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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 (c) 2011, Lawrence Livermore National Security, LLC. Produced at
 * the Lawrence Livermore National Laboratory. Written by Keith Stevens,
 * [email protected] OCEC-10-073 All rights reserved. 
 *
 * 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.similarity;

import edu.ucla.sspace.vector.DoubleVector;
import edu.ucla.sspace.vector.IntegerVector;
import edu.ucla.sspace.vector.Vector;
import edu.ucla.sspace.vector.VectorMath;


/**
 * Returns the dot product of the two vectors.
 *
 * 
 *
 * This metric is symmetric.
 *
 * @author Keith Stevens
 */
public class DotProduct extends AbstractSymmetricSimilarityFunction {

    /**
     * {@inheritDoc}
     */
    public double sim(DoubleVector v1, DoubleVector v2) {
        return VectorMath.dotProduct(v1, v2);
    }

    /**
     * {@inheritDoc}
     */
    public double sim(IntegerVector v1, IntegerVector v2) {
        return VectorMath.dotProduct(v1, v2);
    }

    /**
     * {@inheritDoc}
     */
    public double sim(Vector v1, Vector v2) {
        return VectorMath.dotProduct(v1, v2);
    }
}