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    "info": {
        "author": "Kazuaki Tanida",
        "author_email": "UNKNOWN",
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        "classifiers": [
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        "description": "universal-divergence\n--------------------\n\nuniversal-divergence is a Python module for estimating divergence of two sets of samples generated from the two underlying distributions.\nThe theory of the estimator is based on `a paper\n<http://www.princeton.edu/~verdu/reprints/WanKulVer.May2009.pdf>`_ written by Q.Wang et al [1]_.\n\nInstall\n-------\n\n::\n\n  pip install universal-divergence\n\nExample\n-------\n\n::\n\n  from __future__ import print_function\n\n  import numpy as np\n  from universal_divergence import estimate\n\n  mean = [0, 0]\n  cov = [[1, 0], [0, 10]]\n  x = np.random.multivariate_normal(mean, cov, 100)\n  y = np.random.multivariate_normal(mean, cov, 100)\n  print(estimate(x, y))  # will be close to 0.0\n\n  mean2 = [10, 0]\n  cov2 = [[5, 0], [0, 5]]\n  z = np.random.multivariate_normal(mean2, cov2, 100)\n  print(estimate(x, z))  # will be bigger than 0.0\n\nReferences\n----------\n\n.. [1] Qing Wang, Sanjeev R. Kulkarni, and Sergio Verd\u00fa. \"Divergence estimation for multidimensional densities via k-nearest-neighbor distances.\" Information Theory, IEEE Transactions on 55.5 (2009): 2392-2405.\n",
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