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        "author": "Agne Matuseviciute",
        "author_email": "agne.matuseviciute@tum.de",
        "bugtrack_url": null,
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            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy",
            "Topic :: Utilities"
        ],
        "description": "========\nOverview\n========\n\n\n\n\n\n* Free software: MIT license\n\nActivate virtual environment\n==================\nTogether with the autoCorrection package you will get\n\n        'tensorflow',\n        'keras',\n        'numpy',\n        'kopt',\n        'scipy',\n        'h5py',\n        'sklearn',\n        'pandas',\n        'statsmodels',\n        'pytest'\n\npackages automatically installed, if not present.\n\nIf you don't wannt to install these packages globally, please use virtual environment.\n\nIf you have problems with virtualenv, installing using conda may help:\n\n(Installation of conda: https://conda.io/docs/user-guide/install/index.html)\n\nMake sure you are using python 3.\n\n    conda create -n mypyth3 python=3.6\n\n    source activate mypyth3\n\n    conda install virtualenv\n\nactivate new environment in active python 3 environment:\n\n    virtualenv env-with-autoCorrection\n\n    source env-with-autoCorrection/bin/activate\n\nCheck if you are still using python 3:\n\n    python --version\n\n\nPackage Installation\n============\n\n::\n\n    pip install autoCorrection\n\n\nDeactivate virtual environment\n============\n\n::\n\n    deactivate\n\nUsage\n============\n\n::\n\n    #in python:\n    python\n    import autoCorrection\n    import numpy as np\n    counts = np.random.negative_binomial(n = 20, p=0.2, size = (10,8))\n    sf = np.ones((10,8))\n    corrector = autoCorrection.correctors.AECorrector()\n    c = corrector.correct(counts = counts, size_factors = sf)\n\n    #in R:\n    library(reticulate)\n    autoCorrection <- import(\"autoCorrection\")\n    corrected <- autoCorrection$correctors$AECorrector(model_name, model_directory)$correct(COUNTS, SIZE_FACTORS, only_predict=FALSE)\n\nDocumentation\n=============\n\nhttps://i12g-gagneurweb.in.tum.de/public/docs/autocorrection/\n\n\n\n\nChangelog\n=========\n\n0.0.1 (2017-11-01)\n------------------\n\n* First release on PyPI.",
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