{
    "info": {
        "author": "Saman Fekri",
        "author_email": "samanf74@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "# StreamOutlierDetector\nDetect outliers of sequence in stream.  \nIn this project we have some assumption:\n-   This project works online that means has no idea about the future data\n-   This project forget older data (more than size of sample array)\n-   If more than half of sample array be in outlier then this project assume the majority is not outlier and calculate outlier detection for the sample again\n\n\n## Usage\n\n### install\n```bash\npip install pyood\npip install --upgrade pyood # if you want update package\n```\n\n### How use it\n```python\nfrom pyood import OutlierDetector\n\noutlier_detector = OutlierDetector(bound_factor_standard_deviation=3, window_size=20, size_initial_ignore=10)\n\nis_outlier = outlier_detector.push(your_value)\n```\n\nif you want, you can use it with callback function\n```python\nfrom pyood import OutlierDetector\n\ndef result(is_outlier):\n    print(is_outlier)\n\noutlier_detector = OutlierDetector(bound_factor_standard_deviation=3, window_size=20, size_initial_ignore=10)\n\nis_outlier = outlier_detector.push(value=your_value, callback=result)\n```\n### Help\n``bound_factor_standard_deviation`` is the factor that multiple with standard deviation. ``|value - mean| > bound_factor_standard_deviation * satandard deviation`` is the outlier.  \n``window_size`` is the size of array is effective for finding outlier.  \n``first_learning_number`` is the number of first value we ignore and learn from them.  \n\nWarning \u26a0                               |\n----------------------------------------| \nif the outlier be in the first ``first_learning_number`` we return it is not outlier and more dangerous we learn it and ruined the mean and variance for a while| \n\n\n## Result\nI test this class and show the functionality of it on a chart.  \n\u274c are the outliers we detect.  \n\ud83d\udd35 are the normal values.  \n<span> - </span> are the bound of outlier detection.  \n\nWithout bound                           | With bounds\n----------------------------------------| ------------------------------ \n![]( ./images/test_result_normal.png)   | ![]( ./images/test_result.png) \n\n\n\n\n",
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        "home_page": "https://github.com/SamanFekri/StreamOutlierDetector",
        "keywords": "",
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        "maintainer": "",
        "maintainer_email": "",
        "name": "pyood",
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        "summary": "Detect outliers of sequence in stream.",
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