de.jstacs.utils
Class StatisticalTest

java.lang.Object
  de.jstacs.utils.StatisticalTest

public class StatisticalTest
extends Object

This class enables the user to compute some divergences. In Latex notation the divergence is

Y_{\epsilon}(p,q) = \frac{\left[\sum_{i,j} p_{i,j} \cdot \left(\frac{p_{i,j}}{q_{i,j}}\right)^{\epsilon-1}\right] - 1}{\frac{\epsilon(\epsilon-1)}{2}}

NOTE: There is no checking if the user given matrices and vectors are stochastic.

Author:

Jens Keilwagen

Constructor Summary

StatisticalTest()

Method Summary

static double	getGeneralizedDivergence(double[][] p, double epsilon) Computes the generalized divergence for two stochastic matrices over the same domain, i.e.
static double	getGeneralizedDivergence(double[][] p, double[][] q, double epsilon) Computes the generalized divergence for two given stochastic matrices over the same domain, i.e.
static double	getGeneralizedDivergence(double[][] p, double[] r, double[] s, double epsilon) Computes the generalized divergence for two stochastic matrices over the same domain, i.e.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

StatisticalTest

public StatisticalTest()

Method Detail

getGeneralizedDivergence

public static double getGeneralizedDivergence(double[][] p,
                                              double[][] q,
                                              double epsilon)
                                       throws IllegalArgumentException

Computes the generalized divergence for two given stochastic matrices over the same domain, i.e. the matrices have to have the same dimensionality.

For epsilon=1 this method returns 2*(mutual information).

Parameters:

p - one stochastic matrix

q - another stochastic matrix

epsilon - the positive divergence parameter

Returns:

the value of the generalized divergence for the two matrices

Throws:

IllegalArgumentException - if some arguments are not correct

getGeneralizedDivergence

public static double getGeneralizedDivergence(double[][] p,
                                              double[] r,
                                              double[] s,
                                              double epsilon)

Computes the generalized divergence for two stochastic matrices over the same domain, i.e. the matrices have to have the same dimensionality. The second matrix is computed as joint probability of the given stochastic vectors.

For epsilon=1 this method returns 2*(mutual information).

Parameters:

p - a stochastic matrix

r - one stochastic vector

s - another stochastic vector

epsilon - the divergence parameter

Returns:

the value of the generalized divergence for two stochastic matrices

getGeneralizedDivergence

public static double getGeneralizedDivergence(double[][] p,
                                              double epsilon)

Computes the generalized divergence for two stochastic matrices over the same domain, i.e. the matrices have to have the same dimensionality. The second matrix is computed as joint probability of the marginal distributions.

For epsilon=1 this method returns 2*(mutual information).

Parameters:

p - a stochastic matrix

epsilon - the divergence parameter

Returns:

the value of the generalized divergence for two stochastic matrices