synthval.metrics.KLDivergenceEstimation
=======================================

.. py:class:: synthval.metrics.KLDivergenceEstimation(drop_duplicates = True)

   Bases: :py:obj:`SimilarityMetric`


   Similarity Metric computing an estimation of the Kullback-Leibler divergence based on the methodology proposed in
   the referenced paper. It should be noted that the algorithm used may cause a division-by-zero error if duplicates
   are present in the distributions considered.

   .. attribute:: drop_duplicates

      Flag controlling if the duplicates in the distribution can be dropped automatically (default: True).

      :type: bool, Optional

   .. rubric:: References

   Pérez-Cruz, F. - Kullback-Leibler divergence estimation of continuous distributions - IEEE International Symposium
   on Information Theory, 2008.


   .. py:method:: calculate(real_dist_df, synth_dist_df)

      Compute an estimation of the Kullback-Leibler divergence between two set of samples originating from two
      multivariate distribution real_dist and synth_dist.

      :param real_dist_df: Set of samples representing distribution real_dist.
      :type real_dist_df: pandas.DataFrame
      :param synth_dist_df: Set of samples representing distribution synth_dist.
      :type synth_dist_df: pandas.DataFrame

      :returns: A numpy array containing the estimated value of the Kullback-Leibler divergence.
      :rtype: numpy.ndarray