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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// REQUIRES: long_tests10 11// <random>12 13// template<class RealType = double>14// class exponential_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 <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::exponential_distribution<> D;39        typedef D::param_type P;40        typedef std::mt19937 G;41        G g;42        D d(.75);43        P p(2);44        const int N = 1000000;45        std::vector<D::result_type> u;46        for (int i = 0; i < N; ++i)47        {48            D::result_type v = d(g, p);49            assert(d.min() < v);50            u.push_back(v);51        }52        double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();53        double var = 0;54        double skew = 0;55        double kurtosis = 0;56        for (std::size_t 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 = 1/p.lambda();70        double x_var = 1/sqr(p.lambda());71        double x_skew = 2;72        double x_kurtosis = 6;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  return 0;80}81