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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 according to the Term frequency-Inverse
 * Document Frequency weighting.  The input matrix is assumed to be
 * formatted as rows representing documents and columns representing rows.  Each
 * matrix cell indicates the number of times the columns's word occurs within
 * the rows's document.  For full details see:
 *
 * Spärck Jones, Karen
 *      (1972). "A statistical interpretation of term specificity and its
 *      application in retrieval". Journal of Documentation 28
 *      (1): 11–21.
 *
 * @see TfIdfTransform
 *
 * @author Keith Stevens
 */
public class TfIdfDocStripedTransform extends BaseTransform {

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

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

    /**
     * Returns the name of this transform.
     */
    public String toString() {
        return "TF-IDF-DOC-STRIPED";
    }

    public class TfIdfGlobalTransform implements GlobalTransform {

        /**
         * The total number of terms that occur in each document.
         */
        private double[] docTermCount;

        /**
         * The total number of documents that each term occurs in.
         */
        private double[] termDocCount;

        /**
         * The total number of documents present in the matrix.
         */
        private int totalDocCount;

        /**
         * Creates an instance of {@code TfIdfGlobalTransform} from a {@link
         * Matrix}.
         */
        public TfIdfGlobalTransform(Matrix matrix) {
            MatrixStatistics stats =
                TransformStatistics.extractStatistics(matrix, false, true);
            docTermCount = stats.rowSums;
            termDocCount = stats.columnSums;
            totalDocCount = docTermCount.length;
        }
        
        /**
         * Creates an instance of {@code TfIdfGlobalTransform} from a {@code
         * File} in the format {@link Format}.
         */
        public TfIdfGlobalTransform(File inputMatrixFile, Format format) {
            MatrixStatistics stats = TransformStatistics.extractStatistics(
                    inputMatrixFile, format, false, true);
            docTermCount = stats.rowSums;
            termDocCount = stats.columnSums;
            totalDocCount = docTermCount.length;
        }

        /**
         * 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) {
            double tf = value / docTermCount[row];
            double idf =
                Math.log(totalDocCount / (1 + termDocCount[column]));
            return tf * idf;
        }
    }
}