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The S-Space Package is a Natural Language Processing library for distributional semantics representations. Distributional semantics representations model the meaning of words, phrases, and sentences as high dimensional vectors or probability distributions. The library includes common algorithms such as Latent Semantic Analysis, Random Indexing, and Latent Dirichlet Allocation. The S-Space package also includes software libraries for matrices, vectors, graphs, and numerous clustering algorithms.

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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.matrix;

import edu.ucla.sspace.vector.SparseDoubleVector;
import edu.ucla.sspace.vector.SparseVector;

/**
 * An interface for sparse {@code Matrix} implementations whose backing data
 * storage permits accessing rows and columns with {@link SparseVector} objects.
 *
 * @see Matrix
 * @see SparseDoubleVector
 *
 * @author DavidJurgens
 */
public interface SparseMatrix extends Matrix {

    /**
     * Returns the column as a sparse vector.  Whether updates to the vector are
     * written through to the backing matrix is left open to the implementation.
     *
     * @param column The column to return a {@code DoubleVector} for
     *
     * @return A {@code DoubleVector} representing the column at {@code column}
     */
    SparseDoubleVector getColumnVector(int column);

    /**
     * Returns the row as a sparse vector.  Whether updates to the vector are
     * written through to the backing matrix is left open to the implementation.
     *
     * @param row the index of row to return
     *
     * @return A {@code SparseDoubleVector} of the row's data
     */
    SparseDoubleVector getRowVector(int row);

}