de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared

Class SharedStructureClassifier

java.lang.Object

de.jstacs.classifiers.AbstractClassifier

de.jstacs.classifiers.AbstractScoreBasedClassifier

de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier

de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureClassifier

All Implemented Interfaces:

Storable, Cloneable

public class SharedStructureClassifier
extends TrainSMBasedClassifier

This class enables you to learn the structure on all classes of the classifier together. A special case is, for instance, a Tree Augmented Naive Bayes (TAN).

Author:

Jens Keilwagen

Nested Class Summary

Nested classes/interfaces inherited from class de.jstacs.classifiers.AbstractScoreBasedClassifier

AbstractScoreBasedClassifier.DoubleTableResult

Field Summary

Fields inherited from class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier

models

Constructor Summary

Constructors

Constructor and Description
SharedStructureClassifier(int length, StructureLearner.ModelType model, byte order, StructureLearner.LearningType method, FSDAGTrainSM... models) Creates a new SharedStructureClassifier from given FSDAGTrainSMs.
SharedStructureClassifier(StringBuffer xml) The standard constructor for the interface Storable.

Method Summary

Methods

Modifier and Type	Method and Description
SharedStructureClassifier	clone()
protected void	extractFurtherClassifierInfosFromXML(StringBuffer xml) Extracts further information of a classifier from an XML representation.
CategoricalResult[]	getClassifierAnnotation() Returns an array of Results of dimension AbstractClassifier.getNumberOfClasses() that contains information about the classifier and for each class. res[0] = new CategoricalResult( "classifier", "the kind of classifier", getInstanceName() ); res[1] = new CategoricalResult( "class info 0", "some information about the class", "info0" ); res[2] = new CategoricalResult( "class info 1", "some information about the class", "info1" ); ...
protected StringBuffer	getFurtherClassifierInfos() This method returns further information of a classifier as a StringBuffer.
String	getInstanceName() Returns a short description of the classifier.
void	train(DataSet[] data, double[][] weights) This method trains a classifier over an array of weighted DataSet s.

Methods inherited from class de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier

classify, getCharacteristics, getModel, getNumericalCharacteristics, getPossibleLength, getScore, getScores, getXMLTag, isInitialized, toString

Methods inherited from class de.jstacs.classifiers.AbstractScoreBasedClassifier

check, check, classify, classify, createDefaultClassWeights, getClassWeight, getClassWeights, getMultiClassScores, getNumberOfClasses, getPValue, getPValue, getResults, getScore, setClassWeights, setClassWeights, setThresholdClassWeights

Methods inherited from class de.jstacs.classifiers.AbstractClassifier

evaluate, evaluate, getAlphabetContainer, getLength, toXML, train

Methods inherited from class java.lang.Object

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

Constructor Detail

SharedStructureClassifier

public SharedStructureClassifier(int length,
                         StructureLearner.ModelType model,
                         byte order,
                         StructureLearner.LearningType method,
                         FSDAGTrainSM... models)
                          throws IllegalArgumentException,
                                 CloneNotSupportedException,
                                 ClassDimensionException

Creates a new SharedStructureClassifier from given FSDAGTrainSMs. This is the main constructor.

Parameters:

length - the sequence length

model - the type of the model

order - the order of the model

method - the learning method

models - the class models

Throws:

IllegalArgumentException - if order is below 0

CloneNotSupportedException - if at least one model could not be cloned

ClassDimensionException - if the class dimension is wrong (below 2)

See Also:

StructureLearner.ModelType, StructureLearner.LearningType, TrainSMBasedClassifier.TrainSMBasedClassifier(boolean, de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModel...)

SharedStructureClassifier

public SharedStructureClassifier(StringBuffer xml)
                          throws NonParsableException

The standard constructor for the interface Storable. Creates a new SharedStructureClassifier out of its XML representation.

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the SharedStructureClassifier could not be reconstructed out of the XML representation (the StringBuffer could not be parsed)

See Also:

Storable, TrainSMBasedClassifier.TrainSMBasedClassifier(StringBuffer)

Method Detail

clone

public SharedStructureClassifier clone()
                                throws CloneNotSupportedException

Overrides:

clone in class TrainSMBasedClassifier

Throws:

CloneNotSupportedException

train

public void train(DataSet[] data,
         double[][] weights)
           throws IllegalArgumentException,
                  Exception

Description copied from class: AbstractClassifier

This method trains a classifier over an array of weighted DataSet s. That is why the following has to be fulfilled:

• s.length == weights.length
• and for all i: weights[i] == null || s[i].getNumberOfElements() == weights[i].length.

This method should work non-incrementally as the method AbstractClassifier.train(DataSet...).

This method should check that the DataSets are defined over the underlying alphabet and length.

Overrides:

train in class TrainSMBasedClassifier

Parameters:

data - an array of DataSets

weights - the weights for the DataSets

Throws:

Exception - if the weights are incorrect or the training did not succeed

IllegalArgumentException

See Also:

AbstractClassifier.train(DataSet...)

getInstanceName

public String getInstanceName()

Description copied from class: AbstractClassifier

Returns a short description of the classifier.

Overrides:

getInstanceName in class TrainSMBasedClassifier

Returns:

a short description of the classifier

extractFurtherClassifierInfosFromXML

protected void extractFurtherClassifierInfosFromXML(StringBuffer xml)
                                             throws NonParsableException

Description copied from class: AbstractClassifier

Extracts further information of a classifier from an XML representation. This method is used by the method AbstractClassifier.fromXML(StringBuffer) and should not be made public.

Overrides:

extractFurtherClassifierInfosFromXML in class TrainSMBasedClassifier

Parameters:

xml - the XML representation as StringBuffer

Throws:

NonParsableException - if the information could not be parsed out of the XML representation (the StringBuffer could not be parsed)

See Also:

AbstractClassifier.fromXML(StringBuffer)

getFurtherClassifierInfos

protected StringBuffer getFurtherClassifierInfos()

Description copied from class: AbstractClassifier

This method returns further information of a classifier as a StringBuffer. This method is used by the method AbstractClassifier.toXML() and should not be made public.

Overrides:

getFurtherClassifierInfos in class TrainSMBasedClassifier

Returns:

further information of a classifier as a StringBuffer

See Also:

AbstractClassifier.toXML()

getClassifierAnnotation

public CategoricalResult[] getClassifierAnnotation()

Description copied from class: AbstractClassifier

Returns an array of Results of dimension AbstractClassifier.getNumberOfClasses() that contains information about the classifier and for each class.

res[0] = new CategoricalResult( "classifier", "the kind of classifier", getInstanceName() );
res[1] = new CategoricalResult( "class info 0", "some information about the class", "info0" );
res[2] = new CategoricalResult( "class info 1", "some information about the class", "info1" );
...

Overrides:

getClassifierAnnotation in class TrainSMBasedClassifier

Returns:

an array of Results that contains information about the classifier