1 /* 2 * Licensed to the Apache Software Foundation (ASF) under one or more 3 * contributor license agreements. See the NOTICE file distributed with 4 * this work for additional information regarding copyright ownership. 5 * The ASF licenses this file to You under the Apache License, Version 2.0 6 * (the "License"); you may not use this file except in compliance with 7 * the License. You may obtain a copy of the License at 8 * 9 * http://www.apache.org/licenses/LICENSE-2.0 10 * 11 * Unless required by applicable law or agreed to in writing, software 12 * distributed under the License is distributed on an "AS IS" BASIS, 13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 * See the License for the specific language governing permissions and 15 * limitations under the License. 16 */ 17 package org.apache.commons.rng.sampling.distribution; 18 19 import org.apache.commons.rng.UniformRandomProvider; 20 21 /** 22 * <a href="https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform"> 23 * Box-Muller algorithm</a> for sampling from Gaussian distribution with 24 * mean 0 and standard deviation 1. 25 * 26 * <p>Sampling uses {@link UniformRandomProvider#nextDouble()}.</p> 27 * 28 * @since 1.1 29 */ 30 public class BoxMullerNormalizedGaussianSampler 31 implements NormalizedGaussianSampler, SharedStateContinuousSampler { 32 /** Next gaussian. */ 33 private double nextGaussian = Double.NaN; 34 /** Underlying source of randomness. */ 35 private final UniformRandomProvider rng; 36 37 /** 38 * @param rng Generator of uniformly distributed random numbers. 39 */ 40 public BoxMullerNormalizedGaussianSampler(UniformRandomProvider rng) { 41 this.rng = rng; 42 } 43 44 /** {@inheritDoc} */ 45 @Override 46 public double sample() { 47 double random; 48 if (Double.isNaN(nextGaussian)) { 49 // Generate a pair of Gaussian numbers. 50 51 final double x = rng.nextDouble(); 52 final double y = rng.nextDouble(); 53 final double alpha = 2 * Math.PI * x; 54 final double r = Math.sqrt(-2 * Math.log(y)); 55 56 // Return the first element of the generated pair. 57 random = r * Math.cos(alpha); 58 59 // Keep second element of the pair for next invocation. 60 nextGaussian = r * Math.sin(alpha); 61 } else { 62 // Use the second element of the pair (generated at the 63 // previous invocation). 64 random = nextGaussian; 65 66 // Both elements of the pair have been used. 67 nextGaussian = Double.NaN; 68 } 69 70 return random; 71 } 72 73 /** {@inheritDoc} */ 74 @Override 75 public String toString() { 76 return "Box-Muller normalized Gaussian deviate [" + rng.toString() + "]"; 77 } 78 79 /** 80 * {@inheritDoc} 81 * 82 * @since 1.3 83 */ 84 @Override 85 public SharedStateContinuousSampler withUniformRandomProvider(UniformRandomProvider rng) { 86 return new BoxMullerNormalizedGaussianSampler(rng); 87 } 88 89 /** 90 * Create a new normalised Gaussian sampler. 91 * 92 * @param <S> Sampler type. 93 * @param rng Generator of uniformly distributed random numbers. 94 * @return the sampler 95 * @since 1.3 96 */ 97 @SuppressWarnings("unchecked") 98 public static <S extends NormalizedGaussianSampler & SharedStateContinuousSampler> S 99 of(UniformRandomProvider rng) { 100 return (S) new BoxMullerNormalizedGaussianSampler(rng); 101 } 102 }