public class WEKAClassifier extends AbstractClassifier implements MultiClassClassifier
Modifier and Type | Field and Description |
---|---|
WEKAClassOption |
baseLearnerOption |
protected weka.classifiers.Classifier |
classifier |
protected SamoaToWekaInstanceConverter |
instanceConverter |
protected weka.core.Instances |
instancesBuffer |
protected boolean |
isBufferStoring |
protected boolean |
isClassificationEnabled |
protected int |
numberInstances |
IntOption |
sampleFrequencyOption |
IntOption |
widthInitOption |
IntOption |
widthOption |
classifierRandom, downSampleRatio, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel
config
Constructor and Description |
---|
WEKAClassifier() |
Modifier and Type | Method and Description |
---|---|
void |
buildClassifier() |
void |
createWekaClassifier(String[] options) |
void |
getModelDescription(StringBuilder out,
int indent)
Returns a string representation of the model.
|
protected Measurement[] |
getModelMeasurementsImpl()
Gets the current measurements of this classifier.
The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. |
String |
getPurposeString()
Dictionary with option texts and objects
|
double[] |
getVotesForInstance(Instance samoaInstance)
Predicts the class memberships for a given instance.
|
boolean |
isRandomizable()
Gets whether this learner needs a random seed.
|
void |
resetLearningImpl()
Resets this classifier.
|
void |
trainOnInstanceImpl(Instance samoaInstance)
Trains this classifier incrementally using the given instance.
The reason for ...Impl methods: ease programmer burden by not requiring them to remember calls to super in overridden methods. |
contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getDownSampleRatio, getModel, getModelContext, getModelMeasurements, getNominalValueString, getPredictionForInstance, getPredictionForInstance, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance, updateDownSampleRatio
getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse
copy, measureByteSize, measureByteSize, toString
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
getCLICreationString, getOptions, prepareForUse, prepareForUse
measureByteSize
protected SamoaToWekaInstanceConverter instanceConverter
public WEKAClassOption baseLearnerOption
public IntOption widthOption
public IntOption widthInitOption
public IntOption sampleFrequencyOption
protected weka.classifiers.Classifier classifier
protected int numberInstances
protected weka.core.Instances instancesBuffer
protected boolean isClassificationEnabled
protected boolean isBufferStoring
public String getPurposeString()
AbstractOptionHandler
getPurposeString
in interface OptionHandler
getPurposeString
in class AbstractClassifier
public void resetLearningImpl()
AbstractClassifier
resetLearningImpl
in class AbstractClassifier
public void trainOnInstanceImpl(Instance samoaInstance)
AbstractClassifier
trainOnInstanceImpl
in class AbstractClassifier
samoaInstance
- the instance to be used for trainingpublic void buildClassifier()
public double[] getVotesForInstance(Instance samoaInstance)
Classifier
getVotesForInstance
in interface Classifier
getVotesForInstance
in class AbstractClassifier
samoaInstance
- the instance to be classifiedpublic boolean isRandomizable()
Learner
isRandomizable
in interface Learner<Example<Instance>>
public void getModelDescription(StringBuilder out, int indent)
AbstractClassifier
getModelDescription
in class AbstractClassifier
out
- the stringbuilder to add the descriptionindent
- the number of characters to indentprotected Measurement[] getModelMeasurementsImpl()
AbstractClassifier
getModelMeasurementsImpl
in class AbstractClassifier
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