/*
* 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.matrix.factorization;
import org.apache.mahout.math.function.Functions;
import org.apache.mahout.math.function.Mult;
import org.apache.mahout.math.matrix.DoubleMatrix2D;
import org.apache.mahout.math.matrix.impl.DenseDoubleMatrix2D;
import org.carrot2.matrix.MatrixUtils;
/**
* Performs matrix factorization using the K-means clustering algorithm. This kind of
* factorization is sometimes referred to as Concept Decomposition Factorization.
*/
@SuppressWarnings("deprecation")
public class KMeansMatrixFactorization extends IterativeMatrixFactorizationBase
{
/**
* Creates the KMeansMatrixFactorization object for matrix A. Before accessing
* results, perform computations by calling the {@link #compute()} method.
*
* @param A matrix to be factorized. The matrix must have Euclidean length-normalized
* columns.
*/
public KMeansMatrixFactorization(DoubleMatrix2D A)
{
super(A);
}
public void compute()
{
int n = A.columns();
// Distances to centroids
DoubleMatrix2D D = new DenseDoubleMatrix2D(k, n);
// Object-cluster assignments
V = new DenseDoubleMatrix2D(n, k);
// Initialize the centroids with some document vectors
U = new DenseDoubleMatrix2D(A.rows(), k);
U.assign(A.viewPart(0, 0, A.rows(), k));
int [] minIndices = new int [D.columns()];
double [] minValues = new double [D.columns()];
for (iterationsCompleted = 0; iterationsCompleted < maxIterations; iterationsCompleted++)
{
// Calculate cosine distances
U.zMult(A, D, 1, 0, true, false);
V.assign(0);
U.assign(0);
// For each object
MatrixUtils.maxInColumns(D, minIndices, minValues);
for (int i = 0; i < minIndices.length; i++)
{
V.setQuick(i, minIndices[i], 1);
}
// Update centroids
for (int c = 0; c < V.columns(); c++)
{
// Sum
int count = 0;
for (int d = 0; d < V.rows(); d++)
{
if (V.getQuick(d, c) != 0)
{
count++;
U.viewColumn(c).assign(A.viewColumn(d), Functions.PLUS);
}
}
// Divide
U.viewColumn(c).assign(Mult.div(count));
MatrixUtils.normalizeColumnL2(U, null);
}
}
}
public String toString()
{
return "KMMF";
}
}