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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// template<class _IntType = int>12// class uniform_int_distribution13 14// template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);15 16#include <random>17#include <cassert>18#include <cmath>19#include <cstddef>20#include <numeric>21#include <vector>22 23#include "test_macros.h"24 25template <class T>26inline27T28sqr(T x)29{30    return x * x;31}32 33int main(int, char**)34{35    {36        typedef std::uniform_int_distribution<> D;37        typedef std::minstd_rand G;38        typedef D::param_type P;39        G g;40        D d(5, 100);41        P p(-10, 20);42        const int N = 100000;43        std::vector<D::result_type> u;44        for (int i = 0; i < N; ++i)45        {46            D::result_type v = d(g, p);47            assert(p.a() <= v && v <= p.b());48            u.push_back(v);49        }50        double mean = std::accumulate(u.begin(), u.end(),51                                              double(0)) / u.size();52        double var = 0;53        double skew = 0;54        double kurtosis = 0;55        for (std::size_t i = 0; i < u.size(); ++i)56        {57            double dbl = (u[i] - mean);58            double d2 = sqr(dbl);59            var += d2;60            skew += dbl * d2;61            kurtosis += d2 * d2;62        }63        var /= u.size();64        double dev = std::sqrt(var);65        skew /= u.size() * dev * var;66        kurtosis /= u.size() * var * var;67        kurtosis -= 3;68        double x_mean = ((double)p.a() + p.b()) / 2;69        double x_var = (sqr((double)p.b() - p.a() + 1) - 1) / 12;70        double x_skew = 0;71        double x_kurtosis = -6. * (sqr((double)p.b() - p.a() + 1) + 1) /72                            (5. * (sqr((double)p.b() - p.a() + 1) - 1));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) < 0.01);76        assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);77    }78 79  return 0;80}81