weka.classifiers.pmml.consumer

Class SupportVectorMachineModel

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.pmml.consumer.PMMLClassifier

weka.classifiers.pmml.consumer.SupportVectorMachineModel

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, PMMLModel, RevisionHandler

public class SupportVectorMachineModel
extends PMMLClassifier
implements java.io.Serializable

Implements a PMML SupportVectorMachineModel

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected java.lang.String	m_algorithmName The algorithm name (if defined)
protected int	m_alternateBinaryTargetCategory The other class index (in the case of a single binary SVM - PMML 3.2).
protected weka.classifiers.pmml.consumer.SupportVectorMachineModel.classificationMethod	m_classificationMethod The classification method (PMML 4.0)
protected weka.classifiers.pmml.consumer.NeuralNetwork.MiningFunction	m_functionType The mining function
protected weka.classifiers.pmml.consumer.SupportVectorMachineModel.Kernel	m_kernel The kernel function to use
protected java.util.List<weka.classifiers.pmml.consumer.SupportVectorMachineModel.SupportVectorMachine>	m_machines The individual binary SVMs
protected java.lang.String	m_modelName The model name (if defined)
protected weka.classifiers.pmml.consumer.SupportVectorMachineModel.SVM_representation	m_svmRepresentation Do we have support vectors, or just attribute coefficients for a linear machine?
protected double	m_threshold PMML 4.0 threshold value
protected VectorDictionary	m_vectorDictionary The dictionary of support vectors

Fields inherited from class weka.classifiers.pmml.consumer.PMMLClassifier

m_creatorApplication, m_dataDictionary, m_fieldsMap, m_initialized, m_log, m_miningSchema, m_pmmlVersion

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
SupportVectorMachineModel(org.w3c.dom.Element model, Instances dataDictionary, MiningSchema miningSchema) Construct a new SupportVectorMachineModel encapsulating the information provided in the PMML document.

Method Summary

Modifier and Type	Method and Description
double[]	distributionForInstance(Instance inst) Classifies the given test instance.
java.lang.String	getRevision() Returns the revision string.
java.lang.String	toString() Get a textual description of this SupportVectorMachineModel

Methods inherited from class weka.classifiers.pmml.consumer.PMMLClassifier

buildClassifier, done, getCreatorApplication, getDataDictionary, getFieldsMappingString, getLog, getMiningSchema, getPMMLVersion, mapToMiningSchema, setCreatorApplication, setLog, setPMMLVersion

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getCapabilities, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, getOptions, implementsMoreEfficientBatchPrediction, listOptions, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces, setOptions

Methods inherited from class java.lang.Object

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

Field Detail

m_functionType

protected weka.classifiers.pmml.consumer.NeuralNetwork.MiningFunction m_functionType

The mining function

m_classificationMethod

protected weka.classifiers.pmml.consumer.SupportVectorMachineModel.classificationMethod m_classificationMethod

The classification method (PMML 4.0)

m_modelName

protected java.lang.String m_modelName

The model name (if defined)

m_algorithmName

protected java.lang.String m_algorithmName

The algorithm name (if defined)

m_vectorDictionary

protected VectorDictionary m_vectorDictionary

The dictionary of support vectors

m_kernel

protected weka.classifiers.pmml.consumer.SupportVectorMachineModel.Kernel m_kernel

The kernel function to use

m_machines

protected java.util.List<weka.classifiers.pmml.consumer.SupportVectorMachineModel.SupportVectorMachine> m_machines

The individual binary SVMs

m_alternateBinaryTargetCategory

protected int m_alternateBinaryTargetCategory

The other class index (in the case of a single binary SVM - PMML 3.2).

m_svmRepresentation

protected weka.classifiers.pmml.consumer.SupportVectorMachineModel.SVM_representation m_svmRepresentation

Do we have support vectors, or just attribute coefficients for a linear machine?

m_threshold

protected double m_threshold

PMML 4.0 threshold value

Constructor Detail

SupportVectorMachineModel

public SupportVectorMachineModel(org.w3c.dom.Element model,
                                 Instances dataDictionary,
                                 MiningSchema miningSchema)
                          throws java.lang.Exception

Construct a new SupportVectorMachineModel encapsulating the information provided in the PMML document.

Parameters:

model - the SVM element from the PMML document

dataDictionary - the data dictionary

miningSchema - the mining schema

Throws:

java.lang.Exception - if the model can't be constructed from the PMML

Method Detail

distributionForInstance

public double[] distributionForInstance(Instance inst)
                                 throws java.lang.Exception

Classifies the given test instance. The instance has to belong to a dataset when it's being classified.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

inst - the instance to be classified

Returns:

the predicted most likely class for the instance or Utils.missingValue() if no prediction is made

Throws:

java.lang.Exception - if an error occurred during the prediction

getRevision

public java.lang.String getRevision()

Description copied from class: AbstractClassifier

Returns the revision string.

Specified by:

getRevision in interface RevisionHandler

Overrides:

getRevision in class AbstractClassifier

Returns:

the revision

toString

public java.lang.String toString()

Get a textual description of this SupportVectorMachineModel

Overrides:

toString in class java.lang.Object

Returns:

a description of this SupportVectorMachineModel as a string