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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 2009 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.matrix;

import edu.ucla.sspace.vector.DoubleVector;


/**
 * A {@code Matrix} decorator class that tranposes the data in the backing
 * matrix.  This class provides a way to quickly tranpose the matrix data
 * without the need for copying it.
 */
public class TransposedMatrix implements Matrix {
    
    /**
     * The backing instance of the matrix.
     */
    final Matrix m;

    /**
     * Creates a {@code Matrix} that provides a transposed view of the original
     * matrix.
     */
    public TransposedMatrix(Matrix matrix) {
        this.m = matrix;
    }
    
    /**
     * {@inheritDoc}
     */
    public int columns() {
        return m.rows();
    }
           
    /**
     * {@inheritDoc}
     */
    public double add(int row, int col, double delta) {
        double old = get(row, col);
        set(row, col, delta+old);
        return old;
    }

    /**
     * {@inheritDoc}
     */
    public double get(int row, int col) {
        return m.get(col, row);
    }
           
    /**
     * {@inheritDoc}
     */
    public double[] getColumn(int column) {
        return m.getRow(column);
    }
           
    /**
     * {@inheritDoc}
     */
    public DoubleVector getColumnVector(int column) {
        return m.getRowVector(column);
    }

    /**
     * {@inheritDoc}
     */
    public double[] getRow(int row) {
        return m.getColumn(row);
    }
           
    /**
     * {@inheritDoc}
     */
    public DoubleVector getRowVector(int row) {
        return m.getColumnVector(row);
    }

    /**
     * {@inheritDoc}
     */
    public int rows() {
        return m.columns();
    }
           
    /**
     * {@inheritDoc}
     */
    public void set(int row, int col, double val) {
        m.set(col, row, val);
    }

    /**
     * {@inheritDoc}
     */
    public void setColumn(int column, double[] values) {
        m.setRow(column, values);
    }

    /**
     * {@inheritDoc}
     */
    public void setColumn(int column, DoubleVector values) {
        m.setRow(column, values);
    }

    /**
     * {@inheritDoc}
     */
    public void setRow(int row, double[] values) {
        m.setColumn(row, values);
    }

    /**
     * {@inheritDoc}
     */
    public void setRow(int row, DoubleVector values) {
        m.setColumn(row, values);
    }
     
    /**
     * {@inheritDoc}
     */
    public double[][] toDenseArray() {
        double[][] arr = new double[columns()][0];
        int cols = columns();
        for (int col = 0; col < cols; ++col)
            arr[col] = m.getRow(col);
        return arr;
    }
}