edu.ucla.sspace.clustering.CKVWSpectralClustering03 Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of sspace-wordsi Show documentation

The S-Space Package is a collection of algorithms for building Semantic Spaces as well as a highly-scalable library for designing new distributional semantics algorithms. Distributional algorithms process text corpora and represent the semantic for words as high dimensional feature vectors. This package also includes matrices, vectors, and numerous clustering algorithms. These approaches are known by many names, such as word spaces, semantic spaces, or distributed semantics and rest upon the Distributional Hypothesis: words that appear in similar contexts have similar meanings.

The newest version!

/*
 * Copyright 2011 Keith Stevens 
 *
 * This file is part of the S-Space package and is covered under the terms and
 * conditions therein.
 *
 * The S-Space package is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License version 2 as published
 * by the Free Software Foundation and distributed hereunder to you.
 *
 * THIS SOFTWARE IS PROVIDED "AS IS" AND NO REPRESENTATIONS OR WARRANTIES,
 * EXPRESS OR IMPLIED ARE MADE.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, WE MAKE
 * NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY
 * PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE OR DOCUMENTATION
 * WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER
 * RIGHTS.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program. If not, see .
 */

package edu.ucla.sspace.clustering;

import edu.ucla.sspace.common.Statistics;

import edu.ucla.sspace.matrix.Matrix;
import edu.ucla.sspace.matrix.RowScaledMatrix;
import edu.ucla.sspace.matrix.RowScaledSparseMatrix;
import edu.ucla.sspace.matrix.SparseMatrix;

import edu.ucla.sspace.util.Generator;

import edu.ucla.sspace.vector.DenseVector;
import edu.ucla.sspace.vector.DoubleVector;

import java.util.Properties;


/**
 * A spectral clustering implementation based on the following paper:
 *
 *  David Cheng ,  Ravi Kannan ,
 * Santosh Vempala ,  Grant Wang (2003) On a Recursive Spectral Algorithm for
 * Clustering from Pairwise Similaritie. Available
 * here
 *
 *   This implementation implements a subclass of the {@link
 * BaseSpectralCut} and simply computes the second eigen vector for a data set.
 *
 * @see BaseSpectralCut
 * @see SpectralClustering
 *
 * @author Keith Stevens
 */
public class CKVWSpectralClustering03 implements Clustering {

    /**
     * The proper prefix.
     */
    public static final String PROPERTY_PREFIX =
        "edu.ucla.sspace.clustering.CKVWSpectralClustering03";

    /**
     * The property used to use K-Means as the objective function.
     */
    public static final String USE_KMEANS =
        PROPERTY_PREFIX + ".useKMeans";

    /**
     * {@inheritDoc}
     */
    public Assignments cluster(Matrix matrix, Properties props) {
        SpectralClustering cluster = new SpectralClustering(
                .2, new SpectralCutGenerator());
        return cluster.cluster(matrix);
    }

    /**
     * {@inheritDoc}
     */
    public Assignments cluster(Matrix matrix,
                                int numClusters,
                                Properties props) {
        SpectralClustering cluster = new SpectralClustering(
                .2, new SpectralCutGenerator());
        return cluster.cluster(
                matrix, numClusters, props.getProperty(USE_KMEANS) != null);
    }

    /**
     * An internal spectral cut implementation that is based on the referred to
     * paper.  See paper for details.
     */
    public class SpectralCut extends BaseSpectralCut {

        /**
         * {@inheritDoc}
         */
        protected DoubleVector computeSecondEigenVector(Matrix matrix,
                                                        int vectorLength) {
            DoubleVector Rinv = new DenseVector(vectorLength);
            DoubleVector baseVector = new DenseVector(vectorLength);
            for (int i = 0; i < vectorLength; ++i) {
                Rinv.set(i, 1/Math.sqrt(rho.get(i)));
                baseVector.set(i, rho.get(i) * Rinv.get(i));
            }
     
            // Step 1, generate a random vector, v,  that is orthogonal to
            // pi*D-Inverse.
            DoubleVector v = new DenseVector(vectorLength);
            for (int i = 0; i < v.length(); ++i)
                v.set(i, Math.random());

            Matrix RinvData = (matrix instanceof SparseMatrix)
                ? new RowScaledSparseMatrix((SparseMatrix) matrix, Rinv)
                : new RowScaledMatrix(matrix, Rinv);

            // Make log(matrix.rows()) passes.
            int log = (int) Statistics.log2(vectorLength);
            for (int k = 0; k < log; ++k) {
                // start the orthonormalizing the eigen vector.
                v = orthonormalize(v, baseVector);
                DoubleVector newV = computeMatrixTransposeV(RinvData, v);
                computeMatrixDotV(RinvData, newV, v);
            }

            return v;
        }
    }

    public String toString() {
        return "CKVWSpectralClustering03";
    }

    /**
     * A simple generator for creating instances of the {@link SpectralCut}
     * class.
     */
    public class SpectralCutGenerator implements Generator {

        /**
         * {@inheritDoc}
         */
        public EigenCut generate() {
            return new SpectralCut();
        }
    }
}