/*
* Carrot2 project.
*
* Copyright (C) 2002-2014, Dawid Weiss, Stanisław Osiński.
* All rights reserved.
*
* Refer to the full license file "carrot2.LICENSE"
* in the root folder of the repository checkout or at:
* http://www.carrot2.org/carrot2.LICENSE
*/
package org.carrot2.clustering.lingo;
import org.apache.mahout.math.matrix.*;
import org.carrot2.text.preprocessing.PreprocessingContext;
import org.carrot2.util.Pair;
import org.carrot2.util.attribute.Bindable;
import com.carrotsearch.hppc.*;
/**
* Assigns unique labels to each base vector using a greedy algorithm. For each base
* vector chooses the label that maximizes the base vector--label term vector cosine
* similarity and has not been previously selected. Once a label is selected, it will not
* be used to label any other vector. This algorithm does not create duplicate cluster
* labels, which usually means that this assignment method will create more clusters than
* {@link SimpleLabelAssigner}. This method is slightly slower than
* {@link SimpleLabelAssigner}.
*/
@SuppressWarnings("deprecation")
@Bindable
public class UniqueLabelAssigner implements ILabelAssigner
{
public void assignLabels(LingoProcessingContext context, DoubleMatrix2D stemCos,
IntIntOpenHashMap filteredRowToStemIndex, DoubleMatrix2D phraseCos)
{
final PreprocessingContext preprocessingContext = context.preprocessingContext;
final int firstPhraseIndex = preprocessingContext.allLabels.firstPhraseIndex;
final int [] labelsFeatureIndex = preprocessingContext.allLabels.featureIndex;
final int [] mostFrequentOriginalWordIndex = preprocessingContext.allStems.mostFrequentOriginalWordIndex;
final int desiredClusterCount = stemCos.columns();
final IntArrayList clusterLabelFeatureIndex = new IntArrayList(
desiredClusterCount);
final DoubleArrayList clusterLabelScore = new DoubleArrayList(desiredClusterCount);
for (int label = 0; label < desiredClusterCount; label++)
{
final Pair<Integer, Integer> stemMax = max(stemCos);
final Pair<Integer, Integer> phraseMax = max(phraseCos);
if (stemMax == null && phraseMax == null)
{
break;
}
double stemScore = stemMax != null ? stemCos.getQuick(stemMax.objectA,
stemMax.objectB) : -1;
double phraseScore = phraseMax != null ? phraseCos.getQuick(
phraseMax.objectA, phraseMax.objectB) : -1;
if (phraseScore > stemScore)
{
phraseCos.viewRow(phraseMax.objectA).assign(0);
phraseCos.viewColumn(phraseMax.objectB).assign(0);
stemCos.viewColumn(phraseMax.objectB).assign(0);
clusterLabelFeatureIndex.add(labelsFeatureIndex[phraseMax.objectA
+ firstPhraseIndex]);
clusterLabelScore.add(phraseScore);
}
else
{
stemCos.viewRow(stemMax.objectA).assign(0);
stemCos.viewColumn(stemMax.objectB).assign(0);
if (phraseCos != null)
{
phraseCos.viewColumn(stemMax.objectB).assign(0);
}
clusterLabelFeatureIndex
.add(mostFrequentOriginalWordIndex[filteredRowToStemIndex
.get(stemMax.objectA)]);
clusterLabelScore.add(stemScore);
}
}
context.clusterLabelFeatureIndex = clusterLabelFeatureIndex.toArray();
context.clusterLabelScore = clusterLabelScore.toArray();
}
private Pair<Integer, Integer> max(DoubleMatrix2D matrix)
{
if (matrix == null)
{
return null;
}
int row = 0;
int column = 0;
double value = 0;
for (int r = 0; r < matrix.rows(); r++)
{
for (int c = 0; c < matrix.columns(); c++)
{
final double currentValue = matrix.getQuick(r, c);
if (currentValue > value)
{
value = currentValue;
row = r;
column = c;
}
}
}
if (value > 0)
{
return new Pair<Integer, Integer>(row, column);
}
else
{
return null;
}
}
}