latentmi.ksg
============

.. py:module:: latentmi.ksg


Functions
---------

.. autoapisummary::

   latentmi.ksg.add_noise
   latentmi.ksg.query_neighbors
   latentmi.ksg.count_neighbors
   latentmi.ksg.avgdigamma
   latentmi.ksg.build_tree
   latentmi.ksg.lnc_correction
   latentmi.ksg.entropy
   latentmi.ksg.mi
   latentmi.ksg.entropyd
   latentmi.ksg.centropyd
   latentmi.ksg.midd
   latentmi.ksg.out_of_sample_avgdigamma
   latentmi.ksg.out_of_sample_pmi


Module Contents
---------------

.. py:function:: add_noise(x, intens=1e-10)

.. py:function:: query_neighbors(tree, x, k)

.. py:function:: count_neighbors(tree, x, r)

.. py:function:: avgdigamma(points, dvec)

   tweaked this to return list of psi(nx) for pointwise estimate


.. py:function:: build_tree(points)

.. py:function:: lnc_correction(tree, points, k, alpha)

   in my experience this doesn't work in practice for N_dims > 1


.. py:function:: entropy(x, k=3, base=2)

   The classic K-L k-nearest neighbor continuous entropy estimator
   x should be a list of vectors, e.g. x = [[1.3], [3.7], [5.1], [2.4]]
   if x is a one-dimensional scalar and we have four samples


.. py:function:: mi(x, y, z=None, k=3, base=2, alpha=0)

   Mutual information of x and y (conditioned on z if z is not None)
   x, y should be a list of vectors, e.g. x = [[1.3], [3.7], [5.1], [2.4]]
   if x is a one-dimensional scalar and we have four samples


.. py:function:: entropyd(sx, base=2)

   Discrete entropy estimator
   sx is a list of samples


.. py:function:: centropyd(x, y, base=2)

   The classic K-L k-nearest neighbor continuous entropy estimator for the
   entropy of X conditioned on Y.


.. py:function:: midd(x, y, base=2)

   Discrete mutual information estimator
   Given a list of samples which can be any hashable object


.. py:function:: out_of_sample_avgdigamma(obs_points, est_points, dvec)

   here we compute psi(nx) for query points that are not
   necessarily part of observed samples

   :param obs_points:
   :param est_points:
   :param dvec:


.. py:function:: out_of_sample_pmi(obs_x, obs_y, est_x, est_y, k=3, base=2)