| 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-exception |
| 6 | // |
| 7 | //===----------------------------------------------------------------------===// |
| 8 | // |
| 9 | // REQUIRES: long_tests |
| 10 | |
| 11 | // <random> |
| 12 | |
| 13 | // template<class RealType = double> |
| 14 | // class piecewise_constant_distribution |
| 15 | |
| 16 | // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm); |
| 17 | |
| 18 | #include <random> |
| 19 | #include <algorithm> |
| 20 | #include <cassert> |
| 21 | #include <cmath> |
| 22 | #include <cstddef> |
| 23 | #include <iterator> |
| 24 | #include <numeric> |
| 25 | #include <vector> |
| 26 | |
| 27 | #include "test_macros.h" |
| 28 | |
| 29 | template <class T> |
| 30 | inline |
| 31 | T |
| 32 | sqr(T x) |
| 33 | { |
| 34 | return x*x; |
| 35 | } |
| 36 | |
| 37 | int main(int, char**) |
| 38 | { |
| 39 | { |
| 40 | typedef std::piecewise_constant_distribution<> D; |
| 41 | typedef D::param_type P; |
| 42 | typedef std::mt19937_64 G; |
| 43 | G g; |
| 44 | double b[] = {10, 14, 16, 17}; |
| 45 | double p[] = {25, 62.5, 12.5}; |
| 46 | const std::size_t Np = sizeof(p) / sizeof(p[0]); |
| 47 | D d; |
| 48 | P pa(b, b+Np+1, p); |
| 49 | const int N = 1000000; |
| 50 | std::vector<D::result_type> u; |
| 51 | for (int i = 0; i < N; ++i) |
| 52 | { |
| 53 | D::result_type v = d(g, pa); |
| 54 | assert(10 <= v && v < 17); |
| 55 | u.push_back(v); |
| 56 | } |
| 57 | std::vector<double> prob(std::begin(p), std::end(p)); |
| 58 | double s = std::accumulate(prob.begin(), prob.end(), 0.0); |
| 59 | for (std::size_t i = 0; i < prob.size(); ++i) |
| 60 | prob[i] /= s; |
| 61 | std::sort(u.begin(), u.end()); |
| 62 | for (std::size_t i = 0; i < Np; ++i) |
| 63 | { |
| 64 | typedef std::vector<D::result_type>::iterator I; |
| 65 | I lb = std::lower_bound(u.begin(), u.end(), b[i]); |
| 66 | I ub = std::lower_bound(u.begin(), u.end(), b[i+1]); |
| 67 | const std::size_t Ni = ub - lb; |
| 68 | if (prob[i] == 0) |
| 69 | assert(Ni == 0); |
| 70 | else |
| 71 | { |
| 72 | assert(std::abs((double)Ni/N - prob[i]) / prob[i] < .01); |
| 73 | double mean = std::accumulate(lb, ub, 0.0) / Ni; |
| 74 | double var = 0; |
| 75 | double skew = 0; |
| 76 | double kurtosis = 0; |
| 77 | for (I j = lb; j != ub; ++j) |
| 78 | { |
| 79 | double dbl = (*j - mean); |
| 80 | double d2 = sqr(dbl); |
| 81 | var += d2; |
| 82 | skew += dbl * d2; |
| 83 | kurtosis += d2 * d2; |
| 84 | } |
| 85 | var /= Ni; |
| 86 | double dev = std::sqrt(x: var); |
| 87 | skew /= Ni * dev * var; |
| 88 | kurtosis /= Ni * var * var; |
| 89 | kurtosis -= 3; |
| 90 | double x_mean = (b[i+1] + b[i]) / 2; |
| 91 | double x_var = sqr(b[i+1] - b[i]) / 12; |
| 92 | double x_skew = 0; |
| 93 | double x_kurtosis = -6./5; |
| 94 | assert(std::abs((mean - x_mean) / x_mean) < 0.01); |
| 95 | assert(std::abs((var - x_var) / x_var) < 0.01); |
| 96 | assert(std::abs(skew - x_skew) < 0.01); |
| 97 | assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); |
| 98 | } |
| 99 | } |
| 100 | } |
| 101 | |
| 102 | return 0; |
| 103 | } |
| 104 | |