Java tutorial
/* * 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.rng.sampling.distribution; import org.apache.commons.rng.UniformRandomProvider; /** * <a href="https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform"> * Box-Muller algorithm</a> for sampling from Gaussian distribution with * mean 0 and standard deviation 1. * * @since 1.1 */ public class BoxMullerNormalizedGaussianSampler extends SamplerBase implements NormalizedGaussianSampler { /** Next gaussian. */ private double nextGaussian = Double.NaN; /** * @param rng Generator of uniformly distributed random numbers. */ public BoxMullerNormalizedGaussianSampler(UniformRandomProvider rng) { super(rng); } /** {@inheritDoc} */ @Override public double sample() { final double random; if (Double.isNaN(nextGaussian)) { // Generate a pair of Gaussian numbers. final double x = nextDouble(); final double y = nextDouble(); final double alpha = 2 * Math.PI * x; final double r = Math.sqrt(-2 * Math.log(y)); // Return the first element of the generated pair. random = r * Math.cos(alpha); // Keep second element of the pair for next invocation. nextGaussian = r * Math.sin(alpha); } else { // Use the second element of the pair (generated at the // previous invocation). random = nextGaussian; // Both elements of the pair have been used. nextGaussian = Double.NaN; } return random; } /** {@inheritDoc} */ @Override public String toString() { return "Box-Muller normalized Gaussian deviate [" + super.toString() + "]"; } }