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Declarative Machine Learning

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/*
 * 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());
	}
	
	public void compute(long seed) {
		compute(new Random(seed));
	}

}