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 uniform_real_distribution 14 15 // template<class _URNG> result_type operator()(_URNG& g); 16 17 #include <random> 18 #include <cassert> 19 #include <vector> 20 #include <numeric> 21 22 template <class T> 23 inline 24 T 25 sqr(T x) 26 { 27 return x * x; 28 } 29 30 int main() 31 { 32 { 33 typedef std::uniform_real_distribution<> D; 34 typedef std::minstd_rand0 G; 35 G g; 36 D d; 37 const int N = 100000; 38 std::vector<D::result_type> u; 39 for (int i = 0; i < N; ++i) 40 { 41 D::result_type v = d(g); 42 assert(d.a() <= v && v < d.b()); 43 u.push_back(v); 44 } 45 D::result_type mean = std::accumulate(u.begin(), u.end(), 46 D::result_type(0)) / u.size(); 47 D::result_type var = 0; 48 D::result_type skew = 0; 49 D::result_type kurtosis = 0; 50 for (int i = 0; i < u.size(); ++i) 51 { 52 D::result_type d = (u[i] - mean); 53 D::result_type d2 = sqr(d); 54 var += d2; 55 skew += d * d2; 56 kurtosis += d2 * d2; 57 } 58 var /= u.size(); 59 D::result_type dev = std::sqrt(var); 60 skew /= u.size() * dev * var; 61 kurtosis /= u.size() * var * var; 62 kurtosis -= 3; 63 D::result_type x_mean = (d.a() + d.b()) / 2; 64 D::result_type x_var = sqr(d.b() - d.a()) / 12; 65 D::result_type x_skew = 0; 66 D::result_type x_kurtosis = -6./5; 67 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 68 assert(std::abs((var - x_var) / x_var) < 0.01); 69 assert(std::abs(skew - x_skew) < 0.01); 70 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 71 } 72 { 73 typedef std::uniform_real_distribution<> D; 74 typedef std::minstd_rand G; 75 G g; 76 D d; 77 const int N = 100000; 78 std::vector<D::result_type> u; 79 for (int i = 0; i < N; ++i) 80 { 81 D::result_type v = d(g); 82 assert(d.a() <= v && v < d.b()); 83 u.push_back(v); 84 } 85 D::result_type mean = std::accumulate(u.begin(), u.end(), 86 D::result_type(0)) / u.size(); 87 D::result_type var = 0; 88 D::result_type skew = 0; 89 D::result_type kurtosis = 0; 90 for (int i = 0; i < u.size(); ++i) 91 { 92 D::result_type d = (u[i] - mean); 93 D::result_type d2 = sqr(d); 94 var += d2; 95 skew += d * d2; 96 kurtosis += d2 * d2; 97 } 98 var /= u.size(); 99 D::result_type dev = std::sqrt(var); 100 skew /= u.size() * dev * var; 101 kurtosis /= u.size() * var * var; 102 kurtosis -= 3; 103 D::result_type x_mean = (d.a() + d.b()) / 2; 104 D::result_type x_var = sqr(d.b() - d.a()) / 12; 105 D::result_type x_skew = 0; 106 D::result_type x_kurtosis = -6./5; 107 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 108 assert(std::abs((var - x_var) / x_var) < 0.01); 109 assert(std::abs(skew - x_skew) < 0.01); 110 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 111 } 112 { 113 typedef std::uniform_real_distribution<> D; 114 typedef std::mt19937 G; 115 G g; 116 D d; 117 const int N = 100000; 118 std::vector<D::result_type> u; 119 for (int i = 0; i < N; ++i) 120 { 121 D::result_type v = d(g); 122 assert(d.a() <= v && v < d.b()); 123 u.push_back(v); 124 } 125 D::result_type mean = std::accumulate(u.begin(), u.end(), 126 D::result_type(0)) / u.size(); 127 D::result_type var = 0; 128 D::result_type skew = 0; 129 D::result_type kurtosis = 0; 130 for (int i = 0; i < u.size(); ++i) 131 { 132 D::result_type d = (u[i] - mean); 133 D::result_type d2 = sqr(d); 134 var += d2; 135 skew += d * d2; 136 kurtosis += d2 * d2; 137 } 138 var /= u.size(); 139 D::result_type dev = std::sqrt(var); 140 skew /= u.size() * dev * var; 141 kurtosis /= u.size() * var * var; 142 kurtosis -= 3; 143 D::result_type x_mean = (d.a() + d.b()) / 2; 144 D::result_type x_var = sqr(d.b() - d.a()) / 12; 145 D::result_type x_skew = 0; 146 D::result_type x_kurtosis = -6./5; 147 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 148 assert(std::abs((var - x_var) / x_var) < 0.01); 149 assert(std::abs(skew - x_skew) < 0.01); 150 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 151 } 152 { 153 typedef std::uniform_real_distribution<> D; 154 typedef std::mt19937_64 G; 155 G g; 156 D d; 157 const int N = 100000; 158 std::vector<D::result_type> u; 159 for (int i = 0; i < N; ++i) 160 { 161 D::result_type v = d(g); 162 assert(d.a() <= v && v < d.b()); 163 u.push_back(v); 164 } 165 D::result_type mean = std::accumulate(u.begin(), u.end(), 166 D::result_type(0)) / u.size(); 167 D::result_type var = 0; 168 D::result_type skew = 0; 169 D::result_type kurtosis = 0; 170 for (int i = 0; i < u.size(); ++i) 171 { 172 D::result_type d = (u[i] - mean); 173 D::result_type d2 = sqr(d); 174 var += d2; 175 skew += d * d2; 176 kurtosis += d2 * d2; 177 } 178 var /= u.size(); 179 D::result_type dev = std::sqrt(var); 180 skew /= u.size() * dev * var; 181 kurtosis /= u.size() * var * var; 182 kurtosis -= 3; 183 D::result_type x_mean = (d.a() + d.b()) / 2; 184 D::result_type x_var = sqr(d.b() - d.a()) / 12; 185 D::result_type x_skew = 0; 186 D::result_type x_kurtosis = -6./5; 187 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 188 assert(std::abs((var - x_var) / x_var) < 0.01); 189 assert(std::abs(skew - x_skew) < 0.01); 190 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 191 } 192 { 193 typedef std::uniform_real_distribution<> D; 194 typedef std::ranlux24_base G; 195 G g; 196 D d; 197 const int N = 100000; 198 std::vector<D::result_type> u; 199 for (int i = 0; i < N; ++i) 200 { 201 D::result_type v = d(g); 202 assert(d.a() <= v && v < d.b()); 203 u.push_back(v); 204 } 205 D::result_type mean = std::accumulate(u.begin(), u.end(), 206 D::result_type(0)) / u.size(); 207 D::result_type var = 0; 208 D::result_type skew = 0; 209 D::result_type kurtosis = 0; 210 for (int i = 0; i < u.size(); ++i) 211 { 212 D::result_type d = (u[i] - mean); 213 D::result_type d2 = sqr(d); 214 var += d2; 215 skew += d * d2; 216 kurtosis += d2 * d2; 217 } 218 var /= u.size(); 219 D::result_type dev = std::sqrt(var); 220 skew /= u.size() * dev * var; 221 kurtosis /= u.size() * var * var; 222 kurtosis -= 3; 223 D::result_type x_mean = (d.a() + d.b()) / 2; 224 D::result_type x_var = sqr(d.b() - d.a()) / 12; 225 D::result_type x_skew = 0; 226 D::result_type x_kurtosis = -6./5; 227 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 228 assert(std::abs((var - x_var) / x_var) < 0.01); 229 assert(std::abs(skew - x_skew) < 0.02); 230 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 231 } 232 { 233 typedef std::uniform_real_distribution<> D; 234 typedef std::ranlux48_base G; 235 G g; 236 D d; 237 const int N = 100000; 238 std::vector<D::result_type> u; 239 for (int i = 0; i < N; ++i) 240 { 241 D::result_type v = d(g); 242 assert(d.a() <= v && v < d.b()); 243 u.push_back(v); 244 } 245 D::result_type mean = std::accumulate(u.begin(), u.end(), 246 D::result_type(0)) / u.size(); 247 D::result_type var = 0; 248 D::result_type skew = 0; 249 D::result_type kurtosis = 0; 250 for (int i = 0; i < u.size(); ++i) 251 { 252 D::result_type d = (u[i] - mean); 253 D::result_type d2 = sqr(d); 254 var += d2; 255 skew += d * d2; 256 kurtosis += d2 * d2; 257 } 258 var /= u.size(); 259 D::result_type dev = std::sqrt(var); 260 skew /= u.size() * dev * var; 261 kurtosis /= u.size() * var * var; 262 kurtosis -= 3; 263 D::result_type x_mean = (d.a() + d.b()) / 2; 264 D::result_type x_var = sqr(d.b() - d.a()) / 12; 265 D::result_type x_skew = 0; 266 D::result_type x_kurtosis = -6./5; 267 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 268 assert(std::abs((var - x_var) / x_var) < 0.01); 269 assert(std::abs(skew - x_skew) < 0.01); 270 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 271 } 272 { 273 typedef std::uniform_real_distribution<> D; 274 typedef std::ranlux24 G; 275 G g; 276 D d; 277 const int N = 100000; 278 std::vector<D::result_type> u; 279 for (int i = 0; i < N; ++i) 280 { 281 D::result_type v = d(g); 282 assert(d.a() <= v && v < d.b()); 283 u.push_back(v); 284 } 285 D::result_type mean = std::accumulate(u.begin(), u.end(), 286 D::result_type(0)) / u.size(); 287 D::result_type var = 0; 288 D::result_type skew = 0; 289 D::result_type kurtosis = 0; 290 for (int i = 0; i < u.size(); ++i) 291 { 292 D::result_type d = (u[i] - mean); 293 D::result_type d2 = sqr(d); 294 var += d2; 295 skew += d * d2; 296 kurtosis += d2 * d2; 297 } 298 var /= u.size(); 299 D::result_type dev = std::sqrt(var); 300 skew /= u.size() * dev * var; 301 kurtosis /= u.size() * var * var; 302 kurtosis -= 3; 303 D::result_type x_mean = (d.a() + d.b()) / 2; 304 D::result_type x_var = sqr(d.b() - d.a()) / 12; 305 D::result_type x_skew = 0; 306 D::result_type x_kurtosis = -6./5; 307 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 308 assert(std::abs((var - x_var) / x_var) < 0.01); 309 assert(std::abs(skew - x_skew) < 0.01); 310 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 311 } 312 { 313 typedef std::uniform_real_distribution<> D; 314 typedef std::ranlux48 G; 315 G g; 316 D d; 317 const int N = 100000; 318 std::vector<D::result_type> u; 319 for (int i = 0; i < N; ++i) 320 { 321 D::result_type v = d(g); 322 assert(d.a() <= v && v < d.b()); 323 u.push_back(v); 324 } 325 D::result_type mean = std::accumulate(u.begin(), u.end(), 326 D::result_type(0)) / u.size(); 327 D::result_type var = 0; 328 D::result_type skew = 0; 329 D::result_type kurtosis = 0; 330 for (int i = 0; i < u.size(); ++i) 331 { 332 D::result_type d = (u[i] - mean); 333 D::result_type d2 = sqr(d); 334 var += d2; 335 skew += d * d2; 336 kurtosis += d2 * d2; 337 } 338 var /= u.size(); 339 D::result_type dev = std::sqrt(var); 340 skew /= u.size() * dev * var; 341 kurtosis /= u.size() * var * var; 342 kurtosis -= 3; 343 D::result_type x_mean = (d.a() + d.b()) / 2; 344 D::result_type x_var = sqr(d.b() - d.a()) / 12; 345 D::result_type x_skew = 0; 346 D::result_type x_kurtosis = -6./5; 347 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 348 assert(std::abs((var - x_var) / x_var) < 0.01); 349 assert(std::abs(skew - x_skew) < 0.01); 350 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 351 } 352 { 353 typedef std::uniform_real_distribution<> D; 354 typedef std::knuth_b G; 355 G g; 356 D d; 357 const int N = 100000; 358 std::vector<D::result_type> u; 359 for (int i = 0; i < N; ++i) 360 { 361 D::result_type v = d(g); 362 assert(d.a() <= v && v < d.b()); 363 u.push_back(v); 364 } 365 D::result_type mean = std::accumulate(u.begin(), u.end(), 366 D::result_type(0)) / u.size(); 367 D::result_type var = 0; 368 D::result_type skew = 0; 369 D::result_type kurtosis = 0; 370 for (int i = 0; i < u.size(); ++i) 371 { 372 D::result_type d = (u[i] - mean); 373 D::result_type d2 = sqr(d); 374 var += d2; 375 skew += d * d2; 376 kurtosis += d2 * d2; 377 } 378 var /= u.size(); 379 D::result_type dev = std::sqrt(var); 380 skew /= u.size() * dev * var; 381 kurtosis /= u.size() * var * var; 382 kurtosis -= 3; 383 D::result_type x_mean = (d.a() + d.b()) / 2; 384 D::result_type x_var = sqr(d.b() - d.a()) / 12; 385 D::result_type x_skew = 0; 386 D::result_type x_kurtosis = -6./5; 387 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 388 assert(std::abs((var - x_var) / x_var) < 0.01); 389 assert(std::abs(skew - x_skew) < 0.01); 390 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 391 } 392 { 393 typedef std::uniform_real_distribution<> D; 394 typedef std::minstd_rand G; 395 G g; 396 D d(-1, 1); 397 const int N = 100000; 398 std::vector<D::result_type> u; 399 for (int i = 0; i < N; ++i) 400 { 401 D::result_type v = d(g); 402 assert(d.a() <= v && v < d.b()); 403 u.push_back(v); 404 } 405 D::result_type mean = std::accumulate(u.begin(), u.end(), 406 D::result_type(0)) / u.size(); 407 D::result_type var = 0; 408 D::result_type skew = 0; 409 D::result_type kurtosis = 0; 410 for (int i = 0; i < u.size(); ++i) 411 { 412 D::result_type d = (u[i] - mean); 413 D::result_type d2 = sqr(d); 414 var += d2; 415 skew += d * d2; 416 kurtosis += d2 * d2; 417 } 418 var /= u.size(); 419 D::result_type dev = std::sqrt(var); 420 skew /= u.size() * dev * var; 421 kurtosis /= u.size() * var * var; 422 kurtosis -= 3; 423 D::result_type x_mean = (d.a() + d.b()) / 2; 424 D::result_type x_var = sqr(d.b() - d.a()) / 12; 425 D::result_type x_skew = 0; 426 D::result_type x_kurtosis = -6./5; 427 assert(std::abs(mean - x_mean) < 0.01); 428 assert(std::abs((var - x_var) / x_var) < 0.01); 429 assert(std::abs(skew - x_skew) < 0.01); 430 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 431 } 432 { 433 typedef std::uniform_real_distribution<> D; 434 typedef std::minstd_rand G; 435 G g; 436 D d(5.5, 25); 437 const int N = 100000; 438 std::vector<D::result_type> u; 439 for (int i = 0; i < N; ++i) 440 { 441 D::result_type v = d(g); 442 assert(d.a() <= v && v < d.b()); 443 u.push_back(v); 444 } 445 D::result_type mean = std::accumulate(u.begin(), u.end(), 446 D::result_type(0)) / u.size(); 447 D::result_type var = 0; 448 D::result_type skew = 0; 449 D::result_type kurtosis = 0; 450 for (int i = 0; i < u.size(); ++i) 451 { 452 D::result_type d = (u[i] - mean); 453 D::result_type d2 = sqr(d); 454 var += d2; 455 skew += d * d2; 456 kurtosis += d2 * d2; 457 } 458 var /= u.size(); 459 D::result_type dev = std::sqrt(var); 460 skew /= u.size() * dev * var; 461 kurtosis /= u.size() * var * var; 462 kurtosis -= 3; 463 D::result_type x_mean = (d.a() + d.b()) / 2; 464 D::result_type x_var = sqr(d.b() - d.a()) / 12; 465 D::result_type x_skew = 0; 466 D::result_type x_kurtosis = -6./5; 467 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 468 assert(std::abs((var - x_var) / x_var) < 0.01); 469 assert(std::abs(skew - x_skew) < 0.01); 470 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 471 } 472 } 473
