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    "info": {
        "author": "Yusuke Hanada",
        "author_email": "hyusuk9872@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "rtchange    [![Build Status](https://travis-ci.org/hyusuk/rtchange.svg?branch=master)](https://travis-ci.org/hyusuk/rtchange)\n========\n\nAn implementation of real-time change-point detection in time series.\n(See [this paper](http://dl.acm.org/citation.cfm?id=2022866))\n\n\n## Sample\n<img src=\"images/changepoint_jumping_means.png\" width=\"600px\">\n\n\n## Dependencies\n\nThe required dependencies to use rtchange are\n\n- Python >= 3.3\n- NumPy\n\n\n## Installtion\n\n```\n$ pip install -U rtchange\n```\n\n\n## Example\n\n```python\n>>> from rtchange import Finder\n>>> f = Finder()\n>>> # `score` method returns the iterator of score of the samples.\n>>> scores = f.score([0, 0, 0, 0])\n<generator object Finder.score at 0x7faa143c4620>\n>>> print(list(scores))\n[0.0, 0.3938543257165724, 0.42281301105991476, 0.4718002708508469]\n>>> print(list(f.score([1, 1])))\n[1.0910027829221285, 1.2528868802573792]\n>>> # You can also use `score_one` method for the one sample. \n>>> f.score_one(2)\n1.4303444070633553\n```\n\n## Testing\n\n```\n$ virtualenv venv\n$ source ./venv/bin/activate\n$ pip install -U pip\n$ pip install -e .[testing]\n$ make test\n```\n",
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        "keywords": "change detection realtime",
        "license": "The MIT License (https://opensource.org/licenses/MIT)",
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        "name": "rtchange",
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