[
  {
    "path": ".gitignore",
    "content": "# sbt specific\ntarget/\nproject/project/\n"
  },
  {
    "path": "CONTRIBUTING.md",
    "content": "## How do I submit a contribution?\n\n1. Create a GitHub issue describing the bug or enhancement at https://github.com/huawei-noah/streamDM/issues, please use the appropriate template. \n2. Once you have your contribution ready, that means that follows the coding convention, with sufficient tests, and is organized into logical commits, you may submit a pull request (PR). Prepend the GitHub issue ID to the title of the pull request (e.g, StreamDM-XX: Fancy new feature).\n3. **Make sure not to include any merge commits in your PR and always rebase on the current master to make it easier for project committers to apply your changes**\n4. Prepend each commit log in a branch with the issue ID. Assuming that you are developing your feature on a branch named after the issue ID (StreamDM-XX):\n`git filter-branch -f --msg-filter 'printf \"StreamDM-XX: \" && cat' StreamDM-XX master..HEAD`\n5. The PR will be reviewed and voted upon.\n\n## General guidelines for contributions\n\n1. Contributions should be self-contained, do not create a general issue and append several changes to it that are unrelated. For example, an issue like \"update streamDM\" that requires changes to the whole framework accompanied by a PR with commits that affect how data sources are used, the instances' representation, add new synthetic data generators, etc, is quite difficult to test as it simply touches every part of the framework. If you want to contribute several changes, create separate issues and separate pull requests. \n2. Follow the best practices for Scala programming. \n3. Follow the framework coding style. For example, learners are parametrized using Option classes, if you try to submit a new learner that receives its configuration through a configuration file, it will require changes to make it similar to how other learners are used. \n\n## Issues\nIssues can be created to describe enhancements or bug reports. Two templates will be available whenever you attempt to create a new Issue. \n\n1. Before creating an issue make sure to search for it in the [closed issues](https://github.com/huawei-noah/streamDM/issues?utf8=✓&q=is%3Aissue%20is%3Aclosed). If a similar one already exists, but the answer does not address your bug/enhancement, then you shall still create yours, but please refer to the previous issue in it.  \n2. Is this a functionality request? E.g. please add support to ...? If so, then use the *Enhancement* template, otherwise use the *Bug report* template. \n3. Fillout the appropriate template below depending on whether you are proposing an *enhancement* (e.g., please add support to ...) or you are creating a *bug report* (e.g., cannot execute learner...). Delete the template that was not used. \n4. Add a descriptive title for the issue. \n"
  },
  {
    "path": "ISSUE_TEMPLATE.md",
    "content": "# Warning\n1. Before submitting the issue, read the [CONTRIBUTING guidelines](https://github.com/huawei-noah/streamDM/blob/master/CONTRIBUTING.md).\n2. Use only one of the templates below to create either an Enhancement or Bug Report issue. \n\n-----------------------------------------------\n\n# Enhancement template\n\n## Description\n_<< What is the purpose of this enhancement >>_\n\n## Resources\n_<< Is it implemented in another machine learning library? Are there academic papers describing the method? >>_\n\n-----------------------------------------------\n\n# Bug Report template\n\n## Expected behavior\n_<< What were you trying to do? In other words, what did you expected to achieve? Describe it in here. >>_\n\n## Observed behavior\n_<< What results have you obtained? Is there an error message? If so, please paste it in here (not all issues produce error messages) >>_\n\n## Steps to reproduce the issue\n\n**Command line**\n_<< What is the command line to reproduce the behavior? >>_\n\n**Data source**\n_<< What data was used? Did you use Kafka? >>_\n\n_<< Does the problem applies to one specific data source only? If so, Include more information about it, such as the features' domain and dimensionality, the data format, and a sample of data. >>_\n\n**Infrastructure details**\n* **Java Version:** \n* **Scala Version:** \n* **Spark version:** \n* **OS version:** \n* **Cluster mode or local mode?** \n"
  },
  {
    "path": "LICENSE",
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  },
  {
    "path": "PULL_REQUEST_TEMPLATE.md",
    "content": "## Warning\n**Make sure you have read the [CONTRIBUTION guidelines](https://github.com/huawei-noah/streamDM/blob/master/CONTRIBUTING.md) and to have an open issue already created before submitting a Pull Request**\n\n## Summary of the changes\n_<< Explain the overall functionality addition or corrections. >>_\n\n_<< Optionally, list all the classes affected by the changes and explain the changes >>_\n\n## Tests\n_<< Any dependency for the tests should be listed in here, such as specific datasets to be used that are not part of the `\\data` directory. >>_\n\n1. _<< Description of test1 >>_\n\n_<< steps to execute test1 >>_\n\n_<< expected output for test1 >>_\n\n2. _<< Description of test2 >>_\n\n_<< steps to execute test2 >>_\n\n_<< expected output for test2 >>_\n\n_<< Include as many tests as necessary >>_\n"
  },
  {
    "path": "README.md",
    "content": "# streamDM for Spark Streaming\r\n\r\nstreamDM is a new open source software for mining big data streams using [Spark Streaming](https://spark.apache.org/streaming/), started at [Huawei Noah's Ark\r\nLab](http://www.noahlab.com.hk/). streamDM is licensed under Apache Software License v2.0.\r\n\r\n## Big Data Stream Learning \r\n\r\nBig Data stream learning is more challenging than batch or offline learning,\r\nsince the data may not keep the same distribution over the lifetime of the\r\nstream. Moreover, each example coming in a stream can only be processed once, or\r\nthey need to be summarized with a small memory footprint, and the learning\r\nalgorithms must be very efficient. \r\n\r\n### Spark Streaming\r\n\r\n[Spark Streaming](https://spark.apache.org/streaming/) is an extension of the\r\ncore [Spark](https://spark.apache.org)  API that enables stream processing from\r\na variety of sources. Spark is a extensible and programmable framework for\r\nmassive distributed processing of datasets, called Resilient Distributed\r\nDatasets (RDD). Spark Streaming receives input data streams and divides the data\r\ninto batches, which are then processed by the Spark engine to generate the\r\nresults.\r\n\r\nSpark Streaming data is organized into a sequence of DStreams, represented\r\ninternally as a sequence of RDDs.\r\n\r\n### <a name=\"methods\"></a>Included Methods\r\n\r\nIn this current release of StreamDM v0.2, we have implemented:\r\n\r\n* [SGD Learner](http://huawei-noah.github.io/streamDM/docs/SGD.html) and [Perceptron](http://huawei-noah.github.io/streamDM/docs/SGD.html#perceptron)\r\n* [Naive Bayes](http://huawei-noah.github.io/streamDM/docs/NB.html)\r\n* [CluStream](http://huawei-noah.github.io/streamDM/docs/CluStream.html)\r\n* [Hoeffding Decision Trees](http://huawei-noah.github.io/streamDM/docs/HDT.html)\r\n* [Bagging](http://huawei-noah.github.io/streamDM/docs/Bagging.html)\r\n* [Stream KM++](http://huawei-noah.github.io/streamDM/docs/StreamKM.html)\r\n\r\nwe also implemented following [data generators](http://huawei-noah.github.io/streamDM/docs/generators.html):\r\n\r\n* HyperplaneGenerator\r\n* RandomTreeGenerator\r\n* RandomRBFGenerator\r\n* RandomRBFEventsGenerator\r\n\r\nWe have also implemented [SampleDataWriter](http://huawei-noah.github.io/streamDM/docs/SampleDataWriter.html), which can call data generators \r\nto create sample data for simulation or test.\r\n\r\nIn the next release of streamDM, we are going to add:\r\n\r\n* Classification: Random Forests\r\n* Multi-label: Hoeffding Tree ML, Random Forests ML\r\n* Frequent Itemset Miner: IncMine\r\n\r\nFor future works, we are considering:\r\n* Regression: Hoeffding Regression Tree, Bagging, Random Forests\r\n* Clustering: Clustree, DenStream\r\n* Frequent Itemset Miner: IncSecMine\r\n\r\n## Going Further\r\n\r\nFor a quick introduction to running StreamDM, refer to the [Getting\r\nStarted](http://huawei-noah.github.io/streamDM/docs/GettingStarted.html) document. The StreamDM [Programming\r\nGuide](http://huawei-noah.github.io/streamDM/docs/Programming.html) presents a detailed view of StreamDM. The full API\r\ndocumentation can be consulted [here](http://huawei-noah.github.io/streamDM/api/index.html). \r\n\r\n## Environment\r\n* Spark 2.3.2\r\n* Scala 2.11\r\n* SBT 0.13\r\n* Java 8+\r\n\r\n## Mailing lists\r\n### User support and questions mailing list:\r\n<a href=\"mailto:streamdm-user@googlegroups.com\">streamdm-user@googlegroups.com</a>\r\n### Development related discussions:\r\n<a href=\"mailto:streamdm-dev@googlegroups.com\">streamdm-dev@googlegroups.com</a>"
  },
  {
    "path": "data/electNormNew.arff",
    "content": "\n\n@attribute date numeric\n@attribute day {1,2,3,4,5,6,7}\n@attribute period numeric\n@attribute nswprice numeric\n@attribute nswdemand numeric\n@attribute vicprice numeric\n@attribute vicdemand numeric\n@attribute transfer numeric\n@attribute class 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0.8433231808257144,-1.8285298482027321,2.9492855980367283,5.858550514495381,-1.6800477423107487,2.979955182152631,3.0511131885804823,3.8740435394361095,2.0083246017038165,-0.8272012286004696\n1.0 3.90884251806584,-0.7593742352627781,-5.203388850869145,-3.6631795020853373,0.8871069672546338,-0.1904373824206381,-4.264041778998914,1.7143973857854933,5.427530781737358,-2.409290618584158\n1.0 0.27572034686328023,1.8014746690850445,0.2032698266301792,1.3386507887822165,-3.0064359322199077,-1.431473411005331,1.8938703020350114,4.921562267979115,-2.900266067280517,-0.027246651198155336\n1.0 1.8383966949490316,4.0254970794068905,-0.44567837617699496,4.980130492888658,0.5150162628061533,4.000015166102082,-1.94849073598736,1.873569315795025,1.5256400885732013,2.141883731256308\n1.0 3.1942221302175695,-2.650658793144979,0.5775124378673134,3.345127242700104,-1.8147432584444996,1.6687338648990226,1.8710780361921415,4.545903632374574,1.3480902089000975,0.32023695184178014\n1.0 2.1845081213678124,-4.501882067673481,-3.884410869229355,2.2806291274440027,-0.678641678239519,-3.19789446116247,1.4555724998915038,-4.012746269234101,0.07295712371801563,1.088268417539073\n1.0 4.807802974249693,-1.1264116019027164,1.2941121828994033,-2.6914364629845107,-5.737263727744244,4.813645602536625,3.031406113563494,-2.8169016819738397,0.11416154649501009,4.621479627855903\n1.0 4.6461986980182,2.849404256020918,-2.1124416564523276,-2.421496986202268,0.2567374568682048,0.8405078017854528,-3.7736932001426227,2.5809078749335437,-4.833267406295731,1.662688700043441\n1.0 1.5824262051883915,4.9230105962904,5.691290443546887,2.3112006127050564,-2.244292001231525,3.752538913807231,-1.008657070421142,1.5708267263813922,-2.9019874213509254,-0.0018665441792759252\n0.0 -1.5047463456687322,-3.989058466576017,0.6901567525963336,-4.359017000677359,-3.2975867821440916,4.9117706840414606,-4.03282077695707,-0.6695664824559351,-4.283010946608917,-2.2438558179235333\n1.0 4.460822044864257,-2.8117154785538787,1.1084805723070663,2.133262443004416,-1.4621495482233193,-3.468436161999969,-1.6831355694764256,-1.1470493575420477,-4.4045018779292855,-4.720938635456944\n0.0 -1.668625330745455,4.55091528708429,0.26918921983937566,3.876019462061013,1.0535155997438654,-3.694121639475719,2.971128572540753,-2.9747092294198207,-4.56295244918775,-1.403245059646414\n0.0 -0.18174755624412103,-5.536961107384564,0.45258960430917056,-4.949743935637655,-1.586947208804014,-3.7899143967638826,-3.7504578489989946,-2.106440740129739,0.9442402633852147,-0.6246635782377676\n0.0 -4.072763748762803,-1.4839883046807847,-4.216495174720168,2.8279677299758705,2.488053414044118,1.8761724558958026,3.8219895445095715,-1.1055412641572415,-1.483574000111422,-1.7445449896117142\n1.0 2.252367372025822,-1.522738579127978,1.8384908108058589,1.494486525070562,2.290364105629293,0.8500397097636444,4.5100135817884786,-3.55285499480278,-4.299402764105944,4.995091567554575\n"
  },
  {
    "path": "data/hyperplanesampledata.arff.head",
    "content": "@relation sample-data\n@attribute NumericFeature0 numeric\n@attribute NumericFeature1 numeric\n@attribute NumericFeature2 numeric\n@attribute NumericFeature3 numeric\n@attribute NumericFeature4 numeric\n@attribute NumericFeature5 numeric\n@attribute NumericFeature6 numeric\n@attribute NumericFeature7 numeric\n@attribute NumericFeature8 numeric\n@attribute NumericFeature9 numeric\n@attribute class {false,true}\n"
  },
  {
    "path": "data/iris.arff",
    "content": "% 1. Title: Iris Plants Database\n% \n% 2. Sources:\n%      (a) Creator: R.A. Fisher\n%      (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n%      (c) Date: July, 1988\n% \n% 3. Past Usage:\n%    - Publications: too many to mention!!!  Here are a few.\n%    1. Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\n%       Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions\n%       to Mathematical Statistics\" (John Wiley, NY, 1950).\n%    2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\n%       (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n%    3. Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n%       Structure and Classification Rule for Recognition in Partially Exposed\n%       Environments\".  IEEE Transactions on Pattern Analysis and Machine\n%       Intelligence, Vol. PAMI-2, No. 1, 67-71.\n%       -- Results:\n%          -- very low misclassification rates (0% for the setosa class)\n%    4. Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE \n%       Transactions on Information Theory, May 1972, 431-433.\n%       -- Results:\n%          -- very low misclassification rates again\n%    5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II\n%       conceptual clustering system finds 3 classes in the data.\n% \n% 4. Relevant Information:\n%    --- This is perhaps the best known database to be found in the pattern\n%        recognition literature.  Fisher's paper is a classic in the field\n%        and is referenced frequently to this day.  (See Duda & Hart, for\n%        example.)  The data set contains 3 classes of 50 instances each,\n%        where each class refers to a type of iris plant.  One class is\n%        linearly separable from the other 2; the latter are NOT linearly\n%        separable from each other.\n%    --- Predicted attribute: class of iris plant.\n%    --- This is an exceedingly simple domain.\n% \n% 5. Number of Instances: 150 (50 in each of three classes)\n% \n% 6. Number of Attributes: 4 numeric, predictive attributes and the class\n% \n% 7. Attribute Information:\n%    1. sepal length in cm\n%    2. sepal width in cm\n%    3. petal length in cm\n%    4. petal width in cm\n%    5. class: \n%       -- Iris Setosa\n%       -- Iris Versicolour\n%       -- Iris Virginica\n% \n% 8. Missing Attribute Values: None\n% \n% Summary Statistics:\n%  \t           Min  Max   Mean    SD   Class Correlation\n%    sepal length: 4.3  7.9   5.84  0.83    0.7826   \n%     sepal width: 2.0  4.4   3.05  0.43   -0.4194\n%    petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)\n%     petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)\n% \n% 9. Class Distribution: 33.3% for each of 3 classes.\n\n@RELATION iris\n\n@ATTRIBUTE sepallength\tREAL\n@ATTRIBUTE sepalwidth \tREAL\n@ATTRIBUTE petallength \tREAL\n@ATTRIBUTE petalwidth\tREAL\n@ATTRIBUTE class \t{Iris-setosa,Iris-versicolor,Iris-virginica}\n\n@DATA\n5.1,3.5,1.4,0.2,Iris-setosa\n4.9,3.0,1.4,0.2,Iris-setosa\n4.7,3.2,1.3,0.2,Iris-setosa\n4.6,3.1,1.5,0.2,Iris-setosa\n5.0,3.6,1.4,0.2,Iris-setosa\n5.4,3.9,1.7,0.4,Iris-setosa\n4.6,3.4,1.4,0.3,Iris-setosa\n5.0,3.4,1.5,0.2,Iris-setosa\n4.4,2.9,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5.4,3.7,1.5,0.2,Iris-setosa\n4.8,3.4,1.6,0.2,Iris-setosa\n4.8,3.0,1.4,0.1,Iris-setosa\n4.3,3.0,1.1,0.1,Iris-setosa\n5.8,4.0,1.2,0.2,Iris-setosa\n5.7,4.4,1.5,0.4,Iris-setosa\n5.4,3.9,1.3,0.4,Iris-setosa\n5.1,3.5,1.4,0.3,Iris-setosa\n5.7,3.8,1.7,0.3,Iris-setosa\n5.1,3.8,1.5,0.3,Iris-setosa\n5.4,3.4,1.7,0.2,Iris-setosa\n5.1,3.7,1.5,0.4,Iris-setosa\n4.6,3.6,1.0,0.2,Iris-setosa\n5.1,3.3,1.7,0.5,Iris-setosa\n4.8,3.4,1.9,0.2,Iris-setosa\n5.0,3.0,1.6,0.2,Iris-setosa\n5.0,3.4,1.6,0.4,Iris-setosa\n5.2,3.5,1.5,0.2,Iris-setosa\n5.2,3.4,1.4,0.2,Iris-setosa\n4.7,3.2,1.6,0.2,Iris-setosa\n4.8,3.1,1.6,0.2,Iris-setosa\n5.4,3.4,1.5,0.4,Iris-setosa\n5.2,4.1,1.5,0.1,Iris-setosa\n5.5,4.2,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5.0,3.2,1.2,0.2,Iris-setosa\n5.5,3.5,1.3,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n4.4,3.0,1.3,0.2,Iris-setosa\n5.1,3.4,1.5,0.2,Iris-setosa\n5.0,3.5,1.3,0.3,Iris-setosa\n4.5,2.3,1.3,0.3,Iris-setosa\n4.4,3.2,1.3,0.2,Iris-setosa\n5.0,3.5,1.6,0.6,Iris-setosa\n5.1,3.8,1.9,0.4,Iris-setosa\n4.8,3.0,1.4,0.3,Iris-setosa\n5.1,3.8,1.6,0.2,Iris-setosa\n4.6,3.2,1.4,0.2,Iris-setosa\n5.3,3.7,1.5,0.2,Iris-setosa\n5.0,3.3,1.4,0.2,Iris-setosa\n7.0,3.2,4.7,1.4,Iris-versicolor\n6.4,3.2,4.5,1.5,Iris-versicolor\n6.9,3.1,4.9,1.5,Iris-versicolor\n5.5,2.3,4.0,1.3,Iris-versicolor\n6.5,2.8,4.6,1.5,Iris-versicolor\n5.7,2.8,4.5,1.3,Iris-versicolor\n6.3,3.3,4.7,1.6,Iris-versicolor\n4.9,2.4,3.3,1.0,Iris-versicolor\n6.6,2.9,4.6,1.3,Iris-versicolor\n5.2,2.7,3.9,1.4,Iris-versicolor\n5.0,2.0,3.5,1.0,Iris-versicolor\n5.9,3.0,4.2,1.5,Iris-versicolor\n6.0,2.2,4.0,1.0,Iris-versicolor\n6.1,2.9,4.7,1.4,Iris-versicolor\n5.6,2.9,3.6,1.3,Iris-versicolor\n6.7,3.1,4.4,1.4,Iris-versicolor\n5.6,3.0,4.5,1.5,Iris-versicolor\n5.8,2.7,4.1,1.0,Iris-versicolor\n6.2,2.2,4.5,1.5,Iris-versicolor\n5.6,2.5,3.9,1.1,Iris-versicolor\n5.9,3.2,4.8,1.8,Iris-versicolor\n6.1,2.8,4.0,1.3,Iris-versicolor\n6.3,2.5,4.9,1.5,Iris-versicolor\n6.1,2.8,4.7,1.2,Iris-versicolor\n6.4,2.9,4.3,1.3,Iris-versicolor\n6.6,3.0,4.4,1.4,Iris-versicolor\n6.8,2.8,4.8,1.4,Iris-versicolor\n6.7,3.0,5.0,1.7,Iris-versicolor\n6.0,2.9,4.5,1.5,Iris-versicolor\n5.7,2.6,3.5,1.0,Iris-versicolor\n5.5,2.4,3.8,1.1,Iris-versicolor\n5.5,2.4,3.7,1.0,Iris-versicolor\n5.8,2.7,3.9,1.2,Iris-versicolor\n6.0,2.7,5.1,1.6,Iris-versicolor\n5.4,3.0,4.5,1.5,Iris-versicolor\n6.0,3.4,4.5,1.6,Iris-versicolor\n6.7,3.1,4.7,1.5,Iris-versicolor\n6.3,2.3,4.4,1.3,Iris-versicolor\n5.6,3.0,4.1,1.3,Iris-versicolor\n5.5,2.5,4.0,1.3,Iris-versicolor\n5.5,2.6,4.4,1.2,Iris-versicolor\n6.1,3.0,4.6,1.4,Iris-versicolor\n5.8,2.6,4.0,1.2,Iris-versicolor\n5.0,2.3,3.3,1.0,Iris-versicolor\n5.6,2.7,4.2,1.3,Iris-versicolor\n5.7,3.0,4.2,1.2,Iris-versicolor\n5.7,2.9,4.2,1.3,Iris-versicolor\n6.2,2.9,4.3,1.3,Iris-versicolor\n5.1,2.5,3.0,1.1,Iris-versicolor\n5.7,2.8,4.1,1.3,Iris-versicolor\n6.3,3.3,6.0,2.5,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n7.1,3.0,5.9,2.1,Iris-virginica\n6.3,2.9,5.6,1.8,Iris-virginica\n6.5,3.0,5.8,2.2,Iris-virginica\n7.6,3.0,6.6,2.1,Iris-virginica\n4.9,2.5,4.5,1.7,Iris-virginica\n7.3,2.9,6.3,1.8,Iris-virginica\n6.7,2.5,5.8,1.8,Iris-virginica\n7.2,3.6,6.1,2.5,Iris-virginica\n6.5,3.2,5.1,2.0,Iris-virginica\n6.4,2.7,5.3,1.9,Iris-virginica\n6.8,3.0,5.5,2.1,Iris-virginica\n5.7,2.5,5.0,2.0,Iris-virginica\n5.8,2.8,5.1,2.4,Iris-virginica\n6.4,3.2,5.3,2.3,Iris-virginica\n6.5,3.0,5.5,1.8,Iris-virginica\n7.7,3.8,6.7,2.2,Iris-virginica\n7.7,2.6,6.9,2.3,Iris-virginica\n6.0,2.2,5.0,1.5,Iris-virginica\n6.9,3.2,5.7,2.3,Iris-virginica\n5.6,2.8,4.9,2.0,Iris-virginica\n7.7,2.8,6.7,2.0,Iris-virginica\n6.3,2.7,4.9,1.8,Iris-virginica\n6.7,3.3,5.7,2.1,Iris-virginica\n7.2,3.2,6.0,1.8,Iris-virginica\n6.2,2.8,4.8,1.8,Iris-virginica\n6.1,3.0,4.9,1.8,Iris-virginica\n6.4,2.8,5.6,2.1,Iris-virginica\n7.2,3.0,5.8,1.6,Iris-virginica\n7.4,2.8,6.1,1.9,Iris-virginica\n7.9,3.8,6.4,2.0,Iris-virginica\n6.4,2.8,5.6,2.2,Iris-virginica\n6.3,2.8,5.1,1.5,Iris-virginica\n6.1,2.6,5.6,1.4,Iris-virginica\n7.7,3.0,6.1,2.3,Iris-virginica\n6.3,3.4,5.6,2.4,Iris-virginica\n6.4,3.1,5.5,1.8,Iris-virginica\n6.0,3.0,4.8,1.8,Iris-virginica\n6.9,3.1,5.4,2.1,Iris-virginica\n6.7,3.1,5.6,2.4,Iris-virginica\n6.9,3.1,5.1,2.3,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n6.8,3.2,5.9,2.3,Iris-virginica\n6.7,3.3,5.7,2.5,Iris-virginica\n6.7,3.0,5.2,2.3,Iris-virginica\n6.3,2.5,5.0,1.9,Iris-virginica\n6.5,3.0,5.2,2.0,Iris-virginica\n6.2,3.4,5.4,2.3,Iris-virginica\n5.9,3.0,5.1,1.8,Iris-virginica\n%\n%\n%\n"
  },
  {
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  },
  {
    "path": "data/randomtreesampledata.arff.head",
    "content": "@relation sample-data\n@attribute NominalFeature0 {0,1,2,3,4}\n@attribute NominalFeature1 {0,1,2,3,4}\n@attribute NominalFeature2 {0,1,2,3,4}\n@attribute NominalFeature3 {0,1,2,3,4}\n@attribute NominalFeature4 {0,1,2,3,4}\n@attribute NumericFeature5 numeric\n@attribute NumericFeature6 numeric\n@attribute NumericFeature7 numeric\n@attribute NumericFeature8 numeric\n@attribute NumericFeature9 numeric\n@attribute class {false,true}\n"
  },
  {
    "path": "data/stream2000_7anom.arff",
    "content": "@relation synthetic_anomaly_detection_stream_data_7anomalies\n\n@attribute x numeric\n@attribute y numeric\n@attribute is_anomaly 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  },
  {
    "path": "data/stream2500_51anom.arff",
    "content": "@relation synthetic_anomaly_detection_stream_data_51anomalies\n\n@attribute x numeric\n@attribute y numeric\n@attribute is_anomaly 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  },
  {
    "path": "learn.sbt",
    "content": "name := \"streamDM (Spark Streaming)\"\n\nversion := \"0.2\"\n\nscalaVersion := \"2.11.8\"\n\nlibraryDependencies += \"org.apache.spark\" %% \"spark-core\" % \"2.3.2\"\n\nlibraryDependencies += \"org.apache.spark\" % \"spark-streaming_2.11\" % \"2.3.2\"\n\nlibraryDependencies += \"org.apache.spark\" %% \"spark-streaming-kafka\" % \"1.6.3\"\n\nlibraryDependencies += \"org.scalatest\"  %% \"scalatest\"   % \"3.0.8\" % Test\n"
  },
  {
    "path": "project/plugins.sbt",
    "content": "addSbtPlugin(\"com.typesafe.sbteclipse\" % \"sbteclipse-plugin\" % \"5.2.4\")\naddSbtPlugin(\"com.github.mpeltonen\" % \"sbt-idea\" % \"1.6.0\")\naddSbtPlugin(\"org.netbeans.nbsbt\" % \"nbsbt-plugin\" % \"1.1.4\")\naddSbtPlugin(\"com.orrsella\" % \"sbt-sublime\" % \"1.1.1\")\n"
  },
  {
    "path": "scripts/generateData.sh",
    "content": "java -cp ../target/scala-2.10/streamdm-spark-streaming-_2.10-0.*.jar:$SPARK_HOME/lib/spark-assembly-1.*.0-hadoop2.*.0.jar org.apache.spark.streamdm.streams.SampleDataWriter $1\n"
  },
  {
    "path": "scripts/instance_server/generate_dataset.py",
    "content": "#!/usr/bin/env python\n\nimport socket\nimport sys\nimport time\nimport random\nfrom thread import *\n\n'''\ncreates a file generating dense instances randomly, by also adding noise\n'''\n\n\nfeatures = 3\nnumInstances = 1000000\nfileoutput = 'syn.dat'\n\ndef label(feat, func):\n  value = 1 if sum([x*y for (x,y) in zip(feat,func[:features-1])])>\\\n      -func[features] else 0\n  return value\n\ndef generate():\n  print 'generating function'\n  func = [random.uniform(-1,1) for i in range(features+1)]\n  fstring = '\\t'\n  for i in range(features): fstring+='%+.3fx_%d' % (func[i],i)\n  fstring += '%+.3f' % func[features]\n  print fstring\n  print 'generating examples'\n  f = open(fileoutput,'w')\n  for x in range (0, numInstances):\n    featgen = [random.uniform(-5,5) for i in range(features)]\n    lab = label(featgen,func)\n    sendstr = '%d ' % lab\n    for feat in featgen: sendstr += '%.3f,' % random.gauss(feat,0.5)\n    f.write(sendstr[:-1]+'\\n')\n  f.close()\n\nif __name__=='__main__':\n  random.seed()\n  generate() \n\n  print str(numInstances)+' instances succesfully generated in file '+fileoutput+'!'\n\n"
  },
  {
    "path": "scripts/instance_server/server.py",
    "content": "#!/usr/bin/env python\n\nimport socket\nimport sys\nimport time\nimport random\nfrom thread import *\n\n'''\ncreates a text socket sending dense instance\n- clientthread_file takes the contents of a file and sends it\n- clientthread generates dense instances randomly, by also adding noise\n- by modifying the sltime value (in second) one can control the speed at which instances get sent\n'''\n\n\nHOST = ''\nPORT = 9999\nfeatures = 3\nsltime = 1.0 / float(sys.argv[2] if len(sys.argv) > 2 else 1000)\nfilein = sys.argv[1] if len(sys.argv) > 1 else 'syn.dat'\n\ndef label(feat, func):\n  value = 1 if sum([x*y for (x,y) in zip(feat,func[:features-1])])>\\\n      -func[features] else 0\n  return value\n\ndef clientthread_file(conn):\n  f = open(filein,'r')\n  print 'sending instances'\n  idx = 0\n  while True:\n    line = f.readline().strip()\n    if not line: pass\n    else:\n      idx += 1\n      if idx%100000==0: print '\\tinstace number %d' % idx\n      try:\n        #print line\n        conn.sendall(line+'\\n')\n        time.sleep(sltime)\n      except socket.error as msg:\n        print 'Error Code: '+str(msg[0]) + ' Message ' + msg[1]\n        break\n  print 'finished instances'\n  conn.close()\n  f.close()\n  sys.exit(0)\n\ndef clientthread(conn):\n  print 'generating function'\n  func = [random.uniform(-1,1) for i in range(features+1)]\n  fstring = '\\t'\n  for i in range(features): fstring+='%+.3fx_%d' % (func[i],i)\n  fstring += '%+.3f' % func[features]\n  print fstring\n  print 'generating examples'\n  f = open('out_instances.dat','w')\n  while True:\n    try:\n      featgen = [random.uniform(-5,5) for i in range(features)]\n      lab = label(featgen,func)\n      sendstr = '%d' % lab\n      for feat in featgen: sendstr += ',%.3f' % random.gauss(feat,0.5)\n      conn.sendall(sendstr+'\\n')\n      f.write(sendstr+'\\n')\n      time.sleep(sltime)\n    except socket.error as msg:\n      print 'Error Code: '+str(msg[0]) + ' Message ' + msg[1]\n      break\n  conn.close()\n  f.close()\n  sys.exit(0)\n\nif __name__=='__main__':\n  random.seed()\n\n  s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n  print 'Socket created'\n\n  try:\n    s.bind((HOST, PORT))\n  except socket.error as msg:\n    print 'Bind failed. Error Code: '+str(msg[0]) + ' Message ' + msg[1]\n    sys.exit()\n\n  print 'Socket bind complete'\n\n  s.listen(10)\n  print 'Socket now listening'\n\n  while 1:\n    conn, addr = s.accept()\n    print 'Connected with ' + addr[0] + ':' + str(addr[1])\n    start_new_thread(clientthread_file, (conn,))\n  \n  s.close()\n"
  },
  {
    "path": "scripts/spark.sh",
    "content": "#!/usr/bin/env bash\n$SPARK_HOME/bin/spark-submit \\\n  --class \"org.apache.spark.streamdm.streamDMJob\" \\\n  --master local[2] \\\n  ../target/scala-2.11/streamdm-spark-streaming-_2.11-0.2.jar \\\n  $1\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/AbstractClassOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\nimport java.io.File;\n\n/**\n * Abstract class option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision$\n */\npublic abstract class AbstractClassOption extends AbstractOption {\n\n    private static final long serialVersionUID = 1L;\n\n    /** The prefix text to use to indicate file.  */\n    public static final String FILE_PREFIX_STRING = \"file:\";\n\n    /** The prefix text to use to indicate inmem.  */\n    public static final String INMEM_PREFIX_STRING = \"inmem:\";\n\n    /** The current object */\n    protected Object currentValue;\n\n    /** The class type */\n    protected Class<?> requiredType;\n\n    /** The default command line interface text. */\n    protected String defaultCLIString;\n\n    /** The null text. */\n    protected String nullString;\n\n    /**\n     * Creates a new instance of an abstract option given its class name,\n     * command line interface text, its purpose, its class type and its default\n     * command line interface text.\n     *\n     * @param name the name of this option\n     * @param cliChar the command line interface text\n     * @param purpose the text describing the purpose of this option\n     * @param requiredType the class type\n     * @param defaultCLIString the default command line interface text\n     */\n    public AbstractClassOption(String name, char cliChar, String purpose,\n            Class<?> requiredType, String defaultCLIString) {\n        this(name, cliChar, purpose, requiredType, defaultCLIString, null);\n    }\n\n    /**\n     * Creates a new instance of an abstract option given its class name,\n     * command line interface text, its purpose, its class type, default\n     * command line interface text, and its null text.\n     *\n     * @param name the name of this option\n     * @param cliChar the command line interface text\n     * @param purpose the text describing the purpose of this option\n     * @param requiredType the class type\n     * @param defaultCLIString the default command line interface text\n     * @param nullString the null text\n     */\n    public AbstractClassOption(String name, char cliChar, String purpose,\n            Class<?> requiredType, String defaultCLIString, String nullString) {\n        super(name, cliChar, purpose);\n        this.requiredType = requiredType;\n        this.defaultCLIString = defaultCLIString;\n        this.nullString = nullString;\n        resetToDefault();\n    }\n\n    /**\n     * Sets current object.\n     *\n     * @param obj the object to set as current.\n     */\n    public void setCurrentObject(Object obj) {\n        if (((obj == null) && (this.nullString != null))\n                || this.requiredType.isInstance(obj)\n                || (obj instanceof String)\n                || (obj instanceof File)\n             //   || ((obj instanceof Task) && this.requiredType.isAssignableFrom(((Task) obj).getTaskResultType()))\n                ) {\n            this.currentValue = obj;\n        } else {\n            throw new IllegalArgumentException(\"Object not of required type.\");\n        }\n    }\n\n    /**\n     * Returns the current object.\n     *\n     * @return the current object\n     */\n    public <T> T getValue() {\n        return (T) this.currentValue;\n    }\n\n    /**\n     * Gets the class type of this option.\n     *\n     * @return the class type of this option\n     */\n    public Class<?> getRequiredType() {\n        return this.requiredType;\n    }\n\n    /**\n     * Gets the null string of this option.\n     *\n     * @return the null string of this option\n     */\n    public String getNullString() {\n        return this.nullString;\n    }\n\n    /**\n     * Gets a materialized object of this option.\n     * \n     * @return the materialized object\n     */\n    public Object materializeObject() {\n        if ((this.currentValue == null)\n                || this.requiredType.isInstance(this.currentValue)) {\n            return this.currentValue;\n        } else if (this.currentValue instanceof File) {\n            File inputFile = (File) this.currentValue;\n            Object result = null;\n            try {\n                result = SerializeUtils.readFromFile(inputFile);\n            } catch (Exception ex) {\n                throw new RuntimeException(\"Problem loading \"\n                        + this.requiredType.getName() + \" object from file '\"\n                        + inputFile.getName() + \"':\\n\" + ex.getMessage(), ex);\n            }\n            return result;\n        } else {\n            throw new RuntimeException(\n                    \"Could not materialize object of required type \"\n                    + this.requiredType.getName() + \", found \"\n                    + this.currentValue.getClass().getName()\n                    + \" instead.\");\n        }\n    }\n\n    @Override\n    public String getDefaultCLIString() {\n        return this.defaultCLIString;\n    }\n\n    /**\n     * Gets the command line interface text of the class.\n     *\n     * @param aClass the class\n     * @param requiredType the class type\n     * @return the command line interface text of the class\n     */\n    public static String classToCLIString(Class<?> aClass, Class<?> requiredType) {\n        String className = aClass.getName();\n        String packageName = requiredType.getPackage().getName();\n        if (className.startsWith(packageName)) {\n            // cut off package name\n            className = className.substring(packageName.length() + 1, className.length());\n        } \n        return className;\n    }\n\n    @Override\n    public abstract String getValueAsCLIString();\n\n    @Override\n    public abstract void setValueViaCLIString(String s);\n\n    /**\n     * Gets the class name without its package name prefix.\n     *\n     * @param className the name of the class\n     * @param expectedType the type of the class\n     * @return the class name without its package name prefix\n     */\n    public static String stripPackagePrefix(String className, Class<?> expectedType) {\n        if (className.startsWith(expectedType.getPackage().getName())) {\n            return className.substring(expectedType.getPackage().getName().length() + 1);\n        }\n        return className;\n    }\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/AbstractOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\npackage com.github.javacliparser;\n\n\n/**\n * Abstract option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic abstract class AbstractOption implements Option {\n\n    /** Array of characters not valid to use in option names. */\n    public static final char[] illegalNameCharacters = new char[]{' ', '-',\n        '(', ')'};\n\n    /** Name of this option. */\n    protected String name;\n\n    /** Command line interface text of this option. */\n    protected char cliChar;\n\n    /** Text of the purpose of this option. */\n    protected String purpose;\n\n    /**\n     * Gets whether the name is valid or not.\n     *\n     * @param optionName the name of the option\n     * @return true if the name that not contain any illegal character\n     */\n    public static boolean nameIsLegal(String optionName) {\n        for (char illegalChar : illegalNameCharacters) {\n            if (optionName.indexOf(illegalChar) >= 0) {\n                return false;\n            }\n        }\n        return true;\n    }\n\n    /**\n     * Creates a new instance of an abstract option given its class name,\n     * command line interface text and its purpose.\n     *\n     * @param name the name of this option\n     * @param cliChar the command line interface text\n     * @param purpose the text describing the purpose of this option\n     */\n    public AbstractOption(String name, char cliChar, String purpose) {\n        if (!nameIsLegal(name)) {\n            throw new IllegalArgumentException(\"Illegal option name: \" + name);\n        }\n        this.name = name;\n        this.cliChar = cliChar;\n        this.purpose = purpose;\n    }\n\n    @Override\n    public String getName() {\n        return this.name;\n    }\n\n    @Override\n    public char getCLIChar() {\n        return this.cliChar;\n    }\n\n    @Override\n    public String getPurpose() {\n        return this.purpose;\n    }\n\n    @Override\n    public void resetToDefault() {\n        setValueViaCLIString(getDefaultCLIString());\n    }\n\n    @Override\n    public String getStateString() {\n        return getValueAsCLIString();\n    }\n\n   \n    @Override\n    public Option copy() {\n        try {\n            return (Option) SerializeUtils.copyObject(this);\n        } catch (Exception e) {\n            throw new RuntimeException(\"Object copy failed.\", e);\n        }\n    }\n    \n    //@Override\n    //public Option copy() {\n    //    return (Option) super.copy();\n    //}\n\n    \n    //@Override\n    //public void getDescription(StringBuilder sb, int indent) {\n        // TODO Auto-generated method stub\n    //}\n\n    //@Override\n    //public JComponent getEditComponent() {\n    //    return new StringOptionEditComponent(this);\n    //}\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/ClassOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\nimport java.io.File;\n\n/** \n * Class option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class ClassOption extends AbstractClassOption {\n\n    private static final long serialVersionUID = 1L;\n\n    public ClassOption(String name, char cliChar, String purpose,\n            Class<?> requiredType, String defaultCLIString) {\n        super(name, cliChar, purpose, requiredType, defaultCLIString);\n    }\n\n    public ClassOption(String name, char cliChar, String purpose,\n            Class<?> requiredType, String defaultCLIString, String nullString) {\n        super(name, cliChar, purpose, requiredType, defaultCLIString, nullString);\n    }\n\n    @Override\n    public String getValueAsCLIString() {\n        if ((this.currentValue == null) && (this.nullString != null)) {\n            return this.nullString;\n        }\n        return objectToCLIString(this.currentValue, this.requiredType);\n    }\n\n    @Override\n    public void setValueViaCLIString(String s) {\n        if ((this.nullString != null)\n                && ((s == null) || (s.length() == 0) || s.equals(this.nullString))) {\n            this.currentValue = null;\n        } else {\n            try {\n                this.currentValue = cliStringToObject(s, this.requiredType,\n                        null);\n            } catch (Exception e) {\n                throw new IllegalArgumentException(\"Problems with option: \" + getName(), e);\n            }\n        }\n    }\n\n    public static String objectToCLIString(Object obj, Class<?> requiredType) {\n        if (obj == null) {\n            return \"\";\n        }\n        if (obj instanceof File) {\n            return (FILE_PREFIX_STRING + ((File) obj).getPath());\n        }\n        if (obj instanceof String) {\n            return (INMEM_PREFIX_STRING + obj);\n        }\n        String className = classToCLIString(obj.getClass(), requiredType);\n        if (obj instanceof Configurable) {\n            //Add cli parser\n            JavaCLIParser config = new JavaCLIParser(obj, \"\");\n            String subOptions = config.getOptions().getAsCLIString();\n            //String subOptions = ((Configurable) obj).getOptions().getAsCLIString();\n            if (subOptions.length() > 0) {\n                return (className + \" \" + subOptions);\n            }\n        }\n        return className;\n    }\n\n    public static <T> T createObject(String cliString,\n            Class<T> requiredType) throws Exception {\n        return cliStringToObject(cliString, requiredType, null);\n    }\n    \n    public static <T> T createObject(String[] args,\n            Class<T> requiredType) throws Exception {\n            // build a single string by concatenating cli options\n            StringBuilder cliString = new StringBuilder();\n            for (int i = 0; i < args.length; i++) {\n                cliString.append(\" \").append(args[i]);\n            }\n            return cliStringToObject(cliString.toString(), requiredType, null);\n    }\n    \n    public static <T> T cliStringToObject(String cliString,\n            Class<T> requiredType, Option[] externalOptions) throws Exception {\n       /* if (cliString.startsWith(FILE_PREFIX_STRING)) {\n            return new File(cliString.substring(FILE_PREFIX_STRING.length()));\n        }\n        if (cliString.startsWith(INMEM_PREFIX_STRING)) {\n            return cliString.substring(INMEM_PREFIX_STRING.length());\n        }*/\n        cliString = cliString.trim();\n        int firstSpaceIndex = cliString.indexOf(' ', 0);\n        String className;\n        String classOptions;\n        if (firstSpaceIndex > 0) {\n            className = cliString.substring(0, firstSpaceIndex);\n            classOptions = cliString.substring(firstSpaceIndex + 1, cliString.length());\n            classOptions = classOptions.trim();\n        } else {\n            className = cliString;\n            classOptions = \"\";\n        }\n        Class<?> classObject;\n        try {\n            classObject = Class.forName(className);\n        } catch (Throwable t1) {\n            try {\n                // try prepending default package\n                classObject = Class.forName(requiredType.getPackage().getName()\n                        + \".\" + className);\n            } catch (Throwable t3) {\n                    throw new Exception(\"Class not found: \" + className);\n            }\n        }\n        Object classInstance;\n        try {\n            classInstance = classObject.newInstance();\n        } catch (Exception ex) {\n            throw new Exception(\"Problem creating instance of class: \"\n                    + className, ex);\n        }\n        if (requiredType.isInstance(classInstance)\n                 ) {\n            Options options = new Options();\n            JavaCLIParser config = null;\n            if (externalOptions != null) {\n                for (Option option : externalOptions) {\n                    options.addOption(option);\n                }\n            }\n            if (classInstance instanceof Configurable) {\n                 config = new JavaCLIParser(classInstance, \"\");\n                 Option[] objectOptions = config.getOptions().getOptionArray();\n                for (Option option : objectOptions) {\n                    options.addOption(option);\n                }\n            }\n            try {\n                options.setViaCLIString(classOptions);\n            } catch (Exception ex) {\n                throw new Exception(\"Problem with options to '\"\n                        + className\n                        + \"'.\"\n                        + \"\\n\\nValid options for \"\n                        + className\n                        + \":\\n\"\n                        + config == null ? \"\": config.getOptions().getHelpString(), ex);\n            } finally {\n                options.removeAllOptions(); // clean up listener refs\n            }\n        } else {\n            throw new Exception(\"Class named '\" + className\n                    + \"' is not an instance of \" + requiredType.getName() + \".\");\n        }\n        return requiredType.cast(classInstance);\n    }\n\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/Configurable.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\nimport java.io.Serializable;\n\n/** \n * Configurable Interface.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic interface Configurable extends Serializable {\n\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/FileOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\nimport java.io.File;\n\n/**\n * File option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class FileOption extends StringOption {\n\n    private static final long serialVersionUID = 1L;\n\n    protected String defaultFileExtension;\n\n    protected boolean isOutputFile;\n\n    public FileOption(String name, char cliChar, String purpose,\n            String defaultFileName, String defaultExtension, boolean isOutput) {\n        super(name, cliChar, purpose, defaultFileName);\n        this.defaultFileExtension = defaultExtension;\n        this.isOutputFile = isOutput;\n    }\n\n    public String getDefaultFileExtension() {\n        return this.defaultFileExtension;\n    }\n\n    public boolean isOutputFile() {\n        return this.isOutputFile;\n    }\n\n    public File getFile() {\n        if ((getValue() != null) && (getValue().length() > 0)) {\n            return new File(getValue());\n        }\n        return null;\n    }\n\n    //@Override\n    //public JComponent getEditComponent() {\n    //    return new FileOptionEditComponent(this);\n    //}\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/FlagOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\n/**\n * Flag option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class FlagOption extends AbstractOption {\n\n    private static final long serialVersionUID = 1L;\n\n    protected boolean isSet = false;\n\n    public FlagOption(String name, char cliChar, String purpose) {\n        super(name, cliChar, purpose);\n    }\n\n    public void setValue(boolean v) {\n        this.isSet = v;\n    }\n\n    public void set() {\n        setValue(true);\n    }\n\n    public void unset() {\n        setValue(false);\n    }\n\n    public boolean isSet() {\n        return this.isSet;\n    }\n\n    @Override\n    public String getDefaultCLIString() {\n        return null;\n    }\n\n    @Override\n    public String getValueAsCLIString() {\n        return this.isSet ? \"\" : null;\n    }\n\n    @Override\n    public void setValueViaCLIString(String s) {\n        this.isSet = (s != null);\n    }\n\n    @Override\n    public String getStateString() {\n        return this.isSet ? \"true\" : \"false\";\n    }\n\n    //@Override\n    //public JComponent getEditComponent() {\n    //    return new FlagOptionEditComponent(this);\n    //}\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/FloatOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\n\n/**\n * Float option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class FloatOption extends AbstractOption {\n\n    private static final long serialVersionUID = 1L;\n\n    protected double currentVal;\n\n    protected double defaultVal;\n\n    protected double minVal;\n\n    protected double maxVal;\n\n    public FloatOption(String name, char cliChar, String purpose,\n            double defaultVal) {\n        this(name, cliChar, purpose, defaultVal, Double.NEGATIVE_INFINITY,\n                Double.POSITIVE_INFINITY);\n    }\n\n    public FloatOption(String name, char cliChar, String purpose,\n            double defaultVal, double minVal, double maxVal) {\n        super(name, cliChar, purpose);\n        this.defaultVal = defaultVal;\n        this.minVal = minVal;\n        this.maxVal = maxVal;\n        resetToDefault();\n    }\n\n    public void setValue(double v) {\n        if (v < this.minVal) {\n            throw new IllegalArgumentException(\"Option \" + getName()\n                    + \" cannot be less than \" + this.minVal\n                    + \", out of range: \" + v);\n        }\n        if (v > this.maxVal) {\n            throw new IllegalArgumentException(\"Option \" + getName()\n                    + \" cannot be greater than \" + this.maxVal\n                    + \", out of range: \" + v);\n        }\n        this.currentVal = v;\n    }\n\n    public double getValue() {\n        return this.currentVal;\n    }\n\n    public double getMinValue() {\n        return this.minVal;\n    }\n\n    public double getMaxValue() {\n        return this.maxVal;\n    }\n\n    @Override\n    public String getDefaultCLIString() {\n        return doubleToCLIString(this.defaultVal);\n    }\n\n    @Override\n    public String getValueAsCLIString() {\n        return doubleToCLIString(this.currentVal);\n    }\n\n    @Override\n    public void setValueViaCLIString(String s) {\n        setValue(cliStringToDouble(s));\n    }\n\n    public static double cliStringToDouble(String s) {\n        return Double.parseDouble(s.trim());\n    }\n\n    public static String doubleToCLIString(double d) {\n        return Double.toString(d);\n    }\n\n    //@Override\n    //public JComponent getEditComponent() {\n    //    return new FloatOptionEditComponent(this);\n    //}\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/IntOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\n\n/**\n * Integer option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class IntOption extends AbstractOption {\n\n    private static final long serialVersionUID = 1L;\n\n    protected int currentVal;\n\n    protected int defaultVal;\n\n    protected int minVal;\n\n    protected int maxVal;\n\n    public IntOption(String name, char cliChar, String purpose, int defaultVal) {\n        this(name, cliChar, purpose, defaultVal, Integer.MIN_VALUE,\n                Integer.MAX_VALUE);\n    }\n\n    public IntOption(String name, char cliChar, String purpose, int defaultVal,\n            int minVal, int maxVal) {\n        super(name, cliChar, purpose);\n        this.defaultVal = defaultVal;\n        this.minVal = minVal;\n        this.maxVal = maxVal;\n        resetToDefault();\n    }\n\n    public void setValue(int v) {\n        if (v < this.minVal) {\n            throw new IllegalArgumentException(\"Option \" + getName()\n                    + \" cannot be less than \" + this.minVal\n                    + \", out of range: \" + v);\n        }\n        if (v > this.maxVal) {\n            throw new IllegalArgumentException(\"Option \" + getName()\n                    + \" cannot be greater than \" + this.maxVal\n                    + \", out of range: \" + v);\n        }\n        this.currentVal = v;\n    }\n\n    public int getValue() {\n        return this.currentVal;\n    }\n\n    public int getMinValue() {\n        return this.minVal;\n    }\n\n    public int getMaxValue() {\n        return this.maxVal;\n    }\n\n    @Override\n    public String getDefaultCLIString() {\n        return intToCLIString(this.defaultVal);\n    }\n\n    @Override\n    public String getValueAsCLIString() {\n        return intToCLIString(this.currentVal);\n    }\n\n    @Override\n    public void setValueViaCLIString(String s) {\n        setValue(cliStringToInt(s));\n    }\n\n    public static int cliStringToInt(String s) {\n        return Integer.parseInt(s.trim());\n    }\n\n    public static String intToCLIString(int i) {\n        return Integer.toString(i);\n    }\n\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/JavaCLIParser.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\nimport java.io.Serializable;\nimport java.lang.reflect.Field;\nimport java.util.HashMap;\nimport java.util.LinkedList;\nimport java.util.List;\nimport java.util.Map;\n\n/**\n * Java Command Line Interface Parser.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class JavaCLIParser implements Serializable {\n\n    public Object handler;\n    \n    public JavaCLIParser(Object c, String cliString) {\n        this.handler = c;\n    }\n      \n    private static final long serialVersionUID = 1L;\n\n    /** Options to handle */\n    protected Options options;\n\n    /** Dictionary with option texts and objects */\n    protected Map<String, Object> classOptionNamesToPreparedObjects;\n\n\n    public String getPurposeString() {\n        return \"Anonymous object: purpose undocumented.\";\n    }\n\n    public Options getOptions() {\n        if (this.options == null) {\n            this.options = new Options();\n            Option[] myOptions = discoverOptionsViaReflection();\n            for (Option option : myOptions) {\n                this.options.addOption(option);\n            }\n        }\n        return this.options;\n    }\n\n    /**\n     * Gets the options of this class via reflection.\n     *\n     * @return an array of options\n     */\n    public Option[] discoverOptionsViaReflection() {\n        //Class<? extends AbstractOptionHandler> c = this.getClass();\n        Class c = this.handler.getClass();\n        Field[] fields = c.getDeclaredFields();\n        List<Option> optList = new LinkedList<Option>();\n        for (Field field : fields) {\n            String fName = field.getName();\n            Class<?> fType = field.getType();\n            if (fType.getName().endsWith(\"Option\")) {\n                if (Option.class.isAssignableFrom(fType)) {\n                    Option oVal = null;\n                    try {\n                        field.setAccessible(true);\n                        oVal = (Option) field.get(this.handler);\n                    } catch (IllegalAccessException ignored) {\n                        // cannot access this field\n                    }\n                    if (oVal != null) {\n                        optList.add(oVal);\n                    }\n                }\n            }\n        }\n        return optList.toArray(new Option[optList.size()]);\n    }\n\n    /**\n     * Prepares the options of this class.\n     * \n     */\n    public void prepareClassOptions() { \n        this.classOptionNamesToPreparedObjects = null;\n        Option[] optionArray = getOptions().getOptionArray();\n        for (Option option : optionArray) {\n            if (option instanceof ClassOption) {\n                ClassOption classOption = (ClassOption) option;\n                Object optionObj = classOption.materializeObject(); //monitor,\n                if (optionObj instanceof Configurable) {\n                    JavaCLIParser config = new JavaCLIParser(optionObj, \"\");\n                }\n                if (this.classOptionNamesToPreparedObjects == null) {\n                    this.classOptionNamesToPreparedObjects = new HashMap<String, Object>();\n                }\n                this.classOptionNamesToPreparedObjects.put(option.getName(),\n                        optionObj);\n            }\n        }\n    }\n\n    /**\n     *  Gets a prepared option of this class.\n     *\n     * @param opt the class option to get\n     * @return an option stored in the dictionary\n     */\n    public Object getPreparedClassOption(ClassOption opt) {\n        return this.classOptionNamesToPreparedObjects.get(opt.getName());\n    }\n\n    \n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/ListOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\n/**\n * List option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class ListOption extends AbstractOption {\n\n    private static final long serialVersionUID = 1L;\n\n    protected Option[] currentList;\n\n    protected Option expectedType;\n\n    protected Option[] defaultList;\n\n    protected char separatorChar;\n\n    public ListOption(String name, char cliChar, String purpose,\n            Option expectedType, Option[] defaultList, char separatorChar) {\n        super(name, cliChar, purpose);\n        this.expectedType = expectedType;\n        this.defaultList = defaultList.clone();\n        this.separatorChar = separatorChar;\n        resetToDefault();\n    }\n\n    public void setList(Option[] optList) {\n        Option[] newArray = new Option[optList.length];\n        for (int i = 0; i < optList.length; i++) {\n            newArray[i] = this.expectedType.copy();\n            newArray[i].setValueViaCLIString(optList[i].getValueAsCLIString());\n        }\n        this.currentList = newArray;\n    }\n\n    public Option[] getList() {\n        return this.currentList.clone();\n    }\n\n    @Override\n    public String getDefaultCLIString() {\n        return optionArrayToCLIString(this.defaultList, this.separatorChar);\n    }\n\n    @Override\n    public String getValueAsCLIString() {\n        return optionArrayToCLIString(this.currentList, this.separatorChar);\n    }\n\n    @Override\n    public void setValueViaCLIString(String s) {\n        this.currentList = cliStringToOptionArray(s, this.separatorChar,\n                this.expectedType);\n    }\n\n    public static Option[] cliStringToOptionArray(String s, char separator,\n            Option expectedType) {\n\t if (s == null || s.length() < 1) {\n             return new Option[0];\n         }\n        String[] subStrings = s.split(Character.toString(separator));\n        Option[] options = new Option[subStrings.length];\n        for (int i = 0; i < options.length; i++) {\n            options[i] = expectedType.copy();\n            options[i].setValueViaCLIString(subStrings[i]);\n        }\n        return options;\n    }\n\n    public static String optionArrayToCLIString(Option[] os, char separator) {\n        StringBuilder sb = new StringBuilder();\n        for (int i = 0; i < os.length; i++) {\n            if (i > 0) {\n                sb.append(separator);\n            }\n            sb.append(os[i].getValueAsCLIString());\n        }\n        return sb.toString();\n    }\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/MultiChoiceOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\n\n/**\n * Multi choice option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class MultiChoiceOption extends AbstractOption {\n\n    private static final long serialVersionUID = 1L;\n\n    protected String[] optionLabels;\n\n    protected String[] optionDescriptions;\n\n    protected int defaultOptionIndex;\n\n    protected int chosenOptionIndex;\n\n    public MultiChoiceOption(String name, char cliChar, String purpose,\n            String[] optionLabels, String[] optionDescriptions,\n            int defaultOptionIndex) {\n        super(name, cliChar, purpose);\n        if (optionLabels.length != optionDescriptions.length) {\n            throw new IllegalArgumentException(\"Labels/descriptions mismatch.\");\n        }\n        this.optionLabels = optionLabels.clone();\n        this.optionDescriptions = optionDescriptions.clone();\n        this.defaultOptionIndex = defaultOptionIndex;\n        resetToDefault();\n    }\n\n    @Override\n    public String getDefaultCLIString() {\n        return this.optionLabels[this.defaultOptionIndex];\n    }\n\n    @Override\n    public String getValueAsCLIString() {\n        return getChosenLabel();\n    }\n\n    @Override\n    public void setValueViaCLIString(String s) {\n        try {\n            setChosenIndex(Integer.parseInt(s.trim()));\n        } catch (NumberFormatException nfe) {\n            setChosenLabel(s);\n        }\n    }\n\n    public void setChosenLabel(String label) {\n        label = label.trim();\n        for (int i = 0; i < this.optionLabels.length; i++) {\n            if (this.optionLabels[i].equals(label)) {\n                this.chosenOptionIndex = i;\n                return;\n            }\n        }\n        throw new IllegalArgumentException(\"Label not recognised: \" + label);\n    }\n\n    public void setChosenIndex(int index) {\n        if ((index < 0) || (index >= this.optionLabels.length)) {\n            throw new IndexOutOfBoundsException();\n        }\n        this.chosenOptionIndex = index;\n    }\n\n    public String[] getOptionLabels() {\n        return this.optionLabels.clone();\n    }\n\n    public String getChosenLabel() {\n        return this.optionLabels[this.chosenOptionIndex];\n    }\n\n    public int getChosenIndex() {\n        return this.chosenOptionIndex;\n    }\n\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/Option.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\nimport java.io.Serializable;\n\n/**\n * Interface representing an option or parameter. \n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $ \n */\npublic interface Option extends Serializable {\n\n    /**\n     * Gets the name of this option\n     *\n     * @return the name of this option\n     */\n    public String getName();\n\n    /**\n     * Gets the Command Line Interface text of this option\n     *\n     * @return the Command Line Interface text\n     */\n    public char getCLIChar();\n\n    /**\n     * Gets the purpose of this option\n     *\n     * @return the purpose of this option\n     */\n    public String getPurpose();\n\n    /**\n     * Gets the Command Line Interface text\n     *\n     * @return the Command Line Interface text\n     */\n    public String getDefaultCLIString();\n\n    /**\n     * Sets value of this option via the Command Line Interface text\n     *\n     * @param s the Command Line Interface text\n     */\n    public void setValueViaCLIString(String s);\n\n    /**\n     * Gets the value of a Command Line Interface text as a string\n     *\n     * @return the string with the value of the Command Line Interface text\n     */\n    public String getValueAsCLIString();\n\n    /**\n     * Resets this option to the default value\n     *\n     */\n    public void resetToDefault();\n\n    /**\n     * Gets the state of this option in human readable form\n     *\n     * @return the string with state of this option in human readable form\n     */\n    public String getStateString();\n\n    /**\n     * Gets a copy of this option\n     *\n     * @return the copy of this option\n     */\n    public Option copy();\n\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/Options.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\nimport java.io.Serializable;\nimport java.util.LinkedList;\nimport java.util.List;\n\n/**\n * Option List.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class Options implements Serializable {//extends AbstractMOAObject {\n\n    private static final long serialVersionUID = 1L;\n\n    protected List<Option> optionList = new LinkedList<Option>();\n\n    public void addOption(Option opt) {\n        if (getOption(opt.getName()) != null) {\n            throw new IllegalArgumentException(\"Duplicate option name: \"\n                    + opt.getName());\n        }\n        if (getOption(opt.getCLIChar()) != null) {\n            throw new IllegalArgumentException(\n                    \"Duplicate option command line character: \"\n                    + opt.getCLIChar());\n        }\n        this.optionList.add(opt);\n    }\n\n    public int numOptions() {\n        return this.optionList.size();\n    }\n\n    public Option getOption(String optName) {\n        for (Option option : this.optionList) {\n            if (optName.equals(option.getName())) {\n                return option;\n            }\n        }\n        return null;\n    }\n\n    public Option getOption(char cliChar) {\n        for (Option option : this.optionList) {\n            if (option.getCLIChar() == cliChar) {\n                return option;\n            }\n        }\n        return null;\n    }\n\n    public Option[] getOptionArray() {\n        return this.optionList.toArray(new Option[this.optionList.size()]);\n    }\n\n    public void removeOption(String optName) {\n        removeOption(getOption(optName));\n    }\n\n    public void removeOption(Option opt) {\n        this.optionList.remove(opt);\n    }\n\n    public void removeAllOptions() {\n        this.optionList = new LinkedList<Option>();\n    }\n\n    public void resetToDefaults() {\n        for (Option option : this.optionList) {\n            option.resetToDefault();\n        }\n    }\n\n    public void setViaCLIString(String cliString) {\n        cliString = cliString.trim();\n        while (cliString.length() > 0) {\n            if (cliString.startsWith(\"-\")) {\n                boolean flagClusterFound = false;\n                String optionString = null;\n                int nextSpaceIndex = cliString.indexOf(' ', 1);\n                Option opt;\n                if (nextSpaceIndex > 0) {\n                    optionString = cliString.substring(1, nextSpaceIndex);\n                } else {\n                    optionString = cliString.substring(1, cliString.length());\n                    nextSpaceIndex = cliString.length() - 1;\n                }\n                if (optionString.length() == 1) {\n                    opt = getOption(optionString.charAt(0));\n                } else {\n                    opt = getOption(optionString);\n                    if (opt == null) {\n                        // check for cluster of flags\n                        flagClusterFound = true;\n                        for (int i = 0; i < optionString.length(); i++) {\n                            opt = getOption(optionString.charAt(i));\n                            if (!(opt instanceof FlagOption)) {\n                                flagClusterFound = false;\n                                opt = null;\n                                break;\n                            }\n                        }\n                        if (flagClusterFound) {\n                            for (int i = 0; i < optionString.length(); i++) {\n                                opt = getOption(optionString.charAt(i));\n                                opt.setValueViaCLIString(\"\");\n                            }\n                            cliString = cliString.substring(nextSpaceIndex + 1,\n                                    cliString.length());\n                        }\n                    }\n                }\n                if (!flagClusterFound) {\n                    if (opt != null) {\n                        String parameters = cliString.substring(\n                                nextSpaceIndex + 1, cliString.length());\n                        if (opt instanceof FlagOption) {\n                            opt.setValueViaCLIString(\"\");\n                            cliString = parameters;\n                        } else {\n                            String[] paramSplit = splitParameterFromRemainingOptions(parameters);\n                            opt.setValueViaCLIString(paramSplit[0]);\n                            cliString = paramSplit[1];\n                        }\n                    } else {\n                        throw new IllegalArgumentException(\"Unknown option: -\"\n                                + optionString);\n                    }\n                }\n            } else {\n                throw new IllegalArgumentException(\"Expecting option, found: '\"\n                        + cliString + \"'.\");\n            }\n            cliString = cliString.trim();\n        }\n    }\n\n    public String getAsCLIString() {\n        StringBuilder commandLine = new StringBuilder();\n        for (Option option : this.optionList) {\n            String value = option.getValueAsCLIString();\n            if ((value != null) && !value.equals(option.getDefaultCLIString())) {\n                if (commandLine.length() > 0) {\n                    commandLine.append(\" \");\n                }\n                commandLine.append(\"-\" + option.getCLIChar());\n                if (value.length() > 0) {\n                    if (value.indexOf(' ') < 0) {\n                        commandLine.append(\" \" + value);\n                    } else {\n                        commandLine.append(\" (\" + value + \")\");\n                    }\n                }\n            }\n        }\n        return commandLine.toString();\n    }\n\n    public String getHelpString() {\n        StringBuilder sb = new StringBuilder();\n        getHelp(sb, 0);\n        return sb.toString();\n    }\n\n    public void getHelp(StringBuilder sb, int indent) {\n        if (optionList.size() > 0) {\n            for (Option option : optionList) {\n                StringUtils.appendIndent(sb, indent);\n                sb.append('-');\n                sb.append(option.getCLIChar());\n                sb.append(' ');\n                sb.append(option.getName());\n                String defaultString = option.getDefaultCLIString();\n                if (defaultString != null && defaultString.length() > 0) {\n                    sb.append(\" (default: \");\n                    sb.append(defaultString);\n                    sb.append(')');\n                }\n                StringUtils.appendNewline(sb);\n                StringUtils.appendIndent(sb, indent);\n                sb.append(option.getPurpose());\n                StringUtils.appendNewline(sb);\n            }\n        } else {\n            StringUtils.appendIndented(sb, indent, \"No options.\");\n        }\n    }\n\n    /**\n     * Internal method that splits a string into two parts - the parameter for\n     * the current option, and the remaining options.\n     *\n     * @param cliString\n     *            the command line string, beginning at an option parameter\n     * @return an array of two strings - the first is the option paramter, the\n     *         second is the remaining cli string\n     */\n    protected static String[] splitParameterFromRemainingOptions(\n            String cliString) {\n        String[] paramSplit = new String[2];\n        cliString = cliString.trim();\n        if (cliString.startsWith(\"\\\"\") || cliString.startsWith(\"'\")) {\n            int endQuoteIndex = cliString.indexOf(cliString.charAt(0), 1);\n            if (endQuoteIndex < 0) {\n                throw new IllegalArgumentException(\n                        \"Quotes not terminated correctly.\");\n            }\n            paramSplit[0] = cliString.substring(1, endQuoteIndex);\n            paramSplit[1] = cliString.substring(endQuoteIndex + 1, cliString.length());\n        } else if (cliString.startsWith(\"(\")) {\n            int bracketsOpen = 1;\n            int currPos = 1;\n            int nextCloseIndex = cliString.indexOf(\")\", currPos);\n            int nextOpenIndex = cliString.indexOf(\"(\", currPos);\n            while (bracketsOpen != 0) {\n                if (nextCloseIndex < 0) {\n                    throw new IllegalArgumentException(\"Brackets do not match.\");\n                } else if ((nextOpenIndex < 0)\n                        || (nextCloseIndex < nextOpenIndex)) {\n                    bracketsOpen--;\n                    currPos = nextCloseIndex + 1;\n                    nextCloseIndex = cliString.indexOf(\")\", currPos);\n                } else {\n                    bracketsOpen++;\n                    currPos = nextOpenIndex + 1;\n                    nextOpenIndex = cliString.indexOf(\"(\", currPos);\n                }\n            }\n            paramSplit[0] = cliString.substring(1, currPos - 1);\n            paramSplit[1] = cliString.substring(currPos, cliString.length());\n        } else {\n            int firstSpaceIndex = cliString.indexOf(\" \", 0);\n            if (firstSpaceIndex >= 0) {\n                paramSplit[0] = cliString.substring(0, firstSpaceIndex);\n                paramSplit[1] = cliString.substring(firstSpaceIndex + 1,\n                        cliString.length());\n            } else {\n                paramSplit[0] = cliString;\n                paramSplit[1] = \"\";\n            }\n        }\n        return paramSplit;\n    }\n\n    public void getDescription(StringBuilder sb, int indent) {\n        // TODO Auto-generated method stub\n    }\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/SerializeUtils.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\nimport java.io.BufferedInputStream;\nimport java.io.BufferedOutputStream;\nimport java.io.ByteArrayInputStream;\nimport java.io.ByteArrayOutputStream;\nimport java.io.File;\nimport java.io.FileInputStream;\nimport java.io.FileOutputStream;\nimport java.io.IOException;\nimport java.io.ObjectInputStream;\nimport java.io.ObjectOutputStream;\nimport java.io.OutputStream;\nimport java.io.Serializable;\nimport java.util.zip.GZIPInputStream;\nimport java.util.zip.GZIPOutputStream;\n\n/**\n * Class implementing some serialize utility methods.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class SerializeUtils {\n\n    public static class ByteCountingOutputStream extends OutputStream {\n\n        protected int numBytesWritten = 0;\n\n        public int getNumBytesWritten() {\n            return this.numBytesWritten;\n        }\n\n        @Override\n        public void write(int b) throws IOException {\n            this.numBytesWritten++;\n        }\n\n        @Override\n        public void write(byte[] b, int off, int len) throws IOException {\n            this.numBytesWritten += len;\n        }\n\n        @Override\n        public void write(byte[] b) throws IOException {\n            this.numBytesWritten += b.length;\n        }\n    }\n\n    public static void writeToFile(File file, Serializable obj)\n            throws IOException {\n        ObjectOutputStream out = new ObjectOutputStream(new GZIPOutputStream(\n                new BufferedOutputStream(new FileOutputStream(file))));\n        out.writeObject(obj);\n        out.flush();\n        out.close();\n    }\n\n    public static Object readFromFile(File file) throws IOException,\n            ClassNotFoundException {\n        ObjectInputStream in = new ObjectInputStream(new GZIPInputStream(\n                new BufferedInputStream(new FileInputStream(file))));\n        Object obj = in.readObject();\n        in.close();\n        return obj;\n    }\n\n    public static Object copyObject(Serializable obj) throws Exception {\n        ByteArrayOutputStream baoStream = new ByteArrayOutputStream();\n        ObjectOutputStream out = new ObjectOutputStream(\n                new BufferedOutputStream(baoStream));\n        out.writeObject(obj);\n        out.flush();\n        out.close();\n        byte[] byteArray = baoStream.toByteArray();\n        ObjectInputStream in = new ObjectInputStream(new BufferedInputStream(\n                new ByteArrayInputStream(byteArray)));\n        Object copy = in.readObject();\n        in.close();\n        return copy;\n    }\n\n    public static int measureObjectByteSize(Serializable obj) throws Exception {\n        ByteCountingOutputStream bcoStream = new ByteCountingOutputStream();\n        ObjectOutputStream out = new ObjectOutputStream(\n                new BufferedOutputStream(bcoStream));\n        out.writeObject(obj);\n        out.flush();\n        out.close();\n        return bcoStream.getNumBytesWritten();\n    }\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/StringOption.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\n/**\n * String option.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class StringOption extends AbstractOption {\n\n    private static final long serialVersionUID = 1L;\n\n    protected String currentVal;\n\n    protected String defaultVal;\n\n    public StringOption(String name, char cliChar, String purpose,\n            String defaultVal) {\n        super(name, cliChar, purpose);\n        this.defaultVal = defaultVal;\n        resetToDefault();\n    }\n\n    public void setValue(String v) {\n        this.currentVal = v;\n    }\n\n    public String getValue() {\n        return this.currentVal;\n    }\n\n    @Override\n    public String getDefaultCLIString() {\n        return this.defaultVal;\n    }\n\n    @Override\n    public String getValueAsCLIString() {\n        return this.currentVal;\n    }\n\n    @Override\n    public void setValueViaCLIString(String s) {\n        setValue(s);\n    }\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/StringUtils.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser;\n\nimport java.text.DecimalFormat;\n\n/**\n * Class implementing some string utility methods.\n *\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 7 $\n */\npublic class StringUtils {\n\n    public static final String newline = System.getProperty(\"line.separator\");\n\n    public static String doubleToString(double value, int fractionDigits) {\n        return doubleToString(value, 0, fractionDigits);\n    }\n\n    public static String doubleToString(double value, int minFractionDigits,\n            int maxFractionDigits) {\n        DecimalFormat numberFormat = new DecimalFormat();\n        numberFormat.setMinimumFractionDigits(minFractionDigits);\n        numberFormat.setMaximumFractionDigits(maxFractionDigits);\n        return numberFormat.format(value);\n    }\n\n    public static void appendNewline(StringBuilder out) {\n        out.append(newline);\n    }\n\n    public static void appendIndent(StringBuilder out, int indent) {\n        for (int i = 0; i < indent; i++) {\n            out.append(' ');\n        }\n    }\n\n    public static void appendIndented(StringBuilder out, int indent, String s) {\n        appendIndent(out, indent);\n        out.append(s);\n    }\n\n    public static void appendNewlineIndented(StringBuilder out, int indent,\n            String s) {\n        appendNewline(out);\n        appendIndented(out, indent, s);\n    }\n\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/examples/DoTask.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser.examples;\n\n\nimport com.github.javacliparser.ClassOption;\nimport com.github.javacliparser.Configurable;\nimport com.github.javacliparser.IntOption;\n\n\npublic class DoTask implements Configurable { \n\n   public IntOption sizeOption = new IntOption(\"size\", 's',\n            \"Size\", 10, 1, Integer.MAX_VALUE);\n\n   public ClassOption taskClassOption = new ClassOption(\"taskClass\", 'l',\n            \"Task to execute.\", Task1.class, \"Task1 -s 4\");\n      \n   public void init() {\n        System.out.println(this.sizeOption.getValue());\n        Task1 task = this.taskClassOption.getValue();\n        task.init();\n   }\n    public static void main(String[] args) throws Exception {\n        //Main main = ClassOption.createObject(args, Main.class);\n        DoTask main =  ClassOption.createObject(\"DoTask -s 5 -l (Task1 -s 3 -l (Task2 -s 1))\", DoTask.class);\n        main.init();\n    }\n    \n    \n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/examples/Task1.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser.examples;\n\nimport com.github.javacliparser.ClassOption;\nimport com.github.javacliparser.Configurable;\nimport com.github.javacliparser.IntOption;\n\n\npublic class Task1 implements Configurable {\n\n    public IntOption ensembleSizeOption = new IntOption(\"ensembleSize\", 's',\n            \"The number of models in the bag.\", 10, 1, Integer.MAX_VALUE);\n    \n    public ClassOption taskClassOption = new ClassOption(\"taskClass\", 'l',\n            \"Task to execute.\", Task2.class, \"Task2 -s 100\");\n\n    public void init() {\n        System.out.println(\"Task1:\" + this.ensembleSizeOption.getValue());\n        Task2 task = this.taskClassOption.getValue();\n        task.init();\n    }\n}\n"
  },
  {
    "path": "src/main/java/com/github/javacliparser/examples/Task2.java",
    "content": "/*\n * Copyright 2007 University of Waikato.\n * \n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n * \t        http://www.apache.org/licenses/LICENSE-2.0\n * \n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,\n * either express or implied. See the License for the specific\n * language governing permissions and limitations under the\n * License.  \n */\n\npackage com.github.javacliparser.examples;\n\nimport com.github.javacliparser.Configurable;\nimport com.github.javacliparser.IntOption;\n\npublic class Task2 implements Configurable { \n\n    public IntOption ensembleSizeOption = new IntOption(\"ensembleSize\", 's',\n            \"The number of models in the bag.\", 10, 1, Integer.MAX_VALUE);\n\n    public void init() {\n        System.out.println(\"Task2:\" + this.ensembleSizeOption.getValue());\n\n    }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/Classifier.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers\n\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streaming.dstream._\n\n/**\n * A Classifier trait defines the needed operations on any implemented\n * classifier. It is a subtrait of Learner and it adds a method for predicting\n * the class of and input stream of Examples.\n */\ntrait Classifier extends Learner with Serializable {\n\n  /* Predict the label of the Example stream, given the current Model\n   *\n   * @param instance the input Example stream \n   * @return a stream of tuples containing the original instance and the\n   * predicted value\n   */\n  def predict(input: DStream[Example]): DStream[(Example, Double)]\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/MultiClassLearner.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers\n\nimport com.github.javacliparser.ClassOption\nimport org.apache.spark.streamdm.classifiers.model._\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streaming.dstream._\nimport org.apache.spark.streamdm.utils.Utils\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n * The MultiClassLearner trains a model for each class.\n * The class predicted is the one that its model predicts with highest\n * confidence.\n *\n * <p>It uses the following option:\n * <ul>\n *  <li> Base Classifier to use (<b>-l</b>)\n * </ul>\n */\n\nclass MultiClassLearner extends Classifier {\n\n  type T = LinearModel\n\n  val baseClassifierOption: ClassOption = new ClassOption(\"baseClassifier\", 'l',\n    \"Base Classifier to use\", classOf[Classifier], \"SGDLearner\")\n\n  var classifiers: Array[Classifier] = null\n\n  var exampleLearnerSpecification: ExampleSpecification = null\n\n  var sizeEnsemble: Int = 0\n\n  /* Init the model based on the algorithm implemented in the learner,\n   * from the stream of instances given for training.\n   *\n   */\n  override def init(exampleSpecification: ExampleSpecification): Unit = {\n    exampleLearnerSpecification = exampleSpecification\n\n    //Create the learner members of the ensemble\n    val baseClassifier: Classifier = baseClassifierOption.getValue()\n    sizeEnsemble = exampleSpecification.outputFeatureSpecification(0).range\n    classifiers = new Array[Classifier](sizeEnsemble)\n\n    for (i <- 0 until sizeEnsemble) {\n      classifiers(i) = Utils.copyClassifier(baseClassifier)\n      classifiers(i).init(exampleSpecification)\n    }\n\n  }\n\n  /* Train the model using the members of the ensemble\n     *\n     * @param input a stream of instances\n     * @return the updated Model\n     */\n  override def train(input: DStream[Example]): Unit = {\n    for (labelClass <- 0 until sizeEnsemble) {\n      classifiers(labelClass).train(input.map(convertInstance))\n\n      def convertInstance(example: Example): Example = {\n        new Example(example.in, new DenseInstance(Array(if (example.labelAt(0) == labelClass) 1 else 0)))\n      }\n    }\n  }\n\n  /* Builds a stream of examples and predictions based on the algorithm implemented in the classifier,\n    * from the stream of instances given for testing.\n    *\n    * @param input a stream of examples\n    * @return a stream of examples and numeric values\n    */\n  override def predict(input: DStream[Example]): DStream[(Example, Double)] =\n    input.map(x => (x, ensemblePredict(x)))\n\n  /* Gets the current Model used for the Learner.\n  *\n  * @return the Model object used for training\n  */\n  override def getModel: LinearModel = null\n\n  /* Predict the label of an example combining the predictions of the members of the ensemble\n   *\n   * @param example the Example which needs a class predicted\n   * @return the predicted value\n   */\n  def ensemblePredict(example: Example): Double = {\n    val predictions: Array[Double] = new Array(sizeEnsemble)\n    for (labelClass <- 0 until sizeEnsemble) {\n      predictions(labelClass) = classifiers(labelClass).getModel.asInstanceOf[ClassificationModel].prob(convertExample(example))\n\n      def convertExample(example: Example): Example = {\n        new Example(example.in, new DenseInstance(Array(if (example.labelAt(0) == labelClass) 1 else 0)))\n      }\n    }\n    Utils.argmax(predictions)\n  }\n\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/PerceptronLearner.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers\n\nimport org.apache.spark.streamdm.classifiers.model._\n\n/** The PerceptronLearner trains a LinearModel which is a perceptron. It\n * currently is implemented as an SGDLearner with a PerceptronLoss function.\n * The lambda learning rate parameter, and the number of features need to be\n * specified in the associated Task configuration file.\n */\nclass PerceptronLearner extends SGDLearner {\n\n  override val loss = new PerceptronLoss()  \n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/SGDLearner.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers\n\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.classifiers.model._\nimport org.apache.spark.streaming.dstream._\nimport com.github.javacliparser.{ClassOption, FloatOption, IntOption}\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n * The SGDLearner trains a LinearModel using the stochastic gradient descent\n * algorithm. The type of loss function, the lambda learning\n * reate parameter, and the number of features need to be specified in the\n * associated Task configuration file.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Number of features (<b>-f</b>)\n *  <li> Rate of learning parameter (<b>-l</b>)\n *  <li> Loss function (<b>-o</b>), an object of type <tt>Loss</tt>\n *  <li> Regularizer (<b>-r</b>), an object of type <tt>Regularizer</tt>\n *  <li> Regularization parameter (<b>-p</b>)\n * </ul>\n */\nclass SGDLearner extends Classifier {\n\n  type T = LinearModel\n\n  val numFeaturesOption: IntOption = new IntOption(\"numFeatures\", 'f',\n    \"Number of Features\", 3, 1, Integer.MAX_VALUE)\n\n  val lambdaOption: FloatOption = new FloatOption(\"lambda\", 'l',\n    \"Lambda\", .01, 0, Float.MaxValue)\n\n  val lossFunctionOption: ClassOption = new ClassOption(\"lossFunction\", 'o',\n    \"Loss function to use\", classOf[Loss], \"LogisticLoss\")\n\n  val regularizerOption:ClassOption = new ClassOption(\"regularizer\",\n    'r', \"Regularizer to use\", classOf[Regularizer], \"ZeroRegularizer\")\n\n  val regularizerParameter: FloatOption = new FloatOption(\"regParam\", 'p',\n    \"Regularization parameter\", .001, 0, Float.MaxValue)\n\n\n  var model: LinearModel = null\n  val regularizer: Regularizer = regularizerOption.getValue()\n  val loss: Loss = lossFunctionOption.getValue()\n  var exampleLearnerSpecification: ExampleSpecification = null\n\n  /* Init the model based on the algorithm implemented in the learner,\n   * from the stream of instances given for training.\n   *\n   */\n  override def init(exampleSpecification: ExampleSpecification): Unit = {\n    exampleLearnerSpecification = exampleSpecification\n    model = new LinearModel(loss, new DenseInstance(Array.fill[Double]\n      (numFeaturesOption.getValue + 1)(0.0)), numFeaturesOption.getValue)\n  }\n\n  /* Train the model using stochastic gradient descent.\n   *\n   * @param input a stream of instances\n   */\n  override def train(input: DStream[Example]): Unit = {\n    input.foreachRDD(rdd=> {\n      val chModels = rdd.aggregate(\n        //initialize with the previous model\n        (new LinearModel(loss,model.modelInstance,model.numFeatures),0.0))(\n        (mod,inst) => {\n          //for each instance in the RDD,\n          //add the gradient and the regularizer and update the model\n          val grad = mod._1.gradient(inst)\n          val reg = mod._1.regularize(regularizer).map(f =>\n              f*regularizerParameter.getValue)\n          val change = grad.add(reg).map(f => f*lambdaOption.getValue)\n          (mod._1.update(new Example(change)),1.0)\n        },\n        (mod1,mod2) =>\n          //add all the models together, keeping the count of the RDDs used\n          (mod1._1.update(new Example(mod2._1.modelInstance)), \n                           mod1._2+mod2._2)\n        )\n      if(chModels._2>0)\n        //finally, take the average of the models as the new model\n        model = new LinearModel(loss,\n          chModels._1.modelInstance.map(f => f/chModels._2),\n          model.numFeatures)\n    })\n  }\n\n  /* Predict the label of the Example stream, given the current Model\n   *\n   * @param instance the input Example stream \n   * @return a stream of tuples containing the original instance and the\n   * predicted value\n   */\n  override def predict(input: DStream[Example]): DStream[(Example, Double)] =\n    input.map(x => (x, model.predict(x)))\n\n  /* Gets the current LinearModel used for the SGDLearner.\n   * \n   * @return the LinearModel object used for training\n   */\n  override def getModel: LinearModel = model\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/bayes/MultinomialNaiveBayes.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.bayes\n\nimport scala.math.log10\nimport com.github.javacliparser.IntOption\nimport org.apache.spark.streaming.dstream._\nimport org.apache.spark.streamdm.classifiers.Classifier\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.classifiers.trees._\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n/**\n * Incremental Multinomial Naive Bayes learner. Builds a bayesian text\n * classifier making the naive assumption that all inputs are independent and\n * that feature values represent the frequencies with words occur. For more\n * information see,<br/> <br/> Andrew Mccallum, Kamal Nigam: A Comparison of\n * Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on\n * 'Learning for Text Categorization', 1998.<br/> <br/>\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Number of features (<b>-f</b>)\n *  <li> Number of classes (<b>-c</b>)\n *  <li> Laplace smoothing parameter (<b>-s</b>)\n * </ul>\n */\nclass MultinomialNaiveBayes extends Classifier {\n\n  type T = MultinomialNaiveBayesModel\n\n  val numClassesOption: IntOption = new IntOption(\"numClasses\", 'c',\n    \"Number of Classes\", 2, 2, Integer.MAX_VALUE)\n\n  val numFeaturesOption: IntOption = new IntOption(\"numFeatures\", 'f',\n    \"Number of Features\", 3, 1, Integer.MAX_VALUE)\n\n  val laplaceSmoothingFactorOption: IntOption = new IntOption(\n    \"laplaceSmoothingFactor\", 's', \"Laplace Smoothing Factor\", 1, 1, \n    Integer.MAX_VALUE)\n\n  var model: MultinomialNaiveBayesModel = null\n\n  var exampleLearnerSpecification: ExampleSpecification = null\n\n  /**\n   * Init the model based on the algorithm implemented in the learner.\n   *\n   * @param exampleSpecification the ExampleSpecification of the input stream.\n   */\n  override def init(exampleSpecification: ExampleSpecification): Unit = {\n    exampleLearnerSpecification = exampleSpecification\n    model = new MultinomialNaiveBayesModel(\n      numClassesOption.getValue, numFeaturesOption.getValue,\n          laplaceSmoothingFactorOption.getValue)\n  }\n\n  /** \n   * Train the model based on the algorithm implemented in the learner, \n   * from the stream of Examples given for training.\n   * \n   * @param input a stream of Examples\n   */\n  override def train(input: DStream[Example]): Unit = {\n    input.foreachRDD {\n      rdd =>\n        val tmodel = rdd.aggregate(\n          new MultinomialNaiveBayesModel(model.numClasses, model.numFeatures,\n            model.laplaceSmoothingFactor))(\n            (mod, example) => { mod.update(example) }, (mod1, mod2) => \n              mod1.merge(mod2))\n        model = model.merge(tmodel)\n    }\n  }\n\n  /* Predict the label of the Example stream, given the current Model\n   *\n   * @param instance the input Example stream \n   * @return a stream of tuples containing the original instance and the\n   * predicted value\n   */\n  override def predict(input: DStream[Example]): DStream[(Example, Double)] = {\n    input.map { x => (x, model.predict(x)) }\n  }\n\n  /* Gets the current Model used for the Learner.\n   * \n   * @return the Model object used for training\n   */\n  override def getModel: MultinomialNaiveBayesModel = model\n}\n\n/**\n * The Model used for the multinomial Naive Bayes. It contains the class\n * statistics and the class feature statistics.\n */\nclass MultinomialNaiveBayesModel(val numClasses: Int, val numFeatures: Int, \n                                 val laplaceSmoothingFactor: Int)\n  extends Model with Serializable {\n  type T = MultinomialNaiveBayesModel\n\n  var classStatistics: Array[Double] = new Array[Double](numClasses)\n  var classFeatureStatistics: Array[Array[Double]] = Array.fill(numClasses)(\n      new Array[Double](numFeatures))\n\n  @transient var isReady: Boolean = false\n\n  // variables used for prediction\n  @transient var logNumberDocuments: Double = 0\n  @transient var logProbability: Array[Double] = null\n  @transient var logConditionalProbability: Array[Array[Double]] = null\n  @transient var logNumberDocumentsOfClass: Double = 0\n\n  def this(numClasses: Int, numFeatures: Int, laplaceSmoothingFactor: Int,\n           classStatistics: Array[Double],\n           classFeatureStatistics: Array[Array[Double]]) {\n    this(numClasses, numFeatures, laplaceSmoothingFactor)\n    this.classStatistics = classStatistics\n    this.classFeatureStatistics = classFeatureStatistics\n  }\n\n  /**\n   * Update the model, depending on the Instance given for training.\n   *\n   * @param instance the example based on which the Model is updated\n   * @return the updated Model\n   */\n  override def update(instance: Example): MultinomialNaiveBayesModel = {\n    if (isReady) {\n      isReady = false\n    }\n    classStatistics(instance.labelAt(0).toInt) += 1\n    for (i <- 0 until numFeatures) {\n      classFeatureStatistics(instance.labelAt(0).toInt)(i) +=\n        instance.featureAt(i)\n    }\n    this\n  }\n\n  /**\n   * Prepare the model.\n   *\n   * @return a boolean indicating whether the model is ready\n   */\n  private def prepare(): Boolean = {\n    if (!isReady) {\n      logProbability = new Array[Double](numClasses)\n      logConditionalProbability = Array.fill(numClasses)(new Array[Double](\n        numFeatures))\n      val totalnum = classFeatureStatistics.map { x => x.sum }.sum\n      logNumberDocuments = math.log(totalnum + numClasses *\n        laplaceSmoothingFactor)\n      for (i <- 0 until numClasses) {\n        val logNumberDocumentsOfClass = math.log(classFeatureStatistics(i).sum +\n          numFeatures * laplaceSmoothingFactor)\n        logProbability(i) = math.log(classStatistics(i) +\n          laplaceSmoothingFactor) - logNumberDocuments\n        for (j <- 0 until numFeatures) {\n          logConditionalProbability(i)(j) =\n            math.log(classFeatureStatistics(i)(j) + laplaceSmoothingFactor) -\n              logNumberDocumentsOfClass\n        }\n      }\n      isReady = true\n    }\n    return isReady\n  }\n\n  /**\n   * Predict the label of the Instance, given the current Model.\n   *\n   * @param instance the Example which needs a class predicted\n   * @return a Double representing the class predicted\n   */\n  def predict(instance: Example): Double = {\n    if (prepare()) {\n      val predictlogProbability = new Array[Double](numClasses)\n      for (i <- 0 until numClasses) {\n        predictlogProbability(i) = logProbability(i)\n        for (j <- 0 until numFeatures) {\n          predictlogProbability(i) += logConditionalProbability(i)(j) *\n            instance.featureAt(j)\n        }\n      }\n      argMax(predictlogProbability)\n    } else 0\n  }\n\n  /**\n   * Index corresponding to the maximum value of an array of Doubles.\n   * \n   * @param array the input Array\n   * @return the argmax index\n   */\n  private def argMax(array: Array[Double]): Double = array.zipWithIndex.\n    maxBy(_._1)._2\n\n  /**\n   * Merge the statistics of another model into the current model.\n   *\n   * @param mod2 MultinomialNaiveBayesModel\n   * @return MultinomialNaiveBayesModel\n   */\n  def merge(mod2: MultinomialNaiveBayesModel): MultinomialNaiveBayesModel = {\n    val mergeClassStatistics = this.classStatistics.\n      zip(mod2.classStatistics).map { case (x, y) => x + y }\n    val mergeClassFeatureStatistics = this.classFeatureStatistics.\n      zip(mod2.classFeatureStatistics).map { case (a1, a2) => a1.zip(a2).map {\n        case (x, y) => x + y } }\n    new MultinomialNaiveBayesModel(\n      mod2.numClasses, mod2.numFeatures, mod2.laplaceSmoothingFactor,\n      mergeClassStatistics, mergeClassFeatureStatistics)\n  }\n\n}\n\nobject NaiveBayes {\n  // predict the probabilities of all features for Hoeffding Tree\n  def predict(point: Example, classDistribution: Array[Double], featureObservers: Array[FeatureClassObserver]): Array[Double] = {\n    val votes: Array[Double] = classDistribution.map { _ / classDistribution.sum }\n    for (i <- 0 until votes.length; j <- 0 until featureObservers.length) {\n      votes(i) *= featureObservers(j).probability(i, point.featureAt(j))\n    }\n    votes\n  }\n\n  // predict the log10 probabilities of all features for Hoeffding Tree\n  def predictLog10(point: Example, classDistribution: Array[Double], featureObservers: Array[FeatureClassObserver]): Array[Double] = {\n    val votes: Array[Double] = classDistribution.map { x => log10(x / classDistribution.sum) }\n    for (i <- 0 until votes.length; j <- 0 until featureObservers.length) {\n      votes(i) += log10(featureObservers(j).probability(i, point.featureAt(j)))\n    }\n    votes\n  }\n}\n\n\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/meta/Bagging.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.meta\n\nimport java.util.Random\nimport com.github.javacliparser.{ClassOption, IntOption}\nimport org.apache.spark.streamdm.classifiers.Classifier\nimport org.apache.spark.streamdm.classifiers.model._\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streaming.dstream._\nimport org.apache.spark.streamdm.utils.Utils\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n * The Bagging classifier trains an ensemble of classifier to improve performance.\n * It is based on doing sampling with replacement at the input of each classifier.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Base Classifier to use (<b>-l</b>)\n *  <li> Size of the ensemble (<b>-s</b>)\n * </ul>\n */\n\nclass Bagging extends Classifier {\n\n  type T = LinearModel\n\n  val baseClassifierOption: ClassOption = new ClassOption(\"baseClassifier\", 'l',\n    \"Base Classifier to use\", classOf[Classifier], \"trees.HoeffdingTree\")\n\n  val ensembleSizeOption: IntOption = new IntOption(\"ensembleSize\", 's',\n    \"The number of models in the bag.\", 10, 1, Integer.MAX_VALUE)\n\n  var classifiers: Array[Classifier] = null\n\n  var exampleLearnerSpecification: ExampleSpecification = null\n\n  val classifierRandom: Random = new Random()\n\n  /* Init the model based on the algorithm implemented in the learner,\n   * from the stream of instances given for training.\n   *\n   */\n  override def init(exampleSpecification: ExampleSpecification): Unit = {\n    exampleLearnerSpecification = exampleSpecification\n\n    //Create the learner members of the ensemble\n    val baseClassifier: Classifier = baseClassifierOption.getValue()\n    val sizeEnsemble = ensembleSizeOption.getValue\n    classifiers = new Array[Classifier](sizeEnsemble)\n\n    for (i <- 0 until sizeEnsemble) {\n      classifiers(i) = Utils.copyClassifier(baseClassifier)\n      classifiers(i).init(exampleSpecification)\n    }\n  }\n\n  /* Train the ensemble training each of the members of the ensemble\n     *\n     * @param input a stream of instances\n     * @return the updated Model\n     */\n  override def train(input: DStream[Example]): Unit = {\n    for (i <- 0 until ensembleSizeOption.getValue) {\n      classifiers(i).train(input.map(onlineSampling))\n    }\n    //Online Sampling with replacement\n    def onlineSampling(example: Example): Example = {\n      val weight = Utils.poisson(1.0, classifierRandom);\n      new Example(example.in, example.out, weight * example.weight)\n    }\n  }\n\n  /* Builds a stream of examples and predictions based on the algorithm implemented in the classifier,\n    * from the stream of instances given for testing.\n    *\n    * @param input a stream of examples\n    * @return a stream of examples and numeric values\n    */\n  override def predict(input: DStream[Example]): DStream[(Example, Double)] =\n    input.map(x => (x, ensemblePredict(x)))\n\n  /* Gets the current Model used for the Learner.\n  *\n  * @return the Model object used for training\n  */\n  override def getModel: LinearModel = null\n\n  /* Predict the label of an example combining the predictions of the members of the ensemble\n   *\n   * @param example the Example which needs a class predicted\n   * @return the predicted value\n   */\n  def ensemblePredict(example: Example): Double = {\n    val sizeEnsemble = ensembleSizeOption.getValue\n    val predictions: Array[Double] = new Array(sizeEnsemble)\n    for (i <- 0 until sizeEnsemble) {\n      predictions(i) = classifiers(i).getModel.asInstanceOf[ClassificationModel].predict(example)\n    }\n    Utils.majorityVote(predictions, numberClasses)\n  }\n\n  def numberClasses(): Integer = {\n    if (exampleLearnerSpecification == null) 2\n    else exampleLearnerSpecification.out(0).range\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/meta/RandomForest.scala",
    "content": "/*\n * Copyright (C) 2018 Télécom ParisTech LTCI lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.meta\n\nimport java.util.Random\n\nimport com.github.javacliparser.{ClassOption, FloatOption, IntOption}\nimport org.apache.spark.internal.Logging\nimport org.apache.spark.streamdm.classifiers.Classifier\nimport org.apache.spark.streamdm.classifiers.model._\nimport org.apache.spark.streamdm.classifiers.trees.HoeffdingTree\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\nimport org.apache.spark.streamdm.utils.Utils\nimport org.apache.spark.streaming.dstream._\n\n/**\n  * The streaming version of a Random Forest classifier.\n  * The 2 most important aspects of this ensemble classifier are:\n  * (1) inducing diversity through online bagging;\n  * (2) inducing diversity through randomly selecting subsets of features for node splits;</p>\n  *\n  * <p>See details in:<br> Heitor Murilo Gomes, Albert Bifet, Jesse Read,\n  * Jean Paul Barddal, ..., Bernhard Pfharinger, Geoff Holmes,\n  * Talel Abdessalem. Adaptive random forests for evolving data stream classification.\n  * In Machine Learning, DOI: 10.1007/s10994-017-5642-8, Springer, 2017.</p>\n  *\n  * <p>It uses the following options:\n  * <ul>\n  *  <li> Hyper-parameters to the base classifier (HoeffdingTree) (<b>-l</b>)\n  *  <li> Size of the ensemble (<b>-s</b>)\n  *  <li> The subset of features mode (how to interpret -m) (<b>-o</b>)\n  *  <li> Size of the subset of randomly selected features per split (<b>-m</b>)\n  *  <li> The lambda value for online bagging (<b>-a</b>)\n  * </ul>\n  */\nclass RandomForest extends Classifier with Logging {\n\n  type T = LinearModel\n\n  val baseClassifierOption: ClassOption = new ClassOption(\"baseClassifier\", 'l',\n    \"Base Classifier to use\", classOf[HoeffdingTree], \"HoeffdingTree\")\n\n  val ensembleSizeOption: IntOption = new IntOption(\"ensembleSize\", 's',\n    \"The number of trees in the forest.\", 10, 1, Int.MaxValue)\n\n  import com.github.javacliparser.MultiChoiceOption\n\n  val mFeaturesModeOption = new MultiChoiceOption(\"mFeaturesMode\", 'o',\n    \"Defines how m, defined by mFeaturesPerTreeSize, is interpreted. M represents the total number of features.\",\n    Array[String](\"Specified m\", \"sqrt(M)+1\", \"M-(sqrt(M)+1)\", \"Percentage\"),\n    Array[String](\"SpecifiedM\", \"SqrtM1\", \"MSqrtM1\", \"Percentage\"), 1)\n\n  val mFeaturesPerTreeSizeOption = new IntOption(\"mFeaturesPerTreeSize\", 'm',\n    \"Number of features allowed considered for each split. Negative values corresponds to M - m\", 2,\n    Int.MinValue, Int.MaxValue)\n\n  val lambdaOption = new FloatOption(\"lambda\", 'a',\n    \"The lambda parameter for bagging.\", 6.0, 1.0, Float.MaxValue)\n\n  var trees: Array[HoeffdingTree] = null\n\n  var exampleLearnerSpecification: ExampleSpecification = null\n\n  val classifierRandom: Random = new Random()\n\n  // The number of features to consider at every split.\n  var numSplitFeatures : Int = 0\n\n  protected val FEATURES_M = 0\n  protected val FEATURES_SQRT = 1\n  protected val FEATURES_SQRT_INV = 2\n  protected val FEATURES_PERCENT = 3\n\n  /** Init the model based on the algorithm implemented in the learner,\n   * from the stream of instances given for training.\n   *\n   */\n  override def init(exampleSpecification: ExampleSpecification): Unit = {\n    exampleLearnerSpecification = exampleSpecification\n\n    //Create the learner members of the ensemble\n    val baseClassifier: HoeffdingTree = baseClassifierOption.getValue()\n    val sizeEnsemble = ensembleSizeOption.getValue\n    trees = new Array[HoeffdingTree](sizeEnsemble)\n\n    // m depends on mFeaturesModeOption and mFeaturesPerTreeSizeOption\n    // M represents the total number of features\n    val M = exampleLearnerSpecification.in.size()\n    numSplitFeatures = mFeaturesPerTreeSizeOption.getValue()\n\n    this.mFeaturesModeOption.getChosenIndex() match {\n      case FEATURES_SQRT =>\n        numSplitFeatures = Math.sqrt(M).round.toInt + 1\n      case FEATURES_SQRT_INV =>\n        numSplitFeatures = (M - (Math.sqrt(M) + 1)).round.toInt\n      case FEATURES_PERCENT =>\n        // If numSplitFeatures is negative, then first find out the actual percent, i.e., 100% - numSplitFeatures.\n        val percent = if (numSplitFeatures < 0) (100 + numSplitFeatures) / 100.0\n        else numSplitFeatures / 100.0\n        numSplitFeatures = (M * percent).round.toInt\n      case FEATURES_M =>\n        numSplitFeatures = numSplitFeatures\n    }\n\n    // m is negative, use size(features) + m\n    if (numSplitFeatures < 0) numSplitFeatures = M + numSplitFeatures\n    // Other sanity checks to avoid runtime errors.\n    //  m <= 0 (m can be negative if m was negative and\n    //  abs(m) > M), then use m = 1\n    if (numSplitFeatures <= 0) numSplitFeatures = 1\n    // m > M, then use m = M\n    if (numSplitFeatures > M) numSplitFeatures = M\n\n    logInfo(\"mode = %s, m = %d, M = %d\".format(mFeaturesModeOption.getChosenLabel(), numSplitFeatures, M))\n\n    // Create each tree of the forest.\n    for (i <- 0 until sizeEnsemble) {\n      trees(i) = Utils.copyClassifier(baseClassifier).asInstanceOf[HoeffdingTree]\n      trees(i).init(exampleSpecification, numSplitFeatures)\n    }\n\n  }\n\n  /** Train the ensemble training each of the members of the ensemble\n   *\n   * @param input a stream of instances\n   * @return the updated Model\n   */\n  override def train(input: DStream[Example]): Unit = {\n    for (i <- 0 until ensembleSizeOption.getValue) {\n      trees(i).train(input.map(onlineSampling))\n    }\n    //Online Sampling with replacement\n    def onlineSampling(example: Example): Example = {\n      val weight = Utils.poisson(lambdaOption.getValue, classifierRandom);\n      new Example(example.in, example.out, weight * example.weight)\n    }\n  }\n\n  /** Builds a stream of examples and predictions based on the algorithm implemented in the classifier,\n    * from the stream of instances given for testing.\n    *\n    * @param input a stream of examples\n    * @return a stream of examples and numeric values\n    */\n  override def predict(input: DStream[Example]): DStream[(Example, Double)] =\n    input.map(x => (x, ensemblePredict(x)))\n\n /** Gets the current Model used for the Learner.\n  *\n  * @return the Model object used for training\n  */\n  override def getModel: LinearModel = null\n\n  /** Predict the label of an example combining the predictions of the members of the ensemble\n   *\n   * @param example the Example which needs a class predicted\n   * @return the predicted value\n   */\n  def ensemblePredict(example: Example): Double = {\n    val sizeEnsemble = ensembleSizeOption.getValue\n    val predictions: Array[Double] = new Array(sizeEnsemble)\n    for (i <- 0 until sizeEnsemble) {\n      predictions(i) = trees(i).getModel.asInstanceOf[ClassificationModel].predict(example)\n    }\n    val predictionsStr: String = predictions.map(p => p + \",\").reduce((p1, p2) => p1 + p2)\n    logInfo(predictionsStr)\n\n    Utils.majorityVote(predictions, numberClasses)\n  }\n\n  def numberClasses(): Integer = {\n    if (exampleLearnerSpecification == null) 2\n    else exampleLearnerSpecification.out(0).range\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/HingeLoss.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\n/**\n * Implementation of the squared loss function.\n */\n\nclass HingeLoss extends Loss with Serializable {\n  /** Computes the value of the loss function\n   * @param label the label against which the loss is computed\n   * @param dot the dot product of the linear model and the instance\n   * @return the loss value \n   */\n  def loss(label: Double, dot: Double): Double = {\n    val l = if (label==0) -1.0 else 1\n    val v = 1.0 - l*dot\n    if (v<0) 0 else v\n  }\n\n  /** Computes the value of the gradient function\n   * @param label the label against which the gradient is computed\n   * @param dot the dot product of the linear model and the instance\n   * @return the gradient value \n   */\n  def gradient(label: Double, dot: Double): Double = {\n    val l = if (label==0) -1.0 else 1\n    val d = l*dot\n    if (d<1) -l else 0\n  }\n\n  /** Computes the binary prediction based on a dot prodcut\n   * @param dot the dot product of the linear model and the instance\n   * @return the predicted binary class\n   */\n  def predict(dot: Double): Double =\n    if (dot>=0) 1 else 0\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/L1Regularizer.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\n/**\n * The L1Regularizer gradient return the sign as the weight for the\n * regularization.\n */\nclass L1Regularizer extends Regularizer with Serializable {\n  /** Computes the value of the gradient function\n   * @param weight the weight for which the gradient is computed\n   * @return the gradient value \n   */\n  def gradient(weight: Double): Double = if (weight>=0) 1.0 else -1.0\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/L2Regularizer.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\n/**\n * The L2Regularizer gradient returns the weight as the gradient for the\n * regularization.\n */\nclass L2Regularizer extends Regularizer with Serializable {\n  /** Computes the value of the gradient function\n   * @param valweightue the weight for which the gradient is computed\n   * @return the gradient value \n   */\n  def gradient(weight: Double): Double = weight \n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/LinearModel.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\nimport org.apache.spark.streamdm.core._\n\n/**\n * A Model trait defines the needed operations on any learning Model. It\n * provides methods for updating the model and for predicting the label of a\n * given Instance\n */\nclass LinearModel(lossFunction: Loss, initialModel: Instance,numberFeatures:Int)\n  extends ClassificationModel with Serializable {\n\n  type T = LinearModel\n  \n  val loss = lossFunction\n  val modelInstance = initialModel\n  val numFeatures = numberFeatures\n  /* Update the model, depending on an Instance given for training\n   *\n   * @param instance the Instance based on which the Model is updated\n   * @return the updated Model\n   */\n  override def update(change: Example): LinearModel =\n    new LinearModel(loss, modelInstance.add(change.in), numFeatures) \n\n  /* Predict the label of the Instance, given the current Model\n   *\n   * @param instance the Instance which needs a class predicted\n   * @return a Double representing the class predicted\n   */\n  override def predict(instance: Example): Double =\n    loss.predict(modelInstance.dot(instance.in.set(numFeatures,1.0)))\n\n  /* Compute the loss of the direction of the change\n   * @param instance the Instance for which the gradient is computed\n   * @return an instance containging the gradients for every feature\n   */\n  def gradient(instance: Example): Instance = {\n    //compute the gradient based on the dot product, then compute the changes\n    val ins = instance.in.set(numFeatures,1.0)\n    val ch = -loss.gradient(instance.labelAt(0), modelInstance.dot(ins))\n    ins.map(x => ch*x)\n  }\n\n  def regularize(regularizer: Regularizer): Instance = \n    modelInstance.map(x => -regularizer.gradient(x))\n\n  /** Computes the probability for a given label class, given the current Model\n    *\n    * @param instance the Instance which needs a class predicted\n    * @return the predicted probability\n    */\n\n  def prob(instance: Example): Double =\n    loss.prob(modelInstance.dot(instance.in.set(numFeatures,1.0)))\n  \n  override def toString = \"Model %s\".format(modelInstance.toString)\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/LogisticLoss.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\n/**\n * Implementation of the logistic loss function.\n */\n\nclass LogisticLoss extends Loss with Serializable {\n  /** Computes the value of the loss function\n   * @param label the label against which the loss is computed\n   * @param dot the dot product of the linear model and the instance\n   * @return the loss value \n   */\n  def loss(label: Double, dot: Double): Double = {\n    val y = if(label<=0) -1 else 1\n    math.log(1+math.exp(-y*dot))\n  }\n\n  /** Computes the value of the gradient function\n   * @param label the label against which the gradient is computed\n   * @param dot the dot product of the linear model and the instance\n   * @return the gradient value \n   */\n  def gradient(label: Double, dot: Double): Double = {\n    val y = if(label<=0) -1 else 1\n    -y*(1.0-1.0/(1.0+math.exp(-y*dot)))\n  }\n\n  /** Computes the binary prediction based on a dot product\n   * @param dot the dot product of the linear model and the instance\n   * @return the predicted binary class\n   */\n  def predict(dot: Double): Double = {\n    val f = 1.0 / (1.0+math.exp(-dot))\n    if (f>0.5) 1 else 0\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/Loss.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\n/**\n * A Loss trait defines the operation needed to compute the loss function, the\n * prediction function, and the gradient for use in a LinearModel. \n */\ntrait Loss extends Serializable {\n  /** Computes the value of the loss function\n   * @param label the label against which the loss is computed\n   * @param dot the dot product of the linear model and the instance\n   * @return the loss value \n   */\n  def loss(label: Double, dot: Double): Double\n\n  /** Computes the value of the gradient function\n   * @param label the label against which the gradient is computed\n   * @param dot the dot product of the linear model and the instance\n   * @return the gradient value \n   */\n  def gradient(label: Double, dot: Double): Double\n\n  /** Computes the binary prediction based on a dot product\n   * @param dot the dot product of the linear model and the instance\n   * @return the predicted binary class\n   */\n  def predict(dot: Double): Double\n\n  /** Computes the probability of a binary prediction based on a dot product\n    * @param dot the dot product of the linear model and the instance\n    * @return the predicted probability\n    */\n  def prob(dot: Double): Double = loss(1, dot)\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/PerceptronLoss.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\n/**\n * Implementation of the perceptron loss function. Essentially, the perceptron\n * is using the squared loss function except for the \"gradient\".\n */\n\nclass PerceptronLoss extends SquaredLoss with Serializable {\n  /** Computes the value of the perceptron update function\n   * @param label the label against which the update is computed\n   * @param dot the dot product of the linear model and the instance\n   * @return the update value \n   */\n  override def gradient(label: Double, dot: Double): Double =\n    label-predict(dot)\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/Regularizer.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\n/**\n * A regularizer trait defines the gradient operation for computing regularized\n * models. \n */\ntrait Regularizer extends Serializable {\n  /** Computes the value of the gradient function\n   * @param weight the weight for which the gradient is computed\n   * @return the gradient value \n   */\n  def gradient(weight: Double): Double\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/SquaredLoss.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\n/**\n * Implementation of the squared loss function.\n */\n\nclass SquaredLoss extends Loss with Serializable {\n  /** Computes the value of the loss function\n   * @param label the label against which the loss is computed\n   * @param dot the dot product of the linear model and the instance\n   * @return the loss value \n   */\n  def loss(label: Double, dot: Double): Double =\n    0.5*(dot-label)*(dot-label)\n\n  /** Computes the value of the gradient function\n   * @param label the label against which the gradient is computed\n   * @param dot the dot product of the linear model and the instance\n   * @return the gradient value \n   */\n  def gradient(label: Double, dot: Double): Double =\n    dot-label\n\n  /** Computes the binary prediction based on a dot prodcut\n   * @param dot the dot product of the linear model and the instance\n   * @return the predicted binary class\n   */\n  def predict(dot: Double): Double =\n    if (dot>=0) 1 else 0\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/model/ZeroRegularizer.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\n/**\n * The ZeroRegularizer gradient simply returns 0 as the gradient.\n */\nclass ZeroRegularizer extends Regularizer with Serializable {\n  /** Computes the value of the gradient function\n   * @param weight the weight for which the gradient is computed\n   * @return the gradient value \n   */\n  def gradient(weight: Double): Double = 0.0\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/ConditionalTest.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees\n\nimport org.apache.spark.streamdm.core.Example\n\n/**\n * ConditionalTest is an abstract class for  conditional tests, used for\n * splitting nodes in Hoeffding trees.\n */\nabstract class ConditionalTest(var fIndex: Int) extends Serializable {\n\n  /**\n   *  Returns the number of the branch for an example, -1 if unknown.\n   *\n   * @param example the input Example \n   * @return the number of the branch for an example, -1 if unknown.\n   */\n  def branch(example: Example): Int\n\n  /**\n   * Gets whether the number of the branch for an example is known.\n   *\n   * @param example the input Example\n   * @return true if the number of the branch for an example is known\n   */\n  def hasResult(example: Example): Boolean = { branch(example) >= 0 }\n\n  /**\n   * Gets the number of maximum branches, -1 if unknown.\n   *\n   * @return the number of maximum branches, -1 if unknown.\n   */\n  def maxBranches(): Int\n\n  /**\n   * Returns the index of the feature\n   *\n   * @return the index of the feature\n   */\n  def featureIndex(): Int = fIndex\n\n  /**\n   * Get the conditional test description.\n   *\n   * @return an Array containing the description\n   */\n  def description(): Array[String]\n\n}\n\n/**\n * Numeric binary conditional test for splitting nodes in Hoeffding trees.\n */\n\nclass NumericBinaryTest(fIndex: Int, val value: Double, val isequalTest: Boolean)\n  extends ConditionalTest(fIndex) with Serializable {\n\n  /**\n   *  Returns the number of the branch for an example, -1 if unknown.\n   *\n   * @param example the input Example. \n   * @return the number of the branch for an example, -1 if unknown.\n   */\n  override def branch(example: Example): Int = {\n    // todo process missing value\n    val v = example.featureAt(fIndex)\n    if (isequalTest) {\n      if (v == value) 0 else 1\n    } else {\n      if (v < value) 0 else 1\n    }\n  }\n\n  /**\n   * Gets the number of maximum branches, -1 if unknown.\n   *\n   * @return the number of maximum branches, -1 if unknown..\n   */\n  override def maxBranches(): Int = 2\n\n  /**\n   * Returns the index of the tested feature\n   *\n   * @return the index of the tested feature\n   */\n  override def featureIndex(): Int = fIndex\n\n  /**\n   * Get the conditional test description.\n   *\n   * @return an Array containing the description\n   */\n  override def description(): Array[String] = {\n    val des = new Array[String](2)\n    val ops = if (isequalTest) Array(\"==\", \"!=\") else Array(\"<\", \">=\")\n    des(0) = \"[feature \" + fIndex + \" numeric 0] \" + ops(0) + \" \" + value\n    des(1) = \"[feature \" + fIndex + \" numeric 1] \" + ops(1) + \" \" + value\n    des\n  }\n\n  override def toString = \"NumericBinaryTest(\" + isequalTest + \") feature[\" + fIndex + \"] = \" +\n                           value\n}\n/**\n * Nominal binary conditional test for splitting nodes in Hoeffding trees.\n */\nclass NominalBinaryTest(fIndex: Int, val value: Double)\n  extends ConditionalTest(fIndex) with Serializable {\n\n  /**\n   *  Returns the number of the branch for an example, -1 if unknown.\n   *\n   * @param example the input example\n   * @return the number of the branch for an example, -1 if unknown.\n   */\n  override def branch(example: Example): Int = {\n    // todo process missing value\n    if (example.featureAt(fIndex) == value) 0 else 1\n  }\n\n  /**\n   * Gets the number of maximum branches, -1 if unknown.\n   *\n   * @return the number of maximum branches, -1 if unknown.\n   */\n  override def maxBranches(): Int = 2\n\n  override def toString(): String = {\n    \"NominalBinaryTest feature[\" + fIndex + \"] = \" + value\n\n  }\n\n  /**\n   * Get the conditional test description.\n   *\n   * @return an Array containing the description\n   */\n  override def description(): Array[String] = {\n    val des = new Array[String](2)\n    des(0) = \"[feature \" + fIndex + \" nominal 0] == \" + value\n    des(1) = \"[feature \" + fIndex + \" nominal 1] != \" + value\n    des\n  }\n}\n\n/**\n * Nominal multi-way conditional test for splitting nodes in Hoeffding trees.\n */\nclass NominalMultiwayTest(fIndex: Int, val numFeatureValues: Int)\n  extends ConditionalTest(fIndex) with Serializable {\n\n  /**\n   *  Returns the number of the branch for an example, -1 if unknown.\n   *\n   * @param example the input example \n   * @return the number of the branch for an example, -1 if unknown.\n   */\n  override def branch(example: Example): Int = {\n    // todo process missing value\n    example.featureAt(fIndex).toInt\n  }\n\n  /**\n   * Gets the number of maximum branches, -1 if unknown.\n   *\n   * @return the number of maximum branches, -1 if unknown.\n   */\n  override def maxBranches(): Int = numFeatureValues\n\n  override def toString(): String = \"NominalMultiwayTest\" + \"feature[\" + fIndex + \"] \" +\n                                      numFeatureValues\n\n  /**\n   * Get the conditional test description.\n   *\n   * @return an Array containing the description\n   */\n  override def description(): Array[String] = {\n    val des = new Array[String](numFeatureValues)\n    for (i <- 0 until numFeatureValues)\n      des(i) = \"[feature \" + fIndex + \" nominal \" + i + \"] == \" + i\n    des\n  }\n}\n\n/**\n * Numeric binary rule predicate test for splitting nodes in Hoeffding trees.\n */\nclass NumericBinaryRulePredicate(fIndex: Int, val value: Double, val operator: Int)\n  extends ConditionalTest(fIndex) with Serializable {\n\n  /**\n   *  Returns the number of the branch for an example, -1 if unknown.\n   *\n   * @param example the input example \n   * @return the number of the branch for an example, -1 if unknown.\n   */\n  override def branch(example: Example): Int = {\n    // todo process missing value\n    val v = example.featureAt(fIndex)\n    operator match {\n      // operator: 0 ==, 1 <=, 2 < different from MOA which is > \n      case 0 => if (v == value) 0 else 1\n      case 1 => if (v <= value) 0 else 1\n      case 2 => if (v < value) 0 else 1\n      case _ => if (v == value) 0 else 1\n    }\n  }\n\n  /**\n   * Gets the number of maximum branches, -1 if unknown.\n   *\n   * @return the number of maximum branches, -1 if unknown.\n   */\n  override def maxBranches(): Int = 2\n\n  /**\n   * Get the conditional test description.\n   *\n   * @return an Array containing the description\n   */\n  override def description(): Array[String] = {\n    val des = new Array[String](2)\n    val ops = if (operator == 0) Array(\"==\", \"!=\") else if (operator == 1) \n                Array(\"<=\", \">\") else Array(\"<\", \">=\")\n    des(0) = \"[feature \" + fIndex + \" numeric 0] \" + ops(0) + \" \" + value\n    des(1) = \"[feature \" + fIndex + \" numeric 1] \" + ops(1) + \" \" + value\n    des\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/FeatureClassObserver.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees\n\nimport scala.collection.mutable.TreeSet\nimport scala.math.{ min, max }\nimport org.apache.spark.internal.Logging\nimport org.apache.spark.streamdm.core.specification._\nimport org.apache.spark.streamdm.utils.Utils.{ normal, transpose, splitTranspose }\n/**\n * Trait FeatureClassObserver for observing the class distribution of one feature.\n * The observer monitors the class distribution of a given feature.\n * Used in Naive Bayes and decision trees to monitor data statistics on leaves.\n */\ntrait FeatureClassObserver extends Serializable with Logging {\n\n  /**\n   * Updates statistics of this observer given a feature value, a class index\n   * and the weight of the example observed\n   *\n   * @param cIndex the index of class\n   * @param fValue the value of the feature\n   * @param weight the weight of the example\n   */\n  def observeClass(cIndex: Double, fValue: Double, weight: Double): Unit\n\n  /**\n   * Gets the probability for an attribute value given a class\n   *\n   * @param cIndex the index of class\n   * @param fValue the value of the feature\n   * @return probability for a feature value given a class\n   */\n  def probability(cIndex: Double, fValue: Double): Double\n\n  /**\n   * Gets the best split suggestion given a criterion and a class distribution\n   *\n   * @param criterion the split criterion to use\n   * @param pre the class distribution before the split\n   * @param fValue the value of the feature\n   * @param isBinarySplit true to use binary splits\n   * @return suggestion of best feature split\n   */\n  def bestSplit(criterion: SplitCriterion, pre: Array[Double], fValue: Double,\n    isBinarySplit: Boolean): FeatureSplit\n\n  /**\n   * Merge the FeatureClassObserver to current FeatureClassObserver\n   *\n   * @param that the FeatureClassObserver will be merged\n   * @param trySplit whether called when a Hoeffding tree try to split\n   * @return current FeatureClassObserver\n   */\n  def merge(that: FeatureClassObserver, trySplit: Boolean): FeatureClassObserver\n\n  /**\n   * Not yet supported.\n   */\n  def observeTarget(fValue: Double, weight: Double): Unit = {}\n\n}\n/**\n * NullFeatureClassObserver is a null class for observers.\n */\nclass NullFeatureClassObserver extends FeatureClassObserver with Serializable {\n\n  /**\n   * Updates statistics of this observer given a feature value, a class index\n   * and the weight of the example observed\n   *\n   * @param cIndex the index of class\n   * @param fValue the value of the feature\n   * @param weight the weight of the example\n   */\n  override def observeClass(cIndex: Double, fValue: Double, weight: Double): Unit = {}\n\n  /**\n   * Gets the probability for an attribute value given a class\n   *\n   * @param cIndex the index of class\n   * @param fValue the value of the feature\n   * @return probability for a feature value given a class\n   */\n  override def probability(cIndex: Double, fValue: Double): Double = 0.0\n\n  /**\n   * Gets the best split suggestion given a criterion and a class distribution\n   *\n   * @param criterion the split criterion to use\n   * @param pre the class distribution before the split\n   * @param fValue the value of the feature\n   * @param isBinarySplit true to use binary splits\n   * @return suggestion of best feature split\n   */\n  override def bestSplit(criterion: SplitCriterion, pre: Array[Double], fValue: Double,\n    isBinarySplit: Boolean): FeatureSplit = { null }\n\n  /**\n   * Merge the FeatureClassObserver to current FeatureClassObserver\n   *\n   * @param that the FeatureClassObserver will be merged\n   * @param trySplit whether called when a Hoeffding tree try to split\n   * @return current FeatureClassObserver\n   */\n  override def merge(that: FeatureClassObserver, trySplit: Boolean): FeatureClassObserver = this\n}\n/**\n * Class for observing the class distribution of a nominal feature.\n * The observer monitors the class distribution of a given feature.\n * Used in Naive Bayes and decision trees to monitor data statistics on leaves.\n */\nclass NominalFeatureClassObserver(val numClasses: Int, val fIndex: Int, val numFeatureValues: Int,\n    val laplaceSmoothingFactor: Int = 1) extends FeatureClassObserver with Serializable {\n\n  var classFeatureStatistics: Array[Array[Double]] = Array.fill(numClasses)(new Array[Double](numFeatureValues))\n\n  var blockClassFeatureStatistics: Array[Array[Double]] = Array.fill(numClasses)(new Array[Double](numFeatureValues))\n\n  var totalWeight: Double = 0.0\n  var blockWeight: Double = 0.0\n\n  def this(that: NominalFeatureClassObserver) {\n    this(that.numClasses, that.fIndex, that.numFeatureValues, that.laplaceSmoothingFactor)\n    for (i <- 0 until numClasses; j <- 0 until numFeatureValues) {\n      classFeatureStatistics(i)(j) = that.classFeatureStatistics(i)(j) +\n        that.blockClassFeatureStatistics(i)(j)\n    }\n    totalWeight = that.totalWeight + that.blockWeight\n  }\n  /**\n   * Updates statistics of this observer given a feature value, a class index\n   * and the weight of the example observed\n   *\n   * @param cIndex the index of class\n   * @param fValue the value of the feature\n   * @param weight the weight of the example\n   */\n  override def observeClass(cIndex: Double, fValue: Double, weight: Double): Unit = {\n    blockClassFeatureStatistics(cIndex.toInt)(fValue.toInt) += weight\n    blockWeight += weight\n  }\n\n  /**\n   * Gets the probability for an attribute value given a class\n   *\n   * @param cIndex the index of class\n   * @param fValue the value of the feature\n   * @return probability for a feature value given a class\n   */\n  override def probability(cIndex: Double, fValue: Double): Double = {\n    val sum = classFeatureStatistics(cIndex.toInt).sum\n    if (sum == 0) 0.0 else {\n      (classFeatureStatistics(cIndex.toInt)(fValue.toInt) + laplaceSmoothingFactor) /\n        (sum + numFeatureValues * laplaceSmoothingFactor)\n    }\n  }\n\n  /**\n   * Gets the best split suggestion given a criterion and a class distribution\n   *\n   * @param criterion the split criterion to use\n   * @param pre the class distribution before the split\n   * @param fValue the value of the feature\n   * @param isBinarySplit true to use binary splits\n   * @return suggestion of best feature split\n   */\n  override def bestSplit(criterion: SplitCriterion, pre: Array[Double],\n    fValue: Double, isBinarySplit: Boolean): FeatureSplit = {\n    var fSplit: FeatureSplit = null\n    if (!isBinarySplit) {\n      val post = multiwaySplit()\n      val merit = criterion.merit(pre, post)\n      fSplit = new FeatureSplit(new NominalMultiwayTest(fIndex, numFeatureValues), merit, post)\n    }\n\n    for (i <- 0 until numFeatureValues) {\n      val post: Array[Array[Double]] = binarySplit(i)\n      val merit = criterion.merit(normal(pre), normal(post))\n      if (fSplit == null || fSplit.merit < merit) {\n        fSplit = new FeatureSplit(new NominalBinaryTest(fIndex, i), merit, post)\n      }\n    }\n    fSplit\n  }\n  /**\n   * Merge the FeatureClassObserver to current FeatureClassObserver\n   *\n   * @param that the FeatureClassObserver will be merged\n   * @param trySplit whether called when a Hoeffding tree try to split\n   * @return current FeatureClassObserver\n   */\n  override def merge(that: FeatureClassObserver, trySplit: Boolean): FeatureClassObserver = {\n    if (!that.isInstanceOf[NominalFeatureClassObserver])\n      this\n    else {\n      val observer = that.asInstanceOf[NominalFeatureClassObserver]\n      if (numClasses != observer.numClasses || fIndex != observer.fIndex ||\n        numFeatureValues != observer.numFeatureValues ||\n        laplaceSmoothingFactor != observer.laplaceSmoothingFactor) this\n      else {\n        if (!trySplit) {\n          totalWeight += observer.blockWeight\n          for (\n            i <- 0 until blockClassFeatureStatistics.length; j <- 0 until\n              blockClassFeatureStatistics(0).length\n          ) {\n            blockClassFeatureStatistics(i)(j) += observer.blockClassFeatureStatistics(i)(j)\n          }\n        } else {\n          totalWeight += observer.totalWeight\n          for (\n            i <- 0 until classFeatureStatistics.length; j <- 0 until\n              classFeatureStatistics(0).length\n          ) {\n            classFeatureStatistics(i)(j) += observer.blockClassFeatureStatistics(i)(j)\n          }\n        }\n        this\n      }\n    }\n  }\n  /**\n   * Binary split the tree data depending on the input value\n   * @param fValue the input value\n   * @return an Array encoding the split\n   */\n  private[trees] def binarySplit(fValue: Double): Array[Array[Double]] =\n    { splitTranspose(classFeatureStatistics, fValue.toInt) }\n  /**\n   * Split the data globally.\n   * @return an Array encoding the split\n   */\n  private[trees] def multiwaySplit(): Array[Array[Double]] =\n    { transpose(classFeatureStatistics) }\n}\n/**\n * Trait for the numeric feature observers.\n */\ntrait NumericFeatureClassObserver extends FeatureClassObserver\n\n/**\n * Class GuassianNumericFeatureClassObserver for observing the class data distribution for a numeric feature using gaussian estimators.\n * This observer monitors the class distribution of a given feature.\n * Used in naive Bayes and decision trees to monitor data statistics on leaves.\n */\n\nclass GaussianNumericFeatureClassObserver(val numClasses: Int, val fIndex: Int, val numBins: Int = 10)\n    extends NumericFeatureClassObserver with Serializable {\n\n  val estimators: Array[GaussianEstimator] = Array.fill(numClasses)(new GaussianEstimator())\n  val minValuePerClass: Array[Double] = Array.fill(numClasses)(Double.PositiveInfinity)\n  val maxValuePerClass: Array[Double] = Array.fill(numClasses)(Double.NegativeInfinity)\n\n  def this(that: GaussianNumericFeatureClassObserver) {\n    this(that.numClasses, that.fIndex, that.numBins)\n    for (i <- 0 until numClasses) estimators(i) = new GaussianEstimator(that.estimators(i))\n  }\n\n  /**\n   * Updates statistics of this observer given a feature value, a class index\n   * and the weight of the example observed\n   *\n   * @param cIndex the index of class\n   * @param fValue the value of the feature\n   * @param weight the weight of the example\n   */\n  override def observeClass(cIndex: Double, fValue: Double, weight: Double): Unit = {\n    if (false) {\n      // todo, process missing value\n\n    } else {\n      if (minValuePerClass(cIndex.toInt) > fValue)\n        minValuePerClass(cIndex.toInt) = fValue\n      if (maxValuePerClass(cIndex.toInt) < fValue)\n        maxValuePerClass(cIndex.toInt) = fValue\n      estimators(cIndex.toInt).observe(fValue, weight)\n    }\n  }\n\n  /**\n   * Gets the probability for an attribute value given a class\n   *\n   * @param cIndex the index of class\n   * @param fValue the value of the feature\n   * @return probability for a feature value given a class\n   */\n  override def probability(cIndex: Double, fValue: Double): Double = {\n    if (estimators(cIndex.toInt) == null) 0.0\n    else estimators(cIndex.toInt).probabilityDensity(fValue)\n  }\n\n  /**\n   * Gets the best split suggestion given a criterion and a class distribution\n   *\n   * @param criterion the split criterion to use\n   * @param pre the class distribution before the split\n   * @param fValue the value of the feature\n   * @param isBinarySplit true to use binary splits\n   * @return suggestion of best feature split\n   */\n  override def bestSplit(criterion: SplitCriterion, pre: Array[Double],\n    fValue: Double, isBinarySplit: Boolean): FeatureSplit = {\n    var fSplit: FeatureSplit = null\n    val points: Array[Double] = splitPoints()\n    /**\n      * avoid empty fSplit return when splitPoints is null.\n      */\n\n    if (points.length == 0 ){\n      logInfo(\"Warning! No split point detected!\")\n      val post: Array[Array[Double]] = null\n      fSplit = new FeatureSplit(new NumericBinaryTest(fIndex, 0, false), Double.NegativeInfinity, post)\n    }\n    else {\n      for (splitValue: Double <- points) {\n        val post: Array[Array[Double]] = binarySplit(splitValue)\n        val merit = criterion.merit(normal(pre), normal(post))\n        if (fSplit == null || fSplit.merit < merit)\n          fSplit = new FeatureSplit(new NumericBinaryTest(fIndex, splitValue, false), merit, post)\n      }\n    }\n    fSplit\n  }\n\n  /**\n   * Binary split the tree data depending on the input value\n   * @param splitValue the input value\n   * @return an Array encoding the split\n   */\n  private[trees] def binarySplit(splitValue: Double): Array[Array[Double]] = {\n    val rst: Array[Array[Double]] = Array.fill(2)(new Array(numClasses))\n    estimators.zipWithIndex.foreach {\n      case (es, i) => {\n        if (splitValue < minValuePerClass(i)) {\n          rst(1)(i) += es.totalWeight()\n        } else if (splitValue >= maxValuePerClass(i)) {\n          rst(0)(i) += es.totalWeight()\n        } else {\n          val weights: Array[Double] = es.tripleWeights(splitValue)\n          rst(0)(i) += weights(0) + weights(1)\n          rst(1)(i) += weights(2)\n        }\n      }\n    }\n    rst\n  }\n\n  private[trees] def splitPoints(): Array[Double] = {\n    var minValue = Double.PositiveInfinity\n    var maxValue = Double.NegativeInfinity\n    val points = new TreeSet[Double]()\n    minValuePerClass.foreach { x => minValue = min(minValue, x) }\n    maxValuePerClass.foreach { x => maxValue = max(maxValue, x) }\n    if (minValue < Double.PositiveInfinity) {\n      val range = maxValue - minValue\n      for (i <- 0 until numBins) {\n        val splitValue = range * (i + 1) / ((numBins) + 1) + minValue\n        if (splitValue > minValue && splitValue < maxValue)\n          points.add(splitValue)\n      }\n    }\n    points.toArray\n  }\n\n  /**\n   * Merge the FeatureClassObserver to current FeatureClassObserver\n   *\n   * @param that the FeatureClassObserver will be merged\n   * @param trySplit whether called when a Hoeffding tree try to split\n   * @return current FeatureClassObserver\n   */\n  override def merge(that: FeatureClassObserver, trySplit: Boolean): FeatureClassObserver = {\n    if (!that.isInstanceOf[GaussianNumericFeatureClassObserver]) this\n    else {\n      val observer = that.asInstanceOf[GaussianNumericFeatureClassObserver]\n      if (numClasses == observer.numClasses && fIndex == observer.fIndex) {\n        for (i <- 0 until numClasses) {\n          estimators(i) = estimators(i).merge(observer.estimators(i), trySplit)\n          minValuePerClass(i) = min(minValuePerClass(i), observer.minValuePerClass(i))\n          maxValuePerClass(i) = max(maxValuePerClass(i), observer.maxValuePerClass(i))\n        }\n      }\n      this\n    }\n  }\n}\n\n/**\n * object FeatureClassObserver for create FeatureClassObserver.\n */\nobject FeatureClassObserver {\n  def createFeatureClassObserver(numClasses: Int, fIndex: Int,\n    featureSpec: FeatureSpecification): FeatureClassObserver = {\n    if (featureSpec.isNominal())\n      new NominalFeatureClassObserver(numClasses, fIndex, featureSpec.range())\n    else\n      new GaussianNumericFeatureClassObserver(numClasses, fIndex)\n  }\n\n  def createFeatureClassObserver(observer: FeatureClassObserver): FeatureClassObserver = {\n    if (observer.isInstanceOf[NominalFeatureClassObserver])\n      new NominalFeatureClassObserver(observer.asInstanceOf[NominalFeatureClassObserver])\n    else if (observer.isInstanceOf[GaussianNumericFeatureClassObserver])\n      new GaussianNumericFeatureClassObserver(observer.asInstanceOf[GaussianNumericFeatureClassObserver])\n    else new NullFeatureClassObserver\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/FeatureSplit.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees\n\nimport scala.math.Ordered\n\n/**\n * Class for recording a split suggestion.\n */\nclass FeatureSplit(val conditionalTest: ConditionalTest, val merit: Double, \n  val result: Array[Array[Double]]) extends Ordered[FeatureSplit] {\n\n  /** Compares two FeatureSplit objects.\n   * @param that the comparison feature \n   * @return comparison result\n   */\n  override def compare(that: FeatureSplit): Int = {\n    if (this.merit < that.merit) -1\n    else if (this.merit > that.merit) 1\n    else 0\n  }\n\n  /**\n   * Returns the number of the split.\n   * @return the number of the split\n   */\n  def numSplit(): Int = result.length\n\n  /**\n   * Returns the distribution of the split index.\n   *\n   * @param splitIndex the split index\n   * @return an Array containing the distribution   \n   */\n  def distributionFromSplit(splitIndex: Int): Array[Double] = result(splitIndex)\n\n  override def toString(): String = \"FeatureSplit, merit=\" + merit + \", \" + conditionalTest\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/GaussianEstimator.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees\n\nimport scala.math.{ sqrt, Pi, pow, exp, max }\nimport org.apache.spark.streamdm.utils.Statistics\n\n/**\n * Gaussian incremental estimator that uses incremental method, more resilient\n * to floating point imprecision.\n * For more info see Donald Knuth's \"The Art of Computer Programming, Volume 2:\n * Seminumerical Algorithms\", section 4.2.2.\n */\n\nclass GaussianEstimator(var weightSum: Double = 0.0, var mean: Double = 0.0,\n                        var varianceSum: Double = 0.0) extends Serializable {\n  val normal_constant: Double = sqrt(2 * Pi)\n  var blockWeightSum: Double = 0.0\n  var blockMean: Double = 0.0\n  var blockVarianceSum: Double = 0.0\n\n  def this(that: GaussianEstimator) {\n    this(that.weightSum, that.mean, that.varianceSum)\n  }\n  /**\n   * Observe the data and update the Gaussian estimator\n   *\n   * @param value value of a feature\n   * @param weight weight of the Example\n   */\n  def observe(value: Double, weight: Double): Unit = {\n    if (!value.isInfinite() && !value.isNaN() && weight > 0) {\n      if (blockWeightSum == 0) {\n        blockMean = value\n        blockWeightSum = weight\n      } else {\n        blockWeightSum += weight\n        val lastMean = blockMean\n        blockMean += weight * (value - lastMean) / blockWeightSum\n        blockVarianceSum += weight * (value - lastMean) * (value - blockMean)\n      }\n    }\n  }\n  /**\n   * Merge current GaussianEstimator with another one.\n   *\n   * @param that the GaussianEstimator to be merged\n   * @param trySplit flag indicating whether the Hoeffding Tree tries to split\n   * @return the new GaussianEstimator\n   */\n  def merge(that: GaussianEstimator, trySplit: Boolean): GaussianEstimator = {\n    if (!trySplit) {\n      //add to block variables\n      if (this.blockWeightSum == 0) {\n        blockWeightSum = that.blockWeightSum\n        blockMean = that.blockMean\n        blockVarianceSum = that.blockVarianceSum\n      } else {\n        val newBlockWeightSum = blockWeightSum + that.blockWeightSum\n        val newBlockMean = (this.blockMean * blockWeightSum + that.blockMean *\n          that.blockWeightSum) / newBlockWeightSum\n        val newBlockVarianceSum = this.blockVarianceSum + that.blockVarianceSum +\n          pow(this.blockMean - that.blockMean, 2) *\n          this.blockWeightSum * that.blockWeightSum / (this.blockWeightSum + that.blockWeightSum)\n        blockWeightSum = newBlockWeightSum\n        blockMean = newBlockMean\n        blockVarianceSum = newBlockVarianceSum\n      }\n    } else {\n      //add to the total variables\n      if (this.weightSum == 0) {\n        weightSum = that.blockWeightSum\n        mean = that.blockMean\n        varianceSum = that.blockVarianceSum\n      } else {\n        val newWeightSum = weightSum + that.blockWeightSum\n        val newMean = (this.mean * weightSum + that.blockMean * that.blockWeightSum) / \n                        newWeightSum\n        val newVarianceSum = this.varianceSum + that.blockVarianceSum + pow(this.mean -\n                              that.blockMean, 2) * this.weightSum * that.blockWeightSum /\n                              (this.weightSum + that.blockWeightSum)\n        weightSum = newWeightSum\n        mean = newMean\n        varianceSum = newVarianceSum\n      }\n    }\n    this\n  }\n\n  /**\n   * Returns the total weight\n   *\n   * @return the total weight\n   */\n  def totalWeight(): Double = {\n    weightSum\n  }\n  /**\n   * Returns the mean value\n   *\n   * @return the mean value\n   */\n  def getMean(): Double = {\n    mean\n  }\n  /**\n   * Returns the standard deviation\n   *\n   * @return the standard deviation\n   */\n  def stdDev(): Double = {\n    sqrt(variance())\n  }\n\n  /**\n   * Returns the variance\n   *\n   * @return the variance\n   */\n  def variance(): Double = {\n    if (weightSum <= 1.0) 0\n    else varianceSum / (weightSum - 1)\n  }\n\n  /**\n   * Returns the cumulative probability of the input value in the current\n   * distribution.\n   *\n   * @param value the value\n   * @return the cumulative probability\n   */\n\n  def probabilityDensity(value: Double): Double = {\n    if (weightSum == 0) 0.0\n    else {\n      val stddev = stdDev()\n      if (stddev > 0) {\n        val diff = value - mean\n        exp(-pow(diff / stddev, 2) / 2) / (normal_constant * stddev)\n      } else {\n        if (value == mean) 1.0 else 0\n      }\n    }\n  }\n\n  /**\n   * Returns an array of weights  which have the sum less than, equal to, and\n   * greater than the split value. \n   *\n   * @param splitValue the value of the split \n   * @return the resulting Array of values\n   */\n  def tripleWeights(splitValue: Double): Array[Double] = {\n    //equal weights sum\n    val eqWeight = probabilityDensity(splitValue) * weightSum\n    //less than weights sum\n    val lsWeight = {\n      if (stdDev() > 0) {\n        Statistics.normalProbability((splitValue - getMean()) / stdDev()) * weightSum - eqWeight\n      } else {\n        if (splitValue < getMean()) weightSum - eqWeight\n        else 0.0\n      }\n    }\n    //greater than weights sum\n    val gtWeight = max(0, weightSum - eqWeight - lsWeight)\n    Array[Double](lsWeight, eqWeight, gtWeight)\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/HoeffdingTree.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees\n\nimport scala.math.{sqrt, log => math_log}\nimport scala.collection.mutable.Queue\nimport org.apache.spark.internal.Logging\nimport com.github.javacliparser._\nimport org.apache.spark.streaming.dstream._\nimport org.apache.spark.streamdm.utils.Utils.{argmax, normal}\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.classifiers._\nimport org.apache.spark.streamdm.classifiers.trees.nodes._\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n  *\n  * The Hoeffding tree is an incremental decision tree learner for large data\n  * streams, that assumes that the data distribution is not changing over time.\n  * It grows incrementally a decision tree based on the theoretical guarantees of\n  * the Hoeffding bound (or additive Chernoff bound). A node is expanded as soon\n  * as there is sufficient statistical evidence that an optimal splitting feature\n  * exists, a decision based on the distribution-independent Hoeffding bound. The\n  * model learned by the Hoeffding tree is asymptotically nearly identical to the\n  * one built by a non-incremental learner, if the number of training instances\n  * is large enough.\n  *\n  * <p>It is controlled by the following options:\n  * <ul>\n  * <li> numeric observer to use (<b>-n</b>); for the moment, only Gaussian\n  * approximation is supported; class of type FeatureClassObserver;\n  * <li> number of examples a leaf should observe before a split attempt\n  * (<b>-g</b>);\n  * <li> Number of examples a leaf should observe before applying NaiveBayes\n  * (<b>-q</b>);\n  * <li> split criterion to use (<b>-s</b>), an object of type SplitCriterionType;\n  * <li> allowable error in split decision (<b>-c</b>);\n  * <li> threshold of allowable error in breaking ties (<b>-t</b>);\n  * <li> allow only binary splits (<b>-b</b>), boolean flag;\n  * <li> disable poor attributes (<b>-r</b>);\n  * <li> allow growth (<b>-o</b>);\n  * <li> disable pre-pruning (<b>-p</b>);\n  * <li> leaf prediction to use (<b>-l</b>), either MajorityClass (0), NaiveBayes\n  * (1) or adaptive NaiveBayes (2, default);\n  * </ul>\n  */\nclass HoeffdingTree extends Classifier with Logging {\n\n  type T = HoeffdingTreeModel\n\n  val numericObserverTypeOption: IntOption = new IntOption(\"numericObserverType\", 'n',\n    \"numeric observer type, 0: gaussian\", 0, 0, 2)\n\n  val splitCriterionOption: ClassOption = new ClassOption(\"splitCriterion\", 's',\n    \"Split criterion to use.\", classOf[SplitCriterion], \"InfoGainSplitCriterion\")\n\n  val growthAllowedOption: FlagOption = new FlagOption(\"growthAllowed\", 'o',\n    \"Allow to grow\")\n\n  val binaryOnlyOption: FlagOption = new FlagOption(\"binaryOnly\", 'b',\n    \"Only allow binary splits\")\n\n  val numGraceOption: IntOption = new IntOption(\"numGrace\", 'g',\n    \"The number of examples a leaf should observe between split attempts.\",\n    200, 1, Int.MaxValue)\n\n  val tieThresholdOption: FloatOption = new FloatOption(\"tieThreshold\", 't',\n    \"Threshold below which a split will be forced to break ties.\", 0.05, 0, 1)\n\n  val splitConfidenceOption: FloatOption = new FloatOption(\"splitConfidence\", 'c',\n    \"The allowable error in split decision, values closer to 0 will take longer to decide.\",\n    0.0000001, 0.0, 1.0)\n\n  val learningNodeOption: IntOption = new IntOption(\"learningNodeType\", 'l',\n    \"Learning node type of leaf\", 2, 0, 2)\n\n  val nbThresholdOption: IntOption = new IntOption(\"nbThreshold\", 'q',\n    \"The number of examples a leaf should observe between permitting Naive Bayes\", 0, 0, Int.MaxValue)\n\n  val noPrePruneOption: FlagOption = new FlagOption(\"noPrePrune\", 'p', \"Disable pre-pruning.\")\n\n  val removePoorFeaturesOption: FlagOption = new FlagOption(\"removePoorFeatures\", 'r', \"Disable poor features.\")\n\n  val maxDepthOption: IntOption = new IntOption(\"MaxDepth\", 'h',\n    \"The max depth of tree to stop growing\",\n    20, 0, Int.MaxValue)\n\n  var model: HoeffdingTreeModel = null\n\n  var espec: ExampleSpecification = null\n\n  var numSplitFeatures: Int = 0\n\n  /* Init the model used for the Learner*/\n  override def init(exampleSpecification: ExampleSpecification): Unit = {\n    espec = exampleSpecification\n    numSplitFeatures = espec.numberInputFeatures()\n    model = new HoeffdingTreeModel(espec, numericObserverTypeOption.getValue, splitCriterionOption.getValue(),\n      true, binaryOnlyOption.isSet(), numGraceOption.getValue(),\n      tieThresholdOption.getValue, splitConfidenceOption.getValue(),\n      learningNodeOption.getValue(), nbThresholdOption.getValue(),\n      noPrePruneOption.isSet(), removePoorFeaturesOption.isSet(),\n      maxDepthOption.getValue(), this.numSplitFeatures)\n    model.init()\n  }\n\n  def init(exampleSpecification: ExampleSpecification, numSplitFeatures: Int): Unit = {\n    espec = exampleSpecification\n    this.numSplitFeatures = numSplitFeatures\n    model = new HoeffdingTreeModel(espec, numericObserverTypeOption.getValue, splitCriterionOption.getValue(),\n      true, binaryOnlyOption.isSet(), numGraceOption.getValue(),\n      tieThresholdOption.getValue, splitConfidenceOption.getValue(),\n      learningNodeOption.getValue(), nbThresholdOption.getValue(),\n      noPrePruneOption.isSet(), removePoorFeaturesOption.isSet(),\n      maxDepthOption.getValue(), this.numSplitFeatures)\n    model.init()\n  }\n\n  /** Gets the current model used for the Learner.\n   *\n   * @return the Model object used for training\n   */\n  override def getModel: HoeffdingTreeModel = model\n\n  /** Train the model from the stream of instances given for training.\n   *\n   * @param input a stream of instances\n   * @return Unit\n   */\n  override def train(input: DStream[Example]): Unit = {\n    input.foreachRDD {\n      rdd =>\n        val latestModel = rdd.aggregate(\n          new HoeffdingTreeModel(model))(\n          (updatedModel, example) => { updatedModel.update(example) },\n          (mergeModel1, mergeModel2) => { mergeModel1.merge(mergeModel2, false) })\n        model = model.merge(latestModel, true)\n        logInfo(\"training \" + model.description())\n    }\n  }\n\n  /** Predict the label of the Instance, given the current model\n   *\n   * @param input the Instance which needs a class predicted\n   * @return a tuple containing the original instance and the predicted value\n   */\n  def predict(input: DStream[Example]): DStream[(Example, Double)] = {\n    input.map { x => (x, model.predict(x)) }\n  }\n}\n\nclass HoeffdingTreeModel(val espec: ExampleSpecification, val numericObserverType: Int = 0,\n                         val splitCriterion: SplitCriterion = new InfoGainSplitCriterion(),\n                         var growthAllowed: Boolean = true, val binaryOnly: Boolean = true,\n                         val graceNum: Int = 200, val tieThreshold: Double = 0.05,\n                         val splitConfedence: Double = 0.0000001, val learningNodeType: Int = 0,\n                         val nbThreshold: Int = 0, val noPrePrune: Boolean = true,\n                         val removePoorFeatures: Boolean = false/*, val splitAll: Boolean = false*/, val maxDepth: Int = 20,\n                         val numSplitFeatures: Int = -1)\n  extends ClassificationModel with Serializable with Logging {\n\n  type T = HoeffdingTreeModel\n\n  val outputSpec = espec.outputFeatureSpecification(0)\n  val numClasses = outputSpec.range()\n\n  var activeNodeCount: Int = 0\n  var inactiveNodeCount: Int = 0\n  var deactiveNodeCount: Int = 0\n  var decisionNodeCount: Int = 0\n\n  var baseNumExamples: Int = 0\n  var blockNumExamples: Int = 0\n\n  var root: Node = null\n\n  def this(model: HoeffdingTreeModel) {\n    this(model.espec, model.numericObserverType, model.splitCriterion, model.growthAllowed,\n      model.binaryOnly, model.graceNum, model.tieThreshold, model.splitConfedence,\n      model.learningNodeType, model.nbThreshold, model.noPrePrune, model.removePoorFeatures,\n      model.maxDepth, model.numSplitFeatures)\n    activeNodeCount = model.activeNodeCount\n    this.inactiveNodeCount = model.inactiveNodeCount\n    this.deactiveNodeCount = model.deactiveNodeCount\n    this.decisionNodeCount = model.decisionNodeCount\n    baseNumExamples = model.baseNumExamples + model.blockNumExamples\n    this.root = model.root\n  }\n\n  /* init the model */\n  def init(): Unit = {\n    //create an ActiveLearningNode for root\n    root = createLearningNode(learningNodeType, numClasses)\n    activeNodeCount += 1\n  }\n\n  /** Update the model from the stream of instances given for training.\n   *\n   * @param example an example instance\n   * @return current model\n   */\n  override def update(example: Example): HoeffdingTreeModel = {\n    blockNumExamples += 1\n\n    if (root == null) {\n      init\n    }\n    // filter the example instance to the responding FoundNode\n    val foundNode = root.filterToLeaf(example, null, -1)\n    var leafNode = foundNode.node\n\n    if (leafNode.isInstanceOf[LearningNode]) {\n      val learnNode = leafNode.asInstanceOf[LearningNode]\n      //update the learning node\n      learnNode.learn(this, example)\n    }\n    this\n  }\n\n  /** Try to split the learning node\n   *\n   * @param learnNode the node which may be split\n   * @param parent parent of the learnNode\n   * @param pIndex learnNode's index of the parent\n   * @return Unit\n   */\n  def attemptToSplit(learnNode: LearningNode, parent: SplitNode, pIndex: Int): Unit = {\n    logInfo(\"attemptToSplit(...)\")\n    if (growthAllowed && learnNode.isInstanceOf[ActiveLearningNode]) {\n      logInfo(\"\\tattemptToSplit(...) inside the if\")\n      // split only happened when the tree is allowed to grow and the node is a ActiveLearningNode\n      val activeNode = learnNode.asInstanceOf[ActiveLearningNode]\n      val isPure = activeNode.isPure()\n      if (!isPure) {\n        // one best suggestion for each feature\n        var bestSuggestions: Array[FeatureSplit] = activeNode.getBestSplitSuggestions(splitCriterion, this)\n        //sort the suggestion based on the merit\n        bestSuggestions = bestSuggestions.sorted\n        if (shouldSplit(activeNode, bestSuggestions)) {\n          val best: FeatureSplit = bestSuggestions.last\n          if (best.conditionalTest == null) {\n            logWarning(\"Deactivating learning node\")\n            //deactivate a learning node\n            deactiveLearningNode(activeNode, parent, pIndex)\n          } else {\n            logInfo(\"Split! \")\n            //replace the ActiveLearningNode with a SplitNode and create children\n            val splitNode: SplitNode = new SplitNode(activeNode.classDistribution, best.conditionalTest)\n            for (index <- 0 until best.numSplit) {\n              splitNode.setChild(index,\n                createLearningNode(learningNodeType, best.distributionFromSplit(index)))\n            }\n            // replace the node\n            addSplitNode(splitNode, parent, pIndex)\n          }\n          val tree_size_nodes = activeNodeCount + decisionNodeCount + inactiveNodeCount\n          val tree_size_leaves = activeNodeCount + inactiveNodeCount\n          logInfo(\"{\" + tree_size_nodes + \",\" + tree_size_leaves + \",\" + activeNodeCount + \",\" + treeHeight() + \"}\")\n\n        }\n        // todo manage memory\n      }\n    }\n  }\n\n  /**\n    * Check whether split the activeNode or not according to Heoffding bound and merit\n    * @param activeNode the node which may be splitted\n    * @param bestSuggestions array of FeatureSplit\n    * @return Boolean\n    */\n  def shouldSplit(activeNode: ActiveLearningNode, bestSuggestions: Array[FeatureSplit]): Boolean = {\n    logInfo(\"bestSuggestions.length: \" + bestSuggestions.length)\n    if (bestSuggestions.length < 2) {\n      bestSuggestions.length > 0\n    } else {\n      val hoeffdingBound = computeHoeffdingBound(activeNode)\n      val length = bestSuggestions.length\n\n      logInfo(\"hoeffdingBound: \" + hoeffdingBound + \" tieThreshold: \" + tieThreshold +\n        \" bestSuggestions.last.merit: \" + bestSuggestions.last.merit + \" bestSuggestions(\" + (length - 2) + \").merit: \" + bestSuggestions(length - 2).merit)\n\n      if (hoeffdingBound < tieThreshold ||\n          bestSuggestions.last.merit - bestSuggestions(length - 2).merit > hoeffdingBound) {\n        true\n      } else false\n    }\n  }\n\n  /**\n    * Merge function: merge with another model's FeatureObservers and root, and try to split\n    * @param that : other HoeffdingTree Model\n    * @param trySplit: (false: only update statistics, true: attempt to split).\n    * @return this\n    */\n  def merge(that: HoeffdingTreeModel, trySplit: Boolean): HoeffdingTreeModel = {\n    // merge root with another root\n    root.merge(that.root, trySplit)\n    if (trySplit) {\n      if(this.treeHeight() < maxDepth){\n        //try to split all leaf nodes\n        val queue = new Queue[FoundNode]()\n        queue.enqueue(new FoundNode(root, null, -1))\n        var numLeaves = 0\n        var toSplit = 0\n        var totalAddOnWeight = 0.0\n        while (queue.size > 0) {\n          val foundNode = queue.dequeue()\n          foundNode.node match {\n            case splitNode: SplitNode => {\n              logInfo(\"case splitNode: ...\")\n              for (i <- 0 until splitNode.children.length)\n                queue.enqueue(new FoundNode(splitNode.children(i), splitNode, i))\n            }\n            case activeNode: ActiveLearningNode => {\n              totalAddOnWeight = totalAddOnWeight + activeNode.addOnWeight()\n              numLeaves = numLeaves + 1\n              logInfo(\"case activeNode: activeLearningNode, blockAddonWeight: \" + activeNode.blockAddonWeight +  \" totalAddOnWeight: \" + totalAddOnWeight + \" addOnWeight: \" + activeNode.addOnWeight() + \" graceNum: \" + graceNum + \" addOnWeight > graceNum? \" + (activeNode.addOnWeight() > graceNum))\n              if(activeNode.addOnWeight() > graceNum){\n                attemptToSplit(activeNode, foundNode.parent, foundNode.index)\n                toSplit = toSplit + 1\n              }\n            }\n            case _: Node => {}\n          }\n        }\n      }\n      else{\n        logInfo(\"Tree's height exceeds maxDepth\")\n      }\n    }\n    this\n  }\n\n  /* predict the class of example */\n  def predict(example: Example): Double = {\n    if (root != null) {\n      val foundNode = root.filterToLeaf(example, null, -1)\n      var leafNode = foundNode.node\n      if (leafNode == null) {\n        leafNode = foundNode.parent\n      }\n      argmax(leafNode.classVotes(this, example))\n    } else {\n      logInfo(\"default prediction\")\n      0.0\n    }\n  }\n\n\n /** create a new ActiveLearningNode\n *\n * @param nodeType, (0,ActiveLearningNode),(1,LearningNodeNB),(2,LearningNodeNBAdaptive)\n * @param classDistribution, the classDistribution to init node\n * @return a new LearningNode\n */\n  def createLearningNode(nodeType: Int, classDistribution: Array[Double]): LearningNode = nodeType match {\n    case 0 => new ActiveLearningNode(classDistribution, espec.in, this.numSplitFeatures)\n    case 1 => new LearningNodeNB(classDistribution, espec.in, this.numSplitFeatures)\n    case 2 => new LearningNodeNBAdaptive(classDistribution, espec.in, this.numSplitFeatures)\n    case _ => new ActiveLearningNode(classDistribution, espec.in, this.numSplitFeatures)\n  }\n\n/** create a new ActiveLearningNode\n *\n * @param nodeType, (0,ActiveLearningNode),(1,LearningNodeNB),(2,LearningNodeNBAdaptive)\n * @param numClasses, the number of the classes\n * @return a new LearningNode\n */\n  def createLearningNode(nodeType: Int, numClasses: Int): LearningNode = nodeType match {\n    case 0 => new ActiveLearningNode(new Array[Double](numClasses), espec.in, this.numSplitFeatures)\n    case 1 => new LearningNodeNB(new Array[Double](numClasses), espec.in, this.numSplitFeatures)\n    case 2 => new LearningNodeNBAdaptive(new Array[Double](numClasses), espec.in, this.numSplitFeatures)\n    case _ => new ActiveLearningNode(new Array[Double](numClasses), espec.in, this.numSplitFeatures)\n  }\n\n/** create a new ActiveLearningNode with another LearningNode\n *\n * @param nodeType, (0,ActiveLearningNode),(1,LearningNodeNB),(2,LearningNodeNBAdaptive)\n * @param that, a default LearningNode to init the LearningNode\n * @return a new LearningNode\n */\n  def createLearningNode(nodeType: Int, that: LearningNode): LearningNode = nodeType match {\n    case 0 => new ActiveLearningNode(that.asInstanceOf[ActiveLearningNode])\n    case 1 => new LearningNodeNB(that.asInstanceOf[LearningNodeNB])\n    case 2 => new LearningNodeNBAdaptive(that.asInstanceOf[LearningNodeNBAdaptive])\n  }\n\n /** replace an InactiveLearningNode with an ActiveLearningNode\n  *\n  * @param inactiveNode which will be replaced\n  * @param parent parent of the node which will be replaced\n  * @param pIndex the index of node\n  * @return Unit\n  */\n  def activeLearningNode(inactiveNode: InactiveLearningNode, parent: SplitNode, pIndex: Int): Unit = {\n    val activeNode = createLearningNode(learningNodeType, inactiveNode.classDistribution)\n    if (parent == null) {\n      root = activeNode\n    } else {\n      parent.setChild(pIndex, activeNode)\n    }\n    activeNodeCount += 1\n    inactiveNodeCount -= 1\n  }\n\n  /** replace an ActiveLearningNode with an InactiveLearningNode\n   * @param parent parent of the node which will be replaced\n   * @param pIndex the index of node\n   * @return Unit\n   */\n  def deactiveLearningNode(activeNode: ActiveLearningNode, parent: SplitNode, pIndex: Int): Unit = {\n    val deactiveNode = new InactiveLearningNode(activeNode.classDistribution)\n    if (parent == null) {\n      root = deactiveNode\n    } else {\n      parent.setChild(pIndex, deactiveNode)\n    }\n    activeNodeCount -= 1\n    inactiveNodeCount += 1\n\n  }\n\n  /**\n   * Replace a activeNode with the splitNode\n   * @param splitNode the new SplitNode\n   * @param parent parent of the node which will be replaced\n   * @param pIndex the index of node\n   * @return Unit\n   */\n  def addSplitNode(splitNode: SplitNode, parent: SplitNode, pIndex: Int): Unit = {\n    if (parent == null) {\n      root = splitNode\n    } else {\n      parent.setChild(pIndex, splitNode)\n    }\n    activeNodeCount += splitNode.numChildren() - 1\n    decisionNodeCount += 1\n  }\n\n  /** Computes Heoffding Bound withe activeNode's class distribution\n   * @param activeNode\n   * @return double value\n   */\n  def computeHoeffdingBound(activeNode: ActiveLearningNode): Double = {\n    val rangeMerit = splitCriterion.rangeMerit(activeNode.classDistribution)\n    val hoeffdingBound = sqrt(rangeMerit * rangeMerit * math_log(1.0 / this.splitConfedence)\n      / (activeNode.weight() * 2))\n    hoeffdingBound\n  }\n  /**\n   * Returns the height of Hoeffding Tree\n   */\n  def treeHeight(): Int = {\n    if (root == null) -1\n    else root.height()\n  }\n\n  /**\n   * Description of the Hoeffding Tree Model\n   * @return a multi-line String\n   */\n  def description(): String = {\n    \"[Hoeffding Tree Model description] blockNumExamples = \" + blockNumExamples + \" root: \" + root.description()\n  }\n\n  /** Computes the probability for a given label class, given the current Model\n    *\n    * @param example the Example which needs a class predicted\n    * @return the predicted probability\n    */\n  override def prob(example: Example): Double = {\n    if (root != null) {\n      val foundNode = root.filterToLeaf(example, null, -1)\n      var leafNode = foundNode.node\n      if (leafNode == null) {\n        leafNode = foundNode.parent\n      }\n      argmax(normal(leafNode.classVotes(this, example)))\n    } else {\n      0.0\n    }\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/SplitCriterion.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees\n\nimport scala.math.max\n\nimport org.apache.spark.streamdm.utils.Utils.log2\n\ntrait SplitCriterionType\n\ncase class InfoGainSplitCriterionType() extends SplitCriterionType\n\ncase class GiniSplitCriterionType() extends SplitCriterionType\n\ncase class VarianceReductionSplitCriterionType() extends SplitCriterionType\n\n/**\n * Trait for computing splitting criteria with respect to distributions of class values.\n * The split criterion is used as a parameter on decision trees and decision stumps.\n * The two split criteria most used are Information Gain and Gini.\n */\n\ntrait SplitCriterion extends Serializable {\n\n  /**\n   * Computes the merit of splitting for a given distribution before and after the split.\n   *\n   * @param pre the class distribution before the split\n   * @param post the class distribution after the split\n   * @return value of the merit of splitting\n   */\n  def merit(pre: Array[Double], post: Array[Array[Double]]): Double\n\n  /**\n   * Computes the range of splitting merit\n   *\n   * @param pre the class distribution before the split\n   * @return value of the range of splitting merit\n   */\n  def rangeMerit(pre: Array[Double]): Double\n\n}\n\n/**\n * Class for computing splitting criteria using information gain with respect to\n * distributions of class values.\n */\nclass InfoGainSplitCriterion extends SplitCriterion with Serializable {\n\n  var minBranch: Double = 0.01\n\n  def this(minBranch: Double) {\n    this()\n    this.minBranch = minBranch\n  }\n\n  /**\n   * Computes the merit of splitting for a given distribution before and after the split.\n   *\n   * @param pre the class distribution before the split\n   * @param post the class distribution after the split\n   * @return value of the merit of splitting\n   */\n  override def merit(pre: Array[Double], post: Array[Array[Double]]): Double = {\n    val num = numGTFrac(post, minBranch)\n    if (numGTFrac(post, minBranch) < 2) Double.NegativeInfinity\n    else {\n      val merit = entropy(pre) - entropy(post)\n      merit\n    }\n  }\n\n  /**\n   * Computes the range of splitting merit\n   *\n   * @param pre the class distribution before the split\n   * @return value of the range of splitting merit\n   */\n  override def rangeMerit(pre: Array[Double]): Double = log2(max(pre.length, 2))\n\n  /**\n   * Returns the entropy of a distribution\n    *\n   * @param pre an Array containing the distribution\n   * @return the entropy\n   */\n  def entropy(pre: Array[Double]): Double = {\n    if (pre == null || pre.sum <= 0 || hasNegative(pre)) {\n      0.0\n    }\n    else {\n      log2(pre.sum) - pre.filter(_ > 0).map(x => x * log2(x)).sum / pre.sum\n    }\n  }\n\n  /**\n   * Computes the entropy of an matrix\n   *\n   * @param post the matrix as an Array of Array\n   * @return the entropy\n   */\n  def entropy(post: Array[Array[Double]]): Double = {\n    if (post == null || post.length == 0 || post(0).length == 0) 0.0\n    else {\n      post.map { row => (row.sum * entropy(row)) }.sum / post.map(_.sum).sum\n    }\n\n  }\n\n  /**\n   * Returns number of subsets which have values greater than minFrac\n   *\n   * @param post the matrix as an Array of Array\n   * @param minFrac the min threshold\n   * @return number of subsets\n   */\n  def numGTFrac(post: Array[Array[Double]], minFrac: Double): Int = {\n    if (post == null || post.length == 0) {\n      0\n    } else {\n      val sums = post.map { _.sum }\n      sums.filter(_ > sums.sum * minFrac).length\n    }\n  }\n\n  /**\n   * Returns whether a array has negative value\n   *\n   * @param pre an Array to be valued\n   * @return whether a array has negative value\n   */\n  private[trees] def hasNegative(pre: Array[Double]): Boolean = pre.filter(x => x < 0).length > 0\n\n}\n\n/**\n * Class for computing splitting criteria using Gini with respect to\n * distributions of class values.\n */\n\nclass GiniSplitCriterion extends SplitCriterion with Serializable {\n\n  /**\n   * Computes the merit of splitting for a given distribution before and after the split.\n   *\n   * @param pre the class distribution before the split\n   * @param post the class distribution after the split\n   * @return value of the merit of splitting\n   */\n  override def merit(pre: Array[Double], post: Array[Array[Double]]): Double = {\n    val sums = post.map(_.sum)\n    val totalWeight = sums.sum\n    val ginis: Array[Double] = post.zip(sums).map {\n      case (x, y) => computeGini(x, y) * y / totalWeight\n    }\n    1.0 - ginis.sum\n  }\n\n  /**\n   * Computes the range of splitting merit\n   *\n   * @param pre the class distribution before the split\n   * @return value of the range of splitting merit\n   */\n  override def rangeMerit(pre: Array[Double]): Double = 1.0\n\n  /**\n   * Computes the gini of an array\n   *\n   * @param dist an array to be computed\n   * @param sum the sum of the array\n   * @return the gini of an array\n   */\n  private[trees] def computeGini(dist: Array[Double], sum: Double): Double =\n    1.0 - dist.map { x => x * x / sum / sum }.sum\n\n}\n\n/**\n * Class for computing splitting criteria using variance reduction with respect\n * to distributions of class values.\n */\nclass VarianceReductionSplitCriterion extends SplitCriterion with Serializable {\n\n  val magicNumber = 5.0\n\n  /**\n   * Computes the merit of splitting for a given distribution before and after the split.\n   *\n   * @param pre the class distribution before the split\n   * @param post the class distribution after the split\n   * @return value of the merit of splitting\n   */\n  override def merit(pre: Array[Double], post: Array[Array[Double]]): Double = {\n    val count = post.map { row => if (row(0) >= magicNumber) 1 else 0 }.sum\n    if (count != post.length) 0\n    else {\n      var sdr = computeSD(pre)\n      post.foreach { row => sdr -= (row(0) / pre(0)) * computeSD(row) }\n      sdr\n    }\n  }\n\n  /**\n   * Computes the range of splitting merit\n   *\n   * @param pre the class distribution before the split\n   * @return value of the range of splitting merit\n   */\n  override def rangeMerit(pre: Array[Double]): Double = 1.0\n\n  /**\n   * Computes the standard deviation of a distribution\n   *\n   * @param pre an Array containing the distribution\n   * @return the standard deviation\n   */\n  private[trees] def computeSD(pre: Array[Double]): Double = {\n    val n = pre(0).toInt\n    val sum = pre(1)\n    val sumSq = pre(2)\n    (sumSq - ((sum * sum) / n)) / n\n  }\n}\n\nobject SplitCriterion {\n\n  /**\n   * Return a new SplitCriterion, by default InfoGainSplitCriterion.\n   * @param scType the type of the split criterion\n   * @param minBranch branch parameter\n   * @return the new SplitCriterion\n   */\n  def createSplitCriterion(\n    scType: SplitCriterionType, minBranch: Double = 0.01): SplitCriterion = scType match {\n    case infoGrain: InfoGainSplitCriterionType   => new InfoGainSplitCriterion(minBranch)\n    case gini: GiniSplitCriterionType            => new GiniSplitCriterion()\n    case vr: VarianceReductionSplitCriterionType => new VarianceReductionSplitCriterion()\n    case _                                       => new InfoGainSplitCriterion(minBranch)\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/Utils.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.classifiers.trees\n\nimport scala.math.min\n\nobject Utils {\n  /*\n   * Add two arrays and return the min length of two input arrays.\n   * \n   * @param array1 double Array\n   * @param array2 double Array\n   * @return Array which added in each index\n   */\n  def addArrays(array1: Array[Double], array2: Array[Double]): Array[Double] = {\n    val merge = new Array[Double](min(array1.length, array2.length))\n    for (i <- 0 until merge.length)\n      merge(i) = array1(i) + array2(i)\n    merge\n  }\n\n  /*\n   * Return a string for a matrix\n   * \n   * @param m  matrix in form of 2-D array\n   * @param split string of the split\n   * @param head string of matrix head and each line's head\n   * @param tail string of matrix tail and each line's tail\n   * @return string format of input matrix\n   */\n  def matrixtoString[T](m: Array[Array[T]], split: String = \",\", head: String = \"{\", tail: String = \"}\"): String = {\n    val sb = new StringBuffer(head)\n    for (i <- 0 until m.length) {\n      sb.append(head)\n      for (j <- 0 until m(i).length) {\n        sb.append(m(i)(j))\n        if (j < m(i).length - 1)\n          sb.append(split)\n      }\n      sb.append(tail)\n      if (i < m.length - 1)\n        sb.append(split)\n    }\n    sb.append(tail).toString()\n  }\n\n  /*\n   * Return a string for an array\n   * \n   * @param m  array\n   * @param split string of the split\n   * @param head string of array head\n   * @param tail string of array tail\n   * @return string format of input array\n   */\n  def arraytoString[T](pre: Array[T], split: String = \",\", head: String = \"{\", tail: String = \"}\"): String = {\n    val sb = new StringBuffer(head)\n    for (i <- 0 until pre.length) {\n      sb.append(pre(i))\n      if (i < pre.length - 1)\n        sb.append(split)\n    }\n    sb.append(tail).toString()\n  }\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/nodes/ActiveLearningNode.scala",
    "content": "package org.apache.spark.streamdm.classifiers.trees.nodes\n\nimport org.apache.spark.streamdm.classifiers.trees._\nimport org.apache.spark.streamdm.core.Example\nimport org.apache.spark.streamdm.core.specification.{FeatureSpecification, InstanceSpecification}\n\nimport scala.collection.mutable.ArrayBuffer\n\n/**\n  * Basic majority class active learning node for Hoeffding tree\n  */\nclass ActiveLearningNode(classDistribution: Array[Double])\n  extends LearningNode(classDistribution) with Serializable {\n\n  var addonWeight: Double = 0\n\n  var blockAddonWeight: Double = 0\n\n  var instanceSpecification: InstanceSpecification = null\n\n  var featureObservers: Array[FeatureClassObserver] = null\n\n  var numSplitFeatures: Int = -1\n\n  def this(classDistribution: Array[Double], instanceSpecification: InstanceSpecification, numSplitFeatures: Int) {\n    this(classDistribution)\n    this.numSplitFeatures = numSplitFeatures\n    this.instanceSpecification = instanceSpecification\n    init()\n  }\n\n  def this(that: ActiveLearningNode) {\n    this(Utils.addArrays(that.classDistribution, that.blockClassDistribution),\n      that.instanceSpecification, that.numSplitFeatures)\n    this.addonWeight = that.addonWeight\n  }\n  /**\n    * init featureObservers array\n    */\n  def init(): Unit = {\n    if (featureObservers == null) {\n\n      val allFeatureIndexes = (0 to this.instanceSpecification.size()-1).toArray\n      val selectedFeatureIndexes = Array.fill(this.numSplitFeatures)(-1)\n      val randomSelector = (l: Array[Int]) => l(classifierRandom.nextInt(l.length))\n      for(i <- 0 to selectedFeatureIndexes.length-1) {\n        selectedFeatureIndexes(i) = randomSelector(allFeatureIndexes.filter(!selectedFeatureIndexes.contains(_)))\n      }\n\n      logInfo(selectedFeatureIndexes.mkString(\" \"))\n\n      featureObservers = new Array(this.numSplitFeatures)\n\n      for (i <- 0 until featureObservers.length) {\n        val featureSpec: FeatureSpecification = instanceSpecification( selectedFeatureIndexes(i) )\n        featureObservers(i) = FeatureClassObserver.createFeatureClassObserver(\n          classDistribution.length, selectedFeatureIndexes(i), featureSpec)\n      }\n    }\n  }\n\n  /**\n    * Learn and update the node\n    *\n    * @param ht a Hoeffding tree model\n    * @param example the input example\n    */\n  override def learn(ht: HoeffdingTreeModel, example: Example): Unit = {\n    init()\n    addonWeight += 1\n    blockClassDistribution(example.labelAt(0).toInt) += example.weight\n    featureObservers.zipWithIndex.foreach {\n      x => x._1.observeClass(example.labelAt(0).toInt, example.featureAt(x._2), example.weight)\n    }\n  }\n  /**\n    * Disable a feature at a given index\n    *\n    * @param fIndex the index of the feature\n    */\n  def disableFeature(fIndex: Int): Unit = {\n    //not support yet\n  }\n\n  /**\n    * Returns whether a node is active.\n    *\n    */\n  override def isActive(): Boolean = true\n\n  /**\n    * Returns whether a node is pure, which means it only has examples belonging\n    * to a single class.\n    */\n  def isPure(): Boolean = {\n    this.classDistribution.filter(_ > 0).length <= 1 &&\n      this.blockClassDistribution.filter(_ > 0).length <= 1\n  }\n\n  def weight(): Double = { classDistribution.sum }\n\n  def blockWeight(): Double = blockClassDistribution.sum\n\n  def addOnWeight(): Double = {\n    addonWeight\n  }\n\n  /**\n    * Merge two nodes\n    *\n    * @param that the node which will be merged\n    * @param trySplit flag indicating whether the node will be split\n    * @return new node\n    */\n  override def merge(that: Node, trySplit: Boolean): Node = {\n    if (that.isInstanceOf[ActiveLearningNode]) {\n      val node = that.asInstanceOf[ActiveLearningNode]\n      //merge addonWeight and class distribution\n      if (!trySplit) {\n        this.blockAddonWeight += node.blockClassDistribution.sum\n        for (i <- 0 until blockClassDistribution.length)\n          this.blockClassDistribution(i) += node.blockClassDistribution(i)\n      } else {\n        this.addonWeight = node.blockAddonWeight\n        for (i <- 0 until classDistribution.length)\n          this.classDistribution(i) += node.blockClassDistribution(i)\n      }\n      //merge feature class observers\n      for (i <- 0 until featureObservers.length)\n        featureObservers(i) = featureObservers(i).merge(node.featureObservers(i), trySplit)\n    }\n    this\n  }\n  /**\n    * Returns Split suggestions for all features.\n    *\n    * @param splitCriterion the SplitCriterion used\n    * @param ht a Hoeffding tree model\n    * @return an array of FeatureSplit\n    */\n  def getBestSplitSuggestions(splitCriterion: SplitCriterion, ht: HoeffdingTreeModel): Array[FeatureSplit] = {\n    val bestSplits = new ArrayBuffer[FeatureSplit]()\n    featureObservers.zipWithIndex.foreach(x =>\n      bestSplits.append(x._1.bestSplit(splitCriterion, classDistribution, x._2, ht.binaryOnly)))\n    if (!ht.noPrePrune) {\n      bestSplits.append(new FeatureSplit(null, splitCriterion.merit(classDistribution,\n        Array.fill(1)(classDistribution)), new Array[Array[Double]](0)))\n    }\n    bestSplits.toArray\n  }\n\n  override def toString(): String = \"level[\" + dep + \"]ActiveLearningNode:\" + Utils.arraytoString(classDistribution)\n\n  override def description(): String = {\n    \"[ActiveLearningNode] \" + super.description()\n  }\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/nodes/FoundNode.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees.nodes\n\n/**\n  * The container of a node.\n  */\nclass FoundNode(val node: Node, val parent: SplitNode, val index: Int) extends Serializable {\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/nodes/InactiveLearningNode.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees.nodes\n\nimport org.apache.spark.streamdm.classifiers.trees.{HoeffdingTreeModel, Utils}\nimport org.apache.spark.streamdm.core.Example\n\n/**\n  * Inactive learning node for Hoeffding trees\n  */\nclass InactiveLearningNode(classDistribution: Array[Double])\n  extends LearningNode(classDistribution) with Serializable {\n\n  def this(that: InactiveLearningNode) {\n    this(Utils.addArrays(that.classDistribution, that.blockClassDistribution))\n  }\n\n  /**\n    * Learn and update the node. No action is taken for InactiveLearningNode\n    *\n    * @param ht HoeffdingTreeModel\n    * @param example an Example will be processed\n    */\n  override def learn(ht: HoeffdingTreeModel, example: Example): Unit = {}\n\n  /**\n    * Return whether a learning node is active\n    */\n  override def isActive(): Boolean = false\n\n  /**\n    * Merge two nodes\n    *\n    * @param that the node which will be merged\n    * @param trySplit flag indicating whether the node will be split\n    * @return new node\n    */\n  override def merge(that: Node, trySplit: Boolean): Node = this\n\n  override def toString(): String = \"level[\" + dep + \"] InactiveLearningNode\"\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/nodes/LearningNode.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees.nodes\n\nimport org.apache.spark.streamdm.classifiers.trees.HoeffdingTreeModel\nimport org.apache.spark.streamdm.core.Example\n\n/**\n  * Learning node class type for Hoeffding trees.\n  */\nabstract class LearningNode(classDistribution: Array[Double]) extends Node(classDistribution)\n  with Serializable {\n\n  /**\n    * Learn and update the node\n    *\n    * @param ht a Hoeffding tree model\n    * @param example the input Example\n    */\n  def learn(ht: HoeffdingTreeModel, example: Example): Unit\n\n  /**\n    * Return whether a learning node is active\n    */\n  def isActive(): Boolean\n\n  /**\n    * Filter the data to the related leaf node\n    *\n    * @param example the input example\n    * @param parent the parent of current node\n    * @param index the index of current node in the parent children\n    * @return FoundNode containing the leaf node\n    */\n  override def filterToLeaf(example: Example, parent: SplitNode, index: Int): FoundNode =\n    new FoundNode(this, parent, index)\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/nodes/LearningNodeNB.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees.nodes\n\nimport org.apache.spark.streamdm.classifiers.bayes.NaiveBayes\nimport org.apache.spark.streamdm.classifiers.trees.{HoeffdingTreeModel, NullFeatureClassObserver, Utils}\nimport org.apache.spark.streamdm.core.Example\nimport org.apache.spark.streamdm.core.specification.InstanceSpecification\n\n/**\n  * Naive Bayes based learning node.\n  */\nclass LearningNodeNB(classDistribution: Array[Double], instanceSpecification: InstanceSpecification,\n                     numSplitFeatures: Int)\n  extends ActiveLearningNode(classDistribution, instanceSpecification, numSplitFeatures) with Serializable {\n\n  def this(that: LearningNodeNB) {\n    this(Utils.addArrays(that.classDistribution, that.blockClassDistribution),\n      that.instanceSpecification, that.numSplitFeatures)\n    //init()\n  }\n\n  /**\n    * Returns the predicted class distribution\n    *\n    * @param ht a Hoeffding tree model\n    * @param example  the Example to be evaluated\n    * @return the predicted class distribution\n    */\n  override def classVotes(ht: HoeffdingTreeModel, example: Example): Array[Double] = {\n    if (weight() > ht.nbThreshold)\n      NaiveBayes.predict(example, classDistribution, featureObservers)\n    else super.classVotes(ht, example)\n  }\n\n  /**\n    * Disable a feature having an index\n    *\n    * @param fIndex the index of the feature\n    */\n  override def disableFeature(fIndex: Int): Unit = {\n    featureObservers(fIndex) = new NullFeatureClassObserver()\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/nodes/LearningNodeNBAdaptive.scala",
    "content": "package org.apache.spark.streamdm.classifiers.trees.nodes\n\nimport org.apache.spark.streamdm.classifiers.bayes.NaiveBayes\nimport org.apache.spark.streamdm.classifiers.trees.{HoeffdingTreeModel, Utils}\nimport org.apache.spark.streamdm.core.Example\nimport org.apache.spark.streamdm.core.specification.InstanceSpecification\nimport org.apache.spark.streamdm.utils.Utils.argmax\n/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\n/**\n  * Adaptive Naive Bayes learning node.\n  * This learning node maintains two way of prediction:\n  * - MajorityClass, represented by mcCorrectWeight\n  * - nbCorrectWeight, represented by nbCorrectWeight\n  * Those counters will be incremented respectively depending on which one provides a correct prediction.\n  * If one of two counters is dominant, the Vote (Prediction) will be executed by that winner.\n  * For example: mcCorrectWeight = 500, nbCorrectWeight = 550. The prediction will be made by NaiveBayesClassifier.\n  * On the contrary, the prediction will be made by MajorityClass (super)\n  */\n\nclass LearningNodeNBAdaptive(classDistribution: Array[Double],\n                             instanceSpecification: InstanceSpecification, numSplitFeatures: Int)\n  extends ActiveLearningNode(classDistribution, instanceSpecification, numSplitFeatures) with Serializable {\n\n  var mcCorrectWeight: Double = 0\n  var nbCorrectWeight: Double = 0\n\n  var mcBlockCorrectWeight: Double = 0\n  var nbBlockCorrectWeight: Double = 0\n\n  def this(that: LearningNodeNBAdaptive) {\n    this(Utils.addArrays(that.classDistribution, that.blockClassDistribution),\n      that.instanceSpecification, that.numSplitFeatures)\n    addonWeight = that.addonWeight\n    mcCorrectWeight = that.mcCorrectWeight\n    nbCorrectWeight = that.nbCorrectWeight\n    init()\n  }\n\n  /**\n    * Learn and update the node.\n    *\n    * @param ht a Hoeffding tree model\n    * @param example an input example\n    */\n  override def learn(ht: HoeffdingTreeModel, example: Example): Unit = {\n\n    if (argmax(classDistribution) == example.labelAt(0))\n      mcBlockCorrectWeight += example.weight\n    if (argmax(NaiveBayes.predict(example, classDistribution, featureObservers)) ==\n      example.labelAt(0))\n      nbBlockCorrectWeight += example.weight\n    super.learn(ht, example)\n  }\n\n  /**\n    * Merge two nodes\n    *\n    * @param that the node which will be merged\n    * @param trySplit flag indicating whether the node will be split\n    * @return new node\n    */\n  override def merge(that: Node, trySplit: Boolean): Node = {\n    if (that.isInstanceOf[LearningNodeNBAdaptive]) {\n      val nbaNode = that.asInstanceOf[LearningNodeNBAdaptive]\n      //merge weights and class distribution\n      if (!trySplit) {\n        this.blockAddonWeight += nbaNode.blockClassDistribution.sum\n        mcBlockCorrectWeight += nbaNode.mcBlockCorrectWeight\n        nbBlockCorrectWeight += nbaNode.nbBlockCorrectWeight\n        for (i <- 0 until blockClassDistribution.length)\n          this.blockClassDistribution(i) += that.blockClassDistribution(i)\n      } else {\n        this.addonWeight = nbaNode.blockAddonWeight\n        mcCorrectWeight += nbaNode.mcBlockCorrectWeight\n        nbCorrectWeight += nbaNode.nbBlockCorrectWeight\n        for (i <- 0 until classDistribution.length)\n          this.classDistribution(i) += that.blockClassDistribution(i)\n      }\n      //merge feature class observers\n      for (i <- 0 until featureObservers.length)\n        featureObservers(i) = featureObservers(i).merge(nbaNode.featureObservers(i), trySplit)\n\n    }\n    this\n  }\n\n  /**\n    * Returns the predicted class distribution\n    *\n    * @param ht a Hoeffding tree model\n    * @param example the input example\n    * @return the predicted class distribution\n    */\n  override def classVotes(ht: HoeffdingTreeModel, example: Example): Array[Double] = {\n    if (mcCorrectWeight > nbCorrectWeight) super.classVotes(ht, example)\n    else NaiveBayes.predict(example, classDistribution, featureObservers)\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/nodes/Node.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees.nodes\n\nimport java.util.Random\n\nimport org.apache.spark.internal.Logging\nimport org.apache.spark.streamdm.classifiers.trees._\nimport org.apache.spark.streamdm.core._\n\n/**\n  * Abstract class containing the node information for the Hoeffding trees.\n  */\nabstract class Node(val classDistribution: Array[Double]) extends Serializable with Logging {\n\n  var dep: Int = 0\n  // stores class distribution of a block of RDD\n  val blockClassDistribution: Array[Double] = new Array[Double](classDistribution.length)\n\n  val classifierRandom: Random = new Random()\n\n  /**\n    * Filter the data to the related leaf node\n    *\n    * @param example the input Example\n    * @param parent the parent of current node\n    * @param index the index of current node in the parent children\n    * @return a FoundNode containing the leaf node\n    */\n  def filterToLeaf(example: Example, parent: SplitNode, index: Int): FoundNode\n\n  /**\n    * Return the class distribution\n    * @return an Array containing the class distribution\n    */\n  def classVotes(ht: HoeffdingTreeModel, example: Example): Array[Double] =\n    classDistribution.clone()\n\n  /**\n    * Checks whether a node is a leaf\n    * @return <i>true</i> if a node is a leaf, <i>false</i> otherwise\n    */\n  def isLeaf(): Boolean = true\n\n  /**\n    * Returns height of the tree\n    *\n    * @return the height\n    */\n  def height(): Int = 0\n\n  /**\n    * Returns depth of current node in the tree\n    *\n    * @return the depth\n    */\n  def depth(): Int = dep\n\n  /**\n    * Set the depth of current node\n    *\n    * @param depth the new depth\n    */\n  def setDepth(depth: Int): Unit = {\n    dep = depth\n    if (this.isInstanceOf[SplitNode]) {\n      val splitNode = this.asInstanceOf[SplitNode]\n      splitNode.children.foreach { _.setDepth(depth + 1) }\n    }\n  }\n\n  /**\n    * Merge two nodes\n    *\n    * @param that the node which will be merged\n    * @param trySplit flag indicating whether the node will be split\n    * @return new node\n    */\n  def merge(that: Node, trySplit: Boolean): Node\n\n  /**\n    * Returns number of children\n    *\n    * @return number of children\n    */\n  def numChildren(): Int = 0\n\n  /**\n    * Returns the node description\n    * @return String containing the description\n    */\n  def description(): String = {\n    \"  \" * dep + \"depth: \"+ dep + \" | numChildren: \"  + numChildren() + \" | observedClassDistribution: \" +\n      Utils.arraytoString(this.classDistribution) + \" | hashCode: \" + this.hashCode() + \"\\n\"\n  }\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/classifiers/trees/nodes/SplitNode.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.trees.nodes\n\nimport org.apache.spark.streamdm.classifiers.trees.{ConditionalTest, Utils}\nimport org.apache.spark.streamdm.core.Example\n\nimport scala.collection.mutable.ArrayBuffer\nimport scala.math.max\n\n/**\n  * Branch node of the Hoeffding tree.\n  */\nclass SplitNode(classDistribution: Array[Double], val conditionalTest: ConditionalTest)\n  extends Node(classDistribution) with Serializable {\n\n  val children: ArrayBuffer[Node] = new ArrayBuffer[Node]()\n\n  def this(that: SplitNode) {\n    this(Utils.addArrays(that.classDistribution, that.blockClassDistribution),\n      that.conditionalTest)\n  }\n\n  /**\n    * Filter the data to the related leaf node\n    *\n    * @param example input example\n    * @param parent the parent of current node\n    * @param index the index of current node in the parent children\n    * @return FoundNode cotaining the leaf node\n    */\n  override def filterToLeaf(example: Example, parent: SplitNode, index: Int): FoundNode = {\n    val cIndex = childIndex(example)\n    if (cIndex >= 0) {\n      if (cIndex < children.length && children(cIndex) != null) {\n        children(cIndex).filterToLeaf(example, this, cIndex)\n      } else new FoundNode(null, this, cIndex)\n    } else new FoundNode(this, parent, index)\n  }\n\n  def childIndex(example: Example): Int = {\n    conditionalTest.branch(example)\n  }\n\n  def setChild(index: Int, node: Node): Unit = {\n    if (children.length > index) {\n      children(index) = node\n      node.setDepth(dep + 1)\n    } else if (children.length == index) {\n      children.append(node)\n      node.setDepth(dep + 1)\n    } else {\n      assert(children.length < index)\n    }\n  }\n  /**\n    * Returns whether a node is a leaf\n    */\n  override def isLeaf() = false\n\n  /**\n    * Returns height of the tree\n    *\n    * @return the height\n    */\n  override def height(): Int = {\n    var height = 0\n    for (child: Node <- children) {\n      height = max(height, child.height()) + 1\n    }\n    height\n  }\n\n  /**\n    * Returns number of children\n    *\n    * @return  number of children\n    */\n  override def numChildren(): Int = children.filter { _ != null }.length\n\n  /**\n    * Merge two nodes\n    *\n    * @param that the node which will be merged\n    * @param trySplit flag indicating whether the node will be split\n    * @return new node\n    */\n  override def merge(that: Node, trySplit: Boolean): Node = {\n    if (!that.isInstanceOf[SplitNode]) this\n    else {\n      val splitNode = that.asInstanceOf[SplitNode]\n      for (i <- 0 until children.length)\n        this.children(i) = (this.children(i)).merge(splitNode.children(i), trySplit)\n      this\n    }\n  }\n\n  /**\n    * Returns the node description\n    * @return String containing the description\n    */\n  override def description(): String = {\n    val sb = new StringBuffer(\"  \" * dep + \"\\n\")\n    val testDes = conditionalTest.description()\n    for (i <- 0 until children.length) {\n      sb.append(\"  \" * dep + \" if \" + testDes(i) + \"\\n\")\n      sb.append(\"  \" * dep + children(i).description())\n    }\n    sb.toString()\n  }\n\n  override def toString(): String = \"level[\" + dep + \"] SplitNode\"\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/clusterers/Clusterer.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.clusterers\n\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streaming.dstream._\n\n/**\n * A Clusterer trait defines the needed operations for any implemented\n * clustering algorithm. It provides methods for clustering and for returning\n * the computed cluster.\n */\ntrait Clusterer extends Learner  with Serializable {\n\n  /* Get the currently computed clusters\n   * @return an Array of Examples representing the clusters\n   */\n  def getClusters: Array[Example]\n\n  /* Assigns examples to clusters, given the current Clusters data structure. \n   *\n   * @param input the DStream of Examples to be assigned a cluster\n   * @return a DStream of tuples containing the original Example and the\n   * assigned cluster.\n   */\n  def assign(input: DStream[Example]): DStream[(Example,Double)]\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/clusterers/Clustream.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.clusterers\n\nimport org.apache.spark.streamdm.clusterers.clusters._\nimport org.apache.spark.streamdm.clusterers.utils._\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streaming.dstream._\nimport org.apache.spark.rdd._\nimport com.github.javacliparser._\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n * A Clusterer trait defines the needed operations for any implemented\n * clustering algorithm. It provides methods for clustering and for returning\n * the computed cluster.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Number of microclusters (<b>-m</b>)\n *  <li> Initial buffer size (<b>-b</b>)\n *  <li> Number of clusters (<b>-k</b>)\n *  <li> Iterations (<b>-i</b>), number of iterations of the k-means alforithm \n * </ul>\n */\nclass Clustream extends Clusterer {\n\n  type T = MicroClusters\n\n  var microclusters: MicroClusters = null\n  var initialBuffer: Array[Example] = null\n  var clusters: Array[Example] = null\n \n  val kOption: IntOption = new IntOption(\"numClusters\", 'k',\n    \"Number of clusters for output\", 10, 1, Integer.MAX_VALUE)\n\n  val mcOption: IntOption = new IntOption(\"numMicroclusters\", 'm',\n    \"Size of microcluster buffer\", 100, 1, Integer.MAX_VALUE)\n\n  val initOption: IntOption = new IntOption(\"initialBuffer\", 'b',\n    \"Size of initial buffer\", 1000, 1, Integer.MAX_VALUE)\n\n  val repOption: IntOption = new IntOption(\"kMeansIters\", 'i',\n    \"Number of k-means iterations\", 100, 1, Integer.MAX_VALUE)\n\n  var exampleLearnerSpecification: ExampleSpecification = null\n\n  /* Init the Clustream algorithm.\n   *\n   */\n  def init(exampleSpecification: ExampleSpecification): Unit = {\n    exampleLearnerSpecification = exampleSpecification\n    microclusters = new MicroClusters(Array[MicroCluster]())\n    initialBuffer = Array[Example]()\n  }\n\n  /* Maintain the micro-clusters, given an input DStream of Example.\n   *\n   * @param input a stream of instances\n   */\n  def train(input: DStream[Example]): Unit = {\n    input.foreachRDD(rdd => {\n      val numInstances: Long = initialBuffer.length + 1\n      if (numInstances<initOption.getValue) {\n        val neededInstances = (initOption.getValue - numInstances).toDouble\n        val rddCount = rdd.count.toDouble\n        var procRDD = rdd\n        val fractionNeeded = neededInstances/rddCount\n        val fractionRatio = 1.25\n        //we are conservative: we get a bit more than we need\n        if (fractionRatio*fractionNeeded<1.0) {\n          procRDD = rdd.sample(false, fractionRatio*fractionNeeded)\n        }\n        initialBuffer = initialBuffer ++ ClusterUtils.fromRDDToArray(procRDD)\n      }\n      else if(microclusters.microclusters.length==0) {\n        val timestamp = System.currentTimeMillis / 1000\n        microclusters = new MicroClusters(Array.fill[MicroCluster]\n                (mcOption.getValue)(new MicroCluster(new NullInstance(), \n                                    new NullInstance, 0, 0.0, 0)))\n        //cluster the initial buffer to get the \n        //centroids of themicroclusters\n        val centr = KMeans.cluster(initialBuffer, mcOption.getValue,\n                                    repOption.getValue)\n        //for every instance in the initial buffer, add it \n        //to the closest microcluster\n        initialBuffer.foreach(iex => {\n          val closest = ClusterUtils.assignToCluster(iex,centr)\n          microclusters = microclusters.addToMicrocluster(closest, iex, \n                                                          timestamp)\n        })\n        microclusters = processMicroclusters(rdd, microclusters)\n      }\n      else {\n        microclusters = processMicroclusters(rdd, microclusters)\n      }\n      //perform \"offline\" clustering\n      if(initialBuffer.length<initOption.getValue) {\n        clusters = KMeans.cluster(initialBuffer, kOption.getValue, \n                                  repOption.getValue)\n      }\n      else {\n        val examples = microclusters.toExampleArray\n        clusters = KMeans.cluster(examples, kOption.getValue,\n                                  repOption.getValue)\n      }\n    })\n  }\n\n  /* Gets the current MicroClusters.\n   * \n   * @return the current MicroClusters object\n   */\n  def getModel: MicroClusters = microclusters\n\n  /**\n   * Processes the new microclusters from an input RDD and given an initial\n   * state of the microclusters.\n   * @param rdd the input RDD of Example\n   * @param microclusters the initial MicroClusters data structure\n   * @return the updated microclusters\n   */\n  private def processMicroclusters(rdd: RDD[Example], input: MicroClusters):\n    MicroClusters =\n    rdd.aggregate(input)((mic,ex) =>mic.update(ex), (mic1,mic2) => mic1)\n\n\n\n  /* Compute the output cluster centroids, based on the current microcluster\n   * buffer; if no buffer is started, compute using k-means on the entire init\n   * buffer.\n   * @return an Array of Examples representing the clusters\n   */\n  def getClusters: Array[Example] =\n    if (clusters==null) Array[Example]() else clusters\n\n  \n  /* Assigns examples to clusters, given the current microclusters. \n   *\n   * @param input the DStream of Examples to be assigned a cluster\n   * @return a DStream of tuples containing the original Example and the\n   * assigned cluster.\n   */\n  def assign(input: DStream[Example]): DStream[(Example,Double)] = {\n    input.map(x => {\n      val assignedCl = ClusterUtils.assignToCluster(x,getClusters)\n      (x,assignedCl)\n    })\n  }\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/clusterers/StreamKM.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.clusterers\n\nimport org.apache.spark.streamdm.clusterers.clusters._\nimport org.apache.spark.streamdm.clusterers.utils._\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streaming.dstream._\nimport com.github.javacliparser._\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n * Implements the StreamKM++ algorithm for data streams. StreamKM++ computes a\n * small (weighted) sample of the stream by using <i>coresets</i>, and then uses\n * it as an input to a k-means++ algorithm. It uses a data structure called\n * <tt>BucketManager</tt> to handle the coresets.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Number of microclusters (<b>-m</b>)\n *  <li> Initial buffer size (<b>-b</b>)\n *  <li> Size of coresets (<b>-s</b>)\n *  <li> Learning window (<b>-w</b>) * </ul>\n */\nclass StreamKM extends Clusterer {\n  \n  type T = BucketManager\n  var bucketmanager: BucketManager = null\n  var numInstances: Long = 0\n  var clusters: Array[Example] = null\n\n  val kOption: IntOption = new IntOption(\"numClusters\", 'k',\n    \"Number of clusters for output\", 10, 1, Integer.MAX_VALUE)\n  \n  val repOption: IntOption = new IntOption(\"kMeansIters\", 'i',\n    \"Number of k-means iterations\", 1000, 1, Integer.MAX_VALUE)\n\n  val sizeCoresetOption: IntOption = new IntOption(\"sizeCoreset\", 's',\n    \"Size of coreset\", 10000, 1, Integer.MAX_VALUE)\n  \n  val widthOption: IntOption = new IntOption(\"width\",\n      'w', \"Size of window for training learner.\", 100000, 1, Integer.MAX_VALUE);\n  \n  var exampleLearnerSpecification: ExampleSpecification = null\n  \n  /** \n   * Init the StreamKM++ algorithm.\n   */\n  def init(exampleSpecification: ExampleSpecification) : Unit = {\n    exampleLearnerSpecification = exampleSpecification\n    bucketmanager = new BucketManager(widthOption.getValue, sizeCoresetOption.getValue)\n  }\n  \n\n  def train(input: DStream[Example]): Unit = {}\n \n  /**\n   *  Gets the current Model used for the Learner.\n   * @return the Model object used for training\n   */\n  def getModel: BucketManager = bucketmanager\n  \n  /** \n   * Get the currently computed clusters\n   * @return an Array of Examples representing the clusters\n   */\n  def getClusters: Array[Example] = {\n    if(numInstances <= sizeCoresetOption.getValue) {\n      bucketmanager.buckets(0).points.toArray\n    } \n    else {\n     val streamingCoreset = bucketmanager.getCoreset\n     KMeans.cluster(streamingCoreset, kOption.getValue, repOption.getValue)  \n    }\n  }\n  \n    /** \n   *  Maintain the BucketManager for coreset extraction, given an input DStream of Example.\n   * @param input a stream of instances\n   * @return a DStream of tuples containing the original Example and the\n   * assigned cluster.\n   */\n  def assign(input: DStream[Example]): DStream[(Example,Double)] = {\n     input.map(ex=> {\n       numInstances += 1\n       bucketmanager = bucketmanager.update(ex)\n         if(numInstances <= sizeCoresetOption.getValue){\n           clusters  = KMeans.cluster(bucketmanager.buckets(0).points.toArray,kOption.getValue,repOption.getValue)\n         }\n         else\n         {\n            val streamingCoreset = bucketmanager.getCoreset\n            clusters = KMeans.cluster(streamingCoreset,kOption.getValue,repOption.getValue)\n         }\n\n        val assignedCl = clusters.foldLeft((0, Double.MaxValue, 0))(\n          (cl, centr) => {\n            val dist = centr.in.distanceTo(ex.in)\n            if (dist < cl._2) ((cl._3, dist, cl._3 + 1))\n            else ((cl._1, cl._2, cl._3 + 1))\n          })._1\n        (ex,assignedCl)\n     })\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/clusterers/clusters/BucketManager.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.clusterers.clusters\n\nimport org.apache.spark.streamdm.core._\nimport scala.collection.mutable.Queue\nimport scala.util.control.Breaks._\n\n\n/**\n * Data structure for managing all buckets for streamKM++ algorithm. The\n * structure will extract <i>maxsize</i> coreset examples from <i>n</i>\n * examples. When a new example occurs on the stream, the BucketManager is\n * tasked with updating its corresponding buckets.\n */\nclass BucketManager(val n : Int, val maxsize : Int) extends Clusters {\n\n  type T = BucketManager\n  \n  /** \n   * Inner class Bucket for new instance management, this class has two buffers for\n   * recursively computing the coresets.\n   */\n  class Bucket(val bucketsize : Int = maxsize) extends Serializable {\n    val points = Queue[Example]()\n    val spillover = Queue[Example]()\n    def isFull : Boolean = if(points.length == bucketsize) true else false\n    override def toString : String = \"bucket buffers in bucketmanger\"\n  }\n \n  val L = (math.ceil(math.log(n.toDouble/maxsize.toDouble)/math.log(2))+2).toInt\n  val buckets = new Array[Bucket](L)\n  for(i <- 0 until L)\n    buckets(i) = new Bucket\n\n  /** Update the BucketManager, depending on the Example given\n   *\n   * @param change the input Example\n   * @return the updated BucketManger object\n   */\n  override def update(change: Example): BucketManager = {\n    // Check if there is enough space in the first bucket\n    if(buckets(0).isFull){\n      var curbucket : Int = 0\n      var nextbucket : Int =1\n      // Check if the next bucket is empty\n      if(!buckets(nextbucket).isFull) {\n        // Copy curbucket points to nextbucket points  \n        val backpoints = buckets(curbucket).points.clone()\n        for(point <- backpoints) buckets(nextbucket).points.enqueue(point)\n        // Clear curbucket to empty\n        buckets(curbucket).points.clear()\n      } else {\n        // Copy curbucket points to nextbucket spillover and continue \n        val backspillover = buckets(curbucket).points.clone()\n        buckets(nextbucket).spillover.clear()\n        for(point <- backspillover) buckets(nextbucket).spillover.enqueue(point)\n        // Clear curbucket to empty\n        buckets(curbucket).points.clear()\n        curbucket += 1\n        nextbucket += 1\n        /*\n         * As long as the nextbucket is full, output the coreset to the spillover \n         * of the next bucket\n         */\n        while(buckets(nextbucket).isFull) {\n          val examples = (buckets(curbucket).points union buckets(curbucket).spillover).toArray \n          val tree = new TreeCoreset\n          val coreset = tree.retrieveCoreset(tree.buildCoresetTree(examples, maxsize),\n                          new Array[Example](0))\n          // Copy coreset to nextbucket spillover\n          buckets(nextbucket).spillover.clear()\n          for(point <- coreset) buckets(nextbucket).spillover.enqueue(point)\n          // Clear curbucket\n          buckets(curbucket).points.clear()\n          buckets(curbucket).spillover.clear()\n          curbucket += 1\n          nextbucket += 1\n        }\n        val examples = (buckets(curbucket).points union buckets(curbucket).spillover).toArray \n        val tree = new TreeCoreset\n        val coreset = tree.retrieveCoreset(tree.buildCoresetTree(examples, maxsize),\n                        new Array[Example](0))\n        // Copy coreset to nextbucket points\n        buckets(nextbucket).points.clear()\n        for(point <- coreset) buckets(nextbucket).points.enqueue(point)\n        // Clear curbucket\n        buckets(curbucket).points.clear()\n        buckets(curbucket).spillover.clear()\n      }\n    }\n    \n    buckets(0).points.enqueue(change)\n    this\n  }\n\n  /**\n   * Return an array of weighted examples corresponding to the coreset extracted from\n   * the TreeCoreset data structure, in order to be used in k-means.\n   *\n   * <p>The following two cases can occur:\n   * <ul>\n   *  <li> if the last bucket is full, return the contents of the last bucket\n   *  <li> if the last bucket is not full, recursively compute a coreset from all\n   *          nonempty buckets\n   * </ul> \n   * @return the coreset for the examples entered into the buckets.\n   */\n  def getCoreset: Array[Example] = {\n    if(buckets(L-1).isFull) {\n     buckets(L-1).points.toArray \n    }else {\n      var i = 0\n      var coreset = Array[Example]()\n      for(i <- 0 until L) {\n        if(buckets(i).isFull) {\n          coreset = buckets(i).points.toArray\n          break\n        }\n      }\n      val start = i+1\n      for(j <- start until L) {\n        val examples = buckets(j).points.toArray ++ coreset\n        val tree = new TreeCoreset\n        coreset = tree.retrieveCoreset(tree.buildCoresetTree(examples, maxsize),\n                    new Array[Example](0))\n      }\n      coreset\n    }\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/clusterers/clusters/Clusters.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.clusterers.clusters\n\nimport org.apache.spark.streamdm.core._\n\n\n/**\n * A Clusters trait defines the needed operations for maintaining a clustering\n * data structure. It mainly provides a method to update the data structure\n * based on an Instance.\n */\ntrait Clusters extends Model {\n\n  type T <: Clusters\n\n  /**\n   * Update the clustering data structure, depending on the Example given.\n   *\n   * @param inst the Example based on which the Model is updated\n   * @return the updated Clusters object\n   */\n  override def update(change: Example): T\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/clusterers/clusters/MicroCluster.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.clusterers.clusters\n\nimport org.apache.spark.streamdm.core._\n\n/**\n * A MicroCluster contains the underlying structure for the Clustream processing\n * framework, and summarizes a number of instances belonging to the same\n * MicroCluster. It is composed of: \n * - the number of instance\n * - the sum of instances\n * - the sum of squared instance\n * - the sum of timestamps\n * - the squared sum of timestamps.\n */\ncase class MicroCluster(val sum:Instance, val sqSum: Instance, \n  val timeSum: Long, val sqTimeSum: Double, val num: Int)\n  extends Serializable {\n \n  /**\n   * Insert an instance into the microcluster.\n   *\n   * @param inst the Instance which needs a class predicted\n   * @param time the timestamp of the Instance\n   * @return the new MicroCluster\n   */\n  def insert(inst: Instance, time: Long): MicroCluster = sum match {\n    case NullInstance() =>\n      new MicroCluster(inst.map(x=>x),inst.map(x=>x*x), time,\n        time.toDouble*time.toDouble, 1)\n    case _ =>\n      new MicroCluster(sum.add(inst), sqSum.add(inst.map(x=>x*x)),timeSum+time,\n                       sqTimeSum+time.toDouble*time.toDouble, num+1)\n  }\n   \n  /**\n   * Merges two microclusters together.\n   *\n   * @param other the MicroCluster which gets added\n   * @return the new MicroCluster\n   */\n  def merge(other: MicroCluster): MicroCluster =\n    new MicroCluster(sum.add(other.sum), sqSum.add(other.sqSum),\n      timeSum+other.timeSum, sqTimeSum+other.sqTimeSum, num+other.num)\n   \n  /**\n   * Compute the centroid of a Microcluster.\n   * \n   * @return an instance representing the centroid\n   */ \n  def centroid: Instance =\n    if(num>1)\n      sum.map(x=>x/num.toDouble)\n    else\n      sum.map(x=>x)\n\n  /**\n   * Compute the RMSE of a microcluster.\n   *\n   * @return the RMSE of a microcluster\n   */\n  def rmse: Double = {\n    if(num<=1)\n      Double.MaxValue\n    else {\n      val centr = this.centroid\n      math.sqrt((sqSum.add(centr.map{case x=> x*x}).add(sum.hadamard(centr).\n        map(-2*_)).reduce(_+_))/num)\n    }\n  }\n\n  /**\n   * Compute the threshold timestamp of the microcluster.\n   * \n   * @return the threshold timestamp\n   */\n  def threshold(m: Int): Double = {\n    if(num<=1)\n      Double.MaxValue\n    else {\n      val mu = timeSum / num\n      if(num<2*m)\n        mu\n      else {\n        val z = 2*m/num\n        val sd = sqTimeSum/num - mu*mu\n        mu+sd*math.sqrt(2)*MicroClusterUtils.inverr(2*z-1)\n      }\n    }\n  }\n\n  /**\n   * Return the microcluster as a weighted Example, containing the centroid and\n   * the number of elements.\n   *\n   * @return the output Example \n   */ \n  def toExample: Example =\n    new Example(this.centroid, new NullInstance(), this.num)\n\n  override def toString: String =\n    \"%s %d\".format(centroid.toString, num)\n\n}\n\n/* Helper functions for MicroClusters. */\nobject MicroClusterUtils {\n  /**\n   * Compute the inverse error functions, for computing the timestamp threshold\n   * in a MicroCluster.\n   */\n  def inverr(x:Double): Double = {\n    val pi = 3.14159\n    val a = 0.140012\n    val sgn = if(x<0) -1 else 1\n    val lg = math.log(1-x*x)\n    val fr = 2/(pi*a)+lg/2\n    sgn*math.sqrt(math.sqrt(fr*fr-lg/a)-fr)\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/clusterers/clusters/MicroClusters.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.clusterers.clusters\n\nimport com.github.javacliparser._\n\nimport org.apache.spark.streamdm.core._\n\n/**\n * The MicroClusters object contains a number of microclusters in an Array. It\n * provides operations for inserting, appending and removing microclusters.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Time horizon (<b>-h</b>)\n *  <li> Cluster radius multiplier (<b>-r</b>)\n *  <li> Value of m (<b>-m</b>), controlling number of standard deviations for\n *  expiry\n * </ul>\n */\ncase class MicroClusters(val microclusters: Array[MicroCluster]) \n  extends Clusters {\n\n  type T = MicroClusters\n\n  val horizonOption: IntOption = new IntOption(\"timeHorizon\", 'h',\n    \"Size of horizon window (in seconds)\", 1000, 1, Integer.MAX_VALUE)\n\n  val radiusOption: FloatOption = new FloatOption(\"clusterRadius\", 'r',\n    \"The kernel radius multiplier\", 2.0, 1, Float.MaxValue)\n\n  val mOption: IntOption = new IntOption(\"mValue\", 'm', \"Value of m\",\n    100, 1, Integer.MAX_VALUE)\n\n  /**\n   * Update the clustering data structure, depending on the Example given.\n   *\n   * @param inst the Example based on which the Model is updated\n   * @return the updated MicroClusters object\n   */\n  override def update(change: Example): MicroClusters = {\n    val timestamp = System.currentTimeMillis / 1000\n    //Find the closest kernel to the instance\n    val clTuple = microclusters.foldLeft((0,Double.MaxValue,0))((cl,mcl) => {\n      val dist = change.in.distanceTo(mcl.centroid)\n      if(dist<cl._2) ((cl._3,dist,cl._3+1))\n       else ((cl._1,cl._2,cl._3+1))\n    })\n    val closest = clTuple._1\n    val distance = clTuple._2\n    //Compute the radius of the closest microcluster\n    val radius = radiusOption.getValue * microclusters(closest).rmse\n    if(distance<=radius) {\n      //if within the radius, add it to the closest\n      addToMicrocluster(closest,change,timestamp)\n    }\n    else {\n      val mc = new MicroCluster(change.in.map(x=>x), change.in.map(x=>x*x),\n                                timestamp, timestamp*timestamp, 1)\n      //Find whether an expired microcluster exists\n      val threshold = timestamp - horizonOption.getValue\n      var i: Int = 0\n      var found: Boolean = false\n      var tmc: Int = 0\n      while (i<microclusters.length && !found) {\n        if(microclusters(i).threshold(mOption.getValue)<threshold) {\n          found = true\n          tmc = i\n        }\n        i += 1\n      }\n      if (found) {\n        removeMicrocluster(tmc).appendMicrocluster(mc)\n      }\n      else {\n        //find the two closest microclusters\n        var sm: Int = 0\n        var tm: Int = 0\n        var dist: Double = Double.MaxValue\n        var i: Int = 0\n        var j: Int = 0\n        while(i<microclusters.length) {\n          j = i+1\n          while(j<microclusters.length) {\n            val dst = this.distance(i,j)\n            if(dst<=dist) {\n              sm = i\n              tm = j\n              dist = dst\n            }\n            j += 1\n          }\n          i += 1\n        }\n        if(sm!=tm)\n          mergeMicroclusters(sm,tm).appendMicrocluster(mc)\n        else\n          new MicroClusters(microclusters)    \n      }\n    }\n  }\n\n  /**\n   * Add an instance to the microcluster at a given instance. \n   *\n   * @param index the index where the microcluster is\n   * @param change the Example containing the changed instance\n   * @return the updated MicroClusters object\n   */\n  def addToMicrocluster(index: Int, change: Example, timestamp:Long)\n    : MicroClusters = {\n    new MicroClusters(microclusters.updated(index,\n                      microclusters(index).insert(change.in, timestamp)))\n  }\n\n  /**\n   * Append a microcluster.\n   *\n   * @param mc the microcluster to be appended\n   * @return the updated MicroClusters object\n   */\n  def appendMicrocluster(mc: MicroCluster): MicroClusters = {\n    new MicroClusters(microclusters:+mc)\n  }\n\n  /**\n   * Remove a microcluster from the buffer.\n   *\n   * @param index the index where the microcluster to be removed is\n   * @return the updated MicroClusters object\n   */\n  private def removeMicrocluster(index: Int): MicroClusters =\n    new MicroClusters(microclusters.take(index)++microclusters.drop(index+1))\n\n  /**\n   * Merge the source microcluster into the target microcluster.\n   *\n   * @param target the index of the target microcluster\n   * @param source the index of the source microcluster\n   * @return the updated MicroClusters object\n   */\n  private def mergeMicroclusters(target: Int, source: Int): MicroClusters = {\n    if(target!=source) {\n      var mergedMicroclusters = microclusters.updated(target,\n                            microclusters(target).merge(microclusters(source)))\n      new MicroClusters(mergedMicroclusters.take(source)++\n                        mergedMicroclusters.drop(source+1))\n    }\n    else\n      this\n  }\n\n  /**\n   * Return the distance between two microclusters.\n   *\n   * @param source the index of the source microcluster\n   * @param target the index of the target microcluster\n   * @return the distane between microclusters \n   */\n  private def distance(source: Int, target: Int): Double =\n    microclusters(source).centroid.distanceTo(microclusters(target).centroid)\n\n  /**\n   * Return an array of weighted centroids corresponding to the microclusters.\n   *\n   * @return the output Example array\n   */ \n  def toExampleArray: Array[Example] = microclusters.map(_.toExample)\n\n  override def toString: String = {\n    var str:String = \"\"\n    var idx:Int = 0\n    microclusters.foreach(mc => {\n      str += \"%d: %s\\n\".format(idx,mc.toString)\n      idx += 1\n    })\n    str\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/clusterers/clusters/TreeCoreset.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.clusterers.clusters\n\nimport org.apache.spark.streamdm.core._\n\nimport scala.util.Random\n\n/**\n * A TreeCoreset contains the underlying tree structure for the coreset\n * extraction framework. It constructs a tree structure for efficient coreset\n * extraction from an  Example stream.\n */\nclass TreeCoreset {\n  \n  /**\n   * Wrap the information in CoresetTreeNode, which will not be modified after\n   * a CoresetTreeNode construction. \n   *\n   * <p>It is composed of:\n   * <ul>\n   *  <li> the number of examples in a tree node\n   *  <li> the examples associated with a tree node\n   *  <li> the cluster centre of the associated examples\n   *  <li> the parent of the tree node\n   * </ul>\n   */\n   class CoresetTreeElem(val n : Int, val points : Array[Example], \n    val centre : Example) {\n    }\n\n  sealed trait CoresetTree {\n    def elem : CoresetTreeElem\n    def cost : Double\n  }\n\n  \n  /**\n   * CoresetTreeLeaf node data structure. A leaf does not have any children.\n   */\n  case class CoresetTreeLeaf(val elem : CoresetTreeElem, val cost : Double) extends CoresetTree {\n    /**\n     * Compute the distance between the given point and the leaf centre.\n     * @param point the given Example point\n     * @return the distance between the point and the leaf centre\n     */\n    def costOfPoint(point : Example) : Double = {\n      val weight = point.weight\n      val instance = if(weight != 0.0) point.in.map(x=>x/weight) else point.in\n      val centre = if(elem.centre.weight != 0.0) elem.centre.in.map(x=>x/elem.centre.weight) \n                    else elem.centre.in\n      instance.distanceTo(centre)*weight\n    }\n\n    /**\n     * Compute the leaf node cost attribute, which is the sum of the squared distances over\n     * all points associated with the leaf node to its centre.\n     *\n     * @return a new leaf node with the computed cost value\n     */\n    def weightedLeaf() : CoresetTreeLeaf = {\n      val points = elem.points\n      new CoresetTreeLeaf(elem, points.map(point=>costOfPoint(point)).sum)\n    }\n\n    /**\n     * Select a new centre from the leaf node for splitting. \n     * @return the new centre\n     */\n    def chooseCentre() : Example = {\n      val funcost = this.weightedLeaf().cost\n      val points = elem.points\n      var sum = 0.0\n\n      for(point <- points) {\n        sum += costOfPoint(point)/funcost\n        if(sum >= Random.nextDouble) \n          return point\n      }\n      elem.centre\n    }\n  }\n\n  /**\n   * CoresetTreeNode data structure, a component of the CoresetTree structure.\n   * Each CoresetTreeNode is associated with a cluster of Example, and has its\n   * properties stored.\n   *\n   * <p> It is composed of:\n   * <ul>\n   *  <li> wrapped properties of the node\n   *  <li> left and right children of the node\n   *  <li> the cost of the node\n   * </ul>\n   */\n  case class CoresetTreeNode(val elem : CoresetTreeElem, val left : CoresetTree, \n    val right : CoresetTree, val cost : Double) extends CoresetTree {\n  }\n\n  /**\n   * Split the coreset tree leaf to generate a coreset tree node with two\n   * leaves.\n   * \n   * @param leaf coreset tree leaf for spliting\n   * @return a coreset tree node with two leaves\n   */\n  private def splitCoresetTreeLeaf(leaf : CoresetTreeLeaf) : CoresetTreeNode = {\n    // Select a example from the points associated with the leaf  as a new centre \n    // for one of the new leaf \n    val newcentre = leaf.chooseCentre\n    // The original centre as the other leaf centre\n    val oldcentre = leaf.elem.centre\n    // The points associated with the orignial leaf, the points will be assigned the new leaves\n    val points = leaf.elem.points\n    \n    // Assign points to leftpoints and rightpoints\n    var leftpoints = new Array[Example](0)\n    var rightpoints = new Array[Example](0)\n    for(point <- points) {\n      if(squaredDistance(point, newcentre) < squaredDistance(point, oldcentre))\n        leftpoints = leftpoints :+ point\n      else\n        rightpoints = rightpoints :+ point\n    }\n    \n    // Create new leaves \n    val leftElem = new CoresetTreeElem(leftpoints.length, leftpoints, newcentre)\n    val leftleaf = CoresetTreeLeaf(leftElem, 0.0).weightedLeaf\n    \n    val rightElem = new CoresetTreeElem(rightpoints.length, rightpoints, oldcentre)\n    val rightleaf = CoresetTreeLeaf(rightElem, 0.0).weightedLeaf\n    \n    // Return a coreset tree node with two leaves\n    new CoresetTreeNode(leaf.elem, leftleaf, rightleaf, leftleaf.cost+rightleaf.cost)\n  }\n\n  /**\n   * Split a coreset tree to construct a new coreset tree with recursive schemes\n   * @param input the coreset tree root\n   * @return the new coreset tree\n   */\n  private def splitCoresetTree(root : CoresetTree) : CoresetTree = root match {\n    case CoresetTreeLeaf(e, c) => {\n      splitCoresetTreeLeaf(CoresetTreeLeaf(e, c))\n    }\n    case CoresetTreeNode(e, l, r, c) => {\n      if (Random.nextDouble > 0.5) {\n        val lchild = splitCoresetTree(l)\n        val newcost = lchild.cost + r.cost\n        CoresetTreeNode(e, lchild, r, newcost)\n      }\n      else {\n        val rchild = splitCoresetTree(r)\n        val newcost = l.cost + rchild.cost\n        CoresetTreeNode(e, l, rchild, newcost)\n      }\n    }\n  }\n  \n  /**\n   * Retrieve a new coreset from a given CoresetTree recursively. During this\n   * process, the returned coreset will be combined with the given coreset.\n   * @param root the CoresetTree root node\n   * @param coreset the current coreset\n   * @return the new coreset\n   */\n  def retrieveCoreset(root :  CoresetTree, coreset : Array[Example]) : Array[Example] = \n    root match {\n      case CoresetTreeNode(e, l, r, c) => {\n        retrieveCoreset(r, retrieveCoreset(l, coreset))\n      }\n      case CoresetTreeLeaf(e, c) => coreset :+ e.centre.setWeight(e.n)\n  }\n\n  /**\n   * Build a coreset tree with the points and having the given coreset size\n   * @param points for coreset tree construction\n   * @param m is the coreset size\n   * @return a coreset tree\n   */\n  def buildCoresetTree(points : Array[Example], m : Int) : CoresetTree = {\n    // Random select a point from points as the initial centre for the tree root\n    val initcentre = points(Random.nextInt(points.length))\n    val elem = new CoresetTreeElem(points.length, points, initcentre)\n    \n    var root : CoresetTree = new CoresetTreeLeaf(elem, 0.0) \n    for(i <- 1 to m-1) {\n      root = splitCoresetTree(root)\n    }\n    root\n  }\n\n  /**\n   * A help function for computing the squared distance between two examples\n   * @param p1 the source Example\n   * @param p2 the target Example\n   * @return the squared distance \n   */\n  def squaredDistance(p1 : Example, p2 : Example) : Double = {\n    val inst1 = if(p1.weight != 0.0) p1.in.map(x=>x/p1.weight) else p1.in\n    val inst2 = if(p2.weight != 0.0) p2.in.map(x=>x/p2.weight) else p2.in\n    inst1.distanceTo(inst2)\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/clusterers/utils/KMeans.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.clusterers.utils\n\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.rdd._\n\nimport scala.util.Random\nimport scala.io.Source\n\n/**\n * Clustering helper functions.\n */\nobject ClusterUtils extends Serializable {\n  \n  /**\n   * Transforms an Example RDD into an array of RDD\n   * @param input the input RDD\n   * @return the output Array\n   */\n  def fromRDDToArray(input: RDD[Example]): Array[Example] =\n    input.aggregate(Array[Example]())((arr, ex) => arr:+ex, \n                                    (arr1,arr2) => arr1++arr2)\n\n  /**\n   * Assigns the input example to the cluster corresponding to the closest\n   * centroid.\n   * @param example the input Example\n   * @param centroids the Array of centroids\n   * @return the assigned cluster index\n   */\n  def assignToCluster(example: Example, centroids: Array[Example]): Int =\n    centroids.foldLeft((0,Double.MaxValue,0))((cl,centr) => {\n      val dist = centr.in.distanceTo(example.in)\n      if(dist<cl._2) ((cl._3,dist,cl._3+1))\n      else ((cl._1,cl._2,cl._3+1))\n    })._1\n}\n\n/**\n * The KMeans object computes the weighted k-means clustering given an array of\n * Examples. It assumes that the inputs are weighted. Each instance will\n * contribute a weight number of instances to the cluster.\n */\nobject KMeans extends Serializable {\n\n  /**\n   * Init the model based on the algorithm implemented in the learner,\n   * from the stream of instances given for training.\n   *\n   * @param input an Array of Example containing the instances to be clustered\n   * @param k the number of clusters (default 10)\n   * @param iterations the number of loops of k-means (default 1000)\n   */\n  def cluster(input: Array[Example], k: Int = 10, iterations: Int = 1000)\n      : Array[Example] = {\n    //sample k centroids from the input array\n    //uses reservoir sampling to sample in one go\n    var centroids = input.foldLeft((Array[Instance](),0))((a,e) => {\n      if(a._2<k)\n        (a._1:+e.in, a._2+1)\n      else {\n        var dice = Random.nextInt(a._2)\n        if(dice<k) a._1(dice) = e.in\n        (a._1, a._2+1)\n      }\n    })._1\n    for(i <- 0 until iterations) {\n      //initialize new empty clusters\n      //each cluster will contain the sum of the instances in the cluster and\n      //the number of instances\n      var clusters: Array[(Instance,Double)] = \n                      Array.fill[(Instance,Double)](k)((new NullInstance,0.0))\n      //find the closest centroid for each instance\n      //assign the instance to the corresponding cluster\n      input.foreach(ex => {\n        val closest = ClusterUtils.assignToCluster(ex, centroids.map(\n                        new Example(_)))\n        clusters(closest) = addInstancesToCluster(clusters(closest),\n                                                  (ex.in,ex.weight))\n      })\n      //recompute centroids\n      centroids = clusters.foldLeft(Array[Instance]())((a,cl) => {\n        val centroid = \n          if(cl._2==0) cl._1\n          else cl._1.map(x => x/cl._2)\n        a:+centroid\n      })\n    }\n    centroids.map(new Example(_))\n  }\n\n  private def addInstancesToCluster(left: (Instance,Double), \n                                    right: (Instance,Double))\n                                    : (Instance,Double) = \n    left._1 match {\n      case NullInstance() => \n        (right._1.map(x=>x*right._2), right._2)\n      case _ => \n        (left._1.add(right._1.map(x=>x*right._2)),left._2+right._2)\n    }\n\n}\n\n\n/**\n * TestKmeans is used to test offline the k-means algorithm, and can be run via:\n * {{{\n *   sbt \"run-main org.apache.spark.streamdm.clusterers.utils.TestKMeans\n *    <input_file> <k> <iterations> <instance_type=dense|sparse>\"\n * }}}\n */\nobject TestKMeans {\n\n  private def printCentroids(input: Array[Example]): Unit =\n    input.foreach{ case x => println(x) }\n\n  def main(args: Array[String]) {\n\n    var data: Array[Example] = Array[Example]()\n\n    for(line <- Source.fromFile(args(0)).getLines())\n      data = data :+ Example.parse(line,args(3),\"dense\")\n\n    var centroids = KMeans.cluster(data, args(1).toInt, args(2).toInt)\n\n    printCentroids(centroids)\n\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/ClassificationModel.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\n/**\n * A ClassificationModel trait defines the needed operations on any classification Model. It\n * provides methods for updating the model and for predicting the label of a\n * given Instance\n */\ntrait ClassificationModel extends Model {\n\n  /** Predict the label of the Instance, given the current Model\n   *\n   * @param instance the Instance which needs a class predicted\n   * @return a Double representing the class predicted\n   */\n  def predict(instance: Example): Double\n\n  /** Computes the probability for a given label class, given the current Model\n    *\n    * @param example the Example which needs a class predicted\n    * @return the predicted probability\n    */\n  def prob(example: Example): Double\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/DenseInstance.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\n/**\n * A DenseInstance is an Instance in which the features are dense, i.e., there\n * exists a value for (almost) every feature.\n * The DenseInstance will keep an Array of the values and the\n * corresponding operations will be based on this data structure.\n */\ncase class DenseInstance(inVector: Array[Double])\n    extends Instance with Serializable {\n\n  type T = DenseInstance\n\n  val features = inVector\n\n  /* Get the feature value present at position index\n  * In case the index does not exist or is invalid, then NaN is returned.\n  *\n  * @param index the index of the desired value \n  * @return a Double representing the feature value\n  */\n  override def apply(index: Int): Double =\n    if (index >= features.length || index < 0) Double.NaN else features(index)\n\n  /*\n   * Return an array of features and indexes\n   *\n   * @return an array of turple2(value,index)\n   */\n  def getFeatureIndexArray(): Array[(Double, Int)] = features.zipWithIndex\n\n  /* Perform a dot product between two instances\n  *\n  * @param input an DenseSingleLabelInstance with which the dot\n  * product is performed\n  * @return a Double representing the dot product \n  */\n  override def dot(input: Instance): Double = input match {\n    case DenseInstance(f) => {\n      var sum: Double = 0.0\n      var i: Int = 0\n      while (i < features.length) {\n        sum += f(i) * this(i)\n        i += 1\n      }\n      sum\n    }\n    //using imperative version for efficiency reasons\n    //normally it should be implemented as below\n    //  (0 until features.length).foldLeft(0.0)((d,i) => d + features(i)*f(i))\n    case SparseInstance(i, v) =>\n      input.dot(this)\n    case _ => 0.0\n  }\n\n  /**\n   * Perform an element by element addition between two instances\n   *\n   * @param input an Instance which is added up\n   * @return an Instance representing the added Instances\n   */\n  override def add(input: Instance): DenseInstance = input match {\n    case DenseInstance(f) => {\n      var newF: Array[Double] = Array.fill(features.length)(0.0)\n      var i: Int = 0\n      while (i < features.length) {\n        newF(i) = features(i) + f(i)\n        i += 1\n      }\n      new DenseInstance(newF)\n    }\n    //val addedInstance = (0 until features.length).map(i => features(i)+f(i))\n    //new DenseSingleLabelInstance(addedInstance.toArray, label)\n    case SparseInstance(ind, v) => {\n      var newF: Array[Double] = Array.fill(features.length)(0.0)\n      var i: Int = 0\n      while (i < features.length) {\n        newF(i) = features(i)\n        i += 1\n      }\n      i = 0\n      while (i < ind.length) {\n        newF(ind(i)) += v(i)\n        i += 1\n      }\n      new DenseInstance(newF)\n    }\n    case _ => new DenseInstance(features)\n  }\n\n  /**\n   * Perform an element by element multiplication between two instances\n   *\n   * @param input an Instance which is multiplied\n   * @return an Instance representing the Hadamard product\n   */\n  override def hadamard(input: Instance): DenseInstance = input match {\n    case DenseInstance(f) => {\n      var newF: Array[Double] = Array.fill(features.length)(0.0)\n      var i: Int = 0\n      while (i < features.length) {\n        newF(i) = features(i) * f(i)\n        i += 1\n      }\n      new DenseInstance(newF)\n    }\n    case SparseInstance(ind, v) => {\n      var newF: Array[Double] = Array.fill(features.length)(0.0)\n      var i: Int = 0\n      while (i < ind.length) {\n        newF(ind(i)) = features(ind(i)) * v(i)\n        i += 1\n      }\n      new DenseInstance(newF)\n    }\n    case _ => new DenseInstance(features)\n  }\n\n  /**\n   * Compute the Euclidean distance to another Instance\n   *\n   * @param input the Instance to which the distance is computed\n   * @return a Double representing the distance value\n   */\n  override def distanceTo(input: Instance): Double = input match {\n    case DenseInstance(f) => {\n      var sum: Double = 0.0\n      var i: Int = 0\n      while (i < features.length) {\n        sum += math.pow(features(i) - f(i), 2.0)\n        i += 1\n      }\n      math.sqrt(sum)\n    }\n    case SparseInstance(ind, v) => {\n      var sum: Double = 0.0\n      var i: Int = 0\n      while (i < ind.length) {\n        if (v(i) != 0) sum += math.pow(features(ind(i)) - v(i), 2.0)\n        i += 1\n      }\n      i = 0\n      while (i < features.length) {\n        if (input(i) == 0) sum += math.pow(features(i), 2.0)\n        i += 1\n      }\n      math.sqrt(sum)\n    }\n    case _ => Double.MaxValue\n  }\n\n  /**\n   * Add a feature to the instance\n   *\n   * @param index the index at which the value is added\n   * @param input the feature value which is added up\n   * @return an Instance representing the new feature vector\n   */\n  override def set(index: Int, input: Double): DenseInstance = {\n    var returnInstance = this\n    if (index >= 0 && index < features.length) {\n      features(index) = input\n      returnInstance = new DenseInstance(features)\n    } else if (index == features.length)\n      returnInstance = new DenseInstance(features :+ input)\n    returnInstance\n  }\n\n  /**\n   * Apply an operation to every feature of the Instance\n   * @param func the function for the transformation\n   * @return a new Instance with the transformed features\n   */\n  override def map(func: Double => Double): DenseInstance =\n    new DenseInstance(features.map { case x => func(x) })\n\n  /**\n   * Aggregate the values of an instance\n   *\n   * @param func the function for the transformation\n   * @return the aggregated value\n   */\n  override def reduce(func: (Double, Double) => Double): Double =\n    features.reduce(func)\n\n  override def toString = features.mkString(\",\")\n}\n\nobject DenseInstance extends Serializable {\n\n  /**\n   * Parse the input string as an DenseInstance class, where each features is\n   * separated by a comma (CSV format).\n   *\n   * @param input the String line to be read\n   * @return a DenseInstance which is parsed from input\n   */\n  def parse(input: String): DenseInstance = {\n    val tokens = input.split(\",\")\n    new DenseInstance(tokens.map(_.toDouble))\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/Example.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\n/**\n * An Example is a wrapper on top of the Instance class hierarchy. It contains a\n * reference to an input Instance and an output Instance, and provides setters\n * and getters for the features and labels. This is done so that the DStream\n * accepts any type of Instance in the parameters, and that the same DStream can\n * be allowed to contain multiple types of Instance.\n */\nclass Example(inInstance: Instance, outInstance: Instance = new NullInstance, \n              weightValue: Double=1.0) \n  extends Serializable {\n  \n  val in = inInstance\n  val out = outInstance\n  val weight = weightValue\n\n  /** Get the input value present at position index\n   *\n   * @param index the index of the value\n   * @return a Double representing the feature value\n   */\n  def featureAt(index: Int): Double = in(index)\n  \n  /** Get the output value present at position index\n   *\n   * @param index the index of the value\n   * @return a Double representing the value\n   */\n  def labelAt(index: Int): Double = out(index)\n\n    /** Set the weight of the Example \n   *\n   * @param value the weight value\n   * @return an Example containing the new weight\n   */\n  def setWeight(value: Double): Example =\n    new Example(in, out, value)\n\n    /** Add a feature to the instance in the example\n   *\n   * @param index the index at which the value is added\n   * @param input the feature value which is added up\n   * @return an Example containing an Instance with the new features\n   */\n  def setFeature(index: Int, input: Double): Example =\n    new Example(in.set(index, input), out, weight)\n\n   /** Add a feature to the instance in the example\n   *\n   * @param index the index at which the value is added\n   * @param input the label value which is added up\n   * @return an Example containing an Instance with the new labels\n   */\n  def setLabel(index: Int, input: Double): Example =\n    new Example(in, out.set(index, input), weight)\n\n  override def toString = {\n    val inString = in.toString\n    val weightString = if (weight==1.0) \"\" else \" %f\".format(weight)\n    val outString = out match {\n      case NullInstance() => \"\"\n      case _ => \"%s \".format(out.toString)\n    }\n    \"%s%s%s\".format(outString, inString, weightString)\n  }\n}\n\nobject Example extends Serializable {\n  \n  /** Parse the input string as an SparseInstance class. The input and output\n   * instances are separated by a whitespace character, of the form\n   * \"output_instance<whitespace>input_instance<whitespace>weight\". The output\n   * and the weight can be missing.\n   *\n   * @param input the String line to be read\n   * @param outType String specifying the format of the output instance\n   * @return a DenseInstance which is parsed from input\n   */\n  def parse(input: String, inType: String, outType: String): Example = {\n    val tokens = input.split(\"\\\\s+\")\n    val numTokens = tokens.length\n    if (numTokens==1)\n      new Example(getInstance(tokens.head, inType))\n    else if (numTokens==2)\n      new Example(getInstance(tokens.last, inType), \n                  getInstance(tokens.head, outType))\n    else\n      new Example(getInstance(tokens.tail.head, inType), \n                  getInstance(tokens.head, outType), tokens.last.toDouble)\n  }\n\n  /** Parse the input string based on the type of Instance, by calling the\n   * associated .parse static method\n   * @param input the String to be parsed\n   * @param instType the type of instance to be parsed (\"dense\" or \"sparse\")\n   * @return the parsed Instance, or null if the type is not properly specified\n   */\n  private def getInstance(input: String, instType: String): Instance = \n    instType match {\n      case \"dense\" => DenseInstance.parse(input)\n      case \"sparse\" => SparseInstance.parse(input)\n      case _ => null\n    }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/ExampleParser.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\nimport org.apache.spark.internal.Logging\nimport org.apache.spark.streamdm.core.specification._\n\n/*\n * object ExampleParser helps to parse example from/to different data format.\n */\nobject ExampleParser extends Logging {\n\n  /*\n   * create Example from arff format string\n   * \n   * @param e arff format string\n   * @spec ExampleSpecification\n   * @return Example\n   */\n  def fromArff(e: String, spec: ExampleSpecification): Example = {\n    var input: String = e\n    var output: String = null\n    val isSparse: Boolean = e.contains(\"{\") && e.contains(\"}\")\n    if (isSparse) {\n      // sparse data  \n      input = e.substring(e.indexOf(\"{\") + 1, e.indexOf(\"}\")).trim()\n    }\n    if (spec.out.size() == 1) {\n      val pos = input.lastIndexOf(\",\")\n      output = input.substring(pos + 1).trim()\n      input = input.substring(0, pos)\n    }\n    new Example(arffToInstace(input, spec.in, isSparse), arffToInstace(output, spec.out, isSparse))\n  }\n\n  /*\n   * create Instance from arff format string\n   * \n   * @param e arff format string\n   * @spec InstanceSpecification\n   * @return Instance\n   */\n  private def arffToInstace(data: String, spec: InstanceSpecification, isSparse: Boolean): Instance = {\n    if (data == null || data.length() == 0) null\n    else {\n      if (isSparse)\n        arffToSparceInstace(data, spec)\n      else\n        arffToDenseInstace(data, spec)\n    }\n  }\n\n  /*\n   * create SparseInstance from arff format string\n   * \n   * @param e arff format string\n   * @spec InstanceSpecification\n   * @return SparseInstance\n   */\n  private def arffToSparceInstace(data: String, spec: InstanceSpecification): SparseInstance = {\n    val tokens = data.split(\",[\\\\s]?\")\n    val values = Array[Double](tokens.length)\n    val features = tokens.map(_.split(\"\\\\s+\"))\n    for (index <- 0 until tokens.length) {\n      values(index) = spec(index) match {\n        case nominal: NominalFeatureSpecification => nominal(features(index)(1))\n        case _ => features(index)(1).toDouble\n      }\n    }\n    new SparseInstance(features.map(_(0).toInt), values)\n  }\n\n  /*\n   * create DenseInstance from arff format string\n   * \n   * @param e arff format string\n   * @spec InstanceSpecification\n   * @return DenseInstance\n   */\n  private def arffToDenseInstace(data: String, spec: InstanceSpecification): DenseInstance = {\n    val stringValues = data.split(\",[\\\\s]?\")\n    val values = new Array[Double](stringValues.length)\n    for (index <- 0 until stringValues.length) {\n      values(index) = spec(index) match {\n        case nominal: NominalFeatureSpecification => nominal(stringValues(index))\n        case _ => stringValues(index).toDouble\n      }\n    }\n    new DenseInstance(values)\n  }\n\n  /*\n   * create arff format string for Example\n   * \n   * @param e Exmaple\n   * @spec ExampleSpecification\n   * @return arff format string\n   */\n  def toArff(e: Example, spec: ExampleSpecification): String = {\n    val isSparse = e.in.isInstanceOf[SparseInstance]\n    val sb = new StringBuffer()\n    sb.append(instanceToArff(e.in, spec.in, 0))\n    if (spec.out.size() != 0)\n      sb.append(\", \" + instanceToArff(e.out, spec.out, spec.in.size()))\n    sb.delete(sb.lastIndexOf(\",\"), sb.length())\n    if (isSparse) {\n      sb.insert(0, \"{\")\n      sb.append(\"}\")\n    }\n    sb.toString()\n  }\n  /*\n   * create part arff format string for Instance\n   * \n   * @param instance: Instance\n   * @spec ExampleSpecification\n   * @return part arff format string\n   */\n  def instanceToArff(instance: Instance, insSpec: InstanceSpecification, startIndex: Int): String = {\n    val isSparse = instance.isInstanceOf[SparseInstance]\n    val sb = new StringBuffer()\n    instance.getFeatureIndexArray().foreach(token =>\n      {\n        if (isSparse) {\n          sb.append(startIndex + token._2 + \" \")\n        }\n        val value = insSpec(token._2) match {\n          case nominal: NominalFeatureSpecification => nominal(token._1.toInt)\n          case numeric: NumericFeatureSpecification => token._1\n        }\n        sb.append(value + \", \")\n      })\n    sb.toString()\n  }\n\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/Instance.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\n/**\n * An Instance represents the input or output of any learning algorithm. It is\n * normally composed of a feature vector (having various implementations).\n */\n\ntrait Instance extends Serializable {\n\n  type T <: Instance\n\n  /**\n   * Get the value present at position index\n   *\n   * @param index the index of the features\n   * @return a Double representing the feature value, or 0 if the index is not\n   * present in the underlying data structure\n   */\n  def apply(index: Int): Double\n\n  /**\n   * Return an array of features and indexes\n   *\n   * @return an array of turple2(value,index)\n   */\n  def getFeatureIndexArray(): Array[(Double, Int)]\n\n  /**\n   * Perform a dot product between two instances\n   *\n   * @param input an Instance with which the dot product is performed\n   * @return a Double representing the dot product\n   */\n  def dot(input: Instance): Double\n\n  /**\n   * Compute the Euclidean distance to another Instance\n   *\n   * @param input the Instance to which the distance is computed\n   * @return a Double representing the distance value\n   */\n  def distanceTo(input: Instance): Double\n\n  /**\n   * Perform an element by element addition between two instances\n   *\n   * @param input an Instance which is added up\n   * @return an Instance representing the added Instances\n   */\n  def add(input: Instance): T\n\n  /**\n   * Perform an element by element multiplication between two instances\n   *\n   * @param input an Instance which is multiplied\n   * @return an Instance representing the Hadamard product\n   */\n  def hadamard(input: Instance): T\n\n  /**\n   * Add a feature to the instance\n   *\n   * @param index the index at which the value is added\n   * @param input the feature value which is added up\n   * @return an Instance representing the new feature vector\n   */\n  def set(index: Int, input: Double): T\n\n  /**\n   * Apply an operation to every feature of the Instance\n   *\n   * @param func the function for the transformation\n   * @return a new Instance with the transformed features\n   */\n  def map(func: Double => Double): T\n\n  /**\n   * Aggregate the values of an instance\n   *\n   * @param func the function for the transformation\n   * @return the reduced value\n   */\n  def reduce(func: (Double, Double) => Double): Double\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/Learner.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\nimport org.apache.spark.streaming.dstream._\nimport com.github.javacliparser.Configurable\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n\n/**\n * A Learner trait defines the needed operations for any learner algorithm\n * implemented. It provides methods for training the model for a stream of\n * Example RDDs.\n * Any Learner will contain a data structure derived from Model.\n */\ntrait Learner extends Configurable  with Serializable {\n\n  type T <: Model\n  \n  /**\n   * Init the model based on the algorithm implemented in the learner.\n   *\n   * @param exampleSpecification the ExampleSpecification of the input stream.\n   */\n  def init(exampleSpecification: ExampleSpecification): Unit\n\n  /** \n   * Train the model based on the algorithm implemented in the learner, \n   * from the stream of Examples given for training.\n   * \n   * @param input a stream of Examples\n   */\n  def train(input: DStream[Example]): Unit\n\n  /**\n   * Gets the current Model used for the Learner.\n   * \n   * @return the Model object used for training\n   */\n  def getModel: T\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/Model.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\n/**\n * A Model trait defines the needed operations on any learning Model. It\n * provides a method for updating the model.\n */\ntrait Model extends Serializable {\n\n  type T <: Model\n\n  /**\n   * Update the model, depending on the Instance given for training.\n   *\n   * @param change the example based on which the Model is updated\n   * @return the updated Model\n   */\n  def update(change: Example): T\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/NullInstance.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\n/**\n * A NullInstance is an Instance which does not contain anything in it. It is\n * present to aid the design of Example, and to allow cases when we have\n * instances which do not have output values.\n * Every operation on a NullInstance with either return a NullInstance or a\n * value of 0.\n */\n\ncase class NullInstance() extends Instance with Serializable {\n\n  type T = NullInstance\n\n  /* Get the feature value present at position index\n  *\n  * @param index the index of the desired value \n  * @return a value of 0 \n  */\n  override def apply(index: Int): Double = 0.0\n\n  /*\n   * Return an array of features and indexes\n   *\n   * @return an array of turple2(value,index)\n   */\n  def getFeatureIndexArray(): Array[(Double, Int)] = new Array[(Double, Int)](0)\n\n  /* Perform a dot product between two instances\n  *\n  * @param input an Instance with which the dot\n  * product is performed\n  * @return a value of 0 \n  */\n  override def dot(input: Instance): Double = 0.0\n\n  /**\n   * Compute the Euclidean distance to another Instance\n   *\n   * @param input the Instance to which the distance is computed\n   * @return an infinite distance (implemented as Double.MaxValue)\n   */\n  def distanceTo(input: Instance): Double = Double.MaxValue\n\n  /**\n   * Perform an element by element addition between two instances\n   *\n   * @param input an Instance which is added up\n   * @return a NullInstance\n   */\n  override def add(input: Instance): NullInstance = this\n\n  /**\n   * Perform an element by element multiplication between two instances\n   *\n   * @param input an Instance which is multiplied\n   * @return a NullInstance\n   */\n  override def hadamard(input: Instance): NullInstance = this\n\n  /**\n   * Add a feature to the instance\n   *\n   * @param index the index at which the value is added\n   * @param input the feature value which is added up\n   * @return a NullInstance\n   */\n  override def set(index: Int, input: Double): NullInstance = this\n\n  /**\n   * Apply an operation to every feature of a NullInstance\n   * @param func the function for the transformation\n   * @return a NullInstance\n   */\n  override def map(func: Double => Double): NullInstance = this\n\n  /**\n   * Aggregate the values of a NullInstance\n   *\n   * @param func the function for the transformation\n   * @return a value of 0\n   */\n  override def reduce(func: (Double, Double) => Double): Double = 0.0\n\n  override def toString = \"\"\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/SparseInstance.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\nimport math._\n\n/**\n  * A SparseInstance is an Instance in which the features are sparse, i.e., most\n  * features will not have any value.\n  * The SparseInstance will keep two Arrays: one with the values and one with the\n  * corresponding indexes. The implementation will be based on these two data\n  * structures.\n  */\ncase class SparseInstance(inIndexes:Array[Int], inValues:Array[Double])\n  extends Instance with Serializable {\n\n  type T = SparseInstance\n\n  val indexes = inIndexes\n  val values = inValues\n\n  /** Get the value present at position index\n    * In case the index does not exist or is invalid, then NaN is returned.\n    *\n    * @param index the index of the features\n    * @return a Double representing the value, or 0.0 if not found\n    */\n  def apply(index: Int): Double = {\n    var i: Int = 0\n    var value: Double = Double.NaN\n    var found = false\n    while(i<indexes.length && !found) {\n      if(indexes(i)==index) {\n        value = values(i)\n        found = true\n      }\n      i += 1\n    }\n    value\n  }\n\n  /**\n    * Return an array of features and indexes\n    *\n    * @return an array of turple2(value,index)\n    */\n  def getFeatureIndexArray(): Array[(Double, Int)] = inValues.zip(inIndexes)\n\n  /** Perform a dot product between two instances\n    *\n    * @param input an Instance with which the dot\n    * product is performed\n    * @return a Double representing the dot product\n    */\n  override def dot(input: Instance): Double = {\n    var i: Int = 0\n    var dot: Double = 0.0\n    while(i<indexes.length) {\n      val value = if(values(i).isNaN()) 0.0 else values(i)\n      val inputValue = if(input(indexes(i)).isNaN()) 0.0 else input(indexes(i))\n      dot += value * inputValue\n      i += 1\n    }\n    dot\n  }\n\n  /** Perform an element by element addition between two instances\n    *\n    * @param input an Instance which is added up\n    * @return a SparseInstance representing the added Instances\n    */\n  override def add(input: Instance): SparseInstance = input match {\n    case SparseInstance(inputIndexes, _) => {\n      val bothIndexes: Array[Int] = indexes.toSet.union(inputIndexes.toSet).toArray\n      val addedFeatures: Array[Double] = Array.fill(bothIndexes.length){0}\n\n      var i: Int = 0\n      for(index <- bothIndexes) {\n        val value1 = if(apply(index).isNaN()) 0.0 else apply(index)\n        val value2 = if(input(index).isNaN()) 0.0 else input(index)\n        addedFeatures(i) = value1 + value2\n        i += 1\n      }\n      new SparseInstance(bothIndexes, addedFeatures)\n    }\n    case DenseInstance(_) => {\n      // Range(0 to inputValues.length) are the indexes from the DenseInstance\n      val bothIndexes: Array[Int] = indexes.toSet.union(Range(0, input.getFeatureIndexArray().length).toSet).toArray\n      val addedFeatures: Array[Double] = Array.fill(bothIndexes.length){0}\n\n      var i: Int = 0\n      for(index <- bothIndexes) {\n        val value1 = if(apply(index).isNaN()) 0.0 else apply(index)\n        val value2 = if(input(index).isNaN()) 0.0 else input(index)\n        addedFeatures(i) = value1 + value2\n        i += 1\n      }\n      new SparseInstance(bothIndexes, addedFeatures)\n    }\n    case _ => new SparseInstance(indexes, values)\n  }\n\n  /** Perform an element by element multiplication between two instances\n    *\n    * @param input an Instance which is multiplied\n    * @return a SparseInstance representing the Hadamard product\n    */\n  override def hadamard(input: Instance): SparseInstance = input match {\n    case SparseInstance(ind,v) => {\n      var i: Int = 0\n      var addedFeatures: Array[Double] = Array()\n      var addedIndexes: Array[Int] = Array()\n      while(i<ind.length) {\n        val sum = v(i) * apply(ind(i))\n        if(v(i)!=0 && sum!=0) {\n          addedIndexes :+= ind(i)\n          addedFeatures :+= sum\n        }\n        i += 1\n      }\n      new SparseInstance(addedIndexes, addedFeatures)\n    }\n    case DenseInstance(f) => {\n      var i: Int = 0\n      var addedFeatures: Array[Double] = Array()\n      var addedIndexes: Array[Int] = Array()\n      while(i<f.length) {\n        val sum = f(i) * this(i)\n        if(sum!=0) {\n          addedIndexes :+= i\n          addedFeatures :+= sum\n        }\n        i += 1\n      }\n      new SparseInstance(addedIndexes, addedFeatures)\n    }\n    case _ => new SparseInstance(indexes, values)\n  }\n\n  /** Compute the Euclidean distance to another Instance\n    *\n    * @param input the Instance to which the distance is computed\n    * @return a Double representing the distance value\n    */\n  override def distanceTo(input: Instance): Double = input match {\n    case SparseInstance(inputIndexes, _) => {\n      val bothIndexes: Array[Int] = indexes.toSet.union(inputIndexes.toSet).toArray\n      val powSubFeatures: Array[Double] = Array.fill(bothIndexes.length){0}\n\n      var i: Int = 0\n      for(index <- bothIndexes) {\n        val value1 = if(apply(index).isNaN()) 0.0 else apply(index)\n        val value2 = if(input(index).isNaN()) 0.0 else input(index)\n        powSubFeatures(i) = math.pow(value1 - value2, 2.0)\n        i += 1\n      }\n      math.sqrt(powSubFeatures.reduce(_+_))\n    }\n    case DenseInstance(_) => {\n      // Range(0 to inputValues.length) are the indexes from the DenseInstance\n      val bothIndexes: Array[Int] = indexes.toSet.union(Range(0, input.getFeatureIndexArray().length).toSet).toArray\n      val powSubFeatures: Array[Double] = Array.fill(bothIndexes.length) {\n        0\n      }\n\n      var i: Int = 0\n      for (index <- bothIndexes) {\n        val value1 = if (apply(index).isNaN()) 0.0 else apply(index)\n        val value2 = if (input(index).isNaN()) 0.0 else input(index)\n        powSubFeatures(i) = math.pow(value1 - value2, 2.0)\n        i += 1\n      }\n      math.sqrt(powSubFeatures.reduce(_ + _))\n    }\n    case _ => Double.MaxValue\n  }\n\n  /** Append a feature to the instance\n    *\n    * @param input the value which is added up\n    * @return a SparseInstance representing the new feature vector\n    */\n  override def set(index: Int, input: Double): SparseInstance =\n    new SparseInstance(indexes:+index,values:+input)\n\n  /** Apply an operation to every feature of the Instance (essentially a map)\n    * @param func the function for the transformation\n    * @return a new SparseInstance with the transformed features\n    */\n  override def map(func: Double=>Double): SparseInstance =\n    new SparseInstance(indexes, values.map{case x => func(x)})\n\n  /** Aggregate the values of an instance \n    *\n    * @param func the function for the transformation\n    * @return the aggregated value\n    */\n  override def reduce(func: (Double,Double)=>Double): Double =\n    values.reduce(func)\n\n  override def toString = (indexes zip values).map{ case (i,v) =>\n    \"%d:%f\".format(i+1,v)}.mkString(\",\")\n\n}\n\nobject SparseInstance extends Serializable {\n\n  /** Parse the input string as an SparseInstance class, in LibSVM\n    * comma-separated format, where each feature is of the form \"i:v\" where i is\n    * the index of the feature (starting at 1), and v is the value of the\n    * feature.\n    *\n    * @param input the String line to be read, in LibSVM format\n    * @return a DenseInstance which is parsed from input\n    */\n  def parse(input: String): SparseInstance = {\n    val tokens = input.split(\",\")\n    val features = tokens.map(_.split(\":\"))\n    new SparseInstance(features.map(_(0).toInt-1),features.map(_(1).toDouble))\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/TextInstance.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\n/**\n * A TextInstance is an Instance in which the features are a map of text keys,\n * each associated with a value.\n */\n\ncase class TextInstance(inFeatures: Map[String, Double])\n    extends Instance with Serializable {\n\n  type T = TextInstance\n\n  val features = inFeatures\n\n  /* Get the feature value for a given index \n  *\n  * @param index the key of the features\n  * @return a Double representing the feature value\n  */\n  def apply(index: Int): Double =\n    valueAt(index.toString)\n\n  /*\n   * Return an array of features and indexes\n   *\n   * @return an array of turple2(value,index)\n   */\n  def getFeatureIndexArray(): Array[(Double, Int)] = features.toArray.map{x =>(x._2,x._1.toInt)}\n\n  /* Get the feature value for a given key\n  *\n  * @param key the key of the features\n  * @return a Double representing the feature value\n  */\n  def valueAt(key: String): Double =\n    features.getOrElse(key, 0.0)\n\n  /* Perform a dot product between two instances\n  *\n  * @param input an Instance with which the dot\n  * product is performed\n  * @return a Double representing the dot product \n  */\n  override def dot(input: Instance): Double = input match {\n    case TextInstance(f) =>\n      dotTupleArrays(f.toArray, features.toArray)\n    case _ => 0.0\n  }\n\n  /**\n   * Perform an element by element addition between two instances\n   *\n   * @param input an Instance which is added up\n   * @return an Instance representing the added Instances\n   */\n  override def add(input: Instance): TextInstance = input match {\n    case TextInstance(f) => {\n      val addedInstance = addTupleArrays(f.toArray, features.toArray)\n      new TextInstance(arrayToMap(addedInstance))\n    }\n    case _ => this\n  }\n\n  /**\n   * Perform an element by element multiplication between two instances\n   *\n   * @param input an Instance which is multiplied\n   * @return an Instance representing the Hadamard product\n   */\n  override def hadamard(input: Instance): TextInstance = input match {\n    case TextInstance(f) => {\n      val addedInstance = mulTupleArrays(f.toArray, features.toArray)\n      new TextInstance(arrayToMap(addedInstance))\n    }\n    case _ => this\n\n  }\n\n  /**\n   * Compute the Euclidean distance to another Instance\n   *\n   * @param input the Instance to which the distance is computed\n   * @return a Double representing the distance value\n   */\n  override def distanceTo(input: Instance): Double = input match {\n    case TextInstance(f) => {\n      var sum: Double = 0.0\n      for ((k, v) <- f)\n        if (v != 0) sum += math.pow(valueAt(k) - v, 2.0)\n      for ((k, v) <- features)\n        if (f.getOrElse(k, 0.0) == 0) sum += math.pow(v, 2.0)\n      math.sqrt(sum)\n    }\n    case _ => Double.MaxValue\n  }\n\n  /**\n   * Append a feature to the instance\n   *\n   * @param key the key on which the feature is set\n   * @param value the value on which the feature is set\n   * @return an Instance representing the new feature vector\n   */\n  def setFeature(key: String, value: Double): TextInstance =\n    new TextInstance((features - key) + (key -> value))\n\n  /**\n   * Append a feature to the instance\n   *\n   * @param index the index at which the feature is set\n   * @param input the new value of the feature\n   * @return an Instance representing the new feature vector\n   */\n  def set(index: Int, input: Double): TextInstance =\n    setFeature(index.toString, input)\n\n  /**\n   * Apply an operation to every feature of the Instance (essentially a map)\n   * TODO try to extend map to this case\n   * @param func the function for the transformation\n   * @return a new Instance with the transformed features\n   */\n  override def map(func: Double => Double): TextInstance =\n    new TextInstance(features.mapValues { case x => func(x) })\n\n  /**\n   * Aggregate the values of an instance\n   *\n   * @param func the function for the transformation\n   * @return the reduced value\n   */\n  override def reduce(func: (Double, Double) => Double): Double =\n    features.valuesIterator.reduce(func)\n\n  private def dotTupleArrays(l1: Array[(String, Double)],\n    l2: Array[(String, Double)]): Double =\n    (l1 ++ l2).groupBy(_._1).filter { case (k, v) => v.length == 2 }.\n      map { case (k, v) => (k, v.map(_._2).reduce(_ * _)) }.toArray.unzip._2.sum\n\n  private def addTupleArrays(l1: Array[(String, Double)],\n    l2: Array[(String, Double)]): Array[(String, Double)] =\n    (l1 ++ l2).groupBy(_._1).map { case (k, v) => (k, v.map(_._2).sum) }.\n      toArray.filter(_._2 != 0)\n\n  private def mulTupleArrays(l1: Array[(String, Double)],\n    l2: Array[(String, Double)]): Array[(String, Double)] =\n    (l1 ++ l2).groupBy(_._1).map { case (k, v) => (k, v.map(_._2).product) }.\n      toArray.filter(_._2 != 0)\n\n  private def arrayToMap(l: Array[(String, Double)]): Map[String, Double] =\n    l.groupBy(_._1).map { case (k, v) => (k, v.head._2) }\n\n}\n\nobject TextInstance extends Serializable {\n\n  /**\n   * Parse the input string as an SparseInstance class\n   *\n   * @param input the String line to be read\n   * @return a DenseInstance which is parsed from input\n   */\n  def parse(input: String): TextInstance = {\n    val tokens = input.split(\",\")\n    val features = tokens.map(_.split(\":\"))\n    val featMap = features.groupBy(_.head).map {\n      case (k, v) => (k,\n        v.head.tail.head.toDouble)\n    }\n    new TextInstance(featMap)\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/specification/ExampleSpecification.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core.specification\n\n/**\n * An ExampleSpecification contains information about the input and output\n * features.  It contains a reference to an input InstanceSpecification and an\n * output InstanceSpecification, and provides setters and getters for the\n * feature specification properties.\n */\n\nclass ExampleSpecification(inInstanceSpecification: InstanceSpecification,\n  outInstanceSpecification: InstanceSpecification)\n    extends Serializable {\n\n  val in = inInstanceSpecification\n  val out = outInstanceSpecification\n\n  /**\n   * Gets the input FeatureSpecification value present at position index\n   *\n   * @param index the index of the specification\n   * @return a FeatureSpecification representing the specification for the\n   * feature\n   */\n  def inputFeatureSpecification(index: Int): FeatureSpecification = in(index)\n\n  /**\n   * Gets the output FeatureSpecification value present at position index\n   *\n   * @param index the index of the specification\n   * @return a FeatureSpecification representing the specification for the\n   * feature\n   */\n  def outputFeatureSpecification(index: Int): FeatureSpecification = out(index)\n\n  /**\n   * Evaluates whether an input feature is numeric\n   *\n   * @param index the index of the feature\n   * @return true if the feature is numeric\n   */\n  def isNumericInputFeature(index: Int): Boolean = in.isNumeric(index)\n\n  /**\n   * Evaluates whether an output feature is numeric\n   *\n   * @param index the index of the feature\n   * @return true if the feature is numeric\n   */\n  def isNumericOutputFeature(index: Int): Boolean = out.isNumeric(index)\n\n  /**\n   * Gets the input name of the feature at position index\n   *\n   * @param index the index of the class\n   * @return a string representing the name of the feature\n   */\n  def nameInputFeature(index: Int): String = in.name(index)\n\n  /**\n   * Gets the output name of the feature at position index\n   *\n   * @param index the index of the class\n   * @return a string representing the name of the feature\n   */\n  def nameOutputFeature(index: Int): String = out.name(index)\n\n  /**\n   * Gets the number of input features\n   *\n   * @return an Integer representing the number of input features\n   */\n  def numberInputFeatures(): Int = in.size\n\n  /**\n   * Gets the number of output features\n   *\n   * @return an Integer representing the number of output features\n   */\n  def numberOutputFeatures(): Int = out.size\n}\n\n\n\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/specification/FeatureSpecification.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\n\npackage org.apache.spark.streamdm.core.specification\n\nimport scala.collection.mutable.Map\n\n/**\n * trait FeatureSpecification.\n *\n */\ntrait FeatureSpecification {\n\n  /**\n   * whether the feature is nominal\n   *\n   * @return true if the feature is nominal\n   */\n  def isNominal(): Boolean\n\n  /**\n   * whether the feature is numeric\n   *\n   * @return true if the feature is discrete\n   */\n  def isNumeric(): Boolean\n  /**\n   * if a feature is numeric, return -1, else return the nominal values size\n   */\n  def range(): Int\n}\n\n/**\n * class NumericFeatureSpecification.\n *\n */\nclass NumericFeatureSpecification extends FeatureSpecification with Serializable {\n  /**\n   * whether the feature is nominal\n   *\n   * @return true if the feature is nominal\n   */\n  override def isNominal(): Boolean = false\n  /**\n   * whether the feature is numeric\n   *\n   * @return true if the feature is discrete\n   */\n  override def isNumeric(): Boolean = !isNominal()\n  override def range(): Int = -1\n}\n\n/**\n * A NominalFeatureSpecification contains information about its nominal values.\n *\n */\n\nclass NominalFeatureSpecification(nominalValues: Array[String]) extends FeatureSpecification with Serializable {\n  val values = nominalValues\n\n  val nominalToNumericMap = Map[String,Int]()\n  values.zipWithIndex.map{ case (element, index) => \n                            (nominalToNumericMap += (element -> (index))) }\n\n  /** Get the nominal string value present at position index\n    *\n    * @param index the index of the feature value\n    * @return a string containing the nominal value of the feature\n    */\n  def apply(index: Int): String =  values(index)\n\n  /** Get the position index given the nominal string value\n    *\n    * @param string a string containing the nominal value of the feature\n    * @return the index of the feature value\n    */\n  def apply(string: String): Int = nominalToNumericMap(string)\n\n  /**\n   * whether the feature is nominal\n   *\n   * @return true if the feature is nominal\n   */\n  override def isNominal(): Boolean = true\n  /**\n   * whether the feature is numeric\n   *\n   * @return true if the feature is discrete\n   */\n  override def isNumeric(): Boolean = !isNominal()\n  /**\n   * return the nominal values size\n   */\n  override def range(): Int = values.length\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/specification/InstanceSpecification.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core.specification\n\nimport scala.collection.mutable.Map\n\n/**\n * An InstanceSpecification contains information about the features.\n * It returns information of features that are not numeric,\n * and the names of all features, numeric and discrete,\n */\n\nclass InstanceSpecification extends Serializable {\n  val nominalFeatureSpecificationMap = Map[Int, FeatureSpecification]()\n  val featureNameMap = Map[Int, String]()\n  val numericFeatureSpecification: NumericFeatureSpecification = new NumericFeatureSpecification\n\n  /**\n   * Gets the FeatureSpecification value present at position index\n   *\n   * @param index the index of the position\n   * @return a FeatureSpecification representing the specification for the\n   * feature\n   */\n  def apply(index: Int): FeatureSpecification = {\n    if (nominalFeatureSpecificationMap.contains(index))\n      nominalFeatureSpecificationMap(index)\n    else numericFeatureSpecification\n  }\n\n  /**\n   * Removes the FeatureSpecification value present at position index\n   *\n   * @param index the index of the position\n   * @return Unit\n   */\n  def removeFeatureSpecification(index: Int): Unit = {\n    if (nominalFeatureSpecificationMap.contains(index))\n      nominalFeatureSpecificationMap.remove(index)\n    featureNameMap.remove(index)\n  }\n\n  /**\n   * Adds a specification for the instance feature\n   *\n   * @param index the index at which the value is added\n   * @param input the feature specification which is added up\n   */\n  def addFeatureSpecification(index: Int, name: String, fSpecification: FeatureSpecification = null): Unit = {\n    if (fSpecification != null && fSpecification.isInstanceOf[NominalFeatureSpecification]) {\n      nominalFeatureSpecificationMap += (index -> fSpecification)\n    }\n    featureNameMap += (index -> name)\n  }\n  /**\n   * Evaluates whether a feature is nominal or discrete\n   *\n   * @param index the index of the feature\n   * @return true if the feature is discrete\n   */\n  def isNominal(index: Int): Boolean =\n    this(index).isNominal()\n\n  /**\n   * Evaluates whether a feature is numeric\n   *\n   * @param index the index of the feature\n   * @return true if the feature is numeric\n   */\n  def isNumeric(index: Int): Boolean =\n    !isNominal(index)\n\n  /**\n   * Gets the name of the feature at position index\n   *\n   * @param index the index of the class\n   * @return a string representing the name of the feature\n   */\n  def name(index: Int): String = featureNameMap(index)\n\n  /**\n   * Gets the number of features\n   *\n   * @return the number of features\n   */\n  def size(): Int = featureNameMap.size\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/core/specification/SpecificationParser.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core.specification\n\nimport scala.io.Source\nimport org.apache.spark.internal.Logging\nimport org.apache.spark.streamdm.classifiers.trees.Utils.arraytoString\n\n/*\n * class SpecificationParser helps to generate head for data\n */\nclass SpecificationParser extends Logging {\n\n  /*\n   * Get string head from ExampleSpecification by head type, which will be saved to head file.\n   * \n   * @param spec ExampleSpecification\n   * @param t data head type\n   * @return string of head\n   * \n   */\n  def getHead(spec: ExampleSpecification, t: String = \"arff\"): String = {\n    if (t.equalsIgnoreCase(\"arff\"))\n      toArff(spec)\n    else if (t.equalsIgnoreCase(\"csv\"))\n      toCsv(spec)\n    else\n      toArff(spec)\n  }\n\n  /*\n   * Get ExampleSpecification from head file by head type.\n   * \n   * @param fileName name of head file\n   * @param t data head type\n   * @return ExampleSpecification of data\n   * \n   */\n  def getSpecification(fileName: String, t: String = \"arff\"): ExampleSpecification = {\n    if (t.equalsIgnoreCase(\"arff\"))\n      fromArff(fileName)\n    else if (t.equalsIgnoreCase(\"csv\"))\n      fromCsv(fileName)\n    else fromArff(fileName)\n  }\n\n  def toArff(spec: ExampleSpecification): String = {\n    val sb = new StringBuffer()\n    sb.append(\"@relation sample-data\\n\")\n    val inputIS = spec.in\n    val outputIs = spec.out\n    val atr = \"@attribute\"\n    val nu = \"numeric\"\n    // add arff attributes of input\n    for (index <- 0 until inputIS.size()) {\n      val featureName: String = inputIS.name(index)\n      val featureSpec: FeatureSpecification = inputIS(index)\n\n      val line = featureSpec match {\n        case numeric: NumericFeatureSpecification => { nu }\n        case nominal: NominalFeatureSpecification => { arraytoString(nominal.values) }\n      }\n      sb.append(atr + \" \" + featureName + \" \" + line + \"\\n\")\n    }\n    // add arff attributes of outnput\n    sb.append(atr + \" \" + outputIs.name(0) + \" \" +\n      arraytoString(outputIs(0).asInstanceOf[NominalFeatureSpecification].values))\n    sb.toString()\n  }\n\n  def fromArff(fileName: String): ExampleSpecification = {\n    val lines = Source.fromFile(fileName).getLines()\n    var line: String = lines.next()\n    while (line == null || line.length() == 0 || line.startsWith(\" \") ||\n      line.startsWith(\"%\") || \"@relation\".equalsIgnoreCase(line.substring(0, 9))) {\n      line = lines.next()\n    }\n    var finished: Boolean = false\n    var index: Int = 0\n    val inputIS = new InstanceSpecification()\n    val outputIS = new InstanceSpecification()\n    while (!finished && line.startsWith(\"@\")) {\n      if (\"@data\".equalsIgnoreCase(line.substring(0, 5))) {\n        finished = true\n      } else if (\"@attribute\".equalsIgnoreCase(line.substring(0, 10))) {\n\n        val featureInfos: Array[String] = line.split(\"\\\\s+\")\n        val name: String = featureInfos(1)\n        if (!isArffNumeric(featureInfos(2))) {\n          val featurevalues: Array[String] = featureInfos(2).substring(\n            featureInfos(2).indexOf(\"{\") + 1, featureInfos(2).indexOf(\"}\")).\n            trim().split(\",[\\\\s]?\")\n\n          val fSpecification = new NominalFeatureSpecification(featurevalues)\n\n          inputIS.addFeatureSpecification(index, \"Norminal\" + index, fSpecification)\n        } else {\n          inputIS.addFeatureSpecification(index, \"Numeric\" + index)\n        }\n        index += 1\n      }\n      if (lines.hasNext)\n        line = lines.next()\n      else\n        finished = true\n    }\n    val fSpecification: FeatureSpecification = inputIS(index - 1)\n    outputIS.addFeatureSpecification(0, \"class\", fSpecification)\n    inputIS.removeFeatureSpecification(index - 1)\n    new ExampleSpecification(inputIS, outputIS)\n\n  }\n\n  def isArffNumeric(t: String): Boolean = {\n    if (\"numeric\".equalsIgnoreCase(t)) true\n    else if (\"integer\".equalsIgnoreCase(t)) true\n    else if (\"real\".equalsIgnoreCase(t)) true\n    else false\n  }\n\n  def toCsv(spec: ExampleSpecification): String = {\n    //todo\n    \"\"\n  }\n\n  def fromCsv(fileName: String): ExampleSpecification = {\n    //todo\n    null\n  }\n\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/evaluation/BasicClassificationEvaluator.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.evaluation\n\nimport java.io.Serializable\n\nimport com.github.javacliparser.FlagOption\nimport org.apache.spark.internal.Logging\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\nimport com.github.javacliparser.FloatOption\nimport org.apache.spark.streamdm.core.Example\nimport org.apache.spark.streaming.dstream.DStream\n\n/**\n * The basic classification evaluator output metrics for both binary and\n * multiclass problems.\n * Input: testing Examples and doubles predicted by the learners.\n * Output:\n * (Binary) Accuracy, Recall, Precision, Fbeta-score,\n * Specificity, Confusion Matrix\n * (Multiclass) Accuracy, Recall-macro-avg, Precision-macro-avg,\n * Fbeta-score-macro-avg, Per class recall and precision (Optional),\n * Confusion Matrix (Optional)\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> beta (<b>-b</b>), representing the beta value for f-score calculation\n *  <li> supressPerClassMetrics (<b>-c</b>), when true prevent output of recall and precision per class\n *  <li> supressConfusionMatrix (<b>-m</b>), when true prevent output of the confusion matrix\n * </ul>\n */\nclass BasicClassificationEvaluator extends Evaluator with Logging {\n\n  val betaOption = new FloatOption(\"beta\", 'b',\n    \"Beta value for fbeta-score calculation.\", 1.0, Double.MinValue, Double.MaxValue)\n\n  val supressPerClassMetricsOption = new FlagOption(\"supressPerClassMetrics\",\n    'c', \"Do not output the per class precision and recall for multi-class problems.\")\n\n  val supressConfusionMatrixOption = new FlagOption(\"supressConfusionMatrix\",\n    'm', \"Do not output the confusion matrix.\")\n\n  override def setExampleSpecification(exampleSpecification: ExampleSpecification) = {\n    exampleLearnerSpecification = exampleSpecification\n  }\n\n  // This stores the time in ns for the original call to addResult\n  var startTime: Long = 0\n\n  /**\n   * Process the result of a predicted stream of Examples and Doubles.\n   * The second value of the tuple (Double) contains the predicted value.\n   * The first value contain the original Example.\n   *\n   * @param input the input stream containing (Example,Double) tuples\n   * @return a stream of String with the processed evaluation\n   */\n  override def addResult(input: DStream[(Example, Double)]): DStream[String] = {\n    this.startTime = System.nanoTime()\n\n    val numClasses = exampleLearnerSpecification.outputFeatureSpecification(0).range\n    if(numClasses == 2) {\n      val confusionMatrix = ConfusionMatrix.computeMatrix(input)\n      confusionMatrix.map(calculateMetricsBinary)\n    }\n    else {\n      val confusionMatrixMultiClass = ConfusionMatrixMultiClass.computeMatrix(input, numClasses)\n      confusionMatrixMultiClass.map(calculateMetricsMultiClass)\n    }\n  }\n\n  /**\n    * Calculate several evaluation metrics for binary classification based\n    * on a confusion matrix\n    * @param confMat the binary confusion matrix\n    * @return one string containing the metrics\n    */\n  def calculateMetricsBinary(confMat : Map[String,Double] ): String = {\n    val timeTaken = (System.nanoTime() - startTime)/1e+9 // divide by 1e+9 to obtain the estimation in seconds.\n    val accuracy = (confMat{\"tp\"}+confMat{\"tn\"})/(confMat{\"tp\"}+confMat{\"tn\"}+confMat{\"fp\"}+confMat{\"fn\"})\n    val recall = confMat{\"tp\"} / (confMat{\"tp\"}+confMat{\"fn\"})\n    val precision = confMat{\"tp\"} / (confMat{\"tp\"}+confMat{\"fp\"})\n    val specificity = confMat{\"tn\"} / (confMat{\"tn\"}+confMat{\"fp\"})\n    val f_beta_score = (1 + scala.math.pow(this.betaOption.getValue(),2)) * ((precision * recall) /\n      ((scala.math.pow(this.betaOption.getValue(),2) * precision) + recall))\n\n     \"%.3f,%.3f,%.3f,%.3f,%.3f,%.3f,%.0f,%.0f,%.0f,%.0f\".format(timeTaken, accuracy, recall, precision, f_beta_score, specificity,\n        confMat{\"tp\"}, confMat{\"fn\"}, confMat{\"fp\"}, confMat{\"tn\"})\n  }\n\n  /**\n    * Calculate several evaluation metrics for multiclass classification based\n    * on a confusion matrix\n    * @param confMat the multiclass confusion matrix\n    * @return one string containing the metrics\n    */\n  def calculateMetricsMultiClass(confMat : Map[(Int, Int), Double]): String = {\n    val timeTaken = (System.nanoTime() - startTime)/1e+9 // divide by 1e+9 to obtain the estimation in seconds.\n    val numClasses = exampleLearnerSpecification.outputFeatureSpecification(0).range\n    val instancesSeenBatch = confMat.values.reduce(_+_)\n    val accuracy = confMat.filter(t => t._1._1 == t._1._2).values.reduce(_+_) / instancesSeenBatch\n    val recallPerClass = calculateRecallMultiClass(confMat)\n    val precisionPerClass = calculatePrecisionMultiClass(confMat)\n    // Ignore NaN values while calculating the macro average recall and precision.\n    val recallMacro = recallPerClass.filter(!_.isNaN()).reduce((v,k) => v + k) / (numClasses - recallPerClass.count(_.isNaN))\n    val precisionMacro = precisionPerClass.filter(!_.isNaN()).reduce((v,k) => v + k) / (numClasses - precisionPerClass.count(_.isNaN))\n    val squaredBeta = scala.math.pow(this.betaOption.getValue(),2)\n    // Avoid division by zero in fscore\n    val fscoreMacro = if ((squaredBeta * precisionMacro + recallMacro) != 0) ((squaredBeta +1) * precisionMacro * recallMacro) / (squaredBeta * precisionMacro + recallMacro) else 0.0\n\n    var output = \"%.3f,%.3f,%.3f,%.3f,%.3f,\".format(timeTaken, accuracy, recallMacro, precisionMacro, fscoreMacro)\n\n    if(!this.supressPerClassMetricsOption.isSet()) {\n      val strRecallPerClass = recallPerClass.foldLeft(\"\")((r : String, c : Double) => r+\"%.4f,\".format(c))\n      val strPrecisionPerClass = precisionPerClass.foldLeft(\"\")((r : String, c : Double) => r+\"%.4f,\".format(c))\n      output += \"%s%s\".format(strRecallPerClass, strPrecisionPerClass)\n    }\n\n    if(!this.supressConfusionMatrixOption.isSet()) {\n      output += \"%s\".format(confMat.map( x => \"(%d %d)=%.1f\".format(x._1._1, x._1._2, x._2) ).mkString(\",\"))\n    }\n    output\n  }\n\n  /***\n    * Calculates recall for each class label.\n    * @param confMat confusion matrix (predicted groundtruth) => instance counter\n    * @return array containing the recall for each class label\n    */\n  def calculateRecallMultiClass(confMat : Map[(Int, Int), Double]): Array[Double] = {\n    val numClasses = exampleLearnerSpecification.outputFeatureSpecification(0).range\n    var recallPerClassIndex = Array.fill(numClasses){0.0}\n\n    for(groundTruth <- Range(0, numClasses)) {\n      var TPi = 0.0\n      var FNi = 0.0\n      for(predicted <- Range(0, numClasses)) {\n        if(predicted == groundTruth) {\n          TPi += confMat( (predicted, groundTruth) )\n        }\n        else {\n          FNi += confMat( (predicted, groundTruth) )\n        }\n      }\n      recallPerClassIndex(groundTruth) = TPi / (TPi + FNi)\n    }\n    recallPerClassIndex\n  }\n\n  /***\n    * Calculates precision for each class label.\n    * @param confMat confusion matrix (predicted groundtruth) => instance counter\n    * @return array containing the precision for each class label\n    */\n  def calculatePrecisionMultiClass(confMat : Map[(Int, Int), Double]): Array[Double] = {\n    val numClasses = exampleLearnerSpecification.outputFeatureSpecification(0).range\n    var precisionPerClassIndex = Array.fill(numClasses){0.0}\n\n    for(predicted <- Range(0, numClasses)) {\n      var TPi = 0.0\n      var FPi = 0.0\n\n      for(groundTruth <- Range(0, numClasses)) {\n        if(predicted == groundTruth) {\n          TPi += confMat( (predicted, groundTruth) )\n        }\n        else {\n          FPi += confMat( (predicted, groundTruth) )\n        }\n      }\n      precisionPerClassIndex(predicted) = TPi / (TPi + FPi)\n    }\n    precisionPerClassIndex\n  }\n\n  /**\n    * The header changes according to the number of classes.\n    * @return a String representing the measurements header\n    */\n  override def header(): String = {\n    val numClasses = exampleLearnerSpecification.outputFeatureSpecification(0).range\n    if(numClasses == 2) {\n      \"Runtime,Accuracy,Recall,Precision,F(beta=%.1f)-score,Specificity,TP,FN,FP,TN\".format(this.betaOption.getValue())\n    }\n    else {\n      var output = \"Runtime,Accuracy,Recall-avg-macro,Precision-avg-macro,F(beta=%.1f)-score-avg-macro,\".format(this.betaOption.getValue())\n      if(!this.supressPerClassMetricsOption.isSet()) {\n        val perClassRecall = Range(0, numClasses).foldLeft(\"\")( (s,i) => s + \"Recall(\" + i + \"),\")\n        val perClassPrecision = Range(0, numClasses).foldLeft(\"\")( (s,i) => s + \"Precision(\" + i + \"),\")\n        output += perClassRecall + perClassPrecision\n      }\n      if(!this.supressConfusionMatrixOption.isSet()) {\n        output += \"ConfusionMatrix(predicted ground-truth)\"\n      }\n      output\n    }\n  }\n}\n\n/**\n * Helper class for computing the confusion matrix for binary classification.\n */\nobject ConfusionMatrix extends Serializable with Logging {\n  def confusion(x: (Example,Double)):\n  Map[String, Double] = {\n    val tp = if ((x._1.labelAt(0)==x._2)&&(x._2==0.0)) 1.0 else 0.0\n    val fn = if ((x._1.labelAt(0)!=x._2)&&(x._2==0.0)) 1.0 else 0.0\n    val fp = if ((x._1.labelAt(0)!=x._2)&&(x._2==1.0)) 1.0 else 0.0\n    val tn = if ((x._1.labelAt(0)==x._2)&&(x._2==1.0)) 1.0 else 0.0\n    Map(\"tp\" -> tp, \"fn\" -> fn, \"fp\" -> fp, \"tn\" -> tn)\n  }\n\n  def computeMatrix(input: DStream[(Example,Double)]):\n  DStream[Map[String, Double]] =\n      input.map(x => confusion(x)).reduce( (x,y) =>\n        Map(\"tp\" -> (x{\"tp\"} + y{\"tp\"}),\n            \"fn\" -> (x{\"fn\"} + y{\"fn\"}),\n            \"fp\" -> (x{\"fp\"} + y{\"fp\"}),\n            \"tn\" -> (x{\"tn\"} + y{\"tn\"})))\n}\n\n/**\n  * Helper class for computing the confusion matrix for multi-class classification.\n  */\nobject ConfusionMatrixMultiClass extends Serializable {\n  def confusion(x: (Example,Double), numClasses: Int):\n  Map[(Int, Int), Double] = {\n    var outMat: Map[(Int, Int), Double] = Map()\n    for(predicted <- Range(0, numClasses)) {\n      for(groundTruth <- Range(0, numClasses)) {\n        if (x._2 == predicted && x._1.labelAt(0) == groundTruth)\n          outMat += (predicted, groundTruth) -> 1.0\n        else\n          outMat += (predicted, groundTruth) -> 0.0\n      }\n    }\n    outMat\n  }\n\n  def computeMatrix(input: DStream[(Example,Double)], numClasses: Int):\n  DStream[Map[(Int, Int), Double]] =\n    input.map(x => confusion(x, numClasses)).reduce( (x,y) =>\n      y ++ x.map{ case (k,v) => k -> (v + y.getOrElse(k,0.0))} )\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/evaluation/ClusteringEvaluator.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.evaluation\n\nimport math._\n\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streaming.dstream.DStream\nimport org.apache.spark.rdd.RDD\n\n/**\n * Clustering evaluator which computes, for each RDD of Example-Double tuples,\n * the clustering cohesion measure (SSE). This is the sum of square distances\n * between Examples and the centroid of their assigned cluster.\n */\nclass ClusteringCohesionEvaluator extends Evaluator {\n\n  /**\n   * Process the result of a predicted stream of Examples and Doubles.\n   *\n   * @param input the input stream containing (Example,Double) tuples\n   * @return a stream of String with the processed evaluation\n   */\n  override def addResult(input: DStream[(Example, Double)]): DStream[String] =\n    input.transform(rdd => {\n      val inv=rdd.map{case (e,c)=>(c,e)}\n      val centr = ClusteringEvaluationUtil.computeAllCentroids(rdd).\n                    map{case (k,c,s) => (k,c)}\n      inv.join(centr).map{case (k,(e,c))=>pow(e.in.distanceTo(c.in),2)}  \n    }).reduce(_+_).map(x=>\"SSE=%.5f\".format(x))\n}\n\n/**\n * Clustering evaluator which computes, for each RDD of Example-Double tuples,\n * the clustering separation measure (SSB). This is the sum of square distances\n * between cluster centroids and the overall data centroid.\n */\nclass ClusteringSeparationEvaluator extends Evaluator {\n\n  /**\n   * Process the result of a predicted stream of Examples and Doubles.\n   *\n   * @param input the input stream containing (Example,Double) tuples\n   * @return a stream of String with the processed evaluation\n   */\n  override def addResult(input: DStream[(Example, Double)]): DStream[String] =\n    input.transform(rdd => {\n      val inv=rdd.map{case (e,c) => (c,e)}\n      val centr = ClusteringEvaluationUtil.computeAllCentroids(rdd)\n      val sumAll=inv.map{case (c,e)=>(e,1)}.reduce((x,y)=>\n          (new Example(x._1.in.add(y._1.in)),x._2+y._2))\n      val centrAll = {\n        if(sumAll._2>1) new Example(sumAll._1.in.map(x=>x/sumAll._2))\n        else sumAll._1\n      }\n      centr.map{case (k,c,s)=>s*pow(c.in.distanceTo(centrAll.in),2)}\n    }).reduce(_+_).map(x=>\"SSB=%.5f\".format(x))\n}\n\n/**\n * Helper class which contains util functions for clustering evaluation.\n */\nobject ClusteringEvaluationUtil {\n\n  def computeAllCentroids(input: RDD[(Example,Double)]): \n                RDD[(Double,Example, Int)] =\n    input.map{case (e,c) => (c,e)}.map{case (c,e) => (c,Array(e))}.\n      reduceByKey((x,y) => x++y).map{case (c,e) => {\n        val clSize = e.length\n        val clSum = e.foldLeft(new Example(new NullInstance))(\n          (a,x) =>  a.in match {\n            case NullInstance() => new Example(x.in.map(x=>x))\n            case _ => new Example(a.in.add(x.in))\n          })        \n        if(clSize>1)\n          (c,new Example(clSum.in.map(x=>x/clSize)),clSize)\n        else\n          (c,clSum,1)\n      }}\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/evaluation/Evaluator.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.evaluation\n\nimport java.io.Serializable\n\nimport com.github.javacliparser.Configurable\nimport org.apache.spark.streamdm.core.Example\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\nimport org.apache.spark.streaming.dstream.DStream\n\n/**\n * Abstract class which defines the operations needed to evaluate learners.\n */\nabstract class Evaluator extends Configurable with Serializable{\n\n  var exampleLearnerSpecification: ExampleSpecification = null\n\n  def setExampleSpecification(exampleSpecification: ExampleSpecification) = {\n    exampleLearnerSpecification = exampleSpecification\n  }\n\n  /**\n   * Process the result of a predicted stream of Examples and Doubles.\n   *\n   * @param input the input stream containing (Example,Double) tuples\n   * @return a stream of String with the processed evaluation\n   */\n  def addResult(input: DStream[(Example, Double)]):  DStream[String]\n\n  /**\n    * Obtains the header definition\n    *\n    * @return a String representing the measurements header\n    */\n  def header(): String = {\n    \"\"\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/outlier/Outlier.scala",
    "content": "/*\n * Copyright (C) 2019 Télécom ParisTech LTCI lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.outlier\n\nimport org.apache.spark.streamdm.core.{Example, Learner}\nimport org.apache.spark.streaming.dstream.DStream\n\ntrait Outlier extends Learner {\n\n  /**\n    * The property or degree of being an outlier.\n    * Values closer to 1.0 indicate a higher outlierness. If the outlier method outputs binary predictions, i.e.,\n    * normal or outlier, then 0.0 => normal, 1.0 => outlier.\n    *\n    * @param input a data stream of examples\n    * @return the property or degree of being an outlier\n    */\n  def outlierness(input: DStream[Example]): DStream[(Example, Double)]\n\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/outlier/OutlierModel.scala",
    "content": "/*\n * Copyright (C) 2019 Télécom ParisTech LTCI lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.outlier\n\nimport org.apache.spark.streamdm.core.{Example, Model}\n\n/**\n  * The OutlierModel trait defines the needed operations for updating an outlier/anomaly\n  * detection model using a single Example.\n  */\ntrait OutlierModel extends Model {\n\n  type T <: OutlierModel\n\n  /**\n    * Update the outlier model structure, depending on the Example given.\n    *\n    * @param example the Example based on which the Model is updated\n    * @return the updated OutlierModel object\n    */\n  override def update(example: Example): T\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/outlier/SWNearestNeighbors.scala",
    "content": "/*\n * Copyright (C) 2019 Télécom ParisTech LTCI lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.outlier\n\nimport com.github.javacliparser.{FlagOption, IntOption}\nimport org.apache.spark.internal.Logging\nimport org.apache.spark.streamdm.core.Example\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\nimport org.apache.spark.streaming.dstream.DStream\n\nimport scala.collection.mutable.Queue\n\n/**\n  * The Sliding Window Nearest Neighbors (SWNearestNeighbors) is a global point anomaly detection technique.\n  * The intuition behind this method is that data points 'far' from the majority of the data points may be\n  * considered outliers (i.e., anomalies).\n  * The implementation uses a sliding window per cluster node.\n  * See [[org.apache.spark.streamdm.outlier.SWNearestNeighborsModel]].\n  */\nclass SWNearestNeighbors extends Outlier with Logging {\n\n  type T = SWNearestNeighborsModel\n\n  val windowMaximumSizeOption: IntOption = new IntOption(\"windowMaximumSize\", 'n',\n    \"maximum size of the sliding window\", 1000, 0, Int.MaxValue)\n\n  val debugOption: FlagOption = new FlagOption(\"debug\", 'd',\n    \"write debug information to the log as [INFO]\")\n\n  var model: T = null\n\n  override def init(exampleSpecification: ExampleSpecification): Unit = {\n    this.model = new SWNearestNeighborsModel(windowMaximumSizeOption.getValue(), debugOption.isSet())\n  }\n\n  /**\n    * This is an unsupervised outlier detection method, therefore there is no training\n    * @param input a stream of Examples\n    */\n  override def train(input: DStream[Example]): Unit = {\n  }\n\n  override def getModel: T = this.model\n\n  /**\n    * Calculates the outlierness score of the examples from the stream and store them.\n    * @param input a data stream of examples\n    * @return the 'outlierness' score\n    */\n  override def outlierness(input: DStream[Example]): DStream[(Example, Double)] = {\n    val inputDStreamCached = input.cache()\n\n    inputDStreamCached.map(e => {\n      val output = (e, this.model.outlierness(e))\n      this.model.update(e)\n      output\n    })\n  }\n}\n\nclass SWNearestNeighborsModel(\n                               val windowMaximumSize: Int,\n                               val debug: Boolean\n                             ) extends OutlierModel with Logging {\n\n  var window = new Queue[Example]()\n\n  type T = SWNearestNeighborsModel\n\n  /**\n    * Update the sliding window with the new example\n    * @param example the example added to the window\n    * @return the updated SWNearestNeighborsModel (one example added to its window).\n    */\n  override def update(example: Example): T = {\n    // If maximum size has been reached, remove the oldest instance.\n    if(window.size >= windowMaximumSize)\n      window.dequeue()\n    window.enqueue(example)\n    this\n  }\n\n  /**\n    * Calculates the global 'outlierness' score of an example.\n    * This function uses the build-in function distanceTo (Euclidean distance) from Instance.scala.\n    *\n    * @param example\n    * @return The 'outlierness' score of example\n    */\n  def outlierness(example: Example): Double = {\n    val distances = window.map(p => p.in.distanceTo(example.in))\n\n    if(!distances.isEmpty) {\n      val aggDistance = distances.reduce((d1, d2) => (d1 + d2)) / distances.size\n\n      if(debug)\n        logInfo(\"outlierness, %f, {%s}, %s, %d\".format(aggDistance,\n          example.in.getFeatureIndexArray().map(ins => ins._1).mkString(\";\"),\n          example.out.getFeatureIndexArray().map(ins => ins._1).mkString(\" \"),\n          distances.size))\n\n      aggDistance\n    } else {\n      0.0\n    }\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streamDMJob.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm\n\nimport org.apache.spark._\nimport org.apache.spark.streamdm.tasks.Task\nimport org.apache.spark.streaming._\nimport com.github.javacliparser.ClassOption\n\nimport scala.util.Try\n\n/**\n * The main entry point for testing StreamDM by running tasks on Spark\n * Streaming.\n */\nobject streamDMJob {\n\n  def main(args: Array[String]) {\n\n    //configuration and initialization of model\n    val conf = new SparkConf().setAppName(\"streamDM\")\n\n    var paramsArgs = args.clone()\n    var batchInterval: Int = 1000\n    if(args.length > 0){\n      val firstArg = args(0)\n      if(Try(firstArg.toInt).isSuccess){\n        if(firstArg.toInt > 0 && firstArg.toInt < Int.MaxValue){\n          batchInterval = firstArg.toInt\n        }\n        paramsArgs = paramsArgs.drop(1)\n      }\n    }\n\n    val ssc = new StreamingContext(conf, Milliseconds(batchInterval))\n\n    //run task\n    val string = if (paramsArgs.length > 0) paramsArgs.mkString(\" \")\n    else \"EvaluatePrequential\"\n    val task:Task = ClassOption.cliStringToObject(string, classOf[Task], null)\n    task.run(ssc)\n\n    //start the loop\n    ssc.start()\n    ssc.awaitTermination()\n  }\n}\n\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/FileReader.scala",
    "content": "/*\r\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\r\n *\r\n * Licensed under the Apache License, Version 2.0 (the \"License\");\r\n * you may not use this file except in compliance with the License.\r\n * You may obtain a copy of the License at\r\n *\r\n * http://www.apache.org/licenses/LICENSE-2.0\r\n *\r\n * Unless required by applicable law or agreed to in writing, software\r\n * distributed under the License is distributed on an \"AS IS\" BASIS,\r\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n * See the License for the specific language governing permissions and\r\n * limitations under the License.\r\n *\r\n */\r\n\r\npackage org.apache.spark.streamdm.streams\r\n\r\nimport java.io.File\r\nimport scala.io.Source\r\n\r\nimport org.apache.spark.internal.Logging\r\nimport org.apache.spark.rdd.RDD\r\nimport org.apache.spark.streaming.dstream.{ DStream, InputDStream }\r\nimport org.apache.spark.streaming.{ Time, Duration, StreamingContext }\r\n\r\nimport com.github.javacliparser.{ IntOption, StringOption}\r\n\r\nimport org.apache.spark.streamdm.core._\r\nimport org.apache.spark.streamdm.core.specification._\r\n\r\n/**\r\n  * FileReader is used to read data from one file of full data to simulate a stream data.\r\n  *\r\n  * <p>It uses the following options:\r\n  * <ul>\r\n  *  <li> Chunk size (<b>-k</b>)\r\n  *  <li> Slide duration in milliseconds (<b>-d</b>)\r\n  *  <li> Type of the instance to use, it should be \"dense\" or \"sparse\" (<b>-t</b>)\r\n  *  <li> Data File Name (<b>-f</b>)\r\n  *  <li> Data Header Format,uses weka's arff as default.(<b>-h</b>)\r\n  * </ul>\r\n  */\r\n\r\nclass FileReader extends StreamReader with Logging {\r\n\r\n  val chunkSizeOption: IntOption = new IntOption(\"chunkSize\", 'k',\r\n    \"Chunk Size\", 10000, 1, Integer.MAX_VALUE)\r\n\r\n  val slideDurationOption: IntOption = new IntOption(\"slideDuration\", 'd',\r\n    \"Slide Duration in milliseconds\", 100, 1, Integer.MAX_VALUE)\r\n\r\n  val instanceOption: StringOption = new StringOption(\"instanceType\", 't',\r\n    \"Type of the instance to use\", \"dense\")\r\n\r\n  val instanceLimitOption: IntOption = new IntOption(\"instanceLimit\", 'i',\r\n    \"Limit of number of instance\", 100000, 1, Integer.MAX_VALUE)\r\n\r\n  val fileNameOption: StringOption = new StringOption(\"fileName\", 'f',\r\n    \"File Name\", \"../data/hyperplanesampledata\")\r\n\r\n  val dataHeadTypeOption: StringOption = new StringOption(\"dataHeadType\", 'h',\r\n    \"Data Head Format\", \"arff\")\r\n\r\n  val headParser = new SpecificationParser\r\n  var fileName: String = null\r\n  var headFileName: String = null\r\n  var isInited: Boolean = false\r\n  var hasHeadFile: Boolean = false\r\n  var lines: Iterator[String] = null\r\n  var spec: ExampleSpecification = null\r\n  var counter: Int = 0\r\n\r\n  def init() {\r\n    if (!isInited) {\r\n      fileName = fileNameOption.getValue\r\n      val file = new File(fileName)\r\n      if (!file.exists()) {\r\n        logError(\"file does not exists, input a new file name\")\r\n        sys.exit()\r\n      }\r\n      headFileName = fileNameOption.getValue() + \".\" +\r\n        dataHeadTypeOption.getValue + \".head\"\r\n      val hfile: File = new File(headFileName)\r\n      if (hfile.exists()) {\r\n        // has a head file\r\n        hasHeadFile = true\r\n      }\r\n      spec = headParser.getSpecification(\r\n        if (hasHeadFile) headFileName else fileName, dataHeadTypeOption.getValue)\r\n      for (index <- 0 until spec.out(0).range()) {\r\n        logInfo(spec.out(0).asInstanceOf[NominalFeatureSpecification](index))\r\n      }\r\n\r\n      isInited = true\r\n    }\r\n  }\r\n\r\n  /**\r\n    * Obtains the specification of the examples in the stream.\r\n    *\r\n    * @return an ExampleSpecification of the features\r\n    */\r\n  override def getExampleSpecification(): ExampleSpecification = {\r\n    init()\r\n    spec\r\n  }\r\n\r\n  /**\r\n    * Get one Example from file\r\n    *\r\n    * @return an Example\r\n    */\r\n  def getExampleFromFile(): Example = {\r\n    var exp: Example = null\r\n    // start to read file from its beginning.\r\n    if (lines == null || !lines.hasNext) {\r\n      //get the whole file\r\n      lines = Source.fromFile(fileName).getLines()\r\n    }\r\n    // if reach the end of file, will go to the head again\r\n    if (!lines.hasNext) {\r\n      exp\r\n    }\r\n    var line = lines.next()\r\n    while (!hasHeadFile && (line == \"\" || line.startsWith(\" \") ||\r\n      line.startsWith(\"%\") || line.startsWith(\"@\"))) {\r\n      line = lines.next()\r\n    }\r\n    if (!hasHeadFile) {\r\n      if (\"arff\".equalsIgnoreCase(dataHeadTypeOption.getValue())) {\r\n        exp = ExampleParser.fromArff(line, spec)\r\n      } else {\r\n        if (\"csv\".equalsIgnoreCase(dataHeadTypeOption.getValue())) {\r\n          //for the csv format, we assume the first is the classification\r\n          val index: Int = line.indexOf(\",\")\r\n          line = line.substring(0, index) + \" \" + line.substring(index + 1).trim()\r\n          exp = Example.parse(line, instanceOption.getValue, \"dense\")\r\n        }\r\n      }\r\n    } else {\r\n      exp = Example.parse(line, instanceOption.getValue, \"dense\")\r\n    }\r\n    exp\r\n  }\r\n\r\n  /**\r\n    * Obtains a stream of examples.\r\n    *\r\n    * @param ssc a Spark Streaming context\r\n    * @return a stream of Examples\r\n    */\r\n  override def getExamples(ssc: StreamingContext): DStream[Example] = {\r\n    init()\r\n    new InputDStream[Example](ssc) {\r\n      override def start(): Unit = {\r\n        logInfo(\"File reading gets started.\")\r\n      }\r\n\r\n      override def stop(): Unit = {\r\n        logInfo(\"Reading file stopped.\")\r\n      }\r\n\r\n      override def compute(validTime: Time): Option[RDD[Example]] = {\r\n        val examples: Array[Example] = Array.fill[Example](chunkSizeOption.getValue)(getExampleFromFile())\r\n        val examplesRDD = ssc.sparkContext.parallelize(examples)\r\n        // stop the stream when it gets over N instances.\r\n        counter = counter + 1\r\n        val limit = instanceLimitOption.getValue/ chunkSizeOption.getValue\r\n        if(counter > limit){\r\n          logInfo(\"Over limit instances. STOP!\" )\r\n          ssc.stop(stopSparkContext = false, stopGracefully = false)\r\n        }\r\n        Some(examplesRDD)\r\n      }\r\n\r\n      override def slideDuration = {\r\n        new Duration(slideDurationOption.getValue)\r\n      }\r\n    }\r\n  }\r\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/KafkaReader.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.streams\n\nimport com.github.javacliparser.StringOption\nimport kafka.serializer.StringDecoder\nimport org.apache.spark.streamdm.core.Example\nimport org.apache.spark.streamdm.core.specification.{ExampleSpecification, InstanceSpecification, NominalFeatureSpecification}\nimport org.apache.spark.streaming.StreamingContext\nimport org.apache.spark.streaming.dstream.DStream\nimport org.apache.spark.streaming.kafka.KafkaUtils\n\n/**\n  * Stream reader that gets instances from kafka.\n  *\n  * <p>It uses the following options:\n  * <ul>\n  *  <li> kafka brokers (<b>-b</b>)\n  *  <li> kafka topics (<b>-p</b>)\n  *  <li> Instance type (<b>-t</b>), either <i>dense</i> or <i>sparse</i>\n  * </ul>\n  */\nclass KafkaReader extends StreamReader{\n\n  /** kafka brokers */\n  val brokersOption: StringOption = new StringOption(\"brokers\", 'b', \"kafka brokers\", \"unset\")\n  /** topics name */\n  val topicsOption: StringOption = new StringOption(\"topics\", 'p', \"topics name\", \"unset\")\n  /** data format*/\n  val instanceOption: StringOption = new StringOption(\"instanceType\", 't', \"Type of the instance to use\", \"dense\")\n\n  /**\n    * Obtains a stream of examples.\n    *\n    * @param ssc a Spark Streaming context\n    * @return a stream of Examples\n    */\n  override def getExamples(ssc: StreamingContext): DStream[Example] = {\n    val brokers = brokersOption.getValue\n    val topics = topicsOption.getValue\n\n    assert(!(brokers+topics).contains(\"unset\"),\"brokers or topics should be set\")\n\n    val kafkaParams = Map[String, String](\"metadata.broker.list\" -> brokers)\n    val topicMap = topics.split(\",\").toSet\n\n    KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc,kafkaParams,topicMap)\n      .map(x=>Example.parse(x._2, instanceOption.getValue, \"dense\"))\n\n  }\n\n  /**\n    * Obtains the specification of the examples in the stream.\n    *\n    * @return an ExampleSpecification of the features\n    */\n  override def getExampleSpecification(): ExampleSpecification = {\n\n    //Prepare specification of class attributes\n    val outputIS = new InstanceSpecification()\n    val classFeature = new NominalFeatureSpecification(Array(\"+\", \"-\"))\n    outputIS.addFeatureSpecification(0, \"class\", classFeature)\n\n    new ExampleSpecification(new InstanceSpecification(), outputIS)\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/PrintStreamWriter.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.streams\n\nimport org.apache.spark.streaming.dstream.DStream\n\n/**\n * Stream writer that outputs the stream to the standard output.\n */\nclass PrintStreamWriter extends StreamWriter{\n\n  /**\n   * Process the output.\n   * <p>\n   * In the PrintStreamWriter, each String in the stream is sent to stdout.\n   * \n   * @param stream a DStream of Strings for which the output is processed \n   */\n  def output(stream: DStream[String]) = {\n    stream.foreachRDD(rdd => {\n      rdd.collect().foreach(x => println(x))\n    })\n  }\n\n  /**\n    * Prints a single line of text to the output only once.\n    * @param text\n    */\n  override def output(text: String) = println(text)\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/SampleDataWriter.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.streams\n\nimport java.io._\n\nimport com.github.javacliparser.{ Configurable, IntOption, StringOption, ClassOption }\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.core.specification._\nimport org.apache.spark.streamdm.streams.generators._\n\n/**\n * FileWriter use generator to generate data and save to file or HDFS for simulation or test.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Chunk number (<b>-n</b>)\n *  <li> File Name (<b>-f</b>)\n *  <li> Data Head Type, default is \"arff\" (<b>-f</b>)\n *  <li> Generator (<b>-g</b>)\n * </ul>\n */\n\nclass FileWriter extends Configurable with Serializable {\n\n  val chunkNumberOption: IntOption = new IntOption(\"chunkNumber\", 'n',\n    \"Number of chunks to be generated\", 10, 1, Integer.MAX_VALUE)\n\n  val fileNameOption: StringOption = new StringOption(\"fileName\", 'f',\n    \"File Name\", \"./sampleData\")\n\n  val dataHeadTypeOption: StringOption = new StringOption(\"dataHeadType\", 'h',\n    \"Data Head Format\", \"arff\")\n\n  val generatorOption: ClassOption = new ClassOption(\"generator\", 'g',\n    \"generator to use\", classOf[Generator], \"RandomRBFGenerator\")\n\n  val headParser = new SpecificationParser\n\n  var generator: Generator = null\n\n  var headType: String = \"arff\"\n  /**\n   * writes sample data to file or HDFS file\n   */\n  def write(): Unit = {\n    generator = generatorOption.getValue()\n    headType = dataHeadTypeOption.getValue()\n    if (generator != null)\n      write(fileNameOption.getValue, chunkNumberOption.getValue)\n  }\n  /**\n   * writes sample data to file or HDFS file\n   * @param fileName file name to be stored\n   * @param chunkSize chunk size of RDD\n   * @param chunkNumber chunk number would be stored\n   *\n   * @return Unit\n   */\n  private def write(fileName: String, chunkNumber: Int): Unit = {\n    if (fileName.startsWith(\"HDFS\")) writeToHDFS(fileName, chunkNumber)\n    else writeToFile(fileName, chunkNumber)\n  }\n\n  /**\n   * writes sample data to file\n   * @param fileName file name to be stored\n   * @param chunkSize chunk size of RDD\n   * @param chunkNumber chunk number would be stored\n   *\n   * @return Unit\n   */\n  private def writeToFile(fileName: String, chunkNumber: Int): Unit = {\n\n    val headFile: File = new File(fileName + \".\" + headType + \".head\")\n    val headWriter = new PrintWriter(headFile)\n    val file: File = new File(fileName)\n    val writer = new PrintWriter(file)\n    //   val writerArrf = new PrintWriter(fileArrf)\n    try {\n      //write to head file\n      val head = headParser.getHead(generator.getExampleSpecification(), headType)\n      headWriter.write(head)\n      //write to data file\n      for (i <- 0 until chunkNumber) {\n        //println(i)\n        val examples: Array[Example] = generator.getExamples()\n        val length: Int = examples.length\n        var str: String = new String\n        for (i <- 0 until length) {\n          str = examples(i).toString()\n          writer.append(str + \"\\n\")\n          writer.flush()\n        }\n      }\n    } finally {\n      headWriter.close()\n      writer.close()\n    }\n  }\n\n  /**\n   * writes sample data to HDFS file\n   * @param fileName file name to be stored\n   * @param chunkSize chunk size of RDD\n   * @param chunkNumber chunk number would be stored\n   *\n   * @return Unit\n   */\n  private def writeToHDFS(fileName: String, chunkNumber: Int): Unit = {\n    //todo\n  }\n\n}\n\nobject SampleDataWriter {\n\n  def main(args: Array[String]) {\n    var params = \"FileWriter -n 10 -f ./rbfevents2 -g (RandomRBFEventsGenerator -f 3)\"\n    if (args.length > 2) {\n      params = args.mkString(\" \")\n    }\n    try {\n      println(params)\n      val writer: FileWriter = ClassOption.cliStringToObject(params, classOf[FileWriter], null)\n      writer.write()\n    } catch {\n      case e: Exception => e.printStackTrace()\n    }\n  }\n\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/SocketTextStreamReader.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.streams\n\nimport com.github.javacliparser.{ StringOption, IntOption }\nimport org.apache.spark.streamdm.core.specification._\nimport org.apache.spark.streamdm.core.Example\nimport org.apache.spark.streaming.StreamingContext\nimport org.apache.spark.streaming.dstream.DStream\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n * Stream reader that gets instances from a socket stream.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Socket host (<b>-h</b>)\n *  <li> Socket port (<b>-p</b>)\n *  <li> Instance type (<b>-t</b>), either <i>dense</i> or <i>sparse</i>\n * </ul>\n */\nclass SocketTextStreamReader extends StreamReader {\n\n  val portOption: IntOption = new IntOption(\"port\", 'p',\n    \"Socket port\", 9999, 0, Integer.MAX_VALUE)\n\n  val hostOption: StringOption = new StringOption(\"host\", 'h', \"Host\",\n    \"localhost\")\n\n  val instanceOption: StringOption = new StringOption(\"instanceType\", 't',\n    \"Type of the instance to use\", \"dense\")\n\n  /**\n   * Obtains a stream of examples.\n   *\n   * @param ssc a Spark Streaming context\n   * @return a stream of Examples\n   */\n  def getExamples(ssc: StreamingContext): DStream[Example] = {\n    //stream is a localhost socket stream\n    val text = ssc.socketTextStream(hostOption.getValue, portOption.getValue)\n    //transform stream into stream of instances\n    //instances come as whitespace delimited lines, where the first item is the\n    //instance of the label(s) and the second is the instance of the features\n    text.map(x => Example.parse(x, instanceOption.getValue, \"dense\"))\n  }\n\n  /**\n   * Obtains the specification of the examples in the stream.\n   *\n   * @return an ExampleSpecification of the features\n   */\n  override def getExampleSpecification(): ExampleSpecification = {\n\n    //Prepare specification of class attributes\n    val outputIS = new InstanceSpecification()\n    val classFeature = new NominalFeatureSpecification(Array(\"+\", \"-\"))\n    outputIS.addFeatureSpecification(0, \"class\", classFeature)\n\n    new ExampleSpecification(new InstanceSpecification(),\n      outputIS)\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/StreamReader.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.streams\n\nimport org.apache.spark.streamdm.core.Example\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\nimport org.apache.spark.streaming.StreamingContext\nimport org.apache.spark.streaming.dstream.DStream\nimport com.github.javacliparser.Configurable\n\n/**\n * Abstract class for outputing a DStream of Examples to be used \n * inside tasks.\n */\nabstract class StreamReader extends Configurable {\n  /**\n   * Obtains a stream of examples.\n   *\n   * @param ssc a Spark Streaming context\n   * @return a stream of Examples\n   */\n  def getExamples(ssc:StreamingContext): DStream[Example]\n\n  /**\n   * Obtains the specification of the examples in the stream.\n   *\n   * @return an ExampleSpecification of the features\n   */\n  def getExampleSpecification(): ExampleSpecification\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/StreamWriter.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.streams\n\nimport org.apache.spark.streaming.dstream.DStream\nimport com.github.javacliparser.Configurable\n\n/**\n * Abstract class that defines the output from a DStream of Strings.\n */\nabstract class StreamWriter extends Configurable {\n  /**\n   * Process the output.\n   * \n   * @param stream a DStream of Strings for which the output is processed \n   */\n  def output(stream: DStream[String]): Unit\n\n  /**\n    * Writes a single line on the output. Useful for writing one-time text, such as headers.\n    *\n    * @param text\n    */\n  def output(text: String): Unit\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/TextStreamReader.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.streams\n\nimport com.github.javacliparser._\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.core.specification._\nimport org.apache.spark.streaming.StreamingContext\nimport org.apache.spark.streaming.dstream.DStream\nimport org.apache.spark.streamdm.core.Instance\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n * Stream reader that gets instances from a file stream.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> File location (<b>-f</b>)\n *  <li> Instance type (<b>-t</b>), either <i>dense</i> or <i>sparse</i>\n * </ul>\n */\nclass TextStreamReader extends StreamReader{\n\n  val fileOption: StringOption = new StringOption(\"file\", 'f',\n    \"Directory where the files are\", \".\")\n\n  val instanceOption: StringOption = new StringOption(\"instanceType\", 't',\n    \"Type of the instance to use\", \"dense\")\n\n  /**\n   * Obtains a stream of examples.\n   *\n   * @param ssc a Spark Streaming context\n   * @return a stream of Examples\n   */\n  def getExamples(ssc:StreamingContext): DStream[Example] = {\n    //stream is a localhost socket stream\n    val text = ssc.textFileStream(fileOption.getValue)\n    //transform stream into stream of instances\n    //instances come as whitespace delimited lines, where the first item is the\n    //instance of the label(s) and the second is the instance of the features\n    text.map(x => Example.parse(x, instanceOption.getValue, \"dense\"))\n  }\n\n  /**\n   * Obtains the specification of the examples in the stream.\n   *\n   * @return an ExampleSpecification of the features\n   */\n  def getExampleSpecification(): ExampleSpecification = new ExampleSpecification(\n    new InstanceSpecification(), new InstanceSpecification())\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/generators/Cluster.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.streams.generators\n\nimport scala.collection.mutable.HashMap\nimport org.apache.spark.streamdm.core.Instance\nimport scala.util.Random\n\n\nabstract class Cluster {\n\n  private var id: Double = -1\n  private var gtLabel: Double = -1\n\n  private var measure_values: HashMap[String, String] = _\n\n  def Cluster() {\n    measure_values = new HashMap[String, String]()\n  }\n  /**\n   * @return the center of the cluster\n   */\n  def getCenter(): Array[Double]\n\n  /**\n   * Returns the weight of this cluster, not neccessarily normalized.\n   * It could, for instance, simply return the number of points contined\n   * in this cluster.\n   * @return the weight\n   */\n  def getWeight(): Double\n\n  /**\n   * Returns the probability of the given point belonging to\n   * this cluster.\n   *\n   * @param instance\n   * @return a value between 0 and 1\n   */\n  def getInclusionProbability(instance: Instance): Double\n\n  def setId(id: Double): Unit = {\n    this.id = id\n  }\n\n  def getId(): Double = {\n    this.id\n  }\n\n  def isGroundTruth(): Boolean = {\n    gtLabel != -1\n  }\n\n  def setGroundTruth(truth: Double): Unit = {\n    gtLabel = truth\n  }\n\n  def getGroundTruth(): Double = {\n    gtLabel\n  }\n\n  /**\n   * Samples this cluster by returning a point from inside it.\n   * @param random a random number source\n   * @return an Instance that lies inside this cluster\n   */\n  def sample(random: Random): Instance\n\n  def setMeasureValue(measureKey: String, value: String) {\n    measure_values.put(measureKey, value)\n  }\n\n  def setMeasureValue(measureKey: String, value: Double) {\n    measure_values.put(measureKey, value.toString)\n  }\n\n  def getMeasureValue(measureKey: String): String = {\n    measure_values.getOrElse(measureKey, \"\")\n  }\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/generators/Generator.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.streams.generators\n\nimport org.apache.spark.rdd.RDD\nimport org.apache.spark.streaming.{ Duration, Time, StreamingContext }\nimport org.apache.spark.streaming.dstream.{ InputDStream, DStream }\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.streams.StreamReader\n\nabstract class Generator extends StreamReader {\n\n  /**\n   * returns chunk size\n   */\n  def getChunkSize(): Int\n\n  /**\n   * returns slide duration\n   */\n  def getslideDuration(): Int\n\n  /**\n   * initializes the generator\n   */\n  def init(): Unit\n  // call initialization\n  \n   var inited:Boolean = false\n  \n  /**\n   * generates a random example.\n   *\n   * @return a random example\n   */\n\n  def getExample(): Example\n\n  /**\n   * Obtains a stream of random examples.\n   *\n   * @param ssc a Spark Streaming context\n   * @return a stream of Examples\n   */\n  def getExamples(ssc: StreamingContext): DStream[Example] = {\n    init()\n    new InputDStream[Example](ssc) {\n\n      override def start(): Unit = {}\n\n      override def stop(): Unit = {}\n\n      override def compute(validTime: Time): Option[RDD[Example]] = {\n        Some(ssc.sparkContext.parallelize(getExamples()))\n      }\n      override def slideDuration = {\n        new Duration(getslideDuration)\n      }\n    }\n  }\n\n  /**\n   * obtains an array of examples\n   *\n   * @param length size of the array; default chunkSizeOption.getValue\n   * @return an array of Examples\n   */\n  def getExamples(length: Int = getChunkSize()): Array[Example] = {\n    init()\n    Array.fill[Example](length)(getExample())\n  }\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/generators/HyperplaneGenerator.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.streams.generators\n\nimport com.github.javacliparser.IntOption\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.core.specification._\nimport scala.util.Random\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n * Stream generator for generating data from a hyperplane.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Chunk size (<b>-k</b>)\n *  <li> Slide duration in milliseconds (<b>-d</b>)\n *  <li> Number of features (<b>-f</b>)\n * </ul>\n */\n\nclass HyperplaneGenerator extends Generator {\n\n  val chunkSizeOption: IntOption = new IntOption(\"chunkSize\", 'k',\n    \"Chunk Size\", 1000, 1, Integer.MAX_VALUE)\n\n  val slideDurationOption: IntOption = new IntOption(\"slideDuration\", 'd',\n    \"Slide Duration in milliseconds\", 1000, 1, Integer.MAX_VALUE)\n\n  val numFeaturesOption: IntOption = new IntOption(\"numFeatures\", 'f',\n    \"Number of Features\", 3, 1, Integer.MAX_VALUE)\n\n  /**\n   * returns chunk size\n   */\n  override def getChunkSize(): Int = {\n    chunkSizeOption.getValue\n  }\n\n  /**\n   * returns slide duration\n   */\n  override def getslideDuration(): Int = {\n    slideDurationOption.getValue\n  }\n\n  def init(): Unit = {}\n\n  def getExample(): Example = {\n    val inputInstance = new DenseInstance(Array.fill[Double](\n      numFeaturesOption.getValue)(5.0 * getRandomNumber()))\n    val noiseInstance = new DenseInstance(Array.fill[Double](\n      numFeaturesOption.getValue)(getNoise()))\n    new Example(inputInstance.add(noiseInstance), new DenseInstance(\n      Array.fill[Double](1)(label(inputInstance))))\n  }\n\n  def getRandomNumber(): Double = 2.0 * Random.nextDouble() - 1.0\n\n  def getNoise(): Double = 0.5 * Random.nextGaussian()\n\n  val weight = new DenseInstance(Array.fill[Double](\n    numFeaturesOption.getValue)(getRandomNumber()))\n\n  val bias: Double = getRandomNumber()\n\n  def label(inputInstance: Instance): Double = {\n    val sum = weight.dot(inputInstance)\n    if (sum > bias) 1\n    else 0\n  }\n\n  /**\n   * Obtains the specification of the examples in the stream.\n   *\n   * @return an ExampleSpecification of the examples\n   */\n  override def getExampleSpecification(): ExampleSpecification = {\n\n    //Prepare specification of class attributes\n    val outputIS = new InstanceSpecification()\n    val classFeature = new NominalFeatureSpecification(Array(\"false\", \"true\"))\n    outputIS.addFeatureSpecification(0, \"class\", classFeature)\n\n    //Prepare specification of input attributes\n    val inputIS = new InstanceSpecification()\n    for (i <- 0 until numFeaturesOption.getValue)\n      inputIS.addFeatureSpecification(i, \"NumericFeature\" + i)\n\n    new ExampleSpecification(inputIS, outputIS)\n  }\n\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/generators/RandomRBFEventsGenerator.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\")\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.streams.generators\n\nimport com.github.javacliparser.{ IntOption, FloatOption, FlagOption }\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.core.specification._\nimport scala.collection.mutable.ArrayBuffer\nimport scala.util.Random\nimport scala.math._\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\n\n/**\n * RandomRBFEventsGenerator generates data stream for Clustream  via a random radial basis function.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Chunk size (<b>-k</b>)\n *  <li> Slide duration (<b>-d</b>)\n *  <li> Seed for random generation of model (<b>-m</b>)\n *  <li> Seed for random generation of instances (<b>-i</b>)\n *  <li> The average number of centroids in the model (<b>-C</b>)\n *  <li> Deviation of the number of centroids in the model (<b>-c</b>)\n *  <li> The average radii of the centroids in the model (<b>-R</b>)\n *  <li> Deviation of average radii of the centroids in the model (<b>-r</b>)\n *  <li> Offset of the average weight a cluster has(<b>-D</b>)\n *  <li> Kernels move a predefined distance of 0.01 every X points (<b>-V</b>)\n *  <li> Speed/Velocity point offset (<b>-v</b>)\n *  <li> Noise level (<b>-N</b>)\n *  <li> Allow noise to be placed within a cluster(<b>-n</b>)\n *  <li> Event frequency (<b>-E</b>)\n *  <li> Enable merging and splitting of clusters(<b>-M</b>)\n *  <li> Enable emerging and disappearing of clusters (<b>-e</b>)\n *  <li> The number of features to generate (<b>-f</b>)\n *  <li> Decay horizon (<b>-h</b>)\n * </ul>\n */\n\nclass RandomRBFEventsGenerator extends Generator {\n\n  val chunkSizeOption: IntOption = new IntOption(\"chunkSize\", 'k',\n    \"Chunk Size\", 50, 1, Integer.MAX_VALUE)\n\n  val slideDurationOption: IntOption = new IntOption(\"slideDuration\", 'd',\n    \"Slide Duration in milliseconds\", 1000, 1, Integer.MAX_VALUE)\n\n  val modelRandomSeedOption: IntOption = new IntOption(\"modelRandomSeed\",\n    'm', \"Seed for random generation of model.\", 1)\n\n  val instanceRandomSeedOption: IntOption = new IntOption(\"instanceRandomSeed\", 'i',\n    \"Seed for random generation of instances.\", 5)\n\n  val numClusterOption: IntOption = new IntOption(\"numCluster\", 'C',\n    \"The average number of centroids in the model.\", 5, 1, Integer.MAX_VALUE)\n\n  val numClusterRangeOption: IntOption = new IntOption(\"numClusterRange\", 'c',\n    \"Deviation of the number of centroids in the model.\", 3, 0, Integer.MAX_VALUE)\n\n  val kernelRadiiOption: FloatOption = new FloatOption(\"kernelRadius\", 'R',\n    \"The average radii of the centroids in the model.\", 0.07, 0, 1)\n\n  val kernelRadiiRangeOption: FloatOption = new FloatOption(\"kernelRadiusRange\", 'r',\n    \"Deviation of average radii of the centroids in the model.\", 0, 0, 1)\n\n  val densityRangeOption: FloatOption = new FloatOption(\"densityRange\", 'D',\n    \"Offset of the average weight a cluster has. Value of 0 means all cluster \" +\n      \"contain the same amount of points.\", 0, 0, 1)\n\n  val speedOption: IntOption = new IntOption(\"speed\", 'V',\n    \"Kernels move a predefined distance of 0.01 every X points\", 500, 1, Integer.MAX_VALUE)\n\n  val speedRangeOption: IntOption = new IntOption(\"speedRange\", 'v',\n    \"Speed/Velocity point offset\", 0, 0, Integer.MAX_VALUE)\n\n  val noiseLevelOption: FloatOption = new FloatOption(\"noiseLevel\", 'N',\n    \"Noise level\", 0.1, 0, 1)\n\n  val noiseInClusterOption: FlagOption = new FlagOption(\"noiseInCluster\", 'n',\n    \"Allow noise to be placed within a cluster\")\n\n  val eventFrequencyOption: IntOption = new IntOption(\"eventFrequency\", 'E',\n    \"Event frequency. Enable at least one of the events below and set numClusterRange!\", 30000, 0, Integer.MAX_VALUE)\n\n  val eventMergeSplitOption: FlagOption = new FlagOption(\"eventMergeSplitOption\", 'M',\n    \"Enable merging and splitting of clusters. Set eventFrequency and numClusterRange!\")\n\n  val eventDeleteCreateOption: FlagOption = new FlagOption(\"eventDeleteCreate\", 'e',\n    \"Enable emering and disapperaing of clusters. Set eventFrequency and numClusterRange!\")\n\n  val numFeaturesOption: IntOption = new IntOption(\"numFeatures\", 'f',\n    \"The number of attributes to generate.\", 2, 0, Integer.MAX_VALUE)\n\n  val decayHorizonOption: IntOption = new IntOption(\"decayHorizon\", 'h',\n    \"Decay horizon\", 1000, 0, Integer.MAX_VALUE)\n\n  val merge_threshold = 0.7\n  val kernelMovePointFrequency = 10\n  val maxDistanceMoveThresholdByStep = 0.01\n  val maxOverlapFitRuns = 50\n  val eventFrequencyRange = 0\n  var kernels: ArrayBuffer[GeneratorCluster] = null\n  var instanceRandom: Random = null\n  var numGeneratedInstances: Int = 0\n  var numActiveKernels: Int = 0\n  var nextEventCounter: Int = 0\n  var nextEventChoice = -1\n  var clusterIdCounter: Int = 0\n  var mergeClusterA: GeneratorCluster = null\n  var mergeClusterB: GeneratorCluster = null\n  var mergeKernelsOverlapping = false\n\n  /**\n   * A GeneratorCluster represents the cluster which contains\n   *  some MicroCluster(represented by SphereCluster) in Clustream\n   *  Algorithm.\n   */\n  class GeneratorCluster {\n\n    var generator: SphereCluster = null\n    var kill: Int = -1\n    var merging = false\n    var moveVector = new Array[Double](numFeaturesOption.getValue())\n    var totalMovementSteps: Int = 0\n    var currentMovementSteps: Int = 0\n    var isSplitting = false\n    val microClusters = new ArrayBuffer[SphereCluster]()\n    val microClustersDecay = new ArrayBuffer[Int]()\n\n    def this(label: Int) {\n      this()\n      var outofbounds = true\n      var tryCounter = 0\n      while (outofbounds && tryCounter < maxOverlapFitRuns) {\n        tryCounter = tryCounter + 1\n        outofbounds = false\n        val center = new Array[Double](numFeaturesOption.getValue())\n        var radius = kernelRadiiOption.getValue() + (if (instanceRandom.nextBoolean()) -1 else 1) *\n          kernelRadiiRangeOption.getValue() * instanceRandom.nextDouble()\n        while (radius <= 0) {\n          radius = kernelRadiiOption.getValue() + (if (instanceRandom.nextBoolean()) -1 else 1) *\n            kernelRadiiRangeOption.getValue() * instanceRandom.nextDouble()\n        }\n        for (j <- 0 until numFeaturesOption.getValue() if !outofbounds) {\n          center(j) = instanceRandom.nextDouble()\n          if (center(j) - radius < 0 || center(j) + radius > 1) {\n            outofbounds = true\n          }\n        }\n        generator = new SphereCluster(center, radius)\n      }\n      if (tryCounter < maxOverlapFitRuns) {\n        generator.setId(label)\n        val avgWeight = 1.0 / numClusterOption.getValue()\n        val weight = avgWeight + (if (instanceRandom.nextBoolean()) -1 else 1) * avgWeight *\n          densityRangeOption.getValue() * instanceRandom.nextDouble()\n        generator.setWeight(weight)\n        setDesitnation(null)\n      } else {\n        generator = null\n        kill = 0\n        //println(\"Tried \" + maxOverlapFitRuns + \" times to create kernel. Reduce average radii.\")\n      }\n    }\n\n    def this(label: Int, cluster: SphereCluster) {\n      this()\n      this.generator = cluster\n      this.generator.setId(label)\n      setDesitnation(null)\n    }\n\n    /* *\n     *  update the cluster(remove the invalid microcluster. etc.)\n     */\n    def updateKernel(): Unit = {\n      if (kill == 0) {\n        var flag = true\n        for (i <- 0 until kernels.length if flag) {\n          if (kernels(i) == this) {\n            kernels.remove(i)\n            flag = false\n          }\n        }\n      }\n      if (kill > 0) {\n        kill = kill - 1\n      }\n      //we could be lot more precise if we would keep track of timestamps of points\n      //then we could remove all old points and rebuild the cluster on up to date point base\n      //BUT worse the effort??? so far we just want to avoid overlap with this, so its more\n      //konservative as needed. Only needs to change when we need a thighter representation\n      for (m <- 0 until microClusters.length) {\n        if (numGeneratedInstances - microClustersDecay(m) > decayHorizonOption.getValue()) {\n          microClusters.remove(m)\n          microClustersDecay.remove(m)\n        }\n      }\n    }\n\n    /* *\n   * set the move-path for the cluster \n   *\n   * @param destination_t move-destination Array\n   */\n    def setDesitnation(destination_t: Array[Double]): Unit = {\n\n      var destination: Array[Double] = null\n      if (destination_t == null) {\n        destination = Array.fill[Double](numFeaturesOption.getValue())(instanceRandom.nextDouble())\n      } else {\n        destination = destination_t\n      }\n      val center = generator.getCenter()\n      val dim = center.length\n\n      for (d <- 0 until dim) {\n        moveVector(d) = destination(d) - center(d)\n      }\n      var speedInPoints = speedOption.getValue()\n      if (speedRangeOption.getValue() > 0)\n        speedInPoints = speedInPoints + (if (instanceRandom.nextBoolean()) -1 else 1) *\n          instanceRandom.nextInt(speedRangeOption.getValue())\n      if (speedInPoints < 1) speedInPoints = speedOption.getValue()\n\n      var length: Double = moveVector.foldLeft(0.0)((sum, i) => sum + pow(i, 2))\n      length = Math.sqrt(length)\n\n      totalMovementSteps = (length / (maxDistanceMoveThresholdByStep * kernelMovePointFrequency) * speedInPoints).toInt\n      moveVector = moveVector.map { v => v / totalMovementSteps.toDouble }\n      currentMovementSteps = 0\n    }\n\n    /* *\n    * try to merge two cluster.\n    * \n    * @param merge the Cluster want to be merged\n    * @return info string for merging\n    */\n    def tryMerging(merge: GeneratorCluster): String = {\n      var message = \"\"\n      val overlapDegree: Double = generator.overlapRadiusDegree(merge.generator)\n      if (overlapDegree > merge_threshold) {\n        val mcluster = merge.generator\n        val radius = Math.max(generator.getRadius(), mcluster.getRadius())\n        generator.combine(mcluster)\n        //adjust radius, get bigger and bigger with high dim data\n        generator.setRadius(radius)\n        message = \"Clusters merging: \" + mergeClusterB.generator.getId() + \" into \" + mergeClusterA.generator.getId()\n\n        //clean up and restet merging stuff\n        //mark kernel so it gets killed when it doesn't contain any more instances\n        merge.kill = decayHorizonOption.getValue()\n        //set weight to 0 so no new instances will be created in the cluster\n        mcluster.setWeight(0.0)\n        normalizeWeights()\n        numActiveKernels = numActiveKernels - 1\n        mergeClusterB = null\n        mergeClusterA = null\n        merging = false\n        mergeKernelsOverlapping = false\n      } else {\n        if (overlapDegree > 0 && !mergeKernelsOverlapping) {\n          mergeKernelsOverlapping = true\n          message = \"Merge overlapping started\"\n        }\n      }\n      message\n    }\n\n    /* *\n    * split a cluster from the cluster\n    * \n    * @return message for splitting\n    */\n    def splitKernel(): String = {\n      isSplitting = true\n      //todo radius range\n      val radius: Double = kernelRadiiOption.getValue()\n      val avgWeight: Double = 1.0 / numClusterOption.getValue()\n      val weight: Double = avgWeight + avgWeight * densityRangeOption.getValue() * instanceRandom.nextDouble()\n\n      val center = generator.getCenter()\n      var spcluster: SphereCluster = new SphereCluster(center, radius, weight)\n\n      if (spcluster != null) {\n        val gc: GeneratorCluster = new GeneratorCluster(clusterIdCounter + 1, spcluster)\n        clusterIdCounter = clusterIdCounter + 1\n        gc.isSplitting = true\n        kernels += gc\n        normalizeWeights()\n        numActiveKernels = numActiveKernels + 1\n        \"Split from Kernel \" + generator.getId()\n      } else {\n        //System.out.println(\"Tried to split new kernel from C\" + generator.getId() +\n        //  \". Not enough room for new cluster, decrease average radii, number of clusters or enable overlap.\")\n        \"\"\n      }\n    }\n\n    /* *\n     *  fade out the cluster.\n     *  \n     * @return message for fading out\n     */\n    def fadeOut(): String = {\n      kill = decayHorizonOption.getValue()\n      generator.setWeight(0.0)\n      numActiveKernels = numActiveKernels - 1\n      normalizeWeights()\n      \"Fading out C\" + generator.getId()\n    }\n\n    /* *\n     * add an instance.\n     * \n     * @param instance the instance want to be added\n     */\n    def addInstance(instance: Instance): Unit = {\n      var minMicroIndex = -1\n      var minHullDist = Double.MaxValue\n      var inserted = false\n      //we favour more recently build clusters so we can remove earlier cluster sooner\n      var m = microClusters.length - 1\n\n      while (m >= 0 && !inserted) {\n        val micro = microClusters(m)\n        val hulldist = micro.getCenterDistance(instance) - micro.getRadius()\n        //point fits into existing cluster\n        if (hulldist <= 0) {\n          //  microClustersPoints.get(m).add(point)\n          microClustersDecay.insert(m, numGeneratedInstances)\n          inserted = true\n\n        } //if not, check if its at least the closest cluster\n        else {\n          if (hulldist < minHullDist) {\n            minMicroIndex = m\n            minHullDist = hulldist\n          }\n        }\n        m = m - 1\n      }\n      //Reseting index choice for alternative cluster building\n      val alt = 1\n      if (alt == 1)\n        minMicroIndex = -1\n      if (!inserted) {\n        if (minMicroIndex == -1) {\n          var s: SphereCluster = null\n          s = new SphereCluster(generator.getCenter(), generator.getRadius(), 1)\n          microClusters += s\n          microClustersDecay += numGeneratedInstances\n          var id = 0\n          var exit_flag = false\n          while (id < kernels.size && !exit_flag) {\n            if (kernels(id) == this)\n              exit_flag = true\n            id = id + 1\n          }\n          s.setGroundTruth(id)\n        }\n      }\n\n    }\n\n    /* *\n     * move the cluster.\n     */\n    def move(): Unit = {\n      if (currentMovementSteps < totalMovementSteps) {\n        currentMovementSteps = currentMovementSteps + 1\n        if (moveVector == null) {\n          return\n        } else {\n          var center = generator.getCenter()\n          var outofbounds = true\n          while (outofbounds) {\n            val radius = generator.getRadius()\n            outofbounds = false\n            center = generator.getCenter()\n            for (d <- 0 until center.length if !outofbounds) {\n              center(d) = center(d) + moveVector(d)\n              if (center(d) - radius < 0 || center(d) + radius > 1) {\n                outofbounds = true\n                setDesitnation(null)\n              }\n            }\n          }\n          generator.setCenter(center)\n        }\n      } else {\n        if (!merging) {\n          setDesitnation(null)\n          isSplitting = false\n        }\n      }\n    }\n\n  }\n\n  /* \n   * normalize weights of the cluster.\n   */\n  def normalizeWeights(): Unit = {\n\n    val sumWeights = kernels.foldLeft(0.0)((sum, k) => sum + k.generator.getWeight())\n    kernels.foreach(k => {\n      k.generator.setWeight(k.generator.getWeight() / sumWeights)\n    })\n  }\n\n  /* *\n  * init the clusters.\n  */\n  def initKernels(): Unit = {\n    for (i <- 0 until numClusterOption.getValue()) {\n      kernels += (new GeneratorCluster(clusterIdCounter))\n      numActiveKernels = numActiveKernels + 1\n      clusterIdCounter = clusterIdCounter + 1\n    }\n    normalizeWeights()\n  }\n\n  /* *\n   * get the code for next event.\n   * \n   * @return the code for next event.\n   */\n  def getNextEvent(): Int = {\n    var choice: Int = -1\n    var lowerLimit = numActiveKernels <= numClusterOption.getValue() - numClusterRangeOption.getValue()\n    var upperLimit = numActiveKernels >= numClusterOption.getValue() + numClusterRangeOption.getValue()\n\n    if (!lowerLimit || !upperLimit) {\n      var mode = -1\n      if (eventDeleteCreateOption.isSet() && eventMergeSplitOption.isSet()) {\n        mode = instanceRandom.nextInt(2)\n      }\n\n      if (mode == 0 || (mode == -1 && eventMergeSplitOption.isSet())) {\n        //have we reached a limit? if not free choice\n        if (!lowerLimit && !upperLimit)\n          choice = instanceRandom.nextInt(2)\n        else //we have a limit. if lower limit, choose split\n        if (lowerLimit)\n          choice = 1\n        //otherwise we reached upper level, choose merge\n        else\n          choice = 0\n      }\n\n      if (mode == 1 || (mode == -1 && eventDeleteCreateOption.isSet())) {\n        //have we reached a limit? if not free choice\n        if (!lowerLimit && !upperLimit)\n          choice = instanceRandom.nextInt(2) + 2\n        else //we have a limit. if lower limit, choose create\n        if (lowerLimit)\n          choice = 3\n        //otherwise we reached upper level, choose delete\n        else\n          choice = 2\n      }\n    }\n    choice\n  }\n\n  /* *\n   * get a Weighted-Element.\n   * \n   * @return index of the choosen element\n   */\n  def chooseWeightedElement(): Int = {\n    var r = instanceRandom.nextDouble()\n\n    // Determine index of choosen element\n    var i = 0 // 0 or 1 kenny?\n    while (r > 0.0) {\n      r = r - kernels(i).generator.getWeight()\n      i = i + 1\n    }\n    i = i - 1 // Overcounted once\n    i\n  }\n\n  /* *\n   * get a noise.\n   * \n   * @return the noise vector\n   */\n  def getNoisePoint(): Array[Double] = {\n    var sample = new Array[Double](numFeaturesOption.getValue())\n    var incluster = true\n    var counter = 20\n    while (incluster) {\n      sample = Array.fill(numFeaturesOption.getValue())(instanceRandom.nextDouble())\n      incluster = false\n      if (!noiseInClusterOption.isSet() && counter > 0) {\n        counter = counter - 1\n        for (c <- 0 until kernels.length if !incluster) {\n          for (m <- 0 until kernels(c).microClusters.length if !incluster) {\n            val inst = new DenseInstance(sample)\n            if (kernels(c).microClusters(m).getInclusionProbability(inst) > 0) {\n              incluster = true\n            }\n          }\n        }\n      }\n    }\n    sample\n  }\n\n  /* *\n   *  merge clusters.\n   * \n   *  @param steps used for choosing clusters to merge\n   *  @return info string for merging\n   */\n  def mergeKernels(steps: Int): String = {\n\n    if (numActiveKernels > 1 && ((mergeClusterA == null && mergeClusterB == null))) {\n      //choose clusters to merge\n      val diseredDist = steps / speedOption.getValue() * maxDistanceMoveThresholdByStep\n      var minDist = Double.MaxValue\n      for (i <- 0 until kernels.length) {\n        for (j <- 0 until i) {\n          if (kernels(i).kill != -1 || kernels(j).kill != -1) {\n            //    continue\n          } else {\n            val kernelDist = kernels(i).generator.getCenterDistance(kernels(j).generator)\n            val d = kernelDist - 2 * diseredDist\n            if (Math.abs(d) < minDist &&\n              (minDist != Double.MaxValue || d > 0 || Math.abs(d) < 0.001)) {\n              minDist = Math.abs(d)\n              mergeClusterA = kernels(i)\n              mergeClusterB = kernels(j)\n            }\n          }\n        }\n      }\n\n      if (mergeClusterA != null && mergeClusterB != null) {\n        val merge_point = mergeClusterA.generator.getCenter()\n        val v = mergeClusterA.generator.getDistanceVector(mergeClusterB.generator)\n        for (i <- 0 until v.length) {\n          merge_point(i) = merge_point(i) + v(i) * 0.5\n        }\n\n        mergeClusterA.merging = true\n        mergeClusterB.merging = true\n        mergeClusterA.setDesitnation(merge_point)\n        mergeClusterB.setDesitnation(merge_point)\n        \"Init merge\"\n      } else \"\"\n    } else if (mergeClusterA != null && mergeClusterB != null) {\n      //movekernels will move the kernels close to each other,\n      //we just need to check and merge here if they are close enough\n      mergeClusterA.tryMerging(mergeClusterB)\n    } else \"\"\n  }\n\n  /* *\n   * split a cluster.\n   * \n   * @return message for splitting\n   */\n  def splitKernel(): String = {\n\n    var id: Int = instanceRandom.nextInt(kernels.size)\n    while (kernels(id).kill != -1)\n      id = instanceRandom.nextInt(kernels.size)\n\n    val message: String = kernels(id).splitKernel()\n    message\n  }\n\n  /* *\n   * fade out a cluster.\n   * \n   * @return message for fading out\n   */\n  def fadeOut(): String = {\n    var id = instanceRandom.nextInt(kernels.size)\n    while (kernels(id).kill != -1)\n      id = instanceRandom.nextInt(kernels.size)\n\n    val message = kernels(id).fadeOut()\n    message\n  }\n\n  /* *\n   * add a cluster.\n   * \n   * @return message for fading in\n   */\n  def fadeIn(): String = {\n    val gc = new GeneratorCluster(clusterIdCounter + 1)\n    clusterIdCounter = clusterIdCounter + 1\n    kernels += (gc)\n    numActiveKernels = numActiveKernels + 1\n    normalizeWeights()\n    \"Creating new cluster\"\n  }\n\n  /* *\n   * eventScheduler\n   */\n  def eventScheduler(): Unit = {\n\n    kernels.foreach { k => k.updateKernel() }\n    nextEventCounter = nextEventCounter - 1\n    if (nextEventCounter % kernelMovePointFrequency == 0) {\n      kernels.foreach { k => k.move() }\n    }\n    if (eventFrequencyOption.getValue() == 0) {\n      return\n    }\n    var type_s = \"\"\n    var message = \"\"\n    var eventFinished = false\n    nextEventChoice match {\n      case 0 =>\n        if (numActiveKernels > 1 && numActiveKernels > numClusterOption.getValue() - numClusterRangeOption.getValue()) {\n          message = mergeKernels(nextEventCounter)\n          type_s = \"Merge\"\n        }\n        if (mergeClusterA == null && mergeClusterB == null && message.startsWith(\"Clusters merging\")) {\n          eventFinished = true\n        }\n      case 1 =>\n        if (nextEventCounter <= 0) {\n          if (numActiveKernels < numClusterOption.getValue() + numClusterRangeOption.getValue()) {\n            type_s = \"Split\"\n            message = splitKernel()\n          }\n          eventFinished = true\n        }\n      case 2 =>\n        if (nextEventCounter <= 0) {\n          if (numActiveKernels > 1 && numActiveKernels > numClusterOption.getValue() - numClusterRangeOption.getValue()) {\n            message = fadeOut()\n            type_s = \"Delete\"\n          }\n          eventFinished = true\n        }\n      case 3 =>\n        if (nextEventCounter <= 0) {\n          if (numActiveKernels < numClusterOption.getValue() + numClusterRangeOption.getValue()) {\n            message = fadeIn()\n            type_s = \"Create\"\n          }\n          eventFinished = true\n        }\n      case _ => ()\n\n    }\n    if (eventFinished) {\n      nextEventCounter = (eventFrequencyOption.getValue() + (if (instanceRandom.nextBoolean()) -1 else 1) *\n        eventFrequencyOption.getValue() * eventFrequencyRange * instanceRandom.nextDouble()).toInt\n      nextEventChoice = getNextEvent()\n    }\n    if (!message.isEmpty()) {\n      message += \" (numKernels = \" + numActiveKernels + \" at \" + numGeneratedInstances + \")\"\n      if (type_s != \"Merge\" || message.startsWith(\"Clusters merging\")) {\n\n      }\n    }\n  }\n\n  /* *\n   *  initializes the generator\n   */\n  def init(): Unit = {\n    noiseInClusterOption.set()\n    instanceRandom = new Random(instanceRandomSeedOption.getValue())\n    nextEventCounter = eventFrequencyOption.getValue()\n    nextEventChoice = getNextEvent()\n    numActiveKernels = 0\n    numGeneratedInstances = 0\n    clusterIdCounter = 0\n    mergeClusterA = null\n    mergeClusterB = null\n    kernels = new ArrayBuffer[GeneratorCluster]()\n    initKernels()\n  }\n\n  /* *\n   * @return chunk size\n   */\n  def getChunkSize(): Int = {\n    chunkSizeOption.getValue\n  }\n\n  /* *\n   * @return slide duration\n   */\n  def getslideDuration(): Int = {\n    slideDurationOption.getValue\n  }\n\n  /* *\n   *  Obtains a single example.\n   *  \n   *  @return a Example.\n   */\n  def getExample(): Example = {\n\n    numGeneratedInstances = numGeneratedInstances + 1\n    eventScheduler()\n\n    //make room for the classlabel\n    val values_new = new Array[Double](numFeaturesOption.getValue())\n    var values: Array[Double] = null\n    var clusterChoice = -1\n\n    if (instanceRandom.nextDouble() > noiseLevelOption.getValue()) {\n      clusterChoice = chooseWeightedElement()\n      val ins: Instance = kernels(clusterChoice).generator.sample(instanceRandom)\n      values = ins match {\n        case d: DenseInstance => d.features\n        case s: SparseInstance => null //todo\n      }\n    } else {\n      //get ranodm noise point\n      values = getNoisePoint()\n    }\n    Array.copy(values, 0, values_new, 0, values.length)\n\n    val inputInstance: Instance = new DenseInstance(values_new)\n\n    if (clusterChoice == -1) {\n      new Example(inputInstance, new DenseInstance(Array.fill[Double](1)(numClusterOption.getValue().toDouble)))\n    } else {\n      new Example(inputInstance, new DenseInstance(Array.fill[Double](1)(kernels(clusterChoice).generator.getId())))\n    }\n  }\n\n  /* *\n   *  Obtains the specification of the examples in the stream.\n   *  \n   *  @return an ExampleSpecification of the features\n   */\n  override def getExampleSpecification(): ExampleSpecification = {\n    //Prepare specification of class attributes\n    val outputIS = new InstanceSpecification()\n    val classFeature = new NominalFeatureSpecification(Array(\"false\", \"true\"))\n    outputIS.addFeatureSpecification(0, \"class\", classFeature)\n    //Prepare specification of input attributes\n    val inputIS = new InstanceSpecification()\n    for (i <- 0 until numFeaturesOption.getValue)\n      inputIS.addFeatureSpecification(i, \"NumericFeature\" + i)\n    new ExampleSpecification(inputIS, outputIS)\n  }\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/generators/RandomRBFGenerator.scala",
    "content": "/*\r\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\r\n *\r\n * Licensed under the Apache License, Version 2.0 (the \"License\");\r\n * you may not use this file except in compliance with the License.\r\n * You may obtain a copy of the License at\r\n *\r\n * http://www.apache.org/licenses/LICENSE-2.0\r\n *\r\n * Unless required by applicable law or agreed to in writing, software\r\n * distributed under the License is distributed on an \"AS IS\" BASIS,\r\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n * See the License for the specific language governing permissions and\r\n * limitations under the License.\r\n *\r\n */\r\n\r\npackage org.apache.spark.streamdm.streams.generators\r\n\r\nimport com.github.javacliparser.IntOption\r\nimport org.apache.spark.streamdm.core._\r\nimport org.apache.spark.streamdm.core.specification._\r\nimport scala.util.Random\r\nimport java.io._\r\nimport org.apache.spark.streamdm.core.specification.ExampleSpecification\r\n\r\n/**\r\n * RandomRBFGenerator generates via radial basis function.\r\n *\r\n * <p>It uses the following options:\r\n * <ul>\r\n *  <li> Chunk size (<b>-k</b>)\r\n *  <li> Slid duration (<b>-d</b>)\r\n *  <li> Seed for random generation of model (<b>-m</b>)\r\n *  <li> Seed for random generation of instances (<b>-i</b>)\r\n *  <li> The number of classes to generate (<b>-n</b>)\r\n *  <li> The number of features to generate (<b>-f</b>)\r\n *  <li> The number of centroids in the model (<b>-c</b>)\r\n * </ul>\r\n */\r\n\r\nclass RandomRBFGenerator extends Generator {\r\n  \r\n  val chunkSizeOption: IntOption = new IntOption(\"chunkSize\", 'k',\r\n    \"Chunk Size\", 10000, 1, Integer.MAX_VALUE)\r\n\r\n  val slideDurationOption: IntOption = new IntOption(\"slideDuration\", 'd',\r\n    \"Slide Duration in milliseconds\", 1000, 1, Integer.MAX_VALUE)\r\n \r\n  val modelRandomSeedOption: IntOption = new IntOption(\"modelRandomSeed\",'m',\r\n      \"Seed for random generation of model.\", 1, 1, Integer.MAX_VALUE)\r\n\r\n  val instanceRandomSeedOption: IntOption = new IntOption(\"instanceRandomSeed\", 'i',\r\n      \"Seed for random generation of instances.\", 1, 1, Integer.MAX_VALUE)\r\n\r\n  val numClassesOption: IntOption = new IntOption(\"numClasses\", 'n',\r\n    \"The number of classes to generate.\", 2, 2, Integer.MAX_VALUE)\r\n\r\n  val numFeaturesOption: IntOption = new IntOption(\"numFeatures\", 'f',\r\n    \"The number of features to generate.\", 4, 0, Integer.MAX_VALUE)\r\n\r\n  val numCentroidsOption: IntOption = new IntOption(\"numCentroids\", 'c',\r\n    \"The number of centroids in the model.\", 50, 1, Integer.MAX_VALUE)\r\n\r\n\r\n  class Centroid(center: Array[Double], classLab: Int, stdev: Double) \r\n  extends Serializable {\r\n    val centre = center\r\n    val classLabel = classLab\r\n    val stdDev = stdev\r\n  }\r\n\r\n  val centroids = new Array[Centroid](numCentroidsOption.getValue)\r\n\r\n  val centroidWeights = new Array[Double](centroids.length)\r\n\r\n  val instanceRandom: Random = new Random(instanceRandomSeedOption.getValue())\r\n  \r\n  override def init(): Unit = {\r\n    if(!inited) {\r\n      generateCentroids\r\n      inited = true\r\n    }\r\n  }\r\n  \r\n  override def getChunkSize(): Int = {\r\n    chunkSizeOption.getValue\r\n  }\r\n  \r\n  def getslideDuration(): Int = {\r\n    slideDurationOption.getValue\r\n  }\r\n  \r\n  /**\r\n   * Get a single example.\r\n   * @return an example\r\n   */\r\n  def getExample(): Example = {\r\n    val index = chooseRandomIndexBasedOnWeights(centroidWeights, instanceRandom)\r\n    val centroid: Centroid = centroids(index)\r\n    val numFeatures = numFeaturesOption.getValue()\r\n\r\n    val initFeatureVals:Array[Double] = Array.fill[Double](numFeatures)(\r\n        instanceRandom.nextDouble()*2.0-1.0)\r\n    val magnitude = Math.sqrt(initFeatureVals.foldLeft(0.0){(a,x) => a + x*x})\r\n    \r\n    val desiredMag = instanceRandom.nextGaussian() * centroid.stdDev\r\n    val scale = desiredMag / magnitude\r\n    \r\n    val featureVals = centroid.centre zip initFeatureVals map {case (a,b) => a + b*scale}\r\n\r\n    val inputInstance: Instance = new DenseInstance(featureVals)\r\n    //new Example(inputInstance, new DenseInstance(Array(centroid.classLabel)), centroidWeights(index))\r\n    val example = new Example(inputInstance, new DenseInstance(Array(centroid.classLabel)))\r\n    example\r\n  }\r\n  \r\n  /**\r\n   * choose an index of the weight array randomly.\r\n   * @param weights Weight Array\r\n   * @param random Random value generator\r\n   * @return an index of the weight array\r\n   */\r\n  def chooseRandomIndexBasedOnWeights(weights: Array[Double], random: Random): Int = {\r\n    val probSum = weights.reduceLeft[Double](_ + _)\r\n    val ran = random.nextDouble() * probSum;\r\n    var index: Int = 0\r\n    var sum: Double = 0.0\r\n    while ((sum <= ran) && (index < weights.length)) {\r\n      sum += weights(index)\r\n      index += 1\r\n    }\r\n    index - 1\r\n  }\r\n\r\n  def generateCentroids(): Unit = {\r\n    val modelRand: Random = new Random(modelRandomSeedOption.getValue());\r\n\r\n    for (i <- 0 until centroids.length) {\r\n      val randCentre: Array[Double] = Array.fill[Double](\r\n          numFeaturesOption.getValue)(modelRand.nextDouble())\r\n      centroids.update(i, new Centroid(randCentre, modelRand.nextInt(\r\n          numClassesOption.getValue), modelRand.nextDouble()))\r\n      centroidWeights.update(i, modelRand.nextDouble())\r\n    }\r\n  }\r\n\r\n  /**\r\n   * Obtains the specification of the examples in the stream.\r\n   *\r\n   * @return an ExampleSpecification of the features\r\n   */\r\n  override def getExampleSpecification(): ExampleSpecification = {\r\n\r\n    //Prepare specification of class attributes\r\n    val outputIS = new InstanceSpecification()\r\n    val classFeature = new NominalFeatureSpecification(Array(\"+\", \"-\"))\r\n    outputIS.addFeatureSpecification(0,\"class\", classFeature)\r\n\r\n    //Prepare specification of input attributes\r\n    val inputIS = new InstanceSpecification()\r\n    for (i <- 0 until numFeaturesOption.getValue)\r\n      inputIS.addFeatureSpecification(i, \"NumericFeature\" + i)\r\n\r\n    new ExampleSpecification(inputIS, outputIS)\r\n\r\n  }\r\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/generators/RandomTreeGenerator.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.streams.generators\n\nimport scala.collection.immutable.List\nimport scala.util.Random\nimport org.apache.spark.internal.Logging\nimport com.github.javacliparser.{ IntOption, FloatOption }\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.core.specification._\n\n/**\n * Stream generator for generating data from a randomly generated tree.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Chunk size (<b>-k</b>)\n *  <li> Slid duration (<b>-d</b>)\n *  <li> The number of features to generate (<b>-f</b>)\n *  <li> The number of classes to generate (<b>-n</b>)\n *  <li> The number of nominal attributes to generate (<b>-o</b>)\n *  <li> The number of numeric attributes to generate (<b>-u</b>)\n *  <li> The number of values to generate per nominal attribute (<b>-v</b>)\n *  <li> The maximum depth of the tree concept (<b>-x</b>)\n *  <li> The first level of the tree above maxTreeDepth that can have leaves (<b>-l</b>)\n *  <li> The fraction of leaves per level from firstLeafLevel onwards (<b>-r</b>)\n * </ul>\n */\n\nclass RandomTreeGenerator extends Generator with Logging {\n\n  val chunkSizeOption: IntOption = new IntOption(\"chunkSize\", 'k',\n    \"Chunk Size\", 1000, 1, Integer.MAX_VALUE)\n\n  val slideDurationOption: IntOption = new IntOption(\"slideDuration\", 'd',\n    \"Slide Duration in milliseconds\", 1000, 1, Integer.MAX_VALUE)\n\n  val numFeaturesOption: IntOption = new IntOption(\"numFeatures\", 'f',\n    \"Number of Features\", 3, 1, Integer.MAX_VALUE)\n\n  //  val treeRandomSeedOption: IntOption = new IntOption(\"treeRandomSeed\",\n  //    'r', \"Seed for random generation of tree.\", 1);\n\n  //  val instanceRandomSeedOption = new IntOption(\n  //    \"instanceRandomSeed\", 'i',\n  //    \"Seed for random generation of instances.\", 1)\n\n  val numClassesOption = new IntOption(\"numClasses\", 'n', //default is 'c'\n    \"The number of classes to generate.\", 2, 2, Integer.MAX_VALUE)\n\n  val numNominalsOption = new IntOption(\"numNominals\", 'o',\n    \"The number of nominal attributes to generate.\", 5, 0,\n    Integer.MAX_VALUE)\n\n  val numNumericsOption = new IntOption(\"numNumerics\", 'u',\n    \"The number of numeric attributes to generate.\", 5, 0,\n    Integer.MAX_VALUE)\n\n  val numValsPerNominalOption = new IntOption(\"numValsPerNominal\", 'v',\n    \"The number of values to generate per nominal attribute.\", 5, 2,\n    Integer.MAX_VALUE)\n\n  val maxTreeDepthOption = new IntOption(\"maxTreeDepth\", 'x',\n    \"The maximum depth of the tree concept.\", 5, 0, Integer.MAX_VALUE)\n\n  val firstLeafLevelOption = new IntOption(\"firstLeafLevel\", 'l',\n    \"The first level of the tree above maxTreeDepth that can have leaves.\",\n    3, 0, Integer.MAX_VALUE)\n\n  val leafFractionOption = new FloatOption(\"leafFraction\", 'r',\n    \"The fraction of leaves per level from firstLeafLevel onwards.\",\n    0.15, 0.0, 1.0)\n\n  var treeRoot: Node = _\n\n  /**\n   * returns chunk size\n   */\n  override def getChunkSize(): Int = {\n    chunkSizeOption.getValue\n  }\n\n  /**\n   * returns slide duration\n   */\n  override def getslideDuration(): Int = {\n    slideDurationOption.getValue\n  }\n\n  def init(): Unit = { generateRandomTree() }\n\n  def getExample(): Example = {\n    if (treeRoot == null)\n      init()\n    val featureVals = new Array[Double](numNominalsOption.getValue()\n      + numNumericsOption.getValue())\n\n    for (i <- 0 until featureVals.length) {\n      featureVals(i) =\n        if (i < numNominalsOption.getValue())\n          Random.nextInt(numValsPerNominalOption.getValue())\n        else Random.nextDouble()\n    }\n    val inputInstance = new DenseInstance(featureVals)\n    //val noiseInstance = new DenseInstance(Array.fill[Double](numFeaturesOption.getValue)(getNoise()))\n    new Example(inputInstance, new DenseInstance(Array[Double](getLabel(treeRoot, featureVals))))\n  }\n\n  def getLabel(node: Node, featureVals: Array[Double]): Int = node match {\n    case leaf: LeafNode => leaf.label\n    case branch: BranchNode => {\n      if (branch.fIndex < numNominalsOption.getValue()) {\n        getLabel(\n          branch.children(featureVals(branch.fIndex).toInt), featureVals); ;\n      } else getLabel(\n        branch.children(if (featureVals(branch.fIndex) < branch.fValue) 0 else 1), featureVals)\n    }\n  }\n\n  /**\n   * Obtains the specification of the examples in the stream\n   * @return an specification of the examples\n   */\n  override def getExampleSpecification(): ExampleSpecification = {\n\n    //Prepare specification of class feature\n    val outputIS = new InstanceSpecification()\n    val classvalues: Array[String] = new Array[String](numClassesOption.getValue())\n\n    if (numClassesOption.getValue() == 2) {\n      classvalues(0) = \"false\"\n      classvalues(1) = \"true\"\n    } else {\n      for (index <- 0 until classvalues.length) {\n        classvalues(index) = \"C\" + index\n      }\n    }\n    for (i <- 0 until numClassesOption.getValue) {\n      logInfo(classvalues(i))\n    }\n\n    val classFeature = new NominalFeatureSpecification(classvalues)\n    outputIS.addFeatureSpecification(0, \"class\", classFeature)\n\n    //Prepare specification of input Nominal features for \n    val inputIS = new InstanceSpecification()\n    val nominal = new NominalFeatureSpecification(Array.range(0,\n      numValsPerNominalOption.getValue).map { _.toString() })\n    for (i <- 0 until numNominalsOption.getValue) {\n      inputIS.addFeatureSpecification(i, \"NominalFeature\" + i, nominal)\n    }\n\n    for (i <- numNominalsOption.getValue until numNominalsOption.getValue + numNumericsOption.getValue) {\n      inputIS.addFeatureSpecification(i, \"NumericFeature\" + i)\n    }\n\n    new ExampleSpecification(inputIS, outputIS)\n  }\n\n  def generateRandomTree(): Unit = {\n    val minNumericVals: Array[Double] = Array.fill(numNumericsOption.getValue())(0.0)\n    val maxNumericVals: Array[Double] = Array.fill(numNumericsOption.getValue())(1.0)\n    treeRoot = generateRandomTreeNode(0, List.range(0, numNominalsOption.getValue),\n      minNumericVals, maxNumericVals);\n  }\n\n  def generateRandomTreeNode(currentDepth: Int, nominalFeatureCandidates: List[Int],\n    minNumericVals: Array[Double], maxNumericVals: Array[Double]): Node = {\n    if ((currentDepth >= this.maxTreeDepthOption.getValue())\n      || ((currentDepth >= this.firstLeafLevelOption.getValue()) &&\n        (this.leafFractionOption.getValue() >= (1.0 - Random.nextDouble())))) {\n      val label = Random.nextInt(this.numClassesOption.getValue())\n      new LeafNode(label)\n    } else {\n      val chosenFeature = Random.nextInt(nominalFeatureCandidates.length\n        + this.numNumericsOption.getValue());\n      if (chosenFeature < nominalFeatureCandidates.length) {\n        val splitFeatureIndex = nominalFeatureCandidates(chosenFeature)\n        val node = new BranchNode(splitFeatureIndex, numValsPerNominalOption.getValue())\n        val newNominalCandidates = nominalFeatureCandidates.filter { _ != splitFeatureIndex }\n        for (i <- 0 until numValsPerNominalOption.getValue()) {\n          node.children(i) = generateRandomTreeNode(currentDepth + 1,\n            newNominalCandidates, minNumericVals, maxNumericVals);\n        }\n        node\n      } else {\n        val numericIndex = chosenFeature - nominalFeatureCandidates.length;\n        val splitFeatureIndex = this.numNominalsOption.getValue() + numericIndex;\n        val minVal = minNumericVals(numericIndex);\n        val maxVal = maxNumericVals(numericIndex);\n        val splitFeatureValue = (maxVal - minVal) * Random.nextDouble()\n        +minVal;\n        val node = new BranchNode(splitFeatureIndex, 2, splitFeatureValue)\n        val newMaxVals = maxNumericVals.clone()\n        newMaxVals(numericIndex) = splitFeatureValue\n        node.children(0) = generateRandomTreeNode(currentDepth + 1,\n          nominalFeatureCandidates, minNumericVals, newMaxVals)\n        val newMinVals = minNumericVals.clone()\n        newMinVals(numericIndex) = splitFeatureValue\n        node.children(1) = generateRandomTreeNode(currentDepth + 1,\n          nominalFeatureCandidates, newMinVals, maxNumericVals)\n        node\n      }\n    }\n  }\n\n}\n\nsealed abstract class Node\n\ncase class BranchNode(val fIndex: Int, numChild: Int, val fValue: Double = 0)\n    extends Node with Serializable {\n  val children = if (numChild <= 0) null else new Array[Node](numChild)\n}\n\ncase class LeafNode(val label: Int) extends Node with Serializable {\n\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/streams/generators/SphereCluster.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.streams.generators\nimport scala.util.Random\nimport org.apache.spark.streamdm.core._\n\n/**\n * A class for defining a kind of MicroCluster\n */\nclass SphereCluster extends Cluster {\n\n  private var center: Array[Double] = _\n  private var radius: Double = 0.0\n  private var weight: Double = 0.0\n\n  def this(center: Array[Double], radius: Double, weightedSize: Double) {\n    this()\n    this.center = center\n    this.radius = radius\n    this.weight = weightedSize\n  }\n\n  def this(center: Array[Double], radius: Double) {\n    this(center, radius, 1.0)\n  }\n\n  def this(dimensions: Int, radius: Double, random: Random) {\n    this()\n    this.radius = radius\n\n    // Position randomly but keep hypersphere inside the boundaries\n    val interval: Double = 1.0 - 2 * radius\n    this.center = Array.fill[Double](dimensions)(random.nextDouble()*interval + radius)\n    this.weight = 0.0\n  }\n\n  /**\n   * Checks whether two <code>SphereCluster</code> overlap based on radius\n   * NOTE: overlapRadiusDegree only calculates the overlap based\n   * on the centers and the radi, so not the real overlap\n   */\n\n  def overlapRadiusDegree(other: SphereCluster): Double = {\n    val center0: Array[Double] = getCenter()\n    val radius0: Double = getRadius()\n\n    val center1: Array[Double] = other.getCenter()\n    val radius1: Double = other.getRadius()\n\n    var radiusBig: Double = 0.0\n    var radiusSmall: Double = 0.0\n    if (radius0 < radius1) {\n      radiusBig = radius1\n      radiusSmall = radius0\n    } else {\n      radiusBig = radius0\n      radiusSmall = radius1\n    }\n\n    val dist:Double = Math.sqrt((center0 zip center1 map {case(a,b)=>a-b}).foldLeft(0.0)((a,x)=>a+x*x))\n    if (dist > radiusSmall + radiusBig)\n      0\n    else if (dist + radiusSmall <= radiusBig) {\n      //one lies within the other\n      1\n    } else {\n      (radiusSmall + radiusBig - dist) / (2 * radiusSmall)\n    }\n  }\n\n  def combine(cluster: SphereCluster): Unit = {\n    var center: Array[Double] = getCenter()\n    val newcenter: Array[Double] = new Array[Double](center.length)\n    val other_center: Array[Double] = cluster.getCenter()\n    val other_weight: Double = cluster.getWeight()\n    val other_radius: Double = cluster.getRadius()\n\n    for (i <- 0 until center.length)\n      newcenter(i) = (center(i) * getWeight() + other_center(i) * other_weight) / (getWeight() + other_weight)\n\n    center = newcenter\n    val r_0: Double = getRadius() + Math.abs(distance(center, newcenter))\n    val r_1: Double = other_radius + Math.abs(distance(other_center, newcenter))\n    radius = Math.max(r_0, r_1)\n    weight += other_weight\n  }\n\n  def merge(cluster: SphereCluster): Unit = {\n    val c0 = getCenter()\n    val w0 = getWeight()\n    val r0 = getRadius()\n\n    var c1 = cluster.getCenter()\n    var w1 = cluster.getWeight()\n    var r1 = cluster.getRadius()\n\n    val v:Array[Double] = c0 zip c1 map{case (a,b)=>a-b}\n    val d:Double = Math.sqrt(v.foldLeft(0.0){(a,x)=> a + x*x})\n    \n    var r: Double = 0\n    var c: Array[Double] = new Array[Double](c0.length)\n\n    //one lays within the others\n    if (d + r0 <= r1 || d + r1 <= r0) {\n      if (d + r0 <= r1) {\n        r = r1\n        c = c1\n      } else {\n        r = r0\n        c = c0\n      }\n    } else {\n      r = (r0 + r1 + d) / 2.0\n      for (i <- 0 until c.length) {\n        c(i) = c1(i) - v(i) / d * (r1 - r)\n      }\n    }\n\n    setCenter(c)\n    setRadius(r)\n    setWeight(w0 + w1)\n  }\n\n  @Override\n  def getCenter(): Array[Double] = {\n    val copy: Array[Double] = new Array[Double](center.length)\n    Array.copy(center, 0, copy, 0, center.length)\n    copy\n  }\n\n  def setCenter(center: Array[Double]): Unit = {\n    this.center = center\n  }\n\n  def getRadius(): Double = {\n    radius\n  }\n\n  def setRadius(radius: Double): Unit = {\n    this.radius = radius\n  }\n\n  @Override\n  def getWeight(): Double = {\n    weight\n  }\n\n  def setWeight(weight: Double): Unit = {\n    this.weight = weight\n  }\n\n  @Override\n  def getInclusionProbability(instance: Instance): Double = {\n    if (getCenterDistance(instance) <= getRadius()) {\n      1.0\n    } else\n      0.0\n  }\n\n  def getCenterDistance(instance: Instance): Double = {\n    var distance: Double = 0.0\n    //get the center through getCenter so subclass have a chance\n    val center: Array[Double] = getCenter()\n    for (i <- 0 until center.length) {\n      val d: Double = center(i) - instance(i)\n      distance += d * d\n    }\n    Math.sqrt(distance)\n  }\n\n  def getCenterDistance(other: SphereCluster): Double = {\n    distance(getCenter(), other.getCenter())\n  }\n\n  /*\n   * the minimal distance between the surface of two clusters.\n   * is negative if the two clusters overlap\n   * \n   * @param sphereCluster\n   * @return the minimal distance\n   */\n  def getHullDistance(other: SphereCluster): Double = {\n    var dist: Double = 0\n    //get the center through getCenter so subclass have a chance\n    val center0: Array[Double] = getCenter()\n    val center1: Array[Double] = other.getCenter()\n    dist = distance(center0, center1)\n    dist = distance(center0, center1) - (this.getRadius() + other.getRadius())\n    dist\n  }\n\n\n  /**\n   * When a clusters looses points the new minimal bounding sphere can be\n   * partly outside of the originating cluster. If a another cluster is\n   * right next to the original cluster (without overlapping), the new\n   * cluster can be overlapping with this second cluster. OverlapSave\n   * will tell you if the current cluster can degenerate so much that it\n   * overlaps with cluster 'other'\n   *\n   * @param other the potentially overlapping cluster\n   * @return true if cluster can potentially overlap\n   */\n  def overlapSave(other: SphereCluster): Boolean = {\n    //use basic geometry to figure out the maximal degenerated cluster\n    //comes down to Max(radius *(sin alpha + cos alpha)) which is\n    val minDist: Double = Math.sqrt(2) * (getRadius() + other.getRadius())\n    val diff: Double = getCenterDistance(other) - minDist\n    if (diff > 0)\n      true\n    else\n      false\n  }\n\n  def distance(v1: Array[Double], v2: Array[Double]): Double = {\n    val distance:Double = Math.sqrt((v1 zip v2 map{case (a,b)=>a-b}).foldLeft(0.0)((a,x)=>a+x*x))\n    distance\n  }\n\n/*  def getDistanceVector(instance: Instance): Array[Double] = {\n    val vec: Array[Double] = instance match {\n      case d: DenseInstance  => d.features\n      case s: SparseInstance => null //todo\n    }\n    if (vec != null) {\n      distanceVector(getCenter(), instance match {\n        case d: DenseInstance  => d.features\n        case s: SparseInstance => null //todo\n      })\n    } else null\n  }*/\n\n  def getDistanceVector(other: SphereCluster): Array[Double] = {\n    distanceVector(getCenter(), other.getCenter())\n  }\n\n  def distanceVector(v1: Array[Double], v2: Array[Double]): Array[Double] = {\n    v1 zip v2 map{case (a,b)=> a-b}   \n  }\n\n  /**\n   * Samples this cluster by returning a point from inside it.\n   * @param random a random number source\n   * @return a point that lies inside this cluster\n   */\n  def sample(random: Random): Instance = {\n    // Create sample in hypersphere coordinates\n    //get the center through getCenter so subclass have a chance\n    val center: Array[Double] = getCenter()\n\n    val dimensions: Int = center.length\n\n    var sin: Array[Double] = new Array[Double](dimensions - 1)\n    var cos: Array[Double] = new Array[Double](dimensions - 1)\n    val length = random.nextDouble() * getRadius()\n\n    var lastValue: Double = 1.0\n    for (i <- 0 until dimensions - 1) {\n      val angle: Double = random.nextDouble() * 2 * Math.PI\n      sin(i) = lastValue * Math.sin(angle) // Store cumulative values\n      cos(i) = Math.cos(angle)\n      lastValue = sin(i)\n    }\n\n    // Calculate cartesian coordinates\n    var res: Array[Double] = new Array[Double](dimensions)\n\n    // First value uses only cosines\n    res(0) = center(0) + length * cos(0)\n\n    // Loop through 'middle' coordinates which use cosines and sines\n    for (i <- 1 until dimensions - 1) {\n      res(i) = center(i) + length * sin(i - 1) * cos(i)\n    }\n\n    // Last value uses only sines\n    res(dimensions - 1) = center(dimensions - 1) + length * sin(dimensions - 2)\n\n    new DenseInstance(res)\n  }\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/tasks/ClusteringTrainEvaluate.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.tasks\n\nimport com.github.javacliparser.ClassOption\n\nimport org.apache.spark.streamdm.core._\nimport org.apache.spark.streamdm.clusterers._\nimport org.apache.spark.streamdm.streams._\nimport org.apache.spark.streaming.StreamingContext\nimport org.apache.spark.streamdm.evaluation.Evaluator\n\n/**\n * Task for evaluating a clustering on a stream by first applying the clustering\n * and then evaluating the cluster statistics.\n * \n * <p>It uses the following options:\n * <ul>\n *  <li> Clusterer (<b>-c</b>), an object of type <tt>Clusterer</tt>\n *  <li> Evaluator (<b>-e</b>), an object of type <tt>Evaluator</tt>\n *  <li> Reader (<b>-s</b>), a reader object of type <tt>StreamReader</tt>\n *  <li> Writer (<b>-w</b>), a writer object of type <tt>StreamWriter</tt>\n * </ul>\n */\nclass ClusteringTrainEvaluate extends Task {\n\n  val clustererOption: ClassOption = new ClassOption(\"clusterer\", 'c',\n    \"Clusterer to use\", classOf[Clusterer], \"Clustream\")\n\n  val evaluatorOption:ClassOption = new ClassOption(\"evaluator\", 'e',\n    \"Evaluator to use\", classOf[Evaluator], \"ClusteringCohesionEvaluator\")\n\n  val streamReaderOption:ClassOption = new ClassOption(\"streamReader\", 's',\n    \"Stream reader to use\", classOf[StreamReader], \"SocketTextStreamReader\")\n\n  val resultsWriterOption:ClassOption = new ClassOption(\"resultsWriter\", 'w',\n    \"Stream writer to use\", classOf[StreamWriter], \"PrintStreamWriter\")\n  /**\n   * Run the task.\n   * @param ssc The Spark Streaming context in which the task is run.\n   */\n  def run(ssc:StreamingContext): Unit = {\n  \n    val reader:StreamReader = this.streamReaderOption.getValue()\n\n    val clusterer: Clusterer = this.clustererOption.getValue()\n    clusterer.init(reader.getExampleSpecification())\n\n    val evaluator:Evaluator = this.evaluatorOption.getValue()\n\n    val writer:StreamWriter = this.resultsWriterOption.getValue()\n\n    val instances = reader.getExamples(ssc)\n\n    //Train\n    clusterer.train(instances)\n\n    //Assign\n    val clpairs = clusterer.assign(instances)\n    \n    //Print statistics\n    writer.output(evaluator.addResult(clpairs))\n    \n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/tasks/EvaluateOutlierDetection.scala",
    "content": "/*\n * Copyright (C) 2019 Télécom ParisTech LTCI lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.tasks\n\nimport com.github.javacliparser._\nimport org.apache.spark.streamdm.evaluation.Evaluator\nimport org.apache.spark.streamdm.outlier.Outlier\nimport org.apache.spark.streamdm.streams._\nimport org.apache.spark.streaming.StreamingContext\n\n/**\n * Task for evaluating a outlier (i.e., anomaly) detector on a stream by evaluating then training with\n * each example in sequence. The outlier detector method output either the property or degree of being an outlier\n * (i.e., outlierness score). A threshold is used to obtain the binary prediction (is_anomaly?) in case the\n * outlier detector outputs the degree of an example being an outlier. In case the outlier detector outputs the\n * property of an example being an outlier, then the values will be either 0.0 (normal) or 1.0 (outlier) and\n * the default threshold (t=0.5) will work as expected.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> outlierDetector (<b>-c</b>), an object of type <tt>Outlier</tt>\n *  <li> Evaluator (<b>-e</b>), an object of type <tt>Evaluator</tt>\n *  <li> Reader (<b>-s</b>), a reader object of type <tt>StreamReader</tt>\n *  <li> Writer (<b>-w</b>), a writer object of type <tt>StreamWriter</tt>\n *  <li> Threshold (<b>-t</b>), a value between 0.0 and Float.MaxValue of type <tt>Float</tt>\n * </ul>\n */\nclass EvaluateOutlierDetection extends Task {\n\n  val outlierDetectorOption:ClassOption = new ClassOption(\"outlierDetector\", 'o',\n    \"Outlier detector to use\", classOf[Outlier], \"SWNearestNeighbors\")\n\n  // The default behavior is to use the same evaluator as in classification tasks (i.e., BasicClassificationEvaluator)\n  val evaluatorOption:ClassOption = new ClassOption(\"evaluator\", 'e',\n    \"Evaluator to use\", classOf[Evaluator], \"BasicClassificationEvaluator\")\n\n  val streamReaderOption:ClassOption = new ClassOption(\"streamReader\", 's',\n    \"Stream reader to use\", classOf[StreamReader], \"org.apache.spark.streamdm.streams.generators.RandomTreeGenerator\")\n\n  val resultsWriterOption:ClassOption = new ClassOption(\"resultsWriter\", 'w',\n    \"Stream writer to use\", classOf[StreamWriter], \"PrintStreamWriter\")\n\n  val shouldPrintHeaderOption:FlagOption = new FlagOption(\"shouldPrintHeader\", 'h',\n    \"Whether or not to print the evaluator header on the output file\")\n\n  val thresholdOutliernessOption: FloatOption = new FloatOption(\"thresholdOutlierness\", 't',\n    \"threshold for given the outlierness value if the example is an outlier or not\", 0.5, 0.0, Float.MaxValue)\n\n  /**\n   * Run the task.\n   * @param ssc The Spark Streaming context in which the task is run.\n   */\n  def run(ssc:StreamingContext): Unit = {\n\n    val reader:StreamReader = this.streamReaderOption.getValue()\n\n    val outlierDetector:Outlier = this.outlierDetectorOption.getValue()\n    outlierDetector.init(reader.getExampleSpecification())\n\n    val evaluator:Evaluator = this.evaluatorOption.getValue()\n    evaluator.setExampleSpecification(reader.getExampleSpecification())\n\n    val writer:StreamWriter = this.resultsWriterOption.getValue()\n\n    val threshold: Double = thresholdOutliernessOption.getValue()\n\n    val examples = reader.getExamples(ssc)\n\n    if(shouldPrintHeaderOption.isSet) {\n      writer.output(evaluator.header())\n    }\n\n    // Obtain the outlierness of the examples and update the outlier detector\n    val outlierness = outlierDetector.outlierness(examples)\n\n    // Evaluate using the threshold parameter.\n    writer.output(\n      evaluator.addResult(\n        outlierness.map( e => (e._1, if(e._2 > threshold) 1.0 else 0.0)\n        )\n      )\n    )\n\n  }\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/tasks/EvaluatePrequential.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.tasks\n\nimport com.github.javacliparser._\nimport org.apache.spark.streamdm.classifiers._\nimport org.apache.spark.streamdm.streams._\nimport org.apache.spark.streaming.StreamingContext\nimport org.apache.spark.streamdm.evaluation.Evaluator\n\n/**\n * Task for evaluating a classifier on a stream by testing then training with\n * each example in sequence.\n *\n * <p>It uses the following options:\n * <ul>\n *  <li> Learner (<b>-c</b>), an object of type <tt>Classifier</tt>\n *  <li> Evaluator (<b>-e</b>), an object of type <tt>Evaluator</tt>\n *  <li> Reader (<b>-s</b>), a reader object of type <tt>StreamReader</tt>\n *  <li> Writer (<b>-w</b>), a writer object of type <tt>StreamWriter</tt>\n * </ul>\n */\nclass EvaluatePrequential extends Task {\n\n  val learnerOption:ClassOption = new ClassOption(\"learner\", 'l',\n    \"Learner to use\", classOf[Classifier], \"SGDLearner\")\n\n  val evaluatorOption:ClassOption = new ClassOption(\"evaluator\", 'e',\n    \"Evaluator to use\", classOf[Evaluator], \"BasicClassificationEvaluator\")\n\n  val streamReaderOption:ClassOption = new ClassOption(\"streamReader\", 's',\n    \"Stream reader to use\", classOf[StreamReader], \"org.apache.spark.streamdm.streams.generators.RandomTreeGenerator\")\n\n  val resultsWriterOption:ClassOption = new ClassOption(\"resultsWriter\", 'w',\n    \"Stream writer to use\", classOf[StreamWriter], \"PrintStreamWriter\")\n\n  val shouldPrintHeaderOption:FlagOption = new FlagOption(\"shouldPrintHeader\", 'h',\n    \"Whether or not to print the evaluator header on the output file\")\n\n  /**\n   * Run the task.\n   * @param ssc The Spark Streaming context in which the task is run.\n   */\n  def run(ssc:StreamingContext): Unit = {\n\n    val reader:StreamReader = this.streamReaderOption.getValue()\n\n    val learner:Classifier = this.learnerOption.getValue()\n    learner.init(reader.getExampleSpecification())\n\n    val evaluator:Evaluator = this.evaluatorOption.getValue()\n    evaluator.setExampleSpecification(reader.getExampleSpecification())\n\n    val writer:StreamWriter = this.resultsWriterOption.getValue()\n\n    val instances = reader.getExamples(ssc)\n\n    if(shouldPrintHeaderOption.isSet) {\n      writer.output(evaluator.header())\n    }\n\n    //Predict\n    val predPairs = learner.predict(instances)\n\n    //Train\n    learner.train(instances)\n\n    //Evaluate\n    writer.output(evaluator.addResult(predPairs))\n\n  }\n}"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/tasks/Task.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.tasks\n\nimport java.io.Serializable\n\nimport com.github.javacliparser.Configurable\nimport org.apache.spark.streaming.StreamingContext\n\n/**\n * Abstract Task. All runnable tasks in streamDM extend this class.\n *\n */\nabstract class Task extends Configurable with Serializable{\n\n  /**\n   * Run the task.\n   * @param ssc The Spark Streaming context in which the task is run.\n   */\n  def run(ssc:StreamingContext): Unit\n}\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/utils/Statistics.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.utils\n\nimport scala.math.{ log, sqrt, Pi, sin, exp, floor, abs, pow }\n\n/**\n * Class implementing some distributions, tests, etc. The code is mostly adapted from the CERN\n * Jet Java libraries:\n *\n * Copyright 2001 University of Waikato\n * Copyright 1999 CERN - European Organization for Nuclear Research.\n * Permission to use, copy, modify, distribute and sell this software and its documentation for\n * any purpose is hereby granted without fee, provided that the above copyright notice appear\n * in all copies and that both that copyright notice and this permission notice appear in\n * supporting documentation.\n * CERN and the University of Waikato make no representations about the suitability of this\n * software for any purpose. It is provided \"as is\" without expressed or implied warranty.\n *\n * @author peter.gedeck@pharma.Novartis.com\n * @author wolfgang.hoschek@cern.ch\n * @author Eibe Frank (eibe@cs.waikato.ac.nz)\n * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)\n * @version $Revision: 5619 $\n */\nobject Statistics {\n\n  /** Some constants */\n  val MACHEP: Double = 1.11022302462515654042E-16\n  val MAXLOG: Double = 7.09782712893383996732E2\n  val MINLOG: Double = -7.451332191019412076235E2\n  val MAXGAM: Double = 171.624376956302725\n  val SQTPI: Double = 2.50662827463100050242E0\n  val SQRTH: Double = 7.07106781186547524401E-1\n  val LOGPI: Double = 1.14472988584940017414\n\n  val big: Double = 4.503599627370496e15\n  val biginv: Double = 2.22044604925031308085e-16\n\n  /**\n   * ***********************************************\n   *    COEFFICIENTS FOR METHOD  normalInverse()   *\n   * ***********************************************\n   */\n  /* approximation for 0 <= |y - 0.5| <= 3/8 */\n  val P0: Array[Double] = Array[Double](\n    -5.99633501014107895267E1,\n    9.80010754185999661536E1,\n    -5.66762857469070293439E1,\n    1.39312609387279679503E1,\n    -1.23916583867381258016E0)\n  val Q0: Array[Double] = Array[Double](\n    /*  1.00000000000000000000E0,*/\n    1.95448858338141759834E0,\n    4.67627912898881538453E0,\n    8.63602421390890590575E1,\n    -2.25462687854119370527E2,\n    2.00260212380060660359E2,\n    -8.20372256168333339912E1,\n    1.59056225126211695515E1,\n    -1.18331621121330003142E0)\n\n  /* Approximation for interval z = sqrt(-2 log y ) between 2 and 8\n     * i.e., y between exp(-2) = .135 and exp(-32) = 1.27e-14.\n     */\n  val P1 = Array[Double](\n    4.05544892305962419923E0,\n    3.15251094599893866154E1,\n    5.71628192246421288162E1,\n    4.40805073893200834700E1,\n    1.46849561928858024014E1,\n    2.18663306850790267539E0,\n    -1.40256079171354495875E-1,\n    -3.50424626827848203418E-2,\n    -8.57456785154685413611E-4)\n  val Q1 = Array[Double](\n    /*  1.00000000000000000000E0,*/\n    1.57799883256466749731E1,\n    4.53907635128879210584E1,\n    4.13172038254672030440E1,\n    1.50425385692907503408E1,\n    2.50464946208309415979E0,\n    -1.42182922854787788574E-1,\n    -3.80806407691578277194E-2,\n    -9.33259480895457427372E-4)\n\n  /* Approximation for interval z = sqrt(-2 log y ) between 8 and 64\n     * i.e., y between exp(-32) = 1.27e-14 and exp(-2048) = 3.67e-890.\n     */\n  val P2 = Array[Double](\n    3.23774891776946035970E0,\n    6.91522889068984211695E0,\n    3.93881025292474443415E0,\n    1.33303460815807542389E0,\n    2.01485389549179081538E-1,\n    1.23716634817820021358E-2,\n    3.01581553508235416007E-4,\n    2.65806974686737550832E-6,\n    6.23974539184983293730E-9)\n  val Q2 = Array[Double](\n    /*  1.00000000000000000000E0,*/\n    6.02427039364742014255E0,\n    3.67983563856160859403E0,\n    1.37702099489081330271E0,\n    2.16236993594496635890E-1,\n    1.34204006088543189037E-2,\n    3.28014464682127739104E-4,\n    2.89247864745380683936E-6,\n    6.79019408009981274425E-9)\n\n  /**\n   * Computes standard error for observed values of a binomial\n   * random variable.\n   *\n   * @param p the probability of success\n   * @param n the size of the sample\n   * @return the standard error\n   */\n  def binomialStandardError(p: Double, n: Int): Double = {\n    if (n == 0) 0.0\n    else sqrt((p * (1 - p)) / n)\n  }\n\n  /**\n   * Returns chi-squared probability for given value and degrees\n   * of freedom. (The probability that the chi-squared variate\n   * will be greater than x for the given degrees of freedom.)\n   *\n   * @param x the value\n   * @param v the number of degrees of freedom\n   * @return the chi-squared probability\n   */\n  def chiSquaredProbability(x: Double, v: Double): Double = {\n    if (x < 0.0 || v < 1.0) 0.0\n    else incompleteGammaComplement(v / 2.0, x / 2.0)\n  }\n\n  /**\n   * Computes probability of F-ratio.\n   *\n   * @param F the F-ratio\n   * @param df1 the first number of degrees of freedom\n   * @param df2 the second number of degrees of freedom\n   * @return the probability of the F-ratio.\n   */\n  def FProbability(F: Double, df1: Int, df2: Int): Double = {\n    incompleteBeta(df2 / 2.0, df1 / 2.0, df2 / (df2 + df1 * F))\n  }\n\n  /**\n   * Returns the area under the Normal (Gaussian) probability density\n   * function, integrated from minus infinity to <tt>x</tt>\n   * (assumes mean is zero, variance is one).\n   * <pre>\n   *                            x\n   *                             -\n   *                   1        | |          2\n   *  normal(x)  = ---------    |    exp( - t /2 ) dt\n   *               sqrt(2pi)  | |\n   *                           -\n   *                          -inf.\n   *\n   *             =  ( 1 + erf(z) ) / 2\n   *             =  erfc(z) / 2\n   * </pre>\n   * where <tt>z = x/sqrt(2)</tt>.\n   * Computation is via the functions <tt>errorFunction</tt> and <tt>errorFunctionComplement</tt>.\n   *\n   * @param a the z-value\n   * @return the probability of the z value according to the normal pdf\n   */\n  def normalProbability(a: Double): Double = {\n    val x: Double = a * SQRTH\n    val z: Double = abs(x)\n    var y: Double = 0.0\n    if (z < SQRTH) y = 0.5 + 0.5 * errorFunction(x)\n    else {\n      y = 0.5 * errorFunctionComplemented(z)\n      if (x > 0) y = 1.0 - y\n    }\n    y\n  }\n\n  /**\n   * Returns the value, <tt>x</tt>, for which the area under the\n   * Normal (Gaussian) probability density function (integrated from\n   * minus infinity to <tt>x</tt>) is equal to the argument <tt>y</tt>\n   * (assumes mean is zero, variance is one).\n   * <p>\n   * For small arguments <tt>0 < y < exp(-2)</tt>, the program computes\n   * <tt>z = sqrt( -2.0 * log(y) )</tt>;  then the approximation is\n   * <tt>x = z - log(z)/z  - (1/z) P(1/z) / Q(1/z)</tt>.\n   * There are two rational functions P/Q, one for <tt>0 < y < exp(-32)</tt>\n   * and the other for <tt>y</tt> up to <tt>exp(-2)</tt>.\n   * For larger arguments,\n   * <tt>w = y - 0.5</tt>, and  <tt>x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2))</tt>.\n   *\n   * @param y0 the area under the normal pdf\n   * @return the z-value\n   */\n  def normalInverse(y0: Double): Double = {\n\n    var x: Double = 0.0\n    var y: Double = 0.0\n    var z: Double = 0.0\n    var y2: Double = 0.0\n    var x0: Double = 0.0\n    var x1: Double = 0.0\n\n    val s2pi = sqrt(2.0 * Pi)\n\n    if (y0 <= 0.0 || y0 >= 1.0) throw new IllegalArgumentException()\n\n    var code: Int = 1\n    y = y0\n    if (y > (1.0 - 0.13533528323661269189)) { /* 0.135... = exp(-2) */\n      y = 1.0 - y\n      code = 0\n    }\n\n    if (y > 0.13533528323661269189) {\n      y = y - 0.5\n      y2 = y * y\n      x = y + y * (y2 * polevl(y2, P0, 4) / p1evl(y2, Q0, 8))\n      x = x * s2pi\n      x\n    } else {\n      x = Math.sqrt(-2.0 * Math.log(y))\n      x0 = x - Math.log(x) / x\n\n      z = 1.0 / x\n      if (x < 8.0) /* y > exp(-32) = 1.2664165549e-14 */\n        x1 = z * polevl(z, P1, 8) / p1evl(z, Q1, 8)\n      else\n        x1 = z * polevl(z, P2, 8) / p1evl(z, Q2, 8)\n      x = x0 - x1\n      if (code != 0)\n        x = -x\n      x\n    }\n  }\n\n  /**\n   * Returns natural logarithm of gamma function.\n   *\n   * @param x the value\n   * @return natural logarithm of gamma function\n   */\n  def lnGamma(what: Double): Double = {\n\n    var p: Double = 0.0\n    var q: Double = 0.0\n    var w: Double = 0.0\n    var z: Double = 0.0\n    var x: Double = what\n    val A = Array[Double](\n      8.11614167470508450300E-4,\n      -5.95061904284301438324E-4,\n      7.93650340457716943945E-4,\n      -2.77777777730099687205E-3,\n      8.33333333333331927722E-2)\n    val B = Array[Double](\n      -1.37825152569120859100E3,\n      -3.88016315134637840924E4,\n      -3.31612992738871184744E5,\n      -1.16237097492762307383E6,\n      -1.72173700820839662146E6,\n      -8.53555664245765465627E5)\n    val C = Array[Double](\n      /* 1.00000000000000000000E0, */\n      -3.51815701436523470549E2,\n      -1.70642106651881159223E4,\n      -2.20528590553854454839E5,\n      -1.13933444367982507207E6,\n      -2.53252307177582951285E6,\n      -2.01889141433532773231E6)\n    if (x < -34.0) {\n      q = -x\n      w = lnGamma(q)\n      p = floor(q)\n      if (p == q) throw new ArithmeticException(\"lnGamma: Overflow\")\n      z = q - p\n      if (z > 0.5) {\n        p += 1.0\n        z = p - q\n      }\n      z = q * sin(Pi * z)\n      if (z == 0.0) throw new ArithmeticException(\"lnGamma: Overflow\")\n      z = LOGPI - Math.log(z) - w\n      z\n    } else if (x < 13.0) {\n      z = 1.0\n      while (x >= 3.0) {\n        x -= 1.0\n        z *= x\n      }\n      while (x < 2.0) {\n        if (x == 0.0) throw new ArithmeticException(\"lnGamma: Overflow\")\n        else {\n          z /= x\n          x += 1.0\n        }\n        if (z < 0.0) z = -z\n        if (x == 2.0) Math.log(z) else {\n          x -= 2.0\n          p = x * polevl(x, B, 5) / p1evl(x, C, 6)\n          Math.log(z) + p\n        }\n      }\n    }\n\n    if (x > 2.556348e305) throw new ArithmeticException(\"lnGamma: Overflow\")\n\n    q = (x - 0.5) * log(x) - x + 0.91893853320467274178\n\n    if (x > 1.0e8) q\n    else {\n\n      p = 1.0 / (x * x)\n      if (x >= 1000.0)\n        q += ((7.9365079365079365079365e-4 * p\n          - 2.7777777777777777777778e-3) * p\n          + 0.0833333333333333333333) / x\n      else\n        q += polevl(p, A, 4) / x\n      q\n    }\n  }\n\n  /**\n   * Returns the error function of the normal distribution.\n   * The integral is\n   * <pre>\n   *                           x\n   *                            -\n   *                 2         | |          2\n   *   erf(x)  =  --------     |    exp( - t  ) dt.\n   *              sqrt(pi)   | |\n   *                          -\n   *                           0\n   * </pre>\n   * <b>Implementation:</b>\n   * For <tt>0 <= |x| < 1, erf(x) = x * P4(x**2)/Q5(x**2)</tt>; otherwise\n   * <tt>erf(x) = 1 - erfc(x)</tt>.\n   * <p>\n   * Code adapted from the <A HREF=\"http://www.sci.usq.edu.au/staff/leighb/graph/Top.html\">\n   * Java 2D Graph Package 2.4</A>,\n   * which in turn is a port from the\n   * <A HREF=\"http://people.ne.mediaone.net/moshier/index.html#Cephes\">Cephes 2.2</A>\n   * Math Library (C).\n   *\n   * @param a the argument to the function.\n   */\n  def errorFunction(x: Double): Double = {\n    var y: Double = 0.0\n    var z: Double = 0.0\n    val T = Array[Double](\n      9.60497373987051638749E0,\n      9.00260197203842689217E1,\n      2.23200534594684319226E3,\n      7.00332514112805075473E3,\n      5.55923013010394962768E4)\n    val U = Array[Double](\n      //1.00000000000000000000E0,\n      3.35617141647503099647E1,\n      5.21357949780152679795E2,\n      4.59432382970980127987E3,\n      2.26290000613890934246E4,\n      4.92673942608635921086E4)\n\n    if (abs(x) > 1.0) 1.0 - errorFunctionComplemented(x)\n    else {\n      z = x * x\n      y = x * polevl(z, T, 4) / p1evl(z, U, 5)\n      y\n    }\n  }\n\n  /**\n   * Returns the complementary Error function of the normal distribution.\n   * <pre>\n   *  1 - erf(x) =\n   *\n   *                           inf.\n   *                             -\n   *                  2         | |          2\n   *   erfc(x)  =  --------     |    exp( - t  ) dt\n   *               sqrt(pi)   | |\n   *                           -\n   *                            x\n   * </pre>\n   * <b>Implementation:</b>\n   * For small x, <tt>erfc(x) = 1 - erf(x)</tt>; otherwise rational\n   * approximations are computed.\n   * <p>\n   * Code adapted from the <A HREF=\"http://www.sci.usq.edu.au/staff/leighb/graph/Top.html\">\n   * Java 2D Graph Package 2.4</A>,\n   * which in turn is a port from the\n   * <A HREF=\"http://people.ne.mediaone.net/moshier/index.html#Cephes\">Cephes 2.2</A>\n   * Math Library (C).\n   *\n   * @param a the argument to the function.\n   */\n  def errorFunctionComplemented(a: Double): Double = {\n    var x: Double = 0.0\n    var y: Double = 0.0\n    var z: Double = 0.0\n    var p: Double = 0.0\n    var q: Double = 0.0\n\n    val P = Array[Double](\n      2.46196981473530512524E-10,\n      5.64189564831068821977E-1,\n      7.46321056442269912687E0,\n      4.86371970985681366614E1,\n      1.96520832956077098242E2,\n      5.26445194995477358631E2,\n      9.34528527171957607540E2,\n      1.02755188689515710272E3,\n      5.57535335369399327526E2)\n    val Q = Array[Double](\n      //1.0\n      1.32281951154744992508E1,\n      8.67072140885989742329E1,\n      3.54937778887819891062E2,\n      9.75708501743205489753E2,\n      1.82390916687909736289E3,\n      2.24633760818710981792E3,\n      1.65666309194161350182E3,\n      5.57535340817727675546E2)\n\n    val R = Array[Double](\n      5.64189583547755073984E-1,\n      1.27536670759978104416E0,\n      5.01905042251180477414E0,\n      6.16021097993053585195E0,\n      7.40974269950448939160E0,\n      2.97886665372100240670E0)\n    val S = Array[Double](\n      //1.00000000000000000000E0, \n      2.26052863220117276590E0,\n      9.39603524938001434673E0,\n      1.20489539808096656605E1,\n      1.70814450747565897222E1,\n      9.60896809063285878198E0,\n      3.36907645100081516050E0)\n\n    if (a < 0.0) x = -a\n    else x = a\n\n    if (x < 1.0) 1.0 - errorFunction(a)\n    else {\n      z = -a * a\n      if (z < -MAXLOG) {\n        if (a < 0) 2.0\n        else 0.0\n      } else {\n        z = exp(z)\n\n        if (x < 8.0) {\n          p = polevl(x, P, 8)\n          q = p1evl(x, Q, 8)\n        } else {\n          p = polevl(x, R, 5)\n          q = p1evl(x, S, 6)\n        }\n\n        y = (z * p) / q\n\n        if (a < 0) y = 2.0 - y\n\n        if (y == 0.0) {\n          if (a < 0) 2.0\n          else 0.0\n        } else y\n      }\n    }\n  }\n\n  /**\n   * Evaluates the given polynomial of degree <tt>N</tt> at <tt>x</tt>.\n   * Evaluates polynomial when coefficient of N is 1.0.\n   * Otherwise same as <tt>polevl()</tt>.\n   * <pre>\n   *                     2          N\n   * y  =  C  + C x + C x  +...+ C x\n   *        0    1     2          N\n   *\n   * Coefficients are stored in reverse order:\n   *\n   * coef[0] = C  , ..., coef[N] = C  .\n   *            N                   0\n   * </pre>\n   * The function <tt>p1evl()</tt> assumes that <tt>coef[N] = 1.0</tt> and is\n   * omitted from the array.  Its calling arguments are\n   * otherwise the same as <tt>polevl()</tt>.\n   * <p>\n   * In the interest of speed, there are no checks for out of bounds arithmetic.\n   *\n   * @param x argument to the polynomial.\n   * @param coef the coefficients of the polynomial.\n   * @param N the degree of the polynomial.\n   */\n  def p1evl(x: Double, coef: Array[Double], N: Int): Double = {\n    var ans = x + coef(0)\n    for (i <- 1 until N)\n      ans = ans * x + coef(i)\n    ans\n  }\n\n  /**\n   * Evaluates the given polynomial of degree <tt>N</tt> at <tt>x</tt>.\n   * <pre>\n   *                     2          N\n   * y  =  C  + C x + C x  +...+ C x\n   *        0    1     2          N\n   *\n   * Coefficients are stored in reverse order:\n   *\n   * coef[0] = C  , ..., coef[N] = C  .\n   *            N                   0\n   * </pre>\n   * In the interest of speed, there are no checks for out of bounds arithmetic.\n   *\n   * @param x argument to the polynomial.\n   * @param coef the coefficients of the polynomial.\n   * @param N the degree of the polynomial.\n   */\n\n  def polevl(x: Double, coef: Array[Double], N: Int): Double = {\n    var ans = coef(0)\n    for (i <- 1 until N+1)\n      ans = ans * x + coef(i)\n    ans\n  }\n\n  /**\n   * Returns the Incomplete Gamma function.\n   * @param a the parameter of the gamma distribution.\n   * @param x the integration end point.\n   */\n  def incompleteGamma(a: Double, x: Double): Double = {\n    if (x <= 0 || a <= 0) 0.0\n    else if (x > 1.0 && x > a) 1.0 - incompleteGammaComplement(a, x)\n    else {\n      var ans: Double = 0.0\n      var ax: Double = 0.0\n      var c: Double = 0.0\n      var r: Double = 0.0\n\n      /* Compute  x**a * exp(-x) / gamma(a)  */\n      ax = a * Math.log(x) - x - lnGamma(a)\n      if (ax < -MAXLOG) 0.0\n      else {\n        ax = exp(ax)\n\n        /* power series */\n        r = a\n        c = 1.0\n        ans = 1.0\n\n        do {\n          r += 1.0\n          c *= x / r\n          ans += c\n        } while (c / ans > MACHEP)\n\n        (ans * ax / a)\n      }\n    }\n  }\n\n  /**\n   * Returns the Complemented Incomplete Gamma function.\n   * @param a the parameter of the gamma distribution.\n   * @param x the integration start point.\n   */\n  def incompleteGammaComplement(a: Double, x: Double): Double = {\n\n    var ans: Double = 0.0\n    var ax: Double = 0.0\n    var c: Double = 0.0\n    var yc: Double = 0.0\n    var r: Double = 0.0\n    var t: Double = 0.0\n    var y: Double = 0.0\n    var z: Double = 0.0\n    var pk: Double = 0.0\n    var pkm1: Double = 0.0\n    var pkm2: Double = 0.0\n    var qk: Double = 0.0\n    var qkm1: Double = 0.0\n    var qkm2: Double = 0.0\n\n    if (x <= 0 || a <= 0) 1.0\n    else if (x < 1.0 || x < a) 1.0 - incompleteGamma(a, x)\n    else {\n      ax = a * log(x) - x - lnGamma(a)\n      if (ax < -MAXLOG) 0.0\n      else {\n        ax = exp(ax)\n        /* continued fraction */\n        y = 1.0 - a\n        z = x + y + 1.0\n        c = 0.0\n        pkm2 = 1.0\n        qkm2 = x\n        pkm1 = x + 1.0\n        qkm1 = z * x\n        ans = pkm1 / qkm1\n\n        do {\n          c += 1.0\n          y += 1.0\n          z += 2.0\n          yc = y * c\n          pk = pkm1 * z - pkm2 * yc\n          qk = qkm1 * z - qkm2 * yc\n          if (qk != 0) {\n            r = pk / qk\n            t = abs((ans - r) / r)\n            ans = r\n          } else\n            t = 1.0\n\n          pkm2 = pkm1\n          pkm1 = pk\n          qkm2 = qkm1\n          qkm1 = qk\n          if (abs(pk) > big) {\n            pkm2 *= biginv\n            pkm1 *= biginv\n            qkm2 *= biginv\n            qkm1 *= biginv\n          }\n        } while (t > MACHEP)\n\n        ans * ax\n      }\n    }\n  }\n\n  /**\n   * Returns the Gamma function of the argument.\n   */\n  def gamma(what: Double): Double = {\n    var x: Double = what\n    val P = Array[Double](\n      1.60119522476751861407E-4,\n      1.19135147006586384913E-3,\n      1.04213797561761569935E-2,\n      4.76367800457137231464E-2,\n      2.07448227648435975150E-1,\n      4.94214826801497100753E-1,\n      9.99999999999999996796E-1)\n    val Q = Array[Double](\n      -2.31581873324120129819E-5,\n      5.39605580493303397842E-4,\n      -4.45641913851797240494E-3,\n      1.18139785222060435552E-2,\n      3.58236398605498653373E-2,\n      -2.34591795718243348568E-1,\n      7.14304917030273074085E-2,\n      1.00000000000000000320E0)\n\n    var p: Double = 0.0\n    var z: Double = 0.0\n    var i: Int = 0\n\n    var q: Double = abs(x)\n\n    if (q > 33.0) {\n      if (x < 0.0) {\n        p = floor(q)\n        if (p == q) throw new ArithmeticException(\"gamma: overflow\")\n        i = p.toInt\n        z = q - p\n        if (z > 0.5) {\n          p += 1.0\n          z = q - p\n        }\n        z = q * sin(Pi * z)\n        if (z == 0.0) throw new ArithmeticException(\"gamma: overflow\")\n        z = abs(z)\n        z = Pi / (z * stirlingFormula(q))\n        -z\n      } else {\n        stirlingFormula(x)\n      }\n    } else {\n      z = 1.0\n      while (x >= 3.0) {\n        x -= 1.0\n        z *= x\n      }\n      if (x < 2.0) {\n        while (abs(x) >= 1.0E-9) {\n          if (x == 0.0) {\n            throw new ArithmeticException(\"gamma: singular\")\n          } else {\n            z /= x\n            x += 1.0\n          }\n        }\n        z / ((1.0 + 0.5772156649015329 * x) * x)\n      } else if ((x == 2.0) || (x == 3.0)) z\n      else {\n        x -= 2.0\n        p = polevl(x, P, 6)\n        q = polevl(x, Q, 7)\n        z * p / q\n      }\n    }\n  }\n\n  /**\n   * Returns the Gamma function computed by Stirling's formula.\n   * The polynomial STIR is valid for 33 <= x <= 172.\n   */\n  def stirlingFormula(x: Double): Double = {\n\n    val STIR = Array[Double](\n      7.87311395793093628397E-4,\n      -2.29549961613378126380E-4,\n      -2.68132617805781232825E-3,\n      3.47222221605458667310E-3,\n      8.33333333333482257126E-2)\n    val MAXSTIR: Double = 143.01608\n\n    var w: Double = 1.0 / x\n    var y: Double = exp(x)\n\n    w = 1.0 + w * polevl(w, STIR, 4)\n\n    if (x > MAXSTIR) {\n      /* Avoid overflow in Math.pow() */\n      val v: Double = Math.pow(x, 0.5 * x - 0.25)\n      y = v * (v / y)\n    } else {\n      y = pow(x, x - 0.5) / y\n    }\n    y = SQTPI * y * w\n    y\n  }\n\n  /**\n   * Returns the Incomplete Beta Function evaluated from zero to <tt>xx</tt>.\n   *\n   * @param aa the alpha parameter of the beta distribution.\n   * @param bb the beta parameter of the beta distribution.\n   * @param xx the integration end point.\n   */\n  def incompleteBeta(aa: Double, bb: Double, xx: Double): Double = {\n    if (aa <= 0.0 || bb <= 0.0) throw new ArithmeticException(\"ibeta: Domain error!\")\n\n    else if ((xx <= 0.0) || (xx >= 1.0)) {\n      if (xx == 0.0) 0.0\n      else if (xx == 1.0) 1.0\n      else throw new ArithmeticException(\"ibeta: Domain error!\")\n    } else {\n      var a: Double = 0.0\n      var b: Double = 0.0\n      var t: Double = 0.0\n      var x: Double = 0.0\n      var xc: Double = 0.0\n      var w: Double = 0.0\n      var y: Double = 0.0\n      var flag = false\n      if ((bb * xx) <= 1.0 && xx <= 0.95) {\n        t = powerSeries(aa, bb, xx)\n        t\n      } else {\n        w = 1.0 - xx\n\n        /* Reverse a and b if x is greater than the mean. */\n        if (xx > (aa / (aa + bb))) {\n          flag = true\n          a = bb\n          b = aa\n          xc = xx\n          x = w;\n        } else {\n          a = aa\n          b = bb\n          xc = w\n          x = xx\n        }\n\n        if (flag && (b * x) <= 1.0 && x <= 0.95) {\n          t = powerSeries(a, b, x)\n          if (t <= MACHEP) t = 1.0 - MACHEP\n          else t = 1.0 - t\n          t\n        } else {\n          /* Choose expansion for better convergence. */\n          y = x * (a + b - 2.0) - (a - 1.0)\n          if (y < 0.0)\n            w = incompleteBetaFraction1(a, b, x)\n          else\n            w = incompleteBetaFraction2(a, b, x) / xc\n\n          /* Multiply w by the factor\n         a      b   _             _     _\n         x  (1-x)   | (a+b) / ( a | (a) | (b) ) .   */\n\n          y = a * log(x)\n          t = b * log(xc)\n          if ((a + b) < MAXGAM && abs(y) < MAXLOG && abs(t) < MAXLOG) {\n            t = pow(xc, b)\n            t *= pow(x, a)\n            t /= a\n            t *= w\n            t *= gamma(a + b) / (gamma(a) * gamma(b))\n            if (flag) {\n              if (t <= MACHEP) t = 1.0 - MACHEP\n              else t = 1.0 - t\n            }\n            t\n          } else {\n            /* Resort to logarithms.  */\n            y += t + lnGamma(a + b) - lnGamma(a) - lnGamma(b)\n            y += log(w / a)\n            if (y < MINLOG)\n              t = 0.0\n            else\n              t = exp(y)\n\n            if (flag) {\n              if (t <= MACHEP) t = 1.0 - MACHEP\n              else t = 1.0 - t\n            }\n            t\n          }\n        }\n      }\n    }\n  }\n\n  /**\n   * Continued fraction expansion #1 for incomplete beta integral.\n   */\n  def incompleteBetaFraction1(a: Double, b: Double, x: Double): Double = {\n\n    var xk: Double = 0.0\n    var pk: Double = 0.0\n    var qk: Double = 0.0\n    var t: Double = 0.0\n    var k1 = a\n    var k2 = a + b\n    var k3 = a\n    var k4 = a + 1.0\n    var k5 = 1.0\n    var k6 = b - 1.0\n    var k7 = k4\n    var k8 = a + 2.0\n\n    var pkm2 = 0.0\n    var qkm2 = 1.0\n    var pkm1 = 1.0\n    var qkm1 = 1.0\n    var ans = 1.0\n    var r = 1.0\n    var n = 0\n    var thresh = 3.0 * MACHEP\n    do {\n      xk = -(x * k1 * k2) / (k3 * k4)\n      pk = pkm1 + pkm2 * xk\n      qk = qkm1 + qkm2 * xk\n      pkm2 = pkm1\n      pkm1 = pk\n      qkm2 = qkm1\n      qkm1 = qk\n\n      xk = (x * k5 * k6) / (k7 * k8)\n      pk = pkm1 + pkm2 * xk\n      qk = qkm1 + qkm2 * xk\n      pkm2 = pkm1\n      pkm1 = pk\n      qkm2 = qkm1\n      qkm1 = qk\n\n      if (qk != 0) r = pk / qk\n      if (r != 0) {\n        t = abs((ans - r) / r)\n        ans = r\n      } else\n        t = 1.0\n      k1 += 1.0\n      k2 += 1.0\n      k3 += 2.0\n      k4 += 2.0\n      k5 += 1.0\n      k6 -= 1.0\n      k7 += 2.0\n      k8 += 2.0\n\n      if ((abs(qk) + abs(pk)) > big) {\n        pkm2 *= biginv\n        pkm1 *= biginv\n        qkm2 *= biginv\n        qkm1 *= biginv\n      }\n      if ((abs(qk) < biginv) || (abs(pk) < biginv)) {\n        pkm2 *= big\n        pkm1 *= big\n        qkm2 *= big\n        qkm1 *= big\n      }\n      n += 1\n    } while (n < 300 && t >= thresh)\n\n    ans\n  }\n\n  /**\n   * Continued fraction expansion #2 for incomplete beta integral.\n   */\n  def incompleteBetaFraction2(a: Double, b: Double, x: Double): Double = {\n\n    var xk = 0.0\n    var pk = 0.0\n    var qk = 0.0\n    var t = 0.0\n    var k1 = a\n    var k2 = b - 1.0\n    var k3 = a\n    var k4 = a + 1.0\n    var k5 = 1.0\n    var k6 = a + b\n    var k7 = a + 1.0\n    var k8 = a + 2.0\n\n    var pkm2 = 0.0\n    var qkm2 = 1.0\n    var pkm1 = 1.0\n    var qkm1 = 1.0\n    var z = x / (1.0 - x)\n    var ans = 1.0\n    var r = 1.0\n    var n = 0\n    var thresh = 3.0 * MACHEP\n    do {\n      xk = -(z * k1 * k2) / (k3 * k4)\n      pk = pkm1 + pkm2 * xk\n      qk = qkm1 + qkm2 * xk\n      pkm2 = pkm1\n      pkm1 = pk\n      qkm2 = qkm1\n      qkm1 = qk\n\n      xk = (z * k5 * k6) / (k7 * k8)\n      pk = pkm1 + pkm2 * xk\n      qk = qkm1 + qkm2 * xk\n      pkm2 = pkm1\n      pkm1 = pk\n      qkm2 = qkm1\n      qkm1 = qk\n\n      if (qk != 0) r = pk / qk\n      if (r != 0) {\n        t = abs((ans - r) / r)\n        ans = r\n      } else\n        t = 1.0\n      k1 += 1.0\n      k2 -= 1.0\n      k3 += 2.0\n      k4 += 2.0\n      k5 += 1.0\n      k6 += 1.0\n      k7 += 2.0\n      k8 += 2.0\n\n      if ((abs(qk) + abs(pk)) > big) {\n        pkm2 *= biginv\n        pkm1 *= biginv\n        qkm2 *= biginv\n        qkm1 *= biginv\n      }\n      if ((abs(qk) < biginv) || (abs(pk) < biginv)) {\n        pkm2 *= big\n        pkm1 *= big\n        qkm2 *= big\n        qkm1 *= big\n      }\n      n += 1\n    } while (n < 300 && t >= thresh)\n    ans\n  }\n\n  /**\n   * Power series for incomplete beta integral.\n   * Use when b*x is small and x not too close to 1.\n   */\n  def powerSeries(a: Double, b: Double, x: Double): Double = {\n\n    var s: Double = 0.0\n    var t: Double = 0.0\n    var u: Double = 0.0\n    var v: Double = 0.0\n    var n: Double = 0.0\n    var t1: Double = 0.0\n    var z: Double = 0.0\n    var ai: Double = 0.0\n\n    ai = 1.0 / a\n    u = (1.0 - b) * x\n    v = u / (a + 1.0)\n    t1 = v\n    t = u\n    n = 2.0\n    s = 0.0\n    z = MACHEP * ai\n    while (Math.abs(v) > z) {\n      u = (n - b) * x / n\n      t *= u\n      v = t / (a + n)\n      s += v\n      n += 1.0\n    }\n    s += t1\n    s += ai\n\n    u = a * log(x)\n    if ((a + b) < MAXGAM && Math.abs(u) < MAXLOG) {\n      t = gamma(a + b) / (gamma(a) * gamma(b))\n      s = s * t * Math.pow(x, a)\n    } else {\n      t = lnGamma(a + b) - lnGamma(a) - lnGamma(b) + u + Math.log(s)\n      if (t < MINLOG) s = 0.0\n      else s = exp(t)\n    }\n    s\n  }\n}\n\n"
  },
  {
    "path": "src/main/scala/org/apache/spark/streamdm/utils/Utils.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.utils\n\nimport java.io._\nimport java.util.Random\nimport scala.math.{ min, max, log }\n\nimport org.apache.spark.streamdm.classifiers.Classifier\n\n/**\n * Utility methods.\n *\n */\nobject Utils {\n\n  /* Copy a classifier using serialization\n    *\n    * @param classifier the original classifier to copy\n    * @return the copy of the classifier\n    */\n  def copyClassifier(classifier: Classifier): Classifier = {\n    val baoStream: ByteArrayOutputStream = new ByteArrayOutputStream()\n    val out: ObjectOutputStream = new ObjectOutputStream(\n      new BufferedOutputStream(baoStream))\n    out.writeObject(classifier)\n    out.flush()\n    out.close()\n    val byteArray: Array[Byte] = baoStream.toByteArray()\n    val in: ObjectInputStream = new ObjectInputStream(new BufferedInputStream(\n      new ByteArrayInputStream(byteArray)))\n    val copy: Classifier = in.readObject().asInstanceOf[Classifier]\n    in.close()\n    copy\n  }\n\n  /* Compute a random value from a Poisson distribution\n   *\n   * @param lambda the mean of the Poisson distribution\n   * @param r the random generator\n   * @return a random value sampled from the distribution\n   */\n  def poisson(lambda: Double, r: Random) = {\n    if (lambda < 100.0) {\n      var product = 1.0\n      var sum = 1.0\n      val threshold = r.nextDouble() * Math.exp(lambda)\n      var i = 1.0\n      var max = Math.max(100, 10 * Math.ceil(lambda).toInt)\n      while ((i < max) && (sum <= threshold)) {\n        product *= (lambda / i)\n        sum += product\n        i += 1.0\n      }\n      i - 1.0\n    } else {\n      val x = lambda + Math.sqrt(lambda) * r.nextGaussian()\n      if (x < 0.0) 0.0 else Math.floor(x)\n    }\n  }\n\n  /* Get the most frequent value of an array of numeric values\n  *\n  * @param array the Array of numeric values\n  * @return the argument of the most frequent value\n  */\n  def majorityVote(array: Array[Double], size: Integer): Double = {\n    val frequencyArray: Array[Double] = Array.fill(size)(0)\n    for (i <- 0 until array.length)\n      frequencyArray(array(i).toInt) += 1\n    argmax(frequencyArray)\n  }\n\n  /* Get the argument of the minimum value of an array of numeric values\n *\n * @param array the Array of numeric values\n * @return the argument of the minimum value\n */\n  def argmax(array: Array[Double]): Double = array.zipWithIndex.maxBy(_._1)._2\n  /*\n   * Get the log2 of input\n   * \n   * @param v double value\n   * @return the log2 of v\n   */\n  def log2(v: Double): Double = log(v) / log(2)\n\n  /* Transpose a matrix\n   * \n   * @param input matrix in form of 2-D array\n   * @return the transpose of input matrix\n   */\n  def transpose(input: Array[Array[Double]]): Array[Array[Double]] = {\n    val output: Array[Array[Double]] = Array.fill(input(0).length)(new Array[Double](input.length))\n    input.zipWithIndex.map {\n      row =>\n        row._1.zipWithIndex.map {\n          col => output(col._2)(row._2) = input(row._2)(col._2)\n        }\n    }\n    output\n  }\n  /*\n   * Split a matrix with the input index, merge other columns into one column and transpose\n   * \n   * @param input matrix in form of 2-D array\n   * @param fIndex index of columns\n   * @return a matrix of 2 rows\n   */\n  def splitTranspose(input: Array[Array[Double]], fIndex: Int): Array[Array[Double]] = {\n    val output: Array[Array[Double]] = Array.fill(2)(new Array[Double](input.length))\n    input.zipWithIndex.map {\n      row =>\n        row._1.zipWithIndex.map {\n          col =>\n            if (col._2 == fIndex) output(0)(row._2) = input(row._2)(col._2)\n            else output(1)(row._2) += input(row._2)(col._2)\n        }\n    }\n    output\n  }\n\n  /*\n   * Normalize input matrix\n   * \n   * @param input matrix in form of 2-D array\n   * @return normalized matrix\n   */\n  def normal(input: Array[Array[Double]]): Array[Array[Double]] = {\n    val total = input.map(_.sum).sum\n    input.map { row => row.map { _ / total } }\n  }\n\n  /*\n   * Normalize input array\n   * \n   * @param input double array\n   * @return normalized array\n   */\n  def normal(input: Array[Double]): Array[Double] = {\n    val total = input.sum\n    input.map { { _ / total } }\n  }\n\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/bayes/MultinomialNaiveBayesSuite.scala",
    "content": "package org.apache.spark.streamdm.classifiers.bayes\n\n//import org.apache.spark.streamdm.classifiers.bayes.MultinomialNaiveBayes\nimport org.apache.spark.streamdm.classifiers.bayes._\nimport org.apache.spark.streamdm.core._\n\nimport org.scalatest.FunSuite\n\n/**\n * Test suite for the Multinomial NaiveBayes.\n */\nclass MultinomialNaiveBayesSuite extends FunSuite {\n\n  test(\"the merge function must merge statistics and return a new model\") {\n    val ct1: Array[Double] = Array(1, 0)\n    val ct2: Array[Double] = Array(0, 1)\n    val cfs1: Array[Array[Double]] = Array(Array(1.0, 0, 0), Array(0, 2, 0))\n    val cfs2: Array[Array[Double]] = Array(Array(-1, 0, 0), Array(0, -2, 0))\n    val mod1 = new MultinomialNaiveBayesModel(2, 3, 1, ct1, cfs1)\n    val mod2 = new MultinomialNaiveBayesModel(2, 3, 1, ct2, cfs2)\n    val mergemodel = mod1.merge(mod2)\n    assert(mod1 != mergemodel)\n\n    assert(mergemodel.classStatistics(0) == 1.0)\n    assert(mergemodel.classStatistics(1) == 1.0)\n    mergemodel.classFeatureStatistics.foreach { x => x.foreach { y => assert(y == 0) } }\n\n  }\n\n  test(\"the create function must init as the input\") {\n    val ct1: Array[Double] = Array(1, 0)\n    val ct2: Array[Double] = Array(0, 1)\n    val cfs1: Array[Array[Double]] = Array(Array(1.0, 0, 0), Array(0, 2, 0))\n    val cfs2: Array[Array[Double]] = Array(Array(-1, 0, 0), Array(0, -2, 0))\n    val mod1 = new MultinomialNaiveBayesModel(2, 3, 1)\n    val mod2 = new MultinomialNaiveBayesModel(2, 3, 1, ct2, cfs2)\n    assert(mod1.numClasses == 2)\n    assert(mod1.numFeatures == 3)\n    assert(mod1.laplaceSmoothingFactor == 1)\n\n    assert(mod1.classStatistics(0) == 0)\n    assert(mod1.classStatistics(1) == 0)\n    mod1.classFeatureStatistics.foreach { x => x.foreach { y => assert(y == 0) } }\n    assert(mod2.classStatistics == ct2)\n    assert(mod2.classFeatureStatistics == cfs2)\n\n  }\n\n  test(\"predict must return the right label\") {\n    val ct1: Array[Double] = Array(1, 1)\n    val cfs1: Array[Array[Double]] = Array(Array(1, 0), Array(0, 1))\n    val mod1 = new MultinomialNaiveBayesModel(2, 2, 1, ct1, cfs1)\n    val instance: Example = Example.parse(\"1 1,1\", \"dense\", \"dense\")\n\n    assert(mod1.predict(instance) == 0.0)\n    val instance1: Example = Example.parse(\"0 1,0\", \"dense\", \"dense\")\n    assert(mod1.predict(instance1) == 0.0)\n    val instance2: Example = Example.parse(\"1 0,1\", \"dense\", \"dense\")\n    assert(mod1.predict(instance2) == 1)\n  }\n\n  test(\"updated labeled data must be added to the statistics\") {\n    val ct1: Array[Double] = Array(1, 1)\n    val cfs1: Array[Array[Double]] = Array(Array(1, 0), Array(0, 1))\n    val mod1 = new MultinomialNaiveBayesModel(2, 2, 1, ct1, cfs1)\n\n    val instance1: Example = Example.parse(\"0 1,0\", \"dense\", \"dense\")\n    mod1.update(instance1)\n    assert(mod1.classStatistics(0) == 2)\n    assert(mod1.classStatistics(1) == 1)\n    assert(mod1.classFeatureStatistics(0)(0) == 2)\n    assert(mod1.classFeatureStatistics(0)(1) == 0)\n    assert(mod1.classFeatureStatistics(1)(0) == 0)\n    assert(mod1.classFeatureStatistics(1)(1) == 1)\n    val instance2: Example = Example.parse(\"1 0,1\", \"dense\", \"dense\")\n    mod1.update(instance2)\n    assert(mod1.classStatistics(0) == 2)\n    assert(mod1.classStatistics(1) == 2)\n    assert(mod1.classFeatureStatistics(0)(0) == 2)\n    assert(mod1.classFeatureStatistics(0)(1) == 0)\n    assert(mod1.classFeatureStatistics(1)(0) == 0)\n    assert(mod1.classFeatureStatistics(1)(1) == 2)\n  }\n\n}"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/model/HingeLossSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\nimport org.scalatest.FunSuite\n\n/**\n * Test suite for the Hinge Loss.\n */\nclass HingeLossSuite extends FunSuite {\n\n  test(\"The Hinge Loss function must compute the value of the loss function\") {\n    val loss = new HingeLoss\n    assert (loss.loss(1, 0) == 1)\n    assert (loss.loss(1, 1) == 0)\n\n  }\n\n  test(\"It must compute the gradient of the loss function\") {\n    val loss = new HingeLoss\n    assert (loss.gradient(0, .5) == 1)\n    assert (loss.gradient(1, .5) == -1)\n  }\n\n  test(\"It must compute the prediction using the loss function\") {\n    val loss = new HingeLoss\n    assert (loss.predict(1) == 1)\n    assert (loss.predict(-1) == 0)\n\n  }\n\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/model/L1RegularizerSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\nimport org.scalatest.FunSuite\n\n/**\n * Test suite for the L1Regularizer\n */\nclass L1RegularizerSuite extends FunSuite {\n\n  test(\"The L1 Regularizer function must compute the gradient of the regularizer\") {\n    val regularizer = new L1Regularizer\n    assert (regularizer.gradient(.5) == 1)\n    assert (regularizer.gradient(-.5) == -1)\n  }\n\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/model/L2RegularizerSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\nimport org.scalatest.FunSuite\n\n/**\n * Test suite for the L2Regularizer\n */\nclass L2RegularizerSuite extends FunSuite {\n\n  test(\"The L1 Regularizer function must compute the gradient of the regularizer\") {\n    val regularizer = new L2Regularizer\n    assert (regularizer.gradient(.5) == .5)\n    assert (regularizer.gradient(-.5) == -.5)\n  }\n\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/model/LogisticLossSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\nimport org.scalatest.FunSuite\n\n/**\n * Test suite for the Logistic Loss.\n */\nclass LogisticLossSuite extends FunSuite {\n\n  test(\"The Logistic Loss function must compute the value of the loss function\") {\n    val loss = new LogisticLoss\n    assert (loss.loss(1, 0) == math.log(2))\n    assert (loss.loss(1, 1) == math.log(1+math.exp(-1)))\n  }\n\n  test(\"It must compute the gradient of the loss function\") {\n    val loss = new LogisticLoss\n    assert (loss.gradient(0, 0) == .5)\n    assert (loss.gradient(1, 0) == -.5)\n  }\n\n  test(\"It must compute the prediction using the loss function\") {\n    val loss = new LogisticLoss\n    assert (loss.predict(1) == 1)\n    assert (loss.predict(0) == 0)\n\n  }\n\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/model/PerceptronLossSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\nimport org.scalatest.FunSuite\n\n/**\n * Test suite for the Perceptron Loss.\n */\nclass PerceptronLossSuite extends FunSuite {\n\n  test(\"The PerceptronLoss function must compute the value of the loss function\") {\n    val loss = new PerceptronLoss\n    assert (loss.loss(1, 0) == .5)\n    assert (loss.loss(1, 1) == 0)\n\n  }\n\n  test(\"It must compute the gradient of the loss function\") {\n    val loss = new PerceptronLoss\n    assert (loss.gradient(0, .5) == -1)\n    assert (loss.gradient(1, .5) == 0)\n  }\n\n  test(\"It must compute the prediction using the loss function\") {\n    val loss = new PerceptronLoss\n    assert (loss.predict(1) == 1)\n    assert (loss.predict(-1) == 0)\n\n  }\n\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/model/SquaredLossSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\nimport org.scalatest.FunSuite\n\n/**\n * Test suite for the Squared Loss.\n */\nclass SquaredLossSuite extends FunSuite {\n\n  test(\"The SquaredLoss function must compute the value of the loss function\") {\n    val loss = new SquaredLoss\n    assert (loss.loss(1, 0) == .5)\n    assert (loss.loss(1, 1) == 0)\n\n  }\n\n  test(\"It must compute the gradient of the loss function\") {\n    val loss = new SquaredLoss\n    assert (loss.gradient(0, .5) == .5)\n    assert (loss.gradient(1, .5) == -.5)\n  }\n\n  test(\"It must compute the prediction using the loss function\") {\n    val loss = new SquaredLoss\n    assert (loss.predict(1) == 1)\n    assert (loss.predict(-1) == 0)\n\n  }\n\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/model/ZeroRegularizerSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.classifiers.model\n\nimport org.scalatest.FunSuite\n\n/**\n * Test suite for the ZeroRegularizer\n */\nclass ZeroRegularizerSuite extends FunSuite {\n\n  test(\"The Zero Regularizer function must compute the gradient of the regularizer\") {\n    val regularizer = new ZeroRegularizer\n    assert (regularizer.gradient(.5) == 0)\n    assert (regularizer.gradient(-.5) == 0)\n  }\n\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/trees/ConditionalTestSuite.scala",
    "content": "package org.apache.spark.streamdm.classifiers.trees\n\nimport org.scalatest.FunSuite\nimport org.apache.spark.streamdm.core._\nclass ConditionalTestSuite extends FunSuite {\n\n  test(\"test NumericBinaryTest\") {\n    //nb1 not equal test\n    val nb1: NumericBinaryTest = new NumericBinaryTest(0, 1, false)\n    //nb2 equal test\n    val nb2: NumericBinaryTest = new NumericBinaryTest(0, 1, true)\n    val example0: Example = new Example(new DenseInstance(Array[Double](0, 5, 5)), null)\n    val example1: Example = new Example(new DenseInstance(Array[Double](1, 5, 5)), null)\n    val example2: Example = new Example(new DenseInstance(Array[Double](2, 5, 5)), null)\n    assert(nb1.branch(example0) == 0)\n    assert(nb1.branch(example1) == 1)\n    assert(nb1.branch(example2) == 1)\n    assert(nb2.branch(example0) == 1)\n    assert(nb2.branch(example1) == 0)\n    assert(nb2.branch(example2) == 1)\n    assert(nb2.maxBranches() == 2)\n  }\n\n  test(\"test NominalBinaryTest\") {\n    val nb1: NominalBinaryTest = new NominalBinaryTest(0, 1)\n    val example0: Example = new Example(new DenseInstance(Array[Double](0, 5, 5)), null)\n    val example1: Example = new Example(new DenseInstance(Array[Double](1, 5, 5)), null)\n    val example2: Example = new Example(new DenseInstance(Array[Double](2, 5, 5)), null)\n    assert(nb1.branch(example0) == 1)\n    assert(nb1.branch(example1) == 0)\n    assert(nb1.branch(example2) == 1)\n    assert(nb1.maxBranches() == 2)\n  }\n\n  test(\"test NominalMultiwayTest\") {\n    val nb1: NominalMultiwayTest = new NominalMultiwayTest(0, 3)\n    val example0: Example = new Example(new DenseInstance(Array[Double](0, 5, 5)), null)\n    val example1: Example = new Example(new DenseInstance(Array[Double](1, 5, 5)), null)\n    val example2: Example = new Example(new DenseInstance(Array[Double](2, 5, 5)), null)\n    assert(nb1.branch(example0) == 0)\n    assert(nb1.branch(example1) == 1)\n    assert(nb1.branch(example2) == 2)\n    assert(nb1.maxBranches() == 3)\n  }\n\n  test(\"test NumericBinaryRulePredicate\") {\n    //nb1 ==\n    val nb1: NumericBinaryRulePredicate = new NumericBinaryRulePredicate(0, 1, 0)\n    //nb2 <=\n    val nb2: NumericBinaryRulePredicate = new NumericBinaryRulePredicate(0, 1, 1)\n    //nb3 <\n    val nb3: NumericBinaryRulePredicate = new NumericBinaryRulePredicate(0, 1, 2)\n    val example0: Example = new Example(new DenseInstance(Array[Double](0, 5, 5)), null)\n    val example1: Example = new Example(new DenseInstance(Array[Double](1, 5, 5)), null)\n    val example2: Example = new Example(new DenseInstance(Array[Double](2, 5, 5)), null)\n    assert(nb1.branch(example0) == 1)\n    assert(nb1.branch(example1) == 0)\n    assert(nb1.branch(example2) == 1)\n    assert(nb2.branch(example0) == 0)\n    assert(nb2.branch(example1) == 0)\n    assert(nb2.branch(example2) == 1)\n    assert(nb3.branch(example0) == 0)\n    assert(nb3.branch(example1) == 1)\n    assert(nb3.branch(example2) == 1)\n    assert(nb2.maxBranches() == 2)\n  }\n}"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/trees/FeatureClassObserverSuite.scala",
    "content": "package org.apache.spark.streamdm.classifiers.trees\n\nimport org.scalatest.FunSuite\nimport org.apache.spark.streamdm.core._\n\nclass FeatureClassObserverSuite extends FunSuite {\n\n  test(\"test NullFeatureClassObserver\") {\n\n    val fco: NullFeatureClassObserver = new NullFeatureClassObserver()\n\n  }\n}"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/trees/FeatureSplitSuite.scala",
    "content": "package org.apache.spark.streamdm.classifiers.trees\n\nimport org.scalatest.FunSuite\n\nclass FeatureSplitSuite extends FunSuite {\n\n  test(\"test compare\") {\n    val fs1: FeatureSplit = new FeatureSplit(null, 1, null)\n    val fs2: FeatureSplit = new FeatureSplit(null, 2, null)\n    val fs3: FeatureSplit = new FeatureSplit(null, 2, null)\n    assert(fs1 < fs2)\n    assert(fs2 > fs1)\n    assert(fs3 == fs3)\n  }\n\n  test(\"test sort\") {\n    var fss: Array[FeatureSplit] = new Array[FeatureSplit](4)\n    fss(0) = new FeatureSplit(null, 3, null)\n    fss(1) = new FeatureSplit(null, 2, null)\n    fss(2) = new FeatureSplit(null, 2, null)\n    fss(3) = new FeatureSplit(null, 4, null)\n\n    fss = fss.sorted\n    for (i <- 0 until fss.length - 1)\n      assert(fss(i) <= fss(i + 1))\n    assert(fss(0) <= fss(1))\n    assert(fss(0) >= fss(1))\n    assert(fss(2).merit == 3)\n\n  }\n}"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/trees/GaussianEstimatorSuite.scala",
    "content": "package org.apache.spark.streamdm.classifiers.trees\n\nimport org.scalatest.FunSuite\nimport scala.math.{ abs }\nclass GaussianEstimatorSuite extends FunSuite {\n\n  test(\"test observe,stddev,variance,merge\") {\n    val gs: GaussianEstimator = new GaussianEstimator()\n    gs.observe(2.0, 0.5)\n    gs.observe(1, 2)\n    var gs2: GaussianEstimator = new GaussianEstimator()\n    gs2 = gs2.merge(gs, true)\n    assert(gs2.totalWeight() == 2.5)\n    assert(gs2.getMean() == 1.2)\n    assert(gs2.stdDev() == 0.5163977794943222)\n    assert(gs2.variance() == 0.2666666666666666)\n  }\n\n  test(\"test variance\") {\n    val gs: GaussianEstimator = new GaussianEstimator()\n    gs.observe(2.0, 0.5)\n    gs.observe(1, 0.5)\n    gs.observe(3, 0.5)\n    var gs2: GaussianEstimator = new GaussianEstimator()\n    gs2 = gs2.merge(gs, true)\n    assert(gs2.variance() == 2.0)\n  }\n\n  test(\"test probalilityDensity\") {\n    val gs: GaussianEstimator = new GaussianEstimator()\n    assert(gs.probabilityDensity(0) == 0)\n    assert(gs.probabilityDensity(1) == 0)\n    assert(gs.probabilityDensity(2) == 0)\n    gs.observe(2.0, 1)\n    var gs2: GaussianEstimator = new GaussianEstimator()\n    gs2 = gs2.merge(gs, true)\n    assert(gs2.probabilityDensity(0) == 0)\n    assert(gs2.probabilityDensity(1) == 0)\n    assert(gs2.probabilityDensity(2) == 1)\n  }\n\n}"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/classifiers/trees/SplitCriterionSuite.scala",
    "content": "package org.apache.spark.streamdm.classifiers.trees\n\nimport org.scalatest.FunSuite\nimport org.apache.spark.streamdm.classifiers.trees._\nimport scala.math.{ log, abs }\nimport org.apache.spark.streamdm.utils.Utils.{ log2 }\nclass SplitCriterionSuite extends FunSuite {\n\n  test(\"InfoGainSplitCriterion, test negtive function\") {\n    val ig: InfoGainSplitCriterion = new InfoGainSplitCriterion()\n    assert(ig.hasNegative(Array(0, -1.0, 2)))\n  }\n\n  test(\"InfoGainSplitCriterion, test entropy of array\") {\n    val ig: InfoGainSplitCriterion = new InfoGainSplitCriterion()\n    assert(ig.entropy(Array[Double](2.0, 4, 8)) == (log2(2.0 + 4 + 8) - (2.0 * log2(2.0) + 4 * log2(4.0) + 8 * log2(8.0)) / (2.0 + 4 + 8)))\n    assert(ig.entropy(Array(0.5, 0.5)) == 1.0)\n    assert(ig.entropy(Array(0.4, 0.6)) == 0.9709505944546686)\n  }\n\n  test(\"InfoGainSplitCriterion, test entropy of matrix \") {\n    val ig: InfoGainSplitCriterion = new InfoGainSplitCriterion()\n    assert(ig.entropy(Array(Array(1.0, 1, 1), Array(1.0, 1, 1))) == 1.5849625007211563)\n  }\n\n  test(\"InfoGainSplitCriterion, test nunFrac\") {\n    val ig: InfoGainSplitCriterion = new InfoGainSplitCriterion()\n    assert(ig.numGTFrac(Array(new Array(0)), 0.01) == 0)\n    assert(ig.numGTFrac(Array(Array(1, -1, 1), Array(-1, 1, 0)), 0.01) == 1)\n    assert(ig.numGTFrac(Array(Array(1, 1, 1), Array(-1, 1, 1)), 0.01) == 2)\n    assert(ig.numGTFrac(Array(Array(1, 2, 3, 4, 5), Array(5, 4, 3, 2, 1)), 0.01) == 2)\n  }\n  test(\"InfoGainSplitCriterion, test rangeMerit\") {\n    val ig: InfoGainSplitCriterion = new InfoGainSplitCriterion()\n    assert(ig.rangeMerit(Array(1.0, 1)) == log2(2))\n  }\n  test(\"InfoGainSplitCriterion, test merit\") {\n    val ig: InfoGainSplitCriterion = new InfoGainSplitCriterion()\n    assert(ig.merit(Array(1.0, 1, 1), Array(Array(1, -1, 1), Array(-1, 1, 0))) == Double.NegativeInfinity)\n  }\n\n  test(\"GiniSplitCriterion, test computeGini\") {\n    val gc: GiniSplitCriterion = new GiniSplitCriterion()\n    assert(gc.computeGini(Array[Double](1, 1, 1), 3) == 1.0 - 3.0 * 1.0 / 9)\n  }\n  test(\"GiniSplitCriterion, test merit\") {\n    val gc: GiniSplitCriterion = new GiniSplitCriterion()\n    assert(abs(gc.merit(Array(1.0 / 3, 1.0 / 3, 1.0 / 3),\n      Array(Array(1.0 / 6, 1.0 / 6, 1.0 / 6), Array(1.0 / 6, 1.0 / 6, 1.0 / 6))) - 0.33333333333) < 0.000001)\n  }\n\n}"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/core/DenseInstanceSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\nimport org.scalatest.FunSuite\nimport math._\n\n/**\n  * Test suite for the DenseInstance.\n  */\nclass DenseInstanceSuite extends FunSuite {\n\n  test(\"return its features given indices\") {\n    val instance = DenseInstance(Array(1.4,1.3,2.1))\n    assert(instance(0) == 1.4)\n    assert(instance(1) == 1.3)\n    assert(instance(2) == 2.1)\n  }\n\n  test(\"return Double.NaN for a out of bounds index\") {\n    val instance = DenseInstance(Array(1.4,1.3,2.1))\n    assert(instance(-1).isNaN())\n    assert(instance(3).isNaN())\n  }\n\n  test(\"have a dot operation with another DenseInstance\") {\n    val instance1 = DenseInstance(Array(1.4, 1.3, 2.1))\n    val instance2 = DenseInstance(Array(0.4, 0.3, 1.1))\n    assert(instance1.dot(instance2) == 1.4*0.4+1.3*0.3+2.1*1.1)\n  }\n\n  test(\"add another DenseInstance\") {\n    val instance1 = DenseInstance(Array(1.4, 1.3, 2.1))\n    val instance2 = DenseInstance(Array(0.4, 0.3, 1.1))\n    val sumInstance = instance1.add(instance2);\n    val instance3 = DenseInstance(Array(1.4+0.4, 1.3+0.3, 2.1+1.1))\n    (sumInstance.features zip instance3.features).map{case (x,y)=> assert(x==y)}\n  }\n\n  test(\"add a SparseInstance\") {\n    val instance1 = DenseInstance(Array(1.4, 1.3, 2.1))\n    val instance2 = SparseInstance(Array(1), Array(1.2))\n    val sumInstance = instance1.add(instance2);\n    val instance3 = DenseInstance(Array(1.4+0, 1.3+1.2, 2.1+0))\n    (sumInstance.features zip instance3.features).map{case (x,y)=> assert(x==y)}\n  }\n\n  test(\"compute the distance to another DenseInstance\") {\n    val instance1 = DenseInstance(Array(1.4, 1.3, 2.1))\n    val instance2 = DenseInstance(Array(0.4, 0.3, 1.1))\n    val dist = instance1.distanceTo(instance2);\n    val trueDist = math.sqrt(math.pow(1.4-0.4,2)+math.pow(1.3-0.3,2)+\n      math.pow(2.1-1.1,2))\n    assert(dist==trueDist)\n  }\n\n  test(\"set the value of a Feature\") {\n    val instance1 = DenseInstance(Array(1.1, 1.3, 2.1))\n    val instance2 = instance1.set(1,2.0)\n    val instance3 = DenseInstance(Array(1.1, 2.0, 2.1))\n    (instance2.features zip instance3.features).map{case (x,y) => assert(x==y)}\n  }\n\n  test(\"append a Feature\") {\n    val instance1 = DenseInstance(Array(1.1, 1.3, 2.1))\n    val instance2 = instance1.set(3,2.0)\n    val instance3 = DenseInstance(Array(1.1, 1.3, 2.1, 2.0))\n    (instance2.features zip instance3.features).map{case (x,y) => assert(x==y)}\n  }\n\n  test(\"function for features\") {\n    val instance1 = DenseInstance(Array(1.4, 1.3, 2.1))\n    val instance2 = instance1.map(f=>f+2.0)\n    (instance1.features zip instance2.features).map{case (x,y)=>\n      assert(y==x+2.0)}\n  }\n\n  test(\"parse from CSV format\") {\n    val input = \"1.1,1.3,2.1\"\n    val parsedInstance = DenseInstance.parse(input)\n    val testInstance = DenseInstance(Array(1.1,1.3,2.1))\n    (parsedInstance.features zip testInstance.features).map{ case (x,y) =>\n      assert(x==y)}\n  }\n\n  test(\"have a .toString override\") {\n    val instance1 = DenseInstance(Array(1.4, 1.3, 2.1))\n    assert(instance1.toString == \"1.4,1.3,2.1\")\n  }\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/core/ExampleSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\nimport org.scalatest.FunSuite\n\n/**\n  * Test suite for the Example.\n  */\nclass ExampleSuite extends FunSuite {\n\n  test(\"return its features given indices\") {\n    val example =  new Example(SparseInstance(Array(1,2), Array(1.4, 1.3)),\n      DenseInstance(Array(1.0)))\n    assert(example.featureAt(0).isNaN())\n    assert(example.featureAt(1)==1.4)\n    assert(example.featureAt(2)==1.3)\n  }\n\n  test(\"return its labels given indices\") {\n    val example =  new Example(SparseInstance(Array(1,2), Array(1.4, 1.3)),\n      DenseInstance(Array(1.0)))\n    assert(example.labelAt(0)==1.0)\n    assert(example.labelAt(1).isNaN())\n  }\n\n  test(\"able to parse an input-only format\")\n  {\n    val input = \"1:1.1,3:2.1\"\n    val parsedExample = Example.parse(input,\"sparse\",\"dense\")\n    val testExample = new Example(SparseInstance(Array(0,2),Array(1.1,2.1)))\n    assert(parsedExample.featureAt(0)==testExample.featureAt(0))\n    assert(parsedExample.featureAt(1).isNaN())\n    assert(parsedExample.featureAt(2)==testExample.featureAt(2))\n    assert(parsedExample.labelAt(0)==0)\n    assert(parsedExample.weight==1.0)\n  }\n\n  test(\"able to parse an input-output format\")\n  {\n    val input = \"1 1:1.1,3:2.1\"\n    val parsedExample = Example.parse(input,\"sparse\",\"dense\")\n    val testExample = new Example(SparseInstance(Array(0,2),Array(1.1,2.1)),\n      DenseInstance(Array(1.0)))\n    assert(parsedExample.featureAt(0)==testExample.featureAt(0))\n    assert(parsedExample.featureAt(1).isNaN())\n    assert(parsedExample.featureAt(2)==testExample.featureAt(2))\n    assert(parsedExample.labelAt(0)==testExample.labelAt(0))\n    assert(parsedExample.weight==1.0)\n  }\n\n  test(\"able to parse an input-output-weight format\")\n  {\n    val input = \"1 1:1.1,3:2.1 3.2\"\n    val parsedExample = Example.parse(input,\"sparse\",\"dense\")\n    val testExample = new Example(SparseInstance(Array(0,2),Array(1.1,2.1)),\n      DenseInstance(Array(1.0)))\n    assert(parsedExample.featureAt(0)==testExample.featureAt(0))\n    assert(parsedExample.featureAt(1).isNaN())\n    assert(parsedExample.featureAt(2)==testExample.featureAt(2))\n    assert(parsedExample.labelAt(0)==testExample.labelAt(0))\n    assert(parsedExample.weight==3.2)\n  }\n\n  test(\"should have a .toString override for input-only Examples\") {\n    val instance1 =  new Example(SparseInstance(Array(1,2), Array(1.4, 1.3)))\n    assert(instance1.toString == \"2:%f,3:%f\".format(1.4,1.3))\n  }\n\n  test(\"should have a .toString override for input-output Examples\") {\n    val instance1 =  new Example(SparseInstance(Array(1,2), Array(1.4, 1.3)),\n      DenseInstance(Array(1.0)))\n    assert(instance1.toString == \"1.0 2:%f,3:%f\".format(1.4,1.3))\n  }\n\n  test(\"should have a .toString override for input-output-weight Examples\") {\n    val instance1 =  new Example(SparseInstance(Array(1,2), Array(1.4, 1.3)),\n      DenseInstance(Array(1.0)),2.1)\n    assert(instance1.toString == \"1.0 2:%f,3:%f %f\".format(1.4,1.3,2.1))\n  }\n\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/core/SparseInstanceSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\n\npackage org.apache.spark.streamdm.core\n\nimport org.scalatest.FunSuite\n\n/**\n  * Test suite for the SparseInstance.\n  */\nclass SparseInstanceSuite extends FunSuite {\n\n  test(\"return its features given indices\") {\n    val instance = SparseInstance(Array(0,1,2), Array(1.4,1.3,2.1))\n    assert(instance(0) == 1.4)\n    assert(instance(1) == 1.3)\n    assert(instance(2) == 2.1)\n  }\n\n  test(\"return NaN for a out of bounds index\") {\n    val instance = SparseInstance(Array(0,2), Array(1.4,2.1))\n    assert(instance(-1).isNaN())\n    assert(instance(1).isNaN())\n    assert(instance(3).isNaN())\n  }\n\n  test(\"have a dot operation with another SparseInstance\") {\n    val instance1 = SparseInstance(Array(0,2), Array(1.4,2.1))\n    val instance2 = SparseInstance(Array(1,2),Array(1.2,3.2))\n    assert(instance1.dot(instance2) == 2.1*3.2)\n  }\n\n  test(\"have a dot operation with a DenseInstance\") {\n    val instance1 = SparseInstance(Array(0,2), Array(1.4,2.1))\n    val instance2 = DenseInstance(Array(1.2,2.1,3.2))\n    assert(instance1.dot(instance2) == 1.4*1.2+2.1*3.2)\n  }\n\n  test(\"able to add another SparseInstance\") {\n    val instance1 = SparseInstance(Array(0,2), Array(1.4, 2.1))\n    val instance2 = SparseInstance(Array(1,2), Array(1.6, 2.0))\n    val sumInstance = instance1.add(instance2);\n    val instance3 = SparseInstance(Array(0,1,2), Array(1.4, 1.6, 2.1+2.0))\n    sumInstance.indexes.map{case x => assert(sumInstance(x)==instance3(x))}\n  }\n\n  test(\"able to add a DenseInstance\") {\n    val instance1 = SparseInstance(Array(0,2), Array(1.4, 2.1))\n    val instance2 = DenseInstance(Array(0.0, 1.6, 2.0))\n    val sumInstance = instance1.add(instance2);\n    val instance3 = SparseInstance(Array(0,1,2), Array(1.4, 1.6, 2.1+2.0))\n    sumInstance.indexes.map{ case x => assert(sumInstance(x)==instance3(x))}\n  }\n\n  test(\"able to compute the distance to another SparseInstance\") {\n    val instance1 = SparseInstance(Array(0,2), Array(1.4, 2.1))\n    val instance2 = SparseInstance(Array(1,2), Array(1.6, 2.0))\n    val dist = instance1.distanceTo(instance2);\n    val trueDist = math.sqrt(math.pow(1.4,2)+math.pow(1.6,2)+\n      math.pow(2.1-2.0,2))\n    assert(dist==trueDist)\n  }\n\n  test(\"able to compute the distance to a DenseInstance\") {\n    val instance1 = SparseInstance(Array(0,2), Array(1.4, 2.1))\n    val instance2 = DenseInstance(Array(0.0, 1.6, 2.0))\n    val dist = instance1.distanceTo(instance2);\n    val trueDist = math.sqrt(math.pow(1.4,2)+math.pow(1.6,2)+\n      math.pow(2.1-2.0,2))\n    assert(dist==trueDist)\n  }\n\n  test(\"able to append a Feature\") {\n    val instance1 = SparseInstance(Array(1), Array(1.1))\n    val instance2 = instance1.set(2,2.1)\n    val instance3 = SparseInstance(Array(1,2), Array(1.1, 2.1))\n    instance2.indexes.map{ case x => assert(instance2(x)==instance3(x))}\n  }\n\n  test(\"have a map function for features\") {\n    val instance1 = SparseInstance(Array(1,2), Array(1.4, 1.3))\n    val instance2 = instance1.map(f=>f+2.0)\n    instance2.indexes.map{ case x => assert(instance2(x)==instance1(x)+2.0)}\n  }\n\n  test(\"have a parser in comma separated LibSVM format\")\n  {\n    val input = \"1:1.1,3:2.1\"\n    val parsedInstance = SparseInstance.parse(input)\n    val testInstance = SparseInstance(Array(0,2),Array(1.1,2.1))\n    parsedInstance.indexes.map{ case x =>\n      assert(parsedInstance(x)==testInstance(x))}\n  }\n\n  test(\"have a .toString override\") {\n    val instance1 = SparseInstance(Array(1,2), Array(1.4, 1.3))\n    assert(instance1.toString == \"2:%f,3:%f\".format(1.4,1.3))\n  }\n}\n"
  },
  {
    "path": "src/test/scala/org/apache/spark/streamdm/utils/UtilsSuite.scala",
    "content": "/*\n * Copyright (C) 2015 Holmes Team at HUAWEI Noah's Ark Lab.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n *\n */\npackage org.apache.spark.streamdm.utils\n\nimport org.scalatest.FunSuite\n\n/**\n * Test suite for Utils\n */\nclass UtilsSuite extends FunSuite {\n\n  test(\"The Majority Vote should return the most frequent item\") {\n    assert(Utils.majorityVote(Array(1, 2, 1, 0), 3) == 1)\n    assert(Utils.majorityVote(Array(1, 2, 2, 0), 3) == 2)\n    assert(Utils.majorityVote(Array(1, 2, 0, 0), 3) == 0)\n    assert(Utils.majorityVote(Array(1, 0, 1, 2), 3) == 1)\n\n  }\n\n  test(\"test transpose\") {\n    val input: Array[Array[Double]] = Array(Array(1, 2, 3), Array(4, 5, 6))\n    val output = Utils.transpose(input)\n    assert(output.length == 3)\n    assert(output(0).length == 2)\n    assert(output(0)(0) == input(0)(0))\n    assert(output(0)(1) == input(1)(0))\n    assert(output(1)(0) == input(0)(1))\n    assert(output(1)(1) == input(1)(1))\n    assert(output(2)(0) == input(0)(2))\n    assert(output(2)(1) == input(1)(2))\n  }\n\n  test(\"test splitTranspose\") {\n    val input: Array[Array[Double]] = Array(Array(1, 2, 3), Array(4, 5, 6))\n    val output = Utils.splitTranspose(input, 1)\n    assert(output.length == 2)\n    assert(output(0).length == 2)\n    assert(output(0)(0) == 2)\n    assert(output(0)(1) == 5)\n    assert(output(1)(0) == 4)\n    assert(output(1)(1) == 10)\n  }\n\n}\n"
  },
  {
    "path": "website/.editorconfig",
    "content": "# editorconfig.org\nroot = true\n\n[*]\nindent_style = space\nindent_size = 2\nend_of_line = lf\ncharset = utf-8\ntrim_trailing_whitespace = true\ninsert_final_newline = true\n\n[*.md]\ntrim_trailing_whitespace = false"
  },
  {
    "path": "website/.gitignore",
    "content": "_site\n.DS_Store\n*.sublime-project\n*.sublime-workspace\ncodekit-config.json\nnode_modules\n.sass-cache"
  },
  {
    "path": "website/.jshintrc",
    "content": "{\n  \"bitwise\": true,\n  \"browser\": true,\n  \"curly\": true,\n  \"eqeqeq\": true,\n  \"eqnull\": true,\n  \"es5\": false,\n  \"esnext\": true,\n  \"immed\": true,\n  \"jquery\": true,\n  \"latedef\": true,\n  \"newcap\": true,\n  \"noarg\": true,\n  \"node\": true,\n  \"strict\": false,\n  \"trailing\": false,\n  \"undef\": true,\n  \"multistr\": true,\n  \"expr\": true\n}"
  },
  {
    "path": "website/.travis.yml",
    "content": "language: ruby\nrvm:\n  - 2.1\n\nbefore_install:\n  - export NOKOGIRI_USE_SYSTEM_LIBRARIES=true\n\nscript:\n  - bundle exec jekyll build --drafts\n#  - bundle exec htmlproof ./_site\n\nnotifications:\n  email: false\n"
  },
  {
    "path": "website/404.md",
    "content": "---\nlayout: page\ntitle: \"Page Not Found\"\ndescription: \"Page not found.\"\nsitemap: false\nsearch_omit: true\npermalink: /404.html\n---  \n\nSorry, but the page you were trying to view does not exist --- perhaps you can try searching for it below.\n\n<script type=\"text/javascript\">\n  var GOOG_FIXURL_LANG = 'en';\n  var GOOG_FIXURL_SITE = '{{ site.url }}'\n</script>\n<script type=\"text/javascript\"\n  src=\"//linkhelp.clients.google.com/tbproxy/lh/wm/fixurl.js\">\n</script>\n"
  },
  {
    "path": "website/Gemfile",
    "content": "source 'https://rubygems.org'\n\ngem 'jekyll'\ngem 'jekyll-sitemap'\ngem 'octopress', '~> 3.0.0.rc.12'\ngem 'rouge'"
  },
  {
    "path": "website/Gruntfile.js",
    "content": "'use strict';\nmodule.exports = function(grunt) {\n\n  grunt.initConfig({\n    jshint: {\n      options: {\n        jshintrc: '.jshintrc'\n      },\n      all: [\n        'Gruntfile.js',\n        'assets/js/*.js',\n        'assets/js/plugins/*.js',\n        '!assets/js/scripts.min.js'\n      ]\n    },\n    uglify: {\n      dist: {\n        files: {\n          'assets/js/scripts.min.js': [\n            'assets/js/plugins/*.js',\n            'assets/js/_*.js'\n          ]\n        }\n      }\n    },\n    imagemin: {\n      dist: {\n        options: {\n          optimizationLevel: 7,\n          progressive: true\n        },\n        files: [{\n          expand: true,\n          cwd: 'images/',\n          src: '{,*/}*.{png,jpg,jpeg}',\n          dest: 'images/'\n        }]\n      }\n    },\n    svgmin: {\n      dist: {\n        files: [{\n          expand: true,\n          cwd: 'images/',\n          src: '{,*/}*.svg',\n          dest: 'images/'\n        }]\n      }\n    },\n    watch: {\n      js: {\n        files: [\n          '<%= jshint.all %>'\n        ],\n        tasks: ['uglify']\n      }\n    },\n    clean: {\n      dist: [\n        'assets/js/scripts.min.js'\n      ]\n    }\n  });\n\n  // Load tasks\n  grunt.loadNpmTasks('grunt-contrib-clean');\n  grunt.loadNpmTasks('grunt-contrib-jshint');\n  grunt.loadNpmTasks('grunt-contrib-uglify');\n  grunt.loadNpmTasks('grunt-contrib-watch');\n  grunt.loadNpmTasks('grunt-contrib-imagemin');\n  grunt.loadNpmTasks('grunt-svgmin');\n\n  // Register tasks\n  grunt.registerTask('default', [\n    'clean',\n    'uglify',\n    'imagemin',\n    'svgmin'\n  ]);\n  grunt.registerTask('dev', [\n    'watch'\n  ]);\n\n};"
  },
  {
    "path": "website/LICENSE",
    "content": "The MIT License (MIT)\n\nCopyright (c) 2014 Michael Rose\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."
  },
  {
    "path": "website/README.md",
    "content": "# streamDM Website and Documentation\nThis is the source for the streamDM website and documentation. It is statically generated using [jekyll](http://jekyllrb.com).\n\n## Site Generation\nFirst install jekyll (assuming you have ruby installed):\n\n```\ngem install jekyll\ngem install bundler\nbundle install\n```\n\nGenerate the site, and start a server locally:\n```\nbundle exec jekyll serve -w\n```\n\nThe `-w` option tells jekyll to watch for changes to files and regenerate the site automatically when any content changes.\n\nPoint your browser to http://localhost:4000\n\nBy default, jekyll will generate the site in a `_site` directory.\n\n\n## Publishing the Website\nIn order to publish the website, you must have committer access to the streamDM website.\n\n\nTo publish changes, copy the content of the `_site` directory to the streamDM web hosting folder.\n\n## Testing locally\n\nTo test the website locally, change `url: http://streamdm.noahlab.com.hk` to `url: http://localhost:4000` in the file _config.yml.\n"
  },
  {
    "path": "website/_config.yml",
    "content": "# Site wide configuration\n\ntitle: streamDM\nlocale: en_US\ndescription: stream Data Mining for Spark Streaming\nlogo: site-logo.png\nsearch: true\nurl: http://huawei-noah.github.io/streamDM\n\n# Jekyll configuration\n\npermalink:   /:categories/:title/\nsass:\n  sass_dir: _sass\n  style: compressed\ngems:\n  - jekyll-sitemap\n\n# Build settings\nmarkdown: redcarpet\nredcarpet:\n  extensions: [\"no_intra_emphasis\", \"fenced_code_blocks\", \"autolink\", \"tables\", \"with_toc_data\"]\n\ninclude: [\".htaccess\"]\nexclude: [\"lib\", \"config.rb\", \"Capfile\", \"config\", \"log\", \"Rakefile\", \"Rakefile.rb\", \"tmp\", \"less\", \"*.sublime-project\", \"*.sublime-workspace\", \"test\", \"spec\", \"Gruntfile.js\", \"package.json\", \"node_modules\", \"Gemfile\", \"Gemfile.lock\", \"LICENSE\", \"README.md\", \"vendor\"]\n\n"
  },
  {
    "path": "website/_data/navigation.yml",
    "content": "# Site navigation links\n\n- title: About\n  url: /\n\n- title: Github\n  url: https://github.com/huawei-noah/streamDM\n\n- title: Getting Started\n  url: /docs/GettingStarted.html\n\n- title: Methods\n  url: /index.html#methods\n\n- title: Programming Guide\n  url: /docs/Programming.html\n\n- title: Contributors\n  url: /docs/Contributors.html\n\n\n"
  },
  {
    "path": "website/_includes/ad-footer.html",
    "content": "<div class=\"google-ads\" style=\"margin:10px 0;\">\n  <script async src=\"http://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js\"></script>\n  <!-- 320x50 ad -->\n  <ins class=\"adsbygoogle\"\n       style=\"display:inline-block;width:320px;height:50px\"\n       data-ad-client=\"{{ site.owner.google.ad-client }}\"\n       data-ad-slot=\"{{ site.owner.google.ad-slot }}\"></ins>\n  <script>\n  (adsbygoogle = window.adsbygoogle || []).push({});\n  </script>\n</div><!-- /.google-ads -->"
  },
  {
    "path": "website/_includes/ad-sidebar.html",
    "content": "<div class=\"google-ads\" style=\"margin-top:40px; text-align:center;\">\n  <script async src=\"http://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js\"></script>\n  <!-- 160x600 ad -->\n  <ins class=\"adsbygoogle\"\n       style=\"display:inline-block;width:160px;height:600px\"\n       data-ad-client=\"{{ site.owner.google.ad-client }}\"\n       data-ad-slot=\"{{ site.owner.google.ad-slot }}\"></ins>\n  <script>\n  (adsbygoogle = window.adsbygoogle || []).push({});\n  </script>\n</div><!-- /.google-ads -->"
  },
  {
    "path": "website/_includes/browser-upgrade.html",
    "content": "<!--[if lt IE 9]><div class=\"upgrade\"><strong><a href=\"http://whatbrowser.org/\">Your browser is quite old!</strong> Why not upgrade to a different browser to better enjoy this site?</a></div><![endif]-->"
  },
  {
    "path": "website/_includes/disqus-comments.html",
    "content": "{% if site.owner.disqus-shortname %}\n<script type=\"text/javascript\">\n    /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */\n    var disqus_shortname = '{{ site.owner.disqus-shortname }}'; // required: replace example with your forum shortname\n\n    /* * * DON'T EDIT BELOW THIS LINE * * */\n    (function() {\n        var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;\n        dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js';\n        (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);\n    })();\n\n    /* * * DON'T EDIT BELOW THIS LINE * * */\n    (function () {\n        var s = document.createElement('script'); s.async = true;\n        s.type = 'text/javascript';\n        s.src = '//' + disqus_shortname + '.disqus.com/count.js';\n        (document.getElementsByTagName('HEAD')[0] || document.getElementsByTagName('BODY')[0]).appendChild(s);\n    }());\n</script>\n<noscript>Please enable JavaScript to view the <a href=\"http://disqus.com/?ref_noscript\">comments powered by Disqus.</a></noscript>\n<a href=\"http://disqus.com\" class=\"dsq-brlink\">comments powered by <span class=\"logo-disqus\">Disqus</span></a>\n{% endif %}"
  },
  {
    "path": "website/_includes/feed-footer.html",
    "content": "&lt;p&gt;&lt;a href=&quot;{{ site.url }}{{ post.url }}&quot;&gt;{{ post.title | xml_escape }}&lt;/a&gt; was originally published by {{ site.owner.name }} at &lt;a href=&quot;{{ site.url }}&quot;&gt;{{ site.title }}&lt;/a&gt; on {{ post.date | date: \"%B %d, %Y\" }}.&lt;/p&gt;"
  },
  {
    "path": "website/_includes/footer.html",
    "content": "{% if site.owner.google.ad-client and site.owner.google.ad-slot %}{% include ad-footer.html %}{% endif %}\n\n<span>&copy; {{ site.time | date: '%Y' }} HUAWEI Noah's Ark Lab.</span>\n<div class=\"social-icons\">\n\t{% if site.owner.twitter %}<a href=\"http://twitter.com/{{ site.owner.twitter }}\" title=\"{{ site.owner.name}} on Twitter\" target=\"_blank\"><i class=\"fa fa-twitter-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.facebook %}<a href=\"http://facebook.com/{{ site.owner.facebook }}\" title=\"{{ site.owner.name}} on Facebook\" target=\"_blank\"><i class=\"fa fa-facebook-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.google.plus %}<a href=\"http://plus.google.com/+{{ site.owner.google.plus }}\" title=\"{{ site.owner.name}} on Google+\" target=\"_blank\"><i class=\"fa fa-google-plus-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.linkedin %}<a href=\"http://linkedin.com/in/{{ site.owner.linkedin }}\" title=\"{{ site.owner.name}} on LinkedIn\" target=\"_blank\"><i class=\"fa fa-linkedin-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.stackexchange %}<a href=\"{{ site.owner.stackexchange }}\" title=\"{{ site.owner.name}} on StackExchange\" target=\"_blank\"><i class=\"fa fa-stack-exchange fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.instagram %}<a href=\"http://instagram.com/{{ site.owner.instagram }}\" title=\"{{ site.owner.name}} on Instagram\" target=\"_blank\"><i class=\"fa fa-instagram fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.flickr %}<a href=\"http://www.flickr.com/photos/{{ site.owner.flickr }}\" title=\"{{ site.owner.name}} on Flickr\" target=\"_blank\"><i class=\"fa fa-flickr fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.github %}<a href=\"http://github.com/{{ site.owner.github }}\" title=\"{{ site.owner.name}} on Github\" target=\"_blank\"><i class=\"fa fa-github-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.tumblr %}<a href=\"http://{{ site.owner.tumblr }}.tumblr.com\" title=\"{{ site.owner.name}} on Tumblr\" target=\"_blank\"><i class=\"fa fa-tumblr-square fa-2x\"></i></a>{% endif %}\n  {% if site.owner.pinterest %}<a href=\"https://www.pinterest.com/{{ site.owner.pinterest }}/\" title=\"{{ site.owner.name}} on Pinterest\" target=\"_blank\"><i class=\"fa fa-pinterest fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.weibo %}<a href=\"https://www.weibo.com/u/{{ site.owner.weibo }}/\" title=\"{{ site.owner.name}} on Weibo\" target=\"_blank\"><i class=\"fa fa-weibo fa-2x\"></i></a>{% endif %}\n  <a href=\"{{ site.url }}/feed.xml\" title=\"Atom/RSS feed\"><i class=\"fa fa-rss-square fa-2x\"></i></a>\n</div><!-- /.social-icons -->\n"
  },
  {
    "path": "website/_includes/footer.html~",
    "content": "{% if site.owner.google.ad-client and site.owner.google.ad-slot %}{% include ad-footer.html %}{% endif %}\n\n<span>&copy; {{ site.time | date: '%Y' }} {{ site.owner.name }}. Powered by <a href=\"http://jekyllrb.com\" rel=\"nofollow\">Jekyll</a> using the <a href=\"http://mademistakes.com/so-simple/\" rel=\"nofollow\">So Simple Theme</a>.</span>\n<div class=\"social-icons\">\n\t{% if site.owner.twitter %}<a href=\"http://twitter.com/{{ site.owner.twitter }}\" title=\"{{ site.owner.name}} on Twitter\" target=\"_blank\"><i class=\"fa fa-twitter-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.facebook %}<a href=\"http://facebook.com/{{ site.owner.facebook }}\" title=\"{{ site.owner.name}} on Facebook\" target=\"_blank\"><i class=\"fa fa-facebook-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.google.plus %}<a href=\"http://plus.google.com/+{{ site.owner.google.plus }}\" title=\"{{ site.owner.name}} on Google+\" target=\"_blank\"><i class=\"fa fa-google-plus-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.linkedin %}<a href=\"http://linkedin.com/in/{{ site.owner.linkedin }}\" title=\"{{ site.owner.name}} on LinkedIn\" target=\"_blank\"><i class=\"fa fa-linkedin-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.stackexchange %}<a href=\"{{ site.owner.stackexchange }}\" title=\"{{ site.owner.name}} on StackExchange\" target=\"_blank\"><i class=\"fa fa-stack-exchange fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.instagram %}<a href=\"http://instagram.com/{{ site.owner.instagram }}\" title=\"{{ site.owner.name}} on Instagram\" target=\"_blank\"><i class=\"fa fa-instagram fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.flickr %}<a href=\"http://www.flickr.com/photos/{{ site.owner.flickr }}\" title=\"{{ site.owner.name}} on Flickr\" target=\"_blank\"><i class=\"fa fa-flickr fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.github %}<a href=\"http://github.com/{{ site.owner.github }}\" title=\"{{ site.owner.name}} on Github\" target=\"_blank\"><i class=\"fa fa-github-square fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.tumblr %}<a href=\"http://{{ site.owner.tumblr }}.tumblr.com\" title=\"{{ site.owner.name}} on Tumblr\" target=\"_blank\"><i class=\"fa fa-tumblr-square fa-2x\"></i></a>{% endif %}\n  {% if site.owner.pinterest %}<a href=\"https://www.pinterest.com/{{ site.owner.pinterest }}/\" title=\"{{ site.owner.name}} on Pinterest\" target=\"_blank\"><i class=\"fa fa-pinterest fa-2x\"></i></a>{% endif %}\n\t{% if site.owner.weibo %}<a href=\"https://www.weibo.com/u/{{ site.owner.weibo }}/\" title=\"{{ site.owner.name}} on Weibo\" target=\"_blank\"><i class=\"fa fa-weibo fa-2x\"></i></a>{% endif %}\n  <a href=\"{{ site.url }}/feed.xml\" title=\"Atom/RSS feed\"><i class=\"fa fa-rss-square fa-2x\"></i></a>\n</div><!-- /.social-icons -->\n"
  },
  {
    "path": "website/_includes/head.html",
    "content": "<meta charset=\"utf-8\">\n<title>{% if page.title %}{{ page.title }} &#8211; {% endif %}{{ site.title }}</title>\n{% if page.excerpt %}<meta name=\"description\" content=\"{{ page.excerpt | strip_html }}\">{% endif %}\n{% if page.tags %}<meta name=\"keywords\" content=\"{{ page.tags | join: ', ' }}\">{% endif %}\n{% if page.author %}\n  {% assign author = site.data.authors[page.author] %}{% else %}{% assign author = site.owner %}\n{% endif %}\n\n{% include open-graph.html %}\n\n{% if site.owner.google.verify %}<!-- Webmaster Tools verfication -->\n<meta name=\"google-site-verification\" content=\"{{ site.owner.google.verify }}\">{% endif %}\n{% if site.owner.bing-verify %}<meta name=\"msvalidate.01\" content=\"{{ site.owner.bing-verify }}\">{% endif %}\n\n{% capture canonical %}{{ site.url }}{% if site.permalink contains '.html' %}{{ page.url }}{% else %}{{ page.url | remove:'index.html' | strip_slash }}{% endif %}{% endcapture %}\n<link rel=\"canonical\" href=\"{{ canonical }}\">\n<link href=\"{{ site.url }}/feed.xml\" type=\"application/atom+xml\" rel=\"alternate\" title=\"{{ site.title }} Feed\">\n\n<!-- http://t.co/dKP3o1e -->\n<meta name=\"HandheldFriendly\" content=\"True\">\n<meta name=\"MobileOptimized\" content=\"320\">\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n\n<!-- For all browsers -->\n<link rel=\"stylesheet\" href=\"{{ site.url }}/assets/css/main.css\">\n<!-- Webfonts -->\n<script src=\"//use.edgefonts.net/source-sans-pro:n2,i2,n3,i3,n4,i4,n6,i6,n7,i7,n9,i9;source-code-pro:n4,n7;volkhov.js\"></script>\n\n<meta http-equiv=\"cleartype\" content=\"on\">\n\n<!-- HTML5 Shiv and Media Query Support -->\n<!--[if lt IE 9]>\n  <script src=\"{{ site.url }}/assets/js/vendor/html5shiv.min.js\"></script>\n  <script src=\"{{ site.url }}/assets/js/vendor/respond.min.js\"></script>\n<![endif]-->\n\n<!-- Modernizr -->\n<script src=\"{{ site.url }}/assets/js/vendor/modernizr-2.7.1.custom.min.js\"></script>\n\n<!-- Icons -->\n<!-- 16x16 -->\n<link rel=\"shortcut icon\" href=\"{{ site.url }}/favicon.ico\">\n<!-- 32x32 -->\n<link rel=\"shortcut icon\" href=\"{{ site.url }}/favicon.png\">\n<!-- 57x57 (precomposed) for iPhone 3GS, pre-2011 iPod Touch and older Android devices -->\n<link rel=\"apple-touch-icon-precomposed\" href=\"{{ site.url }}/images/apple-touch-icon-precomposed.png\">\n<!-- 72x72 (precomposed) for 1st generation iPad, iPad 2 and iPad mini -->\n<link rel=\"apple-touch-icon-precomposed\" sizes=\"72x72\" href=\"{{ site.url }}/images/apple-touch-icon-72x72-precomposed.png\">\n<!-- 114x114 (precomposed) for iPhone 4, 4S, 5 and post-2011 iPod Touch -->\n<link rel=\"apple-touch-icon-precomposed\" sizes=\"114x114\" href=\"{{ site.url }}/images/apple-touch-icon-114x114-precomposed.png\">\n<!-- 144x144 (precomposed) for iPad 3rd and 4th generation -->\n<link rel=\"apple-touch-icon-precomposed\" sizes=\"144x144\" href=\"{{ site.url }}/images/apple-touch-icon-144x144-precomposed.png\">\n"
  },
  {
    "path": "website/_includes/navigation.html",
    "content": "<div class=\"navigation-wrapper\">\n\t<nav role=\"navigation\" id=\"site-nav\" class=\"animated drop\">\n\t    <ul>\n      {% for link in site.data.navigation %}\n\t\t    {% if link.url contains 'http' %}\n\t\t        {% assign domain = '' %}\n\t\t        {% else %}\n\t\t        {% assign domain = site.url %}\n\t\t    {% endif %}\n\t\t    <li><a href=\"{{ domain }}{{ link.url }}\" {% if link.url contains 'http' %}target=\"_blank\"{% endif %}>{{ link.title }}</a></li>\n\t\t  {% endfor %}\n\t    </ul>\n\t</nav>\n</div><!-- /.navigation-wrapper -->\n\n{% include browser-upgrade.html %}\n\n{% if page.image.feature %}<header class=\"masthead\">\n\t{% if site.logo != null %}\n\t\t<div class=\"wrap\">\n\t\t\t<a href=\"{{ site.url }}/\" class=\"site-logo\" rel=\"home\" title=\"{{ site.title }}\"><img src=\"{{ site.url }}/images/{{ site.logo }}\" width=\"200\" height=\"200\" alt=\"{{ site.title }} logo\" class=\"animated fadeInDown\"></a>\n\t\t</div>\n\t{% endif %}\n</header><!-- /.masthead -->\n{% else %}<header class=\"masthead\">\n\t<div class=\"wrap\">\n      {% if site.logo != null %}\n  \t\t<a href=\"{{ site.url }}/\" class=\"site-logo\" rel=\"home\" title=\"{{ site.title }}\"><img src=\"{{ site.url }}/images/{{ site.logo }}\" width=\"200\" height=\"200\" alt=\"{{ site.title }} logo\" class=\"animated fadeInDown\"></a>\n      {% endif %}\n      <h1 class=\"site-title animated fadeIn\"><a href=\"{{ site.url }}/\">{{ site.title }}</a></h1>\n\t\t<h2 class=\"site-description animated fadeIn\" itemprop=\"description\">{{ site.description }}</h2>\n\t</div>\n</header><!-- /.masthead -->{% endif %}\n\n<div class=\"js-menu-screen menu-screen\"></div>\n"
  },
  {
    "path": "website/_includes/open-graph.html",
    "content": "<!-- Twitter Cards -->\n<meta name=\"twitter:title\" content=\"{% if page.title %}{{ page.title }}{% else %}{{ site.title }}{% endif %}\">\n{% if page.excerpt %}<meta name=\"twitter:description\" content=\"{{ page.excerpt | strip_html }}\">{% endif %}\n{% if site.owner.twitter %}<meta name=\"twitter:site\" content=\"@{{ site.owner.twitter }}\">{% endif %}\n{% if author.twitter %}<meta name=\"twitter:creator\" content=\"@{{ author.twitter }}\">{% endif %}\n{% if page.image.feature %}\n<meta name=\"twitter:card\" content=\"summary_large_image\">\n<meta name=\"twitter:image\" content=\"{{ site.url }}/images/{{ page.image.feature }}\">\n{% else %}\n<meta name=\"twitter:card\" content=\"summary\">\n<meta name=\"twitter:image\" content=\"{% if page.image.thumb %}{{ site.url }}/images/{{ page.image.thumb }}{% else %}{{ site.url }}/images/{{ site.logo }}{% endif %}\">\n{% endif %}\n<!-- Open Graph -->\n<meta property=\"og:locale\" content=\"{{ site.locale }}\">\n<meta property=\"og:type\" content=\"article\">\n<meta property=\"og:title\" content=\"{% if page.title %}{{ page.title }}{% else %}{{ site.title }}{% endif %}\">\n{% if page.excerpt %}<meta property=\"og:description\" content=\"{{ page.excerpt | strip_html }}\">{% endif %}\n<meta property=\"og:url\" content=\"{{ page.url | replace:'index.html','' | prepend: site.url }}\">\n<meta property=\"og:site_name\" content=\"{{ site.title }}\">"
  },
  {
    "path": "website/_includes/scripts.html",
    "content": "<script type=\"text/javascript\">\n  var BASE_URL = '{{ site.url }}';\n</script>\n\n<script src=\"//ajax.googleapis.com/ajax/libs/jquery/1.9.1/jquery.min.js\"></script>\n<script>window.jQuery || document.write('<script src=\"{{ site.url }}/assets/js/vendor/jquery-1.9.1.min.js\"><\\/script>')</script>\n<script src=\"{{ site.url }}/assets/js/scripts.min.js\"></script>\n\n{% if site.owner.google.analytics %}\n<!-- Asynchronous Google Analytics snippet -->\n<script>\n  var _gaq = _gaq || [];\n  var pluginUrl = \n '//www.google-analytics.com/plugins/ga/inpage_linkid.js';\n  _gaq.push(['_require', 'inpage_linkid', pluginUrl]);\n  _gaq.push(['_setAccount', '{{ site.owner.google.analytics }}']);\n  _gaq.push(['_trackPageview']);\n\n  (function() {\n    var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;\n    ga.src = ('https:' == document.location.protocol ? 'https://' : 'http://') + 'stats.g.doubleclick.net/dc.js';\n    var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);\n  })();\n</script>\n{% endif %}\n"
  },
  {
    "path": "website/_includes/social-share.html",
    "content": "<span class=\"social-share-twitter\">\n  <a href=\"https://twitter.com/intent/tweet?hashtags={{ page.tags | join: ',' | remove: ' ' }}&amp;text={{ page.title | escape | replace:' ','%20' }}&amp;url={{ site.url }}{{ page.url }}{% if author.twitter %}&amp;via={{ author.twitter }}{% endif %}\" title=\"Share on Twitter\" itemprop=\"Twitter\"><i class=\"fa fa-twitter-square\"></i> Tweet</a>\n</span>\n<span class=\"social-share-facebook\">\n  <a href=\"https://www.facebook.com/sharer/sharer.php?u={{ site.url }}{{ page.url }}\" title=\"Share on Facebook\" itemprop=\"Facebook\"><i class=\"fa fa-facebook-square\"></i> Like</a>\n</span>\n<span class=\"social-share-googleplus\">\n  <a href=\"https://plus.google.com/share?url={{ site.url }}{{ page.url }}\" title=\"Share on Google Plus\" itemprop=\"GooglePlus\"><i class=\"fa fa-google-plus-square\"></i> +1</a>\n</span>\n<!-- /.social-share -->"
  },
  {
    "path": "website/_layouts/page.html",
    "content": "<!doctype html>\n<!--[if lt IE 7]><html class=\"no-js lt-ie9 lt-ie8 lt-ie7\" lang=\"en\"> <![endif]-->\n<!--[if (IE 7)&!(IEMobile)]><html class=\"no-js lt-ie9 lt-ie8\" lang=\"en\"><![endif]-->\n<!--[if (IE 8)&!(IEMobile)]><html class=\"no-js lt-ie9\" lang=\"en\"><![endif]-->\n<!--[if gt IE 8]><!--> <html class=\"no-js\" lang=\"en\"><!--<![endif]-->\n<head>\n{% include head.html %}\n<script type=\"text/javascript\"\n    src=\"http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n</script>\n</head>\n\n<body id=\"page\">\n\n{% include navigation.html %}\n\n<div id=\"main\" role=\"main\">\n  <article class=\"entry\">\n    {% if page.image.feature %}<img src=\"{{ site.url }}/images/{{ page.image.feature }}\" class=\"entry-feature-image\" alt=\"{{ page.title }}\" {% if site.logo == null %}style=\"margin-top:0;\"{% endif %}>{% if page.image.credit %}<p class=\"image-credit\">Photo Credit: <a href=\"{{ page.image.creditlink }}\">{{ page.image.credit }}</a></p>{% endif %}{% endif %}\n    <div class=\"entry-wrapper\">\n      <header class=\"entry-header\">\n        <h1 class=\"entry-title\">{% if page.headline %}{{ page.headline }}{% else %}{{ page.title }}</h1>{% endif %}\n      </header>\n      <div class=\"entry-content\">\n        {{ content }}\n      </div><!-- /.entry-content -->\n    </div><!-- /.entry-wrapper -->\n  </article>\n</div><!-- /#main -->\n\n<div class=\"footer-wrapper\">\n  <footer role=\"contentinfo\" class=\"entry-wrapper\">\n    {% include footer.html %}\n  </footer>\n</div><!-- /.footer-wrapper -->\n\n{% include scripts.html %}\n\n</body>\n</html>\n"
  },
  {
    "path": "website/_layouts/post.html",
    "content": "<!doctype html>\n<!--[if lt IE 7]><html class=\"no-js lt-ie9 lt-ie8 lt-ie7\" lang=\"en\"> <![endif]-->\n<!--[if (IE 7)&!(IEMobile)]><html class=\"no-js lt-ie9 lt-ie8\" lang=\"en\"><![endif]-->\n<!--[if (IE 8)&!(IEMobile)]><html class=\"no-js lt-ie9\" lang=\"en\"><![endif]-->\n<!--[if gt IE 8]><!--> <html class=\"no-js\" lang=\"en\"><!--<![endif]-->\n<head>\n{% include head.html %}\n</head>\n\n<body id=\"post\">\n\n{% include navigation.html %}\n\n<div id=\"main\" role=\"main\">\n  <article class=\"hentry\">\n    {% if page.image.feature %}<img src=\"{{ site.url }}/images/{{ page.image.feature }}\" class=\"entry-feature-image\" alt=\"{{ page.title }}\" {% if site.logo == null %}style=\"margin-top:0;\"{% endif %}>{% if page.image.credit %}<p class=\"image-credit\">Image credit: <a href=\"{{ page.image.creditlink }}\">{{ page.image.credit }}</a></p>{% endif %}{% endif %}\n    <div class=\"entry-wrapper\">\n      <header class=\"entry-header\">\n        <span class=\"entry-tags\">{% for tag in page.tags %}<a href=\"{{ site.url }}/tags/#{{ tag }}\" title=\"Pages tagged {{ tag }}\">{{ tag }}</a>{% unless forloop.last %}&nbsp;&bull;&nbsp;{% endunless %}{% endfor %}</span>\n        {% if page.link %}\n          <h1 class=\"entry-title\"><a href=\"{{ page.link }}\">{% if page.headline %}{{ page.headline }}{% else %}{{ page.title }}{% endif %} <span class=\"link-arrow\">&rarr;</span></a></h1>\n        {% else %}\n          <h1 class=\"entry-title\">{% if page.headline %}{{ page.headline }}{% else %}{{ page.title }}{% endif %}</h1>\n        {% endif %}\n      </header>\n      <footer class=\"entry-meta\">\n        {% if page.author %}\n          {% assign author = site.data.authors[page.author] %}{% else %}{% assign author = site.owner %}\n        {% endif %}\n        {% if author.avatar contains 'http' %}\n          <img src=\"{{ author.avatar }}\" class=\"bio-photo\" alt=\"{{ author.name }} bio photo\"></a>\n        {% else %}\n          <img src=\"{{ site.url }}/images/{{ author.avatar }}\" class=\"bio-photo\" alt=\"{{ author.name }} bio photo\"></a>\n        {% endif %}\n        <span class=\"author vcard\">By <span class=\"fn\">{{ author.name }}</span></span>\n        <span class=\"entry-date date published\"><time datetime=\"{{ page.date | date_to_xmlschema }}\"><i class=\"fa fa-calendar-o\"></i> {{ page.date | date: \"%B %d, %Y\" }}</time></span>\n        {% if page.modified %}<span class=\"entry-date date modified\"><time datetime=\"{{ page.modified }}\"><i class=\"fa fa-pencil\"></i> {{ page.modified | date: \"%B %d, %Y\" }}</time></span>{% endif %}\n        {% if (site.owner.disqus-shortname and page.comments) or site.comments %}<span class=\"entry-comments\"><i class=\"fa fa-comment-o\"></i> <a href=\"#disqus_thread\">Comment</a></span>{% endif %}\n        {% if page.share %}{% include social-share.html %}{% endif %}\n        {% if page.ads == true %}{% include ad-sidebar.html %}<!-- /.google-ads -->{% endif %}\n      </footer>\n      <div class=\"entry-content\">\n        {{ content }}\n        {% if (site.owner.disqus-shortname and page.comments) or site.comments %}\n          <div id=\"disqus_thread\"></div><!-- /#disqus_thread -->\n          {% include disqus-comments.html %}\n        {% endif %}\n      </div><!-- /.entry-content -->\n    </div><!-- /.entry-wrapper -->\n    <nav class=\"pagination\" role=\"navigation\">\n      {% if page.previous %}\n        <a href=\"{{ site.url }}{{ page.previous.url }}\" class=\"btn\" title=\"{{ page.previous.title }}\">Previous</a>\n      {% endif %}\n      {% if page.next %}\n        <a href=\"{{ site.url }}{{ page.next.url }}\" class=\"btn\" title=\"{{ page.next.title }}\">Next</a>\n      {% endif %}\n    </nav><!-- /.pagination -->\n  </article>\n</div><!-- /#main -->\n\n<div class=\"footer-wrapper\">\n  <footer role=\"contentinfo\" class=\"entry-wrapper\">\n    {% include footer.html %}\n  </footer>\n</div><!-- /.footer-wrapper -->\n\n{% include scripts.html %}\n\n</body>\n</html>\n"
  },
  {
    "path": "website/_octopress.yml",
    "content": "# Default extensions\npost_ext: md\npage_ext: md\n\n# Found in _templates/\npost_layout: post\npage_layout: page\n\n# Format titles with titlecase?\ntitlecase: true\n"
  },
  {
    "path": "website/_sass/_animations.scss",
    "content": "/* ==========================================================================\n   Animations\n   ========================================================================== */\n\n/* add .animated class to elements you wish to animate\n * along with the type of animation (eg: <div class=\"animated fadeIn\"></div>)\n */\n\n.animated{-webkit-animation-fill-mode:both;-moz-animation-fill-mode:both;-ms-animation-fill-mode:both;-o-animation-fill-mode:both;animation-fill-mode:both;-webkit-animation-duration:1s;-moz-animation-duration:1s;-ms-animation-duration:1s;-o-animation-duration:1s;animation-duration:1s;}.animated.hinge{-webkit-animation-duration:2s;-moz-animation-duration:2s;-ms-animation-duration:2s;-o-animation-duration:2s;animation-duration:2s;}@-webkit-keyframes fadeIn {\n  0% {opacity: 0;}  100% {opacity: 1;}\n}\n\n/*\n   Fade-in animation .fadeIn\n   ========================================================================== */\n\n@-webkit-keyframes fadeIn {\n  0% {opacity: 0;}\n  100% {opacity: 1;}\n}\n@-moz-keyframes fadeIn {\n  0% {opacity: 0;}\n  100% {opacity: 1;}\n}\n@-o-keyframes fadeIn {\n  0% {opacity: 0;}\n  100% {opacity: 1;}\n}\n@keyframes fadeIn {\n  0% {opacity: 0;}\n  100% {opacity: 1;}\n}\n\n.fadeIn {\n  -webkit-animation-name: fadeIn;\n  -moz-animation-name: fadeIn;\n  -o-animation-name: fadeIn;\n  animation-name: fadeIn;\n}\n\n/*\n   Fade-in down animation .fadeInDown\n   ========================================================================== */\n\n@-webkit-keyframes fadeInDown {\n  0% {\n    opacity: 0;\n    -webkit-transform: translateY(-20px);\n  }\n  100% {\n    opacity: 1;\n    -webkit-transform: translateY(0);\n  }\n}\n@-moz-keyframes fadeInDown {\n  0% {\n    opacity: 0;\n    -moz-transform: translateY(-20px);\n  }\n  100% {\n    opacity: 1;\n    -moz-transform: translateY(0);\n  }\n}\n@-o-keyframes fadeInDown {\n  0% {\n    opacity: 0;\n    -o-transform: translateY(-20px);\n  }\n  100% {\n    opacity: 1;\n    -o-transform: translateY(0);\n  }\n}\n@keyframes fadeInDown {\n  0% {\n    opacity: 0;\n    transform: translateY(-20px);\n  }\n  100% {\n    opacity: 1;\n    transform: translateY(0);\n  }\n}\n\n.fadeInDown {\n  -webkit-animation-name: fadeInDown;\n  -moz-animation-name: fadeInDown;\n  -o-animation-name: fadeInDown;\n  animation-name: fadeInDown;\n}\n\n/*\n   Fade-in down big animation .fadeInDownBig\n   ========================================================================== */\n\n@-webkit-keyframes fadeInDownBig {\n  0% {\n    opacity: 0;\n    -webkit-transform: translateY(-2000px);\n  }\n  100% {\n    opacity: 1;\n    -webkit-transform: translateY(0);\n  }\n}\n@-moz-keyframes fadeInDownBig {\n  0% {\n    opacity: 0;\n    -moz-transform: translateY(-2000px);\n  }\n  100% {\n    opacity: 1;\n    -moz-transform: translateY(0);\n  }\n}\n@-o-keyframes fadeInDownBig {\n  0% {\n    opacity: 0;\n    -o-transform: translateY(-2000px);\n  }\n  100% {\n    opacity: 1;\n    -o-transform: translateY(0);\n  }\n}\n@keyframes fadeInDownBig {\n  0% {\n    opacity: 0;\n    transform: translateY(-2000px);\n  }\n  100% {\n    opacity: 1;\n    transform: translateY(0);\n  }\n}\n\n.fadeInDownBig {\n  -webkit-animation-name: fadeInDownBig;\n  -moz-animation-name: fadeInDownBig;\n  -o-animation-name: fadeInDownBig;\n  animation-name: fadeInDownBig;\n}\n\n/*\n   Bounce-in animation .bounceIn\n   ========================================================================== */\n\n@-webkit-keyframes bounceIn {\n  0% {\n    opacity: 0;\n    -webkit-transform: scale(.3);\n  }\n  50% {\n    opacity: 1;\n    -webkit-transform: scale(1.05);\n  }\n  70% {\n    -webkit-transform: scale(.9);\n  }\n  100% {\n    -webkit-transform: scale(1);\n  }\n}\n@-moz-keyframes bounceIn {\n  0% {\n    opacity: 0;\n    -moz-transform: scale(.3);\n  }\n  50% {\n    opacity: 1;\n    -moz-transform: scale(1.05);\n  }\n  70% {\n    -moz-transform: scale(.9);\n  }\n  100% {\n    -moz-transform: scale(1);\n  }\n}\n@-o-keyframes bounceIn {\n  0% {\n    opacity: 0;\n    -o-transform: scale(.3);\n  }\n  50% {\n    opacity: 1;\n    -o-transform: scale(1.05);\n  }\n  70% {\n    -o-transform: scale(.9);\n  }\n  100% {\n    -o-transform: scale(1);\n  }\n}\n@keyframes bounceIn {\n  0% {\n    opacity: 0;\n    transform: scale(.3);\n  }\n  50% {\n    opacity: 1;\n    transform: scale(1.05);\n  }\n  70% {\n    transform: scale(.9);\n  }\n  100% {\n    transform: scale(1);\n  }\n}\n\n.bounceIn {\n  -webkit-animation-name: bounceIn;\n  -moz-animation-name: bounceIn;\n  -o-animation-name: bounceIn;\n  animation-name: bounceIn;\n}\n\n/*\n   Bounce-in down animation .bounceInDown\n   ========================================================================== */\n\n@-webkit-keyframes bounceInDown {\n  0% {\n    opacity: 0;\n    -webkit-transform: translateY(-2000px);\n  }\n  60% {\n    opacity: 1;\n    -webkit-transform: translateY(30px);\n  }\n  80% {\n    -webkit-transform: translateY(-10px);\n  }\n  100% {\n    -webkit-transform: translateY(0);\n  }\n}\n@-moz-keyframes bounceInDown {\n  0% {\n    opacity: 0;\n    -moz-transform: translateY(-2000px);\n  }\n  60% {\n    opacity: 1;\n    -moz-transform: translateY(30px);\n  }\n  80% {\n    -moz-transform: translateY(-10px);\n  }\n  100% {\n    -moz-transform: translateY(0);\n  }\n}\n@-o-keyframes bounceInDown {\n  0% {\n    opacity: 0;\n    -o-transform: translateY(-2000px);\n  }\n  60% {\n    opacity: 1;\n    -o-transform: translateY(30px);\n  }\n  80% {\n    -o-transform: translateY(-10px);\n  }\n  100% {\n    -o-transform: translateY(0);\n  }\n}\n@keyframes bounceInDown {\n  0% {\n    opacity: 0;\n    transform: translateY(-2000px);\n  }\n  60% {\n    opacity: 1;\n    transform: translateY(30px);\n  }\n  80% {\n    transform: translateY(-10px);\n  }\n  100% {\n    transform: translateY(0);\n  }\n}\n\n.bounceInDown {\n  -webkit-animation-name: bounceInDown;\n  -moz-animation-name: bounceInDown;\n  -o-animation-name: bounceInDown;\n  animation-name: bounceInDown;\n}\n\n/*\n   Drop animation .drop\n   ========================================================================== */\n\n@-webkit-keyframes drop {\n  0% {\n    -webkit-transform: translateY(-500px);\n  }\n  100% {\n    -webkit-transform: translateY(0);\n  }\n}\n@-moz-keyframes drop {\n  0% {\n    -moz-transform: translateY(-500px);\n  }\n  100% {\n    -moz-transform: translateY(0);\n  }\n}\n@-o-keyframes drop {\n  0% {\n    -o-transform: translateY(-500px);\n  }\n  100% {\n    -o-transform: translateY(0);\n  }\n}\n@keyframes drop {\n  0% {\n    transform: translateY(-500px);\n  }\n  100% {\n    transform: translateY(0);\n  }\n}\n.drop {\n  -webkit-animation-name: drop;\n  -moz-animation-name: drop;\n  -o-animation-name: drop;\n  animation-name: drop;\n}\n"
  },
  {
    "path": "website/_sass/_archives.scss",
    "content": "/* ==========================================================================\n   Archive pages\n   ========================================================================== */\n\n\n\n\n/*\n   Post archive\n   ========================================================================== */\n\n.post-list {\n  margin: 0;\n  padding: 0;\n  list-style-type: none;\n  li {\n    padding: 8px 0;\n    border-bottom: 1px solid lighten($black,80);\n    border-bottom: 1px solid rgba($black,.10);\n    @include font-size(18,no);\n    @include clearfix;\n    > a {\n      border-bottom-width: 0; /* remove underlines from items in post indexes */\n    }\n  }\n  a > span {\n    float: right;\n  }\n  .entry-date {\n    @include font-size(14,no);\n    text-transform: uppercase;\n    display: none;\n    @include media($medium) {\n      display: inline;\n    }\n  }\n  /* post excerpt */\n  .excerpt {\n    display: block;\n    float: none;\n    @include font-size(14, no, 16);\n    @include media($medium) {\n      width: 70%;\n    }\n    @include media($large) {\n      width: 60%;\n    }\n  }\n}\n\n/*\n   Tag archive\n   ========================================================================== */\n\n.tag-box {\n  list-style: none;\n  margin: 0;\n  padding: 4px 0;\n  overflow: hidden;\n  @include clearfix;\n  &.inline li {\n    float: left;\n    @include font-size(14);\n    line-height: 2.5;\n  }\n  a {\n    padding: 4px 6px;\n    margin: 2px;\n    background-color: lighten($black, 90);\n    @include rounded(4px);\n    text-decoration: none;\n    span {\n      vertical-align: super;\n      @include font-size(10);\n    }\n  }\n}"
  },
  {
    "path": "website/_sass/_base.scss",
    "content": "/* ==========================================================================\n   Base elements\n   ========================================================================== */\n\n/*\n   Selections\n   ========================================================================== */\n\n::-moz-selection {\n\tbackground-color: lighten($base-color, 65%);\n\tcolor: $base-color;\n\ttext-shadow: none;\n}\n::selection {\n\tbackground-color: lighten($base-color, 65%);\n\tcolor: $base-color;\n\ttext-shadow: none;\n}\n\n\n/*\n   Body\n   ========================================================================== */\n\nbody {\n  margin: 0;\n  padding: 0;\n  width: 100%;\n  font-family: $base-font;\n  color: $text-color;\n  background-color: $body-color;\n  border-top: 5px solid $black;\n}\n\n/*\n   Typography\n   ========================================================================== */\n\nbody {\n\tfont-family: $base-font;\n\tcolor: $text-color;\n\t@include font-size(16);\n}\n\np {\n\t@include font-size(18,yes,28);\n}\n\nli {\n\t@include font-size(16,12,24);\n}\n\n/* headings */\n\nh1, h2, h3, h4, h5, h6 {\n\tfont-family: $heading-font;\n\ttext-rendering: optimizeLegibility; // Fix the character spacing for headings\n}\nh1 {\n\t@include font-size(36);\n}\nh2 {\n\t@include font-size(32);\n}\nh3 {\n\t@include font-size(28);\n}\nh4 {\n\t@include font-size(24);\n}\nh5 {\n\t@include font-size(20);\n}\nh6 {\n\t@include font-size(18);\n}\n\n/* underlined text */\n\nu,\nins {\n\ttext-decoration: none;\n\tborder-bottom: 1px solid $text-color;\n}\n\n/*\n   Links\n   ========================================================================== */\n\na {\n\ttext-decoration: none;\n\tcolor: $link-color;\n\t&:visited {\n\t\tcolor: lighten($link-color, 20);\n\t}\n\t&:hover {\n\t\tcolor: darken($link-color, 20);\n\t}\n\t&:focus {\n\t\toutline: thin dotted;\n\t\tcolor: darken($link-color, 20);\n\t}\n\t&:hover,\n\t&:active {\n\t\toutline: 0;\n\t}\n}\n\n/*\n   Horizontal rules\n   ========================================================================== */\n\nhr {\n    display: block;\n    margin: 1em 0;\n    padding: 0;\n    height: 1px;\n    border: 0;\n    border-top: 1px solid #ccc;\n    border-bottom: 1px solid #fff;\n}\n\n/*\n   Figures and Images\n   ========================================================================== */\n\nfigure {\n  margin: 0;\n  padding-top: 10px;\n  padding-bottom: 10px;\n  @include clearfix;\n  img {\n    margin-bottom: 10px;\n  }\n  a {\n    img {\n      @include transform(translateY(0));\n      -webkit-transition-duration: 0.25s;\n      -moz-transition-duration: 0.25s;\n      -o-transition-duration: 0.25s;\n      opacity: 0.7;\n      &:hover {\n        @include transform(translateY(-5px));\n        @include box-shadow(0 0 10px rgba($black, .20));\n        opacity: 1;\n      }\n    }\n  }\n}\n\nsvg:not(:root) {\n  overflow: hidden;\n}\n\n// Figure captions\n\nfigcaption {\n\tpadding-top: 10px;\n\t@include font-size(14);\n\tline-height: 1.3;\n\tcolor: lighten($text-color, 10);\n}\n\n\n\n// Tables\n// --------------------------------------------------\ntable {\n  width: 100%;\n}\n\n\n// Blockquotes\n// --------------------------------------------------\n\nblockquote {\n\tfont-family: $alt-font;\n\tfont-style: italic;\n\t@include font-size(16);\n\tpadding-left: 20px;\n\tborder-left: 8px solid $black;\n}\n\n\n\n\n\n// Code\n// --------------------------------------------------\n\ntt, code, kbd, samp, pre {\n\tfont-family: $code-font;\n}\np,\nli {\n\tcode {\n\t\t@include font-size(14);\n\t\tfont-family: $code-font;\n\t\twhite-space: nowrap;\n\t\tmargin: 0 2px;\n\t\tpadding: 0 5px;\n\t\tborder: 1px solid lighten($black, 90);\n\t\tbackground-color: lighten($black, 95);\n\t\t@include rounded(3px);\n\t}\n}\npre {\n\t@include font-size(14);\n\toverflow-x: auto;\n}\n\n\n\n\n\n// Global Transition\n// ---------------------------------------------------\n\nb, i, strong, em, blockquote, p, q, span, figure, img, h1, h2, header, input, a, .highlight {\n\t@include transition(all .2s ease);\n}\n"
  },
  {
    "path": "website/_sass/_buttons.scss",
    "content": "/* ==========================================================================\n   Buttons\n   ========================================================================== */\n\n/*\n   Default button\n   ========================================================================== */\n\n.btn {\n  display: inline-block;\n  margin-bottom: 20px;\n  padding: 8px 20px;\n  @include font-size(14);\n  background-color: $black;\n  color: $white;\n  border: 2px solid $black !important;\n  border-radius: $border-radius;\n  &:visited {\n    color: $white;\n  }\n  &:hover {\n    background-color: $white;\n    color: $black;\n  }\n}\n\n/*\n   Close button\n   ========================================================================== */\n\n.close-btn {\n  position: absolute;\n  top: 0;\n  right: 0;\n  z-index: 1;\n  background-color: transparent;\n  border: 0 solid transparent;\n  color: $white;\n}\n"
  },
  {
    "path": "website/_sass/_footer.scss",
    "content": "/* ==========================================================================\n   Site footer\n   ========================================================================== */\n\n.footer-wrapper {\n  @include clearfix;\n  margin: 2em auto;\n  text-align: center;\n  font-family: $alt-font;\n  @include font-size(14);\n  font-style: italic;\n  color: lighten($text-color,20);\n  a {\n    color: lighten($text-color,20);\n  }\n}\n\n/*\n   Social media icons\n   ========================================================================== */\n\n.social-icons {\n  margin: 1em 0 2em;\n  a {\n    padding: 4px 8px;\n    &:hover {\n      color: $black;\n    }\n  }\n}\n"
  },
  {
    "path": "website/_sass/_footnotes.scss",
    "content": "/* ==========================================================================\n   Footnotes (class generated by Kramdown)\n   ========================================================================== */\n\n.footnotes {\n  @include font-size(14);\n  font-family: $base-font;\n}"
  },
  {
    "path": "website/_sass/_forms.scss",
    "content": "form { \n\tmargin: 0 0 5px 0;\n\tfieldset { \n\t\tmargin-bottom: 5px; \n\t\tpadding: 0; \n\t\tborder-width: 0; \n\t}\n\tlegend { \n\t\tdisplay: block;\n\t\twidth: 100%;\n\t\tmargin-bottom: 5px * 2;\n\t\t*margin-left: -7px; \n\t\tpadding: 0;\n\t\tcolor: $text-color;\n\t\tborder: 0;\n\t\tborder-bottom: 1px solid lighten($black, 80);\n\t\twhite-space: normal; \n\t}\n\tp {\n\t\tmargin-bottom: 5px / 2; \n\t}\n\tul {\n\t\tlist-style-type: none; \n\t\tmargin: 0 0 5px 0;\n\t\tpadding: 0; \n\t}\n\tbr {\n\t\tdisplay: none; \n\t}\n}\nlabel,\ninput,\nbutton,\nselect,\ntextarea {\n\tvertical-align: baseline; \n\t*vertical-align: middle; \n}\ninput,\nbutton,\nselect,\ntextarea {\n\tfont-family: $base-font; \n\t@include box-sizing(border-box); \n}\nlabel {\n\tdisplay: block;\n\tmargin-bottom: 10px / 8;\n\tfont-weight: bold;\n\tcolor: $text-color;\n\tcursor: pointer; \n\tinput,\n\ttextarea,\n\tselect {\n\t\tdisplay: block; \n\t}\n}\ninput,\ntextarea,\nselect {\n\tdisplay: inline-block;\n\twidth: 100%;\n\tpadding: 4px;\n\tmargin-bottom: 5px / 4;\n\tbackground-color: $white;\n\tborder: 1px solid lighten($black, 80);\n\tcolor: $text-color; \n\t&:hover {\n\t\tborder-color: lighten($accent-color, 50); \n\t}\n}\n.input-mini { \n\twidth: 60px; \n}\n.input-small { \n\twidth: 90px; \n}\ninput[type=\"image\"],\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n\twidth: auto;\n\theight: auto;\n\tpadding: 0;\n\tmargin: 3px 0;\n\t*margin-top: 0;\n\tline-height: normal;\n\tcursor: pointer;\n\t@include rounded(0);\n\tborder: 0 \\9; \n}\ninput[type=\"checkbox\"], \ninput[type=\"radio\"] {  \n\t@include box-sizing(border-box);\n\tpadding: 0; \n\t*width: 13px; \n\t*height: 13px; \n}\ninput[type=\"image\"] {\n\tborder: 0; \n\t@include box-shadow(none); \n}\ninput[type=\"file\"] {\n\twidth: auto;\n\tpadding: initial;\n\tline-height: initial;\n\tborder: initial;\n\tbackground-color: transparent;\n\tbackground-color: initial;\n\t@include box-shadow(none); \n}\ninput[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n\twidth : auto;\n\theight : auto;\n\tcursor : pointer;\n\t*overflow : visible; \n}\nselect,\ninput[type=\"file\"] {\n\t*margin-top : 4px;  \n}\nselect {\n\twidth : auto;\n\tbackground-color : $white;\n}\nselect[multiple],\nselect[size] {\n\theight : auto; \n}\ntextarea {\n\t@include resize(vertical);\n\theight : auto;\n\toverflow : auto; \n\tvertical-align : top; \n}\ninput[type=\"hidden\"] {\n\tdisplay : none; \n}\n.radio,\n.checkbox {\n\tpadding-left : 18px; \n\tfont-weight : normal; \n}\n.radio input[type=\"radio\"],\n.checkbox input[type=\"checkbox\"] {\n\tfloat : left;\n\tmargin-left : -18px; \n}\n.radio.inline,\n.checkbox.inline {\n\tdisplay : inline-block;\n\tpadding-top : 5px;\n\tmargin-bottom : 0;\n\tvertical-align : middle; \n}\n.radio.inline + .radio.inline,\n.checkbox.inline + .checkbox.inline {\n\tmargin-left : 10px; \n}\n\n// disabled\n// --------------------------------------------------\n\ninput[disabled],\nselect[disabled],\ntextarea[disabled],\ninput[readonly],\nselect[readonly],\ntextarea[readonly] {\n\topacity: 0.5;\n\tcursor : not-allowed; \n}\n\n// focus and active\n// --------------------------------------------------\n\ninput:focus,\ntextarea:focus {\n\tborder-color : $accent-color;\n\toutline : 0;\n\toutline : thin dotted \\9; \n}\ninput[type=\"file\"]:focus,\ninput[type=\"radio\"]:focus,\ninput[type=\"checkbox\"]:focus,\nselect:focus {\n\t@include box-shadow(none); \n}\n\n// help text\n// --------------------------------------------------\n\n.help-block,\n.help-inline {\n\tcolor : lighten($black, 50); \n}\n.help-block {\n\tdisplay : block; \n\tmargin-bottom : 1em; \n\tline-height : 1em;\n}\n.help-inline {\n\tdisplay : inline-block;\n\tvertical-align : middle;\n\tpadding-left : 5px; \n}\n\n// .form-inline\n// --------------------------------------------------\n\n.form-inline input,\n.form-inline textarea,\n.form-inline select {\n\tdisplay : inline-block;\n\tmargin-bottom : 0; \n}\n.form-inline label {\n\tdisplay : inline-block; \n}\n.form-inline .radio,\n.form-inline .checkbox,\n.form-inline .radio {\n\tpadding-left : 0;\n\tmargin-bottom : 0;\n\tvertical-align : middle; \n}\n.form-inline .radio input[type=\"radio\"],\n.form-inline .checkbox input[type=\"checkbox\"] {\n\tfloat : left;\n\tmargin-left : 0;\n\tmargin-right : 3px; }\n\n\n// Search\n// --------------------------------------------------\n\n.simple-search {\n\ttext-align: center;\n\tlabel {\n\t\tdisplay: none;\n\t}\n\n\t/* input field */\n\t#goog-wm-qt {\n\t\t@include media($medium) {\n\t\t\twidth: 80%;\n\t\t}\n\t}\n\n\t/* submit button */\n\t#goog-wm-sb {}\n}"
  },
  {
    "path": "website/_sass/_grid-settings.scss",
    "content": "@import \"vendor/neat/neat-helpers\";\n\n// Change the grid settings\n$visual_grid: false;\n$max-width: em(960);\n\n// Define your breakpoints\n$medium: new-breakpoint(min-width em(768) 12);\n$large: new-breakpoint(min-width em(1000) 12);"
  },
  {
    "path": "website/_sass/_helpers.scss",
    "content": "/* ==========================================================================\n   Helpers and Utility Classes\n   ========================================================================== */\n\n.wrap {\n  margin: 0 auto;\n}\n\n/*\n   Typography\n   ========================================================================== */\n\n/* capitalize all letters */\n\n.all-caps {\n  text-transform: uppercase;\n}\n\n/* center text */\n\n.center {\n  text-align: center;\n}\n\n/* remove bullets from list items */\n\n/**\n *  <ul class=\"unstyled-list\">\n *    <li>Item 1</li>\n *    <li>Item 2</li>\n *  </ul>\n */\n\n.unstyled-list {\n  list-style: none;\n  margin-left: 0;\n  padding-left: 0;\n  li {\n    list-style-type: none;\n  }\n}\n\n/*\n   Image alignment\n   ========================================================================== */\n\n/* float left */\n\n.pull-left {\n  float: left;\n  padding-right: $gutter;\n}\n\n/* float right */\n\n.pull-right {\n  float: right;\n  padding-left: $gutter;\n}\n\n/*\n   Image layout in <figure>\n   ========================================================================== */\n\nfigure {\n\n  /* 2 images in a row. should be of equal height/width to maintain layout */\n\n  &.half {\n    @include media($large) {\n      img {\n        width: 47.5%;\n        float: left;\n        margin-right: 2.5%;\n        margin-bottom: 2.5%;\n      }\n      figcaption {\n        clear: left;\n      }\n    }\n  }\n\n  /* 3 images in a row. should be of equal height/width to maintain layout */\n\n  &.third {\n    @include media($large) {\n      img {\n        width: 30.8%;\n        float: left;\n        margin-right: 2.5%;\n        margin-bottom: 2.5%;\n      }\n      figcaption {\n        clear: left;\n      }\n    }\n  }\n}\n\n/*\n   No scrollbars\n   ========================================================================== */\n\n.no-scroll {\n  overflow: hidden;\n}\n\n/*\n   Link arrow (used in link post titles)\n   ========================================================================== */\n\n.link-arrow {\n  font-weight: 100;\n  text-decoration: underline;\n  font-style: normal;\n}\n"
  },
  {
    "path": "website/_sass/_masthead.scss",
    "content": "/* ==========================================================================\n   Masthead\n   ========================================================================== */\n\n.masthead {\n  margin-top: 40px;\n  @include clearfix;\n  @include media($medium) {\n    margin-top: 0;\n  }\n  .wrap {\n    text-align: center;\n    max-width: em(440);\n    padding-right: 20px;\n    padding-left: 20px;\n    @include media($medium) {\n      max-width: em(728);\n      padding-right: 60px;\n      padding-left: 60px;\n    }\n    @include media($large) {\n      max-width: em(960);\n    }\n  }\n}\n\n/*\n   Site logo\n   ========================================================================== */\n\n.site-logo {\n  img {\n    width: 100px;\n    height: 100px;\n    @include animation-duration(1s);\n      @include animation-delay(.05s);\n      border: 4px solid $white;\n      @include rounded(100px); /* crops in a circle */\n    @include media($medium) {\n      width: 150px;\n      height: 150px;\n      @include rounded(150px); /* crops in a circle */\n    }\n    @include media($large) {\n      width: 200px;\n      height: 200px;\n      @include rounded(200px); /* crops in a circle */\n    }\n  }\n}\n\n/*\n   Site title/name\n   ========================================================================== */\n\n.site-title {\n  margin-bottom: 0;\n  @include animation-duration(1s);\n    @include animation-delay(.75s);\n  a {\n    color: $black;\n  }\n}\n\n/*\n   Site description text\n   ========================================================================== */\n\n.site-description {\n  margin-top: 0;\n  font-family: $alt-font;\n  @include font-size(16);\n  font-weight: 400;\n  font-style: italic;\n  @include animation-duration(1s);\n    @include animation-delay(1s);\n  @include media($medium) {\n    @include font-size(20);\n  }\n}\n"
  },
  {
    "path": "website/_sass/_mixins.scss",
    "content": "// UTILITY MIXINS\n// --------------------------------------------------\n\n// Webkit-style focus\n// --------------------\n@mixin tab-focus() {\n  // Default\n  outline: thin dotted #333;\n  // Webkit\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n\n// Center-align a block level element\n// ----------------------------------\n@mixin center-block() {\n  display: block;\n  margin-left: auto;\n  margin-right: auto;\n}\n\n@mixin text-overflow() {\noverflow: hidden;\ntext-overflow: ellipsis;\nwhite-space: nowrap; }\n\n// MIXINS\n// --------------------------------------------------\n\n%tab-focus {\n  // Default\n  outline: thin dotted #333;\n  // Webkit\n  outline: 5px auto -webkit-focus-ring-color;\n  outline-offset: -2px;\n}\n\n\n//  Typography\n// --------------------------------------------------\n\n/*  Vertical Rhythm\n  https://github.com/sturobson/Sassifaction\n\n  In this mixin you can specify the font size in PX and \n  it will calculate the REM based on your $doc-font-size \n  & $doc-line-height variables. \n\n    @include font-size(24);\n\n  It will also create a bottom margin based on the \n  $doc-font-size & $doc-line-height variables unless you \n  specify that it shouldn't have one.\n\n    @include font-size(24, no);\n\n  Or if you want to specify a different bottom margin to \n  be generated.\n\n    @include font-size(24,32);\n\n  This mixin also generates a pixel-less line height by \n  default unless you specify that you either don't want \n  one where I'd suggest declaring 1 within the mixin.\n\n    @include font-size(24, yes, 1);\n\n  There's also the option to specify a different line-height \n  for it to generate to, where you would specify the \n  line-height in (effectively) it's pixel value.\n\n    @include font-size(24, yes, 40);\n*/\n\n@mixin font-size($size, $margin: yes, $line-height: $doc-line-height) {\n    \n  // generates the font-size in REMs with a PX fallback\n  font-size: 0px + $size;\n  font-size: 0rem + $size / $doc-font-size;\n\n  // line-height functions\n  ////////////////////////\n\n  // if you a line-height is specified in the mixin\n  @if $line-height != $doc-line-height and $line-height != 1 {\n    line-height: ceil($size / $line-height) * ($line-height / $size);\n  }\n  \n  // if $line-height == 1\n  // because, typing 1 is quicker than 16\n  @else if $line-height == 1 {\n    line-height: 1;\n  }\n\n  // normal $line-height\n  // if the line-height is left.\n  @else {\n    line-height: ceil($size / $doc-line-height) * ($doc-line-height / $size);\n  }\n\n  // margin-bottom functions\n  //////////////////////////\n\n  // if no is bottom margin is required\n  @if $margin == no {\n    margin-bottom: 0;  \n  }  \n  \n  // if a specific bottom margin is required\n  @else if $margin != yes and $margin != no {\n    margin-bottom: 0px + $margin;\n    margin-bottom: 0rem + ($margin / $doc-font-size);  \n  }\n\n  // if you're keeping the vertical rhythm with the margin\n  @else {\n    margin-bottom: 0px + $doc-line-height;\n    margin-bottom: 0rem + ($doc-line-height / $doc-font-size);  \n  }\n}\n\n// ROUND CORNERS\n// --------------------------------------------------\n\n// @include rounded(VALUE);\n@mixin rounded($radius:4px) {\nborder-radius : $radius; }\n\n// @include border-radius(VALUE,VALUE,VALUE,VALUE);\n@mixin border-radius($topright: 0, $bottomright: 0, $bottomleft: 0, $topleft: 0) {\nborder-top-right-radius : $topright;\nborder-bottom-right-radius : $bottomright;\nborder-bottom-left-radius : $bottomleft;\nborder-top-left-radius : $topleft;\nbackground-clip : padding-box; }\n\n// @include box-shadow(HORIZONTAL VERTICAL BLUR COLOR))\n@mixin box-shadow($shadow: 0 1px 3px rgba(0,0,0,.25)) {\n-webkit-box-shadow : $shadow;\n-moz-box-shadow : $shadow;\nbox-shadow : $shadow; }\n\n// @include drop-shadow(HORIZONTAL, VERTICAL, BLUR, ALPHA);\n@mixin drop-shadow($x-axis: 0, $y-axis: 1px, $blur: 2px, $alpha: 0.1) {\n-webkit-box-shadow : $x-axis $y-axis $blur rgba(0, 0, 0, $alpha);\n-moz-box-shadow : $x-axis $y-axis $blur rgba(0, 0, 0, $alpha);\nbox-shadow : $x-axis $y-axis $blur rgba(0, 0, 0, $alpha); }\n\n// @include text-shadow();\n@mixin text-shadow($shadow: 0 2px 3px rgba(0,0,0,.25)) {\ntext-shadow : $shadow; }\n\n// @include opacity(VALUE);\n@mixin opacity($opacity : .5) {\nopacity : $opacity; }\n\n// For image replacement\n@mixin hide-text() {\ntext-indent : 100%;\nwhite-space : nowrap;\noverflow : hidden; }\n\n// Hide from visual and speaking browsers\n@mixin hidden() {\ndisplay : none !important; \nvisibility : hidden; }\n.hidden {\n  display: none;\n  visibility: hidden;\n}\n\n// Hide but maintain layout\n@mixin invisible() { \nvisibility : hidden; }\n\n// @include resize(VALUE) (none, both, horizontal, vertical, inherit)\n@mixin resize($direction: both) {\nresize : $direction;\noverflow : auto; }\n\n// @include userselect(VALUE) (all, element, none, text)\n@mixin user-select($select) {\n-webkit-user-select : $select;\n-moz-user-select : $select;\n-o-user-select : $select;\nuser-select : $select; }\n\n// Hidden but available to speaking browsers\n@mixin visuallyhidden() { \noverflow : hidden; \nposition : absolute;\nclip : rect(0 0 0 0); \nheight : 1px; \nwidth : 1px;\nmargin : -1px; \npadding : 0; \nborder : 0; }"
  },
  {
    "path": "website/_sass/_navigation.scss",
    "content": "/* ==========================================================================\n   Top navigation\n   ========================================================================== */\n\n.navigation-wrapper {\n  text-align: center;\n  @include clearfix;\n  ul {\n    display: inline-block;\n    width: 100%;\n    vertical-align: top;\n    margin: 0 0 50px;\n    padding: 4px 20px;\n    background-color: $black;\n    @include media($medium) {\n      width: auto;\n      @include rounded(0 0 10px 10px);\n    }\n    @include clearfix;\n  }\n  li {\n    display: block;\n    float: left;\n    list-style: none;\n    text-align: center;\n    @include font-size(24,no);\n    text-transform: uppercase;\n    color: $white;\n    @include media($medium) {\n      @include font-size(14,no);\n    }\n    a:hover {\n      @include box-shadow($shadow: inset 0 0 1px $white);\n    }\n  }\n  a {\n    display: block;\n    margin-bottom: 10px;\n    padding: 12px 20px;\n    @include media($medium) {\n      margin-bottom: 0;\n      padding: 6px 10px;\n    }\n    color: $white;\n    @include rounded(4px);\n    &:hover {\n      background-color: lighten($black, 10);\n    }\n  }\n}\n\n/*\n   Responsive navigation toggle\n   ========================================================================== */\n\n#site-nav {\n  display: none;\n  z-index: 5;\n  @include media($medium) {\n    -webkit-animation-duration: 1s;\n    -moz-animation-duration: 1s;\n    -o-animation-duration: 1s;\n    animation-duration: 1s;\n  }\n  &.closed {\n    @include media($medium) {\n      display: block;\n    }\n  }\n  &.opened {\n    display: block;\n    max-height: 100%;\n    li {\n      float: none;\n      @include media($medium) {\n        float: left;\n      }\n    }\n  }\n}\n\n/* display when JavaScript is disabled */\n.no-js #site-nav {\n  display: block;\n}\n\n#nav-toggle {\n  z-index: 20;\n  display: block;\n  vertical-align: top;\n  margin: 0;\n  padding: 6px 20px;\n  background-color: $black;\n  height: 44px;\n  @include rounded(0);\n  @include media($medium) {\n    display: none;\n  }\n}\n"
  },
  {
    "path": "website/_sass/_notices.scss",
    "content": "/* ==========================================================================\n   Notices\n   ========================================================================== */\n\n/*\n   Default notice\n   ========================================================================== */\n\n.notice {\n  margin-top: 1.5em;\n  padding: .5em 1em;\n  text-indent: 0;\n  @include font-size(16);\n  background-color: lighten($black, 95);\n  border: 1px solid lighten($black, 90);\n  @include rounded(4px);\n}\n\n/*\n   Browser upgrade notice\n   ========================================================================== */\n\n.upgrade {\n  padding: 10px;\n  text-align: center;\n}\n"
  },
  {
    "path": "website/_sass/_page.scss",
    "content": "/* ==========================================================================\n   Page/post layout and styling\n   ========================================================================== */\n\n/*\n   Main content\n   ========================================================================== */\n\n#main {\n\t@include clearfix;\n}\n\n.entry,\n.hentry {\n\t@include clearfix;\n\tborder-bottom: 1px solid lighten($black,80);\n\tborder-bottom: 1px solid rgba($black,.10);\n}\n\n/* feature image */\n\n.entry-feature-image {\n\tmargin: 20px 0 0;\n\twidth: 100%;\n\t@include media($medium) {\n\t\tmargin-top: -75px; /* move up to be overlapped by site logo */\n\t}\n\t@include media($large) {\n\t\tmargin-top: -145px; /* move up further to be overlapped by site logo */\n\t}\n}\n\n/* page header */\n\n.entry-header {\n\t@include fill-parent;\n}\n\n/* tag listing in page header */\n\n.entry-tags {\n\tdisplay: block;\n\tmargin-top: 2em;\n\ttext-transform: uppercase;\n\t@include font-size(16);\n\tfont-weight: 600;\n\ta {\n\t\tcolor: $text-color;\n\t}\n}\n\n/* page title */\n\nspan + .entry-title {\n\tmargin-top: 0;\n}\n\n.entry-title {\n\tfont-family: $alt-font;\n\tfont-style: italic;\n\t@include font-size(36,yes,36);\n\tfont-weight: 400;\n\tline-height: 1;\n\tletter-spacing: -3px;\n\ta {\n\t\tcolor: $black;\n\t\ttext-decoration: underline;\n\t}\n\t@include media($medium) {\n\t\t@include font-size(52,yes,54);\n\t}\n\t@include media($large) {\n\t\t@include font-size(68,yes,72);\n\t}\n}\n\n/* page/post wrapper */\n\n.entry-wrapper {\n\t@include outer-container;\n\tmargin-top: 0;\n\tmargin-bottom: 3em;\n\tpadding-right: $gutter;\n\tpadding-left: $gutter;\n}\n\n/* page/post meta content (date, author, etc) */\n\n.entry-meta {\n\t@include span-columns(12);\n\ttext-transform: uppercase;\n\t@include font-size(14);\n\ta {\n\t\tcolor: $text-color;\n\t}\n\t@include media($large) {\n\t\t@include span-columns(2.5);\n\t}\n\t& > span {\n\t\tpadding: 0 20px 10px 0;\n\t\tdisplay: inline-block;\n\t\t@include media($large) {\n\t\t\tdisplay: block;\n\t\t\tpadding: 8px 0;\n\t\t\tborder-bottom: 1px solid lighten($black,80);\n\t\t\tborder-bottom: 1px solid rgba($black,.10);\n\t\t}\n\t}\n}\n\n/* author avatar (circular) */\n\n.bio-photo {\n\tdisplay: none;\n\t@include media($large) {\n\t\tdisplay: block;\n\t\twidth: 150px;\n\t\theight: 150px;\n\t\tmargin-bottom: 10px;\n\t\t@include rounded(150px);\n\t\t@include clearfix;\n\t}\n}\n\n\n/* feature image credit */\n\n.image-credit {\n  margin: 0 auto;\n  max-width: 440px;\n  padding-top: 5px;\n  padding-right: 20px;\n  padding-left: 20px;\n  text-align: right;\n  @include font-size(12, no);\n  line-height: 1.3;\n  color: lighten($text-color, 30);\n  @include clearfix();\n  @include media($medium) {\n    max-width: 760px;\n    padding-right: 60px;\n    padding-left: 60px;\n    @include font-size(14, no);\n  }\n  @include media($large) {\n    max-width: 960px;\n  }\n  a {\n    color: lighten($text-color, 30);\n  }\n}\n\n/* main content block */\n\n.entry-content {\n\t@include span-columns(12);\n\tp:first-child {\n\t\tmargin-top: 0;\n\t}\n\t@include media($large) {\n\t\t@include span-columns(9.5);\n\t}\n\t/* nice link underlines */\n  p > a,\n\tli > a {\n\t\tborder-bottom: 1px dotted lighten($link-color, 50);\n\t\t&:hover {\n\t\t\tborder-bottom-style: solid;\n\t\t}\n\t}\n}\n\n/*\n   Disqus\n   ========================================================================== */\n\n#disqus_thread {\n\tmargin-top: 2em;\n}\n\n/*\n   Pagination\n   ========================================================================== */\n\n.pagination {\n\tmargin-top: 2em;\n\ttext-align: center;\n}\n\n/*\n   Overrides\n   ========================================================================== */\n\n/* adjust width for lack of meta/author column */\n\n#home,\n#page {\n\t.entry-wrapper {\n\t\tmax-width: em(760);\n\t}\n\t.entry-title {\n\t\ttext-align: center;\n\t\tmax-width: 100%;\n\t}\n\t.entry-content {\n\t\t@include span-columns(12);\n\t}\n}\n\n/*\n   Kramdown generated table of contents\n   ========================================================================== */\n\n#markdown-toc {\n\tfont-family: $alt-font;\n\tmargin-top: $gutter;\n\tmargin-bottom: $gutter;\n\tpadding-left: 0;\n\tborder: 1px solid $border-color;\n\tborder-radius: $border-radius;\n  ul {\n  \tlist-style-type: none;\n  \tpadding-left: 0;\n  }\n  li {\n    @include font-size(16,no,18);\n    border-bottom: 1px solid $border-color;\n    list-style-type: none;\n  }\n  h6 {\n    margin: 0;\n    padding: (.25 * $gutter) (.5 * $gutter);\n    background: $table-stripe-color;\n  }\n  a {\n    display: block;\n    padding: (.25 * $gutter) (.5 * $gutter);\n    border-left: 2px solid transparent;\n    border-bottom: 0 solid transparent;\n    &:hover,\n    &:focus {\n      background: lighten($border-color,5);\n    }\n  }\n}\n\n/*\n   Tables\n   ========================================================================== */\n\n/** For nicer looking tables apply the .table class\n *  Example:\n *  <table class=\"table\">\n *    <tr>\n *      <td>cell1</td>\n *      <td>cell2</td>\n *      <td>cell3</td>\n *    </tr>\n *  </table>\n*/\n\n.table {\n\tborder-collapse: collapse;\n\tmargin: ((0px + $doc-line-height) / 2) 0;\n\tmargin: ((0rem + ($doc-line-height / $doc-font-size)) / 2) 0;\n\twidth: 100%;\n\ttbody {\n\t\ttr:hover > td, tr:hover > th {\n\t\t\tbackground-color: $table-hover-color;\n\t\t}\n\t}\n\tthead {\n\t\ttr:first-child td {\n\t\t\tborder-bottom: 2px solid $table-border-color;\n\t\t}\n\t}\n\tth {\n\t\tpadding: (0px + $doc-line-height) / 2;\n\t\tpadding: (0rem + ($doc-line-height / $doc-font-size)) / 2;\n\t\tfont-family: $alt-font;\n\t\tfont-weight: bold;\n\t\ttext-align: left;\n\t\tbackground-color: $table-header-color;\n\t\tborder-bottom: 1px solid darken($border-color, 15%);\n\t}\n\ttd {\n\t\tborder-bottom: 1px solid $border-color;\n\t\tpadding: (0px + $doc-line-height) / 2;\n\t\tpadding: (0rem + ($doc-line-height / $doc-font-size)) / 2;\n\t\t@include font-size(18);\n\t}\n\ttr, td, th {\n\t\tvertical-align: middle;\n\t}\n}\n"
  },
  {
    "path": "website/_sass/_reset.scss",
    "content": "// STYLE RESETS\n// Adapted from http://github.com/necolas/normalize.css\n// --------------------------------------------------\n\n// Apply a natural box layout model to all elements\n*, *:before, *:after {\n  -moz-box-sizing: border-box; -webkit-box-sizing: border-box; box-sizing: border-box;\n }\n\n// Remove margin\nbody { margin: 0; } \n\n// Display HTML5 elements in IE6-9 and FF3\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nnav,\nsection {\n  display: block;\n}\n\n// Display block in IE6-9 and FF3\naudio,\ncanvas,\nvideo {\n  display: inline-block;\n  *display: inline;\n  *zoom: 1;\n}\n\n// Prevents modern browsers from displaying 'audio' without controls\naudio:not([controls]) {\n  display: none;\n}\n\n// Base font settings\nhtml {\n  font-size: 100%;\n  -webkit-text-size-adjust: 100%;\n  -ms-text-size-adjust: 100%;\n}\n// Apply focus state\na:focus {\n  @extend %tab-focus;\n}\n\n// Remove outline\na:hover,\na:active {\n  outline: 0;\n}\n\n// Prevent sub and sup affecting line-height in all browsers\nsub,\nsup {\n  position: relative;\n  font-size: 75%;\n  line-height: 0;\n  vertical-align: baseline;\n}\nsup {\n  top: -0.5em;\n}\nsub {\n  bottom: -0.25em;\n}\n\n// Normalize blockquotes\nblockquote {\n  margin: 0;\n}\n\n// Img border in a's and image quality\nimg {\n  // Responsive images (ensure images don't scale beyond their parents)\n  max-width: 100%; // Part 1: Set a maximum relative to the parent\n  width: auto\\9; // IE7-8 need help adjusting responsive images\n  height: auto; // Part 2: Scale the height according to the width, otherwise you get stretching\n\n  vertical-align: middle;\n  border: 0;\n  -ms-interpolation-mode: bicubic;\n}\n\n// Prevent max-width from affecting Google Maps\n#map_canvas img,\n.google-maps img {\n  max-width: none;\n}\n\n// Consistent form font size in all browsers, margin changes, misc\nbutton,\ninput,\nselect,\ntextarea {\n  margin: 0;\n  font-size: 100%;\n  vertical-align: middle;\n}\nbutton,\ninput {\n  *overflow: visible; // Inner spacing ie IE6/7\n  line-height: normal; // FF3/4 have !important on line-height in UA stylesheet\n}\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner { // Inner padding and border oddities in FF3/4\n  padding: 0;\n  border: 0;\n}\nbutton,\nhtml input[type=\"button\"], // Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio` and `video` controls.\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n    -webkit-appearance: button; // Corrects inability to style clickable `input` types in iOS.\n    cursor: pointer; // Improves usability and consistency of cursor style between image-type `input` and others.\n}\nlabel,\nselect,\nbutton,\ninput[type=\"button\"],\ninput[type=\"reset\"],\ninput[type=\"submit\"],\ninput[type=\"radio\"],\ninput[type=\"checkbox\"] {\n    cursor: pointer; // Improves usability and consistency of cursor style between image-type `input` and others.\n}\ninput[type=\"search\"] { // Appearance in Safari/Chrome\n  @include box-sizing(content-box);\n  -webkit-appearance: textfield;\n}\ninput[type=\"search\"]::-webkit-search-decoration,\ninput[type=\"search\"]::-webkit-search-cancel-button {\n  -webkit-appearance: none; // Inner-padding issues in Chrome OSX, Safari 5\n}\ntextarea {\n  overflow: auto; // Remove vertical scrollbar in IE6-9\n  vertical-align: top; // Readability and alignment cross-browser\n}"
  },
  {
    "path": "website/_sass/_search.scss",
    "content": "/* ==========================================================================\n   Search\n   ========================================================================== */\n\n.search-wrapper {\n  @include outer-container;\n  margin-top: 0;\n  padding-right: $gutter;\n  padding-left: $gutter;\n  height: 1px;\n}\n\n/* \n   Google search form\n   ========================================================================== */\n\n#goog-fixurl {\n  ul {\n    list-style: none;\n    margin-left: 0;\n    padding-left: 0;\n    li {\n      list-style-type: none;\n    }\n  }\n}\n\n#goog-wm-qt {\n  width: auto;\n  margin-right: 10px;\n  padding: 8px 10px;\n  @include font-size(14);\n  border-width: 2px;\n  border-radius: $border-radius;\n}\n\n/* button */\n#goog-wm-sb {\n  @extend .btn;\n}\n"
  },
  {
    "path": "website/_sass/_syntax.scss",
    "content": "/* ==========================================================================\n   Syntax highlighting and formatting\n   ========================================================================== */\n\n/* \n   Pygments.rb and Rouge\n   ========================================================================== */\n\n.highlight {\n\tbackground-color: #efefef;\n\tfont-family: $code-font;\n\tcolor: #333332;\n\tmargin-bottom: 1.5em;\n\t@include rounded($border-radius);\n\tpre {\n\t\tposition: relative;\n\t\tmargin: 0;\n\t\tpadding: 1em;\n\t}\n\t.lineno { padding-right: 24px; color: lighten(#333332,50);}\n\t.hll { background-color: #ffffcc }\n\t.c { color: #999988; font-style: italic } /* Comment */\n\t.err { color: #a61717; background-color: #e3d2d2 } /* Error */\n\t.k { color: #000000; font-weight: bold } /* Keyword */\n\t.o { color: #000000; font-weight: bold } /* Operator */\n\t.cm { color: #999988; font-style: italic } /* Comment.Multiline */\n\t.cp { color: #999999; font-weight: bold; font-style: italic } /* Comment.Preproc */\n\t.c1 { color: #999988; font-style: italic } /* Comment.Single */\n\t.cs { color: #999999; font-weight: bold; font-style: italic } /* Comment.Special */\n\t.gd { color: #000000; background-color: #ffdddd } /* Generic.Deleted */\n\t.ge { color: #000000; font-style: italic } /* Generic.Emph */\n\t.gr { color: #aa0000 } /* Generic.Error */\n\t.gh { color: #999999 } /* Generic.Heading */\n\t.gi { color: #000000; background-color: #ddffdd } /* Generic.Inserted */\n\t.go { color: #888888 } /* Generic.Output */\n\t.gp { color: #555555 } /* Generic.Prompt */\n\t.gs { font-weight: bold } /* Generic.Strong */\n\t.gu { color: #aaaaaa } /* Generic.Subheading */\n\t.gt { color: #aa0000 } /* Generic.Traceback */\n\t.kc { color: #000000; font-weight: bold } /* Keyword.Constant */\n\t.kd { color: #000000; font-weight: bold } /* Keyword.Declaration */\n\t.kn { color: #000000; font-weight: bold } /* Keyword.Namespace */\n\t.kp { color: #000000; font-weight: bold } /* Keyword.Pseudo */\n\t.kr { color: #000000; font-weight: bold } /* Keyword.Reserved */\n\t.kt { color: #445588; font-weight: bold } /* Keyword.Type */\n\t.m { color: #009999 } /* Literal.Number */\n\t.s { color: #d01040 } /* Literal.String */\n\t.na { color: #008080 } /* Name.Attribute */\n\t.nb { color: #0086B3 } /* Name.Builtin */\n\t.nc { color: #445588; font-weight: bold } /* Name.Class */\n\t.no { color: #008080 } /* Name.Constant */\n\t.nd { color: #3c5d5d; font-weight: bold } /* Name.Decorator */\n\t.ni { color: #800080 } /* Name.Entity */\n\t.ne { color: #990000; font-weight: bold } /* Name.Exception */\n\t.nf { color: #990000; font-weight: bold } /* Name.Function */\n\t.nl { color: #990000; font-weight: bold } /* Name.Label */\n\t.nn { color: #555555 } /* Name.Namespace */\n\t.nt { color: #000080 } /* Name.Tag */\n\t.nv { color: #008080 } /* Name.Variable */\n\t.ow { color: #000000; font-weight: bold } /* Operator.Word */\n\t.w { color: #bbbbbb } /* Text.Whitespace */\n\t.mf { color: #009999 } /* Literal.Number.Float */\n\t.mh { color: #009999 } /* Literal.Number.Hex */\n\t.mi { color: #009999 } /* Literal.Number.Integer */\n\t.mo { color: #009999 } /* Literal.Number.Oct */\n\t.sb { color: #d01040 } /* Literal.String.Backtick */\n\t.sc { color: #d01040 } /* Literal.String.Char */\n\t.sd { color: #d01040 } /* Literal.String.Doc */\n\t.s2 { color: #d01040 } /* Literal.String.Double */\n\t.se { color: #d01040 } /* Literal.String.Escape */\n\t.sh { color: #d01040 } /* Literal.String.Heredoc */\n\t.si { color: #d01040 } /* Literal.String.Interpol */\n\t.sx { color: #d01040 } /* Literal.String.Other */\n\t.sr { color: #009926 } /* Literal.String.Regex */\n\t.s1 { color: #d01040 } /* Literal.String.Single */\n\t.ss { color: #990073 } /* Literal.String.Symbol */\n\t.bp { color: #999999 } /* Name.Builtin.Pseudo */\n\t.vc { color: #008080 } /* Name.Variable.Class */\n\t.vg { color: #008080 } /* Name.Variable.Global */\n\t.vi { color: #008080 } /* Name.Variable.Instance */\n\t.il { color: #009999 } /* Literal.Number.Integer.Long */\n}\n\n/* \n   Coderay\n   ========================================================================== */\n\n.CodeRay {\n  background-color: #efefef;\n  font-family: $code-font;\n  @include font-size(12);\n  color: #333332;\n  margin-bottom: 1.5em;\n  @include rounded(4px);\n  pre {\n    margin: 0px;\n    padding: 1em;\n  }\n}\n\ndiv.CodeRay { }\nspan.CodeRay { white-space: pre; border: 0px; padding: 2px }\n\ntable.CodeRay { border-collapse: collapse; width: 100%; padding: 2px }\ntable.CodeRay td {\n  padding: 1em 0.5em;\n  vertical-align: top;\n}\n\nol.CodeRay { font-size: 10pt }\nol.CodeRay li { white-space: pre }\n\n.CodeRay {\n\n\t.line-numbers, \n\t.no {\n\t  background-color: #ececec;\n\t  color: #aaa;\n\t  text-align: right;\n\t}\n\n\t.line-numbers a {\n\t  color: #aaa;\n\t}\n\n\t.line-numbers tt { font-weight: bold }\n\t.line-numbers .highlighted { color: red }\n\t.line { display: block; float: left; width: 100%; }\n\tspan.line-numbers { padding: 0 24px 0 4px }\n\t.code { width: 100% }\n\n\t.code pre { overflow: auto }\n  .debug { color: white !important; background: blue !important; }\n\t.annotation { color: #007 }\n\t.attribute-name { color: #f08 }\n\t.attribute-value { color: #700 }\n\t.binary { color: #509; font-weight: bold }\n\t.comment  { color: #998; font-style: italic;}\n\t.char { color: #04d }\n\t.char .content { color: #04d }\n\t.char .delimiter { color: #039 }\n\t.class { color: #458; font-weight: bold }\n\t.complex { color: #a08; font-weight: bold }\n\t.constant { color: teal; }\n\t.color { color: #0a0 }\n\t.class-variable { color: #369 }\n\t.decorator { color: #b0b; }\n\t.definition { color: #099; font-weight: bold }\n\t.directive { color: #088; font-weight: bold }\n\t.delimiter { color: black }\n\t.doc { color: #970 }\n\t.doctype { color: #34b }\n\t.doc-string { color: #d42; font-weight: bold }\n\t.escape  { color: #666; font-weight: bold }\n\t.entity { color: #800; font-weight: bold }\n\t.error { color: #f00; background-color: #faa }\n\t.exception { color: #c00; font-weight: bold }\n\t.filename { color: #099; }\n\t.function { color: #900; font-weight: bold }\n\t.global-variable { color: teal; font-weight: bold }\n\t.hex { color: #058; font-weight: bold }\n\t.integer  { color: #099; }\n\t.include { color: #b44; font-weight: bold }\n\t.inline { color: black }\n\t.inline .inline { background: #ccc }\n\t.inline .inline .inline { background: #bbb }\n\t.inline .inline-delimiter { color: #d14; }\n\t.inline-delimiter { color: #d14; }\n\t.important { color: #f00; }\n\t.interpreted { color: #b2b; font-weight: bold }\n\t.instance-variable { color: teal }\n\t.label { color: #970; font-weight: bold }\n\t.local-variable { color: #963 }\n\t.octal { color: #40e; font-weight: bold }\n\t.operator { }\n\t.predefined-constant { font-weight: bold }\n\t.predefined { color: #369; font-weight: bold }\n\t.preprocessor { color: #579; }\n\t.pseudo-class { color: #00c; font-weight: bold }\n\t.predefined-type { color: #074; font-weight: bold }\n\t.reserved, \n\t.keyword { color: #000; font-weight: bold }\n\n\t.key { color: #808; }\n\t.key .delimiter { color: #606; }\n\t.key .char { color: #80f; }\n\t.value { color: #088; }\n\n\t.regexp { background-color: #fff0ff }\n\t.regexp .content { color: #808 }\n\t.regexp .delimiter { color: #404 }\n\t.regexp .modifier { color: #c2c }\n\t.regexp .function  { color: #404; font-weight: bold }\n\n\t.string { color: #d20; }\n\t.string .string { }\n\t.string .string .string { background-color: #ffd0d0 }\n\t.string .content { color: #d14; }\n\t.string .char { color: #d14; }\n\t.string .delimiter { color: #d14; }\n\n\t.shell { color: #d14 }\n\t.shell .content { }\n\t.shell .delimiter { color: #d14 }\n\n\t.symbol { color: #990073 }\n\t.symbol .content { color: #a60 }\n\t.symbol .delimiter { color: #630 }\n\n\t.tag { color: #070 }\n\t.tag-special { color: #d70; font-weight: bold }\n\t.type { color: #339; font-weight: bold }\n\t.variable { color: #036 }\n\n\t.insert { background: #afa; }\n\t.delete { background: #faa; }\n\t.change { color: #aaf; background: #007; }\n\t.head { color: #f8f; background: #505 }\n\n\t.insert .insert { color: #080; font-weight: bold }\n\t.delete .delete { color: #800; font-weight: bold }\n\t.change .change { color: #66f; }\n\t.head .head { color: #f4f; }\n}\n"
  },
  {
    "path": "website/_sass/_variables.scss",
    "content": "// TYPOGRAPHY ================================================\n$base-font: 'source-sans-pro', sans-serif;\n$heading-font: $base-font;\n$caption-font: $base-font;\n$code-font: 'source-code-pro', monospace;\n$alt-font: 'volkhov', serif;\n\n$doc-font-size: 16;\n$doc-line-height: 24;\n\n$border-radius: 3px;\n\n\n// set-up the body font-size / line-height\nbody {\n  margin-top: 0px + $doc-line-height;\n  font-size: 0px + $doc-font-size;  \n}\n\n\n// COLORS =====================================================\n$body-color         : #fff;\n$text-color         : #222;\n$base-color         : #343434;    \n$comp-color         : spin($base-color, 180); \n$border-color       : #ddd;\n$white              : #fff;\n$black              : #000;\n$accent-color       : $black;\n$link-color         : #222;\n\n$table-border-color           : $border-color;\n$table-border                 : 1px solid $table-border-color;\n$table-background             : $body-color;\n$table-header-color           : lighten($table-background, 10);\n$table-hover-color            : darken($table-background, 2);\n$table-stripe-color           : darken($table-background, 4);\n$table-stripe-color-hover     : darken($table-stripe-color, 5);\n\n\n// MEDIA QUERIES ==============================================\n$micro            : \"only screen and (min-width: 30em)\";\n$small            : \"only screen and (min-width: 37.5em)\";\n$medium           : \"only screen and (min-width: 48em)\";\n$large            : \"only screen and (min-width: 62em)\";\n$x-large          : \"only screen and (min-width: 86.375em)\";\n"
  },
  {
    "path": "website/_sass/_wells.scss",
    "content": "/* ==========================================================================\n   Wells\n   ========================================================================== */\n\n/* Used to call out text in a subtle manner */\n\n.well {\n  padding: 20px;\n  border: 1px solid $comp-color;\n  @include rounded(4px);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/_bourbon-deprecated-upcoming.scss",
    "content": "//************************************************************************//\n// These mixins/functions are deprecated\n// They will be removed in the next MAJOR version release\n//************************************************************************//\n@mixin inline-block {\n  display: inline-block;\n  @warn \"inline-block mixin is deprecated and will be removed in the next major version release\";\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/_bourbon.scss",
    "content": "// Settings\n@import \"settings/prefixer\";\n@import \"settings/px-to-em\";\n\n// Custom Helpers\n@import \"helpers/convert-units\";\n@import \"helpers/gradient-positions-parser\";\n@import \"helpers/is-num\";\n@import \"helpers/linear-angle-parser\";\n@import \"helpers/linear-gradient-parser\";\n@import \"helpers/linear-positions-parser\";\n@import \"helpers/linear-side-corner-parser\";\n@import \"helpers/radial-arg-parser\";\n@import \"helpers/radial-positions-parser\";\n@import \"helpers/radial-gradient-parser\";\n@import \"helpers/render-gradients\";\n@import \"helpers/shape-size-stripper\";\n@import \"helpers/str-to-num\";\n\n// Custom Functions\n@import \"functions/assign\";\n@import \"functions/color-lightness\";\n@import \"functions/flex-grid\";\n@import \"functions/golden-ratio\";\n@import \"functions/grid-width\";\n@import \"functions/modular-scale\";\n@import \"functions/px-to-em\";\n@import \"functions/px-to-rem\";\n@import \"functions/strip-units\";\n@import \"functions/tint-shade\";\n@import \"functions/transition-property-name\";\n@import \"functions/unpack\";\n\n// CSS3 Mixins\n@import \"css3/animation\";\n@import \"css3/appearance\";\n@import \"css3/backface-visibility\";\n@import \"css3/background\";\n@import \"css3/background-image\";\n@import \"css3/border-image\";\n@import \"css3/border-radius\";\n@import \"css3/box-sizing\";\n@import \"css3/calc\";\n@import \"css3/columns\";\n@import \"css3/filter\";\n@import \"css3/flex-box\";\n@import \"css3/font-face\";\n@import \"css3/font-feature-settings\";\n@import \"css3/hyphens\";\n@import \"css3/hidpi-media-query\";\n@import \"css3/image-rendering\";\n@import \"css3/keyframes\";\n@import \"css3/linear-gradient\";\n@import \"css3/perspective\";\n@import \"css3/radial-gradient\";\n@import \"css3/transform\";\n@import \"css3/transition\";\n@import \"css3/user-select\";\n@import \"css3/placeholder\";\n\n// Addons & other mixins\n@import \"addons/button\";\n@import \"addons/clearfix\";\n@import \"addons/directional-values\";\n@import \"addons/ellipsis\";\n@import \"addons/font-family\";\n@import \"addons/hide-text\";\n@import \"addons/html5-input-types\";\n@import \"addons/position\";\n@import \"addons/prefixer\";\n@import \"addons/retina-image\";\n@import \"addons/size\";\n@import \"addons/timing-functions\";\n@import \"addons/triangle\";\n@import \"addons/word-wrap\";\n\n// Soon to be deprecated Mixins\n@import \"bourbon-deprecated-upcoming\";\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_button.scss",
    "content": "@mixin button ($style: simple, $base-color: #4294f0, $text-size: inherit, $padding: 7px 18px) {\n\n  @if type-of($style) == string and type-of($base-color) == color {\n    @include buttonstyle($style, $base-color, $text-size, $padding);\n  }\n\n  @if type-of($style) == string and type-of($base-color) == number {\n    $padding: $text-size;\n    $text-size: $base-color;\n    $base-color: #4294f0;\n\n    @if $padding == inherit {\n      $padding: 7px 18px;\n    }\n\n    @include buttonstyle($style, $base-color, $text-size, $padding);\n  }\n\n  @if type-of($style) == color and type-of($base-color) == color {\n    $base-color: $style;\n    $style: simple;\n    @include buttonstyle($style, $base-color, $text-size, $padding);\n  }\n\n  @if type-of($style) == color and type-of($base-color) == number {\n    $padding: $text-size;\n    $text-size: $base-color;\n    $base-color: $style;\n    $style: simple;\n\n    @if $padding == inherit {\n      $padding: 7px 18px;\n    }\n\n    @include buttonstyle($style, $base-color, $text-size, $padding);\n  }\n\n  @if type-of($style) == number {\n    $padding: $base-color;\n    $text-size: $style;\n    $base-color: #4294f0;\n    $style: simple;\n\n    @if $padding == #4294f0 {\n      $padding: 7px 18px;\n    }\n\n    @include buttonstyle($style, $base-color, $text-size, $padding);\n  }\n\n  &:disabled {\n    opacity: 0.5;\n    cursor: not-allowed;\n  }\n}\n\n\n// Selector Style Button\n//************************************************************************//\n@mixin buttonstyle($type, $b-color, $t-size, $pad) {\n  // Grayscale button\n  @if $type == simple and $b-color == grayscale($b-color) {\n    @include simple($b-color, true, $t-size, $pad);\n  }\n\n  @if $type == shiny and $b-color == grayscale($b-color) {\n    @include shiny($b-color, true, $t-size, $pad);\n  }\n\n  @if $type == pill and $b-color == grayscale($b-color) {\n    @include pill($b-color, true, $t-size, $pad);\n  }\n\n  @if $type == flat and $b-color == grayscale($b-color) {\n    @include flat($b-color, true, $t-size, $pad);\n  }\n\n  // Colored button\n  @if $type == simple {\n    @include simple($b-color, false, $t-size, $pad);\n  }\n\n  @else if $type == shiny {\n    @include shiny($b-color, false, $t-size, $pad);\n  }\n\n  @else if $type == pill {\n    @include pill($b-color, false, $t-size, $pad);\n  }\n\n  @else if $type == flat {\n    @include flat($b-color, false, $t-size, $pad);\n  }\n}\n\n\n// Simple Button\n//************************************************************************//\n@mixin simple($base-color, $grayscale: false, $textsize: inherit, $padding: 7px 18px) {\n  $color:         hsl(0, 0, 100%);\n  $border:        adjust-color($base-color, $saturation:  9%,  $lightness: -14%);\n  $inset-shadow:  adjust-color($base-color, $saturation: -8%,  $lightness:  15%);\n  $stop-gradient: adjust-color($base-color, $saturation:  9%,  $lightness: -11%);\n  $text-shadow:   adjust-color($base-color, $saturation:  15%, $lightness: -18%);\n\n  @if is-light($base-color) {\n    $color:       hsl(0, 0, 20%);\n    $text-shadow: adjust-color($base-color, $saturation: 10%, $lightness: 4%);\n  }\n\n  @if $grayscale == true {\n    $border:        grayscale($border);\n    $inset-shadow:  grayscale($inset-shadow);\n    $stop-gradient: grayscale($stop-gradient);\n    $text-shadow:   grayscale($text-shadow);\n  }\n\n  border: 1px solid $border;\n  border-radius: 3px;\n  box-shadow: inset 0 1px 0 0 $inset-shadow;\n  color: $color;\n  display: inline-block;\n  font-size: $textsize;\n  font-weight: bold;\n  @include linear-gradient ($base-color, $stop-gradient);\n  padding: $padding;\n  text-decoration: none;\n  text-shadow: 0 1px 0 $text-shadow;\n  background-clip: padding-box;\n\n  &:hover:not(:disabled) {\n    $base-color-hover:    adjust-color($base-color, $saturation: -4%, $lightness: -5%);\n    $inset-shadow-hover:  adjust-color($base-color, $saturation: -7%, $lightness:  5%);\n    $stop-gradient-hover: adjust-color($base-color, $saturation:  8%, $lightness: -14%);\n\n    @if $grayscale == true {\n      $base-color-hover:    grayscale($base-color-hover);\n      $inset-shadow-hover:  grayscale($inset-shadow-hover);\n      $stop-gradient-hover: grayscale($stop-gradient-hover);\n    }\n\n    box-shadow: inset 0 1px 0 0 $inset-shadow-hover;\n    cursor: pointer;\n    @include linear-gradient ($base-color-hover, $stop-gradient-hover);\n  }\n\n  &:active:not(:disabled),\n  &:focus:not(:disabled) {\n    $border-active:       adjust-color($base-color, $saturation: 9%, $lightness: -14%);\n    $inset-shadow-active: adjust-color($base-color, $saturation: 7%, $lightness: -17%);\n\n    @if $grayscale == true {\n      $border-active:       grayscale($border-active);\n      $inset-shadow-active: grayscale($inset-shadow-active);\n    }\n\n    border: 1px solid $border-active;\n    box-shadow: inset 0 0 8px 4px $inset-shadow-active, inset 0 0 8px 4px $inset-shadow-active;\n  }\n}\n\n\n// Shiny Button\n//************************************************************************//\n@mixin shiny($base-color, $grayscale: false, $textsize: inherit, $padding: 7px 18px) {\n  $color:         hsl(0, 0, 100%);\n  $border:        adjust-color($base-color, $red: -117, $green: -111, $blue: -81);\n  $border-bottom: adjust-color($base-color, $red: -126, $green: -127, $blue: -122);\n  $fourth-stop:   adjust-color($base-color, $red: -79,  $green: -70,  $blue: -46);\n  $inset-shadow:  adjust-color($base-color, $red:  37,  $green:  29,  $blue:  12);\n  $second-stop:   adjust-color($base-color, $red: -56,  $green: -50,  $blue: -33);\n  $text-shadow:   adjust-color($base-color, $red: -140, $green: -141, $blue: -114);\n  $third-stop:    adjust-color($base-color, $red: -86,  $green: -75,  $blue: -48);\n\n  @if is-light($base-color) {\n    $color:       hsl(0, 0, 20%);\n    $text-shadow: adjust-color($base-color, $saturation: 10%, $lightness: 4%);\n  }\n\n  @if $grayscale == true {\n    $border:        grayscale($border);\n    $border-bottom: grayscale($border-bottom);\n    $fourth-stop:   grayscale($fourth-stop);\n    $inset-shadow:  grayscale($inset-shadow);\n    $second-stop:   grayscale($second-stop);\n    $text-shadow:   grayscale($text-shadow);\n    $third-stop:    grayscale($third-stop);\n  }\n\n  border: 1px solid $border;\n  border-bottom: 1px solid $border-bottom;\n  border-radius: 5px;\n  box-shadow: inset 0 1px 0 0 $inset-shadow;\n  color: $color;\n  display: inline-block;\n  font-size: $textsize;\n  font-weight: bold;\n  @include linear-gradient(top, $base-color 0%, $second-stop 50%, $third-stop 50%, $fourth-stop 100%);\n  padding: $padding;\n  text-align: center;\n  text-decoration: none;\n  text-shadow: 0 -1px 1px $text-shadow;\n\n  &:hover:not(:disabled) {\n    $first-stop-hover:  adjust-color($base-color, $red: -13, $green: -15, $blue: -18);\n    $second-stop-hover: adjust-color($base-color, $red: -66, $green: -62, $blue: -51);\n    $third-stop-hover:  adjust-color($base-color, $red: -93, $green: -85, $blue: -66);\n    $fourth-stop-hover: adjust-color($base-color, $red: -86, $green: -80, $blue: -63);\n\n    @if $grayscale == true {\n      $first-stop-hover:  grayscale($first-stop-hover);\n      $second-stop-hover: grayscale($second-stop-hover);\n      $third-stop-hover:  grayscale($third-stop-hover);\n      $fourth-stop-hover: grayscale($fourth-stop-hover);\n    }\n\n    cursor: pointer;\n    @include linear-gradient(top, $first-stop-hover  0%,\n                                  $second-stop-hover 50%,\n                                  $third-stop-hover  50%,\n                                  $fourth-stop-hover 100%);\n  }\n\n  &:active:not(:disabled),\n  &:focus:not(:disabled) {\n    $inset-shadow-active: adjust-color($base-color, $red: -111, $green: -116, $blue: -122);\n\n    @if $grayscale == true {\n      $inset-shadow-active: grayscale($inset-shadow-active);\n    }\n\n    box-shadow: inset 0 0 20px 0 $inset-shadow-active;\n  }\n}\n\n\n// Pill Button\n//************************************************************************//\n@mixin pill($base-color, $grayscale: false, $textsize: inherit, $padding: 7px 18px) {\n  $color:         hsl(0, 0, 100%);\n  $border-bottom: adjust-color($base-color, $hue:  8, $saturation: -11%, $lightness: -26%);\n  $border-sides:  adjust-color($base-color, $hue:  4, $saturation: -21%, $lightness: -21%);\n  $border-top:    adjust-color($base-color, $hue: -1, $saturation: -30%, $lightness: -15%);\n  $inset-shadow:  adjust-color($base-color, $hue: -1, $saturation: -1%,  $lightness:  7%);\n  $stop-gradient: adjust-color($base-color, $hue:  8, $saturation:  14%, $lightness: -10%);\n  $text-shadow:   adjust-color($base-color, $hue:  5, $saturation: -19%, $lightness: -15%);\n\n  @if is-light($base-color) {\n    $color:       hsl(0, 0, 20%);\n    $text-shadow: adjust-color($base-color, $saturation: 10%, $lightness: 4%);\n  }\n\n  @if $grayscale == true {\n    $border-bottom: grayscale($border-bottom);\n    $border-sides:  grayscale($border-sides);\n    $border-top:    grayscale($border-top);\n    $inset-shadow:  grayscale($inset-shadow);\n    $stop-gradient: grayscale($stop-gradient);\n    $text-shadow:   grayscale($text-shadow);\n  }\n\n  border: 1px solid $border-top;\n  border-color: $border-top $border-sides $border-bottom;\n  border-radius: 16px;\n  box-shadow: inset 0 1px 0 0 $inset-shadow;\n  color: $color;\n  display: inline-block;\n  font-size: $textsize;\n  font-weight: normal;\n  line-height: 1;\n  @include linear-gradient ($base-color, $stop-gradient);\n  padding: $padding;\n  text-align: center;\n  text-decoration: none;\n  text-shadow: 0 -1px 1px $text-shadow;\n  background-clip: padding-box;\n\n  &:hover:not(:disabled) {\n    $base-color-hover:    adjust-color($base-color,                                $lightness: -4.5%);\n    $border-bottom:       adjust-color($base-color, $hue:  8, $saturation:  13.5%, $lightness: -32%);\n    $border-sides:        adjust-color($base-color, $hue:  4, $saturation: -2%,    $lightness: -27%);\n    $border-top:          adjust-color($base-color, $hue: -1, $saturation: -17%,   $lightness: -21%);\n    $inset-shadow-hover:  adjust-color($base-color,           $saturation: -1%,    $lightness:  3%);\n    $stop-gradient-hover: adjust-color($base-color, $hue:  8, $saturation: -4%,    $lightness: -15.5%);\n    $text-shadow-hover:   adjust-color($base-color, $hue:  5, $saturation: -5%,    $lightness: -22%);\n\n    @if $grayscale == true {\n      $base-color-hover:    grayscale($base-color-hover);\n      $border-bottom:       grayscale($border-bottom);\n      $border-sides:        grayscale($border-sides);\n      $border-top:          grayscale($border-top);\n      $inset-shadow-hover:  grayscale($inset-shadow-hover);\n      $stop-gradient-hover: grayscale($stop-gradient-hover);\n      $text-shadow-hover:   grayscale($text-shadow-hover);\n    }\n\n    border: 1px solid $border-top;\n    border-color: $border-top $border-sides $border-bottom;\n    box-shadow: inset 0 1px 0 0 $inset-shadow-hover;\n    cursor: pointer;\n    @include linear-gradient ($base-color-hover, $stop-gradient-hover);\n    text-shadow: 0 -1px 1px $text-shadow-hover;\n    background-clip: padding-box;\n  }\n\n  &:active:not(:disabled),\n  &:focus:not(:disabled) {\n    $active-color:         adjust-color($base-color, $hue: 4,  $saturation: -12%,  $lightness: -10%);\n    $border-active:        adjust-color($base-color, $hue: 6,  $saturation: -2.5%, $lightness: -30%);\n    $border-bottom-active: adjust-color($base-color, $hue: 11, $saturation:  6%,   $lightness: -31%);\n    $inset-shadow-active:  adjust-color($base-color, $hue: 9,  $saturation:  2%,   $lightness: -21.5%);\n    $text-shadow-active:   adjust-color($base-color, $hue: 5,  $saturation: -12%,  $lightness: -21.5%);\n\n    @if $grayscale == true {\n      $active-color:         grayscale($active-color);\n      $border-active:        grayscale($border-active);\n      $border-bottom-active: grayscale($border-bottom-active);\n      $inset-shadow-active:  grayscale($inset-shadow-active);\n      $text-shadow-active:   grayscale($text-shadow-active);\n    }\n\n    background: $active-color;\n    border: 1px solid $border-active;\n    border-bottom: 1px solid $border-bottom-active;\n    box-shadow: inset 0 0 6px 3px $inset-shadow-active;\n    text-shadow: 0 -1px 1px $text-shadow-active;\n  }\n}\n\n\n\n// Flat Button\n//************************************************************************//\n@mixin flat($base-color, $grayscale: false, $textsize: inherit, $padding: 7px 18px) {\n  $color:         hsl(0, 0, 100%);\n\n  @if is-light($base-color) {\n    $color:       hsl(0, 0, 20%);\n  }\n\n  background-color: $base-color;\n  border-radius: 3px;\n  border: none;\n  color: $color;\n  display: inline-block;\n  font-size: inherit;\n  font-weight: bold;\n  padding: 7px 18px;\n  text-decoration: none;\n  background-clip: padding-box;\n\n  &:hover:not(:disabled){\n    $base-color-hover:    adjust-color($base-color, $saturation: 4%, $lightness: 5%);\n\n    @if $grayscale == true {\n      $base-color-hover: grayscale($base-color-hover);\n    }\n\n    background-color: $base-color-hover;\n    cursor: pointer;\n  }\n\n  &:active:not(:disabled),\n  &:focus:not(:disabled) {\n    $base-color-active: adjust-color($base-color, $saturation: -4%, $lightness: -5%);\n\n    @if $grayscale == true {\n      $base-color-active: grayscale($base-color-active);\n    }\n\n    background-color: $base-color-active;\n    cursor: pointer;\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_clearfix.scss",
    "content": "// Modern micro clearfix provides an easy way to contain floats without adding additional markup.\n//\n// Example usage:\n//\n//    // Contain all floats within .wrapper\n//    .wrapper {\n//      @include clearfix;\n//      .content,\n//      .sidebar {\n//        float : left;\n//      }\n//    }\n\n@mixin clearfix {\n  &:after {\n    content:\"\";\n    display:table;\n    clear:both;\n  }\n}\n\n// Acknowledgements\n// Beat *that* clearfix: [Thierry Koblentz](http://www.css-101.org/articles/clearfix/latest-new-clearfix-so-far.php)\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_directional-values.scss",
    "content": "// directional-property mixins are shorthands\n// for writing properties like the following\n//\n// @include margin(null 0 10px);\n// ------\n// margin-right: 0;\n// margin-bottom: 10px;\n// margin-left: 0;\n//\n// - or -\n//\n// @include border-style(dotted null);\n// ------\n// border-top-style: dotted;\n// border-bottom-style: dotted;\n//\n// ------\n//\n// Note: You can also use false instead of null\n\n@function collapse-directionals($vals) {\n  $output: null;\n\n  $A: nth( $vals, 1 );\n  $B: if( length($vals) < 2, $A, nth($vals, 2));\n  $C: if( length($vals) < 3, $A, nth($vals, 3));\n  $D: if( length($vals) < 2, $A, nth($vals, if( length($vals) < 4, 2, 4) ));\n\n  @if $A == 0 { $A: 0 }\n  @if $B == 0 { $B: 0 }\n  @if $C == 0 { $C: 0 }\n  @if $D == 0 { $D: 0 }\n\n  @if $A == $B and $A == $C and $A == $D { $output: $A          }\n  @else if $A == $C and $B == $D         { $output: $A $B       }\n  @else if $B == $D                      { $output: $A $B $C    }\n  @else                                  { $output: $A $B $C $D }\n\n  @return $output;\n}\n\n@function contains-falsy($list) {\n  @each $item in $list {\n    @if not $item {\n      @return true;\n    }\n  }\n\n  @return false;\n}\n\n@mixin directional-property($pre, $suf, $vals) {\n  // Property Names\n  $top:    $pre + \"-top\"    + if($suf, \"-#{$suf}\", \"\");\n  $bottom: $pre + \"-bottom\" + if($suf, \"-#{$suf}\", \"\");\n  $left:   $pre + \"-left\"   + if($suf, \"-#{$suf}\", \"\");\n  $right:  $pre + \"-right\"  + if($suf, \"-#{$suf}\", \"\");\n  $all:    $pre +             if($suf, \"-#{$suf}\", \"\");\n\n  $vals: collapse-directionals($vals);\n\n  @if contains-falsy($vals) {\n    @if nth($vals, 1) { #{$top}: nth($vals, 1); }\n\n    @if length($vals) == 1 {\n      @if nth($vals, 1) { #{$right}: nth($vals, 1); }\n    } @else {\n      @if nth($vals, 2) { #{$right}: nth($vals, 2); }\n    }\n\n    // prop: top/bottom right/left\n    @if length($vals) == 2 {\n      @if nth($vals, 1) { #{$bottom}: nth($vals, 1); }\n      @if nth($vals, 2) { #{$left}:   nth($vals, 2); }\n\n    // prop: top right/left bottom\n    } @else if length($vals) == 3 {\n      @if nth($vals, 3) { #{$bottom}: nth($vals, 3); }\n      @if nth($vals, 2) { #{$left}:   nth($vals, 2); }\n\n    // prop: top right bottom left\n    } @else if length($vals) == 4 {\n      @if nth($vals, 3) { #{$bottom}: nth($vals, 3); }\n      @if nth($vals, 4) { #{$left}:   nth($vals, 4); }\n    }\n\n  // prop: top/right/bottom/left\n  } @else {\n    #{$all}: $vals;\n  }\n}\n\n@mixin margin($vals...) {\n  @include directional-property(margin, false, $vals...);\n}\n\n@mixin padding($vals...) {\n  @include directional-property(padding, false, $vals...);\n}\n\n@mixin border-style($vals...) {\n  @include directional-property(border, style, $vals...);\n}\n\n@mixin border-color($vals...) {\n  @include directional-property(border, color, $vals...);\n}\n\n@mixin border-width($vals...) {\n  @include directional-property(border, width, $vals...);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_ellipsis.scss",
    "content": "@mixin ellipsis($width: 100%) {\n  display: inline-block;\n  max-width: $width;\n  overflow: hidden;\n  text-overflow: ellipsis;\n  white-space: nowrap;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_font-family.scss",
    "content": "$georgia: Georgia, Cambria, \"Times New Roman\", Times, serif;\n$helvetica: \"Helvetica Neue\", Helvetica, Roboto, Arial, sans-serif;\n$lucida-grande: \"Lucida Grande\", Tahoma, Verdana, Arial, sans-serif;\n$monospace: \"Bitstream Vera Sans Mono\", Consolas, Courier, monospace;\n$verdana: Verdana, Geneva, sans-serif;\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_hide-text.scss",
    "content": "@mixin hide-text {\n  overflow: hidden;\n\n  &:before {\n    content: \"\";\n    display: block;\n    width: 0;\n    height: 100%;\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_html5-input-types.scss",
    "content": "//************************************************************************//\n// Generate a variable ($all-text-inputs) with a list of all html5\n// input types that have a text-based input, excluding textarea.\n// http://diveintohtml5.org/forms.html\n//************************************************************************//\n$inputs-list: 'input[type=\"email\"]',\n              'input[type=\"number\"]',\n              'input[type=\"password\"]',\n              'input[type=\"search\"]',\n              'input[type=\"tel\"]',\n              'input[type=\"text\"]',\n              'input[type=\"url\"]',\n\n              // Webkit & Gecko may change the display of these in the future\n              'input[type=\"color\"]',\n              'input[type=\"date\"]',\n              'input[type=\"datetime\"]',\n              'input[type=\"datetime-local\"]',\n              'input[type=\"month\"]',\n              'input[type=\"time\"]',\n              'input[type=\"week\"]';\n\n// Bare inputs\n//************************************************************************//\n$all-text-inputs: assign-inputs($inputs-list);\n\n// Hover Pseudo-class\n//************************************************************************//\n$all-text-inputs-hover: assign-inputs($inputs-list, hover);\n\n// Focus Pseudo-class\n//************************************************************************//\n$all-text-inputs-focus: assign-inputs($inputs-list, focus);\n\n\n\n// You must use interpolation on the variable:\n// #{$all-text-inputs}\n// #{$all-text-inputs-hover}\n// #{$all-text-inputs-focus}\n\n// Example\n//************************************************************************//\n//   #{$all-text-inputs}, textarea {\n//     border: 1px solid red;\n//   }\n\n\n\n//************************************************************************//\n// Generate a variable ($all-button-inputs) with a list of all html5\n// input types that have a button-based input, excluding button.\n//************************************************************************//\n$inputs-button-list: 'input[type=\"button\"]',\n                     'input[type=\"reset\"]',\n                     'input[type=\"submit\"]';\n\n// Bare inputs\n//************************************************************************//\n$all-button-inputs: assign-inputs($inputs-button-list);\n\n// Hover Pseudo-class\n//************************************************************************//\n$all-button-inputs-hover: assign-inputs($inputs-button-list, hover);\n\n// Focus Pseudo-class\n//************************************************************************//\n$all-button-inputs-focus: assign-inputs($inputs-button-list, focus);\n\n// Active Pseudo-class\n//************************************************************************//\n$all-button-inputs-active: assign-inputs($inputs-button-list, active);\n\n\n\n// You must use interpolation on the variable:\n// #{$all-button-inputs}\n// #{$all-button-inputs-hover}\n// #{$all-button-inputs-focus}\n// #{$all-button-inputs-active}\n\n// Example\n//************************************************************************//\n//   #{$all-button-inputs}, button {\n//     border: 1px solid red;\n//   }\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_position.scss",
    "content": "@mixin position ($position: relative, $coordinates: null null null null) {\n\n  @if type-of($position) == list {\n    $coordinates: $position;\n    $position: relative;\n  }\n\n  $coordinates: unpack($coordinates);\n\n  $top: nth($coordinates, 1);\n  $right: nth($coordinates, 2);\n  $bottom: nth($coordinates, 3);\n  $left: nth($coordinates, 4);\n\n  position: $position;\n\n  @if ($top and $top == auto) or (type-of($top) == number) {\n    top: $top;\n  }\n\n  @if ($right and $right == auto) or (type-of($right) == number) {\n    right: $right;\n  }\n\n  @if ($bottom and $bottom == auto) or (type-of($bottom) == number) {\n    bottom: $bottom;\n  }\n\n  @if ($left and $left == auto) or (type-of($left) == number) {\n    left: $left;\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_prefixer.scss",
    "content": "//************************************************************************//\n// Example: @include prefixer(border-radius, $radii, webkit ms spec);\n//************************************************************************//\n// Variables located in /settings/_prefixer.scss\n\n@mixin prefixer ($property, $value, $prefixes) {\n  @each $prefix in $prefixes {\n    @if $prefix == webkit {\n      @if $prefix-for-webkit {\n        -webkit-#{$property}: $value;\n      }\n    }\n    @else if $prefix == moz {\n      @if $prefix-for-mozilla {\n        -moz-#{$property}: $value;\n      }\n    }\n    @else if $prefix == ms {\n      @if $prefix-for-microsoft {\n        -ms-#{$property}: $value;\n      }\n    }\n    @else if $prefix == o {\n      @if $prefix-for-opera {\n        -o-#{$property}: $value;\n      }\n    }\n    @else if $prefix == spec {\n      @if $prefix-for-spec {\n        #{$property}: $value;\n      }\n    }\n    @else  {\n      @warn \"Unrecognized prefix: #{$prefix}\";\n    }\n  }\n}\n\n@mixin disable-prefix-for-all() {\n  $prefix-for-webkit:    false !global;\n  $prefix-for-mozilla:   false !global;\n  $prefix-for-microsoft: false !global;\n  $prefix-for-opera:     false !global;\n  $prefix-for-spec:      false !global;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_retina-image.scss",
    "content": "@mixin retina-image($filename, $background-size, $extension: png, $retina-filename: null, $retina-suffix: _2x, $asset-pipeline: false) {\n  @if $asset-pipeline {\n    background-image: image-url(\"#{$filename}.#{$extension}\");\n  }\n  @else {\n    background-image:       url(\"#{$filename}.#{$extension}\");\n  }\n\n  @include hidpi {\n    @if $asset-pipeline {\n      @if $retina-filename {\n        background-image: image-url(\"#{$retina-filename}.#{$extension}\");\n      }\n      @else {\n        background-image: image-url(\"#{$filename}#{$retina-suffix}.#{$extension}\");\n      }\n    }\n\n    @else {\n      @if $retina-filename {\n        background-image: url(\"#{$retina-filename}.#{$extension}\");\n      }\n      @else {\n        background-image: url(\"#{$filename}#{$retina-suffix}.#{$extension}\");\n      }\n    }\n\n    background-size: $background-size;\n\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_size.scss",
    "content": "@mixin size($size) {\n  $height: nth($size, 1);\n  $width: $height;\n\n  @if length($size) > 1 {\n    $height: nth($size, 2);\n  }\n\n  @if $height == auto or (type-of($height) == number and not unitless($height)) {\n    height: $height;\n  }\n\n  @if $width == auto or (type-of($height) == number and not unitless($width)) {\n    width: $width;\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_timing-functions.scss",
    "content": "// CSS cubic-bezier timing functions. Timing functions courtesy of jquery.easie (github.com/jaukia/easie)\n// Timing functions are the same as demo'ed here: http://jqueryui.com/demos/effect/easing.html\n\n// EASE IN\n$ease-in-quad:      cubic-bezier(0.550,  0.085, 0.680, 0.530);\n$ease-in-cubic:     cubic-bezier(0.550,  0.055, 0.675, 0.190);\n$ease-in-quart:     cubic-bezier(0.895,  0.030, 0.685, 0.220);\n$ease-in-quint:     cubic-bezier(0.755,  0.050, 0.855, 0.060);\n$ease-in-sine:      cubic-bezier(0.470,  0.000, 0.745, 0.715);\n$ease-in-expo:      cubic-bezier(0.950,  0.050, 0.795, 0.035);\n$ease-in-circ:      cubic-bezier(0.600,  0.040, 0.980, 0.335);\n$ease-in-back:      cubic-bezier(0.600, -0.280, 0.735, 0.045);\n\n// EASE OUT\n$ease-out-quad:     cubic-bezier(0.250,  0.460, 0.450, 0.940);\n$ease-out-cubic:    cubic-bezier(0.215,  0.610, 0.355, 1.000);\n$ease-out-quart:    cubic-bezier(0.165,  0.840, 0.440, 1.000);\n$ease-out-quint:    cubic-bezier(0.230,  1.000, 0.320, 1.000);\n$ease-out-sine:     cubic-bezier(0.390,  0.575, 0.565, 1.000);\n$ease-out-expo:     cubic-bezier(0.190,  1.000, 0.220, 1.000);\n$ease-out-circ:     cubic-bezier(0.075,  0.820, 0.165, 1.000);\n$ease-out-back:     cubic-bezier(0.175,  0.885, 0.320, 1.275);\n\n// EASE IN OUT\n$ease-in-out-quad:  cubic-bezier(0.455,  0.030, 0.515, 0.955);\n$ease-in-out-cubic: cubic-bezier(0.645,  0.045, 0.355, 1.000);\n$ease-in-out-quart: cubic-bezier(0.770,  0.000, 0.175, 1.000);\n$ease-in-out-quint: cubic-bezier(0.860,  0.000, 0.070, 1.000);\n$ease-in-out-sine:  cubic-bezier(0.445,  0.050, 0.550, 0.950);\n$ease-in-out-expo:  cubic-bezier(1.000,  0.000, 0.000, 1.000);\n$ease-in-out-circ:  cubic-bezier(0.785,  0.135, 0.150, 0.860);\n$ease-in-out-back:  cubic-bezier(0.680, -0.550, 0.265, 1.550);\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_triangle.scss",
    "content": "@mixin triangle ($size, $color, $direction) {\n  height: 0;\n  width: 0;\n\n  $width: nth($size, 1);\n  $height: nth($size, length($size));\n\n  $foreground-color: nth($color, 1);\n  $background-color: if(length($color) == 2, nth($color, 2), transparent);\n\n  @if ($direction == up) or ($direction == down) or ($direction == right) or ($direction == left) {\n\n    $width: $width / 2;\n    $height: if(length($size) > 1, $height, $height/2);\n\n    @if $direction == up {\n      border-left: $width solid $background-color;\n      border-right: $width solid $background-color;\n      border-bottom: $height solid $foreground-color;\n\n    } @else if $direction == right {\n      border-top: $width solid $background-color;\n      border-bottom: $width solid $background-color;\n      border-left: $height solid $foreground-color;\n\n    } @else if $direction == down {\n      border-left: $width solid $background-color;\n      border-right: $width solid $background-color;\n      border-top: $height solid $foreground-color;\n\n    } @else if $direction == left {\n      border-top: $width solid $background-color;\n      border-bottom: $width solid $background-color;\n      border-right: $height solid $foreground-color;\n    }\n  }\n\n  @else if ($direction == up-right) or ($direction == up-left) {\n    border-top: $height solid $foreground-color;\n\n    @if $direction == up-right {\n      border-left:  $width solid $background-color;\n\n    } @else if $direction == up-left {\n      border-right: $width solid $background-color;\n    }\n  }\n\n  @else if ($direction == down-right) or ($direction == down-left) {\n    border-bottom: $height solid $foreground-color;\n\n    @if $direction == down-right {\n      border-left:  $width solid $background-color;\n\n    } @else if $direction == down-left {\n      border-right: $width solid $background-color;\n    }\n  }\n\n  @else if ($direction == inset-up) {\n    border-width: $height $width;\n    border-style: solid;\n    border-color: $background-color $background-color $foreground-color;\n  }\n\n  @else if ($direction == inset-down) {\n    border-width: $height $width;\n    border-style: solid;\n    border-color: $foreground-color $background-color $background-color;\n  }\n\n  @else if ($direction == inset-right) {\n    border-width: $width $height;\n    border-style: solid;\n    border-color: $background-color $background-color $background-color $foreground-color;\n  }\n\n  @else if ($direction == inset-left) {\n    border-width: $width $height;\n    border-style: solid;\n    border-color: $background-color $foreground-color $background-color $background-color;\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/addons/_word-wrap.scss",
    "content": "@mixin word-wrap($wrap: break-word) {\n  word-wrap: $wrap;\n\n  @if $wrap == break-word {\n    overflow-wrap: break-word;\n    word-break: break-all;\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_animation.scss",
    "content": "// http://www.w3.org/TR/css3-animations/#the-animation-name-property-\n// Each of these mixins support comma separated lists of values, which allows different transitions for individual properties to be described in a single style rule. Each value in the list corresponds to the value at that same position in the other properties.\n\n// Official animation shorthand property.\n@mixin animation ($animations...) {\n  @include prefixer(animation, $animations, webkit moz spec);\n}\n\n// Individual Animation Properties\n@mixin animation-name ($names...) {\n  @include prefixer(animation-name, $names, webkit moz spec);\n}\n\n\n@mixin animation-duration ($times...) {\n  @include prefixer(animation-duration, $times, webkit moz spec);\n}\n\n\n@mixin animation-timing-function ($motions...) {\n// ease | linear | ease-in | ease-out | ease-in-out\n  @include prefixer(animation-timing-function, $motions, webkit moz spec);\n}\n\n\n@mixin animation-iteration-count ($values...) {\n// infinite | <number>\n  @include prefixer(animation-iteration-count, $values, webkit moz spec);\n}\n\n\n@mixin animation-direction ($directions...) {\n// normal | alternate\n  @include prefixer(animation-direction, $directions, webkit moz spec);\n}\n\n\n@mixin animation-play-state ($states...) {\n// running | paused\n  @include prefixer(animation-play-state, $states, webkit moz spec);\n}\n\n\n@mixin animation-delay ($times...) {\n  @include prefixer(animation-delay, $times, webkit moz spec);\n}\n\n\n@mixin animation-fill-mode ($modes...) {\n// none | forwards | backwards | both\n  @include prefixer(animation-fill-mode, $modes, webkit moz spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_appearance.scss",
    "content": "@mixin appearance ($value) {\n  @include prefixer(appearance, $value, webkit moz ms o spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_backface-visibility.scss",
    "content": "//************************************************************************//\n// Backface-visibility mixin\n//************************************************************************//\n@mixin backface-visibility($visibility) {\n  @include prefixer(backface-visibility, $visibility, webkit spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_background-image.scss",
    "content": "//************************************************************************//\n// Background-image property for adding multiple background images with\n// gradients, or for stringing multiple gradients together.\n//************************************************************************//\n\n@mixin background-image($images...) {\n  $webkit-images: ();\n  $spec-images: ();\n\n  @each $image in $images {\n    $webkit-image: ();\n    $spec-image: ();\n\n    @if (type-of($image) == string) {\n      $url-str:       str-slice($image, 0, 3);\n      $gradient-type: str-slice($image, 0, 6);\n\n      @if $url-str == \"url\" {\n        $webkit-image: $image;\n          $spec-image: $image;\n      }\n\n      @else if $gradient-type == \"linear\" {\n        $gradients: _linear-gradient-parser($image);\n        $webkit-image:  map-get($gradients, webkit-image);\n        $spec-image:    map-get($gradients, spec-image);\n      }\n\n      @else if $gradient-type == \"radial\" {\n        $gradients: _radial-gradient-parser($image);\n        $webkit-image: map-get($gradients, webkit-image);\n        $spec-image:   map-get($gradients, spec-image);\n      }\n    }\n\n    $webkit-images: append($webkit-images, $webkit-image, comma);\n    $spec-images:   append($spec-images,   $spec-image,   comma);\n  }\n\n  background-image: $webkit-images;\n  background-image: $spec-images;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_background.scss",
    "content": "//************************************************************************//\n// Background property for adding multiple backgrounds using shorthand\n// notation.\n//************************************************************************//\n\n@mixin background($backgrounds...) {\n  $webkit-backgrounds: ();\n  $spec-backgrounds: ();\n\n  @each $background in $backgrounds {\n    $webkit-background: ();\n    $spec-background: ();\n    $background-type: type-of($background);\n\n    @if $background-type == string or list {\n      $background-str: if($background-type == list, nth($background, 1), $background);\n\n      $url-str:       str-slice($background-str, 0, 3);\n      $gradient-type: str-slice($background-str, 0, 6);\n\n      @if $url-str == \"url\" {\n        $webkit-background: $background;\n          $spec-background: $background;\n      }\n\n      @else if $gradient-type == \"linear\" {\n        $gradients: _linear-gradient-parser(\"#{$background}\");\n        $webkit-background: map-get($gradients, webkit-image);\n        $spec-background:   map-get($gradients, spec-image);\n      }\n\n      @else if $gradient-type == \"radial\" {\n        $gradients: _radial-gradient-parser(\"#{$background}\");\n        $webkit-background: map-get($gradients, webkit-image);\n        $spec-background:   map-get($gradients, spec-image);\n      }\n\n      @else {\n        $webkit-background: $background;\n          $spec-background: $background;\n      }\n    }\n\n    @else {\n      $webkit-background: $background;\n        $spec-background: $background;\n    }\n\n    $webkit-backgrounds: append($webkit-backgrounds, $webkit-background, comma);\n    $spec-backgrounds:   append($spec-backgrounds,   $spec-background,   comma);\n  }\n\n  background: $webkit-backgrounds;\n  background: $spec-backgrounds;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_border-image.scss",
    "content": "@mixin border-image($borders...) {\n  $webkit-borders: ();\n  $spec-borders: ();\n\n  @each $border in $borders {\n    $webkit-border: ();\n    $spec-border: ();\n    $border-type: type-of($border);\n\n    @if $border-type == string or list {\n      $border-str: if($border-type == list, nth($border, 1), $border);\n\n      $url-str:       str-slice($border-str, 0, 3);\n      $gradient-type: str-slice($border-str, 0, 6);\n\n      @if $url-str == \"url\" {\n        $webkit-border: $border;\n          $spec-border: $border;\n      }\n\n      @else if $gradient-type == \"linear\" {\n        $gradients: _linear-gradient-parser(\"#{$border}\");\n        $webkit-border: map-get($gradients, webkit-image);\n        $spec-border:   map-get($gradients, spec-image);\n      }\n\n      @else if $gradient-type == \"radial\" {\n        $gradients: _radial-gradient-parser(\"#{$border}\");\n        $webkit-border: map-get($gradients, webkit-image);\n        $spec-border:   map-get($gradients, spec-image);\n      }\n\n      @else {\n        $webkit-border: $border;\n          $spec-border: $border;\n      }\n    }\n\n    @else {\n      $webkit-border: $border;\n        $spec-border: $border;\n    }\n\n    $webkit-borders: append($webkit-borders, $webkit-border, comma);\n    $spec-borders:   append($spec-borders,   $spec-border,   comma);\n  }\n\n  -webkit-border-image: $webkit-borders;\n          border-image: $spec-borders;\n          border-style: solid;\n}\n\n//Examples:\n// @include border-image(url(\"image.png\"));\n// @include border-image(url(\"image.png\") 20 stretch);\n// @include border-image(linear-gradient(45deg, orange, yellow));\n// @include border-image(linear-gradient(45deg, orange, yellow) stretch);\n// @include border-image(linear-gradient(45deg, orange, yellow) 20 30 40 50 stretch round);\n// @include border-image(radial-gradient(top, cover, orange, yellow, orange));\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_border-radius.scss",
    "content": "//************************************************************************//\n// Shorthand Border-radius mixins\n//************************************************************************//\n@mixin border-top-radius($radii) {\n  @include prefixer(border-top-left-radius, $radii, spec);\n  @include prefixer(border-top-right-radius, $radii, spec);\n}\n\n@mixin border-bottom-radius($radii) {\n  @include prefixer(border-bottom-left-radius, $radii, spec);\n  @include prefixer(border-bottom-right-radius, $radii, spec);\n}\n\n@mixin border-left-radius($radii) {\n  @include prefixer(border-top-left-radius, $radii, spec);\n  @include prefixer(border-bottom-left-radius, $radii, spec);\n}\n\n@mixin border-right-radius($radii) {\n  @include prefixer(border-top-right-radius, $radii, spec);\n  @include prefixer(border-bottom-right-radius, $radii, spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_box-sizing.scss",
    "content": "@mixin box-sizing ($box) {\n//  content-box | border-box | inherit\n  @include prefixer(box-sizing, $box, webkit moz spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_calc.scss",
    "content": "@mixin calc($property, $value) {\n  #{$property}: -webkit-calc(#{$value});\n  #{$property}:         calc(#{$value});\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_columns.scss",
    "content": "@mixin columns($arg: auto) {\n// <column-count> || <column-width>\n  @include prefixer(columns, $arg, webkit moz spec);\n}\n\n@mixin column-count($int: auto) {\n// auto || integer\n  @include prefixer(column-count, $int, webkit moz spec);\n}\n\n@mixin column-gap($length: normal) {\n// normal || length\n  @include prefixer(column-gap, $length, webkit moz spec);\n}\n\n@mixin column-fill($arg: auto) {\n// auto || length\n  @include prefixer(column-fill, $arg, webkit moz spec);\n}\n\n@mixin column-rule($arg) {\n// <border-width> || <border-style> || <color>\n  @include prefixer(column-rule, $arg, webkit moz spec);\n}\n\n@mixin column-rule-color($color) {\n  @include prefixer(column-rule-color, $color, webkit moz spec);\n}\n\n@mixin column-rule-style($style: none) {\n// none | hidden | dashed | dotted | double | groove | inset | inset | outset | ridge | solid\n  @include prefixer(column-rule-style, $style, webkit moz spec);\n}\n\n@mixin column-rule-width ($width: none) {\n  @include prefixer(column-rule-width, $width, webkit moz spec);\n}\n\n@mixin column-span($arg: none) {\n// none || all\n  @include prefixer(column-span, $arg, webkit moz spec);\n}\n\n@mixin column-width($length: auto) {\n// auto || length\n  @include prefixer(column-width, $length, webkit moz spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_filter.scss",
    "content": "@mixin filter($function: none) {\n  // <filter-function> [<filter-function]* | none\n  @include prefixer(filter, $function, webkit spec);\n}\n\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_flex-box.scss",
    "content": "// CSS3 Flexible Box Model and property defaults\n\n// Custom shorthand notation for flexbox\n@mixin box($orient: inline-axis, $pack: start, $align: stretch) {\n  @include display-box;\n  @include box-orient($orient);\n  @include box-pack($pack);\n  @include box-align($align);\n}\n\n@mixin display-box {\n  display: -webkit-box;\n  display: -moz-box;\n  display: -ms-flexbox; // IE 10\n  display: box;\n}\n\n@mixin box-orient($orient: inline-axis) {\n// horizontal|vertical|inline-axis|block-axis|inherit\n  @include prefixer(box-orient, $orient, webkit moz spec);\n}\n\n@mixin box-pack($pack: start) {\n// start|end|center|justify\n  @include prefixer(box-pack, $pack, webkit moz spec);\n  -ms-flex-pack: $pack; // IE 10\n}\n\n@mixin box-align($align: stretch) {\n// start|end|center|baseline|stretch\n  @include prefixer(box-align, $align, webkit moz spec);\n  -ms-flex-align: $align; // IE 10\n}\n\n@mixin box-direction($direction: normal) {\n// normal|reverse|inherit\n  @include prefixer(box-direction, $direction, webkit moz spec);\n  -ms-flex-direction: $direction; // IE 10\n}\n\n@mixin box-lines($lines: single) {\n// single|multiple\n  @include prefixer(box-lines, $lines, webkit moz spec);\n}\n\n@mixin box-ordinal-group($int: 1) {\n  @include prefixer(box-ordinal-group, $int, webkit moz spec);\n  -ms-flex-order: $int; // IE 10\n}\n\n@mixin box-flex($value: 0.0) {\n  @include prefixer(box-flex, $value, webkit moz spec);\n  -ms-flex: $value; // IE 10\n}\n\n@mixin box-flex-group($int: 1) {\n  @include prefixer(box-flex-group, $int, webkit moz spec);\n}\n\n// CSS3 Flexible Box Model and property defaults\n// Unified attributes for 2009, 2011, and 2012 flavours.\n\n// 2009 - display (box | inline-box)\n// 2011 - display (flexbox | inline-flexbox)\n// 2012 - display (flex | inline-flex)\n@mixin display($value) {\n//  flex | inline-flex\n    @if $value == \"flex\" {\n        // 2009\n        display: -webkit-box;\n        display: -moz-box;\n        display: box;\n\n        // 2012\n        display: -webkit-flex;\n        display: -moz-flex;\n        display: -ms-flexbox; // 2011 (IE 10)\n        display: flex;\n    }\n\n    @elseif $value == \"inline-flex\" {\n        display: -webkit-inline-box;\n        display: -moz-inline-box;\n        display: inline-box;\n\n        display: -webkit-inline-flex;\n        display: -moz-inline-flex;\n        display: -ms-inline-flexbox;\n        display: inline-flex;\n    }\n\n    @else {\n        display: $value;\n    }\n}\n\n// 2009 - box-flex (integer)\n// 2011 - flex (decimal | width decimal)\n// 2012 - flex (integer integer width)\n@mixin flex($value) {\n\n    // Grab flex-grow for older browsers.\n    $flex-grow: nth($value, 1);\n\n    // 2009\n    @include prefixer(box-flex, $flex-grow, webkit moz spec);\n\n    // 2011 (IE 10), 2012\n    @include prefixer(flex, $value, webkit moz ms spec);\n}\n\n// 2009 - box-orient ( horizontal | vertical | inline-axis | block-axis)\n//      - box-direction (normal | reverse)      \n// 2011 - flex-direction (row | row-reverse | column | column-reverse)\n// 2012 - flex-direction (row | row-reverse | column | column-reverse)\n@mixin flex-direction($value: row) {\n\n    // Alt values.\n    $value-2009: $value;\n    $value-2011: $value;\n    $direction: \"normal\";\n\n    @if $value == row {\n        $value-2009: horizontal;\n    }\n\n    @elseif $value == \"row-reverse\" {\n        $value-2009: horizontal;\n        $direction: reverse;\n    }\n\n    @elseif $value == column {\n        $value-2009: vertical;\n    }\n\n    @elseif $value == \"column-reverse\" {\n        $value-2009: vertical;\n        $direction: reverse;\n    }\n\n    // 2009\n    @include prefixer(box-orient, $value-2009, webkit moz spec);\n    @if $direction == \"reverse\" {\n        @include prefixer(box-direction, $direction, webkit moz spec);\n    }\n\n    // 2012\n    @include prefixer(flex-direction, $value, webkit moz spec);\n\n    // 2011 (IE 10)\n    -ms-flex-direction: $value;\n}\n\n// 2009 - box-lines (single | multiple)\n// 2011 - flex-wrap (nowrap | wrap | wrap-reverse)\n// 2012 - flex-wrap (nowrap | wrap | wrap-reverse)\n@mixin flex-wrap($value: nowrap) {\n\n    // Alt values.\n    $alt-value: $value;\n    @if $value == nowrap {\n        $alt-value: single;\n    }\n\n    @elseif $value == wrap {\n        $alt-value: multiple;\n    }\n\n    @elseif $value == \"wrap-reverse\" {\n        $alt-value: multiple;\n    }\n\n    @include prefixer(box-lines, $alt-value, webkit moz spec);\n    @include prefixer(flex-wrap, $value, webkit moz ms spec);\n}\n\n// 2009 - TODO: parse values into flex-direction/flex-wrap\n// 2011 - TODO: parse values into flex-direction/flex-wrap\n// 2012 - flex-flow (flex-direction || flex-wrap)\n@mixin flex-flow($value) {\n    @include prefixer(flex-flow, $value, webkit moz spec);\n}\n\n// 2009 - box-ordinal-group (integer)\n// 2011 - flex-order (integer)\n// 2012 - order (integer)\n@mixin order($int: 0) {\n    // 2009\n    @include prefixer(box-ordinal-group, $int, webkit moz spec);\n\n    // 2012\n    @include prefixer(order, $int, webkit moz spec);\n\n    // 2011 (IE 10)\n    -ms-flex-order: $int;\n}\n\n// 2012 - flex-grow (number)\n@mixin flex-grow($number: 0) {\n    @include prefixer(flex-grow, $number, webkit moz spec);\n    -ms-flex-positive: $number;\n}\n\n// 2012 - flex-shrink (number)\n@mixin flex-shrink($number: 1) {\n    @include prefixer(flex-shrink, $number, webkit moz spec);\n    -ms-flex-negative: $number;\n}\n\n// 2012 - flex-basis (number)\n@mixin flex-basis($width: auto) {\n    @include prefixer(flex-basis, $width, webkit moz spec);\n    -ms-flex-preferred-size: $width;\n}\n\n// 2009 - box-pack (start | end | center | justify)\n// 2011 - flex-pack (start | end | center | justify)\n// 2012 - justify-content (flex-start | flex-end | center | space-between | space-around)\n@mixin justify-content ($value: flex-start) {\n\n    // Alt values.\n    $alt-value: $value;\n    @if $value == \"flex-start\" {\n        $alt-value: start;\n    }\n\n    @elseif $value == \"flex-end\" {\n        $alt-value: end;\n    }\n\n    @elseif $value == \"space-between\" {\n        $alt-value: justify;\n    }\n\n    @elseif $value == \"space-around\" {\n        $alt-value: center;\n    }\n\n    // 2009\n    @include prefixer(box-pack, $alt-value, webkit moz spec);\n\n    // 2012\n    @include prefixer(justify-content, $value, webkit moz ms o spec);\n\n    // 2011 (IE 10)\n    -ms-flex-pack: $alt-value;\n}\n\n// 2009 - box-align (start | end | center | baseline | stretch)\n// 2011 - flex-align (start | end | center | baseline | stretch)\n// 2012 - align-items (flex-start | flex-end | center | baseline | stretch)\n@mixin align-items($value: stretch) {\n\n    $alt-value: $value;\n\n    @if $value == \"flex-start\" {\n        $alt-value: start;\n    }    \n\n    @elseif $value == \"flex-end\" {\n        $alt-value: end;\n    }\n\n    // 2009\n    @include prefixer(box-align, $alt-value, webkit moz spec);\n\n    // 2012\n    @include prefixer(align-items, $value, webkit moz ms o spec);\n    \n    // 2011 (IE 10)\n    -ms-flex-align: $alt-value;    \n}\n\n// 2011 - flex-item-align (auto | start | end | center | baseline | stretch)\n// 2012 - align-self (auto | flex-start | flex-end | center | baseline | stretch)\n@mixin align-self($value: auto) {\n\n    $value-2011: $value;\n    @if $value == \"flex-start\" {\n        $value-2011: start;\n    }    \n\n    @elseif $value == \"flex-end\" {\n        $value-2011: end;\n    }\n\n    // 2012\n    @include prefixer(align-self, $value, webkit moz spec);\n\n    // 2011 (IE 10)\n    -ms-flex-item-align: $value-2011;\n}\n\n// 2011 - flex-line-pack (start | end | center | justify | distribute | stretch)\n// 2012 - align-content (flex-start | flex-end | center | space-between | space-around | stretch)\n@mixin align-content($value: stretch) {\n\n    $value-2011: $value;\n    @if $value == \"flex-start\" {\n        $value-2011: start;\n    }    \n\n    @elseif $value == \"flex-end\" {\n        $value-2011: end;\n    }\n\n    @elseif $value == \"space-between\" {\n        $value-2011: justify;\n    }\n\n    @elseif $value == \"space-around\" {\n        $value-2011: distribute;\n    }\n\n    // 2012\n    @include prefixer(align-content, $value, webkit moz spec);\n\n    // 2011 (IE 10)\n    -ms-flex-line-pack: $value-2011;\n}\n\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_font-face.scss",
    "content": "// Order of the includes matters, and it is: normal, bold, italic, bold+italic.\n\n@mixin font-face($font-family, $file-path, $weight: normal, $style: normal, $asset-pipeline: false ) {\n  @font-face {\n    font-family: $font-family;\n    font-weight: $weight;\n    font-style: $style;\n\n    @if $asset-pipeline == true {\n      src: font-url('#{$file-path}.eot');\n      src: font-url('#{$file-path}.eot?#iefix')          format('embedded-opentype'),\n           font-url('#{$file-path}.woff')                format('woff'),\n           font-url('#{$file-path}.ttf')                 format('truetype'),\n           font-url('#{$file-path}.svg##{$font-family}') format('svg');\n    } @else {\n      src: url('#{$file-path}.eot');\n      src: url('#{$file-path}.eot?#iefix')               format('embedded-opentype'),\n           url('#{$file-path}.woff')                     format('woff'),\n           url('#{$file-path}.ttf')                      format('truetype'),\n           url('#{$file-path}.svg##{$font-family}')      format('svg');\n    }\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_font-feature-settings.scss",
    "content": "// Font feature settings mixin and property default.\n// Examples: @include font-feature-settings(\"liga\");\n//           @include font-feature-settings(\"lnum\" false);\n//           @include font-feature-settings(\"pnum\" 1, \"kern\" 0);\n//           @include font-feature-settings(\"ss01\", \"ss02\");\n\n@mixin font-feature-settings($settings...) {\n  @if length($settings) == 0 { $settings: none; }\n  @include prefixer(font-feature-settings, $settings, webkit moz ms spec);\n}"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_hidpi-media-query.scss",
    "content": "// HiDPI mixin. Default value set to 1.3 to target Google Nexus 7 (http://bjango.com/articles/min-device-pixel-ratio/)\n@mixin hidpi($ratio: 1.3) {\n  @media only screen and (-webkit-min-device-pixel-ratio: $ratio),\n  only screen and (min--moz-device-pixel-ratio: $ratio),\n  only screen and (-o-min-device-pixel-ratio: #{$ratio}/1),\n  only screen and (min-resolution: #{round($ratio*96)}dpi),\n  only screen and (min-resolution: #{$ratio}dppx) {\n    @content;\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_hyphens.scss",
    "content": "@mixin hyphens($hyphenation: none) {\n// none | manual | auto\n  @include prefixer(hyphens, $hyphenation, webkit moz ms spec);\n}"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_image-rendering.scss",
    "content": "@mixin image-rendering ($mode:auto) {\n\n  @if ($mode == crisp-edges) {\n      -ms-interpolation-mode: nearest-neighbor; // IE8+\n      image-rendering: -moz-crisp-edges;\n      image-rendering: -o-crisp-edges;\n      image-rendering: -webkit-optimize-contrast;\n      image-rendering: crisp-edges;\n  }\n\n  @else {\n      image-rendering: $mode;\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_keyframes.scss",
    "content": "// Adds keyframes blocks for supported prefixes, removing redundant prefixes in the block's content\n@mixin keyframes($name) {\n  $original-prefix-for-webkit:    $prefix-for-webkit;\n  $original-prefix-for-mozilla:   $prefix-for-mozilla;\n  $original-prefix-for-microsoft: $prefix-for-microsoft;\n  $original-prefix-for-opera:     $prefix-for-opera;\n  $original-prefix-for-spec:      $prefix-for-spec;\n\n  @if $original-prefix-for-webkit {\n    @include disable-prefix-for-all();\n    $prefix-for-webkit: true !global;\n    @-webkit-keyframes #{$name} {\n      @content;\n    }\n  }\n  @if $original-prefix-for-mozilla {\n    @include disable-prefix-for-all();\n    $prefix-for-mozilla: true !global;\n    @-moz-keyframes #{$name} {\n      @content;\n    }\n  }\n\n  $prefix-for-webkit:    $original-prefix-for-webkit    !global;\n  $prefix-for-mozilla:   $original-prefix-for-mozilla   !global;\n  $prefix-for-microsoft: $original-prefix-for-microsoft !global;\n  $prefix-for-opera:     $original-prefix-for-opera     !global;\n  $prefix-for-spec:      $original-prefix-for-spec      !global;\n\n  @if $original-prefix-for-spec {\n    @keyframes #{$name} {\n      @content;\n    }\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_linear-gradient.scss",
    "content": "@mixin linear-gradient($pos, $G1, $G2: null,\n                       $G3: null, $G4: null,\n                       $G5: null, $G6: null,\n                       $G7: null, $G8: null,\n                       $G9: null, $G10: null,\n                       $fallback: null) {\n  // Detect what type of value exists in $pos\n  $pos-type: type-of(nth($pos, 1));\n  $pos-spec: null;\n  $pos-degree: null;\n\n  // If $pos is missing from mixin, reassign vars and add default position\n  @if ($pos-type == color) or (nth($pos, 1) == \"transparent\")  {\n    $G10: $G9; $G9: $G8; $G8: $G7; $G7: $G6; $G6: $G5;\n     $G5: $G4; $G4: $G3; $G3: $G2; $G2: $G1; $G1: $pos;\n     $pos: null;\n  }\n\n  @if $pos {\n    $positions: _linear-positions-parser($pos);\n    $pos-degree: nth($positions, 1);\n    $pos-spec:   nth($positions, 2);\n  }\n\n  $full: $G1, $G2, $G3, $G4, $G5, $G6, $G7, $G8, $G9, $G10;\n\n  // Set $G1 as the default fallback color\n  $fallback-color: nth($G1, 1);\n\n  // If $fallback is a color use that color as the fallback color\n  @if (type-of($fallback) == color) or ($fallback == \"transparent\") {\n    $fallback-color: $fallback;\n  }\n\n  background-color: $fallback-color;\n  background-image: -webkit-linear-gradient($pos-degree $full); // Safari 5.1+, Chrome\n  background-image: unquote(\"linear-gradient(#{$pos-spec}#{$full})\");\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_perspective.scss",
    "content": "@mixin perspective($depth: none) {\n  // none | <length>\n  @include prefixer(perspective, $depth, webkit moz spec);\n}\n\n@mixin perspective-origin($value: 50% 50%) {\n  @include prefixer(perspective-origin, $value, webkit moz spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_placeholder.scss",
    "content": "@mixin placeholder {\n  $placeholders: \":-webkit-input\" \":-moz\" \"-moz\" \"-ms-input\";\n  @each $placeholder in $placeholders {\n    &:#{$placeholder}-placeholder {\n      @content;\n    }\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_radial-gradient.scss",
    "content": "// Requires Sass 3.1+\n@mixin radial-gradient($G1,        $G2,\n                       $G3: null, $G4: null,\n                       $G5: null, $G6: null,\n                       $G7: null, $G8: null,\n                       $G9: null, $G10: null,\n                       $pos: null,\n                       $shape-size: null,\n                       $fallback: null) {\n\n  $data: _radial-arg-parser($G1, $G2, $pos, $shape-size);\n  $G1:  nth($data, 1);\n  $G2:  nth($data, 2);\n  $pos: nth($data, 3);\n  $shape-size: nth($data, 4);\n\n  $full: $G1, $G2, $G3, $G4, $G5, $G6, $G7, $G8, $G9, $G10;\n\n  // Strip deprecated cover/contain for spec\n  $shape-size-spec: _shape-size-stripper($shape-size);\n\n  // Set $G1 as the default fallback color\n  $first-color: nth($full, 1);\n  $fallback-color: nth($first-color, 1);\n\n  @if (type-of($fallback) == color) or ($fallback == \"transparent\") {\n    $fallback-color: $fallback;\n  }\n\n  // Add Commas and spaces\n  $shape-size: if($shape-size, '#{$shape-size}, ', null);\n  $pos:        if($pos, '#{$pos}, ', null);\n  $pos-spec:   if($pos, 'at #{$pos}', null);\n  $shape-size-spec: if(($shape-size-spec != ' ') and ($pos == null), '#{$shape-size-spec}, ', '#{$shape-size-spec} ');\n\n  background-color:  $fallback-color;\n  background-image: -webkit-radial-gradient(unquote(#{$pos}#{$shape-size}#{$full}));\n  background-image: unquote(\"radial-gradient(#{$shape-size-spec}#{$pos-spec}#{$full})\");\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_transform.scss",
    "content": "@mixin transform($property: none) {\n//  none | <transform-function>\n  @include prefixer(transform, $property, webkit moz ms o spec);\n}\n\n@mixin transform-origin($axes: 50%) {\n// x-axis - left | center | right  | length | %\n// y-axis - top  | center | bottom | length | %\n// z-axis -                          length\n  @include prefixer(transform-origin, $axes, webkit moz ms o spec);\n}\n\n@mixin transform-style ($style: flat) {\n  @include prefixer(transform-style, $style, webkit moz ms o spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_transition.scss",
    "content": "// Shorthand mixin. Supports multiple parentheses-deliminated values for each variable.\n// Example: @include transition (all 2s ease-in-out);\n//          @include transition (opacity 1s ease-in 2s, width 2s ease-out);\n//          @include transition-property (transform, opacity);\n\n@mixin transition ($properties...) {\n  // Fix for vendor-prefix transform property\n  $needs-prefixes: false;\n  $webkit: ();\n  $moz: ();\n  $spec: ();\n\n  // Create lists for vendor-prefixed transform\n  @each $list in $properties {\n    @if nth($list, 1) == \"transform\" {\n      $needs-prefixes: true;\n      $list1: -webkit-transform;\n      $list2: -moz-transform;\n      $list3: ();\n\n      @each $var in $list {\n        $list3: join($list3, $var);\n\n        @if $var != \"transform\" {\n          $list1: join($list1, $var);\n          $list2: join($list2, $var);\n        }\n      }\n\n      $webkit: append($webkit, $list1);\n         $moz: append($moz,    $list2);\n        $spec: append($spec,   $list3);\n    }\n\n    // Create lists for non-prefixed transition properties\n    @else {\n      $webkit:  append($webkit, $list, comma);\n      $moz:     append($moz,    $list, comma);\n      $spec:    append($spec,   $list, comma);\n    }\n  }\n\n  @if $needs-prefixes {\n    -webkit-transition: $webkit;\n       -moz-transition: $moz;\n            transition: $spec;\n  }\n  @else {\n    @if length($properties) >= 1 {\n      @include prefixer(transition, $properties, webkit moz spec);\n    }\n\n    @else {\n      $properties: all 0.15s ease-out 0s;\n      @include prefixer(transition, $properties, webkit moz spec);\n    }\n  }\n}\n\n@mixin transition-property ($properties...) {\n   -webkit-transition-property: transition-property-names($properties, 'webkit');\n      -moz-transition-property: transition-property-names($properties, 'moz');\n           transition-property: transition-property-names($properties, false);\n}\n\n@mixin transition-duration ($times...) {\n  @include prefixer(transition-duration, $times, webkit moz spec);\n}\n\n@mixin transition-timing-function ($motions...) {\n// ease | linear | ease-in | ease-out | ease-in-out | cubic-bezier()\n  @include prefixer(transition-timing-function, $motions, webkit moz spec);\n}\n\n@mixin transition-delay ($times...) {\n  @include prefixer(transition-delay, $times, webkit moz spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/css3/_user-select.scss",
    "content": "@mixin user-select($arg: none) {\n  @include prefixer(user-select, $arg, webkit moz ms spec);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_assign.scss",
    "content": "@function assign-inputs($inputs, $pseudo: null) {\n  $list : ();\n\n  @each $input in $inputs {\n    $input: unquote($input);\n    $input: if($pseudo, $input + \":\" + $pseudo, $input);\n    $list: append($list, $input, comma);\n  }\n\n  @return $list;\n}"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_color-lightness.scss",
    "content": "// Programatically determines whether a color is light or dark\n// Returns a boolean\n// More details here http://robots.thoughtbot.com/closer-look-color-lightness\n\n@function is-light($hex-color) {\n  $-local-red: red(rgba($hex-color, 1.0));\n  $-local-green: green(rgba($hex-color, 1.0));\n  $-local-blue: blue(rgba($hex-color, 1.0));\n\n  $-local-lightness: ($-local-red * 0.2126 + $-local-green * 0.7152 + $-local-blue * 0.0722) / 255;\n\n  @return $-local-lightness > .6;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_flex-grid.scss",
    "content": "// Flexible grid\n@function flex-grid($columns, $container-columns: $fg-max-columns) {\n  $width: $columns * $fg-column + ($columns - 1) * $fg-gutter;\n  $container-width: $container-columns * $fg-column + ($container-columns - 1) * $fg-gutter;\n  @return percentage($width / $container-width);\n}\n\n// Flexible gutter\n@function flex-gutter($container-columns: $fg-max-columns, $gutter: $fg-gutter) {\n  $container-width: $container-columns * $fg-column + ($container-columns - 1) * $fg-gutter;\n  @return percentage($gutter / $container-width);\n}\n\n// The $fg-column, $fg-gutter and $fg-max-columns variables must be defined in your base stylesheet to properly use the flex-grid function.\n// This function takes the fluid grid equation (target / context = result) and uses columns to help define each.\n//\n// The calculation presumes that your column structure will be missing the last gutter:\n//\n//   -- column -- gutter -- column -- gutter -- column\n//\n//  $fg-column: 60px;             // Column Width\n//  $fg-gutter: 25px;             // Gutter Width\n//  $fg-max-columns: 12;          // Total Columns For Main Container\n//\n//  div {\n//    width: flex-grid(4);        // returns (315px / 995px) = 31.65829%;\n//    margin-left: flex-gutter(); // returns (25px / 995px) = 2.51256%;\n//\n//    p {\n//      width: flex-grid(2, 4);  // returns (145px / 315px) = 46.031746%;\n//      float: left;\n//      margin: flex-gutter(4);  // returns (25px / 315px) = 7.936508%;\n//    }\n//\n//    blockquote {\n//      float: left;\n//      width: flex-grid(2, 4); // returns (145px / 315px) = 46.031746%;\n//    }\n//  }"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_golden-ratio.scss",
    "content": "@function golden-ratio($value, $increment) {\n  @return modular-scale($value, $increment, $golden)\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_grid-width.scss",
    "content": "@function grid-width($n) {\n  @return $n * $gw-column + ($n - 1) * $gw-gutter;\n}\n\n// The $gw-column and $gw-gutter variables must be defined in your base stylesheet to properly use the grid-width function.\n//\n//  $gw-column: 100px;         // Column Width\n//  $gw-gutter: 40px;          // Gutter Width\n//\n//  div {\n//    width: grid-width(4);    // returns 520px;\n//    margin-left: $gw-gutter; // returns 40px;\n//  }\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_modular-scale.scss",
    "content": "// Scaling Variables\n$golden:           1.618;\n$minor-second:     1.067;\n$major-second:     1.125;\n$minor-third:      1.2;\n$major-third:      1.25;\n$perfect-fourth:   1.333;\n$augmented-fourth: 1.414;\n$perfect-fifth:    1.5;\n$minor-sixth:      1.6;\n$major-sixth:      1.667;\n$minor-seventh:    1.778;\n$major-seventh:    1.875;\n$octave:           2;\n$major-tenth:      2.5;\n$major-eleventh:   2.667;\n$major-twelfth:    3;\n$double-octave:    4;\n\n@function modular-scale($value, $increment, $ratio) {\n  $v1: nth($value, 1);\n  $v2: nth($value, length($value));\n  $value: $v1;\n\n  // scale $v2 to just above $v1\n  @while $v2 > $v1 {\n    $v2: ($v2 / $ratio); // will be off-by-1\n  }\n  @while $v2 < $v1 {\n    $v2: ($v2 * $ratio); // will fix off-by-1\n  }\n\n  // check AFTER scaling $v2 to prevent double-counting corner-case\n  $double-stranded: $v2 > $v1;\n\n  @if $increment > 0 {\n    @for $i from 1 through $increment {\n      @if $double-stranded and ($v1 * $ratio) > $v2 {\n        $value: $v2;\n        $v2: ($v2 * $ratio);\n      } @else {\n        $v1: ($v1 * $ratio);\n        $value: $v1;\n      }\n    }\n  }\n\n  @if $increment < 0 {\n    // adjust $v2 to just below $v1\n    @if $double-stranded {\n      $v2: ($v2 / $ratio);\n    }\n\n    @for $i from $increment through -1 {\n      @if $double-stranded and ($v1 / $ratio) < $v2 {\n        $value: $v2;\n        $v2: ($v2 / $ratio);\n      } @else {\n        $v1: ($v1 / $ratio);\n        $value: $v1;\n      }\n    }\n  }\n\n  @return $value;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_px-to-em.scss",
    "content": "// Convert pixels to ems\n// eg. for a relational value of 12px write em(12) when the parent is 16px\n// if the parent is another value say 24px write em(12, 24)\n\n@function em($pxval, $base: $em-base) {\n  @if not unitless($pxval) {\n      $pxval: strip-units($pxval);\n  }\n  @if not unitless($base) {\n      $base: strip-units($base);\n  }\n  @return ($pxval / $base) * 1em;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_px-to-rem.scss",
    "content": "// Convert pixels to rems\n// eg. for a relational value of 12px write rem(12)\n// Assumes $em-base is the font-size of <html>\n\n@function rem($pxval) {\n  @if not unitless($pxval) {\n      $pxval: strip-units($pxval);\n  }\n\n  $base: $em-base;\n  @if not unitless($base) {\n      $base: strip-units($base);\n  }\n  @return ($pxval / $base) * 1rem;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_strip-units.scss",
    "content": "// Srtips the units from a value. e.g. 12px -> 12\n\n@function strip-units($val) {\n  @return ($val / ($val * 0 + 1));\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_tint-shade.scss",
    "content": "// Add percentage of white to a color\n@function tint($color, $percent){\n  @return mix(white, $color, $percent);\n}\n\n// Add percentage of black to a color\n@function shade($color, $percent){\n  @return mix(black, $color, $percent);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_transition-property-name.scss",
    "content": "// Return vendor-prefixed property names if appropriate\n// Example: transition-property-names((transform, color, background), moz) -> -moz-transform, color, background\n//************************************************************************//\n@function transition-property-names($props, $vendor: false) {\n\t$new-props: ();\n\t\n\t@each $prop in $props {\n\t\t$new-props: append($new-props, transition-property-name($prop, $vendor), comma);\n\t}\n\n\t@return $new-props;\n}\n\n@function transition-property-name($prop, $vendor: false) {\n\t// put other properties that need to be prefixed here aswell\n\t@if $vendor and $prop == transform {\n\t\t@return unquote('-'+$vendor+'-'+$prop);\n\t}\n\t@else {\n\t\t@return $prop;\n\t}\n}"
  },
  {
    "path": "website/_sass/vendor/bourbon/functions/_unpack.scss",
    "content": "// Convert shorthand to the 4-value syntax\n\n@function unpack($shorthand) {\n  @if length($shorthand) == 1 {\n    @return nth($shorthand, 1) nth($shorthand, 1) nth($shorthand, 1) nth($shorthand, 1);\n  }\n  @else if length($shorthand) == 2 {\n    @return nth($shorthand, 1) nth($shorthand, 2) nth($shorthand, 1) nth($shorthand, 2);\n  }\n  @else if length($shorthand) == 3 {\n    @return nth($shorthand, 1) nth($shorthand, 2) nth($shorthand, 3) nth($shorthand, 2);\n  }\n  @else {\n    @return $shorthand;\n  }\n}\n\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_convert-units.scss",
    "content": "//************************************************************************//\n// Helper function for str-to-num fn.\n// Source: http://sassmeister.com/gist/9647408\n//************************************************************************//\n@function _convert-units($number, $unit) {\n  $strings: 'px' 'cm' 'mm' '%' 'ch' 'pica' 'in' 'em' 'rem' 'pt' 'pc' 'ex' 'vw' 'vh' 'vmin' 'vmax', 'deg', 'rad', 'grad', 'turn';\n  $units:   1px  1cm  1mm  1%  1ch  1pica  1in  1em  1rem  1pt  1pc  1ex  1vw  1vh  1vmin  1vmax,  1deg,  1rad,  1grad, 1turn;\n  $index: index($strings, $unit);\n\n  @if not $index {\n    @warn \"Unknown unit `#{$unit}`.\";\n    @return false;\n  }\n  @return $number * nth($units, $index);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_gradient-positions-parser.scss",
    "content": "@function _gradient-positions-parser($gradient-type, $gradient-positions) {\n  @if $gradient-positions\n  and ($gradient-type == linear)\n  and (type-of($gradient-positions) != color) {\n    $gradient-positions: _linear-positions-parser($gradient-positions);\n  }\n  @else if $gradient-positions\n  and ($gradient-type == radial)\n  and (type-of($gradient-positions) != color) {\n    $gradient-positions: _radial-positions-parser($gradient-positions);\n  }\n  @return $gradient-positions;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_is-num.scss",
    "content": "//************************************************************************//\n// Helper for linear-gradient-parser\n//************************************************************************//\n@function _is-num($char) {\n  $values: '0' '1' '2' '3' '4' '5' '6' '7' '8' '9' 0 1 2 3 4 5 6 7 8 9;\n  $index: index($values, $char);\n  @return if($index, true, false);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_linear-angle-parser.scss",
    "content": "// Private function for linear-gradient-parser\n@function _linear-angle-parser($image, $first-val, $prefix, $suffix) {\n  $offset: null;\n  $unit-short:  str-slice($first-val, str-length($first-val) - 2, str-length($first-val));\n  $unit-long:   str-slice($first-val, str-length($first-val) - 3, str-length($first-val));\n\n  @if ($unit-long == \"grad\") or\n      ($unit-long == \"turn\") {\n    $offset: if($unit-long == \"grad\", -100grad * 3, -0.75turn);\n  }\n\n  @else if ($unit-short == \"deg\") or\n           ($unit-short == \"rad\") {\n    $offset: if($unit-short == \"deg\", -90 * 3, 1.6rad);\n  }\n\n  @if $offset {\n    $num: _str-to-num($first-val);\n\n    @return (\n      webkit-image: -webkit- + $prefix + ($offset - $num) + $suffix,\n      spec-image: $image\n    );\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_linear-gradient-parser.scss",
    "content": "@function _linear-gradient-parser($image) {\n  $image: unquote($image);\n  $gradients: ();\n  $start: str-index($image, \"(\");\n  $end: str-index($image, \",\");\n  $first-val: str-slice($image, $start + 1, $end - 1);\n\n  $prefix: str-slice($image, 0, $start);\n  $suffix: str-slice($image, $end, str-length($image));\n\n  $has-multiple-vals: str-index($first-val, \" \");\n  $has-single-position: unquote(_position-flipper($first-val) + \"\");\n  $has-angle: _is-num(str-slice($first-val, 0, 0));\n\n  @if $has-multiple-vals {\n    $gradients: _linear-side-corner-parser($image, $first-val, $prefix, $suffix, $has-multiple-vals);\n  }\n\n  @else if $has-single-position != \"\" {\n    $pos: unquote($has-single-position + \"\");\n\n    $gradients: (\n      webkit-image: -webkit- + $image,\n      spec-image: $prefix + \"to \" + $pos + $suffix\n    );\n  }\n\n  @else if $has-angle {\n    // Rotate degree for webkit\n    $gradients: _linear-angle-parser($image, $first-val, $prefix, $suffix);\n  }\n\n  @else {\n    $gradients: (\n      webkit-image: -webkit- + $image,\n      spec-image: $image\n    );\n  }\n\n  @return $gradients;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_linear-positions-parser.scss",
    "content": "@function _linear-positions-parser($pos) {\n  $type: type-of(nth($pos, 1));\n  $spec: null;\n  $degree: null;\n  $side: null;\n  $corner: null;\n  $length: length($pos);\n  // Parse Side and corner positions\n  @if ($length > 1) {\n    @if nth($pos, 1) == \"to\" { // Newer syntax\n      $side: nth($pos, 2);\n\n      @if $length == 2 { // eg. to top\n        // Swap for backwards compatability\n        $degree: _position-flipper(nth($pos, 2));\n      }\n      @else if $length == 3 { // eg. to top left\n        $corner: nth($pos, 3);\n      }\n    }\n    @else if $length == 2 { // Older syntax (\"top left\")\n      $side: _position-flipper(nth($pos, 1));\n      $corner: _position-flipper(nth($pos, 2));\n    }\n\n    @if (\"#{$side} #{$corner}\" == \"left top\") or (\"#{$side} #{$corner}\" == \"top left\") {\n      $degree: _position-flipper(#{$side}) _position-flipper(#{$corner});\n    }\n    @else if (\"#{$side} #{$corner}\" == \"right top\") or (\"#{$side} #{$corner}\" == \"top right\") {\n      $degree: _position-flipper(#{$side}) _position-flipper(#{$corner});\n    }\n    @else if (\"#{$side} #{$corner}\" == \"right bottom\") or (\"#{$side} #{$corner}\" == \"bottom right\") {\n      $degree: _position-flipper(#{$side}) _position-flipper(#{$corner});\n    }\n    @else if (\"#{$side} #{$corner}\" == \"left bottom\") or (\"#{$side} #{$corner}\" == \"bottom left\") {\n      $degree: _position-flipper(#{$side}) _position-flipper(#{$corner});\n    }\n    $spec: to $side $corner;\n  }\n  @else if $length == 1 {\n    // Swap for backwards compatability\n    @if $type == string {\n      $degree: $pos;\n      $spec: to _position-flipper($pos);\n    }\n    @else {\n      $degree: -270 - $pos; //rotate the gradient opposite from spec\n      $spec: $pos;\n    }\n  }\n  $degree: unquote($degree + \",\");\n  $spec:   unquote($spec + \",\");\n  @return $degree $spec;\n}\n\n@function _position-flipper($pos) {\n @return if($pos == left, right, null)\n         if($pos == right, left, null)\n         if($pos == top, bottom, null)\n         if($pos == bottom, top, null);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_linear-side-corner-parser.scss",
    "content": "// Private function for linear-gradient-parser\n@function _linear-side-corner-parser($image, $first-val, $prefix, $suffix, $has-multiple-vals) {\n  $val-1: str-slice($first-val, 0, $has-multiple-vals - 1 );\n  $val-2: str-slice($first-val, $has-multiple-vals + 1, str-length($first-val));\n  $val-3: null;\n  $has-val-3: str-index($val-2, \" \");\n\n  @if $has-val-3 {\n    $val-3: str-slice($val-2, $has-val-3 + 1, str-length($val-2));\n    $val-2: str-slice($val-2, 0, $has-val-3 - 1);\n  }\n\n  $pos: _position-flipper($val-1) _position-flipper($val-2) _position-flipper($val-3);\n  $pos: unquote($pos + \"\");\n\n  // Use old spec for webkit\n  @if $val-1 == \"to\" {\n    @return (\n      webkit-image: -webkit- + $prefix + $pos + $suffix,\n      spec-image: $image\n    );\n  }\n\n  // Bring the code up to spec\n  @else {\n    @return (\n      webkit-image: -webkit- + $image,\n      spec-image: $prefix + \"to \" + $pos + $suffix\n    );\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_radial-arg-parser.scss",
    "content": "@function _radial-arg-parser($G1, $G2, $pos, $shape-size) {\n  @each $value in $G1, $G2 {\n    $first-val: nth($value, 1);\n    $pos-type:  type-of($first-val);\n    $spec-at-index: null;\n\n    // Determine if spec was passed to mixin\n    @if type-of($value) == list {\n      $spec-at-index: if(index($value, at), index($value, at), false);\n    }\n    @if $spec-at-index {\n      @if $spec-at-index > 1 {\n        @for $i from 1 through ($spec-at-index - 1) {\n          $shape-size: $shape-size nth($value, $i);\n        }\n        @for $i from ($spec-at-index + 1) through length($value) {\n          $pos: $pos nth($value, $i);\n        }\n      }\n      @else if $spec-at-index == 1 {\n        @for $i from ($spec-at-index + 1) through length($value) {\n          $pos: $pos nth($value, $i);\n        }\n      }\n      $G1: null;\n    }\n\n    // If not spec calculate correct values\n    @else {\n      @if ($pos-type != color) or ($first-val != \"transparent\") {\n        @if ($pos-type == number)\n        or ($first-val == \"center\")\n        or ($first-val == \"top\")\n        or ($first-val == \"right\")\n        or ($first-val == \"bottom\")\n        or ($first-val == \"left\") {\n\n          $pos: $value;\n\n          @if $pos == $G1 {\n            $G1: null;\n          }\n        }\n\n        @else if\n           ($first-val == \"ellipse\")\n        or ($first-val == \"circle\")\n        or ($first-val == \"closest-side\")\n        or ($first-val == \"closest-corner\")\n        or ($first-val == \"farthest-side\")\n        or ($first-val == \"farthest-corner\")\n        or ($first-val == \"contain\")\n        or ($first-val == \"cover\") {\n\n          $shape-size: $value;\n\n          @if $value == $G1 {\n            $G1: null;\n          }\n\n          @else if $value == $G2 {\n            $G2: null;\n          }\n        }\n      }\n    }\n  }\n  @return $G1, $G2, $pos, $shape-size;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_radial-gradient-parser.scss",
    "content": "@function _radial-gradient-parser($image) {\n  $image: unquote($image);\n  $gradients: ();\n  $start: str-index($image, \"(\");\n  $end: str-index($image, \",\");\n  $first-val: str-slice($image, $start + 1, $end - 1);\n\n  $prefix: str-slice($image, 0, $start);\n  $suffix: str-slice($image, $end, str-length($image));\n\n  $is-spec-syntax: str-index($first-val, \"at\");\n\n  @if $is-spec-syntax and $is-spec-syntax > 1 {\n    $keyword: str-slice($first-val, 1, $is-spec-syntax - 2);\n    $pos: str-slice($first-val, $is-spec-syntax + 3, str-length($first-val));\n    $pos: append($pos, $keyword, comma);\n\n    $gradients: (\n      webkit-image: -webkit- + $prefix + $pos + $suffix,\n      spec-image: $image\n    )\n  }\n\n  @else if $is-spec-syntax == 1 {\n    $pos: str-slice($first-val, $is-spec-syntax + 3, str-length($first-val));\n\n    $gradients: (\n      webkit-image: -webkit- + $prefix + $pos + $suffix,\n      spec-image: $image\n    )\n  }\n\n  @else if str-index($image, \"cover\") or str-index($image, \"contain\") {\n    @warn \"Radial-gradient needs to be updated to conform to latest spec.\";\n\n    $gradients: (\n      webkit-image: null,\n      spec-image: $image\n    )\n  }\n\n  @else {\n    $gradients: (\n      webkit-image: -webkit- + $image,\n      spec-image: $image\n    )\n  }\n\n  @return $gradients;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_radial-positions-parser.scss",
    "content": "@function _radial-positions-parser($gradient-pos) {\n  $shape-size: nth($gradient-pos, 1);\n  $pos:        nth($gradient-pos, 2);\n  $shape-size-spec: _shape-size-stripper($shape-size);\n\n  $pre-spec: unquote(if($pos, \"#{$pos}, \", null))\n             unquote(if($shape-size, \"#{$shape-size},\", null));\n  $pos-spec: if($pos, \"at #{$pos}\", null);\n\n  $spec: \"#{$shape-size-spec} #{$pos-spec}\";\n\n  // Add comma\n  @if ($spec != '  ') {\n    $spec: \"#{$spec},\"\n  }\n\n  @return $pre-spec $spec;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_render-gradients.scss",
    "content": "// User for linear and radial gradients within background-image or border-image properties\n\n@function _render-gradients($gradient-positions, $gradients, $gradient-type, $vendor: false) {\n  $pre-spec: null;\n  $spec: null;\n  $vendor-gradients: null;\n  @if $gradient-type == linear {\n    @if $gradient-positions {\n      $pre-spec: nth($gradient-positions, 1);\n      $spec:     nth($gradient-positions, 2);\n    }\n  }\n  @else if $gradient-type == radial {\n    $pre-spec: nth($gradient-positions, 1);\n    $spec:     nth($gradient-positions, 2);\n  }\n\n  @if $vendor {\n    $vendor-gradients: -#{$vendor}-#{$gradient-type}-gradient(#{$pre-spec} $gradients);\n  }\n  @else if $vendor == false {\n    $vendor-gradients: \"#{$gradient-type}-gradient(#{$spec} #{$gradients})\";\n    $vendor-gradients: unquote($vendor-gradients);\n  }\n  @return $vendor-gradients;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_shape-size-stripper.scss",
    "content": "@function _shape-size-stripper($shape-size) {\n  $shape-size-spec: null;\n  @each $value in $shape-size {\n    @if ($value == \"cover\") or ($value == \"contain\") {\n      $value: null;\n    }\n    $shape-size-spec: \"#{$shape-size-spec} #{$value}\";\n  }\n  @return $shape-size-spec;\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/helpers/_str-to-num.scss",
    "content": "//************************************************************************//\n// Helper function for linear/radial-gradient-parsers.\n// Source: http://sassmeister.com/gist/9647408\n//************************************************************************//\n@function _str-to-num($string) {\n  // Matrices\n  $strings: '0' '1' '2' '3' '4' '5' '6' '7' '8' '9';\n  $numbers:  0   1   2   3   4   5   6   7   8   9;\n\n  // Result\n  $result: 0;\n  $divider: 0;\n  $minus: false;\n\n  // Looping through all characters\n  @for $i from 1 through str-length($string) {\n    $character: str-slice($string, $i, $i);\n    $index: index($strings, $character);\n\n    @if $character == '-' {\n      $minus: true;\n    }\n\n    @else if $character == '.' {\n      $divider: 1;\n    }\n\n    @else {\n      @if not $index {\n        $result: if($minus, $result * -1, $result);\n        @return _convert-units($result, str-slice($string, $i));\n      }\n\n      $number: nth($numbers, $index);\n\n      @if $divider == 0 {\n        $result: $result * 10;\n      }\n\n      @else {\n        // Move the decimal dot to the left\n        $divider: $divider * 10;\n        $number: $number / $divider;\n      }\n\n      $result: $result + $number;\n    }\n  }\n  @return if($minus, $result * -1, $result);\n}\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/settings/_prefixer.scss",
    "content": "// Variable settings for /addons/prefixer.scss\n$prefix-for-webkit:    true !default;\n$prefix-for-mozilla:   true !default;\n$prefix-for-microsoft: true !default;\n$prefix-for-opera:     true !default;\n$prefix-for-spec:      true !default; // required for keyframe mixin\n"
  },
  {
    "path": "website/_sass/vendor/bourbon/settings/_px-to-em.scss",
    "content": "$em-base: 16px !default;\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_bordered-pulled.scss",
    "content": "// Bordered & Pulled\n// -------------------------\n\n.#{$fa-css-prefix}-border {\n  padding: .2em .25em .15em;\n  border: solid .08em $fa-border-color;\n  border-radius: .1em;\n}\n\n.pull-right { float: right; }\n.pull-left { float: left; }\n\n.#{$fa-css-prefix} {\n  &.pull-left { margin-right: .3em; }\n  &.pull-right { margin-left: .3em; }\n}\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_core.scss",
    "content": "// Base Class Definition\n// -------------------------\n\n.#{$fa-css-prefix} {\n  display: inline-block;\n  font-family: FontAwesome;\n  font-style: normal;\n  font-weight: normal;\n  line-height: 1;\n  -webkit-font-smoothing: antialiased;\n  -moz-osx-font-smoothing: grayscale;\n}\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_fixed-width.scss",
    "content": "// Fixed Width Icons\n// -------------------------\n.#{$fa-css-prefix}-fw {\n  width: (18em / 14);\n  text-align: center;\n}\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_icons.scss",
    "content": "/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen\n   readers do not read off random characters that represent icons */\n\n.#{$fa-css-prefix}-glass:before { content: $fa-var-glass; }\n.#{$fa-css-prefix}-music:before { content: $fa-var-music; }\n.#{$fa-css-prefix}-search:before { content: $fa-var-search; }\n.#{$fa-css-prefix}-envelope-o:before { content: $fa-var-envelope-o; }\n.#{$fa-css-prefix}-heart:before { content: $fa-var-heart; }\n.#{$fa-css-prefix}-star:before { content: $fa-var-star; }\n.#{$fa-css-prefix}-star-o:before { content: $fa-var-star-o; }\n.#{$fa-css-prefix}-user:before { content: $fa-var-user; }\n.#{$fa-css-prefix}-film:before { content: $fa-var-film; }\n.#{$fa-css-prefix}-th-large:before { content: $fa-var-th-large; }\n.#{$fa-css-prefix}-th:before { content: $fa-var-th; }\n.#{$fa-css-prefix}-th-list:before { content: $fa-var-th-list; }\n.#{$fa-css-prefix}-check:before { content: $fa-var-check; }\n.#{$fa-css-prefix}-times:before { content: $fa-var-times; }\n.#{$fa-css-prefix}-search-plus:before { content: $fa-var-search-plus; }\n.#{$fa-css-prefix}-search-minus:before { content: $fa-var-search-minus; }\n.#{$fa-css-prefix}-power-off:before { content: $fa-var-power-off; }\n.#{$fa-css-prefix}-signal:before { content: $fa-var-signal; }\n.#{$fa-css-prefix}-gear:before,\n.#{$fa-css-prefix}-cog:before { content: $fa-var-cog; }\n.#{$fa-css-prefix}-trash-o:before { content: $fa-var-trash-o; }\n.#{$fa-css-prefix}-home:before { content: $fa-var-home; }\n.#{$fa-css-prefix}-file-o:before { content: $fa-var-file-o; }\n.#{$fa-css-prefix}-clock-o:before { content: $fa-var-clock-o; }\n.#{$fa-css-prefix}-road:before { content: $fa-var-road; }\n.#{$fa-css-prefix}-download:before { content: $fa-var-download; }\n.#{$fa-css-prefix}-arrow-circle-o-down:before { content: $fa-var-arrow-circle-o-down; }\n.#{$fa-css-prefix}-arrow-circle-o-up:before { content: $fa-var-arrow-circle-o-up; }\n.#{$fa-css-prefix}-inbox:before { content: $fa-var-inbox; }\n.#{$fa-css-prefix}-play-circle-o:before { content: $fa-var-play-circle-o; }\n.#{$fa-css-prefix}-rotate-right:before,\n.#{$fa-css-prefix}-repeat:before { content: $fa-var-repeat; }\n.#{$fa-css-prefix}-refresh:before { content: $fa-var-refresh; }\n.#{$fa-css-prefix}-list-alt:before { content: $fa-var-list-alt; }\n.#{$fa-css-prefix}-lock:before { content: $fa-var-lock; }\n.#{$fa-css-prefix}-flag:before { content: $fa-var-flag; }\n.#{$fa-css-prefix}-headphones:before { content: $fa-var-headphones; }\n.#{$fa-css-prefix}-volume-off:before { content: $fa-var-volume-off; }\n.#{$fa-css-prefix}-volume-down:before { content: $fa-var-volume-down; }\n.#{$fa-css-prefix}-volume-up:before { content: $fa-var-volume-up; }\n.#{$fa-css-prefix}-qrcode:before { content: $fa-var-qrcode; }\n.#{$fa-css-prefix}-barcode:before { content: $fa-var-barcode; }\n.#{$fa-css-prefix}-tag:before { content: $fa-var-tag; }\n.#{$fa-css-prefix}-tags:before { content: $fa-var-tags; }\n.#{$fa-css-prefix}-book:before { content: $fa-var-book; }\n.#{$fa-css-prefix}-bookmark:before { content: $fa-var-bookmark; }\n.#{$fa-css-prefix}-print:before { content: $fa-var-print; }\n.#{$fa-css-prefix}-camera:before { content: $fa-var-camera; }\n.#{$fa-css-prefix}-font:before { content: $fa-var-font; }\n.#{$fa-css-prefix}-bold:before { content: $fa-var-bold; }\n.#{$fa-css-prefix}-italic:before { content: $fa-var-italic; }\n.#{$fa-css-prefix}-text-height:before { content: $fa-var-text-height; }\n.#{$fa-css-prefix}-text-width:before { content: $fa-var-text-width; }\n.#{$fa-css-prefix}-align-left:before { content: $fa-var-align-left; }\n.#{$fa-css-prefix}-align-center:before { content: $fa-var-align-center; }\n.#{$fa-css-prefix}-align-right:before { content: $fa-var-align-right; }\n.#{$fa-css-prefix}-align-justify:before { content: $fa-var-align-justify; }\n.#{$fa-css-prefix}-list:before { content: $fa-var-list; }\n.#{$fa-css-prefix}-dedent:before,\n.#{$fa-css-prefix}-outdent:before { content: $fa-var-outdent; }\n.#{$fa-css-prefix}-indent:before { content: $fa-var-indent; }\n.#{$fa-css-prefix}-video-camera:before { content: $fa-var-video-camera; }\n.#{$fa-css-prefix}-photo:before,\n.#{$fa-css-prefix}-image:before,\n.#{$fa-css-prefix}-picture-o:before { content: $fa-var-picture-o; }\n.#{$fa-css-prefix}-pencil:before { content: $fa-var-pencil; }\n.#{$fa-css-prefix}-map-marker:before { content: $fa-var-map-marker; }\n.#{$fa-css-prefix}-adjust:before { content: $fa-var-adjust; }\n.#{$fa-css-prefix}-tint:before { content: $fa-var-tint; }\n.#{$fa-css-prefix}-edit:before,\n.#{$fa-css-prefix}-pencil-square-o:before { content: $fa-var-pencil-square-o; }\n.#{$fa-css-prefix}-share-square-o:before { content: $fa-var-share-square-o; }\n.#{$fa-css-prefix}-check-square-o:before { content: $fa-var-check-square-o; }\n.#{$fa-css-prefix}-arrows:before { content: $fa-var-arrows; }\n.#{$fa-css-prefix}-step-backward:before { content: $fa-var-step-backward; }\n.#{$fa-css-prefix}-fast-backward:before { content: $fa-var-fast-backward; }\n.#{$fa-css-prefix}-backward:before { content: $fa-var-backward; }\n.#{$fa-css-prefix}-play:before { content: $fa-var-play; }\n.#{$fa-css-prefix}-pause:before { content: $fa-var-pause; }\n.#{$fa-css-prefix}-stop:before { content: $fa-var-stop; }\n.#{$fa-css-prefix}-forward:before { content: $fa-var-forward; }\n.#{$fa-css-prefix}-fast-forward:before { content: $fa-var-fast-forward; }\n.#{$fa-css-prefix}-step-forward:before { content: $fa-var-step-forward; }\n.#{$fa-css-prefix}-eject:before { content: $fa-var-eject; }\n.#{$fa-css-prefix}-chevron-left:before { content: $fa-var-chevron-left; }\n.#{$fa-css-prefix}-chevron-right:before { content: $fa-var-chevron-right; }\n.#{$fa-css-prefix}-plus-circle:before { content: $fa-var-plus-circle; }\n.#{$fa-css-prefix}-minus-circle:before { content: $fa-var-minus-circle; }\n.#{$fa-css-prefix}-times-circle:before { content: $fa-var-times-circle; }\n.#{$fa-css-prefix}-check-circle:before { content: $fa-var-check-circle; }\n.#{$fa-css-prefix}-question-circle:before { content: $fa-var-question-circle; }\n.#{$fa-css-prefix}-info-circle:before { content: $fa-var-info-circle; }\n.#{$fa-css-prefix}-crosshairs:before { content: $fa-var-crosshairs; }\n.#{$fa-css-prefix}-times-circle-o:before { content: $fa-var-times-circle-o; }\n.#{$fa-css-prefix}-check-circle-o:before { content: $fa-var-check-circle-o; }\n.#{$fa-css-prefix}-ban:before { content: $fa-var-ban; }\n.#{$fa-css-prefix}-arrow-left:before { content: $fa-var-arrow-left; }\n.#{$fa-css-prefix}-arrow-right:before { content: $fa-var-arrow-right; }\n.#{$fa-css-prefix}-arrow-up:before { content: $fa-var-arrow-up; }\n.#{$fa-css-prefix}-arrow-down:before { content: $fa-var-arrow-down; }\n.#{$fa-css-prefix}-mail-forward:before,\n.#{$fa-css-prefix}-share:before { content: $fa-var-share; }\n.#{$fa-css-prefix}-expand:before { content: $fa-var-expand; }\n.#{$fa-css-prefix}-compress:before { content: $fa-var-compress; }\n.#{$fa-css-prefix}-plus:before { content: $fa-var-plus; }\n.#{$fa-css-prefix}-minus:before { content: $fa-var-minus; }\n.#{$fa-css-prefix}-asterisk:before { content: $fa-var-asterisk; }\n.#{$fa-css-prefix}-exclamation-circle:before { content: $fa-var-exclamation-circle; }\n.#{$fa-css-prefix}-gift:before { content: $fa-var-gift; }\n.#{$fa-css-prefix}-leaf:before { content: $fa-var-leaf; }\n.#{$fa-css-prefix}-fire:before { content: $fa-var-fire; }\n.#{$fa-css-prefix}-eye:before { content: $fa-var-eye; }\n.#{$fa-css-prefix}-eye-slash:before { content: $fa-var-eye-slash; }\n.#{$fa-css-prefix}-warning:before,\n.#{$fa-css-prefix}-exclamation-triangle:before { content: $fa-var-exclamation-triangle; }\n.#{$fa-css-prefix}-plane:before { content: $fa-var-plane; }\n.#{$fa-css-prefix}-calendar:before { content: $fa-var-calendar; }\n.#{$fa-css-prefix}-random:before { content: $fa-var-random; }\n.#{$fa-css-prefix}-comment:before { content: $fa-var-comment; }\n.#{$fa-css-prefix}-magnet:before { content: $fa-var-magnet; }\n.#{$fa-css-prefix}-chevron-up:before { content: $fa-var-chevron-up; }\n.#{$fa-css-prefix}-chevron-down:before { content: $fa-var-chevron-down; }\n.#{$fa-css-prefix}-retweet:before { content: $fa-var-retweet; }\n.#{$fa-css-prefix}-shopping-cart:before { content: $fa-var-shopping-cart; }\n.#{$fa-css-prefix}-folder:before { content: $fa-var-folder; }\n.#{$fa-css-prefix}-folder-open:before { content: $fa-var-folder-open; }\n.#{$fa-css-prefix}-arrows-v:before { content: $fa-var-arrows-v; }\n.#{$fa-css-prefix}-arrows-h:before { content: $fa-var-arrows-h; }\n.#{$fa-css-prefix}-bar-chart-o:before { content: $fa-var-bar-chart-o; }\n.#{$fa-css-prefix}-twitter-square:before { content: $fa-var-twitter-square; }\n.#{$fa-css-prefix}-facebook-square:before { content: $fa-var-facebook-square; }\n.#{$fa-css-prefix}-camera-retro:before { content: $fa-var-camera-retro; }\n.#{$fa-css-prefix}-key:before { content: $fa-var-key; }\n.#{$fa-css-prefix}-gears:before,\n.#{$fa-css-prefix}-cogs:before { content: $fa-var-cogs; }\n.#{$fa-css-prefix}-comments:before { content: $fa-var-comments; }\n.#{$fa-css-prefix}-thumbs-o-up:before { content: $fa-var-thumbs-o-up; }\n.#{$fa-css-prefix}-thumbs-o-down:before { content: $fa-var-thumbs-o-down; }\n.#{$fa-css-prefix}-star-half:before { content: $fa-var-star-half; }\n.#{$fa-css-prefix}-heart-o:before { content: $fa-var-heart-o; }\n.#{$fa-css-prefix}-sign-out:before { content: $fa-var-sign-out; }\n.#{$fa-css-prefix}-linkedin-square:before { content: $fa-var-linkedin-square; }\n.#{$fa-css-prefix}-thumb-tack:before { content: $fa-var-thumb-tack; }\n.#{$fa-css-prefix}-external-link:before { content: $fa-var-external-link; }\n.#{$fa-css-prefix}-sign-in:before { content: $fa-var-sign-in; }\n.#{$fa-css-prefix}-trophy:before { content: $fa-var-trophy; }\n.#{$fa-css-prefix}-github-square:before { content: $fa-var-github-square; }\n.#{$fa-css-prefix}-upload:before { content: $fa-var-upload; }\n.#{$fa-css-prefix}-lemon-o:before { content: $fa-var-lemon-o; }\n.#{$fa-css-prefix}-phone:before { content: $fa-var-phone; }\n.#{$fa-css-prefix}-square-o:before { content: $fa-var-square-o; }\n.#{$fa-css-prefix}-bookmark-o:before { content: $fa-var-bookmark-o; }\n.#{$fa-css-prefix}-phone-square:before { content: $fa-var-phone-square; }\n.#{$fa-css-prefix}-twitter:before { content: $fa-var-twitter; }\n.#{$fa-css-prefix}-facebook:before { content: $fa-var-facebook; }\n.#{$fa-css-prefix}-github:before { content: $fa-var-github; }\n.#{$fa-css-prefix}-unlock:before { content: $fa-var-unlock; }\n.#{$fa-css-prefix}-credit-card:before { content: $fa-var-credit-card; }\n.#{$fa-css-prefix}-rss:before { content: $fa-var-rss; }\n.#{$fa-css-prefix}-hdd-o:before { content: $fa-var-hdd-o; }\n.#{$fa-css-prefix}-bullhorn:before { content: $fa-var-bullhorn; }\n.#{$fa-css-prefix}-bell:before { content: $fa-var-bell; }\n.#{$fa-css-prefix}-certificate:before { content: $fa-var-certificate; }\n.#{$fa-css-prefix}-hand-o-right:before { content: $fa-var-hand-o-right; }\n.#{$fa-css-prefix}-hand-o-left:before { content: $fa-var-hand-o-left; }\n.#{$fa-css-prefix}-hand-o-up:before { content: $fa-var-hand-o-up; }\n.#{$fa-css-prefix}-hand-o-down:before { content: $fa-var-hand-o-down; }\n.#{$fa-css-prefix}-arrow-circle-left:before { content: $fa-var-arrow-circle-left; }\n.#{$fa-css-prefix}-arrow-circle-right:before { content: $fa-var-arrow-circle-right; }\n.#{$fa-css-prefix}-arrow-circle-up:before { content: $fa-var-arrow-circle-up; }\n.#{$fa-css-prefix}-arrow-circle-down:before { content: $fa-var-arrow-circle-down; }\n.#{$fa-css-prefix}-globe:before { content: $fa-var-globe; }\n.#{$fa-css-prefix}-wrench:before { content: $fa-var-wrench; }\n.#{$fa-css-prefix}-tasks:before { content: $fa-var-tasks; }\n.#{$fa-css-prefix}-filter:before { content: $fa-var-filter; }\n.#{$fa-css-prefix}-briefcase:before { content: $fa-var-briefcase; }\n.#{$fa-css-prefix}-arrows-alt:before { content: $fa-var-arrows-alt; }\n.#{$fa-css-prefix}-group:before,\n.#{$fa-css-prefix}-users:before { content: $fa-var-users; }\n.#{$fa-css-prefix}-chain:before,\n.#{$fa-css-prefix}-link:before { content: $fa-var-link; }\n.#{$fa-css-prefix}-cloud:before { content: $fa-var-cloud; }\n.#{$fa-css-prefix}-flask:before { content: $fa-var-flask; }\n.#{$fa-css-prefix}-cut:before,\n.#{$fa-css-prefix}-scissors:before { content: $fa-var-scissors; }\n.#{$fa-css-prefix}-copy:before,\n.#{$fa-css-prefix}-files-o:before { content: $fa-var-files-o; }\n.#{$fa-css-prefix}-paperclip:before { content: $fa-var-paperclip; }\n.#{$fa-css-prefix}-save:before,\n.#{$fa-css-prefix}-floppy-o:before { content: $fa-var-floppy-o; }\n.#{$fa-css-prefix}-square:before { content: $fa-var-square; }\n.#{$fa-css-prefix}-navicon:before,\n.#{$fa-css-prefix}-reorder:before,\n.#{$fa-css-prefix}-bars:before { content: $fa-var-bars; }\n.#{$fa-css-prefix}-list-ul:before { content: $fa-var-list-ul; }\n.#{$fa-css-prefix}-list-ol:before { content: $fa-var-list-ol; }\n.#{$fa-css-prefix}-strikethrough:before { content: $fa-var-strikethrough; }\n.#{$fa-css-prefix}-underline:before { content: $fa-var-underline; }\n.#{$fa-css-prefix}-table:before { content: $fa-var-table; }\n.#{$fa-css-prefix}-magic:before { content: $fa-var-magic; }\n.#{$fa-css-prefix}-truck:before { content: $fa-var-truck; }\n.#{$fa-css-prefix}-pinterest:before { content: $fa-var-pinterest; }\n.#{$fa-css-prefix}-pinterest-square:before { content: $fa-var-pinterest-square; }\n.#{$fa-css-prefix}-google-plus-square:before { content: $fa-var-google-plus-square; }\n.#{$fa-css-prefix}-google-plus:before { content: $fa-var-google-plus; }\n.#{$fa-css-prefix}-money:before { content: $fa-var-money; }\n.#{$fa-css-prefix}-caret-down:before { content: $fa-var-caret-down; }\n.#{$fa-css-prefix}-caret-up:before { content: $fa-var-caret-up; }\n.#{$fa-css-prefix}-caret-left:before { content: $fa-var-caret-left; }\n.#{$fa-css-prefix}-caret-right:before { content: $fa-var-caret-right; }\n.#{$fa-css-prefix}-columns:before { content: $fa-var-columns; }\n.#{$fa-css-prefix}-unsorted:before,\n.#{$fa-css-prefix}-sort:before { content: $fa-var-sort; }\n.#{$fa-css-prefix}-sort-down:before,\n.#{$fa-css-prefix}-sort-desc:before { content: $fa-var-sort-desc; }\n.#{$fa-css-prefix}-sort-up:before,\n.#{$fa-css-prefix}-sort-asc:before { content: $fa-var-sort-asc; }\n.#{$fa-css-prefix}-envelope:before { content: $fa-var-envelope; }\n.#{$fa-css-prefix}-linkedin:before { content: $fa-var-linkedin; }\n.#{$fa-css-prefix}-rotate-left:before,\n.#{$fa-css-prefix}-undo:before { content: $fa-var-undo; }\n.#{$fa-css-prefix}-legal:before,\n.#{$fa-css-prefix}-gavel:before { content: $fa-var-gavel; }\n.#{$fa-css-prefix}-dashboard:before,\n.#{$fa-css-prefix}-tachometer:before { content: $fa-var-tachometer; }\n.#{$fa-css-prefix}-comment-o:before { content: $fa-var-comment-o; }\n.#{$fa-css-prefix}-comments-o:before { content: $fa-var-comments-o; }\n.#{$fa-css-prefix}-flash:before,\n.#{$fa-css-prefix}-bolt:before { content: $fa-var-bolt; }\n.#{$fa-css-prefix}-sitemap:before { content: $fa-var-sitemap; }\n.#{$fa-css-prefix}-umbrella:before { content: $fa-var-umbrella; }\n.#{$fa-css-prefix}-paste:before,\n.#{$fa-css-prefix}-clipboard:before { content: $fa-var-clipboard; }\n.#{$fa-css-prefix}-lightbulb-o:before { content: $fa-var-lightbulb-o; }\n.#{$fa-css-prefix}-exchange:before { content: $fa-var-exchange; }\n.#{$fa-css-prefix}-cloud-download:before { content: $fa-var-cloud-download; }\n.#{$fa-css-prefix}-cloud-upload:before { content: $fa-var-cloud-upload; }\n.#{$fa-css-prefix}-user-md:before { content: $fa-var-user-md; }\n.#{$fa-css-prefix}-stethoscope:before { content: $fa-var-stethoscope; }\n.#{$fa-css-prefix}-suitcase:before { content: $fa-var-suitcase; }\n.#{$fa-css-prefix}-bell-o:before { content: $fa-var-bell-o; }\n.#{$fa-css-prefix}-coffee:before { content: $fa-var-coffee; }\n.#{$fa-css-prefix}-cutlery:before { content: $fa-var-cutlery; }\n.#{$fa-css-prefix}-file-text-o:before { content: $fa-var-file-text-o; }\n.#{$fa-css-prefix}-building-o:before { content: $fa-var-building-o; }\n.#{$fa-css-prefix}-hospital-o:before { content: $fa-var-hospital-o; }\n.#{$fa-css-prefix}-ambulance:before { content: $fa-var-ambulance; }\n.#{$fa-css-prefix}-medkit:before { content: $fa-var-medkit; }\n.#{$fa-css-prefix}-fighter-jet:before { content: $fa-var-fighter-jet; }\n.#{$fa-css-prefix}-beer:before { content: $fa-var-beer; }\n.#{$fa-css-prefix}-h-square:before { content: $fa-var-h-square; }\n.#{$fa-css-prefix}-plus-square:before { content: $fa-var-plus-square; }\n.#{$fa-css-prefix}-angle-double-left:before { content: $fa-var-angle-double-left; }\n.#{$fa-css-prefix}-angle-double-right:before { content: $fa-var-angle-double-right; }\n.#{$fa-css-prefix}-angle-double-up:before { content: $fa-var-angle-double-up; }\n.#{$fa-css-prefix}-angle-double-down:before { content: $fa-var-angle-double-down; }\n.#{$fa-css-prefix}-angle-left:before { content: $fa-var-angle-left; }\n.#{$fa-css-prefix}-angle-right:before { content: $fa-var-angle-right; }\n.#{$fa-css-prefix}-angle-up:before { content: $fa-var-angle-up; }\n.#{$fa-css-prefix}-angle-down:before { content: $fa-var-angle-down; }\n.#{$fa-css-prefix}-desktop:before { content: $fa-var-desktop; }\n.#{$fa-css-prefix}-laptop:before { content: $fa-var-laptop; }\n.#{$fa-css-prefix}-tablet:before { content: $fa-var-tablet; }\n.#{$fa-css-prefix}-mobile-phone:before,\n.#{$fa-css-prefix}-mobile:before { content: $fa-var-mobile; }\n.#{$fa-css-prefix}-circle-o:before { content: $fa-var-circle-o; }\n.#{$fa-css-prefix}-quote-left:before { content: $fa-var-quote-left; }\n.#{$fa-css-prefix}-quote-right:before { content: $fa-var-quote-right; }\n.#{$fa-css-prefix}-spinner:before { content: $fa-var-spinner; }\n.#{$fa-css-prefix}-circle:before { content: $fa-var-circle; }\n.#{$fa-css-prefix}-mail-reply:before,\n.#{$fa-css-prefix}-reply:before { content: $fa-var-reply; }\n.#{$fa-css-prefix}-github-alt:before { content: $fa-var-github-alt; }\n.#{$fa-css-prefix}-folder-o:before { content: $fa-var-folder-o; }\n.#{$fa-css-prefix}-folder-open-o:before { content: $fa-var-folder-open-o; }\n.#{$fa-css-prefix}-smile-o:before { content: $fa-var-smile-o; }\n.#{$fa-css-prefix}-frown-o:before { content: $fa-var-frown-o; }\n.#{$fa-css-prefix}-meh-o:before { content: $fa-var-meh-o; }\n.#{$fa-css-prefix}-gamepad:before { content: $fa-var-gamepad; }\n.#{$fa-css-prefix}-keyboard-o:before { content: $fa-var-keyboard-o; }\n.#{$fa-css-prefix}-flag-o:before { content: $fa-var-flag-o; }\n.#{$fa-css-prefix}-flag-checkered:before { content: $fa-var-flag-checkered; }\n.#{$fa-css-prefix}-terminal:before { content: $fa-var-terminal; }\n.#{$fa-css-prefix}-code:before { content: $fa-var-code; }\n.#{$fa-css-prefix}-mail-reply-all:before,\n.#{$fa-css-prefix}-reply-all:before { content: $fa-var-reply-all; }\n.#{$fa-css-prefix}-star-half-empty:before,\n.#{$fa-css-prefix}-star-half-full:before,\n.#{$fa-css-prefix}-star-half-o:before { content: $fa-var-star-half-o; }\n.#{$fa-css-prefix}-location-arrow:before { content: $fa-var-location-arrow; }\n.#{$fa-css-prefix}-crop:before { content: $fa-var-crop; }\n.#{$fa-css-prefix}-code-fork:before { content: $fa-var-code-fork; }\n.#{$fa-css-prefix}-unlink:before,\n.#{$fa-css-prefix}-chain-broken:before { content: $fa-var-chain-broken; }\n.#{$fa-css-prefix}-question:before { content: $fa-var-question; }\n.#{$fa-css-prefix}-info:before { content: $fa-var-info; }\n.#{$fa-css-prefix}-exclamation:before { content: $fa-var-exclamation; }\n.#{$fa-css-prefix}-superscript:before { content: $fa-var-superscript; }\n.#{$fa-css-prefix}-subscript:before { content: $fa-var-subscript; }\n.#{$fa-css-prefix}-eraser:before { content: $fa-var-eraser; }\n.#{$fa-css-prefix}-puzzle-piece:before { content: $fa-var-puzzle-piece; }\n.#{$fa-css-prefix}-microphone:before { content: $fa-var-microphone; }\n.#{$fa-css-prefix}-microphone-slash:before { content: $fa-var-microphone-slash; }\n.#{$fa-css-prefix}-shield:before { content: $fa-var-shield; }\n.#{$fa-css-prefix}-calendar-o:before { content: $fa-var-calendar-o; }\n.#{$fa-css-prefix}-fire-extinguisher:before { content: $fa-var-fire-extinguisher; }\n.#{$fa-css-prefix}-rocket:before { content: $fa-var-rocket; }\n.#{$fa-css-prefix}-maxcdn:before { content: $fa-var-maxcdn; }\n.#{$fa-css-prefix}-chevron-circle-left:before { content: $fa-var-chevron-circle-left; }\n.#{$fa-css-prefix}-chevron-circle-right:before { content: $fa-var-chevron-circle-right; }\n.#{$fa-css-prefix}-chevron-circle-up:before { content: $fa-var-chevron-circle-up; }\n.#{$fa-css-prefix}-chevron-circle-down:before { content: $fa-var-chevron-circle-down; }\n.#{$fa-css-prefix}-html5:before { content: $fa-var-html5; }\n.#{$fa-css-prefix}-css3:before { content: $fa-var-css3; }\n.#{$fa-css-prefix}-anchor:before { content: $fa-var-anchor; }\n.#{$fa-css-prefix}-unlock-alt:before { content: $fa-var-unlock-alt; }\n.#{$fa-css-prefix}-bullseye:before { content: $fa-var-bullseye; }\n.#{$fa-css-prefix}-ellipsis-h:before { content: $fa-var-ellipsis-h; }\n.#{$fa-css-prefix}-ellipsis-v:before { content: $fa-var-ellipsis-v; }\n.#{$fa-css-prefix}-rss-square:before { content: $fa-var-rss-square; }\n.#{$fa-css-prefix}-play-circle:before { content: $fa-var-play-circle; }\n.#{$fa-css-prefix}-ticket:before { content: $fa-var-ticket; }\n.#{$fa-css-prefix}-minus-square:before { content: $fa-var-minus-square; }\n.#{$fa-css-prefix}-minus-square-o:before { content: $fa-var-minus-square-o; }\n.#{$fa-css-prefix}-level-up:before { content: $fa-var-level-up; }\n.#{$fa-css-prefix}-level-down:before { content: $fa-var-level-down; }\n.#{$fa-css-prefix}-check-square:before { content: $fa-var-check-square; }\n.#{$fa-css-prefix}-pencil-square:before { content: $fa-var-pencil-square; }\n.#{$fa-css-prefix}-external-link-square:before { content: $fa-var-external-link-square; }\n.#{$fa-css-prefix}-share-square:before { content: $fa-var-share-square; }\n.#{$fa-css-prefix}-compass:before { content: $fa-var-compass; }\n.#{$fa-css-prefix}-toggle-down:before,\n.#{$fa-css-prefix}-caret-square-o-down:before { content: $fa-var-caret-square-o-down; }\n.#{$fa-css-prefix}-toggle-up:before,\n.#{$fa-css-prefix}-caret-square-o-up:before { content: $fa-var-caret-square-o-up; }\n.#{$fa-css-prefix}-toggle-right:before,\n.#{$fa-css-prefix}-caret-square-o-right:before { content: $fa-var-caret-square-o-right; }\n.#{$fa-css-prefix}-euro:before,\n.#{$fa-css-prefix}-eur:before { content: $fa-var-eur; }\n.#{$fa-css-prefix}-gbp:before { content: $fa-var-gbp; }\n.#{$fa-css-prefix}-dollar:before,\n.#{$fa-css-prefix}-usd:before { content: $fa-var-usd; }\n.#{$fa-css-prefix}-rupee:before,\n.#{$fa-css-prefix}-inr:before { content: $fa-var-inr; }\n.#{$fa-css-prefix}-cny:before,\n.#{$fa-css-prefix}-rmb:before,\n.#{$fa-css-prefix}-yen:before,\n.#{$fa-css-prefix}-jpy:before { content: $fa-var-jpy; }\n.#{$fa-css-prefix}-ruble:before,\n.#{$fa-css-prefix}-rouble:before,\n.#{$fa-css-prefix}-rub:before { content: $fa-var-rub; }\n.#{$fa-css-prefix}-won:before,\n.#{$fa-css-prefix}-krw:before { content: $fa-var-krw; }\n.#{$fa-css-prefix}-bitcoin:before,\n.#{$fa-css-prefix}-btc:before { content: $fa-var-btc; }\n.#{$fa-css-prefix}-file:before { content: $fa-var-file; }\n.#{$fa-css-prefix}-file-text:before { content: $fa-var-file-text; }\n.#{$fa-css-prefix}-sort-alpha-asc:before { content: $fa-var-sort-alpha-asc; }\n.#{$fa-css-prefix}-sort-alpha-desc:before { content: $fa-var-sort-alpha-desc; }\n.#{$fa-css-prefix}-sort-amount-asc:before { content: $fa-var-sort-amount-asc; }\n.#{$fa-css-prefix}-sort-amount-desc:before { content: $fa-var-sort-amount-desc; }\n.#{$fa-css-prefix}-sort-numeric-asc:before { content: $fa-var-sort-numeric-asc; }\n.#{$fa-css-prefix}-sort-numeric-desc:before { content: $fa-var-sort-numeric-desc; }\n.#{$fa-css-prefix}-thumbs-up:before { content: $fa-var-thumbs-up; }\n.#{$fa-css-prefix}-thumbs-down:before { content: $fa-var-thumbs-down; }\n.#{$fa-css-prefix}-youtube-square:before { content: $fa-var-youtube-square; }\n.#{$fa-css-prefix}-youtube:before { content: $fa-var-youtube; }\n.#{$fa-css-prefix}-xing:before { content: $fa-var-xing; }\n.#{$fa-css-prefix}-xing-square:before { content: $fa-var-xing-square; }\n.#{$fa-css-prefix}-youtube-play:before { content: $fa-var-youtube-play; }\n.#{$fa-css-prefix}-dropbox:before { content: $fa-var-dropbox; }\n.#{$fa-css-prefix}-stack-overflow:before { content: $fa-var-stack-overflow; }\n.#{$fa-css-prefix}-instagram:before { content: $fa-var-instagram; }\n.#{$fa-css-prefix}-flickr:before { content: $fa-var-flickr; }\n.#{$fa-css-prefix}-adn:before { content: $fa-var-adn; }\n.#{$fa-css-prefix}-bitbucket:before { content: $fa-var-bitbucket; }\n.#{$fa-css-prefix}-bitbucket-square:before { content: $fa-var-bitbucket-square; }\n.#{$fa-css-prefix}-tumblr:before { content: $fa-var-tumblr; }\n.#{$fa-css-prefix}-tumblr-square:before { content: $fa-var-tumblr-square; }\n.#{$fa-css-prefix}-long-arrow-down:before { content: $fa-var-long-arrow-down; }\n.#{$fa-css-prefix}-long-arrow-up:before { content: $fa-var-long-arrow-up; }\n.#{$fa-css-prefix}-long-arrow-left:before { content: $fa-var-long-arrow-left; }\n.#{$fa-css-prefix}-long-arrow-right:before { content: $fa-var-long-arrow-right; }\n.#{$fa-css-prefix}-apple:before { content: $fa-var-apple; }\n.#{$fa-css-prefix}-windows:before { content: $fa-var-windows; }\n.#{$fa-css-prefix}-android:before { content: $fa-var-android; }\n.#{$fa-css-prefix}-linux:before { content: $fa-var-linux; }\n.#{$fa-css-prefix}-dribbble:before { content: $fa-var-dribbble; }\n.#{$fa-css-prefix}-skype:before { content: $fa-var-skype; }\n.#{$fa-css-prefix}-foursquare:before { content: $fa-var-foursquare; }\n.#{$fa-css-prefix}-trello:before { content: $fa-var-trello; }\n.#{$fa-css-prefix}-female:before { content: $fa-var-female; }\n.#{$fa-css-prefix}-male:before { content: $fa-var-male; }\n.#{$fa-css-prefix}-gittip:before { content: $fa-var-gittip; }\n.#{$fa-css-prefix}-sun-o:before { content: $fa-var-sun-o; }\n.#{$fa-css-prefix}-moon-o:before { content: $fa-var-moon-o; }\n.#{$fa-css-prefix}-archive:before { content: $fa-var-archive; }\n.#{$fa-css-prefix}-bug:before { content: $fa-var-bug; }\n.#{$fa-css-prefix}-vk:before { content: $fa-var-vk; }\n.#{$fa-css-prefix}-weibo:before { content: $fa-var-weibo; }\n.#{$fa-css-prefix}-renren:before { content: $fa-var-renren; }\n.#{$fa-css-prefix}-pagelines:before { content: $fa-var-pagelines; }\n.#{$fa-css-prefix}-stack-exchange:before { content: $fa-var-stack-exchange; }\n.#{$fa-css-prefix}-arrow-circle-o-right:before { content: $fa-var-arrow-circle-o-right; }\n.#{$fa-css-prefix}-arrow-circle-o-left:before { content: $fa-var-arrow-circle-o-left; }\n.#{$fa-css-prefix}-toggle-left:before,\n.#{$fa-css-prefix}-caret-square-o-left:before { content: $fa-var-caret-square-o-left; }\n.#{$fa-css-prefix}-dot-circle-o:before { content: $fa-var-dot-circle-o; }\n.#{$fa-css-prefix}-wheelchair:before { content: $fa-var-wheelchair; }\n.#{$fa-css-prefix}-vimeo-square:before { content: $fa-var-vimeo-square; }\n.#{$fa-css-prefix}-turkish-lira:before,\n.#{$fa-css-prefix}-try:before { content: $fa-var-try; }\n.#{$fa-css-prefix}-plus-square-o:before { content: $fa-var-plus-square-o; }\n.#{$fa-css-prefix}-space-shuttle:before { content: $fa-var-space-shuttle; }\n.#{$fa-css-prefix}-slack:before { content: $fa-var-slack; }\n.#{$fa-css-prefix}-envelope-square:before { content: $fa-var-envelope-square; }\n.#{$fa-css-prefix}-wordpress:before { content: $fa-var-wordpress; }\n.#{$fa-css-prefix}-openid:before { content: $fa-var-openid; }\n.#{$fa-css-prefix}-institution:before,\n.#{$fa-css-prefix}-bank:before,\n.#{$fa-css-prefix}-university:before { content: $fa-var-university; }\n.#{$fa-css-prefix}-mortar-board:before,\n.#{$fa-css-prefix}-graduation-cap:before { content: $fa-var-graduation-cap; }\n.#{$fa-css-prefix}-yahoo:before { content: $fa-var-yahoo; }\n.#{$fa-css-prefix}-google:before { content: $fa-var-google; }\n.#{$fa-css-prefix}-reddit:before { content: $fa-var-reddit; }\n.#{$fa-css-prefix}-reddit-square:before { content: $fa-var-reddit-square; }\n.#{$fa-css-prefix}-stumbleupon-circle:before { content: $fa-var-stumbleupon-circle; }\n.#{$fa-css-prefix}-stumbleupon:before { content: $fa-var-stumbleupon; }\n.#{$fa-css-prefix}-delicious:before { content: $fa-var-delicious; }\n.#{$fa-css-prefix}-digg:before { content: $fa-var-digg; }\n.#{$fa-css-prefix}-pied-piper-square:before,\n.#{$fa-css-prefix}-pied-piper:before { content: $fa-var-pied-piper; }\n.#{$fa-css-prefix}-pied-piper-alt:before { content: $fa-var-pied-piper-alt; }\n.#{$fa-css-prefix}-drupal:before { content: $fa-var-drupal; }\n.#{$fa-css-prefix}-joomla:before { content: $fa-var-joomla; }\n.#{$fa-css-prefix}-language:before { content: $fa-var-language; }\n.#{$fa-css-prefix}-fax:before { content: $fa-var-fax; }\n.#{$fa-css-prefix}-building:before { content: $fa-var-building; }\n.#{$fa-css-prefix}-child:before { content: $fa-var-child; }\n.#{$fa-css-prefix}-paw:before { content: $fa-var-paw; }\n.#{$fa-css-prefix}-spoon:before { content: $fa-var-spoon; }\n.#{$fa-css-prefix}-cube:before { content: $fa-var-cube; }\n.#{$fa-css-prefix}-cubes:before { content: $fa-var-cubes; }\n.#{$fa-css-prefix}-behance:before { content: $fa-var-behance; }\n.#{$fa-css-prefix}-behance-square:before { content: $fa-var-behance-square; }\n.#{$fa-css-prefix}-steam:before { content: $fa-var-steam; }\n.#{$fa-css-prefix}-steam-square:before { content: $fa-var-steam-square; }\n.#{$fa-css-prefix}-recycle:before { content: $fa-var-recycle; }\n.#{$fa-css-prefix}-automobile:before,\n.#{$fa-css-prefix}-car:before { content: $fa-var-car; }\n.#{$fa-css-prefix}-cab:before,\n.#{$fa-css-prefix}-taxi:before { content: $fa-var-taxi; }\n.#{$fa-css-prefix}-tree:before { content: $fa-var-tree; }\n.#{$fa-css-prefix}-spotify:before { content: $fa-var-spotify; }\n.#{$fa-css-prefix}-deviantart:before { content: $fa-var-deviantart; }\n.#{$fa-css-prefix}-soundcloud:before { content: $fa-var-soundcloud; }\n.#{$fa-css-prefix}-database:before { content: $fa-var-database; }\n.#{$fa-css-prefix}-file-pdf-o:before { content: $fa-var-file-pdf-o; }\n.#{$fa-css-prefix}-file-word-o:before { content: $fa-var-file-word-o; }\n.#{$fa-css-prefix}-file-excel-o:before { content: $fa-var-file-excel-o; }\n.#{$fa-css-prefix}-file-powerpoint-o:before { content: $fa-var-file-powerpoint-o; }\n.#{$fa-css-prefix}-file-photo-o:before,\n.#{$fa-css-prefix}-file-picture-o:before,\n.#{$fa-css-prefix}-file-image-o:before { content: $fa-var-file-image-o; }\n.#{$fa-css-prefix}-file-zip-o:before,\n.#{$fa-css-prefix}-file-archive-o:before { content: $fa-var-file-archive-o; }\n.#{$fa-css-prefix}-file-sound-o:before,\n.#{$fa-css-prefix}-file-audio-o:before { content: $fa-var-file-audio-o; }\n.#{$fa-css-prefix}-file-movie-o:before,\n.#{$fa-css-prefix}-file-video-o:before { content: $fa-var-file-video-o; }\n.#{$fa-css-prefix}-file-code-o:before { content: $fa-var-file-code-o; }\n.#{$fa-css-prefix}-vine:before { content: $fa-var-vine; }\n.#{$fa-css-prefix}-codepen:before { content: $fa-var-codepen; }\n.#{$fa-css-prefix}-jsfiddle:before { content: $fa-var-jsfiddle; }\n.#{$fa-css-prefix}-life-bouy:before,\n.#{$fa-css-prefix}-life-saver:before,\n.#{$fa-css-prefix}-support:before,\n.#{$fa-css-prefix}-life-ring:before { content: $fa-var-life-ring; }\n.#{$fa-css-prefix}-circle-o-notch:before { content: $fa-var-circle-o-notch; }\n.#{$fa-css-prefix}-ra:before,\n.#{$fa-css-prefix}-rebel:before { content: $fa-var-rebel; }\n.#{$fa-css-prefix}-ge:before,\n.#{$fa-css-prefix}-empire:before { content: $fa-var-empire; }\n.#{$fa-css-prefix}-git-square:before { content: $fa-var-git-square; }\n.#{$fa-css-prefix}-git:before { content: $fa-var-git; }\n.#{$fa-css-prefix}-hacker-news:before { content: $fa-var-hacker-news; }\n.#{$fa-css-prefix}-tencent-weibo:before { content: $fa-var-tencent-weibo; }\n.#{$fa-css-prefix}-qq:before { content: $fa-var-qq; }\n.#{$fa-css-prefix}-wechat:before,\n.#{$fa-css-prefix}-weixin:before { content: $fa-var-weixin; }\n.#{$fa-css-prefix}-send:before,\n.#{$fa-css-prefix}-paper-plane:before { content: $fa-var-paper-plane; }\n.#{$fa-css-prefix}-send-o:before,\n.#{$fa-css-prefix}-paper-plane-o:before { content: $fa-var-paper-plane-o; }\n.#{$fa-css-prefix}-history:before { content: $fa-var-history; }\n.#{$fa-css-prefix}-circle-thin:before { content: $fa-var-circle-thin; }\n.#{$fa-css-prefix}-header:before { content: $fa-var-header; }\n.#{$fa-css-prefix}-paragraph:before { content: $fa-var-paragraph; }\n.#{$fa-css-prefix}-sliders:before { content: $fa-var-sliders; }\n.#{$fa-css-prefix}-share-alt:before { content: $fa-var-share-alt; }\n.#{$fa-css-prefix}-share-alt-square:before { content: $fa-var-share-alt-square; }\n.#{$fa-css-prefix}-bomb:before { content: $fa-var-bomb; }\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_larger.scss",
    "content": "// Icon Sizes\n// -------------------------\n\n/* makes the font 33% larger relative to the icon container */\n.#{$fa-css-prefix}-lg {\n  font-size: (4em / 3);\n  line-height: (3em / 4);\n  vertical-align: -15%;\n}\n.#{$fa-css-prefix}-2x { font-size: 2em; }\n.#{$fa-css-prefix}-3x { font-size: 3em; }\n.#{$fa-css-prefix}-4x { font-size: 4em; }\n.#{$fa-css-prefix}-5x { font-size: 5em; }\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_list.scss",
    "content": "// List Icons\n// -------------------------\n\n.#{$fa-css-prefix}-ul {\n  padding-left: 0;\n  margin-left: $fa-li-width;\n  list-style-type: none;\n  > li { position: relative; }\n}\n.#{$fa-css-prefix}-li {\n  position: absolute;\n  left: -$fa-li-width;\n  width: $fa-li-width;\n  top: (2em / 14);\n  text-align: center;\n  &.#{$fa-css-prefix}-lg {\n    left: -$fa-li-width + (4em / 14);\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_mixins.scss",
    "content": "// Mixins\n// --------------------------\n\n@mixin fa-icon-rotate($degrees, $rotation) {\n  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=#{$rotation});\n  -webkit-transform: rotate($degrees);\n     -moz-transform: rotate($degrees);\n      -ms-transform: rotate($degrees);\n       -o-transform: rotate($degrees);\n          transform: rotate($degrees);\n}\n\n@mixin fa-icon-flip($horiz, $vert, $rotation) {\n  filter: progid:DXImageTransform.Microsoft.BasicImage(rotation=#{$rotation});\n  -webkit-transform: scale($horiz, $vert);\n     -moz-transform: scale($horiz, $vert);\n      -ms-transform: scale($horiz, $vert);\n       -o-transform: scale($horiz, $vert);\n          transform: scale($horiz, $vert);\n}\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_path.scss",
    "content": "/* FONT PATH\n * -------------------------- */\n\n@font-face {\n  font-family: 'FontAwesome';\n  src: url('#{$fa-font-path}/fontawesome-webfont.eot?v=#{$fa-version}');\n  src: url('#{$fa-font-path}/fontawesome-webfont.eot?#iefix&v=#{$fa-version}') format('embedded-opentype'),\n    url('#{$fa-font-path}/fontawesome-webfont.woff?v=#{$fa-version}') format('woff'),\n    url('#{$fa-font-path}/fontawesome-webfont.ttf?v=#{$fa-version}') format('truetype'),\n    url('#{$fa-font-path}/fontawesome-webfont.svg?v=#{$fa-version}#fontawesomeregular') format('svg');\n  //src: url('#{$fa-font-path}/FontAwesome.otf') format('opentype'); // used when developing fonts\n  font-weight: normal;\n  font-style: normal;\n}\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_rotated-flipped.scss",
    "content": "// Rotated & Flipped Icons\n// -------------------------\n\n.#{$fa-css-prefix}-rotate-90  { @include fa-icon-rotate(90deg, 1);  }\n.#{$fa-css-prefix}-rotate-180 { @include fa-icon-rotate(180deg, 2); }\n.#{$fa-css-prefix}-rotate-270 { @include fa-icon-rotate(270deg, 3); }\n\n.#{$fa-css-prefix}-flip-horizontal { @include fa-icon-flip(-1, 1, 0); }\n.#{$fa-css-prefix}-flip-vertical   { @include fa-icon-flip(1, -1, 2); }\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_spinning.scss",
    "content": "// Spinning Icons\n// --------------------------\n\n.#{$fa-css-prefix}-spin {\n  -webkit-animation: spin 2s infinite linear;\n  -moz-animation: spin 2s infinite linear;\n  -o-animation: spin 2s infinite linear;\n  animation: spin 2s infinite linear;\n}\n\n@-moz-keyframes spin {\n  0% { -moz-transform: rotate(0deg); }\n  100% { -moz-transform: rotate(359deg); }\n}\n@-webkit-keyframes spin {\n  0% { -webkit-transform: rotate(0deg); }\n  100% { -webkit-transform: rotate(359deg); }\n}\n@-o-keyframes spin {\n  0% { -o-transform: rotate(0deg); }\n  100% { -o-transform: rotate(359deg); }\n}\n@keyframes spin {\n  0% {\n    -webkit-transform: rotate(0deg);\n    transform: rotate(0deg);\n  }\n  100% {\n    -webkit-transform: rotate(359deg);\n    transform: rotate(359deg);\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_stacked.scss",
    "content": "// Stacked Icons\n// -------------------------\n\n.#{$fa-css-prefix}-stack {\n  position: relative;\n  display: inline-block;\n  width: 2em;\n  height: 2em;\n  line-height: 2em;\n  vertical-align: middle;\n}\n.#{$fa-css-prefix}-stack-1x, .#{$fa-css-prefix}-stack-2x {\n  position: absolute;\n  left: 0;\n  width: 100%;\n  text-align: center;\n}\n.#{$fa-css-prefix}-stack-1x { line-height: inherit; }\n.#{$fa-css-prefix}-stack-2x { font-size: 2em; }\n.#{$fa-css-prefix}-inverse { color: $fa-inverse; }\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/_variables.scss",
    "content": "// Variables\n// --------------------------\n\n$fa-font-path:        \"../fonts\" !default;\n//$fa-font-path:        \"//netdna.bootstrapcdn.com/font-awesome/4.1.0/fonts\" !default; // for referencing Bootstrap CDN font files directly\n$fa-css-prefix:       fa !default;\n$fa-version:          \"4.1.0\" !default;\n$fa-border-color:     #eee !default;\n$fa-inverse:          #fff !default;\n$fa-li-width:         (30em / 14) !default;\n\n$fa-var-adjust: \"\\f042\";\n$fa-var-adn: \"\\f170\";\n$fa-var-align-center: \"\\f037\";\n$fa-var-align-justify: \"\\f039\";\n$fa-var-align-left: \"\\f036\";\n$fa-var-align-right: \"\\f038\";\n$fa-var-ambulance: \"\\f0f9\";\n$fa-var-anchor: \"\\f13d\";\n$fa-var-android: \"\\f17b\";\n$fa-var-angle-double-down: \"\\f103\";\n$fa-var-angle-double-left: \"\\f100\";\n$fa-var-angle-double-right: \"\\f101\";\n$fa-var-angle-double-up: \"\\f102\";\n$fa-var-angle-down: \"\\f107\";\n$fa-var-angle-left: \"\\f104\";\n$fa-var-angle-right: \"\\f105\";\n$fa-var-angle-up: \"\\f106\";\n$fa-var-apple: \"\\f179\";\n$fa-var-archive: \"\\f187\";\n$fa-var-arrow-circle-down: \"\\f0ab\";\n$fa-var-arrow-circle-left: \"\\f0a8\";\n$fa-var-arrow-circle-o-down: \"\\f01a\";\n$fa-var-arrow-circle-o-left: \"\\f190\";\n$fa-var-arrow-circle-o-right: \"\\f18e\";\n$fa-var-arrow-circle-o-up: \"\\f01b\";\n$fa-var-arrow-circle-right: \"\\f0a9\";\n$fa-var-arrow-circle-up: \"\\f0aa\";\n$fa-var-arrow-down: \"\\f063\";\n$fa-var-arrow-left: \"\\f060\";\n$fa-var-arrow-right: \"\\f061\";\n$fa-var-arrow-up: \"\\f062\";\n$fa-var-arrows: \"\\f047\";\n$fa-var-arrows-alt: \"\\f0b2\";\n$fa-var-arrows-h: \"\\f07e\";\n$fa-var-arrows-v: \"\\f07d\";\n$fa-var-asterisk: \"\\f069\";\n$fa-var-automobile: \"\\f1b9\";\n$fa-var-backward: \"\\f04a\";\n$fa-var-ban: \"\\f05e\";\n$fa-var-bank: \"\\f19c\";\n$fa-var-bar-chart-o: \"\\f080\";\n$fa-var-barcode: \"\\f02a\";\n$fa-var-bars: \"\\f0c9\";\n$fa-var-beer: \"\\f0fc\";\n$fa-var-behance: \"\\f1b4\";\n$fa-var-behance-square: \"\\f1b5\";\n$fa-var-bell: \"\\f0f3\";\n$fa-var-bell-o: \"\\f0a2\";\n$fa-var-bitbucket: \"\\f171\";\n$fa-var-bitbucket-square: \"\\f172\";\n$fa-var-bitcoin: \"\\f15a\";\n$fa-var-bold: \"\\f032\";\n$fa-var-bolt: \"\\f0e7\";\n$fa-var-bomb: \"\\f1e2\";\n$fa-var-book: \"\\f02d\";\n$fa-var-bookmark: \"\\f02e\";\n$fa-var-bookmark-o: \"\\f097\";\n$fa-var-briefcase: \"\\f0b1\";\n$fa-var-btc: \"\\f15a\";\n$fa-var-bug: \"\\f188\";\n$fa-var-building: \"\\f1ad\";\n$fa-var-building-o: \"\\f0f7\";\n$fa-var-bullhorn: \"\\f0a1\";\n$fa-var-bullseye: \"\\f140\";\n$fa-var-cab: \"\\f1ba\";\n$fa-var-calendar: \"\\f073\";\n$fa-var-calendar-o: \"\\f133\";\n$fa-var-camera: \"\\f030\";\n$fa-var-camera-retro: \"\\f083\";\n$fa-var-car: \"\\f1b9\";\n$fa-var-caret-down: \"\\f0d7\";\n$fa-var-caret-left: \"\\f0d9\";\n$fa-var-caret-right: \"\\f0da\";\n$fa-var-caret-square-o-down: \"\\f150\";\n$fa-var-caret-square-o-left: \"\\f191\";\n$fa-var-caret-square-o-right: \"\\f152\";\n$fa-var-caret-square-o-up: \"\\f151\";\n$fa-var-caret-up: \"\\f0d8\";\n$fa-var-certificate: \"\\f0a3\";\n$fa-var-chain: \"\\f0c1\";\n$fa-var-chain-broken: \"\\f127\";\n$fa-var-check: \"\\f00c\";\n$fa-var-check-circle: \"\\f058\";\n$fa-var-check-circle-o: \"\\f05d\";\n$fa-var-check-square: \"\\f14a\";\n$fa-var-check-square-o: \"\\f046\";\n$fa-var-chevron-circle-down: \"\\f13a\";\n$fa-var-chevron-circle-left: \"\\f137\";\n$fa-var-chevron-circle-right: \"\\f138\";\n$fa-var-chevron-circle-up: \"\\f139\";\n$fa-var-chevron-down: \"\\f078\";\n$fa-var-chevron-left: \"\\f053\";\n$fa-var-chevron-right: \"\\f054\";\n$fa-var-chevron-up: \"\\f077\";\n$fa-var-child: \"\\f1ae\";\n$fa-var-circle: \"\\f111\";\n$fa-var-circle-o: \"\\f10c\";\n$fa-var-circle-o-notch: \"\\f1ce\";\n$fa-var-circle-thin: \"\\f1db\";\n$fa-var-clipboard: \"\\f0ea\";\n$fa-var-clock-o: \"\\f017\";\n$fa-var-cloud: \"\\f0c2\";\n$fa-var-cloud-download: \"\\f0ed\";\n$fa-var-cloud-upload: \"\\f0ee\";\n$fa-var-cny: \"\\f157\";\n$fa-var-code: \"\\f121\";\n$fa-var-code-fork: \"\\f126\";\n$fa-var-codepen: \"\\f1cb\";\n$fa-var-coffee: \"\\f0f4\";\n$fa-var-cog: \"\\f013\";\n$fa-var-cogs: \"\\f085\";\n$fa-var-columns: \"\\f0db\";\n$fa-var-comment: \"\\f075\";\n$fa-var-comment-o: \"\\f0e5\";\n$fa-var-comments: \"\\f086\";\n$fa-var-comments-o: \"\\f0e6\";\n$fa-var-compass: \"\\f14e\";\n$fa-var-compress: \"\\f066\";\n$fa-var-copy: \"\\f0c5\";\n$fa-var-credit-card: \"\\f09d\";\n$fa-var-crop: \"\\f125\";\n$fa-var-crosshairs: \"\\f05b\";\n$fa-var-css3: \"\\f13c\";\n$fa-var-cube: \"\\f1b2\";\n$fa-var-cubes: \"\\f1b3\";\n$fa-var-cut: \"\\f0c4\";\n$fa-var-cutlery: \"\\f0f5\";\n$fa-var-dashboard: \"\\f0e4\";\n$fa-var-database: \"\\f1c0\";\n$fa-var-dedent: \"\\f03b\";\n$fa-var-delicious: \"\\f1a5\";\n$fa-var-desktop: \"\\f108\";\n$fa-var-deviantart: \"\\f1bd\";\n$fa-var-digg: \"\\f1a6\";\n$fa-var-dollar: \"\\f155\";\n$fa-var-dot-circle-o: \"\\f192\";\n$fa-var-download: \"\\f019\";\n$fa-var-dribbble: \"\\f17d\";\n$fa-var-dropbox: \"\\f16b\";\n$fa-var-drupal: \"\\f1a9\";\n$fa-var-edit: \"\\f044\";\n$fa-var-eject: \"\\f052\";\n$fa-var-ellipsis-h: \"\\f141\";\n$fa-var-ellipsis-v: \"\\f142\";\n$fa-var-empire: \"\\f1d1\";\n$fa-var-envelope: \"\\f0e0\";\n$fa-var-envelope-o: \"\\f003\";\n$fa-var-envelope-square: \"\\f199\";\n$fa-var-eraser: \"\\f12d\";\n$fa-var-eur: \"\\f153\";\n$fa-var-euro: \"\\f153\";\n$fa-var-exchange: \"\\f0ec\";\n$fa-var-exclamation: \"\\f12a\";\n$fa-var-exclamation-circle: \"\\f06a\";\n$fa-var-exclamation-triangle: \"\\f071\";\n$fa-var-expand: \"\\f065\";\n$fa-var-external-link: \"\\f08e\";\n$fa-var-external-link-square: \"\\f14c\";\n$fa-var-eye: \"\\f06e\";\n$fa-var-eye-slash: \"\\f070\";\n$fa-var-facebook: \"\\f09a\";\n$fa-var-facebook-square: \"\\f082\";\n$fa-var-fast-backward: \"\\f049\";\n$fa-var-fast-forward: \"\\f050\";\n$fa-var-fax: \"\\f1ac\";\n$fa-var-female: \"\\f182\";\n$fa-var-fighter-jet: \"\\f0fb\";\n$fa-var-file: \"\\f15b\";\n$fa-var-file-archive-o: \"\\f1c6\";\n$fa-var-file-audio-o: \"\\f1c7\";\n$fa-var-file-code-o: \"\\f1c9\";\n$fa-var-file-excel-o: \"\\f1c3\";\n$fa-var-file-image-o: \"\\f1c5\";\n$fa-var-file-movie-o: \"\\f1c8\";\n$fa-var-file-o: \"\\f016\";\n$fa-var-file-pdf-o: \"\\f1c1\";\n$fa-var-file-photo-o: \"\\f1c5\";\n$fa-var-file-picture-o: \"\\f1c5\";\n$fa-var-file-powerpoint-o: \"\\f1c4\";\n$fa-var-file-sound-o: \"\\f1c7\";\n$fa-var-file-text: \"\\f15c\";\n$fa-var-file-text-o: \"\\f0f6\";\n$fa-var-file-video-o: \"\\f1c8\";\n$fa-var-file-word-o: \"\\f1c2\";\n$fa-var-file-zip-o: \"\\f1c6\";\n$fa-var-files-o: \"\\f0c5\";\n$fa-var-film: \"\\f008\";\n$fa-var-filter: \"\\f0b0\";\n$fa-var-fire: \"\\f06d\";\n$fa-var-fire-extinguisher: \"\\f134\";\n$fa-var-flag: \"\\f024\";\n$fa-var-flag-checkered: \"\\f11e\";\n$fa-var-flag-o: \"\\f11d\";\n$fa-var-flash: \"\\f0e7\";\n$fa-var-flask: \"\\f0c3\";\n$fa-var-flickr: \"\\f16e\";\n$fa-var-floppy-o: \"\\f0c7\";\n$fa-var-folder: \"\\f07b\";\n$fa-var-folder-o: \"\\f114\";\n$fa-var-folder-open: \"\\f07c\";\n$fa-var-folder-open-o: \"\\f115\";\n$fa-var-font: \"\\f031\";\n$fa-var-forward: \"\\f04e\";\n$fa-var-foursquare: \"\\f180\";\n$fa-var-frown-o: \"\\f119\";\n$fa-var-gamepad: \"\\f11b\";\n$fa-var-gavel: \"\\f0e3\";\n$fa-var-gbp: \"\\f154\";\n$fa-var-ge: \"\\f1d1\";\n$fa-var-gear: \"\\f013\";\n$fa-var-gears: \"\\f085\";\n$fa-var-gift: \"\\f06b\";\n$fa-var-git: \"\\f1d3\";\n$fa-var-git-square: \"\\f1d2\";\n$fa-var-github: \"\\f09b\";\n$fa-var-github-alt: \"\\f113\";\n$fa-var-github-square: \"\\f092\";\n$fa-var-gittip: \"\\f184\";\n$fa-var-glass: \"\\f000\";\n$fa-var-globe: \"\\f0ac\";\n$fa-var-google: \"\\f1a0\";\n$fa-var-google-plus: \"\\f0d5\";\n$fa-var-google-plus-square: \"\\f0d4\";\n$fa-var-graduation-cap: \"\\f19d\";\n$fa-var-group: \"\\f0c0\";\n$fa-var-h-square: \"\\f0fd\";\n$fa-var-hacker-news: \"\\f1d4\";\n$fa-var-hand-o-down: \"\\f0a7\";\n$fa-var-hand-o-left: \"\\f0a5\";\n$fa-var-hand-o-right: \"\\f0a4\";\n$fa-var-hand-o-up: \"\\f0a6\";\n$fa-var-hdd-o: \"\\f0a0\";\n$fa-var-header: \"\\f1dc\";\n$fa-var-headphones: \"\\f025\";\n$fa-var-heart: \"\\f004\";\n$fa-var-heart-o: \"\\f08a\";\n$fa-var-history: \"\\f1da\";\n$fa-var-home: \"\\f015\";\n$fa-var-hospital-o: \"\\f0f8\";\n$fa-var-html5: \"\\f13b\";\n$fa-var-image: \"\\f03e\";\n$fa-var-inbox: \"\\f01c\";\n$fa-var-indent: \"\\f03c\";\n$fa-var-info: \"\\f129\";\n$fa-var-info-circle: \"\\f05a\";\n$fa-var-inr: \"\\f156\";\n$fa-var-instagram: \"\\f16d\";\n$fa-var-institution: \"\\f19c\";\n$fa-var-italic: \"\\f033\";\n$fa-var-joomla: \"\\f1aa\";\n$fa-var-jpy: \"\\f157\";\n$fa-var-jsfiddle: \"\\f1cc\";\n$fa-var-key: \"\\f084\";\n$fa-var-keyboard-o: \"\\f11c\";\n$fa-var-krw: \"\\f159\";\n$fa-var-language: \"\\f1ab\";\n$fa-var-laptop: \"\\f109\";\n$fa-var-leaf: \"\\f06c\";\n$fa-var-legal: \"\\f0e3\";\n$fa-var-lemon-o: \"\\f094\";\n$fa-var-level-down: \"\\f149\";\n$fa-var-level-up: \"\\f148\";\n$fa-var-life-bouy: \"\\f1cd\";\n$fa-var-life-ring: \"\\f1cd\";\n$fa-var-life-saver: \"\\f1cd\";\n$fa-var-lightbulb-o: \"\\f0eb\";\n$fa-var-link: \"\\f0c1\";\n$fa-var-linkedin: \"\\f0e1\";\n$fa-var-linkedin-square: \"\\f08c\";\n$fa-var-linux: \"\\f17c\";\n$fa-var-list: \"\\f03a\";\n$fa-var-list-alt: \"\\f022\";\n$fa-var-list-ol: \"\\f0cb\";\n$fa-var-list-ul: \"\\f0ca\";\n$fa-var-location-arrow: \"\\f124\";\n$fa-var-lock: \"\\f023\";\n$fa-var-long-arrow-down: \"\\f175\";\n$fa-var-long-arrow-left: \"\\f177\";\n$fa-var-long-arrow-right: \"\\f178\";\n$fa-var-long-arrow-up: \"\\f176\";\n$fa-var-magic: \"\\f0d0\";\n$fa-var-magnet: \"\\f076\";\n$fa-var-mail-forward: \"\\f064\";\n$fa-var-mail-reply: \"\\f112\";\n$fa-var-mail-reply-all: \"\\f122\";\n$fa-var-male: \"\\f183\";\n$fa-var-map-marker: \"\\f041\";\n$fa-var-maxcdn: \"\\f136\";\n$fa-var-medkit: \"\\f0fa\";\n$fa-var-meh-o: \"\\f11a\";\n$fa-var-microphone: \"\\f130\";\n$fa-var-microphone-slash: \"\\f131\";\n$fa-var-minus: \"\\f068\";\n$fa-var-minus-circle: \"\\f056\";\n$fa-var-minus-square: \"\\f146\";\n$fa-var-minus-square-o: \"\\f147\";\n$fa-var-mobile: \"\\f10b\";\n$fa-var-mobile-phone: \"\\f10b\";\n$fa-var-money: \"\\f0d6\";\n$fa-var-moon-o: \"\\f186\";\n$fa-var-mortar-board: \"\\f19d\";\n$fa-var-music: \"\\f001\";\n$fa-var-navicon: \"\\f0c9\";\n$fa-var-openid: \"\\f19b\";\n$fa-var-outdent: \"\\f03b\";\n$fa-var-pagelines: \"\\f18c\";\n$fa-var-paper-plane: \"\\f1d8\";\n$fa-var-paper-plane-o: \"\\f1d9\";\n$fa-var-paperclip: \"\\f0c6\";\n$fa-var-paragraph: \"\\f1dd\";\n$fa-var-paste: \"\\f0ea\";\n$fa-var-pause: \"\\f04c\";\n$fa-var-paw: \"\\f1b0\";\n$fa-var-pencil: \"\\f040\";\n$fa-var-pencil-square: \"\\f14b\";\n$fa-var-pencil-square-o: \"\\f044\";\n$fa-var-phone: \"\\f095\";\n$fa-var-phone-square: \"\\f098\";\n$fa-var-photo: \"\\f03e\";\n$fa-var-picture-o: \"\\f03e\";\n$fa-var-pied-piper: \"\\f1a7\";\n$fa-var-pied-piper-alt: \"\\f1a8\";\n$fa-var-pied-piper-square: \"\\f1a7\";\n$fa-var-pinterest: \"\\f0d2\";\n$fa-var-pinterest-square: \"\\f0d3\";\n$fa-var-plane: \"\\f072\";\n$fa-var-play: \"\\f04b\";\n$fa-var-play-circle: \"\\f144\";\n$fa-var-play-circle-o: \"\\f01d\";\n$fa-var-plus: \"\\f067\";\n$fa-var-plus-circle: \"\\f055\";\n$fa-var-plus-square: \"\\f0fe\";\n$fa-var-plus-square-o: \"\\f196\";\n$fa-var-power-off: \"\\f011\";\n$fa-var-print: \"\\f02f\";\n$fa-var-puzzle-piece: \"\\f12e\";\n$fa-var-qq: \"\\f1d6\";\n$fa-var-qrcode: \"\\f029\";\n$fa-var-question: \"\\f128\";\n$fa-var-question-circle: \"\\f059\";\n$fa-var-quote-left: \"\\f10d\";\n$fa-var-quote-right: \"\\f10e\";\n$fa-var-ra: \"\\f1d0\";\n$fa-var-random: \"\\f074\";\n$fa-var-rebel: \"\\f1d0\";\n$fa-var-recycle: \"\\f1b8\";\n$fa-var-reddit: \"\\f1a1\";\n$fa-var-reddit-square: \"\\f1a2\";\n$fa-var-refresh: \"\\f021\";\n$fa-var-renren: \"\\f18b\";\n$fa-var-reorder: \"\\f0c9\";\n$fa-var-repeat: \"\\f01e\";\n$fa-var-reply: \"\\f112\";\n$fa-var-reply-all: \"\\f122\";\n$fa-var-retweet: \"\\f079\";\n$fa-var-rmb: \"\\f157\";\n$fa-var-road: \"\\f018\";\n$fa-var-rocket: \"\\f135\";\n$fa-var-rotate-left: \"\\f0e2\";\n$fa-var-rotate-right: \"\\f01e\";\n$fa-var-rouble: \"\\f158\";\n$fa-var-rss: \"\\f09e\";\n$fa-var-rss-square: \"\\f143\";\n$fa-var-rub: \"\\f158\";\n$fa-var-ruble: \"\\f158\";\n$fa-var-rupee: \"\\f156\";\n$fa-var-save: \"\\f0c7\";\n$fa-var-scissors: \"\\f0c4\";\n$fa-var-search: \"\\f002\";\n$fa-var-search-minus: \"\\f010\";\n$fa-var-search-plus: \"\\f00e\";\n$fa-var-send: \"\\f1d8\";\n$fa-var-send-o: \"\\f1d9\";\n$fa-var-share: \"\\f064\";\n$fa-var-share-alt: \"\\f1e0\";\n$fa-var-share-alt-square: \"\\f1e1\";\n$fa-var-share-square: \"\\f14d\";\n$fa-var-share-square-o: \"\\f045\";\n$fa-var-shield: \"\\f132\";\n$fa-var-shopping-cart: \"\\f07a\";\n$fa-var-sign-in: \"\\f090\";\n$fa-var-sign-out: \"\\f08b\";\n$fa-var-signal: \"\\f012\";\n$fa-var-sitemap: \"\\f0e8\";\n$fa-var-skype: \"\\f17e\";\n$fa-var-slack: \"\\f198\";\n$fa-var-sliders: \"\\f1de\";\n$fa-var-smile-o: \"\\f118\";\n$fa-var-sort: \"\\f0dc\";\n$fa-var-sort-alpha-asc: \"\\f15d\";\n$fa-var-sort-alpha-desc: \"\\f15e\";\n$fa-var-sort-amount-asc: \"\\f160\";\n$fa-var-sort-amount-desc: \"\\f161\";\n$fa-var-sort-asc: \"\\f0de\";\n$fa-var-sort-desc: \"\\f0dd\";\n$fa-var-sort-down: \"\\f0dd\";\n$fa-var-sort-numeric-asc: \"\\f162\";\n$fa-var-sort-numeric-desc: \"\\f163\";\n$fa-var-sort-up: \"\\f0de\";\n$fa-var-soundcloud: \"\\f1be\";\n$fa-var-space-shuttle: \"\\f197\";\n$fa-var-spinner: \"\\f110\";\n$fa-var-spoon: \"\\f1b1\";\n$fa-var-spotify: \"\\f1bc\";\n$fa-var-square: \"\\f0c8\";\n$fa-var-square-o: \"\\f096\";\n$fa-var-stack-exchange: \"\\f18d\";\n$fa-var-stack-overflow: \"\\f16c\";\n$fa-var-star: \"\\f005\";\n$fa-var-star-half: \"\\f089\";\n$fa-var-star-half-empty: \"\\f123\";\n$fa-var-star-half-full: \"\\f123\";\n$fa-var-star-half-o: \"\\f123\";\n$fa-var-star-o: \"\\f006\";\n$fa-var-steam: \"\\f1b6\";\n$fa-var-steam-square: \"\\f1b7\";\n$fa-var-step-backward: \"\\f048\";\n$fa-var-step-forward: \"\\f051\";\n$fa-var-stethoscope: \"\\f0f1\";\n$fa-var-stop: \"\\f04d\";\n$fa-var-strikethrough: \"\\f0cc\";\n$fa-var-stumbleupon: \"\\f1a4\";\n$fa-var-stumbleupon-circle: \"\\f1a3\";\n$fa-var-subscript: \"\\f12c\";\n$fa-var-suitcase: \"\\f0f2\";\n$fa-var-sun-o: \"\\f185\";\n$fa-var-superscript: \"\\f12b\";\n$fa-var-support: \"\\f1cd\";\n$fa-var-table: \"\\f0ce\";\n$fa-var-tablet: \"\\f10a\";\n$fa-var-tachometer: \"\\f0e4\";\n$fa-var-tag: \"\\f02b\";\n$fa-var-tags: \"\\f02c\";\n$fa-var-tasks: \"\\f0ae\";\n$fa-var-taxi: \"\\f1ba\";\n$fa-var-tencent-weibo: \"\\f1d5\";\n$fa-var-terminal: \"\\f120\";\n$fa-var-text-height: \"\\f034\";\n$fa-var-text-width: \"\\f035\";\n$fa-var-th: \"\\f00a\";\n$fa-var-th-large: \"\\f009\";\n$fa-var-th-list: \"\\f00b\";\n$fa-var-thumb-tack: \"\\f08d\";\n$fa-var-thumbs-down: \"\\f165\";\n$fa-var-thumbs-o-down: \"\\f088\";\n$fa-var-thumbs-o-up: \"\\f087\";\n$fa-var-thumbs-up: \"\\f164\";\n$fa-var-ticket: \"\\f145\";\n$fa-var-times: \"\\f00d\";\n$fa-var-times-circle: \"\\f057\";\n$fa-var-times-circle-o: \"\\f05c\";\n$fa-var-tint: \"\\f043\";\n$fa-var-toggle-down: \"\\f150\";\n$fa-var-toggle-left: \"\\f191\";\n$fa-var-toggle-right: \"\\f152\";\n$fa-var-toggle-up: \"\\f151\";\n$fa-var-trash-o: \"\\f014\";\n$fa-var-tree: \"\\f1bb\";\n$fa-var-trello: \"\\f181\";\n$fa-var-trophy: \"\\f091\";\n$fa-var-truck: \"\\f0d1\";\n$fa-var-try: \"\\f195\";\n$fa-var-tumblr: \"\\f173\";\n$fa-var-tumblr-square: \"\\f174\";\n$fa-var-turkish-lira: \"\\f195\";\n$fa-var-twitter: \"\\f099\";\n$fa-var-twitter-square: \"\\f081\";\n$fa-var-umbrella: \"\\f0e9\";\n$fa-var-underline: \"\\f0cd\";\n$fa-var-undo: \"\\f0e2\";\n$fa-var-university: \"\\f19c\";\n$fa-var-unlink: \"\\f127\";\n$fa-var-unlock: \"\\f09c\";\n$fa-var-unlock-alt: \"\\f13e\";\n$fa-var-unsorted: \"\\f0dc\";\n$fa-var-upload: \"\\f093\";\n$fa-var-usd: \"\\f155\";\n$fa-var-user: \"\\f007\";\n$fa-var-user-md: \"\\f0f0\";\n$fa-var-users: \"\\f0c0\";\n$fa-var-video-camera: \"\\f03d\";\n$fa-var-vimeo-square: \"\\f194\";\n$fa-var-vine: \"\\f1ca\";\n$fa-var-vk: \"\\f189\";\n$fa-var-volume-down: \"\\f027\";\n$fa-var-volume-off: \"\\f026\";\n$fa-var-volume-up: \"\\f028\";\n$fa-var-warning: \"\\f071\";\n$fa-var-wechat: \"\\f1d7\";\n$fa-var-weibo: \"\\f18a\";\n$fa-var-weixin: \"\\f1d7\";\n$fa-var-wheelchair: \"\\f193\";\n$fa-var-windows: \"\\f17a\";\n$fa-var-won: \"\\f159\";\n$fa-var-wordpress: \"\\f19a\";\n$fa-var-wrench: \"\\f0ad\";\n$fa-var-xing: \"\\f168\";\n$fa-var-xing-square: \"\\f169\";\n$fa-var-yahoo: \"\\f19e\";\n$fa-var-yen: \"\\f157\";\n$fa-var-youtube: \"\\f167\";\n$fa-var-youtube-play: \"\\f16a\";\n$fa-var-youtube-square: \"\\f166\";\n\n"
  },
  {
    "path": "website/_sass/vendor/font-awesome/font-awesome.scss",
    "content": "/*!\n *  Font Awesome 4.1.0 by @davegandy - http://fontawesome.io - @fontawesome\n *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)\n */\n\n@import \"variables\";\n@import \"mixins\";\n@import \"path\";\n@import \"core\";\n@import \"larger\";\n@import \"fixed-width\";\n@import \"list\";\n@import \"bordered-pulled\";\n@import \"spinning\";\n@import \"rotated-flipped\";\n@import \"stacked\";\n@import \"icons\";\n"
  },
  {
    "path": "website/_sass/vendor/magnific-popup/_settings.scss",
    "content": "////////////////////////\n//      Settings      //\n////////////////////////\n\n// overlay\n$mfp-overlay-color:                   $black;                     // Color of overlay screen\n$mfp-overlay-opacity:                 0.8;                        // Opacity of overlay screen\n$mfp-shadow:                          0 0 0 rgba(0, 0, 0, 0);     // Shadow on image or iframe\n\n// spacing\n$mfp-popup-padding-left:              8px;                        // Padding from left and from right side\n$mfp-popup-padding-left-mobile:       6px;                        // Same as above, but is applied when width of window is less than 800px\n\n$mfp-z-index-base:                    1040;                       // Base z-index of popup\n\n// controls\n$mfp-include-arrows:                  true;                       // Include styles for nav arrows\n$mfp-controls-opacity:                1;                          // Opacity of controls\n$mfp-controls-color:                  $white;                     // Color of controls\n$mfp-controls-border-color:           $white;                     // Border color of controls\n$mfp-inner-close-icon-color:          $white;                     // Color of close button when inside\n$mfp-controls-text-color:             #CCC;                       // Color of preloader and \"1 of X\" indicator\n$mfp-controls-text-color-hover:       $white;                     // Hover color of preloader and \"1 of X\" indicator\n$mfp-IE7support:                      true;                       // Very basic IE7 support\n\n// Iframe-type options\n$mfp-include-iframe-type:             true;                       // Enable Iframe-type popups\n$mfp-iframe-padding-top:              40px;                       // Iframe padding top\n$mfp-iframe-background:               #000;                       // Background color of iframes\n$mfp-iframe-max-width:                900px;                      // Maximum width of iframes\n$mfp-iframe-ratio:                    9/16;                       // Ratio of iframe (9/16 = widescreen, 3/4 = standard, etc.)\n\n// Image-type options\n$mfp-include-image-type:              true;                       // Enable Image-type popups\n$mfp-image-background:                transparent;\n$mfp-image-padding-top:               40px;                       // Image padding top\n$mfp-image-padding-bottom:            40px;                       // Image padding bottom\n$mfp-include-mobile-layout-for-image: true;                       // Removes paddings from top and bottom\n\n// Image caption options\n$mfp-caption-title-color:             #F3F3F3;                    // Caption title color\n$mfp-caption-subtitle-color:          #BDBDBD;                    // Caption subtitle color\n.mfp-counter { font-family: $alt-font; }                          // Caption font family\n\n// A11y\n$mfp-use-visuallyhidden:              false; "
  },
  {
    "path": "website/_sass/vendor/magnific-popup/magnific-popup.scss",
    "content": "/* Magnific Popup CSS */\n\n@import \"settings\";\n\n////////////////////////\n//\n// Contents:\n//\n// 1. Default Settings\n// 2. General styles\n//    - Transluscent overlay\n//    - Containers, wrappers\n//    - Cursors\n//    - Helper classes\n// 3. Appearance\n//    - Preloader & text that displays error messages\n//    - CSS reset for buttons\n//    - Close icon\n//    - \"1 of X\" counter\n//    - Navigation (left/right) arrows\n//    - Iframe content type styles\n//    - Image content type styles\n//    - Media query where size of arrows is reduced\n//    - IE7 support\n//\n////////////////////////\n\n\n\n////////////////////////\n// 1. Default Settings\n////////////////////////\n\n$mfp-overlay-color:                   #0b0b0b !default;\n$mfp-overlay-opacity:                 0.8 !default;\n$mfp-shadow:                          0 0 8px rgba(0, 0, 0, 0.6) !default; // shadow on image or iframe\n$mfp-popup-padding-left:              8px !default; // Padding from left and from right side\n$mfp-popup-padding-left-mobile:       6px !default; // Same as above, but is applied when width of window is less than 800px\n\n$mfp-z-index-base:                    1040 !default; // Base z-index of popup\n$mfp-include-arrows:                  true !default; // include styles for nav arrows\n$mfp-controls-opacity:                0.65 !default;\n$mfp-controls-color:                  #FFF !default;\n$mfp-controls-border-color:           #3F3F3F !default;\n$mfp-inner-close-icon-color:          #333 !default;\n$mfp-controls-text-color:             #CCC !default; // Color of preloader and \"1 of X\" indicator\n$mfp-controls-text-color-hover:       #FFF !default;\n$mfp-IE7support:                      true !default; // Very basic IE7 support\n\n// Iframe-type options\n$mfp-include-iframe-type:             true !default;\n$mfp-iframe-padding-top:              40px !default;\n$mfp-iframe-background:               #000 !default;\n$mfp-iframe-max-width:                900px !default;\n$mfp-iframe-ratio:                    9/16 !default;\n\n// Image-type options\n$mfp-include-image-type:              true !default;\n$mfp-image-background:                #444 !default;\n$mfp-image-padding-top:               40px !default;\n$mfp-image-padding-bottom:            40px !default;\n$mfp-include-mobile-layout-for-image: true !default; // Removes paddings from top and bottom\n\n// Image caption options\n$mfp-caption-title-color:             #F3F3F3 !default;\n$mfp-caption-subtitle-color:          #BDBDBD !default;\n\n// A11y\n$mfp-use-visuallyhidden:              false !default; // Hide content from browsers, but make it available for screen readers \n\n\n\n////////////////////////\n// 2. General styles\n////////////////////////\n\n// Transluscent overlay\n.mfp-bg {\n  top: 0;\n  left: 0;\n  width: 100%;\n  height: 100%;\n  z-index: $mfp-z-index-base + 2;\n  overflow: hidden;\n  position: fixed;\n\n  background: $mfp-overlay-color;\n  opacity: $mfp-overlay-opacity;\n  @if $mfp-IE7support {\n    filter: unquote(\"alpha(opacity=#{$mfp-overlay-opacity*100})\");\n  }\n}\n\n// Wrapper for popup\n.mfp-wrap {\n  top: 0;\n  left: 0;\n  width: 100%;\n  height: 100%;\n  z-index: $mfp-z-index-base + 3;\n  position: fixed;\n  outline: none !important;\n  -webkit-backface-visibility: hidden; // fixes webkit bug that can cause \"false\" scrollbar\n}\n\n// Root container\n.mfp-container {\n  text-align: center;\n  position: absolute;\n  width: 100%;\n  height: 100%;\n  left: 0;\n  top: 0;\n  padding: 0 $mfp-popup-padding-left;\n  -webkit-box-sizing: border-box;\n  -moz-box-sizing: border-box;\n  box-sizing: border-box;\n}\n\n// Vertical centerer helper\n.mfp-container {\n  &:before {\n    content: '';\n    display: inline-block;\n    height: 100%;\n    vertical-align: middle;\n  }\n}\n\n// Remove vertical centering when popup has class `mfp-align-top`\n.mfp-align-top {\n  .mfp-container { \n    &:before {\n      display: none;\n    }\n  }\n}\n\n// Popup content holder\n.mfp-content {\n  position: relative;\n  display: inline-block;\n  vertical-align: middle;\n  margin: 0 auto;\n  text-align: left;\n  z-index: $mfp-z-index-base + 5;\n}\n.mfp-inline-holder,\n.mfp-ajax-holder {\n  .mfp-content {\n    width: 100%;\n    cursor: auto;\n  }\n}\n\n// Cursors\n.mfp-ajax-cur {\n  cursor: progress;\n}\n.mfp-zoom-out-cur {\n  &, .mfp-image-holder .mfp-close {\n    cursor: -moz-zoom-out;\n    cursor: -webkit-zoom-out;\n    cursor: zoom-out;\n  }\n}\n.mfp-zoom {\n  cursor: pointer;\n  cursor: -webkit-zoom-in;\n  cursor: -moz-zoom-in;\n  cursor: zoom-in;\n}\n.mfp-auto-cursor {\n  .mfp-content {\n    cursor: auto;\n  }\n}\n\n.mfp-close,\n.mfp-arrow,\n.mfp-preloader,\n.mfp-counter {\n  -webkit-user-select:none;\n  -moz-user-select: none;\n  user-select: none;\n}\n\n// Hide the image during the loading\n.mfp-loading {\n  &.mfp-figure {\n    display: none;\n  }\n}\n\n// Helper class that hides stuff\n@if $mfp-use-visuallyhidden {\n  // From HTML5 Boilerplate https://github.com/h5bp/html5-boilerplate/blob/v4.2.0/doc/css.md#visuallyhidden\n  .mfp-hide {\n    border: 0 !important;\n    clip: rect(0 0 0 0) !important;\n    height: 1px !important;\n    margin: -1px !important;\n    overflow: hidden !important;\n    padding: 0 !important;\n    position: absolute !important;\n    width: 1px !important;\n  }\n} @else {\n  .mfp-hide {\n    display: none !important;\n  }\n}\n\n\n////////////////////////\n// 3. Appearance\n////////////////////////\n\n// Preloader and text that displays error messages\n.mfp-preloader {\n  color: $mfp-controls-text-color;\n  position: absolute;\n  top: 50%;\n  width: auto;\n  text-align: center;\n  margin-top: -0.8em;\n  left: 8px;\n  right: 8px;\n  z-index: $mfp-z-index-base + 4;\n  a {\n    color: $mfp-controls-text-color;\n    &:hover {\n      color: $mfp-controls-text-color-hover;\n    }\n  }\n}\n\n// Hide preloader when content successfully loaded\n.mfp-s-ready {\n  .mfp-preloader {\n    display: none;\n  }\n}\n\n// Hide content when it was not loaded\n.mfp-s-error {\n  .mfp-content {\n    display: none;\n  }\n}\n\n// CSS-reset for buttons\nbutton {\n  &.mfp-close,\n  &.mfp-arrow {\n    overflow: visible;\n    cursor: pointer;\n    background: transparent;\n    border: 0;\n    -webkit-appearance: none;\n    display: block;\n    outline: none;\n    padding: 0;\n    z-index: $mfp-z-index-base + 6;\n    -webkit-box-shadow: none;\n    box-shadow: none;\n  }\n  &::-moz-focus-inner {\n      padding: 0;\n      border: 0\n  }\n}\n\n\n// Close icon\n.mfp-close {\n  width: 44px;\n  height: 44px;\n  line-height: 44px;\n\n  position: absolute;\n  right: 0;\n  top: 0;\n  text-decoration: none;\n  text-align: center;\n  opacity: $mfp-controls-opacity;\n  @if $mfp-IE7support {\n    filter: unquote(\"alpha(opacity=#{$mfp-controls-opacity*100})\");\n  }\n  padding: 0 0 18px 10px;\n  color: $mfp-controls-color;\n\n  font-style: normal;\n  font-size: 28px;\n  font-family: Arial, Baskerville, monospace;\n\n  &:hover,\n  &:focus {\n    opacity: 1;\n    @if $mfp-IE7support {\n      filter: unquote(\"alpha(opacity=#{1*100})\");\n    }\n  }\n\n  &:active {\n    top: 1px;\n  }\n}\n.mfp-close-btn-in {\n  .mfp-close {\n    color: $mfp-inner-close-icon-color;\n  }\n}\n.mfp-image-holder,\n.mfp-iframe-holder {\n  .mfp-close {\n    color: $mfp-controls-color;\n    right: -6px;\n    text-align: right;\n    padding-right: 6px;\n    width: 100%;\n  }\n}\n\n// \"1 of X\" counter\n.mfp-counter {\n  position: absolute;\n  top: 0;\n  right: 0;\n  color: $mfp-controls-text-color;\n  font-size: 12px;\n  line-height: 18px;\n}\n\n// Navigation arrows\n@if $mfp-include-arrows {\n  .mfp-arrow {\n    position: absolute;\n    opacity: $mfp-controls-opacity;\n    @if $mfp-IE7support {\n      filter: unquote(\"alpha(opacity=#{$mfp-controls-opacity*100})\");\n    }\n    margin: 0;\n    top: 50%;\n    margin-top: -55px;\n    padding: 0;\n    width: 90px;\n    height: 110px;\n    -webkit-tap-highlight-color: rgba(0,0,0,0);\n    &:active {\n      margin-top: -54px;\n    }\n    &:hover,\n    &:focus {\n      opacity: 1;\n      @if $mfp-IE7support {\n        filter: unquote(\"alpha(opacity=#{1*100})\");\n      }\n    }\n    &:before,\n    &:after,\n    .mfp-b,\n    .mfp-a {\n      content: '';\n      display: block;\n      width: 0;\n      height: 0;\n      position: absolute;\n      left: 0;\n      top: 0;\n      margin-top: 35px;\n      margin-left: 35px;\n      border: medium inset transparent;\n    }\n\n    &:after,\n    .mfp-a {\n\n      border-top-width: 13px;\n      border-bottom-width: 13px;\n      top:8px;\n    }\n\n    &:before,\n    .mfp-b {\n      border-top-width: 21px;\n      border-bottom-width: 21px;\n      opacity: 0.7;\n    }\n\n  }\n\n  .mfp-arrow-left {\n    left: 0;\n\n    &:after,\n    .mfp-a {\n      border-right: 17px solid $mfp-controls-color;\n      margin-left: 31px;\n    }\n    &:before,\n    .mfp-b {\n      margin-left: 25px;\n      border-right: 27px solid $mfp-controls-border-color; \n    }\n  }\n\n  .mfp-arrow-right {\n    right: 0;\n    &:after,\n    .mfp-a {\n      border-left: 17px solid $mfp-controls-color;\n      margin-left: 39px\n    }\n    &:before,\n    .mfp-b {\n      border-left: 27px solid $mfp-controls-border-color;\n    }\n  }\n}\n\n\n\n// Iframe content type\n@if $mfp-include-iframe-type {\n  .mfp-iframe-holder {\n    padding-top: $mfp-iframe-padding-top;\n    padding-bottom: $mfp-iframe-padding-top;\n    .mfp-content {\n      line-height: 0;\n      width: 100%;\n      max-width: $mfp-iframe-max-width;\n    }\n    .mfp-close {\n      top: -40px;\n    }\n  }\n  .mfp-iframe-scaler {\n    width: 100%;\n    height: 0;\n    overflow: hidden;\n    padding-top: $mfp-iframe-ratio * 100%;\n    iframe {\n      position: absolute;\n      display: block;\n      top: 0;\n      left: 0;\n      width: 100%;\n      height: 100%;\n      box-shadow: $mfp-shadow;\n      background: $mfp-iframe-background;\n    }\n  }\n}\n\n\n\n// Image content type\n@if $mfp-include-image-type {\n\n  /* Main image in popup */\n  img {\n    &.mfp-img {\n      width: auto;\n      max-width: 100%;\n      height: auto;\n      display: block;\n      line-height: 0;\n      -webkit-box-sizing: border-box;\n      -moz-box-sizing: border-box;\n      box-sizing: border-box;\n      padding: $mfp-image-padding-top 0 $mfp-image-padding-bottom;\n      margin: 0 auto;\n    }\n  }\n\n  /* The shadow behind the image */\n  .mfp-figure {\n    line-height: 0;\n    &:after {\n      content: '';\n      position: absolute;\n      left: 0;\n      top: $mfp-image-padding-top;\n      bottom: $mfp-image-padding-bottom;\n      display: block;\n      right: 0;\n      width: auto;\n      height: auto;\n      z-index: -1;\n      box-shadow: $mfp-shadow;\n      background: $mfp-image-background;\n    }\n    small {\n      color: $mfp-caption-subtitle-color;\n      display: block;\n      font-size: 12px;\n      line-height: 14px;\n    }\n    figure {\n      margin: 0;\n    }\n  }\n  .mfp-bottom-bar {\n    margin-top: -$mfp-image-padding-bottom + 4;\n    position: absolute;\n    top: 100%;\n    left: 0;\n    width: 100%;\n    cursor: auto;\n  }\n  .mfp-title {\n    text-align: left;\n    line-height: 18px;\n    color: $mfp-caption-title-color;\n    word-wrap: break-word;\n    padding-right: 36px; // leave some space for counter at right side\n  }\n\n  .mfp-image-holder {\n    .mfp-content {\n      max-width: 100%;\n    }\n  }\n\n  .mfp-gallery {\n    .mfp-image-holder {\n      .mfp-figure {\n        cursor: pointer;\n      }\n    }\n  }\n\n\n  @if $mfp-include-mobile-layout-for-image {\n    @media screen and (max-width: 800px) and (orientation:landscape), screen and (max-height: 300px) {\n      /**\n       * Remove all paddings around the image on small screen\n       */\n      .mfp-img-mobile {\n        .mfp-image-holder {\n          padding-left: 0;\n          padding-right: 0;\n        }\n        img {\n          &.mfp-img {\n            padding: 0;\n          }\n        }\n        .mfp-figure {\n          // The shadow behind the image\n          &:after {\n            top: 0;\n            bottom: 0;\n          }\n          small {\n            display: inline;\n            margin-left: 5px;\n          }\n        }\n        .mfp-bottom-bar {\n          background: rgba(0,0,0,0.6);\n          bottom: 0;\n          margin: 0;\n          top: auto;\n          padding: 3px 5px;\n          position: fixed;\n          -webkit-box-sizing: border-box;\n          -moz-box-sizing: border-box;\n          box-sizing: border-box;\n          &:empty {\n            padding: 0;\n          }\n        }\n        .mfp-counter {\n          right: 5px;\n          top: 3px;\n        }\n        .mfp-close {\n          top: 0;\n          right: 0;\n          width: 35px;\n          height: 35px;\n          line-height: 35px;\n          background: rgba(0, 0, 0, 0.6);\n          position: fixed;\n          text-align: center;\n          padding: 0;\n        }\n      }\n    }\n  }\n}\n\n\n\n// Scale navigation arrows and reduce padding from sides\n@media all and (max-width: 900px) {\n  .mfp-arrow {\n    -webkit-transform: scale(0.75);\n    transform: scale(0.75);\n  }\n  .mfp-arrow-left {\n    -webkit-transform-origin: 0;\n    transform-origin: 0;\n  }\n  .mfp-arrow-right {\n    -webkit-transform-origin: 100%;\n    transform-origin: 100%;\n  }\n  .mfp-container {\n    padding-left: $mfp-popup-padding-left-mobile;\n    padding-right: $mfp-popup-padding-left-mobile;\n  }\n}\n\n\n\n// IE7 support\n// Styles that make popup look nicier in old IE\n@if $mfp-IE7support {\n  .mfp-ie7 {\n    .mfp-img {\n      padding: 0;\n    }\n    .mfp-bottom-bar {\n      width: 600px;\n      left: 50%;\n      margin-left: -300px;\n      margin-top: 5px;\n      padding-bottom: 5px;\n    }\n    .mfp-container {\n      padding: 0;\n    }\n    .mfp-content {\n      padding-top: 44px;\n    }\n    .mfp-close {\n      top: 0;\n      right: 0;\n      padding-top: 0;\n    }\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/_neat-helpers.scss",
    "content": "// Functions\n@import \"functions/private\";\n@import \"functions/new-breakpoint\";\n\n// Settings\n@import \"settings/grid\";\n@import \"settings/visual-grid\";\n"
  },
  {
    "path": "website/_sass/vendor/neat/_neat.scss",
    "content": "// Bourbon Neat 1.6.0.pre\n// MIT Licensed\n// Copyright (c) 2012-2013 thoughtbot, inc.\n\n// Helpers\n@import \"neat-helpers\";\n\n// Grid\n@import \"grid/private\";\n@import \"grid/reset\";\n@import \"grid/grid\";\n@import \"grid/omega\";\n@import \"grid/outer-container\";\n@import \"grid/span-columns\";\n@import \"grid/row\";\n@import \"grid/shift\";\n@import \"grid/pad\";\n@import \"grid/fill-parent\";\n@import \"grid/media\";\n@import \"grid/to-deprecate\";\n@import \"grid/visual-grid\";\n"
  },
  {
    "path": "website/_sass/vendor/neat/functions/_new-breakpoint.scss",
    "content": "@function new-breakpoint($query:$feature $value $columns, $total-columns: $grid-columns) {\n\n  @if length($query) == 1 {\n    $query: $default-feature nth($query, 1) $total-columns;\n  }\n\n  @else if length($query) % 2 == 0 {\n    $query: append($query, $total-columns);\n  }\n\n  @if not belongs-to($query, $visual-grid-breakpoints) {\n    $visual-grid-breakpoints: append($visual-grid-breakpoints, $query, comma) !global;\n  }\n\n  @return $query;\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/functions/_private.scss",
    "content": "// Checks if a number is even\n@function is-even($int) {\n  @if $int%2 == 0   {\n    @return true;\n  }\n\n  @return false;\n}\n\n// Checks if an element belongs to a list\n@function belongs-to($tested-item, $list) {\n  @each $item in $list {\n    @if $item == $tested-item {\n      @return true;\n    }\n  }\n\n  @return false;\n}\n\n// Contains display value\n@function contains-display-value($query) {\n  @if belongs-to(table, $query) or belongs-to(block, $query) or belongs-to(inline-block, $query) or belongs-to(inline, $query) {\n    @return true;\n  }\n\n  @return false;\n}\n\n// Parses the first argument of span-columns()\n@function container-span($span: $span) {\n  @if length($span) == 3 {\n    $container-columns: nth($span, 3);\n    @return $container-columns;\n  }\n\n  @else if length($span) == 2 {\n    $container-columns: nth($span, 2);\n    @return $container-columns;\n  }\n\n  @else {\n    @return $grid-columns;\n  }\n}\n\n@function container-shift($shift: $shift) {\n  $parent-columns: $grid-columns !global !default;\n\n  @if length($shift) == 3 {\n    $container-columns: nth($shift, 3);\n    @return $container-columns;\n  }\n\n  @else if length($shift) == 2 {\n    $container-columns: nth($shift, 2);\n    @return $container-columns;\n  }\n\n  @else {\n    @return $parent-columns;\n  }\n}\n\n// Generates a striped background\n@function gradient-stops($grid-columns, $color: $visual-grid-color) {\n  $transparent: rgba(0,0,0,0);\n\n  $column-width: flex-grid(1, $grid-columns);\n  $gutter-width: flex-gutter($grid-columns);\n  $column-offset: $column-width;\n\n  $values: ($transparent 0, $color 0);\n\n  @for $i from 1 to $grid-columns*2 {\n    @if is-even($i) {\n      $values: append($values, $transparent $column-offset, comma);\n      $values: append($values, $color $column-offset, comma);\n      $column-offset: $column-offset + $column-width;\n    }\n\n    @else {\n      $values: append($values, $color $column-offset, comma);\n      $values: append($values, $transparent $column-offset, comma);\n      $column-offset: $column-offset + $gutter-width;\n    }\n  }\n\n  @return $values;\n}\n\n// Layout direction\n@function get-direction($layout, $default) {\n  $direction: nil;\n  \n  @if $layout == LTR or $layout == RTL {\n    $direction: direction-from-layout($layout);\n  } @else {\n    $direction: direction-from-layout($default);\n  }\n\n  @return $direction;\n}\n\n@function direction-from-layout($layout) {\n  $direction: nil;\n\n  @if $layout == LTR {\n    $direction: right;\n  } @else {\n    $direction: left;\n  }\n\n  @return $direction;\n}\n\n@function get-opposite-direction($direction) {\n  $opposite-direction: left;\n\n  @if $direction == left {\n    $opposite-direction: right;\n  }\n\n  @return $opposite-direction;\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_fill-parent.scss",
    "content": "@mixin fill-parent() {\n  width: 100%;\n\n  @if $border-box-sizing == false {\n    @include box-sizing(border-box);\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_grid.scss",
    "content": "@if $border-box-sizing == true {\n  * {\n    @include box-sizing(border-box);\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_media.scss",
    "content": "@mixin media($query:$feature $value $columns, $total-columns: $grid-columns) {\n  @if length($query) == 1 {\n    @media screen and ($default-feature: nth($query, 1)) {\n      $default-grid-columns: $grid-columns;\n      $grid-columns: $total-columns !global;\n      @content;\n      $grid-columns: $default-grid-columns !global;\n    }\n  }\n\n  @else {\n    $loopTo: length($query);\n    $mediaQuery: 'screen and ';\n    $default-grid-columns: $grid-columns;\n    $grid-columns: $total-columns !global;\n\n    @if length($query) % 2 != 0 {\n      $grid-columns: nth($query, $loopTo) !global;\n      $loopTo: $loopTo - 1;\n    }\n\n    $i: 1;\n    @while $i <= $loopTo {\n      $mediaQuery: $mediaQuery + '(' + nth($query, $i) + ': ' + nth($query, $i + 1) + ') ';\n\n      @if ($i + 1) != $loopTo {\n        $mediaQuery: $mediaQuery + 'and ';\n      }\n\n      $i: $i + 2;\n    }\n\n    @media #{$mediaQuery} {\n      @content;\n      $grid-columns: $default-grid-columns !global;\n    }\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_omega.scss",
    "content": "// Remove last element gutter\n@mixin omega($query: block, $direction: default) {\n  $table: if(belongs-to(table, $query), true, false);\n  $auto: if(belongs-to(auto, $query), true, false);\n\n  @if $direction != default {\n    @warn \"The omega mixin will no longer take a $direction argument. To change the layout direction, use row($direction) or set $default-layout-direction instead.\"\n  } @else {\n    $direction: get-direction($layout-direction, $default-layout-direction);\n  }\n\n  @if $table {\n    @warn \"The omega mixin no longer removes padding in table layouts.\"\n  }\n\n  @if length($query) == 1 {\n    @if $auto {\n      &:last-child {\n        margin-#{$direction}: 0;\n      }\n    }\n\n    @else if contains-display-value($query) and $table == false {\n      margin-#{$direction}: 0;\n    }\n\n    @else {\n      @include nth-child($query, $direction);\n    }\n  }\n\n  @else if length($query) == 2 {\n    @if $auto {\n      &:last-child {\n        margin-#{$direction}: 0;\n      }\n    }\n\n    @else {\n      @include nth-child(nth($query, 1), $direction);\n    }\n  }\n\n  @else {\n    @warn \"Too many arguments passed to the omega() mixin.\"\n  }\n}\n\n@mixin nth-child($query, $direction) {\n  $opposite-direction: get-opposite-direction($direction);\n\n  &:nth-child(#{$query}) {\n    margin-#{$direction}: 0;\n  }\n\n  @if type-of($query) == number {\n    &:nth-child(#{$query}+1) {\n      clear: $opposite-direction;\n    }\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_outer-container.scss",
    "content": "@mixin outer-container {\n  @include clearfix;\n  max-width: $max-width;\n  margin: {\n    left: auto;\n    right: auto;\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_pad.scss",
    "content": "@mixin pad($padding: flex-gutter()) {\n  $padding-list: null;\n  @each $value in $padding {\n    $value: if($value == 'default', flex-gutter(), $value);\n    $padding-list: join($padding-list, $value);\n  }\n  padding: $padding-list;\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_private.scss",
    "content": "$parent-columns: $grid-columns !default;\n$fg-column: $column;\n$fg-gutter: $gutter;\n$fg-max-columns: $grid-columns;\n$container-display-table: false !default;\n$layout-direction: nil !default;\n\n@function flex-grid($columns, $container-columns: $fg-max-columns) {\n  $width: $columns * $fg-column + ($columns - 1) * $fg-gutter;\n  $container-width: $container-columns * $fg-column + ($container-columns - 1) * $fg-gutter;\n  @return percentage($width / $container-width);\n}\n\n@function flex-gutter($container-columns: $fg-max-columns, $gutter: $fg-gutter) {\n  $container-width: $container-columns * $fg-column + ($container-columns - 1) * $fg-gutter;\n  @return percentage($gutter / $container-width);\n}\n\n@function grid-width($n) {\n  @return $n * $gw-column + ($n - 1) * $gw-gutter;\n}\n\n@function get-parent-columns($columns) {\n  @if $columns != $grid-columns {\n    $parent-columns: $columns !global;\n  } @else {\n    $parent-columns: $grid-columns !global;\n  }\n\n  @return $parent-columns;\n}\n\n@function is-display-table($container-is-display-table, $display) {\n  $display-table: false;\n\n  @if $container-is-display-table == true {\n    $display-table: true;\n  } @else if $display == table {\n    $display-table: true;\n  }\n\n  @return $display-table;\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_reset.scss",
    "content": "@mixin reset-display {\n  $container-display-table: false !global;\n}\n\n@mixin reset-layout-direction {\n  $layout-direction: $default-layout-direction !global;\n}\n\n@mixin reset-all {\n  @include reset-display;\n  @include reset-layout-direction;\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_row.scss",
    "content": "@mixin row($display: block, $direction: $default-layout-direction) {\n  @include clearfix;\n  $layout-direction: $direction !global;\n\n  @if $display == table {\n    display: table;\n    @include fill-parent;\n    table-layout: fixed;\n    $container-display-table: true !global;\n  }\n\n  @else {\n    display: block;\n    $container-display-table: false !global;\n  }\n}\n\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_shift.scss",
    "content": "@mixin shift($n-columns: 1) {\n  @include shift-in-context($n-columns);\n}\n\n@mixin shift-in-context($shift: $columns of $container-columns) {\n  $n-columns: nth($shift, 1);\n  $parent-columns: container-shift($shift) !global;\n\n  $direction: get-direction($layout-direction, $default-layout-direction);\n  $opposite-direction: get-opposite-direction($direction);\n\n  margin-#{$opposite-direction}: $n-columns * flex-grid(1, $parent-columns) + $n-columns * flex-gutter($parent-columns);\n\n  // Reset nesting context\n  $parent-columns: $grid-columns !global;\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_span-columns.scss",
    "content": "@mixin span-columns($span: $columns of $container-columns, $display: block) {\n  $columns: nth($span, 1);\n  $container-columns: container-span($span);\n\n  // Set nesting context (used by shift())\n  $parent-columns: get-parent-columns($container-columns) !global;\n\n  $direction: get-direction($layout-direction, $default-layout-direction);\n  $opposite-direction: get-opposite-direction($direction);\n\n  $display-table: is-display-table($container-display-table, $display);\n\n  @if $display-table  {\n    display: table-cell;\n    width: percentage($columns / $container-columns);\n  } @else {\n    float: #{$opposite-direction};\n\n    @if $display != no-display {\n      display: block;\n    }\n\n    @if $display == collapse {\n      @warn \"The 'collapse' argument will be deprecated. Use 'block-collapse' instead.\"\n    }\n\n    @if $display == collapse or $display == block-collapse {\n      width: flex-grid($columns, $container-columns) + flex-gutter($container-columns);\n\n      &:last-child {\n        width: flex-grid($columns, $container-columns);\n      }\n\n    } @else {\n      margin-#{$direction}: flex-gutter($container-columns);\n      width: flex-grid($columns, $container-columns);\n\n      &:last-child {\n        margin-#{$direction}: 0;\n      }\n    }\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_to-deprecate.scss",
    "content": "@mixin breakpoint($query:$feature $value $columns, $total-columns: $grid-columns) {\n  @warn \"The breakpoint() mixin was renamed to media() in Neat 1.0. Please update your project with the new syntax before the next version bump.\";\n\n  @if length($query) == 1 {\n    @media screen and ($default-feature: nth($query, 1)) {\n      $default-grid-columns: $grid-columns;\n      $grid-columns: $total-columns;\n      @content;\n      $grid-columns: $default-grid-columns;\n    }\n  }\n\n  @else if length($query) == 2 {\n    @media screen and (nth($query, 1): nth($query, 2)) {\n      $default-grid-columns: $grid-columns;\n      $grid-columns: $total-columns;\n      @content;\n      $grid-columns: $default-grid-columns;\n    }\n  }\n\n  @else if length($query) == 3 {\n    @media screen and (nth($query, 1): nth($query, 2)) {\n      $default-grid-columns: $grid-columns;\n      $grid-columns: nth($query, 3);\n      @content;\n      $grid-columns: $default-grid-columns;\n    }\n  }\n\n  @else if length($query) == 4 {\n    @media screen and (nth($query, 1): nth($query, 2)) and (nth($query, 3): nth($query, 4)) {\n      $default-grid-columns: $grid-columns;\n      $grid-columns: $total-columns;\n      @content;\n      $grid-columns: $default-grid-columns;\n    }\n  }\n\n  @else if length($query) == 5 {\n    @media screen and (nth($query, 1): nth($query, 2)) and (nth($query, 3): nth($query, 4)) {\n      $default-grid-columns: $grid-columns;\n      $grid-columns: nth($query, 5);\n      @content;\n      $grid-columns: $default-grid-columns;\n    }\n  }\n\n  @else {\n    @warn \"Wrong number of arguments for breakpoint(). Read the documentation for more details.\";\n  }\n}\n\n@mixin nth-omega($nth, $display: block, $direction: default) {\n  @warn \"The nth-omega() mixin is deprecated. Please use omega() instead.\";\n  @include omega($nth $display, $direction);\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/grid/_visual-grid.scss",
    "content": "@mixin grid-column-gradient($values...) {\n  background-image: deprecated-webkit-gradient(linear, left top, left bottom, $values);\n  background-image:  -webkit-linear-gradient(left, $values);\n  background-image:     -moz-linear-gradient(left, $values);\n  background-image:      -ms-linear-gradient(left, $values);\n  background-image:       -o-linear-gradient(left, $values);\n  background-image: unquote(\"linear-gradient(left, #{$values})\");\n}\n\n@if $visual-grid == true or $visual-grid == yes {\n  body:before {\n    content: '';\n    display: inline-block;\n    @include grid-column-gradient(gradient-stops($grid-columns));\n    height: 100%;\n    left: 0;\n    margin: 0 auto;\n    max-width: $max-width;\n    opacity: $visual-grid-opacity;\n    position: fixed;\n    right: 0;\n    width: 100%;\n    pointer-events: none;\n\n    @if $visual-grid-index == back {\n      z-index: -1;\n    }\n\n    @else if $visual-grid-index == front {\n      z-index: 9999;\n    }\n\n    @each $breakpoint in $visual-grid-breakpoints {\n      @if $breakpoint != nil {\n        @include media($breakpoint) {\n          @include grid-column-gradient(gradient-stops($grid-columns));\n        }\n      }\n    }\n  }\n}\n"
  },
  {
    "path": "website/_sass/vendor/neat/settings/_grid.scss",
    "content": "$column: golden-ratio(1em, 3) !default; // Column width\n$gutter: golden-ratio(1em, 1) !default; // Gutter between each two columns\n$grid-columns: 12 !default; // Total number of columns in the grid\n$max-width: em(1088) !default; // Max-width of the outer container\n$border-box-sizing: true !default; // Makes all elements have a border-box layout\n$default-feature: min-width; // Default @media feature for the breakpoint() mixin\n$default-layout-direction: LTR !default;\n"
  },
  {
    "path": "website/_sass/vendor/neat/settings/_visual-grid.scss",
    "content": "$visual-grid: false !default; // Display the base grid\n$visual-grid-color: #EEE !default;\n$visual-grid-index: back !default; // Show grid behind content (back) or overlay it over the content (front)\n$visual-grid-opacity: 0.4 !default;\n$visual-grid-breakpoints: () !default;\n"
  },
  {
    "path": "website/_templates/archive",
    "content": "---\nlayout: post-index\ntitle: {{ title }}\npermalink:\nmodified:\nexcerpt:\nimage:\n  feature:\n---"
  },
  {
    "path": "website/_templates/page",
    "content": "---\nlayout: {{ layout }}\ntitle: {{ title }}\ndate: {{ date }}\nmodified:\nexcerpt:\nimage:\n  feature: \n---\n"
  },
  {
    "path": "website/_templates/post",
    "content": "---\nlayout: {{ layout }}\ntitle: {{ title }}\nmodified:\ncategories: {{ dir }}\nexcerpt:\ntags: []\nimage:\n  feature:\n---\n"
  },
  {
    "path": "website/assets/css/main.scss",
    "content": "---\nsitemap: false\n---\n\n/*\n *\n *  So Simple\n *\n *  Designer: Michael Rose\n *  Twitter: http://twitter.com/mmistakes\n *\n*/\n\n@import \"variables\";\n\n/*\n   Vendor\n   ========================================================================== */\n\n@import \"vendor/bourbon/bourbon\"; /* Bourbon mixins (bourbon.io) */\n@import \"grid-settings\"; /* Bourbon Neat settings */\n@import \"vendor/neat/neat\"; /* Bourbon Neat grid mixins (neat.bourbon.io) */\n@import \"vendor/font-awesome/font-awesome\"; /* Font Awesome icons */\n@import \"vendor/magnific-popup/magnific-popup\"; /* Magnific Popup lightbox */\n\n/*\n   Global\n   ========================================================================== */\n\n@import \"mixins\";\n@import \"reset\";\n@import \"base\";\n\n/*\n   Components\n   ========================================================================== */\n\n@import \"helpers\";\n@import \"buttons\";\n@import \"wells\";\n@import \"syntax\";\n@import \"forms\";\n@import \"notices\"; /* notices and alerts */\n@import \"animations\";\n@import \"masthead\";\n@import \"navigation\";\n@import \"search\";\n@import \"footer\";\n\n/*\n   Page specific\n   ========================================================================== */\n\n@import \"page\"; /* layout and styles that apply to posts and pages */\n@import \"archives\"; /* archive pages (post/tag lists, etc) */\n"
  },
  {
    "path": "website/assets/js/_main.js",
    "content": "/*! Plugin options and other jQuery stuff */\n\n// Responsive Nav\nvar navigation = responsiveNav(\"#site-nav\", { // Selector: The ID of the wrapper\n  animate: true, // Boolean: Use CSS3 transitions, true or false\n  transition: 200, // Integer: Speed of the transition, in milliseconds\n  label: \"<i class='fa fa-bars'></i> Menu\", // String: Label for the navigation toggle\n  insert: \"before\", // String: Insert the toggle before or after the navigation\n  customToggle: \"\", // Selector: Specify the ID of a custom toggle\n  openPos: \"relative\", // String: Position of the opened nav, relative or static\n  jsClass: \"js\", // String: 'JS enabled' class which is added to <html> el\n  init: function(){}, // Function: Init callback\n  open: function(){}, // Function: Open callback\n  close: function(){} // Function: Close callback\n});\n\n$('html').click(function() {\n  //Hide the menus if visible\n  if ($(navigation.wrapper).hasClass('opened')) {\n  \tnavigation.toggle();\n  }\n});\n\n$('#site-nav').click(function(event){\n    event.stopPropagation();\n});\n\n// FitVids options\n$(function() {\n\t$(\"article\").fitVids();\n});\n\n// Add lightbox class to all image links\n$(\"a[href$='.jpg'],a[href$='.jpeg'],a[href$='.JPG'],a[href$='.png'],a[href$='.gif']\").addClass(\"image-popup\");\n\n// Magnific-Popup options\n$(document).ready(function() {\n  $('.image-popup').magnificPopup({\n    type: 'image',\n    tLoading: 'Loading image #%curr%...',\n    gallery: {\n      enabled: true,\n      navigateByImgClick: true,\n      preload: [0,1] // Will preload 0 - before current, and 1 after the current image\n    },\n    image: {\n      tError: '<a href=\"%url%\">Image #%curr%</a> could not be loaded.',\n    },\n    removalDelay: 300, // Delay in milliseconds before popup is removed\n    // Class that is added to body when popup is open. \n    // make it unique to apply your CSS animations just to this exact popup\n    mainClass: 'mfp-fade'\n  });\n});\n"
  },
  {
    "path": "website/assets/js/plugins/jquery.fitvids.js",
    "content": "/*global jQuery */\n/*jshint multistr:true browser:true */\n/*!\n* FitVids 1.0.3\n*\n* Copyright 2013, Chris Coyier - http://css-tricks.com + Dave Rupert - http://daverupert.com\n* Credit to Thierry Koblentz - http://www.alistapart.com/articles/creating-intrinsic-ratios-for-video/\n* Released under the WTFPL license - http://sam.zoy.org/wtfpl/\n*\n* Date: Thu Sept 01 18:00:00 2011 -0500\n*/\n\n(function( $ ){\n\n  \"use strict\";\n\n  $.fn.fitVids = function( options ) {\n    var settings = {\n      customSelector: null\n    };\n\n    if(!document.getElementById('fit-vids-style')) {\n\n      var div = document.createElement('div'),\n          ref = document.getElementsByTagName('base')[0] || document.getElementsByTagName('script')[0],\n          cssStyles = '&shy;<style>.fluid-width-video-wrapper{width:100%;position:relative;padding:0;}.fluid-width-video-wrapper iframe,.fluid-width-video-wrapper object,.fluid-width-video-wrapper embed {position:absolute;top:0;left:0;width:100%;height:100%;}</style>';\n\n      div.className = 'fit-vids-style';\n      div.id = 'fit-vids-style';\n      div.style.display = 'none';\n      div.innerHTML = cssStyles;\n\n      ref.parentNode.insertBefore(div,ref);\n\n    }\n\n    if ( options ) {\n      $.extend( settings, options );\n    }\n\n    return this.each(function(){\n      var selectors = [\n        \"iframe[src*='player.vimeo.com']\",\n        \"iframe[src*='youtube.com']\",\n        \"iframe[src*='youtube-nocookie.com']\",\n        \"iframe[src*='kickstarter.com'][src*='video.html']\",\n        \"object\",\n        \"embed\"\n      ];\n\n      if (settings.customSelector) {\n        selectors.push(settings.customSelector);\n      }\n\n      var $allVideos = $(this).find(selectors.join(','));\n      $allVideos = $allVideos.not(\"object object\"); // SwfObj conflict patch\n\n      $allVideos.each(function(){\n        var $this = $(this);\n        if (this.tagName.toLowerCase() === 'embed' && $this.parent('object').length || $this.parent('.fluid-width-video-wrapper').length) { return; }\n        var height = ( this.tagName.toLowerCase() === 'object' || ($this.attr('height') && !isNaN(parseInt($this.attr('height'), 10))) ) ? parseInt($this.attr('height'), 10) : $this.height(),\n            width = !isNaN(parseInt($this.attr('width'), 10)) ? parseInt($this.attr('width'), 10) : $this.width(),\n            aspectRatio = height / width;\n        if(!$this.attr('id')){\n          var videoID = 'fitvid' + Math.floor(Math.random()*999999);\n          $this.attr('id', videoID);\n        }\n        $this.wrap('<div class=\"fluid-width-video-wrapper\"></div>').parent('.fluid-width-video-wrapper').css('padding-top', (aspectRatio * 100)+\"%\");\n        $this.removeAttr('height').removeAttr('width');\n      });\n    });\n  };\n// Works with either jQuery or Zepto\n})( window.jQuery || window.Zepto );"
  },
  {
    "path": "website/assets/js/plugins/jquery.magnific-popup.js",
    "content": "/*! Magnific Popup - v0.9.9 - 2013-12-27\n* http://dimsemenov.com/plugins/magnific-popup/\n* Copyright (c) 2013 Dmitry Semenov; */\n;(function($) {\n\n/*>>core*/\n/**\n * \n * Magnific Popup Core JS file\n * \n */\n\n\n/**\n * Private static constants\n */\nvar CLOSE_EVENT = 'Close',\n\tBEFORE_CLOSE_EVENT = 'BeforeClose',\n\tAFTER_CLOSE_EVENT = 'AfterClose',\n\tBEFORE_APPEND_EVENT = 'BeforeAppend',\n\tMARKUP_PARSE_EVENT = 'MarkupParse',\n\tOPEN_EVENT = 'Open',\n\tCHANGE_EVENT = 'Change',\n\tNS = 'mfp',\n\tEVENT_NS = '.' + NS,\n\tREADY_CLASS = 'mfp-ready',\n\tREMOVING_CLASS = 'mfp-removing',\n\tPREVENT_CLOSE_CLASS = 'mfp-prevent-close';\n\n\n/**\n * Private vars \n */\nvar mfp, // As we have only one instance of MagnificPopup object, we define it locally to not to use 'this'\n\tMagnificPopup = function(){},\n\t_isJQ = !!(window.jQuery),\n\t_prevStatus,\n\t_window = $(window),\n\t_body,\n\t_document,\n\t_prevContentType,\n\t_wrapClasses,\n\t_currPopupType;\n\n\n/**\n * Private functions\n */\nvar _mfpOn = function(name, f) {\n\t\tmfp.ev.on(NS + name + EVENT_NS, f);\n\t},\n\t_getEl = function(className, appendTo, html, raw) {\n\t\tvar el = document.createElement('div');\n\t\tel.className = 'mfp-'+className;\n\t\tif(html) {\n\t\t\tel.innerHTML = html;\n\t\t}\n\t\tif(!raw) {\n\t\t\tel = $(el);\n\t\t\tif(appendTo) {\n\t\t\t\tel.appendTo(appendTo);\n\t\t\t}\n\t\t} else if(appendTo) {\n\t\t\tappendTo.appendChild(el);\n\t\t}\n\t\treturn el;\n\t},\n\t_mfpTrigger = function(e, data) {\n\t\tmfp.ev.triggerHandler(NS + e, data);\n\n\t\tif(mfp.st.callbacks) {\n\t\t\t// converts \"mfpEventName\" to \"eventName\" callback and triggers it if it's present\n\t\t\te = e.charAt(0).toLowerCase() + e.slice(1);\n\t\t\tif(mfp.st.callbacks[e]) {\n\t\t\t\tmfp.st.callbacks[e].apply(mfp, $.isArray(data) ? data : [data]);\n\t\t\t}\n\t\t}\n\t},\n\t_getCloseBtn = function(type) {\n\t\tif(type !== _currPopupType || !mfp.currTemplate.closeBtn) {\n\t\t\tmfp.currTemplate.closeBtn = $( mfp.st.closeMarkup.replace('%title%', mfp.st.tClose ) );\n\t\t\t_currPopupType = type;\n\t\t}\n\t\treturn mfp.currTemplate.closeBtn;\n\t},\n\t// Initialize Magnific Popup only when called at least once\n\t_checkInstance = function() {\n\t\tif(!$.magnificPopup.instance) {\n\t\t\tmfp = new MagnificPopup();\n\t\t\tmfp.init();\n\t\t\t$.magnificPopup.instance = mfp;\n\t\t}\n\t},\n\t// CSS transition detection, http://stackoverflow.com/questions/7264899/detect-css-transitions-using-javascript-and-without-modernizr\n\tsupportsTransitions = function() {\n\t\tvar s = document.createElement('p').style, // 's' for style. better to create an element if body yet to exist\n\t\t\tv = ['ms','O','Moz','Webkit']; // 'v' for vendor\n\n\t\tif( s['transition'] !== undefined ) {\n\t\t\treturn true; \n\t\t}\n\t\t\t\n\t\twhile( v.length ) {\n\t\t\tif( v.pop() + 'Transition' in s ) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\t\t\t\t\n\t\treturn false;\n\t};\n\n\n\n/**\n * Public functions\n */\nMagnificPopup.prototype = {\n\n\tconstructor: MagnificPopup,\n\n\t/**\n\t * Initializes Magnific Popup plugin. \n\t * This function is triggered only once when $.fn.magnificPopup or $.magnificPopup is executed\n\t */\n\tinit: function() {\n\t\tvar appVersion = navigator.appVersion;\n\t\tmfp.isIE7 = appVersion.indexOf(\"MSIE 7.\") !== -1; \n\t\tmfp.isIE8 = appVersion.indexOf(\"MSIE 8.\") !== -1;\n\t\tmfp.isLowIE = mfp.isIE7 || mfp.isIE8;\n\t\tmfp.isAndroid = (/android/gi).test(appVersion);\n\t\tmfp.isIOS = (/iphone|ipad|ipod/gi).test(appVersion);\n\t\tmfp.supportsTransition = supportsTransitions();\n\n\t\t// We disable fixed positioned lightbox on devices that don't handle it nicely.\n\t\t// If you know a better way of detecting this - let me know.\n\t\tmfp.probablyMobile = (mfp.isAndroid || mfp.isIOS || /(Opera Mini)|Kindle|webOS|BlackBerry|(Opera Mobi)|(Windows Phone)|IEMobile/i.test(navigator.userAgent) );\n\t\t_document = $(document);\n\n\t\tmfp.popupsCache = {};\n\t},\n\n\t/**\n\t * Opens popup\n\t * @param  data [description]\n\t */\n\topen: function(data) {\n\n\t\tif(!_body) {\n\t\t\t_body = $(document.body);\n\t\t}\n\n\t\tvar i;\n\n\t\tif(data.isObj === false) { \n\t\t\t// convert jQuery collection to array to avoid conflicts later\n\t\t\tmfp.items = data.items.toArray();\n\n\t\t\tmfp.index = 0;\n\t\t\tvar items = data.items,\n\t\t\t\titem;\n\t\t\tfor(i = 0; i < items.length; i++) {\n\t\t\t\titem = items[i];\n\t\t\t\tif(item.parsed) {\n\t\t\t\t\titem = item.el[0];\n\t\t\t\t}\n\t\t\t\tif(item === data.el[0]) {\n\t\t\t\t\tmfp.index = i;\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tmfp.items = $.isArray(data.items) ? data.items : [data.items];\n\t\t\tmfp.index = data.index || 0;\n\t\t}\n\n\t\t// if popup is already opened - we just update the content\n\t\tif(mfp.isOpen) {\n\t\t\tmfp.updateItemHTML();\n\t\t\treturn;\n\t\t}\n\t\t\n\t\tmfp.types = []; \n\t\t_wrapClasses = '';\n\t\tif(data.mainEl && data.mainEl.length) {\n\t\t\tmfp.ev = data.mainEl.eq(0);\n\t\t} else {\n\t\t\tmfp.ev = _document;\n\t\t}\n\n\t\tif(data.key) {\n\t\t\tif(!mfp.popupsCache[data.key]) {\n\t\t\t\tmfp.popupsCache[data.key] = {};\n\t\t\t}\n\t\t\tmfp.currTemplate = mfp.popupsCache[data.key];\n\t\t} else {\n\t\t\tmfp.currTemplate = {};\n\t\t}\n\n\n\n\t\tmfp.st = $.extend(true, {}, $.magnificPopup.defaults, data ); \n\t\tmfp.fixedContentPos = mfp.st.fixedContentPos === 'auto' ? !mfp.probablyMobile : mfp.st.fixedContentPos;\n\n\t\tif(mfp.st.modal) {\n\t\t\tmfp.st.closeOnContentClick = false;\n\t\t\tmfp.st.closeOnBgClick = false;\n\t\t\tmfp.st.showCloseBtn = false;\n\t\t\tmfp.st.enableEscapeKey = false;\n\t\t}\n\t\t\n\n\t\t// Building markup\n\t\t// main containers are created only once\n\t\tif(!mfp.bgOverlay) {\n\n\t\t\t// Dark overlay\n\t\t\tmfp.bgOverlay = _getEl('bg').on('click'+EVENT_NS, function() {\n\t\t\t\tmfp.close();\n\t\t\t});\n\n\t\t\tmfp.wrap = _getEl('wrap').attr('tabindex', -1).on('click'+EVENT_NS, function(e) {\n\t\t\t\tif(mfp._checkIfClose(e.target)) {\n\t\t\t\t\tmfp.close();\n\t\t\t\t}\n\t\t\t});\n\n\t\t\tmfp.container = _getEl('container', mfp.wrap);\n\t\t}\n\n\t\tmfp.contentContainer = _getEl('content');\n\t\tif(mfp.st.preloader) {\n\t\t\tmfp.preloader = _getEl('preloader', mfp.container, mfp.st.tLoading);\n\t\t}\n\n\n\t\t// Initializing modules\n\t\tvar modules = $.magnificPopup.modules;\n\t\tfor(i = 0; i < modules.length; i++) {\n\t\t\tvar n = modules[i];\n\t\t\tn = n.charAt(0).toUpperCase() + n.slice(1);\n\t\t\tmfp['init'+n].call(mfp);\n\t\t}\n\t\t_mfpTrigger('BeforeOpen');\n\n\n\t\tif(mfp.st.showCloseBtn) {\n\t\t\t// Close button\n\t\t\tif(!mfp.st.closeBtnInside) {\n\t\t\t\tmfp.wrap.append( _getCloseBtn() );\n\t\t\t} else {\n\t\t\t\t_mfpOn(MARKUP_PARSE_EVENT, function(e, template, values, item) {\n\t\t\t\t\tvalues.close_replaceWith = _getCloseBtn(item.type);\n\t\t\t\t});\n\t\t\t\t_wrapClasses += ' mfp-close-btn-in';\n\t\t\t}\n\t\t}\n\n\t\tif(mfp.st.alignTop) {\n\t\t\t_wrapClasses += ' mfp-align-top';\n\t\t}\n\n\t\n\n\t\tif(mfp.fixedContentPos) {\n\t\t\tmfp.wrap.css({\n\t\t\t\toverflow: mfp.st.overflowY,\n\t\t\t\toverflowX: 'hidden',\n\t\t\t\toverflowY: mfp.st.overflowY\n\t\t\t});\n\t\t} else {\n\t\t\tmfp.wrap.css({ \n\t\t\t\ttop: _window.scrollTop(),\n\t\t\t\tposition: 'absolute'\n\t\t\t});\n\t\t}\n\t\tif( mfp.st.fixedBgPos === false || (mfp.st.fixedBgPos === 'auto' && !mfp.fixedContentPos) ) {\n\t\t\tmfp.bgOverlay.css({\n\t\t\t\theight: _document.height(),\n\t\t\t\tposition: 'absolute'\n\t\t\t});\n\t\t}\n\n\t\t\n\n\t\tif(mfp.st.enableEscapeKey) {\n\t\t\t// Close on ESC key\n\t\t\t_document.on('keyup' + EVENT_NS, function(e) {\n\t\t\t\tif(e.keyCode === 27) {\n\t\t\t\t\tmfp.close();\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\t_window.on('resize' + EVENT_NS, function() {\n\t\t\tmfp.updateSize();\n\t\t});\n\n\n\t\tif(!mfp.st.closeOnContentClick) {\n\t\t\t_wrapClasses += ' mfp-auto-cursor';\n\t\t}\n\t\t\n\t\tif(_wrapClasses)\n\t\t\tmfp.wrap.addClass(_wrapClasses);\n\n\n\t\t// this triggers recalculation of layout, so we get it once to not to trigger twice\n\t\tvar windowHeight = mfp.wH = _window.height();\n\n\t\t\n\t\tvar windowStyles = {};\n\n\t\tif( mfp.fixedContentPos ) {\n            if(mfp._hasScrollBar(windowHeight)){\n                var s = mfp._getScrollbarSize();\n                if(s) {\n                    windowStyles.marginRight = s;\n                }\n            }\n        }\n\n\t\tif(mfp.fixedContentPos) {\n\t\t\tif(!mfp.isIE7) {\n\t\t\t\twindowStyles.overflow = 'hidden';\n\t\t\t} else {\n\t\t\t\t// ie7 double-scroll bug\n\t\t\t\t$('body, html').css('overflow', 'hidden');\n\t\t\t}\n\t\t}\n\n\t\t\n\t\t\n\t\tvar classesToadd = mfp.st.mainClass;\n\t\tif(mfp.isIE7) {\n\t\t\tclassesToadd += ' mfp-ie7';\n\t\t}\n\t\tif(classesToadd) {\n\t\t\tmfp._addClassToMFP( classesToadd );\n\t\t}\n\n\t\t// add content\n\t\tmfp.updateItemHTML();\n\n\t\t_mfpTrigger('BuildControls');\n\n\t\t// remove scrollbar, add margin e.t.c\n\t\t$('html').css(windowStyles);\n\t\t\n\t\t// add everything to DOM\n\t\tmfp.bgOverlay.add(mfp.wrap).prependTo( mfp.st.prependTo || _body );\n\n\t\t// Save last focused element\n\t\tmfp._lastFocusedEl = document.activeElement;\n\t\t\n\t\t// Wait for next cycle to allow CSS transition\n\t\tsetTimeout(function() {\n\t\t\t\n\t\t\tif(mfp.content) {\n\t\t\t\tmfp._addClassToMFP(READY_CLASS);\n\t\t\t\tmfp._setFocus();\n\t\t\t} else {\n\t\t\t\t// if content is not defined (not loaded e.t.c) we add class only for BG\n\t\t\t\tmfp.bgOverlay.addClass(READY_CLASS);\n\t\t\t}\n\t\t\t\n\t\t\t// Trap the focus in popup\n\t\t\t_document.on('focusin' + EVENT_NS, mfp._onFocusIn);\n\n\t\t}, 16);\n\n\t\tmfp.isOpen = true;\n\t\tmfp.updateSize(windowHeight);\n\t\t_mfpTrigger(OPEN_EVENT);\n\n\t\treturn data;\n\t},\n\n\t/**\n\t * Closes the popup\n\t */\n\tclose: function() {\n\t\tif(!mfp.isOpen) return;\n\t\t_mfpTrigger(BEFORE_CLOSE_EVENT);\n\n\t\tmfp.isOpen = false;\n\t\t// for CSS3 animation\n\t\tif(mfp.st.removalDelay && !mfp.isLowIE && mfp.supportsTransition )  {\n\t\t\tmfp._addClassToMFP(REMOVING_CLASS);\n\t\t\tsetTimeout(function() {\n\t\t\t\tmfp._close();\n\t\t\t}, mfp.st.removalDelay);\n\t\t} else {\n\t\t\tmfp._close();\n\t\t}\n\t},\n\n\t/**\n\t * Helper for close() function\n\t */\n\t_close: function() {\n\t\t_mfpTrigger(CLOSE_EVENT);\n\n\t\tvar classesToRemove = REMOVING_CLASS + ' ' + READY_CLASS + ' ';\n\n\t\tmfp.bgOverlay.detach();\n\t\tmfp.wrap.detach();\n\t\tmfp.container.empty();\n\n\t\tif(mfp.st.mainClass) {\n\t\t\tclassesToRemove += mfp.st.mainClass + ' ';\n\t\t}\n\n\t\tmfp._removeClassFromMFP(classesToRemove);\n\n\t\tif(mfp.fixedContentPos) {\n\t\t\tvar windowStyles = {marginRight: ''};\n\t\t\tif(mfp.isIE7) {\n\t\t\t\t$('body, html').css('overflow', '');\n\t\t\t} else {\n\t\t\t\twindowStyles.overflow = '';\n\t\t\t}\n\t\t\t$('html').css(windowStyles);\n\t\t}\n\t\t\n\t\t_document.off('keyup' + EVENT_NS + ' focusin' + EVENT_NS);\n\t\tmfp.ev.off(EVENT_NS);\n\n\t\t// clean up DOM elements that aren't removed\n\t\tmfp.wrap.attr('class', 'mfp-wrap').removeAttr('style');\n\t\tmfp.bgOverlay.attr('class', 'mfp-bg');\n\t\tmfp.container.attr('class', 'mfp-container');\n\n\t\t// remove close button from target element\n\t\tif(mfp.st.showCloseBtn &&\n\t\t(!mfp.st.closeBtnInside || mfp.currTemplate[mfp.currItem.type] === true)) {\n\t\t\tif(mfp.currTemplate.closeBtn)\n\t\t\t\tmfp.currTemplate.closeBtn.detach();\n\t\t}\n\n\n\t\tif(mfp._lastFocusedEl) {\n\t\t\t$(mfp._lastFocusedEl).focus(); // put tab focus back\n\t\t}\n\t\tmfp.currItem = null;\t\n\t\tmfp.content = null;\n\t\tmfp.currTemplate = null;\n\t\tmfp.prevHeight = 0;\n\n\t\t_mfpTrigger(AFTER_CLOSE_EVENT);\n\t},\n\t\n\tupdateSize: function(winHeight) {\n\n\t\tif(mfp.isIOS) {\n\t\t\t// fixes iOS nav bars https://github.com/dimsemenov/Magnific-Popup/issues/2\n\t\t\tvar zoomLevel = document.documentElement.clientWidth / window.innerWidth;\n\t\t\tvar height = window.innerHeight * zoomLevel;\n\t\t\tmfp.wrap.css('height', height);\n\t\t\tmfp.wH = height;\n\t\t} else {\n\t\t\tmfp.wH = winHeight || _window.height();\n\t\t}\n\t\t// Fixes #84: popup incorrectly positioned with position:relative on body\n\t\tif(!mfp.fixedContentPos) {\n\t\t\tmfp.wrap.css('height', mfp.wH);\n\t\t}\n\n\t\t_mfpTrigger('Resize');\n\n\t},\n\n\t/**\n\t * Set content of popup based on current index\n\t */\n\tupdateItemHTML: function() {\n\t\tvar item = mfp.items[mfp.index];\n\n\t\t// Detach and perform modifications\n\t\tmfp.contentContainer.detach();\n\n\t\tif(mfp.content)\n\t\t\tmfp.content.detach();\n\n\t\tif(!item.parsed) {\n\t\t\titem = mfp.parseEl( mfp.index );\n\t\t}\n\n\t\tvar type = item.type;\t\n\n\t\t_mfpTrigger('BeforeChange', [mfp.currItem ? mfp.currItem.type : '', type]);\n\t\t// BeforeChange event works like so:\n\t\t// _mfpOn('BeforeChange', function(e, prevType, newType) { });\n\t\t\n\t\tmfp.currItem = item;\n\n\t\t\n\n\t\t\n\n\t\tif(!mfp.currTemplate[type]) {\n\t\t\tvar markup = mfp.st[type] ? mfp.st[type].markup : false;\n\n\t\t\t// allows to modify markup\n\t\t\t_mfpTrigger('FirstMarkupParse', markup);\n\n\t\t\tif(markup) {\n\t\t\t\tmfp.currTemplate[type] = $(markup);\n\t\t\t} else {\n\t\t\t\t// if there is no markup found we just define that template is parsed\n\t\t\t\tmfp.currTemplate[type] = true;\n\t\t\t}\n\t\t}\n\n\t\tif(_prevContentType && _prevContentType !== item.type) {\n\t\t\tmfp.container.removeClass('mfp-'+_prevContentType+'-holder');\n\t\t}\n\t\t\n\t\tvar newContent = mfp['get' + type.charAt(0).toUpperCase() + type.slice(1)](item, mfp.currTemplate[type]);\n\t\tmfp.appendContent(newContent, type);\n\n\t\titem.preloaded = true;\n\n\t\t_mfpTrigger(CHANGE_EVENT, item);\n\t\t_prevContentType = item.type;\n\t\t\n\t\t// Append container back after its content changed\n\t\tmfp.container.prepend(mfp.contentContainer);\n\n\t\t_mfpTrigger('AfterChange');\n\t},\n\n\n\t/**\n\t * Set HTML content of popup\n\t */\n\tappendContent: function(newContent, type) {\n\t\tmfp.content = newContent;\n\t\t\n\t\tif(newContent) {\n\t\t\tif(mfp.st.showCloseBtn && mfp.st.closeBtnInside &&\n\t\t\t\tmfp.currTemplate[type] === true) {\n\t\t\t\t// if there is no markup, we just append close button element inside\n\t\t\t\tif(!mfp.content.find('.mfp-close').length) {\n\t\t\t\t\tmfp.content.append(_getCloseBtn());\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tmfp.content = newContent;\n\t\t\t}\n\t\t} else {\n\t\t\tmfp.content = '';\n\t\t}\n\n\t\t_mfpTrigger(BEFORE_APPEND_EVENT);\n\t\tmfp.container.addClass('mfp-'+type+'-holder');\n\n\t\tmfp.contentContainer.append(mfp.content);\n\t},\n\n\n\n\t\n\t/**\n\t * Creates Magnific Popup data object based on given data\n\t * @param  {int} index Index of item to parse\n\t */\n\tparseEl: function(index) {\n\t\tvar item = mfp.items[index],\n\t\t\ttype;\n\n\t\tif(item.tagName) {\n\t\t\titem = { el: $(item) };\n\t\t} else {\n\t\t\ttype = item.type;\n\t\t\titem = { data: item, src: item.src };\n\t\t}\n\n\t\tif(item.el) {\n\t\t\tvar types = mfp.types;\n\n\t\t\t// check for 'mfp-TYPE' class\n\t\t\tfor(var i = 0; i < types.length; i++) {\n\t\t\t\tif( item.el.hasClass('mfp-'+types[i]) ) {\n\t\t\t\t\ttype = types[i];\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\titem.src = item.el.attr('data-mfp-src');\n\t\t\tif(!item.src) {\n\t\t\t\titem.src = item.el.attr('href');\n\t\t\t}\n\t\t}\n\n\t\titem.type = type || mfp.st.type || 'inline';\n\t\titem.index = index;\n\t\titem.parsed = true;\n\t\tmfp.items[index] = item;\n\t\t_mfpTrigger('ElementParse', item);\n\n\t\treturn mfp.items[index];\n\t},\n\n\n\t/**\n\t * Initializes single popup or a group of popups\n\t */\n\taddGroup: function(el, options) {\n\t\tvar eHandler = function(e) {\n\t\t\te.mfpEl = this;\n\t\t\tmfp._openClick(e, el, options);\n\t\t};\n\n\t\tif(!options) {\n\t\t\toptions = {};\n\t\t} \n\n\t\tvar eName = 'click.magnificPopup';\n\t\toptions.mainEl = el;\n\t\t\n\t\tif(options.items) {\n\t\t\toptions.isObj = true;\n\t\t\tel.off(eName).on(eName, eHandler);\n\t\t} else {\n\t\t\toptions.isObj = false;\n\t\t\tif(options.delegate) {\n\t\t\t\tel.off(eName).on(eName, options.delegate , eHandler);\n\t\t\t} else {\n\t\t\t\toptions.items = el;\n\t\t\t\tel.off(eName).on(eName, eHandler);\n\t\t\t}\n\t\t}\n\t},\n\t_openClick: function(e, el, options) {\n\t\tvar midClick = options.midClick !== undefined ? options.midClick : $.magnificPopup.defaults.midClick;\n\n\n\t\tif(!midClick && ( e.which === 2 || e.ctrlKey || e.metaKey ) ) {\n\t\t\treturn;\n\t\t}\n\n\t\tvar disableOn = options.disableOn !== undefined ? options.disableOn : $.magnificPopup.defaults.disableOn;\n\n\t\tif(disableOn) {\n\t\t\tif($.isFunction(disableOn)) {\n\t\t\t\tif( !disableOn.call(mfp) ) {\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\t\t\t} else { // else it's number\n\t\t\t\tif( _window.width() < disableOn ) {\n\t\t\t\t\treturn true;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t\n\t\tif(e.type) {\n\t\t\te.preventDefault();\n\n\t\t\t// This will prevent popup from closing if element is inside and popup is already opened\n\t\t\tif(mfp.isOpen) {\n\t\t\t\te.stopPropagation();\n\t\t\t}\n\t\t}\n\t\t\t\n\n\t\toptions.el = $(e.mfpEl);\n\t\tif(options.delegate) {\n\t\t\toptions.items = el.find(options.delegate);\n\t\t}\n\t\tmfp.open(options);\n\t},\n\n\n\t/**\n\t * Updates text on preloader\n\t */\n\tupdateStatus: function(status, text) {\n\n\t\tif(mfp.preloader) {\n\t\t\tif(_prevStatus !== status) {\n\t\t\t\tmfp.container.removeClass('mfp-s-'+_prevStatus);\n\t\t\t}\n\n\t\t\tif(!text && status === 'loading') {\n\t\t\t\ttext = mfp.st.tLoading;\n\t\t\t}\n\n\t\t\tvar data = {\n\t\t\t\tstatus: status,\n\t\t\t\ttext: text\n\t\t\t};\n\t\t\t// allows to modify status\n\t\t\t_mfpTrigger('UpdateStatus', data);\n\n\t\t\tstatus = data.status;\n\t\t\ttext = data.text;\n\n\t\t\tmfp.preloader.html(text);\n\n\t\t\tmfp.preloader.find('a').on('click', function(e) {\n\t\t\t\te.stopImmediatePropagation();\n\t\t\t});\n\n\t\t\tmfp.container.addClass('mfp-s-'+status);\n\t\t\t_prevStatus = status;\n\t\t}\n\t},\n\n\n\t/*\n\t\t\"Private\" helpers that aren't private at all\n\t */\n\t// Check to close popup or not\n\t// \"target\" is an element that was clicked\n\t_checkIfClose: function(target) {\n\n\t\tif($(target).hasClass(PREVENT_CLOSE_CLASS)) {\n\t\t\treturn;\n\t\t}\n\n\t\tvar closeOnContent = mfp.st.closeOnContentClick;\n\t\tvar closeOnBg = mfp.st.closeOnBgClick;\n\n\t\tif(closeOnContent && closeOnBg) {\n\t\t\treturn true;\n\t\t} else {\n\n\t\t\t// We close the popup if click is on close button or on preloader. Or if there is no content.\n\t\t\tif(!mfp.content || $(target).hasClass('mfp-close') || (mfp.preloader && target === mfp.preloader[0]) ) {\n\t\t\t\treturn true;\n\t\t\t}\n\n\t\t\t// if click is outside the content\n\t\t\tif(  (target !== mfp.content[0] && !$.contains(mfp.content[0], target))  ) {\n\t\t\t\tif(closeOnBg) {\n\t\t\t\t\t// last check, if the clicked element is in DOM, (in case it's removed onclick)\n\t\t\t\t\tif( $.contains(document, target) ) {\n\t\t\t\t\t\treturn true;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} else if(closeOnContent) {\n\t\t\t\treturn true;\n\t\t\t}\n\n\t\t}\n\t\treturn false;\n\t},\n\t_addClassToMFP: function(cName) {\n\t\tmfp.bgOverlay.addClass(cName);\n\t\tmfp.wrap.addClass(cName);\n\t},\n\t_removeClassFromMFP: function(cName) {\n\t\tthis.bgOverlay.removeClass(cName);\n\t\tmfp.wrap.removeClass(cName);\n\t},\n\t_hasScrollBar: function(winHeight) {\n\t\treturn (  (mfp.isIE7 ? _document.height() : document.body.scrollHeight) > (winHeight || _window.height()) );\n\t},\n\t_setFocus: function() {\n\t\t(mfp.st.focus ? mfp.content.find(mfp.st.focus).eq(0) : mfp.wrap).focus();\n\t},\n\t_onFocusIn: function(e) {\n\t\tif( e.target !== mfp.wrap[0] && !$.contains(mfp.wrap[0], e.target) ) {\n\t\t\tmfp._setFocus();\n\t\t\treturn false;\n\t\t}\n\t},\n\t_parseMarkup: function(template, values, item) {\n\t\tvar arr;\n\t\tif(item.data) {\n\t\t\tvalues = $.extend(item.data, values);\n\t\t}\n\t\t_mfpTrigger(MARKUP_PARSE_EVENT, [template, values, item] );\n\n\t\t$.each(values, function(key, value) {\n\t\t\tif(value === undefined || value === false) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t\tarr = key.split('_');\n\t\t\tif(arr.length > 1) {\n\t\t\t\tvar el = template.find(EVENT_NS + '-'+arr[0]);\n\n\t\t\t\tif(el.length > 0) {\n\t\t\t\t\tvar attr = arr[1];\n\t\t\t\t\tif(attr === 'replaceWith') {\n\t\t\t\t\t\tif(el[0] !== value[0]) {\n\t\t\t\t\t\t\tel.replaceWith(value);\n\t\t\t\t\t\t}\n\t\t\t\t\t} else if(attr === 'img') {\n\t\t\t\t\t\tif(el.is('img')) {\n\t\t\t\t\t\t\tel.attr('src', value);\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tel.replaceWith( '<img src=\"'+value+'\" class=\"' + el.attr('class') + '\" />' );\n\t\t\t\t\t\t}\n\t\t\t\t\t} else {\n\t\t\t\t\t\tel.attr(arr[1], value);\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t} else {\n\t\t\t\ttemplate.find(EVENT_NS + '-'+key).html(value);\n\t\t\t}\n\t\t});\n\t},\n\n\t_getScrollbarSize: function() {\n\t\t// thx David\n\t\tif(mfp.scrollbarSize === undefined) {\n\t\t\tvar scrollDiv = document.createElement(\"div\");\n\t\t\tscrollDiv.id = \"mfp-sbm\";\n\t\t\tscrollDiv.style.cssText = 'width: 99px; height: 99px; overflow: scroll; position: absolute; top: -9999px;';\n\t\t\tdocument.body.appendChild(scrollDiv);\n\t\t\tmfp.scrollbarSize = scrollDiv.offsetWidth - scrollDiv.clientWidth;\n\t\t\tdocument.body.removeChild(scrollDiv);\n\t\t}\n\t\treturn mfp.scrollbarSize;\n\t}\n\n}; /* MagnificPopup core prototype end */\n\n\n\n\n/**\n * Public static functions\n */\n$.magnificPopup = {\n\tinstance: null,\n\tproto: MagnificPopup.prototype,\n\tmodules: [],\n\n\topen: function(options, index) {\n\t\t_checkInstance();\t\n\n\t\tif(!options) {\n\t\t\toptions = {};\n\t\t} else {\n\t\t\toptions = $.extend(true, {}, options);\n\t\t}\n\t\t\t\n\n\t\toptions.isObj = true;\n\t\toptions.index = index || 0;\n\t\treturn this.instance.open(options);\n\t},\n\n\tclose: function() {\n\t\treturn $.magnificPopup.instance && $.magnificPopup.instance.close();\n\t},\n\n\tregisterModule: function(name, module) {\n\t\tif(module.options) {\n\t\t\t$.magnificPopup.defaults[name] = module.options;\n\t\t}\n\t\t$.extend(this.proto, module.proto);\t\t\t\n\t\tthis.modules.push(name);\n\t},\n\n\tdefaults: {   \n\n\t\t// Info about options is in docs:\n\t\t// http://dimsemenov.com/plugins/magnific-popup/documentation.html#options\n\t\t\n\t\tdisableOn: 0,\t\n\n\t\tkey: null,\n\n\t\tmidClick: false,\n\n\t\tmainClass: '',\n\n\t\tpreloader: true,\n\n\t\tfocus: '', // CSS selector of input to focus after popup is opened\n\t\t\n\t\tcloseOnContentClick: false,\n\n\t\tcloseOnBgClick: true,\n\n\t\tcloseBtnInside: true, \n\n\t\tshowCloseBtn: true,\n\n\t\tenableEscapeKey: true,\n\n\t\tmodal: false,\n\n\t\talignTop: false,\n\t\n\t\tremovalDelay: 0,\n\n\t\tprependTo: null,\n\t\t\n\t\tfixedContentPos: 'auto', \n\t\n\t\tfixedBgPos: 'auto',\n\n\t\toverflowY: 'auto',\n\n\t\tcloseMarkup: '<button title=\"%title%\" type=\"button\" class=\"mfp-close\">&times;</button>',\n\n\t\ttClose: 'Close (Esc)',\n\n\t\ttLoading: 'Loading...'\n\n\t}\n};\n\n\n\n$.fn.magnificPopup = function(options) {\n\t_checkInstance();\n\n\tvar jqEl = $(this);\n\n\t// We call some API method of first param is a string\n\tif (typeof options === \"string\" ) {\n\n\t\tif(options === 'open') {\n\t\t\tvar items,\n\t\t\t\titemOpts = _isJQ ? jqEl.data('magnificPopup') : jqEl[0].magnificPopup,\n\t\t\t\tindex = parseInt(arguments[1], 10) || 0;\n\n\t\t\tif(itemOpts.items) {\n\t\t\t\titems = itemOpts.items[index];\n\t\t\t} else {\n\t\t\t\titems = jqEl;\n\t\t\t\tif(itemOpts.delegate) {\n\t\t\t\t\titems = items.find(itemOpts.delegate);\n\t\t\t\t}\n\t\t\t\titems = items.eq( index );\n\t\t\t}\n\t\t\tmfp._openClick({mfpEl:items}, jqEl, itemOpts);\n\t\t} else {\n\t\t\tif(mfp.isOpen)\n\t\t\t\tmfp[options].apply(mfp, Array.prototype.slice.call(arguments, 1));\n\t\t}\n\n\t} else {\n\t\t// clone options obj\n\t\toptions = $.extend(true, {}, options);\n\t\t\n\t\t/*\n\t\t * As Zepto doesn't support .data() method for objects \n\t\t * and it works only in normal browsers\n\t\t * we assign \"options\" object directly to the DOM element. FTW!\n\t\t */\n\t\tif(_isJQ) {\n\t\t\tjqEl.data('magnificPopup', options);\n\t\t} else {\n\t\t\tjqEl[0].magnificPopup = options;\n\t\t}\n\n\t\tmfp.addGroup(jqEl, options);\n\n\t}\n\treturn jqEl;\n};\n\n\n//Quick benchmark\n/*\nvar start = performance.now(),\n\ti,\n\trounds = 1000;\n\nfor(i = 0; i < rounds; i++) {\n\n}\nconsole.log('Test #1:', performance.now() - start);\n\nstart = performance.now();\nfor(i = 0; i < rounds; i++) {\n\n}\nconsole.log('Test #2:', performance.now() - start);\n*/\n\n\n/*>>core*/\n\n/*>>inline*/\n\nvar INLINE_NS = 'inline',\n\t_hiddenClass,\n\t_inlinePlaceholder, \n\t_lastInlineElement,\n\t_putInlineElementsBack = function() {\n\t\tif(_lastInlineElement) {\n\t\t\t_inlinePlaceholder.after( _lastInlineElement.addClass(_hiddenClass) ).detach();\n\t\t\t_lastInlineElement = null;\n\t\t}\n\t};\n\n$.magnificPopup.registerModule(INLINE_NS, {\n\toptions: {\n\t\thiddenClass: 'hide', // will be appended with `mfp-` prefix\n\t\tmarkup: '',\n\t\ttNotFound: 'Content not found'\n\t},\n\tproto: {\n\n\t\tinitInline: function() {\n\t\t\tmfp.types.push(INLINE_NS);\n\n\t\t\t_mfpOn(CLOSE_EVENT+'.'+INLINE_NS, function() {\n\t\t\t\t_putInlineElementsBack();\n\t\t\t});\n\t\t},\n\n\t\tgetInline: function(item, template) {\n\n\t\t\t_putInlineElementsBack();\n\n\t\t\tif(item.src) {\n\t\t\t\tvar inlineSt = mfp.st.inline,\n\t\t\t\t\tel = $(item.src);\n\n\t\t\t\tif(el.length) {\n\n\t\t\t\t\t// If target element has parent - we replace it with placeholder and put it back after popup is closed\n\t\t\t\t\tvar parent = el[0].parentNode;\n\t\t\t\t\tif(parent && parent.tagName) {\n\t\t\t\t\t\tif(!_inlinePlaceholder) {\n\t\t\t\t\t\t\t_hiddenClass = inlineSt.hiddenClass;\n\t\t\t\t\t\t\t_inlinePlaceholder = _getEl(_hiddenClass);\n\t\t\t\t\t\t\t_hiddenClass = 'mfp-'+_hiddenClass;\n\t\t\t\t\t\t}\n\t\t\t\t\t\t// replace target inline element with placeholder\n\t\t\t\t\t\t_lastInlineElement = el.after(_inlinePlaceholder).detach().removeClass(_hiddenClass);\n\t\t\t\t\t}\n\n\t\t\t\t\tmfp.updateStatus('ready');\n\t\t\t\t} else {\n\t\t\t\t\tmfp.updateStatus('error', inlineSt.tNotFound);\n\t\t\t\t\tel = $('<div>');\n\t\t\t\t}\n\n\t\t\t\titem.inlineElement = el;\n\t\t\t\treturn el;\n\t\t\t}\n\n\t\t\tmfp.updateStatus('ready');\n\t\t\tmfp._parseMarkup(template, {}, item);\n\t\t\treturn template;\n\t\t}\n\t}\n});\n\n/*>>inline*/\n\n/*>>ajax*/\nvar AJAX_NS = 'ajax',\n\t_ajaxCur,\n\t_removeAjaxCursor = function() {\n\t\tif(_ajaxCur) {\n\t\t\t_body.removeClass(_ajaxCur);\n\t\t}\n\t},\n\t_destroyAjaxRequest = function() {\n\t\t_removeAjaxCursor();\n\t\tif(mfp.req) {\n\t\t\tmfp.req.abort();\n\t\t}\n\t};\n\n$.magnificPopup.registerModule(AJAX_NS, {\n\n\toptions: {\n\t\tsettings: null,\n\t\tcursor: 'mfp-ajax-cur',\n\t\ttError: '<a href=\"%url%\">The content</a> could not be loaded.'\n\t},\n\n\tproto: {\n\t\tinitAjax: function() {\n\t\t\tmfp.types.push(AJAX_NS);\n\t\t\t_ajaxCur = mfp.st.ajax.cursor;\n\n\t\t\t_mfpOn(CLOSE_EVENT+'.'+AJAX_NS, _destroyAjaxRequest);\n\t\t\t_mfpOn('BeforeChange.' + AJAX_NS, _destroyAjaxRequest);\n\t\t},\n\t\tgetAjax: function(item) {\n\n\t\t\tif(_ajaxCur)\n\t\t\t\t_body.addClass(_ajaxCur);\n\n\t\t\tmfp.updateStatus('loading');\n\n\t\t\tvar opts = $.extend({\n\t\t\t\turl: item.src,\n\t\t\t\tsuccess: function(data, textStatus, jqXHR) {\n\t\t\t\t\tvar temp = {\n\t\t\t\t\t\tdata:data,\n\t\t\t\t\t\txhr:jqXHR\n\t\t\t\t\t};\n\n\t\t\t\t\t_mfpTrigger('ParseAjax', temp);\n\n\t\t\t\t\tmfp.appendContent( $(temp.data), AJAX_NS );\n\n\t\t\t\t\titem.finished = true;\n\n\t\t\t\t\t_removeAjaxCursor();\n\n\t\t\t\t\tmfp._setFocus();\n\n\t\t\t\t\tsetTimeout(function() {\n\t\t\t\t\t\tmfp.wrap.addClass(READY_CLASS);\n\t\t\t\t\t}, 16);\n\n\t\t\t\t\tmfp.updateStatus('ready');\n\n\t\t\t\t\t_mfpTrigger('AjaxContentAdded');\n\t\t\t\t},\n\t\t\t\terror: function() {\n\t\t\t\t\t_removeAjaxCursor();\n\t\t\t\t\titem.finished = item.loadError = true;\n\t\t\t\t\tmfp.updateStatus('error', mfp.st.ajax.tError.replace('%url%', item.src));\n\t\t\t\t}\n\t\t\t}, mfp.st.ajax.settings);\n\n\t\t\tmfp.req = $.ajax(opts);\n\n\t\t\treturn '';\n\t\t}\n\t}\n});\n\n\n\n\n\n\t\n\n/*>>ajax*/\n\n/*>>image*/\nvar _imgInterval,\n\t_getTitle = function(item) {\n\t\tif(item.data && item.data.title !== undefined) \n\t\t\treturn item.data.title;\n\n\t\tvar src = mfp.st.image.titleSrc;\n\n\t\tif(src) {\n\t\t\tif($.isFunction(src)) {\n\t\t\t\treturn src.call(mfp, item);\n\t\t\t} else if(item.el) {\n\t\t\t\treturn item.el.attr(src) || '';\n\t\t\t}\n\t\t}\n\t\treturn '';\n\t};\n\n$.magnificPopup.registerModule('image', {\n\n\toptions: {\n\t\tmarkup: '<div class=\"mfp-figure\">'+\n\t\t\t\t\t'<div class=\"mfp-close\"></div>'+\n\t\t\t\t\t'<figure>'+\n\t\t\t\t\t\t'<div class=\"mfp-img\"></div>'+\n\t\t\t\t\t\t'<figcaption>'+\n\t\t\t\t\t\t\t'<div class=\"mfp-bottom-bar\">'+\n\t\t\t\t\t\t\t\t'<div class=\"mfp-title\"></div>'+\n\t\t\t\t\t\t\t\t'<div class=\"mfp-counter\"></div>'+\n\t\t\t\t\t\t\t'</div>'+\n\t\t\t\t\t\t'</figcaption>'+\n\t\t\t\t\t'</figure>'+\n\t\t\t\t'</div>',\n\t\tcursor: 'mfp-zoom-out-cur',\n\t\ttitleSrc: 'title', \n\t\tverticalFit: true,\n\t\ttError: '<a href=\"%url%\">The image</a> could not be loaded.'\n\t},\n\n\tproto: {\n\t\tinitImage: function() {\n\t\t\tvar imgSt = mfp.st.image,\n\t\t\t\tns = '.image';\n\n\t\t\tmfp.types.push('image');\n\n\t\t\t_mfpOn(OPEN_EVENT+ns, function() {\n\t\t\t\tif(mfp.currItem.type === 'image' && imgSt.cursor) {\n\t\t\t\t\t_body.addClass(imgSt.cursor);\n\t\t\t\t}\n\t\t\t});\n\n\t\t\t_mfpOn(CLOSE_EVENT+ns, function() {\n\t\t\t\tif(imgSt.cursor) {\n\t\t\t\t\t_body.removeClass(imgSt.cursor);\n\t\t\t\t}\n\t\t\t\t_window.off('resize' + EVENT_NS);\n\t\t\t});\n\n\t\t\t_mfpOn('Resize'+ns, mfp.resizeImage);\n\t\t\tif(mfp.isLowIE) {\n\t\t\t\t_mfpOn('AfterChange', mfp.resizeImage);\n\t\t\t}\n\t\t},\n\t\tresizeImage: function() {\n\t\t\tvar item = mfp.currItem;\n\t\t\tif(!item || !item.img) return;\n\n\t\t\tif(mfp.st.image.verticalFit) {\n\t\t\t\tvar decr = 0;\n\t\t\t\t// fix box-sizing in ie7/8\n\t\t\t\tif(mfp.isLowIE) {\n\t\t\t\t\tdecr = parseInt(item.img.css('padding-top'), 10) + parseInt(item.img.css('padding-bottom'),10);\n\t\t\t\t}\n\t\t\t\titem.img.css('max-height', mfp.wH-decr);\n\t\t\t}\n\t\t},\n\t\t_onImageHasSize: function(item) {\n\t\t\tif(item.img) {\n\t\t\t\t\n\t\t\t\titem.hasSize = true;\n\n\t\t\t\tif(_imgInterval) {\n\t\t\t\t\tclearInterval(_imgInterval);\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\titem.isCheckingImgSize = false;\n\n\t\t\t\t_mfpTrigger('ImageHasSize', item);\n\n\t\t\t\tif(item.imgHidden) {\n\t\t\t\t\tif(mfp.content)\n\t\t\t\t\t\tmfp.content.removeClass('mfp-loading');\n\t\t\t\t\t\n\t\t\t\t\titem.imgHidden = false;\n\t\t\t\t}\n\n\t\t\t}\n\t\t},\n\n\t\t/**\n\t\t * Function that loops until the image has size to display elements that rely on it asap\n\t\t */\n\t\tfindImageSize: function(item) {\n\n\t\t\tvar counter = 0,\n\t\t\t\timg = item.img[0],\n\t\t\t\tmfpSetInterval = function(delay) {\n\n\t\t\t\t\tif(_imgInterval) {\n\t\t\t\t\t\tclearInterval(_imgInterval);\n\t\t\t\t\t}\n\t\t\t\t\t// decelerating interval that checks for size of an image\n\t\t\t\t\t_imgInterval = setInterval(function() {\n\t\t\t\t\t\tif(img.naturalWidth > 0) {\n\t\t\t\t\t\t\tmfp._onImageHasSize(item);\n\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tif(counter > 200) {\n\t\t\t\t\t\t\tclearInterval(_imgInterval);\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tcounter++;\n\t\t\t\t\t\tif(counter === 3) {\n\t\t\t\t\t\t\tmfpSetInterval(10);\n\t\t\t\t\t\t} else if(counter === 40) {\n\t\t\t\t\t\t\tmfpSetInterval(50);\n\t\t\t\t\t\t} else if(counter === 100) {\n\t\t\t\t\t\t\tmfpSetInterval(500);\n\t\t\t\t\t\t}\n\t\t\t\t\t}, delay);\n\t\t\t\t};\n\n\t\t\tmfpSetInterval(1);\n\t\t},\n\n\t\tgetImage: function(item, template) {\n\n\t\t\tvar guard = 0,\n\n\t\t\t\t// image load complete handler\n\t\t\t\tonLoadComplete = function() {\n\t\t\t\t\tif(item) {\n\t\t\t\t\t\tif (item.img[0].complete) {\n\t\t\t\t\t\t\titem.img.off('.mfploader');\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tif(item === mfp.currItem){\n\t\t\t\t\t\t\t\tmfp._onImageHasSize(item);\n\n\t\t\t\t\t\t\t\tmfp.updateStatus('ready');\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\titem.hasSize = true;\n\t\t\t\t\t\t\titem.loaded = true;\n\n\t\t\t\t\t\t\t_mfpTrigger('ImageLoadComplete');\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\t// if image complete check fails 200 times (20 sec), we assume that there was an error.\n\t\t\t\t\t\t\tguard++;\n\t\t\t\t\t\t\tif(guard < 200) {\n\t\t\t\t\t\t\t\tsetTimeout(onLoadComplete,100);\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tonLoadError();\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t},\n\n\t\t\t\t// image error handler\n\t\t\t\tonLoadError = function() {\n\t\t\t\t\tif(item) {\n\t\t\t\t\t\titem.img.off('.mfploader');\n\t\t\t\t\t\tif(item === mfp.currItem){\n\t\t\t\t\t\t\tmfp._onImageHasSize(item);\n\t\t\t\t\t\t\tmfp.updateStatus('error', imgSt.tError.replace('%url%', item.src) );\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\titem.hasSize = true;\n\t\t\t\t\t\titem.loaded = true;\n\t\t\t\t\t\titem.loadError = true;\n\t\t\t\t\t}\n\t\t\t\t},\n\t\t\t\timgSt = mfp.st.image;\n\n\n\t\t\tvar el = template.find('.mfp-img');\n\t\t\tif(el.length) {\n\t\t\t\tvar img = document.createElement('img');\n\t\t\t\timg.className = 'mfp-img';\n\t\t\t\titem.img = $(img).on('load.mfploader', onLoadComplete).on('error.mfploader', onLoadError);\n\t\t\t\timg.src = item.src;\n\n\t\t\t\t// without clone() \"error\" event is not firing when IMG is replaced by new IMG\n\t\t\t\t// TODO: find a way to avoid such cloning\n\t\t\t\tif(el.is('img')) {\n\t\t\t\t\titem.img = item.img.clone();\n\t\t\t\t}\n\n\t\t\t\timg = item.img[0];\n\t\t\t\tif(img.naturalWidth > 0) {\n\t\t\t\t\titem.hasSize = true;\n\t\t\t\t} else if(!img.width) {\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\titem.hasSize = false;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tmfp._parseMarkup(template, {\n\t\t\t\ttitle: _getTitle(item),\n\t\t\t\timg_replaceWith: item.img\n\t\t\t}, item);\n\n\t\t\tmfp.resizeImage();\n\n\t\t\tif(item.hasSize) {\n\t\t\t\tif(_imgInterval) clearInterval(_imgInterval);\n\n\t\t\t\tif(item.loadError) {\n\t\t\t\t\ttemplate.addClass('mfp-loading');\n\t\t\t\t\tmfp.updateStatus('error', imgSt.tError.replace('%url%', item.src) );\n\t\t\t\t} else {\n\t\t\t\t\ttemplate.removeClass('mfp-loading');\n\t\t\t\t\tmfp.updateStatus('ready');\n\t\t\t\t}\n\t\t\t\treturn template;\n\t\t\t}\n\n\t\t\tmfp.updateStatus('loading');\n\t\t\titem.loading = true;\n\n\t\t\tif(!item.hasSize) {\n\t\t\t\titem.imgHidden = true;\n\t\t\t\ttemplate.addClass('mfp-loading');\n\t\t\t\tmfp.findImageSize(item);\n\t\t\t} \n\n\t\t\treturn template;\n\t\t}\n\t}\n});\n\n\n\n/*>>image*/\n\n/*>>zoom*/\nvar hasMozTransform,\n\tgetHasMozTransform = function() {\n\t\tif(hasMozTransform === undefined) {\n\t\t\thasMozTransform = document.createElement('p').style.MozTransform !== undefined;\n\t\t}\n\t\treturn hasMozTransform;\t\t\n\t};\n\n$.magnificPopup.registerModule('zoom', {\n\n\toptions: {\n\t\tenabled: false,\n\t\teasing: 'ease-in-out',\n\t\tduration: 300,\n\t\topener: function(element) {\n\t\t\treturn element.is('img') ? element : element.find('img');\n\t\t}\n\t},\n\n\tproto: {\n\n\t\tinitZoom: function() {\n\t\t\tvar zoomSt = mfp.st.zoom,\n\t\t\t\tns = '.zoom',\n\t\t\t\timage;\n\t\t\t\t\n\t\t\tif(!zoomSt.enabled || !mfp.supportsTransition) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tvar duration = zoomSt.duration,\n\t\t\t\tgetElToAnimate = function(image) {\n\t\t\t\t\tvar newImg = image.clone().removeAttr('style').removeAttr('class').addClass('mfp-animated-image'),\n\t\t\t\t\t\ttransition = 'all '+(zoomSt.duration/1000)+'s ' + zoomSt.easing,\n\t\t\t\t\t\tcssObj = {\n\t\t\t\t\t\t\tposition: 'fixed',\n\t\t\t\t\t\t\tzIndex: 9999,\n\t\t\t\t\t\t\tleft: 0,\n\t\t\t\t\t\t\ttop: 0,\n\t\t\t\t\t\t\t'-webkit-backface-visibility': 'hidden'\n\t\t\t\t\t\t},\n\t\t\t\t\t\tt = 'transition';\n\n\t\t\t\t\tcssObj['-webkit-'+t] = cssObj['-moz-'+t] = cssObj['-o-'+t] = cssObj[t] = transition;\n\n\t\t\t\t\tnewImg.css(cssObj);\n\t\t\t\t\treturn newImg;\n\t\t\t\t},\n\t\t\t\tshowMainContent = function() {\n\t\t\t\t\tmfp.content.css('visibility', 'visible');\n\t\t\t\t},\n\t\t\t\topenTimeout,\n\t\t\t\tanimatedImg;\n\n\t\t\t_mfpOn('BuildControls'+ns, function() {\n\t\t\t\tif(mfp._allowZoom()) {\n\n\t\t\t\t\tclearTimeout(openTimeout);\n\t\t\t\t\tmfp.content.css('visibility', 'hidden');\n\n\t\t\t\t\t// Basically, all code below does is clones existing image, puts in on top of the current one and animated it\n\t\t\t\t\t\n\t\t\t\t\timage = mfp._getItemToZoom();\n\n\t\t\t\t\tif(!image) {\n\t\t\t\t\t\tshowMainContent();\n\t\t\t\t\t\treturn;\n\t\t\t\t\t}\n\n\t\t\t\t\tanimatedImg = getElToAnimate(image); \n\t\t\t\t\t\n\t\t\t\t\tanimatedImg.css( mfp._getOffset() );\n\n\t\t\t\t\tmfp.wrap.append(animatedImg);\n\n\t\t\t\t\topenTimeout = setTimeout(function() {\n\t\t\t\t\t\tanimatedImg.css( mfp._getOffset( true ) );\n\t\t\t\t\t\topenTimeout = setTimeout(function() {\n\n\t\t\t\t\t\t\tshowMainContent();\n\n\t\t\t\t\t\t\tsetTimeout(function() {\n\t\t\t\t\t\t\t\tanimatedImg.remove();\n\t\t\t\t\t\t\t\timage = animatedImg = null;\n\t\t\t\t\t\t\t\t_mfpTrigger('ZoomAnimationEnded');\n\t\t\t\t\t\t\t}, 16); // avoid blink when switching images \n\n\t\t\t\t\t\t}, duration); // this timeout equals animation duration\n\n\t\t\t\t\t}, 16); // by adding this timeout we avoid short glitch at the beginning of animation\n\n\n\t\t\t\t\t// Lots of timeouts...\n\t\t\t\t}\n\t\t\t});\n\t\t\t_mfpOn(BEFORE_CLOSE_EVENT+ns, function() {\n\t\t\t\tif(mfp._allowZoom()) {\n\n\t\t\t\t\tclearTimeout(openTimeout);\n\n\t\t\t\t\tmfp.st.removalDelay = duration;\n\n\t\t\t\t\tif(!image) {\n\t\t\t\t\t\timage = mfp._getItemToZoom();\n\t\t\t\t\t\tif(!image) {\n\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tanimatedImg = getElToAnimate(image);\n\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tanimatedImg.css( mfp._getOffset(true) );\n\t\t\t\t\tmfp.wrap.append(animatedImg);\n\t\t\t\t\tmfp.content.css('visibility', 'hidden');\n\t\t\t\t\t\n\t\t\t\t\tsetTimeout(function() {\n\t\t\t\t\t\tanimatedImg.css( mfp._getOffset() );\n\t\t\t\t\t}, 16);\n\t\t\t\t}\n\n\t\t\t});\n\n\t\t\t_mfpOn(CLOSE_EVENT+ns, function() {\n\t\t\t\tif(mfp._allowZoom()) {\n\t\t\t\t\tshowMainContent();\n\t\t\t\t\tif(animatedImg) {\n\t\t\t\t\t\tanimatedImg.remove();\n\t\t\t\t\t}\n\t\t\t\t\timage = null;\n\t\t\t\t}\t\n\t\t\t});\n\t\t},\n\n\t\t_allowZoom: function() {\n\t\t\treturn mfp.currItem.type === 'image';\n\t\t},\n\n\t\t_getItemToZoom: function() {\n\t\t\tif(mfp.currItem.hasSize) {\n\t\t\t\treturn mfp.currItem.img;\n\t\t\t} else {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t},\n\n\t\t// Get element postion relative to viewport\n\t\t_getOffset: function(isLarge) {\n\t\t\tvar el;\n\t\t\tif(isLarge) {\n\t\t\t\tel = mfp.currItem.img;\n\t\t\t} else {\n\t\t\t\tel = mfp.st.zoom.opener(mfp.currItem.el || mfp.currItem);\n\t\t\t}\n\n\t\t\tvar offset = el.offset();\n\t\t\tvar paddingTop = parseInt(el.css('padding-top'),10);\n\t\t\tvar paddingBottom = parseInt(el.css('padding-bottom'),10);\n\t\t\toffset.top -= ( $(window).scrollTop() - paddingTop );\n\n\n\t\t\t/*\n\t\t\t\n\t\t\tAnimating left + top + width/height looks glitchy in Firefox, but perfect in Chrome. And vice-versa.\n\n\t\t\t */\n\t\t\tvar obj = {\n\t\t\t\twidth: el.width(),\n\t\t\t\t// fix Zepto height+padding issue\n\t\t\t\theight: (_isJQ ? el.innerHeight() : el[0].offsetHeight) - paddingBottom - paddingTop\n\t\t\t};\n\n\t\t\t// I hate to do this, but there is no another option\n\t\t\tif( getHasMozTransform() ) {\n\t\t\t\tobj['-moz-transform'] = obj['transform'] = 'translate(' + offset.left + 'px,' + offset.top + 'px)';\n\t\t\t} else {\n\t\t\t\tobj.left = offset.left;\n\t\t\t\tobj.top = offset.top;\n\t\t\t}\n\t\t\treturn obj;\n\t\t}\n\n\t}\n});\n\n\n\n/*>>zoom*/\n\n/*>>iframe*/\n\nvar IFRAME_NS = 'iframe',\n\t_emptyPage = '//about:blank',\n\t\n\t_fixIframeBugs = function(isShowing) {\n\t\tif(mfp.currTemplate[IFRAME_NS]) {\n\t\t\tvar el = mfp.currTemplate[IFRAME_NS].find('iframe');\n\t\t\tif(el.length) { \n\t\t\t\t// reset src after the popup is closed to avoid \"video keeps playing after popup is closed\" bug\n\t\t\t\tif(!isShowing) {\n\t\t\t\t\tel[0].src = _emptyPage;\n\t\t\t\t}\n\n\t\t\t\t// IE8 black screen bug fix\n\t\t\t\tif(mfp.isIE8) {\n\t\t\t\t\tel.css('display', isShowing ? 'block' : 'none');\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t};\n\n$.magnificPopup.registerModule(IFRAME_NS, {\n\n\toptions: {\n\t\tmarkup: '<div class=\"mfp-iframe-scaler\">'+\n\t\t\t\t\t'<div class=\"mfp-close\"></div>'+\n\t\t\t\t\t'<iframe class=\"mfp-iframe\" src=\"//about:blank\" frameborder=\"0\" allowfullscreen></iframe>'+\n\t\t\t\t'</div>',\n\n\t\tsrcAction: 'iframe_src',\n\n\t\t// we don't care and support only one default type of URL by default\n\t\tpatterns: {\n\t\t\tyoutube: {\n\t\t\t\tindex: 'youtube.com', \n\t\t\t\tid: 'v=', \n\t\t\t\tsrc: '//www.youtube.com/embed/%id%?autoplay=1'\n\t\t\t},\n\t\t\tvimeo: {\n\t\t\t\tindex: 'vimeo.com/',\n\t\t\t\tid: '/',\n\t\t\t\tsrc: '//player.vimeo.com/video/%id%?autoplay=1'\n\t\t\t},\n\t\t\tgmaps: {\n\t\t\t\tindex: '//maps.google.',\n\t\t\t\tsrc: '%id%&output=embed'\n\t\t\t}\n\t\t}\n\t},\n\n\tproto: {\n\t\tinitIframe: function() {\n\t\t\tmfp.types.push(IFRAME_NS);\n\n\t\t\t_mfpOn('BeforeChange', function(e, prevType, newType) {\n\t\t\t\tif(prevType !== newType) {\n\t\t\t\t\tif(prevType === IFRAME_NS) {\n\t\t\t\t\t\t_fixIframeBugs(); // iframe if removed\n\t\t\t\t\t} else if(newType === IFRAME_NS) {\n\t\t\t\t\t\t_fixIframeBugs(true); // iframe is showing\n\t\t\t\t\t} \n\t\t\t\t}// else {\n\t\t\t\t\t// iframe source is switched, don't do anything\n\t\t\t\t//}\n\t\t\t});\n\n\t\t\t_mfpOn(CLOSE_EVENT + '.' + IFRAME_NS, function() {\n\t\t\t\t_fixIframeBugs();\n\t\t\t});\n\t\t},\n\n\t\tgetIframe: function(item, template) {\n\t\t\tvar embedSrc = item.src;\n\t\t\tvar iframeSt = mfp.st.iframe;\n\t\t\t\t\n\t\t\t$.each(iframeSt.patterns, function() {\n\t\t\t\tif(embedSrc.indexOf( this.index ) > -1) {\n\t\t\t\t\tif(this.id) {\n\t\t\t\t\t\tif(typeof this.id === 'string') {\n\t\t\t\t\t\t\tembedSrc = embedSrc.substr(embedSrc.lastIndexOf(this.id)+this.id.length, embedSrc.length);\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tembedSrc = this.id.call( this, embedSrc );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tembedSrc = this.src.replace('%id%', embedSrc );\n\t\t\t\t\treturn false; // break;\n\t\t\t\t}\n\t\t\t});\n\t\t\t\n\t\t\tvar dataObj = {};\n\t\t\tif(iframeSt.srcAction) {\n\t\t\t\tdataObj[iframeSt.srcAction] = embedSrc;\n\t\t\t}\n\t\t\tmfp._parseMarkup(template, dataObj, item);\n\n\t\t\tmfp.updateStatus('ready');\n\n\t\t\treturn template;\n\t\t}\n\t}\n});\n\n\n\n/*>>iframe*/\n\n/*>>gallery*/\n/**\n * Get looped index depending on number of slides\n */\nvar _getLoopedId = function(index) {\n\t\tvar numSlides = mfp.items.length;\n\t\tif(index > numSlides - 1) {\n\t\t\treturn index - numSlides;\n\t\t} else  if(index < 0) {\n\t\t\treturn numSlides + index;\n\t\t}\n\t\treturn index;\n\t},\n\t_replaceCurrTotal = function(text, curr, total) {\n\t\treturn text.replace(/%curr%/gi, curr + 1).replace(/%total%/gi, total);\n\t};\n\n$.magnificPopup.registerModule('gallery', {\n\n\toptions: {\n\t\tenabled: false,\n\t\tarrowMarkup: '<button title=\"%title%\" type=\"button\" class=\"mfp-arrow mfp-arrow-%dir%\"></button>',\n\t\tpreload: [0,2],\n\t\tnavigateByImgClick: true,\n\t\tarrows: true,\n\n\t\ttPrev: 'Previous (Left arrow key)',\n\t\ttNext: 'Next (Right arrow key)',\n\t\ttCounter: '%curr% of %total%'\n\t},\n\n\tproto: {\n\t\tinitGallery: function() {\n\n\t\t\tvar gSt = mfp.st.gallery,\n\t\t\t\tns = '.mfp-gallery',\n\t\t\t\tsupportsFastClick = Boolean($.fn.mfpFastClick);\n\n\t\t\tmfp.direction = true; // true - next, false - prev\n\t\t\t\n\t\t\tif(!gSt || !gSt.enabled ) return false;\n\n\t\t\t_wrapClasses += ' mfp-gallery';\n\n\t\t\t_mfpOn(OPEN_EVENT+ns, function() {\n\n\t\t\t\tif(gSt.navigateByImgClick) {\n\t\t\t\t\tmfp.wrap.on('click'+ns, '.mfp-img', function() {\n\t\t\t\t\t\tif(mfp.items.length > 1) {\n\t\t\t\t\t\t\tmfp.next();\n\t\t\t\t\t\t\treturn false;\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\n\t\t\t\t_document.on('keydown'+ns, function(e) {\n\t\t\t\t\tif (e.keyCode === 37) {\n\t\t\t\t\t\tmfp.prev();\n\t\t\t\t\t} else if (e.keyCode === 39) {\n\t\t\t\t\t\tmfp.next();\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t});\n\n\t\t\t_mfpOn('UpdateStatus'+ns, function(e, data) {\n\t\t\t\tif(data.text) {\n\t\t\t\t\tdata.text = _replaceCurrTotal(data.text, mfp.currItem.index, mfp.items.length);\n\t\t\t\t}\n\t\t\t});\n\n\t\t\t_mfpOn(MARKUP_PARSE_EVENT+ns, function(e, element, values, item) {\n\t\t\t\tvar l = mfp.items.length;\n\t\t\t\tvalues.counter = l > 1 ? _replaceCurrTotal(gSt.tCounter, item.index, l) : '';\n\t\t\t});\n\n\t\t\t_mfpOn('BuildControls' + ns, function() {\n\t\t\t\tif(mfp.items.length > 1 && gSt.arrows && !mfp.arrowLeft) {\n\t\t\t\t\tvar markup = gSt.arrowMarkup,\n\t\t\t\t\t\tarrowLeft = mfp.arrowLeft = $( markup.replace(/%title%/gi, gSt.tPrev).replace(/%dir%/gi, 'left') ).addClass(PREVENT_CLOSE_CLASS),\t\t\t\n\t\t\t\t\t\tarrowRight = mfp.arrowRight = $( markup.replace(/%title%/gi, gSt.tNext).replace(/%dir%/gi, 'right') ).addClass(PREVENT_CLOSE_CLASS);\n\n\t\t\t\t\tvar eName = supportsFastClick ? 'mfpFastClick' : 'click';\n\t\t\t\t\tarrowLeft[eName](function() {\n\t\t\t\t\t\tmfp.prev();\n\t\t\t\t\t});\t\t\t\n\t\t\t\t\tarrowRight[eName](function() {\n\t\t\t\t\t\tmfp.next();\n\t\t\t\t\t});\t\n\n\t\t\t\t\t// Polyfill for :before and :after (adds elements with classes mfp-a and mfp-b)\n\t\t\t\t\tif(mfp.isIE7) {\n\t\t\t\t\t\t_getEl('b', arrowLeft[0], false, true);\n\t\t\t\t\t\t_getEl('a', arrowLeft[0], false, true);\n\t\t\t\t\t\t_getEl('b', arrowRight[0], false, true);\n\t\t\t\t\t\t_getEl('a', arrowRight[0], false, true);\n\t\t\t\t\t}\n\n\t\t\t\t\tmfp.container.append(arrowLeft.add(arrowRight));\n\t\t\t\t}\n\t\t\t});\n\n\t\t\t_mfpOn(CHANGE_EVENT+ns, function() {\n\t\t\t\tif(mfp._preloadTimeout) clearTimeout(mfp._preloadTimeout);\n\n\t\t\t\tmfp._preloadTimeout = setTimeout(function() {\n\t\t\t\t\tmfp.preloadNearbyImages();\n\t\t\t\t\tmfp._preloadTimeout = null;\n\t\t\t\t}, 16);\t\t\n\t\t\t});\n\n\n\t\t\t_mfpOn(CLOSE_EVENT+ns, function() {\n\t\t\t\t_document.off(ns);\n\t\t\t\tmfp.wrap.off('click'+ns);\n\t\t\t\n\t\t\t\tif(mfp.arrowLeft && supportsFastClick) {\n\t\t\t\t\tmfp.arrowLeft.add(mfp.arrowRight).destroyMfpFastClick();\n\t\t\t\t}\n\t\t\t\tmfp.arrowRight = mfp.arrowLeft = null;\n\t\t\t});\n\n\t\t}, \n\t\tnext: function() {\n\t\t\tmfp.direction = true;\n\t\t\tmfp.index = _getLoopedId(mfp.index + 1);\n\t\t\tmfp.updateItemHTML();\n\t\t},\n\t\tprev: function() {\n\t\t\tmfp.direction = false;\n\t\t\tmfp.index = _getLoopedId(mfp.index - 1);\n\t\t\tmfp.updateItemHTML();\n\t\t},\n\t\tgoTo: function(newIndex) {\n\t\t\tmfp.direction = (newIndex >= mfp.index);\n\t\t\tmfp.index = newIndex;\n\t\t\tmfp.updateItemHTML();\n\t\t},\n\t\tpreloadNearbyImages: function() {\n\t\t\tvar p = mfp.st.gallery.preload,\n\t\t\t\tpreloadBefore = Math.min(p[0], mfp.items.length),\n\t\t\t\tpreloadAfter = Math.min(p[1], mfp.items.length),\n\t\t\t\ti;\n\n\t\t\tfor(i = 1; i <= (mfp.direction ? preloadAfter : preloadBefore); i++) {\n\t\t\t\tmfp._preloadItem(mfp.index+i);\n\t\t\t}\n\t\t\tfor(i = 1; i <= (mfp.direction ? preloadBefore : preloadAfter); i++) {\n\t\t\t\tmfp._preloadItem(mfp.index-i);\n\t\t\t}\n\t\t},\n\t\t_preloadItem: function(index) {\n\t\t\tindex = _getLoopedId(index);\n\n\t\t\tif(mfp.items[index].preloaded) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tvar item = mfp.items[index];\n\t\t\tif(!item.parsed) {\n\t\t\t\titem = mfp.parseEl( index );\n\t\t\t}\n\n\t\t\t_mfpTrigger('LazyLoad', item);\n\n\t\t\tif(item.type === 'image') {\n\t\t\t\titem.img = $('<img class=\"mfp-img\" />').on('load.mfploader', function() {\n\t\t\t\t\titem.hasSize = true;\n\t\t\t\t}).on('error.mfploader', function() {\n\t\t\t\t\titem.hasSize = true;\n\t\t\t\t\titem.loadError = true;\n\t\t\t\t\t_mfpTrigger('LazyLoadError', item);\n\t\t\t\t}).attr('src', item.src);\n\t\t\t}\n\n\n\t\t\titem.preloaded = true;\n\t\t}\n\t}\n});\n\n/*\nTouch Support that might be implemented some day\n\naddSwipeGesture: function() {\n\tvar startX,\n\t\tmoved,\n\t\tmultipleTouches;\n\n\t\treturn;\n\n\tvar namespace = '.mfp',\n\t\taddEventNames = function(pref, down, move, up, cancel) {\n\t\t\tmfp._tStart = pref + down + namespace;\n\t\t\tmfp._tMove = pref + move + namespace;\n\t\t\tmfp._tEnd = pref + up + namespace;\n\t\t\tmfp._tCancel = pref + cancel + namespace;\n\t\t};\n\n\tif(window.navigator.msPointerEnabled) {\n\t\taddEventNames('MSPointer', 'Down', 'Move', 'Up', 'Cancel');\n\t} else if('ontouchstart' in window) {\n\t\taddEventNames('touch', 'start', 'move', 'end', 'cancel');\n\t} else {\n\t\treturn;\n\t}\n\t_window.on(mfp._tStart, function(e) {\n\t\tvar oE = e.originalEvent;\n\t\tmultipleTouches = moved = false;\n\t\tstartX = oE.pageX || oE.changedTouches[0].pageX;\n\t}).on(mfp._tMove, function(e) {\n\t\tif(e.originalEvent.touches.length > 1) {\n\t\t\tmultipleTouches = e.originalEvent.touches.length;\n\t\t} else {\n\t\t\t//e.preventDefault();\n\t\t\tmoved = true;\n\t\t}\n\t}).on(mfp._tEnd + ' ' + mfp._tCancel, function(e) {\n\t\tif(moved && !multipleTouches) {\n\t\t\tvar oE = e.originalEvent,\n\t\t\t\tdiff = startX - (oE.pageX || oE.changedTouches[0].pageX);\n\n\t\t\tif(diff > 20) {\n\t\t\t\tmfp.next();\n\t\t\t} else if(diff < -20) {\n\t\t\t\tmfp.prev();\n\t\t\t}\n\t\t}\n\t});\n},\n*/\n\n\n/*>>gallery*/\n\n/*>>retina*/\n\nvar RETINA_NS = 'retina';\n\n$.magnificPopup.registerModule(RETINA_NS, {\n\toptions: {\n\t\treplaceSrc: function(item) {\n\t\t\treturn item.src.replace(/\\.\\w+$/, function(m) { return '@2x' + m; });\n\t\t},\n\t\tratio: 1 // Function or number.  Set to 1 to disable.\n\t},\n\tproto: {\n\t\tinitRetina: function() {\n\t\t\tif(window.devicePixelRatio > 1) {\n\n\t\t\t\tvar st = mfp.st.retina,\n\t\t\t\t\tratio = st.ratio;\n\n\t\t\t\tratio = !isNaN(ratio) ? ratio : ratio();\n\n\t\t\t\tif(ratio > 1) {\n\t\t\t\t\t_mfpOn('ImageHasSize' + '.' + RETINA_NS, function(e, item) {\n\t\t\t\t\t\titem.img.css({\n\t\t\t\t\t\t\t'max-width': item.img[0].naturalWidth / ratio,\n\t\t\t\t\t\t\t'width': '100%'\n\t\t\t\t\t\t});\n\t\t\t\t\t});\n\t\t\t\t\t_mfpOn('ElementParse' + '.' + RETINA_NS, function(e, item) {\n\t\t\t\t\t\titem.src = st.replaceSrc(item, ratio);\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t}\n\n\t\t}\n\t}\n});\n\n/*>>retina*/\n\n/*>>fastclick*/\n/**\n * FastClick event implementation. (removes 300ms delay on touch devices)\n * Based on https://developers.google.com/mobile/articles/fast_buttons\n *\n * You may use it outside the Magnific Popup by calling just:\n *\n * $('.your-el').mfpFastClick(function() {\n *     console.log('Clicked!');\n * });\n *\n * To unbind:\n * $('.your-el').destroyMfpFastClick();\n * \n * \n * Note that it's a very basic and simple implementation, it blocks ghost click on the same element where it was bound.\n * If you need something more advanced, use plugin by FT Labs https://github.com/ftlabs/fastclick\n * \n */\n\n(function() {\n\tvar ghostClickDelay = 1000,\n\t\tsupportsTouch = 'ontouchstart' in window,\n\t\tunbindTouchMove = function() {\n\t\t\t_window.off('touchmove'+ns+' touchend'+ns);\n\t\t},\n\t\teName = 'mfpFastClick',\n\t\tns = '.'+eName;\n\n\n\t// As Zepto.js doesn't have an easy way to add custom events (like jQuery), so we implement it in this way\n\t$.fn.mfpFastClick = function(callback) {\n\n\t\treturn $(this).each(function() {\n\n\t\t\tvar elem = $(this),\n\t\t\t\tlock;\n\n\t\t\tif( supportsTouch ) {\n\n\t\t\t\tvar timeout,\n\t\t\t\t\tstartX,\n\t\t\t\t\tstartY,\n\t\t\t\t\tpointerMoved,\n\t\t\t\t\tpoint,\n\t\t\t\t\tnumPointers;\n\n\t\t\t\telem.on('touchstart' + ns, function(e) {\n\t\t\t\t\tpointerMoved = false;\n\t\t\t\t\tnumPointers = 1;\n\n\t\t\t\t\tpoint = e.originalEvent ? e.originalEvent.touches[0] : e.touches[0];\n\t\t\t\t\tstartX = point.clientX;\n\t\t\t\t\tstartY = point.clientY;\n\n\t\t\t\t\t_window.on('touchmove'+ns, function(e) {\n\t\t\t\t\t\tpoint = e.originalEvent ? e.originalEvent.touches : e.touches;\n\t\t\t\t\t\tnumPointers = point.length;\n\t\t\t\t\t\tpoint = point[0];\n\t\t\t\t\t\tif (Math.abs(point.clientX - startX) > 10 ||\n\t\t\t\t\t\t\tMath.abs(point.clientY - startY) > 10) {\n\t\t\t\t\t\t\tpointerMoved = true;\n\t\t\t\t\t\t\tunbindTouchMove();\n\t\t\t\t\t\t}\n\t\t\t\t\t}).on('touchend'+ns, function(e) {\n\t\t\t\t\t\tunbindTouchMove();\n\t\t\t\t\t\tif(pointerMoved || numPointers > 1) {\n\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tlock = true;\n\t\t\t\t\t\te.preventDefault();\n\t\t\t\t\t\tclearTimeout(timeout);\n\t\t\t\t\t\ttimeout = setTimeout(function() {\n\t\t\t\t\t\t\tlock = false;\n\t\t\t\t\t\t}, ghostClickDelay);\n\t\t\t\t\t\tcallback();\n\t\t\t\t\t});\n\t\t\t\t});\n\n\t\t\t}\n\n\t\t\telem.on('click' + ns, function() {\n\t\t\t\tif(!lock) {\n\t\t\t\t\tcallback();\n\t\t\t\t}\n\t\t\t});\n\t\t});\n\t};\n\n\t$.fn.destroyMfpFastClick = function() {\n\t\t$(this).off('touchstart' + ns + ' click' + ns);\n\t\tif(supportsTouch) _window.off('touchmove'+ns+' touchend'+ns);\n\t};\n})();\n\n/*>>fastclick*/\n _checkInstance(); })(window.jQuery || window.Zepto);"
  },
  {
    "path": "website/assets/js/plugins/respond.js",
    "content": "/*! matchMedia() polyfill - Test a CSS media type/query in JS. Authors & copyright (c) 2012: Scott Jehl, Paul Irish, Nicholas Zakas. Dual MIT/BSD license */\n/*! NOTE: If you're already including a window.matchMedia polyfill via Modernizr or otherwise, you don't need this part */\n\nwindow.matchMedia = window.matchMedia || (function( doc, undefined ) {\n\n  \"use strict\";\n\n  var bool,\n      docElem = doc.documentElement,\n      refNode = docElem.firstElementChild || docElem.firstChild,\n      // fakeBody required for <FF4 when executed in <head>\n      fakeBody = doc.createElement( \"body\" ),\n      div = doc.createElement( \"div\" );\n\n  div.id = \"mq-test-1\";\n  div.style.cssText = \"position:absolute;top:-100em\";\n  fakeBody.style.background = \"none\";\n  fakeBody.appendChild(div);\n\n  return function(q){\n\n    div.innerHTML = \"&shy;<style media=\\\"\" + q + \"\\\"> #mq-test-1 { width: 42px; }</style>\";\n\n    docElem.insertBefore( fakeBody, refNode );\n    bool = div.offsetWidth === 42;\n    docElem.removeChild( fakeBody );\n\n    return {\n      matches: bool,\n      media: q\n    };\n\n  };\n\n}( document ));\n\n\n\n\n\n/*! Respond.js v1.1.0: min/max-width media query polyfill. (c) Scott Jehl. MIT/GPLv2 Lic. j.mp/respondjs  */\n(function( win ){\n\n\t\"use strict\";\n\n\t//exposed namespace\n\tvar respond = {};\n\twin.respond = respond;\n\t\n\t//define update even in native-mq-supporting browsers, to avoid errors\n\trespond.update = function(){};\n\t\n\t//expose media query support flag for external use\n\trespond.mediaQueriesSupported\t= win.matchMedia && win.matchMedia( \"only all\" ).matches;\n\t\n\t//if media queries are supported, exit here\n\tif( respond.mediaQueriesSupported ){\n\t\treturn;\n\t}\n\t\n\t//define vars\n\tvar doc = win.document,\n\t\tdocElem = doc.documentElement,\n\t\tmediastyles = [],\n\t\trules = [],\n\t\tappendedEls = [],\n\t\tparsedSheets = {},\n\t\tresizeThrottle = 30,\n\t\thead = doc.getElementsByTagName( \"head\" )[0] || docElem,\n\t\tbase = doc.getElementsByTagName( \"base\" )[0],\n\t\tlinks = head.getElementsByTagName( \"link\" ),\n\t\trequestQueue = [],\n\t\t\n\t\t//loop stylesheets, send text content to translate\n\t\tripCSS = function(){\n\n\t\t\tfor( var i = 0; i < links.length; i++ ){\n\t\t\t\tvar sheet = links[ i ],\n\t\t\t\thref = sheet.href,\n\t\t\t\tmedia = sheet.media,\n\t\t\t\tisCSS = sheet.rel && sheet.rel.toLowerCase() === \"stylesheet\";\n\n\t\t\t\t//only links plz and prevent re-parsing\n\t\t\t\tif( !!href && isCSS && !parsedSheets[ href ] ){\n\t\t\t\t\t// selectivizr exposes css through the rawCssText expando\n\t\t\t\t\tif (sheet.styleSheet && sheet.styleSheet.rawCssText) {\n\t\t\t\t\t\ttranslate( sheet.styleSheet.rawCssText, href, media );\n\t\t\t\t\t\tparsedSheets[ href ] = true;\n\t\t\t\t\t} else {\n\t\t\t\t\t\tif( (!/^([a-zA-Z:]*\\/\\/)/.test( href ) && !base) ||\n\t\t\t\t\t\t\thref.replace( RegExp.$1, \"\" ).split( \"/\" )[0] === win.location.host ){\n\t\t\t\t\t\t\trequestQueue.push( {\n\t\t\t\t\t\t\t\thref: href,\n\t\t\t\t\t\t\t\tmedia: media\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tmakeRequests();\n\t\t},\n\t\t\n\t\t//recurse through request queue, get css text\n\t\tmakeRequests\t= function(){\n\t\t\tif( requestQueue.length ){\n\t\t\t\tvar thisRequest = requestQueue.shift();\n\t\t\t\t\n\t\t\t\tajax( thisRequest.href, function( styles ){\n\t\t\t\t\ttranslate( styles, thisRequest.href, thisRequest.media );\n\t\t\t\t\tparsedSheets[ thisRequest.href ] = true;\n\n\t\t\t\t\t// by wrapping recursive function call in setTimeout \n\t\t\t\t\t// we prevent \"Stack overflow\" error in IE7\n\t\t\t\t\twin.setTimeout(function(){ makeRequests(); },0);\n\t\t\t\t} );\n\t\t\t}\n\t\t},\n\t\t\n\t\t//find media blocks in css text, convert to style blocks\n\t\ttranslate = function( styles, href, media ){\n\t\t\tvar qs = styles.match(  /@media[^\\{]+\\{([^\\{\\}]*\\{[^\\}\\{]*\\})+/gi ),\n\t\t\t\tql = qs && qs.length || 0;\n\n\t\t\t//try to get CSS path\n\t\t\thref = href.substring( 0, href.lastIndexOf( \"/\" ) );\n\n\t\t\tvar repUrls\t= function( css ){\n\t\t\t\t\treturn css.replace( /(url\\()['\"]?([^\\/\\)'\"][^:\\)'\"]+)['\"]?(\\))/g, \"$1\" + href + \"$2$3\" );\n\t\t\t\t},\n\t\t\t\tuseMedia = !ql && media;\n\n\t\t\t//if path exists, tack on trailing slash\n\t\t\tif( href.length ){ href += \"/\"; }\t\n\t\t\t\t\n\t\t\t//if no internal queries exist, but media attr does, use that\t\n\t\t\t//note: this currently lacks support for situations where a media attr is specified on a link AND\n\t\t\t\t//its associated stylesheet has internal CSS media queries.\n\t\t\t\t//In those cases, the media attribute will currently be ignored.\n\t\t\tif( useMedia ){\n\t\t\t\tql = 1;\n\t\t\t}\n\n\t\t\tfor( var i = 0; i < ql; i++ ){\n\t\t\t\tvar fullq, thisq, eachq, eql;\n\n\t\t\t\t//media attr\n\t\t\t\tif( useMedia ){\n\t\t\t\t\tfullq = media;\n\t\t\t\t\trules.push( repUrls( styles ) );\n\t\t\t\t}\n\t\t\t\t//parse for styles\n\t\t\t\telse{\n\t\t\t\t\tfullq = qs[ i ].match( /@media *([^\\{]+)\\{([\\S\\s]+?)$/ ) && RegExp.$1;\n\t\t\t\t\trules.push( RegExp.$2 && repUrls( RegExp.$2 ) );\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\teachq = fullq.split( \",\" );\n\t\t\t\teql\t= eachq.length;\n\t\t\t\t\t\n\t\t\t\tfor( var j = 0; j < eql; j++ ){\n\t\t\t\t\tthisq = eachq[ j ];\n\t\t\t\t\tmediastyles.push( { \n\t\t\t\t\t\tmedia : thisq.split( \"(\" )[ 0 ].match( /(only\\s+)?([a-zA-Z]+)\\s?/ ) && RegExp.$2 || \"all\",\n\t\t\t\t\t\trules : rules.length - 1,\n\t\t\t\t\t\thasquery : thisq.indexOf(\"(\") > -1,\n\t\t\t\t\t\tminw : thisq.match( /\\(\\s*min\\-width\\s*:\\s*(\\s*[0-9\\.]+)(px|em)\\s*\\)/ ) && parseFloat( RegExp.$1 ) + ( RegExp.$2 || \"\" ), \n\t\t\t\t\t\tmaxw : thisq.match( /\\(\\s*max\\-width\\s*:\\s*(\\s*[0-9\\.]+)(px|em)\\s*\\)/ ) && parseFloat( RegExp.$1 ) + ( RegExp.$2 || \"\" )\n\t\t\t\t\t} );\n\t\t\t\t}\t\n\t\t\t}\n\n\t\t\tapplyMedia();\n\t\t},\n        \n\t\tlastCall,\n\t\t\n\t\tresizeDefer,\n\t\t\n\t\t// returns the value of 1em in pixels\n\t\tgetEmValue = function() {\n\t\t\tvar ret,\n\t\t\t\tdiv = doc.createElement('div'),\n\t\t\t\tbody = doc.body,\n\t\t\t\tfakeUsed = false;\n\t\t\t\t\t\t\t\t\t\n\t\t\tdiv.style.cssText = \"position:absolute;font-size:1em;width:1em\";\n\t\t\t\t\t\n\t\t\tif( !body ){\n\t\t\t\tbody = fakeUsed = doc.createElement( \"body\" );\n\t\t\t\tbody.style.background = \"none\";\n\t\t\t}\n\t\t\t\t\t\n\t\t\tbody.appendChild( div );\n\t\t\t\t\t\t\t\t\n\t\t\tdocElem.insertBefore( body, docElem.firstChild );\n\t\t\t\t\t\t\t\t\n\t\t\tret = div.offsetWidth;\n\t\t\t\t\t\t\t\t\n\t\t\tif( fakeUsed ){\n\t\t\t\tdocElem.removeChild( body );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tbody.removeChild( div );\n\t\t\t}\n\t\t\t\n\t\t\t//also update eminpx before returning\n\t\t\tret = eminpx = parseFloat(ret);\n\t\t\t\t\t\t\t\t\n\t\t\treturn ret;\n\t\t},\n\t\t\n\t\t//cached container for 1em value, populated the first time it's needed \n\t\teminpx,\n\t\t\n\t\t//enable/disable styles\n\t\tapplyMedia = function( fromResize ){\n\t\t\tvar name = \"clientWidth\",\n\t\t\t\tdocElemProp = docElem[ name ],\n\t\t\t\tcurrWidth = doc.compatMode === \"CSS1Compat\" && docElemProp || doc.body[ name ] || docElemProp,\n\t\t\t\tstyleBlocks\t= {},\n\t\t\t\tlastLink = links[ links.length-1 ],\n\t\t\t\tnow = (new Date()).getTime();\n\n\t\t\t//throttle resize calls\t\n\t\t\tif( fromResize && lastCall && now - lastCall < resizeThrottle ){\n\t\t\t\twin.clearTimeout( resizeDefer );\n\t\t\t\tresizeDefer = win.setTimeout( applyMedia, resizeThrottle );\n\t\t\t\treturn;\n\t\t\t}\n\t\t\telse {\n\t\t\t\tlastCall = now;\n\t\t\t}\n\t\t\t\t\t\t\t\t\t\t\n\t\t\tfor( var i in mediastyles ){\n\t\t\t\tif( mediastyles.hasOwnProperty( i ) ){\n\t\t\t\t\tvar thisstyle = mediastyles[ i ],\n\t\t\t\t\t\tmin = thisstyle.minw,\n\t\t\t\t\t\tmax = thisstyle.maxw,\n\t\t\t\t\t\tminnull = min === null,\n\t\t\t\t\t\tmaxnull = max === null,\n\t\t\t\t\t\tem = \"em\";\n\t\t\t\t\t\n\t\t\t\t\tif( !!min ){\n\t\t\t\t\t\tmin = parseFloat( min ) * ( min.indexOf( em ) > -1 ? ( eminpx || getEmValue() ) : 1 );\n\t\t\t\t\t}\n\t\t\t\t\tif( !!max ){\n\t\t\t\t\t\tmax = parseFloat( max ) * ( max.indexOf( em ) > -1 ? ( eminpx || getEmValue() ) : 1 );\n\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t\t// if there's no media query at all (the () part), or min or max is not null, and if either is present, they're true\n\t\t\t\t\tif( !thisstyle.hasquery || ( !minnull || !maxnull ) && ( minnull || currWidth >= min ) && ( maxnull || currWidth <= max ) ){\n\t\t\t\t\t\tif( !styleBlocks[ thisstyle.media ] ){\n\t\t\t\t\t\t\tstyleBlocks[ thisstyle.media ] = [];\n\t\t\t\t\t\t}\n\t\t\t\t\t\tstyleBlocks[ thisstyle.media ].push( rules[ thisstyle.rules ] );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\t\n\t\t\t//remove any existing respond style element(s)\n\t\t\tfor( var j in appendedEls ){\n\t\t\t\tif( appendedEls.hasOwnProperty( j ) ){\n\t\t\t\t\tif( appendedEls[ j ] && appendedEls[ j ].parentNode === head ){\n\t\t\t\t\t\thead.removeChild( appendedEls[ j ] );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\t\n\t\t\t//inject active styles, grouped by media type\n\t\t\tfor( var k in styleBlocks ){\n\t\t\t\tif( styleBlocks.hasOwnProperty( k ) ){\n\t\t\t\t\tvar ss = doc.createElement( \"style\" ),\n\t\t\t\t\t\tcss = styleBlocks[ k ].join( \"\\n\" );\n\t\t\t\t\t\n\t\t\t\t\tss.type = \"text/css\";\t\n\t\t\t\t\tss.media = k;\n\t\t\t\t\t\n\t\t\t\t\t//originally, ss was appended to a documentFragment and sheets were appended in bulk.\n\t\t\t\t\t//this caused crashes in IE in a number of circumstances, such as when the HTML element had a bg image set, so appending beforehand seems best. Thanks to @dvelyk for the initial research on this one!\n\t\t\t\t\thead.insertBefore( ss, lastLink.nextSibling );\n\t\t\t\t\t\n\t\t\t\t\tif ( ss.styleSheet ){ \n\t\t\t\t\t\tss.styleSheet.cssText = css;\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tss.appendChild( doc.createTextNode( css ) );\n\t\t\t\t\t}\n\n\t\t\t\t\t//push to appendedEls to track for later removal\n\t\t\t\t\tappendedEls.push( ss );\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\t\t//tweaked Ajax functions from Quirksmode\n\t\tajax = function( url, callback ) {\n\t\t\tvar req = xmlHttp();\n\t\t\tif (!req){\n\t\t\t\treturn;\n\t\t\t}\t\n\t\t\treq.open( \"GET\", url, true );\n\t\t\treq.onreadystatechange = function () {\n\t\t\t\tif ( req.readyState !== 4 || req.status !== 200 && req.status !== 304 ){\n\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t\tcallback( req.responseText );\n\t\t\t};\n\t\t\tif ( req.readyState === 4 ){\n\t\t\t\treturn;\n\t\t\t}\n\t\t\treq.send( null );\n\t\t},\n\t\t//define ajax obj \n\t\txmlHttp = (function() {\n\t\t\tvar xmlhttpmethod = false;\t\n\t\t\ttry {\n\t\t\t\txmlhttpmethod = new win.XMLHttpRequest();\n\t\t\t}\n\t\t\tcatch( e ){\n\t\t\t\txmlhttpmethod = new win.ActiveXObject( \"Microsoft.XMLHTTP\" );\n\t\t\t}\n\t\t\treturn function(){\n\t\t\t\treturn xmlhttpmethod;\n\t\t\t};\n\t\t})();\n\t\n\t//translate CSS\n\tripCSS();\n\t\n\t//expose update for re-running respond later on\n\trespond.update = ripCSS;\n\t\n\t//adjust on resize\n\tfunction callMedia(){\n\t\tapplyMedia( true );\n\t}\n\tif( win.addEventListener ){\n\t\twin.addEventListener( \"resize\", callMedia, false );\n\t}\n\telse if( win.attachEvent ){\n\t\twin.attachEvent( \"onresize\", callMedia );\n\t}\n})(this);"
  },
  {
    "path": "website/assets/js/plugins/responsive-nav.js",
    "content": "/*! responsive-nav.js v1.0.14\n * https://github.com/viljamis/responsive-nav.js\n * http://responsive-nav.com\n *\n * Copyright (c) 2013 @viljamis\n * Available under the MIT license\n */\n\n/* jshint strict:false, forin:false, noarg:true, noempty:true, eqeqeq:true,\nboss:true, bitwise:true, browser:true, devel:true, indent:2 */\n/* exported responsiveNav */\n\nvar responsiveNav = (function (window, document) {\n\n  var computed = !!window.getComputedStyle;\n\n  // getComputedStyle polyfill\n  if (!window.getComputedStyle) {\n    window.getComputedStyle = function(el) {\n      this.el = el;\n      this.getPropertyValue = function(prop) {\n        var re = /(\\-([a-z]){1})/g;\n        if (prop === \"float\") {\n          prop = \"styleFloat\";\n        }\n        if (re.test(prop)) {\n          prop = prop.replace(re, function () {\n            return arguments[2].toUpperCase();\n          });\n        }\n        return el.currentStyle[prop] ? el.currentStyle[prop] : null;\n      };\n      return this;\n    };\n  }\n\n  var nav,\n    opts,\n    navToggle,\n    docEl = document.documentElement,\n    head = document.getElementsByTagName(\"head\")[0],\n    styleElement = document.createElement(\"style\"),\n    navOpen = false,\n\n    // fn arg can be an object or a function, thanks to handleEvent\n    // read more at: http://www.thecssninja.com/javascript/handleevent\n    addEvent = function (el, evt, fn, bubble) {\n      if (\"addEventListener\" in el) {\n        // BBOS6 doesn't support handleEvent, catch and polyfill\n        try {\n          el.addEventListener(evt, fn, bubble);\n        } catch (e) {\n          if (typeof fn === \"object\" && fn.handleEvent) {\n            el.addEventListener(evt, function (e) {\n              // Bind fn as this and set first arg as event object\n              fn.handleEvent.call(fn, e);\n            }, bubble);\n          } else {\n            throw e;\n          }\n        }\n      } else if (\"attachEvent\" in el) {\n        // check if the callback is an object and contains handleEvent\n        if (typeof fn === \"object\" && fn.handleEvent) {\n          el.attachEvent(\"on\" + evt, function () {\n            // Bind fn as this\n            fn.handleEvent.call(fn);\n          });\n        } else {\n          el.attachEvent(\"on\" + evt, fn);\n        }\n      }\n    },\n\n    removeEvent = function (el, evt, fn, bubble) {\n      if (\"removeEventListener\" in el) {\n        try {\n          el.removeEventListener(evt, fn, bubble);\n        } catch (e) {\n          if (typeof fn === \"object\" && fn.handleEvent) {\n            el.removeEventListener(evt, function (e) {\n              fn.handleEvent.call(fn, e);\n            }, bubble);\n          } else {\n            throw e;\n          }\n        }\n      } else if (\"detachEvent\" in el) {\n        if (typeof fn === \"object\" && fn.handleEvent) {\n          el.detachEvent(\"on\" + evt, function () {\n            fn.handleEvent.call(fn);\n          });\n        } else {\n          el.detachEvent(\"on\" + evt, fn);\n        }\n      }\n    },\n\n    getFirstChild = function (e) {\n      var firstChild = e.firstChild;\n      // skip TextNodes\n      while (firstChild !== null && firstChild.nodeType !== 1) {\n        firstChild = firstChild.nextSibling;\n      }\n      return firstChild;\n    },\n\n    setAttributes = function (el, attrs) {\n      for (var key in attrs) {\n        el.setAttribute(key, attrs[key]);\n      }\n    },\n\n    addClass = function (el, cls) {\n      el.className += \" \" + cls;\n      el.className = el.className.replace(/(^\\s*)|(\\s*$)/g,\"\");\n    },\n\n    removeClass = function (el, cls) {\n      var reg = new RegExp(\"(\\\\s|^)\" + cls + \"(\\\\s|$)\");\n      el.className = el.className.replace(reg, \" \").replace(/(^\\s*)|(\\s*$)/g,\"\");\n    },\n\n    ResponsiveNav = function (el, options) {\n      var i;\n\n      // Default options\n      this.options = {\n        animate: true,        // Boolean: Use CSS3 transitions, true or false\n        transition: 400,      // Integer: Speed of the transition, in milliseconds\n        label: \"Menu\",        // String: Label for the navigation toggle\n        insert: \"after\",      // String: Insert the toggle before or after the navigation\n        customToggle: \"\",     // Selector: Specify the ID of a custom toggle\n        openPos: \"relative\",  // String: Position of the opened nav, relative or static\n        jsClass: \"js\",        // String: 'JS enabled' class which is added to <html> el\n        init: function(){},   // Function: Init callback\n        open: function(){},   // Function: Open callback\n        close: function(){}   // Function: Close callback\n      };\n\n      // User defined options\n      for (i in options) {\n        this.options[i] = options[i];\n      }\n\n      // Adds \"js\" class for <html>\n      addClass(docEl, this.options.jsClass);\n\n      // Wrapper\n      this.wrapperEl = el.replace(\"#\", \"\");\n      if (document.getElementById(this.wrapperEl)) {\n        this.wrapper = document.getElementById(this.wrapperEl);\n      } else {\n        // If el doesn't exists, stop here.\n        throw new Error(\"The nav element you are trying to select doesn't exist\");\n      }\n\n      // Inner wrapper\n      this.wrapper.inner = getFirstChild(this.wrapper);\n\n      // For minification\n      opts = this.options;\n      nav = this.wrapper;\n\n      // Init\n      this._init(this);\n    };\n\n  ResponsiveNav.prototype = {\n    // Public methods\n    destroy: function () {\n      this._removeStyles();\n      removeClass(nav, \"closed\");\n      removeClass(nav, \"opened\");\n      nav.removeAttribute(\"style\");\n      nav.removeAttribute(\"aria-hidden\");\n      nav = null;\n      _instance = null;\n\n      removeEvent(window, \"load\", this, false);\n      removeEvent(window, \"resize\", this, false);\n      removeEvent(navToggle, \"mousedown\", this, false);\n      removeEvent(navToggle, \"touchstart\", this, false);\n      removeEvent(navToggle, \"touchend\", this, false);\n      removeEvent(navToggle, \"keyup\", this, false);\n      removeEvent(navToggle, \"click\", this, false);\n\n      if (!opts.customToggle) {\n        navToggle.parentNode.removeChild(navToggle);\n      } else {\n        navToggle.removeAttribute(\"aria-hidden\");\n      }\n    },\n\n    toggle: function () {\n      if (!navOpen) {\n        removeClass(nav, \"closed\");\n        addClass(nav, \"opened\");\n        nav.style.position = opts.openPos;\n        setAttributes(nav, {\"aria-hidden\": \"false\"});\n\n        navOpen = true;\n        opts.open();\n      } else {\n        removeClass(nav, \"opened\");\n        addClass(nav, \"closed\");\n        setAttributes(nav, {\"aria-hidden\": \"true\"});\n\n  \n\n        navOpen = false;\n        opts.close();\n      }\n    },\n\n    handleEvent: function (e) {\n      var evt = e || window.event;\n\n      switch (evt.type) {\n      case \"mousedown\":\n        this._onmousedown(evt);\n        break;\n      case \"touchstart\":\n        this._ontouchstart(evt);\n        break;\n      case \"touchend\":\n        this._ontouchend(evt);\n        break;\n      case \"keyup\":\n        this._onkeyup(evt);\n        break;\n      case \"click\":\n        this._onclick(evt);\n        break;\n      case \"load\":\n        this._transitions(evt);\n        this._resize(evt);\n        break;\n      case \"resize\":\n        this._resize(evt);\n        break;\n      }\n    },\n\n    // Private methods\n    _init: function () {\n      addClass(nav, \"closed\");\n      this._createToggle();\n\n      addEvent(window, \"load\", this, false);\n      addEvent(window, \"resize\", this, false);\n      addEvent(navToggle, \"mousedown\", this, false);\n      addEvent(navToggle, \"touchstart\", this, false);\n      addEvent(navToggle, \"touchend\", this, false);\n      addEvent(navToggle, \"keyup\", this, false);\n      addEvent(navToggle, \"click\", this, false);\n    },\n\n    _createStyles: function () {\n      if (!styleElement.parentNode) {\n        head.appendChild(styleElement);\n      }\n    },\n\n    _removeStyles: function () {\n      if (styleElement.parentNode) {\n        styleElement.parentNode.removeChild(styleElement);\n      }\n    },\n\n    _createToggle: function () {\n      if (!opts.customToggle) {\n        var toggle = document.createElement(\"a\");\n        toggle.innerHTML = opts.label;\n        setAttributes(toggle, {\n          \"href\": \"#\",\n          \"id\": \"nav-toggle\"\n        });\n\n        if (opts.insert === \"after\") {\n          nav.parentNode.insertBefore(toggle, nav.nextSibling);\n        } else {\n          nav.parentNode.insertBefore(toggle, nav);\n        }\n\n        navToggle = document.getElementById(\"nav-toggle\");\n      } else {\n        var toggleEl = opts.customToggle.replace(\"#\", \"\");\n\n        if (document.getElementById(toggleEl)) {\n          navToggle = document.getElementById(toggleEl);\n        } else {\n          throw new Error(\"The custom nav toggle you are trying to select doesn't exist\");\n        }\n      }\n    },\n\n    _preventDefault: function(e) {\n      if (e.preventDefault) {\n        e.preventDefault();\n        e.stopPropagation();\n      } else {\n        e.returnValue = false;\n      }\n    },\n\n    _onmousedown: function (e) {\n      var evt = e || window.event;\n      // If the user isn't right clicking:\n      if (!(evt.which === 3 || evt.button === 2)) {\n        this._preventDefault(e);\n        this.toggle(e);\n      }\n    },\n\n    _ontouchstart: function (e) {\n      // Touchstart event fires before\n      // the mousedown and can wipe it\n      navToggle.onmousedown = null;\n      this._preventDefault(e);\n      this.toggle(e);\n    },\n\n    _ontouchend: function () {\n      // Prevents ghost click from happening on some Android browsers\n    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\".opened{max-height:\" + savedHeight + \"px}\";\n\n      // Hide from old IE\n      if (computed) {\n        styleElement.innerHTML = innerStyles;\n        innerStyles = \"\";\n      }\n    },\n\n    _resize: function () {\n      if (window.getComputedStyle(navToggle, null).getPropertyValue(\"display\") !== \"none\") {\n        setAttributes(navToggle, {\"aria-hidden\": \"false\"});\n\n        // If the navigation is hidden\n        if (nav.className.match(/(^|\\s)closed(\\s|$)/)) {\n          setAttributes(nav, {\"aria-hidden\": \"true\"});\n          nav.style.position = \"absolute\";\n        }\n\n        this._createStyles();\n        this._calcHeight();\n      } else {\n        setAttributes(navToggle, {\"aria-hidden\": \"true\"});\n        setAttributes(nav, {\"aria-hidden\": \"false\"});\n        nav.style.position = opts.openPos;\n        this._removeStyles();\n      }\n\n      // Init callback\n      opts.init();\n    }\n\n  };\n\n  var _instance;\n  function rn (el, options) 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  {
    "path": "website/assets/js/plugins/search.js",
    "content": "/**\n * A simple JSON search\n * Requires jQuery (v 1.7+)\n *\n * @author  Mat Hayward - Erskine Design\n * @version  0.1\n */\n\n\n /* ==========================================================================\n    Initialisation\n    ========================================================================== */\n\nvar q, jsonFeedUrl = \"/search.json\",\n    $searchForm = $(\"[data-search-form]\"),\n    $searchInput = $(\"[data-search-input]\"),\n    $resultTemplate = $(\"#search-result\"),\n    $resultsPlaceholder = $(\"[data-search-results]\"),\n    $foundContainer = $(\"[data-search-found]\"),\n    $foundTerm = $(\"[data-search-found-term]\"),\n    $foundCount = $(\"[data-search-found-count]\"),\n    allowEmpty = true,\n    showLoader = true,\n    loadingClass = \"is--loading\";\n\n\n$(document).ready( function() {\n    \n    // hide items found string\n    $foundContainer.hide();\n\n    // initiate search functionality\n    initSearch();\n});\n\n\n\n\n /* ==========================================================================\n    Search functions\n    ========================================================================== */\n \n\n/**\n * Initiate search functionality.\n * Shows results based on querystring if present.\n * Binds search function to form submission.\n */\nfunction initSearch() {\n\n    // Get search results if q parameter is set in querystring\n    if (getParameterByName('q')) {\n        q = decodeURIComponent(getParameterByName('q'));\n        $searchInput.val(q);\n        execSearch(q);\n    }\n\n    // Get search results on submission of form\n    $(document).on(\"submit\", $searchForm, function(e) {\n        e.preventDefault();\n        q = $searchInput.val();\n        execSearch(q);\n    });\n}\n\n\n/**\n * Executes search\n * @param {String} q \n * @return null\n */\nfunction execSearch(q) {\n    if (q != '' || allowEmpty) {\n        if (showLoader) {\n            toggleLoadingClass();\n        }\n\n        getSearchResults(processData());\n    }\n}\n\n\n/**\n * Toggles loading class on results and found string\n * @return null\n */\nfunction toggleLoadingClass() {\n    $resultsPlaceholder.toggleClass(loadingClass);\n    $foundContainer.toggleClass(loadingClass);\n}\n\n\n/**\n * Get Search results from JSON\n * @param {Function} callbackFunction \n * @return null\n */\nfunction getSearchResults(callbackFunction) {\n    $.get(BASE_URL + jsonFeedUrl, callbackFunction, 'json');\n}\n\n\n/**\n * Process search result data\n * @return null\n */\nfunction processData() {\n    $results = [];\n    \n    return function(data) {\n        \n        var resultsCount = 0,\n            results = \"\";\n\n        $.each(data, function(index, item) {\n            // check if search term is in content or title \n            if (item.excerpt.toLowerCase().indexOf(q.toLowerCase()) > -1 || item.title.toLowerCase().indexOf(q.toLowerCase()) > -1) {\n                var result = populateResultContent($resultTemplate.html(), item);\n                resultsCount++;\n                results += result;\n            }\n        });\n\n        if (showLoader) {\n            toggleLoadingClass();\n        }\n\n        populateResultsString(resultsCount);\n        showSearchResults(results);\n    }\n}\n\n\n/**\n * Add search results to placeholder\n * @param {String} results\n * @return null\n */\nfunction showSearchResults(results) {\n    // Add results HTML to placeholder\n    $resultsPlaceholder.html(results);\n}\n\n\n/**\n * Add results content to item template\n * @param {String} html \n * @param {object} item\n * @return {String} Populated HTML\n */\nfunction populateResultContent(html, item) {\n    html = injectContent(html, item.title, '##Title##');\n    html = injectContent(html, item.link, '##Url##');\n    html = injectContent(html, item.excerpt, '##Excerpt##');\n    html = injectContent(html, item.date, '##Date##');\n    return html;\n}\n\n\n/**\n * Populates results string\n * @param {String} count \n * @return null\n */\nfunction populateResultsString(count) {\n    $foundTerm.text(q);\n    $foundCount.text(count);\n    $foundContainer.show();\n}\n\n\n\n\n /* ==========================================================================\n    Helper functions\n    ========================================================================== */\n\n\n/**\n * Gets query string parameter - taken from http://stackoverflow.com/questions/901115/how-can-i-get-query-string-values-in-javascript\n * @param {String} name \n * @return {String} parameter value\n */\nfunction getParameterByName(name) {\n    var match = RegExp('[?&]' + name + '=([^&]*)').exec(window.location.search);\n    return match && decodeURIComponent(match[1].replace(/\\+/g, ' '));\n}\n\n\n/**\n * Injects content into template using placeholder\n * @param {String} originalContent\n * @param {String} injection\n * @param {String} placeholder \n * @return {String} injected content\n */\nfunction injectContent(originalContent, injection, placeholder) {\n    var regex = new RegExp(placeholder, 'g');\n    return originalContent.replace(regex, injection);\n}"
  },
  {
    "path": "website/docs/Bagging.md",
    "content": "---\nlayout: page\ntitle: Bagging\nimage:\n  feature: screen_network.png\n---\n\n## Online Bagging\n\n*Nikunj C. Oza, Stuart J. Russell:\nOnline Bagging and Boosting. AISTATS 2001*\n\n###General Description\n\n*Bagging* is an ensemble method that improves the accuracy of a single\nclassifier.  Non-streaming bagging builds a set of base models, training each\nmodel with a bootstrap sample created by drawing random samples with replacement\nfrom the original training set.  Each base model's training set contains each of\nthe original training example a number of times that follows a binomial\ndistribution.  This binomial distribution tends towards a Poisson(1) for large\nvalues. Thus, the online version of the Bagging classifier, instead of using\nsampling with replacement, gives each example a weight according to a Poisson(1)\ndistribution. \n\n###Implementation\n\nIn StreamDM, the main algorithm is implemented in `Bagging`, which\nis controlled by the following options:\n\n* Number of classifiers (**-s**), which sets the number of classifiers used to build the\n  ensemble (10 by default); and\n* Base classifier (**-l**), which sets the base classifier to be used to build the members \n  of the ensemble (`SGDLearner` by default).\n"
  },
  {
    "path": "website/docs/CluStream.md",
    "content": "---\nlayout: page\ntitle: CluStream\nimage:\n  feature: screen_network.png\n---\n\n## CluStream Clustering\n\n*Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu.\nA Framework for Clustering Evolving Data Streams. \nVLDB 2003: 81-92*\n\n###General Description\n\nThe *CluStream* method is a method of clustering data streams, based on the\nconcept of *microclusters*. Microclusters are data structures which summarize a\nset of instances from the stream, and is composed of a set of statistics which\nare easily updated and allow fast analysis.\n\nCluStream has two phases. In the *online* phase, a set of microclusters are kept\nin main memory; each instance coming from the input stream can then be either\nappended to an existing microcluster or created as a new microcluster. Space for\nthe new microcluster is created either by deleting a microcluster (by analyzing\nits expiration timestamp) or by merging the two closest microclusters. The\n*offline* phase will apply a weighted k-means algorithm on the microclusters, to\nobtain the final clusters from the stream.\n\n###Implementation\n\nIn StreamDM, two classes deal with the implementation of the CluStream\nalgorithm: `MicroClusters` and `Clustream`.\n\n`MicroClusters` is the main data structure keeping the online microclusters\nupdated. It is controlled by the following options:\n\n* Horizon parameter (**-h**), which controls the size of horizon window in\n  seconds (1000 by default), which controls which microclusters become expired;\n* Cluster radius multiplier parameter (**-r**), which controls how large the\n  radius of a microcluster is to allow new instances to be appended (2 by\n  default); and\n* m-value parameter (**-m**) which controls the number of standard deviations\n  from the mean timestamp in a microcluster, for expiry analysis (100 by default).\n\n`Clustream` implements the main algorithm, and is controlled by the following\nparameters:\n\n* Initial buffer parameter (**-b**), which controls the initial buffer from which\n  microclusters are created (1000 by default);\n* Number of microclusters parameter (**-m**), which controls how many\n  microclusters are kept (100 by default);\n* Number of clusters parameter (**-k**), which controls how many clusters are\n  output by the offline k-means algorithm (10 by default); and\n* k-means iterations parameter (**-i**), which controls for how many iterations the\n  k-means algorithm iterates (1000 by default).\n"
  },
  {
    "path": "website/docs/Contributors.md",
    "content": "---\nlayout: page\ntitle: Contributors\nimage:\n  feature: screen_finance.png\n---\n\n\n* [Silviu Maniu] (http://silviu.maniu.info/) ([HUAWEI Noah's Ark Lab](http://www.noahlab.com.hk/)) \n* [Albert Bifet] (http://albertbifet.com/) ([HUAWEI Noah's Ark Lab](http://www.noahlab.com.hk/)) \n* Jianfeng Qian ([HUAWEI Noah's Ark Lab](http://www.noahlab.com.hk/)) \n* Guangjian Tian ([HUAWEI Noah's Ark Lab](http://www.noahlab.com.hk/)) \n* Jiantao Wang ([HUAWEI Noah's Ark Lab](http://www.noahlab.com.hk/))\n* Keli Zhang ([HUAWEI Noah's Ark Lab](http://www.noahlab.com.hk/))\n* Cheng He ([HUAWEI Noah's Ark Lab](http://www.noahlab.com.hk/)) \n* Wei Fan ([HUAWEI Noah's Ark Lab](http://www.noahlab.com.hk/)) \n\n\n"
  },
  {
    "path": "website/docs/GettingStarted.md",
    "content": "---\nlayout: page\ntitle: StreamDM Quick Start Guide\nimage:\n  feature: screen_network.png\n---\nThe purpose of this document is to provide a quick entry point for users\ndesiring to quickly run a StreamDM task. We describe how StreamDM can be\ncompiled, and how a quick task can be run.\n\nThe basic requirement for running the example in this documents is to have Spark\n1.4 installed. The example works best on a Linux/Unix machine.\n\n## Compiling The Code\n\nIn the main folder of StreamDM, the following command generates the package\nneeded to run StreamDM:\n\n```bash\nsbt package\n```\n\n## Running The Task\n\nThe task that is run in this example is the EvaluatePrequential. By default, the\ntask connects to a socket open on the localhost at port 9999 which sends dense\ninstances as a stream. Then a linear binary classifier is trained by using\nStochasticGradientDescent and the predictions are evaluted by outputting the\nconfusion matrix.\n\nThe example can be run by executing in command line:\n\n* In the terminal, use the provided spark script to run the task (after\n  modifying the `SPARK_HOME` variable with the folder of your Spark installation):\n\n  {% highlight bash %}\n  cd scripts\n  ./spark.sh\n  {% endhighlight %}\n\n* It is possible to add command-line options to specify task, learner, and evaluation parameters:\n \n  {% highlight bash %}\n  cd scripts\n  ./spark.sh \"EvaluatePrequential -l (SGDLearner -l 0.01 -o LogisticLoss -r ZeroRegularizer) –s (FileReader –k 100 –d 60 –f ../data/mydata)\"\n  {% endhighlight %}\n\n* [Optional] It is advisable to separate the standard and the error output, for\n  better readability:\n\n  {% highlight bash %}\n  cd scripts\n  ./spark.sh 1>results.txt 2>debug.log\n  {% endhighlight %}\n\nThe standard output will contain a confusion matrix aggregating the prediction\nresults for every Spark RDD in the stream.\n\nFour [data generators](http://huawei-noah.github.io/streamDM/docs/generators.html) can generate sample data \nby  [SampleDataWriter](http://huawei-noah.github.io/streamDM/docs/SampleDataWriter.html):\n\n* In the terminal, use the provided sample data generator script to generate sample data (after\n  modifying the `SPARK_HOME` variable with the folder of your Spark installation\n  and setting the correct jar files):\n\n  {% highlight bash %}\n  cd scripts\n  ./generateData.sh \"FileWriter -n 1000 -f ../sampledata/mysampledata -g (HyperPlaneGenerator -k 100 -f 10)\"\n  {% endhighlight %}\n"
  },
  {
    "path": "website/docs/HDT.md",
    "content": "---\nlayout: page\ntitle: Hoeffding Decision Trees\nimage:\n  feature: screen_network.png\n---\n\n## Hoeffding Decision Trees\n\n### General Description\n\nThe Hoeffding tree is an incremental decision tree learner for large data\nstreams, that assumes that the data distribution is not changing over time. It\ngrows incrementally a decision tree based on the theoretical guarantees of the\nHoeffding bound (or additive Chernoff bound). A node is expanded as soon as\nthere is sufficient statistical evidence that an optimal splitting feature\nexists, a decision based on the distribution-independent Hoeffding bound. The\nmodel learned by the Hoeffding tree is asymptotically nearly identical to the\none built by a non-incremental learner, if the number of training instances is\nlarge enough. See for details:\n\n*P. Domingos and G. Hulten. Mining High-Speed Data Streams. In KDD, pages 71-80,\nBoston, MA, 2000. ACM Press.*\n\n*G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams. In\nKDD, pages 97–106, San Francisco, CA, 2001. ACM Press.*\n\n### Implementation\n\nIn StreamDM, Hoeffding trees are implemented in the `HoeffdingTree` class,\nsupported by the model implemented in `HoeffdingTreeModel`. The `HoeffdingTree`\nis controlled by the following parameters:\n\n* Numeric observer to use (**-n**); for the moment, only Gaussian approximation is supported; class of type `FeatureClassObserver`;\n* Number of examples a leaf should observe before a split attempt (**-g**);\n* Number of examples a leaf should observe before applying NaiveBayes (**-q**);\n* Split criterion to use (**-s**), an object of type `SplitCriterionType`;\n* Allowable error in split decision (**-c**);\n* Threshold of allowable error in breaking ties (**-t**);\n* Allow only binary splits (**-b**), boolean flag;\n* Disable poor attributes (**-r**);\n* Allow growth (**-o**);\n* Disable pre-pruning (**-p**);\n* Leaf prediction to use (**-l**), either `MajorityClass` (0), `NaiveBayes` (1)\n  or adaptive `NaiveBayes` (2, default); and\n* Enable splitting at all leaves (**-a**); the original algorithm can split at\n  any time, but in Spark Streaming one can only split once per RDD; the option\n  controls whether to split at leaf of the last `Example` of the RDD, or at\n  every leaf.\n"
  },
  {
    "path": "website/docs/NB.md",
    "content": "---\nlayout: page\ntitle: Naive Bayes\nimage:\n  feature: screen_network.png\n---\n\n## Multinomial Naive Bayes\n\n*Andrew Mccallum and Kamal Nigam.  A comparison of event models for naive bayes\ntext classification. In AAAI-98 Workshop on ’Learning for Text Categorization’,\n1998*\n\n### General Description\n\nMultinomial Naive Bayes models a document as a bag-of-words. For each class\n\\\\(c\\\\), \\\\( \\Pr(w|c)\\\\) (the probability of observing word \\\\(w\\\\) given\n\\\\(c\\\\))  is estimated from the training data, simply by computing the relative\nfrequency of each word in the collection of training documents, for that class.\nThe classifier also requires the prior probability \\\\(\\Pr(c)\\\\), which is\nstraightforward to estimate from the frequency of classes in the training set.\n\nAssuming \\\\(n_{wd}\\\\) is the number of times word \\\\(w\\\\) occurs in document\n\\\\(d\\\\), the probability of class \\\\(c\\\\) given a test document is calculated as\nfollows:\n$$\\Pr(c|d) =\\frac{\\Pr(c) \\prod_{w\\in d} \\Pr(w|c)^{n_{wd}}}{\\Pr(d)},$$\nwhere \\\\(\\Pr(d)\\\\) is a normalization factor. To avoid the zero frequency\nproblem, it is common to use the Laplace correction for all conditional\nprobabilities involved, which means all counts are initialized with a value of 1\ninstead of 0.\n\n### Implementation\n\nIn StreamDM, we have implemented the offline Multinomial Naive Bayes, for use\nin the other online algorithms implemented. The model is handled by the\n`MultinomialNaiveBayesModel` class, which keeps the class and feature\nstatistics. The main algorithm is implemented in `MultinomialNaiveBayes`, which\nis controlled by the following options:\n\n* Number of features (**-f**), which sets the number of features in the input\n  examples (3 by default);\n* Number of classes (**-c**), which sets the number of classes in the input\n  examples (2 by default); and\n* Laplace smoothing (**-s**), which sets the smoothing factor for handling the\n  zero frequency issue (1 by default).\n"
  },
  {
    "path": "website/docs/Programming.md",
    "content": "---\nlayout: page\ntitle: StreamDM Programming Guide\nimage:\n  feature: screen_network.png\n---\n\nEverything in StreamDM is designed around tasks, which describe the flow of a\nstreaming data mining or machine learning algorithm. In a\nnutshell, streaming data is read and parsed into the StreamDM internal\nrepresentation, passed through learners, evaluated and then output to various\nplaces, such as console, files, or as streams for other tasks. \n\n## Basic Data Structure\n\nStreamDM processes multi-dimensional vectors, called _instances_, which are\nimplemented as classes derived from a general class `Instance`. Depending on the\nimplementations of the underlying data structures, and `Instance` can be:\n\n__Instance Type__ | __Data Structure__ | __Format__\n--- | --- | ---\n`DenseInstance` | array of `Double` | `val1,val2,...,valn`\n`SparseInstance` | two arrays: one for indexes, one for values | `idx1:val1,idx2:val2,...`\n`TextInstance` | map of key-value tuples; allows non-numeric keys | `key1:val1,key2:val2,...`\n`NullInstance` | N/A | N/A\n\n`Instance` provides useful operations for use in a `Learner`, such as the\n`dot` and `distanceTo` operations, but also `map` and `reduce` operations. Note\nthat an `Instance` is always immutable; every operation returns a new `Instance`\nwith the modifications. For full details, please refer to the `Instance`\nspecification in the API documentation.\n\nThe input/output data structure which is sent via `DStream` is the `Example`.\nThis data structure  wraps input and output instances, along with a number\nrepresenting its weight. The class signature is specified as:\n\n```scala\nclass Example(inInstance: Instance, outInstance: Instance = new NullInstance, \n              weightValue: Double=1.0) \n```\n\nIts format is the following (note that weight and the output instance are\noptional):\n\n```\n<input_instance> [<output_instance>] [<weight>]  \n```\nThe `Example.parse` method allows StreamDM to create objects of type `Example`\nfrom text lines in the stream.\n\nBy default, every value in each instance is a Double. In cases where the values\nhave different types of values (for example, discrete integers) a helper data\nstructure `ExampleSpecification` is used. \n\nAn `ExampleSpecification` is used in special cases: classifiers such as decision\ntrees and Naive Bayes, which need to know the type of each feature in the\ninstance. In the current implementation, we support two types of features,\nnumeric and discrete. Numeric values are the default, and discrete features are\nstrings (such as colors *Red*, *Green*, *Blue*, etc.) which are internally\nrepresented as doubles, for space efficiency and compatibility with the\n`Instance` classes.\n\nThis information is stored in an `ExampleSpecification` object, which, similarly\nto `Example`, contains two `InstanceSpecification` objects for the input and\noutput instances. Each `InstanceSpecification` stores the name of the features,\nand, for the discrete features, their original string descriptions in an\nassociated `FeatureSpecification` object.\n\n## Task Building Blocks\n\nA `Task` is a sequential algorithm which is tasked with connecting to a\n`StreamingContext` and with processing sequential operations. This is performed\nby using some building blocks, which are generally classes derived from the\nfollowing base classes:\n\n__Base Class__ | __Purpose__ \n--- | ---\n`StreamReader` | read and parse `Example` and create a stream\n`Learner` | provides the `train` method from an input stream\n`Model` | data structure and set of methods used for `Learner`\n`Evaluator` | evaluation of predictions\n`StreamWriter` | output of streams \n\nThe base classes above can also be extended for more specific use cases. For\ninstance, `Learner` is currently extended to `Classifier`, which provides a\n`predict` method, and a `Clusterer`, which provides an `assign` method.\n\nA `Task` will also contain a set of options, which use the\n[JavaCLIParser](https://github.com/abifet/javacliparser/) library. These options\nallow to specify what types of learners, evaluators, writers, and readers are to\nbe used, at *runtime*, without the need to re-compile the task.\n\nAn illustration on how a `Task` can be programmed by using a combination of the\nabove building block is the `EvaluatePrequential` example task, present in the\nsource code of StreamDM.\n\n### Case Study: EvaluatePrequential\n\nConsider the (artificial) stream binary classification scenario, where\nthe stream consists of a stream of single label instances. The objective is to\nfirst predict the instance using the current linear model, and then train (and\nupdate) the model using the true label. The evaluation will be based on the\ndifference between the predicted label and the true label. The code listing of\nthe resulting `EvaluatePrequential` is:\n\n```scala\nclass EvaluatePrequential extends Task {\n  //Task options\n  val learnerOption:ClassOption = new ClassOption(\"learner\", 'l',\n    \"Learner to use\", classOf[Classifier], \"SGDLearner\")\n  val evaluatorOption:ClassOption = new ClassOption(\"evaluator\", 'e',\n    \"Evaluator to use\", classOf[Evaluator], \"BasicClassificationEvaluator\")\n  val streamReaderOption:ClassOption = new ClassOption(\"streamReader\", 's',\n    \"Stream reader to use\", classOf[StreamReader], \"SocketTextStreamReader\")\n  val resultsWriterOption:ClassOption = new ClassOption(\"resultsWriter\", 'w',\n    \"Stream writer to use\", classOf[StreamWriter], \"PrintStreamWriter\")\n  \n  //Run the task\n  def run(ssc:StreamingContext): Unit = {\n    //Parse options and init\n    val reader:StreamReader = this.streamReaderOption.getValue()\n    val learner:SGDLearner = this.learnerOption.getValue()\n    learner.init(reader.getExampleSpecification())\n    val evaluator:Evaluator = this.evaluatorOption.getValue()\n    val writer:StreamWriter = this.resultsWriterOption.getValue()\n\n    //Parse stream and get Examples\n    val instances = reader.getExamples(ssc)\n    //Predict\n    val predPairs = learner.predict(instances)\n    //Train\n    learner.train(instances)\n    //Evaluate and output\n    writer.output(evaluator.addResult(predPairs))\n  }\n}\n```\n\nFirst, `EvaluatePrequential` is created by extending `Task` and implementing its\n`run` method. `run` takes a `StreamingContext` as an argument, and its\nobjective is to process the streams in this context.\n\nThe first step is the processing of the options:\n\n```scala\n    val reader:StreamReader = this.streamReaderOption.getValue()\n    val learner:SGDLearner = this.learnerOption.getValue()\n    learner.init(reader.getExampleSpecification())\n    val evaluator:Evaluator = this.evaluatorOption.getValue()\n    val writer:StreamWriter = this.resultsWriterOption.getValue()\n```\n\nOptions specify the classes used for each of the components of a task; in this\ncase, the type of stream reader, the learner, the evaluator, and the stream\noutput. In addition, each class used, e.g., `SGDLearner`, can also have options,\nsuch as the parameters needed for the algorithms. Then, for example, an\n`EvaluatePrequential` parsing sparse instances and using SGD with a learning\nrate of 0.001 and using hinge loss will use the command line options:\n\n```\n  EvaluatePrequential -s (SocketTextStreamReader -t sparse) -l \n    (SGDLearner -l 0.001 )\n```\n\nThen, the instances get parsed by the reader:\n\n```scala\n    val instances = reader.getExamples(ssc)\n```\n\nAfter the parsing, the evaluate first then train cycle is performed. In this\ncase, our learner is restricted to a `Classifier` so that the method `predict`\nis available:\n\n```scala\n  //Predict\n  val predPairs = learner.predict(instances)\n  //Train\n  learner.train(instances)\n\n```\n\nFinally, the results are output. Here, the evaluator output is combined with the\nfinal output:\n\n```scala\n  writer.output(evaluator.addResult(predPairs))\n```\n\n## Extending StreamDM\n\nStreamDM is designed to be easily extensible. Its purpose is to allow both users\nto run it, but also developers of real-world machine learning workflows to\neasily program task which are more complicated or even contain multiple layers\nof learning and evaluation, and researchers to easily include new learner\nalgorithms in Spark Streaming.\n\n### Adding Tasks\n\nTo define a new task, we have to extend `Task` and implement its `run` method.\nWe illustrate on an example of a task which writes a string to the console: \n\n```scala\nclass HelloWorldTask extends Task {\n\n  val textOption:StringOption = new StringOption(\"text\", 't',\n    \"Text to print\", \"Hello, World!\")\n\n  def run(ssc:StreamingContext): Unit = {\n    print (textOption.getValue)\n}\n```\n\nTo specify the text to print, we can use a `StringOption`, and then pass it from\nthe command line: \n\n```bash\n./spark \"HelloWorldTask -t Bye\"\n```\nAs a general note, tasks like `EvaluatePrequential` allow to test any learner\nwhich inherits `Classifier` - there is no need to create a task for each\nclassifier implemented and tested. In general, tasks should be designed so that\nthey allow as many options as possible at runtime without the need to compile.\n\n## Adding Learners\n\nTo add a new learner, we only need to implement the `Learner` trait and\nimplement its associated methods: `init` for initializing the `Model` inside the\nlearner, and `train` for updating the model with the data from the stream. If\nthe requirements for the learner are more specific, specialized traits need to\nbe implemented instead. For example, the `Classifier` trait also contains a\n`predict` methods which applies the model to a stream:\n \n```scala\ntrait Classifier extends Learner with Serializable {\n\n  /* Predict the label of the Example stream, given the current Model\n   *\n   * @param instance the input Example stream \n   * @return a stream of tuples containing the original instance and the\n   * predicted value\n   */\n  def predict(input: DStream[Example]): DStream[(Example, Double)]\n}\n\ntrait Learner extends Configurable  with Serializable {\n\n  type T <: Model\n  \n  /**\n   * Init the model based on the algorithm implemented in the learner.\n   *\n   * @param exampleSpecification the ExampleSpecification of the input stream.\n   */\n  def init(exampleSpecification: ExampleSpecification): Unit\n\n  /** \n   * Train the model based on the algorithm implemented in the learner, \n   * from the stream of Examples given for training.\n   * \n   * @param input a stream of Examples\n   */\n  def train(input: DStream[Example]): Unit\n\n  /**\n   * Gets the current Model used for the Learner.\n   * \n   * @return the Model object used for training\n   */\n  def getModel: T\n}\n```\n"
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    "path": "website/docs/SGD.md",
    "content": "---\nlayout: page\ntitle: Stochastic Gradient Descent\nimage:\n  feature: screen_network.png\n---\n\n## SGD Learner\n\nThe `SGDLearner` implements the _stochastic gradient descent optimizer_ for\nlearning various linear models: binary class SVM, binary class logistic\nregression, and linear regression.\n\nIts parameters are the following:\n\n* Loss function parameter (**-o**), can be `LogisticLoss` (for logistic\n  regression), `Squaredloss` (for linear regression, default), `HingeLoss` (for\n  SVM), and `PerceptronLoss` (for perceptron);\n* Regularizer parameter (**-r**), can be `ZeroRegularizer` (no regularization,\n  default), `L1Regularizer`, and `L2Regularizer`;\n* Regularization parameter (**-p**), which controls the influence of the\n  regularization; and\n* Learning parameter (**-l**), which controls the rate of update for the\n  gradient descent.\n\nFor example, to instruct `EvaluatePrequential` to use logistic regression as a\nclassifier, we only need to pass `LogisticLoss` as the loss function:\n\n```bash\nEvaluatePrequential -l (SGDLearner -o LogisticRegression -r ZeroRegularizer -p .001 -l .001)\n```\n\n### <a name=\"perceptron\"></a>Perceptron\n\nThe `Perceptron` is a `SGDLearner` with `PerceptronLoss` by default; the other\noptions remain the same as in the `SGDLearner`. \n"
  },
  {
    "path": "website/docs/SampleDataWriter.md",
    "content": "---\nlayout: page\ntitle: SampleDataWriter\nimage:\n  feature: screen_network.png\n---\n\n## SampleDataWriter\n\nCalls data generators to create sample data for simulation or test.\nRuns command line with parameters:\n  {% highlight bash %}\n  ./generateData.sh \"FileWriter -n 1000 -f ./HPData.10.L -g (HyperplaneGenerator -k 100 -f 10)\"\n  {% endhighlight %}\n\n### Parameters:\n * Chunk number (-n)\n * File Name (-f)\n * Generator (-g)\n"
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  {
    "path": "website/docs/StreamKM.md",
    "content": "---\nlayout: page\ntitle: StreamKM++\nimage:\n  feature: screen_network.png\n---\n\n## StreamKM++ Clustering\n\n*Ackermann, Lammersen, Märtens, Raupach, Sohler, Swierkot. StreamKM++: A\nClustering Algorithm for Data Streams. In Proceedings of the 12th Workshop on\nAlgorithm Engineering and Experiments (ALENEX 2010)*\n\n###General Description\n\n*StreamKM++* computes a small weighted sample of the data stream,called the\n*coreset* of the data stream. A new data structure called coreset tree is\ndeveloped in order to significantly speed up the time necessary for sampling\nnon-uniformly during the coreset construction. After the coreset is extracted\nfrom the data stream, a weighted k-means algorithm is applied on the coreset to\nget the final clusters for the original stream data.\n\n###Implementation\n\nIn StreamDM, three classes deal with the implementation of the StreamKM++\nalgorithm: `StreamKM`, `BucketManager`, and `TreeCoreset`.\n\n`BucketManager` and `TreeCoreset` are the data structures to deal with the\ncoreset tree construction process. For the `TreeCoreset` class no parameters are\nrequired; its behaviour is controlled by the `BucketMaanger`. The options for\n`BucketManager` are:\n\n* Stream data window size parameter (**-n**), which controls the stream data size \n(100000 by default), this value is inherited from the parameters of class `StreamKM`;\n* Coreset size parameter (**-maxsize**) which controls the number of examples in \nthe coreset for the stream data (10000 by default).\n\n`StreamKM` implements the main algorithm, and is controlled by the following\nparameters:\n\n* Number of clusters parameter (**-k**), which controls how many clusters are\n  output by the offline k-means algorithm (10 by default); \n* k-means iterations parameter (**-i**), which controls for how many iterations the\n  k-means algorithm iterates (1000 by default);\n* Coreset size parameter(**-s**), which controls the number of examples in the coreset\n  for the StreamKM++ algorithm;\n* Windows size parameter(**-w**), which controls how many examples in the stream data\n  are used for coreset extraction for the StreamKM++ algorithm .\n"
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    "path": "website/docs/generators.md",
    "content": "---\r\nlayout: page\r\ntitle: Data Generators\r\nimage:\r\n   feature: screen_network.png\r\n---\r\n\r\n## Hyper Plan Generator\r\nGenerates a problem of predicting class of a rotating hyperplane.\r\n\r\n###Parameters:\r\n* Chunk size (-k)\r\n* Slide duration in milliseconds (-d)\r\n* Number of features (-f)\r\n\r\n## Random Tree Generator\r\nGenerates a stream based on a randomly generated tree. It constructs a decision tree by splitting a chosen feature randomly, and assigning a random class label to each leaf. Once the tree is built, new examples are generated by assigning uniformly distributed random values to features which then determine the class label via the tree.\r\n\r\n### Parameters:\r\n* Chunk size (-k)\r\n* Slid duration (-d)\r\n* The number of features to generate (-f)\r\n* The number of classes to generate (-n)\r\n* The number of nominal attributes to generate (-o)\r\n* The number of numeric attributes to generate (-u)\r\n* The number of values to generate per nominal attribute (-v)\r\n* The maximum depth of the tree concept (-x)\r\n* The first level of the tree above maxTreeDepth that can have leaves (-l)\r\n* The fraction of leaves per level from firstLeafLevel onwards (-r)\r\n\r\n## Random RBF Generator\r\nGenerates a random radial basis function data. This generator was devised to offer an alternate complex concept type that is not straightforward to approximate with a decision tree model. The RBF (Radial Basis Function) generator works as follows: A fixed number of random centroids are generated. Each center has a random position, a single standard deviation, class label and weight. New examples are generated by selecting a center at random, taking weights into consideration so that centers with higher weight are more likely to be chosen. A random direction is chosen to offset the attribute values from the central point. The length of the displacement is randomly drawn from a Gaussian distribution with standard deviation determined by the chosen centroid. The chosen centroid also determines the class label of the example. This effectively creates a normally distributed hypersphere of examples surrounding each central point with varying densities. This generator only generates numeric examples.\r\n\r\n### Parameters:\r\n* Chunk size (-k)\r\n* Slid duration (-d)\r\n* Seed for random generation of model (-m)\r\n* Seed for random generation of instances (-i)\r\n* The number of classes to generate (-n)\r\n* The number of features to generate (-f)\r\n* The number of centroids in the model (-c)\r\n\r\n## Random RBF Events Generator\r\nGenerates a random radial basis function data for Clustream.\r\n\r\n###Parameters:\r\n* Chunk size (-k)\r\n* Slide duration (-d)\r\n* Seed for random generation of model (-m)\r\n* Seed for random generation of instances (-i)\r\n* The average number of centroids in the model (-C)\r\n* Deviation of the number of centroids in the model (-c)\r\n* The average radii of the centroids in the model (-R)\r\n* Deviation of average radii of the centroids in the model (-r)\r\n* Offset of the average weight a cluster has(-D)\r\n* Kernels move a predefined distance of 0.01 every X points (-V)\r\n* Speed/Velocity point offset (-v)\r\n* Noise level (-N)\r\n* Allow noise to be placed within a cluster(-n)\r\n* Event frequency (-E)\r\n* Enable merging and splitting of clusters(-M)\r\n* Enable emerging and disappearing of clusters (-e)\r\n* The number of features to generate (-f)\r\n* Decay horizon (-h)\r\n"
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    "content": "---\nlayout: page\ntitle: 'streamDM: Data Mining for Spark Streaming' \nimage:\n  feature: screen_finance.png\n---\n\nstreamDM is a new open source software for mining big data streams using [Spark Streaming](https://spark.apache.org/streaming/), developed at [Huawei Noah's Ark\nLab](http://www.noahlab.com.hk/). streamDM is licensed under Apache Software License v2.0.\n\n<p align=\"center\"><img height=\"100\" width=\"100\" src=\"images/huawei-logo.png\" alt=\"Huawei\"></p>\n\n## Big Data Stream Learning \n\nBig Data stream learning is more challenging than batch or offline learning,\nsince the data may not keep the same distribution over the lifetime of the\nstream. Moreover, each example coming in a stream can only be processed once, or\nthey need to be summarized with a small memory footprint, and the learning\nalgorithms must be very efficient. \n\n### Spark Streaming\n\n[Spark Streaming](https://spark.apache.org/streaming/) is an extension of the\ncore [Spark](https://spark.apache.org)  API that enables stream processing from\na variety of sources. Spark is a extensible and programmable framework for\nmassive distributed processing of datasets, called Resilient Distributed\nDatasets (RDD). Spark Streaming receives input data streams and divides the data\ninto batches, which are then processed by the Spark engine to generate the\nresults.\n\nSpark Streaming data is organized into a sequence of DStreams, represented\ninternally as a sequence of RDDs.\n\n### <a name=\"methods\"></a>Included Methods\n\nIn this first release of StreamDM, we have implemented:\n\n* [SGD Learner](docs/SGD.html) and [Perceptron](docs/SGD.html#perceptron)\n* [Naive Bayes](docs/NB.html)\n* [CluStream](docs/CluStream.html)\n* [Hoeffding Decision Trees](docs/HDT.html)\n* [Bagging](docs/Bagging.html)\n* [Stream KM++](docs/StreamKM.html)\n\nWe have implemented following [data generators](docs/generators.html):\n\n* HyperplaneGenerator\n* RandomTreeGenerator\n* RandomRBFGenerator\n* RandomRBFEventsGenerator\n\nWe have also implemented [SampleDataWriter](docs/SampleDataWriter.html), which can call data generators \nto create sample data for simulation or test.\n\nIn the next releases we plan to add:\n\n* Classification: Random Forests\n* Regression: Hoeffding Regression Tree, Bagging, Random Forests\n* Clustering: Clustree, DenStream\n* Frequent Itemset Miner: IncMine, IncSecMine\n\n## Going Further\n\nFor a quick introduction to running StreamDM, refer to the [Getting\nStarted](docs/GettingStarted.html) document. The StreamDM [Programming\nGuide](docs/Programming.html) presents a detailed view of StreamDM. The full API\ndocumentation can be consulted [here](api/index.html). \n\n##Mailing lists\n\n###User support and questions mailing list:\n<a href=\"mailto:streamdm-user@googlegroups.com\">streamdm-user@googlegroups.com</a>\n###Development related discussions:\n<a href=\"mailto:streamdm-dev@googlegroups.com\">streamdm-dev@googlegroups.com</a>\n"
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