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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 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;
}
}