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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 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.vector.DenseVector;
import edu.ucla.sspace.vector.DoubleVector;

import java.util.ArrayList;
import java.util.List;


/**
 * A {@link Matrix} implementation that buildes a matrix out of a list of
 * {@link DoubleVector}s.  
 *
 * @author Keith Stevens
 */
class ListMatrix implements Matrix {

    /**
     * The list of {@code DoubleVector}s providing the values for the {@code
     * Matrix}.
     */
    protected List vectors;

    /**
     * The number of columns in the {@code Matrix}
     */
    protected int columns;

    /**
     * Constructs a matrix from the list of vectors where the first list element
     * is treated as the first row in the matrix and so on.
     *
     * @throws IllegalArgumentException if the list is empty or if the vectors
     * have different lengths
     */
    public ListMatrix(List vectors) {
        if (vectors.size() == 0)
            throw new IllegalArgumentException(
                "Must provide at least one vector");
        // Copy the contents to an ArrayList to guarantee O(1) row access
        this.vectors = new ArrayList(vectors.size());
        columns = vectors.get(0).length();
        for (T t : vectors) {
            if (t.length() != columns)
                throw new IllegalArgumentException(
                    "Cannot create ragged matrix from list of vectors");
            this.vectors.add(t);
        }        
    }

    public ListMatrix(List vectors, int columns) {
        if (vectors.size() == 0)
            throw new IllegalArgumentException(
                "Must provide at least one vector");
        // Copy the contents to an ArrayList to guarantee O(1) row access
        this.vectors = new ArrayList(vectors);
        this.columns = columns;
    }

    /**
     * {@inheritDoc}
     */
    public double add(int row, int column, double delta) {
        double old = get(row, column);
        set(row, column, delta+old);
        return old;
    }

    /**
     * {@inheritDoc}
     */
    public double get(int row, int column) {
        return vectors.get(row).get(column);
    }

    /**
     * {@inheritDoc}
     */
    public double[] getColumn(int column) {
        int i = 0;
        double[] columnValues = new double[vectors.size()];

        for (DoubleVector vector : vectors)
            columnValues[i++] = vector.get(column);
        return columnValues;
    }

    /**
     * {@inheritDoc}
     */
    public DoubleVector getColumnVector(int column) {
        int i = 0;
        DoubleVector columnValues = new DenseVector(vectors.size());

        for (DoubleVector vector : vectors)
            columnValues.set(i++, vector.get(column));
        return columnValues;
    }

    /**
     * {@inheritDoc}
     */
    public double[] getRow(int row) {
        return vectors.get(row).toArray();
    }

    /**
     * {@inheritDoc}
     */
    public T getRowVector(int row) {
        return vectors.get(row);
    }

    /**
     * {@inheritDoc}
     */
    public int columns() {
        return columns;
    }

    /**
     * {@inheritDoc}
     */
    public int rows() {
        return vectors.size();
    }

    /**
     * {@inheritDoc}
     */
    public double[][] toDenseArray() {
        double[][] result = new double[vectors.size()][columns];
        int row = 0;
        for (DoubleVector vector : vectors) {
            for (int i = 0; i < vector.length(); ++i)
                result[row][i] = vector.get(i);
            row++;
        }
        return result;
    }

    /**
     * {@inheritDoc}
     */
    public void set(int row, int column, double value) {
        T vector = vectors.get(row);
        vector.set(column, value);
    }

    /**
     * {@inheritDoc}
     */
    public void setColumn(int column, double[] values) {
        int i = 0;
        for (DoubleVector vector : vectors)
            vector.set(column, values[i++]);
    }

    /**
     * {@inheritDoc}
     */
    public void setColumn(int column, DoubleVector values) {
        int i = 0;
        for (DoubleVector vector : vectors)
            vector.set(column, values.get(i++));
    }

    /**
     * {@inheritDoc}
     */
    public void setRow(int row, double[] values) {
        T v = vectors.get(row);
        for (int i = 0; i < values.length; ++i)
            v.set(i, values[i]);
    }

    /**
     * {@inheritDoc}
     */
    public void setRow(int row, DoubleVector values) {
        T v = vectors.get(row);
        for (int i = 0; i < values.length(); ++i)
            v.set(i, values.get(i));
    }
}