divergence 0.3.2

Compute Statistical Measures of Divergence

Divergence

Divergence is a Python package to compute statistical measures of entropy and divergence from probability distributions and samples.

The following functionality is provided:

• (Information) Entropy
• Cross Entropy
• Relative Entropy or Kullback-Leibler Divergence
• Jensen-Shannon Divergence

The units in which these entropy and divergence measures are calculated can be specified by the user. This is achieved by the argument log_fun, which accepts a function that calculates the logarithm with respect to a particular base. The following units can be realized by the corresponding choice of the argument log_fun in the entropy and divergence calculation functions:

• bits: base 2 via np.log2
• nats: base e via np.log
• dits: base 10 via np.log10

In a Bayesian context, relative entropy can be used as a measure of the information gained by moving from a prior distribution q to a posterior distribution p.

Installation

    pip install divergence

Examples

See the Jupyter notebook Divergence.