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