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 IntType = int> 15 // class geometric_distribution 16 17 // template<class _URNG> result_type operator()(_URNG& g); 18 19 #include <random> 20 #include <numeric> 21 #include <vector> 22 #include <cassert> 23 24 template <class T> 25 inline 26 T 27 sqr(T x) 28 { 29 return x * x; 30 } 31 32 void 33 test1() 34 { 35 typedef std::geometric_distribution<> D; 36 typedef std::mt19937 G; 37 G g; 38 D d(.03125); 39 const int N = 1000000; 40 std::vector<D::result_type> u; 41 for (int i = 0; i < N; ++i) 42 { 43 D::result_type v = d(g); 44 assert(d.min() <= v && v <= d.max()); 45 u.push_back(v); 46 } 47 double mean = std::accumulate(u.begin(), u.end(), 48 double(0)) / u.size(); 49 double var = 0; 50 double skew = 0; 51 double kurtosis = 0; 52 for (unsigned i = 0; i < u.size(); ++i) 53 { 54 double dbl = (u[i] - mean); 55 double d2 = sqr(dbl); 56 var += d2; 57 skew += dbl * d2; 58 kurtosis += d2 * d2; 59 } 60 var /= u.size(); 61 double dev = std::sqrt(var); 62 skew /= u.size() * dev * var; 63 kurtosis /= u.size() * var * var; 64 kurtosis -= 3; 65 double x_mean = (1 - d.p()) / d.p(); 66 double x_var = x_mean / d.p(); 67 double x_skew = (2 - d.p()) / std::sqrt((1 - d.p())); 68 double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p()); 69 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 70 assert(std::abs((var - x_var) / x_var) < 0.01); 71 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 72 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 73 } 74 75 void 76 test2() 77 { 78 typedef std::geometric_distribution<> D; 79 typedef std::mt19937 G; 80 G g; 81 D d(0.05); 82 const int N = 1000000; 83 std::vector<D::result_type> u; 84 for (int i = 0; i < N; ++i) 85 { 86 D::result_type v = d(g); 87 assert(d.min() <= v && v <= d.max()); 88 u.push_back(v); 89 } 90 double mean = std::accumulate(u.begin(), u.end(), 91 double(0)) / u.size(); 92 double var = 0; 93 double skew = 0; 94 double kurtosis = 0; 95 for (unsigned i = 0; i < u.size(); ++i) 96 { 97 double dbl = (u[i] - mean); 98 double d2 = sqr(dbl); 99 var += d2; 100 skew += dbl * d2; 101 kurtosis += d2 * d2; 102 } 103 var /= u.size(); 104 double dev = std::sqrt(var); 105 skew /= u.size() * dev * var; 106 kurtosis /= u.size() * var * var; 107 kurtosis -= 3; 108 double x_mean = (1 - d.p()) / d.p(); 109 double x_var = x_mean / d.p(); 110 double x_skew = (2 - d.p()) / std::sqrt((1 - d.p())); 111 double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p()); 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) < 0.03); 116 } 117 118 void 119 test3() 120 { 121 typedef std::geometric_distribution<> D; 122 typedef std::minstd_rand G; 123 G g; 124 D d(.25); 125 const int N = 1000000; 126 std::vector<D::result_type> u; 127 for (int i = 0; i < N; ++i) 128 { 129 D::result_type v = d(g); 130 assert(d.min() <= v && v <= d.max()); 131 u.push_back(v); 132 } 133 double mean = std::accumulate(u.begin(), u.end(), 134 double(0)) / u.size(); 135 double var = 0; 136 double skew = 0; 137 double kurtosis = 0; 138 for (unsigned i = 0; i < u.size(); ++i) 139 { 140 double dbl = (u[i] - mean); 141 double d2 = sqr(dbl); 142 var += d2; 143 skew += dbl * d2; 144 kurtosis += d2 * d2; 145 } 146 var /= u.size(); 147 double dev = std::sqrt(var); 148 skew /= u.size() * dev * var; 149 kurtosis /= u.size() * var * var; 150 kurtosis -= 3; 151 double x_mean = (1 - d.p()) / d.p(); 152 double x_var = x_mean / d.p(); 153 double x_skew = (2 - d.p()) / std::sqrt((1 - d.p())); 154 double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p()); 155 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 156 assert(std::abs((var - x_var) / x_var) < 0.01); 157 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 158 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02); 159 } 160 161 void 162 test4() 163 { 164 typedef std::geometric_distribution<> D; 165 typedef std::mt19937 G; 166 G g; 167 D d(0.5); 168 const int N = 1000000; 169 std::vector<D::result_type> u; 170 for (int i = 0; i < N; ++i) 171 { 172 D::result_type v = d(g); 173 assert(d.min() <= v && v <= d.max()); 174 u.push_back(v); 175 } 176 double mean = std::accumulate(u.begin(), u.end(), 177 double(0)) / u.size(); 178 double var = 0; 179 double skew = 0; 180 double kurtosis = 0; 181 for (unsigned i = 0; i < u.size(); ++i) 182 { 183 double dbl = (u[i] - mean); 184 double d2 = sqr(dbl); 185 var += d2; 186 skew += dbl * d2; 187 kurtosis += d2 * d2; 188 } 189 var /= u.size(); 190 double dev = std::sqrt(var); 191 skew /= u.size() * dev * var; 192 kurtosis /= u.size() * var * var; 193 kurtosis -= 3; 194 double x_mean = (1 - d.p()) / d.p(); 195 double x_var = x_mean / d.p(); 196 double x_skew = (2 - d.p()) / std::sqrt((1 - d.p())); 197 double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p()); 198 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 199 assert(std::abs((var - x_var) / x_var) < 0.01); 200 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 201 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02); 202 } 203 204 void 205 test5() 206 { 207 typedef std::geometric_distribution<> D; 208 typedef std::mt19937 G; 209 G g; 210 D d(0.75); 211 const int N = 1000000; 212 std::vector<D::result_type> u; 213 for (int i = 0; i < N; ++i) 214 { 215 D::result_type v = d(g); 216 assert(d.min() <= v && v <= d.max()); 217 u.push_back(v); 218 } 219 double mean = std::accumulate(u.begin(), u.end(), 220 double(0)) / u.size(); 221 double var = 0; 222 double skew = 0; 223 double kurtosis = 0; 224 for (unsigned i = 0; i < u.size(); ++i) 225 { 226 double dbl = (u[i] - mean); 227 double d2 = sqr(dbl); 228 var += d2; 229 skew += dbl * d2; 230 kurtosis += d2 * d2; 231 } 232 var /= u.size(); 233 double dev = std::sqrt(var); 234 skew /= u.size() * dev * var; 235 kurtosis /= u.size() * var * var; 236 kurtosis -= 3; 237 double x_mean = (1 - d.p()) / d.p(); 238 double x_var = x_mean / d.p(); 239 double x_skew = (2 - d.p()) / std::sqrt((1 - d.p())); 240 double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p()); 241 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 242 assert(std::abs((var - x_var) / x_var) < 0.01); 243 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 244 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02); 245 } 246 247 void 248 test6() 249 { 250 typedef std::geometric_distribution<> D; 251 typedef std::mt19937 G; 252 G g; 253 D d(0.96875); 254 const int N = 1000000; 255 std::vector<D::result_type> u; 256 for (int i = 0; i < N; ++i) 257 { 258 D::result_type v = d(g); 259 assert(d.min() <= v && v <= d.max()); 260 u.push_back(v); 261 } 262 double mean = std::accumulate(u.begin(), u.end(), 263 double(0)) / u.size(); 264 double var = 0; 265 double skew = 0; 266 double kurtosis = 0; 267 for (unsigned i = 0; i < u.size(); ++i) 268 { 269 double dbl = (u[i] - mean); 270 double d2 = sqr(dbl); 271 var += d2; 272 skew += dbl * d2; 273 kurtosis += d2 * d2; 274 } 275 var /= u.size(); 276 double dev = std::sqrt(var); 277 skew /= u.size() * dev * var; 278 kurtosis /= u.size() * var * var; 279 kurtosis -= 3; 280 double x_mean = (1 - d.p()) / d.p(); 281 double x_var = x_mean / d.p(); 282 double x_skew = (2 - d.p()) / std::sqrt((1 - d.p())); 283 double x_kurtosis = 6 + sqr(d.p()) / (1 - d.p()); 284 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 285 assert(std::abs((var - x_var) / x_var) < 0.01); 286 assert(std::abs((skew - x_skew) / x_skew) < 0.01); 287 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.02); 288 } 289 290 int main() 291 { 292 test1(); 293 test2(); 294 test3(); 295 test4(); 296 test5(); 297 test6(); 298 } 299
