Documentation API of the 'jhplot.stat.MutualInformation' Java class
MutualInformation
jhplot.stat

Class MutualInformation



  • public abstract class MutualInformationextends Object
    Implements common discrete Mutual Information functions. Provides: Mutual Information I(X;Y), Conditional Mutual Information I(X,Y|Z). Defaults to log_2, and so the entropy is calculated in bits.
    • Method Detail

      • calculateMutualInformation

        public static double calculateMutualInformation(double[] firstVector,                                                double[] secondVector)
        Calculates the Mutual Information I(X;Y) between two random variables. Uses histograms to estimate the probability distributions, and thus the information. The mutual information is bounded 0 ≤ I(X;Y) ≤ min(H(X),H(Y)). It is also symmetric, so I(X;Y) = I(Y;X).
        Parameters:
        firstVector - Input vector (X). It is discretised to the floor of each value before calculation.
        secondVector - Input vector (Y). It is discretised to the floor of each value before calculation.
        Returns:
        The Mutual Information I(X;Y).
      • calculateConditionalMutualInformation

        public static double calculateConditionalMutualInformation(double[] firstVector,                                                           double[] secondVector,                                                           double[] conditionVector)
        Calculates the conditional Mutual Information I(X;Y|Z) between two random variables, conditioned on a third. Uses histograms to estimate the probability distributions, and thus the information. The conditional mutual information is bounded 0 ≤ I(X;Y) ≤ min(H(X|Z),H(Y|Z)). It is also symmetric, so I(X;Y|Z) = I(Y;X|Z).
        Parameters:
        firstVector - Input vector (X). It is discretised to the floor of each value before calculation.
        secondVector - Input vector (Y). It is discretised to the floor of each value before calculation.
        conditionVector - Input vector (Z). It is discretised to the floor of each value before calculation.
        Returns:
        The conditional Mutual Information I(X;Y|Z).

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