Package ai.libs.jaicore.ml.weka.rangequery.learner.intervaltree

Class ExtendedRandomForest

  • All Implemented Interfaces:
    RangeQueryPredictor, java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.AdditionalMeasureProducer, weka.core.Aggregateable<weka.classifiers.meta.Bagging>, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.PartitionGenerator, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler
    public class ExtendedRandomForest
extends weka.classifiers.trees.RandomForest
implements RangeQueryPredictor
    See Also:
    Serialized Form

    • Field Summary

      • Fields inherited from class weka.classifiers.trees.RandomForest

        m_computeAttributeImportance

      • Fields inherited from class weka.classifiers.meta.Bagging

        m_BagSizePercent, m_CalcOutOfBag, m_classifiersCache, m_data, m_inBag, m_OutOfBagEvaluationObject, m_random, m_RepresentUsingWeights

      • Fields inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

        m_Seed

      • Fields inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer

        m_numExecutionSlots

      • Fields inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

        m_Classifiers, m_NumIterations

      • Fields inherited from class weka.classifiers.SingleClassifierEnhancer

        m_Classifier

      • Fields inherited from class weka.classifiers.AbstractClassifier

        BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

    • Method Summary

      All Methods Instance Methods Concrete Methods 
      Modifier and Type Method Description
      double computeMarginalVarianceContributionForFeatureSubset​(java.util.Set<java.lang.Integer> features)  
      double computeMarginalVarianceContributionForFeatureSubsetNotNormalized​(java.util.Set<java.lang.Integer> features)  
      protected java.lang.String defaultClassifierString()  
      FeatureSpace getFeatureSpace()  
      int getSize()  
      org.apache.commons.math3.geometry.euclidean.oned.Interval predictInterval​(RQPHelper.IntervalAndHeader intervalAndHeader)  
      org.apache.commons.math3.geometry.euclidean.oned.Interval predictInterval​(weka.core.Instance rangeQuery)  
      void prepareForest​(weka.core.Instances data)
      Needs to be called before predicting marginal variance contributions!
      void printVariances()  

      • Methods inherited from class weka.classifiers.trees.RandomForest

        breakTiesRandomlyTipText, computeAttributeImportanceTipText, computeAverageImpurityDecreasePerAttribute, defaultClassifierOptions, defaultNumberOfIterations, getBreakTiesRandomly, getCapabilities, getComputeAttributeImportance, getMaxDepth, getNumFeatures, getOptions, getRevision, getTechnicalInformation, globalInfo, listOptions, main, maxDepthTipText, numFeaturesTipText, setBatchSize, setBreakTiesRandomly, setClassifier, setComputeAttributeImportance, setDebug, setMaxDepth, setNumDecimalPlaces, setNumFeatures, setOptions, setRepresentCopiesUsingWeights, setSeed, toString

      • Methods inherited from class weka.classifiers.meta.Bagging

        aggregate, bagSizePercentTipText, buildClassifier, calcOutOfBagTipText, distributionForInstance, enumerateMeasures, finalizeAggregation, generatePartition, getBagSizePercent, getCalcOutOfBag, getMeasure, getMembershipValues, getOutOfBagEvaluationObject, getOutputOutOfBagComplexityStatistics, getPrintClassifiers, getRepresentCopiesUsingWeights, getStoreOutOfBagPredictions, getTrainingSet, measureOutOfBagError, numElements, outputOutOfBagComplexityStatisticsTipText, printClassifiersTipText, representCopiesUsingWeightsTipText, setBagSizePercent, setCalcOutOfBag, setOutputOutOfBagComplexityStatistics, setPrintClassifiers, setStoreOutOfBagPredictions, storeOutOfBagPredictionsTipText

      • Methods inherited from class weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

        getSeed, seedTipText

      • Methods inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer

        buildClassifiers, getNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlots

      • Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

        getM_Classifiers, getNumIterations, numIterationsTipText, setNumIterations

      • Methods inherited from class weka.classifiers.SingleClassifierEnhancer

        classifierTipText, getClassifier, getClassifierSpec, postExecution, preExecution

      • Methods inherited from class weka.classifiers.AbstractClassifier

        batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDoNotCheckCapabilities

      • Methods inherited from class java.lang.Object

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

    • Method Detail

      • prepareForest

        public void prepareForest​(weka.core.Instances data)
                   throws java.lang.InterruptedException
        Needs to be called before predicting marginal variance contributions!
        Parameters:
        Instances - for which marginal variance contributions are to be estimated
        Throws:
        java.lang.InterruptedException

      • printVariances

        public void printVariances()

      • computeMarginalVarianceContributionForFeatureSubset

        public double computeMarginalVarianceContributionForFeatureSubset​(java.util.Set<java.lang.Integer> features)

      • computeMarginalVarianceContributionForFeatureSubsetNotNormalized

        public double computeMarginalVarianceContributionForFeatureSubsetNotNormalized​(java.util.Set<java.lang.Integer> features)

      • getSize

        public int getSize()
        Returns:
        Size of

      • getFeatureSpace

        public FeatureSpace getFeatureSpace()
        Returns:
        Feature space on which this forest operates on

      • defaultClassifierString

        protected java.lang.String defaultClassifierString()
        Overrides:
        defaultClassifierString in class weka.classifiers.trees.RandomForest

      • predictInterval

        public org.apache.commons.math3.geometry.euclidean.oned.Interval predictInterval​(weka.core.Instance rangeQuery)
        Specified by:
        predictInterval in interface RangeQueryPredictor