207 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 extreme_value_distribution15 16// template<class _URNG> result_type operator()(_URNG& g);17 18#include <random>19#include <cassert>20#include <cmath>21#include <numeric>22#include <vector>23 24#include "test_macros.h"25 26template <class T>27inline28T29sqr(T x)30{31 return x * x;32}33 34void35test1()36{37 typedef std::extreme_value_distribution<> D;38 typedef std::mt19937 G;39 G g;40 D d(0.5, 2);41 const int N = 1000000;42 std::vector<D::result_type> u;43 for (int i = 0; i < N; ++i)44 {45 D::result_type v = d(g);46 u.push_back(v);47 }48 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();49 double var = 0;50 double skew = 0;51 double kurtosis = 0;52 for (unsigned i = 0; i < u.size(); ++i)53 {54 double dbl = (u[i] - mean);55 double d2 = sqr(dbl);56 var += d2;57 skew += dbl * d2;58 kurtosis += d2 * d2;59 }60 var /= u.size();61 double dev = std::sqrt(var);62 skew /= u.size() * dev * var;63 kurtosis /= u.size() * var * var;64 kurtosis -= 3;65 double x_mean = d.a() + d.b() * 0.577215665;66 double x_var = sqr(d.b()) * 1.644934067;67 double x_skew = 1.139547;68 double x_kurtosis = 12./5;69 assert(std::abs((mean - x_mean) / x_mean) < 0.01);70 assert(std::abs((var - x_var) / x_var) < 0.01);71 assert(std::abs((skew - x_skew) / x_skew) < 0.01);72 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);73}74 75void76test2()77{78 typedef std::extreme_value_distribution<> D;79 typedef std::mt19937 G;80 G g;81 D d(1, 2);82 const int N = 1000000;83 std::vector<D::result_type> u;84 for (int i = 0; i < N; ++i)85 {86 D::result_type v = d(g);87 u.push_back(v);88 }89 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();90 double var = 0;91 double skew = 0;92 double kurtosis = 0;93 for (unsigned i = 0; i < u.size(); ++i)94 {95 double dbl = (u[i] - mean);96 double d2 = sqr(dbl);97 var += d2;98 skew += dbl * d2;99 kurtosis += d2 * d2;100 }101 var /= u.size();102 double dev = std::sqrt(var);103 skew /= u.size() * dev * var;104 kurtosis /= u.size() * var * var;105 kurtosis -= 3;106 double x_mean = d.a() + d.b() * 0.577215665;107 double x_var = sqr(d.b()) * 1.644934067;108 double x_skew = 1.139547;109 double x_kurtosis = 12./5;110 assert(std::abs((mean - x_mean) / x_mean) < 0.01);111 assert(std::abs((var - x_var) / x_var) < 0.01);112 assert(std::abs((skew - x_skew) / x_skew) < 0.01);113 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);114}115 116void117test3()118{119 typedef std::extreme_value_distribution<> D;120 typedef std::mt19937 G;121 G g;122 D d(1.5, 3);123 const int N = 1000000;124 std::vector<D::result_type> u;125 for (int i = 0; i < N; ++i)126 {127 D::result_type v = d(g);128 u.push_back(v);129 }130 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();131 double var = 0;132 double skew = 0;133 double kurtosis = 0;134 for (unsigned i = 0; i < u.size(); ++i)135 {136 double dbl = (u[i] - mean);137 double d2 = sqr(dbl);138 var += d2;139 skew += dbl * d2;140 kurtosis += d2 * d2;141 }142 var /= u.size();143 double dev = std::sqrt(var);144 skew /= u.size() * dev * var;145 kurtosis /= u.size() * var * var;146 kurtosis -= 3;147 double x_mean = d.a() + d.b() * 0.577215665;148 double x_var = sqr(d.b()) * 1.644934067;149 double x_skew = 1.139547;150 double x_kurtosis = 12./5;151 assert(std::abs((mean - x_mean) / x_mean) < 0.01);152 assert(std::abs((var - x_var) / x_var) < 0.01);153 assert(std::abs((skew - x_skew) / x_skew) < 0.01);154 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);155}156 157void158test4()159{160 typedef std::extreme_value_distribution<> D;161 typedef std::mt19937 G;162 G g;163 D d(3, 4);164 const int N = 1000000;165 std::vector<D::result_type> u;166 for (int i = 0; i < N; ++i)167 {168 D::result_type v = d(g);169 u.push_back(v);170 }171 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();172 double var = 0;173 double skew = 0;174 double kurtosis = 0;175 for (unsigned i = 0; i < u.size(); ++i)176 {177 double dbl = (u[i] - mean);178 double d2 = sqr(dbl);179 var += d2;180 skew += dbl * d2;181 kurtosis += d2 * d2;182 }183 var /= u.size();184 double dev = std::sqrt(var);185 skew /= u.size() * dev * var;186 kurtosis /= u.size() * var * var;187 kurtosis -= 3;188 double x_mean = d.a() + d.b() * 0.577215665;189 double x_var = sqr(d.b()) * 1.644934067;190 double x_skew = 1.139547;191 double x_kurtosis = 12./5;192 assert(std::abs((mean - x_mean) / x_mean) < 0.01);193 assert(std::abs((var - x_var) / x_var) < 0.01);194 assert(std::abs((skew - x_skew) / x_skew) < 0.01);195 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);196}197 198int main(int, char**)199{200 test1();201 test2();202 test3();203 test4();204 205 return 0;206}207