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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 David Jurgens
 *
 * 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.tools;

import java.io.*;
import java.util.*;

public class SparseMatrixConverter {

    public static void main(String[] args) {
        try {
            BufferedReader br = new BufferedReader(new FileReader(args[0]));

            Map colToNonZero = 
                new HashMap();

            // read through once to get matrix dimensions
            int rows = 0, cols = 0, nonZero = 0;                
            for (String line = null; (line = br.readLine()) != null; ) {
                String[] rowColVal = line.split("\\s+");
                int row = Integer.parseInt(rowColVal[0]);
                int col = Integer.parseInt(rowColVal[1]);
                if (row > rows)
                    rows = row;
                if (col > cols)
                    cols = col;
                ++nonZero;
                Integer colCount = colToNonZero.get(col);
                colToNonZero.put(col, (colCount == null) ? 1 : colCount + 1);
            }
            br.close();

            // Matlab indices are indexed starting at 1, while SVDLIBC start at
            // 0, so decrement the total number of rows and columns
            --rows;
            --cols;

            br = new BufferedReader(new FileReader(args[0]));

            // loop through a second time and convert each of the rows into its
            // SVDLIBC sparse format
            System.out.println(rows + "\t" + cols + "\t" + nonZero);
            int lastCol = 0;
            for (String line = null; (line = br.readLine()) != null; ) {
                String[] rowColVal = line.split("\\s+");
                int col = Integer.parseInt(rowColVal[1]);
                if (col != lastCol) {
                    // print any missing colums in case not all the columns have
                    // data
                    for (int i = lastCol + 1; i < col; ++i) {
                        System.out.println(0);
                    }
                    // print the new header
                    int colCount = colToNonZero.get(col);
                    lastCol = col;
                    System.out.println(colCount);                    
                }
                System.out.println(rowColVal[0] + "\t" + rowColVal[2]);
            }
            br.close();
            System.out.flush();
            
        } catch (Throwable t) {
            t.printStackTrace();
        }
    }

}