1 //===----------------------------------------------------------------------===// 2 // 3 // The LLVM Compiler Infrastructure 4 // 5 // This file is dual licensed under the MIT and the University of Illinois Open 6 // Source Licenses. See LICENSE.TXT for details. 7 // 8 //===----------------------------------------------------------------------===// 9 // 10 // REQUIRES: long_tests 11 12 // <random> 13 14 // template<class RealType = double> 15 // class piecewise_constant_distribution 16 17 // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm); 18 19 #include <random> 20 #include <vector> 21 #include <iterator> 22 #include <numeric> 23 #include <cassert> 24 25 template <class T> 26 inline 27 T 28 sqr(T x) 29 { 30 return x*x; 31 } 32 33 int main() 34 { 35 { 36 typedef std::piecewise_constant_distribution<> D; 37 typedef D::param_type P; 38 typedef std::mt19937_64 G; 39 G g; 40 double b[] = {10, 14, 16, 17}; 41 double p[] = {25, 62.5, 12.5}; 42 const size_t Np = sizeof(p) / sizeof(p[0]); 43 D d; 44 P pa(b, b+Np+1, p); 45 const int N = 1000000; 46 std::vector<D::result_type> u; 47 for (int i = 0; i < N; ++i) 48 { 49 D::result_type v = d(g, pa); 50 assert(10 <= v && v < 17); 51 u.push_back(v); 52 } 53 std::vector<double> prob(std::begin(p), std::end(p)); 54 double s = std::accumulate(prob.begin(), prob.end(), 0.0); 55 for (int i = 0; i < prob.size(); ++i) 56 prob[i] /= s; 57 std::sort(u.begin(), u.end()); 58 for (int i = 0; i < Np; ++i) 59 { 60 typedef std::vector<D::result_type>::iterator I; 61 I lb = std::lower_bound(u.begin(), u.end(), b[i]); 62 I ub = std::lower_bound(u.begin(), u.end(), b[i+1]); 63 const size_t Ni = ub - lb; 64 if (prob[i] == 0) 65 assert(Ni == 0); 66 else 67 { 68 assert(std::abs((double)Ni/N - prob[i]) / prob[i] < .01); 69 double mean = std::accumulate(lb, ub, 0.0) / Ni; 70 double var = 0; 71 double skew = 0; 72 double kurtosis = 0; 73 for (I j = lb; j != ub; ++j) 74 { 75 double d = (*j - mean); 76 double d2 = sqr(d); 77 var += d2; 78 skew += d * d2; 79 kurtosis += d2 * d2; 80 } 81 var /= Ni; 82 double dev = std::sqrt(var); 83 skew /= Ni * dev * var; 84 kurtosis /= Ni * var * var; 85 kurtosis -= 3; 86 double x_mean = (b[i+1] + b[i]) / 2; 87 double x_var = sqr(b[i+1] - b[i]) / 12; 88 double x_skew = 0; 89 double x_kurtosis = -6./5; 90 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 91 assert(std::abs((var - x_var) / x_var) < 0.01); 92 assert(std::abs(skew - x_skew) < 0.01); 93 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 94 } 95 } 96 } 97 } 98
