{
    "info": {
        "author": "Mike Simms",
        "author_email": "mike@mikesimms.net",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2"
        ],
        "description": "[![C++](https://img.shields.io/badge/cpp-brightgreen.svg)]() [![Rust](https://img.shields.io/badge/rust-brightgreen.svg)](https://www.rust-lang.org) [![Python 2.7|3.7](https://img.shields.io/badge/python-2.7%2F3.7-brightgreen.svg)](https://www.python.org/) [![MIT license](http://img.shields.io/badge/license-MIT-brightgreen.svg)](http://opensource.org/licenses/MIT)\n\n# LibIsolationForest\n\n## Description\n\nThis project contains Rust, C++, and python implementations of the Isolation Forest algorithm. Isolation Forest is an anomaly detection algorithm based around a collection of randomly generated decision trees. For a full description of the algorithm, consult the original paper by the algorithm's creators:\n\nhttps://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/icdm08b.pdf\n\n## Python Example\n\nThe python implementation can be installed via pip:\n\n```pip install IsolationForest```\n\nHere's a short code snipet that shows how to use the Python version of the library. You can also read the file `test.py` for a complete example. As the library matures, I'll add more test examples to this file.\n\n```python\nfrom isolationforest import IsolationForest\n\nforest = IsolationForest.Forest(num_trees, sub_sampling_size)\n\nsample = IsolationForest.Sample(\"Training Sample 1\")\nfeatures = []\nfeatures.append({\"feature 1\": feature_1_value})\n# Add more features to the sample...\nfeatures.append({\"feature N\": feature_N_value})\nsample.add_features(features)\n# Add the features to the sample.\nforest.add_sample(sample)\n# Add more samples to the forest...\n\n# Create the forest.\nforest.create()\n\nsample = IsolationForest.Sample(\"Test Sample 1\")\nfeatures = []\nfeatures.append({\"feature 1\": feature_1_value})\n# Add more features to the sample...\nfeatures.append({\"feature N\": feature_N_value})\n# Add the features to the sample.\nsample.add_features(features)\n\n# Score the sample.\nscore = forest.score(sample)\nnormalized_score = forest.normalized_score(sample)\n```\n\n## Rust Example\n\nAn example of how to use the Rust version of the library can be found in `main.rs`. As the library matures, I'll add more test examples to this file.\n\n## C++ Example\n\nAn example of how to use the C++ version of the library can be found in `main.cpp`. As the library matures, I'll add more test examples to this file.\n\n## Version History\n\n### 1.0\n* Initial version.\n\n### 1.1\n* Added normalized scores.\n* Updated random number generation in rust, because it changed again.\n\n## License\n\nThis library is released under the MIT license, see LICENSE for details.\n\n\n",
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