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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.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.SparseMatrix;
import edu.ucla.sspace.matrix.MatlabSparseMatrixBuilder;

import java.io.BufferedReader;
import java.io.File;
import java.io.InputStreamReader;
import java.io.IOError;
import java.io.IOException;
import java.io.PrintWriter;

import java.util.logging.Logger;
import java.util.logging.Level;


/**
 * A wrapper around the Matlab implementation of the SVD.
 *
 * @author Keith Stevens
 */
public class SingularValueDecompositionMatlab extends AbstractSvd {

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

    public void factorize(SparseMatrix matrix, int dimensions) {
        try {
            File mFile = File.createTempFile("matlab-input", ".dat");
            MatrixIO.writeMatrix(matrix, mFile, Format.MATLAB_SPARSE);
            factorize(new MatrixFile(mFile, Format.MATLAB_SPARSE), dimensions);
        } catch (IOException ioe) {
            LOG.log(Level.SEVERE, "Converting to matlab file", ioe);
        }
    }

    public void factorize(MatrixFile mfile, int dimensions) {
        File matrix;

        try {
            if (mfile.getFormat() == Format.MATLAB_SPARSE)
                matrix = mfile.getFile();
            else
                matrix = MatrixIO.convertFormat(mfile.getFile(), 
                                                mfile.getFormat(),
                                                Format.MATLAB_SPARSE);

            // create the matlab file for executing
            File uOutput = File.createTempFile("matlab-svds-U",".dat");
            File sOutput = File.createTempFile("matlab-svds-S",".dat");
            File vOutput = File.createTempFile("matlab-svds-V",".dat");
            LOG.fine("writing Matlab output to files:\n" + 
                     "  " + uOutput + "\n" +
                     "  " + sOutput + "\n" +
                     "  " + vOutput + "\n");

            uOutput.deleteOnExit();
            sOutput.deleteOnExit();
            vOutput.deleteOnExit();

            String commandLine = "matlab -nodisplay -nosplash -nojvm";
            LOG.fine(commandLine);
            Process matlab = Runtime.getRuntime().exec(commandLine);
            
            // capture the input so we know then Matlab is finished
            BufferedReader br = new BufferedReader(
                new InputStreamReader(matlab.getInputStream()));

            // pipe Matlab the program to execute
            PrintWriter pw = new PrintWriter(matlab.getOutputStream());
            pw.println(
                "Z = load('" + matrix.getAbsolutePath() + "','-ascii');\n" +
                "A = spconvert(Z);\n" + 
                "% Remove the raw data file to save space\n" +
                "clear Z;\n" + 
                "[U, S, V] = svds(A, " + dimensions + " );\n" +
                "save " + uOutput.getAbsolutePath() + " U -ASCII\n" +
                "save " + sOutput.getAbsolutePath() + " S -ASCII\n" +
                "save " + vOutput.getAbsolutePath() + " V -ASCII\n" + 
                "fprintf('Matlab Finished\\n');");
            pw.close();

            // capture the output
            StringBuilder output = new StringBuilder("Matlab svds output:\n");
            for (String line = null; (line = br.readLine()) != null; ) {
                output.append(line).append("\n");
                if (line.equals("Matlab Finished")) {
                    matlab.destroy();
                }
            }
            LOG.fine(output.toString());
            
            int exitStatus = matlab.waitFor();
            LOG.fine("Matlab svds exit status: " + exitStatus);

            // If Matlab was successful in generating the files, return them.
            if (exitStatus == 0) {
                // load U in memory, since that is what most algorithms will be
                // using (i.e. it is the word space)
                dataClasses = MatrixIO.readMatrix(uOutput, Format.DENSE_TEXT, 
                                                  Type.DENSE_IN_MEMORY);
                scaledDataClasses = false;

                // Sigma only has n values for an n^2 matrix, so make it sparse
                Matrix S = MatrixIO.readMatrix(sOutput, Format.DENSE_TEXT, 
                                               Type.SPARSE_ON_DISK);
                singularValues = new double[dimensions];
                for (int s = 0; s < dimensions; ++s)
                    singularValues[s] = S.get(s, s);

                // V could be large, so just keep it on disk.  Furthermore,
                // Octave does not transpose V, so transpose it
                classFeatures = MatrixIO.readMatrix(vOutput, Format.DENSE_TEXT,
                                                    Type.DENSE_ON_DISK, true);
                scaledDataClasses = false;
            }
        } catch (IOException ioe) {
            LOG.log(Level.SEVERE, "Matlab svds", ioe);
        } catch (InterruptedException ie) {
            LOG.log(Level.SEVERE, "Matlab svds", ie);
        }
        
        throw new UnsupportedOperationException(
            "Matlab svds is not correctly installed on this system");
    }

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