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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 2009 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.mains;

import edu.ucla.sspace.beagle.Beagle;
import edu.ucla.sspace.beagle.Beagle.SemanticType;

import edu.ucla.sspace.common.ArgOptions;
import edu.ucla.sspace.common.SemanticSpace;
import edu.ucla.sspace.common.SemanticSpaceIO.SSpaceFormat;

import edu.ucla.sspace.index.DoubleVectorGenerator;
import edu.ucla.sspace.index.GaussianVectorGenerator;

import edu.ucla.sspace.util.SerializableUtil;
import edu.ucla.sspace.util.Generator;
import edu.ucla.sspace.util.GeneratorMap;

import edu.ucla.sspace.vector.DoubleVector;

import java.io.File;

import java.util.Properties;


/**
 * An executable class for running {@link Beagle} from the
 * command line.  This class takes in several command line arguments.
 *
 * 
 *  {@code --dimensions=} how many dimensions to use for the Beagle
 *      vectors.  2048 is the default value.
 * 
 *
 * 
 *
 * An invocation will produce one file as output {@code
 * beagle-semantic-space.sspace}.  If {@code overwrite} was set to {@code true},
 * this file will be replaced for each new semantic space.  Otherwise, a new
 * output file of the format {@code beagle-semantic-space.sspace} will
 * be created, where {@code } is a unique identifier for that program's
 * invocation.  The output file will be placed in the directory specified on the
 * command line.
 *
 * 
 *
 * This main will run with with multiple threads by default.
 *
 * @see Beagle
 *
 * @author Keith Stevens 
 */
public class BeagleMain extends GenericMain {

    /**
     * If no dimension size is given, this will be the size of index vectors and
     * semantic vectors.
     */
    private static final int DEFAULT_DIMENSION = 2048;

    /**
     * The dimensionality of each index and semantic vector.
     */
    private int dimension;

    /**
     * The generator map for automatically creating and retrieving index vectors
     * for beagle.
     */
    private GeneratorMap generatorMap;

    private BeagleMain() {
    }

    public static void main(String[] args) {
        BeagleMain hMain = new BeagleMain();
        try {
            hMain.run(args);
        }
        catch (Throwable t) {
            t.printStackTrace();
        }
    }
    
    /**
     * {@inheritDoc}
     */
    public void addExtraOptions(ArgOptions options) {
        options.addOption('n', "dimension",
                          "the length of each beagle vector",
                          true, "INT", "Options");
        options.addOption('s', "semanticType",
                          "The type of semantic vectors to generate",
                          true, "SemanticType", "Options"); 
        options.addOption('S', "saveVectors", 
                          "save word-to-IndexVector mapping after processing",
                          true, "FILE", "Options");
        options.addOption('L', "loadVectors",
                          "load word-to-IndexVector mapping before processing",
                          true, "FILE", "Options");
    }

    /**
     * {@inheritDoc}
     */
    @SuppressWarnings("unchecked")
    public void handleExtraOptions() {
        dimension = (argOptions.hasOption("dimension"))
          ? argOptions.getIntOption("dimension")
          : DEFAULT_DIMENSION;
        if (argOptions.hasOption("loadVectors")) {
            generatorMap = (GeneratorMap) SerializableUtil.load(
                    new File(argOptions.getStringOption("loadVectors")),
                    GeneratorMap.class);
        } else {
            double stdev = 1 / Math.sqrt(dimension);
            System.setProperty(
                    GaussianVectorGenerator.STANDARD_DEVIATION_PROPERTY,
                    Double.toString(stdev));
            Generator generator =
                new GaussianVectorGenerator(dimension);
            generatorMap = new GeneratorMap(generator);
        }
    }

    /**
     * {@inheritDoc}
     */
    protected void postProcessing() {
        if (argOptions.hasOption("saveVectors")) {
            SerializableUtil.save(
                    generatorMap,
                    new File(argOptions.getStringOption("saveVectors")));
        }
    }

    /**
     * {@inheritDoc}
     */
    public SemanticSpace getSpace() {
        SemanticType type = (argOptions.hasOption('s'))
            ? SemanticType.valueOf(
                    argOptions.getStringOption('s').toUpperCase())
            : SemanticType.COMPOSITE;

        return new Beagle(dimension, type, generatorMap);
    }

    /**
     * {@inheritDoc}
     */
    protected SSpaceFormat getSpaceFormat() {
        return SSpaceFormat.BINARY;
    }
}