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 Summary

All Methods
Modifier and TypeMethod and Description
`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.
`static double``calculateMutualInformation(double[] firstVector, double[] secondVector)`
Calculates the Mutual Information I(X;Y) between two random variables.
• ### Methods inherited from class java.lang.Object

`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`
• ### 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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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.

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