public class ISOUPTree extends AbstractMultiLabelLearner implements MultiTargetRegressor
Modifier and Type | Class and Description |
---|---|
static class |
ISOUPTree.InnerNode |
static class |
ISOUPTree.LeafNode |
class |
ISOUPTree.MultitargetPerceptron |
static class |
ISOUPTree.Node |
static class |
ISOUPTree.SplitNode |
classifierRandom, modelContext, randomSeed, randomSeedOption, trainingWeightSeenByModel
config
Constructor and Description |
---|
ISOUPTree() |
Modifier and Type | Method and Description |
---|---|
protected void |
attemptToSplit(ISOUPTree.LeafNode node,
ISOUPTree.SplitNode parent,
int parentIndex) |
boolean |
buildingModelTree() |
int |
calcByteSize() |
protected void |
checkRoot() |
static double |
computeHoeffdingBound(double range,
double confidence,
double n) |
double |
computeSD(double squaredVal,
double val,
double size) |
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. |
double[] |
getNormalizedError(MultiLabelInstance inst,
double[] prediction) |
Prediction |
getPredictionForInstance(MultiLabelInstance inst) |
String |
getPurposeString()
Dictionary with option texts and objects
|
boolean |
isRandomizable()
Gets whether this learner needs a random seed.
|
protected ISOUPTree.MultitargetPerceptron |
newLeafModel() |
protected ISOUPTree.LeafNode |
newLeafNode() |
NominalStatisticsObserver |
newNominalClassObserver() |
protected NumericStatisticsObserver |
newNumericClassObserver() |
protected ISOUPTree.SplitNode |
newSplitNode(Predicate predicate) |
boolean |
normalize() |
double[] |
normalizedInputVector(MultiLabelInstance inst) |
double[] |
normalizedTargetVector(MultiLabelInstance inst) |
double |
normalizeTargetValue(double value,
int i) |
double |
normalizeTargetValue(MultiLabelInstance inst,
int i) |
double[] |
normalizeTargetVector(double[] pred) |
void |
processInstance(MultiLabelInstance inst,
ISOUPTree.Node node,
double[] prediction,
double[] normalError,
boolean growthAllowed,
boolean inAlternate) |
void |
resetLearningImpl()
Resets this classifier.
|
static double |
scalarProduct(DoubleVector u,
DoubleVector v) |
void |
trainOnInstanceImpl(MultiLabelInstance inst)
Method for updating (training) the model using a new instance
|
getPredictionForInstance, getPredictionForInstance, getVotesForInstance, trainOnInstanceImpl
contextIsCompatible, copy, correctlyClassifies, getAttributeNameString, getAWTRenderer, getClassLabelString, getClassNameString, getDescription, getModel, getModelContext, getModelMeasurements, getNominalValueString, getSubClassifiers, getSublearners, getVotesForInstance, modelAttIndexToInstanceAttIndex, modelAttIndexToInstanceAttIndex, prepareForUseImpl, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance, trainOnInstance
getCLICreationString, getOptions, getPreparedClassOption, prepareClassOptions, prepareForUse, prepareForUse
copy, measureByteSize, measureByteSize, toString
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
copy, correctlyClassifies, getPredictionForInstance, getSubClassifiers, getVotesForInstance, trainOnInstance
getModel, getModelContext, getModelMeasurements, getPredictionForInstance, getSublearners, getVotesForInstance, resetLearning, setModelContext, setRandomSeed, trainingHasStarted, trainingWeightSeenByModel, trainOnInstance
getCLICreationString, getOptions, prepareForUse, prepareForUse
getDescription, measureByteSize
getAWTRenderer
protected ISOUPTree.Node treeRoot
public int maxID
public IntOption gracePeriodOption
public FloatOption splitConfidenceOption
public FloatOption tieThresholdOption
public FloatOption alternateTreeFadingFactorOption
public IntOption alternateTreeTMinOption
public IntOption alternateTreeTimeOption
public FlagOption regressionTreeOption
public FloatOption learningRatioOption
public FloatOption learningRateDecayFactorOption
public FlagOption learningRatioConstOption
public FlagOption doNotNormalizeOption
public String getPurposeString()
AbstractOptionHandler
getPurposeString
in interface OptionHandler
getPurposeString
in class AbstractClassifier
public void resetLearningImpl()
AbstractClassifier
resetLearningImpl
in class AbstractClassifier
public 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
public int calcByteSize()
public Prediction getPredictionForInstance(MultiLabelInstance inst)
getPredictionForInstance
in interface MultiLabelLearner
getPredictionForInstance
in class AbstractMultiLabelLearner
public double[] normalizedInputVector(MultiLabelInstance inst)
public double[] normalizedTargetVector(MultiLabelInstance inst)
public double[] normalizeTargetVector(double[] pred)
public double normalizeTargetValue(MultiLabelInstance inst, int i)
public double normalizeTargetValue(double value, int i)
public double[] getNormalizedError(MultiLabelInstance inst, double[] prediction)
public void trainOnInstanceImpl(MultiLabelInstance inst)
trainOnInstanceImpl
in interface MultiLabelLearner
trainOnInstanceImpl
in class AbstractMultiLabelLearner
public void processInstance(MultiLabelInstance inst, ISOUPTree.Node node, double[] prediction, double[] normalError, boolean growthAllowed, boolean inAlternate)
protected NumericStatisticsObserver newNumericClassObserver()
public NominalStatisticsObserver newNominalClassObserver()
protected ISOUPTree.SplitNode newSplitNode(Predicate predicate)
protected ISOUPTree.LeafNode newLeafNode()
protected ISOUPTree.MultitargetPerceptron newLeafModel()
protected void checkRoot()
public static double computeHoeffdingBound(double range, double confidence, double n)
public boolean buildingModelTree()
public boolean normalize()
protected void attemptToSplit(ISOUPTree.LeafNode node, ISOUPTree.SplitNode parent, int parentIndex)
public double computeSD(double squaredVal, double val, double size)
public static double scalarProduct(DoubleVector u, DoubleVector v)
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