de.jstacs.classifiers.trainSMBased
Class TrainSMBasedClassifier

java.lang.Object
  de.jstacs.classifiers.AbstractClassifier
      de.jstacs.classifiers.AbstractScoreBasedClassifier
          de.jstacs.classifiers.trainSMBased.TrainSMBasedClassifier

All Implemented Interfaces:

Storable, Cloneable

Direct Known Subclasses:

SharedStructureClassifier

public class TrainSMBasedClassifier
extends AbstractScoreBasedClassifier

Classifier that works on TrainableStatisticalModels for each of the different classes. By calling the AbstractClassifier.train(DataSet...) method of this classifier, all internal TrainableStatisticalModels are learned on the DataSet for the corresponding class using their own TrainableStatisticalModel.train(DataSet) method. In addition, the a-priori class probabilities are estimated. After training, the method AbstractScoreBasedClassifier.getScore(Sequence, int) returns the joint probability (likelihood) of the provided Sequence and the specified class. For two-class problems, the method getScores(DataSet) returns the log-likelihood ratios for all Sequences in the provided DataSet. The methods AbstractScoreBasedClassifier.classify(Sequence) and classify(DataSet) use the likelihoods of Sequence and class and report the class yielding the maximum likelihood.

Author:

Jens Keilwagen

See Also:

TrainableStatisticalModel

Nested Class Summary

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

AbstractScoreBasedClassifier.DoubleTableResult

Field Summary

protected TrainableStatisticalModel[]

models The internal TrainableStatisticalModels.

Constructor Summary

protected	TrainSMBasedClassifier(boolean cloneModels, TrainableStatisticalModel... models) This constructor creates a new instance with the given TrainableStatisticalModels and clones these if necessary.
	TrainSMBasedClassifier(StringBuffer xml) The standard constructor for the interface Storable.
	TrainSMBasedClassifier(TrainableStatisticalModel... models) The default constructor that creates a new instance with the given TrainableStatisticalModels.

Method Summary

byte[]	classify(DataSet s) This method classifies all sequences of a sample and returns an array of indices of the classes to which the respective sequences are assigned with for each index i in the array 0 < i < getNumberOfClasses().
TrainSMBasedClassifier	clone()
protected void	extractFurtherClassifierInfosFromXML(StringBuffer xml) Extracts further information of a classifier from an XML representation.
ResultSet	getCharacteristics() Returns some information characterizing or describing the current instance of the classifier.
CategoricalResult[]	getClassifierAnnotation() Returns an array of Results of dimension AbstractClassifier.getNumberOfClasses() that contains information about the classifier and for each class.
protected StringBuffer	getFurtherClassifierInfos() This method returns further information of a classifier as a StringBuffer.
String	getInstanceName() Returns a short description of the classifier.
TrainableStatisticalModel	getModel(int classIndex) Returns a clone of the TrainableStatisticalModel for a specified class.
NumericalResultSet	getNumericalCharacteristics() Returns the subset of numerical values that are also returned by AbstractClassifier.getCharacteristics().
static int	getPossibleLength(TrainableStatisticalModel... models) This method returns the possible length of a classifier that would use the given TrainableStatisticalModels.
protected double	getScore(Sequence seq, int i, boolean check) This method returns the score for a given Sequence and a given class.
double[]	getScores(DataSet s) This method returns the scores of the classifier for any Sequence in the DataSet.
protected String	getXMLTag() Returns the String that is used as tag for the XML representation of the classifier.
boolean	isInitialized() This method gives information about the state of the classifier.
void	train(DataSet[] s, double[][] weights) This method trains a classifier over an array of weighted DataSet s.

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, getAlphabetContainer, getLength, toXML, train

Methods inherited from class java.lang.Object

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

Field Detail

models

protected TrainableStatisticalModel[] models

The internal TrainableStatisticalModels. TrainableStatisticalModel 0 handles class 0; TrainableStatisticalModel 1 handles class 1 ... etc.

Constructor Detail

TrainSMBasedClassifier

protected TrainSMBasedClassifier(boolean cloneModels,
                                 TrainableStatisticalModel... models)
                          throws IllegalArgumentException,
                                 CloneNotSupportedException,
                                 ClassDimensionException

This constructor creates a new instance with the given TrainableStatisticalModels and clones these if necessary.

Parameters:

cloneModels - a switch to decide whether to clone the TrainableStatisticalModel or not

models - the TrainableStatisticalModels

Throws:

IllegalArgumentException - if the TrainableStatisticalModels do not describe a common domain of sequences

CloneNotSupportedException - if at least one TrainableStatisticalModel could not be cloned

ClassDimensionException - if the number of classes is below 2

See Also:

AbstractScoreBasedClassifier.AbstractScoreBasedClassifier(AlphabetContainer, int, int, double)

TrainSMBasedClassifier

public TrainSMBasedClassifier(TrainableStatisticalModel... models)
                       throws IllegalArgumentException,
                              CloneNotSupportedException,
                              ClassDimensionException

The default constructor that creates a new instance with the given TrainableStatisticalModels.

Parameters:

models - the TrainableStatisticalModels

Throws:

IllegalArgumentException - if the TrainableStatisticalModels do not describe a common domain of sequences

CloneNotSupportedException - if at least one TrainableStatisticalModel could not be cloned

ClassDimensionException - if the number of classes is below 2

See Also:

TrainSMBasedClassifier(boolean, TrainableStatisticalModel...)

TrainSMBasedClassifier

public TrainSMBasedClassifier(StringBuffer xml)
                       throws NonParsableException

The standard constructor for the interface Storable. Constructs a TrainSMBasedClassifier out of its XML representation.

Parameters:

xml - the XML representation as StringBuffer

Throws:

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

See Also:

AbstractScoreBasedClassifier.AbstractScoreBasedClassifier(StringBuffer), Storable

Method Detail

getPossibleLength

public static int getPossibleLength(TrainableStatisticalModel... models)
                             throws IllegalArgumentException

This method returns the possible length of a classifier that would use the given TrainableStatisticalModels.

Parameters:

models - the TrainableStatisticalModels that will be tested

Returns:

the length of a classifier that would use the given models

Throws:

IllegalArgumentException - if no classifier could be created since the TrainableStatisticalModels have incompatible lengths

clone

public TrainSMBasedClassifier clone()
                             throws CloneNotSupportedException

Overrides:

clone in class AbstractScoreBasedClassifier

Throws:

CloneNotSupportedException

getCharacteristics

public ResultSet getCharacteristics()
                             throws Exception

Description copied from class: AbstractClassifier

Returns some information characterizing or describing the current instance of the classifier. This could be for instance the number of edges for a Bayesian network or an image showing some representation of the model of a class. The set of characteristics should always include the XML representation of the classifier. The corresponding result type is StorableResult.

Overrides:

getCharacteristics in class AbstractClassifier

Returns:

the characteristics of the current instance of the classifier

Throws:

Exception - if some of the characteristics could not be defined

See Also:

StorableResult, AbstractClassifier.getNumericalCharacteristics(), ResultSet.ResultSet(de.jstacs.results.Result[][])

getInstanceName

public String getInstanceName()

Description copied from class: AbstractClassifier

Returns a short description of the classifier.

Specified by:

getInstanceName in class AbstractClassifier

Returns:

a short description of the classifier

getModel

public TrainableStatisticalModel getModel(int classIndex)
                                   throws CloneNotSupportedException

Returns a clone of the TrainableStatisticalModel for a specified class.

Parameters:

classIndex - the index of the specified class

Returns:

a clone of the TrainableStatisticalModel of the specified class

Throws:

CloneNotSupportedException - if the TrainableStatisticalModel could not be cloned

See Also:

TrainableStatisticalModel.clone()

getNumericalCharacteristics

public NumericalResultSet getNumericalCharacteristics()
                                               throws Exception

Description copied from class: AbstractClassifier

Returns the subset of numerical values that are also returned by AbstractClassifier.getCharacteristics().

Specified by:

getNumericalCharacteristics in class AbstractClassifier

Returns:

the numerical characteristics

Throws:

Exception - if some of the characteristics could not be defined

isInitialized

public boolean isInitialized()

Description copied from class: AbstractClassifier

This method gives information about the state of the classifier.

Specified by:

isInitialized in class AbstractClassifier

Returns:

true if the classifier is initialized and therefore able to classify sequences, otherwise false

train

public void train(DataSet[] s,
                  double[][] weights)
           throws 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.

Specified by:

train in class AbstractClassifier

Parameters:

s - an array of DataSets

weights - the weights for the DataSets

Throws:

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

See Also:

AbstractClassifier.train(DataSet...)

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 AbstractScoreBasedClassifier

Returns:

further information of a classifier as a StringBuffer

See Also:

AbstractClassifier.toXML()

getScore

protected double getScore(Sequence seq,
                          int i,
                          boolean check)
                   throws Exception

Description copied from class: AbstractScoreBasedClassifier

This method returns the score for a given Sequence and a given class.

Specified by:

getScore in class AbstractScoreBasedClassifier

Parameters:

seq - the Sequence

i - the index of the class

check - the switch to decide whether to check AlphabetContainer and the length of the Sequence or not

Returns:

the score for a given Sequence and a given class

Throws:

NotTrainedException - if the classifier is not trained

IllegalArgumentException - if something is wrong with the Sequence seq

Exception - if something went wrong

getScores

public double[] getScores(DataSet s)
                   throws Exception

Description copied from class: AbstractScoreBasedClassifier

This method returns the scores of the classifier for any Sequence in the DataSet. The scores are stored in the array according to the index of the Sequence in the DataSet.

Only for 2-class-classifiers.

Overrides:

getScores in class AbstractScoreBasedClassifier

Parameters:

s - the DataSet

Returns:

the array of scores

Throws:

Exception - if something went wrong

classify

public byte[] classify(DataSet s)
                throws Exception

Description copied from class: AbstractClassifier

This method classifies all sequences of a sample and returns an array of indices of the classes to which the respective sequences are assigned with for each index i in the array 0 < i < getNumberOfClasses().

Overrides:

classify in class AbstractClassifier

Parameters:

s - the sample to be classified

Returns:

an array of class assignments

Throws:

Exception - if something went wrong during the classification

getXMLTag

protected String getXMLTag()

Description copied from class: AbstractClassifier

Returns the String that is used as tag for the XML representation of the classifier. This method is used by the methods AbstractClassifier.fromXML(StringBuffer) and AbstractClassifier.toXML().

Specified by:

getXMLTag in class AbstractClassifier

Returns:

the String that is used as tag for the XML representation 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 AbstractScoreBasedClassifier

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)

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" );
...

Specified by:

getClassifierAnnotation in class AbstractClassifier

Returns:

an array of Results that contains information about the classifier