marf.Classification
Interface IClassification

All Known Implementing Classes:

ChebyshevDistance, Classification, CosineSimilarityMeasure, DiffDistance, Distance, EuclideanDistance, HammingDistance, MahalanobisDistance, Markov, MaxProbabilityClassifier, MinkowskiDistance, NeuralNetwork, RandomClassification, Stochastic, ZipfLaw

public interface IClassification

Classification Interface.

$Id: IClassification.java,v 1.6 2006/07/31 02:19:13 mokhov Exp $

Since:

0.3.0.3

Version:

$Revision: 1.6 $

Author:

Serguei Mokhov

Field Summary

static java.lang.String

MARF_INTERFACE_CODE_REVISION Interface source code revision.

Method Summary

boolean	classify() Generic classification routine.
boolean	classify(double[] padFeatureVector) Generic classification routine.
IFeatureExtraction	getFeatureExtraction() Retrieves the features source.
Result	getResult() Retrieves the likely classification result.
ResultSet	getResultSet() Retrieves the enclosed result set.
void	setFeatureExtraction(IFeatureExtraction poFeatureExtraction) Allows setting the features source.
boolean	train() Generic training routine for building/updating mean vectors in the training set.
boolean	train(double[] padFeatureVector) Generic training routine for building/updating mean vectors in the training set.

Field Detail

MARF_INTERFACE_CODE_REVISION

static final java.lang.String MARF_INTERFACE_CODE_REVISION

Interface source code revision.

See Also:

Constant Field Values

Method Detail

classify

boolean classify()
                 throws ClassificationException

Generic classification routine. Assumes presence of a valid, non-null IFeatureExtraction module.

Returns:

true if classification was successful; false otherwise

Throws:

ClassificationException - if there was an error while classifying

train

boolean train()
              throws ClassificationException

Generic training routine for building/updating mean vectors in the training set. Assumes presence of a valid, non-null IFeatureExtraction module.

Returns:

true if training was successful; false otherwise

Throws:

ClassificationException - if there was a problem while training

classify

boolean classify(double[] padFeatureVector)
                 throws ClassificationException

Generic classification routine.

Parameters:

padFeatureVector - vector of features to compare with the stored ones

Returns:

true if classification was successful; false otherwise

Throws:

ClassificationException - if there was an error while classifying

Since:

0.3.0.6

train

boolean train(double[] padFeatureVector)
              throws ClassificationException

Generic training routine for building/updating mean vectors in the training set.

Parameters:

padFeatureVector - vector of features to train on

Returns:

true if training was successful; false otherwise

Throws:

ClassificationException - if there was a problem while training

Since:

0.3.0.6

getResult

Result getResult()

Retrieves the likely classification result. If there were many, this will return the result with the highest statistical score or probability. The decision of whether to retrieve a maximum result (with maximum probability) or minimum result (with minimum distance) from the sample is left to be made by concrete implementations.

Returns:

Result object

getResultSet

ResultSet getResultSet()

Retrieves the enclosed result set.

Returns:

the enclosed ResultSet object

getFeatureExtraction

IFeatureExtraction getFeatureExtraction()

Retrieves the features source.

Returns:

returns the FeatureExtraction reference

Since:

0.3.0.4

setFeatureExtraction

void setFeatureExtraction(IFeatureExtraction poFeatureExtraction)

Allows setting the features source.

Parameters:

poFeatureExtraction - the FeatureExtraction object to set

Since:

0.3.0.4