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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.similarity;

import edu.ucla.sspace.common.Similarity;

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


/**
 * A functional class for computing Kendall's tau of the
 * values in the two vectors.  This method uses tau-b, which is suitable for
 * vectors with duplicate values.
 *
 * @author David Jurgens
 */
public class KendallsTau extends AbstractSymmetricSimilarityFunction {

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

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

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