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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 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.matrix.MatrixIO.Format;
import edu.ucla.sspace.matrix.TransformStatistics.MatrixStatistics;

import java.io.File;


/**
 * Tranforms a matrix such that the rows sum to 1. 
 *
 * @author Keith Stevens
 */
public class RowSumTransform extends BaseTransform {

    /**
     * {@inheritDoc}
     */
    protected GlobalTransform getTransform(Matrix matrix) {
        return new RowSumGlobalTransform(matrix);
    }

    /**
     * {@inheritDoc}
     */
    protected GlobalTransform getTransform(File inputMatrixFile,
                                           MatrixIO.Format format) {
        return new RowSumGlobalTransform(inputMatrixFile, format);
    }

    /**
     * Returns the name of this transform.
     */
    public String toString() {
        return "RowSum";
    }

    public class RowSumGlobalTransform implements GlobalTransform {

        /**
         * The total sum of occurances for each row (row).
         */
        private double[] rowSums;

        /**
         * Creates an instance of {@code RowSumGlobalTransform} from a
         * {@link Matrix}.
         */
        public RowSumGlobalTransform(Matrix matrix) {
            MatrixStatistics stats =
                TransformStatistics.extractStatistics(matrix);
            rowSums = stats.rowSums;
        }
        
        /**
         * Creates an instance of {@code RowSumGlobalTransform} from a
         * {@code File} in the format {@link Format}.
         */
        public RowSumGlobalTransform(File inputMatrixFile,
                                           Format format) {
            MatrixStatistics stats =
                TransformStatistics.extractStatistics(inputMatrixFile, format);
            rowSums = stats.rowSums;
        }

        /**
         * Computes the Term Frequency-Inverse Document Frequency for a given
         * value where {@code value} is the observed frequency of term {@code
         * row} in document {@code column}.
         *
         * @param row The index speicifying the term being observed
         * @param column The index specifying the document being observed
         * @param value The number of occurances of the term in the document.
         *
         * @return the TF-IDF of the observed value
         */
        public double transform(int row, int column, double value) {
            return (rowSums[row] != 0d) ? value / rowSums[row] : 0d;
        }
    }
}