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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.
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/*
 * Copyright (c) 2011, Lawrence Livermore National Security, LLC. Produced at
 * the Lawrence Livermore National Laboratory. Written by Keith Stevens,
 * [email protected] OCEC-10-073 All rights reserved. 
 *
 * 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.matrix.factorization;

import edu.ucla.sspace.matrix.DiagonalMatrix;
import edu.ucla.sspace.matrix.Matrix;
import edu.ucla.sspace.matrix.Matrix.Type;
import edu.ucla.sspace.matrix.MatrixBuilder;
import edu.ucla.sspace.matrix.MatrixFile;
import edu.ucla.sspace.matrix.MatrixIO;
import edu.ucla.sspace.matrix.MatrixIO.Format;
import edu.ucla.sspace.matrix.MatrixFactorization;
import edu.ucla.sspace.matrix.SparseMatrix;
import edu.ucla.sspace.matrix.SvdlibcSparseBinaryMatrixBuilder;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.InputStreamReader;
import java.io.IOError;
import java.io.IOException;
import java.util.logging.Level;
import java.util.logging.Logger;


/**
 * A wrapper around the LibC implentation of Singular Value Decomposition.
 *
 * @author Keith Stevens
 */
public class SingularValueDecompositionLibC extends AbstractSvd {

    private static final Logger LOG = 
        Logger.getLogger(SingularValueDecompositionLibC.class.getName());

    /**
     * {@inheritDoc}
     */
    public void factorize(SparseMatrix matrix, int dimensions) {
        try {
            File temp = File.createTempFile("svdlibc.svd.matrix", "dat");
            MatrixIO.writeMatrix(matrix, temp, Format.SVDLIBC_SPARSE_TEXT);
            MatrixFile mFile = new MatrixFile(temp, Format.SVDLIBC_SPARSE_TEXT);
            factorize(mFile, dimensions);
        } catch (IOException ioe) {
            throw new IOError(ioe);
        }
    }

    /**
     * {@inheritDoc}
     */
    public void factorize(MatrixFile mFile, int dimensions) {
        try {
            String formatString = "";
            switch (mFile.getFormat()) {
            case SVDLIBC_DENSE_BINARY:
                formatString = " -r db ";
                break;
            case SVDLIBC_DENSE_TEXT:
                formatString = " -r dt ";
                break;
            case SVDLIBC_SPARSE_BINARY:
                formatString = " -r sb ";
                break;
            case SVDLIBC_SPARSE_TEXT:
                // Do nothing since it's the default format.
                break;
            default:
                throw new UnsupportedOperationException(
                    "Format type is not accepted");
            }

            File outputMatrixFile = File.createTempFile("svdlibc", ".dat");
            outputMatrixFile.deleteOnExit();
            String outputMatrixPrefix = outputMatrixFile.getAbsolutePath();

            LOG.info("creating SVDLIBC factor matrices at: " + 
                     outputMatrixPrefix);
            String commandLine = "svd -o " + outputMatrixPrefix + formatString +
                " -w dt " + // output is dense binary
                " -d " + dimensions + " " + mFile.getFile().getAbsolutePath();
            LOG.info(commandLine);
            Process svdlibc = Runtime.getRuntime().exec(commandLine);

            BufferedReader stdout = new BufferedReader(
                new InputStreamReader(svdlibc.getInputStream()));
            BufferedReader stderr = new BufferedReader(
                new InputStreamReader(svdlibc.getErrorStream()));

            StringBuilder output = new StringBuilder("SVDLIBC output:\n");
            for (String line = null; (line = stdout.readLine()) != null; ) {
                output.append(line).append("\n");
            }
            LOG.info(output.toString());
            
            int exitStatus = svdlibc.waitFor();
            LOG.fine("svdlibc exit status: " + exitStatus);

            // If SVDLIBC was successful in generating the files, return them.
            if (exitStatus == 0) {
                File Ut = new File(outputMatrixPrefix + "-Ut");
                File S  = new File(outputMatrixPrefix + "-S");
                File Vt = new File(outputMatrixPrefix + "-Vt");
                    
                // load U in memory, since that is what most algorithms will
                // be using (i.e. it is the word space).  SVDLIBC returns
                // this as U transpose, so correct it by indicating that the
                // read operation should transpose the matrix as it is built
                dataClasses = MatrixIO.readMatrix(
                        Ut, Format.SVDLIBC_DENSE_TEXT, 
                        Type.DENSE_IN_MEMORY, true);
                scaledDataClasses = false;

                // V could be large, so just keep it on disk.  
                classFeatures = MatrixIO.readMatrix(
                        Vt, Format.SVDLIBC_DENSE_TEXT,
                        Type.DENSE_IN_MEMORY);
                scaledClassFeatures = false;

                // Sigma only has n values for an n^2 matrix, so make it sparse.
                // Note that even if we specify the output to be in dense
                // binary, the signular vectors are still reported as text
                singularValues =  readSVDLIBCsingularVector(S, dimensions);
            } else {
                StringBuilder sb = new StringBuilder();
                for (String line = null; (line = stderr.readLine()) != null; )
                    sb.append(line).append("\n");
                // warning or error?
                LOG.warning("svdlibc exited with error status.  " + 
                               "stderr:\n" + sb.toString());
            }
        } catch (IOException ioe) {
            LOG.log(Level.SEVERE, "SVDLIBC", ioe);
        } catch (InterruptedException ie) {
            LOG.log(Level.SEVERE, "SVDLIBC", ie);
        }
    }

    /**
     * {@inheritDoc}
     */
    public MatrixBuilder getBuilder() {
        return new SvdlibcSparseBinaryMatrixBuilder();
    }

    /**
     * Generates a diagonal {@link Matrix} from the special-case file format
     * that SVDLIBC uses to output the Σ matrix.
     */
    private static double[] readSVDLIBCsingularVector(File sigmaMatrixFile,
                                                      int dimensions)
            throws IOException {
        BufferedReader br = new BufferedReader(new FileReader(sigmaMatrixFile));
        double[] m = new double[dimensions];

        // Check that the computed number of dimensions equals the expected
        // number of dimensions.
        int readDimensions = Integer.parseInt(br.readLine());
        if (readDimensions != dimensions)
            throw new RuntimeException(
                    "SVDLIBC generated the incorrect number of " +
                    "dimensions: " + readDimensions + " versus " + dimensions);

        // Read each singular value.
        int i = 0;
        for (String line = null; (line = br.readLine()) != null; )
            m[i++] = Double.parseDouble(line);
        return m;
    }
}