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Java Source Code / Java Documentation » Science » weka » weka.classifiers.meta

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Class Diagram

Java Document (Java Doc)

weka.classifiers .RandomizableMultipleClassifiersCombiner

weka.classifiers.meta .MultiScheme

MultiScheme

public class MultiScheme extends RandomizableMultipleClassifiersCombiner (Code)

Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data. Performance is measured based on percent correct (classification) or mean-squared error (regression).

Valid options are:

 -X <number of folds>
 Use cross validation for model selection using the
 given number of folds. (default 0, is to
 use training error)

 -S <num>
 Random number seed.
 (default 1)

 -B <classifier specification>
 Full class name of classifier to include, followed
 by scheme options. May be specified multiple times.
 (default: "weka.classifiers.rules.ZeroR")

 -D
 If set, classifier is run in debug mode and
 may output additional info to the console

author:
Len Trigg (trigg@cs.waikato.ac.nz)
version:
$Revision: 1.24 $

Field Summary
protected Classifier	m_Classifier The classifier that had the best performance on training data.
protected int	m_ClassifierIndex
protected int	m_NumXValFolds
final static long	serialVersionUID

Method Summary
public void	buildClassifier(Instances data) Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data.
public String	classifiersTipText()
public String	debugTipText()
public double[]	distributionForInstance(Instance instance) Returns class probabilities.
public int	getBestClassifierIndex() Get the index of the classifier that was determined as best during cross-validation.
public Classifier	getClassifier(int index) Gets a single classifier from the set of available classifiers.
protected String	getClassifierSpec(int index) Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier Parameters: index - the index of the classifier string to retrieve, starting from0.
public Classifier[]	getClassifiers() Gets the list of possible classifers to choose from.
public boolean	getDebug()
public int	getNumFolds() Gets the number of folds for cross-validation.
public String[]	getOptions() Gets the current settings of the Classifier.
public int	getSeed() Gets the random number seed.
public String	globalInfo()
public Enumeration	listOptions() Returns an enumeration describing the available options.
public static void	main(String[] argv) Main method for testing this class.
public String	numFoldsTipText()
public String	seedTipText()
public void	setClassifiers(Classifier[] classifiers) Sets the list of possible classifers to choose from.
public void	setDebug(boolean debug)
public void	setNumFolds(int numFolds) Sets the number of folds for cross-validation.
public void	setOptions(String[] options) Parses a given list of options.
public void	setSeed(int seed) Sets the seed for random number generation.
public String	toString()

Field Detail

m_Classifier
protected Classifier m_Classifier(Code)
	The classifier that had the best performance on training data.

m_ClassifierIndex
protected int m_ClassifierIndex(Code)
	The index into the vector for the selected scheme

m_NumXValFolds
protected int m_NumXValFolds(Code)
	Number of folds to use for cross validation (0 means use training error for selection)

serialVersionUID
final static long serialVersionUID(Code)
	for serialization

Method Detail

buildClassifier
public void buildClassifier(Instances data) throws Exception(Code)
	Buildclassifier selects a classifier from the set of classifiers by minimising error on the training data. Parameters: data - the training data to be used for generating theboosted classifier. throws: Exception - if the classifier could not be built successfully

classifiersTipText
public String classifiersTipText()(Code)
	Returns the tip text for this property tip text for this property suitable fordisplaying in the explorer/experimenter gui

debugTipText
public String debugTipText()(Code)
	Returns the tip text for this property tip text for this property suitable fordisplaying in the explorer/experimenter gui

distributionForInstance
public double[] distributionForInstance(Instance instance) throws Exception(Code)
	Returns class probabilities. Parameters: instance - the instance to be classified the distribution for the instance throws: Exception - if instance could not be classifiedsuccessfully

getBestClassifierIndex
public int getBestClassifierIndex()(Code)
	Get the index of the classifier that was determined as best during cross-validation. the index in the classifier array

getClassifier
public Classifier getClassifier(int index)(Code)
	Gets a single classifier from the set of available classifiers. Parameters: index - the index of the classifier wanted the Classifier

getClassifierSpec
protected String getClassifierSpec(int index)(Code)
	Gets the classifier specification string, which contains the class name of the classifier and any options to the classifier Parameters: index - the index of the classifier string to retrieve, starting from0. the classifier string, or the empty string if no classifierhas been assigned (or the index given is out of range).

getClassifiers
public Classifier[] getClassifiers()(Code)
	Gets the list of possible classifers to choose from. the array of Classifiers

getDebug
public boolean getDebug()(Code)
	Get whether debugging is turned on true if debugging output is on

getNumFolds
public int getNumFolds()(Code)
	Gets the number of folds for cross-validation. A number less than 2 specifies using training error rather than cross-validation. the number of folds for cross-validation

getOptions
public String[] getOptions()(Code)
	Gets the current settings of the Classifier. an array of strings suitable for passing to setOptions

getSeed
public int getSeed()(Code)
	Gets the random number seed. the random number seed

globalInfo
public String globalInfo()(Code)
	Returns a string describing classifier a description suitable fordisplaying in the explorer/experimenter gui

listOptions
public Enumeration listOptions()(Code)
	Returns an enumeration describing the available options. an enumeration of all the available options.

main
public static void main(String[] argv)(Code)
	Main method for testing this class. Parameters: argv - should contain the following arguments:-t training file [-T test file] [-c class index]

numFoldsTipText
public String numFoldsTipText()(Code)
	Returns the tip text for this property tip text for this property suitable fordisplaying in the explorer/experimenter gui

seedTipText
public String seedTipText()(Code)
	Returns the tip text for this property tip text for this property suitable fordisplaying in the explorer/experimenter gui

setClassifiers
public void setClassifiers(Classifier[] classifiers)(Code)
	Sets the list of possible classifers to choose from. Parameters: classifiers - an array of classifiers with all options set.

setDebug
public void setDebug(boolean debug)(Code)
	Set debugging mode Parameters: debug - true if debug output should be printed

setNumFolds
public void setNumFolds(int numFolds)(Code)
	Sets the number of folds for cross-validation. A number less than 2 specifies using training error rather than cross-validation. Parameters: numFolds - the number of folds for cross-validation

setOptions

public void setOptions(String[] options) throws Exception(Code)

Parses a given list of options.

Valid options are:

 -X <number of folds>
 Use cross validation for model selection using the
 given number of folds. (default 0, is to
 use training error)

 -S <num>
 Random number seed.
 (default 1)

 -B <classifier specification>
 Full class name of classifier to include, followed
 by scheme options. May be specified multiple times.
 (default: "weka.classifiers.rules.ZeroR")

 -D
 If set, classifier is run in debug mode and
 may output additional info to the console

Parameters:
options - the list of options as an array of strings
throws:
Exception - if an option is not supported

setSeed
public void setSeed(int seed)(Code)
	Sets the seed for random number generation. Parameters: seed - the random number seed

toString
public String toString()(Code)
	Output a representation of this classifier a string representation of the classifier

Fields inherited from weka.classifiers.RandomizableMultipleClassifiersCombiner

protected int m_Seed(Code)(Java Doc)

Methods inherited from weka.classifiers.RandomizableMultipleClassifiersCombiner

public String[] getOptions()(Code)(Java Doc)
public int getSeed()(Code)(Java Doc)
public Enumeration listOptions()(Code)(Java Doc)
public String seedTipText()(Code)(Java Doc)
public void setOptions(String[] options) throws Exception(Code)(Java Doc)
public void setSeed(int seed)(Code)(Java Doc)

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