001    /*
002     * Copyright (C) 2008-2010 by Holger Arndt
003     *
004     * This file is part of the Universal Java Matrix Package (UJMP).
005     * See the NOTICE file distributed with this work for additional
006     * information regarding copyright ownership and licensing.
007     *
008     * UJMP is free software; you can redistribute it and/or modify
009     * it under the terms of the GNU Lesser General Public License as
010     * published by the Free Software Foundation; either version 2
011     * of the License, or (at your option) any later version.
012     *
013     * UJMP is distributed in the hope that it will be useful,
014     * but WITHOUT ANY WARRANTY; without even the implied warranty of
015     * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
016     * GNU Lesser General Public License for more details.
017     *
018     * You should have received a copy of the GNU Lesser General Public
019     * License along with UJMP; if not, write to the
020     * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
021     * Boston, MA  02110-1301  USA
022     */
023    
024    package org.ujmp.core.doublematrix.calculation.entrywise.creators;
025    
026    import org.ujmp.core.Matrix;
027    import org.ujmp.core.MatrixFactory;
028    import org.ujmp.core.doublematrix.calculation.AbstractDoubleCalculation;
029    import org.ujmp.core.enums.ValueType;
030    import org.ujmp.core.exceptions.MatrixException;
031    import org.ujmp.core.util.MathUtil;
032    
033    public class Randn extends AbstractDoubleCalculation {
034            private static final long serialVersionUID = 3846626738610954086L;
035    
036            private double mean = 0.0;
037    
038            private double sigma = 1.0;
039    
040            public Randn(Matrix matrix) {
041                    super(matrix);
042            }
043    
044            public Randn(Matrix matrix, double mean, double sigma) {
045                    super(matrix);
046                    this.mean = mean;
047                    this.sigma = sigma;
048            }
049    
050            public double getDouble(long... coordinates) {
051                    return MathUtil.nextGaussian(mean, sigma);
052            }
053    
054            public static Matrix calc(long... size) throws MatrixException {
055                    return calc(ValueType.DOUBLE, size);
056            }
057    
058            public static Matrix calc(Matrix source) throws MatrixException {
059                    return calc(source, 0.0, 1.0);
060            }
061    
062            public static Matrix calc(Matrix source, double mean, double sigma) throws MatrixException {
063                    Matrix ret = Matrix.factory.zeros(source.getSize());
064                    for (long[] c : source.allCoordinates()) {
065                            ret.setAsDouble(MathUtil.nextGaussian(mean, sigma), c);
066                    }
067                    return ret;
068            }
069    
070            public static Matrix calc(ValueType valueType, long... size) throws MatrixException {
071                    Matrix ret = MatrixFactory.zeros(valueType, size);
072                    for (long[] c : ret.allCoordinates()) {
073                            ret.setAsDouble(MathUtil.nextGaussian(0.0, 1.0), c);
074                    }
075                    return ret;
076            }
077    
078    }