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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 2011 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.matrix;

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


/**
 * A {@code SparseMatrix} backed by vectors that provide amortized O(1) access
 * to their elements.  Each row is implemented using a hashing-based vector.
 * This class provides an alternate implementation to {@link YaleSparseMatrix};
 * this class potentially uses more memory than {@code YaleSparseMatrix}, but
 * provides O(1) access instead of O(log(n)).  The size of this matrix is fixed,
 * and attempts to access rows or columns beyond the size will throw an {@link
 * IndexOutOfBoundsException}.
 *
 * @author David Jurgens
 */
public class SparseHashMatrix extends AbstractMatrix implements SparseMatrix {

    /**
     * The number of rows contained in this {@code SparseMatrix}.
     */
    private final int rows;

    /**
     * The number of columns contained in this {@code SparseMatrix}.
     */
    private final int columns;

    /**
     * Each row is defined as a {@link SparseHashDoubleVector} which does most
     * of the work.
     */
    private final SparseHashDoubleVector[] sparseMatrix;

    /**
     * Constructs a sparse matrix with the specified dimensions.
     */
    public SparseHashMatrix(int rows, int columns) {
        this.rows = rows;
        this.columns = columns;
        sparseMatrix = new SparseHashDoubleVector[rows];
        for (int r = 0; r < rows; ++r)
            sparseMatrix[r] = new SparseHashDoubleVector(columns);
    }

    /**
     * Checks that the indices are within the bounds of this matrix or throws an
     * {@link IndexOutOfBoundsException} if not.
     */        
    private void checkIndices(int row, int col) {
        if (row < 0 || col < 0 || row >= rows || col >= columns) {
            throw new IndexOutOfBoundsException();
        }
    }
    
    /**
     * {@inheritDoc}
     */
    public int columns() {
        return columns;
    }

    /**
     * {@inheritDoc}
     */
    public SparseDoubleVector getColumnVector(int column) {
        SparseHashDoubleVector col = new SparseHashDoubleVector(rows);
        for (int r = 0; r < rows(); ++r)
            col.set(r, getRowVector(r).get(column));
        return col;
    }
    
    /**
     * {@inheritDoc}
     */
    public SparseDoubleVector getRowVector(int row) {
        return Vectors.immutable(sparseMatrix[row]);
    }

    /**
     * {@inheritDoc}
     */
    public int rows() {
        return rows;
    }

    /**
     * {@inheritDoc}
     */
    public void set(int row, int col, double val) {
        checkIndices(row, col);
        sparseMatrix[row].set(col, val);
    }
}