eval.pass.cpp (15835B)
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 uniform_int_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_int_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 double mean = std::accumulate(u.begin(), u.end(), 49 double(0)) / u.size(); 50 double var = 0; 51 double skew = 0; 52 double kurtosis = 0; 53 for (std::size_t i = 0; i < u.size(); ++i) 54 { 55 double dbl = (u[i] - mean); 56 double d2 = sqr(dbl); 57 var += d2; 58 skew += dbl * d2; 59 kurtosis += d2 * d2; 60 } 61 var /= u.size(); 62 double dev = std::sqrt(var); 63 skew /= u.size() * dev * var; 64 kurtosis /= u.size() * var * var; 65 kurtosis -= 3; 66 double x_mean = ((double)d.a() + d.b()) / 2; 67 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 68 double x_skew = 0; 69 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 70 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 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) < 0.01); 74 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 75 } 76 { 77 typedef std::uniform_int_distribution<> D; 78 typedef std::minstd_rand G; 79 G g; 80 D d; 81 const int N = 100000; 82 std::vector<D::result_type> u; 83 for (int i = 0; i < N; ++i) 84 { 85 D::result_type v = d(g); 86 assert(d.a() <= v && v <= d.b()); 87 u.push_back(v); 88 } 89 double mean = std::accumulate(u.begin(), u.end(), 90 double(0)) / u.size(); 91 double var = 0; 92 double skew = 0; 93 double kurtosis = 0; 94 for (std::size_t i = 0; i < u.size(); ++i) 95 { 96 double dbl = (u[i] - mean); 97 double d2 = sqr(dbl); 98 var += d2; 99 skew += dbl * d2; 100 kurtosis += d2 * d2; 101 } 102 var /= u.size(); 103 double dev = std::sqrt(var); 104 skew /= u.size() * dev * var; 105 kurtosis /= u.size() * var * var; 106 kurtosis -= 3; 107 double x_mean = ((double)d.a() + d.b()) / 2; 108 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 109 double x_skew = 0; 110 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 111 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 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) < 0.01); 115 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 116 } 117 { 118 typedef std::uniform_int_distribution<> D; 119 typedef std::mt19937 G; 120 G g; 121 D d; 122 const int N = 100000; 123 std::vector<D::result_type> u; 124 for (int i = 0; i < N; ++i) 125 { 126 D::result_type v = d(g); 127 assert(d.a() <= v && v <= d.b()); 128 u.push_back(v); 129 } 130 double mean = std::accumulate(u.begin(), u.end(), 131 double(0)) / u.size(); 132 double var = 0; 133 double skew = 0; 134 double kurtosis = 0; 135 for (std::size_t i = 0; i < u.size(); ++i) 136 { 137 double dbl = (u[i] - mean); 138 double d2 = sqr(dbl); 139 var += d2; 140 skew += dbl * d2; 141 kurtosis += d2 * d2; 142 } 143 var /= u.size(); 144 double dev = std::sqrt(var); 145 skew /= u.size() * dev * var; 146 kurtosis /= u.size() * var * var; 147 kurtosis -= 3; 148 double x_mean = ((double)d.a() + d.b()) / 2; 149 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 150 double x_skew = 0; 151 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 152 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 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) < 0.01); 156 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 157 } 158 { 159 typedef std::uniform_int_distribution<> D; 160 typedef std::mt19937_64 G; 161 G g; 162 D d; 163 const int N = 100000; 164 std::vector<D::result_type> u; 165 for (int i = 0; i < N; ++i) 166 { 167 D::result_type v = d(g); 168 assert(d.a() <= v && v <= d.b()); 169 u.push_back(v); 170 } 171 double mean = std::accumulate(u.begin(), u.end(), 172 double(0)) / u.size(); 173 double var = 0; 174 double skew = 0; 175 double kurtosis = 0; 176 for (std::size_t i = 0; i < u.size(); ++i) 177 { 178 double dbl = (u[i] - mean); 179 double d2 = sqr(dbl); 180 var += d2; 181 skew += dbl * d2; 182 kurtosis += d2 * d2; 183 } 184 var /= u.size(); 185 double dev = std::sqrt(var); 186 skew /= u.size() * dev * var; 187 kurtosis /= u.size() * var * var; 188 kurtosis -= 3; 189 double x_mean = ((double)d.a() + d.b()) / 2; 190 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 191 double x_skew = 0; 192 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 193 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 194 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 195 assert(std::abs((var - x_var) / x_var) < 0.01); 196 assert(std::abs(skew - x_skew) < 0.01); 197 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 198 } 199 { 200 typedef std::uniform_int_distribution<> D; 201 typedef std::ranlux24_base G; 202 G g; 203 D d; 204 const int N = 100000; 205 std::vector<D::result_type> u; 206 for (int i = 0; i < N; ++i) 207 { 208 D::result_type v = d(g); 209 assert(d.a() <= v && v <= d.b()); 210 u.push_back(v); 211 } 212 double mean = std::accumulate(u.begin(), u.end(), 213 double(0)) / u.size(); 214 double var = 0; 215 double skew = 0; 216 double kurtosis = 0; 217 for (std::size_t i = 0; i < u.size(); ++i) 218 { 219 double dbl = (u[i] - mean); 220 double d2 = sqr(dbl); 221 var += d2; 222 skew += dbl * d2; 223 kurtosis += d2 * d2; 224 } 225 var /= u.size(); 226 double dev = std::sqrt(var); 227 skew /= u.size() * dev * var; 228 kurtosis /= u.size() * var * var; 229 kurtosis -= 3; 230 double x_mean = ((double)d.a() + d.b()) / 2; 231 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 232 double x_skew = 0; 233 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 234 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 235 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 236 assert(std::abs((var - x_var) / x_var) < 0.01); 237 assert(std::abs(skew - x_skew) < 0.01); 238 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 239 } 240 { 241 typedef std::uniform_int_distribution<> D; 242 typedef std::ranlux48_base G; 243 G g; 244 D d; 245 const int N = 100000; 246 std::vector<D::result_type> u; 247 for (int i = 0; i < N; ++i) 248 { 249 D::result_type v = d(g); 250 assert(d.a() <= v && v <= d.b()); 251 u.push_back(v); 252 } 253 double mean = std::accumulate(u.begin(), u.end(), 254 double(0)) / u.size(); 255 double var = 0; 256 double skew = 0; 257 double kurtosis = 0; 258 for (std::size_t i = 0; i < u.size(); ++i) 259 { 260 double dbl = (u[i] - mean); 261 double d2 = sqr(dbl); 262 var += d2; 263 skew += dbl * d2; 264 kurtosis += d2 * d2; 265 } 266 var /= u.size(); 267 double dev = std::sqrt(var); 268 skew /= u.size() * dev * var; 269 kurtosis /= u.size() * var * var; 270 kurtosis -= 3; 271 double x_mean = ((double)d.a() + d.b()) / 2; 272 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 273 double x_skew = 0; 274 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 275 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 276 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 277 assert(std::abs((var - x_var) / x_var) < 0.01); 278 assert(std::abs(skew - x_skew) < 0.01); 279 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 280 } 281 { 282 typedef std::uniform_int_distribution<> D; 283 typedef std::ranlux24 G; 284 G g; 285 D d; 286 const int N = 100000; 287 std::vector<D::result_type> u; 288 for (int i = 0; i < N; ++i) 289 { 290 D::result_type v = d(g); 291 assert(d.a() <= v && v <= d.b()); 292 u.push_back(v); 293 } 294 double mean = std::accumulate(u.begin(), u.end(), 295 double(0)) / u.size(); 296 double var = 0; 297 double skew = 0; 298 double kurtosis = 0; 299 for (std::size_t i = 0; i < u.size(); ++i) 300 { 301 double dbl = (u[i] - mean); 302 double d2 = sqr(dbl); 303 var += d2; 304 skew += dbl * d2; 305 kurtosis += d2 * d2; 306 } 307 var /= u.size(); 308 double dev = std::sqrt(var); 309 skew /= u.size() * dev * var; 310 kurtosis /= u.size() * var * var; 311 kurtosis -= 3; 312 double x_mean = ((double)d.a() + d.b()) / 2; 313 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 314 double x_skew = 0; 315 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 316 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 317 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 318 assert(std::abs((var - x_var) / x_var) < 0.01); 319 assert(std::abs(skew - x_skew) < 0.01); 320 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 321 } 322 { 323 typedef std::uniform_int_distribution<> D; 324 typedef std::ranlux48 G; 325 G g; 326 D d; 327 const int N = 100000; 328 std::vector<D::result_type> u; 329 for (int i = 0; i < N; ++i) 330 { 331 D::result_type v = d(g); 332 assert(d.a() <= v && v <= d.b()); 333 u.push_back(v); 334 } 335 double mean = std::accumulate(u.begin(), u.end(), 336 double(0)) / u.size(); 337 double var = 0; 338 double skew = 0; 339 double kurtosis = 0; 340 for (std::size_t i = 0; i < u.size(); ++i) 341 { 342 double dbl = (u[i] - mean); 343 double d2 = sqr(dbl); 344 var += d2; 345 skew += dbl * d2; 346 kurtosis += d2 * d2; 347 } 348 var /= u.size(); 349 double dev = std::sqrt(var); 350 skew /= u.size() * dev * var; 351 kurtosis /= u.size() * var * var; 352 kurtosis -= 3; 353 double x_mean = ((double)d.a() + d.b()) / 2; 354 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 355 double x_skew = 0; 356 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 357 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 358 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 359 assert(std::abs((var - x_var) / x_var) < 0.01); 360 assert(std::abs(skew - x_skew) < 0.01); 361 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 362 } 363 { 364 typedef std::uniform_int_distribution<> D; 365 typedef std::knuth_b G; 366 G g; 367 D d; 368 const int N = 100000; 369 std::vector<D::result_type> u; 370 for (int i = 0; i < N; ++i) 371 { 372 D::result_type v = d(g); 373 assert(d.a() <= v && v <= d.b()); 374 u.push_back(v); 375 } 376 double mean = std::accumulate(u.begin(), u.end(), 377 double(0)) / u.size(); 378 double var = 0; 379 double skew = 0; 380 double kurtosis = 0; 381 for (std::size_t i = 0; i < u.size(); ++i) 382 { 383 double dbl = (u[i] - mean); 384 double d2 = sqr(dbl); 385 var += d2; 386 skew += dbl * d2; 387 kurtosis += d2 * d2; 388 } 389 var /= u.size(); 390 double dev = std::sqrt(var); 391 skew /= u.size() * dev * var; 392 kurtosis /= u.size() * var * var; 393 kurtosis -= 3; 394 double x_mean = ((double)d.a() + d.b()) / 2; 395 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 396 double x_skew = 0; 397 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 398 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 399 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 400 assert(std::abs((var - x_var) / x_var) < 0.01); 401 assert(std::abs(skew - x_skew) < 0.01); 402 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 403 } 404 { 405 typedef std::uniform_int_distribution<> D; 406 typedef std::minstd_rand0 G; 407 G g; 408 D d(-6, 106); 409 for (int i = 0; i < 10000; ++i) 410 { 411 int u = d(g); 412 assert(-6 <= u && u <= 106); 413 } 414 } 415 { 416 typedef std::uniform_int_distribution<> D; 417 typedef std::minstd_rand G; 418 G g; 419 D d(5, 100); 420 const int N = 100000; 421 std::vector<D::result_type> u; 422 for (int i = 0; i < N; ++i) 423 { 424 D::result_type v = d(g); 425 assert(d.a() <= v && v <= d.b()); 426 u.push_back(v); 427 } 428 double mean = std::accumulate(u.begin(), u.end(), 429 double(0)) / u.size(); 430 double var = 0; 431 double skew = 0; 432 double kurtosis = 0; 433 for (std::size_t i = 0; i < u.size(); ++i) 434 { 435 double dbl = (u[i] - mean); 436 double d2 = sqr(dbl); 437 var += d2; 438 skew += dbl * d2; 439 kurtosis += d2 * d2; 440 } 441 var /= u.size(); 442 double dev = std::sqrt(var); 443 skew /= u.size() * dev * var; 444 kurtosis /= u.size() * var * var; 445 kurtosis -= 3; 446 double x_mean = ((double)d.a() + d.b()) / 2; 447 double x_var = (sqr((double)d.b() - d.a() + 1) - 1) / 12; 448 double x_skew = 0; 449 double x_kurtosis = -6. * (sqr((double)d.b() - d.a() + 1) + 1) / 450 (5. * (sqr((double)d.b() - d.a() + 1) - 1)); 451 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 452 assert(std::abs((var - x_var) / x_var) < 0.01); 453 assert(std::abs(skew - x_skew) < 0.01); 454 assert(std::abs((kurtosis - x_kurtosis) / x_kurtosis) < 0.01); 455 } 456 }