be.ac.ulb.mlg.utils
Interface Measure

All Known Implementing Classes:

BrayCurtis, BrownCorrelation, DirichletEntropy, EmpiricalEntropy, Entropy, Euclidean, Hellinger, HilbertSchmidt, JensenShannon, Kendall, KullbackLeibler, MutualInformation, Pearson, SchurmannGrassbergerEntropy, ShannonEntropy, Spearman, Steinhaus, VarianceOfLogRatios

public interface Measure

Version:

1.00, 24/06/2013

Author:

Jean-Sebastien Lerat (Jean-Sebastien.Lerat@ulb.ac.be)

Field Summary

static double

ERROR_VALUE Value representing an error during measuring

Method Summary

boolean	hasNativeImplementation() Know if the measure owns a native implementation
double	measure(double[] a, double[] b, boolean[] computable) Compute the measure between two data vectors
boolean	requireDataTransformation() Know if the measure need to transform input data (input data are copied to be preserved)
void	transform(double[][] data) The transformation method called before measurement

Field Detail

ERROR_VALUE

static final double ERROR_VALUE

Value representing an error during measuring

See Also:

Constant Field Values

Method Detail

measure

double measure(double[] a,
               double[] b,
               boolean[] computable)

Compute the measure between two data vectors

Parameters:

a - The first data vector

b - The second data vector

computable - Boolean vector set to true except for indexes they must be ignored during the measurement

Returns:

The measure between a and b

hasNativeImplementation

boolean hasNativeImplementation()

Know if the measure owns a native implementation

Returns:

True if the measure is also natively implemented

requireDataTransformation

boolean requireDataTransformation()

Know if the measure need to transform input data (input data are copied to be preserved)

Returns:

True if the input data require some transformation

transform

void transform(double[][] data)

The transformation method called before measurement

Parameters:

data - An input data vector to be measured