edu.stanford.nlp.classify

Class WeightedDataset<L,F>

java.lang.Object

edu.stanford.nlp.classify.GeneralDataset<L,F>

edu.stanford.nlp.classify.Dataset<L,F>

edu.stanford.nlp.classify.WeightedDataset<L,F>

All Implemented Interfaces:

java.io.Serializable, java.lang.Iterable<RVFDatum<L,F>>

public class WeightedDataset<L,F>
extends Dataset<L,F>

Author:

Galen Andrew, Sarah Spikes (sdspikes@cs.stanford.edu) (Templatization)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected float[]	weights

Fields inherited from class edu.stanford.nlp.classify.GeneralDataset

data, featureIndex, labelIndex, labels, size

Constructor Summary

Constructors

Constructor and Description
WeightedDataset()
WeightedDataset(Index<L> labelIndex, int[] labels, Index<F> featureIndex, int[][] data, int size, float[] weights)
WeightedDataset(int initSize)

Method Summary

Methods

Modifier and Type	Method and Description
void	add(java.util.Collection<F> features, L label)
void	add(java.util.Collection<F> features, L label, float weight)
void	add(Datum<L,F> d)
void	add(Datum<L,F> d, float weight)
protected void	ensureSize()
float[]	getFeatureCounts() Get the total count (over all data instances) of each feature
float[]	getWeights()
void	randomize(int randomSeed) Randomizes the data array in place Needs to be redefined here because we need to randomize the weights as well

Methods inherited from class edu.stanford.nlp.classify.Dataset

add, add, addFeatureIndices, addFeatures, addFeatures, addLabel, addLabelIndex, applyFeatureCountThreshold, changeFeatureIndex, changeLabelIndex, getDatum, getFeatureCounter, getInformationGains, getL1NormalizedTFIDFDataset, getL1NormalizedTFIDFDatum, getRandomSubDataset, getRVFDatum, getValuesArray, initialize, printFullFeatureMatrix, printSparseFeatureMatrix, printSparseFeatureMatrix, printSVMLightFormat, readSVMLightFormat, readSVMLightFormat, readSVMLightFormat, readSVMLightFormat, selectFeatures, selectFeaturesBinaryInformationGain, split, split, summaryStatistics, svmLightLineToDatum, toString, toSummaryStatistics, toSummaryString, updateLabels

Methods inherited from class edu.stanford.nlp.classify.GeneralDataset

addAll, applyFeatureCountThreshold, applyFeatureMaxCountThreshold, clear, clear, featureIndex, getDataArray, getLabelsArray, iterator, labelIndex, labelIterator, makeSvmLabelMap, mapDataset, mapDataset, mapDatum, numClasses, numDatumsPerLabel, numFeatures, numFeatureTokens, numFeatureTypes, printSVMLightFormat, printSVMLightFormat, sampleDataset, size, splitOutFold, trimData, trimLabels, trimToSize, trimToSize, trimToSize

Methods inherited from class java.lang.Object

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

Field Detail

weights

protected float[] weights

Constructor Detail

WeightedDataset

public WeightedDataset(Index<L> labelIndex,
               int[] labels,
               Index<F> featureIndex,
               int[][] data,
               int size,
               float[] weights)

WeightedDataset

public WeightedDataset()

WeightedDataset

public WeightedDataset(int initSize)

Method Detail

getWeights

public float[] getWeights()

getFeatureCounts

public float[] getFeatureCounts()

Description copied from class: GeneralDataset

Get the total count (over all data instances) of each feature

Overrides:

getFeatureCounts in class GeneralDataset<L,F>

Returns:

an array containing the counts (indexed by index)

add

public void add(Datum<L,F> d)

Overrides:

add in class Dataset<L,F>

add

public void add(java.util.Collection<F> features,
       L label)

Overrides:

add in class Dataset<L,F>

add

public void add(Datum<L,F> d,
       float weight)

ensureSize

protected void ensureSize()

Overrides:

ensureSize in class Dataset<L,F>

add

public void add(java.util.Collection<F> features,
       L label,
       float weight)

randomize

public void randomize(int randomSeed)

Randomizes the data array in place Needs to be redefined here because we need to randomize the weights as well

Overrides:

randomize in class GeneralDataset<L,F>