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 uniform_real_distribution 16 17 // template<class _URNG> result_type operator()(_URNG& g); 18 19 #include <random> 20 #include <cassert> 21 #include <vector> 22 #include <numeric> 23 #include <cstddef> 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::uniform_real_distribution<> D; 37 typedef std::minstd_rand0 G; 38 G g; 39 D d; 40 const int N = 100000; 41 std::vector<D::result_type> u; 42 for (int i = 0; i < N; ++i) 43 { 44 D::result_type v = d(g); 45 assert(d.a() <= v && v < d.b()); 46 u.push_back(v); 47 } 48 D::result_type mean = std::accumulate(u.begin(), u.end(), 49 D::result_type(0)) / u.size(); 50 D::result_type var = 0; 51 D::result_type skew = 0; 52 D::result_type kurtosis = 0; 53 for (std::size_t i = 0; i < u.size(); ++i) 54 { 55 D::result_type dbl = (u[i] - mean); 56 D::result_type d2 = sqr(dbl); 57 var += d2; 58 skew += dbl * d2; 59 kurtosis += d2 * d2; 60 } 61 var /= u.size(); 62 D::result_type dev = std::sqrt(var); 63 skew /= u.size() * dev * var; 64 kurtosis /= u.size() * var * var; 65 kurtosis -= 3; 66 D::result_type x_mean = (d.a() + d.b()) / 2; 67 D::result_type x_var = sqr(d.b() - d.a()) / 12; 68 D::result_type x_skew = 0; 69 D::result_type x_kurtosis = -6./5; 70 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 71 assert(std::abs((var - x_var) / x_var) < 0.01); 72 assert(std::abs(skew - x_skew) < 0.01); 73 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 74 } 75 { 76 typedef std::uniform_real_distribution<> D; 77 typedef std::minstd_rand G; 78 G g; 79 D d; 80 const int N = 100000; 81 std::vector<D::result_type> u; 82 for (int i = 0; i < N; ++i) 83 { 84 D::result_type v = d(g); 85 assert(d.a() <= v && v < d.b()); 86 u.push_back(v); 87 } 88 D::result_type mean = std::accumulate(u.begin(), u.end(), 89 D::result_type(0)) / u.size(); 90 D::result_type var = 0; 91 D::result_type skew = 0; 92 D::result_type kurtosis = 0; 93 for (std::size_t i = 0; i < u.size(); ++i) 94 { 95 D::result_type dbl = (u[i] - mean); 96 D::result_type d2 = sqr(dbl); 97 var += d2; 98 skew += dbl * d2; 99 kurtosis += d2 * d2; 100 } 101 var /= u.size(); 102 D::result_type dev = std::sqrt(var); 103 skew /= u.size() * dev * var; 104 kurtosis /= u.size() * var * var; 105 kurtosis -= 3; 106 D::result_type x_mean = (d.a() + d.b()) / 2; 107 D::result_type x_var = sqr(d.b() - d.a()) / 12; 108 D::result_type x_skew = 0; 109 D::result_type x_kurtosis = -6./5; 110 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 111 assert(std::abs((var - x_var) / x_var) < 0.01); 112 assert(std::abs(skew - x_skew) < 0.01); 113 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 114 } 115 { 116 typedef std::uniform_real_distribution<> D; 117 typedef std::mt19937 G; 118 G g; 119 D d; 120 const int N = 100000; 121 std::vector<D::result_type> u; 122 for (int i = 0; i < N; ++i) 123 { 124 D::result_type v = d(g); 125 assert(d.a() <= v && v < d.b()); 126 u.push_back(v); 127 } 128 D::result_type mean = std::accumulate(u.begin(), u.end(), 129 D::result_type(0)) / u.size(); 130 D::result_type var = 0; 131 D::result_type skew = 0; 132 D::result_type kurtosis = 0; 133 for (std::size_t i = 0; i < u.size(); ++i) 134 { 135 D::result_type dbl = (u[i] - mean); 136 D::result_type d2 = sqr(dbl); 137 var += d2; 138 skew += dbl * d2; 139 kurtosis += d2 * d2; 140 } 141 var /= u.size(); 142 D::result_type dev = std::sqrt(var); 143 skew /= u.size() * dev * var; 144 kurtosis /= u.size() * var * var; 145 kurtosis -= 3; 146 D::result_type x_mean = (d.a() + d.b()) / 2; 147 D::result_type x_var = sqr(d.b() - d.a()) / 12; 148 D::result_type x_skew = 0; 149 D::result_type x_kurtosis = -6./5; 150 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 151 assert(std::abs((var - x_var) / x_var) < 0.01); 152 assert(std::abs(skew - x_skew) < 0.01); 153 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 154 } 155 { 156 typedef std::uniform_real_distribution<> D; 157 typedef std::mt19937_64 G; 158 G g; 159 D d; 160 const int N = 100000; 161 std::vector<D::result_type> u; 162 for (int i = 0; i < N; ++i) 163 { 164 D::result_type v = d(g); 165 assert(d.a() <= v && v < d.b()); 166 u.push_back(v); 167 } 168 D::result_type mean = std::accumulate(u.begin(), u.end(), 169 D::result_type(0)) / u.size(); 170 D::result_type var = 0; 171 D::result_type skew = 0; 172 D::result_type kurtosis = 0; 173 for (std::size_t i = 0; i < u.size(); ++i) 174 { 175 D::result_type dbl = (u[i] - mean); 176 D::result_type d2 = sqr(dbl); 177 var += d2; 178 skew += dbl * d2; 179 kurtosis += d2 * d2; 180 } 181 var /= u.size(); 182 D::result_type dev = std::sqrt(var); 183 skew /= u.size() * dev * var; 184 kurtosis /= u.size() * var * var; 185 kurtosis -= 3; 186 D::result_type x_mean = (d.a() + d.b()) / 2; 187 D::result_type x_var = sqr(d.b() - d.a()) / 12; 188 D::result_type x_skew = 0; 189 D::result_type x_kurtosis = -6./5; 190 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 191 assert(std::abs((var - x_var) / x_var) < 0.01); 192 assert(std::abs(skew - x_skew) < 0.01); 193 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 194 } 195 { 196 typedef std::uniform_real_distribution<> D; 197 typedef std::ranlux24_base G; 198 G g; 199 D d; 200 const int N = 100000; 201 std::vector<D::result_type> u; 202 for (int i = 0; i < N; ++i) 203 { 204 D::result_type v = d(g); 205 assert(d.a() <= v && v < d.b()); 206 u.push_back(v); 207 } 208 D::result_type mean = std::accumulate(u.begin(), u.end(), 209 D::result_type(0)) / u.size(); 210 D::result_type var = 0; 211 D::result_type skew = 0; 212 D::result_type kurtosis = 0; 213 for (std::size_t i = 0; i < u.size(); ++i) 214 { 215 D::result_type dbl = (u[i] - mean); 216 D::result_type d2 = sqr(dbl); 217 var += d2; 218 skew += dbl * d2; 219 kurtosis += d2 * d2; 220 } 221 var /= u.size(); 222 D::result_type dev = std::sqrt(var); 223 skew /= u.size() * dev * var; 224 kurtosis /= u.size() * var * var; 225 kurtosis -= 3; 226 D::result_type x_mean = (d.a() + d.b()) / 2; 227 D::result_type x_var = sqr(d.b() - d.a()) / 12; 228 D::result_type x_skew = 0; 229 D::result_type x_kurtosis = -6./5; 230 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 231 assert(std::abs((var - x_var) / x_var) < 0.01); 232 assert(std::abs(skew - x_skew) < 0.02); 233 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 234 } 235 { 236 typedef std::uniform_real_distribution<> D; 237 typedef std::ranlux48_base G; 238 G g; 239 D d; 240 const int N = 100000; 241 std::vector<D::result_type> u; 242 for (int i = 0; i < N; ++i) 243 { 244 D::result_type v = d(g); 245 assert(d.a() <= v && v < d.b()); 246 u.push_back(v); 247 } 248 D::result_type mean = std::accumulate(u.begin(), u.end(), 249 D::result_type(0)) / u.size(); 250 D::result_type var = 0; 251 D::result_type skew = 0; 252 D::result_type kurtosis = 0; 253 for (std::size_t i = 0; i < u.size(); ++i) 254 { 255 D::result_type dbl = (u[i] - mean); 256 D::result_type d2 = sqr(dbl); 257 var += d2; 258 skew += dbl * d2; 259 kurtosis += d2 * d2; 260 } 261 var /= u.size(); 262 D::result_type dev = std::sqrt(var); 263 skew /= u.size() * dev * var; 264 kurtosis /= u.size() * var * var; 265 kurtosis -= 3; 266 D::result_type x_mean = (d.a() + d.b()) / 2; 267 D::result_type x_var = sqr(d.b() - d.a()) / 12; 268 D::result_type x_skew = 0; 269 D::result_type x_kurtosis = -6./5; 270 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 271 assert(std::abs((var - x_var) / x_var) < 0.01); 272 assert(std::abs(skew - x_skew) < 0.01); 273 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 274 } 275 { 276 typedef std::uniform_real_distribution<> D; 277 typedef std::ranlux24 G; 278 G g; 279 D d; 280 const int N = 100000; 281 std::vector<D::result_type> u; 282 for (int i = 0; i < N; ++i) 283 { 284 D::result_type v = d(g); 285 assert(d.a() <= v && v < d.b()); 286 u.push_back(v); 287 } 288 D::result_type mean = std::accumulate(u.begin(), u.end(), 289 D::result_type(0)) / u.size(); 290 D::result_type var = 0; 291 D::result_type skew = 0; 292 D::result_type kurtosis = 0; 293 for (std::size_t i = 0; i < u.size(); ++i) 294 { 295 D::result_type dbl = (u[i] - mean); 296 D::result_type d2 = sqr(dbl); 297 var += d2; 298 skew += dbl * d2; 299 kurtosis += d2 * d2; 300 } 301 var /= u.size(); 302 D::result_type dev = std::sqrt(var); 303 skew /= u.size() * dev * var; 304 kurtosis /= u.size() * var * var; 305 kurtosis -= 3; 306 D::result_type x_mean = (d.a() + d.b()) / 2; 307 D::result_type x_var = sqr(d.b() - d.a()) / 12; 308 D::result_type x_skew = 0; 309 D::result_type x_kurtosis = -6./5; 310 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 311 assert(std::abs((var - x_var) / x_var) < 0.01); 312 assert(std::abs(skew - x_skew) < 0.01); 313 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 314 } 315 { 316 typedef std::uniform_real_distribution<> D; 317 typedef std::ranlux48 G; 318 G g; 319 D d; 320 const int N = 100000; 321 std::vector<D::result_type> u; 322 for (int i = 0; i < N; ++i) 323 { 324 D::result_type v = d(g); 325 assert(d.a() <= v && v < d.b()); 326 u.push_back(v); 327 } 328 D::result_type mean = std::accumulate(u.begin(), u.end(), 329 D::result_type(0)) / u.size(); 330 D::result_type var = 0; 331 D::result_type skew = 0; 332 D::result_type kurtosis = 0; 333 for (std::size_t i = 0; i < u.size(); ++i) 334 { 335 D::result_type dbl = (u[i] - mean); 336 D::result_type d2 = sqr(dbl); 337 var += d2; 338 skew += dbl * d2; 339 kurtosis += d2 * d2; 340 } 341 var /= u.size(); 342 D::result_type dev = std::sqrt(var); 343 skew /= u.size() * dev * var; 344 kurtosis /= u.size() * var * var; 345 kurtosis -= 3; 346 D::result_type x_mean = (d.a() + d.b()) / 2; 347 D::result_type x_var = sqr(d.b() - d.a()) / 12; 348 D::result_type x_skew = 0; 349 D::result_type x_kurtosis = -6./5; 350 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 351 assert(std::abs((var - x_var) / x_var) < 0.01); 352 assert(std::abs(skew - x_skew) < 0.01); 353 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 354 } 355 { 356 typedef std::uniform_real_distribution<> D; 357 typedef std::knuth_b G; 358 G g; 359 D d; 360 const int N = 100000; 361 std::vector<D::result_type> u; 362 for (int i = 0; i < N; ++i) 363 { 364 D::result_type v = d(g); 365 assert(d.a() <= v && v < d.b()); 366 u.push_back(v); 367 } 368 D::result_type mean = std::accumulate(u.begin(), u.end(), 369 D::result_type(0)) / u.size(); 370 D::result_type var = 0; 371 D::result_type skew = 0; 372 D::result_type kurtosis = 0; 373 for (std::size_t i = 0; i < u.size(); ++i) 374 { 375 D::result_type dbl = (u[i] - mean); 376 D::result_type d2 = sqr(dbl); 377 var += d2; 378 skew += dbl * d2; 379 kurtosis += d2 * d2; 380 } 381 var /= u.size(); 382 D::result_type dev = std::sqrt(var); 383 skew /= u.size() * dev * var; 384 kurtosis /= u.size() * var * var; 385 kurtosis -= 3; 386 D::result_type x_mean = (d.a() + d.b()) / 2; 387 D::result_type x_var = sqr(d.b() - d.a()) / 12; 388 D::result_type x_skew = 0; 389 D::result_type x_kurtosis = -6./5; 390 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 391 assert(std::abs((var - x_var) / x_var) < 0.01); 392 assert(std::abs(skew - x_skew) < 0.01); 393 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 394 } 395 { 396 typedef std::uniform_real_distribution<> D; 397 typedef std::minstd_rand G; 398 G g; 399 D d(-1, 1); 400 const int N = 100000; 401 std::vector<D::result_type> u; 402 for (int i = 0; i < N; ++i) 403 { 404 D::result_type v = d(g); 405 assert(d.a() <= v && v < d.b()); 406 u.push_back(v); 407 } 408 D::result_type mean = std::accumulate(u.begin(), u.end(), 409 D::result_type(0)) / u.size(); 410 D::result_type var = 0; 411 D::result_type skew = 0; 412 D::result_type kurtosis = 0; 413 for (std::size_t i = 0; i < u.size(); ++i) 414 { 415 D::result_type dbl = (u[i] - mean); 416 D::result_type d2 = sqr(dbl); 417 var += d2; 418 skew += dbl * d2; 419 kurtosis += d2 * d2; 420 } 421 var /= u.size(); 422 D::result_type dev = std::sqrt(var); 423 skew /= u.size() * dev * var; 424 kurtosis /= u.size() * var * var; 425 kurtosis -= 3; 426 D::result_type x_mean = (d.a() + d.b()) / 2; 427 D::result_type x_var = sqr(d.b() - d.a()) / 12; 428 D::result_type x_skew = 0; 429 D::result_type x_kurtosis = -6./5; 430 assert(std::abs(mean - x_mean) < 0.01); 431 assert(std::abs((var - x_var) / x_var) < 0.01); 432 assert(std::abs(skew - x_skew) < 0.01); 433 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 434 } 435 { 436 typedef std::uniform_real_distribution<> D; 437 typedef std::minstd_rand G; 438 G g; 439 D d(5.5, 25); 440 const int N = 100000; 441 std::vector<D::result_type> u; 442 for (int i = 0; i < N; ++i) 443 { 444 D::result_type v = d(g); 445 assert(d.a() <= v && v < d.b()); 446 u.push_back(v); 447 } 448 D::result_type mean = std::accumulate(u.begin(), u.end(), 449 D::result_type(0)) / u.size(); 450 D::result_type var = 0; 451 D::result_type skew = 0; 452 D::result_type kurtosis = 0; 453 for (std::size_t i = 0; i < u.size(); ++i) 454 { 455 D::result_type dbl = (u[i] - mean); 456 D::result_type d2 = sqr(dbl); 457 var += d2; 458 skew += dbl * d2; 459 kurtosis += d2 * d2; 460 } 461 var /= u.size(); 462 D::result_type dev = std::sqrt(var); 463 skew /= u.size() * dev * var; 464 kurtosis /= u.size() * var * var; 465 kurtosis -= 3; 466 D::result_type x_mean = (d.a() + d.b()) / 2; 467 D::result_type x_var = sqr(d.b() - d.a()) / 12; 468 D::result_type x_skew = 0; 469 D::result_type x_kurtosis = -6./5; 470 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 471 assert(std::abs((var - x_var) / x_var) < 0.01); 472 assert(std::abs(skew - x_skew) < 0.01); 473 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 474 } 475 } 476
