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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.matrix;

import edu.ucla.sspace.vector.SparseDoubleVector;

import java.io.OutputStream;
import java.io.PrintStream;


/**
 * @author Keith Stevens
 */
public class MatlabSparseMatrixWriter implements MatrixWriter {

    /**
     * Writes the dense {@link Matrix} to the file using a particular format.
     */
    public void writeMatrix(Matrix m, OutputStream s) {
        // If the matrix is actually sparse, let the sparse method handle it.
        if (m instanceof SparseMatrix) {
            writeMatrix((SparseMatrix) m, s);
            return;
        }

        PrintStream p = new PrintStream(s);

        // Print the row, col, value entrie for each element in the matrix.
        for (int r = 0; r < m.rows(); ++r)
            for (int c = 0; c < m.columns(); ++c)
                p.printf("%d %d %f\n", r+1, c+1, m.get(r,c));

        p.flush();
        p.close();
    }

    /**
     * Writes the {@link SparseMatrix} to the file using a particular format.
     */
    public void writeMatrix(SparseMatrix m, OutputStream s) {
        PrintStream p = new PrintStream(s);

        // Check to see if the last element in the matrix is non zero.  If it
        // has a zero value, print out a single dummy value to bound the total
        // size of the matrix.
        if (m.get(m.rows()-1, m.columns()-1) == 0d)
            p.printf("%d %d %f\n", m.rows(), m.columns(), 0.0);

        // Print the row, col, value entrie for each element in the matrix.
        for (int r = 0; r < m.rows(); ++r) {
            SparseDoubleVector v = m.getRowVector(r);
            for (int c : v.getNonZeroIndices())
                p.printf("%d %d %f\n", r+1, c+1, m.get(r,c));
        }

        p.flush();
        p.close();
    }
}