109 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// <random>10 11// class bernoulli_distribution12 13// template<class _URNG> result_type operator()(_URNG& g);14 15#include <random>16#include <cassert>17#include <cmath>18#include <cstddef>19#include <numeric>20#include <vector>21 22#include "test_macros.h"23 24template <class T>25inline26T27sqr(T x)28{29 return x * x;30}31 32int main(int, char**)33{34 {35 typedef std::bernoulli_distribution D;36 typedef std::minstd_rand G;37 G g;38 D d(.75);39 const int N = 100000;40 std::vector<D::result_type> u;41 for (int i = 0; i < N; ++i)42 u.push_back(d(g));43 double mean = std::accumulate(u.begin(), u.end(),44 double(0)) / u.size();45 double var = 0;46 double skew = 0;47 double kurtosis = 0;48 for (std::size_t i = 0; i < u.size(); ++i)49 {50 double dbl = (u[i] - mean);51 double d2 = sqr(dbl);52 var += d2;53 skew += dbl * d2;54 kurtosis += d2 * d2;55 }56 var /= u.size();57 double dev = std::sqrt(var);58 skew /= u.size() * dev * var;59 kurtosis /= u.size() * var * var;60 kurtosis -= 3;61 double x_mean = d.p();62 double x_var = d.p()*(1-d.p());63 double x_skew = (1 - 2 * d.p())/std::sqrt(x_var);64 double x_kurtosis = (6 * sqr(d.p()) - 6 * d.p() + 1)/x_var;65 assert(std::abs((mean - x_mean) / x_mean) < 0.01);66 assert(std::abs((var - x_var) / x_var) < 0.01);67 assert(std::abs((skew - x_skew) / x_skew) < 0.02);68 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.05);69 }70 {71 typedef std::bernoulli_distribution D;72 typedef std::minstd_rand G;73 G g;74 D d(.25);75 const int N = 100000;76 std::vector<D::result_type> u;77 for (int i = 0; i < N; ++i)78 u.push_back(d(g));79 double mean = std::accumulate(u.begin(), u.end(),80 double(0)) / u.size();81 double var = 0;82 double skew = 0;83 double kurtosis = 0;84 for (std::size_t i = 0; i < u.size(); ++i)85 {86 double dbl = (u[i] - mean);87 double d2 = sqr(dbl);88 var += d2;89 skew += dbl * d2;90 kurtosis += d2 * d2;91 }92 var /= u.size();93 double dev = std::sqrt(var);94 skew /= u.size() * dev * var;95 kurtosis /= u.size() * var * var;96 kurtosis -= 3;97 double x_mean = d.p();98 double x_var = d.p()*(1-d.p());99 double x_skew = (1 - 2 * d.p())/std::sqrt(x_var);100 double x_kurtosis = (6 * sqr(d.p()) - 6 * d.p() + 1)/x_var;101 assert(std::abs((mean - x_mean) / x_mean) < 0.01);102 assert(std::abs((var - x_var) / x_var) < 0.01);103 assert(std::abs((skew - x_skew) / x_skew) < 0.02);104 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.05);105 }106 107 return 0;108}109