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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 can generate a stream of random numbers from standard 
 * normal distribution N(0,1). This class internally makes use of 
 * RandNPair, which uses Box-Muller method.
 */


public class RandN 
{
	//private long seed;
	Random r;
	private RandNPair pair;
	boolean flag = false; // we use pair.N1 if flag=false, and pair.N2 otherwise
	
	public RandN(long s) {
		init(new Random(s));
	}
	
	public RandN(Random random) {
		init(random);
	}
	
	private void init(Random random) {
		//seed = s;
		r = random;
		pair = new RandNPair();
		flag = false;
		pair.compute(r);
	}
	
	public double nextDouble() {
		double d;
		if (!flag) {
			d = pair.getFirst();
		}
		else {
			d = pair.getSecond();
			pair.compute(r);
		}
		flag = !flag;
		return d;
	}

}