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Declarative Machine Learning
/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package org.apache.sysml.runtime.util;
import java.util.Random;
/**
* Class that generates a pair of random numbers from standard normal
* distribution N(0,1). Box-Muller method is used to compute random
* numbers from N(0,1) using two independent random numbers from U[0,1).
*/
public class RandNPair
{
private double N1, N2;
public double getFirst() { return N1; }
public double getSecond() { return N2; }
public void compute(double U1, double U2) {
// Box-Muller transform
double v1 = Math.sqrt(-2*Math.log(U1));
double v2 = 2*Math.PI*U2;
N1 = v1 * Math.cos(v2);
N2 = v1 * Math.sin(v2);
}
public void compute(Random r) {
compute(r.nextDouble(), r.nextDouble());
}
}