001/* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017package org.apache.commons.rng.sampling.distribution; 018 019import org.apache.commons.rng.UniformRandomProvider; 020 021/** 022 * Sampling from an <a href="http://mathworld.wolfram.com/ExponentialDistribution.html">exponential distribution</a>. 023 * 024 * @since 1.0 025 */ 026public class AhrensDieterExponentialSampler 027 extends SamplerBase 028 implements ContinuousSampler { 029 /** 030 * Table containing the constants 031 * \( q_i = sum_{j=1}^i (\ln 2)^j / j! = \ln 2 + (\ln 2)^2 / 2 + ... + (\ln 2)^i / i! \) 032 * until the largest representable fraction below 1 is exceeded. 033 * 034 * Note that 035 * \( 1 = 2 - 1 = \exp(\ln 2) - 1 = sum_{n=1}^\infinity (\ln 2)^n / n! \) 036 * thus \( q_i \rightarrow 1 as i \rightarrow +\infinity \), 037 * so the higher \( i \), the closer we get to 1 (the series is not alternating). 038 * 039 * By trying, n = 16 in Java is enough to reach 1. 040 */ 041 private static final double[] EXPONENTIAL_SA_QI = new double[16]; 042 /** The mean of this distribution. */ 043 private final double mean; 044 /** Underlying source of randomness. */ 045 private final UniformRandomProvider rng; 046 047 /** 048 * Initialize tables. 049 */ 050 static { 051 /** 052 * Filling EXPONENTIAL_SA_QI table. 053 * Note that we don't want qi = 0 in the table. 054 */ 055 final double ln2 = Math.log(2); 056 double qi = 0; 057 058 for (int i = 0; i < EXPONENTIAL_SA_QI.length; i++) { 059 qi += Math.pow(ln2, i + 1) / InternalUtils.factorial(i + 1); 060 EXPONENTIAL_SA_QI[i] = qi; 061 } 062 } 063 064 /** 065 * @param rng Generator of uniformly distributed random numbers. 066 * @param mean Mean of this distribution. 067 */ 068 public AhrensDieterExponentialSampler(UniformRandomProvider rng, 069 double mean) { 070 super(null); 071 this.rng = rng; 072 this.mean = mean; 073 } 074 075 /** {@inheritDoc} */ 076 @Override 077 public double sample() { 078 // Step 1: 079 double a = 0; 080 double u = rng.nextDouble(); 081 082 // Step 2 and 3: 083 while (u < 0.5) { 084 a += EXPONENTIAL_SA_QI[0]; 085 u *= 2; 086 } 087 088 // Step 4 (now u >= 0.5): 089 u += u - 1; 090 091 // Step 5: 092 if (u <= EXPONENTIAL_SA_QI[0]) { 093 return mean * (a + u); 094 } 095 096 // Step 6: 097 int i = 0; // Should be 1, be we iterate before it in while using 0. 098 double u2 = rng.nextDouble(); 099 double umin = u2; 100 101 // Step 7 and 8: 102 do { 103 ++i; 104 u2 = rng.nextDouble(); 105 106 if (u2 < umin) { 107 umin = u2; 108 } 109 110 // Step 8: 111 } while (u > EXPONENTIAL_SA_QI[i]); // Ensured to exit since EXPONENTIAL_SA_QI[MAX] = 1. 112 113 return mean * (a + umin * EXPONENTIAL_SA_QI[0]); 114 } 115 116 /** {@inheritDoc} */ 117 @Override 118 public String toString() { 119 return "Ahrens-Dieter Exponential deviate [" + rng.toString() + "]"; 120 } 121}