cern.colt.matrix.tint.impl.SparseCCMIntMatrix2D Maven / Gradle / Ivy

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Parallel Colt is a multithreaded version of Colt - a library for high performance scientific computing in Java. It contains efficient algorithms for data analysis, linear algebra, multi-dimensional arrays, Fourier transforms, statistics and histogramming.

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/*
Copyright (C) 1999 CERN - European Organization for Nuclear Research.
Permission to use, copy, modify, distribute and sell this software and its documentation for any purpose 
is hereby granted without fee, provided that the above copyright notice appear in all copies and 
that both that copyright notice and this permission notice appear in supporting documentation. 
CERN makes no representations about the suitability of this software for any purpose. 
It is provided "as is" without expressed or implied warranty.
 */
package cern.colt.matrix.tint.impl;

import cern.colt.matrix.tint.IntMatrix2D;

/**
 * Sparse column-compressed-modified 2-d matrix holding int elements.
 * Each column is stored as SparseIntMatrix1D.
 * 
 * @author Piotr Wendykier ([email protected])
 * 
 */
public class SparseCCMIntMatrix2D extends WrapperIntMatrix2D {

    private static final long serialVersionUID = 1L;
    private SparseIntMatrix1D[] elements;

    /**
     * Constructs a matrix with a given number of rows and columns. All entries
     * are initially 0.
     * 
     * @param rows
     *            the number of rows the matrix shall have.
     * @param columns
     *            the number of columns the matrix shall have.
     * @throws IllegalArgumentException
     *             if
     *             rows<0 || columns<0 || (double)columns*rows > Integer.MAX_VALUE
     *             .
     */
    public SparseCCMIntMatrix2D(int rows, int columns) {
        super(null);
        try {
            setUp(rows, columns);
        } catch (IllegalArgumentException exc) { // we can hold rows*columns>Integer.MAX_VALUE cells !
            if (!"matrix too large".equals(exc.getMessage()))
                throw exc;
        }
        elements = new SparseIntMatrix1D[columns];
        for (int i = 0; i < columns; ++i)
            elements[i] = new SparseIntMatrix1D(rows);
    }

    public SparseIntMatrix1D[] elements() {
        return elements;
    }

    public int getQuick(int row, int column) {
        return elements[column].getQuick(row);
    }

    public void setQuick(int row, int column, int value) {
        elements[column].setQuick(row, value);
    }

    public void trimToSize() {
        for (int c = 0; c < columns; c++) {
            elements[c].trimToSize();
        }
    }

    public SparseIntMatrix1D viewColumn(int column) {
        return elements[column];
    }

    protected IntMatrix2D getContent() {
        return this;
    }

    public IntMatrix2D like(int rows, int columns) {
        return new SparseCCMIntMatrix2D(rows, columns);
    }
}