Metadata-Version: 2.1
Name: divergence
Version: 0.2.0
Summary: Compute Statistical Measures of Divergence
Home-page: https://github.com/michaelnowotny/divergence
Author: Michael Christoph Nowotny
Author-email: nowotnym@gmail.com
License: MIT
Description: 
        # 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
        
        <pre>
            pip install divergence
        </pre>
        
        ## Examples
        See the Jupyter notebook [Divergence](notebooks/Divergence.ipynb).
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
