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# Class weka.core.ContingencyTables

java.lang.Object
|
+----weka.core.ContingencyTables

public class ContingencyTables
extends Object
Class implementing some statistical routines for contingency tables.

Version:
\$Revision: 1.2 \$
Author:
Eibe Frank (eibe@cs.waikato.ac.nz)

ContingencyTables()
chiSquared(double[][], boolean)
Returns chi-squared probability for a given matrix.
chiVal(double[][], boolean)
Computes chi-squared statistic for a contingency table.
cochransCriterion(double[][])
Tests if Cochran's criterion is fullfilled for the given contingency table.
CramersV(double[][])
Computes Cramer's V for a contingency table.
entropy(double[])
Computes the entropy of the given array.
entropyConditionedOnColumns(double[][])
Computes conditional entropy of the rows given the columns.
entropyConditionedOnRows(double[][])
Computes conditional entropy of the columns given the rows.
entropyConditionedOnRows(double[][], double[][], double)
Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix.
entropyOverColumns(double[][])
Computes the columns' entropy for the given contingency table.
entropyOverRows(double[][])
Computes the rows' entropy for the given contingency table.
gainRatio(double[][])
Computes gain ratio for contingency table (split on rows).
log2MultipleHypergeometric(double[][])
Returns negative base 2 logarithm of multiple hypergeometric probability for a contingency table.
main(String[])
Main method for testing this class.
reduceMatrix(double[][])
Reduces a matrix by deleting all zero rows and columns.
symmetricalUncertainty(double[][])
Calculates the symmetrical uncertainty for base 2.
tauVal(double[][])
Computes Goodman and Kruskal's tau-value for a contingency table.
ContingencyTables
public ContingencyTables()
chiSquared
public static double chiSquared(double matrix[][],
boolean yates)
Returns chi-squared probability for a given matrix.

Parameters:
matrix - the contigency table
yates - is Yates' correction to be used?
Returns:
the chi-squared probability
chiVal
public static double chiVal(double matrix[][],
boolean useYates)
Computes chi-squared statistic for a contingency table.

Parameters:
matrix - the contigency table
yates - is Yates' correction to be used?
Returns:
the value of the chi-squared statistic
cochransCriterion
public static boolean cochransCriterion(double matrix[][])
Tests if Cochran's criterion is fullfilled for the given contingency table. Rows and columns with all zeros are not considered relevant.

Parameters:
matrix - the contigency table to be tested
Returns:
true if contingency table is ok, false if not
CramersV
public static double CramersV(double matrix[][])
Computes Cramer's V for a contingency table.

Parameters:
matrix - the contingency table
Returns:
Cramer's V
entropy
public static double entropy(double array[])
Computes the entropy of the given array.

Parameters:
array - the array
Returns:
the entropy
entropyConditionedOnColumns
public static double entropyConditionedOnColumns(double matrix[][])
Computes conditional entropy of the rows given the columns.

Parameters:
matrix - the contingency table
Returns:
the conditional entropy of the rows given the columns
entropyConditionedOnRows
public static double entropyConditionedOnRows(double matrix[][])
Computes conditional entropy of the columns given the rows.

Parameters:
matrix - the contingency table
Returns:
the conditional entropy of the columns given the rows
entropyConditionedOnRows
public static double entropyConditionedOnRows(double train[][],
double test[][],
double numClasses)
Computes conditional entropy of the columns given the rows of the test matrix with respect to the train matrix. Uses a Laplace prior. Does NOT normalize the entropy.

Parameters:
train - the train matrix
test - the test matrix
the - number of symbols for Laplace
Returns:
the entropy
entropyOverRows
public static double entropyOverRows(double matrix[][])
Computes the rows' entropy for the given contingency table.

Parameters:
matrix - the contingency table
Returns:
the rows' entropy
entropyOverColumns
public static double entropyOverColumns(double matrix[][])
Computes the columns' entropy for the given contingency table.

Parameters:
matrix - the contingency table
Returns:
the columns' entropy
gainRatio
public static double gainRatio(double matrix[][])
Computes gain ratio for contingency table (split on rows). Returns Double.MAX_VALUE if the split entropy is 0.

Parameters:
matrix - the contingency table
Returns:
the gain ratio
log2MultipleHypergeometric
public static double log2MultipleHypergeometric(double matrix[][])
Returns negative base 2 logarithm of multiple hypergeometric probability for a contingency table.

Parameters:
matrix - the contingency table
Returns:
the log of the hypergeometric probability of the contingency table
reduceMatrix
public static double[][] reduceMatrix(double matrix[][])
Reduces a matrix by deleting all zero rows and columns.

Parameters:
matrix - the matrix to be reduced
the - matrix with all zero rows and columns deleted
symmetricalUncertainty
public static double symmetricalUncertainty(double matrix[][])
Calculates the symmetrical uncertainty for base 2.

Parameters:
matrix - the contingency table
Returns:
the calculated symmetrical uncertainty
tauVal
public static double tauVal(double matrix[][])
Computes Goodman and Kruskal's tau-value for a contingency table.

Parameters:
matrix - the contingency table
Goodman - and Kruskal's tau-value
main
public static void main(String ops[])
Main method for testing this class.

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