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        "author": "Matias Carrasco Kind",
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        "description": "# iso_forest\n\nThis is a simple package implementation for the isolation forest method described (among other places) in this [paper](icdm08b.pdf) for detecting anomalies and outliers from a data point distribution.\n\n## Extended isolation forest\n\nFor an extended version of this algorithm that produces more precise scoring maps please visit this repository\n\n[https://github.com/sahandha/eif](https://github.com/sahandha/eif)/\n\n\n## Installation\n\n\n    pip install iso_forest\n\n\nor directly from the Github repository\n\n\n    pip install git+https://github.com/mgckind/iso_forest.git\n \n\nIt supports python2 and python3 \n\n## Requirements\n\n- numpy\n\nNo extra requirements are needed for the algorithm.\n\nIn addition, it also contains means to draw the trees created using the [igraph](http://igraph.org/) library.\n\n## Use Examples\n\nSee these 2 notebooks examples on how to use it\n\n- [basics](demo_iforest.ipynb)\n- [tree visualization and anomaly PDFs](demo_vis_pdf.ipynb)\n\n## Releases \n\n### v1.0.3\n\n- Initial Release\n\n",
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