Entropy
jhplot.stat

Class Entropy



  • public abstract class Entropyextends Object
    Implements common discrete Shannon Entropy functions. Provides: univariate entropy H(X), conditional entropy H(X|Y), joint entropy H(X,Y). Defaults to log_2, and so the entropy is calculated in bits.
    • Field Detail

      • LOG_BASE

        public static double LOG_BASE
    • Method Detail

      • calculateEntropy

        public static double calculateEntropy(double[] dataVector)
        Calculates the univariate entropy H(X) from a vector. Uses histograms to estimate the probability distributions, and thus the entropy. The entropy is bounded 0 ≤ H(X) ≤ log |X|, where log |X| is the log of the number of states in the random variable X.
        Parameters:
        dataVector - Input vector (X). It is discretised to the floor of each value before calculation.
        Returns:
        The entropy H(X).
      • calculateConditionalEntropy

        public static double calculateConditionalEntropy(double[] dataVector,                                 double[] conditionVector)
        Calculates the conditional entropy H(X|Y) from two vectors. X = dataVector, Y = conditionVector. Uses histograms to estimate the probability distributions, and thus the entropy. The conditional entropy is bounded 0 ≤ H(X|Y) ≤ H(X).
        Parameters:
        dataVector - Input vector (X). It is discretised to the floor of each value before calculation.
        conditionVector - Input vector (Y). It is discretised to the floor of each value before calculation.
        Returns:
        The conditional entropy H(X|Y).
      • calculateJointEntropy

        public static double calculateJointEntropy(double[] firstVector,                           double[] secondVector)
        Calculates the joint entropy H(X,Y) from two vectors. The order of the input vectors is irrelevant. Uses histograms to estimate the probability distributions, and thus the entropy. The joint entropy is bounded 0 ≤ H(X,Y) ≤ log |XY|, where log |XY| is the log of the number of states in the joint random variable XY.
        Parameters:
        firstVector - Input vector. It is discretised to the floor of each value before calculation.
        secondVector - Input vector. It is discretised to the floor of each value before calculation.
        Returns:
        The joint entropy H(X,Y).

SCaVis 2.1 © jWork.ORG