edu.ucla.sspace.clustering.criterion.H2Function Maven / Gradle / Ivy

Go to download

Show more of this group Show more artifacts with this name
Show all versions of sspace-wordsi Show documentation

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.

The newest version!

/*
 * Copyright 2011 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.clustering.criterion;


/**
 * This {@link HybridBaseFunction} uses the {@link E1Function} and the {@link
 * I1Function}.
 *
 * @author Keith Stevens
 */
public class H2Function extends HybridBaseFunction {

    /**
     * {@inheritDoc}
     */
    protected BaseFunction getInternalFunction() {
        return new I2Function(matrix, centroids, i1Costs, 
                              assignments, clusterSizes);
    }

    /**
     * {@inheritDoc}
     */
    protected BaseFunction getExternalFunction() {
        return new E1Function(matrix, centroids, e1Costs,
                              assignments, clusterSizes,
                              completeCentroid, simToComplete);
    }

    /**
     * {@inheritDoc}
     */
    public boolean isMaximize() {
        return true;
    }
}