169 lines · cpp
1//===----------------------------------------------------------------------===//2//3// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.4// See https://llvm.org/LICENSE.txt for license information.5// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception6//7//===----------------------------------------------------------------------===//8//9// REQUIRES: long_tests10 11// <random>12 13// template<class RealType = double>14// class weibull_distribution15 16// template<class _URNG> result_type operator()(_URNG& g);17 18#include <random>19#include <cassert>20#include <cmath>21#include <cstddef>22#include <numeric>23#include <vector>24 25#include "test_macros.h"26 27template <class T>28inline29T30sqr(T x)31{32 return x * x;33}34 35int main(int, char**)36{37 {38 typedef std::weibull_distribution<> D;39 typedef std::mt19937 G;40 G g;41 D d(0.5, 2);42 const int N = 1000000;43 std::vector<D::result_type> u;44 for (int i = 0; i < N; ++i)45 {46 D::result_type v = d(g);47 assert(d.min() <= v);48 u.push_back(v);49 }50 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();51 double var = 0;52 double skew = 0;53 double kurtosis = 0;54 for (std::size_t i = 0; i < u.size(); ++i)55 {56 double dbl = (u[i] - mean);57 double d2 = sqr(dbl);58 var += d2;59 skew += dbl * d2;60 kurtosis += d2 * d2;61 }62 var /= u.size();63 double dev = std::sqrt(var);64 skew /= u.size() * dev * var;65 kurtosis /= u.size() * var * var;66 kurtosis -= 3;67 double x_mean = d.b() * std::tgamma(1 + 1/d.a());68 double x_var = sqr(d.b()) * std::tgamma(1 + 2/d.a()) - sqr(x_mean);69 double x_skew = (sqr(d.b())*d.b() * std::tgamma(1 + 3/d.a()) -70 3*x_mean*x_var - sqr(x_mean)*x_mean) /71 (std::sqrt(x_var)*x_var);72 double x_kurtosis = (sqr(sqr(d.b())) * std::tgamma(1 + 4/d.a()) -73 4*x_skew*x_var*sqrt(x_var)*x_mean -74 6*sqr(x_mean)*x_var - sqr(sqr(x_mean))) / sqr(x_var) - 3;75 assert(std::abs((mean - x_mean) / x_mean) < 0.01);76 assert(std::abs((var - x_var) / x_var) < 0.01);77 assert(std::abs((skew - x_skew) / x_skew) < 0.01);78 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);79 }80 {81 typedef std::weibull_distribution<> D;82 typedef std::mt19937 G;83 G g;84 D d(1, .5);85 const int N = 1000000;86 std::vector<D::result_type> u;87 for (int i = 0; i < N; ++i)88 {89 D::result_type v = d(g);90 assert(d.min() <= v);91 u.push_back(v);92 }93 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();94 double var = 0;95 double skew = 0;96 double kurtosis = 0;97 for (std::size_t i = 0; i < u.size(); ++i)98 {99 double dbl = (u[i] - mean);100 double d2 = sqr(dbl);101 var += d2;102 skew += dbl * d2;103 kurtosis += d2 * d2;104 }105 var /= u.size();106 double dev = std::sqrt(var);107 skew /= u.size() * dev * var;108 kurtosis /= u.size() * var * var;109 kurtosis -= 3;110 double x_mean = d.b() * std::tgamma(1 + 1/d.a());111 double x_var = sqr(d.b()) * std::tgamma(1 + 2/d.a()) - sqr(x_mean);112 double x_skew = (sqr(d.b())*d.b() * std::tgamma(1 + 3/d.a()) -113 3*x_mean*x_var - sqr(x_mean)*x_mean) /114 (std::sqrt(x_var)*x_var);115 double x_kurtosis = (sqr(sqr(d.b())) * std::tgamma(1 + 4/d.a()) -116 4*x_skew*x_var*sqrt(x_var)*x_mean -117 6*sqr(x_mean)*x_var - sqr(sqr(x_mean))) / sqr(x_var) - 3;118 assert(std::abs((mean - x_mean) / x_mean) < 0.01);119 assert(std::abs((var - x_var) / x_var) < 0.01);120 assert(std::abs((skew - x_skew) / x_skew) < 0.01);121 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);122 }123 {124 typedef std::weibull_distribution<> D;125 typedef std::mt19937 G;126 G g;127 D d(2, 3);128 const int N = 1000000;129 std::vector<D::result_type> u;130 for (int i = 0; i < N; ++i)131 {132 D::result_type v = d(g);133 assert(d.min() <= v);134 u.push_back(v);135 }136 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();137 double var = 0;138 double skew = 0;139 double kurtosis = 0;140 for (std::size_t i = 0; i < u.size(); ++i)141 {142 double dbl = (u[i] - mean);143 double d2 = sqr(dbl);144 var += d2;145 skew += dbl * d2;146 kurtosis += d2 * d2;147 }148 var /= u.size();149 double dev = std::sqrt(var);150 skew /= u.size() * dev * var;151 kurtosis /= u.size() * var * var;152 kurtosis -= 3;153 double x_mean = d.b() * std::tgamma(1 + 1/d.a());154 double x_var = sqr(d.b()) * std::tgamma(1 + 2/d.a()) - sqr(x_mean);155 double x_skew = (sqr(d.b())*d.b() * std::tgamma(1 + 3/d.a()) -156 3*x_mean*x_var - sqr(x_mean)*x_mean) /157 (std::sqrt(x_var)*x_var);158 double x_kurtosis = (sqr(sqr(d.b())) * std::tgamma(1 + 4/d.a()) -159 4*x_skew*x_var*sqrt(x_var)*x_mean -160 6*sqr(x_mean)*x_var - sqr(sqr(x_mean))) / sqr(x_var) - 3;161 assert(std::abs((mean - x_mean) / x_mean) < 0.01);162 assert(std::abs((var - x_var) / x_var) < 0.01);163 assert(std::abs((skew - x_skew) / x_skew) < 0.01);164 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);165 }166 167 return 0;168}169