A divergence estimator of two sets of samples.
Project description
universal-divergence
universal-divergence is a Python module for estimating divergence of two sets of samples generated from the two underlying distributions. The theory of the estimator is based on a paper written by Q.Wang et al [1].
Install
pip install universal-divergence
Example
from __future__ import print_function import numpy as np from universal_divergence import estimate mean = [0, 0] cov = [[1, 0], [0, 10]] x = np.random.multivariate_normal(mean, cov, 100) y = np.random.multivariate_normal(mean, cov, 100) print(estimate(x, y)) # will be close to 0.0 mean2 = [10, 0] cov2 = [[5, 0], [0, 5]] z = np.random.multivariate_normal(mean2, cov2, 100) print(estimate(x, z)) # will be bigger than 0.0
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