com.rapidminer.operator.learner.bayes
Class LinearDiscriminantAnalysis

java.lang.Object
  com.rapidminer.operator.Operator
      com.rapidminer.operator.learner.AbstractLearner
          com.rapidminer.operator.learner.bayes.LinearDiscriminantAnalysis

All Implemented Interfaces:

ConfigurationListener, PreviewListener, Learner, ParameterHandler, LoggingHandler

Direct Known Subclasses:

QuadraticDiscriminantAnalysis, RegularizedDiscriminantAnalysis

public class LinearDiscriminantAnalysis
extends AbstractLearner

This operator performs a linear discriminant analysis (LDA). This method tries to find the linear combination of features which best separate two or more classes of examples. The resulting combination is then used as a linear classifier. LDA is closely related to ANOVA (analysis of variance) and regression analysis, which also attempt to express one dependent variable as a linear combination of other features or measurements. In the other two methods however, the dependent variable is a numerical quantity, while for LDA it is a categorical variable (i.e. the class label).

LDA is also closely related to principal component analysis (PCA) and factor analysis in that both look for linear combinations of variables which best explain the data. LDA explicitly attempts to model the difference between the classes of data. PCA on the other hand does not take into account any difference in class.

Author:

Sebastian Land

Field Summary

Fields inherited from class com.rapidminer.operator.learner.AbstractLearner

PROPERTY_RAPIDMINER_GENERAL_CAPABILITIES_WARN

Constructor Summary

LinearDiscriminantAnalysis(OperatorDescription description)

Method Summary

protected Jama.Matrix[]	getInverseCovarianceMatrices(ExampleSet exampleSet, java.lang.String[] labels)
protected Jama.Matrix[]	getMeanVectors(ExampleSet exampleSet, int numberOfAttributes, java.lang.String[] labels)
protected DiscriminantModel	getModel(ExampleSet exampleSet, java.lang.String[] labels, Jama.Matrix[] meanVectors, Jama.Matrix[] inverseCovariances, double[] aprioriProbabilities)
Model	learn(ExampleSet exampleSet) Trains a model.
boolean	supportsCapability(LearnerCapability capability) Checks for Learner capabilities.

Methods inherited from class com.rapidminer.operator.learner.AbstractLearner

apply, getEstimatedPerformance, getInputClasses, getInputDescription, getOptimizationPerformance, getOutputClasses, getWeights, onlyWarnForNonSufficientCapabilities, shouldCalculateWeights, shouldDeliverOptimizationPerformance, shouldEstimatePerformance

Methods inherited from class com.rapidminer.operator.Operator

addError, addValue, addWarning, apply, checkDeprecations, checkForStop, checkIO, checkProperties, clearErrorList, cloneOperator, createExperimentTree, createExperimentTree, createFromXML, createMarkedExperimentTree, createMarkedProcessTree, createProcessTree, createProcessTree, getAddOnlyAdditionalOutput, getApplyCount, getDeliveredOutputClasses, getDeprecationInfo, getDesiredInputClasses, getEncoding, getErrorList, getExperiment, getInnerOperatorsXML, getInput, getInput, getInput, getIOContainerForInApplyLoopBreakpoint, getIODescription, getLog, getName, getOperatorClassName, getOperatorDescription, getParameter, getParameterAsBoolean, getParameterAsColor, getParameterAsDouble, getParameterAsFile, getParameterAsFile, getParameterAsInputStream, getParameterAsInt, getParameterAsMatrix, getParameterAsString, getParameterList, getParameters, getParameterType, getParameterTypes, getParent, getProcess, getStartTime, getStatus, getUserDescription, getValue, getValues, getXML, hasBreakpoint, hasBreakpoint, hasInput, inApplyLoop, isDebugMode, isEnabled, isExpanded, isParallel, isParameterSet, log, logError, logNote, logWarning, performAdditionalChecks, processFinished, processStarts, register, registerOperator, remove, rename, resume, setApplyCount, setBreakpoint, setEnabled, setExpanded, setInput, setListParameter, setOperatorParameters, setParameter, setParameters, setParent, setUserDescription, toString, unregisterOperator, writeXML

Methods inherited from class java.lang.Object

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

Methods inherited from interface com.rapidminer.operator.learner.Learner

getName

Constructor Detail

LinearDiscriminantAnalysis

public LinearDiscriminantAnalysis(OperatorDescription description)

Method Detail

learn

public Model learn(ExampleSet exampleSet)
            throws OperatorException

Description copied from interface: Learner

Trains a model. This method should be called by apply() and is implemented by subclasses.

Throws:

OperatorException

getModel

protected DiscriminantModel getModel(ExampleSet exampleSet,
                                     java.lang.String[] labels,
                                     Jama.Matrix[] meanVectors,
                                     Jama.Matrix[] inverseCovariances,
                                     double[] aprioriProbabilities)
                              throws UndefinedParameterError

Throws:

UndefinedParameterError

getMeanVectors

protected Jama.Matrix[] getMeanVectors(ExampleSet exampleSet,
                                       int numberOfAttributes,
                                       java.lang.String[] labels)

getInverseCovarianceMatrices

protected Jama.Matrix[] getInverseCovarianceMatrices(ExampleSet exampleSet,
                                                     java.lang.String[] labels)
                                              throws UndefinedParameterError

Throws:

UndefinedParameterError

supportsCapability

public boolean supportsCapability(LearnerCapability capability)

Description copied from interface: Learner

Checks for Learner capabilities. Should return true if the given capability is supported.