzFAw5BZAVDdIna8gZ8tnuthv9x9o+ZAqjX5uoJal7p9qsHHcY7B7dskiPGpFC2sK869Ty0Gn16HhtTD3SoUPHV6GFveU7zlLmqodv9xiqHOuKmVKZwdV6weNroO6Fyh0K2+pi2HDe7D1SwUw0d3UUlf3QvA5QXMEchNh8d0Q/82Bj8X0VuUUjgK+osyyx0IawDmbDwzKUuaonSw/GaoPUtBTc6h2o/MJ1iDr4lwoTNN7Dl+pkOmPXBemn6PKtiHzFRwZY059866HnWPggqTj3i5ju5tVPift9dcJ8uPSHdz7bRzd6oXyWbWv8VvxJbS5BM59C3wDKnp55gRwXZfnxq6m8dyHucR3OsX9H8Wn/wMVvSxjjPlTbHezE6UgHaYO1T/6+489+oAI1Eq1Zz50eVdbys84H0KbqF2tOBca36SAqDhfLWm/NoGFtyhM8QkBx4W2z0DGSg28XvWMqkf6/gStSrbrnHuNAqLI9trhavLAsoAopKEqjXJ3wP+zd57hUZVrF773THoCIZDQQ++9994UFLAL9q5HlIPdz3KOvfd2sCAqIkpVQHrvEHovoYeeRkhPZvb3Y00IaBDQJBPIe19Xrkl2m3dPZif7XbOe9Sy6Wh1trlwjgea0QPRe/gtEm1+Hub08osVJCVtZpyTSDIxRSHWNuwpGIAKVU7T9Gq49LEEsM155SJOrwaZXFfpc0ASWh04/S4D7IyeWSCDzKSmByFlCwdUAKfuUy/RHIjrAlasV3H34dyhRVyHWkTdICEvdD6UaApbEzHOVlFkWtBsJfqVh+Z167xkMhssfE1ptMOTJ2KiDPD52A92rBfBj4HsSiLo+C9d9ZQSiYoRlWTzbrwEnur/DRFcnnAtex7X8HJ1jDQaD4RLDiET5idsFi69XK/POE9X960KJX6vsoaqDoGI/WHyN8oM6jIZ9o+T+aPYu7PgMJteUMOATrH0tp9wgPeZD3HI5kuLXgDMA+iyDygO03bb3Yf8YfZ+4XmVHOdR6ELpPVybR9veh9sNyjpSoJcHktED0RP68VqeiYevbMLE8bHwBcKs8znKqJKrPCoVpB58jZ6gg8AuFukPh6u0S+Eo1VTe1XyNhxb1nv14FRf3HzyjjO2NydnAiVOoLARXBdUrrnEFaFz0cjs7787ECIqDnPKhys0LHfUPh8CyoMgiw4dg8vd9wS0CK/jLvMQWEK6A8caNKFw0Gw+VNdhqc3GryiAyGPzBqxX6enrCRgdUtvna9gPPAMrhmOHT/PyOoFkMsy2JIz3ok9v6Ima5WOGc+Q/ban7w9LIPBYPjHGJEoP9nyhjpQtfkSKl5x4ftlp8Ky2yCgLLT8TK6OpO3Q8RdY/4ynBOtamNYQ1jwKIdU1aU/eo/3DWsgBlH4MDk0Bd4ayd65YpZbwqYdhyc3KOgLwLy/XUHYSOAOh+ywo3xtmtlGJW6fx0PpzuVXySyByu+DEclj/LExtAFNq6/v0Y1CmHUR0hfQjyly6ahOEtzn/MQsKywEV+0o0u3q72sHvHwPTGsH8vspGKsgyzebvqV29f4TcQzns/xnKtFapmJ0lR5BPCa1b0BeyUv98LGcAdPwJGj4PWYngToPEDRKOAPaOgqq36vs1/1aHvLyo3F9urq1vQeyqfDtVg8FQBEncqP87RiQyGE4zYsleXvx1M3fUTOXD5Cdx5HQwazbY20MzeJl7utTmeO/PWepqiDV5CFlbpnp7SAaDwfCPMCJRfnF8MWx+CardBjUusiX8uifVarjdd7D7azg4QS3dA8rB0dkSa/aPVgesHrOh7bea0Lszoept0HuxXCUr7tDxqt4C3WZB0g6JQ79WgQNjAQc4Q1XSlbJX4dQD9qscackN6n7Vd706ZbkyYemgfyYQpR2TCLHsNphUAWZ3kJvJt6TKlyxfqPeExhK3QqVe3X4H/zIX/1wFRcm66uI28AA0eRUS1sG83jC9OewbI9dWfuMTBB3HSNSJ6ASBlXPXHfpNZYCOAMDhKT8L1HthWsO8j2c5oOlrKhvDrfea7VYYOW7ljpRuo2Msvu7c59TiIwisBCvulNPAYDBcnuSEVpc2odUGA8DIpXt5depWHq15nJfjnsByZ8Pd06BmD28PzVBEuL1LPfb1/pqN7how7i6yds339pAMBoPhb2NEovwgI06Om+AaEhQuxnIcM1n5QvWe0MR9w3NyeVS8Gma10zZ+paHDT3BllJwl05sov6fBs9BxlPab2Uo5RlUHQ7keMLsdzOkMh6dLdPApAbjBdVJukrpDJcgsGgA7PoY6Q6HXYgipBq50lc0dnAAtPrxwgSgrCQ5Ng3VPw/QWMKm8MpCOzIIKfVQ61/ZbtV53+Mkdtf19OWaujNKYiqpdOyAcGr0AA/epK5o7Q7/zKXVg5+dyg+UnYU2h2VtwZBo0eEqCYQ6J6+UQwi1x0JUGOJVPtGTQuY9Z4y7lU1lOODgOIjoruyo7UefjV1q/m40v5r2/X6jOPWm7p0TQYDBclsSvkVgfVMXbIzEYvM4Py/fx8pStPFt1J48ffQYruCzcOxsqNPH20AxFjFu7NGRHz2/Z7S6P66dBZO43zmuDwXBpYrqb/VNsGxZdA0emK0fnYj55TTsC05pAUGWJQLM7yqlRqT9se1ct0AMqwMC94PSXK2fF3SoDaPi83CEZ8QqfTtwAQZESDDJiIbShcob2jtI6d6ae0xEAncfJnbT8NjmG2o2AKjdqfXaqzufobAlef9XmPv0ExC5XsPLxhZpY2C5w+Ep8qHClWtbndMTa/CpsekmOpex0SN0ncar52x7R4xLCdqu0b+vbeg38w6HuMKgzBPxK5d9zzO8LJxZDt+mw8CoJgaexAFviTmZ87uIWH0O9oec+7qFpsLC/9u0yGdYMlZur8vUQM0nL+yw/d6ZW1MPqBtdrIZTt/M/P02AwFC2mNZMw3WNmoTyd6W5W9Lgk7r8KgZ9WHuC5SZt4pVIUt8d9hFW5NdzyCwSV9vbQDEWY8QtW03reYEr7ZBDw0Bx8y9bx9pAMBoMhT851D2ZEon/Kjk81yW7xEdT794XvZ7vVBe3EEui9DFbcpYwh/zJyhDgCwJ0OV21VePTax2HnZ9q3xj0SdtKOwMwOEltyqDxQwkvZbnIJHf49d11ABeg1H/b/IrGmVGPoNA5Kev55ZZ2SeHB8kRwjNc8om3OlQ8JGiF8NsSskjCRHa53DV23jy3aDct2Vh+QTlLtvVhIsv0sCREhtSN4FITWVq1T+Erdq27Z+h1vfgsPTVEpXewjUe0zB0f+UHCExsBI0f1fvGdx/2MgJviVUnpZDp/EqGzwXByaqxNDygV6LYP4VyqiqOlj5S/7hcM3BvMW7rGSY3lTf99uYG6BuMBgufVzpMLYE1H8Kmr1RKE9pRKKixyVx/1XAjI06yNMTNvBe+bnckPgt1O4DN34PfkHn39lQ7Jk0ZyGdF98GvkGEDV2Is2R5bw/JYDAY/oQRiQqChPUws63ClrtOvrhSqW0fwLonFHIdFwW7v9HykJrqbrbzU5WNdRgNS26Sm8RyKqOm+yx1u1p5j6fUCKjQD1p/CiE11KZ8/pVwfAGqKHR7SrpWQ9QQODwVqt0ObYbnijmZibCgH8StgtZfKosncaMyeOJXQ+JmOZtAAdvhHeQWCu+gcFOfwLzP8+R2WHwtJO0E32AJDPWfhMYvnS0kXQ4krFd4+YHxEldqPQD1n4agiv/suIemSryrMxSCKinMPMdFlPP7BcAHyMkTckC3qQrgPhc7v4DVQ8AZDF1/h/k9JXqVbgnxUSp77PRz3vseWwhzu+l32fzdf3Z+BoOh6BAXpSYG5xOa8xEjEhU9ivz9VwEzcW0MT45bx+fhk+h7agI0vhGu+R84fb09NMMlxKSpk+kTdR8JgZFU/Pc8HIGh3h6SwWAwnMW57sHyJZPIsqwrLcvaYVlWtGVZz+ax/i7Lsk5YlrXe83VffjyvV7HdsPIBlfq0G3lxAlH8OtjwrDJ5MhJyBaIG/wdXroU93wGWypdmtNRNu1+4Aotr3g+z2sKywRKILF8o11OCQEgNZRD9Vk0CkcOP0wJCq89hbk84MgNafQbtv5eLJHGzSoem1JFDyL8MrLpPeUarh0iM8i+rT5U7jYcBe+Hao9BlEjR4Gsp2OrdAdHCixn9qt8YRXEPZQ83fufwEIlBZXaexcn9VuUnOr8k1IOoRSDn4949b6WoJRDs/gRL1oNIAhVFbvoBb4iGQKxCh5QsHwNF55z5unYeh7r/BlaLSw1ZfaL+Tm8AnBA78otK0vCjXVSLY9g9yQ24NBsOlz+nQatPZ7FKhWN6DFSAzNh/hmXFrGRn2vQSiNg/AtV8Zgchw0Vx79QBmN3qXcml72fP5tdhZ6d4eksFgMFwQ/9hJZFmWE9gJ9AZigChgsG3bW8/Y5i6glW3bj1zMsYv0J1nR38Cq+6H9j1D91gvfL/OkhBNXGlQZBDs+ULlWt+lQviesexa2vQ1hLeHkRgiopLKlxA1Qoo6ChS2nnCql20Dccrhqi7KF1j6ubCSQAGT5gZ0px09cFPgEKHcm+xSc3KLuZ/YZwkJQpLYNawqlmuoxsNLFh0m7XbB2WG55nF8paPKaRAVHMbrJSt4LW96Evd/p5xp3SwgMqXbxx3Klw8x2kBYDPRfn5hNlp0jkyXEUWb5gZ+Xu5/CDnvMhokPex7VtueHio6B0K2VZ7f0efD1d8JzBcN0xucD+SGYi/N5A2SVXrCpev1uD4XJl5f1qWnB9XKE1EjBOor9PQd2DFen7rwJk4c4TPPz9MkaEDKddxjLo9hx0fbroNtUwFHls2+b3Hz/k6t0vszmsNw0f/QXL4Tz/jgaDwVAIFKSTqA0Qbdv2Htu2M4GfgYH5cNyiS0a8nEARnaHaLRe+n20reyhlvwSZHR9IzOm9XAJRRpycGViQsAbKdodyXTSBd2dC+lE5PPzC1Zb++Hyo9yTs+ASmNYbjiz3uIZSN419GItCJxco3ykyEPd/qk+KQmlD9dm3nGwq9l8A1B6DTL9DwOah0lQK1L/bGKHELTKnlEYgcUOffch/VGVL8RISQ6tD2K+i/C2reJ4fYlNpyoCXvu7hjOQOg489qPb9miL7PSlRQul84coxZEoh8QnL3c2fC3O4Qd46bfctSlzvfUJUVZsTJrZSVpGWuFAlSeeFXSg61hPWe963BYLjkiV2hjDkzKb5UKH73YAVE1L54/j1qKd8HfiSB6Mq3odsz5low/CMsy+Kq2x5jTqV/0ShhNqtHDPP2kAwGg+G85IdIVAk4s5YmxrPsj1xvWdZGy7LGW5YVmQ/P6z02vijBpdVnF3fzsP0DiPlVwkzOpL39KCjjsfWvvM/jArGh3uOa7O/9AXBIZLDd4BcGPWapq5Z/BGz/UC3sA8prQp/TxSwzHtI8v5ZSTaDNCGUS3ZQMA6IVrHxgvISAPisgouM/e01SD8PiGyVWpeyDsFbQfwe0+ij/un1dqgRXVae4Abuh9kNy6kypDaselGB4oYTWg1afwrH5cGwOtPxIAmDNezytqj2uwOxkBZ/n4M5U57yE9XkfNyBCZXKgvKpSDSVeZp2S6Hh8oZxzeRF5LURepyD0pF0Xfi4Gg6HokXlSLtPw9t4eieHCKX73YAXA5kMneWTkIr73e4eW2etgwKfQ7iFvD8twmWBZFj3vfYNlYdfQ+tAPLPvFZDkaDIaiTb5kEl0AU4Bqtm03AWYD359rQ8uyHrAsa7VlWatPnDhRSMO7COLXQfRwdbAKa3Lh+x1frMBhyxfc2YBL+ULVBmn9/rESkAAavwqHZ0DMRHWZ6joFYn7ThL3Oo8qaSdkHGSc8pUZA2iHAhrAW0PZbcHoyf5q/B/02QK17PAHTQbB3tLpZBVWGPsskPvxdEjfDygfh10g4OF4CWOdfoW+UurIZcgmqLJFnwG6o9aCcRZNrecSiAxd2jBp3Q9VBsPE/ENpE329/D1p+AiUb5G7nTkfh1jk/Z8KM1hB/DqGoQh+o94S+PzhBXepwe96rwKp/QdrRvPdt+Sk4/GHVA3LLGQyGS5O4VYBtRKLLjwu6Byvy918FRPTxZIaMmMe3jtdo4t6Odf030OIObw/LcJlhORy0efhrNgW2pc3WN1g2Y4y3h2QwGAznJD9EokPAmZ9KVfYsO41t23G2bWd4fvwGOGcipm3bX9m23cq27VYREfnQQjw/sd2w+hEJN01evvD90o5J2LFdEBypoOeS9eUEAdj3Cyz1iEXhHdROPWk7+IZBgxdgyY2QmaBys/VPQ+p+iU3V74Jm72iCjg21H4Gmb8DqR8GVKiGi/hNnjN+GTa8qpDi8nUrMgipf/OuQlQy7R8DM9nIO7f4acMtRcu0hiDRO978kqDK0/swjFj0Ae0bKWRT1CKQe+ut9LQtaD5c7adlgaPomhNSCqIeg629Qps0ZG58p2DhVejijJRxbnPexm74OpZrp/XR0lkQnh49nZbaCz/M8n4rqcHZ8gcoZDQbDpUnsCsD6w98RQxEn3+7BivT9VwFx5GQa/x4xm6/cL9HA2o918yhofIO3h2W4TPHx9aP2I+M46FeDJsuHsXLZfG8PyWAwGPIkP0SiKKC2ZVnVLcvyAwYBk8/cwLKsCmf8OADYlg/PW/jsHQWxy6DZ2xdeQuXKgtkdPPkxLfWVEQvtfwBnIGx6GZYN4nQ789hlnG5v7k6HdcMk+ORkDTn8wScYBh6AkrXlTnJnQI17oWQdWNAXcENABWjx4RnjyIQVd8Om/0C126H7LPAvfeHnnp0GMVNgxb0wqYJK41IP6Bycgerw1nmCxma4MIIqQ+vPoX801LgLor+EyTVhzbBzu3YA/EKh4y8SDVc/Cp3G6f216kHoMQ/K9Tpj45xL3AVBVQE3zO0Ce/L4INnp72l575BAmbxbYmRwda1P2gqb38x7TDXvhbJdYN1TkF58PoE2GC4rYpdDaAP9jTFcKhSfe7B8JjE1k6Ffz+LD9Bep7TyCdcsYqHeODD6DIZ8ICA6l7IO/kuYsQbWZd7Nu0yZvD8lgMBj+xD8WiWzbzgYeAWaiG4+xtm1vsSzrFcuyBng2G2pZ1hbLsjYAQ4G7/unzFjpZSXLxlGkH1S/QhmzbEoiS92i/+k/BgXHQ8AV1kVo6WFkulkcAwq0sGFeyJuy2m9O/osDymoi7M+QWinoQNjwP2FDxKoVCrxkqEcqVJldITmv6zASVl+39Hhq/DO2/1/HPR2oM7B4Ji66BCWVg0QCNv1J/qHAFpB2G0EbQd51EDsPfI7gKtPkS+u9UEPrOz2ByDVj3DKTH5r1PmVYqJTw8FY7OVvv6Y/Ng27vQfbp+R8DpQGuQAy3yRn2/4i5Y9bDnPXYGJetCy/chKwF8S8hJlLIXwjtr/cbn885RshxyOGUnSygyGAyXFrYb4lbIZWq4ZCg292D5TFqmi8e/nc0bp56jps8JHLeOg1q9zr+jwZAPBIdH4nfHeIKtDIIm3MrOA4e9PSSDwWA4C8suwhkiRaoF65Y3YcNz0GclhF+gFX/B1XD4dwhtDN1nwvQmChjuMgkW36CuZcHVlC8Eue3LSzaAU9FqXx9YEZq8ChX7qrQrKBIyT2kfywGlGisH6PgChV3H/CZnT9/14HAqM2jRNZB6ENqOgOq35T3W7FQFlsYuhxPL5GhK9WRhBkVCpQFQeSDggFX3SkBq+Dw0eqH4dS0raE5Fy2G2b7ScWXWHqWzwj+4124bF18OhKdB7MewarqDzHrOgXA/93g9NOXsfnxJQ/2nY9KJ+DmumzKszyw5tW6LiiSVgOfWVlQKlm6oznl8ZuP5E3qHt65+DrW9CzwVQrmu+viwGg6EASdoBU+tB22/0gUQhcq72qwbvUaTuv/KZLJebp0fOYsiBx6jmm4DPbeOgemdvD8tQDIndMINSkwaz3GpOjUcnU6l0yPl3MhgMhnzkXPdgRiS6ELKSYXJ15TR0+/3C9llxP+z5RgLL1dGw7GY4PB06joXVD0P6cYkrrlRtX6IeJO/VxDsndLjBs9D4JXD6wYp7JABYPhKF3Fkep4efyr5afykXx5qh0G2aRKUDE2DFnRIGOo2D0PoSd1IP6jFpJyRt09eZ7pCgyhDeESI6qISoVFPIPgXrn4Vd/4OQmtDhR/OJc0FzcqucZgfGgW8paPAU1BkKvmfcRGQmwvTmgA29FsOCK1XO2He9Ot7N7QnH/1DzHlgZ6j4G6z15Vc4Q6PgTVO6fu03KQZjWCAIj9f5w+EKJ+pB2QJ3zKl4N3f4gQIHExt8b5gqVTr8/b2MwGIoee75TSfJVW1RyVogYkajoUWTuv/IZ27Z5dcw8bt0+hCq+ifjePgGq/cPurgbDP+Do3M8ov/h5xvkOpNewbwgLNvdNBoOh8DjXPVhhdTe7tIkerol3oxcvbPsNL0gg8guDfpvhwM/qXFb9Nlh6s8q07GyVheWQHA05uZKWE7pOg2ZvaJJ9ZLbCjW0XhDWX6INbLcozYqHZe9pnw3MQUgMOTYffG8GSGxR1ZDlhfm+YEA7Tm8HC/hD1L4j+n3JtwturDK3TOGUdXXNQ2TR1h8ppcmgKTG0gp0rdYZr8G4Go4AltoNb0fddB2c4qL5xcHbZ9oIwokLuo4y96T61+RCJkVrJKGW0X9Jx7dtczgLQY2PG+yh5B5Y2LBsDaJyU+ggLWW34CSVskHrkz4OR6deTDUpnbjk//PGafIGj1mYSl7R8U2EtjMBjymdgV4BsKJf9Bt0uDoYjz5e/LuHX7ECJ9T+J7569GIDJ4nfI9H+FIvbu4Mes3fh7+CmmZLm8PyWAwGIyT6LxkpyofplQTlfGcj+2fwtqhclJcvUMlYVMbgDMAMo5rm/D2ELc2VxQCwFKXquRoCQNVPN014tbB7HZqYV6yoSb4WSf/egyWUwKBfwSUbgEB5SCgrBwkQTlflVTKZv2FTph2FNb8Gw6MVfZQ228gvO35XwNDwRC7Eja+qAyiwIoq96t5n4TE7R/D2mHqMuZfVg6yhs9D09fkNppcGzL/kG/kU0IZRXvP6EhWpp0EwuCqKjtbdA0cngEVr5BYaDnlcNvyurbvPgsq9P7zWBdfL+fcVVshpFqBvSQGgyGfmNZU7sMeMwv9qY2TqOhRJO6/8pnxi9bSdM5tVPGJx+/OSVhV23t7SAaDcGVz/KtrCTu6lI8rvMWw++/Dx2k+xzcYDAWPcRL9XaK/hvRjF+Yiipkigcjygd5L5cZYchNkJ0kg8g2F1l9DwsY/CETIIZS8C1p+pADRFffAhHIws4UEIoBTO3IFosAKKkXr8BN0/BkcARDRBQIraSLf5ku4/jh0n6Gg6ubvQr1/Q5XrJfQEVT63QOTKgK3vwJQ6ckA1eQ2uXGMEIm8T3lZCZc8FcoytHgJT66pMpPbDEHmDSgKDq0KNe2DLG3B4ptxGPWYBPmcfz86GvSOhbPfcZQnrVb52aKpKH9t8CX4lIPUQlKir92bMZAjz/C1ZeBXERf15rC0+0vtr9aMSmwwGQ9El6xSc3KwPMAyGy5CF67fTaM4dVHHG4rxtnBGIDEULpw9l7x5Nconq3H/kv3w6djpF+UN8g8Fw+WNEor/ClQ7b3oGy3VTu81fErYbF1wIWdPlNE/U53T0t7YHSbdR6PupBcKVoO6enXXzJepCwVvusfUwlafvG5DqPwlpC5PWa1ANUHQQD9kDj/0K1wXBklgKvTyyRY6nPMqj1wMWfr21LAPi9Iax/Bsp1U7lco+dNtkxRolxX6LUIuk0H/zLKEZneGCr1U17U0puh4XNQqhEsv035U6WbQ9vhZx/HlaacquPzIbiGlrnTwaekShI3viQHWuv/6f1ZsZ+64p3cBOV7KWjdnQXz+sDJP3RUDo6Exq+oLC3mt0J5WQwGw98kLkoCsCkjNlyGbIreT8Skm6nuOIZ70Bh8apiQakMRJKAkYfdOxMfXn4HbHmfk3PXeHpHBYCjGGJHor9j9rbJezuciOrkdZndSiVf7HzT5nlpfHcdADozEDbDrc9SSHAlPrhQ5gJK2a5l/ONR7Sp2p3OkqF/MNhbCmcHCCtmnwDHQYLTEI4NgC2POtnrvqTdB3LZRuefHnGr9GHa0WDVQYdrcZ0HUylKx98ccyFDyWBRWvhCuioPNEuddW3ANYkJkAy+/S+8SVpnwid7Y6Fv2xa9HJrVB7iMLPfUpoWep+iOgEm1+GhQOhfG+5lHZ9AU3f1Dbb3lGXNFCo+fw+kHLg7GPXHaoyxbXDVLZpMBiKJrHL9WjcoobLjP2HjmD9eB21rBjSrvuBwLo9vT0kg+HchFUl8LYxVHHEUnvhI0xZd+D8+xgMBkMBYESic+HKhK1vQURHKNf93Nsl7YCZrRTs2+AFODxNgdGnc4Mcamlepi1ggzNIbo8cAcmdLgfRlWtVNnb4d62LvAEyTsj+v8eTGdPyM2j2Vm6Z2KGpEnZA5T0dflLns4shcRMsug5mtIL41dDyY+i3QRk0hqKPZUHktdB3g0Qh26XyxNglsPlVaPOVHGYbPUJnq88kIlpnOMOiv1RouW8pTv9JOLEEqt0BR2bAzDZQ5xGFUh8Ypxwk3LB/DIQ20XOmn4B5vdW1LweHD7T+XJ3ztr5VWK+IwWC4WGLmcjxTAAAgAElEQVSXy9HqF+btkRgM+UZCYiInR1xHXfYS3+9rSjXp6+0hGQznxVGtPfbVH9HZuZn4iU+xam+8t4dkMBiKIUYkOhd7v1er+IYvaiKeF/HrYGZbyE6BSv3h0ETY/wv4hkk0Aqh+O3SZqM4x/uFydiTvRm3HgDKt4aptcizNaKXH9j/BsbkQVFWTdMsBncZD3SHaJysZVj2okiB3JtR7XHlD5xpnXpzcLofJtKZ6rsYvwYC9cn84fP/uq2bwFg4nVLsFrt4Kbb4GnxAJOlvegEoDJdIcmiYHWucJ4BuSKxTZ2bDsdugxR66hHPb9oJDqrFPKHqp2u8onSzaAErUhZY8cR5ZT78OUAzD/Ssg8I1i9bBeoeosyrk7tLtzXxGAwnB/bhrgVJo/IcFmRkZHGvi+up6FrG/u7fUz5Ntd5e0gGwwXj2+p20lv9izudM5jx/ZtEH0/29pAMBkMxw4hEeWG7Ydu7ULo1VOiTx3pb3aRmtpVjKKQ2HF8IyfsBC7ISVP5zxWpo+aHCe50BalfPGUF0Jeqo1f3OTyT4hFSHK1fDwfGamKfuV+5Lj/kKnAY4sVxt7KO/kugUXB2avnGB52XDsYWwcAD83kDdqho8K3Go8X/BL/SfvnIGb+PwhVr3wTUxEhlPboVDv6mUbNmtkHJQzrWOY5RjlYMrGWa3Vwe7Ri/lLt/yGjR6AUIb6n1aog5sehHajtR7M/p/chlhKx8pcZPey2eWlzV/V+NaM9SEWBsMRY1T0ZARZ0Qiw2WD7cpm62eDaJ65mo3NX6FWt9u9PSSD4aIJ6Pc6aVW68xwj+HjEt8QmZ5x/J4PBYMgnjEiUF8fmwaldUG/Yn9056Sc0CV47DBz+Cp9O3qXyNFcK4NJ2ncZCmZaw7hnlvWQnSTjKwScEukzWcdY/C1VuUke0hPUSiexsHf+KlVCuizqObXge5nTSunqPS3Rq8Z7ChP8Kd5aCsGe2hrndVFrQ6EWJQ83eAP/S+fnqGYoCfqHQZ6lyrfzKaFlWojqXJWyU+Nn0tbP3yYyH6U2h+m3QfbayqQBWPwK1hkCVm+HUTr0XN/0HWn0C2AqmDqkFaYegyo0qVVtyo64JgKCK0PhllWIemlJoL4HBYLgA4lbo8Vyh1bYNK+83AfSGSwPbZvOX99D81AKW1BhG82uGentEBsPfw+Ek8JbvyQ6txivp7/D8yKmkZ7m8PSqDwVBMMCJRXuwaLpdO5PVnLz86B6Y10WP5K+S+cKVoneWb+1hpgHJijs6F3V97dnbqIWfi3eYrWHEH7ButFvMdx6hEbdltns2D4KrN6koVv0alaFvegOp3qLPV3h+gbFeofO25zyNplwSoXyNh2S1yJ7UeDgMPQJOXISAiX14uQxElqJJKy7JOyiVQ+VrIjJMQtPwuiT4Vr/Js7BFDM+NhRgsILAdX7/DkFAEr74TyfaDRfyRSHpunErPw9pCyT0HsWCpxa/aWBKEVd4Lbc0NT91G5kdb8G7LTCvVlMBgMf0HscjkNSzbIe33Mr7D7G3W+NBiKMrbNzlHDaHz8N+ZE3EnH21/y9ogMhn9GQCgBd4wjxM9i2ImXeO6XFbjdxpFtMBgKnuInEtk27PwCkvflvT71sG6Ka9wth447Gw5OVDv7eb3BrxS0+QaOztT2Dl+I6AquU1CiHmBBy48geS8svDr3uD4BKjlzZ6ql/bon4eQW6DxJLeYtC2a0BleqXEYDdkFQJGx4QWVtmfHQdSq0Gwk7P1V5QIsP/+x0ykqGvT/CnG4wtQ5se0+fEHedCldvg9oPgk9gAbywhiJJ2U5y/BydJZGmxt1avm80/F4fAitAQAXOKoPMSoIZbSHtCAzY7VkPrLpXImf7UYAFax6DRi/rfb1nhARMOxsOTICmb8H+n2H1EF1zDl+FZqfsg61vF/KLYDAYzknscnU1czj/vC47RcIu6P+RwVCE2T/lLers+Y6ZIQPp+uCHWBeT02gwFFXK1MT35u+o64ih146X+HD2dm+PyGAwFAOKn0h0cqsmrpOrS3zZ9r66L+Wwe4S6NVW4QmG7k2vC4usheQ80exva/wgr79K2ITWh7uNwYqGCq09thwZPQXayynpc6drOt6SOme0Jnkvaoklz72UQeY0m0TPbK9DaGQgD9kHaUZWHbXkdqt0mV1Glq5QfseNjqHGXXEag5zk4CZbcDBPLwvLbITVG7cqvOQhdftW+VvH7dRuAWg9BzXuVL1ThCghrAT7BEHkj7PlOAiR/uJl2p8GcrhAXBf3WQ1AVLd/4gkrG2n0PuGFRf0+GkS2HnX85iF8l8anBs+qctv5ZvcfLdYOqgxSinby3MF8Bg8GQF9kpkLgRypyj1Gzz62rgAJ4OnQZD0eTEku+puvYt5vl0pt3DX+Prk4foaTBcqtTqidXnFfo5V8Gi95iwJsbbIzIYDJc5xU81KNUQ+kerJMadLUfPb9Vgci2YWEFZKwDzesH6ZyCkBnSeKEeFbykJN7YLIrpAg2dg29vKE0qJgcBK6uY0q6PcGDg0Gc9OkZDj8GQHhTaEPisgrInKcWa2US6E5YSrd8GOj7QsI1YOoPbf5bYmXve03BwNnoMD49WVakJZWHwdHJsvp0ivRdB/JzR8VpN1Q/HGsqDV55rkrbw3N4vo1C7ot1Hv39M4AEvvRTsLFvSDo/OgzzKPUGTBgbGw5VWododKJDf9V+64tEMQ0VGHiXoI6j0Jtf8F295RqSRA8/fA4QNrHy/EF8BgMORJXJQaNeQVWp20A7a/B6FN9PPmVwp3bAbDBZKyZSZhcx5jJY2o+cAPhAadJ6fRYLgEsdo/gqvxzTzhO57Zk75lxZ44bw/JYDBcxvicf5PLkBI1JfA0eEbOnAPjlfuTlQRHj6pspmxXtacv1VjBz6sehD3fav9STZXpM6+3clrKdtfEuenbch3hBGxNtLNTAIcChDNOQKlm0HOBWpBnp6oFfXK0jtvyM1jYVx2iqt+hsrUccQhUxhMzSR2mpjXSBN2vNFS5AaoOhnLdNQE3GP6I01/5RDNaQdTD0OIjWHm38rc6/KBrYW4PyDiu7W03+IRBdgIsGwytvoBeC2F2R7ncTu2SA8+/LGTEQ8JaiaAxk6DClXBkBqy4C7r+phLIjS+AbwmoOxQavgAb/g+OzMq7e6DBYCgcji8ELIjocPZy2/Z05QySKxWgfK9CH57BcD6yD67BOf4OdtmV8bn1J6qWNY04DJcploVzwMdkn9jJB0c/5/ZRFfngkcFULRPs7ZEZDIbLEMsuwi2pW7VqZa9evbrwnnDBVeouNnCfysFA3cwWX6eOTQDOEOgxGxZeBQHloPt0ZQmF1ID045B5ErLic4/pDFKZV3ayvr8+VplA6Sfg90a5k/LwDhC3SmHSrb+Eyv01UY9fC4emqsQnYa22DaqqYOzK18i5YYQhw4USuwrmdoXSrVR2tvMT6DROQqMrA8aXVi5WDoGVIc0zSWz8ssKu53RWJhYOIFvrAipA+hHPPhUhM0nB7t2ma3K55CYJSG2/hWq3wLTGui76bgSnX2G+AgaDIYc53fXhSN81Zy8/ME7XbOVrdd0CXBOjMPwCwLKsNbZttyqQgxv+FoV+//U3sON2k/JFDxKzfdjQZzxXdWzu7SEZDAVP0mGyh3flSKrFoyHv88MjV1IywNfbozIYDJco57oHK37lZucieR8cng4178sViBLWw/SWcGJZ7nYt3pUDA6DrFNj+scrCbLc+cT1zwutbWm6fnCyi+k9LIEraAZNrSCBy+Ek8il0GVW+GPlGaPK98ACZVUnnb5lc8k3Kg5ccwcC+0/BDKdTUCkeHiCG8DbUdK9MxKUBbJynvVCc/pryB14PSfhrQYBamDysp2fgo95ihny7LVFcnykUAU2sizz2HlDwEsu1Vh7R3HQPnesOo+ODRZTqakHRKpDAZD4eNKV5lzRJezl2clK5S+RO3cjmYBFQpMIDIY/hap8SR9M5DM7GxmNP/CCESG4kPJivgMHk0lRzxPJL3Do6NXk+1ye3tUBoPhMsOIRDlEf6Xslpr36ecDE2BmB0+orxt8QyGslTqdnYpWTpE7S5PmkvUhPkp5ROlHtb9/WTmKbJfK0Ry+6iy2/xc5iLKTFfLrzpQoVHuIys+m1lFXtP1jIKITtP8Brlyn1uUVr4Y6j/65o5nBcDFUG6RW9ntHqUTR8oElN6g1fcU+EHkD4EZlk069L3PY9Tlsek0OIWeQyin9w8HyhZObIdATcH14qnKKMuM14XT6Q5dJcswtvQWw9X7e9LK6qBkMhsIh6xQc+h2W3SGhaOcnsPxO/V8D2Pyq8sUyk8DfU7qTI/oaDEWB7AwSR95IQOpRRlR+g3sGmLJlQzEjsg2Oq96ns2Mj7fd+zmu/b/P2iAwGw2WGEYkAXJlq4V2pPwRHwt7RsPQmCAgHV4o+ac06qXVHZ0Ob4XLxrH9GTp6krRDeEY7N1fH8IjxlZBa0Hg7xqyHyek2Ilw6ScBQYCRnHwBkssWnX5yo3q3EndJuhsrTO46D67bD1dbUWb/WJEYgM+UPj/0KVG9VprM6j6nC05lGtazNcTiFcni8LgqqBXxmtjxmnvJIuv0r8ST+mzC2A9ENgedx0SdskHu3+ChI2KcS961QIa6bsrio3SCRd90zhnrvBUBzJTIBF16ikdOHVuWVk1W9VednUerDoetj+odrdZ8UrQw/0f8hgKArYNkljH6LUidV8FDKMIXfeisNh7osMxZCWd0Kre3nIZwrxK35i1Ir959/HYDAYLpDiJxKl7IfxEbD7+9xlh35TnlCthyQQrbgDQuqo9W/k9ZCwTjfLMZOg7mNqJ35iqcpmXBkQXANil+pYzhDIPAFY0PFniTrZpyBuNUQPBxyaRKd52go7g6DWA9B7KVxzAFp/ARWv0OQbFO57YBw0fB5CqhfmK2W4nLEc0O47KN1CHYxq3ge7R8Ce78C/DLQ8swzMBan7JOpUv1OLElZL6Gn2nkom04+AT6hEoRI1PLulyoEHsPhalWT6hUL3mVCyLkT9S53V9o06u6TTYDDkLykHYXYnlVTXe0wloxEdJdi2/wEG7IHaj0DMr+pqmHoQmr0Lp3YADhNabSgypM55i5I7J/I/x2DueOAJgvxMyb2hGHPlW9iR7XnX/2vGTpnK0uhYb4/IYDBcJhQ/kejoAsiMhZV3wZphcvHs/VHt69OPSyAKawEpe1SKE1hRpWGndqr8q/k76vyy7mnU4j5E2+a8lK4UwFIYcOWBsOkVrUvereW4wc5Q57P2P8B1h+UQiuigifuZuNIhaoiyIeo/VXivkaF44BMEXSarQ96hqSoFi3oYEjaqu15EZ4k+ADgh+kso10P5QjggKxHWDFH7bMsJ2SflDAquBrWGaLfMWImiybvlUACVsPSYDcFVNSn1j4DVj4Db5YUXwWC4zEncBLPaKzOv+wz9D4voBLErlDW0oD9seV0OV9zo/xTgG6Z9girn5vQZDF4ke/1Ygpa+xSR3F9rd+QYVQgO9PSSDwbv4+GHd/AO+IWX4xu9Dnv9xPvtiU7w9KoPBcBlQ/ESimncqIwhgx8fKBzo8Xa3uV96tiXJGLASUhWbvwK7/yVnh9IcOo1VedniagqZxyyXkCCA3w8WGdh6X0uSaynbAreXYuc6KDqNl4f+r4Omt70JyNLT6PNdZZDDkJ0EVods0uX4y4vX+XHKD3tdthnvcP6UBFzgDYdUDyuDqMRMsz3vy+AI0sfSIoEdmKMOk1oNab2fqcd2zkHZM3weUlZshoKwyjxLWqeTTYDDkH8cWwOzOgA29l+iDD9uGHZ+oqcKR6ZC4AXaPhF2feXaywbcUrP4X4JbjyGDwMvaBFfDbw6x018Nn4Cc0r2pa3RsMAISUxTFoNGUdSbzDh9z/3QqS0rO8PSqDwXCJU/xEIoDWnpthnxA5hOwsTWzDO4Kf59PTjmNh+/vaLiMW2n4DwVUgKw2W3+U5kFPdnU6/jC6ocS/s/kYT7bTDnuWeT2Yr9lO2UZ1H1cnsrzgVDVvfUDlOhd75duoGw58o1UhB7MnRcs4l74EV90gMavCUwqd9S4IrTW63xddB6ZbQa66n85mD0yJoDsvvgKZvQvCZJZLZMP+K3B+DKkHPeXIWWb5y52XEF845GwyXOzFTdL0FVoQ+y/VBSOxKmNMV1j+tbVoPhwF7of6Tnp0siLxJLkFXmhZVuckrwzcYTnPyEOk/3kKMqzSr2nxC/5am9N5gOItKLbD6f0wbtnDLya8ZOmYdLrd9/v0MBoPhHBRPkSi0AVToC84Aj8jjIS4KDk3RDbPDF/b/rMDoWg9BpYFwdC5MraUSGhzgV1J5LG5P9yefEnJDxObkq9iApWM1fx+OL4YybaH5e389PrdL3WYcAdDigwJ4AQyGP1C+p4TQhLXKKTk4QeVhjV5U+ZhfGXVBc6VB8j51RgpvD70WKGcIW+uDPN3N3Gkwo43EVuC0UJq4ARZcldsxLbiqhCK/UhJQVz9SqKdtMFyWJO+D5bdJGOq9RB9wHJ0DczrDqV1Qoi6ENlbHzbQY2PKG9mvyKnT+Bdp8xWnRN2m7t87CYICsdE79MAhXRgojq7zBkH5tvD0ig6Fo0mwwtH2Iu53TCds1kbdnmL/dBoPh71M8RSKA+k/IIZR9CvCUfLnT9bj1LY9FH3UfO7EExobAvF657both8Sc7OTcY/p6BCe/ME6/tE5/dYHa+73Eok5jwen312Pb/oGEplafyW1hMBQGNe6Exi9D/BooWU9ug/g10OpTSNkLkTdoO8uhFvebX/c4ihbndj5LPQjlPe2IU6LVTalif8AGv3AtPzwNpjXODasuUQt6L1aI+/4xEDO5UE/bYLiscGfDslv0fadxcurFr4FF1+q67rseUg9A+R7aZuUDctOGd4AGz2pZ1UG5x9vwvLoTGgyFjW1zavwjlIjbyHvBT/DU7deYTmYGw1/R5zWo2ol3/EewdPFcxq+J8faIDAbDJUrxFYkiOssFBNDyIwgor6/I6z1t6T2CkSsFTm7O/Tnn01U7WwKTOwNwqKV92hE5jjITALfCfHsuUHeyxE3KIQqu8tfjStwCG1+AyOug2i35f94Gw1/R6EWocY/cA35lYMlNEoIir4NDv2qdna0ufpv+o5KWUg2hz1KVtVhOODoLKvTT8dIPweEpKifLjNU1BpByQN2W1j2lgPaSdRVmjQVLboSkaK+9BAbDJc3mVyB2ObT+Uh0xk3bB/L7K1us2Q2WlrjQo21V5fEdnSaDtNA4cTh3j6Fw9BlcD3DCvJ2Snn+sZDYYCIX3JZ5TYMY7/WTdx732PEOJvOpkZDH+J0xdu/A6fEhF8F/Qx705cypr9Cd4elcFguAQpviLR2sfUiQlg348SdrpPh87jIbSRltceAn03Qrfp0PJjTpfMhNSEpm+Rm8HiBodDE+xDU3KX139aLqQ9I6HRC1Dxyr8ekztLWS6+odD6f2CZT8wMhYxlQZsvoVJ/yDiu62LJzSqRtHyU1xXeHlzJ6n607BYJmyVqqawlpJqEoiPT5FrIwc7WY4anVNPOVl7RtvdgeguVekZ0gIYv6Lqc1Q6S93rjFTAYLl2OLVSnshp3QbVBkHbUkwNmQ/dZCqo/tkDbhreHZbfp+w6jtS6H/b/osf7TEnwzTsCCvoV4Iobijit6Pr5z/8Msdyua3foGkaWDvD0kg+HSICQCa9Bowq0khvt/wpBRKzl60oj8BoPh4iieIlH0V7DrC5QXFABxK5TFENYM4tdB/Eq1Bm75MYQ1VqemaE9GQ2BlfeK68YXc49W4Bxo8A5tfVSmOMxAc/powr3tSwZ+NXzr/uLa8oUyYNsPV9clg8AYOH+j4s9pk29lwYjHs/Byavi7XQa2HwT8cshIACxYNgIw4CI5U6VnJ+hKKkraDT3DOQcGnlI5nef7spOyBytdDVpJadG/8LzR8TnkpmfEwp5uyVQwGw/nJiFcOUUhNaPkpZJ6E+VdK7O36O5Sso+2OL4BSTXS9ZcZDxash8prc49g2HJ2t7yM6QNfJ6nBYolahn5KhmJJ4gMyf7yDaXZG4Pp/SvlaEt0dkMFxaVGyO1f9jmrs3MyTrOx4YtZr0LJe3R2UwGC4hip9IlLBR4bgl66OwXY9bp1x3PeZ8str+e1nvT0XDzHZwcgvgkFtiXq9cZ0Tk9SrHiXpY+UN+pXSTXbYLRA2Bst2g/Q+5E+NzEb8GNr8G1W5TaY/B4E18gjQ5LFlfpWLb3wf/cnqvr39SrgTLB7JTIPmASsTcWRBYXmHWYc0Bh9YD4FKYdY6LKIeYCcpCqnqLymRmd4RGzwO2JrdzuxmhyGA4H7YNK++D9GPQcQz4hsDKe/R/q9MECPeE/boylQUW2hB2f63uhJ3Gnn2spG2QGafrPrQhHJkpMalU08I/L0PxIzuDhO9uISsri6kN3mVwpwbeHpHBcGnSdBC0/Re3W9OpdWQqz07YiG2bjmcGg+HCKH4iUalGas3tW1JftkvOnx0fKzA3aatujCv0hgPjYUbL3O4uNe+HhVfrhjmn9KxEbVg9BHxLaVnNB5RjdGKpjtPlV4lHf0VWksSpgHLQ6pOCPHuD4cLxC4PuMyGwggShlR7HXMYJTTA7jEallk44Nh/WPKb9/MtAjzlyIWBx+lpxZ8ilBLrmctjyhq7LzhMUqLvyfghrAa5syEw0QpHBcD72/wIxk6DpGxJyD0yAgxPlkK14Re528avBlQpHZgG2rmGfwLOPdXiGHks1ASxY/4zcSbUeKKyzMRRjYic+RVjiJr4Ke4JHbzhPib7BYPhr+rwK1Trzjv+3RG9YwleL9nh7RAaD4RKh+IlElkPlX3ErJc40+g/UehD2jVHbeVDb+VUPyh1Roq4+pbV8IO2QQqxxQFA1CIpUJ7TASmrf3XGMuj5ZTgiIUMaRX+hfj8d2w7Lb1Za4w4+ezmgGQxEhqKICpX1LKmB63TMSQncNVwh7rQcl/viWgl2fw67/aT+/UOg+A8r3QhldHrHInQk4FJxb8oxPiNc/ozyVvpuU3ZWwVvsF11LZzNzuRigyGPIiO1WdCMOaQ73HVXa2eoiE1vpPnr3t8QV6zIyTe7bygD8fL+Y3wIKIjrD3B/3Pa/rG+btyGgz/kJNRPxO+9Xt+dvbnrvuG4udT/G5RDYZ8xRNk7SwRwQ/Bn/D1jJXM337c26MyGAyXAMXzP/CekXos2QAaPA0Nn5fbJytRgs+Sm2D3CKj3hLqg2Zmy2h+eqv1afwWpe9Xuu2QDiUctPwbbgsSNyjnKcWCcj00vw6HJ0OJDZR8ZDEWNknWg5zx1QErZK0EzoIKE1JYfQ6nGunb8w1VieWia9vMJVslapQFIKLLRnxy31idt9Tjw0DWz8xPlGzV7D9qNlOsocQ1U7HeGo8iEWRsMZ7Htff0vavmRPgRZ94QC4tuNyHXu5XBwih4dfipD+yNpx5RBhq3/eZv+A6VbQ5UbC/w0DMWbzGPb8Zs2jHV2bRrf/RHhIedxYBsMhgsjOBzr5tGEkcS3wZ/z+M9R7D6R7O1RGQyGIk7xFIl2eEq6Ov4EDl9IPyFnA0jwCe8A/TZC09cg2hNwnbBG69t8D7s8+0d00kS37jCVyyy9Scu7/KaW3ufj4ETlsNS4C+o8kp9naDDkL2FNoeccTS6PzVVnpMSNsP1D5RP5BGti6h+h6yDec704A9QxsOogz4HcnC4/A/Ap4dkuWMJTfBRMawSph+CK1SpL2/8TlG4hR9GcbpBs7NIGA6DrZOtbEHmDcvCOzIY936krWVizs7fNSIT4Ffq+6Zvgn4drNeZXTnfnTNqqbobN3zGdNg0Fip2ZQty3g0lzO4nr+yUNK4d7e0gGw+VFxWZY/T+hSfZmnrZ+5P4fVpOUnuXtURkMhiJM8ROJYqZ6rPa9VBa24QWY2YrTLgfLF1p+CKENFCTtSuf0pLbqYDg6XZPjoCqaCJfvA6Waqc2wnQ3lr4AKPc8/jsTNandfpo1pd2+4NAhvC91myq0QM0Hv3c0vQ3YydByvbTKOS0hacFVueZjDF9r/CFU8QpHjjLKVtIMQUhey4uQ4Cqyk5RtfgGWDodnb+vnYAh0nMxHmdFWgvMFQ3Fn/f8rVa/4OZCXDqgegRB1o/J8/b7v8Dj0GVIC6/877eAcnqLTUGQy7v4GKVxmHq6HAif7+Ycql72Veg9fp1a6lt4djMFyeNL0Z2j3MYHsaLRJmMOzn9bjcJsjaYDDkTfETiUIbK2w6eQ9Mawxb3lSQJ0DLT9QVZsU9kJUG2z/w7OSWKJSdDAd+1iKHr9rcl2oCK+9SFyjbBXWGnH8MGfGwaKBcFJ0nym1hMFwKlO+WW6YSvwbwUdlZxSug/lNanpkIWadgQV/ITNAyhxM6joaILsow8j+jpXHyTsAvNwzbN0Qh7hlxsHaYrlfLR9dLdpKWz+kKSTsL8cQNhiJG7ErYN0o5RCHVJaym7IO2I/78P+XILDjsKTXL6dz5RzLiFUDv8IOAcMg+Bc3eKvDTMBRvdswbRe1DvzKt1GCuu/FObw/HYLi86f0qVO/C234jOLFjBe/P2uHtERkMhiJK8ROJUnYrUyXtENQeIku+wx8CKkJtT8ZK7DJYfG1uCZrlI5Ho0BQo00430cm7IbgqbH8Pqt8BwdU0sa3Y96+fPyNOrqPUg+rmFFSpwE/ZYMhXIq+BVl9IFLUz4Ng8lbg0fR3C26GA6iy5fRZd43HjIQdSj7keAeiEOiYBYIPTCbglCnWdJpEpsAJUv1vXq50FgeWhxj26LtOPw+xOcHKbd14Dg8Gb2DasGQYB5aHh/0mw3fEJ1H4YynY6e9uMOHXPBAgoq86dZ+LK1DV6cILcsBmxkHIQqt+pMmqDoYA4vH8nFRY9wzZnHbo+8D4Oh3FUGwwFitMHbvgOZ4nyjAr5hLEL1jJlw2Fvj8pgMBRB8kUksizrSsuydliWFW1Z1hgoNZgAACAASURBVLN5rPe3LOsXz/qVlmVVy4/n/VuU7wFdf4eQaurGlLBWzoaadyvks9ptUKEvHJ2Zu09wNYhbDm2/VamZOxOw9Klty4+h2TtweBpUv/3PQaFnknZEDojETXIQRXQo2HM1GAqKOv+Chi9KDLJ8YM1jciJ0GqfOZpZDYurxRbB0kLYD3aB09TgakndDcA1970rT9gnrJQp1HAMJ6yAzHrrPVte/2GWa8Lb/QU6JjFiY1U7Xk8FQnNg/BuJWqOuYTwisexr8y0CzN8/ezrZh1UO6VkCdCXOIXwtLboaxgfBLoErVTuOG0q20v23Dxv/m5owZihyX1D2Yh9T0DGJH3Y0TFyG3fEeJ4CBvD8lgKB4El4FBowm1TzGqxGc8N34Nmw+d9PaoDAZDEeMfi0SWZTmBz4G+QANgsGVZDf6w2b1Agm3btYAPgbf/6fP+Iyr1gz5RZ7ebD4qE7BRlAwVVzl3uU0Kun1afw9E5uaVpNe6G/tFQdyjsG61PYGvcfe7nTN4HsztLWOo+HSpdXRBnZjAUHk1fgWq3672ffRKiHta102G0hFdQ97KY32DF3WB7upqVaS3HAxak7AHf0lruztTjyvuhwpXq1hQzSQJs/2jwDYVDvykrrP0oZYFlJcGM1nBiWaGfvsHgFVwZsP5ZtbivcSccnS03X8MXlCd0JntHwcHxnA6jrjpIJWXzroAZLeHIDKh6K9R7Shl9fp7AYP9wWD1Ebr3F16vBwoHxhXqahgvjUrwHs22b2V8/R5PszRxs+xKRNRt6czgGQ/GjQhOsgZ9RP2sLL/n+yIOj1hCXnOHtURkMhiJEfjiJ2gDRtm3vsW07E/gZGPiHbQYC33u+Hw/0tCwvJzUfGKO8FL8wdVCKegjGloApdZSNkkP2KeUPRT2kLksArf6n9sIB4fqUdc9IhfiG/vG+zMPJ7brZzoxXuU257gV/fgZDYdD+e3X5A4iZCAfGqeSywbMSVHPyh/aNhqghul5ApWkBZSGoKmQlAGdkpLjT5D6qO1QBuzs+hH0/QqexWp9+FJbcAJX6Qs379ByzO8P+sYV66gaDV9jzrT64yMkLWv+s3K61Hzp7u+S9uuacweAMUmB11MMwtwfEr1bpNE7lGm1/V+WjmR7HUUYshDaEuCgJtaGNobbpwFlEueTuwcZPnky/2JHsLtubelc+dP4dDAZD/tP4BugwlOvdM+iWMp1/jV5Llsvt7VEZDIYzcWWRvfQzcBV+N8L8EIkqAQfP+DnGsyzPbWzbzgZOAmXy4bn/HtkpsOklCG0koajVp2pb3+g/ubb8HCynSmVyWniHNoU6Z9zUxK+Bk5vzdhHZNuz7CeZ0ltui5wJ1iDIYLhcsC7rP0iQVYOmtyjNp8qpHPLIh66TyhaKHw/pndF34lYIWH0DqfmV68YcOGzGT4PgSaP4+VB6orCJXhlp9uzKg0kDY8obK05p6JstLb4aNLxXeuRsMhY0rQ+/7iI5QvpeE0YR10OQVcPrnbud2wfI79X/HlSIBKPsUxC6X2ygzHuJXSaB1+IEjgNO3A5bnOCe3KgsMB5zcImeRoShySd2DLdy0h1ZrniLZtww17vradHY1GLxJr5egZg9e9fmW7H0reHXqVm+PyGAw5JCeRMrIa/GZ/Ty7Fhe+m7vIBVdblvWAZVmrLctafeLEiYJ5kh0fKx8osJJumKsOhsoD1CEmpxuT5Q9XrIGrd6gUbf8vWt7i3bOPtWek8lFyRKQc4tfBnC6w7FYFXPdaBGFNCuZ8DAZv4hMIvRaCT7AmldObqxNgxzHKSwkoC2nHdB1texe2vK79qg6Gcj0h5ldoM/zPx53bXcfpMFqlNUsHQc17lftlZyn4PXk3bHkNGv1XjsDNL8PCgQrjNRguN3aPgNQYaPySPrzY+II6bFa95eztdnwIJxari1lITbntslN13fiV1ocfDn+o/7RKOS2HrmOADqPkHMoRbkMbgm8JKFmnMM/U4AUK4/6r9sHxVHUcJ2jwt1hBYeffwWAwFBwOJ1w/AkepSH4I+ZSZy9fx86oD3h6VwWA4GUP2N1fgH7OM15wPE9ri2kIfQn6IRIeAyDN+ruxZluc2lmX5AKFAXF4Hs237K9u2W9m23SoiIiKvTf4Z6bGw9W2o2A9OLNJE1ScIUg8rNyWHvquhTAsoURMa/5fTN8w7P4d9Y9R9yZUup1Dla+WMAB1n1UPKe0jaAW2/gStWmRtsw+VNcBXoNgOwIDMOpreSaNNxDKQeUg5R6kE5jja+CJte1SfIrb9QaPWxBdB25NnHtLPht6rKKuo6BfxLw6r7oe7j6jRo+ULf9RDWHDb/Fyr0U5bKockwramuRYPhcuFMF1G5nrD7G4mkTd88u6V94ibY8Lw6cmanahsALMABKQf0f69/NDR/G+KjVBqanaL12z+ApG3QdoQ6gJ7c9P/snXd4FGX7he/Z9B6SEAiEFgi9995BqtgpAooK9vazfHb8LJ8NG3YFlSICAor03juh994DIYQkQHp2f3+cjQuIigrZQN77unLNlpnZdyczuzNnz3MeCUuRrfP/PRsuhyt2DnbVz7+AEp2exjZgBj7lW1yV9RsMhr+Jfxj0/pEAK5MxwZ/w+uR1xB1McveoDIbCy7ENOL5pR9apA9yX8xxd73qWyGDffB/GlRCJ1gCxlmWVsyzLG+gF/HrRPL8Cdzlv3wbMdzgcF9WX5BP2LHUvC2+gi9OYe8Bu10VlnhAU0dTV+teeA1vfAmwQGCuL/vI+MLEozGkJ2ckqp5nTUo/9UlIn75Ueg+675HywCpxhy2C48kQ2hwZf6Pa5vTomQmqoFObUKih5k4LbgyrB5ldg40sQFKv8ooNjwL8klL//wnVmp8CUSoANWv6qDmrHZ0FwZYh7XHlH7ebLWXH0ZzmX/KPhzA6YVhUSluTzRjAYrhJ7h0P6Ue3rOecUJh3ZUhlgeeRmqeTT8oC0Q1xYxpkr4dbyUHe0jc+rpOzAWMACz2A1aji1Su698vdAg0/lErQ8IHlD/r5fw+VybZ2D2WxQpolbXtpgMPwBkVWwbv6K8lk7GeI3gvtHxhGfku7uURkMhY9ds+G7LqRmOrgpYzDdbu5DndLucd3+a/XCWd/+CDAL2A6MdzgcWy3Les2yrBudsw0Hwi3L2gP8H/C7Fq35hn8JaD4Wjs1QJlFoTZhRyxXYCVDvE9ftfd9D6jbADtWeg5uOQPslUOF+SN6keRJX6vnom5Wz0mWLOjPluYsMhsJC7P1Quqdup+6Aea2g3F1y7sVPUyelMzvVmWzrm7DhWYlEQbEK1a07BPzyfhR3ZlVknoSpleQSajoaTq0GvxJwbr+cFTZPuf3aLVQJTnqCHEvZKTC3Fez42BWYbTBci1zsItrxIWScgNrvXJjpsqK/nD+5lzi5L94RbjkGPfZBxcfUrWxaNTg8EXCotDMnBep9DGXucC0X2RK6bIIqz1z1t2n4+1xz52AGg6FgUqUbtHqOzjnzuSV7KvePiiMjO9fdozIYCg9bf4GxvTntX4b2Z16hWdMW3FYv+q+Xu0pY7vox6XKoX7++Y+3atVd+xclbYHoNqPFftec+vc71XEA5nUSDfq2dUhEsT/0qe9MRuR1AGSs/l4BKj0LdD034osGQhz0HpsTKNWTzUWh1859g6e16rlgbdVQKbwKnVuiCtWQ3WNBR4fElusLsSwS8e/hDp3XqorbxBQitJQG380YIqaJ5Mk7K6Xd8roSkdGfJWczd0ODLC8N9DYZrhV2fKzi67RwIq68yzGJtoeXPej7rNCy+BRIWKoja7szkCqokUbbUrSr9tHm51pmRqBLrY1NVuunIhlK3KOvrfBwOWHILlOgOFe65Km/Psqw4h8NR/6qs3PCPuGrnXwaDoeBit8P4fth3zqBv5n8oVusGPrijFu5uSG0wXPdsmQgTB3I2si4tjj5ElTIlGHlPQzw9rn410h+dgxXOOqh930n42fPNhQIRQNX/uG7v+EgXmT4REFrDJRCB7PrYocIgIxAZDOdj84QOy3Xhac/WBeyiblD7LchMgPTjchudWqEL3V1D4dA45aRsexu8Q1QGqpW51pubBjNqyklRti8kb9QF8ZoHXE4h36LKRqr2ko5drxA9vu97mNtar20wXEv85iJqrn1/16eQnerMykPH17x2Eog8AlwCUXANCUSeQdBs/IUCEYBvhI4hcHYxA+qfFyDvcCjfaPUDCpc/Nu2qvk2DwWAwuBmbDW7+EltERb71/5S4DesYvnS/u0dlMFzfbBoPE+8jq0RDuiU9TmBwKJ/1qZsvAtGfUfhEotws2DNMt7OcuY0e/ppa3lC2t25nJDgDrrvJvh/V6cL17B8FYfUgpGr+jNtguJbwj4Im3wN2p4Bjg9UPQqUn4cQcBV2X6AYnFqjF/d7hOuZsfroorfO+MlKwS9AFwOmQmNNUglJ4Y4lQCYslAuVh84Bar0OrqYAlsQogaS3MrA9JFwnDBkNB5uIsop0fyW1XpLYEovkdXGKPPUPT4KqQulnfbaVv1Yn/xcTPVpg8uL4Dd38Kiasg7kn4tTxMrwl7vwZsULLr1X6nBoPBYHA3PkHQeww+Xh6MDfqIodPjWLzrKnWbNhgKOxvGwKRB5JZuRr/MZ0jI9OLrfvUpEuDt7pEVQpEofgbkpIJ3EVduQ26apuX6glewbm95Q4+X6KIL0fNFouStciCV7Ze/YzcYriXK9oFSt7uON+8w2PWJjqnt70GZXlCsNRydCrEPQsICuYgSFqpDWcOvtB5b3gdlFvhGgcMOi7pD+QFy+dl8YN3/qdTsfEp2hc7rILiS7jtyICsZZjeDQz/l00YwGP4F9mzY/i5ENJHrbs/XkHkKqr3oEohOb9IxYXmDI1dh7qnb5MbLTfv9Dxwg4XblvbodUA4cFvhEKgx7dmPY/YV+AKn7oY6vCgMVZG0wGAyG65+wGKzbRxCVfYSvA77m0TFrOZB4zt2jMhiuLzaMgV8ewhHTipf8X2bVkQw+uKMWVaKC3T0yoDCKRNE9oOqL6pKEpdySPCoM0jTzlDqUlbsLUraAZ4ACQ/M4MErdXvJcRwaD4dI0HaljLHUbhNWFwBjlBQVVhNWDoNb/lLGyd7jyiDITdVEa9zgUb69Og7lpEOE8/jLiIbSuSjxX368gbBwKqV73f79//cBy0HGFOqsB5J7T8bz0Dtj6tgm0NhRsDo6HcwcV7m7PhB1DJBaFVHY6iDaDd7COGUcWYFPQe8zd4FdSnTWLd/j9eveNgPQjgAX+pcB+TqWgnkH6bmsxEVpPBRx63dgH8/d9GwwGg8G9xLTC6vQ2jXNW8yjjuG/kWs5kZLt7VAbD9cH2KTD5YYhpxcgyb/Pj+kQebxdLp+pR7h7ZbxQ+kSgjAQ6MUICtzccVbBtQDsIb6vbeYXIZVXpCXdCKtXUF3tpz4cAP+nXWN9I978FguFbw8IVWUwALDk9Q16TwBnBmt8rIlt8JTcfItbDtbaj9Hnj6Q3YyrOgHTZ2CbOIyCKmhdSbHqVOT5QU73oOoLoADDoyG4/N/PwavQGg5USIUqMzUp5hagK8eJLeGwVDQcDjkIgqpqmD3fd9DejxUfgYWdJJAFNlSP3jYM/MWkijU8Gs4+ovEVZ+wC9ebnab9Pm/+U6t1s80s6LZdQu7KuyHrDOz+Ui6mIrWMoGowGAyFjYYDoe5d3MfPVEuaw5PjNmC3m+8Cg+FfsW8hTLgHStZjeYNPeG3WfjpWLcbj7WLdPbILKHwikT1bdvzcNLXJzqPMHXIn2HNg12dOYchXbbbPt+snLIS0I1DOlJoZDJdFWF3lqQBsGgwVH1WmSk4qnD0Aax5U2HRgedjwDNT/ArzDIX4mbH8fqj6vZXPTAQ/djp8BZXqCh58uhoOdAtLy3pCb8fsxWDao+V9oPl7iUuYJ8C0uQXhhV8hKucobwWD4m8TPhORNUOVZlZFte1s5XEcmSdip/JRceTgAS38h1aHFBB1XyZvV1SwPhwOOzYJfSrqCqou11zFkecLim+GXaAm4mYkwuTSc2QVBlWFBZ9g/Mv+3gcFgMBjch2VBlyFQuinve39Nwo4VvD9np7tHZTBcuxyNg7F3QngFDnf+ngfH76B80QA+6Fkbm61gNcIqfCKRXwmVm3iHw5kdrsfzTqaPTFaYZ6XHdJIOUOI8kWj/KOUWlbwx/8ZsMFzrVHsBwhpIrFl5N1R7HmIGAHY4Phu2D4G288A/GlYPhCajFKa7+3NdtPpGwdk95x13NjmHyg/U8Zi6GbxC5RRc/SelMaVvhxtWqOtZxnHlsByfD3OawbnD+bAhDIbLZNs7Oh7K9IYDY1R2VrQp7P1GQuueYbi+wh06DtrM0PSws419qVs0Pb1B5WkLO8mlB4AFJ+ZCzhmVnFW4H2q/DY2+heAqKuEE2P+dvguPTM7HN28wGAyGAoGnN/QchUdQJKMDPmLCgjVM2XjM3aMyGK49EnbA6NvAP5yzd4znnnF7ARjWvwGBPp5/sXD+U/hEouTNkLjC6SKygWcg+EUrFwXUjjugrDovxc+CoFjZ7wFy0nTyXfp28PRz0xswGK5BbJ7QdLSz05gFi2+Cqi9A1f/o+R1D4OgUaDdfbeyX94E67+m5Q+N0nAIcn+k8du1a166hUPExiT3ZyYAN9n8Px+f98VjC6kHnDbowzkxQpsu5g+qalrz1qm0Cg+GySVwJCYug8v+p3HLbWxBUSeVfRVtA2jHIOgnY+e1rvNl48C+p24cnQngjpyg7AGbUhVOrLnyNqE7QcrIC3au/BPU+0PFY6iZ1R8NZUhB9M5S/D8remU9v3mAwGAwFioAIrD7jCLJlMibwY16esJotR40D22C4bJIPw6ibwcMLe99feGLacfYlnuPzO+tSOtzf3aO7JIVPJCpSE1pOUXcy7CpNKX2rLJWnN6iddsVHZMc/seDCUrMjv0DOWdPVzGD4JwRX1IVobrpaeS/qApWfhjpD9PzqQRJx282XG2LTS1CiO2BTYLXlpWWDKrrWaXnB1jeg4uMu8QhgUQ+Jun9EYFnoshGK1FGXKByQkwFzmsPJZVfl7RsMl822d9QRsPxAlZel7tR3j1egnEVHnE4hDz/ADuUHQYmOeuzsAUhaK2fdlFi5kEJraHmQoOoVCi0nwbkDeqxYa02PToWpVfWaIDdfo2/0V/q80jWDwWAwFC6KVcO6dRgxOXsY4vUVg0as4eSZzL9ezmAo7GSkwJg7IOss9J3E+3FZzN2ewCvdqtKsQoS7R/eHFD6RCFSmYnmAzVe/ouaVmu0cqpPi8vfAyaW/bx+8fyQElIHIFu4Zt8FwrVPhAYi+SUG75w7C4hsh9iFoNAxwwKJukHYU2i+QeyhxmQSjwPIK8AWVp0V1lZAbWkPH8uYXte6Q6pon9xzMbfvnY/Euos5nJbpItMo5o9ea3x6O/HpVN4PB8Iek7FBpV8VHVBq99W11HUs/BvU+hQ3POWf0kGjqVwIafOZafvv7mh6fJRHUq4iyjQA8ArWfl+mlzL1jM+WW9Q6X42hRd/CJkJsvvInWv/29fH37BoPBYCigVOqM1f5V2tuX0ytjLA+MjiMzJ9fdozIYCi45WTCuHyTugp6jmHy8CJ8t2EvvhqXo36SMu0f3pxQ+kSh1JxwcqxBP/5LqUBbRFDJO6hfXcv118XhsJti8oVgrLZd+HI7PgbJ9ZeE3GAx/H8uCRsPBLwq8w1RWs6IvlLsbGo9QQO/cVpB+AtotUKczRy4kb4SYe1yi7fFZ4BEgR2DlZwALNj6nkpiIpponaRVs+u+fj8fDB1pNVTmNPVMX4v5lYMnNzswXgyGf2f6eBJyKj6pRwul1EnZqDNYPHDmpms/mAVjQZo7KObOSlce1+1M57Ip3hMTl4OnrWnfpWyT8xNwFOemQsABCqsH06srbq/aiXicjAao+A2X76MeTfSN0TBoMBoOhcNPscajVm8dsP1H88Axe+nkLDtP90mD4PQ4HTHkM9i+CGz9hs3cdnp2wiYZlw/jvjdWxrIIVVH0xhU/tCCgLpW4GcnVBGH2zTrb3fqOLxEqPar7jc5T94Bmg+wfHgsMukchgMPxzfMKgyWjlAYXVh8OTYN2T6hjY4Cu5++a2hDN7JRR5BYHNSy3rG3wFATGaJ9dZTrb7U6j7iW5vfF5CUqTTRbTlVTgy/c/HY1kqp6n2gtZ7ZheE1FCAdp4rw2DID9KOwoFREkR9i8KWNwBLre4DysJRp8PNMxjsWVDpCQitCkenw7SqsOdrPe8XJcddxYchK1WiUWhtOLsPgispr+jEfJVbH/lFTqWOK6DWG8o98isJJbtD9cGQm6mw+c2vuGmjGAwGg6HAYFnQ/WMo1YiPfb9i97qFDF+6392jMhgKHgvfho0/QuvnSSh/CwNHriUi0Icv+tbF27PgSzAFf4RXGpsXnN6o7i256er+Ys+GXZ9D8Q4qaclOVTbK+WVlB36AInUhpLL7xm4wXC8UawXVXoKkNRDVGXZ9onDe2EFQ478SaxZ2Vsh8u4UqmclNgxX9oO0c9NHlVOBzziqjpebrur/5FYhoDEWcYfSLu0LiqksM4iJqvQl1PwQcci4VqQ3rn4ZNr+jXAIPharNzqJxzVZ6C01sk5FieUOt/sOYR50w2uYn8SkDN1+QeWtRVZWLl7tIsGcfVIfD0Rh0fjmyo/KTKqGMGOLsA3q95Y+6DTnEQ3gCS1sOJeeruafN0ilLOnK9K/5ffW8NgMBgMBRFPH+g1Bo/g4ozw/5Dvpy9hwc4Ed4/KYCg4rP8BFr0NtfqQ0fRpBo2MIyU9m2/61yc80Mfdo7ssCp9IlLJdLe59wlVWVqyNfklNP6oTY4BTqwGHq2wldZeCQE13F4PhylH9ZSjaXGHxJXvAxhdV4lX9ZSh/P2CHFf3h8E/QfpHygk4uhoPjofITej6kmtaVsACS1kHMvbq/7X8SeX2K6/7sJpcnFFV+QhfXOIPsQ2vDltfldHLYr8JGMBicZKfCni+h1G0QWA7inKJQtRdh8xvK2QL90AESNWfW0zKVn4Iar8OBkcroar9YpdUnl2reMr1UeubhK5fczLpy0obWhMbfuLp1bh8iV1H5QbDxZYmkRZvrudX35d+2MBgMBkPBJiACq894gj1zGOX/Ac+NWc6ehLPuHpXB4H4OLIUpj0O5lji6f8R/Jm1mw+FkPuxZm6olgt09usum8IlEodXgxv1wejOUvFEn3PtHyV4f1VnznFwOWBDRSPcP/KD7ZXq5a9QGw/WHzROa/gAe3gqxLt4B1tyv0N4Gn+n4BIlHOz+Edkt1vG56QceqT1FI2QoxgzTfkZ/h+DwIa6j7Oz+EqA6AB+CQUBQ/96/HVa4vtJqii+3kDbqQ3vkxrBoIdhPQaLhK7B0uoajK05C8BRIWaR/3j4LjMwEHeIaoLDqkmvbH3DRoO08B7ktukQsp9mG5Y7f9T40WbJ5Q9QV9z4XWhsU95E7CobK2PM4dgkPjlM+1+RV1DQyrB6dWSaCNGeCuLWMwGAyGgkhkZazbR1DWfpgh1scM+n4lyWlZ7h6VweA+kvYrqLpIWbhjJJ8vOczkDcd4umNFOlUv7u7R/S0Kn0gE6vSSk6JSs6zTED8TSt/hDAJFJS4h1XRi7HBIJCrWFvxLuHfcBsP1RkBpaDJSYoxvcQhrAMt6qatZsx8l+FgeylrZ9Dw0HYu6oHWF2m9pHUd/gSBn57PsJEharcwWbMp3Kep0BOKABR3h0MS/HlfJrtBmli6mkzepa9q+b2H5nWDPuQobwlCosWfDjo+UPRTeAJb11uO134W1TzpnsvS9Zdkkjpa+DbpsUs7QygHKGgJ9l63oC77FJL5WfQ7iZ0hQOrVS3fzysvdKdHaNYefH+r47u0fln77FIClOuX3d96jrp8FgMBgM51OhHVaXd2nBOvqfGcZDP6wjO9c4rw2FkIwU+LGXKg/6jGPWvkzem7WTG2uV4OE2Fdw9ur9N4RSJjkxSIHVURzj8i07Q81xCDrtEorwLy1Or4exeU2pmMFwtSnZTPtGBUVC6FwTGqBX3mV3QeqoCez0D4NgM2PqmSmHsWRD3JEQ0VwB2REPAkhMj9mEFK2KXwHRyiZyCADhg6W2X17mseDtoO1+hvylbIKiSnBbLeuszw2C4UhyaAGmHoPLTctLl7W8HRoM9XfPY8mrYbdDwa2g6Rg0VVg+EqBuUURRYAXZ/AecOq/uff2k5gDa/qkWrPgctf4YTi3ScBcXq8axkBVZ7BsNRp4vOngPNxkHzcQrRNhgMBoPhUjS4Dxo9yN0eM6hwYAyvTN5qOp4ZChe5OTDhHji1B3qOYltmUZ4ct4Fa0SG8e1vNAt/J7FIUPpHInqsMohJdlc9waBwElNOvtwCpOyA7BSKa6P6BH3RyXuoW943ZYLjeqfGqWnZvfA7qvAdeITC/I2QlQesZOlZ9I3V8xs8C/7JyRiRvAps37B8p8Qjg8ATotEGdCB3O8rD0oxJ78rjczmXFWkCHxfoMOLNTnxWHJ8DSnpBrLNWGK4DDoSyg4EpQrDWsdDp2SnRViDQOsLzBnqF9veMqqDAQdn0Kax5SF7LG36k8LaQKHPxBotHZPRKFZjdVk4YKD8p9Z89WIHZUJ6eYCqx/RsdTTorul7oFum6FMne4Y4sYDAaD4VrjhjehUhde9RpFwtqf+X75AXePyGDIP+a8DHvmQpf3SIhoyMCRawn29eKb/vXx9fJw9+j+EYVPJDqzC3LSoNStkJEIx+dCmZ6uk+XEFZpGNNEvqYfGyengHeK+MRsM1zs2D+UT+RaDtQ9D859UVjO/vXKIWk2VS8g/WhfLWaecQdJ29DFmh1Mr5KbIOKE8lqajoNl4uSJAHZ5cL6hQ3o0v//XYIhpDhyXg4Q/n9suVdORnOZJyM6/8tjAULhIWwul1Cp/e+paE0eDKCqTO6+DncAqS7ZdCeF3Y8SHEPaZSyCyGDgAAIABJREFUsOYT1IXMkSPxJ6y+QqrDGsCml9XpzKco1P9Y6zi5RIJQic4SqNY9DXudzrrAGHUPbD4e/Irl95YwGAwGw7WKzQNuHYYVVZPPfT7j52lTWWg6nhkKA3EjYOXn0OhBMmrdxaCRcSSdy+Kb/vWJDPZ19+j+MYVPJAqpAreehOgecHiinAZlerqeP7kcvMMgqKIEpIwEU2pmMOQHvhHQYgKkx6s8pvUMyDknoSigDLSYJJHGvzT4l0QZLWfBJwywyVVUrr/WtfcbOD4fytwObWZLKPLwO+/F7HJlbH0D1j7612MLbwDtF2od6cfAJ1JlOUtuNUKR4d+xfYhcckVb6DZof83NBM6z61d6AiIaKJ9r3f+pC1rzcQp+3zNcIiY2dUbLOQvJG1Wm6chR1768rmjHZmrf94mEX2Ngh9NRV+5u6LYdirfPxzdvMBgMhusG7wCsPuPxCi7K9z7v878xs9l94oy7R2UwXD0OroBpT0H5dtg7vM5TP21k4xF1MqsRfW0bTAqfSAQqXfHwkUsoqCKE1nI9l7hCLiLLUqmZV6iCPg0Gw9UnvAHUG6ow+UPjJBRlnJBQFFZP7emT1kooyssNy04B/1K6veNDKN1Xt5f3hewzULyt8ltyMyCwvOu17Fm6WN71qea9nLG1navSs8xE8C4Cx6bBkttM6Znhn5G8FY5Nh9hH1MXPkQve4Qqm5rxOej4RUOsNODgOVj+gUrRmYyT8pGxXWHtumoSkQz9pPUVqK1fL5gPlB7rWdWyqvtdmN4JzByR8BlaExt+6hCSDwWAwGP4JQcWw3fkToV65fGG9zaPfLyTxrPkxzXAdknwYxvWFImXgtm/5aMF+pm2K5z+dKl9zncwuReEUiUBuhRMLFVidV2qWmQSp2yUS5aSppKT0bRKUDAZD/lBhEFR4ALa9owyiVlPUwWlhJ2WtNPgcjs+WMFT8BrmNLBt4hak9+NkdcgNmxKukDJStUu9jhdAXqe96LXsW4BSE53f867EVbQptZ+liOjsVPAN10b2slwmzNvx9dnwgkSakuhoqYFfHzd9wlko2Gg4JS2BFPyjaTOVgeYLOtrc1jerqKhsr3hFa/AyHxkPZ3gqezk5V2HvqToW9e4VC9VeVV1T9Bdf3oMFgMBgM/4bIKth6jSLGimfwubd4aMQKMrJz/3o5g+FaISsNxvbRj8S9x/LLjnMMnbeb2+tFc3/LGHeP7opQeEWiQxMAx4WlZqdWaVq0KRz5VRefptTMYMhfLAvqD4VibRUwbXlC84kqJ1twA5TpDTXfgIM/yhkU0cxZhlZSZTpJa3WRDCrNOT5Ptys9ClWfh9NrL3QPeoVpenwOTIqC5O1/Pr7Iluq6hk0lQTZfCcrL71SOmcFwOaTHq3tZubtg8yvnlUM6Wwfb/IBcCaE+4bDkFgipJtHU01/zZJ+FA2PkFspKlDAa2Ur756Fxzu+wfrBpMPxcEnZ+pOWib4abj0vgDCgHZfvk97s3GAwGw/VMTGusHp/SxLaVPsff4dmfNpiOZ4brA4cDJj8MxzfDrcOJOxfBsxM30ahcGG/eXOOa7GR2KQqxSDQOQmtASFXXY4kr5EgIayBngX+0LggNBkP+YvNSeHVAWVhys47T5hPg9HpY0AkqPqKg3z2fqwzNrxSkbHaVnR0aC0XqAhasuEsuCoBab0LMPcpr8YvWY9mnoEw/dVTLOA7Tq8KCLso0+qMTmuLtodVk1HnKUue0Qz/ptezm1zLDZbBzqPKCfCPV8j7X2eo+L6zani7xp8pTsLCbvo9azwTvUNc6VvTXOvxL6UcO/9LQdr7Kzba9o318UXfY8ppyijz8FA7fYqLceElrofpLpszMYDAYDFee2r2h7cvc5LGcqts+5MO5u909IoPh37Pkfdg6Cdq/ysHwZgwcuZYSIb582bce3p7Xj7Ry/byTv8O5w3ByGZTueeHjJ5dDaE2VrMTPdJaiFc5NZDC4HZ8wuSbsObCom9qDNxunC9tFXaH6YKj4GOwaClHtAQ9IO+xyZJxep4vf9KMK+gUJOg2/guibIP2IM+wXODgK6ryvsh+A+Fkwvx1Mrwl7vlH56cWU6KyObLnp4OkH2ODgGFh1r7PzmsHwB2Sfgd1fqERs58euDnyewYBD4hBAlWdg5d0KoG4758KOYycWycGGTe3usel4SdkEc1opyyv9iMogLU+o9pJeJ7qHlt88WE68cv3y730bDAaDoXDR4ikc9e7hAc8ppCz8lEnrjrh7RAbDP2fnDJj/BtS4g+Q6DzLg+zXYHQ6+G9CQIgHe7h7dFaVwKiCHxmt6fqmZPVe/xEY0VSmaI8eUmhkM7ia4ErT4SdlES3tBye7Q7EdIXAmLu8kZFPsQ7PsOIlvIQYGna3m7M1B673A4Ok23bZ5aR2RrhVkDYKm0rc4QfQZgB68i+lxYPQh+KQUbnpfAfD5l7pDolJ2qcGGA/SPUMc3Yqg1/xN5hClzPOaupIxfwgJxUTe2ZEnAOT9I8bWaow18e2amwPK9EzClIRt8I656AGXUgaY3cbQCRzaHrFgirq3WVug2OTJYrr/rLxkVkMBgMhquHZWF1HYK9YhcGe41k/qRvWLXvlLtHZTD8fU7uhIkDIaoWmV0+ZNDodRxJSueb/vUpFxHg7tFdcQqnSHRwrEpUgiq4HkvZqhPoiCZyAwRXvjC3xGAwuIfi7aH+ZxA/Q4JNqVvl4Dm5VI6iWv9T96aEhSq7sRzg7RRsrPMugFcOgEzniYmHr8rFitSWywKH/hZ1hVpvQ/EOkH1aAb9NxyvnZfu78Gs5WHoHJCx1iUAVBmkMmQng77yQ3/05bHzeCEWG32PPVhe+wPKQsIDfwql9wvNm0MQrFM7uVgB1aI0L17HyPkg/5pwvRA6hI79ITPUvBTgUqt5kJLSdJ7H10HgJmZGt5CIKijU/hBgMBoPh6mPzwHbbcOwl6vGB52d8MXI0exLOuntUBsPlk54MP/YGL18cPUfz3K97WL0/ifdur0mDsmHuHt1VwfOvZ7nOOLNX5Sq1373w8cTlmgaUUReZGv813V4MhoJC7P26KN7ymjJVar+lx5ffCQs7Q6tpcv/t+w4sbwiqqpbguWm6MM5MhMyTsOZhaD5Wy3oFy6Ex2xl87bC0jvntnB3MfOHYFFh9H3RcAXU/kPiz5xvlDxWpA5UeU5B21eckQO1wlqylbFEmjEcg1HjJfdvNUPA4OF5lkYB+p8kFz1CJjJY3OLIkIJ2OgyajoXjbC5ff+Skc/sl5x5ITKShWGVxHpoA9DfxKQNft4B2s2XLS4OgUKNtX0+RN0GSUlj+1BpI3Q8o2/ViSsk3His1bfx4+mla4H6o+mz/byGAwGAzXF97+eN45nuxhHfjk9Ns8MjyIIY/cSdEg00HaUMCx58LE+yD5INw1hY/WpPPz+qM81aEiPWqXdPforhqFz0nkV0K5Jhd3c0lcoQDRxJWAQ22DDQZDwaHGq3LtbHsbdnykctHmP0n0XdARar0D5e/TRfbp1VBugJbLTJSwBAqsPzjOtU7fSGW9+BQF7OARAI5smH+DhKlSt6oEaFZ9iVR13oWbj6jEzJ4ld9Lk0rDldajyLMQMkEBUtIXWv/lljdVgAJ1orHvSdT8vE8ueqakjS3lEZ/dCrbeg3HlOH4cdNr8GcY+et0ILAmPVZe/QOAipqIcbDXMJRADHpqnTWenbYdMrCrg+tQYml4JZDZWjtfszBbcXbQ4VH4aYu7X/F+8gh61/9NXYIgaDwWAoLASE43XXZHz8gxmS8V9e+HYKaVmmK6yhgDPvNdgzBzq/y08nS/HxvN3cVi+aR9pW+Otlr2GsgtyOsH79+o61a9fmz4v9Gguh1eDcIWU03LAqf17XYDBcPvZcWHaHslqajNZF9NGpsORWCK4CbWbB1jdh1yeAJZfPwTFa1iMAcs/JFdF9HwScp/6n7IDZjeXK8AxS6anlAU1Hq4zn4FiVpbWYBNHdtYzDASfmqXTo2HRd3JftA2f2qBSu1C1weKLmbfA1xA7M101lKGDkZsr1dmKB9jEc2s88gyDnjPYvh/NkOfZBlVhaFmSlyCG3/T1XiVlQJTizE3VCc6hsstbbsOYBCZ43rLrQCbvkdkhYpFKzwxP0mM0bSnSB0ndAeAMIKAc2j3zcIC4sy4pzOBz13fLihkuSr+dfBoOhcJGwg+xhHTma4cvQsp/y3t0d8LCZ6g1DAWTzBJh4L9QbwKJKL3Lv92toHBPOt3c3uG46mf3ROdj18e7+LRkn1R0msLzCPMv0+etlDAZD/mPzUB5RZCt1fTo2E0p2U1enMzthfluVflV6EnDAkV/Bt7iWzT0HWHIAzWoAOZmu9YZUhnYLVGKWc0ZTmycs6wPF2kDZfrqAX9wD9n6rZSxLeUmtp0HXbXJeHPwRTi5RKdvRqXI+AawZBAfOczAZChfZqbCwiwSivH0sx5nHkHMWsFwCUYmuUG8onNmtAPRfouU+yhOIYgZIiATlDjUeAZ3i1MXv3AGo8cqFAlFavITO7BQJRJ4B0HAY3HICWv4s12xQBbcJRAaDwWAoZERWxqvfBKK9UrjnwDO89fNqCrJpwVBIObYBJj8CpRqztdaLPDQ6jthiQXzRt+51IxD9Gdf/O7wcEldqmnlKLe/L3OHe8RgMhj/GwxdaTobQ6rDkZoifA1EdofUMOHcQ5jjLZcrfC7lnUSj1RWTEq4QsJ931WFgdlZ5ZHmBPl/PDJwJW36/g4HJ3a12r7oUtb1wYSh1SBRp+CT0OqyzOsqmEaO+3UOp2zbO8t6vDmqHwkH4c5raWkwe0/1peTiHSm99C0wGCqyuEfXEPmFoJ9nyljCvQfulbTK4ichVY3eMAxPRXKdrWN9WQoURXzZ+TDtuHwNSKEqD8S+nxJqOgwr3gHZpvm8BgMBgMhgso1RDPXj9QxeMw7TY8wdfzt7p7RAaDi7MJMPZO8A/n6A1fc/eojYT6e/P9gAYE+RaOrrBGJAI4tVoXdQmLIbIN+EW5e0QGg+HP8A6BNnMgqCIsvhGOz4NiraHtfDkm5jSHSo9DsXaQccJZ3gP4RPLbx17KFpjbSi6PPCKbQ4vJqIwHhQmH1oANz+pzoYwzI2bTy7D2YZW/nY9vBNQYDDcdhmov6uL98E/gG4W6p90I8fOu3nYxFCzO7IHZTSF1p7J+bN6QnayW9/YMudry9kebH5ADS26CpDioPhgq/R+cWgkeflomI9EpLAEtJoCPs6PG3uFwdh9Ud7qIEpbA9Bqw/hnlHnmHQ1ayHHjRN7ljSxgMBoPBcCGx7bFu+pJGHtspv/ARJqze5+4RGQyQkwXj+0NaImdu+p7+4/aTmZ3L9wMaUCzY192jyzeMSATq9OJfRh2OLg60NhgMBRPfCLX3DoqFRd3h+HyIaAgdlsh1MaelSm8CK6i8BwsyT0DJG/lNBEpaA3PbyEWYR3RXaPgNcnfYIHkLRHWGbW+BVxCU6qn5dn+hfKTz3Uh5eAZArTc0Fps3ZCU5n7ArZPvE4qu2WQwFhKQ4CUQ5Z+TeSVjscp8FVeK3fTCv5b09XXl4jb+HG/dD1mnY/o5cR7np6qZX9T8SliJbqdQRJP5segkiW0oUjXtC4qfDDq1+lWgaUEb7YN0PTNdOg8FgMBQYbLXuwN75fdp7rMdnykMs2B7v7iEZCjMOB8x4Bg6tIKvrJwyYlc3hpHS+6V+f2GJB7h5dvmJEIoDkjTo5t3krbNZgMFwb5AlFgeVhUTdlvoRUhY7LwLcoLOgE1V9WdkseR3+Byud1mDq9Aea2hLRjrscq3Au130UX8JZKUis8AHu+1GPRzs+Jw5Mk+mQmcUmKNoXm43VhH1YfvCO1/LxWsOPjK7stDAWH43NVYubpDx2WwfFZKHcoW/vqme1cUAYZXE2h6503Kmx9UXfYNVTPOXIgvDHcsMYZyG6DZmNdy255XSJn2f4woxbs/Fjlll02KfTanqkfQmLugrC6+bcNDAaDwWC4DDwb3UtGm8F091hB4o8PseHQaXcPyVBYWTsc4r7H3vQJHtxYjrhDp/mwZ20axYS7e2T5jhGJslKUY5J+TJ1eTE6DwXBt4VsU2s2DwBhY2BXiZ8s50WEpBFdShlDMvfzmDALYN0LtwAGwqyxoTnM4s9e13qrPqK09dpUIJa2BWv9T+Vh2qiv7JXGllj136NLji+4hB0fSWojp5xwLsO4JmNkIktZf+W1icB8HxymkOrAcdFgugXLPt/wmOF4ckVWkLnTbolyt0xsUVH1iHniGQEAMeIdBy0mw4Tm5ksr0Bj9nGHvqTtg5VOtYPVDuoXYLoP4n4BUIh8arVM3mDTXfzOcNYTAYDAbD5eHb6v841/hJbrfNZ/N3j7Iv4Yy7h2QobBxYCjP+gyP2Bp5L7sG8HQm81qM6XWsWzhiafyUSWZYVZlnWHMuydjunRf5gvlzLsjY4/379N695xUnerGnOWVNqZjBcq/hGQrv5yiha1E0X6r6R0H6Rsop2fQwRTfmttCfrlAShgLK6b89SdtGc5iovy6POO1CuP+BQ+dCZvdDoW0hYABkJKu9x5Ehont0ETm+69PgqPQ6xD8OO99VuvP7nejxpNcysC8v7aR2Ga5udn8Cy3nL+tF8M/iVgxwdAjmuec+cJkTZfOYSykmH1g9oXMk9CqVuh0sNwbh80/Eriz86PwPKEBp+7lo97XNPTcfr+6rJJ+ztoncdmqFSt6nMai+G64ro4BzMYDAYnATcMJqXmvfRzTGHBV09xPCXD3UMyFBaS9sO4fhAWw0fBzzJ+XTyPt4ulX+My7h6Z2/i3TqLngHkOhyMWmOe8fynSHQ5Hbeffjf/yNa8syRs19fCHEt3cOxaDwfDP8Y2E9gshooku1Hd9plb0raZJ6ElcDj5FlfECcHqdM8jamdGSmwY5aSo9S1zlWm+TERB1g27vGw65GdBiokp40o5IEMjN0N/cFspGuhjLgnofya249mG5nurllZvZ4MBYmFoZNr+m9RiuLRwO2PgSxD0m51ibWXKlJm2AHR86Z7IgtNaFyzWfCPuGwa8xKmW0PNTSvsozsM0pUJa+FVbeIzEyZgB4B2vZPd9C/CwFWtf9ULlHXueVVR7+Wcv4REKVp/JlMxjynWv/HMxgMBjysCxCbhpCUsWe3Js7jumfP8Xpc1nuHpXheicjFX7sBQ4742Pf5eNlJ+jbuDRPtI9198jcyr8ViXoAI5y3RwDXXtuUpPWApV9uPf3cPRqDwfBv8A6F1jMh+kZY+whsGuwKA672klwaOMDyAWxy8uS1BgfISdUF/7y26piWR+vpUKSebq99GLzCJARkxEPaIbUpz05VadDCTrD/h9+PzeapLJmQ6rD0dijWFmq8BtilU4U3gs2DYVo1ODr1qm0iwxXGngOrB6kFffn7oPlPKk9c8xDMqsdv7rXWM13OVYAitWHzi7D6fsg+C16h0H6JRKEV/cCvJNQbKrHn+GzApiB2gCNTVF6GDdrMgMpP/D6Qevu7mtZ5X9lIhuuRa/8czGAwGM7HZiOs1xecjLmZezJHM/nz/3AuM+evlzMY/gn2XJh4LyTuZnGd93l2QRpdahTnvzdWxyrkjT7+rUhUzOFw5MXQHweK/cF8vpZlrbUsa6VlWQXrJObkEsABZe9090gMBsOVwNMPmk+AmHtgy2uw5kG5LWq9Dg2/VumOIxOJMx6QdlhlPyCXUXYyePjCgs4KpgawbHDDSgiuDjhgfluVqrV3fn6kHYSAcpAeLxFoRV/Y+parm1UeXkHQeqocH4u6Q+z9CtF25MDJZVD7beXHLOoOC7vD2f35t90Mf5+cdFhyK+wdBtVelCCz5XX4tQLs+caVP1S2Lxz4Ech1PmApf+jcYbD5gH9J7V9Fm6ht/Zk9crBh1/6LDcr0Av9o2PstLO6h5xp84XK5nc/JlZC6QwJoOVNGfR1z7Z+DGQwGw8XYPCjadzjxpbpy97lvmfT5C2Tm5P71cgbD32XuYNg9m621X+LuhX40qxDOhz1r42Er3AIRXIZIZFnWXMuytlzir8f58zkcDge/j+TMo4zD4agP9AE+siyr/J+83iDnyczakydP/p338vdx2OHsXgV7Fm93dV/LYDDkHzZPaDQMqj4Pe75Sl7PMJKgwUK4gm4/mc+QiZ5Hzo9CRDX7RahfuFQxLboO9w13r7Byn7lSOHDl+AspBxxXgFyVHkW9RSN0FxTvCxhfkJrFf9AuYfzS0nAwZx2HJLVDzf1DuLq1z44sKGK7zHiQshGnV1QXNbk6OChxZp9XZ7ugUqPsR+JWAqbESJkt2g1J3AHYJkFVfhAPfX7h88Y4SJEOqQMflClk/Oh12fwGV/0/ZQnFPQsZJrafK0ypBW3WvxM3I1tqfL8WqezRtMtK1bxuuSfLzHCxfz78MBoPhz7B5EHX3SI5EdaBfyldM+vJVcnLt7h6V4Xpi/Q+w/BOOxvblptWVqV0qlK/71cfH08PdIysQ/OXZo8PhaO9wOKpf4m8ycMKyrCgA5zThD9Zx1DndBywE6vzJ633tcDjqOxyO+kWLFv0Hb+lvkLxRF4kRTXQBaDAYrh8sC2r/T6VmJ5fArEaQsgNK3ACd4pRDBnIP5aarkxRA+hEo3kHh1j4RsOo+2PaenvPwhq5bwDcKcs/BlBjwLa5OamF1Ie2onEynVquL2Z4vYfHNkHPuwrGFN1D2zMllsOZ+aDhMmWiOXFh2hxwgXbdBZCt1QZvbAlK259umM/wFaUdhTgv9n6v+B/Z8oTLE4CrQcZXKxA4529TXfA1mN3Qta3nJWXR8tlxA7RdLZMxIgFUDILQG1HpDodP7R8hZVrwTHPhBHc78SqiEsuHXvy8xA5Uqpm6H4KquEGvDNUt+noPl6/mXwWAw/BUenkTf9yMHi7amd+JQfvnmdez2P9LCDYa/wcHlMOVxUqKa0XFHZ2Ijg/huQEMCfIwekMe//YnxV+Au5+27gMkXz2BZVhHLsnyctyOAZsC2f/m6V4Y9TodAGVNqZjBct8TcpbbgOakwu5EuvkOrQdetchQ5sp1CURaEOq+djs+BCg9AZqKCfzc8C+v/o/IxD1+4cTd4hej5X8vLjdh2rhwkmYlyBR2dAtVfhfjpMLcVpB+/cFxl7oAar8L+kbDzA2jxE4Q3kcNxWR+1QW89TYHEqTthRm3Y8ubvnUmG/CVlO8xuCmcPQJE6sO1t7Retpmg/C28g5xp27TvbP1br+jzC6sGBURD7kBxlXkFafuU9kJUCTceo297qQeAdDvYM8PBUZ7xi7SH9mMoSgy8RqJiTpvXAecHohuuYa/sczGAwGP4KDy/K3P8T+8JactvxD5g27FUcF5fyGwx/h1N7YWwfMoNK0+XYvRQLDWTkvQ0J8fNy98gKFP9WJHob6GBZ1m6gvfM+lmXVtyxrmHOeKsBay7I2AguAtx0OR8E4QTk2Q9MyPd07DoPBcHUp2lStxgPKwaJuKtsJKA2d1gAWOLIg4wj4FYPAClpmz1dQYZDTURSpIODVAyXSeAbAjfvkRko/ClMqygXUYpIu/nPOqpRo73C5hFK2w+zGkHLRR1/1V6B0TzlE4mdBm2kQXFkOkZUDVHpUri902w7RN8Gml+RgObP3d2/RkA+cXCaBKDMR7Jn6f9Z5D7pslkBoWeqql7xB82enQOZR1/IegXBqpbqR1f/U5WDd/QUcm6Z1hVZXLlHaMYWh+5eSO6jyUwpaj2wJFR+59Pi2vKZw9oAypoS6cHBtn4MZDAbD5eDpTbmHJrCrSGu6H/uIOcNfMUKR4Z+RlgQ/3EaOw+L2M0+CXxFG39uIiEAfd4+swGEV5IOsfv36jrVr116dlaefgJ+LqxvRbaeuzmsYDIaCRc45OS0OjYeozspsiZ8DK84L963wgNw9uWm6H9kGEpep+1RmgsSaZj/KUZR2VE4ie6bKzrpuBe8icn2sf0bZMQFlFTC8or/K2lr+DMXanDemdDmNUrcp38i7CMxqoot9e6aEgypPa96D42D1A3Iq1RsKMXdfuuTIcOU5NBGW9dLtvHb0tf4HfsVd8yRvhem1cAVUX4TNB5qPg+jz4mRStsPMusoYaj0dTsyH+e0hpJpTVHQo8yh+BpxcCl02QWDM79d9egPMcHZTqzcUKj16Zd53PmBZVpwzM8dQQLiq518Gg8HwD3DkZLHts55UOz2fRaUfodU9b7p7SIZriZxMGHkT9qNxDORltnhUYdygJpSNCHD3yNzKH52DFd5Ey0PjNQ1v+OfzGQyG6wfPALWhr/+Zyrlm1IaAUlB+kHMGSzlCpW5xLZOwUGU/2alyFB35ReVEWSnqStVpjcSgjOMwpZKChqs8Dc3H6/Gz+yDucWgzW6HVC26AfSPPG5MftPxF5WuLbpSQ0HaWStg8AyU2bX5NJUllekokCKuvcOKlt0GmEbmvOpte07Z25ChguuMKaPzthQJRTjos7MwFApHN23XbM0DLnS8Q5WbC8j76Pzf+TiLmqvvAvzSkbOU3gcgrUE6z2u9cWiCy58KqgcrM8vCHcv2u9BYwGAwGg8GtWJ7eVH1kPBtD29Hq0Kcs/+45dw/JcK3gcMCvj8Kh5bzEQ2yyVWHMwMaFXiD6MwqvSJR3kVa0uXvHYTAY8hfLgooPQceVEmLmtVZ5TnBV1BzIAw6MhiL1nPPb9Lg9SyVkPkUhYamWSz+hoOGWziiQrESYUgFSd0Pp25VR4xmkIOHld0ooKtoCVt4FmwbrSwvAv4SEovR4iRGBFaD1VLBnKzx782DY+LzmDygF7eZB7XeVezS9lsqgDFee3CyY2wa2DJbgV/NN6LweIhpfOF9WMsyoC2mHXY+F1NA+A4ANOq6BsIvygje9JAdQI6fgtP5pOHfQFXRe83UodbO6nBVrA7EPXnqc29+BpLXaP8r2Bu/QK/L2DQaDwWAoSFgeXtR4ZBxrQzrQ9OAXxA17zHUuZTD8EYvbMWFLAAAgAElEQVTegU3j+NLWm9lWM34c2IjyRQPdPaoCTeEUic7ug9NOG3VoTfeOxWAwuIewOmppX/p22PSiWt7bfPnNCXI6DjyDlTVk84LSd+iiPytJYcMpO2BOM32elOwq0QYUUjy9OpxYrCykzuvUkSplsxxILX9VqdKW1yQc5WZoufAG0Gg4JCyW86hoM7meMk/JWbLtHT3usEu4qvqMU+jyVbna9iHmROlKcmqtSpITFkpE7LYLqr+gfeF8js9TyeGZHa7Hwprq/51HnSEQWuXC5Y7N0v+swgMQ3R2OTlcOln8p5WBFdYEqz8pphENC0qXa2Seugk2vQJG6Kk/8IyHJYDAYDIbrAJunF3UeHcvy0Bupd2QEm76+D+x2dw/LUFBZNxIWvsU0Wxu+5hbGDGxMhcggd4+qwFM4RaIDP7puF6nlvnEYDAb34hWsblKNR8jt48g7yXB+NNqzAMvpELFL4PEOk6PIshRgPLspJK1XiVm5/q7l5rWGvd9DUHm1sw+uKuFgZj2o/yXUegsO/qj8mYyTWq7cnc6W6l8qzLjUTcozSjukjJpdn6jrld0pZIXVhU5xKmFa/wwsvgmyTufb5rsusWfD+udgVkNty7J9ofs+CLqozCsnHdY+rv9fVrJKxkDZVadXueYLrgyVLgqaTo+HFf0gpDrU/QAyEmHVveBVRP9rzyB1u1v/lFxijYZBYNnfjzX7jEQkv5LK0Aqrr+5pBoPBYDBcx3h4etLo0RHMD+9NzfgJbP+iN+Rmu3tYhoLGrlk4pjzBSqs2gx2D+GFgYyoWMwLR5VD4RCKHAw78oJBZr1D9amswGAovlgUx/aHrFiieFyhtB8tLgkFAOT10aLyCqrvvguibFUKdfcZZktQKTiyAhl+rnMzyAmywagCsexa8Q6DLRohoCmd2wtSKcpA0/wmS4pydz5xOlJpvQomusPZRrTP2fqg+WBk1RZurY9qKfhobaN3NJyi75th0lT0lxeX3Vrw+OL1B5Xvb39F+0Wg4NB0Ftou+KpPiFDa9a6jKDz391NEOtE84zsslqjf0QveRww7L+2n+5uPkBFvzIGQkQLZT4Kv7ARyaALs+VVezP+rAufZROHdA86TugNgHrtimMBgMBoOhIOPhYaPVQ58zPXIgVU7OZM9nt0J2hruHZSgoHInDPv4udlCWp6ynGDGwGVWigt09qmuGwicSpWyVY8DDH4rUNJ2BDAaD8I+G1jPUmhwLHNmAQxf9xdprnrUPQvw8aDkJGg7X50f2ac23sDMcmazuZQFlwDtYmUc73oN5HSTqdFwGxTtA2kFlF4VUh3aLlEEzuzHEzwabBzQbA0EVYentcHY/1BgM5Qequ1XJHnIgLb1dwcegcVR+HDoskUAxpzns/8FNG/IaxJ4DW96EmfUltniFQIflUP6ei+bL1XyzGmu/KNFVXehy0s6b6TyBKKozRHW4cB3b3lZoev1PIKSqfrQ4PAGwK7Q8vAkUqQ1r7lfHs9pvX3rMB8bC/hFQ7SU4Mgn8oqDsnVdgYxgMBoPBcG3g4WHjhgfe4+fij1MhaRGHPumCIz3Z3cMyuJtTe8kZfRvHcoJ53PYC393fhmolQtw9qmuKwicShVSDG1arlbXJIzIYDOdjWVDxYWj1K2ABdsg6CenHoOSNmmfZbbDlDYi5CzpvkqCQc1ZCw7JeEmdaT9O8/tHgWwJOzIXJZeDUOmgzC0p0Ue7MjNrqinbDKglLC7vAzk9VbtRyskSJxT0kIjX4XGM4+ivE3CNBanGPCwWKiMbQaS2EN4IVfWHdUxqX4Y85swfmtlSItMMOQbHOcOpGF8537hDMb6v5St0K1V+GY9PQfuLMgrI8XfNbHlB/6IXrOLlM+UFleul/eO6wygdB+wlAvQ8kAPpEyGlk8+R3nD0Aax6AiCZQtCUkLIKqz8uVZDAYDAZDIcLDZtFj0H8ZG/0yUSkbODG0HfaUY+4elsFdnE0g6/ubSM3I5kmvl/nygU6mxOwfUPhEIssCn3Bd1BmRyGAwXIqS3dRuPI/UbZCyE8KdXa02vQzz2oKnv8rI/Es5XYkOWPc4bHkdmk9SKVBwRYhsI8fJrAaw7S09F9VJQcOLe8De76D9EjlT4h6FNQ8pg6b5OLkfV96t0OJmYxVofWA0VH5azqOFnSE71TVW30hoOwcqPgo7PtDzmafyb9tdKzgcsPsrlZflledFddKPCIHlLpz34HjnfOuUX1X9JYWIa0WaeIWA4zxBrtITEFTBdT8zCZb1kRjY4Estt7CryhaDqkDGMXUz2zRYomTzifpfXkxulgLPcUCT0bD1NbmIKgy8QhvGYDAYDIZrC5vNoue9TzG+4vsEph0h+dM25CbscvewDPlN+mnShncn58wJnvV+iSEP3EKM6WL2jyh8IhHA6Y2ahprQaoPB8AdUeQqKtwebt+6f3amAak9ne/FTK2F6TTg2EzoshcAYdUez+UjEWXU31Hjd2R2rNFR8ErDDxhdhfjuJUJGtAAu2/FfOn8bfuoKrF9ygIOLa78HhiSpz8vSTyykoVp2wav0PTi6Hee0lQuRh85KLpdG36pY2swEkb83f7VeQyTgJi26UG8fmo6DxKs9CqynKeMoj+yysvAeW9YTgStB5g1rSL7hBAl8elhdkp7jue4VAzf+67jvssHKAxJ9mY/Uaqx9QkLl/WbnJwhpB6k44PlsljxENLz32uMchcbnyr9IO6v9rXEQGg8FgKORYlkWfPnczufbX5Galkf5lO7IPrPrrBQ3XB5lnSR12E55Je3jJ9wVefbA/ZcID3D2qa5bCKRIlbwIsCK3m7pEYDIaCimWDJiPVAc0vWo85MiEnGbAkLASUldCw4i5o9J3cP5YHhNaGcwdh4/O6+N8/Ajx9oOEwwAaJK5R/U6SO5rU84dgMdT6LvkVulZPL5Dwq3kFd0za/Aod/Ae8i0GYmeIfCzo9Uhpa8SeHZ6ccvfA/lB0D7xXKrzGkqQauwEz9H4l78LHWqyzkHTUZBnXeUB5VH0nr9P/JyfzoskRA4u5nEHgDvcE0deR1VnBl39YaC53knJtvfU5lgnSEQ3gD2j4a934BnMIRU0RhCq8G+4VDtxT92Be3+UgJi1eeg9B1yHfmVMC4ig8FgMBiQUHTnzT2Y12Q0iTl+2Ed0J3Pzr+4eluFqk53ByWG34p+4ibcD/8NzDz9AdBF/d4/qmqbwikRBsReexBsMBsPF+EXJjZN+RJ3JQK4Rv5K6nbIZyg+C0+thQXtduAeUgTO7oPz9midpFVjeCizOPg1tZqhMzbJJ5Mk8rrKiPEfL3OaQlQTtFihvaE4TZRiFNVBXs+QtyjpqM0th2Nvekchxdp+ydc4duvA9RDRyllDFwKKusPMTlVoVNnKzYP0zsKCjtr1lAw8fiT/l+rrmczhgx8cKEs85C23nQa3X9fjUyvqfWx5Q9UXlSoHLbYYDAstDuX6u9Z1YABtf0L5R6TE5ulberXVUexHiZ0DxdrDvW4h9SCVnlyJhibqZRXWGmm9ovSeXGBeRwWAwGAwX0atTK+Laj2N7bjReE/uTtvCjwnnuUxjIzebw1z0penIlX4T+H48/8iSRQea86N9SOEWi0xtNHpHBYLg8ortD7MMq8SndS66R7FQodbue3/s1hNRUXtGW11Tq5VscDozUBb+Hr8uhsv4ZODgOOixTCZrlIRdR+jHl2eSkKYh43ZOwfQi0m6ew/WW9ILIFeAaqTCrzlBworadBejxsfRNaTFIb9TktIHX3he8hoJQyj0p2h7jHYO3DEpgKC6m75aTaPgSCq2h7F20KndZfWNaVkaiMqHVPQPGO0HkjFGsNSRthUjGJfx5+0H0f7P5My3iFSNyznP/jZj/+f3v3HR1lmfZx/PvMTHqBEAi9SweR3puiIvaOrm119V1d2+7a1rK6lnVXXPtaV127rB1FsQFSBKT3XkJCSYCQnpDM5Hn/uCaGkkgEMgnJ73POnCSTZ2buuZODt1euUjY1M3+r/eziOsKA/0BRJnw7xCbQ9fy79aeKaWOBotaX2sSz8iZu5m2BGedboG/IuxbgWvaABSuP+10VbpyIiMix6fzhvUg790O+LulP9LT7yf3oRgjUobNPHeAG/Kx+8TJa7pzG24k3ce2Nd1MvKqy6l1Ur1L0gUXEu5G5QkEhEKq/XeKjfA9K+hbZXgT/bJkq1u8a+v2um9R5qONiCNnmbLJiw/CHrKRQWZxPLvNGWMTJ1jE1RSxoJ+SnW2NoXZ31t0mdCmytg6+d2XY+HoO2V1oQ6viPkp8LMiyzI03CgBYeyV1qgaOSXEMiH74aV9V4rFRZr13a5A9a9YE2Ti7Ko9Ta/C5N7Q84GiGkH2ass+2bUNxDVuOy69Ok2bW7719Dnaev9FF4Plj0Ik0+A4kz7GZ27w/pBFWfu34vIDViPqcR+9nVJsfUy8ufBsI8s2+ibQXZ960thw3/sMXnJ1rB80H8t+HMgfz5MP8d6IA3/zMoM06ZYFlE3ZRGJiIhU5NRe7Ui86l3+wznELn+b3NfOgYLM6l6WHAXFxcUsevZSOu+czKSk6xh3w4NEhnkP/UCplLoXJCrKgMajDh5vLCJSEV8UDP3QSpayV0LLC2BvOuz41gI9uFYylrEI9u6C+M4WDHD9lhXU8x8Q3cy+Dm8IhWmw4FbIWWePT58KCT3guOvBLbIspPiuFkSYNgYiGlk/m52zbDpj2hQbbw/Q7FQY+CbsnGklbSdNsWym70ZaU+t9OR7rvTPgVStX+m6YjWGvjfx5MOcamwQW3dyaRxdlwPCJcMLfy0bLlwQsEPT9KAvsnTLbysJ2z4WvesGy++06bxSMXQolhZYBBPsEaHyAB4ZMKHv9RXfaz6v/K/b7MON8yFkTzGTabuWB/jxoNBSGfmA/swOV+OHHy2DPYhj8LtTrbOnyS/9qWUTtr6mq3RMREakV+rdryPDrn+Nh3x+ISJ1N/gsnwa711b0sOQI5+YX89ORF9M78mpkt/4+x1z+Gz1v3whpVqe7tZkwr+5+opqdU90pE5FgS39GyPXb/BJGNoNEwyN9ipUzRra3Uq2F/G32et9mCEmEJFhiaew20ugQS+0PRLitR8sZYE+r0aRBW3zJZtk6EXv+y0qXsVVYW5YuF1Y/DxteDja9LLINl7bOw4VVbW5tx0O/fln20crw1q45sBFNOhu3fHPxe2l9tvZHykuGbATbavTbJXAFf97c9q3+8TQ2r1xlOW2Dlg6Xyt8HUky0Q1PoSOG2h9RSa9wfL+slaY9c5Hhj5lTWJ/qo34EJEkvUs8kQAfmh3RVlm0ub3Yc2T0PFGaHMJLLoLtk2yTLKE3hYUdAP236FRX1kQ8kCuCz/9H6R+Ar2fhOan2/2b3rDSxx4PKItIRESkEjo2juOam+7j7tiHKMzaQdGLI2FtOecjqfG2ZeQw78mLGJI/hWWdbmboNY/hlFeqL0ek7gWJREQOV8vzoMttVq7V7rdQrwcUbrWsIW+MlaAV7oAz10PnP1kQodTyB6wEqelYa2DtAHmp1hg74QQLGhRsh0W3QauLgYAFOBJOsMdnrbBgU6PhZffNvQ7SfrDPO1wPPR6EzW/B6qfgpOnWoP+HM2DLhwe/lyajrTeSE2YNr7dOqsKNC6ENr9tUuIIdENkYspZD9/vg5JnW06fUtslWXrZrrv0MBr5pE+YmdbGf7769pPo8Yxk/08+Cgq2A1zLJcK0fkTca+r1s12YsgLm/tet7/QvW/wdWjwccKyNMfseua3uFlbRVNEBh8Z1Wmtj9Puh8i91XuMt+PxoNsUCfiIiIVErTelHcd9N1PNz8BdYXNcB99yJKpj+hhtbHkOUpu1n+3MWcWPwDm0+4nR6XVDDsQ46YgkQiIr9Gz0chabg1f+73AsS0DZaWFQMOJL9npWAnPApnb7agkieY8bF7LmyfDA36WKlRIAeW/AUGvAKnzIHmZwAlkPyujWfPXApRTazMqd01lmGU8iFkzIeolnbtlJNh10/2/N3vtUymtc9YY+XR0yx7adbFsP7lg99L/e5w6hyI62QBkHUvhGQLq4Q/D2ZfBXOvtsbhRZk2veykH+D4B8vKuQJFsOgOmHaa7e2Y+dCgF0wZZftEcPJZWH3rA9TxRisDnHe99SsC8IbZz8IbC7jQ+ym7r2CH9Q+KSLI+ROnT4Kfr7DGtxsGGF+3zLrfDwP+WX2IGNrFu1XhrmN7jb2X3L/qzNU3v91L5/YtERESkQvGRYTx2zRl80vt1Pg8MxDPlbxRPuBKK8qp7aXIIkxZuJvU/l3BKySx2DribNufcW91LqtV0yhQR+TU8PhjyvpWMzb0ahn9qPYP2/UvUlvdh5oXWZLjXeDg3BTrfhqUPlVi2iRNmmUWFO+HbERawGPEZnLEWEgdYbyOALR/Awj9ZQOqiXBub7o2EgmAvIbfYxrXPvsp61/R63HrVrHjYAkOjvoGmY6x0adlDB//FLKopjP7BMpzm3WC9dNySEGzkUZS5Aib3g01v2s8ibxO0udSmkyUNLbsud6P1YVo13jKvhn8Ba56yErLM5bbvBVuhQT8rH2x+JvR6EpbcBRtesefwRlmvINeFQK41w+5wLQT2wozzbPLciM/KAka41odoy3v2+J6PQq/Hyp9iBrD+FVh8l5W/9X2m7LodU+z9dbkD6nersq0UERGpzXxeD/ec04fssS/yD/+leFZ/TvGLo2DnmupempQjUOLyxKSFxH/yG8Y4c8kZ8TcanXZndS+r1lOQSETk14pqCkP/Z0GHxXfAiC8sa8UXW3ZN6mc2rt6fb82me4+HszZZw2EIZh65QAkUboPJAyB3E8R3sOyeYR/bNC2AHd/BRw1twlmnm+HcbdAuWG7kibTn2fSGTfH6uDEUpFsp3OI7Yd3zFshqewUs+yvMv8maNe8rLBaGf2KBk1WPwaxLIVBYxZt4lGx8w/oP5SUDrgXQRnwBg9+y6WSlkv9njaiz18Cgd2z0/FfHw4bXoNWFEBYPGfPguP+DrGVW0jf4XSsVWzWen/9zGSiwPlOecPt66AQLGM27AXbNhkFvWKBq2hgLHPnirL+U44XBb0O3u375vcz7vQX1Bv63LFsoUGj3x7a3IKGIiIgckcsGtWHYVQ/xe+4mJ2M7gZdGwpIJh3ychE5WQTG3vPYdJ869hsHeVRSf+Rxxo26t7mXVCQoSiYgcjqRhlt2z/WubRjb0Qxs/H92m7Jod39oY++Js+zq2NZy+zJoXO17wxZdduzcNvuhaNpGs5blw1kbL8AHwZ1tp2qctrISpzaVw4vcQd9z+63K8NoEta5l9veh2+KSFjbtPHGhlaNPG2tf78vig77/hhMdgywQrY9ubcdS266jz58Ps38Kcq6yfUyDfSsNOX1HW5BnKppzNuhjiOlsmzuLbLIDWcJAFhVI+smuHfmDNw8MbwMgvYO1zsORuKx8jmF0VVs9+biWF0PQ0SOxrTcQ3vgbd7rUAz7TTLZMIF/w59pixS6HNbyp+P2uetffS+EQY9iF4w8u+t+JRm4TX74Xym1yLiIjIrzbkuIbce9MN3FTvWRYUtYJPrsOdeDMUF1T30uq8dWk5/N+zn3Jrys10923FO+5twvpcXt3LqjMctwY36+rbt687f/786l6GiEjFFt0Oqx6HPs9CWJz9j35CH9izCAsseKz3z4gvIKalPaYoE6acApmLof11kPIxFG4ve86WF1pJUmwby1JZ+xwsuNWer/7xkJ9q49xj20O7q+yaFQ9bE2XHaxlNvZ+03jwLboLcDTaZqzDNAiqloppbc+u49vZcMW1tAuSexbDwjxDbFkZ+uX/D55ogazX8cCbkBkfYxne1vk6NBu9/XcZCmHUJ5KyFFmdbWVruemv8fNwNFtzZPQfaXG59i344A/JTYPQMC5St+Ls1oN45054vorEF83Bsn8/dCrvmwIxzrTRtyP+sxGz7V2VriO8Mp86zbK3yuC6sfBSW3GNrHPL+/lPLslZbxlOriywTqRZyHGeB67p9q3sdUkbnLxGpSwqKAtz3ySLaLXuaG3wTCSR1w3vhf6FRx+peWp30wfwU/vvZZF71PkrDsCJ8v5kAbYZU97JqpYrOYAoSiYgciZKA9aLZ9gWMmGQBiQW3WEZI2jR+HlkfkWjTrBL72eOKMmHqqRZMGvyeZcbMv94yXwBwoOUF0PlWy3jZswi+G2mZKYmDoP1vYfPbkD7drm00FDKXQXGmfY1rU9L6PAkzL7ax6QPfgITjYd1LsP4FK4WKbQ8FqdaDZz9eew7HC41HWvlVdMvgrYV9jGwU+gbKa/9tATPXbyViJ/wD2l9rmVCl3BJY/aT19vHFWfAufwvU7wHd/2p7uepx6y/U70XLPJoy2oJjI76ArV9Y8++2V8CWT6zBuCfSGlmHN4Ci3dD1L5bt9d0IqNcNTppqvaNKexcBNDvDfuYV9R9yXcsOW/lPyzIa+Pr+zaz9+fDtECulO2M1RCZVzZ5WMwWJah6dv0SkrnFdl3fmbmHaF2/zuO8F4r3FeMY8An2vqfi/43JU5Rf5uffT5WQsnsTzEc8RER2L9/KPoUmP6l5araUgkYhIVSnOhW+HWo+iU360fkRL74Ump8KO4EQsfDZSffDb0OoCu6soywJFGQusx1GLs+HH30Dy+8HHBIM9Cb2g0y3QZAx8O8CCBuEJMOgty1TZ9Kb1JMpLDj4GexzYlK6Br8Pqf1nPnCHv2+unTYXp51qgZOQXENcR8rZYMCUv2T7PWm79kAKFFixy/fu/b0+4ZSPFtILoVhDT2j6PaR3MSmq9f9nUkcjZYFk6WcvtPR53nQWIwuvvf13+Vph9JaR9b6PpA/mWfdXtbvBEwMJb7f21ucyaiofXt/K79On2M9g6yUrHOt1qWTw7JtvzxrSx8jt/NoQnwskz4PuR4I2BU2bDsgdh/fOlGwP9/g0dfl/x+ynxw/wbYf1LcNzv7fp9A26uCz9eZtPyRk6CZqcdnX2sgRQkqnl0/hKRumpxSiZ/ffs7bit4muGepbgdTsU5+zmIrZ1/qKkp1uzI4Ya35zM88yPu872D06QbziXvQ70W1b20Wk1BIhGRqpSXYg2UPeFw8kzr/bPyn9B4lAVkfDFlWUI9/gbd77O/TBVnW9+i3fNgyHsWwNk8AX68lJ/74Dg+C9BENITW42DbVxaQwoW2V9pEs4gGkP4DrH4atn4WXJQPCAZ2EnpZ3ChrqQVDWp4HWSstQFK40+7bt5dPqcJdMP1sy0Tqdi+0OMcmgOWn2C0vJRhY2mL371vO5nggqoWVrcW2t9K2+I72Mfa4yvXXyUu2jKDkCfZ+63WDoR9BvU4HX7v+FVhwszWMxrUpZd3vswDW0ntg2yR7fN9/Q+MRNl1uxvmw9XPo/6oFhLZ8YI8JawCL/hjcuz6wJziRzi2GIRNg6X02gW7Yx7D4L7B7tl3rjYGxyyCubcXvaW8GzLzIAlld74Kefz/4r5Srn7TMpJ6PWICrFlOQqObR+UtE6rKsgmL++slSElb8l7vD3sMbFY/3nH9Dp9r7B5vqUlLi8sbszTz+1XIeCnuD89xvofMZcN7LEB5T3cur9RQkEhGpahkLrSQsujmc9AMs/5tNF2vQzyZnlX4EKw8b/pkFd4qzLVizaw4MehvajIOMRfDdcPDnWqNkf3aw5KkIKLEMoEAR4Fo2TK/x1p/I8Vg2zA9nWXlS4S5+DhRBcIR7kfXPaXWeNVj+4Qwrwer9lDV/PjBgESiE2VdZn572v7MgS3kZQiV+KNgGeZttUlvuxn1u6w8uaYtpbePh47tAveDH+t0s+2nnTFj/Mmx+196vJxL6PW9ldgfaNRt+vNx6L5Xubff7rCfT8ocg5UMrO+vxV8vI8oRZSdqPl0Pyu3D8I5D6KWTMt15QSSPg64H2utGtIX8bRCTA3nRrJO7Pgl3zoMmJsP0byppa17fG2dHNKv4dyVppP5v8FOj/kv3MDrRjCkw9xTLLhn5Y69PcFSSqeXT+EpG6znVdPlm0lTc+m8w/nWfoTDJuj4twxvwDYhKre3m1QuqefG7/YClrN27krXov0XXvYhj2Zxh1L3g0XysUFCQSEQmF9OlWQhbfFU78Dhb9GTa+bpkzeRuh6z3W76Y4y8qhBr1lwZriXPjhdAuODHwT2v4G9mbC5D72uIhGlo2ze44FI7wR1ogabHpWcRY0HAz9X7ZAy/pXbApa+2st6LHm6bIAVanEgRZ4ietgZW5bJ9r1fZ87OAjkllj2zIq/Q9Jwy+aJbPjr9qY426Z0Za+z3k3Zq208fPYaGy1fyvEGM5I8QIll/wz72LKQSgX2WmBnxT+sAThAgz4w4FX7fMUjlhXki7O+Tp1utYAcWB+pn66zsrION9jzFGfB4Hdsr77oAoU7rDyt/vG2Rn+uBdgaDoK0KWVrA/vcG2FlZwk9K37/W7+AWZeCLxqGfQKNBh18TV6y/cwjG8Mpc6yfUi2nIFHNo/OXiIjZsjufP7//E0O2v8FNvs9wourjOf1x6HZurf8jTlVxXZcPFqTy4Ocr6eOu4MWo54kM5OCc+TT0HFfdy6tTFCQSEQmVrV9aiVbDQTYdrLShcUQSBPJg1DcWoNjwGuBC45Msq8jBpnalTbM+Qu2uhJISu2/7lxakOP4h2PKhBYsiGkFxjjVULu1BhAPNz7Lgz5ong5PXnoZON0NBOsy+HHZ8s/96fXHQaLiVf6V8CI2GwbCPrDH1gTa9A3OvsWlpIz63gNSRyN9qwank/1mAzPVbEMyfe3APpJjWdvPnBwM3wfK9iEYw8DUr/1r3ku2NL86yhjr/sSw4BJbtNPtKyyBqeT5s+9IeP+Jza2w9bSxsD/Yhanul9XryRNrI+9IeR2AlgKX3Awz/tPxyvdLXXP4wLH8QGvS2a6PLqbH358G3wyzz6tR5EN/h8Pf1GKIgUc2j85eISBl/oIRXZ27ii2+/41HfS3RnAzx09JUAACAASURBVG7n03HGPg7xv5A9LAdJycjn/okrmLp6B39v+C3j8t7CadAOLnwDmnSv7uXVOQoSiYiEUvIEG7/edIxlwSz6s5We+eJsHPopc6xx9Q+nW+mRJwK63gEdb4Efx8GO7y0r6Ljf2fMtuc/G3AN0vt0yXpbfb02v8drjG4+EtO+CJWlAve4WzMhcYmtoeY7dnz4dfvo/y+QB9utdVNr42hcDfZ61QNWBfynbNdeaSPvzYMi70PyMyu/L3gzrv7RrtvUIygj+Gx/TFpqfDd4wWPOUBdQGvQmxbSB9pmUF7Zptk8UO5I22QJkbsL5NTcdaJlZ8Zys5K82KChTBj5dAyscWFNs5HRoNsb2JTIKV42HxHXZtg362t44P3KKy1/LFQrurrf9T5nIgYCWCbX9T/vvN2WANqHfPsWlp/V6wTKIDlf4u7JoNwydWHHCqhRQkqnl0/hIROdjGnbnc/eFieqS+w+3hH+LzheEZeScMuB58R2lQRy1VHCjhPzM28fT3a0l0cpjQ8DVaZMyG7hfAmU9BRO3PnK6JFCQSEQm10pKvFmfD4PdhyV1W9uUJt6DIyTMhIhEW3wmrn7Aghy8Wut5pgZwd30KfZ6DTTfZ827+H6WdaaVb9E2zsetr3sOgOK0lzwqDvC0CxZa4UbN1nMQ60vsR64DQaaiPjV/3LprC5AWvOHN8Zds6wRtT7Ck+E+E6WGdV4lE0wKymCOb+zAFTPh60Bc+l0rkAh5KcGm1sHP2Yuh90/lfUNwoHEAdDiLMt8Ck+EuVfD9q9sv3o9Abtmweb3LPPJDVgJHw5kr7ASOzdgGUd4bB/dABRlHPxziGhoQaeC7VC8pywDKPY464UUKIDcZMhd98s/z+Ouh77PWE+h7V/ZfX2ehU43Hnyt61oW0vybrHyu34vWa6o8ezOsRHHP4rLm5XWIgkQ1j85fIiLlKylxeeenLbzz5VTu4A1O9CykJLEDnrHjof2o6l5ejfTTpgzu/XQZa9Nyuav1Wq7NeQ7v3hwY+xj0LuePkRIyChKJiFSHNc/BgpugyWjrQ7P8b1YChgca9IXRUyxrJ3+rZR7tnGGP88VDVBPr3XPCPy3LCGDvHvh2KGSvtMeN+BIaDYaV/4Cl9wMl0HAo9H3W+tssuDkY9HH4uSTNG2kBn0bDrM/RknugINWynPo+Ay0vgS1v21SxvbsALxA48J2Z0v5Bjjf4ud/6Fx0ouiUk9ofEfvaxQR8Ii7fvpXxqwbTibGh5LhTuhvSp9lyRTaxEK38bZC6lrEl0PRsL3/wsaDYGwhPs/uJcK9fKT7Em2gXbrZF26udQtMv2wfFARGPrIxQWa/2NctZW/N4AOt4MvZ8M9jIK9j3q8Tdrhn2ggjQbb5/yoWV8DXrTAmvlKUiDqSdD9loY9uGvy8qqJRQkqnl0/hIR+WU7sgr55+TVZC35nAfD36IFabhdz8YZ/QA0aFfdy6sRUjLyGf/1GiYu2Ua3ekW8mjSBJilfQdOecM4L0PgIWxbIEVOQSESkumx8w7JkEgfCiC9g/QsWmAELHo380iZuAaR8YoGIvbv2f45WF1mTa2+4ZaksuBnWPmffa3eNTcraswi+GxFsAu1aT53u98H2r2HZ32w6l+ODZmMtwydzsQV0HI8FiIqz7Pli20PvJ6DpaZZptOoxa+Dc6hLLtslaaRlBe3ce8EYdK9uKbGqNl6NbQGxba4wd0dDeY+nN8dpktWX3W4aRJ7ysTM4bbf2X/LnBfktB4Q2g5QXQ+mJIGla2Z78kL8UmhZWW1jU91Rpz56daydiWDyFredn1EY1hbxpEtbDAGUC738KA/8CSe2Hlo3Zfp1ssaLTvX79K/LDuRduzQL71j+p8G3i85a8tPxWmjLY1jvjMfhfqIAWJah6dv0REKmdBcgaPfLaYwWnvclPYRMKcAJ5+V8PwOyC2nN6OdUBmfhHPTVnPm7OT8XhcxnfeyBmpT+DszYaRd8GQW6y9gFQ7BYlERKrTlo+sH069bjDqaxtTP/cawLXAx9AJZeVaRVkWPFn7fDCI41hWjTca2lwGLc+zsq+0aTDzAvDnWFBm5GS79vsTLeAS2GsBik63QMdbYfV4WP2kvWbSKOj8J8um2TnLmkbvmmWlYqV8sZZxFNnE+vg4jjWIbnWhfd+fB5krbLx92jTY/I4FqMIT7OO+E8sqyxdnQarIJMsAyllrTbL7Pgctzvl1Kck7Z8GUU61ZuOOzLCZ/ngWF3BJ+7r+Ea68bngj+7GBmV4p9q/U46zm05hlY9Ce7r93VMOCVsp8XwK45MO96KxlrMtrWG9+p4rWlT7deRUWZFiRMGvorNql2UZCo5tH5S0Sk8kpKXD5YkMLrk+dw+d73GeebCmFReAffBINvrDP9dvKL/Lw1O5l/T11Pzl4/f+jm50b/f4ncPAWa9YKzn4fGXat7mbIPBYlERKrbtq9hxrlWejRikk3omn6uBYDa/dbGt+8bBMndZCVkm98KNlD2l30MqwfNTocmJ0PyO7DjO8CBjn+wMfZTglkpiYNg20Sb4HX8g9abZ+aFFjwqKbayry53WODJ8VrPpIV/tkyekiJwiw9+H744KxmL7wyx7SCmpQVyPBFWopb2vT1f9/shc5k1Y96zEPYstYBNKW8UtL7UmmNHt4Tw+hbYWv4QrH/JMoW63Gb9jspr9gyWVVWcbQGlvGT7mLsZtn5mZWf7CqsPDQdYL6SoZjD/ZmtKXb8nZK2wsrY9S4IZTS40PR1GTrSJbnOusOfo8AcrySsNEOUlw7IHYON/7Tl7P2lBtIqCWSXFdv2KRy0YNnSCvW4dpiBRzaPzl4jIr1dQFOCtOZv5cup0rvO/y1jvT/gjEvANvgH6XwtRCdW9xCqRlV/Mm7M389qsTezJL2Zs+wgeTviCBivehPBYGHEHDPg9eH3VvVQ5gIJEIiI1QfpMmH4W4FiAwBsD34+ysqq2V8Gg1w9+TOYyWHw3bPvCvo5qBg0GwK4ZZWVpEUnWtNn1Q1iC9ctZ+bC9Tt9nrTQtfTrU62rBj9XjoV4PyxzKXWfBno43Wola5hKbXuaNgXZX2KS2vM2WUeSLsVIzJ8xK3/x5B693314+pcLqWblYYZqVYiWNgtYXWbaS47V1bJsEW7+wIE3SCAuCecOt7Kw4J/gx295zYbqVuxWml5OxFOy/5Hih5UX2OvW6QVx7C+5s+RhmXWx71fJ8SPnI1pM+taypdf3j4bTFsO1L+OFMe77Of4Re/7IAUH4qLH8k2J/IgU43Q/e/Qtgv/LUwex38+BvImAftr4HeT1lPpDpOQaKaR+cvEZHDl7vXz39nbeLH6d9wdeADRnsX4ffF4On/OzyD/gBxjat7iUdFek4hr83czNtzksnd6+fUjvW5p+lcWi19BgqzoM9VMOoeiGlY3UuVCihIJCJSU+RsgOlnWyZRr8ehyRj4dqAFQFqcC8M/Lv9xGYvgp2uDY++xLKJmp1uwY9ePkDY92Jw5yAmzgIbjs8lcjgvJ71tD57hOkLMm2ONnHKx50sqzvJHQ6mIrmVp6nzV+7vFXKAnAxtchb1PZ85YUgTcumB20T7NqX6wFmIr3BK+JtjWWFHHYHJ8FYHxx1t8oMqnsFp5oQZttkyyYBZDQC06aBuHx+z/Portg1T8BDxz/gGX0xHWwpti+eCs3C0+Ac7bZ2Popoy3g1fUu6Pl327uVj1mmEyXWD6r7PdZ/qSL+fFj3vGUQecKh/yvQ6vzD34taRkGimkfnLxGRI5dVUMz7P21h1sypXFD4Iad754LHR6D7hYQPvA6anVDdS/zVSkpcZm/czTtzk/lmRRolrsu53RO4PfFHmix/BXJ3QNvhcOqj0KR7dS9XDkFBIhGRmqQ4B2ZfCamfQNsrLAAxuS8U7rCpYyd9X3Fj5pXjYfGdwdKzYsvQaXUBtBpnE9G2fQnLH4bizMqtxfGVjZQP5P/6YI4n3IJNnW61kfKl2TH+Aphxftm4+OZnwcA3wSmB3Qtg3QuQ+hlW2jUG2l8F9boHG1v7wOOzkjRfrPVOOlDmCtgywYJX+an2GFwLvHW6Zf+Sr4Afpp0KaVMsgDXqG5h/vT3OH2z0XVIIeODMtVbqN21MMEB0DzQ/zfoSpXxkz9fuKuh2L8S2qXhfAoWw7iVrdl2YZo3AB7z8ywGlOkhBoppH5y8RkaPHHyhh8oodfDltJkPT3+Vc7yyinCKyEnsSO+T3eHucB2GR1b3MX5SWXcini7by3k9b2Lw7n4ToMC7rGc/V4d+TsPQ/UJABbYbB8Nug7QiNtT9GKEgkIlLTuCUWzFl2PyT0tgyTWRdbI+i4TnDKLIhILP+xyROs8XFMKyuN2v6NBXiimtn0ssYnWU+e5Q8Fgx8AHgtIJQ2Hgq3WPHv3HPtWZBNofjb4Im1d2WutzK1wW9lrRreCAa9Zk+VAASS/Z4GT0slh3ijLQmr/W+sVtOg2yJhv6ysptsyp2HaWrZS10jKM2v/Oyrh+Kdjy83659hxbPrTgUNZKKx+LaWvvNaYdDPvfwT1+stdaM++CrXbtqfNhyR2w4VUr09u7i58zoQa/Y0GkGefZfa0ugZzVNjkurL6ViXX8g01tq0hRJmx624JDBdug8YlW/leHm1P/EgWJah6dv0REqsbilEwmzV2Fb/kELiiZTHvPdvK98eS0O53EgZfiazuk4qmoIbYts4Cvlu/gq2XbmZ+8B4D+rRO4+bg0BmV9iXf1RPAXQodTLTjUsn81r1h+rSoJEjmOcyHwANAF6O+6brknCsdxxgBPA17gP67r/qMyz69DiojUCakTbdJZcbY1e978HmQvtwyhEROh0ZDyH7dtsk03i2gEwz6GnHUWPNnxfdk4+/huQKAskAMWEOp8q2X2lPit505+spVZDXwTmp9e9heg7LU2tWzdi7A33e5L6G2ZNC3OgpjWkLMZltxlWTauv+x1HJ+tvV4PC+6kTeXnYEyDftD/ZWhQQap1ScBeL3OZTQ7bPddue3cDDiQNg4ZDYPvX1hS79SXQ/0UI26e8LFAMc6+29eNaad7wibD2aVj4p33G3Ad7GHW924Ju834fXH+YZWrV62Y9h9r8xnoylcefB6mfw5b3YdtXlo3VaCgc/xA0Hln+YwRQkOhwVeUZTOcvEZGqtdcfYOqqdFb8+AUdt37MSc4Cop29ZHoTSW81lsR+F5HYaXBImz3nF/mZv3kPszfu5sf1u1iSamfJLk3jGdfezxm+OSSu/R9kbISIeOhxAfS9Gpr0CNka5eiqqiBRF+zE/xJwW3kHFMdxvMBa4GQgFZgHXOK67spDPb8OKSJSZxSm2wj1lI9tcligwAIkONDzEeh65/4j10vtngfTTgdKYPjn0GiQBX4yFkLadzb1bOeP1hi7PBGNrUl03kbL+gFoPBr6PAn196kld13Ljll4qzXILlW/JzQ9xaak7ZgCG/7Dz4EgT/jBpWueCAu0FO0BXMt8iutgfYZKiq0HUsE2K7v7ufm1Yw23Gw60yWRJI2D9y7DmKQuk9X7CAjj7pjZvfBPm3WD9ksLibXJcqwsse2r6WRZYK0wrmxZX/wRbd+ZSe7wvHtpeBm0uhYaDD06bLs6x/dr9k01v2/5tMJOrObS+2Po8NeirdOtKUJDo8FTlGUznLxGR0MkpLGbO6hTSF0ykeeokBpUsJMLxk0M062P6kNNiOHHdTqV1+y4kRIfhHIWzRWFxgPXpuazekcOaHdks3JLJkpRM/CUuPo9D7xaxXNJ0O6M8C6mfMgV2rbUHth4Cva+ALmdBeAWTZ+WYUaXlZo7jTKPiA8og4AHXdU8Nfv0XANd1Hz3U8+qQIiJ1iutaCdf8Gy1IFNnMgjcATU6BQW9CVDkTMXI2wNQxlhUzZIJl+OyrpBiyVlngKPl/sONr9ms0XZGIJAvORCQGp5MlAB7Y8Y1NQPPGWCCoeM8vP090W4jvaK9ZlGVZTkV7wJ9jfXtKJ5F5o+x1wuKsD5EvxppSRzcPZvB4IW+DZVAVZ1rwptVFENHAHuv4LFtpywdQuN3W2uZSOOExCIuxffpuRFnvpdJMof14oOej0PZSW1txlvUtytti4+7zt9heZq+0sjyA2OOg6cmWzdRoSPnBPKmQgkRHpirOYDp/iYhUD9d1WbU5lR0LvyRyyw+0z55LY9eGkmxzG7CadqRGdyY7oTv+xscTFpdEXFQYcZE+4iLCCPd58JeUUBxw8QdcigMl7M4rIj2nkJ3Ze0nP2cu2zAI2786jJBgGCPc5DE/ay+mJ2+nj20Tz/JV4ty+BohzrE9lmKHQcAx1PhQa/UG4vx5zqDBJdAIxxXfd3wa8vBwa4rntjBc91HXAdQKtWrfokJycf8fpERI4p+dtgwU2WVeSJCGYBeSGyofUtanHmwY8pTIdpZ8CeBdDnGeubUxF/PkwbC+k/2NdRLSyI498L2Ssq3/D6sHjswOHxWWDIdcAtsvdYGnQpbVqNLzhFzW/BnH1L2aqDJ8LK0WLbW7ZXw4H2saK+UVIpChIdmaN1BtP5S0SkBnJd9mxZwc6lk/FsXUC9PctptHfLz9/OdSNJdRuR4jYi1W3ETrc+eUSSTwR5rn30UUKMp5ikSJfEyBIaRxTRNmwPzdhJvaI0wnO34hTl2BN6wmwqWbPe0G4ktB8FEXHV8tal6lV0BjtkkaPjON8BTcr51j2u6352NBa3L9d1XwZeBvtL1tF+fhGRGi+6GQz7CHbOhsV3wM6ZQMAyW6afBW0ugz5PWwZNqcgkGD0VZl1imUhZK+0aTzn/zPui4aSpsPIfsORuKNptU79iWkPX2yGwF5Y/aKVdjhd2/WgZPc3OsGyi3T/C7vmAa18XZUB8JzhhPDQcZOvMWgWZiyFjsfUSytsQfPESCwg5HohsaqVm9bpa2Zk/B7JW2+N+7qHklJWEeSKtP5AvxrJ7CrbuX0bn+KzcK6qpNaMu2GoZWQdywi0wVXaHTU9rNc6ylsLiwBdX9jG6ue1NRCOVj0lIhfIMpvOXiEgN5DgktO5OQut9WgAUZsP2JbBjGZEZm2mbkUzbzGR82bPwFudW/Fx+IDd4i6wP9VpCo3Zw3AhIPA6a94HG3Wr8pDWpeocMErmuO/oIX2Mr0HKfr1sE7xMRkV/SaBCMng5bJ8LsK630yRNujZi3TYYBr0DLc8qu98XAsE9gyV9g1XjIWQtDP4Dw+gc/t+NAt7/YtLHZV1oz68imsOQeC7Yk9LJ+O4n9od9LFjTa/IY91httja873mRTyVInwvybLIDV9kro9Rg0O9lupQKFkL0Otn8FqZ/atLDc9Xbb/tUvbIJbVhJWUmiZUhVe6oe9O+2vYDGtoempFjBa81RwgpkD4Q2haGfZYzxh9r5HT7W9EKlBdAYTEZGDRMZD22HQdhjBvGvjupYVXpwPRblQlGc3jxd8URb8CYuG8Bi7iVQgFO3S5wEdHMdpix1MxgGXhuB1RUSOfY4DLc6Gc9Ng+hnWiBqgaBfMONeyffo+D4nBse8erwVp4jvblK5vBsKILyDuuPKfv/XFEN0Cpp8DWSugz3M2Tn7jq4BbNlUMbHx8cbZlHu2eZ5k6sW2sB1KTk2D5I7D6cQsCdbrVAkmlASpvJCT0sFvXO+wgk7Meds2CnbMgfboFtQ7JAzEtIb4rJJxgwazIJIhqYgEhX1xZts/WSTDjAgsuOT4rFysNEIU3sLK7mJZw4vf2UaT20RlMRKSucJxgICgSohsc+nqRChzpdLNzgWeBRkAmsNh13VMdx2mGjVkdG7xuLPAUNn71Ndd1H6nM86txoojIPlwXlt4HKx6BuI42CcwfrCGPTIKWF9qksfrHW+Bn148w4zzr9TP4XWg2pvznLMqwQM38GyE/xTKFAvn2fV9c2Wt4wiFpuGXf7JxtvYuajYUeD0BiP7sma5WVsKV+ak2oO90SDBYl7P+6gb0WfEqbZs2md8220rHwRHvOpOEQXg/wQGQja6Id2cie51CNoYtzYMEfg4EuAK8dnEp7GsV3s3K2+l1h1DcWYJJqo55Eh6cqz2A6f4mIiNR+Vdq4uqrokCIiUo41z8KCW6DRUDju97DyUchaDjhA8N90xwvRLSGyMWSvsYBO/eOhXg+7rCgT8jbbzZ9X9ty+WPDnQlwnGPi6lbzlbIApJ9u1Uc1sRH3p6+ABSqxcLWkkNBxgvXvyNkPKJ1Ye5o2EBv0tkFWcCbmbbFKY67c1J5xgj20y2qaEecIOb19Kp8MtusOynMoT1wly1kCTk4OlePUO77XkqFGQqObR+UtERKT2O+zG1SIiUsN0uskCMXOusFHyIz63ErH5N0L2KhvJ3nCAxXHyN1vJl+uHzKWQs84CR2H1rGl0k5Mhpo2VjSX0trKrDa/DvOth5oUw6A0rJRu7GGaOs/5BLc6B9tfaa+2aAxnzLQNpy/t2O1CgEHZOt899MRDfBZqfBY1HBDOGEg5+zK+1e54FznbNtrKyfXmjIVAEEQkWIGp3NfR/8fCDUSIiIiIitZSCRCIix6I246z8asYF8HU/a1g9dilseMV6A21+xwIwxz9iwRjXhQ2vWiDJDViQpOHA8p+7/W8hoadNSpsyGjr8AXr9E0Z+Aaset+bWGQthyLvQ5c/2GH+BPf/Kf1gWT3RLaH2J9TyKTLIgTeonsPF1CyplLoOsZZC5HBqPhAb9wBv+6/chdwssuBm2fmZ9h2D/iWdNz4D0Kfbce3fC8Q9Dt7s1pUxEREREpBwqNxMROZZlr4UfzrDyrv4vQ7urLHNn/StWhlaw3Uq5uvzZev3sWQwzzrfMn273Qvd7Ks6o8edbf6E1T1t20qA3rfxs10/w4yX2mt0fsKCLx2uPKSmGlI9h3QuQ/oP1MWp5AbS/xoJWjhf2LIRNb0PaFMtuAvBGWV+j2HYWYIpuZR/D4qCkyJ63pNhG2ueuh11zYeeM4NSycnijoec/YMmd9jjHCwNehba/OarbL0dO5WY1j85fIiIitZ96EomI1FZFe2DGhZD2PXS5HXo+akEbfwGsfxlWjQ9OImsPHf8ALc6zBtib34IGfWHw2xDfqeLnT5sKc35rgaVOf4Qef7X7591gGUsJJ0Cfpy0ItK+slbDuRdj0hk1Fi0iE5mdDy/Os/5A3AvbutslmadMgYx7kbYHC7dZs+5A8tu5AIeRtKru79aVw3PUw9WSbbBbT2jKtGvT6tTsrIaAgUc2j85eIiEjtpyCRiEhtVlIMC26Fdc9bn6HBb1uZV+n3Uj6Btc/CzpnWF6j1pRDVHNY+Y9k5JzwGHW+oeHJYcTYsvM3K2SIaQc+Hod01kPoxLPyzBZBaXQS9xkNMq/0f68+H7ZMtw2jr5/Zcvjgrd0vsZ6Vmif0hulnZegu2WcAoUGD9lNJnwLYvLfPIEwkJvWycfc76steJag4jJ9l0s+9PBLcYmp4KQyaoQXUNpiBRzaPzl4iISO2nIJGISF2w/hWYf5Nl7Qx57+DsnoyFFiza8oFNNYtsYtPH8jbbtLR+z0P9HhU/f8YCGy+/c4ZNS+v9BDQcBCsfg1X/BBzLZup4E0Q2PPjxgSIrM0v9DHbPsd5EbsC+F9HQAlARiRCWAASs0XbuRrvG8QElB2QZee269r+zbKa1L8DiO+y6bvfA8Q+p/1ANpyBRzaPzl4iISO2nIJGISF2xZ4lNJsvdaEGSrncenCHkz4PUzyH5Xdj2VXAcvQdwoekYCxbFtin/+V0XUj6CRbdbcClpuJWh1T8eltxlAShPBLQeBx1vhMRf+P9/f4H1ScqYBxmLbGJa3mbrNeQGAAfCEyHuOGvUnZ9i78/x2prjOkKvxy1QNeM8C155wmHwu9Dq/KOxm1LFFCSqeXT+EhERqf0UJBIRqUuKs2HudbBlAjQ9DQa+BlFNyr92b4ZNHkv5DHZ8bY2iAWLaQJNTILEPJPSG+t0t66hUoNB6Dq15CvKSrel0x5stYLPpDbv586yUrO0VkDQC6nW1gJVbYk23d/8Eu+faxz2LLfAT3sCabDc/ExKOt2DW5reDTa4dwLXA0fEPWgZRykfw03Xgz4XoFnDK3LLSNanxFCSqeXT+EhERqf0UJBIRqWtcF9a/CAv/ZNPD+jwLbS795fKrkoAFd5beZ32B8ADB8i7HB3HtbfJYTKv9J5BlLLYx9FnLg5PKBljZWtEeKy8r2BZ8jjD7fklhWTDKGwX1ukG97haYwrUJZtmrrTwO1zKTSvba1LLOt1p2VOYyWHw37Jxuz9N4NIyYCL6oKtlOqRoKEtU8On+JiIjUfhWdwXzVsRgREQkBx4EO10PjE2062ezLIOVD6PdCxVlFHi+0vxraXgnJ78PyByFnrQVvEvoArpV8ZS6FwrTynyNQAOnT7Lbfenw20cz1lwWISq/PmG83u9Caa5dmDYH1S2p3JbQ41/oUzbrEGll7wu37HW+BPk9U3HhbREREREQOSUEiEZHaLr4TjJ4Ba56EJffCpG7Q5yloc1nFWUUeL7T9DbS+GJLfg2UPQupHVi7W/lpoe7kFcvK3gj/HJpgFCuyjWwyeKCjYaoGfnbMsK8j1W0kYgDcGPD4o8dv9biDYFwnAtbK2hF7QeKStM7olpP8Ac66y8jJfrD2HG4D+L8Nx14ZgI0VEREREajeVm4mI1CVZqy2raPccSBxoE8Ea9j/040r8sPldWPdv6x/kiYCW51lwptFwCyod6vF5yZaVlL22bGKZJ8yCRU4YhMVCvR7QoJeNs3ccyN9m5W8bXoXcDRBWD+I7Wx+j+j1gyPsWuJJjlsrNah6dv0RERGo/9SQSERHjlsCmN2HxX6BwB7S5HE54FKKbV+7xe5bChldg09tQnGkj6xuPhqanQJOTIabl4a+tpBh2z4f0qbBjipWsuQFret34REj5BDIXQ4c/QK/x6j9UCyhIVPPo/CUiIlL7KUgkIiL7K86BFY/C6idspHynm6HTrRDVuHKP9xdA6mewfTLs+AYKttv9cR0sVjRsEgAACbNJREFUuyeuY/DWASIbW8aQJ8x6E+FYT6O8ZMjfAnlbIGuljbAvLUmr3wOanW63Da9YYCuyCfR/EVqcXSVbIqGnIFHNo/OXiIhI7afG1SIisr+wODjh71YytuRuWPlPG2ff7hroejvEtP7lx/uioM04u7kuZK2A7d/AzplWVrZtsk0kqwxvJMS2t15HjU+0zKGwOAtgTRtjGUZd74Jud9v9IiIiIiJy1ClIJCJS18W2hSHvQY8HYdVjsOFlWP8StB4H7X8HScMOPTXMcaB+d7t1+ZPdVxKwSWjZa6AoIzjVbJ9G1ZFJFoiKbgURDcuaaBfsgHXP2xoKtttEs96PQ2y7qt0HEREREZE6TkEiEREx8R1gwCvQ435Y9YSVeG1+2wI5bS63LJ/4jpV/Po8XYtvY7VBcF3bNgTXPQsoHljnU9DQY/A40HnW470hERERERH4FBYlERGR/0S2gzxPQ82FI/dR6Aa38O6x4GOr3tKBN41GWYRSecPivU5wNO76H7V9bX6O8ZAiLhw43WGPq+A5H7z2JiIiIiMghKUgkIiLl80VDm0vtlr8Nkt+FbV/CuhesdxEOJJwA9bpbyVpsW4hpa1PS9i1Pc13Yu9tG2JfestfA7nlWeuaLgyYnQbd7ofXF6jkkIiIiIlJNFCQSEZFDi24GXW6zW6AQdv8EaVMhfQak/wDJ74BbUrnnimpmTaq73A7NxkDDQTb1TEREREREqpWCRCIi8ut4IyFpuN1KBYqsSXXuRms2faDw+hYYim1rGUoiIiIiIlLjKEgkIiJHzhsOce3tJiIiIiIix6RDzDQWEREREREREZG6QEEiERERERERERFRkEhERERERERERBQkEhERERERERERFCQSEREREREREREUJBIRERERERERERQkEhERERERERERFCQSEREREREREREUJBIRERERERERERQkEhERERERERERFCQSEREREREREREUJBIRERERERERERQkEhERERERERERFCQSERERERERERGOMEjkOM6FjuOscBynxHGcvr9w3WbHcZY5jrPYcZz5R/KaIiIiInWdzmAiIiJSFXxH+PjlwHnAS5W4dpTruruO8PVERERERGcwERERqQJHFCRyXXcVgOM4R2c1IiIiInJIOoOJiIhIVQhVTyIX+MZxnAWO41wXotcUERERqet0BhMREZFKO2QmkeM43wFNyvnWPa7rflbJ1xnquu5Wx3GSgG8dx1ntuu70Cl7vOqD0EJPrOM6aSr7Gr9UQUOp16Gi/Q0v7HVra79DTnodWVe536yp63mNeKM9gOn/Vatrz0NJ+h5b2O7S036EX8jOY47ruET+z4zjTgNtc1z1kQ0THcR4Acl3XffyIX/gIOI4z33XdChs9ytGl/Q4t7Xdoab9DT3seWtrvmutYO4Ppdyn0tOehpf0OLe13aGm/Q6869rzKy80cx4lxHCeu9HPgFKzZooiIiIhUEZ3BRERE5Nc6oiCR4zjnOo6TCgwCJjmO83Xw/maO43wZvKwxMNNxnCXAT8Ak13UnH8nrioiIiNRlOoOJiIhIVTjS6WafAJ+Uc/82YGzw841AzyN5nSrycnUvoI7RfoeW9ju0tN+hpz0PLe13DXMMn8H0uxR62vPQ0n6HlvY7tLTfoRfyPT8qPYlEREREREREROTYVuU9iUREREREREREpOar1UEix3HGOI6zxnGc9Y7j3FXO9yMcx5kQ/P5cx3HahH6VtUsl9vxPjuOsdBxnqeM43zuOo9HHR+BQ+73Pdec7juM6jqNpBEegMvvtOM5Fwd/xFY7jvBvqNdYmlfj3pJXjOFMdx1kU/DdlbHWss7ZwHOc1x3HSHccpt7GxY54J/jyWOo7TO9RrlGOHzmChpfNX6OkMFlo6g4WWzmChVePOYK7r1sob4AU2AO2AcGAJ0PWAa24AXgx+Pg6YUN3rPpZvldzzUUB08PPrtedVu9/B6+KA6cAcoG91r/tYvVXy97sDsAhICH6dVN3rPlZvldzvl4Hrg593BTZX97qP5RswHOgNLK/g+2OBrwAHGAjMre4161YzbzqD1cj91vkrxHsevE5nsBDtt85gId9vncGO7p7XqDNYbc4k6g+sd113o+u6RcD7wNkHXHM28Ebw8w+BkxzHcUK4xtrmkHvuuu5U13Xzg1/OAVqEeI21SWV+xwEeAv4JFIZycbVQZfb7WuDfruvuAXBdNz3Ea6xNKrPfLhAf/LwesC2E66t1XNedDmT8wiVnA2+6Zg5Q33GcpqFZnRxjdAYLLZ2/Qk9nsNDSGSy0dAYLsZp2BqvNQaLmQMo+X6cG7yv3Gtd1/UAWkBiS1dVOldnzfV2DRUTl8Bxyv4OpiC1d150UyoXVUpX5/e4IdHQcZ5bjOHMcxxkTstXVPpXZ7weAy4JjwL8EbgrN0uqsX/tvvNRdOoOFls5foaczWGjpDBZaOoPVPCE9g/mq6olFfonjOJcBfYER1b2W2spxHA/wBHBVNS+lLvFh6c4jsb/STnccp4frupnVuqra6xLgv67r/stxnEHAW47jdHddt6S6FyYiUhPp/BUaOoNVC53BQktnsFqsNmcSbQVa7vN1i+B95V7jOI4PS5XbHZLV1U6V2XMcxxkN3AOc5bru3hCtrTY61H7HAd2BaY7jbMbqVyeqceJhq8zvdyow0XXdYtd1NwFrsQOL/HqV2e9rgP8BuK47G4gEGoZkdXVTpf6NF0FnsFDT+Sv0dAYLLZ3BQktnsJonpGew2hwkmgd0cBynreM44VhTxIkHXDMRuDL4+QXAFDfYGUoOyyH33HGcXsBL2AFFtcJH5hf323XdLNd1G7qu28Z13TZYD4KzXNedXz3LPeZV5t+UT7G/YOE4TkMs9XljKBdZi1Rmv7cAJwE4jtMFO6DsDOkq65aJwBXBCRsDgSzXdbdX96KkRtIZLLR0/go9ncFCS2ew0NIZrOYJ6Rms1pabua7rdxznRuBrrEP7a67rrnAc50Fgvuu6E4FXsdS49VijqHHVt+JjXyX3fDwQC3wQ7E+5xXXds6pt0cewSu63HCWV3O+vgVMcx1kJBIDbXdfVX8YPQyX3+8/AK47j/BFroHiV/ifz8DmO8x52wG4Y7DFwPxAG4Lrui1jPgbHAeiAf+G31rFRqOp3BQkvnr9DTGSy0dAYLLZ3BQq+mncEc/SxFRERERERERKQ2l5uJiIiIiIiIiEglKUgkIiIiIiIiIiIKEomIiIiIiIiIiIJEIiIiIiIiIiKCgkQiIiIiIiIiIoKCRCIiIiIiIiIigoJEIiIiIiIiIiKCgkQiIiIiIiIiIgL8P1lQTOzB53oWAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# feature = PolynomialFeature(24)\\n\",\n    \"feature = GaussianFeature(np.linspace(0, 1, 24), 0.1)\\n\",\n    \"# feature = SigmoidalFeature(np.linspace(0, 1, 24), 10)\\n\",\n    \"\\n\",\n    \"for a in [1e2, 1., 1e-9]:\\n\",\n    \"    y_list = []\\n\",\n    \"    plt.figure(figsize=(20, 5))\\n\",\n    \"    plt.subplot(1, 2, 1)\\n\",\n    \"    for i in range(100):\\n\",\n    \"        x_train, y_train = create_toy_data(sinusoidal, 25, 0.25)\\n\",\n    \"        X_train = feature.transform(x_train)\\n\",\n    \"        X_test = feature.transform(x_test)\\n\",\n    \"        model = BayesianRegression(alpha=a, beta=1.)\\n\",\n    \"        model.fit(X_train, y_train)\\n\",\n    \"        y = model.predict(X_test)\\n\",\n    \"        y_list.append(y)\\n\",\n    \"        if i < 20:\\n\",\n    \"            plt.plot(x_test, y, c=\\\"orange\\\")\\n\",\n    \"    plt.ylim(-1.5, 1.5)\\n\",\n    \"    \\n\",\n    \"    plt.subplot(1, 2, 2)\\n\",\n    \"    plt.plot(x_test, y_test)\\n\",\n    \"    plt.plot(x_test, np.asarray(y_list).mean(axis=0))\\n\",\n    \"    plt.ylim(-1.5, 1.5)\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3.3 Bayesian Linear Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.3.1 Parameter distribution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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6tY/n3LaMJ8LYtEuxE/DkefWk+vt4e/O+V6YQFX/oGdk0bFGmBX2LBvz0MubiidB2T+gtsngxhA2HQUnCx4bstz4KtMyBzLcTdB93eB33d+DMvS1kGrB2qghEGL4XwwfU9I8BpUGrkchfdzv1n+Mfri2nRNIz3n7/ZJmMipeTHLQd4+9dNhAf48M7t42nX2D4ncRW5ZaX8dOQwPx4+QEZxMaFe3kxq3YYbW7d16M24LjCazWxJSWLR8WOsSojHYDHTLbIRf+nUhdFxLXGxI1qrCiklc7cd4s1fNhLs68X7d4ynXWON++6VHI/P4JEX5hMS6MP/XplGoH/NWz7XtEEBOPA0HH8LbkwDT8ec1zZz5geVFR91Awz4GXSu2vuwmODQs3DsDQjuBQMWgFe04+dqLaVpsHaIkpEZvBzCBtTfXC7CaVBq4FIX3dFTGTzywlyiIwP58MVp+Pl4aO7bYDLx0oK1/LL7GIPbNuPVW0bh56m9n4tJKyrk8727mXfsCOUmE/0aN+G2jp0Z2rS5Q2689U1eWRk/Hz/K7MMHSSrIJ8rXj7u6dmNauw54uNhwk7iIoynneOzbJeQUlfLC1BHc0M22XISDx1J5/OUFxEQH858Xp+Lt9een2WveoOQfhWXtodsH0Orh2h3LGqqSLmNmqKxxWyO3kn+GHXeAi5cKyw0f5NBpWkVJijIm5edhyHJtiZx1gNOg1EBNF11yWi73P/MTPt7ufPzqDIICtDscc4tLefTr39h/Np37RvTi/pF97N7iyigq4r+7d7DgmKrDNLF1G+7q0p2WwQ08MsVGzBYL688m8une3ezNSCfUy5v7u/dkRvuOdjvxc4tL+dt3S9mdkMrdw3ry4Oi+Nn0/2/Ym8PTri+ncrjFvPzMZV9cLb2DXvEEBWN4VhAuM3lX7Y1nDsTfgwD8g7h7o8YntMisFx5VkS1E8dHkLWj1ad5ItJUmwZggYcmDISgjpXTfjasBpUGrg4osur6CEe5/+kdIyA5/++1aiIgI095mcnc/9ny8iM7+IV2aMYnRnjYlXF1FYUc5He3bxzYF9SCmZ2q4D93fvSaN6cq7XBztTU/hg53Z2pKXQyNeXv/Xpz4RWbeyKTDOazby6cB0/7zjCmC6teGX6SNxsMFTL1x/h1f+uYOyQdjz9wOgL/FXXhUE5/i7sfwLGn9CeZFhbHHgajr0ObZ6Ezm/YbgiMhbD9DkhdBDHTodcXtR/RVpyofCaGAiUxE9yjdsezEXvO7atBbdhuDEYT/3zjF7LzSvjPi9NsMibHU89z3+eLsFgsfHH/FDrHNrJ5PhYpmX/0MG9t20JeeRk3tm7LY737Eu1nv/jk1Uav6MbMjopma0oyb27dxOOrlvPtwf28MHgYncJt84O46vU8P2U40UH+fLBsq1IsuGMC3h7afGVjhrQnI6uQr+ZuIzoykNsnN7ynyVoldgYceFL5MDq9XN+zUXR6TRmD42+Bqz+0f8a2flz9lD/m2Otw8BnIPwIDF4FvnGPnW0VRvDImpmIYthaCrs1qz1f/xvwVkFLy9qerOXwynWcfGkO7ltoL4u1LTOOvH8/H3VXPdw9Ns8uYHM/OYsq8n3h63WqaBgbyy/SZvDNyzHVpTKoQQtC/SQyLp8/k7RGjSS8q4qa5s/nX+jUUVlTY3Oddw3ry6oxR7ElI5a5PFlBQWq65n1k392HEgDZ89uMWNu+Kt2kuVy2ekRA+HM7+oPSyGgJCQPf/QOxtysl+8j/29dXuaRiyQjnHV3SHtKWOm2sVhadUoqa5FIatu2aNCVwHBuXn5ftZtv4os27uw9C+2pftu+JTuO/zhYT4evPdg9NsVr01mM28t2MrE+f8QHJBPm+PGM28KdNpH1a/ETRSSkwWCyaLRZMkS22gE4Kb2rRjzW2z+Evnrvx05BCjfviGdWcSbe5zQve2vPeXGziZns1dHy8gr7hMU3shBP+4fyRt4iJ4+cNlnE3NsXkuVyVNZ0LJWcjaWt8z+QOhg95fQfSNsPdhSPzOvv4iR8LovSqJcuN4OPyiCp12BAUnVE6PxQDD1tetinM9cE37UL7+4RcefG4uvbs05d9/v1Gzc3Z3Qir/9/kiooP9+fy+yYT42rbHGp+bw2Mrl3E06zw3tmrDvwYOIdDTPtVcazBYzJwpyia+KIuk4hzSSvI5V15ITnkJeYZSik0VlJkMVD8D9ELg7eKOr6sHgW5ehHn4EuHlR2OvQGJ9g2nhG0YjL/86yX85mHmOv69ZyamcbKa368CzA4fg5WpbNNi2k0k8/NUvxIYF8eX9U/D30haVdz6niDuf/B5/Xw8+e30m3l7u174PBZQ8/MJwZVh6flo3Y1qLuUIZgMx10H++/WKQpjLYfT+c+RYajYO+34NboO39FRxT21wAQ9dCQDv75ldHOJ3yNdClS1cZ2/0eXF31fPnWbfh6a7uBHEzK4O5PfiYy0Jev7r+ZYF/tkixSSuYdPcwLG9fj7erKa8NGMLK5Yyo+1kRWeTG7s8+yNyeZg7mpnCrIxFjtSSvMw5cITz9C3H0IcPfEz9UDD70rrjp9pZaXBYPFTKnJQIGxnHxDKefLikgvy6fI+MfWk5+rB+0CGtE5KJoeITF0CW6Mh97+sN+aqDCZeG/nNj7fu5vYgED+O2Y8bUJtK7q17WQSD331C3ERwXx5/xR8PLQlt+09nMxjL81nWL/WvPDY+OvDoABsvRUylsOkjIaREFgdU4kSVMzdU1mgy07ZEilViPK+R8GridLVCuyovZ/8w7B2GOhcYOg68HdwyeZaxGlQaiAiKk626f8An/z7Flo107atdDojmzv+Nw9/Lw++fXAqoX7WCQZWp8xo5Jl1q1l88jj9Gjfh3ZFjCfV2bBSJlJIj+emsTT/BxszTnCjIBMDLxY2OgVG0C4ikTUAkcb6hxPgE2XzTl1KSbyjjTHE2pwvPcyw/g8N56ZwqzMQsJW46PT1CYhkS0ZLhjVoT7un4KLUdqSk8tnIZeeVlvDh4GNPadbCpn03HEnnk69/o0rQRH989SbOw5DcLtvPFT1vZuvDJ68egpC9XxZ4GLNRWpreuMORXFug6BUNWQVh/+/vM2gZbpqi+e32pTYIm76CSU9F5qG0uv9p7iHQ4FjNC7+I0KBfjH9JEfvHtYm4ep80Bdi6viFv/Mwek5LuHphEVpN1ZnlZYyL1LFnM8O4tHevXlgR697KrfcTFni3NYnHyQpSmHSS3NRy8EXYObMCAsjj5hzWjtH1EniZAlxgr25iSz7XwiGzNPc7ZY+Re6BTdhQuOOjI5qh5+b/QmfVeSUlvLYymVsSUliRvuOPD9oqE36YEv3neAfs5czomMcb902TtN3YzZbePzlBXz44rTrx6BYTLA4CkL7q8iohkj5eVgzUMmZDNsAQV3s77PsnKqvkrUZWj2iclaulKWfu0+tmFy8lQO+tqLG7MVYBIUnofBEtX9PQNFpxIwKp0G5mC5dusp9+/Zq2usvLq/g9v/OIyOvkG8emEqrRtplTvZlpHPvksUYzBY+GD2OwbFNNfdREyaLhXUZJ5iduJtd2WfRIegT1oyx0e0YGtmaALfa98lcicSibFamHWNp6mESirLx0LswNro9M5v1ok2AbSHAF2O2WHhr+xY+27ub3lGN+WjcDQR4aP/s323cx1u/buS2gV15aqK2bOnCojL8/byuH4MCsPdRtRV00zn7/Aq1SUlKZYGuUhi+2THbTBajqgR58gOlJ9Zv7qXl9HN2q+JYbv5qZeLjmGvfZqRFSbxUGYvqP2Xpfxwn9Cogwa81+LVCdH3baVAuRutFZ7ZYeOirX9l+Mon/3XUjfVtpLOwDrEw4zaMrlhHh48MXN9xI8yD766CUmYwsTNrPV/HbSC8tIMorgGmx3ZjYpBNhnrVXe8QeqrbiFpzdx5KUw5SajfQKieXuVv3pG9rMIQ79xSeO8Y81q2js7883EycT5ad9m+31xeuZvfkA/5oyjKl9tO2TXxeJjdXJ2QMreyjHfNw9dTu2FgpPqwJdwkUV6PKJdUy/Z2bDrruVMe2/AEL7XPh+9g5YPwrcgmH4eu2FwezBVKqk7/+02jilfExVuPopo+HbSpVI9mul/u8TB/o/crScPpQa0HrRvbtkM1+v32PTzQVg7pFD/HPdajpHRPLZ+BsJ9rKtrkoVBrOJeWf38unJzWRXlNA1qDGzWvRlSGRL9OLqifYuNJSzIGkf38XvILO8iE6B0Tzabii9Q+1/etuZmsK9S3/B08WVb2+crFmqxmyx8OCXv7DjVDJf3D+Zbs2sFwu87gyKlLC0LbiHwohNdTu2VvIOqVBdt6DKAl3ac89q7vegksIvTVEaZ3H3qVyWrK2wfgx4hKmViXdjx4xXHSmh/NyFBqPqpyQZfo/VFMqYVa421L+Vv3tEWKUs4DQoNaDlolt58BR/+24p0/p24tnJQzWP9eX+vby6eQODYmL5aOwEPG0MbYXKqoTpx3jnyBpSS/PpERLDg20G0zMk1uY+GwIGs4lFyQf45ORmzpUVMiA8jqfajyTOzz715JM52fxl8QIMZjPf3jiFDhrzegrLyrn1gzkUllUw//FbCfO/zis2Xo4jr6pkwglnHPfkX1tk71SOce+mMHyjbQW6asKQB9tmQvoyaHYHxNyidME8o5TPxCvKvv7NFVCcUM1gVDMgxsI/jtN7VTMYVf+2Ad8Wtkn8V8NpUGrA2ovubFYe09/7kbiIYL7+v5txddHm5P1i3x5e27KRMXEteW/UWLuKSCUWZfPywWXsyDpDK79wnmw/gr5hjtkiaihUmE38mLiLT05uptRkYGbznjzYZgjeLrbVowFIys9n5qL5FBkq+H7SzZqNSsK5HGZ88BNtosP48r4puOivvAK8Lg1K8Rn4tRl0ehXa/bPux9dK5nq1cgjsZHuBrpqQFpX8eOQlQKd8JVpXQhU5SqTyYsNRknhhUqVXtNqiunjF4RWlEjxrAadBqQFrLroKo4lbP5xDZn4R8x+fSUSgthPuu4P7eWHjOsbGteT90eNsjqwyWsx8cWorH5/chKfelUfaDmVa025X1daWVnIrSnjv6Fp+TtpPhKc/L3e5gX7hzW3uL7WwgBk/z6PYYOCnyVM1142pivy6e1hPHh57ZTnx69KgAKweABXZMO5Y3Sn02kPqb2oFEToABi+z++n9d6qqSVpMyjfRfx5EjrjwGItJGeHffRrVVhsV1RQXdO7g17LScLRSKw3/Sl+Hq/aUBXtxGpQasOaie2PxBn7YvJ//3TmRgW2baep/4fGj/G31CkY2i+M/Y8bjauPK5ExRNk/tWcSR/HTGRLXjnx1HE+JR9ydRfbE/J4V/7f+VhKJspsV24+8dRuFpY22UlIICpi6Yg0VK5k2ZTkyANhHQ5+auYvHuo3x53xR6xF1+H/y6NSjxn8Gue5VUydWiSXX2R7VN1Wicqi1vS4Gu6qSvhM03qu2lnp/DjllqhdH4JvBprpzhhSegOF5FiVXhEaaMhV+rCx3jXjG213epBZwGpQaudNHtOJXM3Z/+zC39O/P0pCGa+t6UdJa7fltEj0bRfDVhks01PBYnHeDlg8tw07vwYufxjIxqa1M/VzsVZhMfHlvH1/HbaeYbyrs9JtPS3zaNs9M5OUz7eQ7+7h4suHmGpuCI0gojU9/9gQqTmYV/uw1fz0tnhV+3BsWQBwsjoMX/Qbf36mcOtnD6U9h9n5Kq7/OD9hu4tCjn95nv1FaXa4AyBsWJyln+OwJ8m4N/u2oO8TZqBdJQw60vwmlQauByF11xeQU3vf097i4uzH9iJh4asqWPZ2cxdf5PNPEPYM7kafi6a5eiMJhNvHJoOfPP7qNnSCxvdp90QXa52SLRCTT5TqSUWCToHVTPvj7Ydj6Rv+9ZSLGpgpe7TGB8Y9uy4fdmpDFz4QLahoby401TNRn8Q0kZ3P7fuUzo3paXpl1axuO6NSgAm26C7G1wY6qSFrlaOPYmHPg7NL9bhT/XdH0Zi/9YYVT3bRSdAnM1tWpXf/Bv+4fR8G0JOTuVrL5vHAxYpFYgVyHOeigaeX/pFs7lF/H9Q9M1GZPs0lLu/m0RPm7ufDlhkk3GJKeihId2zGV/bgr3tOzPw22HXOArMVskT3oeOHQAACAASURBVM4/iL+XK8+Nb2uVUZFS8tKSYxSUGnnr5k6XNCoWaSHfWEBORQ75xnyKTcWUmcsxWoxIJDqhw03nhpfeC18XH4LcAglxD8Xbxb4QaGvpG9aMhUPv47Fd83lyz0JOFmTyWLthmottdYuM4t1RY3hg2W88vXYV74wcY7Vx7hgTyR2Du/Plut2M7tzKpnyka56mM1VhqnNroNHo+p6N9bR9CowFcPQ1ZUwa31zNr3ESCo9DaeofxwudihLzawXesZC+VG1zDfxVGY2Lz6nGN0LkaNg6FVb2hD7f2i9YeZVx3RmUA2fSmbvtELcN7EqnGOujMkwWCw8vX0JOaRlzp0wjwkd7xMiZomzu2Tab7Ipi3us5hdFRf1Yf1Qnw93Ll661nAa5oVKqMyddbzzKrXyxVtkRKybnyTE4WnSKxJJHk0hTSytIxWAya5+3n4ke0VyNivWJp7tOMVr4t8XVUxMxFhHr48HX/23n14HK+OL2VlJI83ug+CXe9tlN1TFxLHuvdl/d2bKNdWDh3dulmddv7R/Zm7eF4Xv55LYuevF3TQ8d1QaNxasvn7A8N26CYylTCX9FJJSNfteoQrsoXFP+ZOs7FV60ywgZfmLfh20KJYSYvgK0zIKiHqp3idhk5pvBByr+0eQpsngxtnlJRcVfTSs4Oro9PWYnRbObFBWuICPDlwdF9rtygGm9v38KOtBTeGTGGjjZUEjySl87d235Ah+Db/nfQMajmeHUhBM+NV76UKxmVi43JP8e25EjBUfbm7eNgwSFyDXkAeOu9ifFuwuDQQUR4hBPiHkKgWwC+Lj546j1x1bn+rjZcYTFQai6l0FhIniGP8xVZZJRlkFKWyqrMNZjOmQCI9Yqhc2AnegR2I8ozyqGhza46Pc93HkeMTxBvHllN7rYSPuo9Ax9XbSvCB3r05mjWeV7fspFO4RF0b2RdjoC7qwvPTRnGXz9ewGdrdvLwmCtHfV1X6N0hZqqq5GgsrpdIpN+RUul4Va00qhuOkrNQvTiDVxNlLOLug9xdaouqw0vQ/tlLR6wlzYVtt0JwLxiyXEV0XQmvaJX7svdROP4m5O6FfnPAQ1vi7dVIgzAoQojRwAeAHvhCSvn6Re/fAbwFpFW+9F8p5Rdax5m79SDx53J4/44b8HK3Pu9hw9kzfLZ3NzPad2RSG+2O8/05KdyzbTb+bp582e82Ynwun2RljVGpbkxuHeRNszYHePTgZxSbivHQudPevz0TGrWltW9rIjzCrbrh64QOF50L3i5ehLr/+eQ3WoycLUniWOFxDhUc4Ze031ic9itRno3oH9KPASH9HLZyEUIwq0VfQj18eXrvYu7c+j2f9b0Vfw2aZToheGv4aCbM+YFHVixhyYzbra5D0yOuMeO7teab9XuZ2L0tMaG2OVTr6tyuc2Jnqif81MVqC6y2MRsqE/5O/tm/Ycz/4zi9p/JnBPeEprdXy91oeWHNeItJCT8efk4ZgOaz/jzmmdmw43YlijloqTbDqXeHnh+ruvG7/w9WdFMRZkHWr5SvRurdKS+E0AOngBFAKrAbmCGlPFbtmDuA7lLKB63t92LHZX5JGWP//TUdGkfwyT2TrH6izi4tZczsbwnx8mLRtFvw0BjSeiA3lTu3fk+Yhy9f97+dCA3S7hevQKqMipSSF5cc5deEHfTonkK5WyquwoWugV3oHdyb9v7tcLM3NNIKCowF7Mndy9bs7SSUJOIqXOkT0psxEaNo5Ci5C2Bdxkke3TWfVn7hfNX/NnxdtSkYHzmfyeR5PzK0aXM+GnuD1d99VmEx41//ht4tmvDBrAkXvGeN47Kuzu16QVrg1+bq5j10peP6rcj9c4Z44QllTKT5j+M8I6uF3lbpU7UGr8bWJ/yZK2DjDZC5Vok+Npnyx3uJ36pw4PDBMOi3C42RVnL2qO2v8kzo8XHNxqsBcbU75XsC8VLKRAAhxBxgInDssq008snqHZSUG3hy4kCrbyhSSp5Zt4oiQwU/3HSzZmNyouAc92z7gRB3b77t/xfNYo41rVT+Na4N/1i9lHj39XToW4C3WxATw29mYEh/fOp468Hf1Z9h4UMZFj6U1NJU1pxfz5asrWzO2kLPoB7cFH0jER72lzgeGtmKD3pO5eGdc7l/+0983nemplyV9mHhPN6nH29s3czCE8eY3Ma6ynmhfj7cNbQnHy7fyu6EVHo0t17rq5I6ObfrBaGD2Fvh2L+VZLyWBwiLGUrO1KBLdRIqsv44TuemnN8BHdTNvspw+LW6vB/DWvTuMHCRUgjedgu4+CifUMKXsPNuiBgGA38Be4NSgrsrv8rW6bDzr5CzC7q93/CKlTmAhmBQooCUav9PBXrVcNxkIcRA1BPfY1LKlBqOqZHUnALmbjvEpJ7tiIuwfh/zl5MnWJ2YwD/7D6KVRuHBtJJ87t46G28Xd77qf7vNysDVjcq8IwfYp/uOgNBsAk3+zGr6V3oH98KlATj8or2iuSP2Nm6KupEV51axOnMNe/L2MjRsMJOiJuJtzxMeMCSyJW92v4kndi/gb7t/5sPeUzUpCdzVpTvrziTy0sb19G8cQ7iPdcb3tkFdmbvtIO8v2cwPD0/X6iuq9XO7XomdCUdfhaQ50PqxP79vLKw0GhcZjqLTqsZ6Fe4hylhE31i5RVWZLe4dW/sJfy7eMHgprB2ihB9bPgDH34bIUSr011GZ9R4hyqF/8BnlV8nbDwMWqO22a4j6vxNZx2/AT1LKCiHEvcC3wJ9UHIUQ9wD3ADRp0uT31z9etQO9ENw/ynpHfG5ZKS9vWkeXiEhmddaWEVxkLOfe7T9isJj4qv8sory0ZWxfjMFioGXHI3QLXY/R6MrpA53YeOf9uNZS2V178HP1ZWrjyYyKGM6itF9Zk7mOnTm7uSVmOr2DetrlvB8T3Y5cQwmvHFzOG4dX8s+OY6xuq9fpeH34KMbO/o4XNq7j43ETrtwI8HB14f9G9eH5eavZeCyRwe1sl4e5BHad2/WKf2sI6q6e6P3aXigtUniyhpobzZWxaDSumuFoDe72l3mwC7cAGLISlnVUxiSkPwxcDHrHFYcDVKRXlzeUX2XHLOVX6TdPRYZdIzQEg5IGVNe5iOYPByUAUspqwjd8AbxZU0dSys+Az0DtMwMkZeWxZO9xbh3QhXArlWQBXt+6iSKDgdeGjdRW0U9a+NvuhSQV5/BFv5m08LOt/nkV8cUJfJbwBZkV58lIiuXMkfaYjG68tuyU1Xkq9YG/qz93xN7GkNCBfH32ez5J+IzduXuYFXu7XY77W5v1JKU4j28TdhDnG8bUptY7OZsGBPJQz968vX0La88kMKypdcbhhu5t+GLtLv63YjuD2moS66zVc7tOMZVWJvxVRVNVChsWHANphA2V4cOuAcpIRIxQ/o2qLSqf5hfU3GhwJM1RPg69JxSdUBpctZWY2GSKyqTfPEkpInd5C1o9enVoo12BhmBQdgMthBBNURfbdOCW6gcIISKllBmV/50AHLe28y/W7sZVr+OvQ6z3Me3PSGfBsaPc062H5q2u/x3fyKbM0zzXaSy97Kj5YZEWlp9byYKUhejMPhzcOoBJrXqw6qW2vzvq4cp5KvVNjHcMz7X9J8vPrWRh6mL+deRF7o+7h1a+LW3u88kOI0goyuKVg8to6R9O5yDrtw3u7tqdxSeO8XLl1pc1WfSuej33jujFs3NWsfn4GS26b7V6bjscKZU/pErIsCoEt+gklCRVO1Co7Si/1iqaKuFLJWnS9R3wCL/6bozH34H9f4PoSdDxFXWTXze8skBXLVVd9G8Do3bB9jtg3+NKbr/XF/Ubgu0A6t2gSClNQogHgZWo0MqvpJRHhRAvAXuklL8CDwshJgAmIBe4w5q+z+UXsWTvcab27UiIn3V7+BYpeWnTesK8vXmoR29Nn2Xr+QQ+PrmJSU06M72p7aoc5eZyPk/8kj15+/Aub8HKVa25vVeL342HtXkqDQWd0DEucgzt/dryv/hPeP34W8yMmcHQsCE2zVsvdLzVYzJT1n/GE7sXsGjIfVbXrnfV63lu0FBuX7yArw/s477uPa1qN7Zraz5auYMv1u622qDU5rltF+YKKIqvoTTsSTAV/XGci49aXYT2h2Z3qi2uqgp/1X0LpSmQtUWJHzbg87BGquRYmtwMfWcr4cihq1V9+nXDVSlhr0a1M7arHwz4GY69rnwrBUdhwELwa1E749UB9R42XFt0795dznj+Xb7ftI+lT88iKsi6qJAlp07w8IqlvDF8FDe3bW/1eLkVJUxc+zH+bp7MH3yPzYq5+YZ83jn1PimlqQQWDWDR6mBm9Wt62TyU6iHFDZ1SUymfJHzOwYJDDAsbysyYGehslOk/mJvKzE1fM7xRa97tMUXT57/7t0XsTEtlw1/uJMjTuiie2Zv38/riDfzw8HQ6xzZq2FpeUiqZ+ZpCcEvOXFRzo/Ef/oyq8Fu/1qpolDV/07M/quS/4RtV3fWrhaqCYTHToc/3F2azZ++qLNDVBIZvqn0/T8YqlY0vzWou0TfU7niX4WoPG64VLFLy844jDO/QwmpjYjSbeWf7VloFh3BTa+sTGKWUvHhgKQXGcr7od5vNxiSzPJM3T7xLkamI8LwbmbteXNJYXI0rFQAvFy8ebfkQc1Pms+LcKgqNBdzb/G5cbcib6RQUzcNthvDusbUMizyiSUzyqb4DGfPjt3yyZxf/HDDYqjaTerbjfyu38/3GfZrnWmtYjBfW3Ki+2jDk/nGc3kPljAR1hdhbqsmLOKDmRvREFS115oerw6BIqRSDD7+gItV6f/1naZSQnir/ZP1o9TNsrXVZ8rYSObJSsmUybJoA7Z+DDs/XWhGt2uKaNSgFpeUYyyuYOaCL1W0WnzxOUkE+n42fqMkRvzL9GKvSj/N4u2G0slF2/Vx5Jv8+/iZmaSI4azI/bSy/4srjajUqOqFjRpNpBLgGMCdlHsZ4Ew/G3W+TUflry76sO3eSVw4up09YM4LdrdvabBEczKTWbfn+0EHu6tqdMO8r31S93N2Y1LMdszfv1zxPh1KaBpsm/ZHwd0HNjXBlLKryNqrqb3jH1N7NycUbom+C5HnQ/UPHR0c5Einh0HNw9BVVwrfnF5cOTQ4frEJ7N01SCZCDVzgujLgmfGKV32bP/ymDl7Mb+s2+amTvAa4u86eBvOIyWjUKpVOsdQlXJouFj3bvpH1omNXRPwCFhnJePbicdgGRzIrra9Ncsyqyef34W5iliSdbPUlFYbDV21hVRmVWv1gKSo1YrqIdzDGRo7g95lYO5B/ko4RPMVfPhLYSvdDxateJlJoN/PvQCk1tH+zRG5PFzGd7rc86n9qnI+b6/iOXZ6oViF8baP0E9P4GRu6AKXlw0zkYvkHJs7d+TCXq+TSt/SfdpjOVkm/a0todxx6khIP/VMak+Z3Q68sr57lEjVdbUOc3w5YpSgKmNnHxhF5fQY+PIHMNrOgOeYdqd0xHIqW8Jn88wqPlT1sOSGv57eRx2fSDt+WK+FNWt5FSylcPLpNtFr4gj+Sla2pXRaGhUD518Gl5354HZVJJspRSSpPZIi0Wi6Z+LBaLNJm1tWkorMxYLW/f+Vf5ZeI3mj93FR8eWydbL3xB7sw6o6ndYyuWynYffSDzykqtbnPnx/MlyqleL+d2t25dNX3GOsFskvLnCCk33ljfM6kZi0XKvU9IORspd94rpcWsrf3pT1XbzVPVZ60Lzm+TcmEjKed4Spn4Q92MKaVd5/Y1u0IJ9fVmbNdWVh0rpeSzfXtoGhDIiGZxVo+RUJjFj4m7mdq0G+0CtGtXmSwmPjz9P3Iqcnms5cM08VIpC3qd0LxtJYS4aotrjYwYzg2R49iYtYnl52zThbqrRX8aefnz2sEVmKs7nK/APd16UGo0Mvuw9U+Bfx3Sw5YpOpAG+D3r9Mo3k75U6XE1JKSEfY/BiXeg5YNKT0vrii3uHpUvkjxPVX6si2Cm0D7KrxLcA7bPhD2PXLi92QC5Zg1KmL8Pfp7W7eXuzUjnyPlM/tqlm6ZiTu8cXYOn3o2H22grIVzFD8k/car4NHc2m0VL3/oLFZTSgpRmZD1G/E2OnkSPoO7MS1nAwfzDmtt7urjyRLvhnCzM5Ndk641D65BQ+jeO4YdDBzCardtycxbdugSxM9UNL3lefc/kD6SEvQ/DyQ9U8mC3D20PbW7zN2j3LCR8ofJW6uJ68YyAoWug1SNw6kNYOwzKzl25XT1xzTrltfDD4QP4urkzSUNk1/6cFNafO8WjbYcSZKUjuDrbs3ew/vwGxkaOpk9wTfJOjkVKSYkxmezy/eRXnKDQkECpKYMKcy5mWVZ5lA5XnQ+e+lC8XKPwd2tBoEc7Qjy64qZ3gBjfZRBCcHfTv3Ku7ByfJnzOKx1eIMjt8jL/FzMmqh1fn97Of09sYFzjDrhZqQP1l05duHvJYtaeSWR03NWbA1DvBHZWGeBnf4AW99X3bFRo9O4HIP4TZQw6v2l/nkzHl5Sv6MS7qgxwh+ccM9fLoXNVYpLBPWHnXUqypf98CLXNZ1ubXPcGJb+8jBWnTzO9fQe8XK2PMvroxEaC3LyY2Vy7MciqyOKbs9/TwieOKdG1WyK0yHCGpKLfSC9ZS7ExGQAX4Y2fWzOCPDrgoQ/GReeDQIdFGjFaiigzn6fEkExm6TYkJkBHiEdnon1GEe0zGjd97YRPuuvdeSDufp4/+hKfJnzB31v/TVOOihCCh9sO4Z5ts1mUtJ9pViaXDo5tSqSPLz8dOeg0KPYghFqlHHwaihPBx2pFAccjLbDrXrWaaPsP6PSaY5IuhVA3d1MRHH5eGZXWj9jfrzXE3gL+7ZWI5drB0PV9aHF/g0omve4Nyq8nT2CwmJnWzvochqN56Ww5n8DjbYfh7aJNn8giLXyR+DVCCO5rfjd64Xg1VSkl58u2cyrvG7LKdyPQE+rZg+b+txDq2QNf16ZW+WjMlgryKo6RWbaN9OJ1HMj+N4dy3qWJzzhaBt6Bj2vjK/ahlUjPCGbGzODLM9+wJnMtIyNGaGrfP6w5HQOj+PL0NibHdMXFivBvvU7HlLbt+O+uHaQXFdLItxbzDa51Ym9RBuXsj6oSYn1gMcOuuyHxa2j3DHR82bE3XaGDnp+DsQj2PQquvtD8r47r/3IEdoTRu2HbTNjzAOTuhu4f1W44swauWR+KtSw+cZw2IaG0CbVexPGb+B14u7gxvZn2ZNIt2Vs5UXSS6Y1vJqSGqoj2kld+lM3pd7E14wGKjcm0C3qYMTEr6N/oY5r7T8PPzXpxQ73OnRDPLrQLeoDhjRcwJPpHmviMI7l4CauTb+JA1r+pMOc5/DMMCOlPR/8OLEhdRI5GB68Qgrta9iOlJI91GSesbje5TTsk6gHDiR14N1G12c98Xzc+houxmJWSb+LX0OFF6PRK7TzB61yUVEvkKGW8kuc7foxL4Raoki7bPw+J38Dq/lB8tu7GvwzXtUFJLsjnQGYGE1q1trrN+bIiVqQdZUpMV82VA4tNxcxJnk9L3xYMDB2gdbqXxWwp51D226xPu40i41k6hTzNqJjfaBU4Cw8X+w2XEIJA9zZ0DfsXo5osoanfTZwp/JnVyTeRWuzAin2VY90eOxOJ5MfkOZrbD41sRbRXAN8l7LS6TRP/ALpERPLbKadBsZumM5UycW4dV5W0mFQ01Nnvlchjbfs39O5Kiyukj5KeSV9eu+NVR+ig4wvKsBQnKL9Kxuq6G/8SXNcGZUX8aQDGtbAuvBhgQdI+TNLCjGbaQ0d/SVtCqbmU22JutVm/qiYKDQmsT51JfMFsmvndzMjGi2nuPxWdqJ16KZ4uoXQOfZqh0XPwcW3Mrsx/sCfzWUyWsis3tpJQ9xDGR45lT95eThad0tRWL3TMaNaDvTnJnCo8b3W7sS1acTw7i7P5jl91XVc0ngI6dyXFUldYjKrqYtIc6Pw6tH+mbsZ18Vb15qt8G+c31c24VUSNh9F7wLORKiFw9N/1szKs5Lo2KKsSTtMuNIxoP+simCxSsijpAL1DmxLjoy0CKbsim3Xn1zMgtP/v+SaOILN0GxvS7qDCkk+/yI/oHPo0rnrb641owd89joFRX9Em8D6Si5exKf1OykzW38CvxOiIkQS6BjIv5WfNIc2TmnTGVehYlGS9TMqo5ioHaU1igqaxnFyEmz9E3QBJP9VN3oTZoMrrJs+HLu9A27/X/pjVcfNXBbq8Y2HDeFVDvi7xjYNRO6DJVKUEsHmyqpZZD1y3BiWntJT95zI0yazszUkmtTSfSU06ax7vt/RlANzYyHEqoqnFK9mW8TDeLo0YEv0D4V7WV6R0FDrhQpuge+kT8T7FhiQ2pP2FEmPalRtagbvenQlR44kvjudIwVFNbQPdvRgU0ZIlKYcxWaxLdIz286dVcAhrzzgNit00vU3Vh89YVbvjmCtgy82QslBFPbV5vHbHuxQeoUr23j1YrRQKjtXt+C7e0PdH6PoepP0KK3uqImh1zHVrULamJCGBIbHWF9BZlnoET70rwxtZ73MByDcUsCV7KwNC+hHsIBnstOLV7M58hiCPjgyM+govlwiH9Gsrkd4DGRD1BSZLKZvS76LUmH7lRlYwMKQ/ga6BLM3Qvj99Q+MOZFeUsDv7rNVtBsU2ZW9GOsWGWtZsutaJHA1uQSonpbYwl6un8bRfoft/6y5891J4RaskROGqaqkUJ9bt+EJA60dh6Fow5CmjkvxznU7hujUo21KS8Xf3oH2YderAFilZk36cQREt8NIYKrzu/HrM0szoyFG2TPVP5JQfqDQmHegX+V9cddoTK2uDQPc2DGj0KSZLMdvOPYLRUmx3ny46F0ZGDOd40QmSS1M0tR0Y0QIvvSsr06x/WhzQJAaTxcKutFStU3VSHb0bxEyD1MW1s/1iKlMqwOlLlRBmywccP4Yt+DZXKxVzBawdrpSh65rwQUqyxb+9ErTc/3cVsFAHXLcGZWdaKj2joqyWqT+Ul0Z2RQlDI6134IPS69qQtYmO/h2I8LBN2r46Zabz7Mh4HC/XSHpHvIeLzrriUHVFgHtreoW/RZHhDHsyn3WInMug0AG46dxYk7lOUzsPvSv9w+NYf+6U1fPoFtkIN52enWnajJeTGoidqVYRKYsc26+pFDZNhIyVqmxu3D2O7d9eAtrDkBVqy2/dCCjPrvs5eEUr1em4e+H4m6qmSx3M47o0KFklJSQV5NOjkfW1yLdkxqNDMCBcWyb1oYIjFBgLGBw2SOs0/4SUFvac/xcmWUbviPdw1wfY3WdtEObVmw7Bj5NRupHEQvt1nbxdvOkZ1IOdObuoMFdoajsoogXny4usjvbycHGlQ3g4+zIcs2V3XRPSR2XLO3Lby1SiapOcW6MKYzW/03F9O5LgHjBoiaqOuWE0GArqfg56d+j5iZLDz9qiQotrOWDgujQo+8+pm0WXSOsVgrefT6R9YCMC3LRlpG7P2YGviw8d/a0vJ3wpzhQuIKtsFx1D/oafWz3KWlhBc/8ZhHv140jO+w5x0vcP6Uu5pZwD+dpqQ/QLU0EX285b72jvEhHJ4fOZGKwUi3RyCaqkWM6tdczWj7EYNoyD8xtUjZJmf7G/z9okfBD0/xnyDiojaCqtn3k0nwUjt6rfV/eHhK9qbajr0qAcPp+JXgjahliXHV9mMnI4L41eobGaxjFYDBzIP0j3oO64XFxiVCMV5jyO5v6HUM+exPrWrv6XIxBC0CX0Xwh0HMp+y+7+Wvm2xN/Vj925uzW1C/f0I9YnmN3ZSVa36RgegcFs5nROPWxVXGvE3gpIlR9iD8Yi2DBGPWn3mQ1Nb3XI9GqdqLEqoz5riwogqO0CXZciqJvyq4QNgJ13wq77lJ/HwVyXBuV4VhbNA4PwtFIM8nB+GkZpoUtQE23jFJ7AYDHQLcD6MsSX4mTeVxgtpXQKearBl/itwsslnFaBfyWjdCPZZfaVzdUJHV0CunC44AgmjQ7GrsGNOZCbarUfpW2lDM+x7CzN83RyEX4tlUrume9t78NQAOtHQfZ26PcTxE533Pzqgpip0PMzyFihMvkt9bTy9QhRZYzb/gPiP4U1g6DUscEn16VBOZmTTcsQ6+VIDueq5XrnIOt9LgCH8g/jpnOjtZ82R/7FlJtySCycT4zvDfi5WZ830xBo7n8L7voQjud9YndfnQI6Um6p4HRxvLZ2gdHkGUpJKbEuAz7GPwAPFxdOZjtXKA4hdibkHwQb6txgyFeO7ZzdSrK9yc2On19dEHeXSrpMnq+0vzQUgXMoOj10/reSjCk4qvwqmRsc173DerpKKDMaSSsqpEWQ9fkgxwvOEenpR6C7toiq40UnaOnTAledfRIoZwoXYJEVtAyYZVc/9YGLzpMW/reSVbaLggptEioX08avFQLB8UJtelttK6tpHi+wrjCRXqejaUAgCXkNrPLg1UrMdBB67VIsFbkqnyP/gLoBNp5UO/OrK9o8Du2fU8KV+56oV4kUGt8Eo3apXKF1w+H4uw6Zz3VnUJILVbRFbECg1W3iC7No6act5LfEVEpaWbrdlRiltHC2aDFhnn3wdbs6KwXG+N2IDlfOFv1iVz+eek+aeDUmvlhbJnucXygCiNeg69U0IJDkgnyNM3RSIx6hKtEx6Ufrn8zLs2HdMLWqGbAYoifU7hzrig4vqOqLJ9+Hwy/W71z828ConRA9EfY/obTQTCV2dXndGZTUAmVQmmjQ7zpTnE0zX22KvUmlygnczMf6TPyayCk/QJnpHDG+jpNsqWvc9QFEeA8ktWg5Utq3f9zMuylnSs5oym/x0LsS7R1IQpH1W1jR/v6kFRZiqc+nyGuJ2Jlqv/78xisfW54F64Yq6ZCBvyrH9rWCEND1XWg2C468qFYG9YmrH/RfoAQ1k+fByt52dXfdGZT04iIAIn2tE1DMLCvEYDFrFoNMqXR24Wdy0gAAIABJREFUxXhpc+RfTHrJenS4EuE90K5+6ptonxFUWPLIKdcW9nsxTbybUGouI9egbTuqiXeQ1T4UgEY+vhgsZnJK6ynU81ojegK4+F5526ssE9YOgaLTSpq9kWPUJRoUVQW6Gk9RK4OEL+t5PkIJag5eAWX25V9ddwYlq6QEnRAEe1rnD8koUyuaRl7akggzyjLw1nvj52pf9b+ssp0Ee3ZpMPIqthLm2RcQZJXtsqufRh7KH5JenqGpXZSX/+/fpTWE+/gAcL7EfvkYJ4CLFzSZDCkLlGxKTZRlqNK2xWeUJHyktmqdVxU6fWWBrtGw825ImlvfM1J/79F77eriujMo2aUlBHp4Wi25klmmVjThntoMw/mKLMI9rK8CWRNGSwkFhnhCPLra1U9DwE3vi79bHDnlB+3qJ6zyb3q+XFtIb5inHzkVJRisDNmseuDIKXNcjZfrntiZStcr7bc/v1eaBmsGQ2kKDFkOEUPrfHp1jt5NBRuE9lMlfdOW1feMwCfWruY2GxQhRB0XHXAMBRUVBHpYX2kxt0I5qYLdta0Qcg15BLtp2ya7mEJDPCAJcNembtxQ8XdvTYHhtF19BLj6o0NHnkFbEayq7y/fYN0WVkDlOZJf7jQoDiNsMHhG/VmKpSRF5USUZai6ImFX9/auJly8lERLQEfYMhkyrfAxNWCsNihCiHnVfuYDdzlqEkKI0UKIk0KIeCHEP2p4310IMbfy/Z1CiFhbxyo2VODj5m718QVGdUPx01jut9BYgJ+rdY7/S1FsTAbAxzXWrn4aCr6usVSYszFZbPdL6IQOP1dfCkzaFGz9XZVkToHBOgPh46YUpe2Vsa/Lc7vBo9ND7C2qVG7VCrMkSRmTiiwYuko9rV9v/F6gq6mSaMnRpgbRkNCyQimUUk6t/LkZWOOICQgh9MD/gDFAW2CGEKLtRYfdCeRJKeOA94A3bB2v1Gi0OkMeoMRkwF3ngqtOb3Ubi7RQai7Dx8U+v0eZKRNQGefXAp6VNVtKTdblg1wKbxdvSjWGN3pXlhwoMVlnILwqz5FSo+0VB+v63L4qiJ0J0qQiiorPKGNiyFN1RELsizC6qvEIqSzQFaKUgfO1FZRrKFzRoAghqh7NX73oLUcVbe4JxEspE6WUBmAOMPGiYyYC31b+vgAYJmzUHzGYzbi7WG8cKsxG3PXadLiMFiMSibtOW92UP/VjLkQvPNDrtK2OGipuerViM1rsq4/hrnOnwqJt5VD1HRrM1sm2VB1vtE8mo07P7auCwI4Q0AHiP1fGxFgIw9Yodd7rHa8o9bfQu8P6EXVfoMsBWLNC2SWEeAe44C4spXRUGnEUUL34RGrlazUeI6U0AQWATaUPLVKi13C9mqVEL7S5msyVuRZ6Yb3hqrkfA3ph/fZcQ8el8tnEbLFPlM5FuGjW86r6Dk1WJtbpKs8Rs8WuPJQ6PbevGiJHKykWYzEMW6eEC50ofJrBkNVgMcDaYfVToMsOrLlTdgY2AO8JIZYKIcY31CcoIcQ9Qog9Qog9/9/eWYdHdW19+D1xd3cIwYMGihcrUNxKDUpLqbe3clta6qXu3lKq1CjFihR3l6AhhCAhbsRdZmZ/f5zQj0sDOWdmYuS8z5OHSTh7nzXJnlmz117rty5evHoWkFar1jgItF+8sShd202eoni4ULMhC7sN3Ls1rj1NEbdOck1IZa4si6Iyo7ExUeJQ3IBY4DVgOfAeYM69WBoQfNn3QTU/q/UaSZKsAFcg98qJhBALhBBRQogob2/vWm9maWGBXoVHsbawQKeyuvuSVH21MK3tppWFPTpx/WQZ6QzyczG1y2S1qFatj3ZpZ6L0LExfc72VwvTyq9Cga7vJU3hKDnOBrECcsUn7dHc1PKNg8BooTZSVlhujQZcRKHm15AC/ANOAAGAB8LoZbTgEREiS1EqSJBvgNmDVFdesAi5105kKbBVG9pa1sbSkUqf8jd7WwpoKnbqDWWvJGgssVHcX/Nc8Fi4YRBXVBtP0dZoKVXo51ffSWYqxVOgrVZ9PVejlv6Gtwr40lTr5Q4SNpUlhywZd202agpNynQkSDNsObR6EknOQe6CRDWvC+AySG3QVxMCOsY3XoEsFShxKFHAGiAROAZ8JIczW8qsmbvwosAGIA/4UQsRKkjRPkqRLinDfA56SJJ0DngL+lX6pFEdra8pUOAhHaxuqhYFKhYe5IDeXcrRyoERvmiNwuJQVVa2uKrypUqaTn4e9pWlZayW6EhytnFSNKa2WnbuTtbIzqdJq+dDfQUVG4JU09NpusuQfl+VULKxh+A5ZlDBkCljaqVcgbmlcatCVsxd2TqqXpljmpM6Pa0KII8A9kiR5Itee7JQkaa0Q4i1zGSGEWAusveJnL1/2uAIwSyMEZxtbkguVbx/dbOTwTGFVOT72yvS/AFytXSmsMk2t1tlGFpYsrk7A1baNSXM1BYqrE3CwCsDSwvhEA51BR4muBDeVNT4FNfUnSls4F9fUnzirqFmqjYZc202SvCNyPxMrBxi2DZxr1rG1CwROgOQ/ZLFES9MyIq9rQqeBrhgOzIa9d0L/P8DEDrD1hZK04R2SJEUDu5C35m7IW/NmiaudHfkqqp8vVVjnVKrTdPKw8SCn6l+hcFU4W7dCwoqCyjiT5mkq5FfG4WprWrOx/Op8BAIPW3UqBDmVJVgg4arQoRTUSK64qlBV0LiC3Gg5U8nKSd6ZOF/xoajVdPngOWND49jXnAi/V3a8Kcsat0FXHShxc3cBBciFjc0+tuvl4EBBRQXVej3WCuLjvnayhldWedE/jZqU4Gvnw5niMwghjG7Za2lhi7ttRy6WmybY1hQo12VTWp1CKxfTPotkVsjFnr626nTSssuL8bJzUpwCnlsux6u9HExLIGix5ByQD5NtPOTU4No0ovxHgq0nJP4CQc23PUOD0f5J+XD+5Gtg5QI9P5GVgpsQdb66hBBJQojC68GZAPg61qjIlik73whwkEMrqWXqwlcB9gFUGCrJNXGX4uvQj/zKk1Tomnf3wMyyXQD42ptWDZ1WI68dYK/cuQOklOUTqEIxOqNE3pFeWi8aKri4Vw5z2XrB8O1XFxy0sIaQ2yB1VbPJYmp0Il+Bdk/Cmc/gxMt1X9/AtDi1YX8n+RwkvVhZtbanrSMOVjYklahzDCH2cv/5pLJkdQZeQYDjEECQVmoWpZtGI7VkI45WwbjYmNbBMrk0BVdrV9VtAZJL8ghxVB4mSy8uwt7K6h+RSA2FZO+Sdyb2frIzcayjH1Cr6WColEM5GnUjSdDjQzkEFvsGxH1gvrnLUmH3rSZN0eIcSrCrvONIUXgwL0kS4c5eqrr9gdwIylKy5HzJBdU2Xo6LTQSuNhEkmdg+tzEprU7nYvkhQpzHGB3+u0RCaQKtHdV1wSyuriCrophwFV03UwoLCXZ1M9neFkXWdlmHyiFIPjNxCKp7jOcN4NTm3wrEGldHkqDXNxAyDY4+A+cWmDafvhJi34HV7SDtyqx2dbQ4hxLk4oqlJHGhQLn8eYSLD/GFWaraztpY2BDmEEp88RljzPwHSZIIc5lMQeUpciuOmTRXY5FQuBgJC0KdTesLXlhdSEZFJhFO4arGxRfK5y4RrsrPXS4U5BPmqq6pWosmcwtsHy2Ht4ZtA6UhSUmSdylZ22UZew1lWFhC318gYDQcfBASFxk3T8ZGWNsFjs+VG2yNMS0BqMU5FBtLS0Ld3DibqzyE1dHNn/yqMjLK1YkadnBpz4XSC5SZWJAU6jweGwtXTud9a9I8jUGFLo8LRUsJdBqOg7W6c48riS2UF3tHlysFe+sYVyDXvyhNqqjS60kqLCDcw7R+Ni2GjI1y4Z1zmxpn4qdufNidgIAkI98UWyqWNnI/eJ+BsO8uSFujfGxpEuyaIocnETB4LQz6q/EabDVn2nl6czpHuT5OV3d56348L1XVfbq4RaIXek4WmSZFbWXhQFu3e8gq38vF8ubVK+F0/gL0opIO7g+aPNexguO4WLkQWldc/gqO56Xib++Kj52yOqLz+XnoDAbaezVTiZOGJH0d7BgPLu1h6FYwpkupcxvw7KOFvYzByh5uXC1rou2aClnbrn29vgJOvgFrOkD6euj6JoyOgYCbzWJOi3QoHb29SS4qpKiyQtH17Vx9cbC05nBukqr7tHEKx9nKiei8I8aY+T+Eu96Kg5U/xy6+g0EY36OjISmoPM2FoqWEuUzG2SbMpLmqDNUcLzhBd/euWKhQfxZCcDg3me6ewXVfXENsthwi66g5lGuTtgZ2TgTXTjB0i9zTw1hazZAlRvJNaxHdIrF2gSHrZce8YzzkHKz9urS/4e/OcOIlCBgDY+Og0/OyXL6ZaJEOpYuPvCU/kZWl6HprC0u6e4aw/2KiqvtYSpb0dO/J0YJjVOiVOa+rzmVhR1evuRRXJ3Aqb75JczUEekMlh7NfxsbSjU4ej5o834mCE1QYKujlEaVqXGJJLtkVxfT2ClU85nhWJk7WNrRy10JeVyXlL9g1Gdy6yj08VBaa/ouQaSBZabsUY7H1hCEb5R3i9lGydtolShJkR7NjrFxhP3QTDFxSdwaeEbRIh9LVzw8JOJKRrnhMf5/WnC++SEaZunz5/l59qTJUcSgvWqWV/8bfcSChzhM5U/Aj2WX7TZ6vPonJ/ZjCqrP09H4FG0t1Kb61sTtnL67WrnR06aBuXPZ5APr7KD/IP5KRTjc//396omhcQfJS2H0LuPeU35xs3E2f085LPmBO/B1Ma2rWcnEIkDtfWtrLdUD5J+HEq7CmI2RthW7vws0nwG94vZnQIh2Ki60d7by8OZSu/ExkoK9cP7Ej66yqe0U4tcHfzo9t2TtUjbsaXb2ewcW6NQey5lBclWiWOc1NQuESEooW08Z1On6OA02eL7cyl2MFxxng1V9107LtmWdo5eRJkKOyN73CigpO51ykZ0CAMaZe/yQthj23yfLzQzfI/dDNRavpUJ4O2XWcA2hcHadW8k5FVwrru8lV9cGTYWw8dJxT75ppLdKhAPQJCuZwRrpiKftwZy9CHD3YlK4urU6SJIb5DOV8aQLnzdDS08rCgb7+n2KBFXsyHqGsWvkuqyFIK9nM8Zx38XUYQKTnE2aZc3P2VgCG+tyoalxBVTkHLyYy1F+5ftiBtBQE0CdQ+ZlLi+HCb7D3DvDqK8fsVRaX1kngOHlOTYHYeIrOwtGnZTFJIcAhWJZocbiyUWj90GIdSv/gECp0OqIzlLXYlCSJUYEdOXDxAnmV6mTpB3j3w8HSgbUZ64wx9V84WgfSz/9zqg1F7Ey/n9Jqddln9UVqyUYOZs3F3a4zvX3fQTKxBTJAma6Mbdk76OURhZetukPfzelx6ISBmwM7KR6zOzkJB2truvmZluJ83ZGwEPbNAO9BNc5EufK2YiztIOQWuWq+GfT+aFLoSuH4C7C2M1zcIwtJDt0MlTk1DbpMUz5XSot1KH0Cg7GxsGR7ovJK9jFBndELwdrUk3VffBn2lvYM9x1KdP4RkkpNk2K5hLtdJwb4z6faUMy21Bnkljde0aMQgvj8HziY9Swedp3p7/8F1haOZpl7Q+YmyvXljPVXn9a4OiWGUEcPxfUnQgi2JV6gb1AwtlZNUx68UTj/A+y/B3yHwuC/wco8f9taCZsOuhJIbb7KEA2KEJC8TE4Djn0LQm6FcfGykKTfEBi4HApjaxp01X+jvhbrUBxtbOgTFMzmhPOKK+DbuvrSwdWP5Unq37xH+Y3EwdKBpanLVY+9Gu52nRgc+DPWls7sTL+PMwU/IxpY1rpSX8CBrKeJzfucIKdRDPCfj7WFeQQVi6qLWJ+5kSj3noQ6Ks/SAkgtzedgTiITQroqlk85nZtDWnERw1qpq8S/rjm3AA7cC343yfUOVvWsvuwzSA7TaNledVMUL+8+dk8FGzcYvhP6/fy/KgUBo6Df75CzD3ZOrvcGXS3WoQCMbBNBUmGBqiLHqWE9iCvMJCZfWajsEo5WDowLGMOJwhhOFppW6Hg5zjahDAn8BX/HQZzM/Zid6bMpqjpvtvmvhhCC1JKNbE65hYzSnUR6Pkkvn7dMap51JcvTVlItqpkSNEn12D8TD2OBxMSQrorHrD93BgkY1lpzKACc+QoOPiBnX924Ui6iq28kCwi7Q+6RUpFd//drjlSXwLHnYG0k5B6Enp/BqCNyxXxthEyF3t9B5kb5DMygvPusWlq0Q7mpdRssJYm/zyrX2xof3AUHKxt+PX+V4qFr3c93GD62PvyS9BtVBvMVJ9pYunKD7wf08H6VoqrzbEm5lSPZr1OmyzTbPS4hhOBieTQ70u/hYNaz2Ft6MyToVyLc7jKrkGJCyQW2Z+9gqM9g1VL1FfpqliQeYYh/O/wdlGUhCSH4+2w8vQOD8Haox5BOcyH+c4h+RD4oH7hcPt9oKMJmgNDLGWUa/48Q8u9kTXs49a4sWTM2Hto9VncHx/B7oMcnkLJc7vxYT5GMFu1QvBwc6B8cyqozcRgUhr2crG2ZEtqNdaknyVKp7WVtYc1dYXeSWZHF6nQVujsKkEUkJzAiZAWtXW8hqXgVG5LGcTBrLtllB00OhVUbSkksWsn2tJnsSr+Psup0unu/yJCgX3AzsQvjlegMOn5MXIirtatRu5OVyccpqCpnZhvlvVdiL2aTkJ/PuLbtVd/vuuP0x3D4PxA0SdaKMmMltSLcOslSIhd+adj7NmUKYmHrMDll284HbtoLfX4Ee1/lc7R/HCJfgwsL4fDjsoMyMy3+5HFi+448tXEtB9NS6ROkLFX0rvA+/J5wiB/P7uO5LiNV3S/StTP9PfuxJn0t3dy6Eu7U2hizr4qtpQddvZ4lwnUG5wp/J6l4Jakl67G19MLfYRDe9lG423XC0SoI6RoSJnpDJUXVCeRVHCerbB/Z5QcwiEqcrEPp6jWXUOdxWFnUTwhkZfpqkstS+E/EI9hbqruHzmDgh7N7iXQPIMpTeSXw0lMnsbG0ZEyEeZ1js+PU+3Bsjpxt1e83uQlWYxA2XU5/LYoHlxb8N6kugpjXIP4zObOu11cQfr+sNmwMnV+C6kI4/RFYu0LXN8xqbot3KKPatOGV7bb8EXtCsUMJcnRnTFAkixOjubdtf7zt1B1C3xl6O3HFp5l//lvmdX5Z9ZumEhysA+ji9TSdPB4lo2wHaSVbSC3ZQGKxnBRgKdnjYOWPnZUXVhaOSFhgENVUG4op12VTrstEIFcsO1gF0MplMkFOI/CwVX7IbQxxRadZnf43A7z60dO9h+rxq1NOkFyaz+edRyi2s0JXzV/xcYwIb9Oye8jHviWnnobeJkuj1xVGqU9Cb5cdW+Jv0GVe49nRWAghqwYcfRoqsiB8NnR9yzS9NJDbBXT/QHZUsW/KdT8d55jHZjSHgp2VNZM7dGRRzAlyBpYp7iH+cPsb+Ts1hm/id/Ji19Gq7ulo5cBD4ffzdtx7fJfwA4+0eUiV4KEaLC3sCHIaSZDTSAxCR1HVefIrYymuSqBUl06VPp/S6nwQBiTJCmsLJzztuuBgfTOuNhF42HbGwbphqsbzq/KZf34BvnY+TA+9Q/X4Kr2Or07voKObP8NUFDOuORNPUWUld3ZWfoB/3REzD2JekePyfX5qXGcCsoyI7zC5yDHytSbXO71eKYiB6Echeyd49IJBK8Grt/nmlyToNR+qi+HYs7JTiTBdDRw0hwLA9MiuLDx+lD9OnuDR3sri7qFOHkwJ7c7iC4eZHn4DYU6equ7Z1jmCacFT+SPlT1alr2FioGnNp5RgIVnhZtvO7Gce5qDKUM1nZ7+kXF/B0+2eMmrXtuhCNKllBXzbTXlnSCEEPx07QoSHJ70DFXQYvN4QQnYkJ1+HVjPhhu+ND6eYm7DpsH8m5OwF7/6NbU39U1UAJ16Bs1/KacC9v4XwWXLmm7mxsIS+P8s1P4ceBitnaHWn6dOawbQmSXJOAZkFxYquDffw5MbQMH4+cVSxFAvAox2GYGNpxXsxG42ycZTfCPp79mNF2kp25+w1ao7rAYMw8M35BSSUXuD+1rMJVtI69gpyK0v56vQO+vuEM8C3jeJx+1JTOJVzkXu69VDshP46aL6070ZFCDj+vOxMWs+CPj80HWcCEDxJFjq83qVYhAESfoI17eDM59Dmfhh7BtrMrh9ncglLGxiwRK792T8TUlebPOV161CKKypZeeiU4uvv69GLnLIylsUpf7PwtnPi4XaD2JZ5hm0Z6lv9SpLEPa3uoqNLB75P+JGj+c2zxa8pGISBhYm/Ep1/hDtCbiPKQ/25CcBHJzdTpqvi+S6jVI375vBBvBwcmNReWRdIIQS/774O/k5CyGcUp96R38Bu+LZ+37yMwdpZzjRLXgz6qsa2pn7IOwqbBshKBI6tYVS0fPBuajsApVjZw42rwL2HrCCdudWk6ZrYCjIfTrY2LDsQg96gLF22b1Aw3Xz9mX/4IFV65fLZM9r0IdzZm9ePr6W0Wn0VqrWFNf+JeIRQx1A+P/cVx1pQgyGDMPBL0m9sv7iDsf6jGel3k1HzHLh4geXJx5jZpg+tnZUfWh7PzGBXchKzuvVULLVyMiWLuLRmXnAnBBz5L8R9ABEPQ6+vm54zuUSr6VCVD2bSwWsyVObBoUdgQxQUn5NTgEfsASM/UJmEtQsMWSc36NppWui9ia4i03F3sicjv5idp5RpdUmSxGM39CG1qIilp5RrddlYWPJ693Fklhfyfuwmo2y1t7TnmXZPEeIQzGfnvmRf7gGj5mlO6Aw6vkv4ka3Z2xntP4qpQZONmqdUV8VLR1cT4ujOI+0Hqxr70f49uNvZMb1LN8Vj/thzHHubRkqlNQdCyDUI8R9Du8ch6oum60xAlnyx9b5+alKEAc59J4e3zs2XHfq4M9D67sb9O9h6yr1t7PxMmqYJryTTcLa3w9fViV93HVU8ZnBoK3r4+fPFwf1U6JRXsnf3DObuNn1ZfOEwu7LOGWMujlYOzGn3XyKc2jD//ALWZqxXrDHW3CjTlfHp2S/Yk7uXSYETmBY01ehU5PdjNpJams+bPSZgb6X8jX5/agq7kpN4KOoGnGyU9YjIKS5l3dF4xkcpC481OYRBrn4/8zm0fwp6fNz0s6csrOQU4rTV8k6lOZN7CDb0gYP3gUt7WS4l6nP5AL4pYO8vKxSbwHXrUCTgjgHdOHguhVOpylr9SpLEnP6DyCwt4fujh1Xd7/GOQ4lw8eG56BVkVyhLBrgSBysH/tvuSXp5RLE4ZQnfJvxAleH6ih1nlmcy79RbxBad4p6wu5gYON5oZ7Ih7RSLEw8zK6IfUSpa/BqE4O3dO/B3cmJ6F+Wpwot2H0dn0DN9UHdjzG1chAEOPghnv4aOz8q1CE3dmVyi1XQwVMmqus2Rihw4cD9suAHKUuQan+E7wb0Jpqk7hZk0vFEdiiRJHpIkbZIk6WzNv7W21ZMkSS9J0rGar1VK57+lbxccbW34cZvy9ru9A4MY0boNX0cfJKukRPE4W0srPuw1hTJ9Fc8cWo5O4dnNldhYWPNw+ANMCpzAnty9zIt9k/TyDKPmamrsy9nPK7HzKNYV80y7pxissmHW5SSW5PLS0VV0cQ/k8Y5DVY1dcfoUMdlZ/LfvAOwU7mpKK6r4Y88xhnQKJ8y77u6P9b22VWHQy/pN57+FTi9A17ebjzMB8IiSq+WbmwKxQQ9n58vhrYQfoN0TsrR8q+nN6/evgsbeoTwHbBFCRABbar6vjXIhRLeaL8WnRs72ttzarwsbjp8hIStPsVFzB9yIzmDg7T3q2vZGuPjwarexHMxJ5IOTxp2nAFhIFkwMHM9TbZ+goLqAV2LnsTFzM4YGlqY3FyXVJcw/v4D5Cd8S7BDMvE4v08HFeM2s0upKHtu/GEvJgo96TcVaRaprUWUl7+zeSXc/fyYqzOwCWLz3OEXllcweprjArF7XtmIMejmDKOFH6PwKdHm9+b2ZSZJck5K9A8zUT6jeydkPG3rDoYfALRJuPgY9PzJ/l8smRmM7lAnAwprHC4GJ5r7BXTf2xM7aim82KT/oDnVz44GevVgVf5pdyYmq7jchpCszwnuz8Px+liYeUWnt/9LVLZI3Or9Ge+d2/Ja8iDdOvU1yWYpJczYkQgj25OzjuZgXOZgXzaTACcztMAdPW3VFoJejFwaeiV7OhZIcPuw1lUBHdfHn9/fuIr+inFcHD8NC4RtraUUVP26Lpn+7UCJDFB9a1vvarhODTu6ymPiL7Ei6vNr8nMklwmqK7hJ/a1w76qIiG/bPgo19oSIT+i2CYdvArXNjW9YgNLZD8RVCXIrnZAJXk860kyQpWpKk/ZIkqXphejo7cHv/bqw7dpozGTmKxz0cdQNhbu68uHUzZdXqpObndB7JAJ9wXjv2t9GH9Jdws3HjqbaPc3/re8muzOblk6/xw4WF5DfxA8ozxWd5/dRbLEj4Dh9bb17t9BITA8djaUJbYCEEb51Yz7bMM7zQ5Wb6+agT1jyUnsrvMce5q2t3In2Uq7T+uusoBWUVPDyyr5rb1fvaviaGarn3RdIiOcTV+UWzTd0oOLUC7wFy2KspJqsYdBD/BaxuJ2ekdZgDY09D2G3N14kbQb07FEmSNkuSdLKWrwmXXyfklKarrZRQIUQUcAfwiSRJtXZAkiTp/poXZ/TFi//fNGvW0F442dry6d+7Fdtta2XFO8NGkFJUyHt7dioeB2BlYcHHvW8hwsWHxw/8ydFc03YVkiTR36sf73R5k5t8h7E7Zw/PHJ/LouTF5DUxx3Km+Cwfxn/Cm3HvkFOVy6xWd/Nix7mEOCgT3rwWX53ewe8Jh5jVpi+3t+6lamx5dTXPbt5IoIsL/+2jXMYjv6Scn7ZHM7RzOF1C/7cvS1NY27Wir5JlzpOXQPf3odPVom3NjLDpUHgKmloBcPZuWN8TDj8GnlEwOga6vysXZrY0hBD3mHxLAAAgAElEQVSN9gXEA/41j/2BeAVjfgKm1nVdz549xeV8t+Wg6PzUR2L/mSShhtd2bBWtPv1A7Ey8oGqcEEJklxeLERs+E71Wvy1O5qWpHn81ssqzxfxz34qZB+4V9xy8T3x9boE4W3RWGAwGs91DDZX6KrH34j7x2sk3xF0HZomHoh8Ta9L+FhW6CrPd4/sze0T75a+KudF/Cb0Rz/PFrZtE608/EHuT1f3931i2RXR9+mNxPjPnf34ORIsmsrb/B12FEDsmCPEbQsR9rOq5NnkqcoVYZC1E9JONbYlMWYYQe2bIv+sVwUIkLRGikV6D5qSutX2tr8YOea0CZtY8ngmsvPICSZLcJUmyrXnsBfQHlGuq1DB9YHcCPVx4b+UOdHrlh9tz+g0gwsOTpzetJ6esTNU9ve2c+HHAXbhY2zNrzy8cz0tVa3at+Nh580D4bN7v+jZDfQZzNP8Yr8e9zdyYF1mZtpr08vR6r2HRGXTEFp7ihwsLefzoU8xP+JYSXSnTQ+/g427vMyZgNLZmasz0bfxu3j+5iZsDOzGv+zjFZx+X2HT+HL/FHGd2jyj6BivvkXIuM4cl+04wtU8XWvuqPvdpsLX9D/oK2DUFUlfKBYvtnzB6qiaJrQcEjIGk3+u1jW2dGKrh9Cdy9lbyYuj0PIyNk1vttqDwVm1I9f3Gc82bS5In8CcQAiQB04QQeZIkRQEPCiFmS5LUD/gGMCCH6D4RQnxf19xRUVEiOvp/04U3nTjLUwvX8NzEwdw5UHktQVzORSYv/p1eAYH8OGEylhbq/HBaWQH37P6Z3MpSvrjhVvqqjP3XRbm+nAO5h9iTu5ezxecQCHxsfejs2pF2zm2JcGqDh42HSX1M9EJPWnk6Z4vPEld0mtiiU5Tpy7G1sKWHezcGePWno0sHs8rwCyH4KHYL353dw9igSN7uORErlb/71KJCxi76hRBXN5ZMvU2xxIoQgtnzl3E6LZs1z92Du9P/qh9LknRYyKGqWmnotY2+AnZOgoz1sjR5xAOKnmezI2W57DSHbAD/EQ1//6ztEP0YFJ4E/1FyP3eXiIa3ox6pa21fc2xjOpT6pLYXnRCCBxYsJyY5k5VzZuLjqrwx1h8nT/D81k081rsPT6qIwV8iu7yY2Xt/JbE4hzd7TmBccBfVcyghvyqfI/nHOF5wgtPF8VQaZH0xZysnAu0D8bPzw9vWC3cbN5ysnLC3tMfawhoLJHRCT5WhijJ9GcXVxeRV5ZNdeZGM8gzSytOpFnJygoeNO51dOtHNvSudXTqZbSdyOVUGPS8fWc3KlOPcGtaTl7qNxlKls6rQVTNt6WISC/JZfdsMQt2UZ4StPhzH87+v56UpQ5nW798FaKa86EzlX2tbVwY7J0LmZlnkMfzexjCrYdBXwnI/udd9v58b7r5laXKzq6Q/wDEMen4CgeOvyx2JKWu7RfVDkSSJF6cMY9L7P/POX9v4aOY4xWNv7RTJkcx0Pj+4nw5ePoxqo+5TiY+9M78OvIfHDixmTvQKLhTn8miHwarDN3XhbuPOMN8hDPMdgs6gI6UslXOl50kpSyG1LI3o/MOU6JQVbEpIeNi442fnxzDfIYQ5hBLuFI63rVe9dm3Mqyzl8QNLiM5N4tEOg3m43SDV9xNC8PyWTZzMzmLB2AmqnEl+STkfrNpBlxA/pvapH8dvNnSlsGM8ZG2TBQZbz6x7THPG0lZuT5z0O+i+BivH+r2fvgriP4WT8+RQV+eXoeNzskqvxr9oUQ4FIMTLjYdG9OHTtXvYePwMI7q2VTROkiReHzyc83l5/HfjWoJcbqOzitRTABcbO77tP53Xjq3h6/idxBVm8k7Pibja1M/itLKwopVTGK2ukFOo1FeSX11Aia6ECn0FVYZqQGCBBdYW1jhaOeJs5YyrtQtWDdy5LyY/jccP/EluZSnvR01mbHCkUfN8HX2Qv+LjeLJPP4a3Vt4fBeDtv7ZRVF7JK9NuwsKiCX8CrS6BHWPg4m65WVKr6Y1tUcPQarpc9Z/yl1maQl2VzC1y58Si0/KOqOcn4GTecPX1RmMfyjcKdw+OomOQD28s20pOcanicbZWVswfMwF3e3vuXbWC1KJC1fe2sbDkje7jeanraHZnnWPS1m9MTitWi62lLX52vrRxCqezayd6uHejh3t3url3pZNrR8IcQ/G09WhQZyKE4Kdz+7hzxw+AxO+DZhntTFbGx/HBvt2Mb9eeR3sp68B5iU0nzrLuaDz3D7+Btv4m9u+uT6qLYfsouLgH+v7WcpwJyPUojqFywWZ9UJoCu6fB1uGyhtiNq+WeIZozqZMW6VCsLC146/ZRlFZW8eqfm1RlRHk7OvLj+ClU6nXM/GuZ6swvkHc7d7TuxW+DZmEpSczY9SOfndpGlUF5H5briczyIu7b+xvvxmxkoG8Ey4bcTyd34/rY70xKZM6m9fQJDObdYSNVhcqyCkuYt2QznYJ9mT1MXZ1LgyL0sG2kLO/Rf5FcPNeSkCzkyvnMTVCuTPhVEfpKiH0H1rSX1Y0j58GYWAgca757XOe0SIcCEO7nyVNjB7Lj1AUW7VHX1CrC05Pvxk0io6SYu/9aSmFFhVE2dPEIZPnQBxgX3IWv43dyy7YFnMhLM2qu5ohBCBZfiGbc5q84mpvMy11H80WfW3G3dTBqvkPpqTz490oiPDyZP3a84owuAINB8MKi9VTqdLx1+yisLZtQK9wrKT4jS6EP+FM+T2iJhE2XFZSTFplnvvT1sDYSjs8F/5EwJg4iXwJLO/PM30JosQ4FZHn7QR1a8cGqncSmqPukExUQyPwxEziXl8cME5yKs7Udb/ecyFd9bqOwqpzbdnzHK0dXk1+pfufTnDiZn87tO77n1WN/08nNnxVDH+T21r2MPuw/lJ7KrJXLCXB25qeJU3GxVfdGsGDLAQ6cTeG5iUNo7dtA7VeNRVcOA5dBsHFNya4LXDuAR0/TFYhLEuV06+03y98PXg+Dlpss495SadEORZIk3rx9JJ7ODvz35zUUlqlzCoNCw/hqzHjO5OQwfcUSco0If11iiH871gx/hBnhN7As6SgjN33Gd2f2UKFXpyPW1EkrLeDZ6BXcsv1b0ssKeLvnRH4ccBchTsa/ie9PTWHWyuX4ODrx26RpeDmo2+HsjU/iqw37GNuzPZN6dzLajgbDORyCzC9M3OwImw55h6EwTv1YfQXEvA5/d4CMjbLe2egYCBhpfjtbEC3aoQC4Odrz4V1jyC4sZc4vaxX3oL/E0FatmT9W3qncvmwxGcXGNdcCcLK2ZW6XUawY+iDdPUL4MHYzIzZ+xq/nD1CuooNkUyStrIDXjv3NzZs+Z31aLLMj+rP2pkeZGNLVpBTkLQnnuXvlMgKcXfh98jR8nZTXFgGk5BYw59e1hPt68tKU4fWaDm02rF0b24KmQejtIFmqVyBO+xv+7gQxL0PAWFnEsdNzckqyhkm0qMLGa7FsfwyvLtnMjEE9mDNBfeOng2mpzF69AidrG36YMJn2Xt6q57iSQzlJfHZqG9G5SXjYOHBH617c1roXnrb1nHtvRmLz01l4fj/rUmORgMmh3Xmw/SD87E3vC7Ho5Ale2raZTt4+/DhhMh726nYmpRVVTP/8D7IKS/jjiTsI8VJeq9KkChtbMttuhqI4GJ9Qd0/24vNw+AlIXyO34I36HPyGN4ydzQitsNEMTOkTydnMHH7ZeYQwb7daq6OvRe/AIBZPuZVZq1YwbckffHbzWAaHtTLJpl5eofwy6G4O5STx/Zk9fHF6B9/E72J4QAemhHWnj3cr1dXjDUFxdQXr006xNPEIJ/LTcLCy4Y7Wvbi7TV/8HUz/dK03GHh/7y4WHIlmcFgrPh81FkeFfeEvodMbeObXtVzIzuPr+yapciYaTYiw6bBvupw+7TOw9mt0ZXDqHTj1HlhYywrMbf8DlurWjEbdXLcOpaKymqpqHTbWyp/i0+NuJDmnkDeXb8PXzZkbO6rLO+/g7cPyaXdw3+oVzF69gjn9BnJfjyiTwyi9vELp5RXKheIcFl2IZlXycdalxeJj58zIwI6MCOhAd8/gRnUuJdWV7Mo6x4a0U2zPPEOlQUe4sxdzI0cyKbQbztbmyZYpqqzgiQ1r2Z54gTsju/LKjUNV63sJIXhz+VZ2xV3g5anD6NtWeT96gOQ05d0/NeqZ4IlwyFHuQXKlQxFCFso88gSUJskhsu4fgINxKekadXPdhrycPYLFGx8s5PFZ6vqNl1VWcc9XS0jIymPBA5Pp3ipQ9b3LqquZs2k9a8+dYVR4BO8MH4mLrfnis5V6Hdsy41mTcpKdWWepNuhxs7Gnr3dr+vq0ppdXKKGOpolB1kWVQU9sfjqHchLZm53AkdxkqoUBL1tHRgR2ZEJwVyLdA8xqQ2x2Fo+uW0N6cREvDRrC9C7djJrn83V7WbD5ALOH9eLx0QNUjS2vqGL2nF/5/fN7tZBXU2HvDEhbA5Mz//8cpOgsHP6PLJbp2klWX/Yd3KhmNhc0cchaCA5rJ0J73sc7z01kQC910hu5xWXM/GIxeSXlfPfQFDoGqZNYAflT8HdHo3lvzy4CnV34ZNQYuvn51z1QJZd2BjuzzrI76zw5lbJOl7uNA53dA2jr4kOEiw9hTp4EO7rjbuOg6k1eZzCQVV5EcmkeCSU5nCu6SFxBJnGFGf8UYrZz8aW/bzhD/NrWy05JCMEvJ47x1q4deNjb89nNY4kKUO/oARbuOMwHq3YyuXdnXp2m/hD+rS/WsW57LLuXPaM5lKZC+gZZNWDgMrmGJPYtiPtAriGJfA3aPiKHujQUoTmUWujZs6foOvQJ0rMK+P69GQT6qYuRZ+QXMfPLPymrrOb7h6bSLsC4Q/bDGWk8vv5vskpKeLR3Hx6OuqHeiuaEEFwoySU6J4njeanEFmRwoSTnfyrwbSws8bJzwt3GAWdrO+wsrbGxsMRCktAZDFQZ9JTqKimuriCvsoy8ylIMlzUbdLKypb2rH5HuAXTzCCLKKxSPekwSyCop4bktG9iRlMjgsFZ8cNMo1Yfvl/h99zHeXrGNEV0jePfO0VhZqnN8a7bE8M5XG7h7ah/uu2Og5lCaCgYd/BUMjiFQngFlKRA2Q+6aaG/+D3HXO5pDqYWoqCix6u/NzHrmF/x9XPn6zduxs1X3KSUlp4C7v1pCVbWOBQ9MoUOQj1G2FFVW8sr2LayMj6OTtw/vDBtBJ5XCksZSbdCTUppPUkkuKaX5ZFUUk1NRQn5VGaXVlZTrq2scjsBCssDGwhIHKxtcrO1wt3HAx94ZXzsXQpw8aOXkiY+dc4Ok1gohWBoXy5u7tlOl1zN3wI1MjzQ+xfjXnUd4d+UOhnQK58OZY1Q79dPnM3n4xT/o0j6QD1+cgpWVpeZQmgqFcbBtFJQlg0tH6P0N+KgLZWr8P5pDqYVLL7p9hxOY8/ZyhvVvzytPjFH9hpSSU8Csr5dSWlnF1/dNomuo8Z94Npw/y0vbNpNfXs7d3Xrw+A39cFKZndQSOJeXyyvbt7AvNYWogEDeHT6SVm7uRs0lhOD7rYf4dO0ehke24b3po7G2UudM8gtLuXfOr0jAd+/NwN3VQUsbbgpUF8PJ1+H0x3J4S1cin5W0faSxLWvWmLK2m17OqZnp27M1998xkM27T/PzsgOqxwd7ufHTI7fg5mDHffOXsud0otG2jAyPYNP0e7ilY2e+P3qYYT//wLK4WAzXqVNXS2FFBW/s3M7o338m9mI2bw69iT+m3Gq0MzEYBB+u3sWna/cwunt73puh3plUVul4/t2VFBaV89azE3F3NS7cpmFGhIDEP2QRx7j3odUMGHcOXDvKDbA0Go3r3qEATJ/UmxGDOvDtot1s2XNa9fhAD1d+fvRWQrzcefT7lfx1MNZoW1zt7Hhr2AhWTLuDAGdnntm0ngl//MqOxAv13ge+qVKhq+a7I9EM+fl7fjx2mCkdOrFlxixu79zF6AZkVTodz/2+joU7DnN7/268fYd6wUeDQfD2l+uJiU/nxf/cTLvWDROm1LgGBSdhy1DYezvY+cGIfdDnB7D3lWtSLu6GkguNbWWLpUU4FEmSePahkXTpEMibn6/jWKz6/iNeLo789MgtRIUH8dLijXy2bg8Gg/EOoKufP8um3cFHI0ZTVFnJPauWM23pH+xKSmwxjqVCV81Px44wZOEPvLV7B5E+vqy+fQZvDxuBp0o9rsvJLynnvm+Ws+5oPI+P7s/cSYONapT1zW+72Lz7NA/cOZAhfdsZbY+GGagugsNPwbpuUHAcen0NIw+C12X9bsLukP9VK8WiYTau+zOUyyksLufhFxaRm1/K56/fSkSY+kP2ar2eN5dtZdmBkwyLbMObt43E0c60c5AqvZ4/Y2P4OvoAGSUldPb2YXaPKG5u07Zpy6gbSW5ZGb/FHOfn40fJqyind0AQT/bpxw1BwSbPHZ9+kcd/XEVOUSlv3DaSUd2NcwR/rjnMZz9uY8KIrjx9/7/Ti7UzlAZCCFlR+OgzUJEN4bOh61tgd5XmZ5tvlK8bc+q67PfeEGiH8rVwtRdd5sUiHn5hETq9ni9fv53gAPXxeSEEv+06ygerdxLq7c7HM8fS2tfTZJsrdTr+io/j2yOHSMjPx9fRids6RzKtYyT+zs4mz9+YCCE4kpnOopgTrDkbT5Vez5Cw1jzQsxe9A4PMco81h+OYt3Qzzna2fHLPeCJD/IyaZ+3Wk7z15XoG94ngtafGYVlLerHmUBqA/ONyC96Lu8GzN0R9CZ51/MrPfQsH74eRh+q+VqNWNIdSC9d60SWm5vLoS39gY2PFF/NuJcDXOB2nA2eTmfPrWsqrdLw0dRjjenYwxeR/MAjB9sQL/Hz8KLuSEwEYEBLKxHYduSm8TbPKDEspLGT1mdOsOH2K8/l5OFnbMKF9B2Z27U4bD9OdMEBFtY53/9rO0v0x9GgdyIczxuDlYlxtzJY9p3ntk7/pGRnCu3MnXVW6R3Mo9UhVAZx4Gc5+CTYesrR8+Ky6xR8vjV3uCxEPyT3gNVSjOZRaqOtFd/ZCNv959U8c7W34fN6t+PsYJ1qYXVjCM7+u5UhCGuN6duD5yUNwsjOfzEpKYSFL406yPO4UacVF2FhaMigkjOGtwxnSqjXeDk1LeVgIQXxuDlsuJLDh/FlOZsuNy3r6BzC1Y2fGRrRTLeR4Lc5k5PDsr2s5l5nLrCFRPHZzf9UFi5fYsuc08z75m87tAvngxcnYXyOUqTmUekAYIGEhHHsWqnKhzYPQ5XWwVdkrZ9dUuLgLJqaBxXUrV1hvaA6lFpS86OITsnjitSU42Fnz6avTCPI3Lj1Vpzfw7eYDzN90AD83Z964bQS92ph+HnA5BiE4kpHO2nNn2HDuDBklssRKRy9v+gWHcENgMD38A3C3tzfrfetCCEFiYQGH09PYn5rCnpRkskpl27r6+jGqTQRjItoR5GLeHh56g4GF2w/zxfp9uDjY8tbto+jXTp3I4+Vs3BXHm5+tpVO7AD54YQoO9td2eppDMTN5R+TwVs4+8Oor15N49DBurpS/YNckGLwWAm42r50tAM2h1ILSF92ZhCyenLcUKysLPn75FlqHXOWwTwHHEtN5YdEGknMKuLVfF54YM8Csu5VLCCE4dTGb7UmJ7ElO4khG+j/yKqGubnTy9qG9lzdtPT0Jd/cgyMVVVX/1q1FUWUFiQQEJ+Xmczs0h7mI2MdlZFNS0P/aws6dPUDADQ0IZHNZadbMrpZzNyOGVPzcRk5zJsMg2vDx1GB5OxmeFrdp0gve/2Ui3jsG8O3dSnc4ENIdiNirz4MSLcHY+2HlDt3eh1V3KwltXQ18FK/zBfxT01zK+1KI5lFpQ86JLSM7hyXlLqKrW8/7zk+ncznh56/Kqar5Yv5dfdh7B29mRZycO5qYuEfUqV1Khq+ZEVhaHM9I4kZVF7MUs0oqK/lHgkgBvR0f8nJzxsnfA3d4eV1s7HG2ssbW0wtrSAgkJgxBU6fWU66opraqisLKS3LIysstKySwppqiy8p972lhYEu7hQaSPL139/OnpH0AbD0+j60aUUF5VzYLNB/hp22Gc7W15ftIQRnZra/TvVgjBL8sPsOD33fTp3oo3nxmPrUJ5Hs2hmIgwwPnv4fhc+dyj7aMQ+SrYmKkvzcGH4MJCmJwF1s07oaWh0RxKLah90aVnFfDUvKVczCvhlSfGMOiGCJPuH5OcybwlmzmdfpG+bUOYM+FG2vgZv/tRS3FlJQn5eSTk55NcVEB6cTGZJcXklJVRUFFOUWUlZdXV1PbXt5QkHG1scLW1w9PeAW9HB/ycnAlycSHE1Y1wdw9CXd0aLKVZCMHGE2f5cPVOMvKLGR/VkafHDcLdyfjwnk5v4NMftrJi/TFGDOrA84+MwkpFFb3mUEwg9xAcegTyDoH3QDm85d7FvPe4uAc2DYC+P8uV9BqK0RxKLRjzossvLGXO2ys4fS6TR2cOZtrYnibtLHR6A4v3HufLDfsoq6xiyg2RPDSij9EZSOZG1OxIqg0GhBBYSBLWlpZYW1g0md7qxxLT+Wj1Lo4mptPW34vnJw+hZ2vT0ozLyqt49eM17D2cwB0TevHg9EGqCx81h2IEFTnyjuT892DnK3dODLuzfupFhIBV4eDcBoZuNP/81zFaC2Az4e7qyOev3crrn67l85+2k5iay5Ozh6nq+ng5VpYW3DmwO6O7t+frjftZsu8Eqw+f4o4B3bl7cE/cHBv2AP1KJEnC1soK85/ymM7ptGy+3LCP7bEJeDo78Ootw5nYuxOWKrszXklaZgFz3/2LpNRc/nvfcCaNMq5Jl4YKDHo4vwCOvyBXvLd/EiJfAWuX+runJEGr6RD7pixpr8nYNwiNKr0iSdItkiTFSpJkkCTpqh5RkqRRkiTFS5J0TpKk5+rTJjtba15/ejx3TenD6s0xPP7Kn+TklZg0p7uTPc9PHsJfc+5icMdwfth2iJFvfs/Ha3aRU1xqJsuvD04mZ/L4j6u45aPfOHw+jUdH9WPt3HuY0ifSZGdy6Hgi9z37K9m5xXzw4pR6dSZNcW03Chf3wYZecOhhcO8GNx+HHh/WrzO5RNid8llN4qL6v5cG0MghL0mSOgAG4BvgaSHEv/bxkiRZAmeAm4BU4BBwuxDi1LXm7hLZTRw5chgra+Pj/Fv3xvPWF+twsLfh1SfG0iMyxOi5LudsRg4LNh9gw/EzWFtaMq5nB6YP6t6gZyxNCb3BwM5TF/hl5xEOnU/F2d6WOwd0Y8aNPXCxN70XvcEg+Hn5fr7/Yw9hQZ68/exEo1PEAbLS8/EL9LhmWKA+13azCHlVZMv1JAk/gX2g7ERCpjW8HMr63iB0cPORhr1vM6bZhryEEHFAXfH63sA5IURCzbV/ABOAa77oUi5c5O05i5n73q1GO5Wh/doRFuTJi++v4ol5S7j7lr7MnNKnVikONUT4e/H+jDE8OqofC3ccZtWhUyw7cJLebYK5pW8kQzuHY2OGNN+mTk5xKSsPnWLJvhOk5RXh6+rE0+MHMeWGzmZLt84rKOX1z9Zy6HgSIwZ14JkHbrpmwWJdZKTm8ey939d5XX2u7SaNQQdnv4YTL4G+DDo+C51eBOv6SSGvk1Yz5N7yBbHg1qlxbGhBNId3rUDgcnngVOCGugZ5ejuzZ8sp1i2LZtxtdV5+VVqHePHde9P58NvN/LB4L5YWFsyc2qfugQoI9Xbn5anDeWxUf5YdiGHJvhie+WUtPi6OrHth1nXtVA6cTebBBSvQGQxEhQfxxJgBDItsY9bMMSEEz7y1nAspuTzzwE2Mv6mLyckGn81bSXlZlZksNG5tN1myd8nFiQUnwG+4nL3l0sgqzaG3wpEnIfEX6PZO49rSAqj3kJckSZuB2lT6XhBCrKy5ZjtXDwtMBUYJIWbXfD8DuEEI8Wgt194P3F/zbWfgpFmehHnxAnIa24ha0OxSRzvgANravpym+rfS7FJHOyGEUcU79f4RWAgx3MQp0oDLdUyCan5W270WAAsAJEmKbqy0zmuh2aWOJm6XtrYvQ7NLHU3ZLmPHNocGW4eACEmSWkmSZAPcBqxqZJs0NMyBtrY1risaO214kiRJqUBf4G9JkjbU/DxAkqS1AEIIHfAosAGIA/4UQhjfg1dDowHQ1rZGS6Sxs7xWACtq+Xk6MPqy79cCa1VOv8A06+oNzS51NEu7tLXdpNDsUofRdl230isaGhoaGg1LczhD0dDQ0NBoBlw3DqWpSl1IkuQhSdImSZLO1vxba4m2JEl6SZKO1XzV28FsXc9fkiRbSZIW1/z/AUmSwurLFpV23S1J0sXLfkezG8iuHyRJypYkqdY0XUnmsxq7T0iSZGRXqGvaoK1tZfZoa1u5TfWzroUQ18UX0AG5NmA7EHWVayyB80BrwAY4DnSsZ7veA56refwc8O5VritpgN9Rnc8feBiYX/P4NmBxE7HrbuCLRlhXg4AewMmr/P9oYB1y25k+wIF6sEFb2+ZZQ9ra/v971su6vm52KEKIOCFEfB2X/SN1IYSoAi5JXdQnE4CFNY8XAhPr+X7XQsnzv9zepcAwSap3AabG+LsoQgixE8i7xiUTgJ+FzH7ATZIks0rbamtbEdraVkF9revrxqEopDapi8B6vqevECKj5nEm4HuV6+wkSYqWJGm/JEn19cJU8vz/uUbIaa2FgGc92aPGLoApNdvvpZIkBdfy/41BY6yppmKHtrbNYxc0vbVt1HpqVmJRkgIZl8bgWnZd/o0QQkiSdLW0ulAhRJokSa2BrZIkxQghzpvb1mbMamCREKJSkqQHkD9pDm1km8yGtrZbNNfN2m5WDkU0oNSFGq5llyRJWZIk+QshMmq2jNlXmSOt5t8ESdZ/6o4cezUnSp7/pWtSJUmyAlyBXDPbodouIcTlNnyHHL9vCphlTWlr22S0tW1ejFpPLS3k1RhSF5Rh/yEAAAJVSURBVKuAmTWPZwL/+rQpSZK7JEm2NY+9gP7Uj4S5kud/ub1Tga2i5pSuHqnTrivit+ORK8ubAquAu2qyYvoAhZeFgRoSbW1ra9ucGLeuGzKzoJ6zFiYhx/kqgSxgQ83PA4C1V2QvnEH+hPRCA9jlCWwBzgKbAY+an0cB39U87gfEIGeAxAD31qM9/3r+wDxgfM1jO2AJcA44CLRuoL9fXXa9DcTW/I62Ae0byK5FQAZQXbO+7gUeBB6s+X8J+LLG7hiukoWlrW1tbTeltV1f61qrlNfQ0NDQMAstLeSloaGhoVFPaA5FQ0NDQ8MsaA5FQ0NDQ8MsaA5FQ0NDQ8MsaA5FQ0NDQ8MsaA5FQ0NDQ8MsaA5FQ0NDQ8MsaA6lhSFJUpgkSTtrHveQJElIkuQlSZKlJEkxkiQ5NLaNGhoazZNmpeWlYRYKAKeax48B+wE35IrmzUKIssYyTENDo3mjOZSWRxHgUKOr5A/sAdyB+4GnJElyBL4CqoDtQojfGs1SDQ2NZoUW8mphCCEMgABmA98DxUBXwFIIcQaYDCwVQtyHLFSnoaGhoQjNobRMDMjOYgXyjuW/wPya/wvi/xvr6BveNA0NjeaK5lBaJtXAOiF3rSsCHIA1Nf+XiuxUQFsfGhoaKtDUhjX+h5ozlC+ACmC3doaioaGhFM2haGhoaGiYBS2koaGhoaFhFjSHoqGhoaFhFjSHoqGhoaFhFjSHoqGhoaFhFjSHoqGhoaFhFjSHoqGhoaFhFjSHoqGhoaFhFjSHoqGhoaFhFjSHoqGhoaFhFv4PbCHzIOflHm4AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def linear(x):\\n\",\n    \"    return -0.3 + 0.5 * x\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data(linear, 20, 0.1, [-1, 1])\\n\",\n    \"x = np.linspace(-1, 1, 100)\\n\",\n    \"w0, w1 = np.meshgrid(\\n\",\n    \"    np.linspace(-1, 1, 100),\\n\",\n    \"    np.linspace(-1, 1, 100))\\n\",\n    \"w = np.array([w0, w1]).transpose(1, 2, 0)\\n\",\n    \"\\n\",\n    \"feature = PolynomialFeature(degree=1)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X = feature.transform(x)\\n\",\n    \"model = BayesianRegression(alpha=1., beta=100.)\\n\",\n    \"\\n\",\n    \"for begin, end in [[0, 0], [0, 1], [1, 2], [2, 3], [3, 20]]:\\n\",\n    \"    model.fit(X_train[begin: end], y_train[begin: end])\\n\",\n    \"    plt.subplot(1, 2, 1)\\n\",\n    \"    plt.scatter(-0.3, 0.5, s=200, marker=\\\"x\\\")\\n\",\n    \"    plt.contour(w0, w1, multivariate_normal.pdf(w, mean=model.w_mean, cov=model.w_cov))\\n\",\n    \"    plt.gca().set_aspect('equal')\\n\",\n    \"    plt.xlabel(\\\"$w_0$\\\")\\n\",\n    \"    plt.ylabel(\\\"$w_1$\\\")\\n\",\n    \"    plt.title(\\\"prior/posterior\\\")\\n\",\n    \"\\n\",\n    \"    plt.subplot(1, 2, 2)\\n\",\n    \"    plt.scatter(x_train[:end], y_train[:end], s=100, facecolor=\\\"none\\\", edgecolor=\\\"steelblue\\\", lw=1)\\n\",\n    \"    plt.plot(x, model.predict(X, sample_size=6), c=\\\"orange\\\")\\n\",\n    \"    plt.xlim(-1, 1)\\n\",\n    \"    plt.ylim(-1, 1)\\n\",\n    \"    plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 3.3.2 Predictive distribution\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data(sinusoidal, 25, 0.25)\\n\",\n    \"x_test = np.linspace(0, 1, 100)\\n\",\n    \"y_test = sinusoidal(x_test)\\n\",\n    \"\\n\",\n    \"feature = GaussianFeature(np.linspace(0, 1, 9), 0.1)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X_test = feature.transform(x_test)\\n\",\n    \"\\n\",\n    \"model = BayesianRegression(alpha=1e-3, beta=2.)\\n\",\n    \"\\n\",\n    \"for begin, end in [[0, 1], [1, 2], [2, 4], [4, 8], [8, 25]]:\\n\",\n    \"    model.fit(X_train[begin: end], y_train[begin: end])\\n\",\n    \"    y, y_std = model.predict(X_test, return_std=True)\\n\",\n    \"    plt.scatter(x_train[:end], y_train[:end], s=100, facecolor=\\\"none\\\", edgecolor=\\\"steelblue\\\", lw=2)\\n\",\n    \"    plt.plot(x_test, y_test)\\n\",\n    \"    plt.plot(x_test, y)\\n\",\n    \"    plt.fill_between(x_test, y - y_std, y + y_std, color=\\\"orange\\\", alpha=0.5)\\n\",\n    \"    plt.xlim(0, 1)\\n\",\n    \"    plt.ylim(-2, 2)\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 3.5 The Evidence Approximation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def cubic(x):\\n\",\n    \"    return x * (x - 5) * (x + 5)\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data(cubic, 30, 10, [-5, 5])\\n\",\n    \"x_test = np.linspace(-5, 5, 100)\\n\",\n    \"evidences = []\\n\",\n    \"models = []\\n\",\n    \"for i in range(8):\\n\",\n    \"    feature = PolynomialFeature(degree=i)\\n\",\n    \"    X_train = feature.transform(x_train)\\n\",\n    \"    model = EmpiricalBayesRegression(alpha=100., beta=100.)\\n\",\n    \"    model.fit(X_train, y_train, max_iter=100)\\n\",\n    \"    evidences.append(model.log_evidence(X_train, y_train))\\n\",\n    \"    models.append(model)\\n\",\n    \"\\n\",\n    \"degree = np.nanargmax(evidences)\\n\",\n    \"regression = models[degree]\\n\",\n    \"\\n\",\n    \"X_test = PolynomialFeature(degree=int(degree)).transform(x_test)\\n\",\n    \"y, y_std = regression.predict(X_test, return_std=True)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train, y_train, s=50, facecolor=\\\"none\\\", edgecolor=\\\"steelblue\\\", label=\\\"observation\\\")\\n\",\n    \"plt.plot(x_test, cubic(x_test), label=\\\"x(x-5)(x+5)\\\")\\n\",\n    \"plt.plot(x_test, y, label=\\\"prediction\\\")\\n\",\n    \"plt.fill_between(x_test, y - y_std, y + y_std, alpha=0.5, label=\\\"std\\\", color=\\\"orange\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\\n\",\n    \"\\n\",\n    \"plt.plot(evidences)\\n\",\n    \"plt.title(\\\"Model evidence\\\")\\n\",\n    \"plt.xlabel(\\\"degree\\\")\\n\",\n    \"plt.ylabel(\\\"log evidence\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch04_Linear_Models_for_Classfication.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4. Linear Models for Classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.preprocess import PolynomialFeature\\n\",\n    \"from prml.linear import (\\n\",\n    \"    BayesianLogisticRegression,\\n\",\n    \"    LeastSquaresClassifier,\\n\",\n    \"    FishersLinearDiscriminant,\\n\",\n    \"    LogisticRegression,\\n\",\n    \"    Perceptron,\\n\",\n    \"    SoftmaxRegression\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_toy_data(add_outliers=False, add_class=False):\\n\",\n    \"    x0 = np.random.normal(size=50).reshape(-1, 2) - 1\\n\",\n    \"    x1 = np.random.normal(size=50).reshape(-1, 2) + 1.\\n\",\n    \"    if add_outliers:\\n\",\n    \"        x_1 = np.random.normal(size=10).reshape(-1, 2) + np.array([5., 10.])\\n\",\n    \"        return np.concatenate([x0, x1, x_1]), np.concatenate([np.zeros(25), np.ones(30)]).astype(int)\\n\",\n    \"    if add_class:\\n\",\n    \"        x2 = np.random.normal(size=50).reshape(-1, 2) + 3.\\n\",\n    \"        return np.concatenate([x0, x1, x2]), np.concatenate([np.zeros(25), np.ones(25), 2 + np.zeros(25)]).astype(int)\\n\",\n    \"    return np.concatenate([x0, x1]), np.concatenate([np.zeros(25), np.ones(25)]).astype(int)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.1 Discriminant Functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.1.3 Least squares for classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"x1_test, x2_test = np.meshgrid(np.linspace(-5, 5, 100), np.linspace(-5, 5, 100))\\n\",\n    \"x_test = np.array([x1_test, x2_test]).reshape(2, -1).T\\n\",\n    \"\\n\",\n    \"feature = PolynomialFeature(1)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X_test = feature.transform(x_test)\\n\",\n    \"\\n\",\n    \"model = LeastSquaresClassifier()\\n\",\n    \"model.fit(X_train, y_train)\\n\",\n    \"y = model.classify(X_test)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x1_test, x2_test, y.reshape(100, 100), alpha=0.2, levels=np.linspace(0, 1, 3))\\n\",\n    \"plt.xlim(-5, 5)\\n\",\n    \"plt.ylim(-5, 5)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data(add_outliers=True)\\n\",\n    \"x1_test, x2_test = np.meshgrid(np.linspace(-5, 15, 100), np.linspace(-5, 15, 100))\\n\",\n    \"x_test = np.array([x1_test, x2_test]).reshape(2, -1).T\\n\",\n    \"\\n\",\n    \"feature = PolynomialFeature(1)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X_test = feature.transform(x_test)\\n\",\n    \"\\n\",\n    \"least_squares = LeastSquaresClassifier()\\n\",\n    \"least_squares.fit(X_train, y_train)\\n\",\n    \"y_ls = least_squares.classify(X_test)\\n\",\n    \"\\n\",\n    \"logistic_regression = LogisticRegression()\\n\",\n    \"logistic_regression.fit(X_train, y_train)\\n\",\n    \"y_lr = logistic_regression.classify(X_test)\\n\",\n    \"\\n\",\n    \"plt.subplot(1, 2, 1)\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x1_test, x2_test, y_ls.reshape(100, 100), alpha=0.2, levels=np.linspace(0, 1, 3))\\n\",\n    \"plt.xlim(-5, 15)\\n\",\n    \"plt.ylim(-5, 15)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.title(\\\"Least Squares\\\")\\n\",\n    \"plt.subplot(1, 2, 2)\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x1_test, x2_test, y_lr.reshape(100, 100), alpha=0.2, levels=np.linspace(0, 1, 3))\\n\",\n    \"plt.xlim(-5, 15)\\n\",\n    \"plt.ylim(-5, 15)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.title(\\\"Logistic Regression\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data(add_class=True)\\n\",\n    \"x1_test, x2_test = np.meshgrid(np.linspace(-5, 10, 100), np.linspace(-5, 10, 100))\\n\",\n    \"x_test = np.array([x1_test, x2_test]).reshape(2, -1).T\\n\",\n    \"\\n\",\n    \"feature = PolynomialFeature(1)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X_test = feature.transform(x_test)\\n\",\n    \"\\n\",\n    \"least_squares = LeastSquaresClassifier()\\n\",\n    \"least_squares.fit(X_train, y_train)\\n\",\n    \"y_ls = least_squares.classify(X_test)\\n\",\n    \"\\n\",\n    \"logistic_regression = SoftmaxRegression()\\n\",\n    \"logistic_regression.fit(X_train, y_train, max_iter=1000, learning_rate=0.01)\\n\",\n    \"y_lr = logistic_regression.classify(X_test)\\n\",\n    \"\\n\",\n    \"plt.subplot(1, 2, 1)\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x1_test, x2_test, y_ls.reshape(100, 100), alpha=0.2, levels=np.array([0., 0.5, 1.5, 2.]))\\n\",\n    \"plt.xlim(-5, 10)\\n\",\n    \"plt.ylim(-5, 10)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.title(\\\"Least squares\\\")\\n\",\n    \"plt.subplot(1, 2, 2)\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x1_test, x2_test, y_lr.reshape(100, 100), alpha=0.2, levels=np.array([0., 0.5, 1.5, 2.]))\\n\",\n    \"plt.xlim(-5, 10)\\n\",\n    \"plt.ylim(-5, 10)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.title(\\\"Softmax Regression\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.1.4 Fisher's linear discriminant\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"x1_test, x2_test = np.meshgrid(np.linspace(-5, 5, 100), np.linspace(-5, 5, 100))\\n\",\n    \"x_test = np.array([x1_test, x2_test]).reshape(2, -1).T\\n\",\n    \"\\n\",\n    \"model = FishersLinearDiscriminant()\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"y = model.classify(x_test)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x1_test, x2_test, y.reshape(100, 100), alpha=0.2, levels=np.linspace(0, 1, 3))\\n\",\n    \"plt.xlim(-5, 5)\\n\",\n    \"plt.ylim(-5, 5)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 4.3 Probabilistic Discriminative Models\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.3.2 Logistic Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"x1_test, x2_test = np.meshgrid(np.linspace(-5, 5, 100), np.linspace(-5, 5, 100))\\n\",\n    \"x_test = np.array([x1_test, x2_test]).reshape(2, -1).T\\n\",\n    \"\\n\",\n    \"feature = PolynomialFeature(degree=1)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X_test = feature.transform(x_test)\\n\",\n    \"\\n\",\n    \"model = LogisticRegression()\\n\",\n    \"model.fit(X_train, y_train)\\n\",\n    \"y = model.proba(X_test)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x1_test, x2_test, y.reshape(100, 100), np.linspace(0, 1, 5), alpha=0.2)\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.xlim(-5, 5)\\n\",\n    \"plt.ylim(-5, 5)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.3.4 Multiclass logistic regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data(add_class=True)\\n\",\n    \"x1, x2 = np.meshgrid(np.linspace(-5, 10, 100), np.linspace(-5, 10, 100))\\n\",\n    \"x = np.array([x1, x2]).reshape(2, -1).T\\n\",\n    \"\\n\",\n    \"feature = PolynomialFeature(1)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X = feature.transform(x)\\n\",\n    \"\\n\",\n    \"model = SoftmaxRegression()\\n\",\n    \"model.fit(X_train, y_train, max_iter=1000, learning_rate=0.01)\\n\",\n    \"y = model.classify(X)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x1, x2, y.reshape(100, 100), alpha=0.2, levels=np.array([0., 0.5, 1.5, 2.]))\\n\",\n    \"plt.xlim(-5, 10)\\n\",\n    \"plt.ylim(-5, 10)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 4.5 Bayesian Logistic Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"x1_test, x2_test = np.meshgrid(np.linspace(-5, 5, 100), np.linspace(-5, 5, 100))\\n\",\n    \"x_test = np.array([x1_test, x2_test]).reshape(2, -1).T\\n\",\n    \"\\n\",\n    \"feature = PolynomialFeature(degree=1)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X_test = feature.transform(x_test)\\n\",\n    \"\\n\",\n    \"model = BayesianLogisticRegression(alpha=1.)\\n\",\n    \"model.fit(X_train, y_train, max_iter=1000)\\n\",\n    \"y = model.proba(X_test)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x1_test, x2_test, y.reshape(100, 100), np.linspace(0, 1, 5), alpha=0.2)\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.xlim(-5, 5)\\n\",\n    \"plt.ylim(-5, 5)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch05_Neural_Networks.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 5. Neural Networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from sklearn.datasets import fetch_openml, make_moons\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.preprocessing import LabelBinarizer\\n\",\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"\\n\",\n    \"from prml import nn\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5.1 Feed-forward Network Functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class RegressionNetwork(nn.Network):\\n\",\n    \"    \\n\",\n    \"    def __init__(self, n_input, n_hidden, n_output):\\n\",\n    \"        super().__init__()\\n\",\n    \"        with self.set_parameter():\\n\",\n    \"            self.w1 = nn.random.truncnormal(-2, 2, 1, (n_input, n_hidden))\\n\",\n    \"            self.b1 = nn.zeros(n_hidden)\\n\",\n    \"            self.w2 = nn.random.truncnormal(-2, 2, 1, (n_hidden, n_output))\\n\",\n    \"            self.b2 = nn.zeros(n_output)\\n\",\n    \"\\n\",\n    \"    def __call__(self, x):\\n\",\n    \"        h = nn.tanh(x @ self.w1 + self.b1)\\n\",\n    \"        return h @ self.w2 + self.b2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x720 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data(func, n=50):\\n\",\n    \"    x = np.linspace(-1, 1, n)[:, None]\\n\",\n    \"    return x, func(x)\\n\",\n    \"\\n\",\n    \"def sinusoidal(x):\\n\",\n    \"    return np.sin(np.pi * x)\\n\",\n    \"\\n\",\n    \"def heaviside(x):\\n\",\n    \"    return 0.5 * (np.sign(x) + 1)\\n\",\n    \"\\n\",\n    \"func_list = [np.square, sinusoidal, np.abs, heaviside]\\n\",\n    \"plt.figure(figsize=(20, 10))\\n\",\n    \"x = np.linspace(-1, 1, 1000)[:, None]\\n\",\n    \"for i, func, n_iter in zip(range(1, 5), func_list, [1000, 10000, 10000, 10000]):\\n\",\n    \"    plt.subplot(2, 2, i)\\n\",\n    \"    x_train, y_train = create_toy_data(func)\\n\",\n    \"    model = RegressionNetwork(1, 3, 1)\\n\",\n    \"    optimizer = nn.optimizer.Adam(model.parameter, 0.1)\\n\",\n    \"    for _ in range(n_iter):\\n\",\n    \"        model.clear()\\n\",\n    \"        loss = nn.square(y_train - model(x_train)).sum()\\n\",\n    \"        optimizer.minimize(loss)\\n\",\n    \"    y = model(x).value\\n\",\n    \"    plt.scatter(x_train, y_train, s=10)\\n\",\n    \"    plt.plot(x, y, color=\\\"r\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5.3 Error Backpropagation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ClassificationNetwork(nn.Network):\\n\",\n    \"    \\n\",\n    \"    def __init__(self, n_input, n_hidden, n_output):\\n\",\n    \"        super().__init__()\\n\",\n    \"        with self.set_parameter():\\n\",\n    \"            self.w1 = nn.random.truncnormal(-2, 2, 1, (n_input, n_hidden))\\n\",\n    \"            self.b1 = nn.zeros(n_hidden)\\n\",\n    \"            self.w2 = nn.random.truncnormal(-2, 2, 1, (n_hidden, n_output))\\n\",\n    \"            self.b2 = nn.zeros(n_output)\\n\",\n    \"\\n\",\n    \"    def __call__(self, x):\\n\",\n    \"        h = nn.tanh(x @ self.w1 + self.b1)\\n\",\n    \"        return h @ self.w2 + self.b2\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data():\\n\",\n    \"    x = np.random.uniform(-1., 1., size=(100, 2))\\n\",\n    \"    labels = np.prod(x, axis=1) > 0\\n\",\n    \"    return x, labels.reshape(-1, 1)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"model = ClassificationNetwork(2, 4, 1)\\n\",\n    \"optimizer = nn.optimizer.Adam(model.parameter, 1e-3)\\n\",\n    \"history = []\\n\",\n    \"for i in range(10000):\\n\",\n    \"    model.clear()\\n\",\n    \"    logit = model(x_train)\\n\",\n    \"    log_likelihood = -nn.loss.sigmoid_cross_entropy(logit, y_train).sum()\\n\",\n    \"    optimizer.maximize(log_likelihood)\\n\",\n    \"    history.append(log_likelihood.value)\\n\",\n    \"    \\n\",\n    \"plt.plot(history)\\n\",\n    \"plt.xlabel(\\\"iteration\\\")\\n\",\n    \"plt.ylabel(\\\"Log Likelihood\\\")\\n\",\n    \"plt.show()\\n\",\n    \"    \\n\",\n    \"x0, x1 = np.meshgrid(np.linspace(-1, 1, 100), np.linspace(-1, 1, 100))\\n\",\n    \"x = np.array([x0, x1]).reshape(2, -1).T\\n\",\n    \"y = nn.sigmoid(model(x)).value.reshape(100, 100)\\n\",\n    \"\\n\",\n    \"levels = np.linspace(0, 1, 11)\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train.ravel())\\n\",\n    \"plt.contourf(x0, x1, y, levels, alpha=0.2)\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.xlim(-1, 1)\\n\",\n    \"plt.ylim(-1, 1)\\n\",\n    \"plt.gca().set_aspect('equal')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5.5 Regularization in Neural Networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x360 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data(n=10):\\n\",\n    \"    x = np.linspace(0, 1, n)[:, None]\\n\",\n    \"    return x, np.sin(2 * np.pi * x) + np.random.normal(scale=0.25, size=(10, 1))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"x = np.linspace(0, 1, 100)[:, None]\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(20, 5))\\n\",\n    \"for i, m in enumerate([1, 3, 30]):\\n\",\n    \"    plt.subplot(1, 3, i + 1)\\n\",\n    \"    model = RegressionNetwork(1, m, 1)\\n\",\n    \"    optimizer = nn.optimizer.Adam(model.parameter, 0.1)\\n\",\n    \"    for j in range(10000):\\n\",\n    \"        model.clear()\\n\",\n    \"        y = model(x_train)\\n\",\n    \"        optimizer.minimize(nn.square(y - y_train).sum())\\n\",\n    \"        if j % 1000 == 0:\\n\",\n    \"            optimizer.learning_rate *= 0.9\\n\",\n    \"    y = model(x)\\n\",\n    \"    plt.scatter(x_train.ravel(), y_train.ravel(), marker=\\\"x\\\", color=\\\"k\\\")\\n\",\n    \"    plt.plot(x.ravel(), y.value.ravel(), color=\\\"k\\\")\\n\",\n    \"    plt.annotate(\\\"M={}\\\".format(m), (0.7, 0.5))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class RegularizedRegressionNetwork(nn.Network):\\n\",\n    \"    \\n\",\n    \"    def __init__(self, n_input, n_hidden, n_output):\\n\",\n    \"        super().__init__()\\n\",\n    \"        with self.set_parameter():\\n\",\n    \"            self.w1 = nn.random.truncnormal(-2, 2, 1, (n_input, n_hidden))\\n\",\n    \"            self.b1 = nn.zeros(n_hidden)\\n\",\n    \"            self.w2 = nn.random.truncnormal(-2, 2, 1, (n_hidden, n_output))\\n\",\n    \"            self.b2 = nn.zeros(n_output)\\n\",\n    \"        self.prior = nn.Gaussian(0, 1)\\n\",\n    \"\\n\",\n    \"    def __call__(self, x):\\n\",\n    \"        h = nn.tanh(x @ self.w1 + self.b1)\\n\",\n    \"        return h @ self.w2 + self.b2\\n\",\n    \"    \\n\",\n    \"    def log_prior(self):\\n\",\n    \"        logp = 0\\n\",\n    \"        for param in self.parameter.values():\\n\",\n    \"            logp += self.prior.log_pdf(param)\\n\",\n    \"        return logp\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"model = RegularizedRegressionNetwork(1, 30, 1)\\n\",\n    \"optimizer = nn.optimizer.Adam(model.parameter, 0.1)\\n\",\n    \"for i in range(10000):\\n\",\n    \"    model.clear()\\n\",\n    \"    pred = model(x_train)\\n\",\n    \"    log_posterior = -nn.square(pred - y_train).sum() + model.log_prior()\\n\",\n    \"    optimizer.maximize(log_posterior)\\n\",\n    \"    if i % 1000 == 0:\\n\",\n    \"        optimizer.learning_rate *= 0.9\\n\",\n    \"y = model(x).value\\n\",\n    \"plt.scatter(x_train, y_train, marker=\\\"x\\\", color=\\\"k\\\")\\n\",\n    \"plt.plot(x, y, color=\\\"k\\\")\\n\",\n    \"plt.annotate(\\\"M=30\\\", (0.7, 0.5))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/folders/9s/lky4p_js2czgsr4_5962ffbw0000gn/T/ipykernel_10810/1588375823.py:5: DeprecationWarning: `int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `int`, you may wish to use e.g. `int64` or `int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\n\",\n      \"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n      \"  label = label.astype(int)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def load_mnist():\\n\",\n    \"    x, label = fetch_openml(\\\"mnist_784\\\", return_X_y=True, as_frame=False)\\n\",\n    \"    x = x / np.max(x, axis=1, keepdims=True)\\n\",\n    \"    x = x.reshape(-1, 28, 28, 1)\\n\",\n    \"    label = label.astype(int)\\n\",\n    \"\\n\",\n    \"    x_train, x_test, label_train, label_test = train_test_split(x, label, test_size=0.1)\\n\",\n    \"    y_train = LabelBinarizer().fit_transform(label_train)\\n\",\n    \"    return x_train, x_test, y_train, label_test\\n\",\n    \"x_train, x_test, y_train, label_test = load_mnist()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"step 0000, accuracy 0.18, Log Likelihood -4709.11\\n\",\n      \"step 0100, accuracy 0.54, Log Likelihood -356.597\\n\",\n      \"step 0200, accuracy 0.66, Log Likelihood -276.443\\n\",\n      \"step 0300, accuracy 0.64, Log Likelihood -302.26\\n\",\n      \"step 0400, accuracy 0.80, Log Likelihood -71.3797\\n\",\n      \"step 0500, accuracy 0.82, Log Likelihood -102.672\\n\",\n      \"step 0600, accuracy 0.84, Log Likelihood -113.941\\n\",\n      \"step 0700, accuracy 0.84, Log Likelihood -54.7759\\n\",\n      \"step 0800, accuracy 0.84, Log Likelihood -76.342\\n\",\n      \"step 0900, accuracy 0.92, Log Likelihood -13.4575\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class ConvolutionalNeuralNetwork(nn.Network):\\n\",\n    \"    \\n\",\n    \"    def __init__(self):\\n\",\n    \"        super().__init__()\\n\",\n    \"        with self.set_parameter():\\n\",\n    \"            self.conv1 = nn.image.Convolve2d(\\n\",\n    \"                nn.random.truncnormal(-2, 2, 1, (5, 5, 1, 20)),\\n\",\n    \"                stride=(1, 1), pad=(0, 0))\\n\",\n    \"            self.b1 = nn.array([0.1] * 20)\\n\",\n    \"            self.conv2 = nn.image.Convolve2d(\\n\",\n    \"                nn.random.truncnormal(-2, 2, 1, (5, 5, 20, 20)),\\n\",\n    \"                stride=(1, 1), pad=(0, 0))\\n\",\n    \"            self.b2 = nn.array([0.1] * 20)\\n\",\n    \"            self.w3 = nn.random.truncnormal(-2, 2, 1, (4 * 4 * 20, 100))\\n\",\n    \"            self.b3 = nn.array([0.1] * 100)\\n\",\n    \"            self.w4 = nn.random.truncnormal(-2, 2, 1, (100, 10))\\n\",\n    \"            self.b4 = nn.array([0.1] * 10)\\n\",\n    \"        \\n\",\n    \"    def __call__(self, x):\\n\",\n    \"        h = nn.relu(self.conv1(x) + self.b1)\\n\",\n    \"        h = nn.max_pooling2d(h, (2, 2), (2, 2))        \\n\",\n    \"        h = nn.relu(self.conv2(h) + self.b2)\\n\",\n    \"        h = nn.max_pooling2d(h, (2, 2), (2, 2))\\n\",\n    \"        h = h.reshape(-1, 4 * 4 * 20)\\n\",\n    \"        h = nn.relu(h @ self.w3 + self.b3)\\n\",\n    \"        return h @ self.w4 + self.b4\\n\",\n    \"\\n\",\n    \"model = ConvolutionalNeuralNetwork()\\n\",\n    \"optimizer = nn.optimizer.Adam(model.parameter, 1e-3)\\n\",\n    \"\\n\",\n    \"while True:\\n\",\n    \"    indices = np.random.permutation(len(x_train))\\n\",\n    \"    for index in range(0, len(x_train), 50):\\n\",\n    \"        model.clear()\\n\",\n    \"        x_batch = x_train[indices[index: index + 50]]\\n\",\n    \"        y_batch = y_train[indices[index: index + 50]]\\n\",\n    \"        logit = model(x_batch)\\n\",\n    \"        log_likelihood = -nn.loss.softmax_cross_entropy(logit, y_batch).mean(0).sum()\\n\",\n    \"        if optimizer.iter_count % 100 == 0:\\n\",\n    \"            accuracy = accuracy_score(\\n\",\n    \"                np.argmax(y_batch, axis=-1), np.argmax(logit.value, axis=-1)\\n\",\n    \"            )\\n\",\n    \"            print(\\\"step {:04d}\\\".format(optimizer.iter_count), end=\\\", \\\")\\n\",\n    \"            print(\\\"accuracy {:.2f}\\\".format(accuracy), end=\\\", \\\")\\n\",\n    \"            print(\\\"Log Likelihood {:g}\\\".format(log_likelihood.value[0]))\\n\",\n    \"        optimizer.maximize(log_likelihood)\\n\",\n    \"        if optimizer.iter_count == 1000:\\n\",\n    \"            break\\n\",\n    \"    else:\\n\",\n    \"        continue\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"accuracy (test): 0.8594285714285714\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"accuracy (test):\\\", accuracy_score(np.argmax(model(x_test).value, axis=-1), label_test))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5.6 Mixture Density Networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_toy_data(func, n=300):\\n\",\n    \"    t = np.random.uniform(size=(n, 1))\\n\",\n    \"    x = func(t) + np.random.uniform(-0.05, 0.05, size=(n, 1))\\n\",\n    \"    return x, t\\n\",\n    \"\\n\",\n    \"def func(x):\\n\",\n    \"        return x + 0.3 * np.sin(2 * np.pi * x)\\n\",\n    \"\\n\",\n    \"def sample(x, t, n=None):\\n\",\n    \"    assert len(x) == len(t)\\n\",\n    \"    N = len(x)\\n\",\n    \"    if n is None:\\n\",\n    \"        n = N\\n\",\n    \"    indices = np.random.choice(N, n, replace=False)\\n\",\n    \"    return x[indices], t[indices]\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data(func)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class MixtureDensityNetwork(nn.Network):\\n\",\n    \"    \\n\",\n    \"    def __init__(self, n_input, n_hidden, n_components):\\n\",\n    \"        self.n_components = n_components\\n\",\n    \"        super().__init__()\\n\",\n    \"        with self.set_parameter():\\n\",\n    \"            self.w1 = nn.random.truncnormal(-2, 2, 1, (n_input, n_hidden))\\n\",\n    \"            self.b1 = nn.zeros(n_hidden)\\n\",\n    \"            self.w2c = nn.random.truncnormal(-2, 2, 1, (n_hidden, n_components))\\n\",\n    \"            self.b2c = nn.zeros(n_components)\\n\",\n    \"            self.w2m = nn.random.truncnormal(-2, 2, 1, (n_hidden, n_components))\\n\",\n    \"            self.b2m = nn.zeros(n_components)\\n\",\n    \"            self.w2s = nn.random.truncnormal(-2, 2, 1, (n_hidden, n_components))\\n\",\n    \"            self.b2s = nn.zeros(n_components)\\n\",\n    \"\\n\",\n    \"    def __call__(self, x):\\n\",\n    \"        h = nn.tanh(x @ self.w1 + self.b1)\\n\",\n    \"        coef = nn.softmax(h @ self.w2c + self.b2c)\\n\",\n    \"        mean = h @ self.w2m + self.b2m\\n\",\n    \"        std = nn.exp(h @ self.w2s + self.b2s)\\n\",\n    \"        return coef, mean, std\\n\",\n    \"    \\n\",\n    \"def gaussian_mixture_pdf(x, coef, mu, std):\\n\",\n    \"    gauss = (\\n\",\n    \"        nn.exp(-0.5 * nn.square((x - mu) / std))\\n\",\n    \"        / std / np.sqrt(2 * np.pi)\\n\",\n    \"    )\\n\",\n    \"    return (coef * gauss).sum(axis=-1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"model = MixtureDensityNetwork(1, 5, 3)\\n\",\n    \"optimizer = nn.optimizer.Adam(model.parameter, 1e-4)\\n\",\n    \"\\n\",\n    \"for i in range(30000):\\n\",\n    \"    model.clear()\\n\",\n    \"    coef, mean, std = model(x_train)\\n\",\n    \"    log_likelihood = nn.log(gaussian_mixture_pdf(y_train, coef, mean, std)).sum()\\n\",\n    \"    optimizer.maximize(log_likelihood)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x1080 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x = np.linspace(x_train.min(), x_train.max(), 100)[:, None]\\n\",\n    \"y = np.linspace(y_train.min(), y_train.max(), 100)[:, None, None]\\n\",\n    \"coef, mean, std = model(x)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(20, 15))\\n\",\n    \"plt.subplot(2, 2, 1)\\n\",\n    \"plt.plot(x[:, 0], coef.value[:, 0], color=\\\"blue\\\")\\n\",\n    \"plt.plot(x[:, 0], coef.value[:, 1], color=\\\"red\\\")\\n\",\n    \"plt.plot(x[:, 0], coef.value[:, 2], color=\\\"green\\\")\\n\",\n    \"plt.title(\\\"weights\\\")\\n\",\n    \"\\n\",\n    \"plt.subplot(2, 2, 2)\\n\",\n    \"plt.plot(x[:, 0], mean.value[:, 0], color=\\\"blue\\\")\\n\",\n    \"plt.plot(x[:, 0], mean.value[:, 1], color=\\\"red\\\")\\n\",\n    \"plt.plot(x[:, 0], mean.value[:, 2], color=\\\"green\\\")\\n\",\n    \"plt.title(\\\"means\\\")\\n\",\n    \"\\n\",\n    \"plt.subplot(2, 2, 3)\\n\",\n    \"proba = gaussian_mixture_pdf(y, coef, mean, std).value\\n\",\n    \"levels_log = np.linspace(0, np.log(proba.max()), 21)\\n\",\n    \"levels = np.exp(levels_log)\\n\",\n    \"levels[0] = 0\\n\",\n    \"xx, yy = np.meshgrid(x.ravel(), y.ravel())\\n\",\n    \"plt.contour(xx, yy, proba.reshape(100, 100), levels)\\n\",\n    \"plt.xlim(x_train.min(), x_train.max())\\n\",\n    \"plt.ylim(y_train.min(), y_train.max())\\n\",\n    \"\\n\",\n    \"plt.subplot(2, 2, 4)\\n\",\n    \"argmax = np.argmax(coef.value, axis=1)\\n\",\n    \"for i in range(3):\\n\",\n    \"    indices = np.where(argmax == i)[0]\\n\",\n    \"    plt.plot(x[indices, 0], mean.value[(indices, np.zeros_like(indices) + i)], color=\\\"r\\\", linewidth=2)\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5.7 Bayesian Neural Networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x_train, y_train = make_moons(n_samples=500, noise=0.2)\\n\",\n    \"y_train = y_train[:, None]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class Gaussian(nn.Network):\\n\",\n    \"\\n\",\n    \"    def __init__(self, shape):\\n\",\n    \"        super().__init__()\\n\",\n    \"        with self.set_parameter():\\n\",\n    \"            self.m = nn.zeros(shape)\\n\",\n    \"            self.s = nn.zeros(shape)\\n\",\n    \"\\n\",\n    \"    def __call__(self):\\n\",\n    \"        self.q = nn.Gaussian(self.m, nn.softplus(self.s) + 1e-8)\\n\",\n    \"        return self.q.draw()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class BayesianNetwork(nn.Network):\\n\",\n    \"    \\n\",\n    \"    def __init__(self, n_input, n_hidden, n_output=1):\\n\",\n    \"        super().__init__()\\n\",\n    \"        with self.set_parameter():\\n\",\n    \"            self.qw1 = Gaussian((n_input, n_hidden))\\n\",\n    \"            self.qb1 = Gaussian(n_hidden)\\n\",\n    \"            self.qw2 = Gaussian((n_hidden, n_hidden))\\n\",\n    \"            self.qb2 = Gaussian(n_hidden)\\n\",\n    \"            self.qw3 = Gaussian((n_hidden, n_output))\\n\",\n    \"            self.qb3 = Gaussian(n_output)\\n\",\n    \"        self.posterior = [self.qw1, self.qb1, self.qw2, self.qb2, self.qw3, self.qb3]\\n\",\n    \"        self.prior = nn.Gaussian(0, 1)\\n\",\n    \"\\n\",\n    \"    def __call__(self, x):\\n\",\n    \"        h = nn.tanh(x @ self.qw1() + self.qb1())\\n\",\n    \"        h = nn.tanh(h @ self.qw2() + self.qb2())\\n\",\n    \"        return nn.Bernoulli(logit=h @ self.qw3() + self.qb3())\\n\",\n    \"    \\n\",\n    \"    def kl(self):\\n\",\n    \"        kl = 0\\n\",\n    \"        for pos in self.posterior:\\n\",\n    \"            kl += nn.loss.kl_divergence(pos.q, self.prior).mean()\\n\",\n    \"        return kl\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"model = BayesianNetwork(2, 5, 1)\\n\",\n    \"optimizer = nn.optimizer.Adam(model.parameter, 0.1)\\n\",\n    \"for i in range(1, 2001, 1):\\n\",\n    \"    model.clear()\\n\",\n    \"    py = model(x_train)\\n\",\n    \"    elbo = py.log_pdf(y_train).mean(0).sum() - model.kl() / len(x_train)\\n\",\n    \"    optimizer.maximize(elbo)\\n\",\n    \"    if i % 100 == 0:\\n\",\n    \"        optimizer.learning_rate *= 0.9\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_grid = np.mgrid[-2:3:100j, -2:3:100j]\\n\",\n    \"x1, x2 = x_grid[0], x_grid[1]\\n\",\n    \"x_grid = x_grid.reshape(2, -1).T\\n\",\n    \"\\n\",\n    \"y = np.mean([model(x_grid).mean.value.reshape(100, 100) for _ in range(10)], axis=0)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train.ravel(), s=5)\\n\",\n    \"plt.contourf(x1, x2, y, np.linspace(0, 1, 11), alpha=0.2)\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.xlim(-2, 3)\\n\",\n    \"plt.ylim(-2, 3)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch06_Kernel_Methods.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 6. Kernel Methods\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.kernel import (\\n\",\n    \"    PolynomialKernel,\\n\",\n    \"    RBF,\\n\",\n    \"    GaussianProcessClassifier,\\n\",\n    \"    GaussianProcessRegressor\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_toy_data(func, n=10, std=1., domain=[0., 1.]):\\n\",\n    \"    x = np.linspace(domain[0], domain[1], n)\\n\",\n    \"    t = func(x) + np.random.normal(scale=std, size=n)\\n\",\n    \"    return x, t\\n\",\n    \"\\n\",\n    \"def sinusoidal(x):\\n\",\n    \"        return np.sin(2 * np.pi * x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 6.1 Dual Representation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data(sinusoidal, n=10, std=0.1)\\n\",\n    \"x = np.linspace(0, 1, 100)\\n\",\n    \"\\n\",\n    \"model = GaussianProcessRegressor(kernel=PolynomialKernel(3, 1.), beta=int(1e10))\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"\\n\",\n    \"y = model.predict(x)\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", color=\\\"blue\\\", label=\\\"training\\\")\\n\",\n    \"plt.plot(x, sinusoidal(x), color=\\\"g\\\", label=\\\"sin$(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x, y, color=\\\"r\\\", label=\\\"gpr\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 6.4 Gaussian Processes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.4.2 Gaussian processes for regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data(sinusoidal, n=7, std=0.1, domain=[0., 0.7])\\n\",\n    \"x = np.linspace(0, 1, 100)\\n\",\n    \"\\n\",\n    \"model = GaussianProcessRegressor(kernel=RBF(np.array([1., 15.])), beta=100)\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"\\n\",\n    \"y, y_std = model.predict(x, with_error=True)\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", color=\\\"blue\\\", label=\\\"training\\\")\\n\",\n    \"plt.plot(x, sinusoidal(x), color=\\\"g\\\", label=\\\"sin$(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x, y, color=\\\"r\\\", label=\\\"gpr\\\")\\n\",\n    \"plt.fill_between(x, y - y_std, y + y_std, alpha=0.5, color=\\\"pink\\\", label=\\\"std\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.4.3 Learning the hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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4x/J4lOho3hkOHYMECiI6G+fNVhVFBZzKY6F2rNz1r9uSXvb/w7rp3aTu7LS3Lt+ST1p9Qy7ceX3wBc+eq6XOPPQavvQbu7vk9ciGEEIXUYmCYpmk/oxpWx96qH5G4D1nTVHIoPlGtFnabFcvOJ0Uz78QSFpxYxoZzf2HTbZRzC+Txcu1pXKIejYrXpbLnA3cc87mZXXEzu1LWPSDb52NTr/DXhQhWRW1i5ZkNDN/+McP5mPLuZelfoSv9HuhKBc+g25/IoKnKKGsanIiC4t7g41VwehEIIUQhIUkicY39F/bz6spXWX54ORV9KrKg+wIeq/pYvlTBeHmBg6sG7xmDZqBXrV50rd6V77Z9x6iNo6g/pT7+Mb2odupTJkwIxGRSU+jCw2H9ejDJ/41CCCFySNO0OUAo4KdpWiTwHmAG0HV9ErAMaAccBhKBJ/NnpOKuiEuAMxdAR/XruUm8pus6f12I4Jv/ZjDv+FJSbalU9arIWzWH0aVcWx70qXnXYj0viyePBITySEAoMIKoxLOsOL2Bn44tYtSu8Xyw6wualmjAgIqP07t8Z1xuNyXNbAKjTfVcSkgC/+LqMSGEEA4hv1EFABeTLjJi7Qi+2/4d7hZ3xrUZx7AGw7AYpUFgXliMFp5v+DxPBD/BCz9/xg9pY7lSajF/MIKX67/MTz9ZaNwYli6FTp3ye7RCCCHuN7qu33Iitq6WsX3uHg1H3Cu6DhcuqelXFtNNl4lPTU9l9tGFfH1gBhEX9+BhdmdI5d48XbkvNb2r5uycqalw/jwkxENCgmogmZgAZgt4el7dinmDy80TPaVdS/FkpR48WakHpxKi+PHor8w8PI/Bf77O2xGf8EK1gTxbpT/eTsVuPhaDvVdRcgoci1TTz9xdc/Z+hBBCZEuSREWcruv8uPtHXl35KheTLvJMvWd4P/R9/Fz98ntohYqnkye1oj/kiYSBXK75Cm+ueZNpO6cxIXwC7ds/wtatkiQSQgghxB1IS1MrfiUmqURJNhVAabY0fjgyn1G7xnMiIZIaxarwbcOP6VuhCx7mO5jjfuE8/P0X/PcfHD6oegAcP6bmyt+J0gFQtSpUsW+160BQ+RvGWsatNG/VGsabNZ9jw7ktfLp3Iu/u+JQxe77h6cp9eaXGU5R2LZX9OTRNTa9LS4fIc1DCB7w9ZfqZEELkkSwNUIT9F/0frWe1pv+i/lTwqcD2Idv5ut3XkiC6S8qWhcg95VnYYyG/9/kdXdcJnx3OlMs98Cp9Lr+HJ4QoxNavX4+Xlxft2rXLfCw8PJxixYrRIWMZyds4cOAAjRo1wsnJibFjx950vz59+uDj48P8+fPzPG4hxHWSUuB4FCQlZzu9LN2Wzo9HFlBtUSiD/nyN4s6+/N56Fns6rmZo1f43TxClpMAfm2HM/0G7NtAgBJ5/FiZ+DQcPQsWK8MxzMOYz+HoSzJgF8xbC0pWwaAn88BN8MwlGfwqvDYf69eF0JEz5Dl56Hlo2hxZN4f0RsGEdJCddc3pN0wgt1ZhlrWex89GVdCwTxhf/TqHCr014N+LTW6+wZjKqaqpzMWplN5str5+yEOI+l13ck9XAgQMpUaIENWvWvO0xgoODCQ4OZtSoUQCcOnWKFi1aUL16dWrUqMGXX36Z7es3btxISEgIJpPphpho5syZVKpUiUqVKjFz5sxsX79z504eeughgoODqVevHlu3br3luJKSkggODsZisRAdHX3rD+g2pJKoCEpNT2X0ptF8vPljXEwuTGw/kSF1h8hS7XdZx47wxhswYQIMHRrOziF76DdpLL+mj2J03CpK7RpPv9r9ZBU0IUSepaWlYbqu0VmzZs1YsmRJ5v3XX3+dxMREvvvuuzs6po+PDxMmTGDRokW33G/27NkMGDAgx2MWQtxGbLzqP2Q0qATRdTae/YthW99lz6UD1PGuzm8tpvFombCbxxW6Dnt2w7xf4LdFEHcFzGaoVx/eeBOaNofKVcApD6uIpabC0SOw7R9YtxZ+mQMzp4OzM4Q9Ar37QMNG1yS76vhUZ3bzr/nwwdd5d8en/N+eCUw9NIcPH3yNgRV7YjRkM7UuY/pZbDykWiGghDR7FKIIuZO4J6sBAwYwbNiw2y5xn90xTCYT48aNIyQkhLi4OOrWrUtYWBjVq1e/Zr+yZcsyY8aMGy6sXbx4kQ8++IBt27ahaRp169alY8eOeHt7X7PfG2+8wXvvvUfbtm1ZtmwZb7zxBuvXr7/puFxcXNi5cydBQUG3fE93QrICRcyus7toMKUB7294ny7VunBg2AGeqfeMJIjuAScnWLlSrdhWsiSUDXAi8qd3WNhmFzVKVOeJRU8QPjuc45eP5/dQhRAFwIcffkiVKlVo2rQpvXr1ygwyQkNDefHFFwkODqZmzZqZV5bef/99+vXrR5MmTejXr99tj9+qVSs8PDzueDwlSpSgfv36mM3m3L0hIUTu6Pal36POqwbN1zVpPpd0gf6bXuThFd24khrP3IcnEfHocjqWbZN9guhKLEz/XlUMdWqvkkStWsPUGbBjL/w0l9Mdh7H6fG3W/uHEubwUO1ssULUa9O0P38+AHXtgxo/QrQdsWA+9ukOrh1XF0cWL17z0AY9y/NT8G/5qt5iKnkEM2TKc4P+1YcPZLdmfS9PAyQzJqaraKiU1DwMXQtxrdzvuyap58+b4+Pjkapz+/v6EhIQA4OHhQbVq1Th9+vQN+wUFBVG7dm0M1604uWLFCsLCwvDx8cHb25uwsDCWL19+w+s1TePKlSsAxMbGUrp06VyNNzckxV5EWNOtjNk8hlEbR+Hr4stvPX+jY5WO+T2sIqdSJdiwAc6eVdXQ6v/1qnRsspGJ/0zkzTVvUvPbmnzxyBcMDhksVUVCFFH//PMPCxYsYNeuXVitVkJCQqhbt27m84mJiezcuZONGzcycOBA9u7dC8D+/fvZvHkzLrdoGiuEuI/ouppGdemKqh4yXI0L0m3pTDo4i3ciPiUxPYl3ar3A27Wfx/Vmq4NdvgTfT4WZ0yAuTvUJ+mg0PNpJNZy2W7MGdu+G4GAVq8z6ERo3gsaNHfB+nF3g4VC1vfOuWrljzo/w8Ycw9lNVWfTsMCheIvMlDYuHsCn8VxacWMob2z8mdMXjPFO5H5/UfRtPy3WJ7oxEkTUNTpyBMqXAJQ+VUEKIe6Kgxj1btmyhTp06lC5dmrFjx1KjRo1rnj9+/Dg7duygYcOGd3zM06dPU6ZMmcz7gYGB2SaZxo8fzyOPPMJrr72GzWbjzz//vONx5ZUkiYqAfef30X9RfyLORNC7Vm8mhE/A19U3v4dVpJW6rgejQTPwXIPneLTKowz8bSBDlgxh8cHFTH10KiXdS+bPIIUQyksvwc6djj1mcDCMH3/Tp//44w86deqEs7Mzzs7OPProo9c836uXWtCqefPmXLlyhcuXLwPQsWNHSRAJUVik29T0sviEGxpUH7lynH6bX2TLhe209m/G1w0/oopXheyPc/EifD9ZTfNKSIDwdvDc81Cz1g27njkDu3bB0KFXFyirXx8mTYJq1eC62RB54+wCXbup7b8DMG0KzJoJP/8E/Z6Ap58FXxWvappGt6AOtAtsxYgdnzH+36ksPb2G7x4aQ9vAljce22xSDb5PnlFTz2TlMyHunMQ9AISEhHDixAnc3d1ZtmwZnTt35tChQ5nPx8fH07VrV8aPH49nlkS7o0ycOJEvvviCrl27MnfuXAYNGsTq1atvOy5HkDlGhZiu63yz9RvqTq7LqdhTLOi+gNldZkuCqAAr61WWlf1WMv6R8aw6sopaE2ux6MCt+38IIYqe66sMM+67ubnlx3CEEI6Wlg6RZyE+8ZoG1bquM/XgT9T5Xxv2Xz7ErKZfsjLsp+wTRKmpMOkbaN4IJn4DoS1h+WqYODnbBBGoxcxq1752BXtPTzVj7KBj/w1yrSpV4ZNxsHq9SmJNnazG/cXYa5pcu5pcGFd/JH+2XYSHyZ12a/rTf9OLxKZeufGYJhOYDGrlsyu3aHwthCjw8iPu8fT0xN1dNftv164dVqs1syG01Wqla9eu9OnThy5duuTouAEBAZw6dSrzfmRkJAEBATfsN3PmzMxjP/7445nT7G41LkeRSqJC6kLCBQYuHsiSg0toV6kd0ztNp4Rbidu/UOQ7g2bgxYdeJKxCGH1/7ctjvzzGoAcHMaHtBFzNciVMiHvuFle+7pYmTZrw9NNP89Zbb5GWlsaSJUsYMmRI5vO//PILLVq0YPPmzXh5eeHl5XXPxyiEuEvS0uDUOZXkcb7aoPp8UjRPbXmDxadW0rJUE2Y0/YIybjfpUbFpo1pJ7OgRaN1GNaKuVPm2pzYYIS05myFZVb/suy6oPHwxQVU6fTEOJoyHRQth1P+pKWp2DYuHEPHo7/zf7gl8vOdr/ji/jZ8f/ob6fsHXHs9oBDQ4fV5VZnk7/mq/EIWOxD0AnD17lpIlS6JpGlu3bsVms+Hr64uu6wwaNIhq1arxyiuv5Pi4jzzyCG+//TaXLl0CYOXKlYwePfqG/UqXLs2GDRsIDQ1l7dq1VKpU6ZbjciSpJCqEVh5ZSe1JtVl1ZBUTwiewpNcSSRDdh6oXr85fg//iraZvMW3HNOpPqc++8/vye1hCiHugfv36dOzYkdq1a9O2bVtq1ap1TUDk7OzMgw8+yDPPPMP333+fq3M0a9aMxx9/nDVr1hAYGMiKFSsAGDlyJIsXL75h/7NnzxIYGMjnn3/ORx99RGBgYGZDxXbt2hEVFZWrcQghsrCmwcmzaoWuLCuYrT3zB7UWt2bF6Q18Xu89VrWZk32C6PRpGDoE+veG9DSYNhOmTLujBBFAjeqwew/Y/+0CwLlzcOgQVK2a1zeXAxUrwTeT4KdfVKJnQF8YNhTOX+2i7WR0YtSDr7MxfAFpehpNfn+ML/ZNQdf1a49lNKg+RWejVW8nIUSBcy/inqx69epFo0aN+O+//wgMDMw85qRJk5g0aRIA8+fPp2bNmtSpU4cXXniBn3/+GU3T+OOPP5g1axZr167NXIZ+2bJlN5zjn3/+ITAwkHnz5vH0009n9g3y8fFhxIgR1K9fn/r16zNy5MjMJtqDBw9m27ZtAEyZMoVXX32VOnXq8PbbbzN58uRbjsuRtBt+kRYg9erV0zM+JHF7abY0Rq4byejNo6levDpzus6hdsna+T0s4QCrjqyi78K+xKXE8VXbrxj44EBpai3EXfTvv/9SrVq1fB1DfHw87u7uJCYm0rx5cyZPnkxISAihoaGMHTuWevXq3fGx1q9fz9ixY2+6FKyjDRgwgA4dOtCtW7drHs/uc9U0bbuu63f+ZsRdJ/FXPrHae+ikp4NFrSKo6zqf7ZvIWxFjqOJZgbkPT6SmdzbZGl2H+XPhg5Hq9c8+D0OeBifnHA9j+3ZYtVottmGzwZEj8GgHcHBf1DuXkgLfTYRvvlKJs3dGQvee1/RouphyiYF/vMZvp1bwaGAY05t8jq/zdQ2UbDZIsYJ/cSh25ys7ClEUSNxTeAQFBbFt2zb8/PyueTwnMZhUEhUSZ+PPEjYrjNGbRzP4wcFse2qbJIgKkbAKYex6ZheNyzRm8P8G0+fXPsSlxOX3sIQQd9GQIUMIDg4mJCSErl27Zi63mhsWi4W9e/fSrl07B44we3369GHDhg04O+f8H6dCFFmpVpUgSruaIIqzxvP4hqcZvv1jupZtx9b2S7JPEF28CEOfgjdeVb2GVq6F51/MVYIIoG5deO5ZCAqCihXhhefzMUEE4OQEL7wEK1ar9/fm66qq6Eps5i4+Tt4sbDGVLxt8wPKo9YQsCWfXxf3XHsdgryg6cwEuSwwlREFzv8Y9BUVSUhLBwcFYrVYMhryleaSSqBCY+PsGXvmzJ6laLM1iJzFpaP97WxIs7pl0WzpjNo9h5PqRVPatzMIeC6nqJz9sIRytIFxRK4ykkuj+IPHXPZZZQWQDi2oXeiD2MI+tG8yhK8f4pO7bvFJ9SPYVxOvXwhuvqeXtX30DBg+x9+EppGw2mDwRxn0GJUrCl19DvfrX7PJP9E46rx1ErDWOH5tNoHPZ8BuPkWKF0sXBSyqKhACJe4oCqSQqInRdZ+C0T3n2r5Z4WjyZ2exvwkr0p2Xch00AACAASURBVHlzx69aKAoGo8HIO83fYXW/1cQkxlB/Sn0W7F+Q38MSQgghRG5kLNOelp6ZIFpzZjMNlz5KTMolVreZw6s1nr4xQZSWBh+Ngif7q3XpFy2Fp4cW7gQRqGqgZ56DeQvV6mU9usKXn6spdnb1/YL5p8NSqntV4rF1g/l491fX9inKqCiKuiCrngkhRDYkSXSfSkhNoMf8Hkw/NZxmft04/MY2+obV4p134L334P3383uE4m5qUb4FEU9HULNETbrN68Ybq94gzZaW38MSQgghxJ1KT1ermKWlq6QFMOPwXMJX9aWsW2m2tV9GaKnGN74uJgb69YLvJ0P/AbB4KVSvfm/Hnt+CH4Qly6FjZxj/OQwZBHFXp5CVdi3FhvD59C7fmXd2fEKfTcNISksiLg7274djJwzYzPZEUXxiPr4RIYQoeEz5PQCRc0cvHaXzz53Zd2Efrps+Yd3/XsIIkJQCNhvd2+n89qMNYnWw6aqs1mYDG6DbVHPDzA11S5bb62kaoF1NKWqaugqjaWDI8mej/VYz2B/Pup/h6n3hEIGegax/Yj0vr3iZz/78jO1ntjO321x8XR27BKIQQgghHCw9HSLty9w7WdB1nfd3jWPUrvG09m/G/NDv8LJks1z7nt3wzGCVKBo7Hrp2u3GfuyUjnswaP+o66BrYQ0UVB14XA94tHh7wxQQIqasadnftpFZyKxcEgIvJhR+bfUVN76q8HTGGPVFRdDk5nWplixEbC6mpBnr3MOJ7+jyU9QcXp7s3ViGEuI9IkqggsdnUfPR0mwoeMm7T0tRVprR0Vp1cR4+NQwFY2nQ60dZmJO4/jYc79i9pnfSzMLQTcEZHfWNn3GiZdzPvZ3Wz7/Hrc0eZSSX7fzJus54ry91rjqNpqhTaZLDfGsFsUiXDRvtjWW9lBa9bcjI58W37b2kY0JCnlzxNg6kNWNxzMTVK5GeHSSGEEELclM0Gp89DcgpYzKSmpzL4z9eZdXQBAyp0Z3LjTzAbzDe+bsE8ePtN8PNT061q3cUFSmw2FXva9KtxndGgVhcz2WM0Q8at4dq4Nd2mGnGnWFUclxE3mu5SbNfvCahQEZ4dAp07wLffQaMmAGiaxlu1huEWH8Qr/z3P/EpdWdnmRwLc/Nm2Deb+auSZQTpa5FmVKHKyOHZsQghxH5KyjnvFZgOrFZKSIS4BLl2BczHqKtKxSDh4Qm1HT8GJKIg8C1Hn1T4XY9GvxDN+57eEr+lLgGsp/mm/hPDyLSlZzsLi5RaSbBZwNnMlxcJvKyyUqWgBZydwtqjNyaJKmS32zWxWyZmsm+km2/X7ZRzDyayO65zNuTLPmXUzq/n2BlTgkZyiPouYy3A2WgVMJ8/A8dNw+CQcPA6H7Z/HmQtqvyvxkJisgg+bLb9/qgXGE8FPsGHABhKtiTT6vhFLDha95R6FKEwuX77Mt99+m+PXtWvXjsuXL99yn5EjR7J69ercDk0IkRe6rmKaxCSwmElMT6bj2ieZdXQBo4JfY1qTcTcmiHQdPvsEXnsZ6tWDxcscnyDSdXVRMjlVbWk28HBTy8WX9YeKZdVW1h9Kl4CSflDcG3y81HLyvsWghI/aP7AkPBAIlctBUGl136+YShClWNXxU60qAeUojZvAb0vBrzj07wM//XjN0/6nO/D1A7M4mRhJk98f47/YI9StC9ZUOBdjv2Z+6pxqIi6EuOck7ilYZHUzR9F1deUkLQ2s6SohlGqFlFT1hZOW0VAv43IMWcpx7VO0Msp0r2O1WRn297tMPjibx8qG80PTL3E3uwHqgs3y5bBnL3h6QtwVaNgQHn64EBThZJQxZ06Zy+bvqsmoEk8WeyLKYr5anVQEp7ZFXomk88+diTgTwehWo3mjyRvZr4YihLilnK7yYbNBVBR4eakZEHl1/PhxOnTowN69e695PC0tDZPp/i0CltXN7g/3Vfx1P9F1OH8RLl5RF/as8XRYM4DN57cypfGnDKrU68bXWK1qyfdf50PP3vDhx+oCnqPYbCpO1VEX+DzcwM1FxVR3I35IS4NE+0XC+ETVBsFkVBXkjjhfXBy8+BysWwsvvgwvvgKaxuzZalZaYvHdtF3dD13XWdb6B3YuDKZNGyhXDpXAMpugnH/hbwAuxHVyG/cUKwbu7nk/f2GNewoSWd3sbrLZVOInPlFVA525AMej4NAJVf1yPApOn1NBwJV49cVr0FQVjbOq9rlacWOv5jEa7YmiG78cL6Vcpu3qfkw+OJu3ag1jfujkzAQRqJe2bw8vvgBdu8BLL0FoaCFIEMHVXkYmo0r+ZHxuWT8/gwapaeqzPhcDp86qSqSD9p/HqXMQfUkFI8kpKpFXiAV6BrLxyY10r9GdN9e8yROLniAlLSW/hyVEobZwIVSqpC7wBwbC4MGQkJC3Y7755pscOXKE4OBg6tevT7NmzejYsSPV7c1pO3fuTN26dalRowaTJ0/OfF1QUBDR0dEcP36catWq8dRTT1GjRg3atGlDUlISAAMGDGD+/PmZ+7/33nuEhIRQq1YtDhw4AMCFCxcICwujRo0aDB48mHLlyhEdHZ23NyVEUXfpClyMBWczMSmXab2yJ1subGdO82+yTxDFx8PAJ1SC6OXX4ONPHJMg0nUVn2ZUDPl4QfkACApQFUHOTncvkDSZwNMNAkqoyiT/EmA0qQRNigOqizw8YPI06Po4fPkFvPcu2GxUrAQ7IiDEpzZ/tF2Iu9mN0OXd2Z6whYAA+2udzCqmjDov1epC3MK8eVCxItSvDwEB8PTTkJjH/u8S9xQskiS6mYxkUFyimuZ0+hwcOaWSD8dPq6lR52JU8iEtTSV7rk9iZFS15LJx3+Erx3hoWUc2nvubGU2+4OOQNzFo2f/IXFygZElwKko9926WRHLKkkBKTrb//M7DiTP2ZN4p9fO8GKuSfanWmzftvg+5ml2Z03UOo0JHMWv3LNr82IaLSRfze1hCFEp//QXPPgvTp8OZM3DsGCQlwVNP5e24Y8aMoUKFCuzcuZPPPvuMiIgIvvzySw4ePAjAtGnT2L59O9u2bWPChAnExMTccIxDhw7x3HPPsW/fPooVK8aCBQuyPZefnx8REREMHTqUsWPHAvDBBx/QsmVL9u3bR7du3Th58mTe3pAQRV3GxSwnM2eTLxC6ohu7Lx3g1xZT6FG+4437nz8HPbvBlj/g03Hwwkt5T9xkJIdSUlXcFFgSKpaB4j7504vHaAAvdzUlLai0mrZmtVfh5yUuM5nUZzb4aZg1E15+npAaqSQnww+zIO7wA4z1WoR7SgA/+PZnc/SfV1/rZIL4JHWxtxDFhkI4yqZNqihh1iwV9xw9Cpcvw9CheTuuxD0Fi0NqtzRNCwe+BIzAVF3Xx1z3/ADgM+C0/aGvdV2f6ohz55muqzlbGQ32klJUxUmq9dr9MhrzOZnvSZnO5nNb6bRuIBoaq8Pm0LzUQ3f9nIVK5uoa1yXVMqavJSarBGAGg6aunLk4q9uMKq/7tCRL0zRGPDyCij4VGfDbABp934hlvZdRwadCfg9NiELlq6/gnXegeXN138cHJk+GsmVVGXbp0o45T4MGDShfvnzm/QkTJrBw4UIATp06xaFDh/D1vXZlw/LlyxMcHAxA3bp1OX78eLbH7tKlS+Y+v/76KwCbN2/OPH54eDje3t6OeSNCFEWJyWqpdYuZ00nnaLGyO1GJ51jaeiat/JveuH/kKejdA2KiYep0CG2Zt/Prump7kJ6u4pziJdRtQYlxNHsM5uykqpouxqqqKw2VzMrNOA0GePtd8PWBT0ZjvnyZ/l9NYe9RV44dB3eXkqwJm0uPiB60W92fJa1m0tK/iX0sFnV+i1mNRwiRacIEGDkSmqje8Pj6wpQpasrm+fNQooRjziNxT/7Kc5JI0zQj8A0QBkQC/2iatljX9f3X7fqLruvD8no+h0hLh9g49aV9/RQkg30p93uUDMrOvONL6LfpRcq5B7Cs1Q9U8Axy/EkyGhSmp6mrSun2vkm6jlqxLMvVE4N9FTKDfc642d74uqAEFzmhaWC0/4yz9oXUdbUMbVLy1ZXZNE0th+rqYk8c2Vf0uI/0qtWLMl5l6PRzJx76/iF+6/kbjcs0zu9hCVFonDgBQ4Zc+5ibGwQFQWSk45JEbm5XpxmvX7+e1atXs2XLFlxdXQkNDSU5OfmG1zhlKS01Go2ZZdc3289oNJKWJk1bhXColFS1GInJyNnUaFqu7MHZpAusDPuJxiWyacV14rhKEMXHw+xfIPjBvJ0/PV1NoXItgMmh7JhNUNIXfDwhJhYux6mEjzkXPYs0DZ55Dop5wztvYhrSn+BpPxAc7GrfoTjris+l1coetF/Tn/+1nEHr0s3U65zMqprIbFJ9moQQAJw8CbWv65vv6amm20dFOS5JJHFP/nJEJVED4LCu60cBNE37GegEXJ8kKjiSU1TJr9m+7Lq5YDTD0nWdL/ZP4bVtH9KoeF0Wt5yOr/N1WUyrFS5fgosX4dIluHQRrlzJssWq28QESEhUt4kJahWNlBRISVZTsFIc0KfGYk+cWJzUfDdXV3B2VreubmpeeNbNq5jqbubtrb6wvb3Bx1c1nc5vmmZfzS3LYzZdzddPyPJLyGxSSSM356uNsgtysAU0LduUvwb9Rbuf2tFyZkt+7PIj3ap3y+9hCVEo1K8Py5apxQIynDqlpp1VrZr743p4eBAXF5ftc7GxsXh7e+Pq6sqBAwf466+/cn+im2jSpAlz585l+PDhrFy5kkuXLjn8HEIUeunpahVbNC6kXabVip6cTjzL8tY/Zp8gOnYUendXcdpPv0CNmrk/t66rCnmjUfX/8XAr8PHKNcxmKOUH3p4qZk9IUguV5KahdM/eKjZ9+QUYPAC+n6niVqCEix9rH1GJokfXDmBxy+mElW5+NTEVdQHKmdTFQiEE9eqpuKdRo6uPHTumEkSVKuX+uBL3FCyOyI4EAKey3I8EGmazX1dN05oDB4GXdV0/lc0+946h4CSHANIT4hm96h1W71zAF051eDahBeZPvoALFyD6AkRHqz9fib35QTRNpXI9PNWlbDc3cHNXzYqcXVQCx8np6ma22BNl9qXujcar07MypmtlTMfLulmtkJpiv01VCaekJNWxLClRJaTOnoFDB9Vya3Fx6nU34+UFvn7g56eucpUsCSVKQqlS6s/+paGU/71vuGTQwGC6WnGUMVUtLl5VooH6vFyd1UogLk53bzWQPKrkW4ktg7bQ6edOdJ/XnS/Dv+T5hs/n97CEuO+9/LJaUdLNDbp3hyNH4M034dVX1a/j3PL19aVJkybUrFkTFxcXSpYsmflceHg4kyZNolq1alSpUoWHHnL8dOT33nuPXr16MWvWLBo1akSpUqXwcMSybUIUFbquGiBb07ioJRC2shfH4k+yrPUPNC3Z4Mb9Dx+CPj1VZfeceVD1zlcZuuG81nTVW9PHC3y97u+VupwsUKYUXElQySJrau6q/Tt2VrHoqy/BU0+qaXzOKlFU3NmXtW3m0nplTzqtHciKsNk0K9lQfW42HSLPQ5C/Y1eVE+I+9eqr0Lix+mdl165w6BAMH65iH7c8FN1J3FOwaHoem7JpmtYNCNd1fbD9fj+gYdapZZqm+QLxuq6naJr2NNBD1/VsJ1hrmjYEGAJQtmzZuidOnMjT+LIVn6iu7DjfwwqW5CQ19+DkSVVKHHkKok7D6dPokafQsstWuruDX3GVOPHzu7p5+9g3ezWOVzH1rxE3t4K57LuuqyRS7GXV2ezyJXV76ZJKfsVEq9voCyoRdu7sjS3yNU19DqVLQ0CgavhRpiyULacmwZbKpy9vm01NV8yYsmjQVKWRe0ZSrmBVGiVZk+j9a28WHVjE8CbDGd1qNFoBGp8QBUVOloI9cgT+7/9g40aV1x46FPr0KVD/6+dYSkoKRqMRk8nEli1bGDp0KDt37szzcXOy/KrIP/Xq1dO3bduW38O4f2UsdX8plstaEq1X9Wbvpf/4Xyt7lcr1Dv6nppgZDDD7Z6hUOXfntdlU9ZCrM5T0u7dx7r2Qlq5WrL10RV3kzE0LgAXz4fWXoUkzmPJ9ZqII4EJyDM2Xd+V04lnWPTKXur72OTUpqWqaXplS9/cvdiFuISdxz8GD8PHHsHkz+PurBTx6ZbNA4/3kbsU9BUlOYjBH/Kv6NFAmy/1ArjaoBkDX9aztx6cCn97sYLquTwYmgwpSHDC+eyctTTWoOHoEjh9TZcPHjqo/nz177b4uLhAQiLVUSX73MvO3BZoHd+aRkJ72CppSKklUGGiafQqaq6oKuhNxcWplj7Nn4UwUnI60J9WiYN8eWLlcVTJlMJtVwuiBClC+PJR/ACpUVHWPxe5i4zGDvaF5RrWRTVd9jeITrz7v7gLurqrSyJS/zbBdzC7Mf3w+w5YN45M/PiEqLorvO36P2Wi+/YuFENmqUAGmTcvvUTjWyZMn6d69OzabDYvFwpQpU/J7SELcP2Lj4WIsiaZ02q8awO5L/7KoxdTsE0THjqoKIpMRZs9Vv1Byw5qmLliV8FEVRIUxmWEyqilo7q5w5sLVVdpy8l67dgPdBm+8Ck8PhsnTMivVizv7sirsJ5r+3oXwVX3ZGL6AasUqqXMkJMGFi2oluML42QqRA5Urw4wZ+T0Kx5K451qOSBL9A1TSNK08KjnUE+iddQdN0/x1XT9jv9sR+NcB580/aWkq8XPgABw8AIcPw5HD6os+a+LCxweCykPjplAuSFW/lCsHZcqBry9nks7TdnU/9sdeYmaTr3nkgc759pYKnIw+RhUqZv98erpad/HUCZWYO3lCff5Hj8KG9Wo6XAa/4ipZVLGSKt+uVg0qV81bTeTNXD9FzWZTCaMrCep+RgPEjOlp+VD5ZTQY+bb9twR4BjBi3QjOJZxjQfcFuFsKSVJSCJFnlSpVYseOHfk9DCHuP4nJcDYaqxkeX/8Mf0VHMO/hSbQLbHXjvlFR0LcX2NJhTi4TRBm9h8wmCCylYovCzt0VygfAmWi1XL2TKWfxVLfuKo5883V45UWY8E3mlLxAt9KsavMTzX7vStiqXmxuu5Ag9zKqKismVvUm8pR4SYjCRuKea+U5SaTrepqmacOAFYARmKbr+j5N00YB23RdXwy8oGlaRyANuAgMyOt575nLl2D/fti/T23/2ZNCGUkIg0ElfypUgpatVCLigQdUJcstKlgOXzlGm1V9OJ8czZKWM2gT8PBN9xXZMBpVG/3AQGjU5Nrn0tNV1dGRw2qi7OGD6nbhArVaSIay5VTCqEYtqFlTNYgsURKHMhjAYg9cdF0ljS5dUcu7aqiScA93dWu+d1VGmqbxbvN38Xf3Z8iSIbT+oTVLey/F19X39i8WQgghxI2sVog8h240MPiv11h2ei3fNRpDl3Ltbtz3wgXo21NVTs+Zq+LHnEq3QapVNXcu7qMWYykqTCYILKliqvMXc74QTY9eEBsLoz9Sa3h/8FFmDFbJ8wFWtfmJh5d3o/XKnmwK/xV/15KqoigqWt1KI2shRCHmkCYuuq4vA5Zd99jILH9+C3jLEee6q2JiYO9u2L0b9uyGfXtVsiFDiZJquZomTaFKNfXnihXByTlHp9kRs5fw1X1J19NZ2+YXGhTP4/Km4lpGo+pXVKYshGZpfaXratrav/+qZN+/+9W2YvnVfYqXgFq1oU4w1KkDtYNV3ydH0DQ1towGknrG6mnR6r7ZpK5Oubuo4OMeJIwGhQzCx8WHngt60nxGc1b2XUmAZ8BdP68QQghRqNhscPoC6DrDd3/CD0fmMyr4NYZU7nvjvrGXoX9vtcjHDz/lbhUzaxqk6/fnymWOomlqap2rs+o1mmJVK6Dd6Wcx5BnVD3PKd6rq/IWXMp+q5V2NZa1/oPXKXrRf8wQbwufjYXa3/5zPQ7nSueuJJIQQ94Gi26Y/KQl274Cd9m33LpVAyPBABahbD/o9AdVrqM3PL8+n3XTubzqsGUAxiycrw36iilcu556LnNM0CCyjtrA2Vx+Pi1PJon17Ye8e9Xdh3RqVxAE1RfDBuhBi36pUdUyTbE1TiSEzV6uMYi6rzWBQQZ+n212flvZYtcdY3mc5HX/uSNPpTVnZdyWVfPOwhqUQQghRlGQ0qk5OYdyR6Xy2bxLPVXmCd2u/eOO+iYnwZH/Vv3LqDKhXP+fnSrWqOKRMCaloAfUZBJVWyZuklJwtGvLmO+oi8RdjVZzf+2pS76HidZn38CQeXfskj69/mv+1moHZbFa9kM5egICSRTM5J4Qo9IpekujoUXjsMdi37+qy7IFlIPhB6D8AateG6jXztnbxTSw/vY4u656irFsAq9rMoYzbHTZxFneXhwc0aKi2DHFxqqps1y7YtQP+2AyLflXPubqqvy/1G0D9hvBgiHosL66vMrLZ4Eo8xMap59xcwMs+Le0uLGXbonwL1j2xjraz29J0elNW9F1BcKlgh59HCCGEKHRi4+HSFX6KWspr2z7k8XId+LLBqBtXD01Lg+eHwq6d8O130CybRta3klGB7OYCpYvLkuxZmUxq9bFzMXA5TiWK7uQCm8EAYz5T7SVGvK36iYZfnR7YNrAlkx4azVNb3uCZLW8ytfFYNItZ9Zu8GAu+xe7imxJCiPxR9L5d/P3VlYLBz0C9empaUfHid/20844voc+m56lZrAorwmZT3NlBvV8yql10Xa2spWdsWZ7LoGmoJ+w3We7mmGb/j65fPU7WWEjPZl/NPgaDpm4zgqeCeBXGw0P1Osrod5QxVS1iO2zfBtv+gQnj1eMmkyoVf6gRNGoM9RrkvSm2wQBOWXoZJSZdXTHN3UX1MXJzcWipc73S9dj05CbazGpD6IxQfu/zO43KNHLY8YUQeTN+/HiGDBmCazZJ6RkzZrBt2za+/vrrfBiZEEVYUgqci2bT5Qie/PM1Hi75ELOafYnRcN33s67DyHdg7Rr4aDQ80jZn58lY3t7HS61gVhBjp/xmMKjVz5wsqrLLbLqzPk1mM3w9SfWIeul5mFNKXQC0G1y5NycTTvPh7i8p5x7IyDovg8WiVjtzcVYX8IQQDidxT/4pekkiFxf47X9q7rKz5Z6c8vtDcxiyZTiNi9djSasZeFnuoEopI9lj09VSnTb92kRM1iSMwV6FYrJ/GWZUpJgMV5doz0jMZE3QXLNBZiIHrp5IyzKezLHZ/5ORiLp+s2W5tdlUxVb6dbdpNkhPu3pcDdA10LK8T4Mh+6RSfsg6Va2jfRW6K1cgYhv8sxW2/g3TpsJ3E9XPoXYdlWBq2lRNVXPKQzm4pqkmiaA+r6QUtZoHqERRMQ+HVRhV9avK5oGbafVDK8JmhbG412Jalm95+xcKUdgdOw0pKbff7045OanVeXJg/Pjx9O3bN9tgSQiRD9LS4fQ5Diec5LENQwhyD+TXFlNwMmbznf/t1zBnNjw7DPr0y9l5MhpUl/RVTaolQXRzGX2KzCY1/Uw33tkFNRcXmDwNunSEpwbCoiVqcRS7D4Jf42RCFO/tHEcZ19I8WamHivdOn1O/y6WqSxQ2EvcUafIb7S77cv9UXvrnfcIDQlkQOgVXk4t6IjOhYk8A2bIkS0AlSkxGe88a+2YyZUkE2beMJND9ymazJ5JsWRJI6aok25oGqWnqz2lpNybIDPYEUkYy6V4HTZ6eqjF2RnPsxERVZfTXn7DlT5j0DXwzQQUeDR+CJs1UaXnlKrkf6/V9jJJT4LQ9YeTuejVhlIe/E2W9yrJxwEbCZoXRbnY7FnRfQPvK7XN9PCEKhZQUx/b+SL514JWQkED37t2JjIwkPT2dxx9/nKioKFq0aIGfnx/r1q1j+vTpjB49mmLFilGnTh2c8pKMFkLkjK7DmQtcTIyh/YaBACxtNRMfp2wWu1i4AMZ+Ap27wGvDc3aeNHtcFFBCll7PCQ83KOsPp86qn9WdrHzm6wvfz4AunWDwEzBvkaosR60KO6Xxp0QlnmPIluGUcw+kpX8TVd0VdUFNdZPknShMJO4p0iRJdBeN2fM1b0WM4bEyj/BzowlY0oyQlnp1B5NRlatazKo01mxSjxntVz2KwpdNRpLrdn8Tdf1qoJSerhJIKanqylqqVSWRrjmudm0V1b3g6qqSQBk9BuLi4K8tsHkjbN4E60epx/39oXkohLaAxk1z3//q+oRRxpQ0TVOBpJebKoPOxd8jfw9/NgzYQPjscDr/0pnZXWbTvUb33I1TCJFjy5cvp3Tp0ixduhSA2NhYpk+fzrp16/Dz8+PMmTO89957bN++HS8vL1q0aMGDD8pKmULcMxdjSb1ymS5/Psvx+EjWtPmZip7lb9zvzz9g+GvQuAl8MjZn38nWNHURsWwpcHVx3NiLCldnKGdPFKWmqZXPbqdiJdUvakBfeOFZmDI9s0rIbDAzP/Q7Gi3rRLf1Q9jafgkVPYIgIUktOuLnoNVwhSiCJO4pWO7jEpQCJiOJkWJFT0ph5LZPeCtiDL3LdWJui8lYfHxUmXCZUvBAIFQuBxXLqqscpfxU+bC7q8rYmnOwfGdRkZEQcXFSn5O3p/rcyvqrz7FyOfW5likFpXxVk2ezSf1MklMh2apuU1LVY9f3a7obPDzUKmoffARrNsAfW2H0p1DnQVi2BIYOgZBa0OtxtfzqkSO5P1fGlDRne7LxShycPAuHT6o58ympOX7Pvq6+rOm/hocCH6LXgl7M3Dkz9+MTQuRIrVq1WLVqFcOHD2fTpk14eXld8/zff/9NaGgoxYsXx2Kx0KNHj3waqRBFUGIy+vkYhkSMYMO5vxioj+XAkgasXw/JyVn2O3IYnnkKHngAJk5RFwbvVKr94lc5f0kQ5YWzk1qu3mhQVT93omkzGPV/sH4dfPj+NU95Wjz4X6vpGDQDj659ksvWK+pCb/RlSEzO/nhCiNuSuKdgkUqi3Mg6PSqDpoGTBd3dzBvbPmLsvq8YWGcAjkdxqgAAIABJREFUkx+dgtEoH/NdZzCAxWDv3ZMlmNJ19XOyWlXAlZxi31KvbbqdMX3vbk5bK10aevZWm9UKOyJg/VrVxPLjD9UWVB5atYawR9SyuLnpM2RQfxcB9Xf1YizExKrPxtsTPFzveO68p5MnK/quoNPPnXjytydJTU/lqbpP5XxMQogcqVy5MhERESxbtox3332XVq1a5feQhBCgKpejzjPu0DRmHp1Pe+srPN+kC66usGMHTJ8OgwaBJfESDB6gEkPf/5CzquHUNPVdXtb/ak9CkXsWs/osT55RiSKnO/hMe/dVKyJ/P1lVF/V7IvOpBzzKsSB0Mq1X9qLnhmdZ0momJpMRos5DUIBDFxURoqiQuKdgkUqi27Hpqtw3owolOVUlHZydobg3BJbMrAyylSvF8xEfMHb7BIbVH8aUTt9Lgii/aZr6snZxVtVFJX3VFaXK5aBCGQgspVYJcXFWP+sUe8VRcqq9zNt2+3PkhtkMDRrCG2/B8tWwaYuqOCpXDmbNhJ7doMGD8MarsGolJCfl7jwGg0oYOZnVezkXDYdPqUaLCUl3VF3kanblf73+R3jFcIYsGcI3W7/J3ViEEHcsKioKV1dX+vbty+uvv05ERAQeHh7ExcUB0LBhQzZs2EBMTAxWq5V58+bl84iFKAJ0Hc5EsyJyHcN3jKFWUgfm9n6Z6tUgqBx07gTFvGH3disMGwpRUfDdVAjIQbPW1DR10UoSRI5lNqnP1GS884qit96Blq1g1HtqkZIsHi7ViIkPfcyKqA28vu0jddz0dDgbfW+q1YUoZCTuKVgkg5FVRiPptPSrK2xp2tXlLZ2d1Bd2Nv2CbLqNoUuGMjliMq81eo1Pwz5FkyljBVdmPx+TWiHMx+tq1VGqPUmUmKw2W5am2Ubj1YojRwosA/0HqC0+Hjash1XLYfnvMO8X1e8otCW0bQ8tWoKbW87fr8ne60rXVYIoLlG9l2Ke4Ol2tfooG84mZxb2WMj/s3fnYVFWXwDHv3cGhk1AQBDEfWuxzMrc970y7WelZqVmZlpq5pb7vq+lpuWSaWZuLZqZK6SW5lZZmuauue+iCAzMvL8/LuTCMECCw3I+z8Mj8t5552LE3Dn3nnNaLGtB5x86E2eLo3vl7vfyHQshnPjzzz/p1asXJpMJd3d3ZsyYwdatW2nUqBEFChQgMjKSIUOGULlyZfLmzUu5cuVcPWUhcr7L1zh4Zi8tt75LKa8HeM8yCW/vW+sBpeChh8B3/BD4+SeYMBmeLJ/2+ycFiAqFSoAoMyQFik6cSVuNIrMZJk+Bps/C22/Bd6sgNOzfy+1Lt2LP1b/5YN9syuQtTftSL8P1aLh2QzcSEUKkmax7shZlZOFod/ny5Y2dO3dm/I1v3IST5/TpiqR27ElpRxZ3HTTw9rxVTDqVgIDNbqPDdx349PdP6VetHyPqjJAAUU7xb60pK8TE6uBKrPXONLXMLDJutcK2X2DNDzpgdOmibiFZqzY80xjq1k9/wOh2dnti3QNDd2EL8NU1n1LojhZvi6fV161Y9tcyRtUZRd/qff/7cwuRhe3bt4+HHnro1heyQCvYnCDZvyuglNplGEY63kmLzJZp66/sLCaWqEN/U2nDS5yPvcjyJ77n9/WFebvTnUuAvwfO54EF/aBDR+g7IO33j4vX64nCofq0scg88fG6bqPNnrZi1gf+hmZNdHfaL5fq39+JEuwJNN7QhoizW9jUaBmVgh7X66pi4U4334TIamTdk/OlZw2We08SGYmpRV4eEOCt//SwpLsGjM1uo92KdszfPZ/BNQczuOZgCRDlJLefOMrjDcHowEpcvK5tdOOmDh7ZE4OtZgVmt4zrqGax3OqYNnQE7NwBP3wPq1fBmtU67bFOPWj8nD5h5JnO4pYmky52bRg6IHX6gp57Xj+dnnfXAsfd7M6XL3yJxWyhX0Q/bIaNATXSsQgWIruShY0QuZfNhv3UOV7b3pMDUUdYW38hVUIL82ckbP4JqlbRy8ez3/5MqS8GYq1WD0vvdGyiWBMSA0RhaWvVLu6Nu7s+rZXWrmelH9Cnwjp1gKGDYNTYfy+5mdxYWGMaT61szAuRHdj13A+EmgPg1HkoWiDFTTchsjxZ9+RqufOVyNtTF5bzcL+nX94J9gTafNuGhX8uZFitYQysOTADJymyLJNJBxW9PHQhaMMAazzEJAaNbsbeqmWUkSeNzGaoWEl/DBqq8+NXfqeDRqtW6hNFDZ+Gpv/TrXbTWJwauC0Yhp77lWtw+aruqBLgDz6e//6/4mZyY/7z8zErMwMjB2Kz2xhca/C9f39CCCFEVmMYcO4SQ3+fxIqT6/mwwlDqhFUF4OWW8M23+sBvUPxpWi7oRHyh4nhMn5r2Tcf4xCLVhUIlQHQ/WRIDRcdP6/8Gqf3bN3oG3u4C06fCI4/qwtaJAj0C+Lr2LCqvakLzHzuxoeEi3K1WuHBF18IUQohsJne+GiW9yb8HCfYEXvvmNRbtWcTouqPpU61PBk1OZDuJne3wsOgc9KRTajGxiUGjGJ2aBjpglBE1jUymWwGjwUP1CnXFt/DDKvh6GeQL1qeLnm8GZR9L3/MlFbs2DJ1md+qcnnOAH/j7grsbZpOZuU3nYjaZGbJxCHbDzpBaQ+QUnRBCiJwl6gbf7/uOYXun0LZEc7o82O7fS/7+0LYNXD0Xi0+7N3EzW1GfzgbfNNajiU9scy81iFzj30DRGV1aILWuZN17wt4/YchAeLgMlHv830uPBT7MnCoTaLW5Mz13DufDp4bq7rJ5vHUZCyGEyEZyZ5DoHiXYE3j161dZvHcx4+qNo1fVXq6ekshKlNIpXJ4WHVix23Udo5hYXdAw1qrHmRKLSd/rUWQ3N6haTX8MGwGREbD8G1i4AD77VLdubfaCDhiFFUjf93H76aKLV/WHrw8E+GH28mBOkzmYMDFs0zDshp1htYdJoEjkGIZhyM9zBsrKNRCFcCjOytFDv/Hq9u6UCyzD9EojHf5OyDt5IPy1W3cyK1EibfdOsOlU9SJhUrvGlTw9dKDoxBm97jE7WZOZzfDBVGj8tO5e9/1q8M/77+WXiz/Pjku7mfzXLJ4KeoxXizwPp89DsYKpB6CEyAJk3ZNzpXcNJomy6SQBIpFuJpNOcQzKq9McSxaG8BAdbElIDCDFWhO76t3jmygPT30kesYs2PGbzpvPmxfGjYGqFeHVl+HbryE2Jv3fg6dFp2hG39SLqaOnMEVFM6vxJ7R/vD0jNo9gQMQAeSMocgRPT08uXbokP88ZxDAMLl26hKenp6unIkTa2O3E/vMPL/78DqD4qtZMvNwcnAj58gtY/KVORWrQKG33TmqaUihUBymEa3l76nVZfMKtcgEpyRsA02bA+XPQ471k67axT/ajZv5KvLm1N79H7df3O3fx3td3QmQyWffkXP9lDZY7u5v9Rwn2BF75+hWW7F0iASKRMZJSuqJjIOqGTlODjO+advwYfPMVfLUMTv4Dvn7QpCm81CL96WhJ87bZdWDLpLAH+NJxy/vM+n0O/av3Z3jt4bITIbK1+Ph4Tp48SWxsrKunkmN4enpSsGBB3O/q3CTdzbKerLb+conzl+mw5h1mHVnEijpzea5Q/eRjfv8NWrwAFSvD3Plpq0Nkt+s6hgVDdSqSyDquXoczF/SmWGprmM8+1UWs+w7Qnexucz7mIk+sbISn2YNdz67C3+4JBUJ0QxAhsihZ9+Rs6V2DSZAojSRAJO6L+ARdwygqWhfANozEtLQM6phmt8O2rbBksS54HRcHDzwALVrB/5rpHbL/ck9rAnbDzlu7BzP77y8YWH0AQyX1TAiRBhIkynqy0vrLJW7GMPfHqbTb8T79Hu3CyCfeTz7m0iV4rpHuaLpiFQSk4fXTnrgxVCBY1/gTWYth3Eqt93R3HigyDHinI6xdDYuWQfmn7rj807nt1FrzEv8r3Igl1aajbHbdLUpqTwkh0iqxcQKeHrrubSZIaQ0m6WZpkJRiJgEikenc3fTCsVAolCwEBfPrncb4BJ2SFmdN/Si0MyYTVK4Kk6fA9t9g5BidojZsMFQsD+911UWw0xM8TkxFM3l68Em5YbxRrDnDN49g6LoBcrxaCCFE9mKzsXvvRt7+dRB1w6oxrFzP5GPsdujeFS5dhhkz0xYgSjo5HBwAfnKiJEtSCvLlBX8f/d/K2RpGKRgzHgoWhC6ddNDwNtXyV2D0E31Ydvx7ph2cp794VtLOhBDpEBWtuyTey3u//0iCRKmw2W20+bYNi/cuZmy9sRIgEveP2awDRAVCoFRhHTjy971Vxygu/t5+afj56Rauy7+H79dA85awYR20fBHq14a5syHqWtrvpxQmTw9mVhvP68VfYujWUQz9toc+vu2CX25CCCFEuhgG1/85zkubOhFoycvC6tMwmxykkM34CDZthEFDdDv0NNyXWKveCQ7Km3Gp5CLjKQWh+cDLE6wJzsf6+cFHn8DlKzpoeNdap0eZt3iuYH167BzO9qg/dWmBK1GZOHkhRI4RZ9WBZWfF9DORBImcsNlttF3eloV/LmRUnVH0rtrb1VMSuZXJpFuohubTAaPCYZA3z10Bo3vYnXq4DAwfCb/sgvGTdPveYUOg4pPwfg/Y/Xs6pmpmdrWJtC3RnCF/TGb4hkFw6B+4dFXXMBJCCJFhlFKNlFJ/K6UOKaX6OLjeVil1QSn1e+JHe1fMMzswom7QMfI9Dkef4MsaHxHilS/5oF+2wqTx8FxTvdGSFtZ4vemTP0gCRNmByaQLWbuZ9UluZ8o8ooOFmzbCp7PvvI0y8Vm1SRTwyk/zjW9zhWg4f1m/+RNCiJTY7XD6gn69yIhyI/+BBIlSYLPbaLeiHQv+WMCI2iPoW72vq6ckhKaU7sSRP59OSSsUqo+uJySmpFnj//txZm9veLE5fPMdrFwNzzeD71bA8431x9fLdB2ju9jtsPcvWL4CVq+Bc2dNzK4yntYlXmTQng8YvW+6Pi55+B84fwni4+/xH0EIIYRSygx8BDwNPAy8rJR62MHQxYZhlEv8mO3guoiPZ+6WGSw8sYKhj/WgRmil5GMuXoR3O0ORorp7aFoCPnHxYLHoOkQmWXZnG25uOuXfbuhudM60ehUaNIRxo2HPn3dcCvQIYEnNGZyOOUebLT0wlIIzknYmhEiBYSQGk+PA4uayacirlQN2w86b373J/N3zGVprKP1r9Hf1lIRwLOmEUVg+KFlYL2h8vPUR6dg4vQP2XxciZR6B0eP06aLBw+D6dejRDapWgPFj4fRpQAeIFi2GLVugQAHw8oIvFsLvv5n5tMpEWhV7nn6/j2P8oTm65tKVKDh8UncQkd00IYS4FxWAQ4ZhHDEMwwosApq6eE7Zj2Gw96+f6bxrMHXDqtH30c7Jx9hsum7ftavw0ceQJw11hRIS9C5wwZC0dT4TWYuHRZ8osiY4T5tXCsZMgKAg6PoOREffcblC8ONMKD+A706uY8rheXp9djkd6fxCiNzjerR+r+Rhcek0JEh0F7thp+PKjsz9fS6DagxiUM1Brp6SEGljMunj7OEh+oRRWIj+BRMXr08YpbYTlhI/P2jbDtb/CJ9/CY8/CTOmQY3K0LkTR7/aSUy0QbvX4anyULMGtHsd1q2HeKuZedU+oEXRJvTeNZLJ+2brOXm462JsR0/BqXMQEye7akIIkX7hwD+3/f1k4tfu9oJS6g+l1DKlVKH7M7Xs4+b5szSP6ICvex4WVJ/iuA7R9Gnw0ya9afKQo8Nad7HZdUp4wfzgLh2tsq083hASqNdSztYpAQG6KcixozA0+XuHLg+2o0mhBvTeNYrfbuzXp6tlo0wIcTtrvD5paEmlu+J9IEGi2xiGQedVnZn16yz6VuvLkFpDXD0lIf4bsxn88+jaRSUK6QUO6t4KXisF1arDrE9h0xZo1x42baRE7+dpubgx5u++Bqte8AQG6oYfx46Bm8mNBdWn8GKRZ+m+cyhT932q7+Xhrj+iY+D4aTh5Fm7GSrBICCEy1ndAUcMwygLrgHmOBimlOiildiqldl64cOG+TtCl4qx0XduNfVGH+KL6VEK9QpKP2bEdPpgITZ+Hlq1Sv6dh6MV+WLAugCyyt0B/vaaKSyVVvlIVeLsLLF0MK1fccUkpxadVJhLsGUiLze9ww3ZT1xyRxh5CCEisQ3Rev0dyUbHq27l+BlmEYRh0W92NGTtn0LNyT0bWGYmS4oIiJ3B30wuc4gWhaAHdXSXepgNG/zUdrWAh6DcQtu7gz+YjMd24ro/h16isTxldu0pMjC7DADpQtLDGNP5XuBFdtw9ixv75+oJSOlru4a5PEx0/DSfO6MCRBIuEECI1p4DbTwYVTPzavwzDuGQYRlIxudnAk45uZBjGTMMwyhuGUT44ODhTJpvl2O0s2jKbOUeW0O/RLtQrUD35mGtXoVtnKFQIho9OfXc3qZNZUF7w88mceYv7SyldB9LDknrHs3ffg8efgH7vw8l/7rgU5BnAF9WncijqGJ1/G6rTzqTbmRAC4OJV/drhwjpEt5MgETpA1HNtT6Zsn0K3it0YV3+cBIhEzqMUeHro7iqlCuu0tDvS0f7DbpaPD3k7t2HGSz8SPXUelCwF48Zgr1SB8isHUtR0/N+h7iZ3FtWYznMF6/P2tn7MPrDwzrlZ3MHTondfT5zVASMJFgkhhDM7gFJKqWJKKQvQErjjCINSKuy2vzYB9t3H+WVpx4/toePWPlQOfpIh5bonH2AY0Kc3nD8PH36kO3+mxhoPvj4QHODydAGRgcyJHc8wnHdqdXeHD6fpz7t3S5bqXzO0MgPKdmXe4aV8cep7nXYWm7whiBAiF7lxU3eB9sg6qcm5PkhkGAb9NvRj0i+T6PxUZyY1nCQBIpHzmUx6EVs4TJ8wCg64tfuZzu5ohQpBlSompu6ty6LGi/j2zTXsL/o05fZ8jqludXinI/yxGwCL2cLSWh/TKLwWHba+z7xDS++8mVL65JOnuz7l9M8ZOHZa//KUYJEQQtzBMIwEoDOwBh38WWIYxl6l1DClVJPEYV2VUnuVUruBrkBb18w2a0mIjuaVH9phx+CL6lNxMznYvV20EFavgh694bFyqd/UmqCDBGH5JECUE1ncoUCIDhLZnaxJChWGIcNhxzaY9XGyy4Mee49qIRXotL0fh2+e0DVIJO1MiNwpPkE383F3y1KvG8rIwm+8ypcvb+zcuTNTn2Nw5GCGbRpGhyc68HHjjyVAJHIvw9A1ga5cgxsx+mvubmnOi42OhqPHwMMDihcD84Uz8NmnsHCB7oxWuQq81Qlq1CLGFkuTiHZsOPMTn1f/kFeKN0t5Tgk2fcrJw6KDWT5eWeqXqBDi3iildhmGUd7V8xC33I/1l0vZ7Qxb0ZvBuyeyoPoUx69BBw9Ak2fgyadg/hept6+32XSh6qIFXN6VRmSyS1fhwmX93zml9Yhh6E2y9Wvh25XwcJk7Lp+4cYpy3zWgpG9Rfq69GPfQ/DpFUQiRexhGYk3WuJRPEcXGQUiQLh2SCVJag2XISSKlVCOl1N9KqUNKqT4OrnsopRYnXt+mlCqaEc97r0ZsGsGwTcN4vdzrzGg8QwJEIndTSgdgCobqYtfBAXqnLI21i3x84JEyUKpkYqff0DDo0x9+3g59B8CRw9D2NXimAV6r1rC8xkxqhVam9U/dWHx0heObJp0s8nDXrYRPntUniyQNTQghxH+0Zc8ahv3xAa8Wb+Y4QBQXC13e1i9skz5IPUBkGPoUUYFgCRDlBoH+uuuZ1Ukha6Vg5BgICNQ1G+Ni77hcOE84MyuPZcel3Qz7e7p0OxMiN7p8Tb+nySJ1iG53z0EipZQZ+Ah4GngYeFkpdXdv0DeAK4ZhlAQmA2Pv9Xnv1bifxzEwciCvlX2NWc/NwqRyfeadELe4u+kdrRIFdfvepNpFcdb0H4n29YUOHWHTVhg3EeLj4d3OeDdqxA/nG1Er75O8srkL3xz/IeV7/Bsssuhg0YkzUrNICCFEul27co5X1nagsE84H1Uc6XjQ2NHw934YPwlC8ju/YVKqdr4AncYtcj6lIDRY74glOClkHRAA4ybAgb9hfPK3Pi8WbUzbEs0ZtWcaP1/apdPOZE0jRO4QG6eDw1mg3b0jGREZqQAcMgzjiGEYVmAR0PSuMU251XJ1GVBXufDYzuStk3l//fu0fKQlc5vOxWwyu2oqQmRtJpPeLSscBsXCIcDvVmc0Z4UbHbFY4KUWsDYCPp4F/nnxGDSIdSOPMWl3GK+v78R3/6xzfo9/axZZEmsWndUBo5uxsrASIpP8eOxHrsVec/U0hLh3NjvvrOjEPzfP8EX1qfhZHBSi3rQR5s6B1m2hdt3U72mNB19vyCepQrmKm1kXsk6wO988q1kbXmsDc2bBzz8lu/xhhaEU8SnIq790JyrqonQ7EyI3sNnh1Hld0iO1k6oukhGzCgdu7/F4MvFrDsckFlm8BgRlwHOn2+9nf6f72u688NALzH9+vgSIhEgrD4vOiS1ZCELz6V9qsXHpLnSNyQQNn9Y5+p9/ialEKbp+fZKjHxhsG9aedQdWpX6P29PQrFYdKDp5DmKkQ4gQGWn1odU0XNCQXut6uXoqQtyzxb98yhfHvmHQY92oHPJk8gFXrkCv7rpTZ9/+qd8wPgHc3PSpkiy4EywymZcnhATqk9bO1kF9B0Cx4tDrPYi6M+DuZ/FlQfUpnIg+Rdfdw+H8ZUk7EyInMwxd0yw+Qb+XyaKyXOhKKdVBKbVTKbXzwoULGX7/cqHlWN5yOQtfWIi7Oeu0mRMi2zCbIa+vPllUKEwvkv5NRUtHsEgpqFYdFi6BZd+S58kqjFhvo3zTDhwZ1hWuXU3bPdzddbAoJhaOn4JT52SBJUQGWH9kPc8vep4ywWUYW8/lWeJC3JNT5w7RaVNPKuZ7nH6Pdkk+wDCgX2+4chk+mAqeXs5vaE88QRIeok+ViNwpwE+nGcY5STvz8oLJU+D8eRg2JNnlKiHlGVC2K/OOLGPpyR/grKSdCZFj3bipTwxmoXb3jmREkOgUUOi2vxdM/JrDMUopN8AfuOToZoZhzDQMo7xhGOWDg4MzYHrJNXmgCRazFBYU4p4kFbouFKoDRnn9dFQ81qqPUabHk+Vx/+xLrixbxK+l8lB87tckVH0KJo6Hq1fSNheLuz7tdCMGjp7S7SSdFZUUQqTox2M/0uTLJpQOKs2619YR4BXg6ikJ8Z/ZE+J5fXk74mxWPq/+oeN2918thdU/QPdeUOYR5zc0DL05kj8feHpkzqRF9qAUhOUDd7NeA6XksXLQ8R39c7Y+eWr9gLLvUiFfOd7a0Y+Tl47CteuZOGkhhEsktbu3ZK12945kRJBoB1BKKVVMKWUBWgJ3typaAbRJ/PxFIMIwJEQuRI7hYYH8Qbd1RbPfqluUjv/VA56sxqOLf+L5XoX5rqgVpn0I1SunL1jkkXiyKOoGHDkJ5y85LywphLjDTyd+ovHCxhQLKMb61usJ8nZJdrgQGWb6jxNZd2Yzk54aTCm/4skHnDgOQwZCxUrw5lup3zAuHvzz6A8hzGbd2c5md36iums3ePAh6Pe+Tm28jbvJnS+qT8Vqj6fdzr4YZy/pRh9CiJzBMODsBTBIbAOdtd1zkCixxlBnYA2wD1hiGMZepdQwpVSTxGFzgCCl1CGgO9DnXp9XCJEFuZkTu6IVgrBgXX8ozqoXOmkMFoV45WNG22/o80YRKnb24lKFsreCRZPGJ8vnd0gpHbjycIfLUXD4JFy6CrZ0FtsWIpfZdnIbz3zxDOF+4WxovYEQnxBXT0mIe7L/xO/0+mUIz4TXoUPpV5IPSEiA7u+CyQwTP0x98Z5URyJ/UJbfCRb3kZen3iSLs6a83rFYYOIHOqVxyIBkl0v6FWNi+UGsO7OZGYcWwLlLknYmRE5xJUpnO2TBdveOZEhNIsMwVhmGUdowjBKGYYxM/NogwzBWJH4eaxjGS4ZhlDQMo4JhGEcy4nmFEFmUyaR3WJPqFnl46JNFqRV3TBTmnZ+IBou5VCyE4nX/ZO+XH0G1GjD1Q6hWGaZ8ANfTcBRbKd0Jzd2s20wePglXo5x3IhEil9pxagcNFjQgxCeEiNYRhOYJdfWUhLgn8dZYXl3eGh83b+ZUnYDDxrozP4ZdO2HYCAi/u+/KXWx2sBlQMH+22AkW91mgv07Dtzo5vfxwGejSDVYshx++T3a5Q+lXaBRei56/j+Lg6b1wPToTJyyEuC9irboovUfWbHfvSJYrXC2EyEHurluUx/tWketUgkXhPmFENFhCoEdeqh3tx28jusL3a3Q6wOQJUKMyfPwR3LyZ+jxMJh0sMpvg7CVdsygqWnbohEj065lfabCgAUFeQUS2iSTcL5U3y0JkAyPXDmLX5T+ZWXksoV4OTsX99Rd8MBGefhaa/s/5zQxD17kLC9InVYW4m1L6FLXC+cnlTu/Ao2VhQF+4ePGuWyhmVx6Pp9mD1jt6kXD6rKTMC5Gd2e26DpFJZdl2945kn5lmgEuXYPRoaNYMunWDfftcPSMhcgmldHHP8BAoXhD8fXWwKNZ5R7TCecKJbLgEX3cf6q97mT9CFcz6FJZ/D+Ueh7GjoWZVmDcX4uJSn4c5MVhkGHDqPBw/AzdjJVgkcrXdZ3dT//P6+Hn4EdEmgkL+hVJ/kBBZ3K7DPzPi10m8VvwFmhV5JvmAuDjo8S7454URo1Pf3Y2L169dflKHSDjh7qYDRdaElNcW7u4wYTLcuAED+yW7HO4TxkcVR/LLxd8Yv+8TfQJB1ilCZE8Xr+rNcUvW7mZ2t1wTJDp9GsqXhwMH4OWXwd8fataEdckbDAghMpPFHULz6bpFgf63OqKlkAJWNE8hIhsswdPsQd21LdlzZT+UfQzmfg5Lv4HixXXB0To1YOnitO24uZnB013XSjpxRgeM4qwZ/I0KkfXtOb8CFnxRAAAgAElEQVSHep/Xw9vdm8g2kRTNW9TVUxLinsXGRdP6u9cJ9QxmSsVhjgdNmQz798GY8RAY6PyG/9YhCsw2qQLChfJ4Q4C/83VF6QegW3dYvQpWrUx2uWWxpjQv+hyD93zI78e2Q3RMJk5YCJEpbsbC5WtZvt29I7kmSDR6NLzwAsydCy+9BEOHwvz58O67EpwXwiXc3SAk8FZHtARbisGiEn5FiWy4BIvJnbprW/LX1QP6QvmnYNEymPcFBAVB7x7wdH1Y80Pq/2Mrpefg4a4XX0dP6SKRcqxb5BL7Luyj7vy6uJvciWgdQfEAB12fhMiGBv/Ql7+uHWRO1QnktfgnH7BrJ3w8HZq3gLr1nN/Mbte1iMJDpA6RSBulICRAF6qOd7KmeLOjTjsb1B8uX77rForpFUcR5BHAa9t7EnfqlDTfECI7sdng9HlwM2XLzYVcEyRauxbatLnzaw0b6hS0U6dcMychBHd2RLs9WGS7M1hUyq84EQ2XYFIm6qxpwf5rh/QFpaBGTZ2CNmOmDg51fBOaNYFftqT+/ErpQJGHu+48cOSkjvpLcWuRg+2/uJ/a82qjUES0iaBUUClXT0mIDLHlQCTjd0+jQ6lXaBheK/mAmzehZzcIKwADhji/mWFAXILe0PD0yIzpipzKZIICwXotk9J6ws0Nxk2EqCgYNijZ5SDPAGZXGc+ea38zbPcHukurECLrMww4d1m/p3HLHt3M7pZrgkR588LZs3d+7cYNiI2FPJJeLoTrmZOCRYX1gtxmTxYsesC/BBENFmNgUGdNCw5G3dYoUSlo9AysXg9jJ8C5s/Byc2jzCvy1N/XnT+qE5mbW+f9HTkLUDTlqKHKcA5cOUGdeHQwMIttE8mC+B109JSEyRHRMFG1WtqOITzgTnhroeNCYkXDsGEyYBL6+zm9ojQdfLwjwy/C5ilzA00NvfsU5qU/04EPQuSss/xbWrU12+dmCdWlbojlj93/CjoObISYN9ReFEK51/SZcu54t08yS5Jog0RtvwIABcOWK/rvNBv3769NEefO6dm5CiNuYTbpWUYlCDoNFD+UtRUSDxSQYCdRe05xDUUfvfLybGzRvCZGbod9A2P07NG4E3d+FkydTf/6kTmgApy7omkWyKBM5xMFLB6k9rzYJ9gQi20TyUPBDrp6SEBmm76oeHLp+jLnVJuHr7mAHcMvP8Pk8eP0NqFTF+c0SbPr1IDRftkwVEFlEoD94e0C8k1Sxju/oYNGAvnAt+WmhyRUGE+oVTNsdvYk9+Y+cdBYiK4tPgLMXwOKWrV87ck2QqH17qFIFSpSARo2gWDHYuxc+/tjVMxNCOJRSsMhup0zAA2xosIg4m5Vaa15KHigC8PCEN9+CTVvgrU66MGTdGjByOFy9kvrzJxW3jrPC8dNw+oLz2gJCZHGHLx+m9rzaWG1WItpE8HDww66ekhAZ5sd9a5i6ZzZdH2xHrVAHAaAbN3TduqLFoFcf5zczDP37vkBItk0VEFmEUrrbmWFPlkb/L4sFxk+CSxdhRPJC63kt/syqPI6/rh1kyK8TdGq8ECLrMQxd39Qg29ewyzVBIpMJJk+GPXuga1dYtQo2bEi9oYUQwsXuDhYl1ix61F8HimJtcdRe05zDUcccP97PH97vBxGbocnzMGcm1KoGs2fqFsjOKKW7sXm4w/UbcOQfXRNAdvFENpMUIIpNiGVD6w08EvKIq6ckRIaJjomi3ao3KZGnCKOf7Ot40OgRcPqUbj3u5eX8hnHxEOQPPqmMEyItLO6QP59OX0wp7eyRR+Gtt2HZEtgYmezy0wXr8Eaploz/exbb9kdKR1YhsqJrN+B6tD5FlM3lmiBRkgIF4Jln4BFZHwuRvdweLEoscF3WuyQb6n1JjC2W2mubc+T68ZQfX6CA3qlbtRbKPQ4jh0H92rDyu7R1QvOw6G5oF65IvSKRrSQFiKLjo1nfej1l85d19ZSEyFB9V/Xk2I2TzK02CW83B4GdTRth4QJo3wGeLO/8ZvHx+vd9voDMmazInfzzgK+PDhSlpGs3KFES+vXRJ9/uMrH8IMK9Q2m7vRexJ0/JGkSIrMQaD+cu6qBwNk4zS5LrgkRCiGwuqcB18UIQlJfHfB9gfc3PiY6/Se01qQSKQOf9f7YA5n0B3t7QpRO80BR+3ZX6czuqVxQr9YpE1nX48mFqzatFdHw0Ea0jKBdaztVTEiJDbdy/lql7ZtHlwdepnr9i8gFRUdC3l37z3b2n85vZ7WA3dFcqkyyRRQZSCkKD9J8ptbL38IAx4+HMaZgwNtllf4sfsyuPZ3/UYQbuGKU3q4QQrmcYcOYCKJPe1M4BcsZ3IYTIfdzM+kRRiYKUK1GJ9bU+50Z8NLVWv5Ry6tntatSE79foTminTulAUZe34eQ/aXvupHpFx07B2YuQIPWKRNaSFCCKiY8honUEj4U+5uopCZGhomOieOP7DhTPU5hRT6RQZ2jkMN3edvwk8HSSPpbU7j44UJ8kEiKjubnpQuhWJ93Oyj8Fr7WB+Z853LxqEF6TN0u1YtKBT9m2d72sPYTICi5fg5ux4J696xDdToJEQojszc0NQoJ4/MmGbHjuK24mxFBr9UscunYk9ceazboTWsQmfcx7/VqoWwvGjYbr150/9t96RRa4el2noF2JkuPfIku4PUC0ofUGCRCJHKn/D705fOM4n1adiI+7d/IBGyNhySLo0BEef8L5zeITII+ntLsXmcvXR6eeOUs769UHwsLg/Z4OayeOLz+AAl75abftfeLOnJF1hxCuFGeFi1d0/dIckGaWRIJEQoicweJOuUfqEPHqOmKNOGqtac7BSwfStnjy8YH3esKGTfDMszDjI6hdHRZ/mfKx8CRK6RQ0s1l3NDh6Cm7GyKJNuMyBSweo+VlNCRCJHG3zgfVM+XMmnR9oS83QyskHREVB395QshR06+78Zkm/50ODc9QiX2RBSkFIkE5nTGl9kScPjBwDhw7C9KnJLvtb/JhZeSx/RR1k+NYx+gSDEOL+s9t192OlclyKcs76boQQuV7ZQk8S0SYSq7JRc8PL/H1hf9qPYxcoAJOnwLcroWhR6NMLmj4L27el/lhzYr0imw1OnNW5yfFOdgqFyAT7L+6n1me1sNqsRLaJlACRyJFiYqN5Y2UHiuYp6Lyb2blzOs3MwzPlmxmGTv8JDdbNCYTIbG7m1NPOatWBpv/Tm1Z/7092+emCdWhT4iXG7P+E3/ZGgk26rgpx312+pmuTWtxdPZMMJ0EiIUSO82hoWSLbRGIzGdTa9Cp/RR2GmLjUTwUleawcLP0GPpwGly9BixfgnY5w8mTqj3V300dOr9/UKWiXruqdBiEy2d7ze6n1WS3shp3INpE8mv9RV09JiEwxeHUfDl4/yqwq48nj7pN8wOZNsGih7mZW7nHnN4tLAH9f8HWQriZEZsnjrX/u4pxsYg0aCr6+Ou3Mwfpl0lODCPYM4vWtPYk/fyETJyuESCYmDi5ezbE17CRIJITIkcqElCGyTSQoRa0NLfnD7ZzuWhMbl7agjVLQ5HmdgtatO0Ssh3o14cNJEBuT+mM93HXA6MIVnYIWLSloIvP8ce4Pas+rjUmZ+LHtj5QJKePqKQmRKXYc3szEP6bzZqlW1A2rlnzAjRv6FGix4vBeD+c3S7DpU6D5AyXNTNxfSkFIIJiV/jl0JDAQBg+D3b/rQtZ3X/YI4ONKo9l9dR9jtoyVbqtC3C92u84YMJvAlDNfOyRIJITIsR4OfpiNbTdiMVuo/U1jfvW6oDvXxNt0obm0BG28vODd7rB+I9RvCB9Mgnq14YfvU3+8KTEFzTDgxBk4dd55sUoh/oPfzvxGnXl1cDe782PbH3kw34OunpIQmcJqjaHdyjcJ8wxhfPkBjgeNHaVbiKelm1l8gm53b845HWlENpKUdhbvJO3suaZQqzZMGKs7sd6laeGGtCzalOF/TWPPXz/JZpQQ98PFq3o9n4NTlCVIJITI0UoHlWZj243kseSh7hf12R57AIoXTDzmHe+8JsDtwsNh6nT4cqkuKvn2W/BqSzjwd+qPdTPrYFF0DByVFDSRcbad3Ead+XXwdvdmY9uNlA4q7eopCZFpRq0dzJ6rf/Nx5TH4Wxx0Idv6MyyYD+3aw5Plnd8sLh4C/MHHSSBJiMyWx9t5tzOlYPgovU4Z1M/hemVqxeH4u/vyxs89sF26kskTFiKXuxmraxF55Lw6RLeTIJEQIscrEViCTW03EegVSL359fj5zDa9e1c0HLwsEBuf9uLWlSrDytUwdATs3QPPNoQRw+D6deePkxQ0kcE2Hd9Evc/rEegVyObXN1MysKSrpyREpvnz+A5G/jqZVsWep3GheskH3LwJfXrrpgM9eju/WXyC/l0cHJApcxUizZK6nSkn3c4KFtI/0xEb4Pvvkl3O5xnI1IrD2X55N1O2TJITy0JkFtttaWY5PEVZgkRCiFyhSN4ibGy7kTDfMBosaMD6I+v16Z6CoVAov16gxVrT1iHEzQ1at4WIzfBic/h0FtStCV8vS0cKml2noJ25oN+wCJEO64+sp9GCRhT0K8imtpsokreIq6ckRKZJiI+j3Yr2BFj8+bDCMMeDJoyFE8dhzASdJpwSw9A1YAoE64W+EK6Wlm5nbdtB2cdgyCC4djXZ5RZFm/Bcwfr0/2Mihw/9KhtQQmSGi1dubTLkcPLqKITINZLeUJcIKMGzC59lxd8r9E5AHm8oFg75g/ROXmwa6xUFBsLocfDNd1CgAPToBs2bwb6/Un+sm5sOFiV1Qbt8TRZ1Ik1WHlhJ44WNKRVUio1tNxLuF+7qKQmRqT6IGMPOy38wteJw8nkGJh+wayd89im81gYqVnJ+s7h4CMoLXp6ZM1kh/gtfb/DzSfkUkNkMo8fD1SswakSyy0opZlQahbvJnTc3dsOIis7kCQuRy9yMyRVpZkkkSCSEyFXy58nPj21/5LH8j9FscTMW7VmkL5hMEOAHxQvpP9NTr+ixcvD1ChgzHo4chueehuFD0pGCZobzl+HYaYiJvefvUeRci/Ys4n+L/8cjIY8Q2SaSEJ8QV09JiEx16OQeBu4YTZOC9Wle9LnkA+JioXcPCCsAvfs6v1l8AljcIV/ezJmsEP+VUnqjSqmUTzQ//DC8+RYsWQS/bEl2OdwnjAnlBxB5/hfmbPko5fQ1IUT62Gxw+qI+QZTD08ySSJBICJHrBHoFsr71eqoWrkqrr1ox59c5ty66mfVCrVhSvSJryu1pb2cyQYuXYcNG/efcOToFbfk3aU9BS0iA42fg7MW0PafIVWbumkmrr1pRuWBlNrTeQKCXgxMVQuQgRkICHVZ2wGKyML3SKJSjxfmUD3RwfvQ43VQgxZsZeqEfFqx/5wqR1bi5Qf58+jRRSuuGd9+DIkWg7/s6QHqX9qVaUTu0Cj1+G8GpY2k41SyESN2FK3qN7pZ7OmHKq6QQIlfy8/Djh1d+oGHJhrT/rj2Ttk66c4BHYr2igvn1rkGsNW0dyfIGwMgxOgUtLAy6dYFXWsDhQ6k/1t1Nnyy6el2noF27ISloAoDxP4/nrZVv0ahkI1a/uhp/T39XT0mIzGUYzNk8lchzWxlfvj/hPmHJx+z5Ez6ZAS+1gBo1nd/v3zQzj8yZrxAZwc9Hp8BbU6hV6OkFI8bAsaMwbWqyy0opZlYeS7w9gbcjumNEx2TyhIXI4aJj9Lo8l6SZJZEgkRAi1/J29+bbFt/y0sMv0WNtD/pv6I9xe1BGKfD10aeKggN0qkKckx2+2yWloA0fBXv3wtP1YcI4iE1lwaaUPlVkVnD6PPxzFuKs9/aNimzLMAwGRAyg9/retCjTgm9bfou3u7erpyVEpjt99jA9tw6mZv5KtC/VKvkAqxV6dYfAIOg/0PnNktLMgiTNTGRxSkFokP48pY2patXhfy/AJ9Ph4IFkl0v6FWP4471YcXo9y7bPS9sGlxAiOZtNN5hxM+eaNLMkEiQSQuRqHm4efPnCl3R4ogOjfhpFp+87YbPfleplMuk3F8UL6eKScfFp60hmNsOrrWH9j/Dsc/DRFGhQFyI3pO2xnonpbkdP6Y4KstDLVWx2G51XdWbk5pG8+cSbfNHsCyxmi6unJUTmS9A/+3F2K7OqjMOkHCxXP54O+/fBiNHg7yT4Yzd0jRdJMxPZhbs7hAQ6TzvrP0inV/Z73+Ha4N2H3uDJoLJ03jaAy2eOZ/KEhciBDEPXC02w5ao0syTyaimEyPXMJjMfN/6YvtX68smuT2j1dSusNgend9zd9BuNwqE6iJPWFLTgYJg8BRYuBosF2rWBt9+Cc2edP04psLjpHfCLV3WwSI6O5wpxCXG8/NXLTN85nd5VevNJ408wm3LfIkXkQobBV9vm8c3JNQwt14NSfsWTjzl4AKZ9CI2bQIOGzu9nlTQzkQ3l9dUd+OJTqE8YFKQDRTt3wKKFyS67mdyYU2UCl6xX6bmht5xIFiK9cmmaWZJ7ChIppQKVUuuUUgcT/wxIYZxNKfV74seKe3lOIYTIDEopRtUdxYT6E1iydwmNFzbmepyD7mRKgbcXFC2gd/ribXrxlZYUtMpVYdVa6NEbItZDvVq6bXNqHUhMiSlohl2nn525oAvoiRwpKi6KZxY+w9K/ljKxwUTG1h/ruGCvEDnQlQsn6fxTH54IfJTuD3dIPsBmg/d76lMUg4c5v9m/aWZSw0tkM0pBaD69EWVPYX3R7EWoUhVj9Cgil55j/nz4biWcP68vPxb4ML0f6cTco8tY/1sammgIIbSExDSzXNTN7G73epKoD7DBMIxSwIbEvzsSYxhGucSPJvf4nEIIkWl6VOnB3KZziTgaQa15tTh345zjgSYTBPpD8YK6blFaU9AsFujcFdZsgMefhKGDoFkT2Lsn9ce6JRa2vnZDClvnUOdunKP2vNpsOr6Jz//3Od0rd3f1lIS4f+IT6LWmJxfiLjO7ynjcTG7Jx8ybC7/9CoOGQr58Kd9LupmJ7M7DoushWuMdX1eKy91HY4uJ46HFg6laFfL6w7x5cPSYHjKw7LuU8i3GWz/15ual8/dt6kJkW0lpZjYjV6aZJbnXV82mwLzEz+cBz9/j/YQQwuXalmvLipdXsP/ifqp8WoWDlw6mPPj2FDSTCWLi0paCVqQozFsAUz6C06ehyTMwcjjcvOn8cf8WtjbDmcTC1iktIEW2cvjyYap+WpX9F/ezouUKXi37qqunJMT9YxhE/racOUeW0LPMWzwe9EjyMSeOw4SxULsONP2f8/vFWSFQ0sxENhfgp0/DpbAJteFIcU698C6hu1ZS4th6qleHxo1h3Vp93cvNi1lVxnEk+gSDNwyUU8hCpObGTbh2HTwcbFLkIvcaJMpvGMaZxM/PAvlTGOeplNqplPpFKSWBJCFElvdMqWeIbBNJVFwUVT6twvZT21MenJSCViw8MQUtjV3QlILnmsKGH6FlK5j9CTSok8bC1ia9yxhr1aeKLl2VwtbZ2LaT26g8pzJXY6+yofUGni71tKunJMR9FXPpPB0296CEbxEGP/Ze8gGGoYv0ms26BbizFID4BF38N590MxPZnMkEYfl08XUHa4pjxyCgd0co/QAM6g/R0TzwAFy4oBsAAtQMrUyH0q8w6e857NobKSeQhUhJgg3OXNT1QHNpmlmSVINESqn1Sqk9Dj6a3j7O0H2jU/qtU8QwjPJAK+ADpVQJJ8/XITGgtPPChQvp+V6EECJDVQivwJZ2W/C1+FJ7Xm2+P/C98wckdUErVhB8vHQAJyGVekMAfv4wcgws+Rq8vHRh6y5v61WeM/8WtnbTR2OPnYaY2LR/gyJLWL5/ObXn1cbXw5ctb2yhUsFKrp6SEPeXNZ5hEUM4dOM4MyuPw8vNK/mYpYvh55+gT38oUCDlexnSzUzkMF6e+kRRXPJTw97ecO2mRa8hTp+CyROJjgaTWcdTk4x9sh+hnsG88eO7xF93UG9RiNzOMOD8Jb3has69aWZJUn31NAyjnmEYjzj4WA6cU0qFAST+6TDZ1TCMU4l/HgF+BB538nwzDcMobxhG+eDg4P/wLQkhRMYpFVSKrW9s5aF8D9FkUROm75ie+oMs7hAeAgUTD1fGprGw9VMVYOVqeK8nrF0N9WvBkkWpP9Zk0ikVNhscPwPnLqVeDFtkCR9t/4hmS5rxaP5H2frGVkoHlXb1lIS4vwyD3/dEMn7/LNqVbEGdsKrJx5w7CyOGQcVK8PIrzu8XF6/fUHt7Zs58hXCFfHl1fZS7Np6eLA9r18HNh5+CVq9izJ3N1tl/Uu6xO9/n5rX4M63iCHZf3cekyNE6kCqEuOX6TV3rM5d2M7vbvW6xrADaJH7eBlh+9wClVIBSyiPx83xAVeCve3xeIYS4b/Lnyc/Gtht5ttSzvLPqHXqs6YHNnkoQRild0LpYOAQm7gDGpyEFzcMDunbTXdBKP6i7+LzaEo4dTX2i7omFra9EwdFTOq9ajpVnSXbDTu91ven8Q2eeLfUsEa0jCPEJcfW0hLjvEi5cpP1PPQjyCGB8+QHJBxgGDOwP1jgYPc756aAEmy7wn89hs10hsi+zGfIH6Y6qt72uV6wABcNhyhT4PLwv0R5BPPX1+9SrnXyN8r8iT/O/wo0Y8udkDh359X7OXoisLSEBzl6QNLPb3GuQaAxQXyl1EKiX+HeUUuWVUrMTxzwE7FRK7QYigTGGYUiQSAiRrfhYfPimxTd0rdCVSb9M4sWlLxJtjU79gWYzhARB0QK6RkasNW21g0qUhEVL9RHyP/+ARvXg449SLzqZVNgadFHrMxekUGUWE22N5oUlLzB+y3jeLv8237T4Bh+Lj6unJcT9FxPLh1sms+vKHqZWHE6gh4PgzqrvYd0afcKyWPGU72UYOkgUlk/XbBMip8njDX7eYL31mq4UNGwIXbpAjWf9MQ0ZSsDJP3D/cq7DW0yrOAKLycJb67tiSHq6EPq149wl/aekmf1LGVl4l7l8+fLGzp07XT0NIYS4w5RtU3hvzXs8EfYEK1quIMw3LG0PNAx9yufCFf33tO5YnD0DQwbCmtXwyKMwdgI8XCZtz2eN18+RPwj88sgOiYudjDpJky+bsPvcbiY3nEyXCl1Qufy/iVJqV2LdQpFF3Jf1l83GkT+38sjK+tQNq8aKOnOT/79w5QrUrw3h4fDVcn1KKCWx8eDvA6H55PecyLniE3SzCjez42CoYUC71rBjO6yNdFi/65O/F9Dxlz58WmUir9ftJrW7RO4WdQNOndcbrFnxtSM2Tm82B/pnyu1TWoPJbwUhhEinrhW78k2Lb9h3YR9PzXqKnafT+GZKKf1Lvvhtha3TUjsoNAw+ng3TP4GzZ6HJMzB+DMSlsguolO6AZjbD6Qtw8qwOGgmX2Hl6JxVmVeDQ5UN89/J3dK3YNdcHiEQuZRgY5y7R8Ze+mJWZ6ZVGOf5/YfgQuHYVxox3HiCy2cCsIDgway7yhcgo7m5608ea4DidXCkYPkqfWB48wOGYN0u3onpIRXrsHMa504fvw6SFyKISEuCsdDNzRIJEQgjxHzR5oAlb3tiCm8mN6nOrs2jPorQ/2N1NF7YOzw92I+2FrZ9+FtZFQLMXYfo0eLqB3i1Mjdmkd0huxsHRk3D5mtQqus+W7l1Kjbk1sJgt/NzuZ54p9YyrpySE61y/yYLdn7Pu3E+MfqIPhXwcdCv7MQK++Qo6vQMPPZzyvQxDv2HOH6RPVwiR0/nnAW8PXZ/IkYKFoFsPWL8W1vyQ7LJJmZhZZSzRCTG8u667bB6J3Mkw4KykmaVEgkRCCOFEdDT8/jucO5f8Wtn8Zdn+5naeKvAUL3/1MgMiBmA30tgxRCnw89GnivL66kBRWmoH5Q2AcRNh/kJdCLvFC3q3MDqV+khK6aLW7m469/r4GYizpm2u4j+z2W3039Cf5suaUy60HNvab+PR/I+6elpCuE58AheO7ee93SOpFPwEnR5onXzMjRvQvw+UKg3vdHV+P2uCbhLgK3W9RC6hlE6rtNv1RpMj7drrtPQhAyEqKtnlB/1LMqBsVxafWMnKHV/KxpHIfa5H6w+LdDNzRIJEQgiRgkmToHBheO01ePBBePXV5LGYEJ8Q1rdeT/vH2zNy80iaLW7GtdhraX8Ss1kv9ooUAGXSucdpWaxVrwGr10Ob1+Hzebqw9U+bU3+cKfFUkdWqO6Bdupq2Qtoi3a7GXqXJoiaM+mkU7R9vT2SbSPLnye/qaYkcRinVSCn1t1LqkFKqj4PrHkqpxYnXtymlit7/Wd7mShTdfx1BVPwNZlUeh9nkYAd37Gg4cwbGjtcdH1OS1MY7f5CkCojcxcMCQXlTPgXk5gajxsL58zBhrMMh7z/yNmXylqbTz+9z/eKZTJysEFlMfFKambu8dqRAgkRCCOHA0qUwcybs2AF//gn//KNjN126JB9rMVuY+dxMpjSawsoDK6kwuwJ7z+9N3xN6e0KxcL3oi4vXL2Cp8fGBwcNgyde6c9prL8P7PSEqlSCVUvqF0eKui2gfOw0xcembr3Bq34V9VJhVgbWH1zLj2RnMfG4mHm5O3uwK8R8opczAR8DTwMPAy0qpu3Oz3gCuGIZREpgMOH7HeJ+sOb6eBce/pc8jb/NIwIPJB2zfBgvmQdt28PiTKd/o9jQzdyf1ioTIqYL89c9+SqeQHysHrdvCgvnw265kly1mC7Mqj+dUzDn6r+8nnVBF7vBvNzOkE6YT8i8jhBAOTJ8OY8ZA8cSOy3nywLRp8PXXDk9uo5SiS8UuRLSJ4FrsNSrOrsiSvUvS96Qmky68WrSA3gWMiUvbKZ/yT8GqNdDxbVi2BBrWhcgNaXg+pU8V2Wxw/BScvyynijLAsr+WUXF2Ra7FXSOidQQdy3eUAtUis1QADhmGccQwDCuwCGh615imwLzEz5cBdZWLfiCjrdF03NyLB3yL06+sg+vH63wAACAASURBVIh7bIwOdBcqDD3fd36zeJuuy+KfJ3MmK0RWZzLpk8gJ9pRPIPfoDfnzQ9/3dYr6XSqHPMk7D7Zh2oH5/PLnWkk7Ezlf1I3ENLOsv7lgGAZLTnyPzZ6GJjcZTIJEQgjhwJkzULLknV8LCNDBosuXU35cjSI1+PWtXymbvywtlrWgx5oeJNjTuTvn6QFFwiAkUO+Up9TF5I7HeMH7/eCb78DfH9q1gZ7v6c5AqXF300fXL1/VKWg3U+maJhyy2qx0W92Nl5a+xMPBD7PzzZ1UL1Ld1dMSOVs48M9tfz+Z+DWHYwzDSACuAUF330gp1UEptVMptfPChQuZMtkZO2dw7PoJZlUYjafZM/mADyfDsaMwehx4e6d8I7tdf0i7e5Hb+XjpQGlcCmlnvr4wdAT8vR/mzHQ4ZNQTfQj3DuXNje9hTcuaQYjsKj5eF6vOJmlmcw5+SYutXfn2yMr7/twSJBJCCAeqVYOvvrrza7/8oksIFSrk/LEFfAvwY9sfeeepd5j0yyRqfVaLf6794/xBdzOZdOpZsXD9YhYbn7ZTPmUfg+WroPO78O3X0KAurFub+uOU0sEpux1OnNZHcW33f+ciuzpx7QQ1P6vJh9s+pGuFrmx6fROF/FP5QREiCzEMY6ZhGOUNwygfHBycKc/RrVI31jyzhOohFZJf/PMPmPUJNG8JVas5v5E1AYIDdHBbiNwuOECfDLalsEZo0AgaNNRB2BPHk132dc/D9Eqj2HPtAOMjR8lrv8iZDEPXIYJskWZ2NuY8vXaNpFZIRZqVaHLfnz/r/wsJIYQL9O0LH3+s/9yyRdcneuEFnYKWlk6ZFrOFac9M44tmX7D73G7KfVKOlQf+w06Ah0WfKsofCFZb2k4VeXhAj17w7UoIDIQO7eC9rnD1SurPl3Sq6EqUPlUUHZP+Oecyqw+t5olPnmDv+b0seXEJHz79IRazvHkV98Up4PZoZMHErzkco5RyA/yBS/dldndxM7nRoFDt5BesVujdA/Llg/4Dnd8kPkEHzgP8MmeSQmQ3bm6QP58uYp3S+mDIcD1uQD+HY54rVJ+XijRm+J4pHDi4M5MnLIQLXLsBN2KyRZoZQNdtg4hJiGVm+ZEuKVkgQSIhhHCgRAnYulV3M+veHdatg4UL4eWX03efVo+2YleHXRT2L8xzXz5HjzU9sNrS2XpeKQj0h2IFdBv7WGvaThU98igs/x7efQ9WroCG9WDD+rQ9n2dikOOfs3D2guwsOhCXEEePNT14+ounKeBbgJ0ddvJSmZdcPS2Ru+wASimliimlLEBLYMVdY1YAbRI/fxGIMIwsVnjkkxmwfx8MHw1+/imPMwz9uyg0nz5tKYTQ/Hx06ll8Cq/VYQV0na/NG2HFtw6HTKk4DC83LzpEvIs9+mYmTlaI+8war0/Ie2SPNLPv/lnH0uMrGfRYN0r5FnPJHOQVVgghUlC0KEyZotPMli6FmjX/231KB5Vm6xtb6fxUZyb9Molqn1bj4KWD6b+RhwUKh+luPvFpPFVksUC3HrpWUUAAtG8LPbqlrVaRm1m/oF69AUdO6UJ/Wey9pavsv7ifSnMqMemXSbxd/m22td9G6aDSrp6WyGUSawx1BtYA+4AlhmHsVUoNU0olnU+fAwQppQ4B3YE+rpltCg4egGkfwnNNoX4D52Ot8ZDXX3eDFELcopReGyTV63Lk1dZQ7nEYPgSuJD9ZHOoVwvgn+7PxwjY+3TxNGlmInMEw4Eximlk22FyIsl7n7V/68WjAg/R6pKPL5pH1/6WEECIH8HTzZOozU/mq+VccvHyQcp+U4+OdH5PuDf1/TxWFp/9U0YrEWkXLv9Ed0H6MSNvzeVpAASfP6RfahNx7qsgwDGbumskTnzzBP9f+YXnL5Xz07Ed4uXu5emoilzIMY5VhGKUNwyhhGMbIxK8NMgxjReLnsYZhvGQYRknDMCoYhnHEtTO+jc2m08x8fGDwMOdjE2yJHSDz3p+5CZHdeFh0fSJrCs0yzGYYNRauXYPRIxwOeaPUy9QKrUyvX0dy5vj+TJysEPfJlSiIyT5pZv1+HcOpm2eZVXkc7iZ3l81DgkRCCHEfNXuoGXs67aFqoap0+r4Tzy58ljPXz6T/Rhb3W6eK0lqryGLRtYq+XgH+eeH11tCnF1y/nvrzuZl1sCjqBhw9mStPFZ25foami5ry1sq3qFq4Kn90+oMmD9z/YoJC5Bhz58Dvv+kAUVCyhmu3GIauRZQ/X9qKwgmRWwX46dqC8SkEih56GDp0hKWLYcvPyS4rpZhZeSwxtji6RvSEmLhMnrAQmSjOCuevZJtuZlvP72L63/Pp8tDrVAx+wqVzkSCREELcZ+F+4ax+dTVTGk0h8lgkj854lKV7l6b/RnfXKopLRwe0Fd9Dx7f1QrFRPYeLRYfP52nRXVROnoMzFyAhhYVoDmIYBl/88QVlppdh3ZF1TGwwkTWvrqGAbwFXT02I7OvoEZgwFuo1gCbPOx9rTQBfH/D1vj9zEyK7MpkgLFifvEtpI6fLu1CkCPR/H2KTN6co5VecQY91Y9nJH1i+fYGknYnsKSnNzKSyRZqZ1Wblza29KegTxojHe7t6Ov9n797ja67/AI6/vueym11sNjaba+WayiWKSO6R5J4SSshPRBGVSiFEIpfIJbmUUJJrJBXpglxCbrnO7G5X28628/398dnKZZez+znzfj4eHmbf7znfzzR9v3t/3hcJEgkhREkwaAaGNxnOwSEHqVq2Kr3W9aL7mu75yyrK7FXk5612Dy2pNrzGBca+DmvXq2loT/eGt8erlNzcGDOyiuITVa+iuNKbVRSWEEa3Nd3ou74vNX1rcmjIIV5+8GUMmtw+hcg3q1VlMTo5w6T3ct7htVoBXU14dICdYCFKnJsLeHuojaOsuLjC5Glw/jzMnZPlKWPufoF63rUY9vt44kIvFd1ahSgq0bGQnAJmx8g+nXp0HsdiTvLxA1PwMLuX9HIkSCSEECWplm8tfh34K1NaT2Hzqc3UmV+HpQeX5q9XUbmyUDVQpdUmpYDVhvdo0BA2fwfPDoTly6BjO/jzgG3Xc3YCowaXwyAkvFRlFem6zrJDy6g7vy5bT29letvp7Hl2DzV9a5b00oRwfKs/hz9+hzffhgr+OZ9rSQM/HzCXXG8GIRyOr4/KnshuMmmzh6B7T1g4X00WvInZYGbxg9MJSQrjtV3j1Q/bQjiK5BSIuKr6EDnA5sLfMaeZfGQOT1btQqeg1iW9HECCREIIUeLMRjPjHhrHkaFHuKfCPQz8diBtV7TlTPSZvL9ZZlaRr7fKKMquL8H1XF3hrXfgizWQmgo9u8L7UyDFhofCzKyihGtwNlj1LHLwrKK/I/7mkc8e4dkNz1LTtyYHhxxkdNPRGA2OsRslhF27dAmmT4XmD0OPXjmfm5qm/p/m7Vk8axOitDAZM3oW5tCv8PU3wdMTXns1y2BSY7/6jKj9HPPPrOSXI1ul7Ew4BqtVlZkZHaPMzKpbGfTrq7ib3Zjd+J2SXs6/7P9vTgghbhM1ytVgV/9dLOi0gH0h+6g7vy7jfxhPoiUxb29kMKjSsyoV1cfJKbYFbh5oClt3qB/cPp4HXTrB8eO5v+7frCIDXA532KyipNQk3vzhTe5dcC9Hwo7wyWOfsPvZ3dT2q13SSxOidNB1GPOy+vi9aTnv8Oq66qvi7+sQO8FC2B3PMlDGNftpZz4+aoPo0EGVSZyFSfVfpXKZQAb9MoaUyIiiW6sQhSUqRjWsdpDs04UnV/JL+D5mNnqb8q6+Jb2cf0mQSAgh7IhBMzCk0RBODDtB77q9mbx7MrXn1Wbd8XV5L0FzdYZqgaq5dbLlhtH14eFw4iRcvXrTazw8YNoMWLwMoqLgiU7w8dzsU9av56BZRbqus+74OurOr8uk3ZN48u4nOfHiCQY1HCS9h4QoRNEzl8FPP5I6ciwEBeV8ckqq+n+Xq3OxrE2IUkfTVJBV17PPAnr8CXi4pWoiHxx8y2F3cxkWPDCFv+PO8N7Pk9UP30LYq2vJEBmjhrk4gODEEMb++R5tAprT744eJb2cG8jTrxBC2KEAjwCWd13O7md34+PqQ8+1PWm7oi2HQg/l7Y0MBihfTpWgAZaEVD7/XGflSvjzT1i8GNZ/k0UMqHUb+G6nmjz0/lTo3R3On8v9eg6WVbQ/ZD8PL3uYnmt74u7kzs5+O1nedTnly5Qv6aUJUWrEx0P37tB60iPsuXc4y9Oe5uefc4ghp6WroLNv2WJdpxCljpNZZRZnl02kaTBpqvr4rdez/Ef5aFArnq7elSl/L+Do8d1SdibsU3q6mrprMjpE9qmu6wz7fTxp1jQWPjgVzc7WLEEiIYSwYw9Vfoj9g/czr+M8DoYepP7C+vT9ui/nrtoQsLleGVeoFsiXW8pQpYKFkcPTeaoPjBwJCQnwy94sXuPjA/MWwKw5cPq0amq9aoVt2UE3ZBXZ3wS0S7GX6P9Nf+5fdD8nIk+w8LGFHBxykFbVWpX00oQodYYNU61P9oZU5aFtb9G7t4Gjx+Do0SxO1nXVi6hCOfX/ESFEwXh7qmBRdj0Kg4Jg9Kuw6wfYuCHLUz68fwKeZnee3zOa9KibU5CFsAMRV9X3uNlU0iuxyboLm/n20nYm1h9DdY8qJb2cW0iQSAgh7JzJYOJ/9/+Pf0b8w2sPvcbXf39Nzbk1GbltJBGJtvcISNONDH3Xl9ptymPQrWBJxWzSadNatSTIkqZBl66wbQc0vB/GvwYD+0N4WO4XvHkC2pWIG0reSsKV+CuM2DqCO+fcyeqjqxnbbCynh59mcMPB0phaiCIQGwsbNsCsWapHPoC7O7RuBfv3Z/ECSxq4u4GHW7GuU4hSy2BQZWfp6dlv1vR7Fu69D955C6Kjbzns51KO2Y3f4ffoQ8z79SNVwi6EvUi4BlfjHKbMLDrlKi/+Pp4GPvV4qfbAkl5OliRIJIQQDqKsS1nea/0ep4efpv+9/Znzxxyqza7GmO1jCE0IzfX1qamQmqrhUdEdqgWpXh/JqZRxs+Y+3TagIny2EiZMhL2/QIc2sHWzbQvPzCqKT4Rzwer3Ys4qikiMYPT20VT/qDrz982n3z39OPXiKaa2mYqXi1exrkWI20lMjAoKed30z6xsWUi8uSe/1QroKovIzlLvhXBobi5Q1lP1+sqK0QhTp0NcHEzKesLSU9W68mjgI7z+1wwunD4oZWfCPqRllJmZHWPcPcAr+yYSlXKVJc1mYDLYZ+aTBImEEMLBBHoGsujxRRwdepQnaj3BzN9mUnVWVYZvGc7F2IvZvs7VFRo3hi++QN1Mg/yhgg9nT6RRv242D47XMxig/7Ow+TuoVBn+NwReGakeKnOTmVVk0CA4TI0nLYasovMx53lp60tUm12ND3/7kF51e3HyxZMsenwRVcraX3qvEKVNUBA4O8OePTd+/tgxqFL1ppNTUsHXW5XGCCEKl6+3uo9nN4iiVm14YRis/wp+2nXLYU3T+PiBKQC8sPdV9OjYolytELnTdQiLhHSr6kXkAHaE/Myyf9bw6t1Duc+nbkkvJ1tanqflFKNGjRrp+7PMRRZCCJHpTPQZpu6ZyvLDywHofXdvhjceTuPAxrecu28fdOoE/frB/ffDzp1w8DcL3y2LwMfNolJ1bdmJSU2FubNh3hzwD4APZkGTB2xbsK6rHwaNGSnw7m6FvvuzP2Q/M/bOYO3xtRg0A33u7sPrzV+nlm+tQr2OKDhN0w7out6opNch/lPYz19ff636Eo0bB23qRXL1YgK//2lm4HMqowhQvSSMRqhaUf0gK4QofHEJaqiEi1PW992UZOjUAZKS1PAKd/dbTvno7yW89MfbrGgyk74t/wcuMoFQlJDYBDUgJbvvZzuTmHqNu79tjZPBzOHHt+NidMn9RckpagCNT9FkvWf3DCZ3YSGEcHB3+tzJ4scXc2bEGYY2GsqGExtosrgJTRY3YeWRlaSk/VdLdv/9KlDk5ATr1kHlyrD5eyd87gtQN6Bki20ZPmYzjBoNa9errKQ+PWHqZEjJrW4NdSPPvKEHh0FoZPY7m3mQlJrEisMraP5pc+5fdD9bz2zllQdf4dxL51jedbkEiIQoId26wTffwIEDqj+RUYNBz18XINJ19f8df18JEAlRlDzKqI2Z7KadObvA+x/AlRB4f0qWpwyrOYAH/Roy8tBEwv/5W8rORMmwpKrnRycbNzftwJuHpnM+4RKLm063LUBUgiSTSAghSpm4lDiWH17O3D/mcjLqJH5ufjxV7yn63duP+v71cx6zmZgEIRFgTbf9xpuYCJPfhS9WQZ268OFHUKOmbYv9N6vICAEZWUV59FfYXyz6cxErjqwgJjmGO7zvYGijoQxqOAhPZ888v58oXpJJZH+K9PkrNFJlM1xfUpZsAS93CPArmmsKIf5jSVX9Ac2m7IOy774Nny6BL7+Cxk1uOXw85hT1N3aga2A7Vj/2GZT3KeJFC3EdXYeLoSrLxkGaVf8RcZAHt3ZhSI2+zH/gPdtfWEKZRBIkEkKIUkrXdb4/+z0LDixg06lNWNIt1PGrQ797+tGnXh8qe1XO+oVp6arGOy5R/SBntHFn//sdMG40xCfAuNdV/yJbswLS0lW5ibcH+PnkOvr6dNRp1h5fy9rjazkUeggnoxPdandjUINBtKzaEoMm2QiOQoJE9qdYg0TpVpVJWD0ITPbZwFOIUicqRo0Md3HK+nhiohpQYTbDlu/AxfWWUyYdns2bh6bzzUML6fJgP9UcW4jiEBUD4dEOU2ZmSbfQcNOjxFjiONblBzydPGx/8eHD0LyZBImuJ0EiIYQoHNFJ0aw5toYVR1aw99JeAO7zv4/HazzO4zUfp0FAgxszjHRd1XqHRYJmACcbf3iLjISxo+GH76HFwyptvYK/ba/NzCoyGVVGQZn/HkqtupXDoYfZemYr646v42DoQQAeDHqQ3nV78/Q9T+Pr5mvbdYRdkSCR/Sm2IFHmv3l/Xyibh4dmIUTBWK1w4QqkpamMoqzs2Q3P9IGhw+DV1245bEm3cP/mTkQmR3Os8/eUrVE71w0eIQosOQXOh6jnUgcpT37n0EwmHJ7JxlbLeKxSG9telJIMU9+DZUth5efwdJ8iWVuRBIk0TesJTABqA411Xc/yiULTtA7AbMAILNZ1faot7y9BIiGEKHxnos+w/u/1fHvqW/Ze2otVtxLoEUjr6q1pUbkFLaq04E6fO1XQKMWiys9S8tDUWtdV6dnECWqk2pT3of2jti8wLR3dksolpwR2xf3J9vPfs+OfHURciwDggaAH6FmnJz3q9Mg+G0o4DAkS2Z9iCxJZUlWDtCoBDrEbLESpkpQCFy6ryaPZ/fsbOxq+WgvfbIK7691yeH/kYZps6czAar34pO0cqFBO/i2LomO1qgBRenr2wU07c/TqCRpsepSeVTqxqsVc21508gS8NAxOnoRn+sPUaVCxQpGsr6iCRLUBK7AQGJ1VkEjTNCNwCmgLBAP7gD66rh/P7f0lSCSEEEUrIjGCLae3sOn0Jn46/9O/gRh/d38eqvwQ91a4l3v87qaeuQpVLB4YnJxsHzP6zz8wajj8dQR69Ya33oUyZW45zapbCU68wvHYU+yLPMwfkYf4I/IQ4cmRAJR3K0+7O9vRrno72lRvQ4BHQKF9/aLkSZDI/hRLkMhsUllEVSvKdCQhSkpYFFyNy77sLC4W2rYCHx/YsFkFdW8yZv9EZhxbyM6HV9CqYVfVHFuIwqbrEBYNMbEOc89Is6bRdEsXzicGc7zLLnxdcundpevw2acwZTJ4esL0D+CBZiXSk6hAIThd1//OePOcTmsMnNF1/WzGuauBLkCuQSIhhBBFy6+MH/3v60//+/qj6zono07y84Wf+fnCz/wa/Cvrjq/791wPJw9quFelkmsAlTwCqVSmIpXKVMTbyQt3cxncTWXwMJfB1ehCup5OWgUnUj/9EO+Pl1Ju6edc2/sju8Y+yd9V3QlPjuSf+AucjjvHmfjzJKerqWgaGrW87uTRwEdo7HsfzXzqU8+jBoZy3uBb1mFSi4UQuUhJBR9Ph3nYF6JU8i0L8YmqL2BWG0CeXjB5Kgx6FubPgZGv3HLKO/eNZv3FbQw68AZHKjSkzF13SX8xUfgSrsHV2OwDmnZo1vHF7Is6zOoW83MPEEVFwZhRsOsHaNUapn0Avr6qvK4EFMe/4EDg0nV/DgZubZMvhBCiRGmaRi3fWtTyrcXghoMBiE+J51jEMY6EHeGvsL84E3Wa09Hn+SF8L3GpCba9cSVo3h9WrA+jw4jZ/NYS5j5spopnZWp4VqddxYe5y7MatbzuoIFPvVsb+uk6RMeqB4QAP3CVHyqFcGjpVvUDabmyJb0SIW5vRqMqEQsOU0Mqstr4b9MWnugG8+ZAuw5qiul13EyuLG46nUe+68Wbhz9gpsckCKwgZWei8KSmwZUI1YfIQb6vTsWe5c1D0+lSqT29qnbO+eRff4GRIyAmBiZMhH4DSvzrzDVIpGna90BWXUff0HV9Q2EvSNO0wcBggMqVpdeEEEKUJA9nDx4IeoAHgh7475O6DlfjiA0+T3BKOLH6NRJSE0lISyQ+NZGk9GRMmhGTwfTv7+6tyhDxjBM+H3zKpC07mBh3H9qsjyCoUu6L0DS1c5SaBhdC1A+W5bwkq0gIR5WWDoHlpcmtEPbA3Q283FVGkXM2WRpvvwO/7IHRo1R/opvKzlr6N2VozX7MOrmUHoHtaVqmPXh7FsPiRamn6xAaAToOc8+w6lae3zsGZ4Mz8x+YnH3VVVoazJkFc2ZDterw6QqoU6d4F5uNXINEuq7b2II7W5eB638KCMr4XHbX+wT4BFRNfAGvLYQQorBpGvh44eVWC6/LPip4Y2tT63kt4Juv0d56Azq2g4nvQZeutl3XbAKTrkafJlyDAF8pVRHCEZV1l74lQtgLTYPyPuq+mm5VGUU3K+utys4GPwcfz4WXXr7llGkNX2dL8A88t/81Dvncg4ubS/ZBJyFsdTUOEpMc6ntpwckV7A7/nSVNZ1DRLZsJv1dC4KXhsO936NFLZRBl0bezpBTHNuw+4C5N06ppmuYEPAl8WwzXFUIIUZRcnKFqoNqBTLaoh0tbPNENtmyHmrVg5HAYNQLi4mx7bWZWUVqamnARFaN2mYQQjsHLXY28d5CSASFuCyaTKjuzpGZ/T23bTm3qzP0Ijt/aWtbD7M6ipu9zMu4f3jk2W01Gtdr4XCBEVpJTIDxaTcN0kHvG+YRLjD3wHm0DWvDsnb2zPunnn+CxDnDsL5g5G6bPtKsAERQwSKRpWldN04KBB4HNmqZ9l/H5ipqmbQHQdT0NeBH4DvgbWKPr+rGCLVsIIYRdMBrUD3yB5VUJiSXNttcFVYIv1sKo0bBxA3RqDwfyME3JbFK16RHRcOEKpFjyt34hRPFydQGzuaRXIYS4mac7lHGF1PTsz3n7XZVVNGYUpKbecrhtxRYMvOtJpp/4hP1X9kNkTBEuWJRq6elwOVw9ZzpIewFd13l+7xgAFjV9/9Yys/R0+HAGDOgLfn7w7Vbo2r0EVpq7Av2N67q+Xtf1IF3XnXVdr6DrevuMz4fout7xuvO26LpeQ9f1O3Rdn1zQRQshhLAjmqYeLqsFqsBNssW27B6TCUaMhDVfq/fo1Q1mz1RZQrYwGFT6sSUVzl1Wza0lq0gIIYTIO01Tmz5Wa/YZQN7eMHkKHD8G8z7K8pQPGr2Fv6sfz+4biyU8XJUKCZEXmePuU9PUpqCDWHR6FTuv7GFGozep4h5048HISOj/NHw0C7r3hPWb4I47SmahNnCMsJwQQgj752SGygFqrHWyRe2Y2KJBQ9j8nSpDmzUT+vSE4Eu5vw7UQ61TRlZRWBRcvKKCRkIIIYTIGycz+HnnfB9t1+G/srMjh2857OXkycIHp3E05iSTTy2AkHD1w74QtopLgNh41e/SQVxICOaVfRNp5d+MwTWevvHg/n3wWHv1+7QZqrzM1bVkFmojCRIJIYQoPAYDlC8HlfxVj6KUHPobXM/DAz6YBbPmwMkTqqn1xjwM0DQYVK+ilFQ4Fwwx8ZJVJIQQQuSVtyc4O+cc2HlnoiqXeWUkJN+aKdQpqDXPVO/Oe0fn8WfkXxnTqeSeLGyQYoHQKIfqQ6TrOoP2voqOzpJmM/4rM9N1+HSx2vx0dYWvN0KvJ0t2sTaSIJEQQojC5+4G1YJUc+sUG8vPQO1Obv4O7qoBI4apcbsJCba9NjOryGyCKxEQHCa7l0IIIUReGAxqemi6Nft7t1dZmDoDzpyGGdOzPGV243fwcynHgD/GYImNVSXhQuTEalUNzzUt6yl7dmrpmdXsuPIz0xuOp6p7xlD3xER46UV4dwI80ho2bLGb8fa2cJy/fSGEEI7FbILK/lDOW2X4pNlYflapMnz5FQx/CdZ/BZ07wOFDtl83M6voWjKcDYbYBNnBFEIIIWzl4gzlvNS9OzsPt4Snn4Gli+D332457O1clkVN3+evmBNMPPUxRFyFpOSiW7NwbLoO4VchJUVt+DmIS4khvLzvXR7xb8qQmn3VJ8+ehW6Pw+aNMGYcLFgEnp4lu9A8kiCREEKIoqNpqr9BJX/1AGBr+ZnJBC+Pgc/XgMUCPZ6ABfNtH6eraaqW3WRU/RAuh9veEFsIIYS43ZUrqzZ7csrIfW282tgZ83KWWb+dgloz4I5eTDk6j/2xR+FyhO39CsXtJf4aXI1VA0kcROY0s3Q9ncVNp2PQDLBjOzzRCSIj4LNV8L8XHWY62/Ucb8VCCCEcTxlXqBoIrs6qqbXVxsyeJg/Alu3Qtj1Mew/6PQVhobZf15iRVZR4TWUVxSVKVpEQQgiRG4MBAvxUUCe7+2aZMjDjQzVs4r2JWZ7yYeO38Xf1o/+vo0lJuQZXIuU+LG5kSVVtApxMDtOHCOCT2yOmwgAAIABJREFUU6vYHvIT0xuOp3qZSmpC7+DnoGo1Nd7+oeYlvcR8kyCREEKI4mE2qYyizMkptpafeZWFeQtg6nT48wA82hZ2fm/7dTVN7UwZDXA5TD2g2nptIYQQ4nbl5gJlcyk7u78xDBoCX6zK8t5c1smLRQ++z/HYU0w4MQfiE+FqXBEuWjgUq1Vle2saGI0lvRqbnY2/wCv736VNQHNeCOgCgweqCb3de8KaryAwsKSXWCASJBJCCFF8NA18vVWvoryUn2ka9O4DG7dCQEV4fgBMeBNS8tDfwGhUWUVxCWoCWsI12c0UQgghcuJXVpVu57S58vIYqFUbxr4CERG3HH40qBUD73qS9499zO9xRyE8WvUNFLc3XYewKDXgxIH6EFl1K8/+8jJGzchn/sPQunaGn3bBO5PUeHsX+x5vbwsJEgkhhCh+bq5QLR/lZ3fcCV9/C889D599Ck90htOnbL+upqlAkabBpVD1cCL9EYQQQoisGY2q7Cw1LfuNFWdnmD0X4hNg3Ogsz/ug0VsEuQXQb+8ormGRXoFCbdrFxKsekg7ko7+X8nPY73yV1pOKTz8PcXGw6kvoN8ChyuVyIkEiIYQQJcOUz/IzZ2d4cwIs+QzCw+DxjirNPS9ZQaaMrKKYeDgXIjuaQgghRHbcXKCsR85lZzVqwrjX4YedsGrFLYe9nDz5tNlMTsWdZdzhaf+NO5eM3ttTsgWuRIGT2aECKydj/+G1A++x/NAdtJ7wKVS/AzZugcZNSnpphUqCREIIIUpOZvlZ5vQzi43lZwCtWsPWHdDwfnh9LAx7AWJj8nZtFyfAChdDIDzK9ulpQgghxO1C06C8j8oqymlDZ8Bz0OJhmPwu/HPmlsOtAprxUu2BzDnxKTujflMbNJF5uG+L0iE9XfWINGqqX6SDSLOm8cLO4axeY+WZb/5B69IVvlyn2iCUMo7zX0UIIUTplTn9zMVZ1abbGigqXwGWr4Kxr8OO76BjeziwP2/XNplUY+voODh3GZIkq0gIIYS4gdEI/uVyLjvTNNWTxdUVRg4Hi+WWU6Y0GEdNzzsY8MvLxGjXIPKqamYtbg+6rgaIpKapgSYOZMHOKcx6/widj6fD62/CzNmlov9QViRIJIQQwj5kTj8rV1alIdvaK8hggBf+B2vXq4BP7+4wZ3beeg1lZhVZrXAhBCKiJatICCGEuJ67G3h5QEoOvYTKV1DTSI/+BbM+uOWwq8mV5Q/N4kpSOC/tm6AaFodEqA0iUfpFxaigoIP1ITq5fRW9Ry7kjngzhqUr1EQ/ByqTyysJEgkhhLAfmgZ+PipYlG61ffoZwH31YdM2eKwzzJwOfZ+E0Ct5u745I6soKhYuXIHklLx/DUIIIURp9G/ZmZZz2Vm7DvDkU7BgPuz++ZbDjf3q88Y9w1n+zzrWB29XP5EGh8kgidIuPhEirqrnLAcKsFhWfkr1oWOJLWMk7auv4eGWJb2kIidBIiGEEPbH3U2Vnzk75S1Q5OEBH85R6e6HD0HHdrDz+7xdOzOrKC0NzoeoXS9prCmEEEKowQ/+vjmXnQG89Q7ceRe8/BJERNxyePw9L9HApx6Dfx1LaNpVSE1XZUhyvy2dUiwqY8zJBAYHCRClpsLb43F68012VIdLKxdStlb9kl5VsZAgkRBCCPvkZIbK/uDtmVF+ZmP5l6ZBj16wcZtqJvj8AHjnLUjJY1aQ2aQeZiKiVVaRpMILIYQQaiMnt2lnrq7w0TyIj4PRI28p4TYbzKxoPpuEtEQG/jIa3cmoMk2kkXXpk54Ol8PV85nRWNKrsU3MVRjQF5YvY3pT2D5xAI/U6FDSqyo2EiQSQghhvwwGldoeWF6ltlty6INwszvugK83qGkry5ZC9y5w9mzer+/spKaunbsM0bGyyymEEOL2lll2Zspl2lmt2vDmBPj5J1i08JbDdcrWYHrD8Wy5/APzTy1XWbxRMRAnjaxLDV2H0EiVlePkII2qz5yGrp3R9/3ByF6eLO15J1MavVHSqypWEiQSQghh3zQNPN2hakX1QJqch+lnzi7w9ruw6FMIuQydO8BX6/J+faeMrKKwKLh4RQWNhBBCiNuV0QgBfrmXnT3VFzp0hBnT4OCftxweVmsAHQJbMnr/RP6OPaOyeK9ESE/A0kDXVWZYXKLKDncEP+2Cbo+jxyfw9qtNmFf3Giubf4SrqXROMcuOBImEEEI4BmcnqFJRBYySLXmbPtamLWz+Du6up9LeR42AhIS8Xd9gULucyRY4Fwwx8ZJVJIQQ4vZVxhW8vXIuO9M0mPq+mnr20osQF3fTYY1Pm83E3VSGp3cPx0Ka6lkTHKYCUMJxxSVC5FX17GTvjap1HZYuhuf6Q1Alvvrof0w072HCfS/TsNw9Jb26YidBIiGEEI7DaIAAX7V7aUnL2wNkQEX4fA2MegW+/UZlFf11JG/X1zQ1tjVzpzM4VKVQCyGEELcjP291T0zL4X7sVVb1Jwq5DK++fMsGi79reRY3nc7B6KO8dWiGer90a8bEszxsCAn7kZSsnpOczPYfILJYYNwYmDgB2rbjn6Uf8uy5D2hRoQnj7h5W0qsrERIkEkII4Vg0TTXMrFJRZfek5KH8zGiEEaNUsCglRfUp+nRx3jOCMrOKrqXA2csQK1lFQgghbkNGg9q4SbPmfB9s2AjGvg7fbYMli2453KVyewbd9RTvH/2YH0P3qg2ZFIsKNMj91bFYUlWAz2RQ3x/2LDoanukDa1bDsBGkzp1Hn/2vYjKYWPHQRxgNDtJou5DZ+X81IYQQIhuuzipQVMY1o/wsDw+RTR6Azdvh4Ufg3Qnw/LPqQSEvMrOKTEYIiVSTOyQ1XgghxO3GzQV8cik7A3h+MLRrD9Peg/37bjn84f0TuNOzKn13jyAq+aq6x8YnqimjEihyDJmTzHTAZOeNqk+dhCceg0OHYNYcGP0qE47MYl/UYT55cBqV3QNLeoUlRoJEQgghHJfJCIEV1JQViyXnKSs38/aGT5aoxtZ7foaObeG3X/O+BqMBXMyQmKR6FcUlyMOsKHKapvlomrZD07TTGb97Z3NeuqZphzJ+fVvc6xRC3CZ8y6regTmVYGsaTJ8JgYEwfChERt5wuIzZjS9azCM8OYqBe0ejQ8bEs1jVB1DYN6sVQiLU85i9TzL7YafKJk9OhtVroUtXfgr9lSl/zeW5O3vTs+pjJb3CEiVBIiGEEI5N06BcWQjyVw8oue1k3vzaAc/B19+Cqxs83Rtmz1Q7YXldg7NZBYwuh0NIeM79GYQouHHATl3X7wJ2Zvw5K0m6rt+X8evx4lueEOK2YjBART+V1ZvTYAlPL5j/CURfhZHDb7nfNix3D9MavM6GS98x/+Rn/91fw6JUI2Rhn3Rd/TdKuGbfk8x0HRZ/AoOehSpVYcMmqN+A6JSr9N09gjs9qzK78bslvcoSJ0EiIYQQpYO7G1QNVDuZyXnoUwRQ927YuBW6dIVZM1WwKPRK3tdgNKpdz4RrqldRfKJkFYmi0gX4LOPjz4AnSnAtQgih7r9+PpCSlvO9r05deHcS/LJbbczc5KU6A3k08BFe2TeRI9HHVQDKbFIbMNeSi/ALEPmSOeo+Jt6+J5lZLDB2NEx+F9p1gDVfQ0BFdF3n+b1jCE2K4PPmc3E3lynplZY4CRIJIYQoPZzMUNlfNbZOtuS8m3kzd3eYORtmzFJTzzq2U+nIeaVp6kHZqMHlMLgSmbcyOCFsU0HX9cxIZihQIZvzXDRN269p2m+apkkgSQhRtLw9wd0l9x59vZ6EHr1gzmzYsf2GQwbNwLJmH+Lt7MWTPw8jMfWaytQ1GeFSKCSnFOEXIPIsNsH+R91HRUHfPrD2Sxj+EsxbAG5uAMw7sYz1F7cxteFrNPK9t4QXah8kSCSEEKJ0MRigQjmV9m5Jy3sz6e49YOM2CKgIA/vDpHfU7lNeGY0qWBSXqHoVJVyTrCKRJ5qmfa9p2tEsfnW5/jxd13VUm9CsVNF1vRHwFDBL07Q7srnW4Ixg0v6IiIjC/UKEELcPTQN/P/VxTqXbmgYTJ0O9e+DlEXDm9A2Hy7v6suKh2ZyIPcOofRPUJ01GMGgqUGTJQ2m5KDoJ1yA0QpUE2muA6OQJ1aD6yCH4aB68PEY9KwJ/Rv3FK/sn0imoNaPqDCrhhdoPCRIJIYQofTQNvDygakX1IJCSx/Kz6tXh6w3Qb4Aa1duzK1w4n791uGQ8OF0KhbDIvPc7ErctXdfb6Lp+dxa/NgBhmqYFAGT8Hp7Ne1zO+P0s8CNQP5vzPtF1vZGu6438/PyK5OsRQtwmzCYVKLLkUnbm4goLFoOzMwx6DuJibzjcpmJzxt79Pxad/pzV5zb899466p4qvf9KVlIyBIer/yYGOw0rfL9DNai2WGD1Ouj83x5LnCWe3j8Nxc/Fh2XNPsSg2enXUAIK9DehaVpPTdOOaZpm1TStUQ7nndc07a+MyRr7C3JNIYQQwmYuzlClIpRxzSg/y0OgyNkF3pkECxbB+fPwWAfYuCF/6zBl9CqKTYBzl9UkNCEK5lugf8bH/YFbvjk1TfPWNM0542NfoBlwvNhWKIS4fXm4gbdH7sMkKlZUjayDL2XZyPrd+qNp6teIQXtf5WTsP+qTTiZ13kUJFJWY5BT1928yqMxpe6PrsGAeDH4Oqt8B32yE++pfd1hnyK/jOJtwkS9azMPXxacEF2t/ChouOwp0A3624dxHMiZrZBtMEkIIIQqdyQiBFcDPW2UU5TWTp/2jsPk7qFkLRgyDcWMgKR9BnsxeRQAXr0BolGQViYKYCrTVNO000Cbjz2ia1kjTtMUZ59QG9muadhjYBUzVdV2CREKIoqdp4FdO9QrMrey7cRN4+13Y9QN8+MENh8wGM6sfno+z0YmePw3hWlrG/TfzfS+FSd+/4pZiUZlcBk09Y9mblGR4+SWYNgU6dYYvv1ItBK6z5PQXrD6/gXfvG03zCk1KaKH2q0BBIl3X/9Z1/WRhLUYIIYQoEpoGvt5QyR/SrSoFPi+CguCLtfC/F2HNalXbfiqft7/MrKKYODgfAtckq0jkna7rUbqut9Z1/a6MsrTojM/v13X9+YyP9+q6Xk/X9Xszfl9SsqsWQtxWjAbVH9BqzX2QxNPPQO8+MO8j2LzphkOVylRkZfOP+OvqCYb/Pv6/A85mVUYUHCqbLsXFkqoyiECVmdmbiHB4sid887XqPfTRPHB1veGUI9HHGfHHW7QJaM64u4eV0ELtW3EV3unAdk3TDmiaNriYrimEEELcyN0NqgaqB5vkPPYpMpthzDj4bJWaktGlE3yxKn/NqDVNBYp0K1y4AmFRKnglhBBClCYuzlC+nCo7y+l+qWmqxLtBQ3jlJTh08IbDHQIf4Y16I1h65ks+O7P2vwPOTup+HhwmgaKilpqmMoh03T4DREcOw+MdVaPqBYvUFLObmmnHWuLo/uNgyjp5saL5bIwGO8yEsgO5BolsnayRi4d0XW8APAoM0zStRQ7Xk+kaQgghio6TGaoEgKd7Rp+iPAZnmreArduh4f3w+lhVghYfn7+1mEwqWHQ1Ds5fhmvJ+XsfIYQQwl6V9QCPMrlPJHN2hoVLwK+8amR9+fINhyfc9zIt/R9k6G+vcfTqieteZ4akFAkUFaXUVFUqn5auekLZmw3roVc3MJpg3QbVKuAmuq4zYM8oziVcYs3DH+PvWr4EFuoYcg0S5TJZwybXTdYIB9YDjXM4V6ZrCCGEKFoGAwT4QoVyqvQsr/0M/MrD8lUweixs3ayaWh85nL+1XJ9VdPEKhEflPXAlhBBC2CtNA39f1eA4t0bTvr6w9DNISYGB/W/YhDEZTHzefC6eZg96/DiEOEv8f+/vbIYki0w9KwqZJWbpVvX3bE/S02Hae6rp+X31YcNmqFMny1NnHFvAN5e+Y3rD8TxUIdtwhKAYys00TSujaZpH5sdAO1TDayGEEKLkaBr4eEHlAJU6bcklFf5mBgMMG65GqqalQo8nYOni/JWfgcoqcjZDdJyagCZZRUIIIUoLkxEqllebMrlthNxVA+YvhDOnYcT/bgj6BLhV4IsWczkdf44Bv4xCz7znahq4mFVZ28XQ3JtlC9tYMjKI0q32l0EUF6emly2YD337wYovoFy5LE/9MXQv4/6cQo8qnRhZ5/liXqjjKVCQSNO0rpqmBQMPAps1Tfsu4/MVNU3bknFaBWBPxmSNP4DNuq5vK8h1hRBCiELj5qL6FDk75d4zISuN7lfTzx5+BCZOgOefhatX87eWzKwiq2QVCSGEKGXcXMDPx7Z77UPNYeJ78OMudW+9ziMBzXi/4Rusv7iNaUfn3fg654ypZxev5F7eJnKWYlF9E626/QWIzpxWQ0R+/gkmTVHfK+ass5xCroXy5E/DuMujGkuazkC7qU+RuFVBp5ut13U9SNd1Z13XK+i63j7j8yG6rnfM+PhsxlSNe3Vdr6vr+uTCWLgQQghRaMwmNfnM21P1KcprE+my3vDJEnjrHdjzM3RqB3/8XrD1OJshOlZlFSVJVpEQQohSwMdL9SdKsSHTp8/TMPgFWL4MPllww6GX6wymd9XHeePg+2y//NONr3M2q/v4xSsq0CHyLjlF/f1hhwGiHduha2eVSbTqSzUZLxuWdAu9fhpKfFoCX7X8BE8nj2JcqOMqrulmQgghhH0zGKC8T0Y6fFreU9U1DZ4dCF9tUM03+/SEObPz30RT09RUGGvGBDTJKhJCCOHoNE31BDQbbbvPjn0dHnscpkyCr/6baqZpGkuazqCO11302T2Mc/EXb3ydk0llK50PgcSkQv4iSrmEa+q5A+xripnVCrNnqhKz6nfAt1ugcZMcXzLij7f4JXwfS5rOoK53zWJaqOOTIJEQQgiRSdPAyx2qVATNoHYg81p+dnc92LgNOj8OM6dDv6cgPCz/a7o5q0h6FQkhhHBkRiMEllfZPrltfhgMMONDaNYcxo6Gnd//e6iM2Y31jywm3Wql24+DuJZ2UzDIbAKjQTWzjonPf8/A20lsgpoSZzTYV4AoLg5eGASzZkL3nvDlOqhYMceXLDi5goWnVjLu7mE8WS0vg9mFBImEEEKIm7k4Q9WKqn9CfgJF7u7w4RyYNgP+PAAd28FPP+Z/PddnFUmvIiGEEI7OxVlNPMuhP1F8PISEgEVzhgWLoO7d8OILsH/fv+fc6VmNVS3mcDj6OIP2jvmvkXUmk1FlFV2JgMirEijKjq5DVAyEhKu/L5OxpFf0n1MnVf+hXTtVWf/0meDimuNLfg79jeG/v8mjgY8wqf6rxbTQ0kOCREIIIURWTEYI8gefsvnrU6Rp0OtJlQ7t6wsD+qoxrakFaKQpWUVCCCFKCy93KOtxS98giwW++hrmfwzfboQPP4Q9h93RlywH/wAYOABOnvj3/E5BrZlYfwyfn/uGKX/NvfU6BoMaChEZAyEReb+fl3ZWK4RFQXi0GuJhsKMQwaZvVYAoPl71H3p2oHq+ysHFhMv0+GkI1T0q83mLuRgNdhTwchB29B0ghBBC2BlNU32KAjPG9uZnpO5dNeCbTaoB54L58GQPCA4u2JpuziqSB14hhBCORtOgQjl1T0v5bwNla8Yc7FEj4YUhMGQIHD4MR6+UgxWfg4sL9Hsazp/79zWv1xvOU9We4I2D01h/YWvW13JxgvhEuBAiDa0zpabBpTC4Gqf+fgx2MvkrLQ0mvQvD/wd16sKmrbn2HwK4lpbEE7sGkpJuYUOrpZR18iqGxZY+EiQSQgghcuOZ0afIYFBZRXlNV3dxhfemwZyP4fQp6NQetm0p2Jr+zSqKg/OSVSSEEMIBGQwQWEEFJ9LSSUmB48eh46Pg5KROKVsW2rSB/fuBoEqwfBWkWuDp3nBJNazWNI3FTafT2Pc++u4ZwaHoY7deKzNQlJauGlrHJdze5WdJyer5ISVF/b3Yy2j4sFB4qjcs+QT6DYDP10AF/1xfZtWtDNgzikPRx1jVfA61vO4s+rWWUhIkEkIIIWzh4qT6FLm75i9QBPBYZ9i0DapWhaGD4a03IKUAwZ3MB17dqnZGQ6PyP01NCCGEKAlmkwoUpaWTnGTFbAbXm1rO+HhDQkLGH2rWgpWrITER+vT6NzvX1eTKN48swdvJi8d/eJawpIisr5fZc+dyuCqxut16/Om6yhy6cAU0wMlsPwGiPbvVRtrRI/DhR/DOpP+ihbkYf/B91l7YxLSGr/NYpTZFvNDSTYJEQgghhK2MRvUg6+ud0acoHwGZylVg7XoYOBhWfAbdusDZswVbl8mkgkUxcXBOxv0KIYRwMG4u4F8OD6dUzGadizdNtD92XN0+/1WnLqz4Qk29ero3XAkBIMCtAt+2+pTI5Gi67nqe5PRsNmKMGX2KrsbBxdDbp/wsPR2uREJoZEawzE4mmFmt8NGHaiKstw9s2AxPdLP55Z+e/pIpf81l0F1PMbruC0W40NuDBImEEEKIvNA08POGoAqQZs1fI2onJxj/Fiz5TD3Ydu4AX68r+LpcnABdjfsNjZCsIiGEEI7DywODjydd2qWydq3Ovn1w4QLs+F6VmjV/6Kbz692jSs+uRqvypLBQABqUq8fyh2bxa8QB+u8ZhVXPJlNI01TZtiVVDYOIji3d5WeJSerrjEvM6D9kJ6GAyEh49hn48AN4/AnVx/GuGja/fNeVXxj861jaBDRn3gOT0ewlK8qB2cl3hhBCCOFgPMqo8jODEZKzH+Gbo1atYfN3cHc9eGUkjB6l0ucLwmRUD72xCXD2MiRcK90PvUIIIUqHjGERVWu70LdnKhcuwvc7IT0Nnh8IPj5ZvOa++rBsJUSEQ+/uEHwJgB5VH+P9hm+w5vxGxh6YnPM1nUzqV1iUGghhKcAUUnuUOb3s4hX1Zxc7Ki/7ZQ90bAe//QaTp6oSszJlbH75idgzdPtxMDU8q7O25QLMBnMRLvb2IUEiIYQQIr+cC6FPUUBFNdZ1xEiVTfR4R9W1syA0Ta1NQ2UVXYlUjTqFEEIIe2YwQGB5KgSa6NE5jYHPQYcOqnl1tho0VFPPrsZAr27wzxkARtd9gWE1+zPj2ELm/L009+u6OKkpa+cuQ1SM4/cq0nW4lpE9lDm9zGQn4+DT0mD6NHimD3h5wTcb4am+eQpehSdF0un7/pgNJja1XiaTzAqRBImEEEKIgjAaIbB8wfoUmUwwajSsWq06c3btDMuXFTwDyGRUD4VxCXAuWKWYS1aREEIIe2YyqpJudNs3OOo3hNVr1Uj3Xt3g6F9omsbsxu/SpVJ7XvrjbdZf2Jrze2RmFZmNEHFVBVfiHfS+aUmFkHDVnNpqta/pZcHB8GQPmD8HevZW/Ydq18nTW8SnJtBxZz+uJIXxbaulVPOoXESLvT1JkEgIIYQoqMLoUwTwYDNVfta0Kbw9HoYOgtiYgq/NxUmNF74cph4aU9MK9p5CCCFEUXJ2giB/FSSyNaOndh1Y8xW4uMJTvWDfHxgNRj5vMZcmfvV5aveL/Bp+IPf3ycwq0nV137wUCskpBft6iku6FSKvwtlgSEhSX4fZTppT6zp887UqLzt5Aj6aB9NmgJtbnt4mJT2Frrue51D0Mda2XMgDfg2LaMG3LwkSCSGEEIWlMPoU+fqqhtavjYed30PH9nBgf8HXZszIKkq4prKKYhMcc3dUCCHE7cHNBQJ8VQmYrferatXVBFG/8mpS1o7tuJlc+bbVpwS5BfDYD/05dvWkbe9lMqpgVXIKnL+sAkZJKfZ570xPVyVlZy+pIJGTSfUntJfsodgYGDEMRo2AmjXVhljnLnl+G6tupf+eUey8soclTWfQKah1ESxWSJBICCGEKEyF0afIYIDBL6gHXaNRNeOcP7fg/REyexUZjSqjKDgs/1lPQgghRFHzdP+vnNvW+2nFivDlV1CjJgwZCEsW4efsw7Y2K3E2ONN2x1Ocjb9g23tpGjiZ1b0zMQkuhKjMosQk+wgWpaWpoNA/l1RzaoMBXJztZ3IZqObUHdrAti0weiysXgeVq+T5bXRd56U/3uLL89/yfsM36H9nzyJYrAAJEgkhhBCFrzD6FIGa2rJpG7R/FKZPhf59ISKiENaXkUp/LVlNQIuJs4+HXSGEEOJ6mga+ZcHbA1LyECjy9VXBiPYdYNI78NYb3OEWxPa2q0ixptB2+1OEXAvN2zqczCo7J8UCl66onkXRscW/2aLr6v4dGqmCQ5Ex//UgNNrRj/fXrsGEN6Hvk1DGHb7+FoYNV89I+TD5yEfMPbGMl+sMZnTdFwp5seJ6dvRdJIQQQpQit/QpymcfIE9PmPsxvDcN9v2uavl3/1w463M2qwadoZFqNG6KpeDvK4QQQhQmTYMKvuBeJm+BIldXmLcQhgyFlcvh+QHcbQpka+sVhCdH0m7H00QlX837WswmlVlktUJ4NPwTrDKMYuPVvb4oNl10XZW9RUTDmUvqnh2bAGZzRt9BO/ux/tdfVPbQ8mUw4DnYuBXq3ZPvt5t57BPePDSdZ6p3Z3qj8Wj2UkZXStnZd5MQQghRyvzbp8iQ/z5FmgZ9noZvt4CPD/R/Gt6fUji7lwaDetgtTWN/hRBClC6aBhX9wM0VUvKw6WIwwLg31EbLnt3Q4wkax3uxsfWnnIk7T4fv+xJnic/fejKzd5zNaprYlYzMnrPBavMlPlF9Pj/3fatV9T+6GqdKw09fhPMhEBULRu2/6xrsLFiSkABvvg5P9VYZQ19+BW+/qwJ2+TT37095Zf+79KzyGEubfYBBkxBGUdN0O04vb9Sokb5/fyE06xRCCCFKWnq6eoCMTyzYKNqkJJg4Ab5YBfUbqOkgQZUKZ41WqwoWuTiDvy+4OhfO++ZA07QDuq43KvILCZvJ85cQwm6lp8PFULVJ4mTO22t/2QPDh6rXTpvBpnoudN31PE386rO1zQo8zO4FX5+ug1VXU9mu/znbZAJnk1qz+bpYNdBnAAAblUlEQVR1Zz4LpKergJIlTfUZSruuTN1oUAEXewsI3eyHnfDWGxByGZ4bBK+MKVBwCGDRqVUM/nUsXSq1Z23LBZgNefxv7uiSU6B8OfDxKpK3z+4ZTMJwQgghRHG4pU9RPrN1XF3Vjuicj+HMaTX9bMumwlmjwaACQ2lpcOGySqOXrCIhhBD2wmhUZdxGowqo5EWzh2DTd3DnXTDsBR777BdWN53NbxF/0n7H0/nLKLqZpqmgjnNGGdi/GT+oe39sgrq3hkepX2GR6ldUjJo+mpamgkHXv95ssu8A0ZUQGDoIBvZX4+zXrofxbxU4QPTZmbUM+XUcHQNb8eXD82+/AFEJkiCREEIIUVxu6FOUnv8+RQCPdVYjZKtXh2EvwBvjIDmpcNaZ2W8hOlaVoCUlF877CiGEEAVlNkElfxWMyWugKHPyWf9nYckiuo/7lG9rT2Jf5GHaf/80sZa4wl+vpqlNGHNGJpGLk8rYvf6Xs5M6ZjKqcx2h505aGixZBG0fgR93wavj1LCNhgVPDl75z1c8t/cVWgc8xFePfIKzsegzm8V/JEgkhBBCFLfr+xTlpQnnzSpVhjVfw+AX4POV8MRjcOpk4axRy+h5kJ6udj2FEEIIe+FkVoEig5b3QJGTE0yYqMq1/z5Ox4FT+DWlH/sjDtNux1PEWGKLZs2lyd5foPOjanJc4yaw/QcY+qL6uy2ghSdX0m/PSFpWeJANrZbiYnQphAWLvJAgkRBCCFESnJ1UoMjNNf8NrUE9kL02HpathMgo6NJJBYwKq+egvU1MEUIIIUAFiioH5C9QBNC5i8rIrVmTRu8t5eLOewm+eJS2258iOiWPU89uF+fOwuCB8HRv1aT6409gyWdq06oQfHBsIS/8No6OQa3Y3OYz3EwFK1kT+SNPfkIIIURJyeytUM6rYH2KAB5uCVu3Q8P7VenZi0MhTnZDhRBClGLXB4ryM/GzajVYvQ5eG0/A70c5u9CVar8co/nW7gQnhhT+eh1VbAxMngjtW8PePaq07Ptd0KFjoZTG6brOO4dmMnr/RHpWeYyvWy6SDKISJEEiIYQQoiRpGpT3UU2tUwvYp8ivPCxfBWNfg+3bVFPrPw8U3lqFEEIIe5NZeqblo0cRqA2bwS/Axq04V67GmtVpzFjwD31WPMbfMacLf72OJD4ePpoFzZvCkk+gWw/YtVuVljkXThBH13VePTCJCYdnMuCOXnzRYh5OxoKXrYn8kyCREEIIYQ883aFqQMH7FBkM8MIwWPOVCkD16gbz58qUMiGEEKWXs5PKKDIaICUfGUUANWrCVxtg3Bu0u+TE9zPC2TqqA/su7i3ctTqCpCRY+DE83BQ+nAEPPghbtsPU6WpDqpCkpKfQd/dwZhxbyIu1BrCk2QyMBmOhvb/IHwkSCSGEEPbCxRmqVARXF1V+VpC+QvUbql4Lj3aC6VOh31MQHlZ4axVCCCHsSWbpmdmU/80WsxmGDMX4w24sHdrz8o8pBHTuzeFl790emy2xMWpjqUVTmDoZ6t0D32yChUugVu1CvVR0ylXa7XiKz899w3sNxvFR44kYNAlP2AP5ryCEEELYE5NRpc0XRp8iT081vWXqdDiwHx5tC7t2Ft5ahRBCCHtiNqlAkYszWAowFKKCPx5zlxC98lPiPZ259535RD/SCH3Tt/kOFuk67N8PixfD3HmwbZvq/WwXLl9Wk8qaNVEbS7XrwJdfwWer4N77Cv1y5+Iv0mxrV36LOMjnzefyWr0X0Qqht5EoHBIkEkIIIeyNpoGfD1QsD2lpBetTpGnQuw9s3ArlK8Bz/dWDYEpK4a1XCCGEsBcmIwT5g5uLKj0rQFauT7O2BO04wPuD6xF6LRxt+P/QO7SGTRvzHCzauhUOH4ZWraBHd9CBpZ9CcnK+l1cwug6/7YURw6BlM1i2FNq0U1nIy1ep0fZF4I+Igzyw5XHCkiLY0fZz+lR/okiuI/KvQEEiTdOma5p2QtO0I5qmrdc0rWw253XQNO2kpmlnNE0bV5BrCiGEELcFTQMvd6hcUX1cwAdd7rwLvtkI/Z+FJYugexc4e7bw1iuEEELYC6MBAiuo+2iyBaz5v396uHjxythNrJw3lN494EJ8MAwfCi0fggXzICoq1/eIiYG/jkLfvlC9Ovj7w6MdILAiHDyY76XlT2Sk6jfUqgX06QU//Qj9BsCPv8CsOVCnbpFdesnpL2ixrQdlTG7s7biBFv4PFNm1RP4VNJNoB3C3ruv3AKeA124+QdM0IzAPeBSoA/TRNK1OAa8rhBBC3B5cnaFqRZU6X9BAkbMLTJgInyyFy8HQuQOs/bJg7ymEEELYI4MB/H3VBNGUgpVvGw1G3mv8Bp1fmE2dF9IZ9owvCeXLwrQp0PR+GPki/PF7ttlFV0KhciVwdr7x83fVgMsh+V6W7WJjYN0aGNhfrXfqZPD1gw9mwe/74c0JEBRUZJdPSU9h8N5XeX7vGJpXaMwfnTZRy+vOIrueKBhTQV6s6/r26/74G9Aji9MaA2d0XT8LoGnaaqALcLwg1xZCCCFuGyYTVKoA4Vfhaiw4m9XDb361bQdbd8CoEfDqK7D7Z5g0RfUwEkIIIUoLTYNyZVWvoisRoBvUPTWf+t7Rnbs8q/HErudZWuMkK4a8QvdfotG+XgcbvlEpQu06QIdH4f4m/17LyxPCI9SezPWtd8LD1bEiEXwJ9uyGbVvglz2qfD0wCAY8Bz17qwhVMbiUGEL3XYPYF3WY1+q9yMT7xsgEMztXoCDRTZ4Dvszi84HApev+HAwUTYGjEEIIUVoZDFDBB1ycIDQSTAV70MU/AFauhgXz1Xjbg3/C7LnQoGHhrVkIIYSwB57uKlAUHKaycp1MN0Zr8qCJXwMOdt5G390j6BnyAX1adWHByB/x3LUHvtsKa1bD8mXg7Q0tWsL9jQlo0AiPMjXZscNAy5ZqiNrpM/DnnzDwuUL4+nQdrlyBA/tUQOjXvXDxgjoWVAmeex46Pgb33Jvvrzs/tl3exTO7XyLFauHrlovoWuXRYru2yL9cny41Tfse8M/i0Bu6rm/IOOcNIA1YVdAFaZo2GBgMULly5YK+nRBCCFF6aBqU9VCZRIXwoIvRCMOGw4NN4aUXoVc3GPkKDB2mjgkhhBClhasLVA2EkHBISlH30nzeP/1dy/Ndm1VMPTqPtw7NYF/kYb58+GMadO0OSUnw84+wbavK5NmwHg141sOT0IAGHJhfmzjvqlyrUI0nu1WlnLc/NneBSUmG0FD1K/gS/H0cjh9Tv8fEqHM8PKDJgypjqGkzqFGzWANDANfSkhizfxLzT37G3WVrsq7lJ9T0uqNY1yDyT9ML2IdA07QBwBCgta7r17I4/iAwQdf19hl/fg1A1/Upub13o0aN9P379xdofUIIIUSplJpWKA+6/4qLg/GvwcYN8MCDMHM2BFSEtHS1+1qlYuGs+yaaph3Qdb1Rkby5yBd5/hJClGpWK0RchehYcDKrJtcFsDvsd/r8PIyI5Gjeunckr949FPP/27v36CjrO4/j7+9MZiYJCRcTLgmXAIqiolZrlYr1UtCybFesrXvQ1erW1VOsdb2cena9VNfWs3ZXbbutu4qWqrVqq0VlK61W6y7VFRfYWkFUioBchXCLCSSZZPLbP34zZsAgQ2byzGTm8zrnOXN7zjPffDMn8833+f1+TyjiX3QO1q+DJYth6RJYugS3ejXWEe8+QDQK1QOhusrfVlX5oUbx+N7btkbYsWPvN4/F4IiJfrHpo46GY46FScdkN9I4S4u3vcFFf7ialR+u5rqjruCOE26gPFyet3j6tbZ2GFYDhwzqk8PvrwbLqklkZtOBe4DTnXON+9mnDL+o9VRgI7AYuNA599aBjq8iRURE5BN0dcGW7bCrOft1isAXs796Em692Reo37sLpp6tJlGJUf0lIkXPOWje7dcpMvPNoixsb9vJla/fyC/X/iefOuRo5p5yN8fXTOp550TCTw1buwbeX+ubSM3NfmtphuYW6IhDNOYbSLGov62pheEjoK7O39aPhIaGvDaE0sUTce5cfi+3/+kH1FcO56Ep3+fzdVPyHVb/1k+bRKuAGJC67t8i59zXzaweeNA5NyO53wzgB0AYmOucuyOT46tIEREROQDnYNeHsGUHlIX9lq01q/30s2Vvwqy/gVv/CSb2zTBxNYkKj+ovESkZ7XHY1OhvczAq95l1v2X2ohtpbNvODZNm8+3jrimJUTS/3/wq33j9Jt5pWsWF487l3sl3MDjaN42NkpKnJlG2Vzfr8bp1zrlNwIy0xwuABdm8l4iIiPTADIYM8kPONybXKYpld0aUcePhqWfg7n+BOfeBAY89mpNwRURECkYsCg11sH0XbG/yU88ivf8X+dwx0zl9+GSuX/Id/nnZj3ly7a+568RbOGf02VjA6wIFYfOeLVy/5HYeX/Ms46saeG7qw8wYNTXfYUmWshyXLiIiIgWhshzG1vsGUVvcjzDKRjQK/3gzPPRz+PtrcxOjiIhIoQmFYOghMKbO38/yO3RIbDBzp9zNC2c9RlmojHNfvowznz+f/9u+LIdB51drZyt3Lb+PI545nXnv/5Zbj7uW5TNfVIOoSKhJJCIiUiwiEV/kDqzyRW5XV/bHnHIqjKjL/jgiIiKFrDJ59bPB1f47tKMzq8OdVX8ab57zO+49+Q7e2rWSE389g0teuYb3WzbkKODgtSfa+fHbP+XQeafyraXf5dRhn2H5zBe57VPXU1FWke/wJEfUJBIRESkmoRDU1cLwGj/1rDOR74hERET6h3AIRtT6izWEw1mfcImEIlw58RJWnfcKN0yazRNr5nPovClc/IerWbbz7RwG3rfiiTgPrnyMCU9/jm/+7y1MGDiWhdN/xYJpP+OwgePyHV7/5pzfEgnfmIx3+DWyWuOQyMHJvl4ojKXQRUREJHfM/CKHsShs3ArxTojqK19ERCQjqSncu5qhcSeQ8N+jvVxXaFB0IHd++ka+MfFSvr/iAeas/DmPrp7HjJGf54ZJszlt+OSCXLNo/e5N3P/uozzw58fY2raNk2uPZ+4pdzO17tSCjLcgOecbjV3J29RMRqP7figEkbD/jKUuQlKWvF8RCzxkVYwiIiLFakCFL3I3bvVnQ3Nw5RYREZGSEAr5Ey7VA6BxB3y4G0LmF7bu5Xfp6AH13POZW7n52Kv593ce4Ydv/4Qznj+fwweO56uHfoWLx3+ZMVUjc/yDHJzOrk5e/uB/+I93H2H++t/R5br44qhpXDXxUs6qP03NoX2lRgJ91AhyvgEEvglk5ps/sQhEI/7zEynzI9XKwn70WqiwJniZy3Zhyz6kS7CKiIjkQKILtmyDphY/uih0EAVeZ8IXMw31fRLa/i6/Kp/MzM4HbgOOBE5yzvVYMJnZdOCHQBh40Dl354GOrfpLRKQHbe2+WbS7tfsf/CwbJns6W3lizbM8/N6TLNzyOoZx5ohTuGDcTL4w8gxGD+ib7959tSfaeWnzK8xb9xueXfcC29p3UBMbwt9NuICvH3ExY6tGBxJHQUs1ghLJZlC6cNg3gWIRf+GPSJkfCRQJ+wZQgTbW9leDaSSRiIhIsQuHoG6obxA17vRFSzic76gkO8uB84D797eDmYWBe4GzgA3AYjOb75xbEUyIIiJFpDwGo0bAnjbfLGqNQ1koq2ZRZVkFX5swi69NmMXq5vd5dPU8HnnvKS5/7QYAJg46jLPqPsfZ9adz8tDjGVpek5MfpbWzlaXbl/Fa41Jea1zKS5tf5cOOZqojVfzVqGl8acx0vjh6GuXh8py8X7/iXLIRlNzM0kYERaCiAsqje48KKrCRQNlSk0hERKQUmEHNYN8o2rQVEk7rFPVjzrm3gQMN+z8JWOWcW53c9wlgJqAmkYhIb5j5qdyV9X5E0bZdfoRRltPQAMZXN/Dt467llmOvYcWulbywaSEvbP5vHvzz4/zonZ8CMKy8lmOGTOSYwRM5cvBhDCuvpSY25KOtIlxOe1eceCJOvKuDtkQ7m1q3sLZlPWtbNrC2ZT3vNr3HGztX0NHV4d+3qoHzx/4l5435C6bWnUosHPwaOHnhklPDEom9RwaZ+RFB5ZW+ZopGkmsFZff77U9UHYqIiJSSqkp/id8NW/zVM6Jap6iIjQTWpz3eAJycp1hERIqHmf8+HVDhm0Tbm6Blj38tmt3IEjPj6CFHcPSQI7j26MtpS7Tx2talvLFjBct2vcOyne9w/8pHaU20HdRxQxZiVGUdh1Y3cN1Rl/PZoZ9m8tATGF4xtNex9hsfNYOSC0enFo2OlPnfY3nMN4RSI4NKvC5Sk0hERKTURCPQUAcfbPMLcR7sOkUSCDN7ERjRw0s3OeeezfF7XQFcATBmzJhcHlpEpHiZQUU5jCr3J16ammFnM7hOP9U7B+sWlYfLObNuCmfWTfnouURXgo17PmBb+w62t+/8aGvtbCMWjhILR4mGosRCUeoqhzG2ajQjK0cQCUWy/YkL2/5GB4VCfopYRcw3hFJTxYpsmliuqEkkIiJSisJhqB8G5U2wdYc/86l1igqKc25alofYCKSvNjoq+VxP7zUHmAN+4eos31dEpPTEojCsBmqH+FFFOz+E1nb/WuoqVjkaoRIOhRlTNTLvV0LLqy4HXQm/flD66KBocnRQRbmfNhaJ5KRZV0rUJBIRESlV6esUbdQ6RUVoMTDBzMbhm0OzgAvzG5KISJELhWBgld/a475h1NQC7X4NoFw3jIrex64qluwG7Ts6KBLJeqqfeKoERURESl1VJYyt940irVPUL5jZl4AfAUOB58zsDefcF8ysHn+p+xnOuU4zuwp4HggDc51zb+UxbBGR0hKL+u2QQRDv+HjDKGS+aaTGRvdUsa6EP2mVLhaByork2kEaHdTX1CQSERERX8Q21MHmbdCyG6Jap6iQOeeeBp7u4flNwIy0xwuABQGGJiIi+zLrbhjVDIbOTj8VrWU3NLdCV6ffL2R+6nfIircBstcl5vdpBkXK0ppB0aK9xHyhU5NIREREvHAYRg6DHWnrFImIiEhulZVBdRlUD4ARzo8yao/D7jbY0+pHGqXW2AmZb5L0lylqzqWNCuqhEZS6xHws2QxKNYLUDCoYqv5ERESkW2qdomgENjX6Ai+ickFERKRPpI8yGljln+tM+MZRvAPa2v2oo3h87wWaoXvEUcjAQv61vmokpZo/qQZQ+uPUe6biKgv7E03RaPeVxCJlvjnWX5pdJUxVn4iIiHxc9QAYG4ENW1TMiYiIBKks7LfKcqDaP+ecbx51dqY1kTr9447kc11dft/0r+1U4ya9udSTnr7q990/nIwrmrxNNX9Sz4eTi3JrRFC/piaRiIiI9CwW9Qtad3TmOxIREZHSZtbdlNmf1PSu1K3rShv1g7/FdY9I6j64P77RPTrJktPcUtPdrIjXSZK9qEkkIiIi+xdOnhkUERGRwhYKQQj8BS1FekfjwERERERERERERE0iERERERERERFRk0hERERERERERFCTSEREREREREREUJNIRERERERERERQk0hERERERERERFCTSEREREREREREUJNIRERERERERERQk0hERERERERERFCTSEREREREREREAHPO5TuG/TKzRuD9Pjp8LbCtj44tH6d8B0v5DpbyHTzlPFh9me8G59zQPjq29ILqr6KjnAdL+Q6W8h0s5Tt4gddgBd0k6ktmtsQ5d2K+4ygVynewlO9gKd/BU86DpXxLruizFDzlPFjKd7CU72Ap38HLR8413UxERERERERERNQkEhERERERERGR0m4Szcl3ACVG+Q6W8h0s5Tt4ynmwlG/JFX2WgqecB0v5DpbyHSzlO3iB57xk1yQSEREREREREZFupTySSEREREREREREkoq6SWRm083sXTNbZWb/0MPrMTP7RfL1181sbPBRFpcMcn6dma0wszfN7CUza8hHnMXiQPlO2+/LZubMTFcjyEIm+Tazv05+xt8ys8eCjrGYZPD3ZIyZvWxmf0z+TZmRjziLhZnNNbOtZrZ8P6+bmf1b8vfxppmdEHSM0n+oBguW6q/gqQYLlmqwYKkGC1bB1WDOuaLcgDDwHjAeiAJ/Ao7aZ58rgfuS92cBv8h33P15yzDnZwKVyfuzlfO+zXdyv2pgIbAIODHfcffXLcPP9wTgj8CQ5ONh+Y67v24Z5nsOMDt5/yhgbb7j7s8bcBpwArB8P6/PAH4DGDAZeD3fMWsrzE01WEHmW/VXwDlP7qcaLKB8qwYLPN+qwXKb84KqwYp5JNFJwCrn3GrnXBx4Api5zz4zgYeT958CppqZBRhjsTlgzp1zLzvn9iQfLgJGBRxjMcnkMw7wHeB7QFuQwRWhTPJ9OXCvc24ngHNua8AxFpNM8u2Agcn7g4BNAcZXdJxzC4Edn7DLTOAR5y0CBptZXTDRST+jGixYqr+CpxosWKrBgqUaLGCFVoMVc5NoJLA+7fGG5HM97uOc6wSagJpAoitOmeQ83WX4jqj0zgHznRyKONo591yQgRWpTD7fhwOHm9mrZrbIzKYHFl3xySTftwEXmdkGYAHwzWBCK1kH+zdeSpdqsGCp/gqearBgqQYLlmqwwhNoDVbWVwcW+SRmdhFwInB6vmMpVmYWAu4BLs1zKKWkDD/c+Qz8WdqFZnaMc25XXqMqXhcADznn7jazzwI/M7NJzrmufAcmIlKIVH8FQzVYXqgGC5ZqsCJWzCOJNgKj0x6PSj7X4z5mVoYfKrc9kOiKUyY5x8ymATcB5zjn2gOKrRgdKN/VwCTgv8xsLX7+6nwtnNhrmXy+NwDznXMdzrk1wEp8wSIHL5N8Xwb8EsA59xpQDtQGEl1pyuhvvAiqwYKm+it4qsGCpRosWKrBCk+gNVgxN4kWAxPMbJyZRfGLIs7fZ5/5wCXJ+18Bfu+SK0NJrxww52Z2PHA/vkDRXOHsfGK+nXNNzrla59xY59xY/BoE5zjnluQn3H4vk78pz+DPYGFmtfihz6uDDLKIZJLvdcBUADM7El+gNAYaZWmZD3w1eYWNyUCTc25zvoOSgqQaLFiqv4KnGixYqsGCpRqs8ARagxXtdDPnXKeZXQU8j1+hfa5z7i0zux1Y4pybD/wEPzRuFX6hqFn5i7j/yzDn/wpUAU8m16dc55w7J29B92MZ5ltyJMN8Pw+cbWYrgATwLeeczoz3Qob5vh54wMyuxS+geKn+yew9M3scX2DXJtcYuBWIADjn7sOvOTADWAXsAf42P5FKoVMNFizVX8FTDRYs1WDBUg0WvEKrwUy/SxERERERERERKebpZiIiIiIiIiIikiE1iURERERERERERE0iERERERERERFRk0hERERERERERFCTSEREREREREREUJNIRERERERERERQk0hERERERERERFCTSEREREREREREgP8HvhiEdo79kYgAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data(sinusoidal, n=7, std=0.1, domain=[0., 0.7])\\n\",\n    \"x = np.linspace(0, 1, 100)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(20, 5))\\n\",\n    \"plt.subplot(1, 2, 1)\\n\",\n    \"model = GaussianProcessRegressor(kernel=RBF(np.array([1., 1.])), beta=100)\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"y, y_std = model.predict(x, with_error=True)\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", color=\\\"blue\\\", label=\\\"training\\\")\\n\",\n    \"plt.plot(x, sinusoidal(x), color=\\\"g\\\", label=\\\"sin$(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x, y, color=\\\"r\\\", label=\\\"gpr {}\\\".format(model.kernel.params))\\n\",\n    \"plt.fill_between(x, y - y_std, y + y_std, alpha=0.5, color=\\\"pink\\\", label=\\\"std\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"\\n\",\n    \"plt.subplot(1, 2, 2)\\n\",\n    \"model.fit(x_train, y_train, iter_max=100)\\n\",\n    \"y, y_std = model.predict(x, with_error=True)\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", color=\\\"blue\\\", label=\\\"training\\\")\\n\",\n    \"plt.plot(x, sinusoidal(x), color=\\\"g\\\", label=\\\"sin$(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x, y, color=\\\"r\\\", label=\\\"gpr {}\\\".format(np.round(model.kernel.params, 2)))\\n\",\n    \"plt.fill_between(x, y - y_std, y + y_std, alpha=0.5, color=\\\"pink\\\", label=\\\"std\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 6.4.4 Automatic relevance determination\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_toy_data_3d(func, n=10, std=1.):\\n\",\n    \"    x0 = np.linspace(0, 1, n)\\n\",\n    \"    x1 = x0 + np.random.normal(scale=std, size=n)\\n\",\n    \"    x2 = np.random.normal(scale=std, size=n)\\n\",\n    \"    t = func(x0) + np.random.normal(scale=std, size=n)\\n\",\n    \"    return np.vstack((x0, x1, x2)).T, t\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x360 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train, y_train = create_toy_data_3d(sinusoidal, n=20, std=0.1)\\n\",\n    \"x0 = np.linspace(0, 1, 100)\\n\",\n    \"x1 = x0 + np.random.normal(scale=0.1, size=100)\\n\",\n    \"x2 = np.random.normal(scale=0.1, size=100)\\n\",\n    \"x = np.vstack((x0, x1, x2)).T\\n\",\n    \"\\n\",\n    \"model = GaussianProcessRegressor(kernel=RBF(np.array([1., 1., 1., 1.])), beta=100)\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"y, y_std = model.predict(x, with_error=True)\\n\",\n    \"plt.figure(figsize=(20, 5))\\n\",\n    \"plt.subplot(1, 2, 1)\\n\",\n    \"plt.scatter(x_train[:, 0], y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", label=\\\"training\\\")\\n\",\n    \"plt.plot(x[:, 0], sinusoidal(x[:, 0]), color=\\\"g\\\", label=\\\"$\\\\sin(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x[:, 0], y, color=\\\"r\\\", label=\\\"gpr {}\\\".format(model.kernel.params))\\n\",\n    \"plt.fill_between(x[:, 0], y - y_std, y + y_std, color=\\\"pink\\\", alpha=0.5, label=\\\"gpr std.\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.ylim(-1.5, 1.5)\\n\",\n    \"\\n\",\n    \"model.fit(x_train, y_train, iter_max=100, learning_rate=0.001)\\n\",\n    \"y, y_std = model.predict(x, with_error=True)\\n\",\n    \"plt.subplot(1, 2, 2)\\n\",\n    \"plt.scatter(x_train[:, 0], y_train, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", label=\\\"training\\\")\\n\",\n    \"plt.plot(x[:, 0], sinusoidal(x[:, 0]), color=\\\"g\\\", label=\\\"$\\\\sin(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x[:, 0], y, color=\\\"r\\\", label=\\\"gpr {}\\\".format(np.round(model.kernel.params, 2)))\\n\",\n    \"plt.fill_between(x[:, 0], y - y_std, y + y_std, color=\\\"pink\\\", alpha=0.2, label=\\\"gpr std.\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.ylim(-1.5, 1.5)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### 6.4.5 Gaussian processes for classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data():\\n\",\n    \"    x0 = np.random.normal(size=50).reshape(-1, 2)\\n\",\n    \"    x1 = np.random.normal(size=50).reshape(-1, 2) + 2.\\n\",\n    \"    return np.concatenate([x0, x1]), np.concatenate([np.zeros(25), np.ones(25)]).astype(int)[:, None]\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"x0, x1 = np.meshgrid(np.linspace(-4, 6, 100), np.linspace(-4, 6, 100))\\n\",\n    \"x = np.array([x0, x1]).reshape(2, -1).T\\n\",\n    \"\\n\",\n    \"model = GaussianProcessClassifier(RBF(np.array([1., 7., 7.])))\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"y = model.predict(x)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train.ravel())\\n\",\n    \"plt.contourf(x0, x1, y.reshape(100, 100), levels=np.linspace(0,1,3), alpha=0.2)\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.xlim(-4, 6)\\n\",\n    \"plt.ylim(-4, 6)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch07_Sparse_Kernel_Machines.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 7. Sparse Kernel Machines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.kernel import (\\n\",\n    \"    RBF,\\n\",\n    \"    PolynomialKernel,\\n\",\n    \"    SupportVectorClassifier,\\n\",\n    \"    RelevanceVectorRegressor,\\n\",\n    \"    RelevanceVectorClassifier\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 7.1 Maximum Margin Classifiers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x_train = np.array([\\n\",\n    \"        [0., 2.],\\n\",\n    \"        [2., 0.],\\n\",\n    \"        [-1., -1.]])\\n\",\n    \"y_train = np.array([1., 1., -1.])\\n\",\n    \"\\n\",\n    \"model = SupportVectorClassifier(PolynomialKernel(degree=1))\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"x0, x1 = np.meshgrid(np.linspace(-3, 3, 100), np.linspace(-3, 3, 100))\\n\",\n    \"x = np.array([x0, x1]).reshape(2, -1).T\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker=\\\"x\\\")\\n\",\n    \"plt.scatter(model.x[:, 0], model.x[:, 1], s=100, facecolor=\\\"none\\\", edgecolor=\\\"g\\\")\\n\",\n    \"cp = plt.contour(x0, x1, model.distance(x).reshape(100, 100), np.array([-1, 0, 1]), colors=\\\"k\\\", linestyles=(\\\"dashed\\\", \\\"solid\\\", \\\"dashed\\\"))\\n\",\n    \"plt.clabel(cp, fmt='y=%.f', inline=True, fontsize=15)\\n\",\n    \"plt.xlim(-3, 3)\\n\",\n    \"plt.ylim(-3, 3)\\n\",\n    \"plt.gca().set_aspect(\\\"equal\\\", adjustable=\\\"box\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data():\\n\",\n    \"    x = np.random.uniform(-1, 1, 100).reshape(-1, 2)\\n\",\n    \"    y = x < 0\\n\",\n    \"    y = (y[:, 0] * y[:, 1]).astype(float)\\n\",\n    \"    return x, 1 - 2 * y\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"\\n\",\n    \"model = SupportVectorClassifier(RBF(np.ones(3)))\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"\\n\",\n    \"x0, x1 = np.meshgrid(np.linspace(-1, 1, 100), np.linspace(-1, 1, 100))\\n\",\n    \"x = np.array([x0, x1]).reshape(2, -1).T\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker=\\\"x\\\")\\n\",\n    \"plt.scatter(model.x[:, 0], model.x[:, 1], s=100, facecolor=\\\"none\\\", edgecolor=\\\"g\\\")\\n\",\n    \"plt.contour(\\n\",\n    \"    x0, x1, model.distance(x).reshape(100, 100),\\n\",\n    \"    np.arange(-1, 2), colors=\\\"k\\\", linestyles=(\\\"dashed\\\", \\\"solid\\\", \\\"dashed\\\"))\\n\",\n    \"plt.xlim(-1, 1)\\n\",\n    \"plt.ylim(-1, 1)\\n\",\n    \"plt.gca().set_aspect(\\\"equal\\\", adjustable=\\\"box\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 7.1.1 Overlapping class distributions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data():\\n\",\n    \"    x0 = np.random.normal(size=100).reshape(-1, 2) - 1.\\n\",\n    \"    x1 = np.random.normal(size=100).reshape(-1, 2) + 1.\\n\",\n    \"    x = np.concatenate([x0, x1])\\n\",\n    \"    y = np.concatenate([-np.ones(50), np.ones(50)]).astype(int)\\n\",\n    \"    return x, y\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"\\n\",\n    \"model = SupportVectorClassifier(RBF(np.array([1., 0.5, 0.5])), C=1.)\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"\\n\",\n    \"x0, x1 = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100))\\n\",\n    \"x = np.array([x0, x1]).reshape(2, -1).T\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker=\\\"x\\\")\\n\",\n    \"plt.scatter(model.x[:, 0], model.x[:, 1], s=100, facecolor=\\\"none\\\", edgecolor=\\\"g\\\")\\n\",\n    \"plt.contour(x0, x1, model.distance(x).reshape(100, 100), np.arange(-1, 2), colors=\\\"k\\\", linestyles=(\\\"dashed\\\", \\\"solid\\\", \\\"dashed\\\"))\\n\",\n    \"plt.xlim(-4, 4)\\n\",\n    \"plt.ylim(-4, 4)\\n\",\n    \"plt.gca().set_aspect(\\\"equal\\\", adjustable=\\\"box\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 7.2 Relevance Vector Machines\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### 7.2.1 RVM for regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data(n=10):\\n\",\n    \"    x = np.linspace(0, 1, n)\\n\",\n    \"    t = np.sin(2 * np.pi * x) + np.random.normal(scale=0.1, size=n)\\n\",\n    \"    return x, t\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data(n=10)\\n\",\n    \"x = np.linspace(0, 1, 100)\\n\",\n    \"\\n\",\n    \"model = RelevanceVectorRegressor(RBF(np.array([1., 20.])))\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"\\n\",\n    \"y, y_std = model.predict(x)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train, y_train, facecolor=\\\"none\\\", edgecolor=\\\"g\\\", label=\\\"training\\\")\\n\",\n    \"plt.scatter(model.x.ravel(), model.t, s=100, facecolor=\\\"none\\\", edgecolor=\\\"b\\\", label=\\\"relevance vector\\\")\\n\",\n    \"plt.plot(x, y, color=\\\"r\\\", label=\\\"predict mean\\\")\\n\",\n    \"plt.fill_between(x, y - y_std, y + y_std, color=\\\"pink\\\", alpha=0.2, label=\\\"predict std.\\\")\\n\",\n    \"plt.legend(loc=\\\"best\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 7.2.3 RVM for classification\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data():\\n\",\n    \"    x0 = np.random.normal(size=100).reshape(-1, 2) - 1.\\n\",\n    \"    x1 = np.random.normal(size=100).reshape(-1, 2) + 1.\\n\",\n    \"    x = np.concatenate([x0, x1])\\n\",\n    \"    y = np.concatenate([np.zeros(50), np.ones(50)]).astype(int)\\n\",\n    \"    return x, y\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"\\n\",\n    \"model = RelevanceVectorClassifier(RBF(np.array([1., 0.5, 0.5])))\\n\",\n    \"model.fit(x_train, y_train)\\n\",\n    \"\\n\",\n    \"x0, x1 = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100))\\n\",\n    \"x = np.array([x0, x1]).reshape(2, -1).T\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], s=40, c=y_train, marker=\\\"x\\\")\\n\",\n    \"plt.scatter(model.x[:, 0], model.x[:, 1], s=100, facecolor=\\\"none\\\", edgecolor=\\\"g\\\")\\n\",\n    \"plt.contourf(x0, x1, model.predict_proba(x).reshape(100, 100), np.linspace(0, 1, 5), alpha=0.2)\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.xlim(-4, 4)\\n\",\n    \"plt.ylim(-4, 4)\\n\",\n    \"plt.gca().set_aspect(\\\"equal\\\", adjustable=\\\"box\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch08_Graphical_Models.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 8. Graphical Models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import itertools\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.datasets import fetch_openml\\n\",\n    \"from prml import bayesnet as bn\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"b = bn.discrete([0.1, 0.9])\\n\",\n    \"f = bn.discrete([0.1, 0.9])\\n\",\n    \"\\n\",\n    \"g = bn.discrete([[[0.9, 0.8], [0.8, 0.2]], [[0.1, 0.2], [0.2, 0.8]]], b, f)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b: DiscreteVariable(proba=[0.1 0.9])\\n\",\n      \"f: DiscreteVariable(proba=[0.1 0.9])\\n\",\n      \"g: DiscreteVariable(proba=[0.315 0.685])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"b:\\\", b)\\n\",\n    \"print(\\\"f:\\\", f)\\n\",\n    \"print(\\\"g:\\\", g)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"g.observe(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b: DiscreteVariable(proba=[0.25714286 0.74285714])\\n\",\n      \"f: DiscreteVariable(proba=[0.25714286 0.74285714])\\n\",\n      \"g: DiscreteVariable(observed=[1. 0.])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"b:\\\", b)\\n\",\n    \"print(\\\"f:\\\", f)\\n\",\n    \"print(\\\"g:\\\", g)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"b.observe(0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"b: DiscreteVariable(observed=[1. 0.])\\n\",\n      \"f: DiscreteVariable(proba=[0.11111111 0.88888889])\\n\",\n      \"g: DiscreteVariable(observed=[1. 0.])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"b:\\\", b)\\n\",\n    \"print(\\\"f:\\\", f)\\n\",\n    \"print(\\\"g:\\\", g)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 8.3.3 Illustration: Image de-noising\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/folders/9s/lky4p_js2czgsr4_5962ffbw0000gn/T/ipykernel_11247/693879585.py:3: DeprecationWarning: `int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `int`, you may wish to use e.g. `int64` or `int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\\n\",\n      \"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n      \"  binarized_img = (x > 127).astype(int).reshape(28, 28)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x14909fc40>\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x, _ = fetch_openml(\\\"mnist_784\\\", return_X_y=True, as_frame=False)\\n\",\n    \"x = x[0]\\n\",\n    \"binarized_img = (x > 127).astype(int).reshape(28, 28)\\n\",\n    \"plt.imshow(binarized_img, cmap=\\\"gray\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x14918b370>\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"indices = np.random.choice(binarized_img.size, size=int(binarized_img.size * 0.1), replace=False)\\n\",\n    \"noisy_img = np.copy(binarized_img)\\n\",\n    \"noisy_img.ravel()[indices] = 1 - noisy_img.ravel()[indices]\\n\",\n    \"plt.imshow(noisy_img, cmap=\\\"gray\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"markov_random_field = np.array([\\n\",\n    \"        [[bn.discrete([0.5, 0.5], name=f\\\"p(z_({i},{j}))\\\") for j in range(28)] for i in range(28)], \\n\",\n    \"        [[bn.DiscreteVariable(2) for _ in range(28)] for _ in range(28)]])\\n\",\n    \"a = 0.9\\n\",\n    \"b = 0.9\\n\",\n    \"pa = [[a, 1 - a], [1 - a, a]]\\n\",\n    \"pb = [[b, 1 - b], [1 - b, b]]\\n\",\n    \"for i, j in itertools.product(range(28), range(28)):\\n\",\n    \"    bn.discrete(pb, markov_random_field[0, i, j], out=markov_random_field[1, i, j], name=f\\\"p(x_({i},{j})|z_({i},{j}))\\\")\\n\",\n    \"    if i != 27:\\n\",\n    \"        bn.discrete(pa, out=[markov_random_field[0, i, j], markov_random_field[0, i + 1, j]], name=f\\\"p(z_({i},{j}), z_({i+1},{j}))\\\")\\n\",\n    \"    if j != 27:\\n\",\n    \"        bn.discrete(pa, out=[markov_random_field[0, i, j], markov_random_field[0, i, j + 1]], name=f\\\"p(z_({i},{j}), z_({i},{j+1}))\\\")\\n\",\n    \"    markov_random_field[1, i, j].observe(noisy_img[i, j], proprange=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x149605a30>\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for _ in range(10000):\\n\",\n    \"    i, j = np.random.choice(28, 2)\\n\",\n    \"    markov_random_field[1, i, j].send_message(proprange=3)\\n\",\n    \"restored_img = np.zeros_like(noisy_img)\\n\",\n    \"for i, j in itertools.product(range(28), range(28)):\\n\",\n    \"    restored_img[i, j] = np.argmax(markov_random_field[0, i, j].proba)\\n\",\n    \"plt.imshow(restored_img, cmap=\\\"gray\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "notebooks/ch09_Mixture_Models_and_EM.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 9. Mixture Models and EM\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.datasets import fetch_openml\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.clustering import KMeans\\n\",\n    \"from prml.rv import (\\n\",\n    \"    MultivariateGaussianMixture,\\n\",\n    \"    BernoulliMixture\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"np.random.seed(2222)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 9.1 K-means Clustering\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# training data\\n\",\n    \"x1 = np.random.normal(size=(100, 2))\\n\",\n    \"x1 += np.array([-5, -5])\\n\",\n    \"x2 = np.random.normal(size=(100, 2))\\n\",\n    \"x2 += np.array([5, -5])\\n\",\n    \"x3 = np.random.normal(size=(100, 2))\\n\",\n    \"x3 += np.array([0, 5])\\n\",\n    \"x_train = np.vstack((x1, x2, x3))\\n\",\n    \"\\n\",\n    \"x0, x1 = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))\\n\",\n    \"x = np.array([x0, x1]).reshape(2, -1).T\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"kmeans = KMeans(n_clusters=3)\\n\",\n    \"kmeans.fit(x_train)\\n\",\n    \"cluster = kmeans.predict(x_train)\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=cluster)\\n\",\n    \"plt.scatter(kmeans.centers[:, 0], kmeans.centers[:, 1], s=200, marker='X', lw=2, c=['purple', 'cyan', 'yellow'], edgecolor=\\\"white\\\")\\n\",\n    \"plt.contourf(x0, x1, kmeans.predict(x).reshape(100, 100), alpha=0.1)\\n\",\n    \"plt.xlim(-10, 10)\\n\",\n    \"plt.ylim(-10, 10)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## 9.2 Mixture of Gaussians\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"gmm = MultivariateGaussianMixture(n_components=3)\\n\",\n    \"gmm.fit(x_train)\\n\",\n    \"p = gmm.classify_proba(x_train)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=p)\\n\",\n    \"plt.scatter(gmm.mu[:, 0], gmm.mu[:, 1], s=200, marker='X', lw=2, c=['red', 'green', 'blue'], edgecolor=\\\"white\\\")\\n\",\n    \"plt.xlim(-10, 10)\\n\",\n    \"plt.ylim(-10, 10)\\n\",\n    \"plt.gca().set_aspect(\\\"equal\\\")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### 9.3.3 Mixtures of Bernoulli distributions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/folders/9s/lky4p_js2czgsr4_5962ffbw0000gn/T/ipykernel_10929/1003235212.py:6: DeprecationWarning: `float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\\n\",\n      \"Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\\n\",\n      \"  x_train = (x_train > 127).astype(float)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"x, y = fetch_openml(\\\"mnist_784\\\", return_X_y=True, as_frame=False)\\n\",\n    \"x_train = []\\n\",\n    \"for i in [0, 1, 2, 3, 4]:\\n\",\n    \"    x_train.append(x[np.random.choice(np.where(y == str(i))[0], 200)])\\n\",\n    \"x_train = np.concatenate(x_train, axis=0)\\n\",\n    \"x_train = (x_train > 127).astype(float)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x360 with 5 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"bmm = BernoulliMixture(n_components=5)\\n\",\n    \"bmm.fit(x_train)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(20, 5))\\n\",\n    \"for i, mean in enumerate(bmm.mu):\\n\",\n    \"    plt.subplot(1, 5, i + 1)\\n\",\n    \"    plt.imshow(mean.reshape(28, 28), cmap=\\\"gray\\\")\\n\",\n    \"    plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch10_Approximate_Inference.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 10. Approximate Inference\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.animation as animation\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.rv import VariationalGaussianMixture\\n\",\n    \"from prml.preprocess import PolynomialFeature\\n\",\n    \"from prml.linear import (\\n\",\n    \"    VariationalLinearRegression,\\n\",\n    \"    VariationalLogisticRegression\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 10.2 Illustration: Variational Mixture of Gaussians\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"x1 = np.random.normal(size=(100, 2))\\n\",\n    \"x1 += np.array([-5, -5])\\n\",\n    \"x2 = np.random.normal(size=(100, 2))\\n\",\n    \"x2 += np.array([5, -5])\\n\",\n    \"x3 = np.random.normal(size=(100, 2))\\n\",\n    \"x3 += np.array([0, 5])\\n\",\n    \"x_train = np.vstack((x1, x2, x3))\\n\",\n    \"\\n\",\n    \"x0, x1 = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))\\n\",\n    \"x = np.array([x0, x1]).reshape(2, -1).T\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"vgmm = VariationalGaussianMixture(n_components=6)\\n\",\n    \"vgmm.fit(x_train)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=vgmm.classify(x_train))\\n\",\n    \"plt.contour(x0, x1, vgmm.pdf(x).reshape(100, 100))\\n\",\n    \"plt.xlim(-10, 10)\\n\",\n    \"plt.ylim(-10, 10)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<video width=\\\"640\\\" height=\\\"480\\\" controls autoplay loop>\\n\",\n       \"  <source type=\\\"video/mp4\\\" src=\\\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAABiT21kYXQAAAKuBgX//6rcRem9\\n\",\n       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\"<matplotlib.animation.ArtistAnimation at 0x7faf238e1f50>\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"vgmm = VariationalGaussianMixture(n_components=6)\\n\",\n    \"vgmm._init_params(x_train)\\n\",\n    \"params = np.hstack([param.flatten() for param in vgmm.get_params()])\\n\",\n    \"fig = plt.figure()\\n\",\n    \"colors = np.array([\\\"r\\\", \\\"orange\\\", \\\"y\\\", \\\"g\\\", \\\"b\\\", \\\"purple\\\"])\\n\",\n    \"frames = []\\n\",\n    \"for _ in range(100):\\n\",\n    \"    plt.xlim(-10, 10)\\n\",\n    \"    plt.ylim(-10, 10)\\n\",\n    \"    plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"    r = vgmm._variational_expectation(x_train)\\n\",\n    \"    imgs = [plt.scatter(x_train[:, 0], x_train[:, 1], c=colors[np.argmax(r, -1)])]\\n\",\n    \"    for i in range(vgmm.n_components):\\n\",\n    \"        if vgmm.component_size[i] > 1:\\n\",\n    \"            imgs.append(plt.scatter(vgmm.mu[i, 0], vgmm.mu[i, 1], 100, colors[i], \\\"X\\\", lw=2, edgecolors=\\\"white\\\"))\\n\",\n    \"    frames.append(imgs)\\n\",\n    \"    vgmm._variational_maximization(x_train, r)\\n\",\n    \"    new_params = np.hstack([param.flatten() for param in vgmm.get_params()])\\n\",\n    \"    if np.allclose(new_params, params):\\n\",\n    \"        break\\n\",\n    \"    else:\\n\",\n    \"        params = np.copy(new_params)\\n\",\n    \"plt.close()\\n\",\n    \"plt.rcParams['animation.html'] = 'html5'\\n\",\n    \"anim = animation.ArtistAnimation(fig, frames)\\n\",\n    \"anim\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 10.3 Variational Linear Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_toy_data(func, sample_size, std, domain=[0, 1]):\\n\",\n    \"    x = np.linspace(domain[0], domain[1], sample_size)\\n\",\n    \"    np.random.shuffle(x)\\n\",\n    \"    t = func(x) + np.random.normal(scale=std, size=x.shape)\\n\",\n    \"    return x, t\\n\",\n    \"\\n\",\n    \"def cubic(x):\\n\",\n    \"    return x * (x - 5) * (x + 5)\\n\",\n    \"\\n\",\n    \"x_train, y_train = create_toy_data(cubic, 10, 10., [-5, 5])\\n\",\n    \"x = np.linspace(-5, 5, 100)\\n\",\n    \"y = cubic(x)\\n\",\n    \"\\n\",\n    \"feature = PolynomialFeature(degree=3)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X = feature.transform(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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CwYEEiIiK488472bBhA9OmTeP1119n69ataJqGpmlMmzYt3WNs376dZs2aERYWRqFChRgwYAAJCQmpj/fp04dOnToxduxYihcvTqFChRgyZAh2uz2AP9VryYyKuJbdcflCnF3t74OVyajqc1Js6ucTEaY2RRRCBI6uqzcEWS083Ou/d9OnT6d///5s3LiRTZs2MWDAAMqUKcPjjz8OwNixY3n11Vd57bXXADhw4ACtW7fmrbfeYsqUKZw9ezY12PFsN9OnTx9OnDjBmjVrMJvNDB8+nDNnzlx3DAkJCdxzzz2ULFmSRYsWUaxYMf7++29cLhcPPfQQO3bsYPny5axatQpQHeCvlpiYSKtWrWjQoAF//fUXZ86c4bHHHmPo0KGpgQ3AmjVrKF68OGvWrGH//v089NBD1KpVK/X7zQwSqIi07HZ3kOKSlS7XY3AX2todqiNvRFjuWaYtRDBISoJ8+bL+vAkJEBHh1UtKly7Nxx9/jKZpVK5cme3bt/Pxxx+nXribNWvG008/nfr8xx57jJ49e6ZuPFixYkU+/fRT7rnnHiZMmMCRI0dYtmwZGzdupG7dugBMnjyZqlWrXncMM2fO5OzZs/z1118ULFgQgFtvvTX18Xz58mEymW6Y6pk5cyYpKSl88803RLh/Bp999hkdOnTgvffeS+0gX6BAAT777DOMRiNVqlShXbt2rF69OlMDlTw+ny/SsNnVCheXLhfem0lNBWkqsEtKkQZxQuRB9evXR7vib2WDBg3Yt28fTndq+M4770zz/K1btzJt2jTy5cuXemvVqhUul4uDBw+ya9cuTCYTderUSX1NlSpVyJ8//3XHsGXLFmrXrp0apPhi165d1KxZMzVIAdULzeVypemHdvvtt2O8YlFF8eLFbzjbEwgyoyIUm3smRddlVYs3TCbQ3KuCXC41u5LX63mE8Fd4uJrdyI7zBljEVTM0CQkJDBw4kOHDh1/z3DJlyrB3716vzxEWFubz+LxlvirVrWkarkxu3SCBiric7pEgxTdGA2gmtYTb5YKIcFXLIoTwjaZ5nYLJLhs2bEjz9Z9//knFihXTzDpc6Y477mDnzp1pUjNXqlKlCg6Hg82bN6emfvbs2cOlS5euO4YaNWrw9ddfc+HChXRnVSwWS+oMz/VUrVqVadOmkZiYmBpc/e9//8NgMFC5cuUbvjazyVu/vM7hgIRkCVL85dlOwOZuv293ZPeIhBBZ4MiRI4wcOZI9e/Ywa9Ysxo8fz5NPPnnd5z///PP88ccfDB06lC1btrBv3z4WLlzI0KFDAahcuTKtW7dm4MCBbNiwgc2bN/PYY4/dcNakR48eFCtWjE6dOvG///2P//77jx9//JH169cDavXRwYMH2bJlC+fOncNqtV5zjJ49exIaGkrv3r3ZsWMHa9asYdiwYTz66KOp9SnZRQKVvMzhvLy6xyyTa37TNAgxqULk+ESVThNC5Gq9evUiOTmZu+66iyFDhvDkk0+mLkNOT40aNfj111/Zu3cvjRs3pnbt2rz66quUKFEi9TlTp06lRIkS3HPPPXTu3JkBAwYQExNz3WNaLBZWrFhBTEwMbdu2pXr16rz77rupszpdunShdevWNG3alCJFijBr1qxrjhEeHs7PP//MhQsXqFu3Lg8++CDNmzfns88+8+OnExiarufsCsC4uDiio6OJjY2VDQq94XQHKTaHLEHODJ4gJV+4mmkRQqQrJSWFgwcPUr58+Ry3dcq9995LrVq10rS2F2nd6N83o9dvmVHJi1wule6xS5CSaTxLuxOSVO1Kzn4/IIQQ2UYClbzGs8GgzSZ9UjKb2aRqVxKSIVmCFSGE8IUUJuQ1yVZ1kyAla5iMoKF2XwbZI0iIXGTt2rXZPYQ8QQKVvMRqg+QUdxt4mUzLMp5liknJgK42NZRgRQghMkQClbzC7oDEZHWBzAk9PpxOSE5WDdVMJnWxz8kXd0+wkpiiPkqwIoQQGSKBSl7gdKogxeUKrhUoiYmwfRv8/Tds3w4nTsDZM3DmDJw7p8Z7JbMZihSBMmWhdGkoUwbKl4c6daBK1cvBQLC6MljRgXAJVoTwyOELUMV1BOLfVQKV3M5TPOtZ4ZOd7Hb4/Xf4aRH88T/YvfvaYORmrz9xQt3+XJ/2sagoFbDUvQuaNoX6DYIzcDEaAU39m2jIzIrI8zwt2ZOSkrK0FbzIGknuXbCvbr3vDemjktslpajaCM8KlKxmt8PKFbBoESxdCpcupn28WDG4ow7UrKlmSooWhZgY9TEyUnXOdTjAYVe9SU6dgiOH4ehROHIE9uyGzZvV7MyVCheG9u2hQ0e45x6wBNFMEqhZLocTwsOkwFbkeSdPnuTSpUvExMQQHh6eZpM/kTPpuk5SUhJnzpwhf/78FC9e/JrnZPT6LYFKbma3Q1yiClCyui7l/HmYNhW+nAQnT16+v3BhaNceWrWCOndCOr+8XnM4YOdO2LgB1q+HlSvTBkTR0dC9Bzz2GFSu4v/5AsXpBIdLpYAkWBF5mK7rnDp16ob72YicKX/+/BQrVizd4FMClbzO5VJBisOZtSmfvXvgi89h1ixVDAtqdqTLg9ChQ9akZDwppkULYfFPcPr05ceaNIHHHlfBkh9TkQHjcKqW+/nCIDQku0cjRLZyOp3Y7bL1RG5hNpuvuzkjSKCSt+m6Kp5NTlHFs1nxTv3MaXj7LZg+/XLdSc2a8MRQ6NIl+1IvLhesWQOTv1KpJ8/YSpeGp5+BRx7N/rSQwwEuXdrtCyHyFAlU8rIUK8QngSUL6lKSk9UMyodjISFB3demLQwfDg0bBVc649gxmDpFpaTOnlX3lSkDzzwLPR/J3hkWu0MFmJERsou1ECJPkEAlr3I4VMpHQ/UfyUzLlsIzT6vCVoDad8CYMSpACWbJySpg+fijy2mhsmXhtdEqRZVdwZXNrs4dGSG7WQshcj0JVPIiXYeEREixQ2gmphDi4mDUC/DtN+rrkiXhtdehW7cMz+Ak25PZf+kA+y4dYO/F/fwXe5A4WzyJ9iQS7Ykk2pNw6S6iLJFEh0QRZYkif0g0ZaPKULlARSoXrEi5qLIYDX7UuyQnw+TJMO4j1bsF4O674f2xUK2a78f1h9UORgNEhmd+oCmEENlIApW8KCkFkpJUzUVmzQr8vg4GD4LDh9U5hg2HF1+C8PAbvux04mnWHlvHmqO/sfboOvZc3Of3UCxGC1UKVKJxyYbcU/pumpRsRNGIot4fKCkJPv0EPvoQUlJUsPXY4/DSy1CggN/j9Iquq5kVk0kFK8HYC0YIIQJAApW8JrNTPnY7vD4axn+qLqZly8KkL2+Y5tl/8QDf7prFj/sW8u/5Xdc8nj8kP5UK3ErF/Ldwa/4KFAwtQLg5nAhzOBHmCDQ04m0JxNpiibXGcTHlEv/FHmTPxX3su3iAFGfKNcesUrASrcvdR7dKnalXvC4GzYsanSNH4KVRsHCh+rpwYRj3CdzfMePHCARPsGI2qTSQ7MskhMiFJFDJS3Qd4hNV2iAzUj7nz0GvR2HdOvV1r94w5l3VkO3qpyafZ/aeH/l21/f8eXJjmsdqFqlO09JNaFq6CQ2K30XhsMI+N3ZyupwciT/KP2e28uux3/n12O9sO7sDncu/zqUjS9G10gN0q9SZu4rdmfFzrV0Dzz4De/aorx/sCh+MhUKFfBqrT3RdbSIZGqJWAwVTUbIQQgSABCp5SVIKJCZlzlLkf3dA94dUqidfPjWL0uH+a552NP4YYzd9wlfbp5HsUP1TDJqB+8o045Gq3WlT/j4KhWXuhf5C8gV+PfY78/YvYuGBJcTb4lMfq1G4GkNrDeThqt2IMEfc/GBWK7w3Bj76SC1pjomBT8dD23aZ+B1cxeUCm0M1hJN9gYQQuYwEKnlFasonE3ZFXvwTPP6Yak9frjzMng1Vb0vzlL0X9/Hexo/5dtcs7C7VqKlG4Wr0vr0nPSp3pXi+YoEdUwalOFL4+dAqftg7j/n7f0oNnvKH5KdftUcZUnMAFfKXv/mBNm2CwQMvz6706g3vf3DTmpyAcbrU0uWIMBWsCCFELiGBSl6Qmat8xn0Mr76iPr/nHpj2TZrUx9mks4z6fTRTdnyTmm5pWroJL971LM3L3BtUe3VcTLnI1H+/4/MtX/Jf7EEAjJqR3rf15OX6z1E+utyND5CSAu+8DZ+MUz/z226Db76FSpUzfeyA6l7rcklDOCFEriKBSl6QYlO1KRYzGAIUGOg6vPk6jB2rvh44CN4Zk9oMzeFyMHHr17zyx1tcsl4CoEOFtoy662kalKgXmDFkEqfLyfJDK/nkny9YefgXAEwGE31ue4SX6j1LueiyNz7A2jXwWH+1lDkiAsZ9Cg89lAUjR82qgPRYEULkGhKo5HZOp0r56HrgLly6Di88DxO+UF+/+RY8OSL14d+P/8HQX55m69ntANSOqclnzT6kYYn6gTl/Flp/YgOj17/DisOrATAbzAytNZDXGowiOiT6+i88fRr694XfflNf9+kDH3wIIVmwT4/VDiaDClZk2bIQIoeTQCW3S0yGpOTAFdA6nfDUkzBtmvr6w4/g8QEAWB1WXvrf63y4+VMACoQU4J27X+Px6n39a7gWBP53fD2j17/DqiNrACgSVpgxd79O32qPXn9ps9MJ746B999Twd1d9WDGTLX5YmbSdRWsWMyqx4osWxZC5GASqORmdruaTTEaVRdTfzkcMGgg/DBbXfw+nwA9ewKw+8IeHl7aj3/ObAWg3+29eK/JGxQOK+z/eYPIz4dWMWLtc+y+sBeAOkVrM77p2Buns1avhr694dIlKFUKZs1WGzFmJk+wEh4C4WGyEkgIkWNl9Potb8lyGl1Xy5F1PTBBiq7Dk8NVkGIywZSp0LMnuq7z5bYp3PHd3fxzZiuFQguy4P7vmdzqi1wXpAC0KteCbY9u4KN73iXKEsXm0//Q6PsWDP/lGRJsCem/qHlzWL0GKlZUGx62ug8WzM/cgWqa2mwyyQrJ1sw9lxBCBAEJVHIaq03NqARqh93Rr6k9ewwGmP4NdO5Csj2Zh5f2ZeCq4SQ7kmlRpinbem2g463tA3POIGU2mnmqzlD29dtC39sfRUdn/JaJ1Pi2Pr8cWZv+iypWVMFK8xaqFX+vRy+nhDKLwaCWoienqN8HIYTIxSRQyUmcTjWbYjAGZsp//KdqB2GA8Z9Bh/s5mXCKe35ozfd75mIymPigydv83GUhJfIV9/98OURMeAxTWk1gRZdFlI0qw8HYQzSf256BK4cRZ4279gX588OcuTBkqPr6rTdh5Aj175VZTEa1XUJiskrdCSFELiWBSk6SYlMNwALR2G3mTHjpRfX562/Ao73458xW7pp5D3+d3kzB0IKs6vITz9z5pHf75eQi95VtxvZeG3ii5uMAfLl9KrW/a8RfpzZf+2STSW0r8OFHKoicPBke7al2aM4sZrPqr5KYrD4KIUQulDevQDmR3QEpVjAHYDZl+XIYMlh9PnQYjHiKBft/4u7v7+NYwnGqFKzExofXck/pxv6PO4eLtETyefOPWdt1GWWjyvBf7EEaft+csZs+waWnExw8PkA1gwsJgcWLoeP9cOFC5g3QYlYbGCYmZ266SQghsokEKjmBrqsgRdf975/x7w61UsXphB4Pw1tv8+X2qXRe9DBJjiTuK9uM9d1/4Zb8FQIz9lzintKN2fLIH3St9AAOl4Nnf3uJtvM6czrx9LVP7tgJFiyE6Gj4cz20bgknTmTOwDRNBStWmxTXCiFyJQlUcgK7Qy1J9bex2/nz0KO72rvnnnvgs8/5dMtEBq4ajo7OwBr9WPrAPPKH5g/IsHOb/KH5md3uGya1+JRQYyg/H15FzW8bsO7Y/659cqO7YfnPUKIE7N4NrVvBkSOZMzCDQQWwySlqdkUIIXKRLAtU3n33XTRNY8SIEan3paSkMGTIEAoVKkS+fPno0qULp0+n8w41L3O51AVIw78GXw6Hmkk5dAjKlYNp3/D+ls94cu1zADx75wgmNP8Ek0Has9+IpmkMqNGPTT1/4/ZCVTmddIZmc9vx+ZZJXNOS6PZqsGKV2tDx0EE1s3LgQOYMzFO3lJis9gYSQohcIksClb/++otJkyZRo0aNNPc/9dRT/PTTT8yZM4dff/2VEydO0Llz56wYUs5hs6vlyP7OprzyMqxdCxER6DO/5429X/L8OrXp4Cv1nue9xm8G1UaCwe72wrex4eG1PFS5Cw6Xg6G/PE3/FU+Q4khJ+8QyZdTMiqfXSptWsGd35gzKbAKHCxKTpLhWCJFrZHqgkpCQQM+ePfnqq68oUKBA6v2xsbFMnjyZjz76iGbNmlGnTh2mTp3KH3/8wZ9//pnZw8oZnC5Vd+DvcuSZM+Hzz9TnEyfxWux8Xlv/NgBvN3qNNxq9IkGKDyLMEcxqO40PmryNQTMw9d9vaTK7FUfjj6V9YokSsGw53H47nDoFbVrD9u2BH5CmQYgJbA71eyPFtUKIXCDTA5UhQ4bQrl07WrRokeb+zZs3Y7fb09xfpUoVypQpw/r16697PKvVSlxcXJpbrmW1qpSNP8uR//kHnhymPn/uecaVPs6bG94D4MN7xvBivWcDMNC8S9M0nrnzSX7uvJCCoQX56/Rm6s28l82n/0n7xJiisHgp1KoF585Bh3aqsDnwA1IzK8kpqq5JCCFyuEwNVL7//nv+/vtvxowZc81jp06dwmKxkD9//jT3Fy1alFOnTl33mGPGjCE6Ojr1Vrp06UAPOzg4napvisnk+2xKfDz07aMCnjZtmfHArTy19nkA3rl7NCPrDAvceP2h6+r7dThU4bDNrlaxpFxxs3pu7lSYw6lmnFzBMWvQomxTNvX8jWqFbuNk4imazG7FTweWpn1SoUKwaDHUuVMtWb6/A+zdE/jBGA2qnikpWf08hRAiB8u0QOXo0aM8+eSTzJgxg9DQ0IAdd9SoUcTGxqbejh49GrBjB5VANHd79hn47wCUKsWqF3vQZ6XqnfJk7Sd4oe7TARqoD1wu90omd/Bhc6iAQ9PURdZihrBQyBfuvoVBeKi6L9R8eYm2y6WCG09QY3eogCebUh7lo8vxe/eV3Fe2GUmOJDot6s74fyakfVL+/DBvvtq88OxZaN8O9u8P/GDMJvXzSZJmcEKInC3TApXNmzdz5swZ7rjjDkwmEyaTiV9//ZVPP/0Uk8lE0aJFsdlsXLp0Kc3rTp8+TbFixa573JCQEKKiotLcch2H83JzN1/98APMnAEGA/++9xwdfx2Iw+Xg4Srd+Ojed7O+JsXpUjMlKVb1/RkM7mAkAqLzQXTk5VtkBESEQViI+xaqdgqOCHM/PxLyu2/R+dzPD1VBjkt3n8emgpgsDlqiQ6JZ0ulHHqvWB5fuYviaZxmx5jmcritW4hQooPqseGpWOrRTq7ECzdMMzrOJpRBC5ECafs2aysCIj4/n8OHDae7r27cvVapU4fnnn6d06dIUKVKEWbNm0aVLFwD27NlDlSpVWL9+PfXr18/QeTK6TXSOkpikiiFDLL69/uBBuLshxMdzfsRAKhebw/mUC7Qq24JFnX7AYvTxuN7S9cspGoOmZodCLOqjMUD7FV3N6VKzKp4UktOlxmEyZN4506HrOu/99RGjfn8NgIcqd+Gb1l+l/dmfPaMKa/fuhbJlYdnPUKpUYAficqkZq8hwCA0J7LGFEMIPGb1+Z1rTjMjISKpVq5bmvoiICAoVKpR6f//+/Rk5ciQFCxYkKiqKYcOG0aBBgwwHKbmSw+GuTfFxNsVuh/79ID4eR727aFJmNefjLlC3aB3mdvgua4IUl0sFKC5dzQqFh6tUhMGQ+YGC0XA5fRQeernuxWpXgYvmDpj86UmTAZqm8cJdT1M2qjS9lg9g9p4fibXGMbfDd0SYI9STisTAT0tUsPLfAXigIyxfoWpZAsVgUEFaYrIK1Pxd5i6EEFksWzvTfvzxx7Rv354uXbrQpEkTihUrxrx587JzSNlL19VMisuPVvnvvA2b/kKPzs+Ah8LZGbefUvlK8lOnH8hnyRfY8V7Npat6EYdTXRCjIiAqn3onn4WzGak0TW3cFx52OUVkNqnxWW2Zu7uxW48q3fip4xzCTeEsP7SSlj925GLKxctPKF4cfloMJUvCnj3wYBdISAjsIEwm9bsl9SpCiBwo01I/WSVXpX7sDohLUBd1ow8x5F9/wX3NweVi1ov387BlEaHGUNY9tII7i90R+PF66Loau65DiFkFJv6sVspMnnSUp15G1y+nojLRHyf+pN38B7lkvUS1Qrexossiiue7ohZrz25o2RIuXoCmzeCHOWpjw0DR3UFkuLveJxj/bYQQeUpGr9+y10+wSLPxoA//LFar2hHZ5eJQ20Y8bFkEwFf3fZa5QYonrWI0qhmLfBFqFiNYL4SePiMRYaooNyxU1bFk8gxLwxL1+a3bcopHFGPH+Z00nt2SI3FXrFirXAXm/ggREbDmFxj4eGDH49m8MNkq/VWEEDmKBCrBwuEu/vS1huD992D3buyFCtC41t8A9Cg+nLMruvPmm/DBWJVhSE65yXEyyqWrWhpQhZpREapQNlgDlPSYjOkHLJmUHqlepBq/P7SS8tHlOBD7H/f80JpDsVcUnNetCzNmqUBv3jy1vDyQE56GK/qrOKS/ihAiZ5DUTzDQdUhIUhdJX1b6bN8O9zQGh4MhvYvwRfmzFLnQjFI/z6NEMRMFC6hr08GDEBUJ93eEgQNUjatPHO5+JSEWCAvzr9dLMLE71KyW1XZ55iUTAq+j8cdoNqcd+y8doExkadZ0XUqF/OUvP2Hej6pRn67D62/AUyMDOwCrTX1vkRGZXlQshBDXk9HrtwQqwcBTm+LLahS7HZrdC1u38uddJWnQ9jiWhLIMsa+jT9eCVK9++Vp79Ji6Bs6dC2XLwWefQf5oL86lu3uUGAyqDiUsJGfNoGSE53tMTgG7U/2bZEIgdjz+BM3mtmPvxX2UyleSX7ouoWKBWy8/YcIX8Lza2ZopU+HBroE7ua6r9E94qLrltn9DIUSOIDUqOUmKe6WPL+9uP/0Etm7FGhlOp8bH0Vwm3qs+jY/eLEiNGmmvQaVLwZNPwteT4eRJeHqkFx3WXa6078Rz6wVO09RMUVQ+1UTO830HOJ4vGVmCtV2XUbVgZY4lHOeeH1qz9+K+y08Y/AQ8MUR9Pmgg/O/3wJ3cM1uUYlVBmRBCBDEJVLKb3Y/alL174F21j9KQ+xycjoTOEa8w4oG6N3xZ5Uowdixs2Qpr12bgPA6nahoWFnp5iW9uZzCo1TFR+dT3a3XvLxRAxfMVY03XpdxeqConE0/RfE57DsYeuvyEt9+B++8Hmw169AjsvkCegu2klCxZpi2EEL6SQCW72Wzuvile/lPoOox8CqxWfr8tH5Or28h/pilTez2VoZfXrgW1a8PcOTd5ot2uZhUi3C3s81pNg2cGKSIsU2ZXikYU5ZeuS1JnVprPbc+x+OPqQaMRvpoMde+CSxehS2c4czpg58ZsUkGKtNgXQgSxPHbVCTKeLrS+7Okzfx789hs2i5FH70vAYi/M4AJfERmR8X/SLl1g02Y4md5m1Z6+G2hqY8DcmurJCINBff+psyuBXRkUEx7DqgcXc0t0BQ7GHqL53PacSnQHJGFhMHs2lK8Ahw9D9+6QEqClW56GeFarWrYshBBBSAKV7GS1u/umeBmoJCTASy8C8HZDJ4cKQJk/JlHn1utv5pieihXVxzNnrnpA18HqUEWkkRG+7zmU21xZn2N3eFHgc3Ml8hXnl65LKBNZmr0X99FibnvOJZ9TDxYuonqs5C8Am/6CoUMCNwNi0MBochcPy5JlIUTwkUAlu3h2SPZlRcmHY+H4cQ4XNPJ+I3iy9hDyn2zl9WE8EyRprnmeFSFmo7t5Wx6oR/GGp3Yln3u/ngCmgspEleaXrksoEVGcf8/votWPnYizxqkHK1aEb79VQe0Ps9XvQKCYjNJiXwgRtCRQyS5Wm297+uzbp1b6AMNaOikTU5Exd4+mSBH47z/vDnVgv/oYU8R9h2dprsWkmrjllv4ogaZpEGpRTe4CnAq6JX8FVnddTEx4Ef4+s4WOCx8ixeFO9dxzL4z9UH3+xuuwaGFAzgmorrU2u0oBSb2KECKISKCSHZzuTfG8DQR0XfXWsNtZeissrgxTW00gzBxGm7aweAkkJ2f8cD/Og1q1oEQJrphJMamalEze+yZXMLlTQaEhKm1yg9Uz8Qnw/WwYMAC6doWHH4bnn4f166+NcaoUrMzyzguIskSx9tg6eizpg8PlTsv0fwwGDlKfD3gctm4NzPfiqVdJliXLQojgIoFKdrDa1UXN25U+y5bCqpVYjfBkG3iyzhAalqgPqAUhiYkq+MiIHf/Cpk3qonl5JsWsUhoSpGScweAuNg5TLfivqvNwuuDzz6F1a/j4I7WVz131oHoNOHwEhg6Dzl1gw4a0h60dU5NFHWcTYgxhwYHFDFw5nNTejGPehWbNISkJuncL3EogowE0ZMmyECKoSGfarOZyQWyCCg68qf9ISYG77oRDh3jnbpjSpQLbev1JuPlyH/z331ddZ997D5o2vf6h/vsPBg+GokVh8tc6Zpc73SMzKf6x2iDRXedhMePUNV5+CVatgj59oFs3KFLk8tN1HbZth4kT4O+/4Z13oHnztIdcsP8nuvzUE5fu4rk7n+K9Jm+qBy5dghbNYO9eaNgQFi0GSwCKnj1Ba4hF/T7k1ZVeQohMJ51pg5XdoZYle5v2+epLOHSI45HwTmOY0uqLNEEKwMin1YXu2efgzTdh1+60hzh9BiZMhL791AKScePArLsLZyMkSPFbiEWlgkwmsNn5apLOqlUqcBwyJG2QAioGqFkDxn+m/t1eflnFHVfqdGsHvr7vcwDe3/QxH20erx7Inx9mfQ9RUfDHHzDqhcB8D6lda23u5elCCJG9ZEYlK+m6mk1xOlWaJaMuXkSvWR3t0iX63Q8R/QYyvtmH6T7V5YIZM2DWLBWYlCtH6qaEe/dAaCi0b6+6s0da7Gp5qqzuCSynk8SzSTzey06jphaGDL35rITNBg8+qJrwvf76tY9/8Nc4nlv3MgCz202nW+Uu6oFly1T6R9fh8y/g0V6B+R4cDtBRBcMm+d0QQgSezKgEI89sirdBwccfoV26xI4isLZJacbcnc6VzM1ggEcfhUU/wdgPoG5dKFoMbqsKz78Ay39WRZyRIQ717jkiXIKUQDMa+W5BBLsPh9Ctkz1DK4IsFtWAb8UKuHjp2sefufNJhtVSRbSPLn+c34659/5p0wZeUgEMT42Av/4KzPdgMqkCm0TpWiuEyF4yo5JVdB0SElUhrTcN1I4dw1WrBgabjfY9YMBzs7n/lnb+jcXhVBfPfOHSzC2TtGoFJqPOkjkpqjjVbLxpau3iJWjRQq08bpfOP7HT5aTr4keYv/8n8ofk54/uq6haqIr6t3y0J/z0ExQvDr/9rgqQ/OVZCRYRpprcCSFEAMmMSrDxbOzn5eyF/vabGGw21pYFWrWiQ4W2/o3D5VJjCQuVICUTnTsHpcto6gIfEap+5jfZ1LBAftUxPzY2/ceNBiMz2kyhQfF6XLJeos38zpxMOKWm0SZ+CVWqqG2xez2i9mjyl6apWirpWiuEyEYSqGQVqw1ceLep3787YOZMAF5ubeaTZh+g+bMKw7OiIyxE3USmsVhU3QmapoJCz/LlGwQrug52m2pncj1h5jAWdZpNxfy3cjjuCO0WdCHBlgCRkTDTXVy7fj289mpgvhHpWiuEyGYSqGQFh7vBm9m7H7fjlRfRdJ0fboP7Oj/LLfkr+DcOq7tXSl7eYDCLVKoE69a5r+2apgLDfO4dmB3pz07s2KF+VcqUufGxC4cVZlnneRQJK8w/Z7bSc1l/nC4n3HorTJyknvTZeFgwPzDfjMWsZgNl40IhRDaQQCUr2Hxol//br5hW/YLdABM7leK5O5/ycwx29e44Ity7WR3hkwEDYP9+WL3afYemqQ62EeHqdyGdVMqcOVCqpCqAvplb8ldgobsh3KIDS3h+3SvqgfYdYIT7d+WJwbBv7/UPklGapmpsUqRrrRAi68kVK7O5XGomw+TFj1rXSXpV9cWYVAee7jaOMHOY72PwpBsiwmT/nizSsCHUqAGvvqp69aUKdTdS09MGK7t2w4qVauVPRuPIBiXqMbXVBAA+3PwpX26boh549TW4+261y3bPh9VHf3mC7KQUSQEJIbKUBCqZze4Ap8O72ZRffiH87+0km2BTr+a0q9Da9/O7dJVqCAv1rneL8IumwcSJ8M8/qj9KYuIVD4akDVZ27YLhw6BSRejazbvz9KjSjdcbvATAE6ufYtXhNWpp8dTpUKwY7N4Nw4YGZomx2aSKdGXjQiFEFpJAJTPpupou1wwZrwnRdS6Ofg6AL+saeLXjOP/GYLOrd/FSPJvlGjSAhQvh119V+chrr8GRI+rXwmG0sOHfcN59V+epYQ5KlIBPPlHxpLdeqf8CPas8hFN38uDiR9h1frdanjz9GxUg/zgXpkz2/xvybFyYYpVVQEKILCOBSmZyOFTaxYt0i3P1Sgps3UOyCS4M7E2F/OV9P7/doVJOYWFSPJtNWrVS+/h07gwffQRly6oJD7MZ6t9jYemv4QwcoDPpCwcFCvh2Dk3T+Lrl5zQsUZ9Yayz3L3yIC8kXoEFDeMO9N9ALzwdmp2XPRppJyWoVkxBCZDJp+JaZEpMgyapmNDJC1zndqAZFdxzki0YhdJ+3j4JhBX07t8u9k29khPRLCRLx8arz7NmzavnyrbdC48ag2WyQkHR5nx0fnU06S92Z93A47ggtyjRlWef5mDQjdH9I7bxd4Rb4bZ1awuwPTyO48BC17FqCYCGED6ThW3ZzOtXGbuaMz6akrFxK0R0HSTaBYcRTvgcpV/ZLkbqUoBEZqYplBw2Cfv2gSRP3NT7EolYD6fpNm8LdSJHwIizsOJtwUzirjqzh2d9eUieYMBFKl4b/DqhiGH/fm2ia2m072SargIQQmU4Clcxide/xktEiWl3n7KtPAzCzYSR9mz/r+7ntDpVbCJN+KTlGqEWtynK6VJDro5pFqvNN6y8BGPf350zZ8Q0ULKiKa00mmPdjYOpVDAb11yMpxa/xCiHEzUigkhlcLtXgzYuVPheX/kjpncdINkGhF94gxORj8avnohEeKv1ScpoQi/p3szv9qv/oUqkToxu8CMCgVU/yv+Pr4a67Al+vYjKpGaAk2bhQCJF55EqWGexeFtHqOhfcK30WNImhY6PHfDuvrquLXKikfHIkTwfb8FBViO1Hv5JX6r9Al4odsbvsdP7pYY7FH4chQ6FNW7BaoU9vVTTj73gtJhWUSwpICJFJJFAJNF1Xf7gNWobTLkeXzOCWPWdINkH518b5vp+P3b3poSxFzrk090aGYaGqbb2PwYpBMzC99ZfULFKdM0ln6fJTT1KcVlWvUqoUHNgPzzzt/3gNBvW7LikgIUQmkUAl0BxO97LgjKd9Lrz7GgC/3FuW+rXv9+28nlSBpHxyPk+wEmpRMxU+plUizBHMv38WBUMLsvHUJoasHoleoAB8PVn9jsyaCd9/7/94JQUkhMhEckULNLv7wpLBYOHAbwuoue00Tg3Kv/Shb+f0tGMPtfi1vFUEEU1TxbWhFlWY7WMAUD66HN+3m4ZBMzDl32+YtG0yNGwEL4xSTxg5Ag4c8H+skgISQmQSCVQCybOvjzHjP9YT76g9fdbXL8ltdXxsle+phwkNkVU+uYnBoPqUWExgdfgcrNxXthlj7n4dgOFrnlXFtc8+B40aqX2A+vVVG2f6O1aDQVJAQoiAk0AlkLzc12fnPytosP4YAMVGvePbOV0udQsP9W4/IZEzGN07XpsMqmbFR8/eOYKulR7A7rLz4OJHOJF8Br6eAgUKwj9/w+uj/R+rySgpICFEwEmgEiieIlov9vU58PZITDpsrxbDrfd28e28Noda1iqrfHIvk1FtYmjQVGrRB5qmMaXlBKoVuo1Tiad58KdHsBUrAl98oZ4w/lNYtcq/cUoKSAiRCSRQCRSnd0W0W3avpdmaQwBEvzDat3M6nOriFSYpn1zPbFI1Kzo+d6/NZ8nH/PtnER0SzfqTG1Tn2nbt4fEB6gmDB8K5s/6NU1YBCSECTAKVQLF5V0S7/d0RRNjhULkClOnwqPfn03XVayMsRK26ELmfpyGc0/eGcLcWuCW1c+2n/0zg+91z4K23oUoVOH0ahgzxP20jq4CEEAEkgUogeFlEu/HAb7Revh+AkKef9202xOFUF4RQ6ZmSp4SGqB4rdt97rNx/SztG3fUMAI+tHMrOpEMwearaKXHZUv9b7KemgOySAhJC+E0ClUCwO9S73AwWs278aCRFkuBsTD6K9xzk/fl0Xb2jDpOeKXlOgHqsvNHwZZqVvodEeyKdFz1MfOVyMPoN9eCLo2DPbv/GaTCAhqSAhBB+y9Sr3JgxY6hbty6RkZHExMTQqVMn9uzZk+Y5KSkpDBkyhEKFCpEvXz66dOnC6dOnM3NYgaXrammnlrFOtH+f/JtWS9RFQBs6zLe0jc2u3rGGSAFtnuTpsWIx+9xjxWQwMavdVErmK8Gei/vo9/Ng9MGDoVlzSE6G/v1Uq31/mN0poGSrpICEED7L1EDl119/ZciQIfz555+sXLkSu91Oy5YtSUxMTH3OU089xU8//cScOXP49ddfOXHiBJ07d87MYQWW06lW3mSwiPbnr5+n4gVIDDdT+PERPpzP5d4TRnZGztMMBveyZaOa0fNBTHgMc9p/i8lgYu6+BXyydSJMnKR2W962Dd543b8xelJAKbIKSAjhO03Xs+6tztmzZ4mJieHXX3+lSZMmxMbGUqRIEWbOnMmDDz4IwO7du6latSrr16+nfv36Nz1mXFwc0dHRxMbGEhUVldnfwrWSUiAxWU3F38Tei/v4r1ltWh+As/17UOTjr7w/n9Wm6hQiwiRQESoAiE9UK218LKr+9O8veHLtc5gMJtZ1W0H9f85Aj+7q9+unxdDkHv/HaDRAZD6vmiEKIXK3jF6/s/SvRmxsLAAFCxYEYPPmzdjtdlq0aJH6nCpVqlCmTBnWr1+f7jGsVitxcXFpbtnG5VKBQwb/+E5f8DqtD4BLgyLDX/T+fA6neictHWiFh8Wsglany+eVQMNqD+bBip1wuBw8tKQ355s1gD59VLpm0EBw/3/rM7NJzfqkSApICOG9LAtUXC4XI0aMoFGjRlSrVg2AU6dOYbFYyJ8/f5rnFi1alFOnTqV7nDFjxhAdHZ16K126dGYP/focTpX6yUDa51j8cUrMXATApaYNoHx5787lWY4cavFqw0ORB4RYLq8E8iEQ0DSNr1t+zq35b+FI/FF6LR+A6+13oFx5OHYMnn3Gv/FpGpjNKlDxMU0lhMi7sixQGTJkCDt27OB7P3drHTVqFLGxsam3o0ePBmiEPrC690fJwOzGF7+Npdc/6h1vwSd9mE3xBEQhN08xiTxGczf982MlUHRINHPaf0uIMYSlB3/mg11fw1dfqxm872fB/Hn+jdEz65iU4vOyaiFE3pQlgcrQoUNZvHgxa9asoVSpUqn3FytWDJvNxqVLl9I8//Tp0xQrVizdY4WEhBAVFZXmli286ER7Pvk8ju+mE2mDhAql4N57vTuXroPDBSEhsp+PSJ/BoJYtm0w+F67WiqnB+KZjAXjpf6/zW0k7PO2eTXnySThxgpOnVNf9jp2g0d3QpAk89BB8+x3E3iwLazapLQCS/VxNJITIUzI1UNF1naFDhzJ//nx++eUXyl+V7qhTpw5ms5nVq1en3rdnzx6OHDlCgwYNMnNo/rO7u4NmoI/JZ39P5PH1avYlYugz3teXpO6OLLMp4gaMRlWvYjD4nGJ5rHofHqnaHafupMeSvpwd/hjUrg2XLnKw7WDub6/z/fdQ7y4Y8oTqvn/rrfD559CmNXzzzQ0mdDRNBSspVlkFJITIsExd9fPEE08wc+ZMFi5cSOXKlVPvj46OJiwsDIDBgwezdOlSpk2bRlRUFMOGDQPgjz/+yNA5smXVj65DXIIKIG6yGWCCLYH+z9zK7GkJ2PKFY9l3ECIivDuX1Q6R4dKFVmRMig0SEtXsig+rbBLtidSd0YRdF/bQutx9LKo6BhrejdmZwoaHPqTaJwOJCE/7mgsXYPo38N130Kc3uP83Tp/NroKqqAhpWChEHhYUq34mTJhAbGws9957L8WLF0+9zZ49O/U5H3/8Me3bt6dLly40adKEYsWKMW+en/nwzOZ0Xp7luImp/35L33UJAJh69/MuSAFVQGs2yu7IIuNCzH4V10aYI5jdbjqhxlCWH1pJ99XL+LjQWwDUW/QyESf2X/OaggXhqREw8imYNh1WrrzBCa5cBSSEEDeRpX1UMkO2zKhksHeKS3fR6t3bWDnmGLqmoW3Z5t1qH11X7z4jI6SIVnjH5YLEJEixq8DFh+XsX22byoBVw9BcRp7Ql/HZundg7Vq4qx4s//m6fVuGDIWEeJg+/QYH92ysGJVPBS5CiDwnKGZUciUveqcs/m8ZrdceA8DZsoX3S5LtDvVHXGZThLcMBggP86tz7WPV+9Ak8kF0g5P5kf259NG7EBUFGzfAJ+Ou+7puXWHHv7Bz5w0ObjSqQDwpWVYBCSFuSAIVb3nRO+XzDePpvUV9buo/0Lvz6Dq4dGnuJnznKa4F9XvrJU3TuOvIp0Rab+FE0lF6//sm+nvvqwffeRu2b0/3dXc3hshIuE7PxsssZrX9RIrN67EJIfIOCVS8lcHeKVvPbqfQit8pnAyO4sXgvvu8O4/MpohAsLjrVRxOn2YubPFRdLg0HYvRwqIDS/i0yiVo114tMx74eLobFxoNULAAxCfc5OCapgL+5BRpBCeEuC4JVLzhRe+UcX9/zoDN6nNTn37e9T/RdXULk9kUEQCpzeC8L64NDYHo+FqMbfI2AM/9/irbXx0EhQrBjh0w5p10X5ecrNr+3JTJkwJKkfb6Qoh0SaDiDUfGeqecTjzNpnWzufcw6AYD9Ort5XncwZAUGYpA0DRVr2I2qWDFCxUrwdZt0PvWQdx/SztsThtd1o8g+UN3Cmjcx7BxY5rX7NsPZ85CxYoZPInFDHabrAISQqRLApWM0nWw2dQutTeZ5Ziw9Wt6/6UaWmmtWkPJkt6dx+muTZEeEyJQjAb3jtuoQDiDOt4PDjssXaoxpeUXlMxXgn2X9jM4/Bfo9pBKJw0eqKZQ3ObOhcKF4J6MbrqsaWrGMdnq1diEEHmDXAkzyulS70ZvksJJcaQw+e+v6LPFfUefvt6dx9OfRWpTRKCZTSpYcbgyXK9SpAg0bQZTp4IjvhAz2kzGoBmYvnMGcx5rCMWKwb598OYbgFrps/gn6NzZywlBk0n9P5YsOywLIdKSQCWjHO78/k2WJc/aPYe7N5+jcDLoJUt6V0Sr6+qPdZjMpohMEmJRv19ebF749EgVRwwcCOX0xrxS73kA+v31EifecW+w+fln7J3+B8OGwa0Vvc92Aio4T7FdLlgXQggkUMkYXVd/PG+S8tF1nU/++SK1iFbr1fu6TbHSJbMpIrNpmtq80GzO8H47RYrApEkq69n1QYib/zw1IxuSYE+gdfxUjt3bA3Sd0JGDqVImkfGfqoVGXjNo6o1AcooqXBdCCCRQyZgMtsz/8+RGknZtp+khdxHto70yfg7PbEqIRWZTROYyeOpVtAzXhJQqqfbxefZZOHbYhPPbKRitBdh+8R+a5c/PhbCSlLEf4NNCr+FXg2iTUf1/IKuAhBBuckXMCJtDNV+7SQAxYevXPP63+lxr2QpKlcr4OZxO9W5SWuWLrOBDvUp4OHTtCrNnw7wppXir5mcA7Ks1gUMfDwDA+OVEWPeb7+Py7LBstckOy0IIQAKVm/Pst3OT2pTzyeeZt+vH1E609PW2iNalel34sNutED4JsfjUX0XToOKt8ELbjgyorn7P28dPIPnRHuoJQ56AhJt1e7sBg0GlgZIkBSSEkEDl5hwZS/tM/fc7mu22EZMEetGicF/LjJ/D6VR/mC0ymyKyUGp/Fd/3A/r43veoWrAyJxNP0efuc+ilS8OhQzD6Nf/GZjKp/+9SZBWQEHmdBCo341kdcYNCWpfuYuLWyalLkrWHuntfRGsxZ6jjrRABZXRvXgg+7QcUbg5nVrtpWIwWfji5kkUj2qgHvpwEv6/zfVyaBhYTJNukvb4QeZwEKjficqlAxXTjH9Oqw2u4dOI/2u9z3/Hww96dQ9PUFLwQ2SF1PyB3LZaXahapzvuN3wTgoaTpnO/eST3wxBOQmOj7uAwG1aBOdlgWIk+TQOVGPDsl36TJ24StX9FjB1icQO3acNvt3p3DZPJuBkaIQEvdDyjj/VWuNLz2E7Qt3wqr00q7O3apHkKHDsLro/0bl9mkZlSSpb2+EHmVBCo34nSBzg3TPsfij7Pov6WXi2h7eDGbouvqHWyoRTYfFNlL0yAsTM0e+pAC0jTVYj8mvAgbkvYw8fHa6oGJE+B/v/s3LrNJ1arIKiAh8iQJVG7o5u8sv94+jaqnXNx5EtVEq2vXjB/e4VSFjLL5oAgGJqOqV3G5e/p4qWhEUaa2nAjAEyzmaBd3V2Z/U0CeGc3kFEkBCZEHSaDiB7vTzlfbp9F7q/uO1q2hUOGMvdjT4E02HxTBxGJWaSC7bymgthVaMazWIACa1vwHZ8nicPA/eOtN/8bl2flZdlgWIs+RK6QfFv+3jNNxJ+m9zZ226flIxl/sdKkVF2Zply+CiKapQMWS8Rb7V3uv8ZvcXqgqB1zneKOHe+fwLz6HDX/6Ny6ze4dlWQUkRJ4igYofJu/4hpYHICZBh8KFve+dEiIN3kQQMriXLGuaT/UqYeYwZradSogxhDfybWJXm7pqduaJwZCc7Pu4jEZ1nKRk6a0iRB4iV0kfHY8/wbJDK1J7p9DtoYzPjniWJIfIbIoIUmaTWrLs9G3Jco0i1Xiv8RsANKu9DXtMYdi3D94d49+4LGaVApJVQELkGRKo+OibXTOJSnLRaa877fNwz4y/2O5u8HaTZc9CZKuwEAgJ8TkFNKz2YFqVbcEpi5VnH4hQd34yDjZv9n1MmqaKflOsGd5QUQiRs0mg4gNd15my41se2gEWhw7Vq0ONGhl9sbpZzLIkWQQ3TVOzKiaDT3UhBs3A1FYTKRRakE+KH+afeyqr2cQnBoHVjxkRzw7LybLDshB5gQQqPvj9+B/sv3SAR/91//ge6p7xF3v2DZIlySInSF2ynPFdlq9UPF8xvm75OQAt79qDrWA07NoFH7zv37gsZrDa1S7LQohcTQIVH0z591tKxUKjQ+5aky4PZvzFTpcqopUlySKnsJjVMnovd1n26HRrBx6v3odzETC8nTvd+dGHsH2772MyaOr/oaQUnwp+hRA5h1wtvRRnjeOHPfPovsN9R6NGULJkxl7sdLl3SZYiWpGDeFJAfu6yXDH/rUyqcIGNdUuo+pKhT/hXZ2I2SQpIiKyQzZ2hJVDx0g9755HkSKLvrhB1x4PedKJ1yC7JIme6cpdlp/czGBHmCGa0nYzJaKJjkxNYI8Phn39g/Kf+jctighSbtNcXIrPouvp/LBu7Qkug4qUpO76l8lm47ZhVbSTYsVPGXuh5xxciuySLHMqTArI7fZrBqFusDqMbvMipSHiylfuP3jtvw769vo/JYFBBVFKKTwGUEOImHI5s/39LAhUv7Dq/m/UnN9Bzh3u1TvMWUKhQxl4sRbQiNwgLUbMYPs5gvFD3aRqVaMCk21P4s1p+tfpnyBD/3q2ZjOoPaZKkgIQIOLsj2+vAJFDxwtR/vwMd+u92T4F37ZbxFzud6t2oLEkWOZmfXWuNBiPftvmKyJBIurW4hDXMAn+uhy8n+T4mzw7LVkkBCRFQuh4U/09JoJJBDpeDb3bOpM4JKHE6CcLCoG3bjL3Y6VTN3WQ2ReQGnq61Dt9SQOWjy/Fp0w84mh+eae4upn19NBw+7PuYDAYVsCSl+LTzsxAiHQ4nONyLQLKRBCoZtPLwL5xOOsNju0LVHW3bQb58GXuxw6n+uEsnWpFbeDYutPr2bqv3bT3pUrEjn9/h4q9bwiAxEYYP8y91Yzap/9dSJAUkREDY7YCe7ZkACVQy6Ltd32NwQQ9Pk7euGVztI0W0IjfSNIgIVYWsPiwx1jSNSS0+pVhkMXq2ScZmMcKaX2DmDP/GZDZBsk12WBbCX560TxD0/Mr+EeQA8bZ45u//iSaHIfpiEuQvAC3uy9iLpYhW5FYmE4SHqlSLDxsXFgorxLRWk9hXGF5u4q53GfUCnDrl+5iMBtBQOyxn43JKIXI8h1P9vx0E7TQkUMmABfsXk+xIZvDeKHVHx45gyeAMidOlnitFtCI3CrGom48Fdy3LNWd47cF81AC2ljLBpUvwzEj/xmQ2qRmVZKukgITwld2u/v8JgmuXBCoZ8N2u7zE7oMMO974iGU37uDydaGU2ReRSnq61Rt82LgR49+43qBxThV7tHTiMGixaBAvm+zcms1l105QUkBDeC6K0D0igclMnE0+x6sgamh2EsPgUiImBRndn7MWetI8U0YrczGS8IgXkfbolzBzGd20ms6uEmTGN3DMgT4+ECxd8H5PR/actKUVSQEJ4y5P2CZJrl7zVvw6nE5ISYMa+H3HpLoYcLgKchQ73Z+wfT9dV3l56p4i8IMSiZi+sNrUayMvf+doxNXmj4cu8ZnuNrrsMVDl7VtWrTPrS9zGZTWpVUrIVIsJ8P44QeY3dgd2m88sajZ07IUSDWAfUqg+tWmX9RIvMqFzBaoUZM9Q+gyYT1KoFby2fg8kJTf9JVE/q1CljB3O61Ls6k8SCIg/QNDWrYjT43MXy2TtHcFfZBvS934VLA2bNhFWr/BuT2ehOAWV/0yohcoKUZJ3JE+w8/KiBF1+C336Dfftg8WLVOqxiRfj006ydqAyKQOXzzz+nXLlyhIaGUq9ePTZu3JjlY9iwAcqXh0ceUb3cJkyAp9/fSWzUNpoeMpIvMYmLpsL8E5HBtI/TCSHmy1PQQuR2RqPqWuvyLQVkNBj5pvVX/HtLJJ/e5b7zyWEQH+/fmHQkBSREBsTFQeeOTpYsclK/kZE5P8D8+fDxR/D777B+PTRsCCNGQM+e/m1+7o1sv4rOnj2bkSNH8tprr/H3339Ts2ZNWrVqxZkzZ7JsDH/+CU2bqkBl1y71Jm7QIDhaaBYAI4+XBGB72Q48MczE33/f5ICelQZmcyaOWoggZDFfTgP50bX25WZwMD9w9Ci88bqfYzKBzaFmVoQQ6XK5oFs3OHnEzjtv6zz9jEaFCpcf1zSoXx++/RZ++AHmzFEBS1bQdD171+/Vq1ePunXr8tlnnwHgcrkoXbo0w4YN44UXXrjp6+Pi4oiOjiY2NpaoqCivz5+UpAKUihVh5Uo1mwLg0l2U+7gsx2OPkTg+ktCL8djnLmLY/Gbs3QMLF0Hk9RrT2h1qtU90pNSniLzH6YS4RBWo+NA/SNd1HvypJ3E/L2Llt6BrGtrPK6B+A//G5HRBVD7paSREOpYtgw4ddH5fHE/9u676f9dqg3zhqubS7ZNPVKCybx/ceqtv58zo9TtbZ1RsNhubN2+mRYsWqfcZDAZatGjB+vXr032N1WolLi4uzc0fs2bB2bPwzTeXgxSAdYfXcTT+GK1PhBF6MR4KFMTctDFvvA4JCbBs6Q0OKr1TRF5mdK8C8jEFpGkak+77lB01ijKlFmi6DkOHqNb4/oxJ16URnBDX8cUXcE9DB/XqpG3ytujAEvquHsIfx9JekwcOhIIFVZlEZsvWQOXcuXM4nU6KFi2a5v6iRYty6jrdKceMGUN0dHTqrXTp0n6N4YsvoE0b0kxxAXy37TsAnj7iPn779mA2ExMD99yjpr3SnYuS3ilC+J0CKhxWmKktJ/J0KziZD9i7F95/z/8xSQpIiGucOwdLlsDAvg40SPMm++vt05m2exZL/1ue5jWhodC3L0yfnvnjy/YaFW+NGjWK2NjY1NvRo0d9PlZ8PPz9N3Tvfu1jg+sOZkStwdz991l1R8dOqY+1bAX/HYSLF9M5qPROEeKKVUBGn1cBtS5/Hz3rD2CIe5Ny/eOPYNs2/8ZkMqrlytIITohUJ06AyeiiemU7mC6HBeeSz7Hs0AoAet7W45rX1awJ58/7N9mZEdkaqBQuXBij0cjp06fT3H/69GmKFSuW7mtCQkKIiopKc/NVbKz6WKjQtY/dUfwOPo7ojOXcRYiOhnvvTX0sf371MSEhnYO6dPVOUtI+Iq/zMwUE8H6Tt9jZqCJzq4LmdKIPecK/pQYmSQEJcTVdh4JRTow406xUnb3nRxwuB3WK1KJq4SrXvM5zmcvsStdsDVQsFgt16tRh9erVqfe5XC5Wr15NgwZ+FM5lUD53Mex1y1zmu9t4t22XZm+fRHdLlbDwq57vdKd9pFhPCMXPFFC4OZzv2kxmRHsjF0NB27oFPhvv/5gkBSREquLFISa/jSNHSfMm+7tdswF4pHL628b8+6964x6Wyf0Usz31M3LkSL766iumT5/Orl27GDx4MImJifTt2zfTzx0draqVFy1K50GXCxYsUJ9f1eTt17VQrBgUKnjVa5xO9UdQ0j5CKAFIAd1Z7A4G3fciI1upr11vvwX79/s3JkkBCZEqppCLNi0c/Ljw8rVr/8UD/HlyIwbNQPdbO1/zGpsNpkxRvccyW7YHKg899BBjx47l1VdfpVatWmzZsoXly5dfU2CbGTQNBg+GuXPhquyT6gB34oSadmnaLPXu2Dj4+Wfo0vmqNsKelvkW6Z0iRBoBSAG9cNfT7GlzFysrgMFqRR821L/UjaSAhLjM4eCBDi42bjaw2d0nbMZuNZvSsmxzikVcez2ePh3OnFHX0MyW7YEKwNChQzl8+DBWq5UNGzZQr169LDt3nz4qqzP06r97c+eqj63aqPJm1N+1sR+oj1fU1iouaZkvxHX5mQIyGUx82/ZrnnognEQzaP/7HaZN9X9MkgISAmx27rxLo1Ytjeeeg717db7d+T0Aj1S9drXJihUwbBj06gW33Zb5wwuKQCU7FSyo9vf58Ud4+OHLBbY0bQodO0LnLgAkJsHo0bB0Gbz+ejppH4dT1aZIy3whrhWAvYBuyV+Bpzp/wIvN1dfOl0bB8eP+jUlSQCKvczjBZsdkMfLhh1CsKHR79i8OxP5HuCmCTre2T33qzp3qTX27dtCiBXzpx56h3sj2zrT+8rczrce8eWrvAqNRfXzwQSgWnYIrPpllaywsWaz+lo0eDa1bXfViXVe7tEZFqHeNQoj0pdggPlH1GfJhC1Zd13lwQXeeeWkJDY6Bs+V9GOfM82+VndWm3mRERmT9trBCZLcUK8QnQai6diUmQcsvRvKH4UsKHuxO20tfU7a4jc27w1m+NoSYGBgyBF580f8EQkav3xKoXOHECfj6a5g0SX1eoUQK1colk6JbeKAzPPAAFI1J54UOJ6Crlvnyh06I69N1SEhSwYGPQf3ZpLN0+aAOK8ddIMQJTJ4CXbv5NyarHSJC1aaKQuQVug5xCeoa5q6vtDvtFJ90C+dTLvBSzAJCDrYg1GDjgjWcGneF0KVLmkWwfpFAxQ8OBxw6BCmXUihoSaZIacuNVxyn2NS0doT8kRPiphxO9ccRfF7Kv/S/n1n/VBfeXAO2AlFYNm+FwkV8H1PqXkARspmoyDvsDvX/osmY+iZ78X/L6LCgK0XDYzg2YC8mgyndvX4CIUfs9ROsTCa1bLna7VCi+E3+luo6aEjvFCEyyuReBeR0qZVyPmhboRWxg/uxLQYsF+OwPvOUf2NK3QsoRVYBibzDble/91dkAr7dOQuAh6t0U0FKEJBAxV9Op/ojZ5LeKUJkWIhF5cRtdp8P8W7zd3njkdI4NQiZtwD9hjuFZoCsAhJ5iculUp5XLACJtcay6ID6/yi91T7ZRQIVfzlcEGKW2hQhvKFpEBbiXgXk24qbcHM4LwyawbiGqpA2aeiAG7SZTuvCBZg6Fbo8CI0bw92NoXMXjWkzjJw/bvUrgBIiR7A7VBr2igals/f8SIozhaoFK1M7pmY2Di4tubr6Q9fdSxyDY3pMiBzFZHKngJw+bxZyZ7E7cL4wiv0FIOLsJeJeGHHD5+s6TJwEbdrCV1+p9O7AQTB4ENSqBV9NNdK3D3zxYQouh6SARC5ms6uyhStWzE3Z8S0A/ar1Qgui/eqkmPZGklMgITl12dY1HA5Ag+h8MqMihC90HRISIcV+/f/PbsLpcjLijQaM/2in+vqnxRjvuTfdU737Lsz9ER7rDw/3hOir/mTEx8PsH3SWzbdRsWYYY8aFohmC5w+2EAHhcEJsvJpNcad+/j23k2rf3IXJYOLY43so6ulGe/Gimn0pWVyKaXMkh0vltSVIEcI3mgZhoWozTx8bwRkNRkY+9QNT6qrVOrEDH4WkpGuet2ChClJeeUW1/b46SAGIjITH+msMGmZm+19Wvp4gjeBELuRwXO6m7jb13+8AaF++9eUgBeDLSXBbZXh3TFaPMpVcYX0lq32ECAyTSQUrDt9TQOWjyxE25gOORULBExc59eKwNI/rOnz3LbRoDp063vx497Uy0Lw5rFiQjNMmKSCRi+i6aqlxRW2K3Wnnm50zAZX2SfPcH2arHQhLlcrqkaaSQMVXstpHiMAJtahutX4UsXav259vBzUAoMjU2ST/uS71sU2b4NBh6PZQxo/Xur0JR4qDdStTfA6ghAg6jmuLaBf/t4yzyecoFlGUNuVbXn7uli2wb5/a7+7+DET4mUQCFV9J2keIwDEYVFdYTVNvAnygaRoDn5nNvDvCMOpwoX8PsKqlxn/8obpK31E748e77XaN0Egzu/6xySogkXvY3BuDXlF7NeVfVUTbq+rDaXun/KB2UKZVawh0DagX5CrrC0n7CBF4ZpNasmz3PQVUMKwgRT75mjPhUPLoJfa8NACA+AQoVMj7LYEiow3ExqMawfkYQAkRNFwu1WX2ikzAiYSTLD34MwB9qz1y+blOJ8ydoz5/sGtWjvIaEqj4wumStI8QmSE0xJ0C8r2ItXHNjvw8tDUAFb7+kXN//UpICKSkeH+slBSw6yb1RztJUkAih7M73NevtJ1oXbqLRiUaUKVg5cvP/fVXOH0aChSEZs2zYbCXSaDiC6dT0j5CZAaDQRXWavg1g9Ht+e/4pWYUZhdc6N+dSuXtHDykNhvNqHPnYc8eqFhJU7M9KTb1blSInEjXVddlg5Y6tajremrap1+1R9M+35P2eaBT4HYh9JFcab2l66AjaR8hMovFrGZW7A6fZzBCzKGUnPQ9F0Oh0qF4CvzRk4gI+HFexo+xYAGYzNCuLSqAMhrUrIqPnXSFyFYOp7pdkQn448Sf7L24jwhzBF0rPXD5ucnJ8NMi9flD2d9KXwIVbzldYDJI2keIzBQWonYxtvseFFS+rQmbn1LLfO6aupS2DZYwdw4c+O/mrz16FGbNhDZtVG8VQL05ceqSAhI5k82uNgG9IhMwecc3AHSr1JlIS+Tl5y5bqroflikD9epn9UivIYGKtyTtI0TmMxhUe31Qbw581Pz5r9hUvTChDmi5tA9FiyfyxGDYufP6r9m3DwYNhuhoGDr0qgdDTGojt2TZuFDkIE7nNUW0F1Mu8v2euQD0v7J3CsAPP6iPXbsFxbUu+0eQk6SmfczZPRIhcj+zSfVX8SMFpBkMlJ0yn/gQjdoHk+lSpDNFi8KjvWDIUFizBo4dh+MnYN06eHIE9HhYzaJMmAj5o68+oKb+2CengF2WLIscIp0i2m92ziTZkUz1wrfTsMQVsyYXLsDKFerzbt2yeKDpk0ILb3j+oSXtI0Tm0zTVaMrmUHUhPr5BKFK5NtufHUD1tybRYcb/KDT3G5KTejF3DjzzbNrnVqkML70ErVurmt50mYxgdakUUKQxKN5xCnFdqZ1o0xbRTtw2GYDBNR9LuwHhgvkqCK9eHarelh0jvoYEKt5wuiBE0j5CZBmjOwUUn6h6QPj4/171Zz5g78KfqLT9BKFPP0mNdS3p0L4Yhw7D6VNqojSmCJQvn8FeKxaTmkpPtqrxBdFOs0KkYXcH+pbLgf7ao7+x+8Je8pnz8UjVq4plZ3+vPnrTxjmTyRU3o3Rd3SyS9hEiS1nMEOJfCgiDgTJTF5Bk0bj7gJ0fRt2PruuUKwv16kH9elChghfxhqapGZ5kq3StFcHNs6T+il/uCdu+BqBn1W5pi2gPH4b169Vzs7nJ25UkUMkol7vlsKR9hMhamqZmLYwGn3dYBgitdBuXnhsBQO9ZO5m27B3/xmU0qL+g0rVWBCuHUwXSV1y3TiacYv7+nwAYXPPxtM+fOUN9bNIESpbMqlHelAQqGeV0rz+XtI8QWc9ohLAwlX51+b4KqMTTozlRvRyRNij90rtsO7Pdv3GZpGutCGI2u/r/5YoNCKf8+w0Ol4MGxetRs0j1y891uS4HKo9ctQoom8lVN6NcLjX9LLloIbJHiFmtAvKjvT5GI8WnzcNqNtDigM6clzuRZE/y/XieFJDVKkuWRXDx7OtzRZDidDn5cttUQBXRprHuN5X6iYqCDh2ycqQ3JYFKRniK+CTtI0T20TS1FMdo8Ks7rFaxEvaXRgHw7LzTvDV3mH/jMmhgNLmXLEvXWhEk7A5wOtJct5Ye/Jkj8UcpFFowbSdagO++Ux8ffBDCw7NwoDcngUpGeDYhNEqgIkS2MhlV11qn7zssA+R78jku1axClA2avD+b+XsX+j8uXYekZL9SU0IEhGdfH82Qtoh2qyqi7Xv7o4Sarlh/HxsLi9z/D/S8as+fICCBSka4XGo5oqR9hMh+oSFgtvi32sZoJP/kGdjNRlofgF/GPM7R+GP+jctiVmNKtkq9ishedoe6mS53IDkYe4jlh1YCMKBG37TPn/ej2t+ncmW4886sHGmGSKByUzqgySaEQgQLzyogTfNrFRCVKqO9/AoAby1O4qnvHsHp8uN4smRZBANdv7wk2XD5zfX4fyaio9OybHMqFrg17Ws8aZ9HewXlG3IJVG7G5e5GK2kfIYKH2aTqVRz+pYBMw58ipXZ1oq3w2JebeOvPd/0bl9GgLg6Jyf4FUUL4yrMk+Yo313HWOL7eMR2AEXcMSfv8Pbvhr43qGhcEOyWnRwKVm9JkE0IhglGoRaVk/VwFFPrVNJwWE60PwLEv3uW3Y7/7Ny6zSdW1JUq9isgG6eySPO3f74i3xVO5QEValWuR9vme2ZRWraFo0SwcaMbJ1fdmDAZJ+wgRjDw7LGv413CtUmWMr74OwIfLdZ6d0Zvzyef9G1uIu14lRZYsiyzk2SXZnHZJ8if/TADUbIpBu+Kyb7fDrJnq80ceycqRekUClZuRtI8QwctsVsW1/rTXBxgyFGfdO4mywVuzTtP/5yfQ/TmepqmLhdSriKxktatg5Ypr1uL/lvFf7EEKhBTg0dt6pH3+qpVw5gwULqxmVIKUBCo3Yzal2RpbCBFkwkJUwOJPDxOjEePEr3CFWLjvPyj6wxK+2Pqlf+PyXCwSk6XFvsh86TR4Axj39+cADKzRjwhzRNrXfPuN+ti9h8+7k2cFuQLfiMV8g73ehRBBwZMCAlUb4quKFTGMfhOAsSvgkwUv8M+Zrf6NzexpsZ8sS5ZF5rLZVSHtFQ3etpzZxtpj6zBqRobUGpD2+SdOwLJl6vNewdUy/2oSqNyI0SjdaIXICcwmVVzrbwpo8GD0hg2JtMGkeXYeWvQo8bZ434+nuYvxU+yqc60QmcHlUvVQRi3N8mLPbErXSg9QKvKqTQa/ma6C6EaNoErVrByt1yRQEULkfJoGoaHqjYU/KSCDAe2LiehhYTQ9BK1X/MfAlcOlXkUEN7tDbStxRYO3U4mnmbVnDgBP3TE07fMdDpim9vyhX/+sGqXPJFARQuQORncKSNf9WxZcoQLaW28D8N4q2Py/OUx296DwfWxX1KtIfxURSLquguBr2uV/hc1po0HxetxV/Kpusz8vV6mfQoXg/o5ZPGDvSaAihMg9LGa1y7m/KaDHHoemzQhzwPT5MGLl0+w4969/YzObVJCSLP1VRADZ7Or3/Yo2Gkn2JL7Y+hUAI+544trXTJ6sPj7aC0JCsmKUfpFARQiRe3ja6xsN/s1caBp8/gV6VBT1j8OwdVa6Le5Fgi3Bv2OGeOpVZD8gEQCezQcNaWtTvt4+jXPJ56kQXZ7OFa+aMTl4EFavUp/3uWrPnyCVaYHKoUOH6N+/P+XLlycsLIxbbrmF1157DZvNluZ527Zto3HjxoSGhlK6dGnef//9zBqSECIvMBohLEytAPJn5qJUKbT3PgDg9bVg2rmHJ1Y/JfUqInjY7Koz8xWzKTanjbGbPwXgubojMBmualg6fZoKcJq3gAoVsnCwvsu0QGX37t24XC4mTZrEv//+y8cff8zEiRN58cUXU58TFxdHy5YtKVu2LJs3b+aDDz5g9OjRfPmln/0LhBB5W4hZrQLyp70+wMMPQ7v2WJzwzQL4fvuswNSrpO4H5Of4RN51ndmUmbt/4Gj8MYpFFKX3bT3TvsZmU6t9APoHfxGtR6b1hm/dujWtW1/udFehQgX27NnDhAkTGDt2LAAzZszAZrMxZcoULBYLt99+O1u2bOGjjz5iwIAB1zu0EELcmKapHkjprIbw+jiffAJ/rqfWqfOMXgtDQ57mzqJ3UCumhu/jM5tUc67EZIiMkL3EhPfsDnWzXG7U5tJdvPfXR4Ba6RNquqoP2KKFcO4cFC8Ordtk5Wj9kqX/d8TGxlKwYMHUr9evX0+TJk2wWCyp97Vq1Yo9e/Zw8eLFdI9htVqJi4tLcxNCiGuYjKpexenfDsvEFIVP1FT68/+DOgetdF38KHFWP//2WNz7ASWlSL2K8I6uq748WtrZlAX7f2L3hb3kD8nPoBrpzJhMcRfR9u7je/CeDbIsUNm/fz/jx49n4MCBqfedOnWKolft1uj5+tSpU+keZ8yYMURHR6feSpcunXmDFkLkbCEWdbP6WQ9yf0fo8TBGHWYuNHLq9AEeWznE/3oVi1nVq8jmhcIbnpU+VwQbuq4zZuOHAAyp9ThRIVFpX7N7F/z+u0o9eltEq4Pa/TN7eB2ovPDCC2iadsPb7t2707zm+PHjtG7dmq5du/L444/7NeBRo0YRGxubejt69KhfxxNC5GKeFJBB879/yfsfQOnSlD3v5OMVGnP2zuezLRP9O6bBoGZ+klKkuFZkjKc2RdPU77Xb6iNr2XT6b8JMYTxZO50lyZPcv6tt20KJEhk/n9OpVtGZs69Lu9dzP08//TR9+vS54XMqXFFJfOLECZo2bUrDhg2vKZItVqwYp0+fTnOf5+tixYqle+yQkBBCcsC6byFEkDCZVLCSmKz+4Go+vjOMjoaJk6B9Ox7brLOgEjxteJG6RetQv8RdfozPCDaXGp8ncBHiejyzKZa0mwiO2ahqPx+r1psi4UXSvubCBZg5U33+xBDvzudwqllJYw4KVIoUKUKRIkVu/kTUTErTpk2pU6cOU6dOxXBVwViDBg146aWXsNvtmN07N65cuZLKlStToEABb4cmhBDpCwsBu139kQ+x3Pz519O4CQwZCp+N57slIVQsZeXBxY/w9yO/ExMe4/txzSaVnkpMkuJacX0ul6pNuWqlz8aTm/jl6K+YDCaeufPJa183fZpqNFizJjRslPHzeVKb/vw/EwCZ9n/D8ePHuffeeylTpgxjx47l7NmznDp1Kk3tycMPP4zFYqF///78+++/zJ49m08++YSRI0dm1rCEEHmRpkF4mProbwro1degalXyx1mZuTyC4/En6LGkL06Xnw3mQtzFtclSXCuuw2YHu/OaQtjR698BoGeVhygTdVXdpt0OX05Snw8e4t2Momc3ZnP2Ft5mWqCycuVK9u/fz+rVqylVqhTFixdPvXlER0ezYsUKDh48SJ06dXj66ad59dVXZWmyECLwzCY1s+LwcxVQaCh8NRnMZu7bnsgTWy38cvRXXvnjTf/G5ymuTbKqAlshruR0qd+Lq9KX609sYNmhFRg1I6/Uf/7a1y1aCMePQ0wMdOni/TktFt/TpQGi6X6VrWe/uLg4oqOjiY2NJSoq6uYvEELkXS4XxCeqHL+/09mfjINXXsYRaqHq4zb2F4KFHWdz/y3t/Duuw6nGGRlxTR2CyMOSUlRqMCRt4HDf3A6sOrKG/tV683XLz699XfNm8NdGePEleGFUxs/ncqnfxeh8mbaUOaPXb0mECiHyDoN7h2VNU6sZ/DFsODRpginFxqrlMZic0Gv5APZfPODfcT3FtIlJ0rlWKE6nWuljMqUJUn479jurjqzBbDDzcr3nrn3dxo0qSLFYvO9E63CCxZStRbQeEqgIIfIWs9ldXOtnCshggIlfQv78lN13hq/+LkmsNZYHFvXwb/NCUDMpTvdKIKfstJznpdjU78EVK8J0XeeV/6l0Y/9qvSgXXfba133hnmHp9hAU8aLYW9fVLQjSPiCBihAiLwoNUTUrdj9nLEqVgnGfANB72UnuP1OAHed30m/FYP+awYG7c60DkpL921xR5Gx2h5pNuaqPyS9H1/Lb8f9hMVp4Kb3ZlGPHYOEC9fngdPqq3Ehq75Tg6F4rgYoQIu/xpIDA/xRQ5y7wcE80l4vZCywUspmYs3c+H2wa599xPcW1KTZVRJmzywmFL3QdUtyrwIxpZ1Ne/eNtAAZW70epyJLXvvarL9XvdpMmUL26d+d1uItog2SZfHCMQgghsprFrGZW7A7/g4D3P4By5Qg9cZr1G2uADqN+f40Vh1b7d1yDpt7VJqXISqC8yGaHFLtKV17h50Or+OPEn4QaQxl11zPXvi4u7vK+Pt7Oprhc6vcuiAq5JVARQuRdYSHqIuBvCigqCr6eAkYjFVf9zeTzjXDpLrov6cPB2EP+Hdvo7labnKJ2XBZ5g6e5mzFtq3w1m/IWAE/Uepzi+dLp4j5lMsTGQqVK0Katd+f19E4Jog7JEqgIIfKuNCkgP+tA7roLXnoZgL5Tt9BFq8ZF60U6Lezuf3GtyahSQYnJ/gdVImdIsV6z8SDAD3t/5K/Tm4kwR/B83afSeV0KfP6Z+vypkd6lb3QdXDqEhARFEa2HBCpCiLzNbHKngOz+p4CeGgmNG6MlJjLzR51SliJsO7eDPj8PxKX7GQiZTeoikpDkf3ddEdwcDpXqu2o5stVhZdTvowF4vu5T6W/bMHMGnD6tCr27dvPuvE4XmAxqWXIQkUBFCJG3aVrgUkBGo+paW6Aglm3/smF/UyxGCz/uW8ibf77r/1gtJhWkJCbJsuXcStdVkOLSr0m/fL71Sw7GHqJ4RDFG1hl27WsdDhg3Tn0+bLgqiPWG0xlURbQewTUaIYTIDoFMAZUoAV98oT6d8gOLIgYCaj+WH/cu8O/YqXsCOSApSZYt50Y2u6pFumpW40LyBd76830A3mr0KhHmiGtfO38eHDoIhQpB7z7endflurzSLMhIoCKEEBDYFFC79vDY4wC0GvM9r5brC6jOtVvPbvfv2J5gJcWuVgPJsuXcw+lU/6YG7ZpZjbc3fsBF60WqF76d3rf1vPa1ug4ff6Q+HzQYwsO9O7enE20QFdF6SKAihBAQ2BQQwNvvQLVqcPYsr005QOvSzUhyJHH/gm6cSTrj/1gtJpUikGAld9B1VUDruHZ35P8uHeSzLWoH5A+avI3RkE4w8fPPsGMH5MsHAwZ6f25X8HSivZoEKkII4RHIFFBYGEybDhERGH77jXl7alMx/60ciT9Kp4U9SHGk+D/W1B4rEqzkeHYHJLtTPlcFCy/+bzQ2p437yjajVbkW6b/+ow/Vx379oUAB787tWZIcJJ1oryaBihBCXCmQKaBKleGjcQCEjf2YVUWfI39Iftaf3EC/nwPQZt9oUK3VpSFczuZyqa0SNK5J+Ww4+Rez9/yIhsYHTd5O//V//A/+XK9mRIYM9f78TpfalTnIimg9gnNUQgiRXQKdAurRA3o+Ai4XZZ56lUUNP8NkMDFrzxze+HOM/8c3GtUtOUW12xc5i2eVj91xzYyG0+Vk6C9PA9D79p7ULHKdVvhvqwZwPPIIFC/u3fmdwdeJ9moSqAghxNUCmQICGPshVK4Mp07R+I1pTGiqih5Hr3+HWbt/8P/4qQ3hkqR7bU6Tuumg+ZqUz+Qd09l0+m+iLFGMufv19F//61pYt07Npjz9rPfndzhUkBKERbQeEqgIIUR6ApkCioiA6d+oupXVq3hsxTmeqfMkAH1/HswfJ/4MzHg1TTWEs9n9P57IfE6XStuBSuNd4VzyudTmbm82fJliEUWvfb2uw1vu2ZTefaB0ae/O7/m9DvGy30oWk0BFCCHSE+gU0G23q5kVgLff4l1Xczre0h6r00rHhQ+x/+IB/8/hSR1IsBL8dF3VpaST8gF48ffRXEi5QI3C1Xii1oD0j7FqFWz4E0JD4RkfZlM8LfqDtIjWQwIVIYS4njQpoAC0rX+0FzzyKLhcGB/rz8zab1OnaG3OJZ+nzfzOnE066/85LGZ1EZRgJbhZbaqmKJ1VPhtO/sXX26cD8HnzjzAZ0gkkdB3eelN93v8x72tTPEuSQ4NzSfKVJFARQogbsZjdKSBnYJYAj/0wtb9K+OODWNxuFuWiyrL/0gE6LOhGkj0pMGP2BCt2CVaCjsOhUj5GwzUrbZwuJ0N+GYmOTu/benJ3yYbpH2PZUvjnb9XYbeRI78fgdLr39QneIloPCVSEEOJmwkLUO99AzFCEh8M330FkJKxfT7GxE1jWeR4FQgqw4dRfPLy0H05XAGZvJFgJTi6X2gXb5Uo35fLl9ilsPv0P0SHRvNf4jesfw7PSZ+AgKJLO5oQ34wjuJclXCv4RCiFEdvOkgDQtMDsX33orTJioPv/0E6r8bw+LOs0mxBjCwgOLGb7mGf97rIAKVlw6xEsaKCh4liLbHOnOZJxKPM2Lv6vVPW81fIWi6RXQAixaCNu3q2B3+JPej8PpdC9JDu4iWg8JVIQQIiPMZjWz4ghQCuj+jjDUvQPu4EHcnVSY79p8jYbGF1u/4t2/PszwoVwuiE+AS7HpxFFSsxI8bHYVqJivrUvRdZ3Bq57kkvUSd8TUYlDNx9I/hsMB77gbvw1+Qm1A6C2HM+iXJF9JAhUhhMio0ACmgABefwMaNIC4OHi4Bw8Wb8FH974LqFUfX22besOXHz0KH4+DFi3g3nuheXO4+2546SX4Z8sV8ZQnWIlPlD4r2cXuUCkfg3bNUmSA2XvmsuDAYswGM1NbTUy/gBbgu29h927IX+ByoOsNlwvQVBFtDhHca5KEECKYGAwQHgZxCZf3R/GH2azqVZrcDXv2wBODGPHNd5xJOsuYjWMZtPpJCoUVpHPFjmle5nLBp+Ph228hOgo63A/VblcNao8chQULYPljUL8evPuuyhBgMatalQR3sW6Q987IVZxO1YzPpaudr69yJulMagfal+o9S40i1dI/Tnz85ZU+z78A+fN7PxaHU227YMo5l39ND0giNPvExcURHR1NbGwsUVFR2T0cIURekJQCSUlgtqh3yP7asAHatlaBxOjX0Z8aycBVw/hq+zQsRgvLHphHszL3AmpiZMwYmDcfhg6F7t3VRM+VXC7VrPS10VCqFHw5SdXwAuqdva6rmpvQkKBfmprjuVzujsF2FSym8/PutvhR5uydT43C1fir529YjNcJIt96E95/D8pXgL82eV9joutqNjAyIigC1YxevyX1I4QQ3goLUX/oA5UCqlfvcjO410ejrV7NhOaf0PnW+7E5bXRc2J3Np/8BYPnP8OM8eOVl6NP72iAF1MTPPffApElw+BB89PEVD5pN6gmJybLrcmbTdfceTNcPUn7cu4A5e+dj1IxMbTXx+kHK8eMw/lP1+Rtv+FYI63Cqabcgb/B2NQlUhBDCW5qm2uEbDYHpWgvQt59qg67r0K8vxsNHmNl2Ks1K30OCPYHW8zqx8/wuZs1UZS0dO970iFSuBP36wdIlqtA2lcmobokpanZIgpXMkWyFJGu6Td1Atcl/4penAHjhrpHcUbTW9Y/11puQnAz1G6hCbG/pumrZHxaSI5YkXylnjVYIIYKFyajqVVwud4FiAIz9EOrcCZcuQo+HCEm2saDj99xZ9A7OJZ/nnu/bs/nofrp1zfghO3ZUpRE//XTVA0ajuoAmpai6lUB9D0JJsanZFJMx3cBArfIZwZmks9xWqAqv1Hvh+sfatg1mzlCfv/2Ob+k6T4M3c/A3eLuaBCpCCOGrELM7BeQIzKxESAjMmAnFisHOnfBYfyKN4fzceQE1ClfjnPU0+5u3p2T1wxk+ZMGCcOed8PfmdB40GFSwkmJVK4IC0SNGqJVViUmqfuk6Bddfb5/G3H0LMBlMTGs1iRBTOjk8UL9XL41SH7s8CHXr+jYmh1P9rqaz4ijY5bwRCyFEsNA0VZRqNgYuBVSiBMz8XgUty5bCm29QMKwgKx9cRDGtErbwY9w3rx3H4o9n+JDRUZCYeJ0HDYbLwVZ8onSx9ZfNvbJK0667smbn+V08ufY5AN5pNJq6xepc/3jLl8Ovv6qalNGv+zYmT21KEBTQ+kICFSGE8IfRnQICVQMQCHfeCZ9PUJ9/9CHMnk1MeAwv5F9CaEIFDsYeovnc9pxKPJ2hwyUkQnjEDZ6gaWp2yOWCuESVtpC6Fe9dufz7OgWryfZkui/pQ7IjmZZlm/P0ncOvf7zkZHheBTQMfgLKlvVtXE7PbErOaPB2NQlUhBDCX2aTe+PCAKWAALp1g5GqtwZDn4BNm2hSqzi3rFpMsZDS7L24j6Zz2nAy4dQNDxMfD5v+Uvsg3pCmXV6ZkpAkRbbesjsgIVn9zG6w0d+zv73E9nP/EhNehOmtv8Sg3eAy/OFYOHRQzbI997xv43K6LgeiOZQEKkII4S9NC+zGhR6vvgZt2oLVCj0eolaho9xevAztji6hdGQpdl/Yy71z2nA8/sR1D/HTT6rreqdOGTyn2aSKLpNS1FSMU+pWbspmV2kzp/OGQcqC/T/x+dYvAfim9VcUu95ePgD79sE497ry9953d+3zgd29r1AOavB2NQlUhBAiEDxdawO1caHnmF9Phttvh9On0R56kJ73x7FldQXeLb2cslFl2HtxH/f80Jqj8ceuefnRozB5smqxX9ibLWGMRvUOPMWuUkGyR9D1WW1qBkrXb1gDcij2MP1XDAHgmTpP0qpci+sfU9fhmZFgs0GL+3xbjgwqlWfQ0m+2k4NIoCKEEIFiNkFYqHpnHajlvpGR8MNcKFoU/v2XDnN70bKpnU9Gl+PlAsspH1WOA7H/cc8PrTkUe3k10Pbt8PgAiI6GZ5/z4bxX1q3EJ0oqKD0ptss1KTeYSUm0J9Jx4UNcSLlA3aJ1ePvu12583PnzYM0aVVD9wVjfuwenzqbkzNoUDwlUhBAikDxdawNZr1K6tApWwsPRVq/izeSRtG6lM2FMGW75eTkxhls4GHuIBt+1ZuLc/fTtC336QuHCMOlLyB/t43k9dStGdyfbhESVR8rrdF0Fbonu1T036PSq6zp9fx7EtnM7KBoew7z7Z16/+yyoDSpfcNejjHwabrnFtzF6Nh8MseT4bRIkUBFCiEDyLFk2GgN7Ua9dG6ZMBU3DOH0qowt8wrRp0PD2UpRauIyQuIqcsh5l2J77SMy/lbEfwLRpUKRwAM6dmgpyuFcFWfPu7IrLBUnJ7p2QDTdtRz9m41jm7J2P2WDmxw4zKBVZ8sbHH/MOnDql9vN5aqTv47Q7Vc1UDmuXnx4JVIQQItCMRhWsuAjckmWAtu1gzLsAaK++TPV983h9NKxbUoL1PX/mtugaOMLO8vMtbTBV/F9gZ/w1DULNoAHxSSrlkdcaxDmcl9NgFtNNUyqL/1vGy/97A4DPmn1Io5INbnz8TZtgwhfq8w8/gtBQ38bpSTvmkk0nJVARQojMYDGrNFAgU0AATwyBgYPU548/Br/9Sng41K4Ywx+PLKNJyUbE2eJo+WNHFv+3LHDn9TCZ1EXaaoO4BNUmPi+03/es7LE5VDrlJvvl7L6wh4eX9kNHZ3DNxxhQo9+Nj5+SAoMGqJ/lg11VBbSvctFsCkigIoQQmSOzliwDvPse3H+/WhXSo7vaCwaIDolmeecFtK/QhhRnCp0WduebnTMDe2643M1WQ/UOiXevDMqN6SBPqic+UX0ekv4uyFc6mXCKtvO7EG+Lp3HJhoy79/2bn+ftt2DvXlU0/cFY/8YLuWY2BbIoULFardSqVQtN09iyZUuax7Zt20bjxo0JDQ2ldOnSvP9+Bv5BhRAiJ/AsWTYYAluvYjTC11Pg7rtVR7cuD8DBgwCEmcOY12Emj1TtjlN30nv5AF5f/w56ZgQRJpO6cNs9KZHk3JUO8syiJKaogmLLzYOUWGssbeY/wMHYQ9wSXYG5Hb67cfEswIY/4dNP1OeffAqFvFlLfpVcNpsCWRSoPPfcc5QoUeKa++Pi4mjZsiVly5Zl8+bNfPDBB4wePZovv/wyK4YlhBCZz2xS9SqOAO6yDKp+YdZs1XL29Gl4oBOcPaNOaTQzvfWXPF9XFWOOXv8OfX4eiM1pC9z5PTzLmI1GSLKqdFBScs5uFOdyqWJZz0aNnu/vJlIcKXRc+BBbz26naHgMK7osJCY85sYvSkqCQYPUbFSPh1Udkj/jhlw1mwJZEKgsW7aMFStWMHbstVNZM2bMwGazMWXKFG6//Xa6d+/O8OHD+eijjzJ7WEIIkXVCLCoNFKhdlj2io+HH+WoPmP8OwINd1PJWwKAZeLfxG0xq8SlGzcg3O2fS6seOXEy5GLjzX8logFD3UtjEZIhNUEWngSwmzmy6rnqjeIKtDM6iADhdTnou7cevx34n0hLJ8s4LqJC//M3P+eYbcGA/FC+uUnr+yIWzKZDJgcrp06d5/PHH+fbbbwkPD7/m8fXr19OkSRMslsvTYq1atWLPnj1cvJj+/0xWq5W4uLg0NyGECGqaphrBmU0qWAmk4sVh3gKVLvjnH+j2YJqtkgfU6MeSB34k0hLJ2mPraPh9c/Zd3B/YMVzJZLxcv5KYDLHxwZ8S0nVVHBybAAkJ7lqUjG/ip+s6Q355inn7F2ExWlh4//fUiqlx8xf+8T/44nP1+aefQYECvn8PuXQ2BTIxUNF1nT59+jBo0CDuvPPOdJ9z6tQpihZNu9eB5+tTp9LfaGvMmDFER0en3kqXLh3YgQshRGYwGiAiVF3AA33RrlgR5i9UMyx//AEP91CrSNxalWvB7w+tpFS+kuy+sJe6M+9hyX/LAzuGK2na5foVTVM1HrHxwVd063JdXr0U59mrxwLmjM2iALh0F8PXPMOkbVPQ0JjZZgpNy9xz8xeePw+P9Vc/i0d7QatW/n0vdkeunE0BHwKVF154AU3TbnjbvXs348ePJz4+nlGjRgV0wKNGjSI2Njb1dvTo0YAeXwghMo3ZHPgW+x61asHceRARAWt+gd69wH55tVGNItXY+PCvNCxRn1hrLB0WdOWtP9/DpWdiakbT1AxLqHt2wmpTAUFsvEoLBXrpdkbouipsTnKnp+KuqEPJYJrHw6W7GLhyOJ9tmYSGxlf3fUaXSp0yNobBg+DYMbjlVv9TPi73Dsm5cDYFQNO9LAU/e/Ys58+fv+FzKlSoQLdu3fjpp5/QrvihOZ1OjEYjPXv2ZPr06fTq1Yu4uDgWLFiQ+pw1a9bQrFkzLly4QIEMTIPFxcURHR1NbGwsUVFR3nwrQgiR9XRdNUtLsWVoqavXfvtV1aqkpEDnLjB5SpoUhs1pY8Ta55iw9WsAHri1A9Nbf0mkxcfdeb3l0lWg5nRvmGcyqlkMk0GN8yb9SXyi6yoYcTrVjI7docbhOacP/wYOl4N+Pw/m212zMGgGpraaSK/bHs7Yi8d/Ci+9qPbyWb0GamQgTXQjKXbVjC9feI4KVDJ6/fY6UMmoI0eOpKkfOXHiBK1atWLu3LnUq1ePUqVKMWHCBF566SVOnz6N2aw2dHrxxReZN28eu3fvztB5JFARQuQ4TlfaFSWBtmIF9HhIzah07wETJl5TbzF5+3Se+OUpbE4blQpU5Pt206gdUzPwY7kRl8sdtOjqAmtw75tjNqmAxWBQ93kTvOi6Oq7L/dHhVD8Hp3vVlcGggiM/AiK7084jy/rzw955GDUjM9pO5qHKD2bsxRs3QuuWalbno4/hscd9Hgegvi+nE6Ly5bi0T7YHKlc7dOgQ5cuX559//qFWrVoAxMbGUrlyZVq2bMnzzz/Pjh076NevHx9//DEDBgzI0HElUBFC5Eh2u0o7GNz1HIG2aKFK/zid0LWb2p3wqvNsOPkXD/70CMcSjmMxWni/8ZsMr/1EmpnwLKPrl4MJl/uyZNDcAYxB1fgYDKrGR3N/9LzOpYPucm9Z4E6r6e7HNNyvNarj+SneFk+PJX1ZcnA5ZoOZH9p/Q6dbO2TsxRcuQJO74cgReOABmPaN/zMgVptaURZx7YKVYJfR63e2dqaNjo5mxYoVHDx4kDp16vD000/z6quvZjhIEUKIHMtshoiwwPdX8bi/I0ybroKTOT+ows2rms7VK16XLY/+Qcdb2rtTQs/TYUFXziadDfx4bsZTz2Ixq5qWELN75kNTPx+bXbXrT0pRqbPUW7K6P8XursnR1eyRxeQ+jkX9DAIQpByOO0Kj71uw5OByQo2hLLj/+4wHKboOQ55QQUr5CmqVj79BisPp7hIc4t9xglyWzahkFplREULkWKn1Klb3kt5MmMlYshh6Paou4p06weSpKkhKMwydL7Z+ydO/vojVaaVERHG+bvk5bcq3DPx4cqg/T2yk46KHOJN0lqLhMSzq+AN3FU9/RWu6xryjbhYLrPpFFT/7w7OkOiJMdT/OgXLEjIoQQuRpmqYuNBZz4PcD8mjXHr6bqS6QCxZA70fBar1qGBpDag1kw8NrqVKwEicST9J2fmf6LB+YeQ3icpCZu37g3jltOJN0lppFqvNXz9+8C1J+nKuCFFC7IvsbpIBKcXl61uRyEqgIIUR2unI/IHuAm8F5tGkDM2apFMHixaopXHz8NU+rWaQ6m3v+zlN3DEVDY/rOGdw+vS6LDizJnHEFuRRHCsN/eYaey/phdVq5/5Z2/P7QSkpHlsr4QTZtUkuRAYYOg959/B+YrquUYUhIhpvS5WQSqAghRHYzm9TMikvPvJbzrVrBD3PdfVbWQIf2cP7cNU8LN4fz0b3v8nv3lVQuUJGTiafouPAhui/pzdH4Y5kztiC049y/1J3RhPFbJgLw3J1PMa/DTPJZ8mX8IMePq9VXKSnQqjW8+VZgBmd3gNndnyYPkEBFCCGCgcWsVm9kZhO0pk1h8VIoWBD+3gwt74PrNM1sWKI+Wx5dz/N1R2LQDMze8yNVpt7BW3++R4ojJd3X5Aa6rjP+nwncOaMJO87vJCa8CEsfmMd7Td7EaPBi9iIxEbp3UxtG3nbbNf1sfOZyqd+PsNDM6TkThPLGdymEEMFO01SgEmrO3DbzderAipVQqhTs2wf3NYfdu9J9aqgplHcbv8Gmnuu4u2QDkhxJvPLHm1SdVod5+xaSw9diXGPfxf20nteJ4Wuexeq00rZ8K7Y9+qf3RcV2O/TrC1u3QuHCMHsOBGqxh91xuYtuHiGBihBCBAtPvYrJmHn1KgCVKsPK1VC5Mpw4Afe1UOmg66gdU5Pfuq1gZtsplMxXgkNxh+nyU08aft+cnw+tyvEBS6I9kRd/H021b+5ixeHVhBhD+LTpByzuNJeiEUVvfoArOZ0w4HFYthRCQ2HmLLW7dSA4ne5W+aE5qgOtvyRQEUKIYGI0Xl5u6sjEYKVkSVi+AurVh9hY6NwJpk657tM1TaNHlW7s6fsPL9V7ljBTGH+e3EjreZ24e/Z9rDz8S44LWHRdZ87eeVSZegdjNo7F5rTRutx9bOv1J8NqD/a+8Z2uw4gn1Sofsxm+nQH1GwRqsGB3qv18clgHWn9JHxUhhAhGKVbVY8VkUp1VM+08KTBsKMz+Xn09dJgq+rxJPcWpxNO899dHTNw6mRSnqlmpX/wuhtceRJeKnbAYg7fQ0+lyMn//It7ZOJZ/zmwFoFxUWcbd+x7339LOt868ug6jXoAvPlczY9OmQ6cHAjdou0N12Y3Kl2tW+gRdC/3MIoGKECJX0nXVhTUpRdUjBKCz6g3P9f578LZ7VUrbdvDV1xB5840KTyacUgHLtslYnao/S9HwGAbW6MfAGv0pka945o3bS3annRm7Z/Puxg/Zc3EfABHmCJ6pM5zn644kzOxH47S33lQ/Q4AJk6BnzwCM2E3XwWaDfBFqRiWXkEBFCCFyuszeaflqP85VPT9SUqBiRfhuBlS9LUMvPZV4mi+3TWHitsmcTDwFgMlgomXZ5nSr1JmOt7Qjf2j+TBz89W07u4Nvd85ixu7ZqWPLH5Kf4bUHMbz2YAqFFfL94LoOo1+Djz9SX4/9EAYMDMCor2Czq7qlqHy5qjZFAhUhhMgNnE6IT1L1KlnRhXTTJni0p+oBEh4O4z9TmxpmkM1pY/7+RXy2ZRK/H1+fer/ZYKZVuRY8cGsHmpZuQrmospm2+aGu6+y+sIclB3/m252z2HZuR+pjRcNjGFlnGINq9CcqxM9rhsMBTw6Hb79RX7/9Dgwb7t8xr+bZHTkyItet9JFARQghcgu7A+IT1edZUUh57iz073d5JdCAgfDOGNWG3wu7zu9mzt75/LB3Hv+eT7sEunRkKe4pdTf3lLqb2jE1qVygonfN1K6Q4khh/6UD/O/En6w9uo61x9ZxKvF06uNmg5n2FVrzaNUetC3fihBTANInKSnQr4/q9GswqIDu0V7+H/dKnv18wsMgPPet9JFARQghchOrTaWBDAaVBshsTqfan8ZTd3FHHfjqK6hYyafD/XtuJ3P2zufnw6vZdPpvHK5rVzSVzFeCygUqUiG6PPlDookOiSLKEklUSBROl5MkRxKJ9iQS7YlcSLnIvksH2HtxP0fijqKT9lIWYgyhUYn6dK30AN0qdaZgWEGfxp2u2FjVcfb331Ub+6nToH0Gd1H2ht0OmsFdQJv7FulKoCKEELlNUgokJqtZlay6cC1frvqCXLoIYWHw1tvw2ON+vbtPtCfyx4kN/HpsHb8fX8/O87s5m3xtO39vRFmiqFWkOk1LN6Fp6SbUK16XUFOoX8dM1/790LMH7Nqlio2/nw2NmwT+PC6XmkmLjMi1Gw9KoCKEELlNVq4EutLx4/DEoMupoOYt4IsJUDxwK3ouplxkz4V97L64lyNxR4mzxRNrjSPOFkesNQ6jwUiEOZwIcwQRpnDyh0ZzS3QFKha4hUoFbqVIWJFMq3lJtWQxDBwAcXFQtCjMnQc1a2bOuVJsai+ffOG5LuXjIYGKEELkRlm9EsjD5YIvJ8Grr6j6jAIFVd3Kww/n2gtpKqdTLT/+cKz6ukEDmP4tFCuWOedzOEBHpXyyIs2XTTJ6/c59SS8hhMjNNE0VV1pMYM3EPYGuZjDAoMGw7neoVQsuXoDBA6Ft6+vuFZQrnDoFnR+4HKQ8MURt7JhZQYquq8AoPDRXBynekEBFCCFyGqMBIsLBbFQ9NrJS5Sqweg288aZavvy//0HDBqqXSFJS1o4lM+k6zPgO7roT1vyivtcpU+Hd91R7/Buw2+H4CfjvPzh7zstY0upehp5L61J8IakfIYTIqTzLlnU9e3psHDkCzz0LS5eor0uWhFEvwsM9Vev/nOrIEdUfZfUq9XXt2jBx0k2b3x09BnPnwqKFEBd/+f5KFaFrV2jdWsU71+XZiDIqImf//DJIalSEECIvsNlVsKJp2bdZ3dIl8OwzcPSo+rpiRXjlVejYKWfVr1itMPlrVY+SkKCWHr/0str/6AaBg67Dd9/BJ59CVCTc3xHq11ebHJ87pzZS/u03KFgIxo2DqlXSOYjLBTYHRIbnqjb5NyKBihBC5BUpNkjMwh4r6Y4hBb7+CsZ+ABcuqPtq14ZnnlV7BwXzRnpOJ3z/PYx5W82mgCqY/ewLFXTdxLTpMH489OkNjw9IP844cQJeeAEOH4avJ0PFW694UNdVwBmSu1f5XE0CFSGEyCt0HZKtWd9jJT1xcTD+U/j8MzUrAVCuHAwcDI8+CsH0d9rlUkuO33wDdu9W9xUrptJXvfuowO8mdu5UDWkf6w+DB9/4uYlJ8PhjqnffnB+uOHxqY7eI4A7oAkxW/QghRF6haRAWolaKOOzqApxdoqJUumTrNnj6GbWM+dAhGPU8VK2salo2bcq61UrpOXcWPhkHtWtBz4dVkJK/gCoQ3rIN+vbLUJAC8MMc1U4mI/sQRoTDs8+pH8fGv9x3ulzgQv3b5aEgxRsyoyKEELlFmoZwpgxfbDNVUhLMmglffA779l2+v3wF6NJFVZlmcIdmv1itaoXSd9+qalebTd0fFaWijOFPQv78Xh0yNg7atFbpnr59MvYaXYfuPaBUKfjwA89ePqFqyXkeSfl4SOpHCCHyIl1XKaBka9Z2r70ZlwtWr4aZM1R16ZVLmcuWhbsbq1uTJlC6dGDOefQorFwBK1bAr2shMfHyY7XvgP79ocuDEBHh0+H/2gSDBsGPP0K5shl/3cRJMH8e/LzIplJ1kRHBEVRmsYxev3P/+ichhMhLNE29Q9d1SLGqAs1geKduMMB996lbYqJaKTR3LqxaqSpMDx9WfUtATTdUqgy33qqKWW+tCDExKqDIFwER+VQvk/g4Na0RF6f2Ijp4UBWN7N6lPp49m3YMRYtC27bQp58q9PVTsjvWyuflps/58oHZ4FA/k4iwPBmkeEMCFSGEyG08F0DI+lb7GRERAV27qVt8PGzYAL+vg3Xr4O/NcOyYuv2y2r/zGAxwVz13gNQSatQIaFAQ4Q5Q4mKhcKGMvy4xzkWh/LoqWskD/VL8JT8hIYTIjVKDFR1S7MEXrHhERkKLFuoGKnDZsV3tUrxvH+zfpz6/eFHNxHhWEnlEREB0tKo1KVkKbrtN3areBpUr+5zWyYjKldWG0itXwsAMFNMCuJw6Wzc5KFw6NHua9OVAEqgIIURuZXC32seziWGQpIFuJDISGjRUt/S4XJCcrIphIyOzdUYiXwS0awvz5kO//hnot6frbNtkZ9N2Mx9NCgn+f4sgIYkxIYTIzTzBSqhFrTDJ2esn3N9PBBQoEBRpk67d1P6MH3148x/tpfMOPhxnJKRAGI0ay+U3o+QnJYQQuV1uC1aCyK23wKhRqp/KW2/Bpdj0n7dvt4Nnnoa/94Qx/TujTKZ4IfvDUSGEEJkvNQ2ESgNZLMGzdDmHe+ABNbnzzjuwdCm0bAn1G6hW+ufOwy8rnBw9pJNAGAuWmqlQIbtHnLNIHxUhhMhLXC7VZyXFFjxN4XKJixdh0SLVV+X4CXWfxeSi5b12GrUMo1nbUExmCQ49pOGbEEKI9LlcqnttsjX79wbKhXRd7dGYkqyTz2LHHBmiVmBJvicNafgmhBAifZ6ly5qmAhaMss9MAGkahIXqhBncy8LDQyVI8YMEKkIIkRd5OthqqGBFB0wSrASEroPVrlJr4dJ51l/y0xNCiLxKvfWHfOEqHWSzZ/eIcj5PkGI2qZ+rzFT5TQIVIYTIyzRNLU+JjFCfy/Jl30mQkikkUBFCCKHauUdGqPSPzS7Bird0Xf3czEYVpEgaLWAkUBFCCKGYTSpYMZvVzIDLld0jyhk8QYpRgpTMkKmBypIlS6hXrx5hYWEUKFCATp06pXn8yJEjtGvXjvDwcGJiYnj22WdxOByZOSQhhBA3YjRCZDj/b+9+Y6K69zyOv88Mw/BHQNvlT10RxXS311rFglJTo9h1xcY2a9PV7qaxYAxdGzQ1NKtYvWIbq42Q1Sxt1X1Q22i7dsVrSU1tL0lv7YPa6JX2gXZxQ7xUFhylbQT0KjPMnH1wgCvbKiAM58z4eSUnyMww8+UnMh9/f0nwQqAb9Dv5znqHe2JirHZzwLb+0SZsLXrkyBFKSkrYvn07TzzxBN3d3Zw9e7bv/mAwyJIlS8jIyODrr7/m0qVLvPDCC3g8HrZv3x6uskREZCAul7Vaxe22NofrXcES5iW2166DzwcBP4wdCxkZDl/Ve+vqHs1JCZuwbPjW3d3NpEmTeO2111i1atWvPub48eM89dRTtLa2kp6eDsDevXvZsGEDbW1txMbGDuq1tOGbiEgYBQJWWAkEw7aT7dlzcPgw/P7z/guP/vZvYNkyWLwY4uNH/GWHp3eVVFwsJCRo07y7MNj377C0bH19PS0tLbhcLmbOnMkDDzzAk08+2a9H5eTJkzzyyCN9IQWgsLCQjo4Ozp07d9vn7urqoqOjo98lIiJh4umZZBsXC/6RHQoKhmDnTigqgjNn4MV/gXffhYMHoKoS0tPhje3WCcV/+tOIvezwBYNWW8R5rfOTFFLCKiyte+HCBQC2bt3K5s2bOXbsGOPGjaOgoICff/4ZAJ/P1y+kAH2f+3y+2z73jh07SElJ6bsyMzPD8S2IiEiv3kmiSQnWxnAjsITZNOHNHVBTAxvWQ20trCyGGdPhN7+BBQtg1y44+jsrC5S8CM3/OyLfzfAEuq2Eldiz/4w2cwu7IbVweXk5hmHc8WpoaCDUM1N806ZNPPvss+Tm5rJ//34Mw+Dw4cPDKnjjxo20t7f3Xc3NzcN6PhERGYTe/VaSb1kV1B2866f7wx/gd0fht7+F5ctv3ymRmQn7/gOSxsDWirt+ueEzTSuggRVQ4rUt/mgZ0mTaV155heLi4js+Jjs7m0uXLgEwderUvtu9Xi/Z2dlcvHgRgIyMDE6dOtXvay9fvtx33+14vV68Xu9QyhYRkZHSu7rlZpd1qGFX0AourqG9aX/0X5AzA55+euDHjk2BtWvhX9fD+f+x5q6Mqt75KLEe64wkrewZVUNq7dTUVFJTUwd8XG5uLl6vl/PnzzN37lwAAoEATU1NZGVlATBnzhzeeOMNrly5QlpaGgB1dXUkJyf3CzgiIuIwvauCPJ6eY4IDVpdIjHtQvQxNP8Af/wjbtg3+JefNh9RUqDkMmzYNo/ahME1rqMfE6kFJiNNQjw3CEguTk5NZvXo1FRUVZGZmkpWVRWVlJQDLli0DYNGiRUydOpUVK1awc+dOfD4fmzdvprS0VD0mIiKRwBMDMYngCcCNmz3bxw98EvN/f299nD9v8C8V44a5c+H774dR71AEQ1ZIiXFbASXWo6Eem4St/6qyspKYmBhWrFjBjRs3yM/P54svvmDcuHEAuN1ujh07xksvvcScOXNITEykqKiI119/PVwliYjISDMMa0WQxw03/daQ0ABLmf/8557zEIe45Dgx0frasLq1FyXBC3FxWtVjs7AFFY/HQ1VVFVVVVbd9TFZWFp9++mm4ShARkdHidlvzN7wea+6KPwBmt9Xr8v8Cy5gxVh5o77DmnwzW1avW14aFaVqTg4Mhq+aEOOujelFsp5goIiIjJ6Znl9bkMeCNtd78bwasvUd6zJxpdVLU/X7wT3vjJpw4AXl5I1yvaVp7w3QFrFCSlGCtbNJQj2MoqIiIyMgyDKs3ojewxMdaPRU3/dDdTVqqyfz51m60g92O5fPP4do1ePYfR6jG3iGeroA1zJMYDyljrCXYmjDrKPrbEBGR8Lg1sKQkWWEAA7oCFP2zn8uXguzdN3BSuXgRqqth/nyY8NfDrCkYsvZD8QesQJKUYAUUrehxLC0GFxGR8ItxW1ecNRw0LS/AS6sD/OfBAO4APPe8i5Sxrn5hIRSCkydh61brkMItW+7idU3TCifBoNVz4jKsISlvrOagRAgFFRERGT0uF8S6INbDP70Y4gbd/Pu/dVPzcTeLFwWZ/kg3MTHgu+zi2KcGF5oMpj5sUFlpkDLQubOmCSHTSjim+ZdxJbfLCiaxHiss6ZTjiBKW05NHk05PFhGJbFeuwP53TT48EOSnthApiUH+amw3f/93If7haZNpU00MAAOrV+TWj/T8GazeEVfP5XZbE3t7N6LTsI7jDPb9W0FFREQcIxAAvx8SEsCgp3ekt5cEekKJeUtQMayPhtETVFxWUNGQjuMN9v1bQz8iIuIYHo91WXp6RjRSc09TX5iIiIg4loKKiIiIOJaCioiIiDiWgoqIiIg4loKKiIiIOJaCioiIiDiWgoqIiIg4loKKiIiIOJaCioiIiDiWgoqIiIg4loKKiIiIOJaCioiIiDiWgoqIiIg4VsSfnmyaJmAdFy0iIiKRofd9u/d9/HYiPqh0dnYCkJmZaXMlIiIiMlSdnZ2kpKTc9n7DHCjKOFwoFKK1tZWkpCQMw7C7HNt1dHSQmZlJc3MzycnJdpcTtdTOo0PtPDrUzqND7dyfaZp0dnYyfvx4XK7bz0SJ+B4Vl8vFhAkT7C7DcZKTk/UPYRSonUeH2nl0qJ1Hh9r5L+7Uk9JLk2lFRETEsRRURERExLEUVKKM1+uloqICr9drdylRTe08OtTOo0PtPDrUzncn4ifTioiISPRSj4qIiIg4loKKiIiIOJaCioiIiDiWgoqIiIg4loLKPaCrq4ucnBwMw+C7776zu5yo0tTUxKpVq5g8eTLx8fFMmTKFiooK/H6/3aVFvLfffptJkyYRFxdHfn4+p06dsrukqLNjxw5mzZpFUlISaWlpLF26lPPnz9tdVtR78803MQyDdevW2V1KRFBQuQesX7+e8ePH211GVGpoaCAUCrFv3z7OnTvHrl272Lt3L6+++qrdpUW0jz76iLKyMioqKqivr2fGjBkUFhZy5coVu0uLKidOnKC0tJRvvvmGuro6AoEAixYt4vr163aXFrVOnz7Nvn37mD59ut2lRAwtT45yx48fp6ysjCNHjvDwww/z7bffkpOTY3dZUa2yspI9e/Zw4cIFu0uJWPn5+cyaNYu33noLsM70yszMZO3atZSXl9tcXfRqa2sjLS2NEydOMG/ePLvLiTrXrl3j0Ucf5Z133mHbtm3k5OSwe/duu8tyPPWoRLHLly9TUlLCgQMHSEhIsLuce0Z7ezv33Xef3WVELL/fz5kzZ1i4cGHfbS6Xi4ULF3Ly5EkbK4t+7e3tAPr5DZPS0lKWLFnS72dbBhbxhxLKrzNNk+LiYlavXk1eXh5NTU12l3RPaGxspLq6mqqqKrtLiVg//vgjwWCQ9PT0frenp6fT0NBgU1XRLxQKsW7dOh5//HGmTZtmdzlR59ChQ9TX13P69Gm7S4k46lGJMOXl5RiGcceroaGB6upqOjs72bhxo90lR6TBtvOtWlpaWLx4McuWLaOkpMSmykXuTmlpKWfPnuXQoUN2lxJ1mpubefnll/nggw+Ii4uzu5yIozkqEaatrY2ffvrpjo/Jzs5m+fLlfPLJJxiG0Xd7MBjE7Xbz/PPP8/7774e71Ig22HaOjY0FoLW1lYKCAh577DHee+89XC79H+Bu+f1+EhISqKmpYenSpX23FxUVcfXqVWpra+0rLkqtWbOG2tpavvrqKyZPnmx3OVHn448/5plnnsHtdvfdFgwGMQwDl8tFV1dXv/ukPwWVKHXx4kU6Ojr6Pm9tbaWwsJCamhry8/OZMGGCjdVFl5aWFhYsWEBubi4HDx7UL5wRkJ+fz+zZs6murgasYYmJEyeyZs0aTaYdQaZpsnbtWo4ePcqXX37Jgw8+aHdJUamzs5Mffvih320rV67koYceYsOGDRpqG4DmqESpiRMn9vt8zJgxAEyZMkUhZQS1tLRQUFBAVlYWVVVVtLW19d2XkZFhY2WRraysjKKiIvLy8pg9eza7d+/m+vXrrFy50u7SokppaSkffvghtbW1JCUl4fP5AEhJSSE+Pt7m6qJHUlLSL8JIYmIi999/v0LKICioiAxDXV0djY2NNDY2/iIAqrPy7j333HO0tbWxZcsWfD4fOTk5fPbZZ7+YYCvDs2fPHgAKCgr63b5//36Ki4tHvyCRX6GhHxEREXEszfgTERERx1JQEREREcdSUBERERHHUlARERERx1JQEREREcdSUBERERHHUlARERERx1JQEREREcdSUBERERHHUlARERERx1JQEREREcdSUBERERHH+j+4zZ1qYZd/MgAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"vlr = VariationalLinearRegression(beta=0.01)\\n\",\n    \"vlr.fit(X_train, y_train)\\n\",\n    \"y_mean, y_std = vlr.predict(X, return_std=True)\\n\",\n    \"plt.scatter(x_train, y_train, s=100, facecolor=\\\"none\\\", edgecolor=\\\"b\\\")\\n\",\n    \"plt.plot(x, y, c=\\\"g\\\", label=\\\"$\\\\sin(2\\\\pi x)$\\\")\\n\",\n    \"plt.plot(x, y_mean, c=\\\"r\\\", label=\\\"prediction\\\") \\n\",\n    \"plt.fill_between(x, y_mean - y_std, y_mean + y_std, alpha=0.2, color=\\\"pink\\\")\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 10.6 Variational Logistic Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_toy_data(add_outliers=False, add_class=False):\\n\",\n    \"    x0 = np.random.normal(size=50).reshape(-1, 2) - 3.\\n\",\n    \"    x1 = np.random.normal(size=50).reshape(-1, 2) + 3.\\n\",\n    \"    return np.concatenate([x0, x1]), np.concatenate([np.zeros(25), np.ones(25)]).astype(int)\\n\",\n    \"x_train, y_train = create_toy_data()\\n\",\n    \"x0, x1 = np.meshgrid(np.linspace(-7, 7, 100), np.linspace(-7, 7, 100))\\n\",\n    \"x = np.array([x0, x1]).reshape(2, -1).T\\n\",\n    \"feature = PolynomialFeature(degree=1)\\n\",\n    \"X_train = feature.transform(x_train)\\n\",\n    \"X = feature.transform(x)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"vlr = VariationalLogisticRegression()\\n\",\n    \"vlr.fit(X_train, y_train)\\n\",\n    \"y = vlr.proba(X).reshape(100, 100)\\n\",\n    \"\\n\",\n    \"plt.scatter(x_train[:, 0], x_train[:, 1], c=y_train)\\n\",\n    \"plt.contourf(x0, x1, y, np.array([0., 0.01, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1.]), alpha=0.2)\\n\",\n    \"plt.colorbar()\\n\",\n    \"plt.xlim(-7, 7)\\n\",\n    \"plt.ylim(-7, 7)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "notebooks/ch11_Sampling_Methods.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 11. Sampling Methods\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.rv import Gaussian, Uniform\\n\",\n    \"from prml.sampling import metropolis, metropolis_hastings, rejection_sampling, sir\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 11.1.2 Rejection sampling\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def func(x):\\n\",\n    \"    return np.exp(-x ** 2) + 3 * np.exp(-(x - 3) ** 2)\\n\",\n    \"x = np.linspace(-5, 10, 100)\\n\",\n    \"rv = Gaussian(mu=np.array([2.]), var=np.array([2.]))\\n\",\n    \"plt.plot(x, func(x), label=r\\\"$\\\\tilde{p}(z)$\\\")\\n\",\n    \"plt.plot(x, 15 * rv.pdf(x), label=r\\\"$kq(z)$\\\")\\n\",\n    \"plt.fill_between(x, func(x), 15 * rv.pdf(x), color=\\\"gray\\\")\\n\",\n    \"plt.legend(fontsize=15)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"samples = rejection_sampling(func, rv, k=15, n=100)\\n\",\n    \"plt.plot(x, func(x), label=r\\\"$\\\\tilde{p}(z)$\\\")\\n\",\n    \"plt.hist(samples, density=True, alpha=0.2)\\n\",\n    \"plt.scatter(samples, np.random.normal(scale=.03, size=(100, 1)), s=5, label=\\\"samples\\\")\\n\",\n    \"plt.legend(fontsize=15)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 11.1.5 Sampling-importance-resampling\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"samples = sir(func, rv, n=100)\\n\",\n    \"plt.plot(x, func(x), label=r\\\"$\\\\tilde{p}(z)$\\\")\\n\",\n    \"plt.hist(samples, density=True, alpha=0.2)\\n\",\n    \"plt.scatter(samples, np.random.normal(scale=.03, size=(100, 1)), s=5, label=\\\"samples\\\")\\n\",\n    \"plt.legend(fontsize=15)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 11.2 Markov Chain Monte Carlo\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"samples = metropolis(func, Gaussian(mu=np.zeros(1), var=np.ones(1)), n=100, downsample=10)\\n\",\n    \"plt.plot(x, func(x), label=r\\\"$\\\\tilde{p}(z)$\\\")\\n\",\n    \"plt.hist(samples, density=True, alpha=0.2)\\n\",\n    \"plt.scatter(samples, np.random.normal(scale=.03, size=(100, 1)), s=5, label=\\\"samples\\\")\\n\",\n    \"plt.legend(fontsize=15)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### 11.2.2 The Metropolis-Hastings algorithm\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"samples = metropolis_hastings(func, Gaussian(mu=np.ones(1), var=np.ones(1)), n=100, downsample=10)\\n\",\n    \"plt.plot(x, func(x), label=r\\\"$\\\\tilde{p}(z)$\\\")\\n\",\n    \"plt.hist(samples, density=True, alpha=0.2)\\n\",\n    \"plt.scatter(samples, np.random.normal(scale=.03, size=(100, 1)), s=5, label=\\\"samples\\\")\\n\",\n    \"plt.legend(fontsize=15)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch12_Continuous_Latent_Variables.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 12. Continuous Latent Variables\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn import datasets\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"from prml.dimreduction import Autoencoder, BayesianPCA, PCA\\n\",\n    \"\\n\",\n    \"np.random.seed(1234)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"iris = datasets.load_iris()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 12.1 Principal Component Analysis\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pca = PCA(n_components=2)\\n\",\n    \"Z = pca.fit_transform(iris.data)\\n\",\n    \"plt.scatter(Z[:, 0], Z[:, 1], c=iris.target)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 12.1.4 PCA for high-dimensional data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 5 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"x, y = datasets.fetch_openml(\\\"mnist_784\\\", return_X_y=True, as_frame=False)\\n\",\n    \"mnist3 = x[np.random.choice(np.where(y == '3')[0], 200)]\\n\",\n    \"pca = PCA(n_components=4)\\n\",\n    \"pca.fit(mnist3)\\n\",\n    \"plt.subplot(1, 5, 1)\\n\",\n    \"plt.imshow(pca.mean.reshape(28, 28))\\n\",\n    \"plt.axis('off')\\n\",\n    \"for i, w in enumerate(pca.W.T[::-1]):\\n\",\n    \"    plt.subplot(1, 5, i + 2)\\n\",\n    \"    plt.imshow(w.reshape(28, 28))\\n\",\n    \"    plt.axis('off')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 12.2.2 EM algorithm for PCA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pca = PCA(n_components=2)\\n\",\n    \"Z = pca.fit_transform(iris.data, method=\\\"em\\\")\\n\",\n    \"plt.scatter(Z[:, 0], Z[:, 1], c=iris.target)\\n\",\n    \"plt.gca().set_aspect('equal', adjustable='box')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 12.2.3 Bayesian PCA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def create_toy_data(sample_size=100, ndim_hidden=1, ndim_observe=2, std=1.):\\n\",\n    \"    Z = np.random.normal(size=(sample_size, ndim_hidden))\\n\",\n    \"    mu = np.random.uniform(-5, 5, size=(ndim_observe))\\n\",\n    \"    W = np.random.uniform(-5, 5, (ndim_hidden, ndim_observe))\\n\",\n    \"\\n\",\n    \"    X = Z.dot(W) + mu + np.random.normal(scale=std, size=(sample_size, ndim_observe))\\n\",\n    \"    return X\\n\",\n    \"\\n\",\n    \"def hinton(matrix, max_weight=None, ax=None):\\n\",\n    \"    \\\"\\\"\\\"Draw Hinton diagram for visualizing a weight matrix.\\\"\\\"\\\"\\n\",\n    \"    ax = ax if ax is not None else plt.gca()\\n\",\n    \"\\n\",\n    \"    if not max_weight:\\n\",\n    \"        max_weight = 2 ** np.ceil(np.log(np.abs(matrix).max()) / np.log(2))\\n\",\n    \"\\n\",\n    \"    ax.patch.set_facecolor('gray')\\n\",\n    \"    ax.set_aspect('equal', 'box')\\n\",\n    \"    ax.xaxis.set_major_locator(plt.NullLocator())\\n\",\n    \"    ax.yaxis.set_major_locator(plt.NullLocator())\\n\",\n    \"\\n\",\n    \"    for (x, y), w in np.ndenumerate(matrix):\\n\",\n    \"        color = 'white' if w > 0 else 'black'\\n\",\n    \"        size = np.sqrt(np.abs(w) / max_weight)\\n\",\n    \"        rect = plt.Rectangle([y - size / 2, x - size / 2], size, size,\\n\",\n    \"                             facecolor=color, edgecolor=color)\\n\",\n    \"        ax.add_patch(rect)\\n\",\n    \"\\n\",\n    \"    ax.autoscale_view()\\n\",\n    \"    ax.invert_yaxis()\\n\",\n    \"    plt.xlim(-0.5, np.size(matrix, 1) - 0.5)\\n\",\n    \"    plt.ylim(-0.5, len(matrix) - 0.5)\\n\",\n    \"\\n\",\n    \"X = create_toy_data(sample_size=100, ndim_hidden=3, ndim_observe=10, std=1.)\\n\",\n    \"pca = PCA(n_components=9)\\n\",\n    \"pca.fit(X)\\n\",\n    \"bpca = BayesianPCA(n_components=9)\\n\",\n    \"bpca.fit(X, initial=\\\"eigen\\\")\\n\",\n    \"plt.subplot(1, 2, 1)\\n\",\n    \"plt.title(\\\"PCA\\\")\\n\",\n    \"hinton(pca.W)\\n\",\n    \"plt.subplot(1, 2, 2)\\n\",\n    \"plt.title(\\\"Bayesian PCA\\\")\\n\",\n    \"hinton(bpca.W)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### 12.4.2 Autoassociative neural networks\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"autoencoder = Autoencoder(4, 3, 2)\\n\",\n    \"autoencoder.fit(iris.data, 10000, 1e-3)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"Z = autoencoder.transform(iris.data)\\n\",\n    \"plt.scatter(Z[:, 0], Z[:, 1], c=iris.target)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "notebooks/ch13_Sequential_Data.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 13. Sequential Data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"np.random.seed(1234)\\n\",\n    \"\\n\",\n    \"from prml.markov import CategoricalHMM, GaussianHMM, Kalman, Particle\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"gaussian_hmm = GaussianHMM(\\n\",\n    \"    initial_proba=np.ones(3) / 3,\\n\",\n    \"    transition_proba=np.array([[0.9, 0.05, 0.05], [0.05, 0.9, 0.05], [0.05, 0.05, 0.9]]),\\n\",\n    \"    means=np.array([[0, 0], [2, 10], [10, 5]]),\\n\",\n    \"    covs=np.asarray([np.eye(2) for _ in range(3)]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"seq = gaussian_hmm.draw(100)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"plt.scatter(seq[:, 0], seq[:, 1])\\n\",\n    \"plt.plot(seq[:, 0], seq[:, 1], \\\"k\\\", linewidth=0.1)\\n\",\n    \"for i, p in enumerate(seq):\\n\",\n    \"    plt.annotate(str(i), p)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"posterior = gaussian_hmm.fit(seq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 1000x1000 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"plt.scatter(seq[:, 0], seq[:, 1], c=np.argmax(posterior, axis=-1))\\n\",\n    \"plt.plot(seq[:, 0], seq[:, 1], \\\"k\\\", linewidth=0.1)\\n\",\n    \"for i, p in enumerate(seq):\\n\",\n    \"    plt.annotate(str(i), p)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"categorical_hmm = CategoricalHMM(\\n\",\n    \"    initial_proba=np.ones(2) / 2,\\n\",\n    \"    transition_proba=np.array([[0.95, 0.05], [0.05, 0.95]]),\\n\",\n    \"    means=np.array([[0.8, 0.2], [0.2, 0.8]]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"seq = categorical_hmm.draw(100)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"posterior = categorical_hmm.forward_backward(seq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"hidden = categorical_hmm.viterbi(seq)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(posterior[:, 1])\\n\",\n    \"plt.plot(hidden)\\n\",\n    \"for i in range(0, len(seq)):\\n\",\n    \"    plt.annotate(str(seq[i]), (i, seq[i] / 2. + 0.2))\\n\",\n    \"plt.xlim(-1, len(seq))\\n\",\n    \"plt.ylim(-0.1, np.max(seq) + 0.1)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 13.3 Linear Dynamical Systems\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"observed_sequence = np.concatenate(\\n\",\n    \"    (np.arange(50)[:, None] * 0.1 + np.random.normal(size=(50, 1)),\\n\",\n    \"     np.random.normal(loc=5., size=(50, 1)),\\n\",\n    \"     5 - 0.1 * np.arange(50)[:, None] + np.random.normal(size=(50, 1))), axis=0)\\n\",\n    \"plt.plot(observed_sequence)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"kalman = Kalman(\\n\",\n    \"    system=np.array([[1., 1.], [0., 1.]]),\\n\",\n    \"    cov_system=np.eye(2) * 0.001,\\n\",\n    \"    measure=np.array([[1., 0.]]),\\n\",\n    \"    cov_measure=np.eye(1) * 10,\\n\",\n    \"    mu0=np.zeros(2), P0=np.eye(2) * 100\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"mean, cov = kalman.filtering(observed_sequence)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mean = mean[:, 0]\\n\",\n    \"std = np.sqrt(cov[:, 0, 0])\\n\",\n    \"plt.plot(observed_sequence, label=\\\"observation\\\")\\n\",\n    \"plt.plot(mean, label=\\\"estimate\\\")\\n\",\n    \"plt.fill_between(np.arange(len(mean)), mean - std, mean + std, color=\\\"orange\\\", alpha=0.5, label=\\\"std\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mean, cov = kalman.smoothing()\\n\",\n    \"mean = mean[:, 0]\\n\",\n    \"std = np.sqrt(cov[:, 0, 0])\\n\",\n    \"plt.plot(observed_sequence, label=\\\"observation\\\")\\n\",\n    \"plt.plot(mean, label=\\\"estimate\\\")\\n\",\n    \"plt.fill_between(np.arange(len(mean)), mean - std, mean + std, color=\\\"orange\\\", alpha=0.5, label=\\\"std\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"observed_sequence[50:90] = np.nan\\n\",\n    \"plt.plot(observed_sequence)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"kalman = Kalman(\\n\",\n    \"    system=np.array([[1., 1.], [0., 1.]]),\\n\",\n    \"    cov_system=np.eye(2) * 0.001,\\n\",\n    \"    measure=np.array([[1., 0.]]),\\n\",\n    \"    cov_measure=np.eye(1) * 10,\\n\",\n    \"    mu0=np.zeros(2), P0=np.eye(2) * 100\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"mean = []\\n\",\n    \"std = []\\n\",\n    \"for obs in observed_sequence:\\n\",\n    \"    m, s = kalman.predict()\\n\",\n    \"    if np.isfinite(obs):\\n\",\n    \"        m, s = kalman.filter(obs)\\n\",\n    \"    mean.append(m[0])\\n\",\n    \"    std.append(np.sqrt(s[0, 0]))\\n\",\n    \"mean = np.asarray(mean)\\n\",\n    \"std = np.asarray(std)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(observed_sequence, label=\\\"observation\\\")\\n\",\n    \"plt.plot(mean, label=\\\"estimate\\\")\\n\",\n    \"plt.fill_between(np.arange(len(mean)), mean - std, mean + std, color=\\\"orange\\\", alpha=0.5, label=\\\"std\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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bMmMFf//pXqqqqePHFF/n0pz9NXV0dF198MV/96lfZsmULPT09ltCprKwkmUxy1llncfzxx/Pcc8/hdDr5wQ9+wNlnn83GjRv7Pcbxxvj9ZkokhyudG0WUIrJPtOEe730pRgPVKaanxpph910Q2gNzPwKeyrE+sryYwsJnDB0r94tIU28sOWbHVDTJCFw/bWxe+5tN4A4UtWo8Huf666/niSee4Pjjjwdg3rx5PP/889x8882EQiFWrVrF2rVrAZgzZ461bU1NDQBVVVVMnTq139c58sgj+fa3vw3Addddx49+9COqq6u54oorAPjOd77DjTfeyMaNGznuuONwuVx897vftbafO3cuL730En/5y1+4+OKLKSkpwefzEY/Hs177j3/8I5qm8bvf/Q7FEFe333475eXlPP3005x55plF/V3GA1JYSCTjBV2Hg8/BrrvExNCy5ZOuG+UhoShiaqozCK1PQawFFnxSTE8dZ5ipEHOaqfmvuVxy6OzcuZNIJMIZZ5yRtTyRSLBq1Sr+53/+hwsvvJD169dz5pln8r73vY8TTjhh0K9zxBFHWD87HA6qqqpYuXKltWzKFDFYr7W11Vr229/+lttuu43Gxkai0SiJRIKjjjqq39d588032blzJ8Fg9jyZWCxGfX39oI97LJHCQiIZD2hp0fCq8a/G3fniyVFGOhK4SoTfpHc7bP4ZzL0Mqo8bV3+vaE7EwhQW8ZzBZOMSl19EDsbqtYskFAoB8NBDDzF9+vSs5zweDzNnzqShoYGHH36Yxx9/nNNOO42rr76an/3sZ4M7JFe2r0lRlKxlZnRB04RovOeee/jqV7/Kz3/+c44//niCwSA//elPeeWVVwZ8P2vWrOHuu+/u85wZYZkoSGEhkYw1WhL23AP7/ylKSX39h2YlCBNr6TII7xL9LmItMP38cdNQy/JYWMJCzVo+rlGUotMRY8myZcvweDw0NjZyyimn5F2npqaGj33sY3zsYx/j5JNP5mtf+xo/+9nPLL9COj38/x8vvPACJ5xwAp/97GetZbkRB7fb3ee1V69ezb333kttbS2lpRPbUyWFhUQylqTjsPsPcOAR8E4ft56BcYmiiJ4XsRbYc7eYnjrnEnD6xvrIrIiFKSisVEhKpkKGi2AwyFe/+lW+9KUvoWkaJ510Et3d3bzwwguUlpZSX1/PmjVrWL58OfF4nH/9618sXboUgNraWnw+H48++igzZszA6/UOW6npwoULueuuu3jssceYO3cuf/jDH1i3bp1ViQLC7/HYY4+xbds2qqqqKCsr49JLL+WnP/0p733ve/ne977HjBkzaGho4P777+frX/86M2bMGJbjGw1kAlciGStSEdh5KzQ9Ar5ZUlQMFe8U0VCr6SHY8VtIdI71EfUxb3qdjqzlkuHh+9//Pv/1X//FDTfcwNKlSzn77LN56KGHmDt3Lm63m+uuu44jjjiCd7zjHTgcDu655x4AnE4nv/71r7n55puZNm0a733ve4ftmD7zmc/w/ve/nw9+8IMce+yxtLe3Z0UvAK644goWL17M2rVrqamp4YUXXsDv9/Pss88ya9Ys3v/+97N06VI++clPEovFJlwEQ9F1XR/NF+zp6aGsrIzu7u4J98eSSIaNVAR23gKtz4oBYq6SsT6iiU8qAqEdUH4kLPyMMHqOEX9Zt5ev/20j71pSy22XH01rT4xjrn8SVYH6699t5eXHmlgsxu7du5k7dy5er3esD0cyDujvM1Hs9VtGLCSS0cYSFc+IUL4UFcOD0w+lS6H7Ldj6SwjtGrNDyTVvml4LTYdkelTv5SSSUUcKC4lkNMkSFQvE6HDJ8KG6IbjMaKb1S+h6e0wOI2PeFKdYjzNzqo2nZDpEMrmRwkIiGS3SMdH4SoqKkUV1iIqRWCts/w10vD7qh5AxbxoRC6dqVcPKXhaSyY4UFhLJaJBOiJHnLU+J9IcUFSOLoopeIMle2H4jHHxxVF/eFA9mKkRRFCtqIQ2cksmOFBaSScW6PR38+NGt4yvcrKVEOeSBxyEwB5zSUzEqKIqIDGlJkX5qeUp0Nx0FYjnlpuJno0nWePpsSiQjgBQWkknFTx/dxo1P1/PCzraxPhSBrkHjfdD0MPhnHN7DxMYCRYGSuYAqhro1PzEq4sISFs5Mw65MyalMhUgmN1JYSCYVXdEEAL2x1BgfCeIC1vQo7Pu76LXgrhjrIzp8CcwC1SPSUQceHXFxYfWxcNuExUTqvimRHAJSWEgmFeG4OGlHE+Pg5H3wBWi4R0QpPNVjfTQS/wzhbdn9BzGXZQTFRTSnpTfIQWSSwwcpLCSTinBCRCrG/K6wc6O4O0YB3xiNoJb0xTdNTEdt+POIiotc8yZkRMaYfzYlkhFGzgqRTCrCcUNYjOVMhtAeqP8dpMNQsmjsjkOSH18dRBHiAmD6ecM+GTWvedOsCpko5s1EN6Qjo/d6Dj+4h2deR7Hs2bOHuXPn8sYbbww41lxSPFJYSCYN8VTa6mo4ZneF8XaovxUi+6Fs+bga5S2xMcLiIp9502ONTp8AqZBEN7z9fYiPognaUw0r/mtYxMXll19OV1cXf//73w/9uCSDZtDCYv/+/Vx77bU88sgjRCIRFixYwO23387atWtH4vgkkqKJxDNiYkzy2KmIqDzo3iwaNCky0ziusYsL1Ql1Zw+buLBSIXbz5kSKWKQjQlQ4fCKSMFqvl44Aoxu1kAw/gxIWnZ2dnHjiibzzne/kkUceoaamhh07dlBRId3ukrEnFM9Ugox6xEJLw54/QdtLIv2hukb39SVDw1cHUR12/xFQoe7MYREXpnjI18diQpk3HX5wBUfntdLRQW9y33338d3vfpedO3fi9/tZtWoVq1at4s477wSwhr099dRTnHrqqbz66qt85jOfYcuWLaxYsYJvfetbw/oWJIJBCYsf//jHzJw5k9tvv91aZp8xL5GMJRFbJcioNyFqegSa/w3+WeD0je5rSw4N3zSRutr9B9EOfMpphywuzKokr0uWm44UBw4c4MMf/jA/+clPuOCCC+jt7eW5557jox/9KI2NjfT09FjXqsrKSkKhEOeddx5nnHEGf/zjH9m9ezdf+MIXxvhdTE4GJSwefPBBzjrrLC666CKeeeYZpk+fzmc/+1muuOKKgtvE43Hi8bj1e09Pz9CPViLpB3vEYlTLTdtfg71/BVc5uMtH73Ulw4d/OkT2wq67QHHBlFMOaXexZD5hYXospLAYDg4cOEAqleL9738/s2fPBmDlypUA+Hw+4vE4U6dOtda/44470DSN3//+93i9XpYvX86+ffu46qqrxuT4JzODSgLv2rWLG2+8kYULF/LYY49x1VVXcc0111hhp3zccMMNlJWVWY+ZM2ce8kFLJPmIJOypkFEKN4f2wO47RNtuWVY6sfHPBMUBu24XPUgOAfPzl09YjGnF0iTiyCOP5LTTTmPlypVcdNFF3HrrrXR2dhZcf8uWLRxxxBF4vV5r2fHHHz8ah3rYMShhoWkaq1ev5vrrr2fVqlV8+tOf5oorruCmm24quM11111Hd3e39di7d+8hH7REko+w3WMxGqmQZI8wa0abxWAxycQnMFv0tqi/DdpeGdIu0ppOIt23j4VXDiEbVhwOB48//jiPPPIIy5Yt43//939ZvHgxu3fvHutDO+wZlLCoq6tj2bJlWcuWLl1KY2NjwW08Hg+lpaVZD4lkJAhnVYWM8MlbSwnDX/dbEFwkK0AmE4E5Yhrtzt9BxxuD3tzu77GbN2WDrOFHURROPPFEvvvd7/LGG2/gdrt54IEHcLvdpNPZf+elS5eyceNGYrGYtezll18e7UM+LBjU2fDEE09k27ZtWcu2b99u5bckkrEkPJqpkKaHxbTMwDxQ3SP7WpLRRVGgZJ4ofdx5M3S9NajN7f6erD4WVsRCpkKGg1deeYXrr7+e1157jcbGRu6//34OHjzI0qVLmTNnDhs3bmTbtm20tbWRTCa55JJLUBSFK664gs2bN/Pwww/zs5/9bKzfxqRkUObNL33pS5xwwglcf/31XHzxxbz66qvccsst3HLLLSN1fBJJ0YxaxKJjPez9G7grR68UTzK6mCPXe7fBjptg0eehbElRm5oeCrdTRVUz1SUTcmz6aHXeHMLrlJaW8uyzz/LLX/6Snp4eZs+ezc9//nPOOecc1q5dy9NPP83atWsJhUJWuek///lPrrzySlatWsWyZcv48Y9/zIUXXjgCb+jwZlDC4uijj+aBBx7guuuu43vf+x5z587ll7/8JZdeeulIHZ9EUjR2j0V8pAxy0QOiLFFLQkCWWk9qFAWCi6F3K+y4ERZ/HoILBtzMKjV1ZgeEJ1QfC4dfdMKMtw2pv8SQ8FQPqhnX0qVLefTRR/M+V1NTw7///e8+y4877jg2bNiQtUwf4Um3hyOD7rx53nnncd55543EsUgkh8SIl5umY7DrTgg3QNmK4d+/ZPxhioueLbD9RlhyjTB49kO+kekwwfpYuMtEe+1JPitEMjLIWSGSSUNWuelwh5t1HRrvh/ZXIbhQmjUPJxQVSpdCz2bY/ltYfI0YwV6AeKpvDwvI+C0mTLmpuwzZXlsyFOTZUTJpGFGPRdtL0PSQ6FXhkJ01JyO6DulC13xFheBS6K0X4iLaXHA/0UTfUlOQDbIkhw9SWEgmDblVIcOWOw3vFXNAFFXkgSWTjp++OZ3lf13NbzfVFV5JdRiRi22w/f8gdjDvaqao9fQRFhMoFSKRHAJSWEgmDXbzJgyTgTMVhd13CdOmNGtOWhyKTiTloCU6QOmw6hTiovstYeiMd/RZJZqcBOZNieQQkMJCMmmwp0IA4od6Atd12PsAdLwufRWTnCm+JAAt0SKm0qoukRbpfAN23gSJ7qynBzRvjsNyU1kZITEZjs+CPFNKJg32VAgMwwm8/VXRCMs3DRzegdeXTFim+hMAtA4UsTBxuCG4BNrXiSZayV7rKdOcaW+OBeBxmh6L8ROxcLmEkIpERrH6QzKuMT8L5mdjKMiqEMmkITdicUglp9EDwlcB0ldxGGBGLJqLiViYODyinXvbS6A4YeFnwBkgZo1Mz75v89giFrquoxziaPbhwOFwUF5eTmtrKwB+v39cHJdk9NF1nUgkQmtrK+Xl5TgcjoE3KoAUFpJJQ67HYsgRi3RCzAGJ7JX9Kg4Tan0iYtEWc5HSwFlsLNfhg5KFcPB5IS4WXNFPKkT8ruuQSGtWBGOsMUeLm+JCcnhTXl6eNW5+KEhhIZkUpDXdMs35XA6iyfTQTXJND0P7K6Kls/RVHBZUeVI4FJ20rtAeczHFnyx+Y6dfTLdtfRpUJ9HEKQB9hIM9NRJLjh9hoSgKdXV11NbWkkwO4n1LJh0ul+uQIhUmUlhIJgX25lhVJW72dUaHVtbX9Tbs+we4q8UFQ3JY4FChxpukOeqmJTpIYQHgDIiqoeYnibVVAsE+EQuXQ0FVQNONXha+oeewRwKHwzEsFxWJRAoLyaTA9Fc4VYVSrwsYgrBIdIk5IOkI+GcN+zGOSzQNYiGIGg/r515IxMV4+FRK/Js2HroOThc4nKL80mE8XB7wBcBbAr6SzL++EvH8OGeKL2EICzcwBDOjKwiB2cR69gNL8OZcoxVFwetyEEkcQjRNIpkAjP9vu0RSBGZFiN/tsDUiGsTJW9eg4V7o3Q6ly8WMiMlGPALdbdB9MPNvbwdoQ4jsxAezsgIl5VBaZTyqxb8lFaCOn1RTrT8JHUWWnBbCVUpMEW2wvaGNoC0Q4svAEhbjsOTUpCuSIJbUmFomK6EkQ0MKC8mkwDRulnicQxtP3foctDwF/jlZF4IJTaQHWvdC2144uFdEIvKhqJnIghVpCILbm4lG2B+QiV6kbdGMRMyIeoSzox/pFIQ6xaNpZ+Z1HU6omArV08Wjsg6cRZZ7jgBmZUjroQgLIKZ7APCFNkJDEmZ/SHTtJNM0a7x239R1nQv+70VaemK8+q3TKfFMku+CZFSRnxrJpMBMhfg9TmtGQ9HlpuG90HgvqJ6JPV0xmYCW3dDSKMREuLvvOoFyKKuGsprMv/7SkYvQ6LqIlPS0QU87dLdnfk4noW2feIA4hvJaqJ4BU+dB1bRRjWhMMSpDmiOHJm6iaXHMXm8Q9v0dFAfMvhgUddx33zzQHWN3WxiAtt64FBaSISE/NZJJgRmxCNgiFkXdFabjsOduiLZMzNLSZAKad8G+7dCyJzutoSgiIlAzUzwqp45+REBRwBsQj1rbuHFdF2mYtv3Qvl/8G+2Fzhbx2PG68GxMnQt182DKHPH7CDKo7pv9EDOFhccH3imw934RsZh5IW4jYjGoaNoosrmpx/p5WFriSw5LpLCQTApMj0XA7bA1IirixLj/X6J7YnBB1l372x1+ntxfzkcXtVDhGWcXgXRKpBTyiYmSCnEhrpkJVdPBNXaphX5RlIznYt4RYlmkRwiM1gZo3i1SK3u3ioeiivc0cwlMXzAiAsmMWAw4L2QAYilDWDg10VxN16Dxb6C48LpqxDrjNGKxKUtYjLPPvWTCIIWFZMKQTGv0xlJUBvqe+M1UyKAiFl2bhLDw1PQZhX7j5joeaqxkf9jNT47bMyzHf8iEumD3RmjYJC66JiUVMH0RzFgojJET1XjqL4VZpTBrqahW6WiCA7vEI9QpBEdrA2x4EqYvFOvVzBy2XiNmiemheiysVIjDEA/eWkCHxnvxapcCyrj1WGw+kEmfyYiFZKhIYSGZMFz1x/U8u/0gz3z9VOrKsoVAlnnTWUQeO9kDe/6Yt7S0odfDI3srAPjEkpZhfAdDQNPE3fuuN8VF1cQXhFnLYMYicdc/UcVEIVRVeC2qZ8DKd0BvJ+zfDo2bhcBq3CIe3hKYtQTmHgGBQ/PHmBGLjriLeFrB4xjaMKa4ISx8DtvnzzsFdA1vsgmYPo6FhS1iMU6jKpLxjxQWkgnDlgM9JNIa9a3hvsIib7lpgZO3rkPDX6FnK5Qu63NR/t3WKWi6wql1XSwpjw7/GymGdAr2vAXbXxfeA5Mpc8RFtG7u4dUVNFgBS46FxcdAZzM0bIZ920TlyfbXxKNuPsw/yohiDF5olbvTuFWNhKZyMOZiRiAxpEO1PBbOnAuzrw6vQxxXrH0z6DPGlSDsjibZ25H5vMtUiGSoSGEhmTCYJ7pQzkwQyI5Y+AYqN217GZqfEJEKNTvs3R5z8pddIg/+mWXNw3XoxZNKinTH9tdENQWA2wdzlsPclaKq43BGUURZamUdHHGKiObsfktEcw7Ui0dpFcw7SqRKnMWnNRQFan1J9oU9tETcQxYW0VSeiIWB1yMMqPHWddDqgSmnDuk1RoKttmgFyFSIZOhIYSGZMJipjUgij7AwSkv97ozHIm+5abQFGu4RJYDuij5P37m9lnha5cjKEMfV9vbdfqRIJmD3m6IaIm7cNfqCsPhomL18QnSuHHUcTuG1mL5QVJjUbxCRjJ524cPY9LyIYCxYLXpyFMEUX0IIi0PwWcTSIgrhyScsjGWxtAt23SE+h7UnD/m1hpPNfYSFjFhIhoY8W0kmDGZqI3eKqX1ZwOPAYzUhyjmxaylo+HPBqaWRlMofdtQCIloxKlFqLS0iFFtezhgyA2Ui5D9rqdVYSTIAwUo46l2w/ERhbq3fIPp4bH0Fdr4B846EhavB0//8l0MtOdV1iKWNZlj9CQtHpVhQf5sQFzUnDOn1hhN7RQhIj4Vk6EhhIZkQpNIaKU2Y6cJ5IhH2qhCHauSxc++4Wv4DB1+AwLy8/oT7dlXTGXcxuyTGWTM6h/kd5KF5N7z1rLjbBtH2evGxoqRyHLW6nlC4PCJCMX8VNO0QwqK7Dbavg3pTYKwRfTXyUGsJi6GVnMbTGTXqy/VYkIlixNIq+GdDeA/U/058HquPG9JrDhdmDwtzOrBMhUiGihQWkgmBvSdFpN+IReYjnWXeDO0RjYqcQXCV5H2NdQfF8ovnt+EYyet6T7sQFC17xO9uHyw7AeaskIJiuFAUUYI7baEoV936MnS1ilRT/QZYsAoWHd0nRTLVL3wVrRERsajv8fL912fyhZVNrKoOD/iypnET8kcsTGERTyviGANzILwLdv4OUKH6mKG930MkkdLY0SpSf0fMKOOV3R0yFSIZMlJYSCYEdpEQivc94UVsDbLSRmTDSoWk49DwJ4gfhNLC3TVN012VZ5Ajs4slEYMtL4nSUV0Xd6kLVolqhxHuKnnYoigwbb5oGtayR0QwOg4Ic+yet8Xffu4RloclNxXyi43TePpAObW+ZFHCwuxh4VQ0nHk0otcesTCPLzAPQvWw81bxmahae4hvevDsbA2RTOsEvU7m1ZQIYSFTIZIhIoWFZEJgFxb5zJshW8Qimdayt2l6GNpfh+DCfsv7LDd/nhD2IXOgHt54UgzoAlEaufIdIv0hGXkURbQHnzJHpKDefk6koDY+IzwYK06C6Yuyum+GkipP7C8HoDdZnNclNsBnyGv0xrBHNlAUKJkPoZ2w82aj8mXN0N7nEDGNm8vqSq1ybZkKkQwVKSwkEwK7EbNfj4XbaXkr4ikNurfAvn8a3TX7rwyI9mO6GzLxKLz5lOi5AEJIHHUa1M7qdzPJCKEomdkjDZtEBCnSA68+DBWvM2v+mcASWqIu/r2vwjJihooUFmbEolBzLbO3RZawMI+rZIEQFztugYVXQuWqIb3FoWD6K5ZNKx3380wk4x+Z0JVMCOwnubxVIYlMVYjZeTOaSMKeP0EqJDofDsCwRix0XYiJJ+40RIUiTIOnXSZFxXhAVUVfkDM/DkuPF/0uOluY8dof+LHzFpzJCH/eWWOt3lNsxMLqupn/omylQlJ5Tr2muEiFYOdN0PHGIN/U0NnUJFp5L59WhqeYzrUSST9IYSGZEGRFLHKEha7r2S29zc6b8Rh0b+ozYKwQpqM/X2OjwR1sGF7+p7gLjkdFw6ZTPyRSH7IfxfjC6YKlxwmBMXs5AB90Ps1/PF9lQcerKIjPQrERi0zXzQIRC8u8WeDUa4qLZO+oiostRipkaV3QKteWEQvJUJHCQjIoNE3nyj+8zvf/tXlUXzdu81iEczwW8ZSG4dfEbx9CltLANw3U4koHzVTIIUUsWhvgyT8KT4WiiovWuy4VI8sl4xdvANacCad8kHpmUKGEuMH1e/7h+Q4rlF3FCwtzsmkBcdrHvJkPRYGShTZxsX5w72WQpDWdnpj4Tk0r89mEhYxYSIaGFBaSQdHUHeXRTc3c9dKeUX1de0+KSE5ViL3Ft9/lwKOHxDZpJ7q7us++1rWW8LWX59ARy44eRAe4KPSLpsGmF+D5+0Ur7tIqISiWHi+bXE0kqqbx7eB1fDd5Gb26jyOUXTzo/i8+m/6zaLc+ALF8A8hsZITFABE0S1yEYMdN0PH64N7HILAbo70uBx6zJb5MhUiGiBQWkkGRTOvWv7o+tOmPQyHbvJkdsTCFht/tQFXA1/JP67m41vcE/uu3p/HXXTU8bjj+TaKFhkcNRKQHnvsrbHtV/D5nJbzzEijrK2ok458av8bt6XM4I/FTeqYsR1V0PqY+hv7kH+Dg3n63NSMb+dp525f3G7EwsTwXYSEu2l8b3BspEruw8DhVmQqRHDJSWEgGhVnKKX4eTWFhN2/mj1gEPE5ofwVv25PWc/ly2fU9ojqkN5GJJGh6gXHXA9G0U6Q+2pvA6YZj3g2rT5deiglMndEka9FUB57jzuJjiWtp0itRwt3w3H2w4T9itkse7t9dBcDiAlNxzWqRgh6LXCxxEYEdN0Pbq4N8NwMTNb5bHqeKqioyFSI5ZKSwkAyKbGExeiee3IiFPVpiNcdyAQ334nKoOBSjX0CO+z6aUmmKiGZUEdtz9jvIojwWuiZ6Ibz8T0jGoWIKnHYpzFg86PcmGV9csuAg757ZwTdX7cXj0HmJlZwZ/wnhGUeKFXa9CU/+AVoasrZb11rCy62luFSNTyxuybvvojwWuZjiIh2FnbeI6bzDiPndMr1JZlWIFBaSoSKFhWRQ2KMUqTGKWOh65i4LbBELpUfMXiiZa53Aozkn8D29mQ6XkXQmYhG1iYwBPRbJOLz4oOjeCGI2xSkflCPNJwmzg3H+7+R6lhhRh6ArTQg/exedCyddCP5Skf564X7R9MzwXvz67WkAfGBeG9MKjFwv2MdiIMwmWlpciIuDLwzx3fXF/G6Z1VQel0yFSA4NKSwkgyJli1IkRjNikXOSs6dDrOZYehcE5oLiKHhnuLs30yTLLiYyjY001P58daEuePoeaNktTJlHvxuOOEUaNCcxJS7x+QolHaIHyemXwbyjxJO7N8J/7mbb7h6eay7Doeh8dtmBgvuyl5sO2qJktv/W0mK2SOuzQ3g3fTEFRCZiYQgLad6UDBGZCJYMCruYSGljkwoBM/0hog/hUDsAAWcaXKVA4UZEu3oywiIrFZIqwl/R2giv/EtELLwlcPz5UCHLSCc7QUNYWG29nW446p0wbR689hiEOpn/xh1c6QjTPutYZpbkj1ZAdjQsnlYK9rsoiKJAyVxjKurvQU9D7alF9WkpRDRhpEKcMhUiGR5kxEIyKOypkGRq9FIh9j4WYCsx1dKEm9cB4Pdm0hyFQs67eu3CwpYKMY2b+fwVui5Gbr9wv+GnmArv/LAUFYcJZsTCbvYFoHY2nHYZPTWLcZLmG657+F70/0Gkt+C+7MJi0OkQO4E5gAPqb4PmJxh8+CODlQpx50QsZCpEMkSksJAMijFLheQIi4g5L6T1GSLd+wAocWWOp1C/gN09BVIhVsQi52Sq67DxaXjzafHzrKXwjovAl3/0umTyYaVCUnnSXR4ff6m+nK8lP00MD76uvcLYuX9H3n05VTH5FA5RWAAEZoHqgV13QNMjQxYXZprRawgKOYRMcqhIYSEZFMlxkgoJxVMQ2Q+N9xHS/AD4nbZGP3k8FrqeG7HoKyyyelikU6Itd/0G8fuKk2HNWbKU9DCj1O6xyMPBmJu/pk/llmlfFlGsZFykzDY+LfwQOeSdcDpU/DPAGYA9f4R9D4pqpUFSsCpEeiwkQ0QKC8mgGKtUSK55MxKLQ8OfId5ChAoASpx9Ixb2fgGdcSfdiYwoyGfetDwWyQS8+HfYv1205j7m3bBo7SHlsiUTk4KpEIO2mAsAZ7AcTrlYfE5AjGN/9q99UiOmeC26l8VA+KaBs1R8H/beP2hxEc2tCrGlQkazCZ5k8iCFhWRQJMdJKiR8cBO0vQKBeVaIOuCyRyzECdEuHuwVIZBdbhq3eyxiEdFJ8+BeMaTqxPfJ/hSHMSW55s0cDhrCosaXFNVBK06G494DLg90HID/3J3V88KjDlMqxI5vKrgroeEv0HAvaH0nABfC9C/5ciIWmg4pTQoLyeCRwkIyKFJZfSxGT1jk5nvDrW+KChBnwEppBOypkDzmzV1GD4ugS5x0I8m+qZAZtMIz90JXK3h8cPIHhElPctgSdJsei/yny4NRISyqvbaL+bT58K5LoKwWElFh/N3yMug6Hmd+/88h460F7xQRtdhzN2gDzzYBex8LQ1i4Mu9T+iwkQ0EKC8mgSIxxS++A4VyPxGLgmwFkct+BvOZNm7Do8QGwrCIi9pHOToUsVhq5rufnEO4STZDe8UFZ+SGxpULye2vajGF2Nd6cC3mgHE79IMxZIX7f8hK8+A8qVWNIXgGhckh4qkRqZP+DsOtOSMcH3CTXY+F22IRFUlaGSAaPFBaSQWGPUiTHwLxZ6TXuHtVay+9gRiwGMm/uNiIWK0xhYXP5+0PN/Nn9A8q0HiitFp00gxUj9XYkE4ig0zRv9j1dpjVoj4uIRa0vT4TA4YTVZwjTr+qAlt38Iv4jZivNw5sKseOuAN8sUSlSfxuk8s8tMbFmhRiRClVVLHEhIxaSoSCFhWRQZJs3R99jUenoBCCiZXpWdBl3kqXuAYSFUWq6vFIIi3haJa0BXa28Z++tVCoh9rlnyXJSSRZWKiSPx6Iz4UTTFRR0Kj39pB5mLxNi1VvCTP0A/3D/FyWdu0fqkMFdJnpdND8OO28VE1ILEMvxWAByEJnkkJDCQjIo7FGK0UyFmCHZapdw2IdtYeR2wzxXbTuxWx4LYz1Nz5g3lxsRC4B4x0F4/m/4tAgbtPn8pe7T4M42eUoOb/ozb5r+ikpPCudAZ9OKKfDOD1PvmEO5Eub4nXeJUuaRqrxwBcV8kdanYceNkOzJu1puKgTkvBDJoSGFhWRQ2EtMR7WPRVzccVUG3ACEjZN8UlOsiEWVzTyXG7HYH3aT0FTcqsb80igKOsuVPXhfug8SMfa6Z3NZ4joUVyYSIpHAAMLCXhFSDL4SflXxBf6WPgkVDd58CjY8mbffxbDgDIjJqAdfgO3/B4nOPqvkNsgC2ctCcmhIYSEZFFnlpqMVJu2tJ5YQ8xcqvULYhA1/RGdciApV0Sl3FxYWZrRidjCOU4XVzl3c7f4hajIGlXXcVPFZevEXNzJdcljRX4Mss4dFda5xsx+cTgdfSV7FK7XnoAPsfot9/34IErHhONw8L+iH4GJofwW2/QZibVlPxxLZVSEgUyGSQ0MKC8mgGPVUSDoGDfdYAsE8gZuGTdORX+lJYTOz28pNhcHT9FfMDcags4XbnD+iXAkTKZ0OJ15Alx4ABhhCJjksMSMW0bSDpJZdImqmQvpUhPSDx6EBCq+Wv5OHpl1OSPcyI1IvypzD3cN23Fk4vBBcAp3rYfv/QrTFeiqW6iss3HJeiOQQkMJCMihGPRXS9Bh6+3pi6YyAgMzdo+mvqMoxzuVONzVbea/xNMALD1BGmPXaArYtvwRcnsysEBmxkORgn0ETzqkMGXQqBPDYWnr/b/tJXJT4b1r0CujtgKfvgc7mfrcPJVWGFEhweKB0GXS9Bdt+LVriU8hjIVMhkqEjhYVkUNjFxIinQnp2wP4HSbhq0RF3ilU5EYv2WF9/BfSdx1Df42UabVzWejMkomxXZvPRxDcIK2LOiNnS2ysjFpIcXKqO15HfZ5FJhRTf6dKMpr3VEWBbt58t+mzeG/8eWmk1xCOiDfiB+rzbdiccHP/3I/noU0PsBKu6hLjo2SrERbjB1iDL7rGQqRDJ0JHCQjIosoeQjWAqJBUVsw+SPcRcddZiU0CY5s02o4dAlbdAxMIQDM1dKf7gvgF/shtKKviB7xpC+K07UBmxkPSHGbXIFRYHowWaY/WD+dl8vrnUWtZMFU1rPyK6vKZT8NI/M8PvbOzo9tGbdLKxPTDYt5BBdQpx0bsTtv2aaFx4O/J7LGQqRDJ4pLCQDIpR62Nx4FHoehNKFhDXxAlPIWPQDFsRCzMVkhuxyAiLgz1p/p/2/5ivHkDzBeGkC0m7jEiFsZ9YWgoLSWGCBQycQ0mFmJ9NTc/xa6RK4IT3wuwVgC4qRt56Nqsc1YzQhVIO+uuo/8cdNTy4p7LwCqoDypZDuJF4VPg6soWFkQqREQvJEJDCQjIoklktvUfopNOzDfb/C9zV4PBYF32vQyNgmTLFidU80ea68q2TdzKN89UHOULdTRclqCe9H/xBS0CYg8hML4Y0b0ryUUhYDKUqxJ5uM8ufrX2pDlh9Oiw7Uayw43Uxgj0thHNHPNNWPJzqW6UC0BJx8e11c/jqy3P7FR8oKpQuJWZocl9sl/WU1cdCtvSWDAEpLCSDwj6ELDkSqZBUFPaIFAheMacjlhJ3dl6nljXBNJxyZCIWOSd2n1PDQZpvpm+momcPvbqPmyo+C0FxF+c3hYUhKCyPhVOeSCV9ydfLIqkpdMQHXxViFxbvnNbNnBIxz8OMfqAosOQYOPocITSadsJz90E8Yr1e7rHYaQiJXiwJTaWzwHwTC0UlponeMN69d0PnBkB6LCSHhhQWkkGRNYRsJE46TQ9B10bR1MeYBZKJWOi4VR2nkhEFbfEC5k01zQ+ct3Eq60ni5IrkVwjWVlvPm8LCTIVEZcRC0g/5hEWHES1zKDoVnkGYN22fsfNmd1BtpFHaojkiYOYSOPH9mfHrT99LujdTjpqvrwbA/nCmyZspvAuh6zZRrXeJDp0dr8tUiOSQkMJCMihSI2ne7N4C+x8CT60ojTOwp0IUJTPFNJR0FCw3rd77Eh92PkVaV/ih8zO8rC1jaXmmlbfPiEyYg8ikx0LSH/lSIQdt0TJ1EBPQvU7xvfE50pw2vctKo7TlEwE1M8SMEX8phLu4vPnXLFf2AIUjFvvDbuvn9nj/EYukplheD2/pbEiGYPuNeFIHgZExbz6+uYWr715PT6z4KI9kYiGFhWRQ2M2bieH0WKQi0HAPpELgnZL1VNy46HuMO72AJQpUm8fCdse4bzvlO54B4Lupj/KH8AkALLXNCLFHLFKaCBuDjFhI8hPME7EYSnMsgLU1vcwpifGZZc34nZq1fV5hAVBaJcRFWQ2lWi/3uL/PCerbxQmLASIW9iF9XqcuIoXpOJ7eDcDI9LG49bldPPTWAf69qWXglSUTEiksJIMiOVKpkP3/Eo17bCkQk1iOsDBFQVvMRdQwX1oei44D8NqjANyeOou70meR1hXK3Smm2pz7do9F1slVCgtJHkr6iVgMVlhUe1M8/Z63+OLKJuP3ZNb+8uIrgXdcxEZ1MUElyh2uHxNs3pR31X1ZqZD+IxZmZ1oFkWZEUaBkHh5FHFO8d3/R76tYQoZbtLEjMsCakomKFBaSQTEifSy6N0PTwyJS4eg7BCyW07zKNHCaJjWPWS0S7oaXHgQtjTZlHt9PXWbtY2lFJEuvmJGJSEq1/BViX6M3sVUyccgbsTArQgZRapoPM9pWMGJh4vLwOf2r/Ct9LG4lzdo9f4Wd6/ustj8ymIiFeD9mmhEARcHjLQMg3rkNWp8t8p1k8+SWFr71wFt90ilRo9JkrxQWkxYpLCSDYthTIamwqAJJRYS3Ig/mXZUlLIxoQ6MhLKo9SZRkDF78u+hcWFaLeuw5WXlvu78CwG+kU6IpR8Zf4UjnBkskEiC/eTOTCineuJkPswdG2wDRBV2H5riPzyc/z+2ps8TCjc/A289ZvS50HZoG4bGwV1zZ8Rg+kLimQv1t0PJM8W/I4KePbePuVxp5ZVdH1vJIQkYsJjsD1CJJJNkMaypE12HfP6F7E5Qu6ZMCMbFXhUAmYtFozP+o9cRErX9vB3iNJkNON16HTsg459v9FZAxaYZtEQtp3BzH6DroadBT4qGljd9tDzTQNUA3LrQ6YN2Gi74N1sMpHqoTVLd4KIXvs/KlQtoK9FAZLOb2vUknsZRimTtzCadUywv03dRHWVCrcnLHI7D9NYhFYPXptCc8VhQCivdY5HqLzLRjXAkCCuy6HdBhyqlFv6+WHtHRszeWLbwiCRmxmOxIYSEZFIeaCrn12V20hxNce/ZilO5NosOmd6o4sRcgnspJhZgRCyOX/AXtbji4FxwuISp8JWJ9p0bIqPpYWh7N2mfAiliolk9DGjdHEV0DLQlaIvOvbvtdz4kC6IieDooTFIcQBIrD+NkBqgcUlyEQnIZ4MC6welq8np4CLQ7puFiWjkIqKV4vHc9oEBzg9IEjAK4SUL15R6cP1WORS6krjVvVSGgqB2MuZpYk8q5n72EBCk+WnMnJc9Ow/nFo3AzxKE0LLsraZsCIRYEZOR7ViFikVQjMhnCDIS4oSlwkUhqdEWOuTyLzf6nrOlFDWLT2xokm0vjc+U2okomLFBaSQWEXE4PtvNkbS3L9I1vQdXjfinKWdNwjTu6BOf1uZ538nKZ507jjCbm5xPEkp8afFyse824oz6RTzLsup6KxsCxbWPhtnTfNiEVuOFgySHTdEAb5Hkkg5+9rRgpUQxA4S8FVDu5ycJWBMwBOPzh8xsObeage4cdR3YagGMTFyTzOdNQQF2FI9oqmbMkuiDZDpBFibRDZB1qMksQUYBGhBEKkKOqQ2nnnQ1GEONkf8dDWj7DINWL2Jh0wezm4ffDqQ9Cym5mhP1HOfEJKgJSuDhixMD/7ud4iK2JhGptNcVFfnLg4GIpbP5sRChDpU/s5ZF9nhIVTgv3uSzLxkMJCMijs6Y/BCoutzb3W2IMNbz/PksBmkQIZgNy7KjMsvVzbyf+47xArLT8R6uZlbWeuP7801ufE6bNVhUQLhIMl2MRCsoBYSGG71RcXeYc7Ixo8leCuFP+6ysBZYjwCtn+NhzpKpyNFEaLE4QHKC6+XDEH0AMQOUNKwDzAu5j2bAZW26JHAoUcsQKRDTGFRiI6c6IMVPambByd/AF78OxXhffzF/T1+4P8Cz3bNKKIqpEDEwhIWtv/brMiFAlNOKbjfg70ZYRG2RSyiiWwjZ2OHFBaTESksJIMimRWxGFwqZHNTj/Xzht0H+NDaun5TICa5Jz+/U6OWTm50/wq3kmZXcCXzFh3dZztz/SU5aRBzHyDu2GKHm8dC1wqIBTMlkTb8Bsb/rykWFLf4110D7gpDMFTkCIUc4TCYSMJ4w1UCroVQupCgKwI8RW/aB4u/QKx9Ez1J8dmtSW6EaCl4qocsjszKkP5KTnOjD1l9LCrr4B0X0/3UP1jEfn6Z/BHvV77FnmQdibSCu0C1U2400MQU4vF0ju/ELi4UBWrfkXe/rYa/ArLFRCRHWEifxeRECgvJoDiUIWRZwqKzArw1RW2X28eiVI1zk/tmapUutmoz2Tb3fcxTQn22M4VFrnETMukUex+LCd3DQteFRyFtFwrxAmkIJZN+yIosGGLBXW6LJOSKhlGMLIwjgh5xUY+ndBKVJ9LuXgv8B7cDSme+Czpfh96t4u/qnQauwd2FF2zrbcOMWFR4knTGXX1bepdW8cPgV7iy4/+YRzP3ub/LRxPfoCPuZKo/f1QlPpB5U8tjaA3MhvAeUS2CCrUn9Vml1R6xiBcWFo0dfUW/ZOJz+J0hJIdE1hCyQUYsNjVl5hxsD5UTTqpWe+7+yC03fcfBf7BA3Um37ufTyS/zfX9X3u3W1oR4qyPAqdO6+zzns01JDY/3iIWuG9GEGKRjGQOiFseKKoBR5WD4Dhxu8Ey1pSHKMwLBVZIxJjoD4ueJHFkYBQKezN8nFE9Zof7qEi/Kwk9B4iIxwKv1WTGdN7xHmJI91QWrnewM2H2TjLCYVRKnM+7K23lzY2waFyX+m6crv0t1pJl73D+gveX9MDe/iI+mCqRC1DypEDuBORDaLcSF4oCa47OetqdCIgOkQiSTj0MSFj/60Y+47rrr+MIXvsAvf/nLYTokyXgmMcSIRTKtsb1FRCx8jhTRtJO3OgIcN6V3wG1j1slPh10bWdCxDk1XuCb5eRr1KVR52/Ju942j9vGllfvzlu/5bSKi0zhhj6nHQteFaEhHMqZCs1JB1w2x4BW+AHcFeGpExMddAa5ScAbFXbIrmIkuHIaRhZHC6VDxux1EEmlCsYywqAkaDd3cZcJzUHsy9GyHg89B28vQvRE8U0Tzt34ERr/zQgzMVMickjhvtpfkFRb7w2568dN69IfZ/exDHMF2AhvuAe+5fTxI0J/HokAqxE5gDoR307Dxz+wuTXHq0SdbT7X25jdv2kUGyFTIZGXIZ55169Zx8803c8QRRwzn8UjGOakhCov6plYSaQg6E5xYF+LRvZVsaC9SWBgntxnx3bDlKQB+mvogz2jCPFeoj4CiULAngP1Eap6wRyVioesi0pAKGY8wVtRB9YoyR3cp+JaAtw48VUaVRLm4eLnKhcCQnbxGnRKPk0giTW88afVosISFiaJC2RLxqDtbRDBanxYCwzcd3FV5/++KaettRixmB43+EDnCojvhoDcp1qkrU/ls8Go+0nkHpzvegJf/CWvOhFlLs7ax0ow5n31vblVIPhQFAnO55tn5vNnVwyMlL7F0qYhcHOzNeCzsYiJidN0MuB2EE2kaOyLouo4iP8+TiiEJi1AoxKWXXsqtt97KD37wg+E+Jsk4Ja3p2FtXpIpNheg6mzc9A5SwtCLKqqqwJSyKIZ5WqaKbkxv+BLpGW9Uybtx/vvV85SBGVpuoiui0GU07rFr/EfFYaAmjjLFHRCN0XUQdnCXgmwYl88BXZ0QgqsWFx1UqhcM4pMTrpLU3Tm8sxcNvHQBgWV1p4Q0CM2HupaI088Cj0Pq8qDIJzOnjwch03+xHWMQyqRAQ3wu7MdMcPlbpSeJ3apT6VK5s/RKPVPyChT1viBk6yTjMP8ra54ANsgqlQkwUhQNx8V62vv04S6f6oOKonFRIJmJhpkIWTAmycV8X0WSa9nCC6pK+rfwlE5chCYurr76ac889l9NPP31AYRGPx4nHMx+ynp6eftaWjGdyIxRFt/TuepNNjfuBxSyriHFkVRiADW0lRW2eSMEvXb/Fl+qFYCXNS8+F/eKEV+pKFXS8D4TfqRFNO6wT9rBELLQEJLog0Sl+Vp2izLJkHgQXgn+GEBS+KSJ9IQXEhCHoFRf9DXu7eLG+HVWBDx4za+AN/dNh3ieg9lTY9yC0vwIxhxAYqjFvxJoXUviU3G40yJpdkjmfhpIOKh1i2/1Gw7jpAdEHo8qbJIWT+6su4doaHeo3wJtPQSIGS44FRSnssTBTIZqKrvf/MTX30dDrgh03w6Krs82beapCynwu6kq9NHXHaOyISGExyRi0sLjnnntYv34969atK2r9G264ge9+97uDPjDJ+CNXWKSKERbJHmi4l83dCwBYVhFhZWUYVdFpjrppjrgKOtZN3ht/mJMdb5NWXTiOPQ+vnvnYHko7Zb8zTXvcZZ2whxSx0HXxHhMdkA4JA6W7AirXQNlSCMwC/0whLqSImNAEPeJzd9vzuwE4bekUppf7ittYUSA4HxZfA+2vwr6/Q88Ww+BZU1RbbzMVUuNL4nemiaQchFIOKjGFhYhYTA8YxlJDrLTH3XDsqaKR1paXxCOVgBUnWxGJQn0sNF0hpSu4lPziXdchYnS3bUxMg8SbaNtvoq33HGudSNxu3hQ/B9wOZlb6aeqOsbcjwupZFcX9HSUTgkEJi7179/KFL3yBxx9/HK/XW9Q21113HV/+8pet33t6epg5c+bgjlIyLsitAhmwKkTXYe/f0bu3sbn3WEAIi4BLY1FZlK1dfja0Bzi7QFUHAK2NfCj5LwDq55/LotIqApHMHVDVIQyAMg2c5gm76IiFrkOyG+IHhcnSFRR3pZVrRFSiZO6gyw0l458SQ1iYd+OXHTd78DtRjQqK8hWw/yGRIunZTGlgQb9tvWMpxbqAV3pSlLiEsOhJZHwWVsTCnzDWE2KlPeYSwmbpceDywManYcfrkEoST30C6OtFMqtCQKRDXGr+73pcU0jrQpw0hj0QXExX226Stq9SJE/Ewud2MNPj55XdHTS2SwPnZGNQwuL111+ntbWV1atXW8vS6TTPPvssv/nNb4jH4zgc2YYij8eDxyPDXJOB3AjFgKmQzg3Q/DhNzKc74cKlCkEBsKoqZAiLEs6e2ZV/+2gI1j2Cis6fU+9k0dSVQMiaFQIi3DtUTCGRqQpJ97e6mMAaaxamS1ep6BpadYyITPhn9DvESjLxCXozp8u51QFOWlA99J25gjDnQ1BxJDTci9L1FjWe1eyP+vO29Tajai5VI+hKE3SlaY1mzy7JRCzMVIiRXrF37FywCpwuMV9k90Yu8vyZf/C5Pp99e3oxnlYpKVAWbqZBABp6PaAotDqzDaJZ5k1DWPjdDmqD4uZUlpxOPgYlLE477TTeeuutrGUf//jHWbJkCddee20fUSGZXOQKiX5TIckeaPwLaEk2hacDsKA0Zp2wjqwK8+d62NBWwMCpabDuYYhH2MFM/if1Mf7m2AFkmlsBVA3BuGliRiySWj+zQnQd4m0QbxEdKAOzRUOg8iMNMSHTG4cLJTZhcemxs1DVYfi/L1sKy66Fpn9R7YkJYRFV2Bd286EnlnDy1G5uOLbBiqpVelIoin3aaubCbgqLGYYHo8qIWHTk+jbmrACHE157lOPjr/ArF8SUd2etoipYEZT+KkPMKArAwZibSErlYEzcSJY4E4RS7hxhIX72u53MqvQDsLdTCovJxqCERTAYZMWKFVnLAoEAVVVVfZZLJh+5VSCaLipFHLknWF2HvQ9Az1YoXcbmBnECWWbrgHlUtTBwvtURIK2BI/fctflFaNsPTjdfjn+OOG4rD+xUwetIE0s7DiliYRcokOOM19IQaxLeCXcVTD0Dak4QUQq1/8FOksmJad70ulQuWjOM6VynD2ZdRHXVf6ArysHONv6+eyH7wh7+uqua/1qz15r5YVZAmdNWzfJSgP0R07xpCAvLY5Hn8zpzCTicJF9+hPMcr9Dc0AXzzhSCw8DjMIVFYQFlj1gANIY8tEaNfhvBBG93uomndFKRNpz+6kwqxCU8FgB7ZffNSYeM3UqKxjRvuhxKn2VZdG6A5ifANwNUF5s7+wqLhaVR/M404ZSD+p4cv07zbthumINXn862tIh42CMKZjqk+hA8FrmeCp9TAy0lZiH0bBIn/LkfhSO/Dws/A+Urpag4jFk0RVQxfejoWZT5h/9zUFMpUiuPdh7NQ/umApDSVV5tLbFGpptCOjdiEU2pVqnqDFtVCIioQq4AAGDaAn7ouZqY7mJqzzZ46R9ijLyBvTKkEJGc/Tb0emg1jmN2MJPOiWy9FRLdVrmp3+2wIhZN3VESqSL9TZIJwSELi6efflp23ZxEvLyrnYb2cN7nTLOmz+WwLcs5ISS6oeEe0YLaU4Wuw6Y8wsKhwtJy8fuWLn9m+2hI1NsDzDsKbfpiElrfkjgz2mAa1IaCP1dYpFrE5EpXKcz/JBzxA5h5gegzITnsefeKOv71+ZP49rlLB155CJgll8/uF0LbpYjP50vNASudYUYsTGHRY0QsmiIiDVLiTFvRjBKnhtswYbbH8wenX1ZW8rHEN0ipbmhthBfuF70usLf1Li4VAiJicTBqCpw4DqOaJNr2NtTfSsRoPeB3O6guceNzOdB12N8loxaTCRmxkFjs7YjwoVte5jN/eD3v86aI8LudtmW29Iiuw74HoHc7lIjy0t9vncL+sAe3qmUJC8gIDTOiga7BukdEnX15Law8OeukZhcWprltfmmmw99gyW0K5HUpmQjF9HNFx0uJxEBVFVZML8PZJ283PFSXZCb9lnicXHuaELQvNnloj5hCWgiLoBWxEBf2JsNfMS0Qt2w/ipIpx86djGoST6u8oi9l+4pLRcVIexM89zeIR4tqkpUbCWkIea1USK1RFgsQ9iyAgy8Q6W4EwOd2oigKMytFuW6hmxnJxEQKC4lFs9GmuKUn/8U6pYkTjcelYtoqsgycnW/AATMF4uT55lKu3yBy0d9atZcyd7anwRQWZkSDbeugbR84XHD0u8HhtAaQQbaw+NUJ9dx3xhaWVgz9TsfviGf97lvyaZj5PhGxkEhGmWpbe/Cr37mA848RvrW3e6rZ3S1O1dVeIahLLI+FEBYHjIhFnT+7msQUIu0FGm+ZwiBdOQ1O/oDoddHVAs/dR40qmhn2G7FIF06F1HiTVlQwogcgMI9oROzT7xTf66VG59IH32wq+BqSiYcUFhIL01gVS+bPdyZSIjrhcqi4jLs2q1Ik0Q0N9wJp8FTRGPLwuefno+kKF807yEcXtfbZ33JbxEJvaxKNewCOeicERcMcs+WwU9Fw2j6tNb4Ua2v6jkovCl2HcCP+9MGsxb6S2qHtTyIZBhbWit4ns6v8fPzEOUwp9TK/JoCOwtNtomdGpb4f9Ey6w4xY7LciFtnComqA4WZZQ8jKa+EdF4E3AD1t/DjxUyrp6TdiETZeP2BEJhpDHtpsEQtzeSSlgitIRBc3Eb7ul0HX+cSJcwH4x4Ym9rTJqMVkQQoLiYXZFS+eSqPrfRvimBELp6rgNoRFMq0bVSD3ixRIYD66Dp97fj5dCSdHVoX4/tENeasyF5VFcSg6qXgC7dVHxH5mLoFZy6x1siabDgepKHS/DQ43/qlrs57yuWW5tGTsWDw1yN+uOp77rjwBr+FjOmG+MHTGDI9yZdAP3ZsocRiDyBLZEYtp/lxhITbsKOCxiOV23iytgpOFuJijN3G3+4fo8cJRQTPisbhcrLM/7KY5Ko6lxpe0DNJhw4sRTQvR4e98Bpoe5sgZZZy6uIa0pvPbp3b2/weSTBiksJBYmBELTc/fVdP0WLidKk6jMiSV1qBzPTQ/Cb6ZoDoJpVQ2doj+FP93Un1BUeB16swPRrje9TscsR4IlMFR78rqDWHdUR3qHA9dh2gThOuh6mhY/k381dkl0l6XFBaSsWXN7MqsiaknLqjKer5q8UVQtZZgei8AvcaFvamAsOjPY6HrEEuLz3xWS+9gBZx8EZ1KGUvVvRyz7Q4oIC7MVMicYAy3qpHSVcvQWetLWNVbpgAxnwt4PSLCefAFrjltIQD3v7FfjlGfJEhhIbGwt96Npfp2oTRTIU5VyaRCYmYKRANPpdiPER5VFb3PiS6XT3j/w3mOV0ijCl+FK7tLqzXW+VAmj2pp6N0mKlXmXAZLvgSBWX0iFD4pLCTjjGPnVmVF+yorp8LiL1Iy5SgAeuPiO2kKi7pA8R4Le4qjj3APVvCzsi/QopdTFmuB5+/LKy5MwRBwpplpG47mc6QpcWr4TPOmJSyMaaqBKkCFXXewOtjAyQurZdRiEiGFhcQiahcWyb7CwkyF2D0WqaYnoXcHBOZZ60VsJ5t+G1P2tPOB3vsA+EfgPVA5tc8qWTngoZAKQ8/bYhjY0q/AzPeKseVkV7c4VCWrP4dEMh6oCLizRrNXBdzg9FMyR3TKDCUd6L31VlXI9D6pEMNjkadJViyr4qpvVLHLXcuHE98m4gxCdxs8/7c+4sKMQPidWtbU1RpfEkXJ9Jsxbzaixmv6nWnRxTYdgZ2/44snCO/Ffa/vY5/sxDnhkcJCYmGPWMTzGDgzDbJU6yKcbFsP/lliPLhB2HayKUg6BesexqkneTa9kl/Hzsu72iEJi3gbhHZBzTtg6dfF4CcbflvEwutUUWR7bsk45IT5Ih3iUBVKje6fpT4hjnu1IJ1ahZXSmJqbCjEiFn3aepP5bjkUPe+QMY9DY5c+jX/O/DR4/NB9sI+4sCIQTo1ZNmFR6xOCxm+YTCNplaSmWO3z/U5NpDxLFkKshTXRuzhmdgkpTeffm1oG9feRjD+ksJBYRJKZLpbxPKkQ03fhcmSmHSY0VYwJt+8nZTt5FGLzi9Ddhub285XkVewJ++lN9v04Fhrr3C9G1QeJDpj1AVh0NXj7Doyyp0KkcVMyXjnBGHZWFXBb80nMuSWhBDRN+RQA1e4IHrJLxSv7aettCovcfi4mZvqxxTlFVIvkERf2VMjsYOa1a4xIid+RiVjYu3Rar6koEFwCoXqOC24DYFtz7wB/Ecl4RwoLiUUsKxVSOGLhdKg4090ApNzT+6yXERYFpoW2NoqxzYC65nScfhEG3drp77Nq3PJYFFkVomvCT6EoMP9TMPtD4HDnXTUrYiH9FZJxyskLqrn02Fl89czF1jJzbklK09kVFcJjelCH3q0i/WdQZZk3neQWepmioJAx2qPaWnoHK0WfixxxYaZCfDmpkEzEwhAWKdU6LzgVLWt6KqoDSpew1LUFgK0Huor8y0jGK1JYSCwiA3ksjIiFO9WJWxONbpL0vSBbzu98J6xEDF57TPw8dyXUzWdZeU4HThuDSoVoaejZAt5aWPwFmPqufqeP+l2Z8LA0bkrGK06Hyg8vWMnFR2cGn/ldDuujvd24w6+bMgtqTxHpv0QnALXepDWltCE0OGN0pvOmcZkorcoRF/dDQkQu/IVSIY5MHwu7COmD6mbJVOEl2dbcTTo19Fb9krFHCguJRSRZZMQi1mjNMUjmGVAUtvKuOeJE1+GNJyEWgpIKWHkKYGvt3ZVHWKSKFBZaUsz5CMyBJV+EiiP6Xx+ZCpFMXFRVocQjhPG2FiEsplWUwsKrYPp5ED0AsRbcDp2VlSKC8frBkqx9DCTa87b0zhIXrXwl9BtKiOB3aswsiaMgbj6sVIgr08ciOkCKdFaZgs+RJpZW2bPpH/QJsUgmDFJYSCwGqgpJpsUyV7oHp1EWmtT6RgSihSIWe7fA/u2gqHD02eAU4dw+M0NsFBWxSCdEpKJsmRAVJfMKr2tDpkIkE5mgISy2m8Ki3CsqnuZ+FOZ8GJI9EG5kTbXoUPt6W35hUdhjYaRCclt6l1bBSReC28sibQ+/d/+MAFE8jkx5eY0RsbB33hzIe+VQYZHRaGvrtnXQ8p8i/xKS8YYUFhKLSMJu3swTsegRA4Rcbj8u45OTT1iYEQvTES4WdsOGp8TPS4+DikxpqSkstnX5+uzPCtcWMoKm49C7BSqOEukPf1/PRyHs6Q+ZCpFMNEwDZ6PRVGpauRjoheqAGe8TE3rRWV0iekOszxUWA3ksclMhdsqq4cT3E8LHsepWjthxD6RTXL3iAKdP7+S4WiF2rFkhKYetgqSA9wpYagqLUC3svhs61vf3J5CMU6SwkFj0G7GIHSTVuRkAl9NlVYWk8ggLs2bddISja2IUeioBVdNg8dFZ688siVPqSpHQVF5sDma/bH8Ri3RcmNUqVhes/OgPVVUsQSGFhWSiYRo4zYxBXZk386SiCI/Ros+yplZcrLd1+ehJZD7nRadC8nzHAaiYwjV8hZDupaxrN7zyLy6Z18zvTtlp+SgywkK1Ipn9VYstMfxWW8JTQItB/W3CMyKZUEhhIbEo2HlT16DhLyTi4i7Epeq2ctM8wsKKWBgnkO2viXHMTjesPVukQmyoCnxgXhsAv91cl/VcZpZBTr41nRCionKtEBWe7NbHxWKmQ6THQjLRMD0WJtPNiIWdqqOpOfJKZgci6ChsOJhZp8+ckBysqpB+ppuuSy/ik4mvoalOaN4Nrz4MWmZ//kGkQgCWmBGLLr/R46IZdt4KsYMFt5GMP6SwkFhECpWbHnweDj5HylkOgFPVcammebOwsAg409DVCpuNqaVHnirmgeTh00ubcakar7aWsq41E7LNe1elJY30x2pY9FmrlfhQMAWF9FhIJhpBb0ZYuBwK1SWe/CuWLWXNPCHYX2+Kie8P9qqQ/CbJ3FTI77dO4csvzcUcaKzrIsXxir6U7tUXiBRM0054/TFxM0JGRIRTDmuuSMEydDIRi31hDz1Jp+hx0bMN6m+F5BCnGUtGHSksJBbRZJ5USLQZGu8D1U0SEWp12yIW+VIhZufNgJoQKRBdg2kLsqaW5jLVn+QDc0XU4jebMlGLeG5ViJYyjJorhAM+pznXYLEiFlJYSCYYdmExtcxrNc/Kx+r5olR1fe88UT2VjllVGoU8D/aqkFhK4ccbZnD/7mq2douoR0JTSOviNR1TZ8Ox54po5N6twk+l65awiNpTIf0Yscs9aeoMA+j2bp/o6BtcBO2vwe4/WKJIMr6RwkJi0ce8qaXFgLHIPgjMsdIezqxUSN+PkBmxOK7939DTLkrTVp3Wb08JgCuXNaMqOs8cKOftDlEhkhWx0DURqShdLCIVg/RU5MNnzAvxueVXQTKxMD0WAHVledIgNtbMFgL8jc5K0hXHQO826+ah0PRhe1XIhvYS67veZYxgt3fS9DvTUDdfVHsB7N4Im1+yohPhLPNm/6Xjls+iy3hPDi8E5kLLk7D3flmGOgGQZ1MJAJqmZ6U/4sk0tD4DbS+KL7WiWtEJl6pZwiJ/KsTBamU7Sw8+JxasOl2IiwGYHYzzntkdAPzWiFpkhEVahET9c0SkwldXaDeDwi/Nm5IJit1jkddfYWPRlCAlHieheJptZR+DmpOJx0STu4HNmyqv2tKTXQlTWIjvjFvVcJpXkhmL4ajTxM/bXqGq8RWxj7RKKDmweRNsPgt7+bkrCJ4psO8f0PJUv9tLxh4pLCRAdhoEIBbtgb1/A4dPfKnJNMNyDZAKSSWT/D/XjaJZzqxlMG1+0cfx2WVNADy6t4KXWoIZYZE8IAyaCz8tJpUOE37psZBMUOzCIqsiJA8OVWHVrHIAXm9KwsIriXlmA+Cl7zh0AI+aSYW8ejBTrWVGLKKFIhDzjoDlJwLg3/o0FzmeBjKj2/srN4VMxGJrV45Y8taA6oU9d0Pnhn73IRlbpLCQANnGTYBY107Rvc+fuYgnrYiF3m/E4tLY/cxRW4i7S+GIUwZ1HIvKY1w87yA6Cp97fr41DtrjVGH+J0QaZBhZNk20EV48NTjAmhLJ+MLusZg2QMQCYPUskQ5Z39AJTj9R/1IAfHq71QLcjpkKCScdWV07MxGLfqo8Fh0NC9cA8CPnrZylrqMt5iq8vo2l5WZfGz9abtbDP5NUMirKUMMN/e5HMnZIYSEBsntYAMTC7VAyP6s0NGnzWDgLCYvWBt6riVDl3sXngbv/O6l8fHdtI0vLI7THXewJie2900+BqrWD3tdAfPmMRbz6rdM4eWHNsO9bIhlJsoXFwN8z02fxeoMQETHDUuWpWALRJoi3Za1vpkja4y6i6UxEr9vohZGZ/ZEnAqEosOJkmL0Ch6Lza9f/Mj0sGnUNJCzmlsZxqxqhlIP94ewBgn+ur2HFox/ixb067LwF4h0Dvu/RQtd1ntzSwv6u/BGgwwkpLCRA9sh0gLjuBWcga5mZ9nAXKjdNxOD1fwNwZ+oMtJrZQzoWn1Pj5nfspMyVcYB7alYNaV8DoSgKtcHBix+JZKyxmzeLiVgcNascVRGdOvd2RDLmzSnHwMz3Q/wgxFqt9QsNJ+vMSYXkHTYIQlysPo0nWYtHSfGd5G84QqkfMBXiUnUWlImL85ac+UFPNZUTSztYH1kK3Zth122QGh8X8o37uvnkna9x7X0bx/pQxhwpLCRAnlQIgT7r2FMhbitiYfsIbXwaoiH26FP4UerDmQZZQ2CWr5tfHvmkNdTIfhKVSCTZHotihEWp18Wxc0UjuUffbiZmtO33ul0w+4Mw6yJIdECsBejb32J+qbiAW6mQdP/lqgAoKj92XsFz6RX4iXOb+6dUpwZudrWwNAbArp5s0X8gIiIYcc0lGmgdfBH2/ElUsI0xB3vjWf8ezkhhIQHypELydNvrNxXStBMat6Cj8OXEVUTxWgOIBo2Wht7tvHNpHb++eBmfPXU+yw0vhEQiEdQERUOsqoCb0iKF9zkrxYyeRzc1EzO+8z6XQ6Q8Z74fZn8Ikl0QPdAnYnHmjC4AunPKTfvrSwHgdKlcmfwSb2lzqFZ6OG7HnRAL97vNtIDoZWEKCRPz94SmgNMvPGAHHoOmh8a8DNWc/pzUhn5DNVmQwkICQKR7X9bv/QmLPuWm8YgYhw4k5h/Nen0RMHC9el50HXq3Q3ABzPsk56+ex9fPXoIyQA8MieRwY1q5j1996Cj+79LVRW9z5jIhLF5v6GRvpzBJes2JgooKM94Lsy+BVC+exH5ru6ArxfFTRHlql+GxKGb2B4hUSRgfH09cS4NWiy/eBS/8HZKJwu/NL+76m2zCIpFWLAOo1WbcXQ7uSmj8K7S9NPAfYARJGk5TU2AczkhhIYF0jEjT81mL8gmLVFYqRHx5UmmEqIhHoLSKrvknAeBQdGvWwKCINIK7FOZ9AnxTBr+9RHIY8d6jpnPsvOLn5Ewt87LaKDs90C3SDVml1ooC08+DuZfh1nqtxWtrQlR6hA/LTIWEi2x4ZT7fRhkfS15LyuWH7lZ45Z8FUxhWxMJm3myJZqIyWYPRfFMBBXbfBd1b+z2WkSSZMs+JsoGXFBYS2P8vYj0iYhF0iZNHIt03QpCw9bEwUyGrYq+JNIiiwtqzCWviROB3pgdqtNmXeDtocZhzKZQtGeKbkUgk/XHOiuzmclbEwkRRoO5s1HmX4VbFhf+Y2l7KTWERd6LrGfNmf7M/gKyU6B69jgNHXQQOF7Q2CrN3nhSG2dbbHrFotv3cZzBaYC7EO6H+d6JMfgywUiEyYiGFxWFP9xbY/xARRZSimXclA3ksXKrOFDq4JPwX8eTS46C81ipBGyg82odURJS8TTsPagfX+0IikRTP2SumZv2etzmcokDdWVYDuWNKd1HuNm46NJVYWi36u577vKNyKhx7XmauyNvP9dlmmiEsOuIuYilx3jmQkxbpc7yliyC8W0xDTfb0e0yHip5HDJmCIpGSwkIKi8OZZEg4qlNhIoipoxX9CIusclNF4yeuWwjoUaiYIhrikDPZtFi0NIR2Qs3xMOvCAWeKSCSSoTOz0p9lhi7YdVZR+Ma7V3LVGoXVpfsJJPfhVMRFsytR/OyP3IiG35mGqXNgzRliwY7XYef6rHXK3GlrO1NQNEdtEYs8M4pQHFCyCDrfgF13Qrqwh+NQ2NsR4Zjrn+S3T+3MWp5Im92IZSpECovDFV2HfQ9C9yYILrAa4BQVsVB05rev4xTHRhK4YO3ZoIr1w4ONWOg6hHaIZlxzPwaOAqOfJRLJsHGOLWrRXzv7Dx0zi2s/cA7KvI+gpEOUu8XFuivutPWx6P8mIvdcYAmRWctgufBksfEZaKq31lGUvumQAxGbxyLP+QkQ54/AfDHnqPG+EakUWd/YycHeOE9tbc1anpKpEAspLA5Xut6EA4+Ctw5Ut3X3YY9Y5H4nk8aIZH+inSX7HgPgD+4LIFhprdNvm998RPeLRlzzLhezACQSyYhzts1n4R9oTo7huWDOpZS5RLVGV8Jp67xZfCrEoWR64ACwaC3MWSl+XvcwdDRbT03LERbN/aVC7LhKxHlt/z+h+Yn+39sQiKfyC4iMx0LPmyo5nJDC4nAk0QV7/gxa0rqYR3OEhaYrfdp1JzUFFY0ZW/6JU0vwsraUv6unZa0TKdLQJXbYIx6zLoby5Yf4piQSSbEsqC3hmnct4MpT5lMRcA+8gaLAtHdTXiJSKN2hUNE3EfaIRh9Tt6LAUe+CKXMgnYKX/gHhbiATsTArQw70Z97MxVMlblj2/Ak61vd5OpHSeGprK6F4Ks/G/WN6KBI51R/23w/3dIgUFocbug6NfxO9IkoyU0fNuw9TWEBOSRfCY/FJx8P4u/eRVt18NfkZEnr23U7EGI0cGKjrppYURqup74Kppx/KO5JIJEPgy2cu5hvnDKL6SlEoLxcl4F3RpDUReUDzpu1ckDe6oapwzLuhrFqUrb/4d0jErJLTpohIj2ZFLPIMP+yDbzpoCTGwLLQr66kH3tjHx+9Yx6+f3DHwfnJIDBCxyPfc4YYUFocb7a9Ay5OiY52aaQlstuctd6esNtqxVPbHY5bWxFedogqkcd6Z7NNrrfSISaa2vZ+IhdkEq2wZzP4wqHJkuUQyESjzC59DV2AtYWOUj88xgMfC9nzBLp0uDxz/PvAGoLcDXvkXM7yigVdTxE1Kg9ZYER4LO4oCJQsg1iwqRWKZVuK72kTnz70dkYH3k0OigJcimbILCxmxkBwuxNqg4V5AAXdF1lNRW1jTbOWbZeDU0vzYcRMeJUW8Zh6ddUcCkMzJdZrd+AoOJgKI7BUd8+ZeDu6yQ3pLEolk9Cj3iahBl3sxUaUcAL/e1e82A0YsrBWDcML7wOmCg3s5ufl+QOdAxM3BmAvNdhMT789jYUdRILgEerZB/e8hJQRFZ1hEQ3pjg0+FxJOGsMgpK7WnP2TEQnJ4oKWh4R4IN0DJvD5P20cge43hQ/a7An3rq6xQ99ClB4isPAujvD17CBmZiEXB8GiiG9JhMfSodOGhviuJRDKKlBsRi+5okogmBp/50s2Q6Cy4jf1cEHAN4L0qr4VjzgVFYcrBN/m84wGawm6acsanJ/KVmxZCdUJwEbS/Crv/AFqKDktYJAfYuC+JdNr4N9djkXmfh3v3TSksDhcOPgsHn4PAHFHvnYPdiOXNjVh0tsC2VwD4r+THcfgDVufNVE4qJGJFLPKcQLQURPbAlHdC7TuH4U1JJJLRxBQWXZGkNbjQX3eSqO5KdOfdxm7k9g0wsAyAqXPhSHF++IrrPs7QXmJHtxAxZUaTrqJSIXYcXnHua34S9t5vExZDN2/2nwqREQvJZCeyT9R0qz5w5Z8Smi8VEk+rwqn92qMous6/0sfyT+0E3LYhZLllXwWb5pj9KkqXiAmK0lchkUw4ynxCWHRGEkQM82Zg/gUw9Swx5ycZ6rONPS1adBn6vCNh4RoAfuK6ma79ogx1TomYb1KUeTMXVyl4amHf3+noESKo91CqQnJSIdK8mUEKi8lOOiFKS2PNEJhVcLWIzXSZiVgosPlF6O1A8/j5r+THAdHS2xxClluSWrDzZuwAOAIw9zLhr5BIJBOOcr9ISRzsjZM2PAU+rxfmfVREIsO7RHt+G/6cctOiWXEyz6lrcCtpLm3/PfOV/cwKij4ag45YmHhrwOGjIyz2M5RUSME+FlkeC5kKkUxmDvxb5BYD80Vv/gJYI5AdmVSIt3OvaLcL9Cw/i05KUdBxKBRMheTtvJkKixzszPeJShCJRDIhKTciFuZkVDAabDk8MP8TUHOyiEymbc/bzgUDNdPKQlH4U/lHWK8tIEiE21w/ZamvHYC0rjDUkRxJz0x6kkIgxZLaoKMLZqQipelodjEhUyEWUlhMZnp2wL6/g6scnP6Cq+l6ptzU79TwODX8xFiy6+9ihdnLCdcsAsRkU0XBSoUktewOnX0aZOkahOqh+jiYds5wvjuJRDLKmB6LiOGvcDtUnA7jMuL0w4JPQdWxopzcmNXhH0oqxKA6AJ9KfJVGrYbZaisXH7wND2K/gzJw2uhMOLN+7+0a3DTUuD3loeUXE1JYSCYnqQjsuRuSXeCb1u+qcU2xSrl8Tg2vQ+ebzrsJxDvBF4QjTrEGkJmCwmVry2uPWlgTD80Ss9Au8M8Uo9DVTB26RCKZeJjlpiY+d45XyhWEhZ+BiqOgdytoKbwOzeqNM6hUCDAtkKCDUj6e/Do9up/qcCM/dt0C6MWXnObQEc8+D4W2/wmSvUVvnyjQr6LQz4cjUlhMRnQd9v0DujZCycIBp4VGbY2w/M40q9Nv8xHnk2LBmjPB5ckamQ7ZwsLus4gkTY+FBvEOQBNmTV/2qGaJRDLxCHqdWacTf66wANEjZ+GVULoUeragkLYMnINKhZCZF1KvT+fK5BfRFZX3OV7ki86/kRiiz6Ijnh2x6GnbBrvuKHoaapawsP2cXW4qIxaSyUbXm9D0iBjEU8S0UDPK4FY1nKkYHwvdDcCmyhOgVhg+k/1ELLKEhZlSUeMQ3QdTzxBpEIlEMuFRVcWqDIE8EQsTby0sugpK5kLPVqsTb8HOmwUw54UAvKIvRz/yXQB80Xk/jv1bBnn0go5YTirENQdanhZ9fvSBjy+eykRdkgXEREIKC8mkItFpDBhLFD0tNGovEd34NBVaF7u0qbxQlfFE5AoLp5JfWISNWSH+eL0was76wIARE4lEMnEotwmLgNtZeEX/DFh4FfjqCKjCzDnYiIVdWNT6kqjzVnK7fi4AVW8/DG37BrU/gM6ciEWvViLmiuz/F+z754Cj1hMFohT29IdskCWZPOgaNPzFGDC2oOjNokaU4Wx1HTRuQUPhK8mrCOlea52MsBBfJGHgzC45TWkZQ1XA4xa+Clfw0N+XRCIZN5T5Mz6LghELk+B8WHAFPtfQPBZT/UnLnzHVJ0TGzcqFPJI+GkVPw8v/hFDXoPbZnuuxSDrAUwnuSmj8K7Q+2+/2djGRKFAJIs2bksnDweeh5Snwz8kaMDYQkZRKFd18gzsAeKnsXbyhL8yqFTdbd9tTIObPprHTTKkA+GafA2VLh/pOJBLJOMUescjrseizwUpWzKzFoWgs9uwe1Gu5VJ1an+g1YUYvXA74UvKzhEvqIBGzpqEWS5+IRcJ4D76pwmC++y7oeKPg9oXMmwmZCrGQwmKyEGmCxr+A6hn0YK9IUuWHrtuooBfKqnm16gwguwmNWflhFxZmOsSMUkSSirXcPePsob8XiUQybjFLTqFIYQH8+JLTWPdpHwsDByHWMqjXMwXFVONfj0MnhoctSz8kqtZCnSJyoRUXDWnP9Vgkbe/BPwvSUaj/nSjXz0O8iCiFTIVIJj7phCgtjTRBYPaAq+s63FtfzdsdordFRevbnO1YRwoHrDkbl1N8LOzTTc0ppnZh4XaYvSzEc+FeccLwexwozkwaRSKRTB7sEQufq7jIqKoqVM45DWZdBPF2o2KsOGaXiC6ZM41/3YYBNOQoNaahuoXXYv0ToOskNYWn9pcRS+X3dpkRi2qviIRkCQtFEZV0sVbYeZMYh5BDIY9FKqvcVEYsJBOdA49C+yvCV9FPd02TN9oDXPvKXL7y0lyI9LKs4SEAHvCeC+U1fYeQAUk9E40wMX9OaQoke4kmxBfV786udZdIJJMHu8ei2IgFIC7aM94D088XQ8uK7B1xzcomPr+8iffPFV03PaotUlpWDce8G1CgcTNsX8e3183m488s4o7tU/Luz/RYzDbmjtiFRUqDx/dX0OlZAaE9sOMmiLVlbV+o3DQreqHJiIWkCLoiCf6ztWX81Sd3bxU9K9yV/XbXtNPQK6IJ9T0etPWP49ZibNDm81TJaQB4nLZZIQaWedNWLmaaNxNpILyLcMlqQEQsJBLJ5GTQHgs7igqzPwhTT4fwbpF2GID5pTG+cuR+ytxmt0/bkETImobKphcI794JwHZjImouZsRitjF3xC4sHmqs5IpnF/L99XPEwMTuTVB/CyR7rHUKeiwKCI7DESksiuT6h7fwiTte44ktrWN9KBmSvUZ3zV7Rs6JImqPixPBB9SnU1gZSipOvJK/E6xTiwYpYpPKkQmwRC5eZCgkfgMAcIpWnAgOUoEkkkgmN3WMxYFVIPhxuMbTMbP2tDW4QmMeRZ7Ly/CPR568C4Oeum1il7OBgtG+nX13PNMgyUyyWeRPYY9x0vdRaKoycwSXQvg52/s4arhYv4KtIylSIhRQWRWIO3WnuHlhhjwq6Lkahd78NwQWD6hXREnEzS2nhm07RCOv5qndTr0+3aszNUGNc65sKyaoKsVIhwKyLiSBKS4d0spFIJBMCu7AY8k2EMwALroCyldCzFfTiy1A9ZsQiZ1bIQ2Xv5Yn0KjxKklvcP0eNdPfZNpRSrQq3WSV9IxZthrHzQMTNgYhLNBgsWSgq7nbdgZ6K9dPHwua3kKkQSTFEjaE7sfES4mp7GZofB99MUAfnaWiJOPiZ6yYCSpz9/nm8WHIykBkQ5HXmiVgYX0ZnVrmpkQopWw3VxxFOpAAISGEhkUxaygfTx6I/PJVirkjJHOjZNmBjKhO3eeNji1jEUgo3bJjNNcnP0+icQY3Sw3fiv4ZkPGvbjpgQRX5nmhqjjDVkExbtsYxoeqOtRPzg9ENgLjQ/SWLXn7L2Z4oMXdezxERivFwnxggpLIokmhTCwhQYY0r0ADT8GRCNXTQdnjtQ2qeMqhBH9zzLMeo2QrqXu0o+QiQtvkxm29185k2zV4XbLiwQ0ZtU1QmgKETiQlj4PTIVIpFMVg7JY5GLfzos+DR4qsQU5CIwIxb2WSF/rq9hf8RDuV/BecL5NOsVzFea0F5+KKsM1UyDVHhSBF1ieXbEIo+wANHozz+TxL7Hs47FjFLkDh1LaVJYSIrAFBax1BgLC6u0dJ/oww+80FzKZU8t5txHllPfM0CZZ08blyb+DsD3U5fxenS6bdS5EbGwzFGZO4JEzhAy4m1WKiSpCNNoxPgb+V0yYiGRTFbKh1oVUojSxTDv46KpX57yzlw8at/zk2nU/MC8NqZUevl08itEdA/qwQZ48ykrGmIKiypPiqBL3AhlCQtb86z1bYHsF3aXk3BnT4rOCAstZ7lMhUiKIGZEKuLJMVaiTY+INIittPRARHzRm6NuPvjEErZ05ndDo6XRX3sMDyn+kz6Ke9OnsqvHa80KMYWFaY7KF7FwqbqYQxLdj8tXAWS+VJG4+BsFZMRCIpm0lHoz32/fcBm1q4+BOZdAKgzxtn5XtcybNo9F1Oj6W+pK41Ch2TODa5KfE83Ad78FO9cD9ohF0opYhJIOzCxGm83w+VZHINsgCsQdVVm/J4wbzb7CQkYsJEVgRSySYxix6NoE+/4O7qqs0lK7AGiLufjQk0t4qyNP6em2V1G6WunSA3wz9SlAoSPuoskQJrmpkOyW3mYqRIPenVC2Aqe/VjxnqHPTYzEsdzESiWRc4nSoBA1xMazf9alnwIz3QuxAvz0uPHnOT2H7IEWg1pvkCW0NO2edJVZ461lo2ml5LKpsqRAdhXBKJZFW6EmK9+V1pEloKps7s8+jCS1baCTbN4Ku9WnhXaywiKfSaJPQ6CmFRZGMubBIdMGeP0I6At6pWU+ZEYfTp3eyujpEd8LJ91+flb19ZwtsfRWA/0p+HLwlVqvcrV3iy5ObConlERZOPSJEzZwP43IKQWJ+iUz/iRQWEsnkZmFtCaoCMyuK651TFIoCsy6EKadDeBek88//yGfezI26msbM18tOhrlHiJXWPYK75wAgPBYeh24Z0HsTTtqNaIZT0TiuVgibN9qz0yG5EYxk+5uw7x+kclLkydTAYqEnluTEH/2HK+56bcB1JxpSWBSBpunEjBRIdCyEhZaGhntEWVbJwj6lpaYAqPEl+elxYsjPhvZAZpx5KgmvPQq6xoGK5fxTO54p/gTzgsJ8aSp/v6M/YWEMIdNCMP1cKFuG2+h7YQqLsCUsZCpEIpnM3H75MTz+5VOYWjbMrftVF8y9DKqOgd5toKX6rGKZN22pEHMAojk9tcZo130w7hbNs2pnQzrF+5tvo452Kr0pFIUsA6dZEVLpTbGmJgTkGDhzXhMgqQSg4V6S+x7LXl6EeXPrgV7aQgle3VN8e/OJghQWRWAfOhMbC49F69P9Ti01BYDXoTE3GKPUlSKhqWzrMrwWm56H3g7wBni69v2AQp0/wbzS7DsCX065aVpXskaiA7h81TDtPACcqnhdMxViVoUEZOdNiWRSU+Z3Mb+mZOAVh4KrBOZ/SnS+7N0KevY515xRZI9Y5BrQzYmoB6MuUFU49lworaJM6+H37p8xxSmEg11YmD0sqr1JVlWHAVifIyziORGLhBoEdyXJfY9mLS/GvNnaK86/oXhq0qVDpLAoAnuUYtRTIaFd0PhXcPgLTi01W297HTqqAkdWiS/Fm+0BaGmA+g1ixTVn0pAsB2CqL8n8HGFhqn3TdW3fdzIhohuuimXgFILF5TCFhWHeNCIWw2bokkgkhyfeGlGG6psKoZ1ZPS7M81N2xCInFWJELFrN8lGXB054H10EWaY28I5994CmUZIlLDL+iyOrQijo7At7aI1mzme5TbmSmgreWhLObFNnMaMfWntEjw1dz1TUTRaksCiCLGExmo1PkiHYdZdwSftnFVzNbGTlc4jjNIXFjoPA60aIbt5RMGUOLRHx5ZmSJ2KRWxUCRpokHSWZEl8Cpz/zBTJTISlLWMgGWRKJZJgomQvzPgGqB6KZMlR3nnL4PqkQe8TCxF/KNxzXENNd1HZvh7eeyUQsEplUSI03SdClsahM3EzZ0yG5Hgvz95SzImt5MtU3hZNLa2+meVcoNvD6EwkpLIrA3hQrPlrKUteh8S/QtRFKFvXbsttKhTiFIBDCQuddrfdDLAzBSlhxEiBKUgHqfIVTIYqSyWPGUgqE6km6RAWIGaWATCokYVWFSI+FRCIZRipXw9xLhWndKEP1WKmQwhELKxVia3gF8EJiMV9Kflb8Ur+B96SfBHIiFl5xkTfTIW+024RFn4iFEdHNXd67Rxju+8FMhQCE4oOblzLekcKiCOzpj1Ezbx58HpqfAP9MMbSnH+weCxDC4n3qC7xDew1dUWHt2eAUX5pMxCLJNH8CryPzfky1b99XLHwQ3BUkffMAcDkyAqdPKiQuy00lEskwM+U0mJ4pQ83MChHnIl23V4Vkmzdboy4ri5JIK/QmnTyiHUt0sRhj8KHwfZyqvmGYNzMeC4AVlUJYbO/K9AXqUxViHEOfMtRYN2z9leiSXICDtohFj4xYHH6Musci3AB7/gyKC9wVA66eKyxq9XZ+4L4dgL0zT4GKKYD4AprNtKb6EqgKzAlmPtxmxMK+r1g8CjPfT1IVZWX2iIUpMqxUSNJskCWFhUQiGSbMMtTad0J4F25EmbwZsYilVXTEuSi33DSWdhAyREdXQggHh6LjWboGZi9HRec3rv/FG2qxohtVhrAwx7SHbTOTciMW5u/JXGGhlEDXm7Dl/0H3lrxvy/RYwGGeCrnhhhs4+uijCQaD1NbW8r73vY9t27aN1LGNG+ypkBGvCkmFYdedEG+FwJyiNjHVutehCfXw2mOUEOV1bSGP+s6w1utJOoimxUV/ql98eUwDp4JuTTUFWxOakpUw9XQrKuHMEha5VSEyFSKRSEYA1QXzPgYVa/DEG4HMrBD7hd+8OfI7NUqM6IXpszAjEhWeFKqqwKrTaPTOp0SJ8b4Dt6HFRKWIGbEwox/2YYy5VSGZVEjOct0Bpcsh0ijERctTfYasZadCDmNh8cwzz3D11Vfz8ssv8/jjj5NMJjnzzDMJh8MjdXyjwj2vNvKBG1+kI5zI+/yoRSx0HRr+Ap0b8varKETGY6GJ1rVt+0gqbr6U/CxvdpZa65lpkDJ3yvoCzguKD7ffqWW9nFcRajpefRqoLlKGeHDnSYUk0hrJdKb7nEyFSCSSYccVhAWfwl0iGgSaF/mozbyu2s5hZtSi1fCVddoGkAGgOnhy5keo1+ooT3fx7dhv8RGj2vBY+Iybq0g6cz6zDz6DfoSFpoDqgOAS0FOw81bY8ydIGb2DUmk6IxlfxWEdsXj00Ue5/PLLWb58OUceeSR33HEHjY2NvP766yN1fKPCX17by2sNnby8qz3v83YxEU9pI1dzfPA5YxT6DHB4it7MFBaVsQOw6QUAGuadSaM+hQ22znGmcXOqLyOgTAOnPQ1CKoRXNUKJzkoASzRkmTdtqZAu25dERiwkEsmI4JuKZ9b5ACTSZqrCrAjJjiZbTbKMFEd73GiA5cmcqzw+Dx9Pfp1epYSl7OEXrhup8ojzo3lOjGSlQgpFLLIvpeZsJRQFArPFGIa9f4Ot/w/CDVn+ChBdOCcTh+Sx6O7uBqCysrLgOvF4nJ6enqzHeCOR05I6l9zl8ZEoOQ3tzvgqPIX/nvmIp1XcJFm2837RpbNuHlOWLkJBZ3/Yw0GjDrvZ8FdM8Wc+xCsNg9J0v/FB1zUI7cLrFZ4KM/VjRizsqRC3LRWyo1W0wJ1V6cftlNYdiUQyMnjK5gAQTzsgesC68PtyhYUvY+CETMSi0pOJDgRdaRr1Kfy362riupOzHeuorX8KyAiVaB6PhTunl4YpMMyS/9wIBp4qCC6CzvWw6Ue0NryU9fRhnQqxo2kaX/ziFznxxBNZsWJFwfVuuOEGysrKrMfMmTOH+pIjhtnXvdBI9NxKkGFPhyR7DV/FQQjMob7Hy5XPzef1g8V1toumVL7s/Cv+SCt4fLDqDIJu3fJPbOwQUYtmIxVizggBWFAW46+nb+HGk+vFgnAD+KfhKRGGT/O9mh6L7FRIpqX39mYhLBZPDQ7pTyCRSCTFYN64JPBCopNoNAJAwJl9Xs6NWDSGPFnLAatB1sPhFXwt+RkAHDtfg90bLY9FJI/HImBsZ1aJmELCFCN9hAWAwwulKyAVpXXHI1lPHdapEDtXX301b7/9Nvfcc0+/61133XV0d3dbj7179w71JUeM3CFaufQRFgUEyJDQNdhzj3AQlywioalc/fx8Ht1byR931BS1ixXpbXza8ZD4ZdUZYEQbsjpwkkmFTPFle0mOrg0xLZCAZA9ocZh5ER53dsQiaaR/zN4VkIleJNMa21oMYTFFCguJRDJyeAxhEdccMO1swpFOoG/EotY4z5nmzeebhd9srTEHBMSYdRDVIw9qJ3K7473iiQ3/obRjp/Wcmf02K1ECOQLCTJH4rOUFLq2KAiVzOKhNy1ocisXzrz9BGZKw+NznPse//vUvnnrqKWbMmNHvuh6Ph9LS0qzHeMNMhRRKccQSuRGLYUyFtPwHWp4A/2xwuPnNpjpr2mhPogivQiLG95WbURWd0PQjYNp866mjqsQX6KUW8Tc3UyFT/XnyeXoawruh5iSoOQmvy/jyGiIqafxtXM78qZBtMmIhkUhGAUtYpDT02R8m6l8OQMCZfddvT4W0Rl1s7fKjoHPS1Ew6PujK3uYR3zkwaxnoOqVvPMgKZReQ8bGZAsKMdJgCImVFLAqkQnJoTYjzpEMxpqu2bIS2l0UqexIwKGGh6zqf+9zneOCBB/jPf/7D3LlzR+q4RpXBRiwKrTdoureIaIWzBNzlvNXh57ebMkq2NzXAf4+uo69/nDqlg3qtjuiyd2Y9fcq0HpyKxrqDQR7fV06zodyn+vJUv4R2i/LW2R8E1YHXJQxRlsfCmNbnstmunfZUSIsQMVJYSCSSkcTjFOcmXYeU4iFSIZpd+ejJGlhmT4U8d0DcXK2sjFDptXks3Nnn8mpfClafDrWzUNJJbnf/lBnKQSsdYlaFmMIikWPe7DcVYsOcYTIzIM7FvbEEbP0lbPkptL8G2sQ2cw5KWFx99dX88Y9/5E9/+hPBYJDm5maam5uJRqMjdXyjgtmHoZB3YkRSIfF22HWH8Ff4ZhBPK3zlpbmkdcUyUoaSA5RtNmxCadpJQnfwheTVeL3ZEY5ZJXGuWNoCwP+8Pov9YZFjnOrPERaJLkCDWReL4T9gRSwyHgvxN7JHLMwKkT3tYULxFC6HwtzqTBWKRCKRDDcel83zkNIIp0Uk1u92Qe92q1+E2da7Lebi2QNigOPJdd1Z+zJnhZhUeZOiTPTY86CshhqlmztcPyYeFedkU0gEciITRXksbJiGUtMHF9JLRZfljvVCXLz1PdH7ItFZ3B9lnDEoYXHjjTfS3d3NqaeeSl1dnfW49957R+r4RgUzzF+oXXc0kZ36OGTzZjohhov1boeg6Ffxt93VbO/2U+1N8t21DQCE+xMWvR3wpnAv/zx1MW/r8/A6+pbBfn5FE9P9cfaHPXQbqZWpPpsa1lIQaYDaU6D6WGux17grMEWU1SBLtZs3xcdnb4cQlvNrSrLKUSUSiWS4cdvOMYmUZkWQA5ULwBkQTanIRCzaY06eM/wV78gRFn6nhkLmvGn2sDCnoTbrlSxQm6hY/wCkU5bHosSV47FIZ6dCUnqRwsIYdBZKquLYy5aBf46Yar39t/DGN2D7jSJNEjvYp8nWeGVQDQf0CfKmBovpsSjkncgVEvFD8VjoOuz7O7S9ACULQBX/BQ29IprwntntzCwREYWCEQstDeseER/0ylnc0nQuDkXHpfb9//E7Nb6zppHPPLcQEGVSFbZyK8K7xHHMvhiUzBfWvCsw32syTx8L+9wQkGkQiUQy8qiqgsuhkEzrxFNpa5SAr6QW5l4GO2+BWCuVnlpURUfTFTriLgLONKuqsps5qopIa/QmxXm4ylYxgq+Erzq+xP+lb6C0ey+89hjJtDiPBnM8Fn0jFiq6XrjHYYthpDcbFJqvD4DTJ244tTQk2g0f3n/AVQq+aVC+ArxTRAmruwqcfjEB1uERqSA9DSgDzpgaSWQnIzIXzaJTIYcSsWh7CfY9CJ5aoVANehJCRJS7U1b+rreQsNj8InS1gttL6/Lz0ZtUq346H2fO6OJd07r4T1M5U/2JzIc93g6owleRM5PEjFhY5k0zFZKnpbfJIlkRIpFIRgG3QyWZThNPatnDD2tPEWX7DfficHip9iatzpvHT+nBnSeqW2oTFtXebG9Ds3Man4l9ibs9N6Du3857vA/yCJ/okwoxIxT2Jl1JTcn7eikt017cbFAYSuaJ9KoO8NaKh5aGZLdoB9CzBdBBcYLDJ/5VnaA4jIiGLsTGsmvFtmPAYR+3Tmu6VUpUUFjkmDWHPOE0tAt2/1FEBnL+w800RZk7bTMGqX1609PaCNtfEz+vPoOQU+QOvc7CURRFge+ubWBZRZiL54nRw2hJiO6DujOgck2fbXLNm5mIRV/zpskSGbGQSCSjgMc4PyXSGhHj/Oz3OMTJbsYFMPV0CO+hxpPxk72jLn9zRruBs9qbXSXid2q8pC1nyzxRhnpO7Ak+6nisTx8L09RpnxBdKB3SHnOho6AqujUEMpTMlLTmRXWIxokl86BsBZStFGMf3FXg8PN2ZwUb2kpExEJLQrQFUpF+djiyHPbCwrxgQj8eC2O5aS8YUrlpogvqbzOGi/Wtpuk2IhZl7pQ1PAdyfBbxKLz2qPh57kqYtsAqgzKHhhViZkmCh8/ZzOdWGGN8Q/VQugRmXpA3XtfXvNk3FeKWEQuJRDIGmOeeeNImLAyxgeoUKZGqY6hxZcyPJ0/t7rMfyDZw5kYsfMa5eFf5alh2IgD/7byL5bG3AHtVSP6IRT7MipBqb5JStxAyOkpWI66iUJ3gChJ1VPOh547n4udOopWZ4B5c5+aR4LAXFvbeFYUiFubyCr+73/UKYpo1uzdBcHHeC7k9YuFQM8o3ZPTBR9dh/eMQC0OwElaeIo4lZ2R6UcRaRT5uziUib5cHjxWxMMxIA6RCAm4H08t9xR+DRCKRDBHTA5ZIp4kkzFSI3acQgPmfpLbELOuMWdGBXEpswqIqR1hktfVefDSPOk/Goeictv/PrFJ29PFY+IoRFoZxs9abxKPquIz24AVT3wPwVoefUMpBQlN5fF/5kPYx3Bz2wiI7YpH/4mxGLMr94gNRqNx0e0sv3/nH27T2ZMbhouuw935ofQZK5ovxv3kwIxamgs34LIz/ot1vwYF6kUY5+hxwGseSNif7FSks0nGINUPdu0VIrQBmE5pYUkPXdVKaKSzyp0IWTQ2KUcQSiUQywlhNspI5qRA73hrmzFkJwLtqGgoaKc2IhVvVKMlJKWcJC0Xh146P8GR6FU49ye/dP2U2B0hrmQZZblXDqfRfcmoJC19SNOI0Xn/A9gIFeKMtM/rh31JYjA/swiI+gMfCilgUaJB1+wu7ueulBv7ymq1teeszsP9B8NWJRlgFsEcsIOfD1tMObz0jVlxxEpRn/BmxlG1k+kDoOoR2QvkRMOP8fseymx6LeCptGTch/xAykK28JRLJ6GHOC4nbPRbuvhfmy9+5mh+fXcrXl2+GyL68+zKFRY032eeU6LVGpxs3WrqTzyc/T3dgGpVKiLvcPyIZCVspEZeaqc5LFRQW4jpi9tkwU99DjVjYJ1i/2FJqFQKMJVJYpDIXzYE8FuWmsCjQ+rszLD4o+7uMhmHdm2H33aIUyFNd8Bg0PfOhKjMiFkHjwxaO61ZpKbWzYMHqrG0HlQqJHRCpjzmXCNdwP9jNm3bx5S6QCpGlphKJZLQwu2+KiEWeVIiB3+3kg6eeTGDBxZAKG5Vw2Zjmzaoc4yZkUtJRW+fNCF52rvgQu7SpzFDacL10P86UiFLbhUWiwLyQTMQikfX6hxqxcKsaSU3l6WbpsRhzEunBeCxc/a5njr5t7o5BpAnqfw+pHp7pXskpD67kxZb8F9/epAMdoW5zIxaz9zwB3QfB7YM1Z/WJMsQMV/KAwiIVhUQHTH8PlC7qf13Aa6ZCUmnLXwHZ6Q/7zzJiIZFIRgvLvJlKWxHlfBELi6mnw/Tzxc1VMpT1lBmxyDVuQiYVEjG8bmaVnsfn46PJ62jRy3H0tnFVz814SGQJi4HMm1bEYqD2Av1wIOKiOerGoehcsuAgAI/tL3wTO1pMemHxi8e3c97/Pldw3v1AVSHJtGalAioD/Zs3ey1hERFNWkJ7ILiIfzVW0hDy8khjRd7tzDSIz5G26p5LXBpnquuYf/BlsdKaM8HXN5VSVMTCTIFUroVpZxdez4YVsUiks8SXvfOmW0YsJBLJGGCZN1Ma4WKEhaLCrA+IPhfheuE1MziqKoSq6Bxd29tnM58lLMwhZJnzbTNVfCzxDTSnm8WpnfzS9VtcSnrgVEgkW1gEjdcYSsRigxGtWFwe4X1zRDTm6QOV1nVhrJj0wuL+N/bx9v4eNu7ryvt8MitiofXpLmoXEVYqpIDJszcmPigtnd1iDHpwMSgOmsJiO3NWRy49lnEz81ozlVZ+6rpZ/LJwDdTNy7tttBiPRWSvmAEy5xJRDVIEAcMI1dQd48O3CnHjcigotohJdYmHE+ZXce4RdVSVFLdfiUQiOVTsE04zEYsB+j063DDvcqhYA73brEmiJ07tZeMH1nPVsuY+m5im+JiVCjEiFg4Nl6qzVZ9F21EXksDJOY51rN73oGXeTBSMWBgeC++hRyxMf8VRVWGOqAozxZcgnHLyUtuUQe9rOJn0wsKc85Eo4IuwCwvoOzrd3sOi1OfMWpZLKCYiFh1xJ3HfQqulalNEXHT3hfO3WLX3sABAS3N59x2UKRGaPLNg+YkF318mYlGgu0oyBKkQzLwQArMK7ieXedUlXHrsLNwOlZ2tInToVLM/Lqqq8KcrjuO3l6zOtwuJRCIZEdyGxyKSSFkR1X4jFiauICz4FAQXCHGhZyLE+TA9FpF0dsTCbUt59JbP4VfeT6LpCrPbXuNT/B3IH7HQdDgYFdcRK2JxCB4L01+xqjqEqsCZM0TfjseaZwx6X8PJYSAsxMW6kLBIpApHKABihjDxuRyZwVz5hIWuE4pmykxbk+XmYvYbgmJf2JN3hkxuRQibXmBGooFu3c9fqj4muq4VIN5fKkTXRNiv5gSY8s6+z/eDqir88IKVvHTdu7j27CXMrwnw7pV1g9qHRCKRjARmxKIzkvFF+IoRFgC+qTD/U2LWRqi+/1VtHouUBmmjm6bboVlp66Sm8Jy6lu+kLgfgcu1BLnE8mddj0Rl3ktLFsVfnRiwGWc2R1BQ2dggTvjkD5cwZXQA80TyDdL+tPEeWSS0sdF23BtTkRiJMciMWudEI83ef25EpwcyTCkk3P00klfkgNUeEmGiPOy2VG0k56Iz3DddlRSwO7IIdrwPwteRn2Ef/Rpx++1iEdoN/Fsz+sDXsbLBUlXi46tT5PPmVU/n5xUcOaR8SiUQynJjlpl0RUVnhVJU+nYD7pXSRSIsoDog2FVzN3sfCXuXhcWSbNBOawh/TZ9A44x0AfN95G2VtW/vsr8WoCKn0JC1hYppHrWaIRbKty0cs7SDoSlkzR46b0kupK0lbwsvGA9FB7W84mdTCIp7SrAhBsamQXP+EKSy8Lgc+d6ZSIouO9YR2ZI+OP2AIi6ac9Mf+SN90iBmxmOVog9cfA2BL1fH8Wzt6wPBYQY9FohNIiwFjvrHNt0kkEslwYkUsjBJ/n9uR5f8qiupjxVTnZDfEO/KuYjdvJmxzm9yqZnXMTKQVKzrRNOsUHjO6cy7dcT+0ZffOqO8R3Ylnl2TMo2Yfi8GmQt6w+StMT71L1fnZMdt4/NSHOGra2HVCntTCwj48rOiIRU7zK7NGumAqpHsL7PwdoXj2dqYyNf0VJvtCfU2O3QknTlJc3n07JGJQXsuOGWcCA3/Y8s4K0VIQaYQp74Lq4/vdXiKRSCYaVsQiKiIWgYGMm4WYdg5MO1cMZEyF+zxtRoKj6UzEQlV0nCq2iIVq+SlcDp3bvB/msfRaHHoKXvwHdLZY+9veJS72S8ozA8JMj8VgzZsbbP4KO2dOb2dhsGfwQmsYmdTCIpK0C4v8hstEOsdjkbNezJYKMednWOmS3nrYcRMk2gm552dtZ6ZC9udELPIZOLsTDr7svI9Z8T3gdMOx5+L3iA/FQB+2vOWmoZ0QXCTKq8bwwyWRSCQjgdkgq8vwWBRl3MyHooqobu07xHlTS2Q9nWmQ5bB6WLiNSIXblgoxIxZuh47DoXJN8nO0lcyBVAJeeAB6RURkW7cQFovKM2mKoXoszIiF6a8YT0xqYWEaN6GfVEjO8tx23WZVidflsE381CC8F3bcKPJzwcWEUtmKuTlaIBWSp+R0eu92Put8UPyy5gwIlFsu5WIjFpbHwhowdim4y/vdViKRSCYiGfOmEAJFGzfz4fDAvI9D+ZHQs02Y3g38tlSIaZQ3BYUpMITHQjznUnWcqk4cN8/P/YgYv5CIwvN/g0iPFbFYXNZXWAzGYxFLKewy0ipHSGExukSGkArJjVhY5k2Xw9Y0Kgk7/g/Cu8XocUXNDAszyKRChLBYVCZCX30iFtEQl/XcBUB99bEwXXTFLHYwTdasEHPA2LRzoXxlv9tJJBLJRCW3KmTIqRATdxksuAICs6F3u1WG6ssyb2Z6WIBIe4DoV2FGLJyKbgmOmOKDE98vplFHQ2jP/Y1wSJgs7RGL4BCGkPUY66qKTqUnf/PHseQwEhb5UyF9PRb5zZs+lwOfVRWSgp7tEFwqXMVkPhTmh6o5x7x5dI3Ig+2zRyw0DdY9TKkeYpM2m8a5p1tPFfths1Ihqia+EBWrROtamQKRSCSTFFNYmJHoQ4pYmPinw/xPil4X4QaxyEiFJDSVqBFRMKs5rJkgaTUrFZLV0tvjg5MuBH8pariLu9w/Yo6ni2rbXJLMub74y7F5XQg40+PyVD+phYW9dLRQKiQ3ktG3j4Wt3DQlerEnNJV0ydKsEk7zP9os+2mNukQPC8O8eYzRLnZ/2J3pZbHlZWjbTwQPVyevIejN/HeYEYtwykE6/6GL4zNnhaRbRF323EvBOXZuYIlEIhlpTPOmyZA9FrmUL4e5HwU9BbGWrDJ+sy2Ax7h5tAsIy7ypajhtpk5AjGI46UKizhKWqo3c4vyZ8F4Y2KPTxbaeCBsip1Bjr7FmcguLolIh2f+ThfpYeJUo3j03W8tjenZKwxQW80tjKOgkNJUDETdtxsAZU1j0Jp0ijNXaANteAeB72ifYo9dlOm+S+bBB5kOUDytiofeI7pol+Vt/SyQSyWTBNG+aDNjOezDUnAQzL4B4Gx6tGwVxjegy2gKYEQszOp3I8VjkHUJWUs4faq+iSw+wKL0LXv6nmFhN5lyvoxBOZV+SdR2+8tJcbngju5Omaeq3XyfGE5NaWNhTIcX3sSjgseh9E28k0/Akd8iL+R9d4UlZ43dN167PkWaqL2l1WjvQnoR1jwKgz1nBX5InAbbOm4gGLOYHN/fDlnV8pseiagVMPa3gehKJRDJZ8IxUxAJEGnnGe2Hq6SiR3VY6pMtobpgbsYjabvz6m276fHQeH0tcS1J1Q2sjrHsENA2Pqls9MXJT361RF3/bXc3NW+qyItdhWypkPDKphYW9KqRo82ausAiJ9IdP60AtW5Yx5hQQFiWuNFN9Isy1/qCoM54WSKAoMD0QR0Wj5u0HIR6B0mp6l56GZrSJLXVnm3CKMXAa40nwzj53yN01JRKJZCIxYqkQE9UJcz8CVcfiU8X5vCuR32MRsd349ScstnX5eFNfwK7lF4sxDU07Yf3jKOgFPXX2fdurRsz1gjJiMfpEi+pj0U/nzbZXiLW9DYAvUAmKw+oXkSss7P/RU/2GsDAamEwzfp8RSPA5x9+p6t0NDiccey7daS8gnMa5g8RMNVqwl0W8g7gmnvOV1OZfRyKRSCYZI5oKMXEGYP4n8LuEQOg2IhZWHwuHGVHOjlg4lb7CoivuoMVoQTBtzhQ45lwRGWncDBufpsQp7hB7cnpZxG1txO3PhQzBEZDCYvQpKhWSyuOx0DVoegR2/B9RQ2h4nbrxb6b8yI4pLEqcaaYYU+s2dYoBMdMDQlicoG7iC86/iQ2OOg2ClbYBZH1LhvrtZaElSEf2kzCEhVkKK5FIJJOdEY9YmHhr8fnLAOiKiBLR3IiF/VrgVHRLcKT0jLDYbjTGmh6IE3RpMG0+rDlLPFm/gc8pfwX0Puf6mD1ikewbsZAeizFgMC29zZKdWCIBu/8Eu+4ExU1UER8q0x1sRizi/aVCjAiFaeiZ5o9DLMIFbX/Eoei84DkOZi8DMirU7q8wyQ2P7Q+7+fCTi3liXxn07iAWyPSqMJt3SSQSyWSnj8fCM3I3Vj7v/2/vzuPkKstEj/9O7dX7ll6TTgKENSEJ2YhBUYgGRMCgohiQD446YDJsDoPLReZzZxTRERHkgjrX0Rl1dJhLRFB0YkCQMSQhCTuGDAnZ052t02ut571/nDqnzqmlt1R1V59+vp9PPnSfOl113lST89TzPu/zGh8Qu+JGxsHsY2E2yjJr4AIeHU1zLkM1mR037Y2xaD/L+IAJXJ34Hbd4H8tqkmXPjNsz133WB1lZFTLmHH0scuxICunAoiJoZA4GjrwG+x6D4BQoa7OiUbNRSnoqxDl/1psjsDC1lkXgxd9RlujhLb2Nf9KutR4bPGPhDCx+u6eWDR1V/HR7FfiribRdbZ0b8knGQggxOYxZxsL23F20GK+N8e97usbC6/g+11SI2XHT3hgLgFPOhXMvBOA2//9j6r7nHQ9HbfeZHslYlAZHjUWeZhBmjUVVwPhliPYfh4pZRk8Isltmh/PVWCRsNRapqRDToq4/QududI+P1fFbeLuvynrM2jI9xy+I1UM+9Uu0N7WB2bGIF6atJBKeCRj/k3k8JdglRQghiiAzYxH2F69w3QwsTiSNVX4BvRuSESuQ6Es1tjL7V5hTJfapkJwZC9Np5/FExRUAzD30X/A/W62H7PcZx1SI1FiMH8dUSDz3GxBPGJmCSo4aP+OpdTSYytyWPOgbvHgzM2OxUPsL7XueMV7r3IvYoabSHfdZUyAjyVjsTXXxPJqogZYPWCtYQj5Xv41CCOEQzKgpKy/mVEiqMLRrwPjAGCirg5638GvGv795MxapbINSsL3LmE453barqd3G2ou4P3GV8c0rz8LOlwHnfaZbMhalod++CVmujEXvLuLHtwNQlapxiCSdv6ADGRkLc+VGdvGm8X2lP128WUs3Dwa+h6YUTDuT4MyzqQsaj5m7npoZi6ph1Fjs6zFe42g0iNJ8VuAkhZtCiMkk4B27qZBwqn7thBlY1J4NFacSiButCOw1FmDryJnKWHQO+DkR8+HVFKemOjNnqvAnuT/xEf5c9T7jwEtPw+7XHbV89t1PpcZiHOWtsUj0wd618NrXiUe7AagKGn8VmQGDWZVrNknJtdxUV84IstKfpNwX59v+R2jRjkFFLcy/GDSNqakVIuaeIcPLWHhQkaPs6y9PXZNOfyxpLaGVwEIIMZkE/WM5FWI8t7kVQzBcAad+Gr/f6KqcmbHw23Y9BdiTmsKeWh7NailgMj5EajxecSWcOs84uGUdLUdfsc6xT4VI581xFIlnrArRk3BkE7z2j7Dr30DTiHmMVR9VfjNj4fwrsTIWWcWb6fP6Ex4Uxi9Rpd/YFObmwJNc5H2JGD5jzbLPyFC0lUeB9C6nJwZZFWLVWMQ0jpw4ykAy/T/Psb6Y1XMjLIGFEGISycxYFHcqxPncQa8Hqs8iMOU8APpSJXU+j3MZqrl/iDmFUZPjw6Op0qqn88G574UZcwDFsn2PssKzKfVY9lSI1FiMA0cfi3gM3vwWbP8O9L0DVWdBuNWKKisDeQILs8bCLN70ZS83Nd9kn6YT9Co4eoDP6Ea/iv+s+CjUTLHOnVZhBBY7u43GWMPKWESi7PXOdzx2tC+WrrGQpaZCiEkkq3izmKtCMj64mStS/DWzgHR3TH9GYGG2GxhOPYSjUF/TjAx3+9l40HnQ/yDv82wzgo6UPttigVLk6juSvcYiGo/Dsa0QboPKM8BjpLHMwCJXxkIpGEjVXGRmLAZyNC6p8Oto8Qhs+i1edJ5Ins/O+kWOa5pf3wfAxs5KwN7HIjuwqEw1yOrTw+wrW+547Ghv1Fr1klnIJIQQbqZpmmPJaXkxOm+mZAYt5usGvMbx/qRxLzFrLHwZLb3NjEVljqy0KWv7Bk2DBe/nzYp5BLQkj/jvZ3rfm9b5Zk2f7BUyVpSC/gNw4HcMDPRYh2O6F1V1NvgqHKebW9uab7o9YLCvIc5skJWrcUmFLwFbfg8DPfSH6vjn8LV8cPpxx+stSe1y+taJMg4P+GwZixxTIZpR/9GjNbB3oNLx2FHbVIjUWAghJpugbTqkmNPBWVMhqZ5Bfp9xfzD3evKrflDKapxlToUMJ2NhFerbG2RpHtbWr+J3yUUEtTire34AHe+glKwKGVudzxvTHS//L/ifHzCQSAcGCi1rUxgwtrwFqPIbGQN746sBW/BgBhTBHIGF+SZf6/k9HNwJHi9l77qUxz/0Nuc19Dlery6U4KzUkqMXOqs4Ec+TsdATlCf2Gs+fDLHvuHOZ0jHbVEhYpkKEEJOMWcAZ8he3j0/mihNrKiSjzsPv1aB/j5WxMO8t5mqOwaYtrBqLjL1C+nU/fxP/G36fXEiABGz4NdEDe6yaPnPbh1LjnjtSMga7fwFHNoKvAr1qjqPYESA6SGBRaVtualb/mtvhBjw65u9Qrs6bPXEv52pv85nEfxoH5rwHavJvCra0ychE/Lmjku58GYvet6msbjW+jCbYe8xorFJbZqTdjvZGbTUWkrEQQkwuZgFnMadBIHvFifm6WYFFWRPoEfxJ4993M2NhZrQHCyzMKY2+zFWJSY04PtbEb+ZPzAM9SXDz41zgeRWPpqxMeqlxT2CBApWEUBME64jo2TfbWDJ7uGYTkyrbm25OgWSuCLF/bd8cJhqJ8ZD/AXwkofU0OGXuoFe6tMmYDnl6fw3JVBrNkbGIdIA3QMVMoxtbbyTB3lTGYu60GiCjeFPaeQshJhmztqyYhZuQP2ORuTLFH66HlksJxDuAdI3FcLY4L0vdV/oT6Q+2kF4kEMfHbfrN0HwKmp7gn/3/xEW+V6w9rkqNiwILp37HjnO5Nw4DW42F7U03AwprnxBbVJhVY6EU83avZZrnMEe89XDe+xnq3V7c2INHU9Y2ugGPbu2eSmIAIp3QdjkVTecah3TF3mNGYDEvFVgcc9RYuPZtFEKInMyVIcVsjpXr+fNOhfg80P4xfDWnAyPLWJgfWJPKOWVvn3I/Hg+iFl9Gd91phLQ43/N+Bzr3jHZYReXaO1J6mWjStkQ0+4Zvvolhn241NjHfzEiOjIXZ4MR6w3e+zMye14kpL7+svx4CoSGvrTqQZHZtv+37VLZC6dC7A+oXQdvllPm9VoyiK/B5NM5uMfYZOdpry1gU+X8sIYQoNQErsCjuVEjmVLMZ0GRuhOb3esAXxj/1gwDEEs69ngYrtLTfY+y1ffbMeFJpRAjw+ulXsz45nxAx2PA4HN47mmEVlesDi7BPt6p0zXXFdmaNRcCjZy0lzexhYf86kvTA8Q549TkAvpH4JCfK24Z9fWadBdjqK/regbJWmHkdeIN4PBoVtv9pWmvCNFYZgcuxvhiRhEyFCCEmp/HPWDg/qFq1F+VGfV1ceSDSMayMhd+jrA+2zpWJzntWT9xLjx7kpvitvOidDckE/PlXJRdcuDawMNuslnl1ayXHYBkLv1dlTXPkqrEwNyPT4lHY9BvQk7wems2PkpdQMYI1xc7AIgGx46AS0P4JKEsHKBWhdGAxtTZMfbkxfXKkN8pATJabCiEmp7HKWGQ+fzDPVIjPqzmOJ7QwRA/TEzO+H2ppqHn/6c/T8gCMvke9cS8x/Pyfis9B0wxbcLFvZAMrIvcGFsnhZSzMGouAR+XNWIS96V8I4xzF52P/Bn0nIFzJTyquBbRBG6BkWjSl16r9qPInoH8PNC+HKcsc51UE07/U02rLqK8wAotoQud4v7HviNRYCCEmG7OfRLEzFlkNslKNsXJOhZAOMGLKDw3n0xtLLRAY4v5gFnA6pkJy7KJtbtMeDHjg/MuhcXoquFgLR0ojuHDtHSm9eVj+jEVSx1qV4ffoWe26c9dY6HzS+zTv0zeD5oEll9GZNOoeRtKspNyvc269UWdRrR2D6tnQ/rGswk97xmJaXZiygM8KJPYfN5agyl4hQojJxpx6KHpgkVlj4c/Xx8K5WiSRVKjpq+hJGB8Gh7o/hK0PtunXywwseuJea2ql3J8Erw+WXpEOLv77V3D04EiGVxSuDSzMdFLIlzT27yB7vsrc1haMOS5rS/SMVSH2GovqgYPc7ftX45tzlkFdi9UtrXKEzUoubDkBQGtFAmZeC/7KrHPsGYuptWUA1Jcbu+Xt7zICC5kKEUJMNuYNvthTIQGfB5+tAVe6j4WWdZ5xPLVENKkT9TeSUKmVh8mOQV/H/ABrr7GwPtx604Wg5j4h1pbpVnDRDsk4vPBbOH505AMtIBcHFukai0AqMMicCrEv6/F7FCGfc7+QrBqLRIypr68lqMV5Tp8LsxYAo2+v+tlZb/Oduc/y1xeeDlVn5DynMiNjAVjTIb1RYzWJTIUIISabsSreBOd0SL4+FmbwYU6FJHRF94Cx9amGoiyxH5KR/K/hy1VjYXw9JWw8T0/cm/t+4/XB0itTwUUCtm2Eg6+NfKAF4to70oB9KsSTeyokbstgODIWWTUWurEHybb1BPqPcVDV8YX4jda0RXqvkBEEFkqnLLKDlfNaqJ65Iu9pmTUWAHWpAk6TbEImhJhs5k2rxevRrKaBxVSWI7DINxViP3683wgIKkJ+PPXnQe//4OiAZX+NjA+2xtfGPWZKaIjAAozg4vwroKENyiqgZtrIBllAxc0hjSN7tsF8o7KmQlIZC5+m49HSc1w5+1jsfh32/gWlafxNdA2HVTVxXcPvUaPLWPTuhPJ2mPFJa6fVXMpTgUXQ52FKpTEFkhlYSI2FEGKy+eSSdlbObyt6500wp1uiQP4+FrmWoR7rMwrsq0J+Y7q7f59RqF8+Pes1MleFKGVsMQHQEDKy0z2xdGBRnut+4/PD4hXQvx/C1aMaayG4NmPRb2UsktZUSOZeIWYPC39q1Yi13DQjY9GaPAgvPwNA4sxlvKjONM5Lao6d5gZbp+wQPQYomH4NhJsGPbUyFVi01YbRUhmShoqg4xypsRBCTEZjEVSA89/YQTchyzhuBhYVQZ8RTLR/DJL9EO8mk7UqJDWNH7PdrxpsGQtzP5HKfBlynx/8gdyPjRHXBhbmmxP26QQ9eYo3MwILax8QW41FkBgf6vipMW/V2I7vjIXWz0cSHgaSHmtlybAyFnoMBvZBywqoXzzk6eaqEHMaBLIzFlJjIYQQxeOYCslTvGktN7UVeh5LtQSwauUaLzT+9O0C3Xm/CGcsN7VPiZg1Fr2OVSGluQEZuDiw6LfVR1jFm5k1FmYPC68zY5FeFeLlbt+/Uh89BMEyWHgJmkdzbJ1uZis0lBVx5qUU9OyA6nNg2lVD7ikC8N4zGjmjqZKPLphqHavPDCyk86YQQhSNGVgEvB4rc6xpmiO48KUCC/vx42bGwgwsPF6Y/nGoOAX6djpeI5wxFWJ+EPZoitrUtg89cS99o1wsMJZcG1iYwYHRxyJ3xiJrKiRj59LZfdv4pO9pFBosugRC5UD6FyCa9Dj6wA8ZJ0QOgr8qtbS0YljjOL2pkt/f9h4un9tqHTNXhZjGKh0ohBCTkVnHlq8pFkDAFmSYx82pkMqQrY4uWA/tHze+jh1Pv0bGclMzYxHy6lbzxUGLN0uIewMLW42FtSokY7mpmcEwe7Q7MhZ9XXyq7+cA7G69wGhAkmI/b9j1FYl+iB2FqVfmXVo6XHXlGTUWkrEQQoiiMTMWwUECC/vX5nSI2R3ZvroPMDaabL7YKOTUjWyEuSpkwMpYGM8R9OrW/cVeYzGiVYhjzLWBRbpBlm5NdeSbCrFqLFIBQzyhozb9lnIG2KyfjjpzqePn7EWevfFh9IFXOvS9DfXnQ8slJzmy7KmQoNRYCCFE0ZhZ4cEyFo7shc+ZsagKZQQWmgbTPgKVpxtLULF13kytBElnLFQ6sBhqVUiJcO0dySzedLb0Hrx40wwYVpx4Eu14B12qnNviq5lalXD8XMhW5Dmcnevo2wOhZphxDXhPvlrXPhWiadlRtBBCiMIJ+43AIGuJqWP6w1Zv4cmcCsnR2SFQDdM/YbQbiB7NapAVSdinQox7UMdAwJiaZ+SdnseSa+9IVo2FVyfgMTtvZmYszC3T06tC3ufZxvKBpwH42/iN+MorrcDDlKt4M2/GIt4N+gC0fxTKpuY+Z4Ts+4WEfF6rmEgIIUTh2Ys37eyBhiN74cso3sycCjHVzoOmi2BgH2GPsfLDmgpJZdSNqRDjnmO28/ZqyrHVRKlxbWBhrQoZVvGm8QZVJ7v4tv9hALbXL+UP+gJmVmW3YA3nDCxyvMkqaSwrmvJumPKeAowqzdwvRJaaCiFEcZlTIZnTzvmmQvxmxqI/R/GmnabBtJVQcRplSWPzMGu5acJeY+HMmpf7hrFYYBy59q404Ags8k2F2GoslM6i3Y9Sp/Xytmcav6r8MAAzKrMDC/v26oMWb/bugvIZxvIiT2ELLM3pEGmOJYQQxZUvY5E3sEh9HYkb94qKXFMhpkANtH+UUEbGwr4qJPOD67CbMY4T17b0tjYh8yWtqY68UyFeBW9upK7nHXpViK/5bsTbaywHnVkZzXpuszHJ915vobUslerKfKNjXUbGYvrHITSlYOMymQWc0s5bCCGKK+9yU9v3AZ+t3sLnvNfkrLGwq19E2ZTXgdyBhbGXVdJq8V3KhZvg5oxFcjgZC+PNn5P8C/xlIwBfiX+aXaqFXd0hIHfG4rpZnUwJxdjTG+KFziogI7DQk9C/25g7q19S2IGlmEtOZQMyIYQortObKwGY1VjpOB7IUbCZ+TVAZTD/flAAaB7CbRcBMBB3Tt2bU/n2LEXOqfcS4srAIqmn35Qyrz2wyN4rpI5uPtv7Y0BxrPlcHtcvoC/hYU+vceOemSOwOL+ph2cuf5U15xywCkObw7H0CX07oeJUaP/IsLprjkZ6KsSVb6EQQpSM89pr2fjli/n7K85xHM+73DRjymTIjAUQrmgAoD/phUS/I2MBWE2ywKixKGWunAoZsGUmjAZZ5lRIRsYiAf/kf4Qa/QRU1nH8zPfDO9A5YNy0Ax7dmurIVOHX+du5+/nEaYd5sbOCS9pTHdRixwEF7VdDsK7gYzOZUyHSHEsIIYqvqSqUdczZu8Le3nuEUyGkC0QHkn7o20kkcQpgCyxswYTUWIwDc0WIhiLoVXkzFmcd+W+Wel8ijg//4ssIeJ1/He0VUbxDJASmlseYOvOY8Y2eNDqptV5mdFYrovY6Y1OyxqrgEGcKIYQohqGKN02DFm+mlPnNXU09JH1VRGMDQJ6MRYkHFq7Mo5sFLmGfjqZh67xpG+6xQyw+/BQAT9ashOoGq0GJKVd9xaD6dhqbyxRxCsT0/rOb+O4n5vGlS88q6usIIYTIzVGwOUgXzuAwMsv2PZ8GGi4hGjPuP7lrLCSwGHP91j4hRqBgZSzMqZB4FDb9Bi86v0kuZluF0bI7s+FIrhUhecVOADpM+ygEak9uAMPg83q4cl4bzdXZ6TkhhBDFZ6+l8OXpwlmZrzlWhqDPY30e7a+/mIinHrBlLOyBxVA7aY8zVwcWYa/xRlibkCU1Y+vyreugv5suXx1fin8Wc2FFVmCRozlWTkqH/neMRlgNxVkFIoQQorTkK9i0Hx9OfQUY262by1ojKkwkNBOAkGbchyokYzG+7M2xIGMqZNersH8HaB4em3Id3ZTjTz3u86S7cMIIpkL6dkPZNKNtt+bKv1IhhBAZ/HlaetuzF8OprzCZjbj64wmiPiNjEUweAaUcGQupsRgHZnOscMZUyHS1D175o3HSOct4xzcDwFoyCs6sRa6lplkSfZAcgGlXQajx5C9eCCHEhJAvS2E/PmQPCxuzk3J/LEkk1c8i6PdD9LCjeLPUV4W4MrAYyKixCHgUYSLc7/2esXKjaQbMWpC1uymkA4uQN0lTOD74CykFvTuhYTFMuaDwAxFCCFGy/Pl2N7XXWIwiYxGJJYkkjOAhVHcGRA5S6U3fj0q9j8WoAouHHnqIGTNmEAqFWLJkCZs2bSr0dZ0U+86mYGQs/t73r8zy7EcFy2HhCtA0q69FrsBiRmUUz1ALOyIHIVBnFGx6XLlyVwghRB5mlsLn0Ry7TNuzFyOZCgk7MhapwGLKPChrp1J1pJ/TbZ03f/nLX3L77bdz9913s3XrVubOncuKFSvo7OwsxvWNilm8GUplLEIH3uDjvj+iK42++ZdB0OgBkStjYU6fDDkNkoxB9Ai0XQYVMwo8AiGEEKXODCAy+1bYp0Kq8u1smoPVJCueJJpIfTAOVULbh6j09Fjnua5487777uOzn/0sN9xwA2effTaPPPIIZWVl/OhHPyrG9Y2KfQMyeo/jffkPADyYXEl/7XTrPGsTMkeNhRFkzBhqqWnfTqg+B1o+UMhLF0IIMUGYm5L5MzptOoo3h7ncFNIZi4FY0toZNeT3QuO7qahuSz+nmwKLWCzGli1bWL58efoJPB6WL1/Ohg0bcv5MNBqlu7vb8afYIqmMRaU3Bpt+i5aIs1k/gwcSKx0bkeXKWJjVtoNmLGLHQfMZjbB8ZUUYgRBCiFJnZiaydj0dxXJTgLKAce5APEnUnArxe8EbonLahdZ5rqqxOHLkCMlkkqamJsfxpqYmDh06lPNn7rnnHqqrq60/06ZNG/3VDpM5FXLpiV9DVycEQnyZm0jidQQWuWosbjr7IFfNOMKl7cdyP7nSjbbdje+GmrnFG4QQQoiSZmYqMnczHW2NRShXjUVqo8mqpnPTz+mmwGI0vvSlL3HixAnrz969e4v9kvQnPSz3bGFR95+MAwtW0OWpAYwdTU25MhbvaenmvnftojJfcczAfgi1wNQri962WwghROky+1j4fc57gaPz5ghqLMpy1Vik2oE3VIZZNqOMDzTvJ5Q8fFLXXWwjWsrQ0NCA1+ulo6PDcbyjo4Pm5uacPxMMBgkGx3ajrGC0m2/5v298c9p50HKK1W8911SI2UBrSMkoxLrgtM9AOPd4hRBCTA75ijftGYyRTIVYxZuxRFbGwuPR+Nlfvxd27oT9+yFYX7IfbkeUsQgEAixYsID169dbx3RdZ/369SxdurTgFzcqus61x39MrdbL0XAbzDb6SwRSy0gdGYukmbEY5tKdvl1GwWbT+wp7zUIIISYcq8YiI7Cwd+Qc7l4hYCvejCeJJGzFmyZNg5ZLINgAkdzlB6VgxFMht99+Oz/84Q/5yU9+wptvvslNN91EX18fN9xwQzGub+R2vMJpiZ30qDAvzrwaPMabEvRkZyzMGouAZxgZi9gJ0LwwbSV4ZeMvIYSY7PIvNx3dVIiZseiJJEjqqc6bGYWhlLVB83KIdoIqzVqLEXd1+vjHP87hw4f56le/yqFDh5g3bx6/+93vsgo6x8WRQ/D26wB8Kf4ZPlReCxwHbDucDrEqJCeloH+3kamonVfwyxZCCDHx1JQZQUN12Bk82KdCRrNXyPH+dJdNR8bC1LwcOv8E/fuhvH0klzwmRtUucs2aNaxZs6bQ13JyejvhpT8D8JT33TypL+Wj3u3WwzmnQvRhToVEOowOm1OvKNk5LSGEEGPr/FPq+ccPz2bJzDrHccdUyChWhXT1x6xjWRkLMOorWi+Ft/8v6C3gGX5WZCy4Y68QXYfH10AsAhXVPKBdA6T3CoHcUyHDKt7UkxDtgJb3Q/n0/OcJIYSYVLwejWvPn86spkrHcb9tP4jywMgzFsf6jMAi6PM4WoU7NF4IFTOhf98Ir7r43BFY9HVC1x6jnmL+BRxOlAPO7mTpqZD0m5Srj0WW/j1QPgNaVhT+uoUQQriOWXNREfThHXLTqbSwlbEwpkJyZiusF6kwCjmTfaDH8p83DtwRWFQ2w2f+AIveS6K8lqMRI0KcEkrPU5nLTWMjqbFIRo03re1DEKgpzrULIYRwFXMqZCTTIJAu3uyNJoA89RV2U5ZBxSnQV/z+UCPhjsACIFgB9U0ciwVQaHg0RV0wkX44VUcR1W2rQpLZe4U49L8DVWfJluhCCCGGzSzmbKgYWQ+ncEYgMWRg4Sszai30iPFBuES4bq/vwxHjjawPxrGvAApYGYvBO29aEr3GapC2y8E7tg2+hBBCTFxzp1bzv688h7lTa0b0c2UZ9Rhmc6xBNSyFg/8FfXugctaIXq9Y3JOxSDEDi4ZQwnE8c7mpriChBqmx6NsNtfOhbkERr1YIIYTbaJrGp5bOYO60mhH9nFm8aTLbeQ/KG4LWDxp1FslBNs8cQ+4LLKJGYDElHHcctwKLVJYiblt2mhVYxI4bb1bbh6wGW0IIIUQxZU59DCtjAVC/BKrOgP7SqLVwXWBxJBIAnIWbkO6uaRZv2gMLR42FUjCwD+rPh+qzi3y1QgghhCEzYzFkjYXJGzBWLuqxklgh4rrAwsxYNITyZCySQ2QsokfAVw1tH5RmWEIIIcZMZiAx6HLTTPWLjRqL/j0FvqqRc19gEck9FWJmLMxVIfHUfz2aShd5KgWRg9B4gdF4RAghhBgjXo/mCCaCw81YgLHIoET6LbkwsDA2CMucCsks3ozlWhES7TRad5fImyOEEGJyCdumQ0LDKd60a1gClaePe7bddYHFkWiqxiJf8aY5FZLZw0Lpxja0Te8zdo8TQgghxliZLUsx7OJNkzcErSvAX1PYixoh1/axyFu8qTuLN62MReQQhJqMXeOEEEKIcRCyZSyGtdw0U/35ED0GoSkFvKqRcVXGIpr0cCKee1VIZsbCsU+I0iF6GJougnAJbP8uhBBiUrKvDBlxxgLAF4b2j4CvvIBXNTKuCizMaRC/R6c6kHQ8ZnbejCZzZCwGDkKo2QgshBBCiHESdkyFTMw+Su4KLGzTIJm1K+ZeIbGMBlkBjw6xo9B8EYQaxu5ihRBCiAxhW1vvES03LSET86rzSPewSGQ9FsyTsQhoMaO2ovG9Y3ORQgghRB5h2/SHZCxKQL6lpgCBVI1FLLPGgpixEmQcC12EEEIIcG5ENqoaixIwMa86j3zNscC+V0hGjYXPC03vHZsLFEIIIQYRkhqL0nIkmnupKUDQ45wKiaVqO/2hagg1js0FCiGEEIMocyw3nZi36Il51Xmkt0zPn7Ewp0LikR4AAqGasbk4IYQQYgj2VSEjauldQtwVWOTZMh1sxZu6B6Ur4rFeAPyB8NhdoBBCCDGIk2rpXSLcFVjk6boJ6dbdutJIRI8R81QA4PfKDqZCCCFKQ/hkWnqXiIl51XkcGUbGAiDW10E8fAoAfq+r/gqEEEJMYGUn29K7BLjmrtoXTdCXMJbp5KqxsDYbA6JaNfHyWcZxCSyEEEKUiPDJtvQuARPzqnM40hsDIOxNUO7Tsx73esCnpQo4q+cT99YCkrEQQghROqSldwkxA4spoWjereitXha1S4kljakRv09qLIQQQpSGsCw3LR2HU4FFQzCW95yAZrT6joZPIZ40ggzJWAghhCgVzt1NJWMxrg73RAEjY5FTMkLQa3TFiiWxAgupsRBCCFEqpPNmCTEzFlOCeQKL/n1WWimaSBI3p0IksBBCCFEizL1C/F4Nr2diTtX7hj5lYrACi1Ak+0E9AckBQsEy6FWcGIgTk6kQIYQQJWZqbZizWqqYUV823pcyau4JLFJTITlrLAYOQNlU5s1oYvvRQ/zX6x0kdSneFEIIUVr8Xg+/vfkCtHyrECYA13xcP2xbFeKgdIh3QdNFrFwwA4DfvHqQ3qhRyCk1FkIIIUrJRA4qwEWBxZF8gUX0MAQbYMq7WDyjjraaMD2RBM+9dRiQqRAhhBCikFxzV22sDNIYijgDC6Ug0gENSyE0BY9H48p5rQD0pfZNl8BCCCGEKBzX3FXX3riITR/8A1PLBtIH493gq4ApF1iHVs5vc/xcYII2IBFCCCFKkbvvqpEDUDMHKk+zDs1qqmR2W5X1vexuKoQQQhSOewOLZGrZadN7yezxvXL+VOtrKd4UQgghCse9d9WB/VA+E2rmZj10+dwWzL4jUmMhhBBCFI4776oqCYl+aL4IvIGshxsrQ3xswTSCPg9ntVbleAIhhBBCjIZrGmQ5RDoh1AT1i/Ke8vWr5vCPK2dLxkIIIYQoIHfeVaOHYcq7IFCb9xSvR5OgQgghhCgw991Z4yeMJaYNS8f7SoQQQohJx32BRaQDamZDxSnjfSVCCCHEpOO+wCJQA43vyVpiKoQQQojic19gUT4daueN91UIIYQQk5K7AgtfGTS9D7yh8b4SIYQQYlJyz3JTTwBaPwh1C8f7SoQQQohJyz2BhaZB88XjfRVCCCHEpOauqRAhhBBCjCsJLIQQQghRMBJYCCGEEKJgJLAQQgghRMFIYCGEEEKIgpHAQgghhBAFI4GFEEIIIQpGAgshhBBCFIwEFkIIIYQoGAkshBBCCFEwElgIIYQQomAksBBCCCFEwUhgIYQQQoiCGfPdTZVSAHR3d4/1SwshhBBilMz7tnkfz2fMA4uenh4Apk2bNtYvLYQQQoiT1NPTQ3V1dd7HNTVU6FFguq5z4MABKisr0TStYM/b3d3NtGnT2Lt3L1VVVQV73lI22cY82cYLk2/Mk228MPnGPNnGC+4Zs1KKnp4eWltb8XjyV1KMecbC4/EwderUoj1/VVXVhH7jRmOyjXmyjRcm35gn23hh8o15so0X3DHmwTIVJineFEIIIUTBSGAhhBBCiIJxTWARDAa5++67CQaD430pY2ayjXmyjRcm35gn23hh8o15so0XJt+Yx7x4UwghhBDu5ZqMhRBCCCHGnwQWQgghhCgYCSyEEEIIUTASWAghhBCiYFwTWDz00EPMmDGDUCjEkiVL2LRp03hfUkHcc889LFq0iMrKShobG/nwhz/M9u3bHedEIhFWr15NfX09FRUVfOQjH6Gjo2OcrriwvvGNb6BpGrfeeqt1zI3j3b9/P9deey319fWEw2HmzJnDiy++aD2ulOKrX/0qLS0thMNhli9fzo4dO8bxikcvmUxy1113MXPmTMLhMKeeeir/8A//4Nh/YKKP97nnnuPyyy+ntbUVTdP41a9+5Xh8OOM7duwYq1atoqqqipqaGv7qr/6K3t7eMRzFyAw25ng8zp133smcOXMoLy+ntbWVT33qUxw4cMDxHBNpzEO9x3Y33ngjmqZx//33O45PpPGOhCsCi1/+8pfcfvvt3H333WzdupW5c+eyYsUKOjs7x/vSTtqzzz7L6tWreeGFF1i3bh3xeJwPfOAD9PX1WefcdtttPPHEEzz66KM8++yzHDhwgKuuumocr7owNm/ezPe//33OPfdcx3G3jff48eMsW7YMv9/PU089xRtvvMG3v/1tamtrrXO++c1v8sADD/DII4+wceNGysvLWbFiBZFIZByvfHTuvfdeHn74Yb73ve/x5ptvcu+99/LNb36TBx980Dpnoo+3r6+PuXPn8tBDD+V8fDjjW7VqFa+//jrr1q3jySef5LnnnuNzn/vcWA1hxAYbc39/P1u3buWuu+5i69atPPbYY2zfvp0rrrjCcd5EGvNQ77Fp7dq1vPDCC7S2tmY9NpHGOyLKBRYvXqxWr15tfZ9MJlVra6u65557xvGqiqOzs1MB6tlnn1VKKdXV1aX8fr969NFHrXPefPNNBagNGzaM12WetJ6eHjVr1iy1bt06deGFF6pbbrlFKeXO8d55553qggsuyPu4ruuqublZfetb37KOdXV1qWAwqP793/99LC6xoC677DL16U9/2nHsqquuUqtWrVJKuW+8gFq7dq31/XDG98YbbyhAbd682TrnqaeeUpqmqf3794/ZtY9W5phz2bRpkwLU7t27lVITe8z5xrtv3z7V1tamXnvtNTV9+nT1ne98x3psIo93KBM+YxGLxdiyZQvLly+3jnk8HpYvX86GDRvG8cqK48SJEwDU1dUBsGXLFuLxuGP8Z555Ju3t7RN6/KtXr+ayyy5zjAvcOd5f//rXLFy4kI997GM0NjYyf/58fvjDH1qP79q1i0OHDjnGXF1dzZIlSybkmN/1rnexfv163nrrLQBefvllnn/+eS699FLAfePNNJzxbdiwgZqaGhYuXGids3z5cjweDxs3bhzzay6GEydOoGkaNTU1gPvGrOs61113HXfccQfnnHNO1uNuG6/dmG9CVmhHjhwhmUzS1NTkON7U1MRf/vKXcbqq4tB1nVtvvZVly5Yxe/ZsAA4dOkQgELD+5zQ1NTVx6NChcbjKk/eLX/yCrVu3snnz5qzH3DjenTt38vDDD3P77bfz5S9/mc2bN3PzzTcTCAS4/vrrrXHl+h2fiGP+4he/SHd3N2eeeSZer5dkMsnXvvY1Vq1aBeC68WYazvgOHTpEY2Oj43Gfz0ddXZ0r/g4ikQh33nkn11xzjbUpl9vGfO+99+Lz+bj55ptzPu628dpN+MBiMlm9ejWvvfYazz///HhfStHs3buXW265hXXr1hEKhcb7csaErussXLiQr3/96wDMnz+f1157jUceeYTrr79+nK+u8P7jP/6Dn/3sZ/z85z/nnHPO4aWXXuLWW2+ltbXVleMVTvF4nKuvvhqlFA8//PB4X05RbNmyhe9+97ts3boVTdPG+3LG3ISfCmloaMDr9WatCujo6KC5uXmcrqrw1qxZw5NPPskzzzzj2Ha+ubmZWCxGV1eX4/yJOv4tW7bQ2dnJeeedh8/nw+fz8eyzz/LAAw/g8/loampy1XgBWlpaOPvssx3HzjrrLPbs2QNgjcstv+N33HEHX/ziF/nEJz7BnDlzuO6667jtttu45557APeNN9Nwxtfc3JxVfJ5IJDh27NiE/jswg4rdu3ezbt06xxbibhrzn/70Jzo7O2lvb7f+Hdu9ezdf+MIXmDFjBuCu8Waa8IFFIBBgwYIFrF+/3jqm6zrr169n6dKl43hlhaGUYs2aNaxdu5ann36amTNnOh5fsGABfr/fMf7t27ezZ8+eCTn+iy++mFdffZWXXnrJ+rNw4UJWrVplfe2m8QIsW7YsawnxW2+9xfTp0wGYOXMmzc3NjjF3d3ezcePGCTnm/v5+PB7nPz1erxdd1wH3jTfTcMa3dOlSurq62LJli3XO008/ja7rLFmyZMyvuRDMoGLHjh384Q9/oL6+3vG4m8Z83XXX8corrzj+HWttbeWOO+7g97//PeCu8WYZ7+rRQvjFL36hgsGg+vGPf6zeeOMN9bnPfU7V1NSoQ4cOjfelnbSbbrpJVVdXqz/+8Y/q4MGD1p/+/n7rnBtvvFG1t7erp59+Wr344otq6dKlaunSpeN41YVlXxWilPvGu2nTJuXz+dTXvvY1tWPHDvWzn/1MlZWVqZ/+9KfWOd/4xjdUTU2Nevzxx9Urr7yirrzySjVz5kw1MDAwjlc+Otdff71qa2tTTz75pNq1a5d67LHHVENDg/q7v/s765yJPt6enh61bds2tW3bNgWo++67T23bts1aATGc8V1yySVq/vz5auPGjer5559Xs2bNUtdcc814DWlIg405FoupK664Qk2dOlW99NJLjn/LotGo9RwTacxDvceZMleFKDWxxjsSrggslFLqwQcfVO3t7SoQCKjFixerF154YbwvqSCAnH/+5V/+xTpnYGBAff7zn1e1tbWqrKxMrVy5Uh08eHD8LrrAMgMLN473iSeeULNnz1bBYFCdeeaZ6gc/+IHjcV3X1V133aWamppUMBhUF198sdq+ffs4Xe3J6e7uVrfccotqb29XoVBInXLKKeorX/mK4wYz0cf7zDPP5Pz/9vrrr1dKDW98R48eVddcc42qqKhQVVVV6oYbblA9PT3jMJrhGWzMu3btyvtv2TPPPGM9x0Qa81DvcaZcgcVEGu9IyLbpQgghhCiYCV9jIYQQQojSIYGFEEIIIQpGAgshhBBCFIwEFkIIIYQoGAkshBBCCFEwElgIIYQQomAksBBCCCFEwUhgIYQQQoiCkcBCCCGEEAUjgYUQQgghCkYCCyGEEEIUjAQWQgghhCiY/w9ErpO1sdIpfgAAAABJRU5ErkJggg==\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mean, cov = kalman.smoothing()\\n\",\n    \"mean = mean[:, 0]\\n\",\n    \"std = np.sqrt(cov[:, 0, 0])\\n\",\n    \"plt.plot(observed_sequence, label=\\\"observation\\\")\\n\",\n    \"plt.plot(mean, label=\\\"estimate\\\")\\n\",\n    \"plt.fill_between(np.arange(len(mean)), mean - std, mean + std, color=\\\"orange\\\", alpha=0.5, label=\\\"std\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### 13.3.4 Particle Filters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"observed_sequence = np.concatenate(\\n\",\n    \"    (np.arange(50)[:, None] * 0.1 + np.random.normal(size=(50, 1)),\\n\",\n    \"     np.random.normal(loc=5., size=(50, 1)),\\n\",\n    \"     5 - 0.1 * np.arange(50)[:, None] + np.random.normal(size=(50, 1))), axis=0)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def nll(observed, particle):\\n\",\n    \"    diff = observed - particle @ np.array([1., 0.])\\n\",\n    \"    return 0.1 * (diff ** 2)\\n\",\n    \"\\n\",\n    \"particle = Particle(np.random.normal(0, 1, (1000, 2)), system=np.array([[1, 1], [0, 1]]), cov_system=np.eye(2) * 0.001, nll=nll)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"mean, cov = particle.filtering(observed_sequence)\\n\",\n    \"mean = mean[:, 0]\\n\",\n    \"std = np.sqrt(cov[:, 0, 0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(observed_sequence, label=\\\"observation\\\")\\n\",\n    \"plt.plot(mean, label=\\\"estimate\\\")\\n\",\n    \"plt.fill_between(np.arange(len(mean)), mean - std, mean + std, color=\\\"orange\\\", alpha=0.5, label=\\\"std\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mean, cov = particle.smoothing()\\n\",\n    \"mean = mean[:, 0]\\n\",\n    \"std = np.sqrt(cov[:, 0, 0])\\n\",\n    \"plt.plot(observed_sequence, label=\\\"observation\\\")\\n\",\n    \"plt.plot(mean, label=\\\"estimate\\\")\\n\",\n    \"plt.fill_between(np.arange(len(mean)), mean - std, mean + std, color=\\\"orange\\\", alpha=0.5, label=\\\"std\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"observed_sequence[50:90] = np.nan\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"particle = Particle(np.random.normal(0, 1, (1000, 2)), system=np.array([[1, 1], [0, 1]]), cov_system=np.eye(2) * 0.001, nll=nll)\\n\",\n    \"mean = []\\n\",\n    \"std = []\\n\",\n    \"for obs in observed_sequence:\\n\",\n    \"    p, w = particle.predict()\\n\",\n    \"    if np.isfinite(obs):\\n\",\n    \"        p, w = particle.filter(obs)\\n\",\n    \"    mean.append(np.average(p, axis=0, weights=w)[0])\\n\",\n    \"    std.append(np.cov(p, rowvar=False, aweights=w)[0, 0])\\n\",\n    \"mean = np.asarray(mean)\\n\",\n    \"std = np.asarray(std)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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FIykYGIhLJdGH4KCR7INha6T2ZHOzKmAXLIWSZ13wBWHlO5fZprKijpGYAUKQiIpGMAZmakUimC0OHQJ+l/pB0UigfACvOruiunBHO0LvRFBEZiEgk5SIDEYlkujCwS/QPmY3+kOMvQiYNVfXQNIMVn9HMqgCYMjUjkYwBGYhIJNOB1IBQRHwzwLA5Hmw1ZNmmmR1oleMRMRHKlkQiKQsZiEgk04HoYUj3zc5AJNoPfR2AAovWVHpvzozRqmZAdFfNxKZmfySSWYAMRCSS6UD0kJD7Z+PE1rYXxb/Ni7PNy2Yq5aRmVA304anZH0nFePvb3+7MoZGcGTIQkUgqjWlC3w7wzPCLdDFME9r2iseL11Z2XyaCclIzikcqIrOIo0ePoigK27dvz3n+m9/8Jj/+8Y8nfftzIeCR5bsSSaVJnIbYydmZluk7LVIzmgcWzJBeISNRbmpGj0/N/kgqRv6EW8n4kYqIRFJpoocgMzg7G5nZaZkFK8Drq+y+TARO+e4IqRlFA32OKCKmCanhyvyMcV6rYRjceuutLFu2jGAwyFlnncUvf/lLAPr6+rjppptoamoiGAyyatUqfvSjHwGwbNkyALZs2YKiKFx++eVAoVJx+eWX83d/93fccsst1NXVMW/ePG677TaGh4f567/+a6qqqli5ciV33XWX8x5d13nHO97h7NOaNWv45je/6bz+T//0T/zkJz/hN7/5DYqioCgKDz74IABtbW3ceOON1NbWUl9fz/XXX8/Ro0fH+B84PZCKiERSaaJHATPrP5gtGAac2Ccet86CtAy4PCKjKSJJkb6Zbf+n+aRj8IWWymz7E6fAV34689Zbb+VnP/sZ3/nOd1i1ahUPP/wwb3nLW2hqauJ///d/2bNnD3fddReNjY0cPHiQeFyoWk8//TTnn38+f/rTn9iwYQM+X+mA+ic/+Qkf/ehHefrpp/nFL37Be97zHu68805e97rX8YlPfIJ//dd/5a1vfSvHjx8nFAphGAaLFi3if//3f2loaODxxx/n//2//8eCBQu48cYb+fCHP8zevXsZHBx0AqP6+nrS6TRXX301F110EY888ggej4fPf/7zvPKVr+SFF14YcR+nIzIQkUgqTfQQaLPQH3J8LyRj4AvCvCWV3puJoVyPiKmLYMQTnJr9koxIMpnkC1/4An/605+46KKLAFi+fDmPPvoo3/3ud4lGo2zZsoWtW7cCsHTpUue9TU1NADQ0NDB//vwRt3PWWWfxj//4jwB8/OMf54tf/CKNjY28853vBODTn/403/72t3nhhRe48MIL8Xq9fPazn3Xev2zZMp544gn+53/+hxtvvJFIJEIwGCSZTOZs+2c/+xmGYfD9738fxSqH/9GPfkRtbS0PPvggV1111Rn+xaYWGYhIJJUkE4f4KfBEKr0nE0d/F+x6GDqPi98Xr81ewGc65bR4VzTQU1Z31VkeiHhDQpmo1LbL5ODBg8RiMa688sqc51OpFFu2bOGf/umfuOGGG3j++ee56qqreO1rX8vFF1885l3avDk7QVrTNBoaGti0aZPz3Lx58wDo7Ox0nvuP//gPfvjDH3L8+HHi8TipVIqzzz57xO3s2LGDgwcPUlVVlfN8IpHg0KFDY97vSiMDEYmkkiQ6IB2FYIXk7Ynm2B547h7xWNVg+Vmwfuwn9GlLuakZMzM3uqsqypjSI5UiGo0C8Ic//IGFCxfmvOb3+2ltbeXYsWP88Y9/5L777uOKK67g5ptv5qtf/eqYtuP15pbfK4qS85ytXhjW8XPHHXfw4Q9/mK997WtcdNFFVFVV8ZWvfIWnnnpq1M9z7rnncvvttxe8Zis4MwkZiEgklSTeAUYctFlw5zw8ADvuF49bVsKmyyBcW9FdmnDKMauqrtSMZFqwfv16/H4/x48f56UvfWnRZZqamnjb297G2972Ni677DI+8pGP8NWvftXxW+j6SG39x8djjz3GxRdfzHvf+17nuXxFw+fzFWz7nHPO4Re/+AXNzc1UV1dP+H5NNTIQkUgqScLqODqT256DqGB4/j4xT6ahBS54zcz/TMWwUzMjekQ0EYjMBUVkhlBVVcWHP/xhPvjBD2IYBpdeeikDAwM89thjVFdXc+jQIc4991w2bNhAMpnk97//PevWrQOgubmZYDDI3XffzaJFiwgEAhNWurtq1Sp++tOfcs8997Bs2TL+67/+i2eeecap1AHhV7nnnnvYt28fDQ0N1NTUcNNNN/GVr3yF66+/nn/+539m0aJFHDt2jF//+td89KMfZdGiRROyf1PFLLd0SyTTnOgRUGaWw70oR16ArjbRL+Tcq2dnEALlTd91AhGpiEwnPve5z/GpT32KW2+9lXXr1vHKV76SP/zhDyxbtgyfz8fHP/5xNm/ezEte8hI0TeOOO+4AwOPx8K1vfYvvfve7tLS0cP3110/YPr3rXe/i9a9/PW984xu54IIL6OnpyVFHAN75zneyZs0atm7dSlNTE4899hihUIiHH36YxYsX8/rXv55169bxjne8g0QiMSMVEsU0x1iMPYUMDg5SU1PDwMDAjPzjSiQjYmTg+Q+JAWkz2SMS7Yc//wz0NGx+Kaw8p9J7NHnEBuHuH4iA5LXvL76MacLgbtjwSajfMrX7N8kkEgmOHDnCsmXLCAQCld4dSYUZ6XgYy/VbpmYkkkqR7IL0IPjqK70n46f7BDz1BxGENCyEFbPrwltAWWZVRQQjMjUjkZSFDEQkkkoR74DMMARbK70nY8c04dB22Pmw8EtUN8J518zelIyNU4ZsjtywTFFlakYiKRMZiEgklSLRIS7oM7HHxsHnRRACsGgNnHMleGbh5OB8VFfgYRiglbLZSUVEIikXGYhIJJViuC33wjZTyKRh3zPi8bqLYO0Fs18JsXEHjYYBJWNIRZbvSiRlMgPPghLJLMA0IXoQtBnYUfX4HkjFIVQNa86fO0EI5KZiRuolIhURiaRsZCAikVSCdD8ke2dea3fTgAPPiccrz5mZis6Z4A66RuolYiIVEYmkTObYWUQimSbEOyATnXmByKlDooOq1w9LN1R6b6YeRSmvu6qigj48NfskkcxwZCAikVSCRAcYadBmUDMz04T9z4rHy88Czwza94lELXPeTCY2NfsjkcxwpFlVIqkEQ4dLl35OJ3pOiTJdVRX729chFIEVZ1d6zyqHUuYE3rkUiKQGQJ/Cz6uFwDcxbdYllUcGIhLJVJOOQt/z4Kur9J6MTH8XPPZrUSXjZvF6CEz/iauThp2aGXHejAf0+IRt0jRNZ3LrtCM1ALs+B8nuqdumvxE2fmpKg5GjR4+ybNkytm3bxtlnnz1l250LyEBEIplqBvdCoguqVlV6T0oTG4LH/88aYrcQFiwXlTKmCavPq/TeVZZyUjOqNmEKwdfu3cevnjvB/73vEpqrpmFbdT0mghAtKJSKqdqeHgPOPBB5+9vfTn9/P//3f/93xuuSjA8ZiEgkU03fdsAAdZo2AEsn4Yn/g0QUqurhouvANw0vgJWi3NSMnhCB2xkqGfftOc2pgQQ72ga4cv00/n/QQuCtmpptTaDaJKk8MyBJLZHMItJD0LsNfA2V3pPSvPAQDHSDPwSXvE4GIfmUlZrRhBnZSJdepkySGbGdWCpzxuuay/zyl79k06ZNBINBGhoaeMUrXsFHPvIRfvKTn/Cb3/wGRVFQFIUHH3wQgKeffpotW7YQCATYunUr27Ztq+wHmMVIRUQimUoG9ohhd1WrK70nxYlH4fhe8fiC14imZZJc1HIUEY+YNWMkz7gyKpkW2xlOjtRATTIS7e3tvOlNb+LLX/4yr3vd6xgaGuKRRx7hr/7qrzh+/DiDg4P86Ec/AqC+vp5oNMprXvMarrzySn72s59x5MgRPvCBD1T4U8xeZCAikUwlfdvFv9M1LXNou7jTb1gIjQsrvTfTE6ePyCiKiKlPSHfVhFREzpj29nYymQyvf/3rWbJkCQCbNm0CIBgMkkwmmT9/vrP8j3/8YwzD4Ac/+AGBQIANGzZw4sQJ3vOe91Rk/2c7MjUjkUwV6UHom8ZpmUwKjrwgHq86t7L7Mp1RyuwjYmYmpLuqVETOnLPOOosrrriCTZs28YY3vIHbbruNvr6+ksvv3buXzZs3Ewhk05IXXXTRVOzqnEQGIhLJVDGwB5I9ovRwOnJsjzCqhmthwbJK7830xU7NmKOlZnSRmjlDbI/IsFRExo2madx3333cddddrF+/nn/7t39jzZo1HDlypNK7JkEGIhLJ1NG3Q/yrTsOMqGnAwefF45VbZkaztUoxhamZjG6QMUwAhpMyEDkTFEXhkksu4bOf/Szbtm3D5/Nx55134vP50PXcoHLdunW88MILJBIJ57knn3xyqnd5zjANz4gSySwkMwz9L4CvvtJ7Upy2F7MzZJbMwRkyY6Gc8l3VCkTOMDVjqyEAsdTYUzOxVIa97UNsaa1FVSe5IdpUdVYdx3aeeuop/vznP3PVVVfR3NzMU089RVdXF+vWrSORSHDPPfewb98+GhoaqKmp4c1vfjOf/OQneec738nHP/5xjh49yle/+tVJ+DASkIGIRDI1DO4XTZgiKyq9J1kMHY6/CEd2QN9p8dyyzeCZpkba6cIIisg3d7YQy6h8/Ow2oTKNoohsO97Hwc4ob9jaWvR1dyAyHkXki3e9yE+fOMZ33nIur9w4f/Q3jActJNKNye6p6+/hbxxT87Tq6moefvhhvvGNbzA4OMiSJUv42te+xjXXXMPWrVt58MEH2bp1K9FolAceeIDLL7+c3/3ud7z73e9my5YtrF+/ni996UvccMMNk/ih5i4yEJFIpoKBveLCr06jQXFP/xFOHRSPVQ0WrYa151d2n2YCJTqrPnCyhn/dKSqN3rG2g2aFET0ipmny3tufp30gwdmttayaV9gMLJHOqiDjUUQOnI4C0D4wiQGCr0a0W5/Gs2bWrVvH3XffXfS1pqYm7r333oLnL7zwQrZv357znGmaY9pNSXnIQEQimWyMNPQ9B95p1JOj44gIQhQV1l8ESzeKBmaS0XEammUDg5Su8Lnns6pGUlcBZcTUzKmBBO0DwoPQOZQsGojkKCLjMKv2DgtFJq2P4GeZCHw1TES7dcncRAYiEslkEz0M8Q4ITpO+HIYuuqeCMKaukSrImCiiiPx0fzOHh4LO7xnD8mOMkJrZ0dbvPB6MF+/Amsy4FJFxlO/2xuxARN7JS6Yv0hovkUw2g/tE7twzTRSHg9sg2icUkLUXVHpvZh55fUS64h6+uaslZ5G0oQDmiKmZnEAkUTwQSaTHr4iYpkmfpYikMpOsiEgkZ8CkBiInT57kLW95Cw0NDQSDQTZt2sSzzz47mZuUSKYXpgE9z4InXOk9ESSG4cWnxOMNl4oqGcnYyEvNfO2FRQylPWyqH6YxIAKKtDm6IrI9RxEpHmQkXR6RsZpVB+MZp/R30lMzEskZMGmBSF9fH5dccgler5e77rqLPXv28LWvfY26urrJ2qREMv2InYTYcfBNgyZmhg7P3yc6qNbNgyXrK71HMxNXaiapK/zysOiU++lzjuPXxAU/rVvLZIqbRHXDZOfJAef3UopIrkdkbKkZOy0DOAHJRCFNmxKYuONg0jwiX/rSl2htbXUGCQEsWya7NUrmGEP7xMTd0JLK7oeegSd/B6ePijv6s15+xuPp5yxO+a5OR8xHxlQJaDpbm6J4FXFizpiK6K6aiRZdxcHOaE4VTGmPSDYQSWUM0rqBVyvv/rF3OJsWmqjUjNcrSrtjsRjBYHCUpSWznVhMVErZx8V4mbRA5Le//S1XX301b3jDG3jooYdYuHAh733ve3nnO99Z8j3JZJJkMvvlGRwcnKzdk0imhv6dYsBdJS/6qYQIQrpPgOaBC6+D+knqKTEXcHlETsVEOXZLKIWigFcTgUjKUEALwvBxMM2C/3+3PwRgMFE87eIu3wVRwlsTLC8Q6YlmFZGJSs1omkZtbS2dnZ0AhEIhFBnQzjlM0yQWi9HZ2UltbS2app3R+iYtEDl8+DDf/va3+dCHPsQnPvEJnnnmGd7//vfj8/l429veVvQ9t956K5/97Gcna5ckkqklMywamfkqkI7sOSUm6fZ3CmMqgMcHF79WTtU9U5xZMwbtViCyICQu+h5bETEU8EQg1QPpAfDV5qxi+4l+AOrDPnqHU2UpIiA6pdYEy7v7tEt3YWI9IvaUWjsYkcxdamtrc6YWj5dJC0QMw2Dr1q184QtfAGDLli3s2rWL73znOyUDkY9//ON86EMfcn4fHByktbV4x0GJZNoTPQqpPghPcUqy7zQ8+iuRjrGJ1MF51whviOTMcKVmnEAkLC76PtXyiNiByHA3JE4XBCK2InLpykZ+u+MUQyUUEXf5LoxtAm+OR2QCy3cVRWHBggU0NzeTThcPoCSzH6/Xe8ZKiM2kBSILFixg/fpcM9y6dev41a9+VfI9fr8fv1+6+CWTRyyV4bO/3cOV6+fxivWTfFGOHhZVE9oUHtOxIXjiNyIIaWqFVVuhthkC06R0eDbgTs0MZ1MzAB7VqlIxFNFF10iJHjLVa5y3J9I6L3YMAXDZKhGIlFO+C+L4LZdeV2omNQlVM5qmTdiFSDK3mbRA5JJLLmHfvn05z+3fv58lSyps2pPMaZ483MMvnm3jsUPdkx+IDOwCLTC52zB0GB4U3g9FgSf+T5ToVjfAhdfK8tzJwCnfLUzNeJ1ARM36QhIdOW/ffWoA3TBpqvKzZr7oplpOQzOA6BhKeCcrNSORTDSTFoh88IMf5OKLL+YLX/gCN954I08//TTf+973+N73vjdZm5RIRsWWtk/0xWnrjdFaP0lKQWoAokfAO4n+ENOER34p/CBu/CHhBZFByOSgZqfvnioZiFhBiBYUypiL7W2ibPesRbVUB4Tfo5RZNZmviIwzNSM7q0qmM5PWR+S8887jzjvv5L//+7/ZuHEjn/vc5/jGN77BTTfdNFmblEhGxW3+e+JQz+RtaPhIUZPihHJsjxWEKNl0QTACF10PoWk012a24eoj0h7LTc3YgYjT4t0TgdgJ0LNBge0PObu1hmrLeBpNZsgUUS0S+R6RsaRmpCIimSFM6qyZ17zmNbzmNa+ZzE1IJGPCXQ752KFubjxvkszQQ4fAyIjS3ckgnYTdj4rHmy6DVeeKNI3iCkokk4MiUjMZ3WAgJU6htlm1ihjVeEX5LoiOuknLsBoWx9oLVsXM5kW1VAWyp+BoMkNtKHc6c4EiMoamZpNRviuRTAbyjCWZU7gVkccP9UxOh0jTFP1DtEk0iL74FCRjohpmxdniOVWTQchUYCkiqYw4dqq8Gaq8BmTS/GP/F/mz/+8xM5bnwxMGPSYCEYuuIdEraXF9CK+mEvKJwKZYm/f88t2xtHnPUUTkrBnJNEaetSRzCrf5r2soyaGu4p0vz2wjPUKOn6z+IUN9YnAdwOaXZs2TkqkhLxCx/SEc3kGT0U2TMkgw2S+eU1TAdAyrhmE6rdojlhqS9YkUGlbzzarlKiLxlE7cpf6l04my3ieRVAIZiEjmFPnlkI9Phk/E9od4ayZ+3aYJOx4Qw/TmLYP5cmzClGOnZqxApCWUgkwaDmQHenpT7gBXheE2INfjEfFbgUhQ/FuscsZOzfistu7lekTcRlWAVDr392RG57ljfegTPINGIhkPMhCRzCnsO0yvJnL4jx+chEBk6IAIFCZDqTi0DTqPiXVvfsnEr18yOpYiYptLF4RTcHgHJLMD7vxpVyDiiYjKGdN0ym89qoLfo0LsFNUeESSMpIjUh4V3pNyqGXcPEYB0Jnfd3/rzAW749uPcue1kWeuTSCYTGYhI5hT2Heb5y+oBeOJwD8ZE3hVmhqH7ycmpluk7DTsfEY83vRSq6id+G5LRsQJMwwpEFgeisF+oIXFF9I0JZIayy3siosNuqo+oVaYbCXhQ0v1w4D+o1oVaUqyE1/aI1FmBSLkeEffAO4BMJiXUNIuTfSJo2tch53lJKo8MRCRzCvsO89wl9UT8Hgbiafa0T+DJuHcbxE9BYIKHyqVT8PQfhdLSshKWb57Y9UvKx1JETEMECZfEHoFUHMI1vBA5D4BgOi8QyUQh0cGQFUhE/Boc/gkM7KFKFcsWS83YVV71YeEjKTs103MCgCqvWGdaN0X1joXdabVrKCkqvNrvzSkxlkimEhmISOYUtiIS8WtcYKkijx/qHukt5WOa0PWwGP8+0WW7Ox6A4X4IVsE5V1Z2mu9cx9VHxEuGdZ2WSrX2AoYtX1BIdwUimg/MNCROO4pGRB2GrkchspLqoFA7BvsL0yS2IlIfFs3pyjWr9nQfAWB+UAQiKUOBWJvzespab+fpg7Drn+HA9+DUH8tat0Qy0chARDKnsE/sfo/GRSsagAk0rEYPwcCLEGyZmPXZdJ+A43vE4/NeCb5JbhsvGRnLrKqYOper2/FmYhAIQ+s6El7Rsj3kTs2IpaF/J9ETImiJGD3gnweeMNVW9cxg98ECVcIOnBvGkppJD9HbI4Ka+SFLETFUGD6eXW9cqICdQynw1oK/EU78Bvp3lflHkEgmDhmISOYUttTt96isbxHdR9t6YxOz8u6nhQTvqZqY9YFIxex4UDxeugkaF03cuiXjw1JENHSu1x4Tz7WuBVUl4YkAENHzAhFfA3Q+zFD70+J1HxBoAqDaK47JweggdD+W8za7s2qdfhSAWCIOmTglGToE+75FX0wEIPOs0uK0ocLgi2IZ0ySd6AWgKxURZeaBeaLfydHbIdVf/t9ikknrBp+4cyd37Wyv9K5IJhEZiEjmFLYiEvBqTvnkWEarlyRtXUT8jRObNjm6Cwa6xNyYDRdP3Hol48cKRKqI8wr1efFc61oAUj47EMnzHQUXQM1Goh4x9DPiz1ZUVfuEyjGoB+DUXaBne37Yikj9kFBShqM98MKnoPMR0bnXJj0EJ34Lu2+Fvm30GI0AzLNSMxlTheGjwkw9dIBUUgTfAykPCV0Rx2xklQhWjt0hjudpwJOHe/j5U8f5xp8OVHpXJJPIpLZ4l0imG7ZZ1e9RCduByBjmd+RgpMXdYyYKfTtE98yqtRO0p0AqAbsfF4/XXSiG2Ukqj1U1E1KsypSqeqgR6kbaUkTCZgz0jJiK7GI4YwUx3mzwW+2zFBG9CqJHxbHUeAEAyYw4NusjIh0X030QPw37/x16noLGS2BwL/Q+K573N0D1BnpTwlMyz/GIaJipQZThNuh8mJSeHYjYFffSGkmB6oHQUmi/B/pegIatUL8VajdVzJPU3i+Cslh6nN9RyYxABiKSOYXd0MzvVV2KSAbTNFHKOdnqCRjcB/27ofc5SPeJvL6RBC0sTuYTxYtPimqMqnpYftbErVdyZuS30W9d61yoDW+QlKnhU3TRgj9v+GA0LYKYiDfbWM9JzaS9Yj2dD0PD+aAoJKwguS4sgtBoxgNVK0Tw2/UEdD8FmCL1U73WMUn3JsVxOC+Y9ZxkMim8PU9Cz1OkuNJ5vithBSIAvhrwbIBklzCvdj4Kaz8AdZU5/joGRSCSP3NHMruQgYhkTmErIgGP5igihikClKBvlAZkw21w6PtCvjYz4KkBbzX4/KIyQpnABmZdba427pfLNu7Tifz/i9asCubRoItaFtIDicJAZMgKRMJFFJGhlAaBBcIwGj2MGV5KUjcBhfqg6AGS1FUyBng8EajZWLJxXm9CBCS2WRUgjQ/v4H5I9ZIyslVdnfG8Ci/VI1JJwQUwsBdO/g5q1o9aCXake5iP/nIH733ZSl62pnnEZcvFCUTkrJxZjfSISOYUTtWMVyXkzZ7Ao6NVI/S9AHu/CgO7IbwMajZBeLFoXOYJTmwQkozDM3eJx0s2wLwlE7duyZmjZk+b7cElEM628veqJl2m9XtiuOCtWUUkG4hUeS2PSFoTYwEyQ9D9JJn+/RimUFrq/dmAIpaxjjVFKRqEZAzoTxUqImm1BoYOgr+ZtJFV/7ryAxE34aXi2O9+qvQyFn/c2c4zR/v45XMnRl22XDqtQCQlA5FZjQxEJHOKbNWMhqoqzuTTEcsiTz8E+74ByU6oXg/aJJbPmiY8d4+4iEXq4KyXTd62JOPDlZrpbNyU85IIRKxhhyMEIsU8ItG0hoEiDM9dj5FsfzC7jFfHo4iLcSwz8mm7z0rLKJg0BlyKiLdJqBqBeSSN7Dq6EiMEIp6geM/J3wmj6wic7BfVPPEye52UQ1YR0SdnUrZkWiADEcmcIls1Iw79UQ2rA3vhyE9FgBBZXegPmGgObYOOI+JO9/xXg2eCG6NJzhyPl34iRM0AesuanJe8qpFVRJJFAhEriMhVRMRjE0WkbvzzIHGaRM9eZxm/ZhLyiGN3ODOy+mYHIrX+DB5V7BNAWvFD1SpQVFJ6VhHpjPtG/rzhpUJJOf3QiIudsgKR2HjN30U4PSgMwYYJGTmgb9YiAxHJnMI2vfk9lkQ+Uglvqk8EIekohBZPfuXAUC/selQ83vQSqG2a3O1JxoWhaLwp9Slem/pnmmpybXYe1aSLWvFLorA/jZOa8WSPN79mEtAsw2pKE0GoJ0RyuMt63UBRIOwtTxHpSYrgtc4vAgKvKi7gaZcKkjLcgcgowa7qFSmj9j+K70QJ7AqXiVJE0rpBdzQ7M0f6RGYvMhCRzBlM03QaRPk9tiJSIjVj6HD0v4UxtWrV5Achpgnb/iS227xEVslMY3oSHvYarRwyF+aYQQF8qkmnWSt+GdEjkntRdUp4rdcJLyMZFiZYv6VohKzgZTg9siJiV8w05AUibhUkXW5qxia0EGLtYpZSCWxFJJ6emECkayjpntNHcoLWK5l+yKoZyZwhrZvOic1vGVXDPvEVKDCrnr4fTj8ojKkTWZJbiiM7ofskaF7YcoWcJTONaQhkePz6HXTGvc5F3sYzDrMqCA9IZxwGLZMpikrCFF4kvyYCkbCnPEXEDkTqCxQRcUzpBujmGBQREGZs1QfdT8C8lxUcn4OJtDPQr9x5OKNxejCR87s9qE8y+5CKiGTOYJfuglsRyfYScUh0w4n/A09YlOdONvEo7LIGp224OKcKQzL9UBVoCac4u7Ew0BBm1VrxS3KE1Ex+IOJzpWYskro4RgNariISHU0RsRSOesuoagciGSv4SBm5p/3uhJeyrvGB+TC4X3RozcNOy8DEpWbyA5FyeomYpsnHfvUCtz18eEL2QTI1yEBEMmdIuE5kBYGI++R5+n6In4JQ69Ts2I4HIJOCuvmw4uyp2aZkUihIzbhyC0ldcYKAQkXEVcLrLC+W9WtmznvGroiI495Ozbj9ISDUkb5UGaqftxoyg0XTM6cGsvNvJio10zGQF4iU4RE51hPjjmfa+Nb9siX8TEIGIpI5g7u9u91FNZLvEYl3QMefwd80+RUyIBqXnTootnXOlVOzTcmk4VFNurEULUOHdNZs6fZ2uM2qAFWOIpINCBJW4OB3FJHyqmZ6EqVSM+LYcntFGqz+JOWlZxQxqbf7cdCTOS/Z/hAQgchElNqeHsrdhlvRLIUdrMhOrDMLedaTzBmcZmae7GFve0ScQKTjT6JfSGD+5O+QaWarZJZtgprGyd+mZFLxqgZJfAxizQVy+USiVgAR8uhoeWdep817kdSMrYiEPaMrIkld4bHTIp24vDph7VOuR8RWZXyqQVNwDIEIiCm9sRMwsCfnaXdqxjRz1cfxcnocikhat9UfQ/YdmUHIQEQyZ3Cambk6qtqpmWgyI06wpx8UQchUKBOnDkJfhxiMtvaCyd+eZNKxL/rdRXwiQ2krHegpvLMvqJqhmEdkdEXkruN19CW9LAiluGz+ACDSReAKRFxKS7MViJRVOQOimZ+ZEXOWXLgVEZiY9ExHvlm1jEBEd/UakeW+MwcZiEjmDPnNzMBVvhvtg7b/g1QP+CdmTsaIGAbsfkw8XnUuBMKTv03JpGMHIsUqZ4aLdFW1qfZZHpGc1IytiFhVM7ZHJF36tP2zg+LYfdOKTmzhz1NSETFpCtiKyChNzdz4GqHnGaGKWKrDqa72nEVio41MKIMCs2oZqZmMkQ0+ZJXNzEEGIpI5Q34zM4Bw6jgA0Z69wqQaWjo1pbPHdkO0D3xBEYhIZgX2Rb/TaWrmSs2U6CEC2dTMkFsRMcbmEXmxP8izXVVoiskbV3Q7zzudVY1cs6pXNbOKSLmpGYBAE6R6YfeX4OD34fRDnOodzFkkHj1d/vpKYHdVbYyIIKkc30dazyoicj7NzEEGIpI5Q34zM1L9RAaERyNmhMQgO98UlM52HIXdljdk7fng9U/+NiVTgp0G6TRqxROuQGSoROkujFa+m+sRGS7hEbn9gOjEe9WiPua5Gq35SphVfZpBszUUr+zUDIieItXrrW6rd2EcuI2ORBAATRHbivfuL399RYgmM05vn9Z64bcpJ9WSkYHIjEQGIpI5g31HFbA9Iv0vEDZ7ARjO+EoqIY91VHFocAIG3ekZUar7+J2QSkBtMyzbfObrlUwbPNaF+HQRj0ipHiIwWvmunZqxFJEifUSG0yp3HhFm55tWdWVfOLyDfx/4B9YrR0dJzYxxppGigL8eajbRTQspQ0PBZFFYqBix7r05pctjxU7LRPwe6kOWIjLG1Iz0iMwcZGdVyZzBXb6LaULP085dZrSE3H1PWy3vemQVy6vi3H/trvFvvPskbP8zDPaI31ecDRsvE0ZVyazBawUNXUXavBebM8Nz90F3G8taLkFlVY5HJGkpFzUMw0AXIY9YZ7Gqmd8cayCa0VhWleDieVaaxDRh/7PUmIO8XnuElHEZkK+IjDMQsVEU2tNCiZkXTDv+l3i0HWLHIbxkXKu1K2bmVfvxW54uqYjMXuRZUDJnyHpEVNGwrH8PkVALUPwuM5pW+afnxIn08FCQ/qRGrX+M1QDJGOx8BI5b5Y7+EJx7FcxfNv4PIpm2eC1FpKuIR2Q4f/Juzyk4JoLbxQf+yB98u/hK6iZneaGImPxV93fhz8dYv/AlwMqiHpF728T23riiC9UW9ga6ICaCkq3qfrYbLwFyFZExV80U4dSwUCwWhFKOWTeeTEP/zvEHIkMiEJlfE3A8XeUEFjlmVRmIzBhkakYyZ7AVkYBXg/4XIN1PKCiqVYrl3f/1hYW0x7LVBC/2h8a2wVQC7r89G4Qs3Qiv+CsZhMxi7Atxtruqu3w3LzXz4lPi37r5GB4/69Tj3KZ+EaPrJCACkUvVXSxKHgNg8cmH+bznhyRy5+wB2Ym7q2tcZbSnDjoPNyhHMTPijXaKxp2aiWU0oiNU44zEKes70hJOEbR7nZgR6HpcNHUbBx0DIsUzryrgeLrK6yPiUkR0OSRvpiAVEcmcIdvQTIGuJ0ALEdGylQimmbWJ7OoN8aP98wBxp9ce8/Fif5AL5w2Vv8Fdj4o5MuEaOO8aqF8woZ9HMv3QVFBwDb5LxcXFWNVyPSJ9HXD6qDjgzruGlBbgkd8/zZXac+gvPAwv/0sSusp7tN+K9dQ2Y/Z38hbPn2nWh8C4CNRs4NBntXWv9bvKZl2BiFfRqYudAMI5qZmw1yDs0RnOaHTGvUS8ud1My8FWRBaGkxyPCuN1XK2FoSdh29+LYXlaEOa9HJouBXXkzrCQ9YjMqwk4zQbLmb6b00dEdledMUhFRDJnsBuaBRiG4SMQmO/0ZjBMxenboBvwyaeXYJgKr17cy18sE6WQY1JEetvh6E7x+NyrZBAyh/CqJv1EMO2meIPCEB119xF58WnxWutaiNQSCAb4tP43RM0A2kAHnNjHvPhxLtF2Y6DChdfSs/k6UqbGVTwNh7bnbHPAqraptfqREO0XfiRF5YB/DQDN1rA6d2oGcFSR8aZnbNVwQShFyArs42YIAs2QHoZkDwwdhP3/Afv/DWKnRl2nHYjMrx6rIuIyq8o+IjMGGYhI5gyOIpLpgcwweMJObwbAkaZ39YXZ0Rsh7NH5zLnHWVsn5PW9/cHyNmQYsO3P4vHi9dC4aOI+hGTa41VNTFTiTSvFEweeBbKByILMKWg/JF5bc77zvrQvwrcz14lfdj3KldG7AThSezaEqlEWreEzmbcDYL74pEj9IVItQ2lLEbHKgB01pGkRB0IbxHbjR4FsfxKfFTRkDatjaGrm4qSdmgmlCFrfp1hGBX8jhBaK4ZFVqyC0GDofht1fgOiREddpd1WdV+13PCJlmVUNaVadichARDJncAKRdLvogaAoqIq7P4M44XXExJ3hqpo4zcE0a2tF3n1/f7C8cemHtwujoNcPmy6b8M8hmd7YPpGeJZeIJ9r2wVCfFYiYbDptBamLVkNVvfO+Gl+G7+uvIumrhvgQG1O7Adg/TxxDYa/OL/SX8aLRipJOwj6hqgy4eo/U2IqIHYgsWMmpgPAktSSOgWmQspQ/ez/HPG8mj/Ycj4iliBTrdeKNQM1GYRQf2DviOrNVM2NTRDJuRUQGIjMGGYhI5gxOasYcAl/2AmCnZ+w7Vtv412hJ1ksjCfyaQVzXnBx4SfpOw+7HxeONl4oqGcmcwu5kOhRZAPOXAybse5poRuMd2l3M69sLKLAmd75QQyBNEh+7W650nrtH30oqLPqD+FUTRVG4NfNm8eKh7TDcT7/lD6n2ZsQwvXhUpAYBWlbQF5jHkBnEbyZhoDvHrIppsMzbwyKla1ypmaSuOEpKSyhFyArqiwYiIGY4qT4YKt3wzDBMOq3Ju/NrAq7y3XL6iEhFZCYiAxHJnMEp3yUOnojzfNgtJ5Mdo94YEHeXmgprrGqEEX0iQ73w2J2gp6GpFZZumvDPIJn+2EpDxlCywwzb9nJN6n4+6bld/L7pJQXTlhsto+kLoXOgoYU0Hv4t81qnoZmiiMm9DxlnEatfJkywOx8h1dvFleqzvMV7PxzakZ1hVDcfghE8msLzxirxXM8pUrrCq9Qn+cfOz8Cd3+LDJz/Do/4PsKr7iTF/1tNWEOLXDOr9mWxqRh/h0uKthaEDoCeKvtwznCJjmCgKNEb8+DTZR2S2I6tmJHMGp6GZZuR0UXVSM5Yi0m3dGTYEsnWSa2tj7O4NkDh5FHQxnwZFAV8AInWiEuDRX4sqidpmuPDaqZlZI5l2eN0t1Zvmw7ylcPooH+J2UGBw4dlUr9xS8D77eOtJ+uGS1/M39y5hV2Ihfi2rHoQ9BkNpOLn8Clb1fh9OHWT9qYPc5gMMYIdrhS0rAPBpJs8aq3mp9gL0nKTePMG/ev8Tv547mO6awd9D4q/GNIDRrphpCaVQFAjaZtURJgTjrYH4CRg+DtWrC162jaqNET9eTXWmZZc1ayanj4gs350pyEBEMmewUzN+nxfInoSd1EwmLxCxSyGTcd6QuYsP+J9hYXsP5A4azaWqHi55nZwfM4fJn3bL2gtEqS7wmL6BVRtfTrVS2P68wVLgupMe8HjpMEX60FZEAEJeHeLQG1gghiUefJ64FmFfqgnTH2JLU0IExf4gLD8LEIHRo6aonKHrBNdlfoJfybAvuJE1l1/Ewz3zqHny55ylHhZqyrlXlf1ZT7kqZiA7mK9kagZAC4CRgOFjRQOR7qhIyzRFxHco6xEpIzXjUkSkR2TmIAMRyZwhac398Hv9uAORiD3VNJ2bmmkIpGG4Hx74b85LJUCBfiLUzm8CFMAUnTOjfZBJQ6gaLnm99IXMcbz5gUhDC+nl5/LogQwfSN/MU/59QLFAxFJErEDYbvEecAUiYedY1UR6Z+Nl3P7ifD6/bTHXL+hhy4WHi+yPwXZjBToqWjJGNXDIWMDd8/+SNcEemsIGn0i/jTv9nxFToZdthvr5ZX1WRxGxZszY+1qsDb2DoojBeUMHYcGVBS/bNwwhn7gxGFtnVZmamYnIQEQyZ8gGIj4g23o7XxFxzKr+FGx/AFIJ9HAd/9B/I7/TL+K583bmjnI3TdHK3eMDz/hbZUtmBzkeEYve1Vfw13vORlPMnMDCTZOliNiBcP7QO4A6S6XrtQyqKAp9qSLNzFx4VJM4AY57WlmWOUZS8fP/0h/iOmsVzcE028yN/FJ/CX+hPSwGM17+l2WlFm3z9mIrEHHMqiN5RAA8NTD4IhhpUHO/M4m84ZT++AFg7FUzKdlHZMYgzaqSOUMiKXLPAU/u3agdiMTyFJGlQ7uEpK5qaJdczyO+i0jiY19+PxFFEXl1GYRIyAYiKVcg4m7vXur63uDPVUTsBnt+NXu8FpuWa1fNOM3M8rAblz3suwT8QW6v+ysOmQudPiJ1/gwexeBL6TdiaD7R9fXJ35HsbOdPJ2pGTLM4gUjEDkTKSM0A+Gog1QuxEwUvOdVtXhXSg/i6RLlzMl2kt30eUhGZmchARDI3MHSSVgMof94dqSN3ZzQyhmiXHSHG/AP3igXWnAeROtbWCkVlzDNnJHMKj1W+mzGzEUc0f85MEbJmVVsRsVIzrqZ7TUWG1PWPoojYgdGffJfBq97Fdu9mIBugqIqoEOuijpPLrxBvaj+E/9E7qH3y5/zvjtKXiTYrEGm1ApGAU4E2Sht3LQyZmDCs5uF4ubwaDOwVDQiBZLx/1Nk1adlHZEYiAxHJ3CDRnm1opuUpIp5sH5G+pAcThQ97/gctOQyRWlh9HoDT2OzFcjusSuYk9gU+rWcDEWfyrmekQEQEEkNpD4mMkk3NqNkLanOgsAtqf1Jc9OtKKCI5nhVFcfURca3XCnBerLsQrngLLF5PBo2t6n6uOv5jp4urm5SuOGbVfEUkMVpqRlFET5HooYKXEtb3NODRoG8Hfk38zZKpJHTcN+Jqc2bNyEBkxiADEcmM4/Rggvf/9zaeO9Zb/puiR52TY36O3r5LHc5odCe8bFYO8VbNOuGd/XLQxB3nOqmISMrAY1XEpM3iqZlSVHt1pxlae8yHiXi/O3DOKiJZe5+tiNT4i6/bm6fQ2J1VfVphyqcr4YWaJth6Nf8v+AXajCbmm12Yz9wtvFAuTgyLfQx5dKfnTiivJ8+IeKpgYE+ByuGkZjQd+rbj94uePynDA8f/Fwb3lVxlWvYRmZHIQEQy47hz20l+u+MUP378WPlvih4iaVjmt/zUjGU8jaZVeuMqX/J+D00xxUCy5iXOchvqRSCyoyfM6Zj0g0iK49VcfUQsyknNKEq2ZNxWGiD3eG0OijLZrhxFRAQioyoilkKTcndWzVuv23vyQnw+705/kITpRTl9BF58Mme9bn+I7XsJaNnOqmZhYVAuvlpIdkMitx7eMasa/ZDswh+oBiBpeCDVByM0XpNm1ZmJDEQkM45jPSIgiCWLn3gLGDwAvc+LExmlUzOxjEb9scdZp7YxqERg80tzlltZnWBr0xApQ+U7e8srb5TMPbxKYdVMNhAZ+eJo+0RODouLvIKZEzA4ykV87B4Re+pu/tA7KJw3E8uodCe87DaX8sn0O8RCe5+Ejuywunx/CGQVERPF8biUxBOB9BAM5aZnHEUk0wmY+OyGZoYKvgbofQ4y8aKrdJtVk2nZ0GymIAMRyYzjRJ8IRBKjNTgyTeh8BF78GsRPkdDFCbtUaqY+2cHq9ocA+FXVXxT0A1EUuGWTGGH+84PN4x4SJpndFKuasQcqVo2giEDWJ3JyONs63V1lYwcM0YxGLKOS0hVn3aWqZvL7muQPvXOv1zbB2tsH+JXxEp4MWwP8nr1HzLIBjkcDQLZ0F7KdVaEMw6qiitLd3mdy0j5247JA+hT4GhyPTFJXMX2NkOiEoeLpmbRURGYkMhCRzDjaeq1AZKSWz3oSjv0CDnwbMnHMqvXOHWF+aibkMVikdHJz8ido6Nynn8Oxms1FV3vJvEG2Ng2R1FW+vUeqIpJCPEX6iNgekfAIZlXIDlq0UzP56l3EYxC00h9dcS/91uRdBZNqX/F1+/ICkaJm1byy4La84Y5fyLwFaprFCINn7gLTKCjdBTGXyV6vPW/GNOGFnpDTMDAHf5OYxJs47TzlpGbMQfA35vwNUkoATB36dhX9rLos352RyEBEMqPQDZOT/UKWTZSSXuOnYd+3hLHNWweRZSTN7KGeE4i0vcjmnT/kUf8trDOPkFACfCr91zQGi99dSlVEMhr2hThdNDVTZiBiKRIBzRBXcl1c7BUlN40yYBtVfTpqiUyIY1a1FRGj0KxqV83YKZ8TVmrorAahfuwarCZ+7qtB80L3CXjx6WwgUpWEREy0h//TT7neI7wkCcuw+uCpGq67ZwOfeW5xkT9WnfB99O90nnJSM2oGVG/O9zVlKGJoXu8zRYfmSbPqzEQGIpIZRcdgwjnZFA1E+nbAni9B9+MQWQGBJiDbpRIgYJ+AO4/BM3cRHjyBbio8bm7kG1XvoYOGnIF3+UhVRDISBbNmyAYio6Zm/HZqxpqzohkQPQgDu50Lb7MrjdJnG1VL+EPc+5NyUjNWNU6J1IxpZhWRcxujzA+mMEyFF1KLYMvLATD3Psn7Yj/gs54fce6xX8Hd34d9T8NgDx9U70DBcCpnDgyKcvcHT9UWGlgVVcye6XnKSc8kEkNi//zifW6PTFJXwd8oFJTB/eSTcQ29k+W7MwcZiEhmFMctoyoUSc0MHYT9/ylc+DUbwZP1eNiBiKqYTnklJ8SJLNm8kouS/85bUh/nicw6IJurL4ZbFbn9QDNtUV/JZSVzj5zpuxZRKy0xoiKSHqRBEyXpTmpG1cHMQM16iIo5Mm7DqlO6W8IfAu7UjNgHWxHxulIz9jrThkp/SuOEpci0RpKc3ShUkR09EVi8HhavQ8Hk1eoTvM1zH9XtO0UJbt188PppoZtL1V2OR8TuVNyd8HJ4KFC4g/554rsbOw5GhkS0E4BAQJTtKkpWZUrqVuBipqG/MD2TkYrIjEQGIpKK09Yb4zO/2ZUTZJRcti+7TM4dTyYGR2+HVA9EVouhWi6Szl2gZf4zDWgXJ3Zj+dl0UodhKpyMiTtBu912KS6ZN8jF8wZJGSpf2bGonI8pmSMUmzUzYmpGT4m5K/F2JxCxg4WAEoe6c2Dpm8QFONmTbfOe8GZLd0dQRPLNqo5HxJWa8WumY3btjPuc1MyicIqzG8Rcpu3dYbHwOVdxZN3r+Xz6Jn7EtWIK8GV/IebTLBaB/Ju0+515M3anWIBnOiNFdrAa0oMisOh6hERCbM89isFOzziVOE56JpmzKrciIs2qMwcZiEgqzh3PHOcnTxzj9qdH7wtiG1XB6rRopIWke+K3Ii0TWVV0WJfTzMxul93bLgbVef3457WgWNNQu62qgcYRFBEQm/jEljYUTH57rCF7kpbMeQqm7zJCIJLsFRUgVStg3d/TuPjinJf9mgmLroPazTDvcoifcPUSySoipSpmiu2PfTF3m1UhNz3jLs09yw5EeqxjXFV5IbiF7+uv5q7qV4spwE2t4kuxdBMAV6rPocfF+3pd7eif7qoq3EFFAU8Yuh6DtjtJGGJ5d3WbbVh1Uqz+Roh3wNCBnFVJRWRmIgMRScUZiIsT4FBi9L4g7kAkkdZh79eh409w6o8QWACav+j7kvkDxE5ZvQvmL0PVNKf/gc1IHhGbjfUxXr9MzMH4wrbW0Rs4SeYEIwcieRfH+ElYcBVs+CQ0bKVh+ZU5L/sD1VC9VlysF14HoUU0qR2AMKva7d1rSlTMYOp4M93O/phmcbMqQHNABDhHBv1OgLMonGRz/TCqYnIq5i+oqnFXzIgdaeSAthyvojOvczuQTc0APN1ZJBABkZ6JHoFYG0lTpG9yAxHx2N53tKBIzwwfzVlNOqfFu+wjMlOQgYik4sRT1nyKMhoQuVMzaVND734GjvyXUEYsY2oxckaqmyacOiheaFkBZCfw2suEPeXdTX34rBP4NYOnu6q490RtWe+RzG6KmlUzRRQRPSH6aDRe6PiZ6qtye9cEwk1ZhS/QCAuvo9kj0jddCS99qVFSM/EOvDGRgjRRyJiKY1bNV0RsE+zz3RFrnWkiXoOw12B1jahUs1WRY1YgsiQ/EAEeDwlVZ2nPc2Ca9CSzisiJYT/txboSe8IinRpaWnQUQ0FqBgAVYidzVpORQ+9mJDIQkVSceFqcRJMj9QWxON49lPN7IrwJvDVQtXLE9yXsSaaaAUM9MDwAqgbNS4HcYWSNgXTJUe35LAileedacYf6xe2tzkleMncpVr5r99DI6SOS6ITgAqF4WAS8GlWBrILgD+TNNWrYSlOVCAI6414GkqOkZtIDeCMtzq/xjOrMsHFXo0A2NWMHIovCKec1Oz3zaLtot16sh4jNntDZDJohqlK9cPoYvY6PRay/qCqiKOI77KspGojY++qufsMTFoqIS4qUfURmJjIQkVScWMqaTzGKIhJP6XQN5w3IMjTwNxSYU/PJKiJmNi3TvBi8ojog7JLMG0cxqubz7vXtNAbSBDSDzoTsKzLXyVdEDBPiujg+g26lLd0v1JC8dGJjJPu735N3XPvqaGpeDkBP0kt3coT27qYpUjOBOucp9zC6wtSMOO6PWR1TW10dU1+9WKgw/32oiaND/qLt3W00r4df65eKXXji//im8q9crT7NKxeKNGbJ9IxF1s81glkVRCCS7IVM1Hkqv7OqKfOlMwLP6ItIJJOLHYiMlpo5cVqUzFZ50yR1jZShjj5u3CInNWMHIgtWOK+771RHKt0tRsRr8D+v2MuSSBJNhvZznvzy3bjr4u+k/DLDwudQW9jBtyHs40i3UCD8nsIDqqFlEwr9GKbCYatHR1FFJBMFTxhvVavz1LCr7Xops6rNIleQ8ZIFg7x0QT8PtdfyT88upt0qLy6miIQ8Bv+eeR0vjxxkceIwV2rPc6X2PLHuWjLajTzXeW7hvlqYJiOnZlwl0WghSJ2CZBd4RXDjnjVjmqLBmc8jVcrpjjxtSiqOHYCMFoi0HRd9A1rDKefEVG4gYi/XonRDv9VOOicQyZ70yjGq5rO8WgYhEkF++a6tQiiY2YtrohNCrVC1quD9DZFsX5qAt1Dp89RtoMEv0iZ2lVetv8h3J9UHgWaUpovxKuJ1O0WkKib5MU5+IOJWRAA+dU4bmmLyYHstJgohj+40YHMT1Ay6qeH7TX/HvvPexX9mrqOHakKpfr7i/R7/mfgMg6c7C/cXoSIZplVq7w5E7KZs7u+7FgQjAYku5yl31YxYXqZnZgLy1CmpOFlFZKTZMSmOnxSleq2RlHNCT5TpybAl3S36HvFE/QJw5d/dZtWxKiISiZv8oXfutIzoYWNCZkikZdRCUbohJzVT5BTtq6MpnHvcFzWrZgahdhNEljnpIts061ULUxbNeQG42yMCsLImwVtXZQOIxZFkUS9V0J5mraucUhfw5cxf8s7AF2DjZfQTYbnaAdv+XPhGcm8sRjWrij+mCOrsj2zknkOkT2RmIAMRScWJl5Oa6X6Mtj7R4npxJJkNRDJjS82s1a2+A02tOa+7A5GxekQkEjd2596MmauIOCXimSHwRKCu+GDFxnBWESkaiABN1bkm1oLOqqYBmMIA6q3CFlZiliKSn5aBbNWMTTH/xwc2nXS2VSwtA9nPGc9oTjOzSFCD1Vv5jwX/gG4qVMc6hGE8D/vGQsHMMdMW9BFx0CB+yvkto8tAZCYiAxFJxYmlxImtZCCSHoSTv6ctXgOIE6RtZCs8MRXHvtNanbbKdhtzu6FGclIzUhGRjB/bBJrOS83YU3NJnIbIMggvLfr+HEWkSGoGoKm23nmsKSbV+Y3S0oPgqYbIcmufxHpsj0j+VF8Qc3Dc6ZCFeYoIQJ1f55NWI79L5g0W3begE4io9FipI7tT8apmlacN0X2V9kMF701Y6lFAM3LUFr9axCMCwrAaPeJUzqQzud9d2UtkZjBlgcgXv/hFFEXhlltumapNSmYITmqm1N1Lx/0QPcLxhHD/t4aziki5gUhSV1hIF/V6r5B0GxbkvJ6jiIzDIyKR2HiU3PLduFsRMTJgJKH5pWLgWxHcHpFSikhzbbbypMaXKUyRpPpFaXBgHgBeq/pm2IotiikiipJtatYYSOdW+Li4cUU3O/5iG29bU9znEbS+m7GM6pTu2sF9YyDNvYZlVrV7+bhIZKwy+7xtF+8jgghEUtnKGamIzEymJBB55pln+O53v8vmzcWlSMncxTBMp/FQviLy7NFeLvyXe/nRI/swvQ2csMsKI8kxm1WThsp56j7xS+088OQOqovkVM24AhHThKH9MLAr+9O/E4aPifk2Ekke+Z1VbRUi6DHEQMZgCzScV/L9jeUoIq5laj0xcVwO7hXVOCAuzLWbnGDH6xHKxHAynbOP+djpmUXh4mkXm+pSnVyBkPVdiuuqk5qptxSRKq/OvfpWsWD3KTFmwUW2YiZ3/2wFJ5X/fddC4nto+UTcVTMgFZGZwqQHItFolJtuuonbbruNurq60d8gmVO4e4fE03pO3f/D+7voGErz2Rc28uO2sx2j3SK3R6Tc1ExG5QJ1r/ilcWHB6yF3HxF3aibVA6ofVvwtrLkF1n4IlrxB9C6JnxRBSfyUuNOVSCismrEVkbBHF3fvzZc75abFaHRXzZRSRKpdgUhNI6x8F9SfKwLk6OFsgzALj0cEBMNWGjS/h4hNU5mByEgUTc1Y36kqn85JmthrLgFMZ/CkTbHSXXCX7+YpIloQ9Lgo4U31OX/zgJUGSw2fHvfnkEwdk95H5Oabb+bVr341r3jFK/j85z8/4rLJZJJkMvsFGBwsnoOUzB7stAwI8SGlG04Tp3g0K/1+9vmlAMwLpgho5tirZgyV89UXxS95/hDI7SPiVCCYpggyWl4NC652DdO7GFpvEKPLe56Fnidg6EVQPOBrAF+96NoqmZPk9xFxPCJqXBwfTReXfC9AQ3iMikh1HSw4TwzF63wETtwJRhjCy5xlfFZtuZOaMeMiiNYC4KsT02xVj1Mps7w6Uf4HzsMORGIZjV5rzkyDSxEBuFs/j3WeYyI9s3Sj896Eu9+PC3+pVKyigAIkujAUH4bVNTbsMUjoGsmBY8CacX8WydQwqYHIHXfcwfPPP88zzzxT1vK33norn/3sZydzlyTTjHgqVzpNpK1AJB0l1nsAmEfIoxOz1BDbyW9LteUqIt7UICvUdkwUlIaWgtftGSC1vkxWtk6cFheOlqsLJ/qqXqhZJ34WXQd926D3OTHOfWiveD20DDQfkrmFN6/Fu33shhgWJbvB+SO+vyboRVMVdMMsXTVT5QpEQlY3X9UL818OtRsh1iZUO3ufrEAkqovj0ef1wtK/hMEDMHwEBndDZBXvXNtBrS/Dm1Zme3OMFbtqJqGrdFtzZuptRcT6nt2ln8cHPb+EzuOQTjkdjosqIr0d1BgRYGEJT5gHYidIZ9KA39oHnZ6kl1T/QeCqcX8WydQwaamZtrY2PvCBD3D77bcTCATKes/HP/5xBgYGnJ+2trbJ2j3JNCGWznO5p3WhRJz8HfG4UMTes76d85vEjJmV1p3aWFMzrYkjAPQG5oOv8HgsyI2bBiRPw7yXQahQQcnBVyPuRtf9PZz9JZHCiawQ491jbVYppWROED2MJyZMmAVmVS/QfNmoq1BVhXqrhLdYQzOA5ursMVwbzAt2A80iTeMKnr2aFRQFVwPgq14Mi98AGz8hjtm6cyB2nHmhNO/b2H5GlWM5ZlVLEbHTnRGvjoLJfnMRmVAtGDqcPuq81y7HdwKRrhPw4H/z2pO3oWAUmlVBDAwcPobet8d5yh7ZkIq2Q6J73J9FMjVMWiDy3HPP0dnZyTnnnIPH48Hj8fDQQw/xrW99C4/Hg64Xmoj8fj/V1dU5P5LZTb4iEk/rwnjXfjdxUwzfqvVl+P5LD/C5rUe5ZZOYtjnWPiLLkqJUsKtEyeTGuhhfv+gwX73oiLUj7RCYDwteMbYP5K+H5kvFWPcV7xD+kqF9Y1uHZOaSGcbnEz0+8jurBsO1ULW6rNWsahbH/sLa4jdxYZ9G0ApS6kKjzzeyFZFhtRkAn3uGjb9epB5hQgzYTkOzjOY0c7PNqqpiq48K0SYrZeKqnilQRA5tA6Ah2cHL1O2k8st3wamcSQ9l+4nYqkwqHRcqpWRaM2mByBVXXMHOnTvZvn2787N161Zuuukmtm/fjqbJHLqkSGpm73/C4Z+CniBmijkaQY9BtU/nrau7mB8SJ7SxKiIrU+Jk11e1tOjrigKvX9bD2tq4OBmnemHBVeLucjx4grDwVbDynYAK6eiob5HMAhQFT0iUzNqdVWNxUckSql9TmOIrwb+/+Rx+975LWdlc3NSqKIpjWK0dSyCSFN83X/48gvpzoGY9xI6XtX8jEcorvfWpRs4IBTs901Vn9RPpOCKUEbLluQGPAbGh7Fwo4J3aH4srIloYMsNkUtnJ3HblTtLwQtdjUpWc5kxaIFJVVcXGjRtzfsLhMA0NDWzcuHH0FUjmBLH8QGTgJMQ7ILKSuLvsMQ+7z0BZfUSSMVoNcbc0VL145GWNNEQPQNNFsOCVZXyCUajdDHVniRSNZE7gtS6wGQNIDxKzshyhSEPpN+VRH/axaVHNiMu01IhA3e0XKblPVmrGqZrJ956oHksVMSETL3s/ixHMM5o2BtI58ZcdiJwOLgZ/CDIp6BLfj5zy3SM7xf7UNGKgcpG2h/nJE4Ub1AKgJ8hYFXeakjWzp7QGq7R5/xl9JsnkIjurSiqK3VXVJu5rhaoVoHqzrbG1IoFIOYpItB92Pgz3/RSAA8ZClECo9PKmIWTcmk0ireIJju3DFEPVhLKiKNkeD5JZjIk31ARYVTPRQ8Q8whwd8k2sCvzxV63l/Ves4vI1o6t2jlk1WSIQAaGKVK+D2LEz2i9NzW2YVp83B6fK6kEylPZAizV40krP2N/nsJqCozvFa2suoL1e3LxenSgyo0ZRQNHIaKI9hKZk28OnFKvPSOfDZ/SZJJPLlAYiDz74IN/4xjemcpOSaU5iuCf3d5f0GrdOSkUVkZECEdOE/c/CvT+GA89BKk4XdXwlc2NBfwJn+dQADO6B8GKRTvFNYM+b2rNEc6kJkL0l0xhDBzS8tkfEVDGr1hD3CrPzRAcimxfV8qErV5c0tLrxWoFHzErNeIuNila90PJKQIHh42COvxlYaIRp1rYiMpTWoMXqdXLqEJim833ektwump0FwtCygpMtFwJwUeZZkbLJp2Y9Gb8I+Lyq6fRJSemK8Hr1PAWxk+P+PJLJRSoikoln6CAcuV30NIifduZAFCM2mHtycAcW2dbYhSfEkn1E9Aw8dw/segQwoXkxXHgdb/J8iXuN8wr6ExA7CQM7Id0rAoaV74Jw7kC8M0bVLNlbkd1YZzNmGlQvHl/YeSq9+oNZs6pv0ts2lSTbR2QERQSgfiss/yvwRmBgt+ijM8L3txTum4eGfEXECkQGU5oYPun1i6Cjt90xn18QfVQsvHQTqBqp6gU8oa/Hg+EYWPOxq5Q8qukoMklDBX+j8Hx1PTbmzyGZGir3zZDMPgwdOv4Ex38JyU5xh+WtEaWsjRdC3dnCoW9jmsQGjgPZ8li35yM2gkek2DTOE30GQw/+gXWm1Vly80th+dmgKMQNb877xP6mId0PS94o9i+0uGwz4ZipOxtqNoh8dfW6ydmGpLIYIhDx+V2BiLeJWFp0Dw2VoVxMFo5HxE7NFFNEQHhFWq4R34eOP0P7fU6PEbQiXhTTFO3ktYD4vlsEXTcP9YESgUhaE0H6/GXQ9iKcOkhCv4iL1N20Jo+I9vTLNgFi6N139Gu4SNsDx/fCxssKvqv2tGOvaubOplEU0Q+o82FxQ+Ab2XsjmXpkICIZG8PHhdKx5MacEw/pKBz+sfiye6uh9mzAFMO3+neKZl/+Rmg4H1pfJwKS2HHisdzuufGMOxApkZpJJTi3/fecpVxDQrfy46aJ+uxdrDMPM0SIqkteCc1LnLcUbZSU6hNdUFuuEfs8mageWHQ9DB2AZLf4W0hmF0aqQBHJ6CYx6+I/0amZseCxAg97FEupRmkOvjpY/BeiH8mR/xIN+4ILRQdWRRNpm0SHUBo8VWAkxHOqD8LLcnxdBakZnys1A7BghRWIHCIU6uU/vN8Uzy9eD0FRxuzXDB42ziKOj2AyBoPdUNOUs167XDrHI2KX+wYWiNRr95OiQaFkWiFTM5LyMU048Ts4+XvofT73tdP3i59Qq2gApijijsZfD9VrRGmgoor37v2aGN3dv4tYKjfIsAMG3cieRArMqge3saLzCX7u+xdWJA+I5/Y/Q8vQfhKmlzelPkm8flnOW5LFWken+qB69eQHITa1m2D+K8SMGjmbZvZhpEH14fFmA5GUbjiVYcEKBiL5CkjJ1Ew+kWWiUd+i10FmyBoAuVv0xvEEYembYPNnRN+c5W8XA/1ix8tKzTiByPylQhkZ7uc9Pf9GvRKlK7AQzrrctf8maTw8b7dr7yz0W2XcqRm3RwTE+j0RaL8b0kU8JpKKIhURSfkMHYTep8UJqf0ecbekeiDZC+33WjMrSgzzUjQxktxXL05ie78K3mriZm67azsQibtSLgUekQ7RdCysJPnHxL/BvvNg9+MAfDrzdnYZy9jVm+C85mzvDntYlhOImKYYx16zYdx/jjGjKND6WnEiHz4EVXIGxqzCTIOnGkXz4tUU0rpJxjCcwY6hCnpE7NRM9vcx3IN6wrDsraIrbGZYBFwYULUqN4iv2ywa+B34DkGtxDRroNonApOhlPX38PiEl6vjCPVGH6fNWp5YdhOv9WTf57c8H48bG7lE3QmdbbDq3Jz1OqkZxSw+mybUKlSRzodh4avL//ySSUcqIrMY0zRJZSaokY9pQsd9IgVTvR4G9gi5FuD0A+IuP1g4w6UA1QvVG0SVSv9Op3uqjR2I2GkZBTPX15EYhn4xUfNJYx0BUrD7McBke/g8/ke/HIDtPdm7Ut3IDiBzUjN6TIwQd00onRK81SKthSoCOMnswUgLTxTZC306Yzol6pVMzeQHHmUrIjaKItSR2o1Qv0XchBRTEhsvhNBCgkq2VD2/fLc6XxEBp3omhZd3pv4eM5C7bvsc8Ihu9aDqPuE0QbMpZlZNuaf1qh6RWmq/S6ihkmmDDERmMf/vv57j4i/+mcFEevSFRyN6SJTABReK2Q4Ap+4Wzcc6/gS+RqF6lIM9orz2bGKGMMDZY7ttj4i7mVmOJ82aSxGLLOBtqX/gcYSZjeoG/qfqDWBN39zekw1wkq620E5Qk+qFQBOEsz6SKaPuHJj3coi3nVGJpGSaYaTAVwuARxXHYUrXSaTFRXFaBSJjUUTGtKEqmPdyQkq2KVrjSOW7NovXwZrz+Zfg3/GCuaKgzN5WOHbqSzB9QdDT0Nues4xuFkvN5H3O0EKInYKOB4ruftdQko4Ba/Jw9AgMyPbwU4EMRGYppmny8P4uuqMpDnedYSMt0xTu+XQ0218j1CpUkSM/Faa14IKxr1f1OMqHfdeUzFNE8ttF03FUvN64nCQ+Pmh+AC64Fl5yI316tlnZ9u6sIuJuC21LvKQHhGfDbbidKuwUTXgJDJ9Z8yjJNMLMiDtusorDQDyrBkyn1MyYFZGx0HRJjh+moKGZHYikXIGIqsGGS3gOUVFWKhAxUTGarO7IeT4RRxFRTOd7nqOIgLhZ8jUIdTfRmfOSYZhc9++P8oqvP8TxUydg/3/CgX+X/UemABmIzFIG4mmSVlrGLtkbN8NHsmqILU94woAhKmL8zcKIOg5sL0iddbLK94i4c80YBnSKC3e6aTkAQ7ofFq4EX4Co6w7rZMxPZ9yTs06vaqCpZOdOVK8d1z5PCP4GYQDUE7Lj6mzCK5Q4W4EYjAs1QFEg4K3c6XbKFBGAQBPBKnFj4teMgpuJgqoZF/Z31V8wryabnk02LBUP8gIRt1m1WHm/Q3CBuHnquD/n6YF4mvaBBNFkhs/8+nHMoYNCPTnyM9CTI31iyRkiA5FZSsdgwnk8lDjDQKR/F6QHC7uNhpcBquhcOE7ieYpINjVTRBHpPQXpJPiCqPVisFhCV51+S9G8E9sOKz3jVMzYJ7NUf7a/SSVpugQaz4fo4XE1jZJMNxThOwI8lgJhp0WDXg1lsnrUlMEZe0TGSKhGNAVs9MVRyD22i6ZmLIqW2SN6g9jridVb6dS+DnE+sLADEW8pj4iNooK/CTofgmS2s3NfLOU8fuBEmHsGLhD9U3qehlN/HP1DS8aNDERmKacHs1/QM1JETFP0ANHChc2+tIAwsJ3BCTY/EMmaVcVJKuAORKxqGeYtIWBlVAxTcSTZqPWeRWHx2W3DakHpbqpPpJbGO1l3olA90HqDCPDip0ZfXjK9MU1LKcxe+AcsRaSS/hDItni3mexAJBgWjQvr/WnRxE/P3hjZgUjKUAs6I5cKRBQl+/1N+OsgXCv+3t3ZIXh21YymjOARsQnMh8TpnG6r/fFcL8tnt69h2AyLoOXEb4Un7uTv4eD3xWPJhCEDkVnK6YHsF384dQaBSPwUmaE2+pV5E7BXhdgBRzY1Y41OL6aIWP4Q5i/L6Qdin7xsReSy+QMA7LACkYKTmx4TnU4reIfqEFkGi64TDaEG9+acsCUzCNMQx5Nl5PaquamZSvYQAfDle0QmMzVD9vPWNywSw/SGDgjzZyZGxKs76kaOT4Ssn6vYTKicdEuzNYbBlZ4ppogkiykiIFQRb63oHmv1FekfEFVsqyL9LI4kaI/5+MbOhaLtgJGCQ98XTRtP/gZO3SXTNROIDERmKaddqZnomSgig/v4/AtrOe/3L+WFnhEm144TO+Ao8IjkT96NDYluiijQvAS/S6q1T17RtHjPJfNFt9YdPWEMM/u6XzOsplMeiCyf8M8ybhZeC+s+Ilq/Rw9IdWQmYrV3t1MzXo845mxFJFxBoyqAR8091ecrJBPNZasaWd4Y5vpzV4iGaCv+BoLzIdaGOrCTSIn0TPamoTBVmdO2vdkyrHa1Oa+ny/WI2ARbhBG15ykw0vQffwiA+WH47FYR4PxoXzODaY/omVK9AWo2ivYFyS6RUpVMCLKh2SzF7RGJnolHpH8nj/QsJG2o/LGtns0NEzu0zT7x1FslfvbQq+zkXcus2m596evngz+IgjgxJXSNhK5imFlF5NymKAFNZyjt4fBgwDkZ+TRTVMt4a8SU3emCoohmUNVrxJ3W0Z+Dt050rpTMDIw0KF4nNWNf+AemiSJSkJqZZEVkZXMV93/48uwTC18NC66CwX1w4rdUaQmG0hGG0h5AKAtF+/2491l1BRd1VpXeUJ/oJ6JqTmpGlO+O4BGxUT2gBUVFYGqAvp6jwDxqAzovaxmg3p+mN+nlVMxHtS+eVVC1oFAuh/ZDjZwbNRFIRWSW4lZExu0RSQ1g9u/hREx0S33ydImuqeMk42rjXsojEvIYIhd8dKd408JVzvsDrrueWEbFtHqI1Pp0NtWLgOlPJ2t54FSNtbwhjKqhVqffw7RC84sTdt1ZolJJMnOw5szYqRnfNPOIFKRmJlkRKYrqFQ3RWq6hyiv+Lm5FxD15O1B00KWrW2owAppHpMSGRSpWt97icc+aGUkRATGOInoETv6OAb0WgFqr82tzUOxjZ7xIib8nDD3PSpP5BCEDkVmK26waTY6zadbQfrqG4iQNcbLY2Rt20h8TgR1sANT5iqdmgh5DNC4a6BK9BpZkW7I75jVdZdg6oWmKSUAzOLtBlMR+cXsrP9wnqnpaQinQ42LuzXRF9YrheJpfdn+cSZh2asZSROyqGauPSNBbWfE5v2pm1KF3k0ntZqost/lgqkQgUtQj4lI5FAUiwhDLkPie2GpK2YoIiO+ZV7Qi6DNqgWyauCkwQiDia4RYm+goLTljZCAyS+mYCEVkYA9tsWyHUt1UeLYrMsIbxoYdbKiKSbXVWyC/xXvQY8DhHeINi9aAL+C8P+AKROw7q7BHR1HgsgUDznJbGqJ8+pzjfPG8Q8KkVoluqmOhZoMo7Y21yTuumYKRFqk0q0HetKuameLy3RFRNaqqRBAxlMjeJNnffZ9qoBaJH/xqnu+jymonEBWBSM6smfxlRyK0BMLL6UuKYLHGOhc1WYpIV6JIIOKtFibXwf2jr18yKtIjMgvJ6AbdUbciMo5ARE9B7zbaUrklrk92VnN5y2DB4k93Rvjp/mY+fW6bI2mORsxlSLWDinieR6SeQThpTdhdflbO+92BiO0PsUsDX7JgkDuv2kNjIE1rxOoPkOwVJ5DpHogoijCw9j4//q61kqnFnjNj+Qi8eX1EKh2IeM5k6N0kUBWpBzoYimUn4Sbyy+zzyDGrAkSsQMRRRNxmVUsR0UdRRFwMWEP4bEVkxNSMogiPSf8LMP/lZW9DUhypiMxCuqLJnBvpcQUiA7sh2UlbsgmAiGUaLeUT+cn+efz+eAO/PVZf9ibchlQ7J5xNzYgT99nRp4UZrbZZGFVdOIFIRnF6iNhufIAtjcPZIASEUTU4H/yNZe9jxQgthAVXQrJTqiIzASPltHeHQkWk0mbVfHNqRRURoCroA2Aw7RXpUrKl+8XSMoDTGySriFjnmqgou7U9Iprq8ogY5X9OWxGxPSLZ1IwvZ7mDAwGe7oyIVvEDe8UAT8kZIQORWUjHQG4vihFTM9GjcOJ3uV+m/t1w+Adg6rTFRM771UvEl72UT8RWN9qHfQWvlSLuMqQGnJyuqICJZVQUDNb1PSkWzlNDINsG2q2IuAORAvRhkfaYDv1DyqHhAnGXnZYnummPmc7pPOyxLvx2V+NKl+9OaYv3MrA9IkM0QlxM07Yr5koFIo4iYuSlZoZyUzMXDD9BuOcgINLJ5Q4g77cUkdo8RaQrkft/97YHVvOXf15Lpz5PDM8ckumZM0UGIrMQ26hqn2xKKiJGBo7dAQe/C7s+D12Piy6q+/8dEt1QtZa2YTEd94LmIVrDiZI+EfsupSNefiDi9oEEXSefpK4Sz6i8VH2BqnQfeP3CH5KHXTUjPCJiXSUDEXu+THhZ2ftXcUKtULVGdICUTG9MnDkzUDhkrtKKyLTyiABVAXFxH1KbISPSM/Y5JOAprgA6g+zyUzOpOCTjpA2V9cpRXtv3C8LP/YZaxHrLVUX6LUWkzqmaEWpql+ucNpjSOBnzY5gK7YmQOK8M7Clr/ZLSyEBkFmKX7i5rFGpGyUCk+0no2wZVq0UTrX3fgn3/IebKVK0BRaEtKgKR1nCSC+eJL/aTndUFq7Lztu2xcQQiLo8ICJ9IXFf5C000GGLxevAU5mkDRapmIt4Stz/pIfBEplf/kNFQFGi6UMj+Zpm3dZLKoOBUzEC2s6pNpT0iPs/UdlYdjeqgpYgYEWEgN9JOqrZAEUlHwcgUNinzeEUZL0C0j4yhcJG6GwDF0HmtJtq3l+MTSRvZ9G7tCFUzJ12K70BKA2+V6I0i06dnhAxEZiF2ILK8SZwYh5MZzPwvSnoITv5WNGHyVovOgcGFoPogshIUIWnagUVrJMmFzSIQeaqIT8Q+OZyOFTF2lcD2g4Q8OpoqpuPazyvpBFeqz4sFlxQvty1mVi2piKQHxMyImWb8rN0sJvUmuyu9J5LR8GQ7D3vzLvyVDkTyO6tWWhGpthWRTAB89ZDqKz5nRk9AdD8MHy00q0LWJzLUR8aEC9W9zktv0u4HzLIUkf6k+P9RMKm2ziF2amY4ozFsKa4nLIVYvMcjgs9kj5ygfYbIQGQWYpfurmgSdwuGCYl03l1Gx59g6GBuBYm3Spg5lay6oZsKPtWgOZjmAksReaE37Hwxbey87em41zGNjUZOiS7uwELh/PQ2/EqaWKgJapqKvj/ganBkl+/aptoC0oOWP2SGHfL+Bqg9S7SUlkxPTDNn4B0UXviDlfaIuAIPTVXQitXHTiFOaiaFuAlK9RX3iAwfgaq1oMdc82Ncf1s7PRPtRddNLlBfdF5ao57gbOVQbuBSArtiptonbopAqKsh63xil/DmKCJpTfyfZ4aFqVwybmbYWVlSDu7UjO3LHEq6Smpjp8T0SF+D0/egGHZaZlE4iarAonCqpE/E/rJnTJWeZHmqSE73VLInoLiucoX+BACD8zeWNJcGXHdIBYpIZhj6d4rJnwO7QPOJAXMzkYbzxL9GauTlJJXBSAslUcsqIvmKQ8hbaY9I9jtU6bQMuMyqibTotmoks1UzdlfV1AAoHmi6CDwR/IrwvuX0BnEZVpuSp6hWYiTVgOMpe6P2QFm9RPry/CE2+ekZtyIymLJaxBsJSMgbhTOh8kekZMKxzarzawKOW3/Y3V319IMigg+2jLge+0u3yFUCu6pGBDn5plR3V8SOMtMz8RKKiBEd5Bz2YZgKiZbSXVADRapmqry6aOMePQzNl8HaD8Lav4c1t0DD+WXt17SjdoNQquTJbnpid1V1KSL5ZtVKp2bcwUf+vlUCRxFJZKBqJWghEmlxjgpo1liH2HFovBAWXgehRfhNy3zqVjjs7qrRPpYkDwHQHlwKyzcDcJ32OJnU6AF8n6WI1PhzA5HmvKZmBR4RRQFMSEhF5EyQgcgs5LRVvjuvOkDEbwci1hcs1Qddjwq/xChlrG6jqk22d0deasYViJRrWLVTMxvSe+GeH/Jm7gFMqk8Lw9kTxnr8kXDhG00Tho8RULKD8hxFhH7Rdnnha2DVe0SH0qaLoPH8mTtEzhOG+gtEqaBk+pE3ZwYKUzMh//Qp3/V5KhsUgVsRyYj0cKCJREqctwKaIVKRvlrxPVY1aDgPP6LfSE5qxumu2s+qxD4AOsLLoWEhx5lPWEkS6sima0qRXzFjk9/UrMAjAoAmJ2afITIQmWUMJzMMWUHH/JoAYb846TiVM91Pi3LQwLxR12WX7rZGCgORpJEfiGSDmo4yAxHbJX9h9FEYHuA9+i/4hvc/aOx8AYA7jUudtE0OmShkhvFnxF1IQleJWkFNROmHJX8Jy/5KzJGYLdSsFf4Wc5xzgySTh1GoiBSkZiptVnWpIBWdM2NhKyIp3RBqau1mkilxjgooSUi0w7wrsunU6nXOfuekWoJVzvC7NSkRcHRGloGicJd2GQD17c+PWtXSn9dV1cZOzXQ5gYhbEbECEU9YDM6TlTPjpvJHpGRCsf0hYZ9GxO9xFJFoIiPatnc+KO7clNFPjG3RbMWMjdNELJM9sZlm7smh3F4i8YyGhs6SxEHnuddqjxNO9BA3fdyln5/TX8QhPQjB+fgD4sSf0FWiKbE/4XnnQuvrxV3UbCLUCp4qUe0kmV4YadACoGYDX0+eGTRYaY+I6lZEKn/aj/g8Wf9aIgPVa0lYwzUDRr8IQhZdm31D1Qr8AeFLyzGfKopjWFUxGTSDDIZEB+b7PReRML1Eou1w6sCI+2NXzdhzZmzseTOdCS/DaZU+l/9twB7Y5wkLtTIjv5vjpfJHpGRCsStm5tWI4XBhOzWTykD/DuGdCC4sa12OIuJKzdjDpNyekLShYOJWRMo1q6qcpRzCbyTA6+cL4VvoNkWPknuNrSSUAF61yF1GehCqVhNoPlfsSypFNCUClqrWy2dO59SxEGiCQKPssjodMdLgqc457vIbiFVaEVFVxQmOpoNZVVUVIj7bJ5KGqpUkTJHaCjSshVXvElV8zhu8+KtFD6AC86mdngGeMdY6abGEFuG7uhXM7HwE9NIdpvtKKCJ2U7POuC/HHwJZFQXNqpyZBA/Xn/ee5u0/eppO1xDT2Ujlj0jJhNJpGVXnVYlAJEcR6XxIyBdaoOT7bRIZxZmxkJOayZsJA5DMG7U9Fo/Ipeou8UvzYo4EV/Ga5L/wQO2r+EL6zYSsSbqFZKBqBYHalWJf0hmiujiJRUKjf7YZiaKK8mOpiEw/zDT4anKeKjSrVn6+qB0c5fc4qRQ5hlV/Iwm/aCUQqF9ftJrPXyVuoFJ63s1JJBuIPGmsQ7NuXnyaybcz1xL31UBsEPY/W3JfBvLmzNi4UzO2P0RTxPoHbUVEC1iVMxNvWP2vJ4/x4L4uHtw3u43qMhCZZdiKyPyavEBk8DT07Ry1UsbG/tJFPDq1LrnS3bvDJt+4errs1IzKpdpO8UvzEoKaQQcN/D74Sk5T71TT5GCkRElfaBEB6y4z4W1hKCO2GamwKXBSCS8FTJmLnm4Y6ZyBd5CriCgKBLyVP9XawdF0UEQga1gdTKRBUUj4heLhL5HGsgORZCbPJ2U3NQOeNNbjtQIFv2aQwM+ulqvFi/ufEQFJEfry5szYuKtmbEVkZbUwzToeEftuaRJ6/UStWUXJcpszzVCmxxEpmTBsj0hztQgknNRM72GRw/TWlHyvmzandDeZo0qElSRrleN5ikjuYdQe85Z3rcyk2KJY/pDmxU6QY9f0h0r5Q7zVEGolYLn/h6hz7pKq/OV3dp1xhFtF3wI9Vuk9keSg5xhVITv0DoQ/RJkG6UI7OJoOHhHIU0QgW75bKhDxiZurgokV1WKadpQQe8wleGxFxPr3SPVmaFgoUjO7Him67vzJuzZ2INKT8HB0SGx/fZ34/g1nNNK2Gqx4IHZilE88duwig4wMRCQzCTsQmV9tKSIBWxHpEN6QMk+IJ4oYVTFNXn3yR9zt/xjLY9lWys6wKk2cSBK6ljVyjcCq1AG8ik48UA/hGicQ6bVOCkUVkfSgUHW8Nc4Jqyea3Ue7SmhWElwkShqlT2SaoRSkEtypmemQlgF3IDI9viPZQMQuw7f6CZUIRHzFqmYAahrhnCv5SuDd6GjZQMRWb00VzrpcLHtiP/QVDpEcKOERqfdn0BQTE4UdvSLYtAMR8T47PROG4aMTrlbGUuKcmslPR80yZCAyy+hw9RABiFjpi+GUIWY6lEkxoypHXqBlWDQN2hJ/znnadrFXeXXq/WkUDPq6+uHkQTjwHOx9EoYLL56bMyKYGaoTJXq2/2TEQESPQfVaUBRH7u4dFoayoFfLuROddWg+MaAwXVxellSQgkAkexxW2qhqY3tDfNOgoRnk9RLBrYgU/w7bAUpU94kZNG6WbmS7thYAj52asQKSlK5CbTO0itfZ83jBuvusqpn8QERVoNHyiey0ApHWSJIqr1gup4Q31Tfh3027/1PamN2KyPQI1SUTht1V1Q5EwqZoghWlBpTyvyROMzNbEYkN5siam9K7wbwIFNW5Q/GrBi/VdvJm89cse/pY7gr3PwPrLoKV54Dlaj/HFI3LEg1LAQpTM/lzY+y7DWuCrn1iyhji+fBs9ofYVK2ErocrvReSfGZCIDJNUzODtg/CDkRKKDbLrWniJ+MRBmMxqqtyjekZK01SoIjY5b7rLoIT++D0Ueg+CY3Cc5LIKCT04uW7INIzp+M+5zy3KJyixqczlPbklvDGeoRh1Vde+rschq3eKumMVEQkM4iBuIje68M+MA3CMVGVEtXHVk1y0lJEFoZTIgDY9mfIpOkLtzJghqgyh6G3AxBS6SKlk2+aX+Ff0v/KBvUYGcULdfPFzIeGlmx+9oGfw5Gd0NvBUjrQTQWzoRXIBiJD6RKKSGZYzPMILRLL59052Se2WU14segBI+fOTCMUMWvGhbuBWHCaBCK2SXW6mVWd1Ex65NRMXdjHwlrRHXlPd+EydiDizfOIONN3I7WwZIN4vOdx58bGLsPVFFOMiMjDrpyxaY0kHS+JXW2D6hffyQk0rOqG6fxNMlIRkUwJbXeKg3nhq8a9CtM0iVt3FUGvBgO7qUruB+YznBnbyfC01UlwfjAFbXvFXYSqsX/FdXRs28b12uPQfhgaWkhm4Bve/+Rccz8ZNH6cuYr0qgt4z5YBe8fg2B7Y9TAMdMG2Pznb2WGuYGHQB6QJaLlRf4FZNT0gPBJW5Y8/785pVlfM2IRaheE4PQj+xkrvjQQAs0AR8UlFZFQKzKqZkVMzABsXVnOyP86u/iouzHstbRZXRHJm06y9EI7vhe4T0Hkc5i1xApFaX6aohc42rIKoIqz26tTYgYitiEzCzBlbDQFIS4+IZNJJ9UP7vdD2a4geHfdq0rqJbqUpgl4NencQVoWxyp7FUg66Ad3WkKd5wTTse0a8sO5C9EgDf9a3iN87johtDbSxVd1PGg8/X/IhPp95K0dT2dp+FAWWboAr3wbrLxZKicU9+nmO8hHMS8UUKiKDYmS4ddLPv3OaE4GIr0YoQtKwOr1QcgMRd2fVoHd6HJe2SpPfbK1SVAfzFZGRq2YANi0UaY9dA41g5p4fdDs1Y/3p/VqeIgIQqoJlm8RjSxVxKmb8+eU4ArupGWSrCO0UjuMRAXEMTGCr95hrUKmsmpFMPr3PiUg61SuCEWN880QSrvr6gMeE/m2E/SLFMpZApCfpQTcVVMWkIX0ahnpFQ63lZ+HXDB4yzkJHgcFuiA2y5NSjADzku4hAbS1QoqmZPwRrL4CXvYmBK97Nm1Kf5Pv6q1zTd3O/wPmBCaYuPBL2Z8y7c4rMhdQMQPU6yMgS3mmBafV1yfeIeKQiMhrV1ve1L5afmim9fxvsQGSwqaC5X4FHRM3ziNisOV/Mp+nrgPbDzvC6/NJdAEwzJzWzyDLv24pIvzsQ8TeLc3nf8yX3fyxEXXXKtg9utjI9jsjpSucjcOhHsPtLsO0foO3/QE+O+rYxYejQ+bDozhdZDj1PQ8+T41pVwir1UhTwJdsg0UkkKDqODmfK/6+2O6o2+NN4OrJ9PvD6CXgMBojwAqvE8y8+xbzBA+imwj2BV7AgJO4eTo/S5n3YU8MTxgZUVXFyuoG8VEzOwDs9JbwRlj8EClMzVXNBEQGoXi08CTIYmQaYIkjP84i4UzPTpaTcN80CkbXzxTiHHW39JNK6o4jkf6/dbGwRgcihaBWxxHDOa9nUjDhvFFVEAAJhWGGpunsfpz8p3pdTMdN9Au77CfzppyzwZk3+C8Pi/FbjtxUR17766wEDjt4ByTOflB1zpWZSUhGZo8RPw+GfwsnfQf9OiHfAkf+CvV8V8ttEMbQPhg5CcAF4IqIxTtudkBq79O72hyjRw5AZJhIQisjwGBQRe+T1vGAaTlmBSMsKIKtaPGhYX+Sjwgz7e+MiBn2NzLcCkdHavNuTd91D7UYMRFLd4G+CyArnKa+m4J4tNieqZkC0eq9aCfGTld4TiamLADlPEckxq06T1Izd28Q/TVIzq+dFaKkJkMwYPH6om+QofUQAmqr8zKv2Y6Kwtyf37+qYVZVcRSSVr4gArN4KHh8MdNPQLdoI1Ph0yKRhxwPw8P8KJXiol02dDzhvy1dEclIzAOHlED0Ix/+3IHU0VnIUERmIzFEGdolUSc1GcdKPLBP+hL5tsPuLMPDixGyn60lRE+8RkyUJL4XoITj5hzHnGnNc5wO7QPUR9llfRkMtlChLYBtV1/pOZ5v/LBABgN8KFu7NnJPznm9nrsOvGU4gMpj2EBtBhYlbr7mDjUCeJyRn8m6qF+rOEZODLRRFyTlpzZnUjOqBeZeL48YoPchLMgWYuqWIyPLdsaIoCpevbQbg7l0dzvOjtcO3VZGdA/U550g7ELH/9CUVEQBfAFaJoZnndt+Hhs5mcz/8+WdwaLtYZt5SAJpPPcNy5RQgSnfB7RHJ+79VPRBaIuZ6dT8x4ucYjVyPiEzNzD1ME7qfElUsiutPpAWgegMku2F4AlSRZA/0PAWB5uxzqgcCLdBxDwzsGdPqHEXEo8DgPvDVEXb5LMpVRexZMS9RrFxnQ4uQM8mqFnuNRZghIa0eC6/lRXMxfs2gymsQsbY50hReO0hxG1LzFRHHI2KkARXqNhesJycQmSuKCED9VqGiJQq7REqmENMoqoh4VVeL92kWiEwXsyrAy9aIc999e7LH8UiKCLh8IkPNYGRT5Rkzv3y3hEfEZuUW8AVoSHfxU+8X+auO/4DhfghG4OLXwSWvg/nLUEyDT3puB2BhOAk9p1iWFEpxgSICorIP4NTdZ2RczamakR6ROUisDYb25wYINooifiYgB0jP06Lu3N+U+3ygCTJxOPaLXB/A8DHxUwLHda7posufrx6PCkGr9Xq5PhFbEdmS3CGeaHEZRJ1gQSGz8nwI1/JQ/TVA9g5knqWKdMR8mKaowsknG4gUDtSzcdSSZDcEGkVH1TwCrru7OdFHxMZXA82XQqpHDsGrJE5qJjcV6Z5wO10UkRXNQnVdaf07HbhkZQM+TXUMq5qqjBooOZUzg405nUwds6qdmrHORwUt4W28flh9ntgPbTcKpugz8oq/gvlLrY29BBSVK7RtvC/wBzbsuR0e+gUX7vsRK5UTpUdZ+OrFTcIZdFodllUzc5z+ndZwtRId8lQ/JNrHv35DF9Hysf8R21CK/DdEVoj0yqm7hPx+6m7Y+XnY/x8iyCiCrYgE1BSYGecuLewVB3G5lTNdcS91DLIwflg8UTQQgeHWs+Hqv+akZ1HOa7Zh9b2PrmTt/5zLhv89h8dPV+Vsw+5kGBpREbF+T/VC7dngLTyBzllFBKDhQjEAMN1f6T2Zu5iG+P4WlO9Ov9TMLVes4rGPvZwr1s2r9K44hHweLlieHT0RKCNttHGhUGIPDFaTcBlW00auWdXxiBgjpKSXn8VRdREnzEaeXvkWOPcqEaDYVNXDirMB+DC3o3UdBUDB5Drt8eKKCIhUeyYKiY7ir5dBLKePiAxE5haGLnJ7nnDpAXFaQJhXx3MnmhmGwz+CQz+0xtkvLrENv1BKTv0RXvxXOPQDcfc1dACO/7rotu2qmSBR0LIXbTtVUm4gcjru4xXa86iYUNME4WxA5lFFB0LAmcDrtHi3Agl7KFR/ykNSV0noGn8+UZuzDUcRcQUf+X1DQh4j64GoO6vovvrnciASXgJ1Z0P8DIJiyZlh6kBhasZdNROcJkPvVFVxOpNOJ+z0DIyelgEx0LMh7EM3FV4cEJ4xwwSTEn1ErPNTf1Ijkck7p3u8vMvzaS5Nfot08/LiG1x7gZOaZtEaWH8JAK9Rn6S/lCKi+kXa6Ay+m26zqmxoNteIHhZNxfwj3DVoQVHDnhkuvUwpjvxMBBfBFgiNMg03MF9sp/sJcdEJLxaBy+n7oeeZgsXtPiJBJZYz4C5stS2OFumu+uCpar60fVFO+uR03Mv16mPiF5ca4uyWM8PBCkSsOw6/dQfyoU0n+dnL9/Hrq/bwoU1iNLY9QtsmVsysmq+IaIZIy/gboGZdwX5ArrFtzphVbRQFGi8EzHH3npGcISXMqu6qmfA0UUSmKy9bO7ZARFGUrE9kQJzn0i7Vo6CPiKFwctjHy3+/iUt/exYHB3LPRb0pkVYr1dAMXwBe/ha4+q/h/FfBirMxVQ/L1Q5WGm0kinlQFAVQIH5q1M9TCnvyLsz+Fu8yEMmn/wUx4dUTLr2MGgA9PnZJPNUnGt4E5mcNTSOhKKJ5Vc2mbFWNz+pYevx/Cnwq8ZRVP68mhWRvEbECkeF07n+3bsCHn1zOt/cs4KH2Gue5dcm9XKrtxlRUWFzEl2EFDIWKiNUPxGNy6fxBzmkc5pwmEawdjfpz1hEv16ya6hF3/a7Pk7MvnjmsiABEVlot32Wn1cpQwqyao4jIQGQkljWGWWYNtPOPUjFjs7FFnA92DzSAaTr+EHCZVV2KyBe2tdKb9NKd8PLWB9ZwclgEH0ldcdIrtUUG3jkEQhCutTbgczwkr9aeZLBUekYLihvbcSIVkbmKaYoqFm/1yEqFFhClkyW8GiXp3y38Dr6G8t9jm2PdhJdbJb6/z3naNqsGNSPnPRHrYp8/b2ZbT8Rp5b5vQEi2PXGNT3p+BoC5/Ozsl89F6UCkMGpfEhHjutui/hzVJWbti9us6s+fNaNaHQ1ri6dlIE8RmYuBiL9BKGWpCTBPS8aOqYtKNyX32PNqbrPqHDwux8jla4Rhv9Tk3Xxsw+rOwUYwkk7FDGRTx/b56HjUzx+O16MqJq3hBO0xH2+9fzU/O9DEy3+3iZShoikmDf504YZKoCxaDcBr1CcYSJa4jHoiEDspCg/GQSwpPSJzEz0mGolpI6ghIE48GGMPRPqet+6ezvAOSdWEf6Tn6Zz0UNwybuU3ULJTM0N5HpH7XL6NA/0iEEkf3slq9ST9RFDXXVB0834nEBFffrs8rlgg0hJK4VMNUobKKVeTM7uhmXuwnaJYFT8WIbNf3O1Xry66H8Dc7CPiRlGgdrNQ6CRTj6mLipm8mwVFUZx5M9PFrDqdefWmBQAsrg+NsqRgqaWgdCTCoMeLKyJ503ffvLKLX7xiHy2hJIeHgvzjM0s5GfMzL5ji6xcdFmMxymX+cuL4WKx2ke4pMXH3DA2rUdlHZI6SGRajnNWRu4I6pPrLWuxg5xCd3e3Qvwt8EzQx1d8k5tO4eo0komLyY8CXKxNnUzOlA5H9A0FIJWg68jAA/+29TuRGi206ryzO/jd/VgyI5kKLI6LW/4jLJ9Jp9Ripy8vLutcRNHtES3eX3yUfdyBS5R+5rfyspWqFCI711OjLSiYW0xAKaRHqwj5UBerDZZ5P5jBbl9Zz9y2X8fU3llY/3djqZzTjzQlENMV0YkL3jVGNL8Pfbz5BSzjFf718P/OCKWp9GT6xpY2Hrn2B65eOUVH0eHlWE32NwqdL9HvSQuIGIT6+QGQuVc3MwVvIEchERfOssgIRBRIlImEX3dEkr/zGIyyvU7j3wl7h+ZgIVC9gQu82aBC18InhLiBCMC+ydwIRVx+RQ4MBDg8JFaSKGKuHtmE++Xt8mTj7jYVsq7oQKN60bSypGYClVQkODgY5NuQHcePD7j5x57OuLndeitsnEmQIajeOmCazUzOaqozakXHWElkugrV0H2jTpzRzTmDqokKiCN95y7n0DadojBR/XZKLPXumHOxxDgldI5NKkPbkDryDrCIC8OHNJ6iz5sOsqE7w0HUvoClZ9WQ8PO8/h8tiz9LUswfMCwrPU4oCmONu9TCcY1ad3YqIDETcjEUR0YKQGN0R3TGQIGOY7O8xSRg+AsoEyrS+BpHuSQ+CohEf7gEiBQFB2PKIuFMzthrylZr/5vrEH/EpOnSDgcI/Z/6KxaHSEbjdij2RsQOR0qkZgGVVuYpIIqNwcFAEQRvyAhHbvOpTDSFtR5aN+CewB2RF/B6UkXw9sxlPWDR7634CAjIQmVJMAzzFS2LPXVI3xTszd3APEhxOZ8iormZmRgYUlZZwio11w9T5M7xpZe5NYzH1dqwci6whOhwgkh6AgS6oLdYA0z/u2WTDctbMHCUdBfTyPBxaQKRGRimbTGayr5/MLBphyXHgbxTlrf27YXA/8ZQwW+X34wgXSc3cd6KWc5T9vCH5O3yKziFjAUcXXMK/N/09jxqbaA6WNm7ZZbqOImLkVs3ks6RKGFbtEt59AyF0U6HBnxaD9dzrtoKZoJYR/pDw0pH/BJYKMieNqm5q1gk1T3ZZnVpGUEQkk4ffozmG4OGMN9tVVTVhaC8M7cermvz+mj389GX7mYzxOiG/xvOGNYW8t4Tq4Y3A8FFrTMXYiMmqmTmKPkzZfxI1INzQmZFb+CbT2aDgRLpIxHwmqB5Ahd7noX+3ExgE85SJqrxApCvuYUd3kM97fwTAU4ELuCL1Nf5YfR07dNHUZ16wtN/AVkSSjlnVCkTUUoqIHYiIE7adlllfFytQM+3UTEjLjOoPEfuSVUTmNJHlQqXTY6MvK5k4TL2kR0QyudjpmeG0B2twL17FEBVMiuq0V58sobTGl2GbafVZ6i3hA/FERC+oROeY1x+VVTNzlHQUKDPy1IJllfDao60BTgxPQldDfyP074C+Z0gYYv35/TjswXdRK5Vy/6la3qL9ifXqMfD62dlyNQAHB4LOnJl8pcJNoMCsOnJqZqmVmjke9ZMxYHdvNhBxsDqoBhxFJDWqPwSyZtU5WTHjJrxEHAtjreSSnBkjmFUlk4tjWDVDZDLi/OFRDaGk1m6C2PFJ3X6NT2e7YQciJRQRT1ik/MdYOWOaZl5DM6mIzB0yQ+Uvq/nBGD0QsXt7AJwYngQJ198gekjEO0kgToiFqZncWTNPH1f4kOd/xYsbLqG1QTy/3xWIjJSaKWVWzQ+AbBaEUvg1g4ypcirmdxQRxx+SGYbepyHZ7ex7UNNH9YdA1qw65xUR1StOvrKx2dSiIAORCmF/54eNMOmMUHA1MhBqhUWvE1UrkxiY1/oybDdWiF+ifZBKFC6kaIA55lbvyYyRE3xIRWQukewtGF5VEntQ3SglvMlUdkz1ieFJKOOzuzqaKeK62Pf8gMCdmjkd8/KS7t9TrcRJVC2AZZtYXSN6UBwcDDgNzppHSM348wKRhD6yR0RVso3NDg0EeNHqWbKh3gpEUv1QtQriJwmoYl9DXnNUfwjA+gXVKApsXlRiQOFconq18IhIn8jUUu45QzKh2KmZqBFG18WNk1fRhZJavQYaLxKT1Cfp+1Dty9BHNe2KNT2973SJJTUYHps641ZDQPYRmVuk+srvIeJ+zwgkE1Hn8YnoJJnaIqugap3TNr10akbj/l0xrtcex0AhsPVyUFQWR5L4VIOErmGiWF0GS8xdoJgiMnJqBmCJlZ65/1QNCV0j5NEd7wjpQeFxqF5LwBR39EGfd1R/CMAFyxt4/h+v5ENXlm56NmcILRIVHNInMrWoMhCpBI5HRK13FBGPaoo0paJAyzVilEZy9DYL46HGagm/W7FUkVLpGV8NDOwekzrjrpgBqYjMHUxTzI4ZSyCieEZtVpNMZjuftk1GagasoVseJyDIT81EnM6qKhtO3A3AiYYtUDcfEE3HVlRnZcWmQBpthCPDr2XNqhkDMubIfUQga1i9q00EF+tqY6i2/cPMQM0GWPKXTnfDUDBStsusLuybu6W7boILxHiC9BhSjJIzRwYiFSFilfAO00AmIxQRj6pkldTIUpj3cuHPGGeb9ZGo9YlgYZsximE1ME+kZroeL3vdw1YzM/u0ljFMzFmsdMpAxEZPiJ+xBCJacNRmNYlk9u60O+EtHEM9gcR1e35L8T4irzCfYTMHieNj/rlbc5ax0zMwsj8EchURu30ylE7NQNawaqd+HH+IkRIn8tAiqN1MoGqh+AyBUdrsSwrRAuIkPEoll2QiUWQgUiHCPtusWuXcDHk0T3YwKEDrDSJFEz3gGOInClsReSJtlfD2tRdPAymaMNB2/MkqiBidYau9e3Uge2zNZsOqDERsnGZmYzipaAHhK9GTJRdJJnMj8UkxrFqUTM14dfyk+JjnvwHYXn85vkjuhX5VTiAycqtwO+BI6KqjwkB27HYxllblGrkcf0h6UNzFh1pBUQjUibuLYFg2gxoXVatGPB4lE4059nSuZEJwUjNGgIx1jfb4grlKqicIy/9aeEaGXpxQv0iNpYjs1Jdhqpowqw6XMIsHFwqfSM+TZa3bTs3UhlyByCz2iUxqIHLrrbdy3nnnUVVVRXNzM6997WvZt2/fZG5y/IypvbuFak/h7S+5SDLPST0phlULewhdfiDi10z+1nMXrWoXHWYdK85dX/BedyAyUumue/0iELHuRBRjxKZBSyO5F0endDc9CMEWEYwAL9/QyrLGMFdtnODmb3OF0EKRqjNnd055+mBKs2qFcKpmdD8ZU5xXvZ4iN3qBRljxtyJFMnx0wrYf8hh4VYM0HtLVVkfjUj4R1SNKedvvE9eMUbDnzNQGs8dW2pi93+lJDUQeeughbr75Zp588knuu+8+0uk0V111FcPDw6O/eaoZ68A7KKuXSCKZ+1knSxExTTF3AYqU0caGeK/2GwDurXkNzVWF6aGyUzOxNgJJ0bI4qauOYXW0lsnzrRJeEEGLsz09JrqCWncx5y2t54EPX87laya4+dtcIbjQ6l1QngQsOQNME0yZmqkUYdfgu7QiSqg93hK9mqpWiDSNPjxhiqGiZNMzQxGRUi7pEwEILoLoYTE1fRTsybvV7kAkM3sDkUltvnD33Xfn/P7jH/+Y5uZmnnvuOV7ykpcULJ9MJkkmswfJ4OAU5rozUWGaHMssGKtslmQnsLboIslkviIyOYGIO0WS7xFh50OElQTPGavYsHkxUFhVYVfOpAy1dFfV2EkwUgR84jMkdGXUgXc2qgJLIwn2DYRYWZMQ6R37rj3UWt6HlIxOYJ6oFLBTXpLJw9Qto7gMRCqBY1ZNa2Q8ViDiGeFGsukS6PgzDB+Bqompsot4dLrx0h9ppYFnSisiAJpPHCvt90HDheL3EtiKSFXAg6qAYUqPyIQxMCDyZ/X1xcsyb731Vmpqapyf1tYpvEBlhgFlbP2AFUXMmWj7NQwfK3xdT5BMiwOqwS9UhhPR4gdfW9TH6dj4T2hxPftfmaOIdByBkwcwFYXIeZdzbnPx0k5NFZUsAK2RIncM8Q5xN7HkTQR84q4jmVHLKt21sQ2rOY3MtLAMRCYSVRMnWVk5M/mYhtXHR3pEKoGjiKR0Ml5xTfFoI5y/NT8sfJUwreoTU0Vjz/HqCljnsIEu0EcwxYYWw9A+6H1mxPXa7d1DPg9eq4RxNpfwTlkgYhgGt9xyC5dccgkbN24suszHP/5xBgYGnJ+2trap2r3xS9mRlTDcBge+UzhPINVPwjom7fLYYiW8sYzKq+7awPX3rB+3lyqeEXcHXtXl1dAzsOMBAJQVW1izJDLiOr54wVE+t/UoF83Lu4il+kVp85K/hJZr8FeJsl+3R2Skihmby1v60RSTKxdZqaz0oHC4BxeU9RklZRJZBow8jFEyAZh6tqGgZMpxzKrJDJna8wDwjNR3AKDhfKhZX/zGcRzYrRG61XoIhEVwemx36TdoAdH24dQ9oJcuCohZqZmIPxuISLPqBHDzzTeza9cu7rjjjpLL+P1+qqurc36mjDLLqgpQVKheB4P74MB3IeVyTaf6sY4nVlSLCPxkkUCkPeZjKO2hI+7LSbGMhaJG1RefFi7uQATWXTTqOtbVxXnr6q5sfw9n5e3QcAEsfI2obImIwCGpq67JuyWi9cyw+NIbGd60spvdNz7HK1v7rdcGRTdQdY63Z59oggsBdVwTPyVjQaZmKokzayaZIeNrAHAm8pZE9ULLq8TjzJl7FSNWGjya8cBqqyXCrkchPsL1JLQYBl+E3mdLLpJVRDRH5ZGKyBnyvve9j9///vc88MADLFo0TashUr0iUh0Pqgeq1kDfNuh6NPt8ut8JLFbUCEWkO+F1ymxt+pLZ7cYyY/CouLBTM44/xDTh8Hbx+KyXgnec8rGpi5+GrU5b+0BEGEmFR8RKzeSX7hppGDogBk95QjC4B/RkrqnV0EVHVcnEElooG5tNBXZqRlbNVISsIqKTttQCTS3jklZ/LtRtnhBVxFZEohkNVpwtmkRmUrD9/tKlwlpApFDb7yl5s2B7RMJ+Dx7VTs1IRWRcmKbJ+973Pu68807uv/9+li0bfYhZxRhPe3c3ml+U8w7scq2zn6QhvixNgTRVXnFwncwr4e11ByL6+P5LEvk9RIZ6IZ0EzQMLVo5rnYDok+KrF51PLQIROzXjMqt6DPHFSw2IAGToRdGkbM0HYMMnoe4c8VwmKu5E4u0igJP+kInHVy8m8Y5liKNk7MjUTEWxzarRZAbdKm31Fsi5RVA1WHitOGef4VA8JxBJa+JG7Zwrxb/th3j4udOlU+22KtJTXBUZtmbNhH0aPksRycjy3fFx880387Of/Yyf//znVFVV0dHRQUdHB/H4xLfbPSNME9JnGIiA8DsMHcqmZ1J9OSW1C8MiJ5jvE+lNuAKR9DgDEVsRsQMRu4ysbh6Uc5dQimSXmOrqzxqM/aF6Z5vOwDslAwMvCGWpeg2sfDds+jQ0XQyhFlj7fmh+mWjqE28XJ/DGC8uasCsZI4oC1WtlCe9k41TNSLNqJXB7RGy1YESzqpuajdB06RkPxYu4BoqK9TbCGuFXWXfsj7zYXeLcqwXFsdNxb9GeP3ZDs7Df4/heZrMiMqnJ+W9/+9sAXH755TnP/+hHP+Ltb3/7ZG56bBgpMYvgjAORWogeFOVhvrMh3kHSaAREINIaSfJif6igqVnvRKRmbEXETs3YZWT1Z2AENXTAhPpzcp72+8Q+JnUtG4gwDPVbYelN2aFTbrzVsOpd0Hyp+DsFW+T49MkkvFg2NZtsnKoZqYhUAjsQyRimk8oY1axqoyhCFel9HuKnRDpzHGQVEdd215zPqb2HaFG66e46AU0txd8caIGhg+LmtXpVzku2WVUEIpYiIj0i48M0zaI/0yoIAaur6hjbuxdD9YrSsKFD4vdEh5Oa8Wsmi8KifDV/Cm9fMrvdcadm9LzUTJ8ViNSdQSCS6rHSMrmdWANeEYiYKERTViCiZsSAqcjS0iXQml8ENZHlMgiZbALzROpLGlYnD9lHpKLYs2YA+mOuoXflEmoRE3pTPdnviWmOSSHJDhTN3kCaqocnjHUA+AdHaHDmiYg0dd+2gpfcZlWv9IjMETLDY2/vXgpPGPpfEB1X0wMknEDEYJGVmslvataTPPPUTNydmkmnYKBHvFA/f1zrAyDZDbWbc4dIAQFP9ks3YLUc8fuCUL9l/NuSTCz+RtGjJVO8b4xkAjANcc5Q5Gm0EmiqQtC6KeqP24HIGP8v5r8CqlbCwG7h7xvcJf61f0YxtNoDRYddSnZcV9lpiJRzeHiEBmeKAt5a6H6yoNurrfBEGMSji1T/bG7xLusmwaWITEAg4q2F2Alh2NQTJF0ekawikrudiaiaSbhTM/2nAROCVRAcuXdISYwMYBQNLryagqKIG4eBhPhy+CPzpcoxnfA3iKBYHwZqKr03sxNTl8d8hQn7PcTTOgOWIjJq+W4+3gisfBcM7QctZHk3FBEYJDrg+C9FMO8JFX17ldusajGY0pxApHqkQASs+TdHYGBPzrnWbvEeGn4Brx4D6mZ1HxEZiICliGTGX77rxlcDg+0i96gnSBriAPVrBousjqX5iojbIxI/w9RMUDMmxh+S7BZ31a5qGRtFUQh4NOJpncG4+Ex2JY1kmqB6RN67f9foy0rGh6mLzsqSihHxa3RHYcBWRMYaiICYQ1O1ovB5IyOUksEXRXuGIoSLpGYGUxp7zCUYpkIwMyR6ipS6IdT8YrRI7/M5gYijiAw9i1cRVY/SIzLbyURBYWzt3UuhaIApfCJGOqfzaEtIpGZ6kt6cxmXuqpnh9ASYVe2KmTMJRFLdUHeuCKyKEPCK7Q1kxB2hPxAe/7Ykk0N4MRijT/qUjBPTEBcSScWwDav9cXFuLauPSLmoHph/pTDtuyfm6kkn5ZmtmsludyjtIU6AQ6ZlUu3P67idj68Rep9zqi0NwyRmle+GMifxKOJxWs6ameXYc2YmCk8VZAYxTIWUq/NorU/HZzX+6oxnDW5us+qZKiIBVXcpIuNUKTJxoQ41nl9yEduwOmCIQMXvkYfStCMgJxhPLjI1U2mcQMROzYzFrFoO9VtFRUvMGjeSjop+SEP7wTSyqZlMriICsNO0WhOMEIiYJkJ5TnZC/04AYunseIaIlsSjiABkNk/flVcPEB0oz6CWvABfHST7SBnZgzOgGSgKzmTb03HhE0nqSs5BHMucmVl1ntkDyZgw0NWO80KUaIfw0oJqGTd24DFgip4ifq88lKYd/iZEq/cRhnBJxo9pgFpi7LxkSrDbvA8lxli+Wy6aD+ZfJTyEiU7h52i6THiwUr1Zs6o7NWM93mWMHIh8eftCtv76bE7EQ0JJ73kSjDQxq2JGxcQfanR8L7Kh2Wwn1S+67U0Unir4/+29eZhcZZn3/zmn9qreu9Nbks7CkrAGTCAsrkMUHAZUFJVBRHT0pwOvC16IM/6QmXEUUcaZ0WFweWd0fjM6LvOCC77oREAQhyUkBAyBBEJIIEln6U7vtZ/n98dzzqlT1Xt3VVdV1/25rr6SVJ0+9Tzp6nO+dd/f+75TfSRV7iLlDIXrjGrl3mtP2vUaVWHuQsQxq65Mv6wfaGrXXVVni1I6VdX+uinLEp2IyFDKrgryF/H/TygOoSXaZJeVypmSIGbVsuNERBxmbVadCW0bdcuBxCE9p+bk66H5bEgcdlMzKSs3iTwnRFbq7x84POFpf76vlb5kgG1H6/R8qGOPwp5/YWRMNyKM+dMYoZbc9N305EPyqh0RIqC7gRazO6JhQN0JJEK6fblpKDe8VhgR6R8nROZYNWNHRJYm7XKzuaZlUv06olPQxKyQkJOasU1ikpqpQEJtuhJASnhLg7JEiJQZp827g6/YqRnQP+OeK2H1B2D1tdoX1HYeGCYxI/e75VTODNupmWfVSiwMbVZN5P8OJjKGO+rjeNKvGz5GV8Ch/2bsRT0YNubPgGG6UZ5McpDpGIyn+fnTB4mnqmv6ttw9wI6IFLlNc7CZpKFLvpy0DEBHRN+4D8eLGxFxUjOdif36gbkaVROHde+QyNTfH7aFR8Y2UIkQqUB8QYh22SW8QtExDGlmVmbqCiIiRU/NOLRugGVvy00KbzoDosvwpXqJ+m3Dqv0hcihtt54nwjH/En18QXpm73AYZfsSj9tRZQINUHciI0efBSBqv7XchmaJoWmX+c+/fZGP/+dT/GjL/rnts0zI3SOb0mbVEsyLcCfT+nK5vU67cqZ3TL9eXyL/QlY4mXemJDImMeK0xg/oB+YiRLJ26K/t/GkPdSIiDuGApGYqkuiKfMe/UEREiJSbcamZUkREJsIX0l6R9ICnu6q+djsREYB9PnuoZ0F65qXhXCQt78OoP8pYSE8krws483Nss2pq+iGWhwb07/orxytsnts0iBBJD+oSxxL0A3DnsJg5I+xkEZEWhmhgJK9DXyGvjAR5aWjidcazJueZOzGxINaov2bL2MsQW67V/jSECyIgEhGpUMIdwOIt+ysvSgbelZkFi4hMRMt6CDRR59PXdMewOuQxru719ei/FERE9gxOIkSAkYy+xjuRloB9/8gkpk/NOMPynCqiakHuHukhXRdegn4AycL5L0CHHRE5PJbziNQxxm/CN/Gr0GchPfGn16wF7/rvNVzxq5MnjJokMiavM3X5F+0rZr/Y+CEwArDiveCfvhKgMAJSGCERKoSwHRpW1ZUzrhokIlJWCiMis5o1M1+iy6DpdOp8OvpQ6BEBeMFYqf9SIEReGs5dYydLzzuRloDhCJGBaQdZDttCZDBeXcZWESLpQbCSJYmITJiasSMivfEASmkhcp75HC0M023087bkryc816ERg3+3buGXvps42j9erCSyJq83n9H/6JilEEkNapNqzzt13fwMCAckIlIVhJbYhtXqCtVWDSJEyso4IVKKqpnJMAxo3UjMr6/pw+l8jwjALsO+Fo8NQTL3O7hnaPKIiBMVj9qlwX7T6SOS0h2vp8CJiByXiEiVkbbDXSUYXOXtqurgVM0ksj6G0j76kwEuNHNtuN+R3azftAVYu7dxsnmApUYfzc/8XHf789CU6WO12asNUEuWz3yR2aROyXRugu5LZ9xdtrBcV4RIhRJqs2fOSOVMSTBEiJSTwqqZWQ+9my8Na1wvhyMgvO3e+7MxqLOHhh7SU9mVgpemEiK21yRWkJpJZ7O6mGAKRtzUjEREqov09E7kueJ6RDwRkbBf0RjUb5bDYwGOJ/2uEBlQMUJk4Nnf558oPkLXq/+jl6t81A+9AjseyTtkXfY5fWjjUgjMMLqjFIy8qGviV149q14q4yMikpqpSPwRnZ7JSOVMUVEKULoySSgbseAC9BGZilAb9VGdZnFSM0Oe1Ew8a8Iq23P3/ONgZTkcD+R5Ad2qGRvnuVggPyKSyRrTC5GEk5qRiEh1keynqO3dvaeewCMC0GlHRXrjQUiMcLJ5AIXB9emP6wNeeR6O9+a+Yef/ELBSbLNO5AbnmBe3wYEX3EM2KF3ylWhdNYsFHtHN11a8V0+hnAWFHpFCYSJUELEVEhEpOhZgSkSkzBSmZkrSR2QqDINYTHeXHknpa6BXiCSyJqw6E0JRHel+eYcbDWkLa7EwlvGR8Mwey6VmCiIiyoSxA1MuZ8RjVlXF7BZeYuTukThcsgmaE3lEADqiucqZE5O7ARiJdfJ76wz+T/a1+qCtm+HAbug7APu0yPhC+hp+bZ3DtubX28f8NwweBcviPPQx1kyNqlZK7737Emg4edZ7E7NqFRHuQipnioyydFtu8YiUlcKqmcBCVs04a6h3hEiWtGUQz+auhcmsCf4ArN2oH3j+CfYO6OfPbBnFb+h7w4AnPTOWdsyq+jm3aoYgjLw06TrSWYukPY8mYylXlFQDtS1ElILk0ZJ1R0xM4BEBT3fVsSBn2SmVTJsWEHek34PyBWDoGDz+S3joxwA85DuXp9RJAPyy/jJoWwaZFPz+p2QPvEiDMcagiuJvmeF8mZGXoOEUWHrpnPZW6AkRj0gFE2rTf1bRJ6SKR2VFiFQAZa2asamrs4VIMpNXMQO50RusPB0i9ZAYoeXgUwCc2BinKaSjHt4O2yMFERF36B1BHRGJH5pwHaMFwqOaSnhr++6RjdvNzEoVERnvEYFc5cyLg2HOM+0uel1LMVAcopX+C98PJ2+AaIP+Bl+AL2fe637/sWQYzrsM6lsgMYL55P8F4BHrdMIzGS+T7NMX0BVXaiPjHCiMgIgQqWCCzbrfhZUs90oWD8rSBnfpI1JWytpHxFlDWL8HRlNGnlEVyKVcfH5Yq6eZnz/4AHWMsbo+QXPIrnLxCBHnHM5kX7dqhgik+uDQxJWVzuA/h2ryidT23SM9qLtOlqCHCEyVmtERkeH+IbqNfpIqQKijm4hdrjUSWgKnvw4u/iD80dUMv/5ankvmZsf0Jf0QDMOF74BQFMP+pPuwtW5c9GUcmTGIH9ATJZvWzXlv4xuaSWqmYgk26aifdFgtHhIRqQjCARNvEKQsERFnAnA2yFBCi4ewT/+ZUSZpy17TitMg1kiTGuJfg1/lpOggzSEtFrxC5LA9ELXd/sAaNO10izIg3A2HH4aRvePWUZiKOV5FlTM1LkTsZmZmiVMzZmFqRr/Blo1ps+kfjBPB53frxkedcJ5hQFM7+8lPt7ht4aMNcOE7sHxBksrPY5w+dfWtlYGRF2DJBToaMsNS3YnwekQMowxudWHmBJvAFxEhUkxUVkdExKxaVgzDyEvPLGgfERvn9UeyUYbHdHWaIyLAExUxfSTWX8aginKuuYszd/2AzoA+3hEiSsFBu9llt/2B1Y2IWIZOs6YH4cAvx6VaJTVTraQGQaVL9qnGrZrxF6Rm7DfY+eZOAHYG1gC5nGC8oM37fnsugaOy8yb2NrWzb+MHeUfqbxjyN02+GKVgeDfUnwyrPjBvX4w3FRP2+zDmIWqEEmMGINgqQqSYKAuQiEgl4E3PlMOsWm/nw0dVHUN2R9O2cE4UJDydsF/y9XBN6i8YVlECx1/lk8P/TIiUK0QGUz4Sttm1o0CIZCxDf+qLLoe+x2FwZ946hguFiKRmqgS3mVmJynctJyJSkJqJpGhkhPNtf8hLYUeI6OMKJ/DuH9GpozNbdAlmf9KfJ4aHA63sVCuJFKSA8hjdB6EWOOHPINw2903ZeCMiISndrXwiXWBJd9WiobK6744IkbKTFxEpQ2omFxEJM5TVE9cbgxn3g6MTGQfYMxThGXUCfxv9OPiDrE7t4T2+B+lP6veREw1pDaUJ22l2p8W7m+IJNukPFQfuzWtsOS4iMiqpmerAESIlYjKPSOvYq9wb+hyNxhi9qpnhqPZ/OEJidBIhclbbiF62ZeYNVkpM0q/EJXUcVBJW/ik0nDTPXWnyhIgYVSufcLvMmykmKguIWbUSyBciZTCrOkIkbTIU0q0Q6gNZV0gkPULE6SFiNXfBqRcAcI3vNwwk9fXUmcruRM3Bk5pRHpEVWwHHt8Ngriv3SEIiItVJsk8bzkpETiB4whcv78D3ux+z3DjKy1YHH0zdRGNYPx8LTJKasYXIiQ0Jt+1vfyL3y+cMwStMAQF2C/f92py65HXF2Rj5DczEqFoFBJvLvYJFhgWmv6TXD2FmeNu8l8Mj4qRmRpIZhsKnAtBgDrofDON5EREtRE5oSMCKU8mYQU4yD9AxqvuDOBGRLo8QcfuIWJ7btb8OVAZ6N7uD8ArNquIRqRZK2MwMJijfHe6HbZvByvKY7ywuT/0tO9VKWuwSrsgkqZlXbCHSU5ek1e7G54TyICd4xqVmlNLm1OazYcW7i5qCkohIlRFs0n9OM71TmCEqq6Mh4o0qO9427+U0q2YtxVG7kKDeGM6lZjzX833D+lq+qj4BgRC9bbr9++vjvwNyERFXiBzYzbnPf5cO+rVHxEt0OfRvh0Hdi2okob8nYOjXHeh7uWqGXdbuHURZepJhiZqZwQSpGacl+5LlfLfpzxhC9/BosY1NUUdBe964WQteHdVvzp66pCtajnkiIpOmZuIH9PTVVe+fc7+QyfCKD/GIVAHBZl0dJobV4qCskl47hJmTZ1YtQ2omGvC5evTggP79aqhvJmzov3s9IoP2XJlW+5o/uGw9ABuzT0F8JD8iks3A9gdoHn6Zd/p+l/OIOAQawEpA729AKUaOvwxAd0wLkoGhPnjuqzC8p/ibLjK1ewfJjOiLcgkjIuM6qzpCZPlaOmO5sJkjLqJ2asY7EOngWJCMMgmaFp3RFK32sd7KGUe4RApTM+lBaN0IdSuLtieH/IiIhKcrHuklUlxUVoRIheD1iPjKEBExTYM6OypzaFBHIOrbTibs09d4rxBxuqbW2df6aEsLj1tr8WPB3mc8EZG0njmW1Oc7y3xxvBABiCyD/q1w5CFG+3VvkWWOEMnUw8AzsPPLcPDXun1DhVK7QiRV2mZmUDD0bnRAz4UxDOg6wW3zDrhNbZyIiDc146RlltUlMQ2mTM2MN6sqiC4t6p4cwh7xIQPvqoBAI/ij+hOUMH9EiFQMsTJHRLxrOORERBraXG+gV4g4XVMdIdIcyvDvmTcDoPbu4OioFhtdkSS8sNX9vrPMPWQm8poHGnV38AP/1y3fXVZnC5FUABpO09G7Pf8bdt+pPYMVSO3eQdKDuuW1p5nZkXiAd21ey917W4vyEnkekQMv6gfblkEo4jY1A9woh1O+603NOEbVnph+AzlpHG9qJj5RvxIrA5gQ7ijKXgoJiVm1ujBMCC+RiEixUFZJo6nCzCm3WRWgzjasOmKgvn4JYb9ei3M9T2UN957gtG9vCGbZrDZwVDViJEe5MvkLAFbFd2lPoT+IMkzajQFa1PHxL2wYEF0GQ88yqhoBWGbfKwZSfhR235Hocjj22JRD88pJDQuRIdtwlruhP3SogSeP1vPvu2c4OG4akpbHI3LQFiLdunzWW57VPEVqZr/HqAq6vhygP+GJiDipGW9EJDOqfSGlEiJej4iYVauDcLeeryTMH5XV3WqFslPuPiKFawBoiEUIh/T7w/EKetsyONWPpgF1QbgtfRUAH/Hdy5/5fknbq4/rA1eeTiKm70drrElERLAZmjcwktVpHSc1k1UGI85rBhp0886xV+e509JQu3eQCXqIDNq+i8Px4jQpStpvgvrMIPTbExO7TwBybd4NFE3Byc2qhUKkZQKPyIRlwpkRCNTnJq8WGcMwXAEiQqRKCLcBMoG3KIhZtWJwRIDPNMrW4bm+UIiE/YTDukDAuT6P2GmZsC+L95LZFMpwt/V6trRfAsD/G/g+vqP7AANOOItEYzcAp6opohlGbuBeazjtVuwMeLtwY+jGlh6eeXWAf3/0ZZ7Y2z+r/Rab2r2DpAbGXZOP247mI/EA1iyv10rBnc928btDDe5jTmfV1j5dXkVLF0TqAFjdkGDT0uO8/+QjOF2JJyrf3T+iL3Y99XZExPaI9E1oVvUkETOjEFupuz+WCMewKqmZKkF6iRQRJT1EKgSnaqZc0RDvGhwaIgEiYd1lNZHR6xpx/SH5Xj7nw+WPfJfwrcyluSeWngixRtKNXQCcytRpldG0vg/E/FmagrYQSXnWFWiAoefzSvh/u+sot/zsWe556sCM9lkqZjI0fnGSPDquPbOjHjPKpC/hZ0lk5i7jP/RH+erTy1hVn+DBy/6gX8IOyTUcfV4ftDTX1dQ04H+/4cW8czizZsamSs3YHpG+xDRmVZUqSbWMl3DAZDAu5btVQ6AJMHVb6BIKVEFYSJyISDnmzBSuwaE+7CcUrgMSJNL6mu0IEccf4tBkC5GdAzH+K/OnnFg3wkXWE7B2IwDZZkeIvDzl765TkVMfyNIUytAbD+ZHRPz1uoln4ghEdDfvRNqeFlzma3jt3kESR8aFVr3q8XB8dq2bHWHgTesksibNDBEesPNy3SdOeY5YQURkJG26a3IMSN7UjDNvJl6YmlFKR3tK5A9xcCIh3lJeoYIJNuv3vMycERYRjlm1XEZVyHVXBR2ZiQR8hMP1AMTtm/1IQcWMg3NNf2EwAhj8dsnb4U8+Co1L9AF1zQypKBEjBUN9k65h1D5/LGC56f7jXiESqNcpe49PJJHW95xyR7VrU4hYGT1/pcD17vT7B+gdm51PxJn9MpbxkcgYZC09E2aN+SoGCmKN+msK3NSMLSycmvKGQMYN5zlm1bRlujnBcakZyy5LLrEQcVS0eESqBOklUjykoWrF0NmoTaEtsfLN/Yl5Knfqw34MwyBs9xZxbvaFpbsOjmhI26l8b3t3AL8J2y3tLVSO17CAZNYgZX9/nR0RAT3N18XwAVaeEElmJCJSPlRW58mM/O17IyK9s4yIDHq+93jK774peozD+sFY07TncFIzzqyZQxPMHQj7leu47rNLeJOFLd5LXDHjrsX1iNTm26jq8NdBoE6ESNEQNVIJLG2K8L3rzuGuq9eXbQ11odwH14aI/rtzfUzYKXondRLzTxwRceiK5s+ICZiK7coRIocnfP0RzxDUusk8IqA/fA+/4P7TEUnljmrLHcSD94d2ZIrKmYmMrEMe5dmf9LtvvpWOEKlrmvb1nT4ijunIESIdBW/MllB+U7NcHxF7YZkR3TPCXzfta84Hp6lZucN6wgwxDAh1iBApBlJ8VFG8cU07azrry/b6dZ7UjJOmidg396TlByszqUekeZwQKYyIKLZbdlp/koiI0/Ih4sviM3O+k7zUDIC/AUb2uteAhBMRKfOHSREiHrzGHictUsiReIBz7zmLv9m6PO/xIY8iPZ4MuFGKlUavfnBGEZH8aY1OeqjwjekaVu31On1EnJItXTGzuuQDuRyTqphVq4hIl3RXFYQi422q1hB2IiL6upiwwpAZYSSdS514mU6IBDxCxBjph/T47qjDBed20j3jIiKBBt1Da0xXySRt/0pIIiKVQSpr5DUSmyw1s+1YHccSAR482JT3uDc105/wu0JkhXlEPziNPwRyqZm0ZZLKGu4aOiMFQiTkVM7o13SES27WTOlau3vpatRm384G6adQNYSK0zVYkLSMkMObmnEiIk66I67CkBmetHy3UIh0TCBE+mlgv7VEv+uO9457/dGCcztCZLAwIuKL6KaGtk8kmdKvFTbzo+4LjQgRmzxTD3B4ErPqUfvmXxjyKkzN6NJdRQ+2EKmbiRDJvUHjWXP8SGibloJ5M7mIiNL+F4yS+0MAPvfHp/K9687holNK/1pCkQg26T+V5BbmTZmaZwmVR2yCiIiTsk4QLRAik0dEWkPp/MaU6FYPpqHYotboBw7kt32A8RU5bmqmMCJiGPprdD+kBkgMa0ESVqMz32wJECFiM1jwA5usfNcp0x1M+fK8IuM9IibNDFNvjOkHZ5CaCfoUfiNXwut4RDoLhUhBRCThmlWzC2ZUBWiMBnjjmnZ8ZWwkJMySQCNg2oJVmDsi5IQc9ROaVZ3UTGhqj0gwJ0QKP3Q6+A3FT7Jv1P/YvxNS+enVcULENatOkHLx1cHQTnjhn0kkxuy1lvcaLkLExolwOJNwB1J+tyOel2O2EFEYeeJjKO2pmknq1MwKw46GhOvAN7Pecbnuqj56bcNsZ4FZtc0TETk0FnCjOXUByxYiddqsKgiF+Ot0abc18QVPEITZM5VZNaFCEG5nOKmv24VVM43BjG7xwPgPnQ5BU/GYdQqpWBtkM1qMeCisyHHLdwtTM6D7iYzuh74ndbQGCJWxBwuIEHFxTD09saRr+pwoKuKdeus1Ag3mRUQCJC0zV7o7g7SMg9PUbCDp53hyYrOqNyLyd88sJaNMzl0yrI/LjEBs+biusYIAaCFiBkWIzBuJAgo5JkrNOB6RZNaErosZSen3TGFqxmdCox3B6J4sImIqwGBgmV2ivOfpvPRqYbTFa1Ydl4UNNoMRgPo1JLJ25aP0EakMnBBWUyhDpz2QrneCEl5va3Vvlc1QKj8iksgYrJhFDxEHx7C6Z0gbQCO+LA2Fnfjsqpkdx6P8n5f0ULu/PPsVnbK2ElC/ZsavJ9QYgXoRIkVDxIigqZ/KrJrOQudFjFo6+lCYmoFcS4bJIiJaiEB/+xngD8LoABzODbBzKnJiBWbVrMoNw3MxTD3+wxdyiyrCfomIVAQDbmom67qWJyrhPeYRIo4RSKn88t3+pJ+kZbLSnL0QcVIzLw1rIdIVTY3zxDmpmePJAAqDS3v6OattVHeMNXwQWzHj1xNqDDOoPUQiROaJeESEHOGAiWOVG+cRSWch0MCI0n1O6nzjRyw4LRmWxib+vQzYQiTtC8GK0/SDL213ny/0iIT9yo3sFxZieHHnlElEpDJw0ixNwVxEZKKmZt7UzKDdEn40Y2KpnFpwPCJzSc04lTMv2RGRwmZmkN+JL2BafGad3bI3PQj+RhEiwuQYhg7NWuN7EQiCMDcMw3An8BZGRBLpLEqpXIv35Asw8pL+iusGZR8//SDvXn2UTUsHJjy/U8SQyhqwep1+sHcvjOjjndYTdR7/iWNYHdfUzIObmhGPSGXgREQag5lJIyKJrMGw15Rqi5fCipvjdtWMa1adQQ8RByc14wiRrsh4hezMmwF430lHWFFv31TSA7p/iIx7F6Yi2CoRkfkipbtCActbdOplebP+0xEiloJU1mIkqT9A1rWfDg0nQf2JurlYepjXdQ3xlfNedlMrhQTskt6MMqC+GdrtD5svbgMmHqjnGFbHNTWzsRTuKJJyR0RmVspRAzg/rOZQhgY7ulHY1MzrD4GceHGqZ2L+LKMZHynLZGjMot0Y0AfOyiOi34j7RvRAvolyhmG/4pwlw/SOBfhfp3la/mbHoPE0uUgKUxNqkfLdYiC/Z4KHb12znt7BhCtIvDf346Np1zRad+anIOjX886e+zvo2wKNp0557oBhCxHLfs+tOQeO7IOXd8CacyccqOcaVieJiKSyufdvuT0iIkRsnMm7TcGMqz4PF6Rm+hJ+lnKUX4Q+x33ZjbyQ+hMg5w/piKQ4NBYknvWRGhoCYNSIEgvOvPOo4xGZbBKjw483PU/aMgg6zW+sLGCbkARhKko8g6g2EI+IkM+y5ijL7GgIQNBnYhjaQ3hsREetTSNX1othQteboX8bpEf0QMpJcMyqaUeItC2D1m7oOwgvPMloegOQ37V1uoiI4w+B8kdEJDVj4/ywGkPZXNVMQWrmWCLA5b5HaTFGeJfvIUbjWn06FTMn+w+yIjgIgDk2AEC/v21W6yisMZ/IIwJ2qt/bgS8zpOcIiD9EmI5A+YaDCUKtYBiGOxj0qC1E6kJ+DG8krfEMaF4HY/snPolSkE14hIjpnBzWbtR/3/sHfKlRQHHy0d/D5n+D/TvdRmnPD0QmPLUjRPyGhb/MTSlFiNi4qZlghnbbl3EkHsirwT6WCPBG33YAQkaGZWO7Ae1KPtl4hX+K38p3+FuCpAkljuvnArOb7eFt8w6TR0TGkRqASCeEpJGZMA1ORERNnI8WZoKkZYTpcSINx4a1EKkPFxRAmD7oeouOjmQmaLMefxUGniZgm1Xd1Axon0hzJ2QzvCt9H1/1f4vVL/8ahvvhyV/zkcyP8JHlBy+2c8/e8fchR4iEzPKnaUWI2Dh5tKZQhg47IpKyzDzH8fBoivXGbvffpyZ0d7uhlJ8rfQ/hx2K56uWDvvtoSh/TzwVnJ0QiBUKkcODdpGRHxR8izAxpalYk5HdNmBonDXNsRP+uOZU1eTStg6YzYOyV/MetDKSOQ7SHgKG/P+0VIp6oyLvVZq70P4zCgG49qXfV4UfZ3PwFGhnhM4+v5PEj+akft4eIT4RIReCdvNsUzBD0KbcyxdvUrHFgL37DImVba87O/gGUYjhlcLnvf9zjbvD/lNPVHgBGQi2zWos3NRMwLbe+HJh8UJmy9HOxVbN6LaFGkaZm80eGBgozIOwKETs1E55AiJg+6L5E94BKHss9PvaKvqbHluNH/65mVIH47VyFamwHYEhFOb7hXXDeZXDupeDzszq+m99GP8N6nucjD5/EvuGQ+60JESKVhdPwxUC5Xe+cEt7DHp9Iz8jzAGyJXcCICtPGIAwcoWVoLx3GAHEzwqvBFdQZCU4xdc5vbJZCxBsR6YikyUvdje6BwWdtY6qH9LC+uYg/RJgJ/jowZd6MIJSaUIEQiU0UEQFofg10X6r7imTikE3pKPfSt0LzWQTQH0jzUjMAhkFq/R/zL5m38vbU3xDo6tGPLzsZ3vAeqGui2RrgB8Ev8v9Y/8V/7cm1dkjYVTOSmqkQXKNqMIvP/h8Z1+ZdKdYkdSpmoHUNj1hnAGAdeom1w7qWe0/9Oh7puDyvuVkiPLueHl6PSF7prpWFbAIiS2H4+fxPZKk+CLdrj4ggTIc/KoPv5oukQIUZ4HpEbCFSP5kQMQzouQLazoOR3TC6F+pPgrYLIbYKv31fShUKEWAotIQvZK7hJdXtzioDoKkd/uhqWHk6Joo/9/+cU/pykftExvaISESkMnD9IZ5xzGf79vDT4C2sePUhfdM/fphGNcKwihDr6OB+62wArAMvcnriGQAOtJ5JuqmLn2TfAEBcBbFCsyuVzBMiXn9I6phuRLXqfRDp0G/WbBIGdwIGtL9eG54EYToMEwJN+lOXMA9EjAhT41TNHBuewiPi4AvD6muh7gRQGej+Y/BHoG4VAb/+vnEREXJdVWP+LOOKX/xBeM2bebLtzQCsju9yn6qk1Iz0EcFbumsLkf5ePtJ3DyEzAf17YNeIW2HwO+sMVtZZbDFPB8A/fAw/8Iq1hFTTUlrMDLdm3sOp5ss8aa2h0T+7XHLU4xHp8pbuJo9B50XQukFP1t31DR0ZaVoHPVdO2xBHEPIItcLwi+VehSAsaiLBGXhEvITb4YQPwbHHodUuz/VHCYR0yf1EQmSirqqFHG5aA8c205Pepz9YG0bOrFoBqRkRIuR68TcHM7pBzO/vIWSl2G8tocc8Cjv/BxUIYQAPWmdxUzhDJljP0+nVrDNfAuCn1oWcGbQI+BTHaOSy1JcAuNM3u4v9hKkZKwUY0GKPgG5eByd9FJJ90PEGraQFYTaEWkBJREQQSomTmukfm0FExKHxFP3lwR9qAJRbNbN7IMx/7W3jY6cecoVIbAohkq5bQlwFiRKHkeNQ3+KJiGQm/b6FQmL55Myqq31H4Pd3QybFYMMKLkndzveMywAw0lrRPpw9k5ZQmuZQhgeyZ7vn+Gn2QhqC2byBdAAhc4a9GsZegfhBIp6IiCtEEkd1OqbxtNzxrRug+2IRIcLcCMx8/pEgCHPDSc04lr766SIik+BGRLL6/nLXzi6+/VwXX96+nJG0vo3X+ycXIpEA/EHZVZX9eixI0jWrlr+fkAgRcqmZ12R3QCYNjUswL3wbcUL8Vfy9xHvOAmC7dQLpUD1+U/tJ7rXOI2v4eEKdwh61lMZgZpwQCc80NZMahFQfMTM3FdX1iKT6oeVcbTIUhGIgbd7njxhWhWlwqmYcJq2amQYdEYF0Wt8Tjtlzz+7e28ruQd05darUTNRv8ZSl+4vQ3wtUlkdEhAg5s2qnOqofWLKM+oiPkxrjgMEjHW/juTVXckP6f7l9PZpCGfaopfzohM/wgeRNADQEs3mGV5ih2rTSupY82kM0lWv12xVNQzauez60nDXvfQqCi9tdVfphCEKpKJzhMqPUzAQE/Fp4pDP6g6oz8T1tmXzneV0tOdnkXtDew+0FQiQpqZnKwomItGXtZjIxHbY+q1W33H26v57dsTN5VbXTFtYGUkdw7Eq1M4ZOj9QHsgR9ivpA7gcb8s1AiGRG9I1hyQU0+oZoCGRoDaV1q/n4YYguhfo1RdmrIAC674zh1+58QRBKQqQgIjKtWXUSAj4dfctYeoqeYycAOJ7UImW6iIgrRIaOQiZdWxGRO++8k5UrVxIOh9m4cSNPPPFEqV9y1riTdzN9+oGoFiLrbCGyvS/GUTsUlhMi+ofndKoL+7KE7CF0zSGvEJnBJ870MARboPtPCDWdwC9f/0t+dslO/IkDYCWg44/AF5z+PIIwU9w278npjxUEYU6EC4TIpH1EpsFvC5G0EYb0oBsRafe0eKibwiMSC1gcooUjqklHQQeOeGbNLHKPyI9+9CNuvPFGbr31VrZt28a6deu4+OKLOXLkSClfdtboiIiiLqUH1bkRkbYRAJ7ui3HUbmzWGsqlZgD2j2oh0hjMvQm8QiQ8k4hIdgTqT9Q1491vZXn4OMuyO3RVw+oP6Pa/glBMArYQkV4i80A8IsLUjEvNzDEi4jf1edK+JqzEEYbsSpkbzziQO/eUEZEsYLDNTc8cygmRxZ6a+drXvsaHP/xhrrvuOk499VS++c1vEo1G+dd//dcJj08mkwwNDeV9LQQDST+NjOLP2p8ObSGypjFO2JdlOO1ny1HtWl4SyU/NvDqihUiDR4i0hGaZmlEWxOzWvK0boWENBBvgpI/ZMwgkgyYUGRl8Nz/EWiPMgMKIyFw9IkG7tWom2M5IMut27377yj5OaRoDoCk0dWoGyKVnjve6VTOV0EekZHe4VCrF1q1b2bRpU+7FTJNNmzbx6KOPTvg9t912G42Nje7X8uXLS7W8PAZSfnoMO0oTjoFPv1n8JpzRon/ITx2LAbjD8JyoR8rS/4UNHl/IrISIlQEMiHTpf/tCcMKfwak363a/glAKzAAEGkSICEIJKZYQ8dstU9P+ZgZZAuh7S9iv+McL9vC+k45wxapjk35/2GdhoNiucoZVJyISNZPj55ctMCUTIseOHSObzdLR0ZH3eEdHB729vRN+z1/8xV8wODjofr3yyisTHldMnMm7rhCJ5fdXcAyryg7DOh6RxoLqmMkiIuHpPCKZEfDXQ7gr91jdSqiTSbpCiQk2ixCZK5KVEWZAoRCZc/muPQQtQ4jB4MlA7h50clOCvz1nH23hyVMspqGjIs9Yq/W9LD5MNHWcP/Xdz5X7/wV2/fec1lUsKqqzaigUIhQKTX9gERmw3cc9xmH9QKEQsX0iDm1u+W6+gsz3iORaswenMwJlRiDYBOEls1m2IMyfUJsIkXkhakSYmrA/91k/HDAJ+Ob22T9XNaMYCp8GJGgMzM7bEfFbHMuEScaWEB49wl8d/xKRQAIywB/uhnM+Nqe1FYOSRUTa2trw+XwcPnw47/HDhw/T2Vk5U2Id9/GJfnud0Xwh4lTOOLTaEZHmwojIBKmZgGkx7fsuM6yHHIkPRFhogk1A+R3zgrBY8UZE6kKBOZ/HMaumMopBn7YsNAYSszpHzK6qGa5fCkCEBEdUE892vhau+Kc5r60YlOzuFwwGWb9+Pffff7/7mGVZ3H///Zx//vmletlZ4zQzW2lOHBFZGk256RiAJfbfG6ZIzTj+kRlVzKgs1K2Y9boFYd5Id1VBKCleITLX9u7gjYhYDGZ036pG3+hU3zKOiG1Y3dd5AXSu4l8CV/K65D/Q234a+Bc2E1FIST+G33jjjXznO9/h3/7t33juuef42Mc+xujoKNddd10pX3ZWOM3MlmJ3VS0QIoYBZ7Xq9EydP+u2bPeb+VEQr2fEiYhM20NE2eIl0jX1cYJQCgL1Uv0hCCUkkhcRmY8QsT0iWcVg3PYpBpK6K/cMcSIixwLtcMHb+bF5MUmCFVE1U1KPyHve8x6OHj3K5z//eXp7eznrrLP41a9+Nc7AWk6OJ/34ybBE9esHYuOHgZ3VOspvDjS7aRmHplCGobT+L/RGRNY2j7E0mmRj+/DUL54Z1Z9KwyJEhDLgj2mlrbJg+KY/XshHZs0I0+DtIxILzf13zG1olrUYsIVIQyQEY6/OuLDBKeGNZ/Q6cn1EFrkQAbjhhhu44YYbSv0yc2Yo5aPL6MOHpee9hGPjjnlt5xB3PAMnN8XzHm8OZnAmwzR4msnUByx+97ZnMKe7TqWHdQlluH2euxCEOeCL6DbvVgZ8IkQEodgU2yOSsTwRkZYeyGy2Z5VNf25HiIxm9LncFu+LPSJSDeT1EIk1Tvgp56y2UX71xztYHstvh93oqZwp9IxMK0JAV8w0nQZmzf8YhHLgi+gLmMoA5c0RC8JiJOSJiBTDI5LOWjkh0rYaYitnHBVxUjNjdkQkWUuzZiqdwdTkPUS8rG2Kj5tu6J206y3fnRalYOwVwIKGtbNZriAUD19Yi+BZ5JkFL5KaEaYmXGSPSDqrGHKESKwOut6iR4RY05fyOmbVMTciot+/lZCaESFSGBGZBd6ZMg0zFSJWCoZ2Aiac8CHofPOsXlMQioaTmpEJvIJQEvLMqvOIiDgekYw3IhIJwJILIdqjoyJKQTYJmbEJzxELOBERE6UgkdVrC0lqpvwMpPwsn6MQ8VbKNMy0uczwbmg4BVa9HxpOmtXrCUJR8YV1amYGn6YEQZg9AZ+JzzTIWqo4VTNej0g0oIdXdl0Me/4Fhp4FIwBY+kNGdEWe1cD1iKR9JK3c45WQmql5IZKXmonOPSJSP8XkQ5dsQn8CXfFuESFC+TH9WoxkB8u9EkFYtIT9JqOp7LyEiDNrJpWxGE3p+05jxDaotr8eMHQ5fqgV4ofgpf8PRvbYzTL19+aqZkzXHwIiRCqCweTcUzNNdjqmPpCZvoMqQLJPt3KvFxEiVAj+ekhMPixLmAwD8YgIMyEc8M1biOQ8IlbOI+IIEX8Uui/OHdywBnxR2PMdGNkNdSeBYRK1zaqjGZ8rRExDETDK3125Nj0i6TE4+DJkM2TTCZoMu0NdrGFWp3HMqg0ziYYApI9D82v0p1BBqAQCdeIREYQS4hhW5+MRcYTIQDyNZTchdIXIRLSdCydfryMkg89CNk7MY1Z1e4iYVkW0w6nNiMjz98H2/0H5/PyVtRt8kA1G8fmDszrNaS1j1AcynDtd4zKw8/AGNJ0+tzULQinwN4hHZE5IS1phZjhlu01TCYdpcMyqqYwWEyG/OW6y7ziaz4JTPwt7/wP6thCxosBqLUQy+nwzGkOyANSmEEFBJIYRH+Ui31MAGLNMywC0R9JsuWI7IXMGF6VUn1anDWtm/TqCUDIC9blRA4IgFJ2b37qWx/b0sWFly5zPETDzkxdTRkO8RJfB2k/BgZ8T6/s9YKdmrMrpIQK1KkTOeBckf8eRg0M8sHWU1/qeZdnKU+d0qvB082QcUv2w5HW6k6ogVAq+iFgd5oQhLd6FGfGmNe28ac38umc7ERGHGQsRAF8Ilr2D6M4dgDarJjKOEJGISHkxDI5Ee/hs5nW0B1I8serp0r2WsnT4u/nM0r2GIMwFX6TcKxAEYRoCvjlGRBxMP9HGpYCOiOTmzFSGEKlNs6rNYFr/MJuCJc6Rp45DsEm6qAqVhxin54h4RISFIzCfiIhNrGkZUGBWFSFSfhwhMqv27HMheUyX7IaWlPZ1BGG2+CKA0l0ZBUGoSPzzjYgAkXo95T2eMYlXWGqmpoXIQMoRIiWOiKgUNJ0pOWWh8vBHAJ9OHwqzQPqICAuHv2CKasNcIiKNOiKiMBhM6YobESIVwGBqASIiygIMiHSU7jUEYa6Y9uA7JYPvBKFSmbdHBAhHWzHslGJ/Un9/pVTN1LQQGUjrviEljYhk4zr8HWwt3WsIwlzxy+C7uSGpLGHh8JkG3qDIXISI6TOJ2OUp/Un9F/GIVABDCxERyYzpdruhttK9hiDMFV9EBt8JQhXg9YnMRYgARIP6HP0JLUQkNVMBLEjVTDauK2b8sdK9hiDMFV9YIiJzQjwiwsIS8IRE5i5EtAA5nhSzasWwIGbV7BhEl4tRVahMHI+IJR4RQahk8iIi0TkKkXAIgL6ECJGKwTWrhkppVs1AdGnpzi8I88H06dShRERmiXhEhIUlUITUTCykv++4bVYNmSJEys5AusQREac3Q0iMqkIFE6gTj4ggVDjepmZzT83ost3+lI6MSESkAii5WVWldf5djKpCJeNvkPLdWSOzZoSFxV9EIZKy9J8hMzn/hRWBmhUiGctgOFNis2omrsPeUrorVDKBeomICEKF40zgDfpNwgHfnM7hmFUdwgxDsBnM4LzXNx9qdujdUDq39YZSRUSyY+CPSmpGqGwC9aAqo7FR9SAeEWFhcSIic42GQC4i4hBa+XY4vQvC85sOPF9qNiIyaDczi/mzBMwSXVSyYxDp0lUJglCp+CKSZhCECscxq85HiMRCBRGRWLvu+l3m3//aFSKpheghktClu4JQycgE3jkgfUSEhcVfBCESKUjpzDXFU2xqVog4PURKlpYBQJU95CUI0+KLlHsFgiBMg9PQbH4RkUIhUhkSoDJWUQbcrqqhUpXu2mVR4g8RKh1fWFselPgeZo78XwkLS3E8IvmpmZBfIiJlZbDUXVWzCf1JU0p3hUrHFwHDFMOqIFQwxfCIFJpVJSJSZgbTJe4hkh0TISJUB87gO+muOgvEIyIsLI4QaShiREQ8ImWm5GbVzJg97K6uNOcXhGLhc+bNiBARhEolUILy3XCFpGZqtq50IKXLd0vXQyQO0WVlL4sShGnxRewJvNJddeaIR0RYWC49s5t9fWO8cc2SOZ+jUs2qNStEBks5ZyYzAtlRiMiwO6EKcFIzEhERhIrl8nXdXL6ue17nqFSzau0KkVKkZhJHIXFIt3VvOhOa1xXv3IJQKnwhHRGxJCIyc2TWjFB9jOusKhGR8lISs2riEHT/MbS/DupO1CPWBaHSMUzwx7SQFgRh0eKNiBgGhPyVIUQqYxVlwI2IFKuPiJXWg4OWXAANa0SECNVFoE6qZmaFeESE6sPrEQn5TYwKierVrBAZsGfNFC0ikrHLdWXSrlCN+GUCryAsdrxVMpXiD4EaFSKJdJZEVv8QimZWzcbtSbstxTmfICwkgQapmpkV0kdEqD5M03B9IpVSMQM1KkSG4lp8GCjqA0WKiGTHINyhqw8Eodrw1yHpBkFY/OSEiEREyspgQn/yawikMYv1ocbpGyII1YgMvpslItqE6sQxrFZKMzOoVSFiR0Qag8UMRSsdERGEasQvQkQQagEnIlIppbtQo0JkYEwLkKZAkYSIUvpLJu0K1YoZLvcKqgzpIyJUJ25qRiIi5cVJzRQtImIldVMoqZgRqhV/BLAFtSAIi5ZYSKdmJCJSZgbGipyayYzpbqoSERGqFX8dGEGwUuVeSZUggk2oTirRrFqTnVWv2biMy9Sddmi1cf4nzMb1hTzYNP9zCUI5CLfr93BmREf3BEFYlDhm1Urpqgo1GhEJ+k3aI0naw8ninDA7BtGlulW2IFQjgSYtpDOj5V5JlSB9RITqpBIjInLnLAbZpJTuCtWNYUDdahEigrDIkYZmixUDCLWVexWCMD9iy0EVcQjkokY8IkJ1cs7KFoI+kw0rKqcLeE16RIqKsvSfYlQVqp1QuxbVypI0oyAsUt5yWic7/vpiguIRWURk47orpQgRodqJdOjqr2y83CupAsQjIlQvlSRCQITI/HFKd6WHiFDthNrBH9OVM4IgCAuECJH5ko1DsFFfwAWhmvFHINIlhtUZIR4RQSgWIkTmS3ZMV8xIu2dhMRBbraN8giAIC4QIkfliZSCytNyrEITiEO0U68OMkFkzglAsalOImAEwg6Ay8ztP4giYfoh0FmddglBuwh1g+MAq5mRqQRCEyalNIWKYEGzWjcjmytirkB6AnndB63lFW5oglJVwB/hi4hOZFvGICEKxqE0hAnq2hjVHITKyV0dTVl8Hy98JZuW0yhWEeRFshUCDCBFBEBaM2hUioba5pWaU0uWNK66CrjdLnlhYXJg+qFshJbzTIn1EBKFY1K4QCdTP7fuslJ5OWrequOsRhEohtgJUqtyrEAShRqhhIdIwt+/LxsEX1h4TQViMhDsAQ0f/hEmQ/xtBKBa1K0T89YAJ1iyHfGUTuqV7sKkUqxKE8hPp1t2CJT0jCMICULtCJNCgS3hna1jNxrW/xAyUZl2CUG6iPVqMJI+VeyUVjHhEBKFY1LAQqddej9kKESsBYekbIixiTB+0boCsREQEQSg9JREiL7/8Mh/60IdYtWoVkUiEE044gVtvvZVUqoIMcIEGMEPafDobrKyeUioIi5mGU8AIyiTeSRGPiCAUC38pTvr8889jWRbf+ta3OPHEE9mxYwcf/vCHGR0d5Y477ijFS84eM6DFSOLI7L7PQIyqwuKn/kTdayfZp2cpCYIglIiSCJFLLrmESy65xP336tWr2bVrF3fdddeUQiSZTJJM5lIlg4ODAAwNDZVimZCpg+GXQM0wPaOyMJqBRABKtSZBqBQCp8Cx+8BaUu6VVB6jaRgZg4hcBwRhIpz7tppB9V1JhMhEDA4O0tLSMuUxt912G3/913897vHly5eXallz5GflXoAgLCA/L/cCKhT5fxGE6RgeHqaxsXHKYww1E7kyT1588UXWr1/PHXfcwYc//OFJjyuMiFiWRX9/P62trRhF7mA6NDTE8uXLeeWVV2homGNPkSqi1vYLtbfnWtsv1N6ea22/UHt7Xiz7VUoxPDxMd3c3pjm1HXVWEZHPfvaz3H777VMe89xzz7F27Vr33wcOHOCSSy7hyiuvnFKEAIRCIUKhUN5jTU1Ns1nirGloaKjqH/ZsqbX9Qu3tudb2C7W351rbL9TenhfDfqeLhDjMSoh8+tOf5gMf+MCUx6xevdr9+8GDB3nTm97EBRdcwLe//e3ZvJQgCIIgCDXArITIkiVLWLJkZsa1AwcO8KY3vYn169fz3e9+d9rQjCAIgiAItUdJzKoHDhzgjW98IytWrOCOO+7g6NGj7nOdnZXRDCwUCnHrrbeOSwUtVmptv1B7e661/ULt7bnW9gu1t+da2y+UyKz6ve99j+uuu27C5xbAGysIgiAIQpWwIFUzgiAIgiAIEyHGDUEQBEEQyoYIEUEQBEEQyoYIEUEQBEEQyoYIEUEQBEEQykZNCpE777yTlStXEg6H2bhxI0888US5l1QUbrvtNs455xzq6+tpb2/n7W9/O7t27co7JpFIcP3119Pa2kpdXR3vfOc7OXz4cJlWXHy+/OUvYxgGn/zkJ93HFtueDxw4wPve9z5aW1uJRCKcccYZPPnkk+7zSik+//nP09XVRSQSYdOmTbzwwgtlXPH8yGaz3HLLLaxatYpIJMIJJ5zAF77whbwKvGrf88MPP8xll11Gd3c3hmHw05/+NO/5meyvv7+fq6++moaGBpqamvjQhz7EyMjIAu5i5ky133Q6zc0338wZZ5xBLBaju7ub97///Rw8eDDvHNW0X5j+Z+zlox/9KIZh8A//8A95j1fbnmdKzQmRH/3oR9x4443ceuutbNu2jXXr1nHxxRdz5MiRci9t3jz00ENcf/31PPbYY2zevJl0Os1b3vIWRkdH3WM+9alP8Ytf/IKf/OQnPPTQQxw8eJArrriijKsuHlu2bOFb3/oWZ555Zt7ji2nPx48f58ILLyQQCHDfffexc+dO/u7v/o7m5mb3mK985St8/etf55vf/CaPP/44sViMiy++mEQiUcaVz53bb7+du+66i3/6p3/iueee4/bbb+crX/kK3/jGN9xjqn3Po6OjrFu3jjvvvHPC52eyv6uvvppnn32WzZs3c++99/Lwww/zkY98ZKG2MCum2u/Y2Bjbtm3jlltuYdu2bdx9993s2rWLyy+/PO+4atovTP8zdrjnnnt47LHH6O7uHvdcte15xqga49xzz1XXX3+9++9sNqu6u7vVbbfdVsZVlYYjR44oQD300ENKKaUGBgZUIBBQP/nJT9xjnnvuOQWoRx99tFzLLArDw8PqpJNOUps3b1ZveMMb1Cc+8Qml1OLb880336xe+9rXTvq8ZVmqs7NTffWrX3UfGxgYUKFQSP3nf/7nQiyx6Fx66aXqgx/8YN5jV1xxhbr66quVUotvz4C655573H/PZH87d+5UgNqyZYt7zH333acMw1AHDhxYsLXPhcL9TsQTTzyhALVv3z6lVHXvV6nJ9/zqq6+qpUuXqh07dqgVK1aov//7v3efq/Y9T0VNRURSqRRbt25l06ZN7mOmabJp0yYeffTRMq6sNAwODgLQ0tICwNatW0mn03n7X7t2LT09PVW//+uvv55LL700b2+w+Pb885//nA0bNnDllVfS3t7O2WefzXe+8x33+b1799Lb25u338bGRjZu3FiV+wW44IILuP/++9m9ezcATz/9NI888ghvfetbgcW5Zy8z2d+jjz5KU1MTGzZscI/ZtGkTpmny+OOPL/iai83g4CCGYbhDUBfjfi3L4pprruGmm27itNNOG/f8YtyzQ0lavFcqx44dI5vN0tHRkfd4R0cHzz//fJlWVRosy+KTn/wkF154IaeffjoAvb29BIPBcRONOzo66O3tLcMqi8MPf/hDtm3bxpYtW8Y9t9j2/NJLL3HXXXdx44038pd/+Zds2bKFj3/84wSDQa699lp3TxO9x6txv6Cnfg8NDbF27Vp8Ph/ZbJYvfvGLXH311QCLcs9eZrK/3t5e2tvb8573+/20tLRU/f9BIpHg5ptv5qqrrnKn0S7G/d5+++34/X4+/vGPT/j8YtyzQ00JkVri+uuvZ8eOHTzyyCPlXkpJeeWVV/jEJz7B5s2bCYfD5V5OybEsiw0bNvClL30JgLPPPpsdO3bwzW9+k2uvvbbMqysNP/7xj/n+97/PD37wA0477TS2b9/OJz/5Sbq7uxftngVNOp3m3e9+N0op7rrrrnIvp2Rs3bqVf/zHf2Tbtm0YhlHu5Sw4NZWaaWtrw+fzjauYOHz4cMUM4ysGN9xwA/feey8PPvggy5Ytcx/v7OwklUoxMDCQd3w173/r1q0cOXKE17zmNfj9fvx+Pw899BBf//rX8fv9dHR0LKo9d3V1ceqpp+Y9dsopp7B//34gN1RyMb3Hb7rpJj772c/y3ve+lzPOOINrrrmGT33qU9x2223A4tyzl5nsr7Ozc5zhPpPJ0N/fX7X/B44I2bdvH5s3b3ajIbD49vu73/2OI0eO0NPT417H9u3bx6c//WlWrlwJLL49e6kpIRIMBlm/fj3333+/+5hlWdx///2cf/75ZVxZcVBKccMNN3DPPffwwAMPsGrVqrzn169fTyAQyNv/rl272L9/f9Xu/6KLLuIPf/gD27dvd782bNjA1Vdf7f59Me35wgsvHFeSvXv3blasWAHAqlWr6OzszNvv0NAQjz/+eFXuF3QVhWnmX6p8Ph+WZQGLc89eZrK/888/n4GBAbZu3eoe88ADD2BZFhs3blzwNc8XR4S88MIL/OY3v6G1tTXv+cW232uuuYZnnnkm7zrW3d3NTTfdxK9//Wtg8e05j3K7ZReaH/7whyoUCqnvfe97aufOneojH/mIampqUr29veVe2rz52Mc+phobG9Vvf/tbdejQIfdrbGzMPeajH/2o6unpUQ888IB68skn1fnnn6/OP//8Mq66+HirZpRaXHt+4oknlN/vV1/84hfVCy+8oL7//e+raDSq/uM//sM95stf/rJqampSP/vZz9Qzzzyj3va2t6lVq1apeDxexpXPnWuvvVYtXbpU3XvvvWrv3r3q7rvvVm1tbeozn/mMe0y173l4eFg99dRT6qmnnlKA+trXvqaeeuopt0pkJvu75JJL1Nlnn60ef/xx9cgjj6iTTjpJXXXVVeXa0pRMtd9UKqUuv/xytWzZMrV9+/a8a1kymXTPUU37VWr6n3EhhVUzSlXfnmdKzQkRpZT6xje+oXp6elQwGFTnnnuueuyxx8q9pKIATPj13e9+1z0mHo+rP//zP1fNzc0qGo2qd7zjHerQoUPlW3QJKBQii23Pv/jFL9Tpp5+uQqGQWrt2rfr2t7+d97xlWeqWW25RHR0dKhQKqYsuukjt2rWrTKudP0NDQ+oTn/iE6unpUeFwWK1evVp97nOfy7spVfueH3zwwQl/d6+99lql1Mz219fXp6666ipVV1enGhoa1HXXXaeGh4fLsJvpmWq/e/funfRa9uCDD7rnqKb9KjX9z7iQiYRIte15phhKedoTCoIgCIIgLCA15RERBEEQBKGyECEiCIIgCELZECEiCIIgCELZECEiCIIgCELZECEiCIIgCELZECEiCIIgCELZECEiCIIgCELZECEiCIIgCELZECEiCIIgCELZECEiCIIgCELZECEiCIIgCELZ+P8BrvTjujeiXSsAAAAASUVORK5CYII=\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(observed_sequence, label=\\\"observation\\\")\\n\",\n    \"plt.plot(mean, label=\\\"estimate\\\")\\n\",\n    \"plt.ylim(-2, 8)\\n\",\n    \"plt.fill_between(np.arange(len(mean)), mean - std, mean + std, color=\\\"orange\\\", alpha=0.5, label=\\\"std\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mean, cov = particle.smoothing()\\n\",\n    \"mean = mean[:, 0]\\n\",\n    \"std = np.sqrt(cov[:, 0, 0])\\n\",\n    \"plt.plot(observed_sequence, label=\\\"observation\\\")\\n\",\n    \"plt.plot(mean, label=\\\"estimate\\\")\\n\",\n    \"plt.fill_between(np.arange(len(mean)), mean - std, mean + std, color=\\\"orange\\\", alpha=0.5, label=\\\"std\\\")\\n\",\n    \"plt.legend(bbox_to_anchor=(1, 1))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.11.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "prml/__init__.py",
    "content": "from prml import (\n    bayesnet,\n    clustering,\n    dimreduction,\n    kernel,\n    linear,\n    markov,\n    nn,\n    rv,\n    sampling\n)\n\n\n__all__ = [\n    \"bayesnet\",\n    \"clustering\",\n    \"dimreduction\",\n    \"kernel\",\n    \"linear\",\n    \"markov\",\n    \"nn\",\n    \"rv\",\n    \"sampling\"\n]\n"
  },
  {
    "path": "prml/bayesnet/__init__.py",
    "content": "from prml.bayesnet.discrete import discrete, DiscreteVariable\n\n\n__all__ = [\n    \"DiscreteVariable\",\n    \"discrete\"\n]\n"
  },
  {
    "path": "prml/bayesnet/discrete.py",
    "content": "import numpy as np\n\nfrom prml.bayesnet.probability_function import ProbabilityFunction\nfrom prml.bayesnet.random_variable import RandomVariable\n\n\nclass DiscreteVariable(RandomVariable):\n    \"\"\"\n    Discrete random variable\n    \"\"\"\n\n    def __init__(self, n_class:int):\n        \"\"\"\n        intialize a discrete random variable\n\n        parameters\n        ----------\n        n_class : int\n            number of classes\n\n        Attributes\n        ----------\n        parent : DiscreteProbability, optional\n            parent node this variable came out from\n        message_from : dict\n            dictionary of message from neighbor node and itself\n        child : list of DiscreteProbability\n            probability function this variable is conditioning\n        proba : np.ndarray\n            current estimate\n        \"\"\"\n        self.n_class = n_class\n        self.parent = []\n        self.message_from = {self: np.ones(n_class)}\n        self.child = []\n        self.is_observed = False\n\n    def __repr__(self):\n        string = \"DiscreteVariable(\"\n        if self.is_observed:\n            string += f\"observed={self.proba})\"\n        else:\n            string += f\"proba={self.proba})\"\n        return string\n\n    def add_parent(self, parent):\n        self.parent.append(parent)\n\n    def add_child(self, child):\n        self.child.append(child)\n        self.message_from[child] = np.ones(self.n_class)\n\n    @property\n    def proba(self):\n        return self.posterior\n\n    def receive_message(self, message, giver, proprange):\n        self.message_from[giver] = message\n        self.summarize_message()\n        self.send_message(proprange, exclude=giver)\n\n    def summarize_message(self):\n        if self.is_observed:\n            self.prior = self.message_from[self]\n            self.likelihood = self.prior\n            self.posterior = self.prior\n            return\n\n        self.prior = np.ones(self.n_class)\n        for func in self.parent:\n            self.prior *= self.message_from[func]\n        self.prior /= np.sum(self.prior, keepdims=True)\n\n        self.likelihood = np.copy(self.message_from[self])\n        for func in self.child:\n            self.likelihood *= self.message_from[func]\n\n        self.posterior = self.prior * self.likelihood\n        self.posterior /= self.posterior.sum()\n\n    def send_message(self, proprange=-1, exclude=None):\n        for func in self.parent:\n            if func is not exclude:\n                func.receive_message(self.likelihood, self, proprange)\n        for func in self.child:\n            if func is not exclude:\n                func.receive_message(self.prior, self, proprange)\n\n    def observe(self, data:int, proprange=-1):\n        \"\"\"\n        set observed data of this variable\n\n        Parameters\n        ----------\n        data : int\n            observed data of this variable\n            This must be smaller than n_class and must be non-negative\n        propagate : int, optional\n            Range to propagate the observation effect to the other random variable using belief propagation alg.\n            If proprange=1, the effect only propagate to the neighboring random variables.\n            Default is -1, which is infinite range.\n        \"\"\"\n        assert(0 <= data < self.n_class)\n        self.is_observed = True\n        self.receive_message(np.eye(self.n_class)[data], self, proprange=proprange)\n\n\nclass DiscreteProbability(ProbabilityFunction):\n    \"\"\"\n    Discrete probability function\n    \"\"\"\n\n    def __init__(self, table, *condition, out=None, name=None):\n        \"\"\"\n        initialize discrete probability function\n\n        Parameters\n        ----------\n        table : (K, ...) np.ndarray or array-like\n            probability table\n            If a discrete variable A is conditioned with B and C,\n            table[a,b,c] give probability of A=a when B=b and C=c.\n            Thus, the sum along the first axis should equal to 1.\n            If a table is 1 dimensional, the variable is not conditioned.\n        condition : tuple of DiscreteVariable, optional\n            parent node, discrete variable this function is conidtioned by\n            len(condition) should equal to (table.ndim - 1)\n            (Default is (), which means no condition)\n        out : DiscreteVariable or list of DiscreteVariable, optional\n            output of this discrete probability function\n            Default is None which construct a new output instance\n        name : str\n            name of this discrete probability function\n        \"\"\"\n        self.table = np.asarray(table)\n        self.condition = condition\n        if condition:\n            for var in condition:\n                var.add_child(self)\n        self.message_from = {var: var.prior for var in condition}\n\n        if out is None:\n            self.out = [DiscreteVariable(len(table))]\n        elif isinstance(out, DiscreteVariable):\n            self.out = [out]\n        else:\n            self.out = out\n\n        for i, random_variable in enumerate(self.out):\n            random_variable.add_parent(self)\n            self.message_from[random_variable] = np.ones(np.size(self.table, i))\n\n        for random_variable in self.out:\n            self.send_message_to(random_variable, proprange=0)\n\n        self.name = name\n\n    def __repr__(self):\n        if self.name is not None:\n            return self.name\n        else:\n            return super().__repr__()\n\n    def receive_message(self, message, giver, proprange):\n        self.message_from[giver] = message\n        if proprange:\n            self.send_message(proprange, exclude=giver)\n\n    @staticmethod\n    def expand_dims(x, ndim, axis):\n        shape = [-1 if i == axis else 1 for i in range(ndim)]\n        return x.reshape(*shape)\n\n    def compute_message_to(self, destination):\n        proba = np.copy(self.table)\n        for i, random_variable in enumerate(self.out):\n            if random_variable is destination:\n                index = i\n                continue\n            message = self.message_from[random_variable]\n            proba *= self.expand_dims(message, proba.ndim, i)\n        for i, random_variable in enumerate(self.condition, len(self.out)):\n            if random_variable is destination:\n                index = i\n                continue\n            message = self.message_from[random_variable]\n            proba *= self.expand_dims(message, proba.ndim, i)\n        axis = list(range(proba.ndim))\n        axis.remove(index)\n        message = np.sum(proba, axis=tuple(axis))\n        message /= np.sum(message, keepdims=True)\n        return message\n\n    def send_message_to(self, destination, proprange=-1):\n        message = self.compute_message_to(destination)\n        destination.receive_message(message, self, proprange)\n\n    def send_message(self, proprange, exclude=None):\n        proprange = proprange - 1\n\n        for random_variable in self.out:\n            if random_variable is not exclude:\n                self.send_message_to(random_variable, proprange)\n\n        if proprange == 0:\n            return\n\n        for random_variable in self.condition:\n            if random_variable is not exclude:\n                self.send_message_to(random_variable, proprange - 1)\n\n\ndef discrete(table, *condition, out=None, name=None):\n    \"\"\"\n    discrete probability function\n\n    Parameters\n    ----------\n    table : (K, ...) np.ndarray or array-like\n        probability table\n        If a discrete variable A is conditioned with B and C,\n        table[a,b,c] give probability of A=a when B=b and C=c.\n        Thus, the sum along the first axis should equal to 1.\n        If a table is 1 dimensional, the variable is not conditioned.\n    condition : tuple of DiscreteVariable, optional\n        parent node, discrete variable this function is conidtioned by\n        len(condition) should equal to (table.ndim - 1)\n        (Default is (), which means no condition)\n    out : DiscreteVariable, optional\n        output of this discrete probability function\n        Default is None which construct a new output instance\n    name : str\n        name of the discrete probability function\n\n    Returns\n    -------\n    DiscreteVariable\n        output discrete random variable of discrete probability function\n    \"\"\"\n    function = DiscreteProbability(table, *condition, out=out, name=name)\n    if len(function.out) == 1:\n        return function.out[0]\n    else:\n        return function.out\n"
  },
  {
    "path": "prml/bayesnet/probability_function.py",
    "content": "class ProbabilityFunction(object):\n    pass\n"
  },
  {
    "path": "prml/bayesnet/random_variable.py",
    "content": "class RandomVariable(object):\n    \"\"\"\n    Base class for random variable\n    \"\"\"\n"
  },
  {
    "path": "prml/clustering/__init__.py",
    "content": "from prml.clustering.k_means import KMeans\n\n\n__all__ = [\n    \"KMeans\"\n]\n"
  },
  {
    "path": "prml/clustering/k_means.py",
    "content": "import numpy as np\nfrom scipy.spatial.distance import cdist\n\n\nclass KMeans(object):\n\n    def __init__(self, n_clusters):\n        self.n_clusters = n_clusters\n\n    def fit(self, x, iter_max=100):\n        \"\"\"\n        perform k-means algorithm\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input data\n        iter_max : int\n            maximum number of iterations\n\n        Returns\n        -------\n        centers : (n_clusters, n_features) ndarray\n            center of each cluster\n        \"\"\"\n        eye = np.eye(self.n_clusters)\n        centers = x[np.random.choice(len(x), self.n_clusters, replace=False)]\n        for _ in range(iter_max):\n            prev_centers = np.copy(centers)\n            D = cdist(x, centers)\n            cluster_index = np.argmin(D, axis=1)\n            cluster_index = eye[cluster_index]\n            centers = np.sum(x[:, None, :] * cluster_index[:, :, None], axis=0) / np.sum(cluster_index, axis=0)[:, None]\n            if np.allclose(prev_centers, centers):\n                break\n        self.centers = centers\n\n    def predict(self, x):\n        \"\"\"\n        calculate closest cluster center index\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input data\n\n        Returns\n        -------\n        index : (sample_size,) ndarray\n            indicates which cluster they belong\n        \"\"\"\n        D = cdist(x, self.centers)\n        return np.argmin(D, axis=1)\n"
  },
  {
    "path": "prml/dimreduction/__init__.py",
    "content": "from prml.dimreduction.autoencoder import Autoencoder\nfrom prml.dimreduction.bayesian_pca import BayesianPCA\nfrom prml.dimreduction.pca import PCA\n\n\n__all__ = [\n    \"Autoencoder\",\n    \"BayesianPCA\",\n    \"PCA\",\n]\n"
  },
  {
    "path": "prml/dimreduction/autoencoder.py",
    "content": "import numpy as np\n\nfrom prml import nn\n\n\nclass Autoencoder(nn.Network):\n\n    def __init__(self, *args):\n        self.n_unit = len(args)\n        super().__init__()\n        for i in range(self.n_unit - 1):\n            self.parameter[f\"w_encode{i}\"] = nn.asarray(np.random.randn(args[i], args[i + 1]))\n            self.parameter[f\"b_encode{i}\"] = nn.asarray(np.zeros(args[i + 1]))\n            self.parameter[f\"w_decode{i}\"] = nn.asarray(np.random.randn(args[i + 1], args[i]))\n            self.parameter[f\"b_decode{i}\"] = nn.asarray(np.zeros(args[i]))\n\n\n    def transform(self, x):\n        h = x\n        for i in range(self.n_unit - 1):\n            h = nn.tanh(h @ self.parameter[f\"w_encode{i}\"] + self.parameter[f\"b_encode{i}\"])\n        return h.value\n\n    def forward(self, x):\n        h = x\n        for i in range(self.n_unit - 1):\n            h = nn.tanh(h @ self.parameter[f\"w_encode{i}\"] + self.parameter[f\"b_encode{i}\"])\n        for i in range(self.n_unit - 2, 0, -1):\n            h = nn.tanh(h @ self.parameter[f\"w_decode{i}\"] + self.parameter[f\"b_decode{i}\"])\n        x_ = h @ self.parameter[\"w_decode0\"] + self.parameter[\"b_decode0\"]\n        return x_\n\n    def fit(self, x, n_iter=100, learning_rate=1e-3):\n        optimizer = nn.optimizer.Adam(self.parameter, learning_rate)\n        for _ in range(n_iter):\n            self.clear()\n            x_ = self.forward(x)\n            log_likelihood = nn.Gaussian(x_, 1.).log_pdf(x)\n            optimizer.maximize(log_likelihood)\n"
  },
  {
    "path": "prml/dimreduction/bayesian_pca.py",
    "content": "import numpy as np\n\nfrom prml.dimreduction.pca import PCA\n\n\nclass BayesianPCA(PCA):\n\n    def fit(self, X, iter_max=100, initial=\"random\"):\n        \"\"\"\n        empirical bayes estimation of pca parameters\n\n        Parameters\n        ----------\n        X : (sample_size, n_features) ndarray\n            input data\n        iter_max : int\n            maximum number of em steps\n\n        Returns\n        -------\n        mean : (n_features,) ndarray\n            sample mean fo the input data\n        W : (n_features, n_components) ndarray\n            projection matrix\n        var : float\n            variance of observation noise\n        \"\"\"\n        initial_list = [\"random\", \"eigen\"]\n        self.mean = np.mean(X, axis=0)\n        self.eye = np.eye(self.n_components)\n        if initial not in initial_list:\n            print(\"availabel initializations are {}\".format(initial_list))\n        if initial == \"random\":\n            self.W = np.eye(np.size(X, 1), self.n_components)\n            self.var = 1.\n        elif initial == \"eigen\":\n            self.eigen(X)\n        self.alpha = len(self.mean) / np.sum(self.W ** 2, axis=0).clip(min=1e-10)\n        for i in range(iter_max):\n            W = np.copy(self.W)\n            stats = self._expectation(X - self.mean)\n            self._maximization(X - self.mean, *stats)\n            self.alpha = len(self.mean) / np.sum(self.W ** 2, axis=0).clip(min=1e-10)\n            if np.allclose(W, self.W):\n                break\n        self.n_iter = i + 1\n\n    def _maximization(self, X, Ez, Ezz):\n        self.W = X.T @ Ez @ np.linalg.inv(np.sum(Ezz, axis=0) + self.var * np.diag(self.alpha))\n        self.var = np.mean(\n            np.mean(X ** 2, axis=-1)\n            - 2 * np.mean(Ez @ self.W.T * X, axis=-1)\n            + np.trace((Ezz @ self.W.T @ self.W).T) / len(self.mean))\n\n    def maximize(self, D, Ez, Ezz):\n        self.W = D.T.dot(Ez).dot(np.linalg.inv(np.sum(Ezz, axis=0) + self.var * np.diag(self.alpha)))\n        self.var = np.mean(\n            np.mean(D ** 2, axis=-1)\n            - 2 * np.mean(Ez.dot(self.W.T) * D, axis=-1)\n            + np.trace(Ezz.dot(self.W.T).dot(self.W).T) / self.ndim)\n"
  },
  {
    "path": "prml/dimreduction/pca.py",
    "content": "import numpy as np\n\n\nclass PCA(object):\n\n    def __init__(self, n_components):\n        \"\"\"\n        construct principal component analysis\n\n        Parameters\n        ----------\n        n_components : int\n            number of components\n        \"\"\"\n        assert isinstance(n_components, int)\n        self.n_components = n_components\n\n    def fit(self, x, method=\"eigen\", iter_max=100):\n        r\"\"\"Maximum likelihood estimate of pca parameters.\n\n        x ~ \\int_z N(x|Wz+mu,sigma^2)N(z|0,I)dz\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input data\n        method : str\n            method to estimate the parameters\n            [\"eigen\", \"em\"]\n        iter_max : int\n            maximum number of iterations for em algorithm\n\n        Attributes\n        ----------\n        mean : (n_features,) ndarray\n            sample mean of the data\n        W : (n_features, n_components) ndarray\n            projection matrix\n        var : float\n            variance of observation noise\n        C : (n_features, n_features) ndarray\n            variance of the marginal dist N(x|mean,C)\n        Cinv : (n_features, n_features) ndarray\n            precision of the marginal dist N(x|mean, C)\n        \"\"\"\n        method_list = [\"eigen\", \"em\"]\n        if method not in method_list:\n            print(\"availabel methods are {}\".format(method_list))\n        self.mean = np.mean(x, axis=0)\n        getattr(self, method)(x - self.mean, iter_max)\n\n    def eigen(self, x, *arg):\n        sample_size, n_features = x.shape\n        if sample_size >= n_features:\n            cov = np.cov(x, rowvar=False)\n            values, vectors = np.linalg.eigh(cov)\n            index = n_features - self.n_components\n        else:\n            cov = np.cov(x)\n            values, vectors = np.linalg.eigh(cov)\n            vectors = (x.T @ vectors) / np.sqrt(sample_size * values)\n            index = sample_size - self.n_components\n        self.eye = np.eye(self.n_components)\n        if index == 0:\n            self.var = 0\n        else:\n            self.var = np.mean(values[:index])\n\n        self.W = vectors[:, index:].dot(np.sqrt(np.diag(values[index:]) - self.var * self.eye))\n        self.__M = self.W.T @ self.W + self.var * self.eye\n        self.C = self.W @ self.W.T + self.var * np.eye(n_features)\n        if index == 0:\n            self.Cinv = np.linalg.inv(self.C)\n        else:\n            self.Cinv = np.eye(n_features) / np.sqrt(self.var) - self.W @ np.linalg.inv(self.__M) @ self.W.T / self.var\n\n    def em(self, x, iter_max):\n        self.eye = np.eye(self.n_components)\n        self.W = np.eye(np.size(x, 1), self.n_components)\n        self.var = 1.\n        for i in range(iter_max):\n            W = np.copy(self.W)\n            stats = self._expectation(x)\n            self._maximization(x, *stats)\n            if np.allclose(W, self.W):\n                break\n        self.C = self.W @ self.W.T + self.var * np.eye(np.size(x, 1))\n        self.Cinv = np.linalg.inv(self.C)\n\n    def _expectation(self, x):\n        self.__M = self.W.T @ self.W + self.var * self.eye\n        Minv = np.linalg.inv(self.__M)\n        Ez = x @ self.W @ Minv\n        Ezz = self.var * Minv + Ez[:, :, None] * Ez[:, None, :]\n        return Ez, Ezz\n\n    def _maximization(self, x, Ez, Ezz):\n        self.W = x.T @ Ez @ np.linalg.inv(np.sum(Ezz, axis=0))\n        self.var = np.mean(\n            np.mean(x ** 2, axis=1)\n            - 2 * np.mean(Ez @ self.W.T * x, axis=1)\n            + np.trace((Ezz @ self.W.T @ self.W).T) / np.size(x, 1))\n\n    def transform(self, x):\n        \"\"\"\n        project input data into latent space\n        p(Z|x) = N(Z|(x-mu)WMinv, sigma^-2M)\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input data\n\n        Returns\n        -------\n        Z : (sample_size, n_components) ndarray\n            projected input data\n        \"\"\"\n        return np.linalg.solve(self.__M, ((x - self.mean) @ self.W).T).T\n\n    def fit_transform(self, x, method=\"eigen\"):\n        \"\"\"\n        perform pca and whiten the input data\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input data\n\n        Returns\n        -------\n        Z : (sample_size, n_components) ndarray\n            projected input data\n        \"\"\"\n        self.fit(x, method)\n        return self.transform(x)\n\n    def proba(self, x):\n        \"\"\"\n        the marginal distribution of the observed variable\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input data\n\n        Returns\n        -------\n        p : (sample_size,) ndarray\n            value of the marginal distribution\n        \"\"\"\n        d = x - self.mean\n        return (\n            np.exp(-0.5 * np.sum(d @ self.Cinv * d, axis=-1))\n            / np.sqrt(np.linalg.det(self.C))\n            / np.power(2 * np.pi, 0.5 * np.size(x, 1)))\n"
  },
  {
    "path": "prml/kernel/__init__.py",
    "content": "from prml.kernel.gaussian_process_classifier import GaussianProcessClassifier\nfrom prml.kernel.gaussian_process_regressor import GaussianProcessRegressor\nfrom prml.kernel.polynomial import PolynomialKernel\nfrom prml.kernel.rbf import RBF\nfrom prml.kernel.relevance_vector_classifier import RelevanceVectorClassifier\nfrom prml.kernel.relevance_vector_regressor import RelevanceVectorRegressor\nfrom prml.kernel.support_vector_classifier import SupportVectorClassifier\n\n\n__all__ = [\n    \"PolynomialKernel\",\n    \"RBF\",\n    \"GaussianProcessClassifier\",\n    \"GaussianProcessRegressor\",\n    \"RelevanceVectorClassifier\",\n    \"RelevanceVectorRegressor\",\n    \"SupportVectorClassifier\"\n]\n"
  },
  {
    "path": "prml/kernel/gaussian_process_classifier.py",
    "content": "import numpy as np\n\n\nclass GaussianProcessClassifier(object):\n\n    def __init__(self, kernel, noise_level=1e-4):\n        \"\"\"\n        construct gaussian process classifier\n\n        Parameters\n        ----------\n        kernel\n            kernel function to be used to compute Gram matrix\n        noise_level : float\n            parameter to ensure the matrix to be positive\n        \"\"\"\n        self.kernel = kernel\n        self.noise_level = noise_level\n\n    def _sigmoid(self, a):\n        return np.tanh(a * 0.5) * 0.5 + 0.5\n\n    def fit(self, x, t):\n        if x.ndim == 1:\n            x = x[:, None]\n        self.x = x\n        self.t = t\n        Gram = self.kernel(x, x)\n        self.covariance = Gram + np.eye(len(Gram)) * self.noise_level\n        self.precision = np.linalg.inv(self.covariance)\n\n    def predict(self, x):\n        if x.ndim == 1:\n            x = x[:, None]\n        K = self.kernel(x, self.x)\n        a_mean = K @ self.precision @ self.t\n        return self._sigmoid(a_mean)\n"
  },
  {
    "path": "prml/kernel/gaussian_process_regressor.py",
    "content": "import numpy as np\n\n\nclass GaussianProcessRegressor(object):\n\n    def __init__(self, kernel, beta=1.):\n        \"\"\"\n        construct gaussian process regressor\n\n        Parameters\n        ----------\n        kernel\n            kernel function\n        beta : float\n            precision parameter of observation noise\n        \"\"\"\n        self.kernel = kernel\n        self.beta = beta\n\n    def fit(self, X, t, iter_max=0, learning_rate=0.1):\n        \"\"\"\n        maximum likelihood estimation of parameters in kernel function\n\n        Parameters\n        ----------\n        X : ndarray (sample_size, n_features)\n            input\n        t : ndarray (sample_size,)\n            corresponding target\n        iter_max : int\n            maximum number of iterations updating hyperparameters\n        learning_rate : float\n            updation coefficient\n\n        Attributes\n        ----------\n        covariance : ndarray (sample_size, sample_size)\n            variance covariance matrix of gaussian process\n        precision : ndarray (sample_size, sample_size)\n            precision matrix of gaussian process\n\n        Returns\n        -------\n        log_likelihood_list : list\n            list of log likelihood value at each iteration\n        \"\"\"\n        if X.ndim == 1:\n            X = X[:, None]\n        log_likelihood_list = [-np.inf]\n        self.X = X\n        self.t = t\n        eye = np.eye(len(X))\n        gram = self.kernel(X, X)\n        self.covariance = gram + eye / self.beta\n        self.precision = np.linalg.inv(self.covariance)\n        for i in range(iter_max):\n            gradients = self.kernel.derivatives(X, X)\n            updates = np.array(\n                [-np.trace(self.precision.dot(grad)) + t.dot(self.precision.dot(grad).dot(self.precision).dot(t)) for grad in gradients])\n            for j in range(iter_max):\n                self.kernel.update_parameters(learning_rate * updates)\n                gram = self.kernel(X, X)\n                self.covariance = gram + eye / self.beta\n                self.precision = np.linalg.inv(self.covariance)\n                log_like = self.log_likelihood()\n                if log_like > log_likelihood_list[-1]:\n                    log_likelihood_list.append(log_like)\n                    break\n                else:\n                    self.kernel.update_parameters(-learning_rate * updates)\n                    learning_rate *= 0.9\n        log_likelihood_list.pop(0)\n        return log_likelihood_list\n\n    def log_likelihood(self):\n        return -0.5 * (\n            np.linalg.slogdet(self.covariance)[1]\n            + self.t @ self.precision @ self.t\n            + len(self.t) * np.log(2 * np.pi))\n\n    def predict(self, X, with_error=False):\n        \"\"\"\n        mean of the gaussian process\n\n        Parameters\n        ----------\n        X : ndarray (sample_size, n_features)\n            input\n\n        Returns\n        -------\n        mean : ndarray (sample_size,)\n            predictions of corresponding inputs\n        \"\"\"\n        if X.ndim == 1:\n            X = X[:, None]\n        K = self.kernel(X, self.X)\n        mean = K @ self.precision @ self.t\n        if with_error:\n            var = (\n                self.kernel(X, X, False)\n                + 1 / self.beta\n                - np.sum(K @ self.precision * K, axis=1))\n            return mean.ravel(), np.sqrt(var.ravel())\n        return mean\n"
  },
  {
    "path": "prml/kernel/kernel.py",
    "content": "import numpy as np\n\n\nclass Kernel(object):\n    \"\"\"\n    Base class for kernel function\n    \"\"\"\n\n    def _pairwise(self, x, y):\n        \"\"\"\n        all pairs of x and y\n\n        Parameters\n        ----------\n        x : (sample_size, n_features)\n            input\n        y : (sample_size, n_features)\n            another input\n\n        Returns\n        -------\n        output : tuple\n            two array with shape (sample_size, sample_size, n_features)\n        \"\"\"\n        return (\n            np.tile(x, (len(y), 1, 1)).transpose(1, 0, 2),\n            np.tile(y, (len(x), 1, 1))\n        )\n"
  },
  {
    "path": "prml/kernel/polynomial.py",
    "content": "import numpy as np\n\nfrom prml.kernel.kernel import Kernel\n\n\nclass PolynomialKernel(Kernel):\n    \"\"\"\n    Polynomial kernel\n    k(x,y) = (x @ y + c)^M\n    \"\"\"\n\n    def __init__(self, degree=2, const=0.):\n        \"\"\"\n        construct Polynomial kernel\n\n        Parameters\n        ----------\n        const : float\n            a constant to be added\n        degree : int\n            degree of polynomial order\n        \"\"\"\n        self.const = const\n        self.degree = degree\n\n    def __call__(self, x, y, pairwise=True):\n        \"\"\"\n        calculate pairwise polynomial kernel\n\n        Parameters\n        ----------\n        x : (..., ndim) ndarray\n            input\n        y : (..., ndim) ndarray\n            another input with the same shape\n\n        Returns\n        -------\n        output : ndarray\n            polynomial kernel\n        \"\"\"\n        if pairwise:\n            x, y = self._pairwise(x, y)\n        return (np.sum(x * y, axis=-1) + self.const) ** self.degree\n"
  },
  {
    "path": "prml/kernel/rbf.py",
    "content": "import numpy as np\n\nfrom prml.kernel.kernel import Kernel\n\n\nclass RBF(Kernel):\n\n    def __init__(self, params):\n        \"\"\"\n        construct Radial basis kernel function\n\n        Parameters\n        ----------\n        params : (ndim + 1,) ndarray\n            parameters of radial basis function\n\n        Attributes\n        ----------\n        ndim : int\n            dimension of expected input data\n        \"\"\"\n        assert params.ndim == 1\n        self.params = params\n        self.ndim = len(params) - 1\n\n    def __call__(self, x, y, pairwise=True):\n        \"\"\"\n        calculate radial basis function\n        k(x, y) = c0 * exp(-0.5 * c1 * (x1 - y1) ** 2 ...)\n\n        Parameters\n        ----------\n        x : ndarray [..., ndim]\n            input of this kernel function\n        y : ndarray [..., ndim]\n            another input\n\n        Returns\n        -------\n        output : ndarray\n            output of this radial basis function\n        \"\"\"\n        assert x.shape[-1] == self.ndim\n        assert y.shape[-1] == self.ndim\n        if pairwise:\n            x, y = self._pairwise(x, y)\n        d = self.params[1:] * (x - y) ** 2\n        return self.params[0] * np.exp(-0.5 * np.sum(d, axis=-1))\n\n    def derivatives(self, x, y, pairwise=True):\n        if pairwise:\n            x, y = self._pairwise(x, y)\n        d = self.params[1:] * (x - y) ** 2\n        delta = np.exp(-0.5 * np.sum(d, axis=-1))\n        deltas = -0.5 * (x - y) ** 2 * (delta * self.params[0])[:, :, None]\n        return np.concatenate((np.expand_dims(delta, 0), deltas.T))\n\n    def update_parameters(self, updates):\n        self.params += updates\n"
  },
  {
    "path": "prml/kernel/relevance_vector_classifier.py",
    "content": "import numpy as np\n\n\nclass RelevanceVectorClassifier(object):\n\n    def __init__(self, kernel, alpha=1.):\n        \"\"\"\n        construct relevance vector classifier\n\n        Parameters\n        ----------\n        kernel : Kernel\n            kernel function to compute components of feature vectors\n        alpha : float\n            initial precision of prior weight distribution\n        \"\"\"\n        self.kernel = kernel\n        self.alpha = alpha\n\n    def _sigmoid(self, a):\n        return np.tanh(a * 0.5) * 0.5 + 0.5\n\n    def _map_estimate(self, x, t, w, n_iter=10):\n        for _ in range(n_iter):\n            y = self._sigmoid(x @ w)\n            g = x.T @ (y - t) + self.alpha * w\n            H = (x.T * y * (1 - y)) @ x + np.diag(self.alpha)\n            w -= np.linalg.solve(H, g)\n        return w, np.linalg.inv(H)\n\n    def fit(self, x, t, iter_max=100):\n        \"\"\"\n        maximize evidence with respect ot hyperparameter\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input\n        t : (sample_size,) ndarray\n            corresponding target\n        iter_max : int\n            maximum number of iterations\n\n        Attributes\n        ----------\n        x : (N, n_features) ndarray\n            relevance vector\n        t : (N,) ndarray\n            corresponding target\n        alpha : (N,) ndarray\n            hyperparameter for each weight or training sample\n        cov : (N, N) ndarray\n            covariance matrix of weight\n        mean : (N,) ndarray\n            mean of each weight\n        \"\"\"\n        if x.ndim == 1:\n            x = x[:, None]\n        assert x.ndim == 2\n        assert t.ndim == 1\n        Phi = self.kernel(x, x)\n        N = len(t)\n        self.alpha = np.zeros(N) + self.alpha\n        mean = np.zeros(N)\n        for _ in range(iter_max):\n            param = np.copy(self.alpha)\n            mean, cov = self._map_estimate(Phi, t, mean, 10)\n            gamma = 1 - self.alpha * np.diag(cov)\n            self.alpha = gamma / np.square(mean)\n            np.clip(self.alpha, 0, 1e10, out=self.alpha)\n            if np.allclose(param, self.alpha):\n                break\n        mask = self.alpha < 1e8\n        self.x = x[mask]\n        self.t = t[mask]\n        self.alpha = self.alpha[mask]\n        Phi = self.kernel(self.x, self.x)\n        mean = mean[mask]\n        self.mean, self.covariance = self._map_estimate(Phi, self.t, mean, 100)\n\n    def predict(self, x):\n        \"\"\"\n        predict class label\n\n        Parameters\n        ----------\n        x : (sample_size, n_features)\n            input\n\n        Returns\n        -------\n        label : (sample_size,) ndarray\n            predicted label\n        \"\"\"\n        if x.ndim == 1:\n            x = x[:, None]\n        assert x.ndim == 2\n        phi = self.kernel(x, self.x)\n        label = (phi @ self.mean > 0).astype(int)\n        return label\n\n    def predict_proba(self, x):\n        \"\"\"\n        probability of input belonging class one\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input\n\n        Returns\n        -------\n        proba : (sample_size,) ndarray\n            probability of predictive distribution p(C1|x)\n        \"\"\"\n        if x.ndim == 1:\n            x = x[:, None]\n        assert x.ndim == 2\n        phi = self.kernel(x, self.x)\n        mu_a = phi @ self.mean\n        var_a = np.sum(phi @ self.covariance * phi, axis=1)\n        return self._sigmoid(mu_a / np.sqrt(1 + np.pi * var_a / 8))\n"
  },
  {
    "path": "prml/kernel/relevance_vector_regressor.py",
    "content": "import numpy as np\n\n\nclass RelevanceVectorRegressor(object):\n\n    def __init__(self, kernel, alpha=1., beta=1.):\n        \"\"\"\n        construct relevance vector regressor\n\n        Parameters\n        ----------\n        kernel : Kernel\n            kernel function to compute components of feature vectors\n        alpha : float\n            initial precision of prior weight distribution\n        beta : float\n            precision of observation\n        \"\"\"\n        self.kernel = kernel\n        self.alpha = alpha\n        self.beta = beta\n\n    def fit(self, x, t, iter_max=1000):\n        \"\"\"\n        maximize evidence with respect to hyperparameter\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input\n        t : (sample_size,) ndarray\n            corresponding target\n        iter_max : int\n            maximum number of iterations\n\n        Attributes\n        -------\n        x : (N, n_features) ndarray\n            relevance vector\n        t : (N,) ndarray\n            corresponding target\n        alpha : (N,) ndarray\n            hyperparameter for each weight or training sample\n        cov : (N, N) ndarray\n            covariance matrix of weight\n        mean : (N,) ndarray\n            mean of each weight\n        \"\"\"\n        if x.ndim == 1:\n            x = x[:, None]\n        assert x.ndim == 2\n        assert t.ndim == 1\n        N = len(t)\n        Phi = self.kernel(x, x)\n        self.alpha = np.zeros(N) + self.alpha\n        for _ in range(iter_max):\n            params = np.hstack([self.alpha, self.beta])\n            precision = np.diag(self.alpha) + self.beta * Phi.T @ Phi\n            covariance = np.linalg.inv(precision)\n            mean = self.beta * covariance @ Phi.T @ t\n            gamma = 1 - self.alpha * np.diag(covariance)\n            self.alpha = gamma / np.square(mean)\n            np.clip(self.alpha, 0, 1e10, out=self.alpha)\n            self.beta = (N - np.sum(gamma)) / np.sum((t - Phi.dot(mean)) ** 2)\n            if np.allclose(params, np.hstack([self.alpha, self.beta])):\n                break\n        mask = self.alpha < 1e9\n        self.x = x[mask]\n        self.t = t[mask]\n        self.alpha = self.alpha[mask]\n        Phi = self.kernel(self.x, self.x)\n        precision = np.diag(self.alpha) + self.beta * Phi.T @ Phi\n        self.covariance = np.linalg.inv(precision)\n        self.mean = self.beta * self.covariance @ Phi.T @ self.t\n\n    def predict(self, x, with_error=True):\n        \"\"\"\n        predict output with this model\n\n        Parameters\n        ----------\n        x : (sample_size, n_features)\n            input\n        with_error : bool\n            if True, predict with standard deviation of the outputs\n\n        Returns\n        -------\n        mean : (sample_size,) ndarray\n            mean of predictive distribution\n        std : (sample_size,) ndarray\n            standard deviation of predictive distribution\n        \"\"\"\n        if x.ndim == 1:\n            x = x[:, None]\n        assert x.ndim == 2\n        phi = self.kernel(x, self.x)\n        mean = phi @ self.mean\n        if with_error:\n            var = 1 / self.beta + np.sum(phi @ self.covariance * phi, axis=1)\n            return mean, np.sqrt(var)\n        return mean\n"
  },
  {
    "path": "prml/kernel/support_vector_classifier.py",
    "content": "import numpy as np\n\n\nclass SupportVectorClassifier(object):\n\n    def __init__(self, kernel, C=np.inf):\n        \"\"\"\n        construct support vector classifier\n\n        Parameters\n        ----------\n        kernel : Kernel\n            kernel function to compute inner products\n        C : float\n            penalty of misclassification\n        \"\"\"\n        self.kernel = kernel\n        self.C = C\n\n    def fit(self, x: np.ndarray, t: np.ndarray, tol: float = 1e-8):\n        \"\"\"Estimate support vectors and their parameters.\n\n        Parameters\n        ----------\n        x : (N, D) np.ndarray\n            training independent variable\n        t : (N,) np.ndarray\n            training dependent variable\n            binary -1 or 1\n        tol : float, optional\n            numerical tolerance (the default is 1e-8)\n        \"\"\"\n\n        N = len(t)\n        coef = np.zeros(N)\n        grad = np.ones(N)\n        Gram = self.kernel(x, x)\n\n        while True:\n            tg = t * grad\n            mask_up = (t == 1) & (coef < self.C - tol)\n            mask_up |= (t == -1) & (coef > tol)\n            mask_down = (t == -1) & (coef < self.C - tol)\n            mask_down |= (t == 1) & (coef > tol)\n            i = np.where(mask_up)[0][np.argmax(tg[mask_up])]\n            j = np.where(mask_down)[0][np.argmin(tg[mask_down])]\n            if tg[i] < tg[j] + tol:\n                self.b = 0.5 * (tg[i] + tg[j])\n                break\n            else:\n                A = self.C - coef[i] if t[i] == 1 else coef[i]\n                B = coef[j] if t[j] == 1 else self.C - coef[j]\n                direction = (tg[i] - tg[j]) / (Gram[i, i] - 2 * Gram[i, j] + Gram[j, j])\n                direction = min(A, B, direction)\n                coef[i] += direction * t[i]\n                coef[j] -= direction * t[j]\n                grad -= direction * t * (Gram[i] - Gram[j])\n        support_mask = coef > tol\n        self.a = coef[support_mask]\n        self.x = x[support_mask]\n        self.t = t[support_mask]\n\n    def lagrangian_function(self):\n        return (\n            np.sum(self.a)\n            - self.a\n            @ (self.t * self.t[:, None] * self.kernel(self.x, self.x))\n            @ self.a)\n\n    def predict(self, x):\n        \"\"\"\n        predict labels of the input\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input\n\n        Returns\n        -------\n        label : (sample_size,) ndarray\n            predicted labels\n        \"\"\"\n        y = self.distance(x)\n        label = np.sign(y)\n        return label\n\n    def distance(self, x):\n        \"\"\"\n        calculate distance from the decision boundary\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input\n\n        Returns\n        -------\n        distance : (sample_size,) ndarray\n            distance from the boundary\n        \"\"\"\n        distance = np.sum(\n            self.a * self.t\n            * self.kernel(x, self.x),\n            axis=-1) + self.b\n        return distance\n"
  },
  {
    "path": "prml/linear/__init__.py",
    "content": "\"\"\"Linear machine learning models.\"\"\"\n\nfrom prml.linear._bayesian_logistic_regression import (\n    BayesianLogisticRegression,\n)\nfrom prml.linear._bayesian_regression import BayesianRegression\nfrom prml.linear._empirical_bayes_regression import EmpiricalBayesRegression\nfrom prml.linear._fishers_linear_discriminant import FishersLinearDiscriminant\nfrom prml.linear._least_squares_classifier import LeastSquaresClassifier\nfrom prml.linear._linear_regression import LinearRegression\nfrom prml.linear._logistic_regression import LogisticRegression\nfrom prml.linear._perceptron import Perceptron\nfrom prml.linear._ridge_regression import RidgeRegression\nfrom prml.linear._softmax_regression import SoftmaxRegression\nfrom prml.linear._variational_linear_regression import (\n    VariationalLinearRegression,\n)\nfrom prml.linear._variational_logistic_regression import (\n    VariationalLogisticRegression,\n)\n\n\n__all__ = [\n    \"BayesianLogisticRegression\",\n    \"BayesianRegression\",\n    \"EmpiricalBayesRegression\",\n    \"LeastSquaresClassifier\",\n    \"LinearRegression\",\n    \"FishersLinearDiscriminant\",\n    \"LogisticRegression\",\n    \"Perceptron\",\n    \"RidgeRegression\",\n    \"SoftmaxRegression\",\n    \"VariationalLinearRegression\",\n    \"VariationalLogisticRegression\",\n]\n"
  },
  {
    "path": "prml/linear/_bayesian_logistic_regression.py",
    "content": "import numpy as np\n\nfrom prml.linear._logistic_regression import LogisticRegression\n\n\nclass BayesianLogisticRegression(LogisticRegression):\n    \"\"\"Bayesian logistic regression model.\n\n    w ~ Gaussian(0, alpha^(-1)I)\n    y = sigmoid(X @ w)\n    t ~ Bernoulli(t|y)\n\n    Graphical Model\n    ---------------\n\n    ```txt\n    *----------------*\n    |                |\n    |      x_n       |               alpha\n    |       **       |                **\n    |       **       |                **\n    |       |        |                |\n    |       |        |                |\n    |       |        |                |\n    |       |        |                |\n    |       |        |                |\n    |       |        |                |\n    |       |        |                |\n    |       v        |                v\n    |      ****      |               ****  w\n    |    **    **    |             **    **\n    |   *        *   |            *        *\n    |   *        *<--|------------*        *\n    |    **    **    |             **    **\n    |  t_n ****      |               ****\n    |             N  |\n    *----------------*\n    ```\n    \"\"\"\n\n    def __init__(self, alpha: float = 1.):\n        \"\"\"Initialize bayesian logistic regression model.\n\n        Parameters\n        ----------\n        alpha : float, optional\n            Precision parameter of the prior, by default 1.\n        \"\"\"\n        self.alpha = alpha\n\n    def fit(\n        self,\n        x_train: np.ndarray,\n        y_train: np.ndarray,\n        max_iter: int = 100,\n    ):\n        \"\"\"Bayesian estimation of the posterior using Laplace approximation.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            Training data independent variable (N, D)\n        y_train : np.ndarray\n            training data dependent variable (N,)\n            binary 0 or 1\n        max_iter : int, optional\n            maximum number of paramter update iteration (the default is 100)\n        \"\"\"\n        w = np.zeros(np.size(x_train, 1))\n        eye = np.eye(np.size(x_train, 1))\n        self.w_mean = np.copy(w)\n        self.w_precision = self.alpha * eye\n        for _ in range(max_iter):\n            w_prev = np.copy(w)\n            y = self._sigmoid(x_train @ w)\n            grad = (\n                x_train.T @ (y - y_train)\n                + self.w_precision @ (w - self.w_mean)\n            )\n            hessian = (x_train.T * y * (1 - y)) @ x_train + self.w_precision\n            try:\n                w -= np.linalg.solve(hessian, grad)\n            except np.linalg.LinAlgError:\n                break\n            if np.allclose(w, w_prev):\n                break\n        self.w_mean = w\n        self.w_precision = hessian\n\n    def proba(self, x: np.ndarray):\n        \"\"\"Return probability of input belonging class 1.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            training data independent variable (N, D)\n\n        Returns\n        -------\n        np.ndarray\n            probability of positive (N,)\n        \"\"\"\n        mu_a = x @ self.w_mean\n        var_a = np.sum(np.linalg.solve(self.w_precision, x.T).T * x, axis=1)\n        return self._sigmoid(mu_a / np.sqrt(1 + np.pi * var_a / 8))\n"
  },
  {
    "path": "prml/linear/_bayesian_regression.py",
    "content": "import numpy as np\n\nfrom prml.linear._regression import Regression\n\n\nclass BayesianRegression(Regression):\n    \"\"\"Bayesian regression model.\n\n    w ~ N(w|0, alpha^(-1)I)\n    y = X @ w\n    t ~ N(t|X @ w, beta^(-1))\n    \"\"\"\n\n    def __init__(self, alpha: float = 1., beta: float = 1.):\n        \"\"\"Initialize bayesian linear regression model.\n\n        Parameters\n        ----------\n        alpha : float, optional\n            Precision parameter of the prior, by default 1.\n        beta : float, optional\n            Precision parameter of the likelihood, by default 1.\n        \"\"\"\n        self.alpha = alpha\n        self.beta = beta\n        self.w_mean = None\n        self.w_precision = None\n\n    def _is_prior_defined(self) -> bool:\n        return self.w_mean is not None and self.w_precision is not None\n\n    def _get_prior(self, ndim: int) -> tuple:\n        if self._is_prior_defined():\n            return self.w_mean, self.w_precision\n        else:\n            return np.zeros(ndim), self.alpha * np.eye(ndim)\n\n    def fit(self, x_train: np.ndarray, y_train: np.ndarray):\n        \"\"\"Bayesian update of parameters given training dataset.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            training data independent variable (N, n_features)\n        y_train :  np.ndarray\n            training data dependent variable\n        \"\"\"\n        mean_prev, precision_prev = self._get_prior(np.size(x_train, 1))\n\n        w_precision = precision_prev + self.beta * x_train.T @ x_train\n        w_mean = np.linalg.solve(\n            w_precision,\n            precision_prev @ mean_prev + self.beta * x_train.T @ y_train,\n        )\n        self.w_mean = w_mean\n        self.w_precision = w_precision\n        self.w_cov = np.linalg.inv(self.w_precision)\n\n    def predict(\n        self,\n        x: np.ndarray,\n        return_std: bool = False,\n        sample_size: int = None,\n    ):\n        \"\"\"Return mean (and standard deviation) of predictive distribution.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            independent variable (N, n_features)\n        return_std : bool, optional\n            flag to return standard deviation (the default is False)\n        sample_size : int, optional\n            number of samples to draw from the predictive distribution\n            (the default is None, no sampling from the distribution)\n\n        Returns\n        -------\n        y : np.ndarray\n            mean of the predictive distribution (N,)\n        y_std : np.ndarray\n            standard deviation of the predictive distribution (N,)\n        y_sample : np.ndarray\n            samples from the predictive distribution (N, sample_size)\n        \"\"\"\n        if sample_size is not None:\n            w_sample = np.random.multivariate_normal(\n                self.w_mean, self.w_cov, size=sample_size,\n            )\n            y_sample = x @ w_sample.T\n            return y_sample\n        y = x @ self.w_mean\n        if return_std:\n            y_var = 1 / self.beta + np.sum(x @ self.w_cov * x, axis=1)\n            y_std = np.sqrt(y_var)\n            return y, y_std\n        return y\n"
  },
  {
    "path": "prml/linear/_classifier.py",
    "content": "class Classifier(object):\n    \"\"\"Base class for classifiers.\"\"\"\n\n    pass\n"
  },
  {
    "path": "prml/linear/_empirical_bayes_regression.py",
    "content": "import numpy as np\n\nfrom prml.linear._bayesian_regression import BayesianRegression\n\n\nclass EmpiricalBayesRegression(BayesianRegression):\n    \"\"\"Empirical Bayes Regression model.\n\n    a.k.a.\n    type 2 maximum likelihood,\n    generalized maximum likelihood,\n    evidence approximation\n\n    w ~ N(w|0, alpha^(-1)I)\n    y = X @ w\n    t ~ N(t|X @ w, beta^(-1))\n    evidence function p(t|X,alpha,beta) = S p(t|w;X,beta)p(w|0;alpha) dw\n    \"\"\"\n\n    def __init__(self, alpha: float = 1., beta: float = 1.):\n        \"\"\"Initialize empirical bayesian linear regression model.\n\n        Parameters\n        ----------\n        alpha : float, optional\n            Precision parameter of the prior, by default 1.\n        beta : float, optional\n            Precision parameter of the likelihood, by default 1.\n        \"\"\"\n        super().__init__(alpha, beta)\n\n    def fit(\n        self,\n        x_train: np.ndarray,\n        y_train: np.ndarray,\n        max_iter: int = 100,\n    ):\n        \"\"\"Maximize of evidence function with respect to the hyperparameters.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            training independent variable (N, D)\n        y_train : np.ndarray\n            training dependent variable (N,)\n        max_iter : int\n            maximum number of iteration\n        \"\"\"\n        xtx = x_train.T @ x_train\n        eigenvalues = np.linalg.eigvalsh(xtx)\n        eye = np.eye(np.size(x_train, 1))\n        n = len(y_train)\n        for _ in range(max_iter):\n            params = [self.alpha, self.beta]\n\n            w_precision = self.alpha * eye + self.beta * xtx\n            w_mean = self.beta * np.linalg.solve(\n                w_precision, x_train.T @ y_train)\n\n            gamma = np.sum(eigenvalues / (self.alpha + eigenvalues))\n            self.alpha = float(gamma / np.sum(w_mean ** 2).clip(min=1e-10))\n            self.beta = float(\n                (n - gamma) / np.sum(np.square(y_train - x_train @ w_mean)),\n            )\n            if np.allclose(params, [self.alpha, self.beta]):\n                break\n        self.w_mean = w_mean\n        self.w_precision = w_precision\n        self.w_cov = np.linalg.inv(w_precision)\n\n    def _log_prior(self, w):\n        return -0.5 * self.alpha * np.sum(w ** 2)\n\n    def _log_likelihood(self, x, y, w):\n        return -0.5 * self.beta * np.square(y - x @ w).sum()\n\n    def _log_posterior(self, x, y, w):\n        return self._log_likelihood(x, y, w) + self._log_prior(w)\n\n    def log_evidence(self, x: np.ndarray, y: np.ndarray):\n        \"\"\"Return logarithm or the evidence function.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            indenpendent variable (N, D)\n        y : np.ndarray\n            dependent variable (N,)\n        Returns\n        -------\n        float\n            log evidence\n        \"\"\"\n        n = len(y)\n        d = np.size(x, 1)\n        return 0.5 * (\n            d * np.log(self.alpha) + n * np.log(self.beta)\n            - np.linalg.slogdet(self.w_precision)[1] - d * np.log(2 * np.pi)\n        ) + self._log_posterior(x, y, self.w_mean)\n"
  },
  {
    "path": "prml/linear/_fishers_linear_discriminant.py",
    "content": "import typing as tp\n\nimport numpy as np\n\nfrom prml.linear._classifier import Classifier\nfrom prml.rv.gaussian import Gaussian\n\n\nclass FishersLinearDiscriminant(Classifier):\n    \"\"\"Fisher's Linear discriminant model.\"\"\"\n\n    def __init__(\n        self,\n        w: tp.Optional[np.ndarray] = None,\n        threshold: tp.Optional[float] = None,\n    ):\n        \"\"\"Initialize fisher's linear discriminant model.\n\n        Parameters\n        ----------\n        w : tp.Optional[np.ndarray], optional\n            Initial parameter, by default None\n        threshold : tp.Optional[float], optional\n            Initial threshold, by default None\n        \"\"\"\n        self.w = w\n        self.threshold = threshold\n\n    def fit(self, x_train: np.ndarray, y_train: np.ndarray):\n        \"\"\"Estimate parameter given training dataset.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            training dataset independent variable (N, D)\n        y_train : np.ndarray\n            training dataset dependent variable (N,)\n            binary 0 or 1\n        \"\"\"\n        x0 = x_train[y_train == 0]\n        x1 = x_train[y_train == 1]\n        m0 = np.mean(x0, axis=0)\n        m1 = np.mean(x1, axis=0)\n        cov_inclass = np.cov(x0, rowvar=False) + np.cov(x1, rowvar=False)\n        self.w = np.linalg.solve(cov_inclass, m1 - m0)\n        self.w /= np.linalg.norm(self.w).clip(min=1e-10)\n\n        g0 = Gaussian()\n        g0.fit((x0 @ self.w))\n        g1 = Gaussian()\n        g1.fit((x1 @ self.w))\n        root = np.roots([\n            g1.var - g0.var,\n            2 * (g0.var * g1.mu - g1.var * g0.mu),\n            g1.var * g0.mu ** 2 - g0.var * g1.mu ** 2\n            - g1.var * g0.var * np.log(g1.var / g0.var),\n        ])\n        if g0.mu < root[0] < g1.mu or g1.mu < root[0] < g0.mu:\n            self.threshold = root[0]\n        else:\n            self.threshold = root[1]\n\n    def transform(self, x: np.ndarray):\n        \"\"\"Project data.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            independent variable (N, D)\n\n        Returns\n        -------\n        y : np.ndarray\n            projected data (N,)\n        \"\"\"\n        return x @ self.w\n\n    def classify(self, x: np.ndarray):\n        \"\"\"Classify input data.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            independent variable to be classified (N, D)\n\n        Returns\n        -------\n        np.ndarray\n            binary class for each input (N,)\n        \"\"\"\n        return (x @ self.w > self.threshold).astype(int)\n"
  },
  {
    "path": "prml/linear/_least_squares_classifier.py",
    "content": "import numpy as np\n\nfrom prml.linear._classifier import Classifier\nfrom prml.preprocess.label_transformer import LabelTransformer\n\n\nclass LeastSquaresClassifier(Classifier):\n    \"\"\"Least squares classifier model.\n\n    X : (N, D)\n    W : (D, K)\n    y = argmax_k X @ W\n    \"\"\"\n\n    def __init__(self, w: np.ndarray = None):\n        \"\"\"Initialize least squares classifier model.\n\n        Parameters\n        ----------\n        w : np.ndarray, optional\n            Initial parameter, by default None\n        \"\"\"\n        self.w = w\n\n    def fit(self, x_train: np.ndarray, y_train: np.ndarray):\n        \"\"\"Least squares fitting for classification.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            training independent variable (N, D)\n        y_train : np.ndarray\n            training dependent variable\n            in class index (N,) or one-of-k coding (N,K)\n        \"\"\"\n        if y_train.ndim == 1:\n            y_train = LabelTransformer().encode(y_train)\n        self.w = np.linalg.pinv(x_train) @ y_train\n\n    def classify(self, x: np.ndarray):\n        \"\"\"Classify input data.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            independent variable to be classified (N, D)\n\n        Returns\n        -------\n        np.ndarray\n            class index for each input (N,)\n        \"\"\"\n        return np.argmax(x @ self.w, axis=-1)\n"
  },
  {
    "path": "prml/linear/_linear_regression.py",
    "content": "import numpy as np\n\nfrom prml.linear._regression import Regression\n\n\nclass LinearRegression(Regression):\n    \"\"\"Linear regression model.\n\n    y = X @ w\n    t ~ N(t|X @ w, var)\n    \"\"\"\n\n    def fit(self, x_train: np.ndarray, y_train: np.ndarray):\n        \"\"\"Perform least squares fitting.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            training independent variable (N, D)\n        y_train : np.ndarray\n            training dependent variable (N,)\n        \"\"\"\n        self.w = np.linalg.pinv(x_train) @ y_train\n        self.var = np.mean(np.square(x_train @ self.w - y_train))\n\n    def predict(self, x: np.ndarray, return_std: bool = False):\n        \"\"\"Return prediction given input.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            samples to predict their output (N, D)\n        return_std : bool, optional\n            returns standard deviation of each predition if True\n\n        Returns\n        -------\n        y : np.ndarray\n            prediction of each sample (N,)\n        y_std : np.ndarray\n            standard deviation of each predition (N,)\n        \"\"\"\n        y = x @ self.w\n        if return_std:\n            y_std = np.sqrt(self.var) + np.zeros_like(y)\n            return y, y_std\n        return y\n"
  },
  {
    "path": "prml/linear/_logistic_regression.py",
    "content": "import numpy as np\n\nfrom prml.linear._classifier import Classifier\n\n\nclass LogisticRegression(Classifier):\n    \"\"\"Logistic regression model.\n\n    y = sigmoid(X @ w)\n    t ~ Bernoulli(t|y)\n    \"\"\"\n\n    @staticmethod\n    def _sigmoid(a):\n        return np.tanh(a * 0.5) * 0.5 + 0.5\n\n    def fit(\n        self,\n        x_train: np.ndarray,\n        y_train: np.ndarray,\n        max_iter: int = 100,\n    ):\n        \"\"\"Maximum likelihood estimation of logistic regression model.\n\n        Parameters\n        ----------\n        x_train : (N, D) np.ndarray\n            training data independent variable\n        y_train : (N,) np.ndarray\n            training data dependent variable\n            binary 0 or 1\n        max_iter : int, optional\n            maximum number of parameter update iteration (the default is 100)\n        \"\"\"\n        w = np.zeros(np.size(x_train, 1))\n        for _ in range(max_iter):\n            w_prev = np.copy(w)\n            y = self._sigmoid(x_train @ w)\n            grad = x_train.T @ (y - y_train)\n            hessian = (x_train.T * y * (1 - y)) @ x_train\n            try:\n                w -= np.linalg.solve(hessian, grad)\n            except np.linalg.LinAlgError:\n                break\n            if np.allclose(w, w_prev):\n                break\n        self.w = w\n\n    def proba(self, x: np.ndarray):\n        \"\"\"Return probability of input belonging class 1.\n\n        Parameters\n        ----------\n        x : (N, D) np.ndarray\n            Input independent variable\n\n        Returns\n        -------\n        (N,) np.ndarray\n            probability of positive\n        \"\"\"\n        return self._sigmoid(x @ self.w)\n\n    def classify(self, x: np.ndarray, threshold: float = 0.5):\n        \"\"\"Classify input data.\n\n        Parameters\n        ----------\n        x : (N, D) np.ndarray\n            Input independent variable to be classified\n        threshold : float, optional\n            threshold of binary classification (default is 0.5)\n\n        Returns\n        -------\n        (N,) np.ndarray\n            binary class for each input\n        \"\"\"\n        return (self.proba(x) > threshold).astype(int)\n"
  },
  {
    "path": "prml/linear/_perceptron.py",
    "content": "import numpy as np\n\nfrom prml.linear._classifier import Classifier\n\n\nclass Perceptron(Classifier):\n    \"\"\"Perceptron model.\"\"\"\n\n    def fit(\n        self,\n        x_train: np.ndarray,\n        y_train: np.ndarray,\n        max_epoch: int = 100,\n    ):\n        \"\"\"Fit perceptron model on given input pair.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            training independent variable (N, D)\n        y_train : np.ndarray\n            training dependent variable (N,)\n            binary -1 or 1\n        max_epoch : int, optional\n            maximum number of epoch (the default is 100)\n        \"\"\"\n        self.w = np.zeros(np.size(x_train, 1))\n        for _ in range(max_epoch):\n            prediction = self.classify(x_train)\n            error_indices = prediction != y_train\n            x_error = x_train[error_indices]\n            y_error = y_train[error_indices]\n            idx = np.random.choice(len(x_error))\n            self.w += x_error[idx] * y_error[idx]\n            if ((x_train @ self.w) * y_train > 0).all():\n                break\n\n    def classify(self, x: np.ndarray):\n        \"\"\"Classify input data.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            independent variable to be classified (N, D)\n\n        Returns\n        -------\n        np.ndarray\n            binary class (-1 or 1) for each input (N,)\n        \"\"\"\n        return np.sign(x @ self.w).astype(int)\n"
  },
  {
    "path": "prml/linear/_regression.py",
    "content": "class Regression(object):\n    \"\"\"Base class for regressors.\"\"\"\n\n    pass\n"
  },
  {
    "path": "prml/linear/_ridge_regression.py",
    "content": "import numpy as np\n\nfrom prml.linear._regression import Regression\n\n\nclass RidgeRegression(Regression):\n    \"\"\"Ridge regression model.\n\n    w* = argmin |t - X @ w| + alpha * |w|_2^2\n    \"\"\"\n\n    def __init__(self, alpha: float = 1.):\n        \"\"\"Initialize ridge linear regression model.\n\n        Parameters\n        ----------\n        alpha : float, optional\n            Coefficient of the prior term, by default 1.\n        \"\"\"\n        self.alpha = alpha\n\n    def fit(self, x_train: np.ndarray, y_train: np.ndarray):\n        \"\"\"Maximum a posteriori estimation of parameter.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            training data independent variable (N, D)\n        y_train : np.ndarray\n            training data dependent variable (N,)\n        \"\"\"\n        eye = np.eye(np.size(x_train, 1))\n        self.w = np.linalg.solve(\n            self.alpha * eye + x_train.T @ x_train,\n            x_train.T @ y_train,\n        )\n\n    def predict(self, x: np.ndarray):\n        \"\"\"Return prediction.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            samples to predict their output (N, D)\n\n        Returns\n        -------\n        np.ndarray\n            prediction of each input (N,)\n        \"\"\"\n        return x @ self.w\n"
  },
  {
    "path": "prml/linear/_softmax_regression.py",
    "content": "import numpy as np\n\nfrom prml.linear._classifier import Classifier\nfrom prml.preprocess.label_transformer import LabelTransformer\n\n\nclass SoftmaxRegression(Classifier):\n    \"\"\"Softmax regression model.\n\n    aka\n    multinomial logistic regression,\n    multiclass logistic regression,\n    maximum entropy classifier.\n\n    y = softmax(X @ W)\n    t ~ Categorical(t|y)\n    \"\"\"\n\n    @staticmethod\n    def _softmax(a):\n        a_max = np.max(a, axis=-1, keepdims=True)\n        exp_a = np.exp(a - a_max)\n        return exp_a / np.sum(exp_a, axis=-1, keepdims=True)\n\n    def fit(\n        self,\n        x_train: np.ndarray,\n        y_train: np.ndarray,\n        max_iter: int = 100,\n        learning_rate: float = 0.1,\n    ):\n        \"\"\"Maximum likelihood estimation of the parameter.\n\n        Parameters\n        ----------\n        X : (N, D) np.ndarray\n            training independent variable\n        t : (N,) or (N, K) np.ndarray\n            training dependent variable\n            in class index or one-of-k encoding\n        max_iter : int, optional\n            maximum number of iteration (the default is 100)\n        learning_rate : float, optional\n            learning rate of gradient descent (the default is 0.1)\n        \"\"\"\n        if y_train.ndim == 1:\n            y_train = LabelTransformer().encode(y_train)\n        self.n_classes = np.size(y_train, 1)\n        w = np.zeros((np.size(x_train, 1), self.n_classes))\n        for _ in range(max_iter):\n            w_prev = np.copy(w)\n            y = self._softmax(x_train @ w)\n            grad = x_train.T @ (y - y_train)\n            w -= learning_rate * grad\n            if np.allclose(w, w_prev):\n                break\n        self.w = w\n\n    def proba(self, x: np.ndarray):\n        \"\"\"Return probability of input belonging each class.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            Input independent variable (N, D)\n\n        Returns\n        -------\n        np.ndarray\n            probability of each class (N, K)\n        \"\"\"\n        return self._softmax(x @ self.w)\n\n    def classify(self, x: np.ndarray):\n        \"\"\"Classify input data.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            independent variable to be classified (N, D)\n\n        Returns\n        -------\n        np.ndarray\n            class index for each input (N,)\n        \"\"\"\n        return np.argmax(self.proba(x), axis=-1)\n"
  },
  {
    "path": "prml/linear/_variational_linear_regression.py",
    "content": "import numpy as np\n\nfrom prml.linear._regression import Regression\n\n\nclass VariationalLinearRegression(Regression):\n    \"\"\"Variational bayesian linear regression model.\n\n    p(w,alpha|X,t)\n    ~ q(w)q(alpha)\n    = N(w|w_mean, w_var)Gamma(alpha|a,b)\n\n    Attributes\n    ----------\n    a : float\n        a parameter of variational posterior gamma distribution\n    b : float\n        another parameter of variational posterior gamma distribution\n    w_mean : (n_features,) ndarray\n        mean of variational posterior gaussian distribution\n    w_var : (n_features, n_features) ndarray\n        variance of variational posterior gaussian distribution\n    n_iter : int\n        number of iterations performed\n    \"\"\"\n\n    def __init__(self, beta: float = 1., a0: float = 1., b0: float = 1.):\n        \"\"\"Initialize variational linear regression model.\n\n        Parameters\n        ----------\n        beta : float\n            precision of observation noise\n        a0 : float\n            a parameter of prior gamma distribution\n            Gamma(alpha|a0,b0)\n        b0 : float\n            another parameter of prior gamma distribution\n            Gamma(alpha|a0,b0)\n        \"\"\"\n        self.beta = beta\n        self.a0 = a0\n        self.b0 = b0\n\n    def fit(\n        self,\n        x_train: np.ndarray,\n        y_train: np.ndarray,\n        iter_max: int = 100,\n    ):\n        \"\"\"Variational bayesian estimation of parameter.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            training independent variable (N, D)\n        y_train : np.ndarray\n            training dependent variable (N,)\n        iter_max : int, optional\n            maximum number of iteration (the default is 100)\n        \"\"\"\n        xtx = x_train.T @ x_train\n        d = np.size(x_train, 1)\n        self.a = self.a0 + 0.5 * d\n        self.b = self.b0\n        eye = np.eye(d)\n        for _ in range(iter_max):\n            param = self.b\n            self.w_var = np.linalg.inv(self.a * eye / self.b + self.beta * xtx)\n            self.w_mean = self.beta * self.w_var @ x_train.T @ y_train\n            self.b = (\n                self.b0\n                + 0.5 * (np.sum(self.w_mean ** 2) + np.trace(self.w_var))\n            )\n            if np.allclose(self.b, param):\n                break\n\n    def predict(self, x: np.ndarray, return_std: bool = False):\n        \"\"\"Return predictions.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            Input independent variable (N, D)\n        return_std : bool, optional\n            return standard deviation of predictive distribution if True\n            (the default is False)\n\n        Returns\n        -------\n        y :  np.ndarray\n            mean of predictive distribution (N,)\n        y_std : np.ndarray\n            standard deviation of predictive distribution (N,)\n        \"\"\"\n        y = x @ self.w_mean\n        if return_std:\n            y_var = 1 / self.beta + np.sum(x @ self.w_var * x, axis=1)\n            y_std = np.sqrt(y_var)\n            return y, y_std\n        return y\n"
  },
  {
    "path": "prml/linear/_variational_logistic_regression.py",
    "content": "import typing as tp\n\nimport numpy as np\n\nfrom prml.linear._logistic_regression import LogisticRegression\n\n\nclass VariationalLogisticRegression(LogisticRegression):\n    \"\"\"Variational logistic regression model.\n\n    Graphical Model\n    ---------------\n\n    ```txt\n    *----------------*\n    |                |               ****  alpha\n    |     phi_n      |             **    **\n    |       **       |            *        *\n    |       **       |            *        *\n    |       |        |             **    **\n    |       |        |               ****\n    |       |        |                |\n    |       |        |                |\n    |       |        |                |\n    |       |        |                |\n    |       |        |                |\n    |       v        |                v\n    |      ****      |               ****  w\n    |    **    **    |             **    **\n    |   *        *   |            *        *\n    |   *        *<--|------------*        *\n    |    **    **    |             **    **\n    |  t_n ****      |               ****\n    |             N  |\n    *----------------*\n    ```\n    \"\"\"\n\n    def __init__(\n        self,\n        alpha: tp.Optional[float] = None,\n        a0: float = 1.,\n        b0: float = 1.,\n    ):\n        \"\"\"Construct variational logistic regression model.\n\n        Parameters\n        ----------\n        alpha : tp.Optional[float]\n            precision parameter of the prior\n            if None, this is also the subject to estimate\n        a0 : float\n            a parameter of hyper prior Gamma dist.\n            Gamma(alpha|a0,b0)\n            if alpha is not None, this argument will be ignored\n        b0 : float\n            another parameter of hyper prior Gamma dist.\n            Gamma(alpha|a0,b0)\n            if alpha is not None, this argument will be ignored\n        \"\"\"\n        if alpha is not None:\n            self.__alpha = alpha\n        else:\n            self.a0 = a0\n            self.b0 = b0\n\n    def fit(self, x_train: np.ndarray, t: np.ndarray, iter_max: int = 1000):\n        \"\"\"Variational bayesian estimation of the parameter.\n\n        Parameters\n        ----------\n        x_train : np.ndarray\n            training independent variable (N, D)\n        t : np.ndarray\n            training dependent variable (N,)\n        iter_max : int, optional\n            maximum number of iteration (the default is 1000)\n        \"\"\"\n        n, d = x_train.shape\n        if hasattr(self, \"a0\"):\n            self.a = self.a0 + 0.5 * d\n        xi = np.random.uniform(-1, 1, size=n)\n        eye = np.eye(d)\n        param = np.copy(xi)\n        for _ in range(iter_max):\n            lambda_ = np.tanh(xi) * 0.25 / xi\n            self.w_var = np.linalg.inv(\n                eye / self.alpha + 2 * (lambda_ * x_train.T) @ x_train)\n            self.w_mean = self.w_var @ np.sum(x_train.T * (t - 0.5), axis=1)\n            xi = np.sqrt(np.sum(\n                (\n                    x_train\n                    @ (self.w_var + self.w_mean * self.w_mean[:, None])\n                    * x_train\n                ),\n                axis=-1,\n            ))\n            if np.allclose(xi, param):\n                break\n            else:\n                param = np.copy(xi)\n\n    @property\n    def alpha(self) -> float:\n        \"\"\"Return expectation of variational distribution of alpha.\n\n        Returns\n        -------\n        float\n            Expectation of variational distribution of alpha.\n        \"\"\"\n        if hasattr(self, \"__alpha\"):\n            return self.__alpha\n        else:\n            try:\n                self.b = self.b0 + 0.5 * (\n                    np.sum(self.w_mean ** 2) + np.trace(self.w_var))\n            except AttributeError:\n                self.b = self.b0\n            return self.a / self.b\n\n    def proba(self, x: np.ndarray):\n        \"\"\"Return probability of input belonging class 1.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            Input independent variable (N, D)\n\n        Returns\n        -------\n        np.ndarray\n            probability of positive (N,)\n        \"\"\"\n        mu_a = x @ self.w_mean\n        var_a = np.sum(x @ self.w_var * x, axis=1)\n        y = self._sigmoid(mu_a / np.sqrt(1 + np.pi * var_a / 8))\n        return y\n"
  },
  {
    "path": "prml/markov/__init__.py",
    "content": "from prml.markov.categorical_hmm import CategoricalHMM\nfrom prml.markov.gaussian_hmm import GaussianHMM\nfrom prml.markov.kalman import Kalman, kalman_filter, kalman_smoother\nfrom prml.markov.particle import Particle\n\n\n__all__ = [\n    \"GaussianHMM\",\n    \"CategoricalHMM\",\n    \"Kalman\",\n    \"kalman_filter\",\n    \"kalman_smoother\",\n    \"Particle\"\n]\n"
  },
  {
    "path": "prml/markov/categorical_hmm.py",
    "content": "import numpy as np\n\nfrom prml.markov.hmm import HiddenMarkovModel\n\n\nclass CategoricalHMM(HiddenMarkovModel):\n    \"\"\"\n    Hidden Markov Model with categorical emission model\n    \"\"\"\n\n    def __init__(self, initial_proba, transition_proba, means):\n        \"\"\"\n        construct hidden markov model with categorical emission model\n\n        Parameters\n        ----------\n        initial_proba : (n_hidden,) np.ndarray\n            probability of initial latent state\n        transition_proba : (n_hidden, n_hidden) np.ndarray\n            transition probability matrix\n            (i, j) component denotes the transition probability from i-th to j-th hidden state\n        means : (n_hidden, ndim) np.ndarray\n            mean parameters of categorical distribution\n\n        Returns\n        -------\n        ndim : int\n            number of observation categories\n        n_hidden : int\n            number of hidden states\n        \"\"\"\n        assert initial_proba.size == transition_proba.shape[0] == transition_proba.shape[1] == means.shape[0]\n        assert np.allclose(means.sum(axis=1), 1)\n        super().__init__(initial_proba, transition_proba)\n        self.ndim = means.shape[1]\n        self.means = means\n\n    def draw(self, n=100):\n        \"\"\"\n        draw random sequence from this model\n\n        Parameters\n        ----------\n        n : int\n            length of the random sequence\n\n        Returns\n        -------\n        seq : (n,) np.ndarray\n            generated random sequence\n        \"\"\"\n        hidden_state = np.random.choice(self.n_hidden, p=self.initial_proba)\n        seq = []\n        while len(seq) < n:\n            seq.append(np.random.choice(self.ndim, p=self.means[hidden_state]))\n            hidden_state = np.random.choice(self.n_hidden, p=self.transition_proba[hidden_state])\n        return np.asarray(seq)\n\n    def likelihood(self, x):\n        return self.means[x]\n\n    def maximize(self, seq, p_hidden, p_transition):\n        self.initial_proba = p_hidden[0] / np.sum(p_hidden[0])\n        self.transition_proba = np.sum(p_transition, axis=0) / np.sum(p_transition, axis=(0, 2))\n        x = p_hidden[:, None, :] * (np.eye(self.ndim)[seq])[:, :, None]\n        self.means = np.sum(x, axis=0) / np.sum(p_hidden, axis=0)\n"
  },
  {
    "path": "prml/markov/gaussian_hmm.py",
    "content": "import numpy as np\n\nfrom prml.markov.hmm import HiddenMarkovModel\nfrom prml.rv import MultivariateGaussian\n\n\nclass GaussianHMM(HiddenMarkovModel):\n    \"\"\"\n    Hidden Markov Model with Gaussian emission model\n    \"\"\"\n\n    def __init__(self, initial_proba, transition_proba, means, covs):\n        \"\"\"\n        construct hidden markov model with Gaussian emission model\n\n        Parameters\n        ----------\n        initial_proba : (n_hidden,) np.ndarray or None\n            probability of initial states\n        transition_proba : (n_hidden, n_hidden) np.ndarray or None\n            transition probability matrix\n            (i, j) component denotes the transition probability from i-th to j-th hidden state\n        means : (n_hidden, ndim) np.ndarray\n            mean of each gaussian component\n        covs : (n_hidden, ndim, ndim) np.ndarray\n            covariance matrix of each gaussian component\n\n        Attributes\n        ----------\n        ndim : int\n            dimensionality of observation space\n        n_hidden : int\n            number of hidden states\n        \"\"\"\n        assert initial_proba.size == transition_proba.shape[0] == transition_proba.shape[1] == means.shape[0] == covs.shape[0]\n        assert means.shape[1] == covs.shape[1] == covs.shape[2]\n        super().__init__(initial_proba, transition_proba)\n        self.ndim = means.shape[1]\n        self.means = means\n        self.covs = covs\n        self.precisions = np.linalg.inv(self.covs)\n        self.gaussians = [MultivariateGaussian(m, cov) for m, cov in zip(means, covs)]\n\n    def draw(self, n=100):\n        \"\"\"\n        draw random sequence from this model\n\n        Parameters\n        ----------\n        n : int\n            length of the random sequence\n\n        Returns\n        -------\n        seq : (n, ndim) np.ndarray\n            generated random sequence\n        \"\"\"\n        hidden_state = np.random.choice(self.n_hidden, p=self.initial_proba)\n        seq = []\n        while len(seq) < n:\n            seq.extend(self.gaussians[hidden_state].draw())\n            hidden_state = np.random.choice(self.n_hidden, p=self.transition_proba[hidden_state])\n        return np.asarray(seq)\n\n    def likelihood(self, x):\n        diff = x[:, None, :] - self.means\n        exponents = np.sum(\n            np.einsum('nki,kij->nkj', diff, self.precisions) * diff, axis=-1)\n        return np.exp(-0.5 * exponents) / np.sqrt(np.linalg.det(self.covs) * (2 * np.pi) ** self.ndim)\n\n    def maximize(self, seq, p_hidden, p_transition):\n        self.initial_proba = p_hidden[0] / np.sum(p_hidden[0])\n        self.transition_proba = np.sum(p_transition, axis=0) / np.sum(p_transition, axis=(0, 2))\n        Nk = np.sum(p_hidden, axis=0)\n        self.means = (seq.T @ p_hidden / Nk).T\n        diffs = seq[:, None, :] - self.means\n        self.covs = np.einsum('nki,nkj->kij', diffs, diffs * p_hidden[:, :, None]) / Nk[:, None, None]\n"
  },
  {
    "path": "prml/markov/hmm.py",
    "content": "import numpy as np\n\n\nclass HiddenMarkovModel(object):\n    \"\"\"\n    Base class of Hidden Markov models\n    \"\"\"\n\n    def __init__(self, initial_proba, transition_proba):\n        \"\"\"\n        construct hidden markov model\n\n        Parameters\n        ----------\n        initial_proba : (n_hidden,) np.ndarray\n            initial probability of each hidden state\n        transition_proba : (n_hidden, n_hidden) np.ndarray\n            transition probability matrix\n            (i, j) component denotes the transition probability from i-th to j-th hidden state\n\n        Attribute\n        ---------\n        n_hidden : int\n            number of hidden state\n        \"\"\"\n        self.n_hidden = initial_proba.size\n        self.initial_proba = initial_proba\n        self.transition_proba = transition_proba\n\n    def fit(self, seq, iter_max=100):\n        \"\"\"\n        perform EM algorithm to estimate parameter of emission model and hidden variables\n\n        Parameters\n        ----------\n        seq : (N, ndim) np.ndarray\n            observed sequence\n        iter_max : int\n            maximum number of EM steps\n\n        Returns\n        -------\n        posterior : (N, n_hidden) np.ndarray\n            posterior distribution of each latent variable\n        \"\"\"\n        params = np.hstack(\n            (self.initial_proba.ravel(), self.transition_proba.ravel()))\n        for i in range(iter_max):\n            p_hidden, p_transition = self.expect(seq)\n            self.maximize(seq, p_hidden, p_transition)\n            params_new = np.hstack(\n                (self.initial_proba.ravel(), self.transition_proba.ravel()))\n            if np.allclose(params, params_new):\n                break\n            else:\n                params = params_new\n        return self.forward_backward(seq)\n\n    def expect(self, seq):\n        \"\"\"\n        estimate posterior distributions of hidden states and\n        transition probability between adjacent latent variables\n\n        Parameters\n        ----------\n        seq : (N, ndim) np.ndarray\n            observed sequence\n\n        Returns\n        -------\n        p_hidden : (N, n_hidden) np.ndarray\n            posterior distribution of each hidden variable\n        p_transition : (N - 1, n_hidden, n_hidden) np.ndarray\n            posterior transition probability between adjacent latent variables\n        \"\"\"\n        likelihood = self.likelihood(seq)\n\n        f = self.initial_proba * likelihood[0]\n        constant = [f.sum()]\n        forward = [f / f.sum()]\n        for like in likelihood[1:]:\n            f = forward[-1] @ self.transition_proba * like\n            constant.append(f.sum())\n            forward.append(f / f.sum())\n        forward = np.asarray(forward)\n        constant = np.asarray(constant)\n\n        backward = [np.ones(self.n_hidden)]\n        for like, c in zip(likelihood[-1:0:-1], constant[-1:0:-1]):\n            backward.insert(0, self.transition_proba @ (like * backward[0]) / c)\n        backward = np.asarray(backward)\n\n        p_hidden = forward * backward\n        p_transition = self.transition_proba * likelihood[1:, None, :] * backward[1:, None, :] * forward[:-1, :, None]\n        return p_hidden, p_transition\n\n    def forward_backward(self, seq):\n        \"\"\"\n        estimate posterior distributions of hidden states\n\n        Parameters\n        ----------\n        seq : (N, ndim) np.ndarray\n            observed sequence\n\n        Returns\n        -------\n        posterior : (N, n_hidden) np.ndarray\n            posterior distribution of hidden states\n        \"\"\"\n        likelihood = self.likelihood(seq)\n\n        f = self.initial_proba * likelihood[0]\n        constant = [f.sum()]\n        forward = [f / f.sum()]\n        for like in likelihood[1:]:\n            f = forward[-1] @ self.transition_proba * like\n            constant.append(f.sum())\n            forward.append(f / f.sum())\n\n        backward = [np.ones(self.n_hidden)]\n        for like, c in zip(likelihood[-1:0:-1], constant[-1:0:-1]):\n            backward.insert(0, self.transition_proba @ (like * backward[0]) / c)\n\n        forward = np.asarray(forward)\n        backward = np.asarray(backward)\n        posterior = forward * backward\n        return posterior\n\n    def filtering(self, seq):\n        \"\"\"\n        bayesian filtering\n\n        Parameters\n        ----------\n        seq : (N, ndim) np.ndarray\n            observed sequence\n\n        Returns\n        -------\n        posterior : (N, n_hidden) np.ndarray\n            posterior distributions of each latent variables\n        \"\"\"\n        likelihood = self.likelihood(seq)\n        p = self.initial_proba * likelihood[0]\n        posterior = [p / np.sum(p)]\n        for like in likelihood[1:]:\n            p = posterior[-1] @ self.transition_proba * like\n            posterior.append(p / np.sum(p))\n        posterior = np.asarray(posterior)\n        return posterior\n\n    def viterbi(self, seq):\n        \"\"\"\n        viterbi algorithm (a.k.a. max-sum algorithm)\n\n        Parameters\n        ----------\n        seq : (N, ndim) np.ndarray\n            observed sequence\n\n        Returns\n        -------\n        seq_hid : (N,) np.ndarray\n            the most probable sequence of hidden variables\n        \"\"\"\n        nll = -np.log(self.likelihood(seq))\n        cost_total = nll[0]\n        from_list = []\n        for i in range(1, len(seq)):\n            cost_temp = cost_total[:, None] - np.log(self.transition_proba) + nll[i]\n            cost_total = np.min(cost_temp, axis=0)\n            index = np.argmin(cost_temp, axis=0)\n            from_list.append(index)\n        seq_hid = [np.argmin(cost_total)]\n        for source in from_list[::-1]:\n            seq_hid.insert(0, source[seq_hid[0]])\n        return seq_hid\n"
  },
  {
    "path": "prml/markov/kalman.py",
    "content": "import numpy as np\n\nfrom prml.markov.state_space_model import StateSpaceModel\n\n\nclass Kalman(StateSpaceModel):\n    \"\"\"A class to perform kalman filtering or smoothing\n\n    :math:`z` : internal state (random variable)\\n\n    :math:`x` : observation (random variable)\n\n    prior distributions:\n\n    :math:`p(z_0) = \\\\mathcal{N}(\\\\mu_0, P_0)`\n\n    :math:`p(z_n) = \\int p(z_n|z_{n-1})p(z_{n-1}) {\\\\rm d}z_{n-1} = \\\\mathcal{N}(A\\\\mu_{n-1},AP_{n-1}A^{\\\\rm T}+\\\\Gamma) = \\\\mathcal{N}(\\\\mu_n, P_n)`\n\n    :math:`p(x_n)=\\int p(x_n|z_n)p(z_n){\\\\rm d}z_n=\\\\mathcal{N}(C\\\\mu_n,CP_nC^{\\\\rm T}+\\\\Sigma)`\n\n    Parameters\n    ----------\n    system : (Dz, Dz) np.ndarray\n        system matrix aka transition matrix (:math:`A`)\n    cov_system : (Dz, Dz) np.ndarray\n        covariance matrix of process noise (:math:`\\\\Gamma`)\n    measure : (Dx, Dz) np.ndarray\n        measurement matrix aka observation matrix (:math:`C`)\n    cov_measure : (Dx, Dx) np.ndarray\n        covariance matrix of measurement noise (:math:`\\\\Sigma`)\n    mu0 : (Dz,) np.ndarray\n        mean parameter of initial hidden variable (:math:`\\mu_0`)\n    P0 : (Dz, Dz) np.ndarray\n        covariance parameter of initial hidden variable (:math:`P_0`)\n\n    Attributes\n    ----------\n    Dz : int\n        dimensionality of hidden variable\n    Dx : int\n        dimensionality of observed variable\n    \"\"\"\n\n    def __init__(self, system, cov_system, measure, cov_measure, mu0, P0):\n        \"\"\"\n        construct Kalman model\n\n        z_1 ~ N(z_1|mu_0, P_0)\\n\n        z_n ~ N(z_n|A z_n-1, P)\\n\n        x_n ~ N(x_n|C z_n, S)\n\n        Parameters\n        ----------\n        system : (Dz, Dz) np.ndarray\n            system matrix aka transition matrix (A)\n        cov_system : (Dz, Dz) np.ndarray\n            covariance matrix of process noise\n        measure : (Dx, Dz) np.ndarray\n            measurement matrix aka observation matrix (C)\n        cov_measure : (Dx, Dx) np.ndarray\n            covariance matrix of measurement noise\n        mu0 : (Dz,) np.ndarray\n            mean parameter of initial hidden variable\n        P0 : (Dz, Dz) np.ndarray\n            covariance parameter of initial hidden variable\n\n        Attributes\n        ----------\n        hidden_mean : list of (Dz,) np.ndarray\n            list of mean of hidden state starting from the given hidden state\n        hidden_cov : list of (Dz, Dz) np.ndarray\n            list of covariance of hidden state starting from the given hidden state\n        \"\"\"\n        self.system = system\n        self.cov_system = cov_system\n        self.measure = measure\n        self.cov_measure = cov_measure\n\n        self.hidden_mean = [mu0]\n        self.hidden_cov = [P0]\n        self.hidden_cov_predicted = [None]\n\n        self.smoothed_until = -1\n        self.smoothing_gain = [None]\n\n    def predict(self):\n        \"\"\"\n        predict hidden state at current step given estimate at previous step\n\n        Returns\n        -------\n        tuple ((Dz,) np.ndarray, (Dz, Dz) np.ndarray)\n            tuple of mean and covariance of the estimate at current step\n        \"\"\"\n        mu_prev, cov_prev = self.hidden_mean[-1], self.hidden_cov[-1]\n        mu = self.system @ mu_prev\n        cov = self.system @ cov_prev @ self.system.T + self.cov_system\n        self.hidden_mean.append(mu)\n        self.hidden_cov.append(cov)\n        self.hidden_cov_predicted.append(np.copy(cov))\n        return mu, cov\n\n    def filter(self, observed):\n        \"\"\"\n        bayesian update of current estimate given current observation\n\n        Parameters\n        ----------\n        observed : (Dx,) np.ndarray\n            current observation\n\n        Returns\n        -------\n        tuple ((Dz,) np.ndarray, (Dz, Dz) np.ndarray)\n            tuple of mean and covariance of the updated estimate\n        \"\"\"\n        mu, cov = self.hidden_mean[-1], self.hidden_cov[-1]\n        innovation = observed - self.measure @ mu\n        cov_innovation = self.cov_measure + self.measure @ cov @ self.measure.T\n        kalman_gain = np.linalg.solve(cov_innovation, self.measure @ cov).T\n        mu += kalman_gain @ innovation\n        cov -= kalman_gain @ self.measure @ cov\n        return mu, cov\n\n    def filtering(self, observed_sequence):\n        \"\"\"\n        perform kalman filtering given observed sequence\n\n        Parameters\n        ----------\n        observed_sequence : (T, Dx) np.ndarray\n            sequence of observations\n\n        Returns\n        -------\n        tuple ((T, Dz) np.ndarray, (T, Dz, Dz) np.ndarray)\n            seuquence of mean and covariance of hidden variable at each time step\n        \"\"\"\n        for obs in observed_sequence:\n            self.predict()\n            self.filter(obs)\n        mean_sequence = np.asarray(self.hidden_mean[1:])\n        cov_sequence = np.asarray(self.hidden_cov[1:])\n        return mean_sequence, cov_sequence\n\n    def smooth(self):\n        \"\"\"\n        bayesian update of current estimate with future observations\n        \"\"\"\n        mean_smoothed_next = self.hidden_mean[self.smoothed_until]\n        cov_smoothed_next = self.hidden_cov[self.smoothed_until]\n        cov_pred_next = self.hidden_cov_predicted[self.smoothed_until]\n\n        self.smoothed_until -= 1\n        mean = self.hidden_mean[self.smoothed_until]\n        cov = self.hidden_cov[self.smoothed_until]\n        gain = np.linalg.solve(cov_pred_next, self.system @ cov).T\n        mean += gain @ (mean_smoothed_next - self.system @ mean)\n        cov += gain @ (cov_smoothed_next - cov_pred_next) @ gain.T\n        self.smoothing_gain.insert(0, gain)\n\n    def smoothing(self, observed_sequence:np.ndarray=None):\n        \"\"\"\n        perform Kalman smoothing (given observed sequence)\n\n        Parameters\n        ----------\n        observed_sequence : (T, Dx) np.ndarray, optional\n            sequence of observation\n            run Kalman filter if given (the default is None)\n\n        Returns\n        -------\n        tuple ((T, Dz) np.ndarray, (T, Dz, Dz) np.ndarray)\n            sequence of mean and covariance of hidden variable at each time step\n        \"\"\"\n        if observed_sequence is not None:\n            self.filtering(observed_sequence)\n        while self.smoothed_until != -len(self.hidden_mean):\n            self.smooth()\n        mean_sequence = np.asarray(self.hidden_mean[1:])\n        cov_sequence = np.asarray(self.hidden_cov[1:])\n        return mean_sequence, cov_sequence\n\n    def update_parameter(self, observation_sequence):\n        \"\"\"\n        maximization step of EM algorithm\n        \"\"\"\n        mu0 = self.hidden_mean[1]\n        P0 = self.hidden_cov[1]\n\n        Ezn = np.asarray(self.hidden_mean)\n        Eznzn = np.asarray(self.hidden_cov) + Ezn[..., None] * Ezn[:, None, :]\n        Eznzn_1 = np.einsum(\"nij,nkj->nik\", self.hidden_cov[2:], self.smoothing_gain[1:-1]) + Ezn[2:, :, None] * Ezn[1:-1, None, :]\n        self.system = np.linalg.solve(np.sum(Eznzn[2:], axis=0), np.sum(Eznzn_1, axis=0).T).T\n        self.cov_system = np.mean(\n            Eznzn[2:]\n            - np.einsum(\"ij,nkj->nik\", self.system, Eznzn_1)\n            - np.einsum(\"nij,kj->nik\", Eznzn_1, self.system)\n            + np.einsum(\"ij,njk,lk->nil\", self.system, Eznzn[1:-1], self.system),\n            axis=0\n        )\n        self.measure = np.linalg.solve(\n            np.sum(Eznzn[1:], axis=0),\n            np.sum(np.einsum(\"ni,nj->nij\", Ezn[1:], observation_sequence), axis=0)\n        ).T\n        self.cov_measure = np.mean(\n            np.einsum(\"ni,nj->nij\", observation_sequence, observation_sequence)\n            - np.einsum(\"ij,nj,nk->nik\", self.measure, Ezn[1:], observation_sequence)\n            - np.einsum(\"ni,nj,kj->nik\", observation_sequence, Ezn[1:], self.measure)\n            + np.einsum(\"ij,njk,lk->nil\", self.measure, Eznzn[1:], self.measure),\n            axis=0\n        )\n        return self.system, self.cov_system, self.measure, self.cov_measure, mu0, P0\n\n    def fit(self, sequence, max_iter=10):\n        for _ in range(max_iter):\n            kalman_smoother(self, sequence)\n            param = self.update_parameter(sequence)\n            self.__init__(*param)\n        return kalman_smoother(self, sequence)\n\n\ndef kalman_filter(kalman: Kalman, observed_sequence: np.ndarray) -> tuple:\n    \"\"\"Perform kalman filtering given Kalman model and observed sequence.\n\n    Parameters\n    ----------\n    kalman : Kalman\n        Kalman model\n    observed_sequence : (T, Dx) np.ndarray\n        sequence of observations\n\n    Returns\n    -------\n    tuple ((T, Dz) np.ndarray, (T, Dz, Dz) np.ndarray)\n        seuquence of mean and covariance of hidden variable at each time step\n    \"\"\"\n    for obs in observed_sequence:\n        kalman.predict()\n        kalman.filter(obs)\n    mean_sequence = np.asarray(kalman.hidden_mean[1:])\n    cov_sequence = np.asarray(kalman.hidden_cov[1:])\n    return mean_sequence, cov_sequence\n\n\ndef kalman_smoother(kalman:Kalman, observed_sequence:np.ndarray=None):\n    \"\"\"Perform Kalman smoothing given Kalman model (and observed sequence).\n\n    Parameters\n    ----------\n    kalman : Kalman\n        Kalman model\n    observed_sequence : (T, Dx) np.ndarray, optional\n        sequence of observation\n        run Kalman filter if given (the default is None)\n\n    Returns\n    -------\n    tuple ((T, Dz) np.ndarray, (T, Dz, Dz) np.ndarray)\n        seuqnce of mean and covariance of hidden variable at each time step\n    \"\"\"\n\n    if observed_sequence is not None:\n        kalman_filter(kalman, observed_sequence)\n    while kalman.smoothed_until != -len(kalman.hidden_mean):\n        kalman.smooth()\n    mean_sequence = np.asarray(kalman.hidden_mean[1:])\n    cov_sequence = np.asarray(kalman.hidden_cov[1:])\n    return mean_sequence, cov_sequence\n"
  },
  {
    "path": "prml/markov/particle.py",
    "content": "import numpy as np\nfrom scipy.special import logsumexp\nfrom scipy.spatial.distance import cdist\n\nfrom prml.markov.state_space_model import StateSpaceModel\n\n\nclass Particle(StateSpaceModel):\n    \"\"\"\n    A class to perform particle filtering, smoothing\n\n    z_1 ~ p(z_1)\\n\n    z_n ~ p(z_n|z_n-1)\\n\n    x_n ~ p(x_n|z_n)\n\n    Parameters\n    ----------\n    init_particle : (n_particle, ndim_hidden)\n        initial hidden state\n    sampler : callable (particles)\n        function to sample particles at current step given previous state\n    nll : callable (observation, particles)\n        function to compute negative log likelihood for each particle\n\n    Attribute\n    ---------\n    hidden_state : list of (n_paticle, ndim_hidden) np.ndarray\n        list of particles\n    \"\"\"\n\n    def __init__(self, init_particle, system, cov_system, nll, pdf=None):\n        \"\"\"\n        construct state space model to perform particle filtering or smoothing\n\n        Parameters\n        ----------\n        init_particle : (n_particle, ndim_hidden) np.ndarray\n            initial hidden state\n        system : (ndim_hidden, ndim_hidden) np.ndarray\n            system matrix aka transition matrix\n        cov_system : (ndim_hidden, ndim_hidden) np.ndarray\n            covariance matrix of process noise\n        nll : callable (observation, particles)\n            function to compute negative log likelihood for each particle\n\n        Attribute\n        ---------\n        particle : list of (n_paticle, ndim_hidden) np.ndarray\n            list of particles at each step\n        weight : list of (n_particle,) np.ndarray\n            list of importance of each particle at each step\n        n_particle : int\n            number of particles at each step\n        \"\"\"\n        self.particle = [init_particle]\n        self.n_particle, self.ndim_hidden = init_particle.shape\n        self.weight = [np.ones(self.n_particle) / self.n_particle]\n        self.system = system\n        self.cov_system = cov_system\n        self.nll = nll\n        self.smoothed_until = -1\n\n    def resample(self):\n        index = np.random.choice(self.n_particle, self.n_particle, p=self.weight[-1])\n        return self.particle[-1][index]\n\n    def predict(self):\n        predicted = self.resample() @ self.system.T\n        predicted += np.random.multivariate_normal(np.zeros(self.ndim_hidden), self.cov_system, self.n_particle)\n        self.particle.append(predicted)\n        self.weight.append(np.ones(self.n_particle) / self.n_particle)\n        return predicted, self.weight[-1]\n\n    def weigh(self, observed):\n        logit = -self.nll(observed, self.particle[-1])\n        logit -= logsumexp(logit)\n        self.weight[-1] = np.exp(logit)\n\n    def filter(self, observed):\n        self.weigh(observed)\n        return self.particle[-1], self.weight[-1]\n\n    def filtering(self, observed_sequence):\n        mean = []\n        cov = []\n        for obs in observed_sequence:\n            self.predict()\n            p, w = self.filter(obs)\n            mean.append(np.average(p, axis=0, weights=w))\n            cov.append(np.cov(p, rowvar=False, aweights=w))\n        return np.asarray(mean), np.asarray(cov)\n\n    def transition_probability(self, particle, particle_prev):\n        dist = cdist(\n            particle,\n            particle_prev @ self.system.T,\n            \"mahalanobis\",\n            VI=np.linalg.inv(self.cov_system))\n        matrix = np.exp(-0.5 * np.square(dist))\n        matrix /= np.sum(matrix, axis=1, keepdims=True)\n        matrix[np.isnan(matrix)] = 1 / self.n_particle\n        return matrix\n\n    def smooth(self):\n        particle_next = self.particle[self.smoothed_until]\n        weight_next = self.weight[self.smoothed_until]\n\n        self.smoothed_until -= 1\n        particle = self.particle[self.smoothed_until]\n        weight = self.weight[self.smoothed_until]\n        matrix = self.transition_probability(particle_next, particle).T\n        weight *= matrix @ weight_next / (weight @ matrix)\n        weight /= np.sum(weight, keepdims=True)\n\n    def smoothing(self, observed_sequence:np.ndarray=None):\n        if observed_sequence is not None:\n            self.filtering(observed_sequence)\n        while self.smoothed_until != -len(self.particle):\n            self.smooth()\n        mean = []\n        cov = []\n        for p, w in zip(self.particle, self.weight):\n            mean.append(np.average(p, axis=0, weights=w))\n            cov.append(np.cov(p, rowvar=False, aweights=w))\n        return np.asarray(mean), np.asarray(cov)\n"
  },
  {
    "path": "prml/markov/state_space_model.py",
    "content": "class StateSpaceModel(object):\n    \"\"\"\n    Base class for state-space models\n    \"\"\"\n\n    pass\n"
  },
  {
    "path": "prml/nn/__init__.py",
    "content": "# flake8: noqa\n\nfrom prml.nn.config import config\nfrom prml.nn.network import Network\nfrom prml.nn import array\nfrom prml.nn import io\nfrom prml.nn import loss\nfrom prml.nn import optimizer\nfrom prml.nn import random\nfrom prml.nn.array.array import array, asarray\nfrom prml.nn.array.ones import ones\nfrom prml.nn.array.reshape import reshape\nfrom prml.nn.array.zeros import zeros\nfrom prml.nn.distribution.bernoulli import Bernoulli\nfrom prml.nn.distribution.categorical import Categorical\nfrom prml.nn.distribution.gaussian import Gaussian, GaussianRadial\nfrom prml.nn.image.convolve2d import convolve2d\nfrom prml.nn.image.deconvolve2d import deconvolve2d\nfrom prml.nn.image.max_pooling2d import max_pooling2d\nfrom prml.nn.math.exp import exp\nfrom prml.nn.math.log import log\nfrom prml.nn.math.sqrt import sqrt\nfrom prml.nn.math.square import square\nfrom prml.nn.math.sum import sum\nfrom prml.nn.nonlinear.log_softmax import log_softmax\nfrom prml.nn.nonlinear.relu import relu\nfrom prml.nn.nonlinear.sigmoid import sigmoid\nfrom prml.nn.nonlinear.softmax import softmax\nfrom prml.nn.nonlinear.softplus import softplus\nfrom prml.nn.nonlinear.tanh import tanh\nfrom prml.nn.normalization.batch_normalization import BatchNormalization\n"
  },
  {
    "path": "prml/nn/array/__init__.py",
    "content": "# flake8: noqa\n\nfrom prml.nn.array.array import Array, array, asarray\nfrom prml.nn.array.reshape import reshape_method\nfrom prml.nn.function import broadcast, broadcast_to\n\n\nArray.reshape = reshape_method\n"
  },
  {
    "path": "prml/nn/array/array.py",
    "content": "import numpy as np\n\nfrom prml.nn.config import config\nfrom prml.nn.queue import backprop_queue\n\n\nclass Array(object):\n    __array_ufunc__ = None\n\n    def __init__(self, value, parent=None):\n        self.value = np.atleast_1d(value)\n        self.parent = parent\n        self.grad = None\n        self.gradtmp = None\n        self.depth = 0 if parent is None else parent._out_depth()\n        self.is_in_queue = False\n\n    def __repr__(self):\n        return f\"Array(shape={self.value.shape}, dtype={self.value.dtype})\"\n\n    @property\n    def ndim(self):\n        return self.value.ndim\n\n    @property\n    def shape(self):\n        return self.value.shape\n\n    @property\n    def size(self):\n        return self.value.size\n\n    @property\n    def dtype(self):\n        return self.value.dtype\n\n    def backward(self, delta=None):\n        if delta is None:\n            delta = np.ones_like(self.value).astype(config.dtype)\n        assert(delta.shape == self.value.shape)\n        self._backward(delta)\n        backprop_queue.enqueue(self)\n        depth = self.depth\n        while(len(backprop_queue)):\n            queue = backprop_queue.dequeue(depth)\n            if queue.parent is not None:\n                queue.parent.backward(queue.gradtmp)\n            queue.update_grad(queue.gradtmp)\n            queue.gradtmp = None\n            depth = queue.depth\n\n    def update_grad(self, grad):\n        if self.grad is None:\n            self.grad = np.copy(grad)\n        else:\n            self.grad += grad\n\n    def cleargrad(self):\n        self.grad = None\n        self.gradtmp = None\n\n    def _backward(self, delta):\n        if delta is None:\n            return\n        assert(delta.shape == self.shape)\n        if self.gradtmp is None:\n            self.gradtmp = np.copy(delta)\n        else:\n            self.gradtmp += delta\n\n    def __add__(self, arg):\n        raise NotImplementedError\n\n    def __radd__(self, arg):\n        raise NotImplementedError\n\n    def __truediv__(self, arg):\n        raise NotImplementedError\n\n    def __rtruediv__(self, arg):\n        raise NotImplementedError\n\n    def __matmul__(self, arg):\n        raise NotImplementedError\n\n    def __rmatmul__(self, arg):\n        raise NotImplementedError\n\n    def __mul__(self, arg):\n        raise NotImplementedError\n\n    def __rmul__(self, arg):\n        raise NotImplementedError\n\n    def __neg__(self):\n        raise NotImplementedError\n\n    def __pow__(self, arg):\n        raise NotImplementedError\n\n    def __rpow__(self, arg):\n        raise NotImplementedError\n\n    def __sub__(self, arg):\n        raise NotImplementedError\n\n    def __rsub__(self, arg):\n        raise NotImplementedError\n\n    def flatten(self):\n        raise NotImplementedError\n\n    def reshape(self, *args):\n        raise NotImplementedError\n\n    def swapaxes(self, *args):\n        raise NotImplementedError\n\n    def mean(self, axis=None, keepdims=False):\n        raise NotImplementedError\n\n    def prod(self):\n        raise NotImplementedError\n\n    def sum(self, axis=None, keepdims=False):\n        raise NotImplementedError\n\n\ndef array(array_like):\n    return Array(np.array(array_like, dtype=config.dtype))\n\n\ndef asarray(array_like):\n    if isinstance(array_like, Array):\n        return array_like\n    return Array(np.asarray(array_like, dtype=config.dtype))\n"
  },
  {
    "path": "prml/nn/array/broadcast.py",
    "content": "from prml.nn.function import Function, broadcast, broadcast_to  # noqa\n"
  },
  {
    "path": "prml/nn/array/ones.py",
    "content": "import numpy as np\n\nfrom prml.nn.array.array import Array\nfrom prml.nn.config import config\n\n\ndef ones(size):\n    return Array(np.ones(size, dtype=config.dtype))\n"
  },
  {
    "path": "prml/nn/array/reshape.py",
    "content": "from prml.nn.function import Function\n\n\nclass Reshape(Function):\n\n    @staticmethod\n    def _forward(x, shape):\n        return x.reshape(*shape)\n\n    @staticmethod\n    def _backward(delta, x, shape):\n        return delta.reshape(*x.shape)\n\n\ndef reshape(x, shape):\n    return Reshape().forward(x, shape=shape)\n\n\ndef reshape_method(x, *shape):\n    return Reshape().forward(x, shape=shape)\n"
  },
  {
    "path": "prml/nn/array/zeros.py",
    "content": "import numpy as np\n\nfrom prml.nn.array.array import Array\nfrom prml.nn.config import config\n\n\ndef zeros(size):\n    return Array(np.zeros(size, dtype=config.dtype))\n"
  },
  {
    "path": "prml/nn/config.py",
    "content": "import numpy as np\n\n\nclass Config(object):\n    __dtype = np.float32\n    __is_updating_bn = False\n    __available_dtypes = (np.float16, np.float32, np.float64)\n    __enable_backprop = True\n\n    @property\n    def dtype(self):\n        return self.__dtype\n\n    @dtype.setter\n    def dtype(self, dtype):\n        if dtype in self.__available_dtypes:\n            self.__dtype = dtype\n        else:\n            raise ValueError\n\n    @property\n    def is_updating_bn(self):\n        return self.__is_updating_bn\n\n    @is_updating_bn.setter\n    def is_updating_bn(self, flag):\n        assert(isinstance(flag, bool))\n        self.__is_updating_bn = flag\n\n    @property\n    def enable_backprop(self):\n        return self.__enable_backprop\n\n    @enable_backprop.setter\n    def enable_backprop(self, flag):\n        assert(isinstance(flag, bool))\n        self.__enable_backprop = flag\n\n\nconfig = Config()\n"
  },
  {
    "path": "prml/nn/distribution/__init__.py",
    "content": ""
  },
  {
    "path": "prml/nn/distribution/bernoulli.py",
    "content": "import numpy as np\n\nfrom prml.nn.array.array import asarray\nfrom prml.nn.distribution.distribution import Distribution\nfrom prml.nn.loss.sigmoid_cross_entropy import sigmoid_cross_entropy\nfrom prml.nn.math.log import log\nfrom prml.nn.nonlinear.logit import logit as logit_func\nfrom prml.nn.nonlinear.sigmoid import sigmoid\n\n\nclass Bernoulli(Distribution):\n    is_categorical = True\n\n    def __init__(self, mean=None, logit=None):\n        super().__init__()\n        if mean is not None:\n            self.mean = asarray(mean)\n            assert((self.mean.value >= 0).all() and (self.mean.value <= 1).all())\n            self.logit = logit_func(mean)\n            self._log_pdf = self._log_pdf_mu\n        elif logit is not None:\n            self.mean = sigmoid(logit)\n            self.logit = asarray(logit)\n            self._log_pdf = self._log_pdf_logit\n        else:\n            raise ValueError\n\n    def forward(self):\n        binary = (np.random.uniform(size=self.mean.shape) < self.mean.value)\n        return asarray(binary)\n\n    def _pdf(self, x):\n        return (self.mean ** x) * (1 - self.mean) ** (1 - x)\n\n    def _log_pdf_mu(self, x):\n        return x * log(self.mean) + (1 - x) * log(1 - self.mean)\n\n    def _log_pdf_logit(self, x):\n        return -sigmoid_cross_entropy(self.logit, x)\n"
  },
  {
    "path": "prml/nn/distribution/categorical.py",
    "content": "import numpy as np\n\nfrom prml.nn.array.array import asarray\nfrom prml.nn.distribution.distribution import Distribution\nfrom prml.nn.function import Function\nfrom prml.nn.loss.softmax_cross_entropy import softmax_cross_entropy\nfrom prml.nn.math.log import log\nfrom prml.nn.nonlinear.softmax import softmax\n\n\nclass Categorical(Distribution):\n    \"\"\"Categorical distribution.\"\"\"\n\n    is_categorical = True\n\n    def __init__(\n        self,\n        mean=None,\n        logit=None,\n        use_gumbel_softmax=True,\n        tau=0.1,\n    ):\n        \"\"\"Initialize a categorical distribution.\"\"\"\n        super().__init__()\n        if mean is not None:\n            self.mean = asarray(mean)\n            v = self.mean.value\n            assert ((v >= 0).all() and np.allclose(v.sum(axis=-1), 1))\n            self.logit = log(self.mean)\n            self._log_pdf = self._log_pdf_mean\n        elif logit is not None:\n            self.mean = softmax(logit)\n            self.logit = asarray(logit)\n            self._log_pdf = self._log_pdf_logit\n        else:\n            raise ValueError\n        self.n_category = self.mean.shape[-1]\n        if use_gumbel_softmax:\n            self.forward = self._forward_gumbel_softmax\n        else:\n            self.forward = self._forward\n        self.tau = tau\n        self.eye = np.eye(self.n_category)\n\n    def _forward_gumbel_softmax(self):\n        g = np.random.gumbel(size=self.mean.shape)\n        return softmax((self.logit + g) / self.tau)\n\n    def _forward(self):\n        if self.mean.ndim == 1:\n            index = np.random.choice(self.n_category, p=self.mean.value)\n            return asarray(self.eye[index])\n        else:\n            mean = self.mean.value.reshape(-1, self.n_category)\n            indices = [np.random.choice(self.n_category, p=p) for p in mean]\n            onehot = self.eye[np.array(indices)]\n            return asarray(onehot)\n\n    def _pdf(self, x):\n        return CategoricalPDF().forward(self.mean, x)\n\n    def _log_pdf_mean(self, x):\n        return x * log(self.mean)\n\n    def _log_pdf_logit(self, x):\n        return -softmax_cross_entropy(self.logit, x)\n\n\nclass CategoricalPDF(Function):\n    \"\"\"Probability density function of a categorical distribution.\"\"\"\n\n    def _forward(self, mean, x):\n        proba = np.ones_like(mean)\n        self.indices = np.where(x == 1)\n        proba[self.indices] = mean[self.indices]\n        return proba\n\n    def _backward(self, delta, mean, x):\n        dmean = np.zeros_like(mean)\n        dmean[self.indices] = delta[self.indices]\n        return dmean\n"
  },
  {
    "path": "prml/nn/distribution/distribution.py",
    "content": "from prml.nn.function import Function\n\n\nclass Distribution(Function):\n    is_categorical = False\n\n    def __init__(self, data=None):\n        self.data = data\n\n    def draw(self):\n        self.data = self.forward()\n        return self.data\n\n    def pdf(self, x=None):\n        if x is not None:\n            return self._pdf(x)\n        elif self.data is not None:\n            return self._pdf(self.data)\n        else:\n            raise ValueError\n\n    def _pdf(self, x):\n        raise NotImplementedError\n\n    def log_pdf(self, x=None):\n        if x is not None:\n            return self._log_pdf(x)\n        elif self.data is not None:\n            return self._log_pdf(self.data)\n        else:\n            raise ValueError\n\n    def _log_pdf(self, *args, **kwargs):\n        raise NotImplementedError\n"
  },
  {
    "path": "prml/nn/distribution/gaussian.py",
    "content": "import numpy as np\nimport scipy.special as sp\nfrom prml.nn.array.array import asarray\nfrom prml.nn.distribution.distribution import Distribution\nfrom prml.nn.function import Function\nfrom prml.nn.math.log import log\nfrom prml.nn.math.sqrt import sqrt\nfrom prml.nn.math.square import square\nfrom prml.nn.random.normal import normal\n\n\nclass Gaussian(Distribution):\n\n    def __init__(self, mean, std):\n        super().__init__()\n        self.mean = asarray(mean)\n        self.std = asarray(std)\n        assert((self.std.value > 0).all())\n\n    def forward(self):\n        if np.prod(self.mean.shape) > np.prod(self.std.shape):\n            eps = normal(0, 1, self.mean.shape)\n        else:\n            eps = normal(0, 1, self.std.shape)\n        return self.mean + self.std * eps\n\n    def _log_pdf(self, x):\n        return GaussianLogPDF().forward(x, self.mean, self.std)\n\n\nclass GaussianLogPDF(Function):\n    enable_auto_broadcast = True\n    log2pi = np.log(2 * np.pi)\n\n    def _forward(self, x, mean, std):\n        self.mahalanobis_distance = np.square((x - mean) / std)\n        log_pdf = -0.5 * (\n            self.mahalanobis_distance\n            + 2 * np.log(std)\n            + self.log2pi\n        )\n        return log_pdf\n\n    def _backward(self, delta, x, mean, std):\n        dx = -0.5 * delta * (x - mean) / np.square(std)\n        dmean = -dx\n        dstd = (self.mahalanobis_distance - 1) / std\n        return dx, dmean, dstd\n\n\nclass GaussianRadial(Distribution):\n\n    def __init__(self, std, ndim: int):\n        super().__init__()\n        self.std = asarray(std)\n        self.ndim = ndim\n        assert((self.std.value >= 0).all())\n\n    def forward(self):\n        eps = normal(0, 1, (self.ndim,) + self.std.shape)\n        return sqrt(square(self.std * eps).sum(axis=0))\n\n    def _log_pdf(self, x):\n        return (\n            (self.ndim - 1) * log(x)\n            - 0.5 * square(x / self.std)\n            - self.ndim * log(self.std)\n            - np.log(sp.gamma(0.5 * self.ndim))\n        )\n"
  },
  {
    "path": "prml/nn/function.py",
    "content": "import numpy as np\n\nfrom prml.nn.array.array import Array, asarray\nfrom prml.nn.config import config\nfrom prml.nn.queue import backprop_queue\n\n\nclass Function(object):\n    enable_auto_broadcast = False\n\n    def forward(self, *args, **kwargs):\n        self.args = [self._convert2array(arg) for arg in args]\n        if self.enable_auto_broadcast:\n            self.args = self._autobroadcast(self.args)\n        self.kwargs = kwargs\n        out = self._forward(*tuple(arg.value for arg in self.args), **kwargs)\n        if config.enable_backprop:\n            return Array(out, parent=self)\n        else:\n            return Array(out, parent=None)\n\n    def backward(self, delta):\n        dargs = self._backward(delta, *tuple(arg.value for arg in self.args), **self.kwargs)\n        if isinstance(dargs, tuple):\n            for arg, darg in zip(self.args, dargs):\n                arg._backward(darg)\n                if not arg.is_in_queue:\n                    backprop_queue.enqueue(arg)\n        else:\n            self.args[0]._backward(dargs)\n            if not self.args[0].is_in_queue:\n                backprop_queue.enqueue(self.args[0])\n\n    def _out_depth(self):\n        return max([arg.depth for arg in self.args]) + 1\n\n    @staticmethod\n    def _autobroadcast(arg):\n        return broadcast(arg)\n\n    def _forward(self, *args, **kwargs):\n        raise NotImplementedError\n\n    def _backward(self, *args, **kwargs):\n        raise NotImplementedError\n\n    @staticmethod\n    def _convert2array(arg):\n        if not isinstance(arg, Array):\n            return asarray(arg)\n        else:\n            return arg\n\n\nclass BroadcastTo(Function):\n    \"\"\"\n    Broadcast a tensor to an new shape\n    \"\"\"\n\n    def __init__(self, shape):\n        self.shape = shape\n\n    def _forward(self, x):\n        output = np.broadcast_to(x, self.shape)\n        return output\n\n    @staticmethod\n    def _backward(delta, x):\n        dx = delta\n        xdim = getattr(x, \"ndim\", 0)\n        xshape = getattr(x, \"shape\", ())\n        if delta.ndim != xdim:\n            dx = dx.sum(axis=tuple(range(dx.ndim - xdim)))\n            if isinstance(dx, np.number):\n                dx = np.array(dx)\n        axis = tuple(i for i, len_ in enumerate(xshape) if len_ == 1)\n        if axis:\n            dx = dx.sum(axis=axis, keepdims=True)\n        return dx\n\n\ndef broadcast_to(x, shape):\n    \"\"\"\n    Broadcast a tensor to an new shape\n    \"\"\"\n    return BroadcastTo(shape).forward(x)\n\n\ndef broadcast(args):\n    \"\"\"\n    broadcast list of tensors to make them have the same shape\n\n    Parameters\n    ----------\n    args : list\n        list of Tensor to be aligned\n\n    Returns\n    -------\n    list\n        list of Tensor whose shapes are aligned\n    \"\"\"\n    shape = np.broadcast(*(arg.value for arg in args)).shape\n    for i, arg in enumerate(args):\n        if arg.shape != shape:\n            args[i] = BroadcastTo(shape).forward(arg)\n    return args\n"
  },
  {
    "path": "prml/nn/image/__init__.py",
    "content": "# flake8: noqa\n\nfrom prml.nn.image.convolve2d import convolve2d, Convolve2d\nfrom prml.nn.image.deconvolve2d import deconvolve2d, Deconvolve2d\nfrom prml.nn.image.max_pooling2d import max_pooling2d\nfrom prml.nn.image.util import img2patch, patch2img\n"
  },
  {
    "path": "prml/nn/image/convolve2d.py",
    "content": "import numpy as np\n\nfrom prml.nn.array.array import Array\nfrom prml.nn.network import Network\nfrom prml.nn.function import Function\nfrom prml.nn.image.util import img2patch, patch2img\n\n\nclass Convolve2dFunction(Function):\n\n    def __init__(self, kernel_size, stride, pad):\n        \"\"\"\n        construct 2 dimensional convolution function\n        Parameters\n        ----------\n        kernel_size : tuple of ints\n            size of convolution kernel\n        stride : tuple of ints\n            stride of kernel application\n        pad : tuple of ints\n            padding image\n        \"\"\"\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.pad = (0,) + pad + (0,)\n\n    def _forward(self, x, y):\n        img = np.pad(x, [(p,) for p in self.pad], \"constant\")\n        self.paddedshape = img.shape\n        self.patch = img2patch(img, self.kernel_size, self.stride)\n        self.outshape = self.patch.shape[:3] + (y.shape[1],)\n        self.patch_flattened = self.patch.reshape(-1, y.shape[0])\n        return np.matmul(self.patch_flattened, y).reshape(self.outshape)\n\n    def _backward(self, delta, x, y):\n        delta_flattened = delta.reshape(-1, delta.shape[-1])\n        dpatch_flattened = delta_flattened @ y.T\n        dpatch = dpatch_flattened.reshape(self.patch.shape)\n        dimg = patch2img(dpatch, self.stride, self.paddedshape)\n        slices = tuple(slice(p, len_ - p) for p, len_ in zip(self.pad, self.paddedshape))\n        dx = dimg[slices]\n        dy = self.patch_flattened.T @ delta_flattened\n        return dx, dy\n\n\nclass Convolve2d(Network):\n\n    def __init__(self, kernel, stride, pad):\n        super().__init__()\n        self.in_ch = kernel.shape[-2]\n        self.out_ch = kernel.shape[-1]\n        self.kernel_size = kernel.shape[:2]\n        self.stride = stride\n        self.pad = pad\n        kernel = kernel.value\n        with self.set_parameter():\n            self.w = Array(kernel.reshape(-1, kernel.shape[-1]))\n\n    @property\n    def kernel(self):\n        return self.w.reshape(*self.kernel_size, self.in_ch, self.out_ch)\n\n    def __call__(self, x):\n        func = Convolve2dFunction(self.kernel_size, self.stride, self.pad)\n        return func.forward(x, self.w)\n\n\ndef convolve2d(x, y, stride=(1, 1), pad=(0, 0)):\n    \"\"\"\n    returns convolution of two tensors\n    Parameters\n    ----------\n    x : (n_batch, xlen, ylen, in_chaprml.nnel) Tensor\n        input tensor to be convolved\n    y : (kx, ky, in_chaprml.nnel, out_chaprml.nnel) Tensor\n        convolution kernel\n    stride : tuple of ints (sx, sy)\n        stride of kernel application\n    pad : tuple of ints (px, py)\n        padding image\n    Returns\n    -------\n    output : (n_batch, xlen', ylen', out_chaprml.nnel) Tensor\n        input convolved with kernel\n        len' = (len + 2p - k) // s + 1\n    \"\"\"\n    conv = Convolve2dFunction(y.shape[:2], stride, pad)\n    return conv.forward(x, y.reshape(-1, y.shape[-1]))\n"
  },
  {
    "path": "prml/nn/image/deconvolve2d.py",
    "content": "import numpy as np\n\nfrom prml.nn.array.array import Array\nfrom prml.nn.function import Function\nfrom prml.nn.network import Network\nfrom prml.nn.image.util import img2patch, patch2img\n\n\nclass Deconvolve2dFunction(Function):\n\n    def __init__(self, kernel_size, out_ch, stride, pad, shape):\n        \"\"\"\n        construct 2 dimensional convolution function\n        Parameters\n        ----------\n        stride : tuple of ints\n            stride of kernel application\n        pad : tuple of ints\n            padding image\n        shape : tuple of ints, optional\n            desired output image shape\n        \"\"\"\n        self.kernel_size = kernel_size\n        self.out_ch = out_ch\n        self.stride = stride\n        self.pad = (0,) + pad + (0,)\n        if shape is None:\n            self.shape = None\n        else:\n            self.shape = shape\n\n    def _forward(self, x, y):\n        if self.shape is None:\n            shape = (len(x),) + tuple(\n                s * (imlen - 1) + klen for s, imlen, klen\n                in zip(self.stride, x.shape[1:], self.kernel_size)\n            ) + (self.out_ch,)\n        else:\n            shape = (len(x),) + self.shape + (self.out_ch,)\n        patch_flat = np.matmul(x, y.T)   # (N, Hx, Wx, kx * ky * out_ch)\n        output = patch2img(patch_flat.reshape(*patch_flat.shape[:3], *self.kernel_size, -1), self.stride, shape)\n        output = output[\n            :,\n            self.pad[1]: output.shape[1] - self.pad[1],\n            self.pad[2]: output.shape[2] - self.pad[2]\n        ]\n        return output\n\n    def _backward(self, delta, x, y):\n        delta = np.pad(delta, [(p,) for p in self.pad], \"constant\")\n        dpatch = img2patch(delta, self.kernel_size, self.stride)\n        dpatch_flat = dpatch.reshape(-1, y.shape[0])    # (N * Hx * Wx, Hk * Wk * out_ch)\n        dx = np.matmul(dpatch_flat, y).reshape(x.shape)\n        dy = np.matmul(x.reshape(-1, x.shape[-1]).T, dpatch_flat).T\n        return dx, dy\n\n\nclass Deconvolve2d(Network):\n\n    def __init__(self, kernel, stride, pad, shape=None):\n        super().__init__()\n        self.kernel_size = kernel.shape[:2]\n        self.out_ch = kernel.shape[2]\n        self.in_ch = kernel.shape[3]\n        self.stride = stride\n        self.pad = pad\n        self.shape = shape\n        kernel = kernel.value\n        with self.set_parameter():\n            self.w = Array(kernel.reshape(-1, kernel.shape[-1]))\n\n    @property\n    def kernel(self):\n        return self.w.reshape(*self.kernel_size, self.out_ch, self.in_ch)\n\n    def __call__(self, x):\n        func = Deconvolve2dFunction(self.kernel_size, self.out_ch, self.stride, self.pad, self.shape)\n        return func.forward(x, self.w)\n\ndef deconvolve2d(x, y, stride=1, pad=0, shape=None):\n    \"\"\"\n    deconvolution of two tensors\n    aka transposed convolution\n\n    Parameters\n    ----------\n    x : (n_batch, xlen, ylen, in_chaprml.nnel) Tensor\n        input tensor to be deconvolved\n    y : (kx, ky, out_chaprml.nnel, in_chaprml.nnel) Tensor\n        deconvolution kernel\n    stride : int or tuple of ints (sx, sy)\n        stride of kernel application\n    pad : int or tuple of ints (px, py)\n        padding image\n    shape : tuple of ints (xlen', ylen')\n        desired shape of output image\n        If not specified, the output has the following length\n        len' = s * (len - 1) - 2p + k\n\n    Returns\n    -------\n    output : (n_batch, xlen', ylen', out_chaprml.nnel) Tensor\n        The first argument deconvolved with the second one\n        len' will be the following if not specified\n        len' = s * (len - 1) - 2p + k\n    \"\"\"\n    deconv = Deconvolve2dFunction(y.shape[:2], y.shape[2], stride, pad, shape)\n    return deconv.forward(x, y.reshape(-1, y.shape[-1]))\n"
  },
  {
    "path": "prml/nn/image/max_pooling2d.py",
    "content": "import numpy as np\n\nfrom prml.nn.config import config\nfrom prml.nn.function import Function\nfrom prml.nn.image.util import img2patch, patch2img, patch2img_no_overlap\n\n\nclass MaxPooling2d(Function):\n\n    def __init__(self, pool_size, stride, pad):\n        \"\"\"\n        construct 2 dimensional max-pooling function\n        Parameters\n        ----------\n        pool_size : int or tuple of ints\n            pooling size\n        stride : int or tuple of ints\n            stride of kernel application\n        pad : int or tuple of ints\n            padding image\n        \"\"\"\n        self.pool_size = self._check_tuple(pool_size, \"pool_size\")\n        self.stride = self._check_tuple(stride, \"stride\")\n        self.pad = self._check_tuple(pad, \"pad\")\n        self.pad = (0,) + self.pad + (0,)\n\n    def _check_tuple(self, tup, name):\n        if isinstance(tup, int):\n            tup = (tup,) * 2\n        if not isinstance(tup, tuple):\n            raise TypeError(\n                \"Unsupported type for {}: {}\".format(name, type(tup))\n            )\n        if len(tup) != 2:\n            raise ValueError(\n                \"the length of {} must be 2, not {}\".format(name, len(tup))\n            )\n        if not all([isinstance(n, int) for n in tup]):\n            raise TypeError(\n                \"Unsuported type for {}\".format(name)\n            )\n        if not all([n >= 0 for n in tup]):\n            raise ValueError(\n                \"{} must be non-negative values\".format(name)\n            )\n        return tup\n\n    def _forward(self, x):\n        img = np.pad(x, [(p,) for p in self.pad], \"constant\")\n        patch = img2patch(img, self.pool_size, self.stride)\n        n_batch, xlen_out, ylen_out, _, _, in_channels = patch.shape\n        patch = patch.reshape(n_batch, xlen_out, ylen_out, -1, in_channels)\n        self.shape = img.shape\n        self.index = patch.argmax(axis=3)\n        return patch.max(axis=3)\n\n    def _backward(self, delta, x):\n        delta_patch = np.zeros(delta.shape + (np.prod(self.pool_size),), dtype=config.dtype)\n        index = np.where(delta == delta) + (self.index.ravel(),)\n        delta_patch[index] = delta.ravel()\n        delta_patch = np.reshape(delta_patch, delta.shape + self.pool_size)\n        delta_patch = delta_patch.transpose(0, 1, 2, 4, 5, 3)\n        if self.pool_size == self.stride:\n            dx = patch2img_no_overlap(delta_patch, self.stride, self.shape)\n        else:\n            dx = patch2img(delta_patch, self.stride, self.shape)\n        slices = tuple(slice(p, len_ - p) for p, len_ in zip(self.pad, self.shape))\n        dx = dx[slices]\n        return dx\n\n\ndef max_pooling2d(x, pool_size, stride=1, pad=0):\n    \"\"\"\n    spatial max pooling\n    Parameters\n    ----------\n    x : (n_batch, xlen, ylen, in_chaprml.nnel) Tensor\n        input tensor\n    pool_size : int or tuple of ints (kx, ky)\n        pooling size\n    stride : int or tuple of ints (sx, sy)\n        stride of pooling application\n    pad : int or tuple of ints (px, py)\n        padding input\n    Returns\n    -------\n    output : (n_batch, xlen', ylen', out_chaprml.nnel) Tensor\n        max pooled image\n        len' = (len + p - k) // s + 1\n    \"\"\"\n    return MaxPooling2d(pool_size, stride, pad).forward(x)\n"
  },
  {
    "path": "prml/nn/image/util.py",
    "content": "import itertools\n\nimport numpy as np\nfrom numpy.lib.stride_tricks import as_strided\n\n\ndef img2patch(img, size, step=1):\n    \"\"\"Convert batch of image array into patches.\n\n    Parameters\n    ----------\n    img : (n_batch, xlen_in, ylen_in, in_chaprml.nnels) ndarray\n        batch of images\n    size : tuple or int\n        patch size\n    step : tuple or int\n        stride of patches\n    Returns\n    -------\n    patch : (n_batch, xlen_out, ylen_out, size, size, in_chaprml.nnels) ndarray\n        batch of patches at all points\n        len_out = (len_in - size) // step + 1\n    \"\"\"\n    ndim = img.ndim\n    if isinstance(size, int):\n        size = (size,) * (ndim - 2)\n    if isinstance(step, int):\n        step = (step,) * (ndim - 2)\n\n    slices = tuple(slice(None, None, s) for s in step)\n    window_strides = img.strides[1:]\n    index_strides = img[(slice(None),) + slices].strides[:-1]\n\n    out_shape = tuple(\n        np.subtract(img.shape[1: -1], size) // np.array(step) + 1)\n    out_shape = (len(img),) + out_shape + size + (np.size(img, -1),)\n    strides = index_strides + window_strides\n    patch = as_strided(img, shape=out_shape, strides=strides)\n    return patch\n\n\ndef _patch2img(x, stride, shape):\n    \"\"\"Sum up patches and form an image.\n\n    Parameters\n    ----------\n    x : (n_batch, xlen_in, ylen_in, kx, ky, in_chaprml.nnels) ndarray\n        batch of patches at all points\n    stride : tuple\n        applied stride to take patches\n    shape : (n_batch, xlen_out, ylen_out, in_chaprml.nnels) tuple\n        output image shape\n    Returns\n    -------\n    img : (n_batch, xlen_out, ylen_out, in_chaprml.nnels) ndarray\n        image\n    \"\"\"\n    img = np.zeros(shape, dtype=x.dtype)\n    kx, ky = x.shape[3: 5]\n    for i, j in itertools.product(range(kx), range(ky)):\n        slices = tuple(\n            slice(b, b + s * len_, s)\n            for b, s, len_ in zip([i, j], stride, x.shape[1: 3])\n        )\n        img[(slice(None),) + slices] += x[..., i, j, :]\n    return img\n\n\ndef patch2img(x, stride, shape):\n    \"\"\"Transform patches to an image.\"\"\"\n    img = np.zeros(shape, dtype=x.dtype)\n    kx, ky = x.shape[3:5]\n    patch = img2patch(img, (kx, ky), stride)\n    for i, j in itertools.product(range(kx), range(ky)):\n        patch[..., i, j, :] += x[..., i, j, :]\n    return img\n\n\ndef patch2img_no_overlap(x, stride, shape):\n    \"\"\"Transform patches to an image without overlaps.\"\"\"\n    img = np.zeros(shape, dtype=x.dtype)\n    patch = img2patch(img, x.shape[3:5], stride)\n    patch += x\n    return img\n"
  },
  {
    "path": "prml/nn/io/__init__.py",
    "content": "# flake8: noqa\n\nfrom prml.nn.io.io import save_parameter, load_parameter, save_object, load_object\n"
  },
  {
    "path": "prml/nn/io/io.py",
    "content": "import pickle\nimport numpy as np\n\n\ndef save_parameter(filename: str, parameter: dict):\n    dict_ = {key: param.value for key, param in parameter.items()}\n    np.savez_compressed(filename, **dict_)\n\n\ndef load_parameter(filename: str, parameter: dict):\n    loaded = np.load(filename)\n    for key in parameter:\n        np.copyto(parameter[key].value, loaded[key])\n\n\ndef save_object(filename: str, obj):\n    with open(filename, \"wb\") as f:\n        pickle.dump(obj, f)\n\n\ndef load_object(filename: str, obj):\n    with open(filename, \"rb\") as f:\n        return pickle.load(f)\n"
  },
  {
    "path": "prml/nn/loss/__init__.py",
    "content": "from prml.nn.loss.kl import kl_divergence\nfrom prml.nn.loss.sigmoid_cross_entropy import sigmoid_cross_entropy\nfrom prml.nn.loss.softmax_cross_entropy import softmax_cross_entropy\n\n\n_functions = [kl_divergence, sigmoid_cross_entropy, softmax_cross_entropy]\n\n\n__all__ = [_f.__name__ for _f in _functions]\n\n\ndel _functions\n"
  },
  {
    "path": "prml/nn/loss/kl.py",
    "content": "from prml.nn.distribution.bernoulli import Bernoulli\nfrom prml.nn.distribution.categorical import Categorical\nfrom prml.nn.distribution.gaussian import Gaussian\nfrom prml.nn.math.log import log\nfrom prml.nn.math.square import square\nfrom prml.nn.nonlinear.log_softmax import log_softmax\nfrom prml.nn.nonlinear.softplus import softplus\n\n\ndef kl_divergence(q, p, data=None):\n    \"\"\"\n    compute sample approximation of kl divergence from p to q\n    KL(q||p)\n\n    Parameters\n    ----------\n    q : Distribution\n        one generated a sample\n    p : Distribution\n    data : Array\n    \"\"\"\n    if isinstance(q, Bernoulli) and isinstance(p, Bernoulli):\n        return kl_bernoulli(q, p)\n    elif isinstance(q, Categorical) and isinstance(p, Categorical):\n        return kl_categorical(q, p)\n    elif isinstance(q, Gaussian) and isinstance(p, Gaussian):\n        return kl_gaussian(q, p)\n    elif q.data.depth > 0:\n        return q.log_pdf() - p.log_pdf(q.data)\n    else:\n        return q.pdf() * (q.log_pdf() - p.log_pdf(q.data))\n\n\ndef kl_bernoulli(q, p):\n    return (\n        (q.mean - 1) * (q.logit - p.logit)\n        - softplus(-q.logit) + softplus(p.logit)\n    )\n\n\ndef kl_categorical(q, p):\n    return (q.mean * (log_softmax(q.logit) - log_softmax(p.logit))).sum(axis=-1)\n\n\ndef kl_gaussian(q, p):\n    qvar = square(q.std)\n    pvar = square(p.std)\n    return log(p.std) - log(q.std) + 0.5 * (qvar + square(p.mean - q.mean)) / pvar - 0.5\n"
  },
  {
    "path": "prml/nn/loss/sigmoid_cross_entropy.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass SigmoidCrossEntropy(Function):\n    enable_auto_broadcast = True\n\n    @staticmethod\n    def _forward(x, t):\n        return np.maximum(x, 0) - t * x + np.log1p(np.exp(-np.abs(x)))\n\n    @staticmethod\n    def _backward(delta, x, t):\n        y = np.tanh(x * 0.5) * 0.5 + 0.5\n        dx = delta * (y - t)\n        dt = -delta * x\n        return dx, dt\n\n\ndef sigmoid_cross_entropy(x, t):\n    return SigmoidCrossEntropy().forward(x, t)\n"
  },
  {
    "path": "prml/nn/loss/softmax_cross_entropy.py",
    "content": "import numpy as np\nfrom scipy.special import logsumexp\n\nfrom prml.nn.function import Function\n\n\nclass SoftmaxCrossEntropy(Function):\n\n    def _forward(self, x, t):\n        self.log_softmax = x - logsumexp(x, axis=-1, keepdims=True)\n        return -t * self.log_softmax\n\n    def _backward(self, delta, x, t):\n        dx = delta * (np.exp(self.log_softmax) - t)\n        dt = -delta * self.log_softmax\n        return dx, dt\n\n\ndef softmax_cross_entropy(x, t):\n    return SoftmaxCrossEntropy().forward(x, t)\n"
  },
  {
    "path": "prml/nn/math/__init__.py",
    "content": "from prml.nn.math.negative import negative\nfrom prml.nn.math.add import add\nfrom prml.nn.math.subtract import subtract, rsubtract\nfrom prml.nn.math.divide import divide, rdivide\nfrom prml.nn.math.mean import mean\nfrom prml.nn.math.multiply import multiply\nfrom prml.nn.math.matmul import matmul, rmatmul\nfrom prml.nn.math.power import power, rpower\nfrom prml.nn.math.sum import sum\nfrom prml.nn.array import Array\n\n\nArray.__neg__ = negative\nArray.__add__ = add\nArray.__radd__ = add\nArray.__sub__ = subtract\nArray.__rsub__ = rsubtract\nArray.__truediv__ = divide\nArray.__rtruediv__ = rdivide\nArray.__mul__ = multiply\nArray.__rmul__ = multiply\nArray.__matmul__ = matmul\nArray.__rmatmul__ = rmatmul\nArray.__pow__ = power\nArray.__rpow__ = rpower\nArray.sum = sum\nArray.mean = mean\n"
  },
  {
    "path": "prml/nn/math/add.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Add(Function):\n    enable_auto_broadcast = True\n\n    @staticmethod\n    def _forward(x, y):\n        return x + y\n\n    @staticmethod\n    def _backward(delta, x, y):\n        return delta, delta\n\n\nclass AddBias(Function):\n\n    @staticmethod\n    def _forward(x, y):\n        return x + y\n\n    @staticmethod\n    def _backward(delta, x, y):\n        dx = delta\n        dy = np.sum(delta, axis=tuple(i for i in range(x.ndim - 1)))\n        return dx, dy\n\n\nclass AddScalar(Function):\n\n    @staticmethod\n    def _forward(x, y):\n        return x + y\n\n    @staticmethod\n    def _backward(delta, x, y):\n        dx = delta\n        dy = np.atleast_1d(np.sum(delta))\n        return dx, dy\n\n\ndef add(x, y):\n    return Add().forward(x, y)\n    # x = Function._convert2array(x)\n    # y = Function._convert2array(y)\n    # if x.shape == y.shape:\n    #     return Add().forward(x, y)\n    # elif x.size == 1:\n    #     return AddScalar().forward(y, x)\n    # elif y.size == 1:\n    #     return AddScalar().forward(x, y)\n    # elif x.shape[-1] == y.shape[-1]:\n    #     if x.ndim == 1:\n    #         return AddBias().forward(y, x)\n    #     elif y.ndim == 1:\n    #         return AddBias().forward(x, y)\n    # else:\n    #     raise ValueError\n"
  },
  {
    "path": "prml/nn/math/divide.py",
    "content": "from prml.nn.function import Function\n\n\nclass Divide(Function):\n    enable_auto_broadcast = True\n\n    @staticmethod\n    def _forward(x, y):\n        return x / y\n\n    @staticmethod\n    def _backward(delta, x, y):\n        dx = delta / y\n        dy = -delta * x / (y ** 2)\n        return dx, dy\n\n\ndef divide(x, y):\n    return Divide().forward(x, y)\n\n\ndef rdivide(x, y):\n    return Divide().forward(y, x)\n"
  },
  {
    "path": "prml/nn/math/exp.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Exp(Function):\n\n    def _forward(self, x):\n        self.output = np.exp(x)\n        return self.output\n\n    def _backward(self, delta, x):\n        return delta * self.output\n\n\ndef exp(x):\n    return Exp().forward(x)\n"
  },
  {
    "path": "prml/nn/math/log.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Log(Function):\n\n    @staticmethod\n    def _forward(x):\n        return np.log(x)\n\n    @staticmethod\n    def _backward(delta, x):\n        return delta / x\n\n\ndef log(x):\n    return Log().forward(x)\n"
  },
  {
    "path": "prml/nn/math/matmul.py",
    "content": "from prml.nn.function import Function\n\n\nclass Matmul(Function):\n\n    @staticmethod\n    def _forward(x, y):\n        return x @ y\n\n    @staticmethod\n    def _backward(delta, x, y):\n        dx = delta @ y.T\n        dy = x.T @ delta\n        return dx, dy\n\n\ndef matmul(x, y):\n    return Matmul().forward(x, y)\n\n\ndef rmatmul(x, y):\n    return Matmul().forward(y, x)\n"
  },
  {
    "path": "prml/nn/math/mean.py",
    "content": "from prml.nn.math.sum import sum\n\n\ndef mean(x, axis=None, keepdims=False):\n    \"\"\"\n    returns arithmetic mean of the elements along given axis\n    \"\"\"\n    if axis is None:\n        return sum(x, axis=None, keepdims=keepdims) / x.size\n    elif isinstance(axis, int):\n        N = x.shape[axis]\n        return sum(x, axis=axis, keepdims=keepdims) / N\n    elif isinstance(axis, tuple):\n        N = 1\n        for ax in axis:\n            N *= x.shape[ax]\n        return sum(x, axis=axis, keepdims=keepdims) / N\n    else:\n        raise TypeError(f\"Unsupported type for axis: {type(axis)}\")\n"
  },
  {
    "path": "prml/nn/math/multiply.py",
    "content": "from prml.nn.function import Function\n\n\nclass Multiply(Function):\n    enable_auto_broadcast = True\n\n    @staticmethod\n    def _forward(x, y):\n        return x * y\n\n    @staticmethod\n    def _backward(delta, x, y):\n        dx = delta * y\n        dy = delta * x\n        return dx, dy\n\n\ndef multiply(x, y):\n    return Multiply().forward(x, y)\n"
  },
  {
    "path": "prml/nn/math/negative.py",
    "content": "from prml.nn.function import Function\n\n\nclass Negative(Function):\n\n    @staticmethod\n    def _forward(x):\n        return -x\n\n    @staticmethod\n    def _backward(delta, x):\n        return -delta\n\n\ndef negative(x):\n    return Negative().forward(x)\n"
  },
  {
    "path": "prml/nn/math/power.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Power(Function):\n    \"\"\"\n    First array elements raised to powers from second array\n    \"\"\"\n\n    def _forward(self, x, y):\n        self.output = np.power(x, y)\n        return self.output\n\n    def _backward(self, delta, x, y):\n        dx = y * np.power(x, y - 1) * delta\n        if (x > 0).all():\n            dy = self.output * np.log(x) * delta\n            return dx, dy\n        return dx\n\n\ndef power(x, y):\n    \"\"\"\n    First array elements raised to powers from second array\n    \"\"\"\n    return Power().forward(x, y)\n\n\ndef rpower(x, y):\n    return Power().forward(y, x)\n"
  },
  {
    "path": "prml/nn/math/product.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Product(Function):\n\n    def __init__(self, axis=None, keepdims=False):\n        if isinstance(axis, int):\n            axis = (axis,)\n        elif isinstance(axis, tuple):\n            axis = tuple(sorted(axis))\n        self.axis = axis\n        self.keepdims = keepdims\n\n    def _forward(self, x):\n        self.output = np.prod(x, axis=self.axis, keepdims=True)\n        if not self.keepdims:\n            return np.squeeze(self.output)\n        else:\n            return self.output\n\n    def backward(self, delta, x):\n        if not self.keepdims and self.axis is not None:\n            for ax in self.axis:\n                delta = np.expand_dims(delta, ax)\n        dx = delta * self.output / x\n        return dx\n\n\ndef prod(x, axis=None, keepdims=False):\n    \"\"\"\n    product of all element in the array\n    Parameters\n    ----------\n    x : tensor_like\n        input array\n    axis : int, tuple of ints\n        axis or axes along which a product is performed\n    keepdims : bool\n        keep dimensionality or not\n    Returns\n    -------\n    product : tensor_like\n        product of all element\n    \"\"\"\n    return Product(axis=axis, keepdims=keepdims).forward(x)\n"
  },
  {
    "path": "prml/nn/math/sqrt.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Sqrt(Function):\n\n    def _forward(self, x):\n        self.output = np.sqrt(x)\n        return self.output\n\n    def _backward(self, delta, x):\n        return 0.5 * delta / self.output\n\n\ndef sqrt(x):\n    return Sqrt().forward(x)\n"
  },
  {
    "path": "prml/nn/math/square.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Square(Function):\n\n    @staticmethod\n    def _forward(x):\n        return np.square(x)\n\n    @staticmethod\n    def _backward(delta, x):\n        return 2 * delta * x\n\n\ndef square(x):\n    return Square().forward(x)\n"
  },
  {
    "path": "prml/nn/math/subtract.py",
    "content": "from prml.nn.function import Function\n\n\nclass Subtract(Function):\n    \"\"\"Subtraction function.\"\"\"\n\n    enable_auto_broadcast = True\n\n    @staticmethod\n    def _forward(x, y):\n        return x - y\n\n    @staticmethod\n    def _backward(delta, x, y):\n        return delta, -delta\n\n\ndef subtract(x, y):\n    \"\"\"Subtract.\"\"\"\n    return Subtract().forward(x, y)\n\n\ndef rsubtract(x, y):\n    \"\"\"Reverse subtract.\"\"\"\n    return Subtract().forward(y, x)\n"
  },
  {
    "path": "prml/nn/math/sum.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass SumAxisOrKeepdims(Function):\n    \"\"\"\n    summation along given axis\n    y = sum_i=1^N x_i\n    \"\"\"\n\n    def __init__(self, axis=None, keepdims=False):\n        if isinstance(axis, int):\n            axis = (axis,)\n        self.axis = axis\n        self.keepdims = keepdims\n\n    def _forward(self, x):\n        return x.sum(axis=self.axis, keepdims=self.keepdims)\n\n    def _backward(self, delta, x):\n        if isinstance(delta, np.ndarray) and (not self.keepdims) and (self.axis is not None):\n            axis_positive = []\n            for axis in self.axis:\n                if axis < 0:\n                    axis_positive.append(x.ndim + axis)\n                else:\n                    axis_positive.append(axis)\n            for axis in sorted(axis_positive):\n                delta = np.expand_dims(delta, axis)\n        dx = np.broadcast_to(delta, x.shape)\n        return dx\n\nclass SumSimple(Function):\n\n    @staticmethod\n    def _forward(x):\n        return x.sum()\n\n    @staticmethod\n    def _backward(delta, x):\n        return np.broadcast_to(delta, x.shape)\n\n\ndef sum(x, axis=None, keepdims=False):\n    \"\"\"\n    returns summation of the elements along given axis\n    y = sum_i=1^N x_i\n    \"\"\"\n    x = Function._convert2array(x)\n    if x.ndim == 1:\n        return SumAxisOrKeepdims(axis=axis, keepdims=True).forward(x)\n    elif axis is None and keepdims == False:\n        return SumSimple().forward(x)\n    return SumAxisOrKeepdims(axis=axis, keepdims=keepdims).forward(x)\n"
  },
  {
    "path": "prml/nn/network.py",
    "content": "from contextlib import contextmanager\nfrom prml.nn.array.array import Array\n\n\nclass Network(object):\n\n    def __init__(self):\n        self._setting_parameter = False\n        self.parameter = {}\n\n    @property\n    def setting_parameter(self):\n        return getattr(self, \"_setting_parameter\", False)\n\n    @contextmanager\n    def set_parameter(self):\n        prev_scope = self._setting_parameter\n        object.__setattr__(self, \"_setting_parameter\", True)\n        try:\n            yield\n        finally:\n            object.__setattr__(self, \"_setting_parameter\", prev_scope)\n\n    def __setattr__(self, key, value):\n        if self.setting_parameter:\n            if isinstance(value, Array):\n                self.parameter[self.__class__.__name__ + \".\" + key] = value\n            elif isinstance(value, Network):\n                for name, param in value.parameter.items():\n                    self.parameter[self.__class__.__name__ + \".\" + key + \".\" + name] = param\n\n        object.__setattr__(self, key, value)\n\n    def clear(self):\n        for param in self.parameter.values():\n            param.cleargrad()\n"
  },
  {
    "path": "prml/nn/nonlinear/__init__.py",
    "content": ""
  },
  {
    "path": "prml/nn/nonlinear/log_softmax.py",
    "content": "import numpy as np\nfrom scipy.special import logsumexp\n\nfrom prml.nn.function import Function\n\n\nclass LogSoftmax(Function):\n    \"\"\"Log-softmax function.\"\"\"\n\n    def _forward(self, x):\n        self.output = x - logsumexp(x, axis=-1, keepdims=True)\n        return self.output\n\n    def _backward(self, delta, x):\n        softmax = np.exp(self.output)\n        dx = delta - softmax * delta.sum(axis=-1, keepdims=True)\n        return dx\n\n\ndef log_softmax(x):\n    \"\"\"Return log-softmax transformation of the input.\"\"\"\n    return LogSoftmax().forward(x)\n"
  },
  {
    "path": "prml/nn/nonlinear/logit.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Logit(Function):\n\n    @staticmethod\n    def _forward(x):\n        return np.arctanh(2 * x - 1) * 2\n\n    @staticmethod\n    def _backward(delta, x):\n        return delta / x / (1 - x)\n\n\ndef logit(x):\n    return Logit().forward(x)\n"
  },
  {
    "path": "prml/nn/nonlinear/relu.py",
    "content": "from prml.nn.function import Function\n\n\nclass ReLU(Function):\n\n    @staticmethod\n    def _forward(x):\n        return x.clip(min=0)\n\n    @staticmethod\n    def _backward(delta, x):\n        return delta * (x > 0)\n\n\ndef relu(x):\n    return ReLU().forward(x)\n"
  },
  {
    "path": "prml/nn/nonlinear/sigmoid.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Sigmoid(Function):\n\n    def _forward(self, x):\n        self.out = np.tanh(x * 0.5) * 0.5 + 0.5\n        return self.out\n\n    def _backward(self, delta, x):\n        return delta * self.out * (1 - self.out)\n\n\ndef sigmoid(x):\n    return Sigmoid().forward(x)\n"
  },
  {
    "path": "prml/nn/nonlinear/softmax.py",
    "content": "import numpy as np\nfrom scipy.special import logsumexp\nfrom prml.nn.function import Function\n\n\nclass Softmax(Function):\n\n    def _forward(self, x):\n        self.output = np.exp(x - logsumexp(x, axis=-1, keepdims=True))\n        return self.output\n\n    def _backward(self, delta, x):\n        dx = self.output * delta\n        dx -= self.output * dx.sum(axis=-1, keepdims=True)\n        return dx\n\n\ndef softmax(x):\n    return Softmax().forward(x)\n"
  },
  {
    "path": "prml/nn/nonlinear/softplus.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Softplus(Function):\n\n    @staticmethod\n    def _forward(x):\n        return np.maximum(x, 0) + np.log1p(np.exp(-np.abs(x)))\n\n    @staticmethod\n    def _backward(delta, x):\n        return (np.tanh(0.5 * x) * 0.5 + 0.5) * delta\n\n\ndef softplus(x):\n    \"\"\"\n    y = log(1 + exp(x))\n    \"\"\"\n    return Softplus().forward(x)\n"
  },
  {
    "path": "prml/nn/nonlinear/tanh.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass Tanh(Function):\n\n    def _forward(self, x):\n        self.out = np.tanh(x)\n        return self.out\n\n    def _backward(self, delta, x):\n        dx = delta * (1 - self.out ** 2)\n        return dx\n\n\ndef tanh(x):\n    return Tanh().forward(x)\n"
  },
  {
    "path": "prml/nn/normalization/__init__.py",
    "content": "from prml.nn.normalization.batch_normalization import BatchNormalization\n\n\n__all__ = ['BatchNormalization']\n"
  },
  {
    "path": "prml/nn/normalization/batch_normalization.py",
    "content": "import numpy as np\n\nfrom prml.nn.array.ones import ones\nfrom prml.nn.array.zeros import zeros\nfrom prml.nn.config import config\nfrom prml.nn.function import Function\nfrom prml.nn.network import Network\n\n\nclass BatchNormalizationFunction(Function):\n\n    def _forward(self, x):\n        self.mean = x.mean(axis=0)\n        self.xc = x - self.mean\n        self.var = np.mean(self.xc ** 2, axis=0)\n        self.std = np.sqrt(self.var + 1e-7)\n        return self.xc / self.std\n\n    def _backward(self, delta, x):\n        # dstd = -np.mean((delta * self.xc) / (self.std ** 2), axis=0)\n        dxc = delta / self.std - self.xc * np.mean((delta * self.xc) / (self.std ** 3), axis=0)\n        return dxc - np.mean(dxc, axis=0)\n\n        # dstd = -np.mean((delta * self.xc) / (self.std ** 2), axis=0)\n        # dxc = delta / self.std + self.xc * dstd / self.std\n        # return dxc - np.mean(dxc, axis=0)\n\n        # dxn = delta\n        # dxc = dxn / self.std\n        # dstd = -np.sum((dxn * self.xc) / (self.std ** 2), axis=0)\n        # dvar = 0.5 * dstd / self.std\n        # dxc += 2.0 * self.xc * dvar / delta.shape[0]\n        # dmu = np.sum(dxc, axis=0)\n        # dx = dxc - dmu / delta.shape[0]\n        # return dx\n\n\nclass BatchNormalization(Network):\n\n    def __init__(self, ndim, scale=None, bias=None, momentum=0.9):\n        super().__init__()\n        self.momentum = momentum\n        with self.set_parameter():\n            self.mean = zeros(ndim)\n            self.var = ones(ndim)\n\n    def __call__(self, x):\n        shape = x.shape\n        x = x.reshape(-1, x.shape[-1])\n        if config.is_updating_bn:\n            func = BatchNormalizationFunction()\n            out = func.forward(x)\n            self.mean.value = self.momentum * self.mean.value + (1 - self.momentum) * func.mean\n            self.var.value = self.momentum * self.var.value + (1 - self.momentum) * func.var\n            del func.mean\n            del func.var\n        else:\n            xc = x - self.mean\n            out = xc / np.sqrt(self.var.value + 1e-7)\n        return out.reshape(*shape)\n"
  },
  {
    "path": "prml/nn/optimizer/__init__.py",
    "content": "# flake8: noqa\n\nfrom prml.nn.optimizer.ada_delta import AdaDelta\nfrom prml.nn.optimizer.ada_grad import AdaGrad\nfrom prml.nn.optimizer.adam import Adam\nfrom prml.nn.optimizer.gradient import Gradient\nfrom prml.nn.optimizer.momentum import Momentum\nfrom prml.nn.optimizer.rmsprop import RMSProp\n"
  },
  {
    "path": "prml/nn/optimizer/ada_delta.py",
    "content": "import numpy as np\n\nfrom prml.nn.config import config\nfrom prml.nn.optimizer.optimizer import Optimizer\n\n\nclass AdaDelta(Optimizer):\n    \"\"\"\n    AdaDelta optimizer\n    \"\"\"\n\n    def __init__(self, parameter: dict, rho=0.95, epsilon=1e-8):\n        super().__init__(parameter, None)\n        self.rho = rho\n        self.epsilon = epsilon\n        self.mean_squared_deriv = {}\n        self.mean_squared_update = {}\n        for key, param in self.parameter.items():\n            self.mean_squared_deriv[key] = np.zeros(param.shape, dtype=config.dtype)\n            self.mean_squared_update[key] = np.zeros(param.shape, dtype=config.dtype)\n\n    def update(self):\n        for key in self.parameter:\n            param = self.parameter[key]\n            if param.grad is None:\n                continue\n            msd = self.mean_squared_deriv[key]\n            msu = self.mean_squared_update[key]\n            grad = param.grad\n            msd *= self.rho\n            msd += (1 - self.rho) * grad ** 2\n            delta = np.sqrt((msu + self.epsilon) / (msd + self.epsilon)) * grad\n            msu *= self.rho\n            msu *= (1 - self.rho) * delta ** 2\n            param.value += delta\n"
  },
  {
    "path": "prml/nn/optimizer/ada_grad.py",
    "content": "import numpy as np\n\nfrom prml.nn.config import config\nfrom prml.nn.optimizer.optimizer import Optimizer\n\n\nclass AdaGrad(Optimizer):\n    \"\"\"\n    AdaGrad optimizer\n    initialization\n    G = 0\n    update rule\n    G += gradient ** 2\n    param -= learning_rate * gradient / sqrt(G + eps)\n    \"\"\"\n\n    def __init__(self, parameter: dict, learning_rate=0.001, epsilon=1e-8):\n        super().__init__(parameter, learning_rate)\n        self.epsilon = epsilon\n        self.G = []\n        for key, param in self.parameter.items():\n            self.G[key] = np.zeros(param.shape, dtype=config.dtype)\n\n    def update(self):\n        \"\"\"\n        update parameters\n        \"\"\"\n        for key in self.parameter:\n            param, G = self.parameter[key], self.G[key]\n            if param.grad is None:\n                continue\n            grad = param.grad\n            G += grad ** 2\n            param.value += self.learning_rate * grad / (np.sqrt(G) + self.epsilon)\n"
  },
  {
    "path": "prml/nn/optimizer/adam.py",
    "content": "import numpy as np\n\nfrom prml.nn.config import config\nfrom prml.nn.optimizer.optimizer import Optimizer\n\n\nclass Adam(Optimizer):\n    \"\"\"\n    Adam optimizer\n    initialization\n    m1 = 0 (Initial 1st moment of gradient)\n    m2 = 0 (Initial 2nd moment of gradient)\n    n_iter = 0\n    update rule\n    n_iter += 1\n    learning_rate *= sqrt(1 - beta2^n) / (1 - beta1^n)\n    m1 = beta1 * m1 + (1 - beta1) * gradient\n    m2 = beta2 * m2 + (1 - beta2) * gradient^2\n    param += learning_rate * m1 / (sqrt(m2) + epsilon)\n    \"\"\"\n\n    def __init__(self, parameter, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8):\n        \"\"\"\n        construct Adam optimizer\n        Parameters\n        ----------\n        parameters : dict\n            dict of parameters to be optimized\n        learning_rate : float\n        beta1 : float\n            exponential decay rate for the 1st moment\n        beta2 : float\n            exponential decay rate for the 2nd moment\n        epsilon : float\n            small constant to be added to denominator for numerical stability\n        Attributes\n        ----------\n        n_iter : int\n            number of iterations performed\n        moment1 : dict\n            1st moment of each learnable parameter\n        moment2 : dict\n            2nd moment of each learnable parameter\n        \"\"\"\n        super().__init__(parameter, learning_rate)\n        self.beta1 = beta1\n        self.beta2 = beta2\n        self.epsilon = epsilon\n        self.moment1 = {}\n        self.moment2 = {}\n        for key, param in self.parameter.items():\n            self.moment1[key] = np.zeros(param.shape, dtype=config.dtype)\n            self.moment2[key] = np.zeros(param.shape, dtype=config.dtype)\n\n    def update(self):\n        \"\"\"\n        update parameter of the neural network\n        \"\"\"\n        lr = (\n            self.learning_rate\n            * (1 - self.beta2 ** self.iter_count) ** 0.5\n            / (1 - self.beta1 ** self.iter_count))\n        for kp in self.parameter:\n            p, m1, m2 = self.parameter[kp], self.moment1[kp], self.moment2[kp]\n            if p.grad is None:\n                continue\n            m1 += (1 - self.beta1) * (p.grad - m1)\n            m2 += (1 - self.beta2) * (p.grad ** 2 - m2)\n            p.value += lr * m1 / (np.sqrt(m2) + self.epsilon)\n"
  },
  {
    "path": "prml/nn/optimizer/gradient.py",
    "content": "from prml.nn.optimizer.optimizer import Optimizer\n\n\nclass Gradient(Optimizer):\n\n    def __init__(self, parameter, learning_rate=1e-3):\n        super().__init__(parameter, learning_rate)\n\n    def update(self):\n        for param in self.parameter.values():\n            param.value += self.learning_rate * param.grad\n"
  },
  {
    "path": "prml/nn/optimizer/momentum.py",
    "content": "import numpy as np\n\nfrom prml.nn.optimizer.optimizer import Optimizer\n\n\nclass Momentum(Optimizer):\n\n    def __init__(self, parameter: dict, learning_rate=1e-3, momentum=0.9):\n        super().__init__(parameter, learning_rate)\n        self.momentum = momentum\n        self.inertia = {key: np.zeros(value.shape) for key, value in parameter.items()}\n\n    def update(self):\n        for key in self.parameter:\n            param, inertia = self.parameter[key], self.inertia[key]\n            if param.grad is None:\n                continue\n            inertia *= self.momentum\n            inertia += self.learning_rate * (1 - self.momentum) * param.grad\n            param.value += inertia\n"
  },
  {
    "path": "prml/nn/optimizer/optimizer.py",
    "content": "class Optimizer(object):\n\n    def __init__(self, parameter: dict, learning_rate: float):\n        if isinstance(parameter, list):\n            self.parameter = {f\"parameter{i}\": param for i, param in enumerate(parameter)}\n        elif isinstance(parameter, dict):\n            self.parameter = parameter\n        self.learning_rate = learning_rate\n        self.iter_count = 0\n\n    def increment_iter_count(self):\n        self.iter_count += 1\n\n    def minimize(self, loss):\n        if self.learning_rate > 0:\n            self.learning_rate *= -1\n        self.optimize(loss)\n\n    def maximize(self, score):\n        if self.learning_rate < 0:\n            self.learning_rate *= -1\n        self.optimize(score)\n\n    def optimize(self, array):\n        self.increment_iter_count()\n        array.backward()\n        self.update()\n\n    def update(self):\n        raise NotImplementedError\n"
  },
  {
    "path": "prml/nn/optimizer/rmsprop.py",
    "content": "import numpy as np\n\nfrom prml.nn.optimizer.optimizer import Optimizer\n\n\nclass RMSProp(Optimizer):\n\n    def __init__(self, parameter: dict, learning_rate=1e-3, rho=0.9, epsilon=1e-8):\n        super().__init__(parameter, learning_rate)\n        self.rho = rho\n        self.epsilon = epsilon\n        self.mean_squared_grad = {key: np.zeros(value.shape) for key, value in parameter.items()}\n\n    def update(self):\n        for key in self.parameter:\n            param, msg = self.parameter[key], self.mean_squared_grad[key]\n            if param.grad is None:\n                continue\n            msg *= self.rho\n            msg += (1 - self.rho) * (param.grad ** 2)\n            param.value += self.learning_rate * param.grad / (np.sqrt(msg) + self.epsilon)\n"
  },
  {
    "path": "prml/nn/queue.py",
    "content": "class BackPropQueue(object):\n\n    def __init__(self):\n        self.queue = []\n\n    def __len__(self):\n        return len(self.queue)\n\n    def enqueue(self, array):\n        array.is_in_queue = True\n        self.queue.append(array)\n\n    def dequeue(self, depth_to_dequeue):\n        queue = self.queue[0]\n        for candidate in self.queue:\n            if candidate.depth == depth_to_dequeue:\n                queue = candidate\n                break\n            elif candidate.depth > queue.depth:\n                queue = candidate\n        self.queue.remove(queue)\n        queue.is_in_queue = False\n        return queue\n\n\nbackprop_queue = BackPropQueue()\n"
  },
  {
    "path": "prml/nn/random/__init__.py",
    "content": "from prml.nn.random.dropout import dropout\nfrom prml.nn.random.normal import normal, truncnormal\nfrom prml.nn.random.uniform import uniform\n\n_functions = [dropout, normal, truncnormal, uniform]\n\n\n__all__ = [_f.__name__ for _f in _functions]\n\n\ndel _functions\n"
  },
  {
    "path": "prml/nn/random/dropout.py",
    "content": "import numpy as np\n\nfrom prml.nn.function import Function\n\n\nclass DropoutFunction(Function):\n\n    def _forward(self, x, drop_ratio=.5):\n        self.coef = 1 / (1 - drop_ratio)\n        self.mask = (np.random.rand(*x.shape) > drop_ratio) * self.coef\n        return x * self.mask\n\n    def _backward(self, delta, x, drop_ratio):\n        return delta * self.mask\n\n\ndef dropout(x, drop_ratio=.5):\n    return DropoutFunction().forward(x, drop_ratio=drop_ratio)\n"
  },
  {
    "path": "prml/nn/random/normal.py",
    "content": "import numpy as np\nfrom scipy.stats import truncnorm\n\nfrom prml.nn.array.array import asarray\n\n\ndef normal(mean, std, size):\n    \"\"\"Return a random sample from normal distribution.\"\"\"\n    return asarray(np.random.normal(mean, std, size))\n\n\ndef truncnormal(min_, max_, scale, size):\n    \"\"\"Return a random sample from trunc-normal distribution.\"\"\"\n    return asarray(truncnorm(a=min_, b=max_, scale=scale).rvs(size))\n"
  },
  {
    "path": "prml/nn/random/random.py",
    "content": "from prml.nn.function import Function\n\n\nclass RandomVariable(Function):\n\n    def __init__(self, data=None, p=None):\n        if data is not None and p is not None:\n            raise ValueError\n        if data is not None:\n            data = self._convert2array(data)\n        self.data = data\n        self.observed = (data is not None)\n        self.p = p\n\n    def draw(self):\n        if self.observed:\n            raise ValueError\n        self.data = self.forward()\n        return self.data\n\n    def pdf(self, x=None):\n        if x is not None:\n            return self._pdf(x)\n        if self.data is not None:\n            return self._pdf(self.data)\n        raise ValueError\n\n    def _pdf(self, *args):\n        raise NotImplementedError\n\n    def log_pdf(self, x=None):\n        if x is not None:\n            return self._log_pdf(x)\n        if self.data is not None:\n            return self._log_pdf(self.data)\n        raise ValueError\n\n    def _log_pdf(self, *args):\n        raise ValueError\n\n    def KLqp(self):\n        if self.p is None or self.data is None:\n            raise ValueError\n        return self._log_pdf(self.data) - self.p._log_pdf(self.data)\n"
  },
  {
    "path": "prml/nn/random/uniform.py",
    "content": "import numpy as np\n\nfrom prml.nn.array.array import asarray\n\n\ndef uniform(min_, max_, size):\n    return asarray(np.random.uniform(min_, max_, size))\n"
  },
  {
    "path": "prml/preprocess/__init__.py",
    "content": "from prml.preprocess.gaussian import GaussianFeature\nfrom prml.preprocess.label_transformer import LabelTransformer\nfrom prml.preprocess.polynomial import PolynomialFeature\nfrom prml.preprocess.sigmoidal import SigmoidalFeature\n\n\n__all__ = [\n    \"GaussianFeature\",\n    \"LabelTransformer\",\n    \"PolynomialFeature\",\n    \"SigmoidalFeature\"\n]\n"
  },
  {
    "path": "prml/preprocess/gaussian.py",
    "content": "import numpy as np\n\n\nclass GaussianFeature(object):\n    \"\"\"\n    Gaussian feature\n\n    gaussian function = exp(-0.5 * (x - m) / v)\n    \"\"\"\n\n    def __init__(self, mean, var):\n        \"\"\"\n        construct gaussian features\n\n        Parameters\n        ----------\n        mean : (n_features, ndim) or (n_features,) ndarray\n            places to locate gaussian function at\n        var : float\n            variance of the gaussian function\n        \"\"\"\n        if mean.ndim == 1:\n            mean = mean[:, None]\n        else:\n            assert mean.ndim == 2\n        assert isinstance(var, float) or isinstance(var, int)\n        self.mean = mean\n        self.var = var\n\n    def _gauss(self, x, mean):\n        return np.exp(-0.5 * np.sum(np.square(x - mean), axis=-1) / self.var)\n\n    def transform(self, x):\n        \"\"\"\n        transform input array with gaussian features\n\n        Parameters\n        ----------\n        x : (sample_size, ndim) or (sample_size,)\n            input array\n\n        Returns\n        -------\n        output : (sample_size, n_features)\n            gaussian features\n        \"\"\"\n        if x.ndim == 1:\n            x = x[:, None]\n        else:\n            assert x.ndim == 2\n        assert np.size(x, 1) == np.size(self.mean, 1)\n        basis = [np.ones(len(x))]\n        for m in self.mean:\n            basis.append(self._gauss(x, m))\n        return np.asarray(basis).transpose()\n"
  },
  {
    "path": "prml/preprocess/label_transformer.py",
    "content": "import numpy as np\n\n\nclass LabelTransformer(object):\n    \"\"\"\n    Label encoder decoder\n\n    Attributes\n    ----------\n    n_classes : int\n        number of classes, K\n    \"\"\"\n\n    def __init__(self, n_classes:int=None):\n        self.n_classes = n_classes\n\n    @property\n    def n_classes(self):\n        return self.__n_classes\n\n    @n_classes.setter\n    def n_classes(self, K):\n        self.__n_classes = K\n        self.__encoder = None if K is None else np.eye(K)\n\n    @property\n    def encoder(self):\n        return self.__encoder\n\n    def encode(self, class_indices:np.ndarray):\n        \"\"\"\n        encode class index into one-of-k code\n\n        Parameters\n        ----------\n        class_indices : (N,) np.ndarray\n            non-negative class index\n            elements must be integer in [0, n_classes)\n\n        Returns\n        -------\n        (N, K) np.ndarray\n            one-of-k encoding of input\n        \"\"\"\n        if self.n_classes is None:\n            self.n_classes = np.max(class_indices) + 1\n\n        return self.encoder[class_indices]\n\n    def decode(self, onehot:np.ndarray):\n        \"\"\"\n        decode one-of-k code into class index\n\n        Parameters\n        ----------\n        onehot : (N, K) np.ndarray\n            one-of-k code\n\n        Returns\n        -------\n        (N,) np.ndarray\n            class index\n        \"\"\"\n\n        return np.argmax(onehot, axis=1)\n"
  },
  {
    "path": "prml/preprocess/polynomial.py",
    "content": "import itertools\nimport functools\nimport numpy as np\n\n\nclass PolynomialFeature(object):\n    \"\"\"\n    polynomial features\n\n    transforms input array with polynomial features\n\n    Example\n    =======\n    x =\n    [[a, b],\n    [c, d]]\n\n    y = PolynomialFeatures(degree=2).transform(x)\n    y =\n    [[1, a, b, a^2, a * b, b^2],\n    [1, c, d, c^2, c * d, d^2]]\n    \"\"\"\n\n    def __init__(self, degree=2):\n        \"\"\"\n        construct polynomial features\n\n        Parameters\n        ----------\n        degree : int\n            degree of polynomial\n        \"\"\"\n        assert isinstance(degree, int)\n        self.degree = degree\n\n    def transform(self, x):\n        \"\"\"\n        transforms input array with polynomial features\n\n        Parameters\n        ----------\n        x : (sample_size, n) ndarray\n            input array\n\n        Returns\n        -------\n        output : (sample_size, 1 + nC1 + ... + nCd) ndarray\n            polynomial features\n        \"\"\"\n        if x.ndim == 1:\n            x = x[:, None]\n        x_t = x.transpose()\n        features = [np.ones(len(x))]\n        for degree in range(1, self.degree + 1):\n            for items in itertools.combinations_with_replacement(x_t, degree):\n                features.append(functools.reduce(lambda x, y: x * y, items))\n        return np.asarray(features).transpose()\n"
  },
  {
    "path": "prml/preprocess/sigmoidal.py",
    "content": "import numpy as np\n\n\nclass SigmoidalFeature(object):\n    \"\"\"Sigmoidal features.\n\n    1 / (1 + exp((m - x) @ c)\n    \"\"\"\n\n    def __init__(self, mean, coef=1):\n        \"\"\"Initialize sigmoidal features.\n\n        Parameters\n        ----------\n        mean : (n_features, ndim) or (n_features,) ndarray\n            center of sigmoid function\n        coef : (ndim,) ndarray or int or float\n            coefficient to be multplied with the distance\n        \"\"\"\n        if mean.ndim == 1:\n            mean = mean[:, None]\n        else:\n            assert mean.ndim == 2\n        if isinstance(coef, int) or isinstance(coef, float):\n            if np.size(mean, 1) == 1:\n                coef = np.array([coef])\n            else:\n                raise ValueError(\"mismatch of dimension\")\n        else:\n            assert coef.ndim == 1\n            assert np.size(mean, 1) == len(coef)\n        self.mean = mean\n        self.coef = coef\n\n    def _sigmoid(self, x, mean):\n        return np.tanh((x - mean) @ self.coef * 0.5) * 0.5 + 0.5\n\n    def transform(self, x):\n        \"\"\"Transform input array with sigmoidal features.\n\n        Parameters\n        ----------\n        x : (sample_size, ndim) or (sample_size,) ndarray\n            input array\n\n        Returns\n        -------\n        output : (sample_size, n_features) ndarray\n            sigmoidal features\n        \"\"\"\n        if x.ndim == 1:\n            x = x[:, None]\n        else:\n            assert x.ndim == 2\n        assert np.size(x, 1) == np.size(self.mean, 1)\n        basis = [np.ones(len(x))]\n        for m in self.mean:\n            basis.append(self._sigmoid(x, m))\n        return np.asarray(basis).transpose()\n"
  },
  {
    "path": "prml/rv/__init__.py",
    "content": "from prml.rv.bernoulli import Bernoulli\nfrom prml.rv.bernoulli_mixture import BernoulliMixture\nfrom prml.rv.beta import Beta\nfrom prml.rv.categorical import Categorical\nfrom prml.rv.dirichlet import Dirichlet\nfrom prml.rv.gamma import Gamma\nfrom prml.rv.gaussian import Gaussian\nfrom prml.rv.multivariate_gaussian import MultivariateGaussian\nfrom prml.rv.multivariate_gaussian_mixture import MultivariateGaussianMixture\nfrom prml.rv.students_t import StudentsT\nfrom prml.rv.uniform import Uniform\nfrom prml.rv.variational_gaussian_mixture import VariationalGaussianMixture\n\n\n__all__ = [\n    \"Bernoulli\",\n    \"BernoulliMixture\",\n    \"Beta\",\n    \"Categorical\",\n    \"Dirichlet\",\n    \"Gamma\",\n    \"Gaussian\",\n    \"MultivariateGaussian\",\n    \"MultivariateGaussianMixture\",\n    \"StudentsT\",\n    \"Uniform\",\n    \"VariationalGaussianMixture\"\n]\n"
  },
  {
    "path": "prml/rv/bernoulli.py",
    "content": "import numpy as np\n\nfrom prml.rv.rv import RandomVariable\nfrom prml.rv.beta import Beta\n\n\nclass Bernoulli(RandomVariable):\n    \"\"\"\n    Bernoulli distribution\n    p(x|mu) = mu^x (1 - mu)^(1 - x)\n    \"\"\"\n\n    def __init__(self, mu=None):\n        \"\"\"\n        construct Bernoulli distribution\n\n        Parameters\n        ----------\n        mu : np.ndarray or Beta\n            probability of value 1 for each element\n        \"\"\"\n        super().__init__()\n        self.mu = mu\n\n    @property\n    def mu(self):\n        return self.parameter[\"mu\"]\n\n    @mu.setter\n    def mu(self, mu):\n        if isinstance(mu, (int, float, np.number)):\n            if mu > 1 or mu < 0:\n                raise ValueError(f\"mu must be in [0, 1], not {mu}\")\n            self.parameter[\"mu\"] = np.asarray(mu)\n        elif isinstance(mu, np.ndarray):\n            if (mu > 1).any() or (mu < 0).any():\n                raise ValueError(\"mu must be in [0, 1]\")\n            self.parameter[\"mu\"] = mu\n        elif isinstance(mu, Beta):\n            self.parameter[\"mu\"] = mu\n        else:\n            if mu is not None:\n                raise TypeError(f\"{type(mu)} is not supported for mu\")\n            self.parameter[\"mu\"] = None\n\n    @property\n    def ndim(self):\n        if hasattr(self.mu, \"ndim\"):\n            return self.mu.ndim\n        else:\n            return None\n\n    @property\n    def size(self):\n        if hasattr(self.mu, \"size\"):\n            return self.mu.size\n        else:\n            return None\n\n    @property\n    def shape(self):\n        if hasattr(self.mu, \"shape\"):\n            return self.mu.shape\n        else:\n            return None\n\n    def _fit(self, x):\n        if isinstance(self.mu, Beta):\n            self._bayes(x)\n        elif isinstance(self.mu, RandomVariable):\n            raise NotImplementedError\n        else:\n            self._ml(x)\n\n    def _ml(self, x):\n        n_zeros = np.count_nonzero((x == 0).astype(int))\n        n_ones = np.count_nonzero((x == 1).astype(int))\n        assert x.size == n_zeros + n_ones, (\n            \"{x.size} is not equal to {n_zeros} plus {n_ones}\"\n        )\n        self.mu = np.mean(x, axis=0)\n\n    def _map(self, x):\n        assert isinstance(self.mu, Beta)\n        assert x.shape[1:] == self.mu.shape\n        n_ones = (x == 1).sum(axis=0)\n        n_zeros = (x == 0).sum(axis=0)\n        assert x.size == n_zeros.sum() + n_ones.sum(), (\n            f\"{x.size} is not equal to {n_zeros} plus {n_ones}\"\n        )\n        n_ones = n_ones + self.mu.n_ones\n        n_zeros = n_zeros + self.mu.n_zeros\n        self.prob = (n_ones - 1) / (n_ones + n_zeros - 2)\n\n    def _bayes(self, x):\n        assert isinstance(self.mu, Beta)\n        assert x.shape[1:] == self.mu.shape\n        n_ones = (x == 1).sum(axis=0)\n        n_zeros = (x == 0).sum(axis=0)\n        assert x.size == n_zeros.sum() + n_ones.sum(), (\n            \"input x must only has 0 or 1\"\n        )\n        self.mu.n_zeros += n_zeros\n        self.mu.n_ones += n_ones\n\n    def _pdf(self, x):\n        assert isinstance(self.mu, np.ndarray)\n        return np.prod(self.mu ** x * (1 - self.mu) ** (1 - x))\n\n    def _draw(self, sample_size=1):\n        if isinstance(self.mu, np.ndarray):\n            return (\n                self.mu > np.random.uniform(size=(sample_size,) + self.shape)\n            ).astype(int)\n        elif isinstance(self.mu, Beta):\n            return (\n                self.mu.n_ones / (self.mu.n_ones + self.mu.n_zeros)\n                > np.random.uniform(size=(sample_size,) + self.shape)\n            ).astype(int)\n        elif isinstance(self.mu, RandomVariable):\n            return (\n                self.mu.draw(sample_size)\n                > np.random.uniform(size=(sample_size,) + self.shape)\n            )\n"
  },
  {
    "path": "prml/rv/bernoulli_mixture.py",
    "content": "import numpy as np\nfrom scipy.special import logsumexp\nfrom prml.rv.rv import RandomVariable\n\n\nclass BernoulliMixture(RandomVariable):\n    \"\"\"\n    p(x|pi,mu)\n    = sum_k pi_k mu_k^x (1 - mu_k)^(1 - x)\n    \"\"\"\n\n    def __init__(self, n_components=3, mu=None, coef=None):\n        \"\"\"\n        construct mixture of Bernoulli\n\n        Parameters\n        ----------\n        n_components : int\n            number of bernoulli component\n        mu : (n_components, ndim) np.ndarray\n            probability of value 1 for each component\n        coef : (n_components,) np.ndarray\n            mixing coefficients\n        \"\"\"\n        super().__init__()\n        assert isinstance(n_components, int)\n        self.n_components = n_components\n        self.mu = mu\n        self.coef = coef\n\n    @property\n    def mu(self):\n        return self.parameter[\"mu\"]\n\n    @mu.setter\n    def mu(self, mu):\n        if isinstance(mu, np.ndarray):\n            assert mu.ndim == 2\n            assert np.size(mu, 0) == self.n_components\n            assert (mu >= 0.).all() and (mu <= 1.).all()\n            self.ndim = np.size(mu, 1)\n            self.parameter[\"mu\"] = mu\n        else:\n            assert mu is None\n            self.parameter[\"mu\"] = None\n\n    @property\n    def coef(self):\n        return self.parameter[\"coef\"]\n\n    @coef.setter\n    def coef(self, coef):\n        if isinstance(coef, np.ndarray):\n            assert coef.ndim == 1\n            assert np.allclose(coef.sum(), 1)\n            self.parameter[\"coef\"] = coef\n        else:\n            assert coef is None\n            self.parameter[\"coef\"] = np.ones(self.n_components) / self.n_components\n\n    def _log_bernoulli(self, x):\n        np.clip(self.mu, 1e-10, 1 - 1e-10, out=self.mu)\n        return (\n            x[:, None, :] * np.log(self.mu)\n            + (1 - x[:, None, :]) * np.log(1 - self.mu)\n        ).sum(axis=-1)\n\n    def _fit(self, x):\n        self.mu = np.random.uniform(0.25, 0.75, size=(self.n_components, np.size(x, 1)))\n        params = np.hstack((self.mu.ravel(), self.coef.ravel()))\n        while True:\n            resp = self._expectation(x)\n            self._maximization(x, resp)\n            new_params = np.hstack((self.mu.ravel(), self.coef.ravel()))\n            if np.allclose(params, new_params):\n                break\n            else:\n                params = new_params\n\n    def _expectation(self, x):\n        log_resps = np.log(self.coef) + self._log_bernoulli(x)\n        log_resps -= logsumexp(log_resps, axis=-1)[:, None]\n        resps = np.exp(log_resps)\n        return resps\n\n    def _maximization(self, x, resp):\n        Nk = np.sum(resp, axis=0)\n        self.coef = Nk / len(x)\n        self.mu = (x.T @ resp / Nk).T\n\n    def classify(self, x):\n        \"\"\"\n        classify input\n        max_z p(z|x, theta)\n\n        Parameters\n        ----------\n        x : (sample_size, ndim) ndarray\n            input\n\n        Returns\n        -------\n        output : (sample_size,) ndarray\n            corresponding cluster index\n        \"\"\"\n        return np.argmax(self.classify_proba(x), axis=1)\n\n    def classfiy_proba(self, x):\n        \"\"\"\n        posterior probability of cluster\n        p(z|x,theta)\n\n        Parameters\n        ----------\n        x : (sample_size, ndim) ndarray\n            input\n\n        Returns\n        -------\n        output : (sample_size, n_components) ndarray\n            posterior probability of cluster\n        \"\"\"\n        return self._expectation(x)\n"
  },
  {
    "path": "prml/rv/beta.py",
    "content": "import numpy as np\nfrom scipy.special import gamma\nfrom prml.rv.rv import RandomVariable\n\n\nnp.seterr(all=\"ignore\")\n\n\nclass Beta(RandomVariable):\n    \"\"\"\n    Beta distribution\n    p(mu|n_ones, n_zeros)\n    = gamma(n_ones + n_zeros)\n      * mu^(n_ones - 1) * (1 - mu)^(n_zeros - 1)\n      / gamma(n_ones) / gamma(n_zeros)\n    \"\"\"\n\n    def __init__(self, n_zeros, n_ones):\n        \"\"\"\n        construct beta distribution\n\n        Parameters\n        ----------\n        n_zeros : int, float, or np.ndarray\n            pseudo count of zeros\n        n_ones : int, float, or np.ndarray\n            pseudo count of ones\n        \"\"\"\n        super().__init__()\n        if not isinstance(n_ones, (int, float, np.number, np.ndarray)):\n            raise ValueError(\n                \"{} is not supported for n_ones\"\n                .format(type(n_ones))\n            )\n        if not isinstance(n_zeros, (int, float, np.number, np.ndarray)):\n            raise ValueError(\n                \"{} is not supported for n_zeros\"\n                .format(type(n_zeros))\n            )\n        n_ones = np.asarray(n_ones)\n        n_zeros = np.asarray(n_zeros)\n        if n_ones.shape != n_zeros.shape:\n            raise ValueError(\n                \"the sizes of the arrays don't match: {}, {}\"\n                .format(n_ones.shape, n_zeros.shape)\n            )\n        self.n_ones = n_ones\n        self.n_zeros = n_zeros\n\n    @property\n    def ndim(self):\n        return self.n_ones.ndim\n\n    @property\n    def size(self):\n        return self.n_ones.size\n\n    @property\n    def shape(self):\n        return self.n_ones.shape\n\n    def _pdf(self, mu):\n        return (\n            gamma(self.n_ones + self.n_zeros)\n            * np.power(mu, self.n_ones - 1)\n            * np.power(1 - mu, self.n_zeros - 1)\n            / gamma(self.n_ones)\n            / gamma(self.n_zeros)\n        )\n\n    def _draw(self, sample_size=1):\n        return np.random.beta(\n            self.n_ones, self.n_zeros, size=(sample_size,) + self.shape\n        )\n"
  },
  {
    "path": "prml/rv/categorical.py",
    "content": "import numpy as np\n\nfrom prml.rv.dirichlet import Dirichlet\nfrom prml.rv.rv import RandomVariable\n\n\nclass Categorical(RandomVariable):\n    \"\"\"\n    Categorical distribution\n    p(x|mu) = prod_k mu_k^x_k\n    \"\"\"\n\n    def __init__(self, mu=None):\n        \"\"\"\n        construct categorical distribution\n\n        Parameters\n        ----------\n        mu : (n_classes,) np.ndarray or Dirichlet\n            probability of each class\n        \"\"\"\n        super().__init__()\n        self.mu = mu\n\n    @property\n    def mu(self):\n        return self.parameter[\"mu\"]\n\n    @mu.setter\n    def mu(self, mu):\n        if isinstance(mu, np.ndarray):\n            if mu.ndim != 1:\n                raise ValueError(\"dimensionality of mu must be 1\")\n            if (mu < 0).any():\n                raise ValueError(\"mu must be non-negative\")\n            if not np.allclose(mu.sum(), 1):\n                raise ValueError(\"sum of mu must be 1\")\n            self.n_classes = mu.size\n            self.parameter[\"mu\"] = mu\n        elif isinstance(mu, Dirichlet):\n            self.n_classes = mu.size\n            self.parameter[\"mu\"] = mu\n        else:\n            if mu is not None:\n                raise TypeError(f\"{type(mu)} is not supported for mu\")\n            self.parameter[\"mu\"] = None\n\n    @property\n    def ndim(self):\n        if hasattr(self.mu, \"ndim\"):\n            return self.mu.ndim\n        else:\n            return None\n\n    @property\n    def size(self):\n        if hasattr(self.mu, \"size\"):\n            return self.mu.size\n        else:\n            return None\n\n    @property\n    def shape(self):\n        if hasattr(self.mu, \"shape\"):\n            return self.mu.shape\n        else:\n            return None\n\n    def _check_input(self, x):\n        assert x.ndim == 2\n        assert (x >= 0).all()\n        assert (x.sum(axis=-1) == 1).all()\n\n    def _fit(self, x):\n        if isinstance(self.mu, Dirichlet):\n            self._bayes(x)\n        elif isinstance(self.mu, RandomVariable):\n            raise NotImplementedError\n        else:\n            self._ml(x)\n\n    def _ml(self, x):\n        self._check_input(x)\n        self.mu = np.mean(x, axis=0)\n\n    def _map(self, x):\n        self._check_input(x)\n        assert isinstance(self.mu, Dirichlet)\n        alpha = self.mu.alpha + x.sum(axis=0)\n        self.mu = (alpha - 1) / (alpha - 1).sum()\n\n    def _bayes(self, x):\n        self._check_input(x)\n        assert isinstance(self.mu, Dirichlet)\n        self.mu.alpha += x.sum(axis=0)\n\n    def _pdf(self, x):\n        self._check_input(x)\n        assert isinstance(self.mu, np.ndarray)\n        return np.prod(self.mu ** x, axis=-1)\n\n    def _draw(self, sample_size=1):\n        assert isinstance(self.mu, np.ndarray)\n        return np.eye(self.n_classes)[\n            np.random.choice(self.n_classes, sample_size, p=self.mu)\n        ]\n"
  },
  {
    "path": "prml/rv/dirichlet.py",
    "content": "import numpy as np\nfrom scipy.special import gamma\nfrom prml.rv.rv import RandomVariable\n\n\nclass Dirichlet(RandomVariable):\n    \"\"\"\n    Dirichlet distribution\n    p(mu|alpha)\n    = gamma(sum(alpha))\n      * prod_k mu_k ^ (alpha_k - 1)\n      / gamma(alpha_1) / ... / gamma(alpha_K)\n    \"\"\"\n\n    def __init__(self, alpha):\n        \"\"\"\n        construct dirichlet distribution\n\n        Parameters\n        ----------\n        alpha : (size,) np.ndarray\n            pseudo count of each outcome, aka concentration parameter\n        \"\"\"\n        super().__init__()\n        self.alpha = alpha\n\n    @property\n    def alpha(self):\n        return self.parameter[\"alpha\"]\n\n    @alpha.setter\n    def alpha(self, alpha):\n        assert isinstance(alpha, np.ndarray)\n        assert alpha.ndim == 1\n        assert (alpha >= 0).all()\n        self.parameter[\"alpha\"] = alpha\n\n    @property\n    def ndim(self):\n        return self.alpha.ndim\n\n    @property\n    def size(self):\n        return self.alpha.size\n\n    @property\n    def shape(self):\n        return self.alpha.shape\n\n    def _pdf(self, mu):\n        return (\n            gamma(self.alpha.sum())\n            * np.prod(mu ** (self.alpha - 1), axis=-1)\n            / np.prod(gamma(self.alpha), axis=-1)\n        )\n\n    def _draw(self, sample_size=1):\n        return np.random.dirichlet(self.alpha, sample_size)\n"
  },
  {
    "path": "prml/rv/gamma.py",
    "content": "import numpy as np\nfrom scipy.special import gamma\nfrom prml.rv.rv import RandomVariable\n\n\nnp.seterr(all=\"ignore\")\n\n\nclass Gamma(RandomVariable):\n    \"\"\"\n    Gamma distribution\n    p(x|a, b)\n    = b^a x^(a-1) exp(-bx) / gamma(a)\n    \"\"\"\n\n    def __init__(self, a, b):\n        \"\"\"\n        construct Gamma distribution\n\n        Parameters\n        ----------\n        a : int, float, or np.ndarray\n            shape parameter\n        b : int, float, or np.ndarray\n            rate parameter\n        \"\"\"\n        super().__init__()\n        a = np.asarray(a)\n        b = np.asarray(b)\n        assert a.shape == b.shape\n        self.a = a\n        self.b = b\n\n    @property\n    def a(self):\n        return self.parameter[\"a\"]\n\n    @a.setter\n    def a(self, a):\n        if isinstance(a, (int, float, np.number)):\n            if a <= 0:\n                raise ValueError(\"a must be positive\")\n            self.parameter[\"a\"] = np.asarray(a)\n        elif isinstance(a, np.ndarray):\n            if (a <= 0).any():\n                raise ValueError(\"a must be positive\")\n            self.parameter[\"a\"] = a\n        else:\n            if a is not None:\n                raise TypeError(f\"{type(a)} is not supported for a\")\n            self.parameter[\"a\"] = None\n\n    @property\n    def b(self):\n        return self.parameter[\"b\"]\n\n    @b.setter\n    def b(self, b):\n        if isinstance(b, (int, float, np.number)):\n            if b <= 0:\n                raise ValueError(\"b must be positive\")\n            self.parameter[\"b\"] = np.asarray(b)\n        elif isinstance(b, np.ndarray):\n            if (b <= 0).any():\n                raise ValueError(\"b must be positive\")\n            self.parameter[\"b\"] = b\n        else:\n            if b is not None:\n                raise TypeError(f\"{type(b)} is not supported for b\")\n            self.parameter[\"b\"] = None\n\n    @property\n    def ndim(self):\n        return self.a.ndim\n\n    @property\n    def shape(self):\n        return self.a.shape\n\n    @property\n    def size(self):\n        return self.a.size\n\n    def _pdf(self, x):\n        return (\n            self.b ** self.a\n            * x ** (self.a - 1)\n            * np.exp(-self.b * x)\n            / gamma(self.a))\n\n    def _draw(self, sample_size=1):\n        return np.random.gamma(\n            shape=self.a,\n            scale=1 / self.b,\n            size=(sample_size,) + self.shape\n        )\n"
  },
  {
    "path": "prml/rv/gaussian.py",
    "content": "import numpy as np\n\nfrom prml.rv.rv import RandomVariable\nfrom prml.rv.gamma import Gamma\n\n\nclass Gaussian(RandomVariable):\n    \"\"\"\n    The Gaussian distribution\n    p(x|mu, var)\n    = exp{-0.5 * (x - mu)^2 / var} / sqrt(2pi * var)\n    \"\"\"\n\n    def __init__(self, mu=None, var=None, tau=None):\n        super().__init__()\n        self.mu = mu\n        if var is not None:\n            self.var = var\n        elif tau is not None:\n            self.tau = tau\n        else:\n            self.var = None\n            self.tau = None\n\n    @property\n    def mu(self):\n        return self.parameter[\"mu\"]\n\n    @mu.setter\n    def mu(self, mu):\n        if isinstance(mu, (int, float, np.number)):\n            self.parameter[\"mu\"] = np.array(mu)\n        elif isinstance(mu, np.ndarray):\n            self.parameter[\"mu\"] = mu\n        elif isinstance(mu, Gaussian):\n            self.parameter[\"mu\"] = mu\n        else:\n            if mu is not None:\n                raise TypeError(f\"{type(mu)} is not supported for mu\")\n            self.parameter[\"mu\"] = None\n\n    @property\n    def var(self):\n        return self.parameter[\"var\"]\n\n    @var.setter\n    def var(self, var):\n        if isinstance(var, (int, float, np.number)):\n            assert var > 0\n            var = np.array(var)\n            assert var.shape == self.shape\n            self.parameter[\"var\"] = var\n            self.parameter[\"tau\"] = 1 / var\n        elif isinstance(var, np.ndarray):\n            assert (var > 0).all()\n            assert var.shape == self.shape\n            self.parameter[\"var\"] = var\n            self.parameter[\"tau\"] = 1 / var\n        else:\n            assert var is None\n            self.parameter[\"var\"] = None\n            self.parameter[\"tau\"] = None\n\n    @property\n    def tau(self):\n        return self.parameter[\"tau\"]\n\n    @tau.setter\n    def tau(self, tau):\n        if isinstance(tau, (int, float, np.number)):\n            assert tau > 0\n            tau = np.array(tau)\n            assert tau.shape == self.shape\n            self.parameter[\"tau\"] = tau\n            self.parameter[\"var\"] = 1 / tau\n        elif isinstance(tau, np.ndarray):\n            assert (tau > 0).all()\n            assert tau.shape == self.shape\n            self.parameter[\"tau\"] = tau\n            self.parameter[\"var\"] = 1 / tau\n        elif isinstance(tau, Gamma):\n            assert tau.shape == self.shape\n            self.parameter[\"tau\"] = tau\n            self.parameter[\"var\"] = None\n        else:\n            assert tau is None\n            self.parameter[\"tau\"] = None\n            self.parameter[\"var\"] = None\n\n    @property\n    def ndim(self):\n        if hasattr(self.mu, \"ndim\"):\n            return self.mu.ndim\n        else:\n            return None\n\n    @property\n    def size(self):\n        if hasattr(self.mu, \"size\"):\n            return self.mu.size\n        else:\n            return None\n\n    @property\n    def shape(self):\n        if hasattr(self.mu, \"shape\"):\n            return self.mu.shape\n        else:\n            return None\n\n    def _fit(self, x):\n        mu_is_gaussian = isinstance(self.mu, Gaussian)\n        tau_is_gamma = isinstance(self.tau, Gamma)\n        if mu_is_gaussian and tau_is_gamma:\n            raise NotImplementedError\n        elif mu_is_gaussian:\n            self._bayes_mu(x)\n        elif tau_is_gamma:\n            self._bayes_tau(x)\n        else:\n            self._ml(x)\n\n    def _ml(self, x):\n        self.mu = np.mean(x, axis=0)\n        self.var = np.var(x, axis=0)\n\n    def _map(self, x):\n        assert isinstance(self.mu, Gaussian)\n        assert isinstance(self.var, np.ndarray)\n        N = len(x)\n        mu = np.mean(x, 0)\n        self.mu = (\n            (self.tau * self.mu.mu + N * self.mu.tau * mu)\n            / (N * self.mu.tau + self.tau)\n        )\n\n    def _bayes_mu(self, x):\n        N = len(x)\n        mu = np.mean(x, 0)\n        tau = self.mu.tau + N * self.tau\n        self.mu = Gaussian(\n            mu=(self.mu.mu * self.mu.tau + N * mu * self.tau) / tau,\n            tau=tau\n        )\n\n    def _bayes_tau(self, x):\n        N = len(x)\n        var = np.var(x, axis=0)\n        a = self.tau.a + 0.5 * N\n        b = self.tau.b + 0.5 * N * var\n        self.tau = Gamma(a, b)\n\n    def _bayes(self, x):\n        N = len(x)\n        mu_is_gaussian = isinstance(self.mu, Gaussian)\n        tau_is_gamma = isinstance(self.tau, Gamma)\n        if mu_is_gaussian and not tau_is_gamma:\n            mu = np.mean(x, 0)\n            tau = self.mu.tau + N * self.tau\n            self.mu = Gaussian(\n                mu=(self.mu.mu * self.mu.tau + N * mu * self.tau) / tau,\n                tau=tau\n            )\n        elif not mu_is_gaussian and tau_is_gamma:\n            var = np.var(x, axis=0)\n            a = self.tau.a + 0.5 * N\n            b = self.tau.b + 0.5 * N * var\n            self.tau = Gamma(a, b)\n        elif mu_is_gaussian and tau_is_gamma:\n            raise NotImplementedError\n        else:\n            raise NotImplementedError\n\n    def _pdf(self, x):\n        d = x - self.mu\n        return (\n            np.exp(-0.5 * self.tau * d ** 2) / np.sqrt(2 * np.pi * self.var)\n        )\n\n    def _draw(self, sample_size=1):\n        return np.random.normal(\n            loc=self.mu,\n            scale=np.sqrt(self.var),\n            size=(sample_size,) + self.shape\n        )\n"
  },
  {
    "path": "prml/rv/multivariate_gaussian.py",
    "content": "import numpy as np\n\nfrom prml.rv.rv import RandomVariable\n\n\nclass MultivariateGaussian(RandomVariable):\n    \"\"\"\n    The multivariate Gaussian distribution\n    p(x|mu, cov)\n    = exp{-0.5 * (x - mu)^T @ cov^-1 @ (x - mu)}\n      / (2pi)^(D/2) / |cov|^0.5\n    \"\"\"\n\n    def __init__(self, mu=None, cov=None, tau=None):\n        super().__init__()\n        self.mu = mu\n        if cov is not None:\n            self.cov = cov\n        elif tau is not None:\n            self.tau = tau\n        else:\n            self.cov = None\n            self.tau = None\n\n    @property\n    def mu(self):\n        return self.parameter[\"mu\"]\n\n    @mu.setter\n    def mu(self, mu):\n        if isinstance(mu, np.ndarray):\n            assert mu.ndim == 1\n            self.parameter[\"mu\"] = mu\n        else:\n            assert mu is None\n            self.parameter[\"mu\"] = None\n\n    @property\n    def cov(self):\n        return self.parameter[\"cov\"]\n\n    @cov.setter\n    def cov(self, cov):\n        if isinstance(cov, np.ndarray):\n            assert cov.ndim == 2\n            self.tau_ = np.linalg.inv(cov)\n            self.parameter[\"cov\"] = cov\n        else:\n            assert cov is None\n            self.tau_ = None\n            self.parameter[\"cov\"] = None\n\n    @property\n    def tau(self):\n        return self.tau_\n\n    @tau.setter\n    def tau(self, tau):\n        if isinstance(tau, np.ndarray):\n            assert tau.ndim == 2\n            self.parameter[\"cov\"] = np.linalg.inv(tau)\n            self.tau_ = tau\n        else:\n            assert tau is None\n            self.tau_ = None\n            self.parameter[\"cov\"] = None\n\n    @property\n    def ndim(self):\n        if hasattr(self.mu, \"ndim\"):\n            return self.mu.ndim\n        else:\n            return None\n\n    @property\n    def size(self):\n        if hasattr(self.mu, \"size\"):\n            return self.mu.size\n        else:\n            return None\n\n    @property\n    def shape(self):\n        if hasattr(self.mu, \"shape\"):\n            return self.mu.shape\n        else:\n            return None\n\n    def _fit(self, x):\n        self.mu = np.mean(x, axis=0)\n        self.cov = np.atleast_2d(np.cov(x.T, bias=True))\n\n    def _pdf(self, x):\n        d = x - self.mu\n        return (\n            np.exp(-0.5 * np.sum(d @ self.tau * d, axis=-1))\n            * np.sqrt(np.linalg.det(self.tau))\n            / np.power(2 * np.pi, 0.5 * self.size))\n\n    def _draw(self, sample_size=1):\n        return np.random.multivariate_normal(self.mu, self.cov, sample_size)\n"
  },
  {
    "path": "prml/rv/multivariate_gaussian_mixture.py",
    "content": "import numpy as np\n\nfrom prml.clustering import KMeans\nfrom prml.rv.rv import RandomVariable\n\n\nclass MultivariateGaussianMixture(RandomVariable):\n    \"\"\"\n    p(x|mu, L, pi(coef))\n    = sum_k pi_k N(x|mu_k, L_k^-1)\n    \"\"\"\n\n    def __init__(self,\n                 n_components,\n                 mu=None,\n                 cov=None,\n                 tau=None,\n                 coef=None):\n        \"\"\"\n        construct mixture of Gaussians\n\n        Parameters\n        ----------\n        n_components : int\n            number of gaussian component\n        mu : (n_components, ndim) np.ndarray\n            mean parameter of each gaussian component\n        cov : (n_components, ndim, ndim) np.ndarray\n            variance parameter of each gaussian component\n        tau : (n_components, ndim, ndim) np.ndarray\n            precision parameter of each gaussian component\n        coef : (n_components,) np.ndarray\n            mixing coefficients\n        \"\"\"\n        super().__init__()\n        assert isinstance(n_components, int)\n        self.n_components = n_components\n        self.mu = mu\n        if cov is not None and tau is not None:\n            raise ValueError(\"Cannot assign both cov and tau at a time\")\n        elif cov is not None:\n            self.cov = cov\n        elif tau is not None:\n            self.tau = tau\n        else:\n            self.cov = None\n            self.tau = None\n        self.coef = coef\n\n    @property\n    def mu(self):\n        return self.parameter[\"mu\"]\n\n    @mu.setter\n    def mu(self, mu):\n        if isinstance(mu, np.ndarray):\n            assert mu.ndim == 2\n            assert np.size(mu, 0) == self.n_components\n            self.ndim = np.size(mu, 1)\n            self.parameter[\"mu\"] = mu\n        elif mu is None:\n            self.parameter[\"mu\"] = None\n        else:\n            raise TypeError(\"mu must be either np.ndarray or None\")\n\n    @property\n    def cov(self):\n        return self.parameter[\"cov\"]\n\n    @cov.setter\n    def cov(self, cov):\n        if isinstance(cov, np.ndarray):\n            assert cov.shape == (self.n_components, self.ndim, self.ndim)\n            self._tau = np.linalg.inv(cov)\n            self.parameter[\"cov\"] = cov\n        elif cov is None:\n            self.parameter[\"cov\"] = None\n            self._tau = None\n        else:\n            raise TypeError(\"cov must be either np.ndarray or None\")\n\n    @property\n    def tau(self):\n        return self._tau\n\n    @tau.setter\n    def tau(self, tau):\n        if isinstance(tau, np.ndarray):\n            assert tau.shape == (self.n_components, self.ndim, self.ndim)\n            self.parameter[\"cov\"] = np.linalg.inv(tau)\n            self._tau = tau\n        elif tau is None:\n            self.parameter[\"cov\"] = None\n            self._tau = None\n        else:\n            raise TypeError(\"tau must be either np.ndarray or None\")\n\n    @property\n    def coef(self):\n        return self.parameter[\"coef\"]\n\n    @coef.setter\n    def coef(self, coef):\n        if isinstance(coef, np.ndarray):\n            assert coef.ndim == 1\n            if np.isnan(coef).any():\n                self.parameter[\"coef\"] = np.ones(self.n_components) / self.n_components\n            elif not np.allclose(coef.sum(), 1):\n                raise ValueError(f\"sum of coef must be equal to 1 {coef}\")\n            self.parameter[\"coef\"] = coef\n        elif coef is None:\n            self.parameter[\"coef\"] = None\n        else:\n            raise TypeError(\"coef must be either np.ndarray or None\")\n\n    @property\n    def shape(self):\n        if hasattr(self.mu, \"shape\"):\n            return self.mu.shape[1:]\n        else:\n            return None\n\n    def _gauss(self, x):\n        d = x[:, None, :] - self.mu\n        D_sq = np.sum(np.einsum('nki,kij->nkj', d, self.cov) * d, -1)\n        return (\n            np.exp(-0.5 * D_sq)\n            / np.sqrt(np.linalg.det(self.cov) * (2 * np.pi) ** self.ndim)\n        )\n\n    def _fit(self, x):\n        cov = np.cov(x.T)\n        kmeans = KMeans(self.n_components)\n        kmeans.fit(x)\n        self.mu = kmeans.centers\n        self.cov = np.array([cov for _ in range(self.n_components)])\n        self.coef = np.ones(self.n_components) / self.n_components\n        params = np.hstack(\n            (self.mu.ravel(),\n             self.cov.ravel(),\n             self.coef.ravel())\n        )\n        while True:\n            stats = self._expectation(x)\n            self._maximization(x, stats)\n            new_params = np.hstack(\n                (self.mu.ravel(),\n                 self.cov.ravel(),\n                 self.coef.ravel())\n            )\n            if np.allclose(params, new_params):\n                break\n            else:\n                params = new_params\n\n    def _expectation(self, x):\n        resps = self.coef * self._gauss(x)\n        resps /= resps.sum(axis=-1, keepdims=True)\n        return resps\n\n    def _maximization(self, x, resps):\n        Nk = np.sum(resps, axis=0)\n        self.coef = Nk / len(x)\n        self.mu = (x.T @ resps / Nk).T\n        d = x[:, None, :] - self.mu\n        self.cov = np.einsum(\n            'nki,nkj->kij', d, d * resps[:, :, None]) / Nk[:, None, None]\n\n    def joint_proba(self, x):\n        \"\"\"\n        calculate joint probability p(x, Z)\n\n        Parameters\n        ----------\n        x : (sample_size, n_features) ndarray\n            input data\n\n        Returns\n        -------\n        joint_prob : (sample_size, n_components) ndarray\n            joint probability of input and component\n        \"\"\"\n        return self.coef * self._gauss(x)\n\n    def _pdf(self, x):\n        joint_prob = self.coef * self._gauss(x)\n        return np.sum(joint_prob, axis=-1)\n\n    def classify(self, x):\n        \"\"\"\n        classify input\n        max_z p(z|x, theta)\n\n        Parameters\n        ----------\n        x : (sample_size, ndim) ndarray\n            input\n\n        Returns\n        -------\n        output : (sample_size,) ndarray\n            corresponding cluster index\n        \"\"\"\n        return np.argmax(self.classify_proba(x), axis=1)\n\n    def classify_proba(self, x):\n        \"\"\"\n        posterior probability of cluster\n        p(z|x,theta)\n\n        Parameters\n        ----------\n        x : (sample_size, ndim) ndarray\n            input\n\n        Returns\n        -------\n        output : (sample_size, n_components) ndarray\n            posterior probability of cluster\n        \"\"\"\n        return self._expectation(x)\n"
  },
  {
    "path": "prml/rv/rv.py",
    "content": "import numpy as np\n\n\nclass RandomVariable(object):\n    \"\"\"Base class for random variables.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initialize a random variable.\"\"\"\n        self.parameter = {}\n\n    def __repr__(self):\n        \"\"\"Representation of the random variable.\"\"\"\n        string = f\"{self.__class__.__name__}(\\n\"\n        for key, value in self.parameter.items():\n            string += (\" \" * 4)\n            if isinstance(value, RandomVariable):\n                string += f\"{key}={value:8}\"\n            else:\n                string += f\"{key}={value}\"\n            string += \"\\n\"\n        string += \")\"\n        return string\n\n    def __format__(self, indent=\"4\"):\n        \"\"\"Format the random variable.\"\"\"\n        indent = int(indent)\n        string = f\"{self.__class__.__name__}(\\n\"\n        for key, value in self.parameter.items():\n            string += (\" \" * indent)\n            if isinstance(value, RandomVariable):\n                string += f\"{key}=\" + value.__format__(str(indent + 4))\n            else:\n                string += f\"{key}={value}\"\n            string += \"\\n\"\n        string += (\" \" * (indent - 4)) + \")\"\n        return string\n\n    def fit(self, x, **kwargs):\n        \"\"\"Estimate parameter(s) of the distribution.\n\n        Parameters\n        ----------\n        x : np.ndarray\n            observed data\n        \"\"\"\n        self._check_input(x)\n        if hasattr(self, \"_fit\"):\n            self._fit(x, **kwargs)\n        else:\n            raise NotImplementedError\n\n    # def ml(self, x, **kwargs):\n    #     \"\"\"\n    #     maximum likelihood estimation of the parameter(s)\n    #     of the distribution given data\n\n    #     Parameters\n    #     ----------\n    #     x : (sample_size, ndim) np.ndarray\n    #         observed data\n    #     \"\"\"\n    #     self._check_input(x)\n    #     if hasattr(self, \"_ml\"):\n    #         self._ml(x, **kwargs)\n    #     else:\n    #         raise NotImplementedError\n\n    # def map(self, x, **kwargs):\n    #     \"\"\"\n    #     maximum a posteriori estimation of the parameter(s)\n    #     of the distribution given data\n\n    #     Parameters\n    #     ----------\n    #     x : (sample_size, ndim) np.ndarray\n    #         observed data\n    #     \"\"\"\n    #     self._check_input(x)\n    #     if hasattr(self, \"_map\"):\n    #         self._map(x, **kwargs)\n    #     else:\n    #         raise NotImplementedError\n\n    # def bayes(self, x, **kwargs):\n    #     \"\"\"\n    #     bayesian estimation of the parameter(s)\n    #     of the distribution given data\n\n    #     Parameters\n    #     ----------\n    #     x : (sample_size, ndim) np.ndarray\n    #         observed data\n    #     \"\"\"\n    #     self._check_input(x)\n    #     if hasattr(self, \"_bayes\"):\n    #         self._bayes(x, **kwargs)\n    #     else:\n    #         raise NotImplementedError\n\n    def pdf(self, x):\n        \"\"\"Compute probability density function p(x|parameter).\n\n        Parameters\n        ----------\n        x : (sample_size, ndim) np.ndarray\n            input of the function\n\n        Returns\n        -------\n        p : (sample_size,) np.ndarray\n            value of probability density function for each input\n        \"\"\"\n        self._check_input(x)\n        if hasattr(self, \"_pdf\"):\n            return self._pdf(x)\n        else:\n            raise NotImplementedError\n\n    def draw(self, sample_size=1):\n        \"\"\"Draw samples from the distribution.\n\n        Parameters\n        ----------\n        sample_size : int\n            sample size\n\n        Returns\n        -------\n        sample : (sample_size, ndim) np.ndarray\n            generated samples from the distribution\n        \"\"\"\n        assert isinstance(sample_size, int)\n        if hasattr(self, \"_draw\"):\n            return self._draw(sample_size)\n        else:\n            raise NotImplementedError\n\n    def _check_input(self, x):\n        assert isinstance(x, np.ndarray)\n"
  },
  {
    "path": "prml/rv/students_t.py",
    "content": "import numpy as np\nfrom scipy.special import gamma, digamma\nfrom prml.rv.rv import RandomVariable\n\n\nclass StudentsT(RandomVariable):\n    \"\"\"\n    Student's t-distribution\n    p(x|mu, tau, dof)\n    = (1 + tau * (x - mu)^2 / dof)^-(D + dof)/2 / const.\n    \"\"\"\n\n    def __init__(self, mu=None, tau=None, dof=None):\n        super().__init__()\n        self.mu = mu\n        self.tau = tau\n        self.dof = dof\n\n    @property\n    def mu(self):\n        return self.parameter[\"mu\"]\n\n    @mu.setter\n    def mu(self, mu):\n        if isinstance(mu, (int, float, np.number)):\n            self.parameter[\"mu\"] = np.array(mu)\n        elif isinstance(mu, np.ndarray):\n            self.parameter[\"mu\"] = mu\n        else:\n            assert mu is None\n            self.parameter[\"mu\"] = None\n\n    @property\n    def tau(self):\n        return self.parameter[\"tau\"]\n\n    @tau.setter\n    def tau(self, tau):\n        if isinstance(tau, (int, float, np.number)):\n            tau = np.array(tau)\n            assert tau.shape == self.shape\n            self.parameter[\"tau\"] = tau\n        elif isinstance(tau, np.ndarray):\n            assert tau.shape == self.shape\n            self.parameter[\"tau\"] = tau\n        else:\n            assert tau is None\n            self.parameter[\"tau\"] = None\n\n    @property\n    def dof(self):\n        return self.parameter[\"dof\"]\n\n    @dof.setter\n    def dof(self, dof):\n        if isinstance(dof, (int, float, np.number)):\n            self.parameter[\"dof\"] = dof\n        else:\n            assert dof is None\n            self.parameter[\"dof\"] = None\n\n    @property\n    def ndim(self):\n        if hasattr(self.mu, \"ndim\"):\n            return self.mu.ndim\n        else:\n            return None\n\n    @property\n    def size(self):\n        if hasattr(self.mu, \"size\"):\n            return self.mu.size\n        else:\n            return None\n\n    @property\n    def shape(self):\n        if hasattr(self.mu, \"shape\"):\n            return self.mu.shape\n        else:\n            return None\n\n    def _fit(self, x, learning_rate=0.01):\n        self.mu = np.mean(x, axis=0)\n        self.tau = 1 / np.var(x, axis=0)\n        self.dof = 1\n        params = np.hstack(\n            (self.mu.ravel(),\n             self.tau.ravel(),\n             self.dof)\n        )\n        while True:\n            E_eta, E_lneta = self._expectation(x)\n            self._maximization(x, E_eta, E_lneta, learning_rate)\n            new_params = np.hstack(\n                (self.mu.ravel(),\n                 self.tau.ravel(),\n                 self.dof)\n            )\n            if np.allclose(params, new_params):\n                break\n            else:\n                params = new_params\n\n    def _expectation(self, x):\n        d = x - self.mu\n        a = 0.5 * (self.dof + 1)\n        b = 0.5 * (self.dof + self.tau * d ** 2)\n        E_eta = a / b\n        E_lneta = digamma(a) - np.log(b)\n        return E_eta, E_lneta\n\n    def _maximization(self, x, E_eta, E_lneta, learning_rate):\n        self.mu = np.sum(E_eta * x, axis=0) / np.sum(E_eta, axis=0)\n        d = x - self.mu\n        self.tau = 1 / np.mean(E_eta * d ** 2, axis=0)\n        N = len(x)\n        self.dof += learning_rate * 0.5 * (\n            N * np.log(0.5 * self.dof) + N\n            - N * digamma(0.5 * self.dof)\n            + np.sum(E_lneta - E_eta, axis=0)\n        )\n\n    def _pdf(self, x):\n        d = x - self.mu\n        D_sq = self.tau * d ** 2\n        return (\n            gamma(0.5 * (self.dof + 1))\n            * self.tau ** 0.5\n            * (1 + D_sq / self.dof) ** (-0.5 * (1 + self.dof))\n            / gamma(self.dof * 0.5)\n            / (np.pi * self.dof) ** 0.5\n        )\n"
  },
  {
    "path": "prml/rv/uniform.py",
    "content": "import numpy as np\n\nfrom prml.rv.rv import RandomVariable\n\n\nclass Uniform(RandomVariable):\n    \"\"\"Random variable that follows a uniform distribution.\n\n    p(x|a, b)\n    = 1 / ((b_0 - a_0) * (b_1 - a_1)) if a <= x <= b else 0\n    \"\"\"\n\n    def __init__(self, low, high):\n        \"\"\"Initialize a uniform distribution.\n\n        Parameters\n        ----------\n        low : int, float, or np.ndarray\n            lower boundary\n        high : int, float, or np.ndarray\n            higher boundary\n        \"\"\"\n        super().__init__()\n        low = np.asarray(low)\n        high = np.asarray(high)\n        assert low.shape == high.shape\n        assert (low <= high).all()\n        self.low = low\n        self.high = high\n        self.value = 1 / np.prod(high - low)\n\n    @property\n    def low(self):\n        \"\"\"Lower bound of the random variable.\"\"\"\n        return self.parameter[\"low\"]\n\n    @low.setter\n    def low(self, low):\n        self.parameter[\"low\"] = low\n\n    @property\n    def high(self):\n        \"\"\"Higher bound of the random variable.\"\"\"\n        return self.parameter[\"high\"]\n\n    @high.setter\n    def high(self, high):\n        self.parameter[\"high\"] = high\n\n    @property\n    def ndim(self):\n        \"\"\"Rank of the random variable.\"\"\"\n        return self.low.ndim\n\n    @property\n    def size(self):\n        \"\"\"Number of elements in the random variable.\"\"\"\n        return self.low.size\n\n    @property\n    def shape(self):\n        \"\"\"Shape of the random variable.\"\"\"\n        return self.low.shape\n\n    @property\n    def mean(self):\n        \"\"\"Mean value of the random variable.\"\"\"\n        return 0.5 * (self.low + self.high)\n\n    def _pdf(self, x):\n        higher = np.logical_and.reduce(x >= self.low, 1)\n        lower = np.logical_and.reduce(x <= self.high, 1)\n        return self.value * np.logical_and(higher, lower)\n\n    def _draw(self, sample_size=1):\n        u01 = np.random.uniform(size=(sample_size,) + self.shape)\n        return u01 * (self.high - self.low) + self.low\n"
  },
  {
    "path": "prml/rv/variational_gaussian_mixture.py",
    "content": "import numpy as np\nfrom scipy.special import digamma, gamma, logsumexp\n\nfrom prml.rv.rv import RandomVariable\n\n\nclass VariationalGaussianMixture(RandomVariable):\n\n    def __init__(\n        self,\n        n_components=1,\n        alpha0=None,\n        m0=None,\n        W0=1.,\n        dof0=None,\n        beta0=1.,\n    ):\n        \"\"\"Initialize variational gaussian mixture model.\n\n        Parameters\n        ----------\n        n_components : int\n            maximum numnber of gaussian components\n        alpha0 : float\n            parameter of prior dirichlet distribution\n        m0 : float\n            mean parameter of prior gaussian distribution\n        W0 : float\n            mean of the prior Wishart distribution\n        dof0 : float\n            number of degrees of freedom of the prior Wishart distribution\n        beta0 : float\n            prior on the precision distribution\n        \"\"\"\n        super().__init__()\n        self.n_components = n_components\n        if alpha0 is None:\n            self.alpha0 = 1 / n_components\n        else:\n            self.alpha0 = alpha0\n        self.m0 = m0\n        self.W0 = W0\n        self.dof0 = dof0\n        self.beta0 = beta0\n\n    def _init_params(self, x):\n        sample_size, self.ndim = x.shape\n        self.alpha0 = np.ones(self.n_components) * self.alpha0\n        if self.m0 is None:\n            self.m0 = np.mean(x, axis=0)\n        else:\n            self.m0 = np.zeros(self.ndim) + self.m0\n        self.W0 = np.eye(self.ndim) * self.W0\n        if self.dof0 is None:\n            self.dof0 = self.ndim\n\n        self.component_size = sample_size / self.n_components + np.zeros(self.n_components)\n        self.alpha = self.alpha0 + self.component_size\n        self.beta = self.beta0 + self.component_size\n        indices = np.random.choice(sample_size, self.n_components, replace=False)\n        self.mu = x[indices]\n        self.W = np.tile(self.W0, (self.n_components, 1, 1))\n        self.dof = self.dof0 + self.component_size\n\n    @property\n    def alpha(self):\n        \"\"\"Alpha parameter.\"\"\"\n        return self.parameter[\"alpha\"]\n\n    @alpha.setter\n    def alpha(self, alpha):\n        self.parameter[\"alpha\"] = alpha\n\n    @property\n    def beta(self):\n        \"\"\"Beta parameter.\"\"\"\n        return self.parameter[\"beta\"]\n\n    @beta.setter\n    def beta(self, beta):\n        self.parameter[\"beta\"] = beta\n\n    @property\n    def mu(self):\n        \"\"\"Mean parameter of posterior Wishart distribution.\"\"\"\n        return self.parameter[\"mu\"]\n\n    @mu.setter\n    def mu(self, mu):\n        self.parameter[\"mu\"] = mu\n\n    @property\n    def W(self):\n        \"\"\"Weight parameter.\"\"\"\n        return self.parameter[\"W\"]\n\n    @W.setter\n    def W(self, W):\n        self.parameter[\"W\"] = W\n\n    @property\n    def dof(self):\n        \"\"\"Degree of freedom.\"\"\"\n        return self.parameter[\"dof\"]\n\n    @dof.setter\n    def dof(self, dof):\n        self.parameter[\"dof\"] = dof\n\n    def get_params(self):\n        \"\"\"Get parameters.\"\"\"\n        return self.alpha, self.beta, self.mu, self.W, self.dof\n\n    def _fit(self, x, iter_max=100):\n        self._init_params(x)\n        for _ in range(iter_max):\n            params = np.hstack([p.flatten() for p in self.get_params()])\n            r = self._variational_expectation(x)\n            self._variational_maximization(x, r)\n            if np.allclose(params, np.hstack([p.flatten() for p in self.get_params()])):\n                break\n\n    def _variational_expectation(self, x):\n        d = x[:, None, :] - self.mu\n        maha_sq = -0.5 * (\n            self.ndim / self.beta\n            + self.dof * np.sum(\n                np.einsum(\"kij,nkj->nki\", self.W, d) * d, axis=-1))\n        ln_pi = digamma(self.alpha) - digamma(self.alpha.sum())\n        ln_Lambda = digamma(0.5 * (self.dof - np.arange(self.ndim)[:, None])).sum(axis=0) + self.ndim * np.log(2) + np.linalg.slogdet(self.W)[1]\n        ln_r = ln_pi + 0.5 * ln_Lambda + maha_sq\n        ln_r -= logsumexp(ln_r, axis=-1)[:, None]\n        r = np.exp(ln_r)\n        return r\n\n    def _variational_maximization(self, x, r):\n        self.component_size = r.sum(axis=0)\n        Xm = (x.T.dot(r) / self.component_size).T\n        d = x[:, None, :] - Xm\n        S = np.einsum('nki,nkj->kij', d, r[:, :, None] * d) / self.component_size[:, None, None]\n        self.alpha = self.alpha0 + self.component_size\n        self.beta = self.beta0 + self.component_size\n        self.mu = (self.beta0 * self.m0 + self.component_size[:, None] * Xm) / self.beta[:, None]\n        d = Xm - self.m0\n        self.W = np.linalg.inv(\n            np.linalg.inv(self.W0)\n            + (self.component_size * S.T).T\n            + (self.beta0 * self.component_size * np.einsum('ki,kj->kij', d, d).T / (self.beta0 + self.component_size)).T)\n        self.dof = self.dof0 + self.component_size\n\n    def classify(self, x):\n        \"\"\"Index of highest posterior of the latent variable.\n\n        Parameters\n        ----------\n        x : (sample_size, ndim) ndarray\n            input\n        Returns\n        -------\n        output : (sample_size, n_components) ndarray\n            index of maximum posterior of the latent variable\n        \"\"\"\n        return np.argmax(self._variational_expectation(x), 1)\n\n    def classify_proba(self, x):\n        \"\"\"Compute posterior of the latent variable.\n\n        Parameters\n        ----------\n        x : (sample_size, ndim) ndarray\n            input\n        Returns\n        -------\n        output : (sample_size, n_components) ndarray\n            posterior of the latent variable\n        \"\"\"\n        return self._variational_expectation(x)\n\n    def student_t(self, x):\n        \"\"\"Student's t probability distribution function.\"\"\"\n        nu = self.dof + 1 - self.ndim\n        L = (nu * self.beta * self.W.T / (1 + self.beta)).T # noqa\n        d = x[:, None, :] - self.mu\n        maha_sq = np.sum(np.einsum('nki,kij->nkj', d, L) * d, axis=-1)\n        return (\n            gamma(0.5 * (nu + self.ndim))\n            * np.sqrt(np.linalg.det(L))\n            * (1 + maha_sq / nu) ** (-0.5 * (nu + self.ndim))\n            / (gamma(0.5 * nu) * (nu * np.pi) ** (0.5 * self.ndim)))\n\n    def _pdf(self, x):\n        return (self.alpha * self.student_t(x)).sum(axis=-1) / self.alpha.sum()\n"
  },
  {
    "path": "prml/sampling/__init__.py",
    "content": "from prml.sampling.metropolis import metropolis\nfrom prml.sampling.metropolis_hastings import metropolis_hastings\nfrom prml.sampling.rejection_sampling import rejection_sampling\nfrom prml.sampling.sir import sir\n\n\n__all__ = [\n    \"metropolis\",\n    \"metropolis_hastings\",\n    \"rejection_sampling\",\n    \"sir\"\n]\n"
  },
  {
    "path": "prml/sampling/metropolis.py",
    "content": "import random\n\nimport numpy as np\n\n\ndef metropolis(func, rv, n, downsample=1):\n    \"\"\"Metropolis algorithm.\n\n    Parameters\n    ----------\n    func : callable\n        (un)normalized distribution to be sampled from\n    rv : RandomVariable\n        proposal distribution which is symmetric at the origin\n    n : int\n        number of samples to draw\n    downsample : int\n        downsampling factor\n\n    Returns\n    -------\n    sample : (n, ndim) ndarray\n        generated sample\n    \"\"\"\n    x = np.zeros((1, rv.ndim))\n    sample = []\n    for i in range(n * downsample):\n        x_new = x + rv.draw()\n        accept_proba = func(x_new) / func(x)\n        if random.random() < accept_proba:\n            x = x_new\n        if i % downsample == 0:\n            sample.append(x[0])\n    sample = np.asarray(sample)\n    assert sample.shape == (n, rv.ndim), sample.shape\n    return sample\n"
  },
  {
    "path": "prml/sampling/metropolis_hastings.py",
    "content": "import random\n\nimport numpy as np\n\n\ndef metropolis_hastings(func, rv, n, downsample=1):\n    \"\"\"\n    Metropolis Hastings algorith\n\n    Parameters\n    ----------\n    func : callable\n        (un)normalized distribution to be sampled from\n    rv : RandomVariable\n        proposal distribution\n    n : int\n        number of samples to draw\n    downsample : int\n        downsampling factor\n\n    Returns\n    -------\n    sample : (n, ndim) ndarray\n        generated sample\n    \"\"\"\n    x = np.zeros((1, rv.ndim))\n    sample = []\n    for i in range(n * downsample):\n        x_new = x + rv.draw()\n        accept_proba = func(x_new) * rv.pdf(x - x_new) / (func(x) * rv.pdf(x_new - x))\n        if random.random() < accept_proba:\n            x = x_new\n        if i % downsample == 0:\n            sample.append(x[0])\n    sample = np.asarray(sample)\n    assert sample.shape == (n, rv.ndim), sample.shape\n    return sample\n"
  },
  {
    "path": "prml/sampling/rejection_sampling.py",
    "content": "import random\nimport numpy as np\n\n\ndef rejection_sampling(func, rv, k, n):\n    \"\"\"\n    perform rejection sampling n times\n\n    Parameters\n    ----------\n    func : callable\n        (un)normalized distribution to be sampled from\n    rv : RandomVariable\n        distribution to generate sample\n    k : float\n        constant to be multiplied with the distribution\n    n : int\n        number of samples to draw\n\n    Returns\n    -------\n    sample : (n, ndim) ndarray\n        generated sample\n    \"\"\"\n    assert hasattr(rv, \"draw\"), \"the distribution has no method to draw random samples\"\n    sample = []\n    while len(sample) < n:\n        sample_candidate = rv.draw()\n        accept_proba = func(sample_candidate) / (k * rv.pdf(sample_candidate))\n        if random.random() < accept_proba:\n            sample.append(sample_candidate[0])\n    sample = np.asarray(sample)\n    assert sample.shape == (n, rv.ndim), sample.shape\n    return sample\n"
  },
  {
    "path": "prml/sampling/sir.py",
    "content": "import numpy as np\n\n\ndef sir(func, rv, n):\n    \"\"\"\n    sampling-importance-resampling\n\n    Parameters\n    ----------\n    func : callable\n        (un)normalized distribution to be sampled from\n    rv : RandomVariable\n        distribution to generate sample\n    n : int\n        number of samples to draw\n\n    Returns\n    -------\n    sample : (n, ndim) ndarray\n        generated sample\n    \"\"\"\n    assert hasattr(rv, \"draw\"), \"the distribution has no method to draw random samples\"\n    sample_candidate = rv.draw(n * 10)\n    weight = np.squeeze(func(sample_candidate) / rv.pdf(sample_candidate))\n    assert weight.shape == (n * 10,), weight.shape\n    weight /= np.sum(weight)\n    index = np.random.choice(n * 10, n, p=weight)\n    sample = sample_candidate[index]\n    return sample\n"
  },
  {
    "path": "setup.cfg",
    "content": "; See:\n; https://setuptools.readthedocs.io/en/latest/userguide/declarative_config.html\n\n[options.extras_require]\ndevelop =\n    jupyter\n    matplotlib\n    pandas\n    scikit-learn\n\n    pre-commit\n\n    # format\n    autopep8\n    flake8\n    flake8-absolute-import\n    flake8-broken-line\n    flake8-builtins\n    flake8-commas\n    flake8-docstrings\n    flake8-import-order\n    flake8-multiline-containers\n    flake8-mutable\n    pep8-naming\n\n[flake8]\nignore =\n    ; variable \"sum\" is shadowing a Python builtin\n    A001\n    ; class attribute is shadowing a Python builtin\n    A003\n    ; import statement \"sum\" is shadowing a Python builtin\n    A004\n    ; No blank lines allowed after function docstring\n    D202\n    ; missing whitespace around arithmetic operator\n    E226\n    ; missing whitespace after ':'\n    E231\n    ; missing whitespace around parameter equals\n    E252\n    ; expected 2 blank lines, found 1\n    E302\n    ; too many blank lines (2)\n    E303\n    ; comparison to False should be 'if cond is False:' or 'if not cond:'\n    E712\n    ; the module is shadowing a Python builtin module\n    A005\n    ; missing trailing comma\n    C812\n    ; Missing docstring in public module\n    D100\n    ; Missing docstring in public class\n    D101\n    ; Missing docstring in public method\n    D102\n    ; Missing docstring in public function\n    D103\n    ; Missing docstring in public package\n    D104\n    ; Missing docstring in magic method\n    D105\n    ; Missing docstring in __init__\n    D107\n    ; One-line docstring should fit on one line with quotes\n    D200\n    ; 1 blank line required between summary line and description\n    D205\n    ; Use r\"\"\" if any backslashes in a docstring\n    D301\n    ; First line should end with a period\n    D400\n    ; First line should not be the function's \"signature\"\n    D402\n    ; First line should be in imperative mood; try rephrasing\n    D401\n    ; First word of the first line should be properly capitalized\n    D403\n    ; missing whitespace after keyword\n    E275\n    ; line too long\n    E501\n    ; Import statements are in the wrong order.\n    I100\n    ;  Imported names are in the wrong order.\n    I101\n    ; Missing newline between import groups.\n    I201\n    ; Multi-line container not broken after opening character\n    JS101\n    ; function name should be lowercase\n    N802\n    ; argument name should be lowercase\n    N803\n    ; variable in function should be lowercase\n    N806\n    ; line break before binary operator\n    W503\n    ; invalid escape sequence\n    W605\nper-file-ignores =\n    ; Ignore 'Missing docstring in public module' and 'variable \"copyright\" is shadowing a python builtin'\n    docs/conf.py:A001,D100\n    ; Ignore missing docstring in public module, class, method, function, package\n    test/*.py:D100,D101,D102,D103,D104\n\napplication-import-names = prml\n\n; https://github.com/PyCQA/flake8-import-order/blob/master/tests/test_cases/complete_google.py\nimport-order-style = google\n"
  },
  {
    "path": "setup.py",
    "content": "\"\"\"PRML setup module.\n\nSee:\nhttps://packaging.python.org/guides/distributing-packages-using-setuptools/\nhttps://github.com/pypa/sampleproject/blob/main/setup.py\n\"\"\"\n\nfrom setuptools import find_packages, setup\n\n\nsetup(\n    name=\"prml\",\n    version=\"0.0.1\",\n    description=\"Collection of PRML algorithms\",\n    author=\"ctgk\",\n    python_requires=\">=3.9\",\n    install_requires=[\"numpy>=2\", \"scipy\"],\n    packages=find_packages(exclude=[\"test\", \"test.*\"]),\n    test_suite=\"test\",\n)\n"
  },
  {
    "path": "test/__init__.py",
    "content": ""
  },
  {
    "path": "test/test_bayesnet/__init__.py",
    "content": ""
  },
  {
    "path": "test/test_bayesnet/test_discrete.py",
    "content": "import unittest\n\nimport numpy as np\n\nfrom prml import bayesnet as bn\n\n\nclass TestDiscrete(unittest.TestCase):\n\n    def test_discrete(self):\n        a = bn.discrete([0.2, 0.8])\n        b = bn.discrete([[0.1, 0.2], [0.9, 0.8]], a)\n        self.assertTrue(np.allclose(b.proba, [0.18, 0.82]))\n        a.observe(0)\n        self.assertTrue(np.allclose(b.proba, [0.1, 0.9]))\n\n        a = bn.discrete([0.1, 0.9])\n        b = bn.discrete([[0.7, 0.2], [0.3, 0.8]], a)\n        c = bn.discrete([[0.8, 0.4], [0.2, 0.6]], b)\n        self.assertTrue(np.allclose(a.proba, [0.1, 0.9]))\n        self.assertTrue(np.allclose(b.proba, [0.25, 0.75]))\n        self.assertTrue(np.allclose(c.proba, [0.5, 0.5]))\n        c.observe(0)\n        self.assertTrue(np.allclose(a.proba, [0.136, 0.864]))\n        self.assertTrue(np.allclose(b.proba, [0.4, 0.6]))\n        self.assertTrue(np.allclose(c.proba, [1, 0]))\n\n        a = bn.discrete([0.1, 0.9], name=\"p(a)\")\n        b = bn.discrete([0.1, 0.9], name=\"p(b)\")\n        c = bn.discrete(\n            [[[0.9, 0.8],\n              [0.8, 0.2]],\n             [[0.1, 0.2],\n              [0.2, 0.8]]],\n            a, b, name=\"p(c|a,b)\"\n        )\n        c.observe(0)\n        self.assertTrue(np.allclose(a.proba, [0.25714286, 0.74285714]))\n        b.observe(0)\n        self.assertTrue(np.allclose(a.proba, [0.11111111, 0.88888888]))\n\n        a = bn.discrete([0.1, 0.9], name=\"p(a)\")\n        b = bn.discrete([0.1, 0.9], name=\"p(b)\")\n        c = bn.discrete(\n            [[[0.9, 0.8],\n              [0.8, 0.2]],\n             [[0.1, 0.2],\n              [0.2, 0.8]]],\n            a, b, name=\"p(c|a,b)\"\n        )\n        c.observe(0, proprange=0)\n        self.assertTrue(np.allclose(a.proba, [0.1, 0.9]))\n        b.observe(0, proprange=1)\n        self.assertTrue(np.allclose(a.proba, [0.1, 0.9]))\n        b.send_message(proprange=2)\n        self.assertTrue(np.allclose(a.proba, [0.11111111, 0.88888888]))\n        a.send_message()\n        c.send_message()\n        self.assertTrue(np.allclose(a.proba, [0.11111111, 0.88888888]), a.message_from)\n\n        a = bn.discrete([0.1, 0.9], name=\"p(a)\")\n        b = bn.discrete([0.1, 0.9], name=\"p(b)\")\n        c = bn.discrete(\n            [[[0.9, 0.8],\n              [0.8, 0.2]],\n             [[0.1, 0.2],\n              [0.2, 0.8]]],\n            a, b, name=\"p(c|a,b)\"\n        )\n        b.observe(0)\n        self.assertTrue(np.allclose(a.proba, [0.1, 0.9]))\n        c.send_message()\n        self.assertTrue(np.allclose(a.proba, [0.1, 0.9]), a.message_from)\n\n    def test_joint_discrete(self):\n        a = bn.DiscreteVariable(2)\n        b = bn.DiscreteVariable(2)\n        bn.discrete([[0.1, 0.2], [0.3, 0.4]], out=[a, b])\n        b.observe(1)\n        self.assertTrue(np.allclose(a.proba, [1/3, 2/3]))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_linear/__init__.py",
    "content": ""
  },
  {
    "path": "test/test_linear/test_bayesian_regression.py",
    "content": "import unittest\n\nimport numpy as np\n\nfrom prml.linear import BayesianRegression\n\n\nclass TestBayesianRegression(unittest.TestCase):\n\n    def test_fit_predict(self):\n        x_train = np.array([-1, 0, 1]).reshape(-1, 1)\n        y_train = np.array([-2, 0, 2])\n        model = BayesianRegression(alpha=1., beta=1.)\n        model.fit(x_train, y_train)\n        self.assertTrue(np.allclose(model.w_mean, np.array([4 / 3])))\n\n        mean, std = model.predict(np.array([[3]]), return_std=True)\n        self.assertTrue(np.allclose(mean, np.array([4])))\n        self.assertTrue(np.allclose(std, np.array([2])))\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "test/test_linear/test_linear_regression.py",
    "content": "import unittest\n\nimport numpy as np\n\nfrom prml.linear import LinearRegression\n\n\nclass TestLinearRegression(unittest.TestCase):\n\n    def test_fit(self):\n        x_train = np.array([-1, 0, 1]).reshape(-1, 1)\n        y_train = np.array([-2, 0, 2])\n        model = LinearRegression()\n\n        model.fit(x_train, y_train)\n\n        self.assertTrue(\n            np.allclose(model.w, np.array([2])),\n        )\n\n    def test_predict(self):\n        x_train = np.array([-1, 0, 1]).reshape(-1, 1)\n        y_train = np.array([-2, 0, 2])\n        model = LinearRegression()\n        model.fit(x_train, y_train)\n\n        actual = model.predict(np.array([[3]]))\n        self.assertTrue(np.allclose(actual, np.array([6])))\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "test/test_linear/test_logistic_regression.py",
    "content": "import unittest\n\nimport numpy as np\n\nfrom prml.linear import LogisticRegression\n\n\nclass TestLogisticRegression(unittest.TestCase):\n\n    def test_fit_classify_proba(self):\n        x_train = np.array([-3, -2, -1, 1, 2, 3]).reshape(-1, 1)\n        y_train = np.array([0, 0, 1, 0, 1, 1])\n        model = LogisticRegression()\n        model.fit(x_train, y_train)\n        self.assertTrue(np.allclose(model.w, np.array([0.73248753])))\n\n        actual = model.classify(np.array([[-5], [5]]))\n        self.assertTrue(np.allclose(actual, np.array([0, 1])))\n\n        actual = model.proba(np.array([[0], [4]]))\n        self.assertTrue(np.allclose(actual, np.array([0.5, 0.94930727])))\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "test/test_linear/test_ridge_regression.py",
    "content": "import unittest\n\nimport numpy as np\n\nfrom prml.linear import RidgeRegression\n\n\nclass TestRidgeRegression(unittest.TestCase):\n\n    def test_fit_predict(self):\n        x_train = np.array([-1, 0, 1]).reshape(-1, 1)\n        y_train = np.array([-2, 0, 2])\n        model = RidgeRegression(alpha=1.)\n        model.fit(x_train, y_train)\n        self.assertTrue(np.allclose(model.w, np.array([4 / 3])))\n\n        actual = model.predict(np.array([[3]]))\n        self.assertTrue(np.allclose(actual, np.array([4])))\n\n\nif __name__ == '__main__':\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/__init__.py",
    "content": ""
  },
  {
    "path": "test/test_nn/test_backward.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestBackward(unittest.TestCase):\n\n    def test_backward(self):\n        def sigmoid(x):\n            return np.tanh(x * 0.5) * 0.5 + 0.5\n\n        x_train = nn.asarray(np.array([[0, 0], [0, 1], [1, 0], [1, 1]]))\n        y_train = nn.asarray(np.array([[0], [1], [1], [0]]))\n        w1 = nn.asarray(np.random.normal(0, 1, (2, 3)))\n        b1 = nn.asarray(np.zeros(3))\n        w2 = nn.asarray(np.random.normal(0, 1, (3, 1)))\n        b2 = nn.asarray(np.zeros(1))\n        parameter = [w1, b1, w2, b2]\n        for _ in range(10):\n            for param in parameter:\n                param.cleargrad()\n            x_train.cleargrad()\n            h = x_train @ w1 + b1\n            a = nn.tanh(h)\n            logit = a @ w2 + b2\n            loss = nn.loss.sigmoid_cross_entropy(logit, y_train)\n            loss.backward()\n            dlogit = sigmoid(logit.value) - y_train.value\n            db2 = np.sum(dlogit, axis=0)\n            dw2 = a.value.T @ dlogit\n            da = dlogit @ w2.value.T\n            dh = da * (1 - a.value ** 2)\n            db1 = np.sum(dh, axis=0)\n            dw1 = x_train.value.T @ dh\n            dx = dh @ w1.value.T\n            self.assertTrue(np.allclose(logit.grad, dlogit))\n            self.assertTrue(np.allclose(b2.grad, db2))\n            self.assertTrue(np.allclose(w2.grad, dw2))\n            self.assertTrue(np.allclose(a.grad, da))\n            self.assertTrue(np.allclose(h.grad, dh))\n            self.assertTrue(np.allclose(b1.grad, db1))\n            self.assertTrue(np.allclose(w1.grad, dw1))\n            self.assertTrue(np.allclose(x_train.grad, dx))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_distribution/__init__.py",
    "content": ""
  },
  {
    "path": "test/test_nn/test_distribution/test_bernoulli.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestBernoulli(unittest.TestCase):\n\n    def test_bernoulli_kl(self):\n        logit = nn.random.normal(0, 1, (5, 3))\n        mu = nn.random.uniform(0, 1, (5, 3))\n        q = nn.Bernoulli(mu)\n        for _ in range(1000):\n            logit.cleargrad()\n            p = nn.Bernoulli(logit=logit)\n            nn.loss.kl_divergence(p, q).backward()\n            logit.value -= 0.1 * logit.grad\n        self.assertTrue(np.allclose(p.mean.value, q.mean.value, 1e-2, 1e-2))\n        self.assertTrue(logit.value.dtype, nn.config.dtype)\n        self.assertEqual(p.mean.value.dtype, nn.config.dtype)\n        self.assertEqual(q.mean.value.dtype, nn.config.dtype)\n        self.assertEqual(nn.config.dtype, np.float32)\n\n    def test_bernoulli_mu(self):\n        npmu = np.random.uniform(0, 1, (5, 3))\n        npt = np.random.uniform(0, 1, (5, 3))\n        p = nn.Bernoulli(npmu)\n        log_likelihood = p.log_pdf(npt)\n        self.assertTrue(np.allclose(log_likelihood.value, npt * np.log(npmu) + (1 - npt) * np.log(1 - npmu)))\n\n    def test_bernoulli_logit(self):\n        nplogit = np.random.randn(5, 3)\n        npmu = np.tanh(nplogit * 0.5) * 0.5 + 0.5\n        npt = np.random.uniform(0, 1, (5, 3))\n        p = nn.Bernoulli(logit=nplogit)\n        log_likelihood = p.log_pdf(npt)\n        self.assertTrue(np.allclose(log_likelihood.value, npt * np.log(npmu) + (1 - npt) * np.log(1 - npmu)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_distribution/test_gaussian.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestGaussian(unittest.TestCase):\n\n    def test_gaussian_draw_forward(self):\n        mu = nn.array(0)\n        sigma = nn.softplus(nn.array(-1))\n        gaussian = nn.Gaussian(mu, sigma)\n        sample = []\n        for _ in range(1000):\n            sample.append(gaussian.draw().value)\n        self.assertTrue(np.allclose(np.mean(sample), 0, rtol=0.1, atol=0.1), np.mean(sample))\n        self.assertTrue(np.allclose(np.std(sample), gaussian.std.value, 0.1, 0.1))\n\n    def test_gaussian_draw_backward(self):\n        mu = nn.array(0)\n        s = nn.array(2)\n        optimizer = nn.optimizer.Gradient({0: mu, 1: s}, 0.01)\n        prior = nn.Gaussian(1, 1)\n        for _ in range(1000):\n            mu.cleargrad()\n            s.cleargrad()\n            gaussian = nn.Gaussian(mu, nn.softplus(s))\n            gaussian.draw()\n            loss = nn.loss.kl_divergence(gaussian, prior).sum()\n            optimizer.minimize(loss)\n        self.assertTrue(np.allclose(gaussian.mean.value, 1, 0.1, 0.1))\n        self.assertTrue(np.allclose(gaussian.std.value, 1, 0.1, 0.1))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_image/__init__.py",
    "content": ""
  },
  {
    "path": "test/test_nn/test_image/test_convolve2d.py",
    "content": "import unittest\n\nimport numpy as np\nfrom scipy.ndimage.filters import correlate\nimport prml.nn as nn\n\n\nclass TestConvolve2d(unittest.TestCase):\n\n    def test_convolve2d_forward(self):\n        img = np.random.randn(1, 5, 5, 1)\n        kernel = np.random.randn(3, 3, 1, 1)\n        output = nn.convolve2d(img, kernel)\n        self.assertTrue(\n            np.allclose(\n                output.value[0, ..., 0],\n                correlate(img[0, ..., 0], kernel[..., 0, 0])[1:-1, 1:-1]\n            )\n        )\n        self.assertEqual(nn.config.dtype, np.float32)\n        self.assertEqual(output.value.dtype, nn.config.dtype)\n\n    def test_convolve2d_backward(self):\n        x = nn.random.normal(0, 1, (1, 5, 5, 1))\n        w = nn.random.normal(0, 1, (3, 3, 1, 1))\n        for _ in range(1000):\n            x.cleargrad()\n            w.cleargrad()\n            output = nn.convolve2d(x, w, (2, 2), (1, 1))\n            output.backward(2 * (output.value - 1))\n            x.value -= x.grad * 0.01\n            w.value -= w.grad * 0.01\n        self.assertTrue(\n            np.allclose(output.value, 1, rtol=0, atol=1e-2),\n            output.value,\n        )\n        self.assertEqual(nn.config.dtype, np.float32)\n        self.assertEqual(x.dtype, nn.config.dtype)\n        self.assertEqual(w.dtype, nn.config.dtype)\n        self.assertEqual(output.dtype, nn.config.dtype)\n\n    def test_convolve2d_network(self):\n        x = nn.random.normal(0, 1, (1, 5, 5, 1))\n        kernel = nn.random.normal(0, 1, (3, 3, 1, 1))\n        conv = nn.image.Convolve2d(kernel, (1, 1), (0, 0))\n        for _ in range(1000):\n            x.cleargrad()\n            conv.clear()\n            output = conv(x)\n            output.backward(2 * (output.value - 1))\n            x.value -= x.grad * 0.01\n            for param in conv.parameter.values():\n                param.value -= param.grad * 0.01\n        self.assertTrue(np.allclose(output.value, 1))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_image/test_deconvolve2d.py",
    "content": "import unittest\n\nimport numpy as np\nfrom scipy.ndimage.filters import correlate\n\nimport prml.nn as nn\n\n\nclass TestDeconvolve2d(unittest.TestCase):\n\n    def test_deconvolve2d_forward(self):\n        img = np.random.randn(1, 3, 3, 1).astype(np.float32)\n        kernel = np.random.randn(3, 3, 1, 1).astype(np.float32)\n        out = nn.deconvolve2d(img, kernel, (1, 1), (0, 0))\n\n        actual = out.value[0, 1:-1, 1:-1, 0]\n        expect = correlate(\n            img[0, :, :, 0],\n            kernel[::-1, ::-1, 0, 0],\n            mode='constant',\n        )\n        self.assertTrue(np.allclose(actual, expect, 0, 1e-2))\n\n    def test_deconvolve2d_backward(self):\n        x = nn.random.normal(0, 1, (1, 3, 3, 1))\n        w = nn.random.normal(0, 1, (3, 3, 1, 1))\n        for _ in range(1000):\n            x.cleargrad()\n            w.cleargrad()\n            output = nn.deconvolve2d(x, w, (2, 2), (1, 1))\n            output.backward(2 * (output.value - 1))\n            x.value -= x.grad * 0.01\n            w.value -= w.grad * 0.01\n        self.assertTrue(\n            np.allclose(output.value, 1, rtol=0, atol=1e-2),\n            output.value,\n        )\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_image/test_max_pooling2d.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestMaxPooling2d(unittest.TestCase):\n\n    def test_max_pooling2d(self):\n        img = np.array([\n            [2, 3, 4, 1],\n            [2, 5, 1, 2],\n            [3, 5, 1, 3],\n            [3, 7, 8, 2]\n        ]).astype(float)\n        img = img[None, :, :, None]\n        expected = np.array([[5, 4], [7, 8]])\n        actual = nn.max_pooling2d(img, 2, 2).value.squeeze()\n        self.assertTrue((expected == actual).all(), actual)\n\n        expected = np.array([\n            [0, 0, 1, 0],\n            [0, 1, 0, 0],\n            [0, 0, 0, 0],\n            [0, 1, 1, 0]\n        ])\n        img = nn.asarray(img)\n        nn.max_pooling2d(img, 2, 2).backward(np.ones((1, 2, 2, 1)))\n        actual = img.grad.squeeze()\n        self.assertTrue((expected == actual).all())\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_loss/__init__.py",
    "content": ""
  },
  {
    "path": "test/test_nn/test_loss/test_sigmoid_cross_entropy.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestSigmoidCrossEntropy(unittest.TestCase):\n\n    def test_sigmoid_cross_entropy(self):\n        npx = np.random.randn(10, 3)\n        npy = np.tanh(npx * 0.5) * 0.5 + 0.5\n        npt = np.random.uniform(0, 1, (10, 3))\n        x = nn.asarray(npx)\n        t = nn.asarray(npt)\n        loss = nn.loss.sigmoid_cross_entropy(x, t)\n        self.assertTrue(np.allclose(loss.value, -npt * np.log(npy) - (1 - npt) * np.log(1 - npy)))\n\n        npg = np.random.randn(10, 3)\n        loss.backward(npg)\n        self.assertTrue(np.allclose(x.grad, npg * (npy - npt), 0, 1e-2))\n        self.assertTrue(np.allclose(t.grad, -npg * npx, 0, 1e-2))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_math/__init__.py",
    "content": ""
  },
  {
    "path": "test/test_nn/test_math/test_add.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestAdd(unittest.TestCase):\n\n    def test_add(self):\n        npa = np.random.randn(4, 5)\n        npb = np.random.randn(4, 5)\n        a = nn.asarray(npa)\n        b = nn.asarray(npb)\n        c = a + b\n        self.assertTrue(np.allclose(c.value, npa + npb))\n\n        npg = np.random.randn(4, 5)\n        c.backward(npg)\n        self.assertTrue(np.allclose(a.grad, npg))\n        self.assertTrue(np.allclose(b.grad, npg))\n\n    def test_add_bias(self):\n        np.random.seed(0)\n        npa = np.random.randn(4, 3)\n        npb = np.random.randn(3)\n        a = nn.asarray(npa)\n        b = nn.asarray(npb)\n        c = a + b\n        self.assertTrue(np.allclose(c.value, npa + npb))\n\n        npg = np.random.randn(4, 3)\n        c.backward(npg)\n        self.assertTrue(np.allclose(a.grad, npg))\n        self.assertTrue(np.allclose(b.grad, npg.sum(axis=0)))\n\n    def test_add_scalar(self):\n        npa = np.random.randn(5, 6)\n        npb = 2\n        a = nn.asarray(npa)\n        b = nn.asarray(npb)\n        c = a + b\n        self.assertTrue(np.allclose(c.value, npa + npb))\n\n        npg = np.random.randn(5, 6)\n        c.backward(npg)\n        self.assertTrue(np.allclose(a.grad, npg))\n        self.assertTrue(np.allclose(b.grad, np.sum(npg)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_math/test_log.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestLog(unittest.TestCase):\n\n    def test_log(self):\n        np.random.seed(0)\n        npx = np.random.uniform(0, 10, (4, 5))\n        x = nn.asarray(npx)\n        y = nn.log(x)\n        self.assertTrue(np.allclose(y.value, np.log(npx)))\n\n        npg = np.random.randn(4, 5)\n        y.backward(npg)\n        self.assertTrue(np.allclose(x.grad, npg / npx))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_math/test_matmul.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestMatmul(unittest.TestCase):\n\n    def test_matmul(self):\n        np.random.seed(0)\n        npa = np.random.randn(4, 6)\n        npb = np.random.randn(6, 3)\n        a = nn.asarray(npa)\n        b = nn.asarray(npb)\n        c = a @ b\n        self.assertTrue(np.allclose(c.value, npa @ npb))\n\n        npg = np.random.randn(4, 3)\n        c.backward(npg)\n        self.assertTrue(np.allclose(a.grad, npg @ npb.T))\n        self.assertTrue(np.allclose(b.grad, npa.T @ npg))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_math/test_multiply.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestMultiply(unittest.TestCase):\n\n    def test_multiply(self):\n        npx = np.random.randn(5, 6)\n        npy = np.random.randn(5, 6)\n        x = nn.asarray(npx)\n        y = nn.asarray(npy)\n        z = x * y\n        self.assertTrue(np.allclose(z.value, npx * npy))\n\n        npg = np.random.randn(5, 6)\n        z.backward(npg)\n        self.assertTrue(np.allclose(x.grad, npg * npy))\n        self.assertTrue(np.allclose(y.grad, npg * npx))\n\n    def test_multiply_vector(self):\n        npx = np.random.randn(5, 6)\n        npy = np.random.randn(6)\n        x = nn.asarray(npx)\n        y = nn.asarray(npy)\n        z = x * y\n        self.assertTrue(np.allclose(z.value, npx * npy))\n\n        npg = np.random.randn(5, 6)\n        z.backward(npg)\n        self.assertTrue(np.allclose(x.grad, npg * npy))\n        self.assertTrue(np.allclose(y.grad, np.sum(npg * npx, axis=0)))\n\n    def test_multiply_scalar(self):\n        npx = np.random.randn(5, 6)\n        npy = 3.5\n        x = nn.asarray(npx)\n        y = nn.asarray(npy)\n        z = x * y\n        self.assertTrue(np.allclose(z.value, npx * npy))\n\n        npg = np.random.randn(5, 6)\n        z.backward(npg)\n        self.assertTrue(np.allclose(x.grad, npg * npy))\n        self.assertTrue(np.allclose(y.grad, np.sum(npg * npx)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_math/test_negative.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestNegative(unittest.TestCase):\n\n    def test_negative(self):\n        npx = np.random.randn(8, 9)\n        x = nn.asarray(npx)\n        y = -x\n        self.assertTrue(np.allclose(y.value, -npx))\n\n        npg = np.random.randn(8, 9)\n        y.backward(npg)\n        self.assertTrue(np.allclose(x.grad, -npg))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_nonlinear/__init__.py",
    "content": ""
  },
  {
    "path": "test/test_nn/test_nonlinear/test_log_softmax.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestLogSoftmax(unittest.TestCase):\n\n    def test_forward(self):\n        npx = np.random.randn(5, 3)\n        npy = np.log(np.exp(npx) / np.exp(npx).sum(axis=-1, keepdims=True))\n        self.assertTrue(np.allclose(npy, nn.log_softmax(npx).value))\n\n    def test_backward(self):\n        npx = np.random.randn(1, 5)\n        x = nn.asarray(npx)\n        nn.softmax(x).backward()\n        grad1 = np.copy(x.grad)\n        x.cleargrad()\n        nn.exp(nn.log_softmax(x)).backward()\n        grad2 = np.copy(x.grad)\n        self.assertTrue(np.allclose(grad1, grad2))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_nonlinear/test_sigmoid.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestSigmoid(unittest.TestCase):\n\n    def test_sigmoid(self):\n        npx = np.random.randn(3, 5)\n        x = nn.asarray(npx)\n        y = nn.sigmoid(x)\n        self.assertTrue(np.allclose(y.value, np.tanh(npx * 0.5) * 0.5 + 0.5))\n\n        npg = np.random.randn(3, 5)\n        y.backward(npg)\n        self.assertTrue(np.allclose(x.grad, npg * y.value * (1 - y.value)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_nonlinear/test_softmax.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestSoftmax(unittest.TestCase):\n\n    def test_forward(self):\n        npx = np.random.randn(5, 3)\n        npy = np.exp(npx) / np.exp(npx).sum(axis=-1, keepdims=True)\n        self.assertTrue(np.allclose(npy, nn.softmax(npx).value))\n\n    def test_backward(self):\n        npx = np.random.randn(1, 4)\n        x = nn.asarray(npx)\n        y = nn.square(nn.softmax(x)).sum()\n        y.backward()\n        grad = x.grad\n\n        eps = np.zeros(4)\n        eps[0] = 1e-3\n        numerical_grad = (nn.square(nn.softmax(npx + eps)).sum() - nn.square(nn.softmax(npx - eps)).sum()) / 2e-3\n        self.assertAlmostEqual(grad[0][0], numerical_grad.value[0], 3)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_nonlinear/test_tanh.py",
    "content": "import unittest\n\nimport numpy as np\n\nimport prml.nn as nn\n\n\nclass TestTanh(unittest.TestCase):\n\n    def test_tanh(self):\n        npx = np.random.randn(4, 7)\n        x = nn.asarray(npx)\n        y = nn.tanh(x)\n        self.assertTrue(np.allclose(y.value, np.tanh(npx)))\n\n        npg = np.random.randn(4, 7)\n        y.backward(npg)\n        self.assertTrue(np.allclose(x.grad, npg * (1 - y.value ** 2)))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n"
  },
  {
    "path": "test/test_nn/test_random/__init__.py",
    "content": ""
  }
]