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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.commons.math4.random;
import org.apache.commons.rng.UniformRandomProvider;
import org.apache.commons.rng.sampling.distribution.NormalizedGaussianSampler;
import org.apache.commons.rng.sampling.distribution.MarsagliaNormalizedGaussianSampler;
/**
* Random generator that generates normally distributed samples.
*
* @since 1.2
*/
public class GaussianRandomGenerator implements NormalizedRandomGenerator {
/** Gaussian distribution sampler. */
private final NormalizedGaussianSampler sampler;
/**
* Creates a new generator.
*
* @param generator Underlying random generator.
*/
public GaussianRandomGenerator(final UniformRandomProvider generator) {
sampler = new MarsagliaNormalizedGaussianSampler(generator);
}
/**
* Generates a random scalar with zero mean and unit standard deviation.
*
* @return a random value sampled from a normal distribution.
*/
@Override
public double nextNormalizedDouble() {
return sampler.sample();
}
}