165 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 IntType = int>14// class negative_binomial_distribution15 16// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);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 34int main(int, char**)35{36 {37 typedef std::negative_binomial_distribution<> D;38 typedef D::param_type P;39 typedef std::minstd_rand G;40 G g;41 D d(16, .75);42 P p(5, .75);43 const int N = 1000000;44 std::vector<D::result_type> u;45 for (int i = 0; i < N; ++i)46 {47 D::result_type v = d(g, p);48 assert(d.min() <= v && v <= d.max());49 u.push_back(v);50 }51 double mean = std::accumulate(u.begin(), u.end(),52 double(0)) / u.size();53 double var = 0;54 double skew = 0;55 double kurtosis = 0;56 for (unsigned i = 0; i < u.size(); ++i)57 {58 double dbl = (u[i] - mean);59 double d2 = sqr(dbl);60 var += d2;61 skew += dbl * d2;62 kurtosis += d2 * d2;63 }64 var /= u.size();65 double dev = std::sqrt(var);66 skew /= u.size() * dev * var;67 kurtosis /= u.size() * var * var;68 kurtosis -= 3;69 double x_mean = p.k() * (1 - p.p()) / p.p();70 double x_var = x_mean / p.p();71 double x_skew = (2 - p.p()) / std::sqrt(p.k() * (1 - p.p()));72 double x_kurtosis = 6. / p.k() + sqr(p.p()) / (p.k() * (1 - p.p()));73 assert(std::abs((mean - x_mean) / x_mean) < 0.01);74 assert(std::abs((var - x_var) / x_var) < 0.01);75 assert(std::abs((skew - x_skew) / x_skew) < 0.01);76 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.03);77 }78 {79 typedef std::negative_binomial_distribution<> D;80 typedef D::param_type P;81 typedef std::mt19937 G;82 G g;83 D d(16, .75);84 P p(30, .03125);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, p);90 assert(d.min() <= v && v <= d.max());91 u.push_back(v);92 }93 double mean = std::accumulate(u.begin(), u.end(),94 double(0)) / u.size();95 double var = 0;96 double skew = 0;97 double kurtosis = 0;98 for (unsigned i = 0; i < u.size(); ++i)99 {100 double dbl = (u[i] - mean);101 double d2 = sqr(dbl);102 var += d2;103 skew += dbl * d2;104 kurtosis += d2 * d2;105 }106 var /= u.size();107 double dev = std::sqrt(var);108 skew /= u.size() * dev * var;109 kurtosis /= u.size() * var * var;110 kurtosis -= 3;111 double x_mean = p.k() * (1 - p.p()) / p.p();112 double x_var = x_mean / p.p();113 double x_skew = (2 - p.p()) / std::sqrt(p.k() * (1 - p.p()));114 double x_kurtosis = 6. / p.k() + sqr(p.p()) / (p.k() * (1 - p.p()));115 assert(std::abs((mean - x_mean) / x_mean) < 0.01);116 assert(std::abs((var - x_var) / x_var) < 0.01);117 assert(std::abs((skew - x_skew) / x_skew) < 0.02);118 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.1);119 }120 {121 typedef std::negative_binomial_distribution<> D;122 typedef D::param_type P;123 typedef std::mt19937 G;124 G g;125 D d(16, .75);126 P p(40, .25);127 const int N = 1000000;128 std::vector<D::result_type> u;129 for (int i = 0; i < N; ++i)130 {131 D::result_type v = d(g, p);132 assert(d.min() <= v && v <= d.max());133 u.push_back(v);134 }135 double mean = std::accumulate(u.begin(), u.end(),136 double(0)) / u.size();137 double var = 0;138 double skew = 0;139 double kurtosis = 0;140 for (unsigned 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 = p.k() * (1 - p.p()) / p.p();154 double x_var = x_mean / p.p();155 double x_skew = (2 - p.p()) / std::sqrt(p.k() * (1 - p.p()));156 double x_kurtosis = 6. / p.k() + sqr(p.p()) / (p.k() * (1 - p.p()));157 assert(std::abs((mean - x_mean) / x_mean) < 0.01);158 assert(std::abs((var - x_var) / x_var) < 0.01);159 assert(std::abs((skew - x_skew) / x_skew) < 0.02);160 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.08);161 }162 163 return 0;164}165