classifier

Class MahalanobisClassifier

java.lang.Object

classifier.Classifier

classifier.MahalanobisClassifier

public class MahalanobisClassifier
extends Classifier

Nested Class Summary

Nested classes/interfaces inherited from class classifier.Classifier

Classifier.ProbabilityType

Constructor Summary

Constructors

Constructor and Description
MahalanobisClassifier()

Method Summary

Methods

Modifier and Type	Method and Description
java.lang.String	getClassifierName()
ClassifierParams	getClassifierParams() Will get called AFTER classifier training to get classifier specific parameters.
Jama.Matrix	getLogLikelihoodsM() Get a matrix of log likelihoods from the most recent call to RunClassification(Matrix )
Jama.Matrix	getProbabilitiesM() This need to be rewritten with real probabilities fromt he chi2 distribution Maniana !
Classifier.ProbabilityType	getProbabilityType() Get the type of probability returned by a classifier.
int[]	runClassification(Jama.Matrix data)
boolean	setClassifierParams(ClassifierParams classifierParams) Will be called to load stored parameters into a classifier.
java.lang.String	trainClassification(Jama.Matrix matrix, int[] truth) /** Train the classifier.

Methods inherited from class classifier.Classifier

getLogLikelihoods1, getLogLikelihoods2, getMinimumProbability, getProbabilities1, getProbabilities2, hasParamsDialog, runClassification, runClassification, setMinimumProbability, showParamsDialog, toString, trainClassification

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

MahalanobisClassifier

public MahalanobisClassifier()

Method Detail

getClassifierName

public java.lang.String getClassifierName()

Specified by:

getClassifierName in class Classifier

Returns:

the classifier name, e.g. Linear Discriminant Analysis

getClassifierParams

public ClassifierParams getClassifierParams()

Description copied from class: Classifier

Will get called AFTER classifier training to get classifier specific parameters.

Specified by:

getClassifierParams in class Classifier

Returns:

classifier specific parameters.

getLogLikelihoodsM

public Jama.Matrix getLogLikelihoodsM()

Description copied from class: Classifier

Get a matrix of log likelihoods from the most recent call to RunClassification(Matrix )

This version will return a matrix of data, which should be from a set of classifications.

Specified by:

getLogLikelihoodsM in class Classifier

Returns:

log likelihoods matrix.

getProbabilitiesM

public Jama.Matrix getProbabilitiesM()

This need to be rewritten with real probabilities fromt he chi2 distribution Maniana !

Specified by:

getProbabilitiesM in class Classifier

Returns:

probabilities matrix.

getProbabilityType

public Classifier.ProbabilityType getProbabilityType()

Description copied from class: Classifier

Get the type of probability returned by a classifier. this will either be NORMALISED, ABSOLUTE or UNAVAILABLE in which case the classifier should still return data (to stop things crashing) but should return arrays / Matrixes with zeros in all columns except the selected item, which should be 1.

Specified by:

getProbabilityType in class Classifier

Returns:

type of probability returned.

runClassification

public int[] runClassification(Jama.Matrix data)

Specified by:

runClassification in class Classifier

setClassifierParams

public boolean setClassifierParams(ClassifierParams classifierParams)

Description copied from class: Classifier

Will be called to load stored parameters into a classifier.

Specified by:

setClassifierParams in class Classifier

Parameters:

classifierParams - classifier parameters.

Returns:

Return true if parameters loaded OK. Reasons for not loading include the classifier being incompatible with the classifier parameters or the parameters not being present (e.t. null matrixes)

trainClassification

public java.lang.String trainClassification(Jama.Matrix matrix,
                                   int[] truth)

Description copied from class: Classifier

/** Train the classifier.

Specified by:

trainClassification in class Classifier

Parameters:

matrix - matrix of training data (each row one training point, each col one parameter

truth - truth

Returns:

null if OK or error string