cern.colt.matrix.tlong.impl.SparseRCMLongMatrix2D 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.tlong.impl;

import cern.colt.matrix.tlong.LongMatrix2D;

/**
 * Sparse row-compressed-modified 2-d matrix holding long elements.
 * Each row is stored as SparseLongMatrix1D.
 * 
 * @author Piotr Wendykier ([email protected])
 * 
 */
public class SparseRCMLongMatrix2D extends WrapperLongMatrix2D {

    private static final long serialVersionUID = 1L;
    private SparseLongMatrix1D[] 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 SparseRCMLongMatrix2D(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 SparseLongMatrix1D[rows];
        for (int i = 0; i < rows; ++i)
            elements[i] = new SparseLongMatrix1D(columns);
    }

    public SparseLongMatrix1D[] elements() {
        return elements;
    }

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

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

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

    public SparseLongMatrix1D viewRow(int row) {
        return elements[row];
    }

    protected LongMatrix2D getContent() {
        return this;
    }

    public LongMatrix2D like(int rows, int columns) {
        return new SparseRCMLongMatrix2D(rows, columns);
    }
}