Calculate the conditional entropy H(data | cond).
Calculates the conditional mutual information I(x, y | z) from a set of observations.
Calculates the joint entropy of a set of observations. Each input range represents a vector of observations. If only one range is given, this reduces to the plain old entropy. Input range must have a length.
This function calculates the Shannon entropy of a forward range that is treated as frequency counts of a set of discrete observations.
Calculates the entropy of any old input range of observations more quickly * than entropy(), provided that all equal values are adjacent. If the input * is sorted by more than one key, i.e. structs, the result will be the joint * entropy of all of the keys. The compFun alias will be used to compare * adjacent elements and determine how many instances of each value exist.
Bind a set of ranges together to represent a joint probability distribution.
Calculates the mutual information of two vectors of discrete observations.
Calculates the mutual information of a contingency table representing a joint discrete probability distribution. Takes a set of finite forward ranges, one for each column in the contingency table. These can be expressed either as a tuple of ranges or a range of ranges.
Much faster implementations of information theory functions for the special but common case where all observations are integers on the range [0, nBin). This is the case, for example, when the observations have been previously binned using, for example, dstats.base.frqBin().
Iterate over a set of ranges by value in lockstep and return an ObsEnt, * which is used internally by entropy functions on each iteration.
Basic information theory. Joint entropy, mutual information, conditional * mutual information. This module uses the base 2 definition of these * quantities, i.e, entropy, mutual info, etc. are output in bits. * * Author: David Simcha