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 * Distribution sampler that uses the 023 * <a href="https://en.wikipedia.org/wiki/Inverse_transform_sampling"> 024 * inversion method</a>. 025 * 026 * It can be used to sample any distribution that provides access to its 027 * <em>inverse cumulative probability function</em>. 028 * 029 * <p>Sampling uses {@link UniformRandomProvider#nextDouble()}.</p> 030 * 031 * <p>Example:</p> 032 * <pre><code> 033 * import org.apache.commons.math3.distribution.RealDistribution; 034 * import org.apache.commons.math3.distribution.ChiSquaredDistribution; 035 * 036 * import org.apache.commons.rng.simple.RandomSource; 037 * import org.apache.commons.rng.sampling.distribution.ContinuousSampler; 038 * import org.apache.commons.rng.sampling.distribution.InverseTransformContinuousSampler; 039 * import org.apache.commons.rng.sampling.distribution.ContinuousInverseCumulativeProbabilityFunction; 040 * 041 * // Distribution to sample. 042 * final RealDistribution dist = new ChiSquaredDistribution(9); 043 * // Create the sampler. 044 * final ContinuousSampler chiSquareSampler = 045 * InverseTransformContinuousSampler.of(RandomSource.XO_RO_SHI_RO_128_PP.create(), 046 * new ContinuousInverseCumulativeProbabilityFunction() { 047 * public double inverseCumulativeProbability(double p) { 048 * return dist.inverseCumulativeProbability(p); 049 * } 050 * }); 051 * 052 * // Generate random deviate. 053 * double random = chiSquareSampler.sample(); 054 * </code></pre> 055 * 056 * @since 1.0 057 */ 058public class InverseTransformContinuousSampler 059 extends SamplerBase 060 implements SharedStateContinuousSampler { 061 /** Inverse cumulative probability function. */ 062 private final ContinuousInverseCumulativeProbabilityFunction function; 063 /** Underlying source of randomness. */ 064 private final UniformRandomProvider rng; 065 066 /** 067 * Create an instance. 068 * 069 * @param rng Generator of uniformly distributed random numbers. 070 * @param function Inverse cumulative probability function. 071 */ 072 public InverseTransformContinuousSampler(UniformRandomProvider rng, 073 ContinuousInverseCumulativeProbabilityFunction function) { 074 super(null); 075 this.rng = rng; 076 this.function = function; 077 } 078 079 /** {@inheritDoc} */ 080 @Override 081 public double sample() { 082 return function.inverseCumulativeProbability(rng.nextDouble()); 083 } 084 085 /** {@inheritDoc} */ 086 @Override 087 public String toString() { 088 return function.toString() + " (inverse method) [" + rng.toString() + "]"; 089 } 090 091 /** 092 * {@inheritDoc} 093 * 094 * <p>Note: The new sampler will share the inverse cumulative probability function. This 095 * must be suitable for concurrent use to ensure thread safety.</p> 096 * 097 * @since 1.3 098 */ 099 @Override 100 public SharedStateContinuousSampler withUniformRandomProvider(UniformRandomProvider rng) { 101 return new InverseTransformContinuousSampler(rng, function); 102 } 103 104 /** 105 * Create a new inverse-transform continuous sampler. 106 * 107 * <p>To use the sampler to 108 * {@link org.apache.commons.rng.sampling.SharedStateSampler share state} the function must be 109 * suitable for concurrent use.</p> 110 * 111 * @param rng Generator of uniformly distributed random numbers. 112 * @param function Inverse cumulative probability function. 113 * @return the sampler 114 * @see #withUniformRandomProvider(UniformRandomProvider) 115 * @since 1.3 116 */ 117 public static SharedStateContinuousSampler of(UniformRandomProvider rng, 118 ContinuousInverseCumulativeProbabilityFunction function) { 119 return new InverseTransformContinuousSampler(rng, function); 120 } 121}