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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 2010 Keith Stevens 
 *
 * 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.MaskedSparseDoubleVectorView;

import java.util.HashMap;
import java.util.Map;


/**
 * This {@link SparseMatrix} decorator allows every row and column index to be
 * remapped to new indices.  The size of this matrix will depend on the number
 * of mapped rows and columns.  Write-through access is allowed for each cell in
 * the underlying matrix through the {@code set} method.  Array based accessors
 * and getters are disabled when using this decorator.
 *
 * @author Keith Stevens
 */
public class CellMaskedSparseMatrix extends CellMaskedMatrix
        implements SparseMatrix, java.io.Serializable {

    private static final long serialVersionUID = 1L;

    /**
     * The original underlying matrix.
     */
    private final SparseMatrix matrix;

    /**
     * An internal mapping from known row indices in this {@link
     * CellMaskedSparseMatrix} to rows in the backing {@link SparseMatrix}.
     */
    private final int[] rowMaskMap;

    /**
     * An internal mapping from row indices in the backing {@link SparseMatrix}
     * to known rows in this {@link CellMaskedSparseMatrix}.
     */
    private final Map reverseRowMaskMap;

    /**
     * An internal mapping from known column indices in this {@link
     * CellMaskedSparseMatrix} to columns in the backing {@link SparseMatrix}.
     */
    private final int[] colMaskMap;

    /**
     * An internal mapping from column indices in the backing {@link
     * SparseMatrix} to known columns in this {@link CellMaskedSparseMatrix}.
     */
    private final Map reverseColMaskMap;

    /**
     * Creates a new {@link CellMaskedSparseMatrix} from a given {@link
     * SparseMatrix} and maps, one for the row indices and one for the column
     * indices.  Only valid mappings should be included.
     *
     * @param matrix The underlying matrix to decorate
     * @param rowMaskMap A mapping from new indices to old indices in the
     *        original map for rows.
     * @param colMaskMap A mapping from new indices to old indices in the
     *        original map for columns.
     */
    public CellMaskedSparseMatrix(SparseMatrix matrix,
                                  int[] rowMaskMap,
                                  int[] colMaskMap) {
        super(matrix, rowMaskMap, colMaskMap);
        this.matrix = matrix;
        this.rowMaskMap = rowMaskMap;
        this.colMaskMap = colMaskMap;
        reverseRowMaskMap = new HashMap();
        for (int i = 0; i < rowMaskMap.length; ++i)
            reverseRowMaskMap.put(rowMaskMap[i], i);
        reverseColMaskMap = new HashMap();
        for (int i = 0; i < colMaskMap.length; ++i)
            reverseColMaskMap.put(colMaskMap[i], i);
    }

    /**
     * {@inheritDoc}
     */
    public SparseDoubleVector getRowVector(int row) {
        row = getIndexFromMap(rowMaskMap, row);
        return new MaskedSparseDoubleVectorView(
                matrix.getRowVector(row), colMaskMap, reverseColMaskMap);
    }

    /**
     * {@inheritDoc}
     */
    public SparseDoubleVector getColumnVector(int col) {
        col = getIndexFromMap(colMaskMap, col);
        return new MaskedSparseDoubleVectorView(
                matrix.getColumnVector(col), rowMaskMap, reverseRowMaskMap);
    }
}