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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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package edu.ucla.sspace.tools;

import edu.ucla.sspace.common.ArgOptions;
import edu.ucla.sspace.common.SemanticSpace;
import edu.ucla.sspace.common.StaticSemanticSpace;

import edu.ucla.sspace.clustering.Assignments;
import edu.ucla.sspace.clustering.Clustering;

import edu.ucla.sspace.matrix.Matrices;
import edu.ucla.sspace.matrix.Matrix;
import edu.ucla.sspace.matrix.SparseMatrix;

import edu.ucla.sspace.util.ReflectionUtil;

import edu.ucla.sspace.vector.DoubleVector;
import edu.ucla.sspace.vector.SparseDoubleVector;
import edu.ucla.sspace.vector.Vector;
import edu.ucla.sspace.vector.Vectors;

import java.util.ArrayList;
import java.util.List;
import java.util.Properties;
import java.util.Set;


/**
 * @author Keith Stevens
 */
public class ClusterSSpace {
    public static void main(String[] args) throws Exception {
        ArgOptions options = new ArgOptions();
        options.addOption('s', "sspace",
                          "The semantic space to be clustered",
                          true, "FILE", "Required");
        options.addOption('a', "clusteringAlgorithm",
                          "The clustering algorithm to use",
                          true, "CLASSNAME", "Required");
        options.addOption('c', "numClusters",
                          "The number of clusters to use",
                          true, "INT", "Optional");
        options.parseOptions(args);

        if (!options.hasOption('a') ||
            !options.hasOption('s')) {
            System.out.println("Usage: ClusterSSpace\n" +
                               options.prettyPrint());
            System.exit(1);
        }

        Clustering clustering = ReflectionUtil.getObjectInstance(
                options.getStringOption('a'));
        SemanticSpace sspace = new StaticSemanticSpace(
                options.getStringOption('s'));
        int numClusters = options.getIntOption('c', 0);

        Set words = sspace.getWords();
        List vectors = new ArrayList();
        List sparseVectors =
            new ArrayList();
        for (String word : words) {
            Vector v = sspace.getVector(word);
            if (v instanceof SparseDoubleVector)
                sparseVectors.add((SparseDoubleVector) v);
            else
                vectors.add(Vectors.asDouble(sspace.getVector(word)));
        }

        Properties props = System.getProperties();
        Assignments assignments = null;
        if (sparseVectors.size() > 0) {
            SparseMatrix matrix = Matrices.asSparseMatrix(sparseVectors);
            assignments = (numClusters > 0) 
                ? clustering.cluster(matrix, numClusters, props)
                : clustering.cluster(matrix, props);
        } else {
            Matrix matrix = Matrices.asMatrix(vectors);
            assignments = (numClusters > 0) 
                ? clustering.cluster(matrix, numClusters, props)
                : clustering.cluster(matrix, props);
        }

        int a = 0;
        for (String word : words) {
            System.out.printf("%s ", word);
            for (int id : assignments.getAll(a++))
                System.out.printf("%d ", id);
            System.out.println();
        }
    }
}