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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 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.vector;


/**
 * A decorator for {@link SparseDoubleVector}s that scales every value in a
 * given {@link DoubleVector} by some non zero scale.
 *
 * 
 *
 * Note that this automatically computes the scaling of a {@link
 * ScaledDoubleVector} so that backing vector is scaled only once, thus
 * preventing any recursive calls to scaling.
 *
 * @author Keith Stevens
 */
public class ScaledSparseDoubleVector extends ScaledDoubleVector 
                                      implements SparseDoubleVector {

    /**
    * The original vector.
    */
    private SparseDoubleVector vector;

    /**
    * Creates a new {@link ScaledSparseDoubleVector} that decorates a given
    * {@link SparseDoubleVector} by scaling each value in {@code vector} by
    * {@code scale}.
    */
    public ScaledSparseDoubleVector(SparseDoubleVector vector, double scale) {
        super(vector, scale);

        // If the vector we are to orthonormalize is already scaled, get its
        // backing data and create a new instance that is rescaled by the
        // product of both scalars.  This avoids unnecessary recursion to
        // multiply all the values together for heavily scaled vectors.
        if (vector instanceof ScaledSparseDoubleVector) {
            ScaledSparseDoubleVector ssdv = (ScaledSparseDoubleVector) vector;
            this.vector = ssdv.vector;
        } else 
            this.vector = vector;
    }

    /**
    * {@inheritDoc}
    */
    public int[] getNonZeroIndices() {
        return vector.getNonZeroIndices();
    }
}