1 //===----------------------------------------------------------------------===//
3 // The LLVM Compiler Infrastructure
5 // This file is dual licensed under the MIT and the University of Illinois Open
6 // Source Licenses. See LICENSE.TXT for details.
8 //===----------------------------------------------------------------------===//
10 // REQUIRES: long_tests
14 // template<class RealType = double>
15 // class chi_squared_distribution
17 // template<class _URNG> result_type operator()(_URNG& g, const param_type& parm);
36 typedef std::chi_squared_distribution<> D;
37 typedef D::param_type P;
38 typedef std::minstd_rand G;
42 const int N = 1000000;
43 std::vector<D::result_type> u;
44 for (int i = 0; i < N; ++i)
46 D::result_type v = d(g, p);
50 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
54 for (std::size_t i = 0; i < u.size(); ++i)
56 double dbl = (u[i] - mean);
63 double dev = std::sqrt(var);
64 skew /= u.size() * dev * var;
65 kurtosis /= u.size() * var * var;
67 double x_mean = p.n();
68 double x_var = 2 * p.n();
69 double x_skew = std::sqrt(8 / p.n());
70 double x_kurtosis = 12 / p.n();
71 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
72 assert(std::abs((var - x_var) / x_var) < 0.01);
73 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
74 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
77 typedef std::chi_squared_distribution<> D;
78 typedef D::param_type P;
79 typedef std::mt19937 G;
83 const int N = 1000000;
84 std::vector<D::result_type> u;
85 for (int i = 0; i < N; ++i)
87 D::result_type v = d(g, p);
91 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
95 for (std::size_t i = 0; i < u.size(); ++i)
97 double dbl = (u[i] - mean);
104 double dev = std::sqrt(var);
105 skew /= u.size() * dev * var;
106 kurtosis /= u.size() * var * var;
108 double x_mean = p.n();
109 double x_var = 2 * p.n();
110 double x_skew = std::sqrt(8 / p.n());
111 double x_kurtosis = 12 / p.n();
112 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
113 assert(std::abs((var - x_var) / x_var) < 0.01);
114 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
115 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);
118 typedef std::chi_squared_distribution<> D;
119 typedef D::param_type P;
120 typedef std::minstd_rand G;
124 const int N = 1000000;
125 std::vector<D::result_type> u;
126 for (int i = 0; i < N; ++i)
128 D::result_type v = d(g, p);
132 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size();
136 for (std::size_t i = 0; i < u.size(); ++i)
138 double dbl = (u[i] - mean);
139 double d2 = sqr(dbl);
145 double dev = std::sqrt(var);
146 skew /= u.size() * dev * var;
147 kurtosis /= u.size() * var * var;
149 double x_mean = p.n();
150 double x_var = 2 * p.n();
151 double x_skew = std::sqrt(8 / p.n());
152 double x_kurtosis = 12 / p.n();
153 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
154 assert(std::abs((var - x_var) / x_var) < 0.01);
155 assert(std::abs((skew - x_skew) / x_skew) < 0.01);
156 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01);