Austin Schuh | 36244a1 | 2019-09-21 17:52:38 -0700 | [diff] [blame] | 1 | // Copyright 2017 The Abseil Authors. |
| 2 | // |
| 3 | // Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | // you may not use this file except in compliance with the License. |
| 5 | // You may obtain a copy of the License at |
| 6 | // |
| 7 | // https://www.apache.org/licenses/LICENSE-2.0 |
| 8 | // |
| 9 | // Unless required by applicable law or agreed to in writing, software |
| 10 | // distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | // See the License for the specific language governing permissions and |
| 13 | // limitations under the License. |
| 14 | |
| 15 | #include "absl/random/distributions.h" |
| 16 | |
| 17 | #include <cmath> |
| 18 | #include <cstdint> |
| 19 | #include <random> |
| 20 | #include <vector> |
| 21 | |
| 22 | #include "gtest/gtest.h" |
| 23 | #include "absl/random/internal/distribution_test_util.h" |
| 24 | #include "absl/random/random.h" |
| 25 | |
| 26 | namespace { |
| 27 | |
| 28 | constexpr int kSize = 400000; |
| 29 | |
| 30 | class RandomDistributionsTest : public testing::Test {}; |
| 31 | |
Austin Schuh | 36244a1 | 2019-09-21 17:52:38 -0700 | [diff] [blame] | 32 | |
| 33 | struct Invalid {}; |
| 34 | |
| 35 | template <typename A, typename B> |
| 36 | auto InferredUniformReturnT(int) |
| 37 | -> decltype(absl::Uniform(std::declval<absl::InsecureBitGen&>(), |
| 38 | std::declval<A>(), std::declval<B>())); |
| 39 | |
| 40 | template <typename, typename> |
| 41 | Invalid InferredUniformReturnT(...); |
| 42 | |
| 43 | template <typename TagType, typename A, typename B> |
| 44 | auto InferredTaggedUniformReturnT(int) |
| 45 | -> decltype(absl::Uniform(std::declval<TagType>(), |
| 46 | std::declval<absl::InsecureBitGen&>(), |
| 47 | std::declval<A>(), std::declval<B>())); |
| 48 | |
| 49 | template <typename, typename, typename> |
| 50 | Invalid InferredTaggedUniformReturnT(...); |
| 51 | |
| 52 | // Given types <A, B, Expect>, CheckArgsInferType() verifies that |
| 53 | // |
| 54 | // absl::Uniform(gen, A{}, B{}) |
| 55 | // |
| 56 | // returns the type "Expect". |
| 57 | // |
| 58 | // This interface can also be used to assert that a given absl::Uniform() |
| 59 | // overload does not exist / will not compile. Given types <A, B>, the |
| 60 | // expression |
| 61 | // |
| 62 | // decltype(absl::Uniform(..., std::declval<A>(), std::declval<B>())) |
| 63 | // |
| 64 | // will not compile, leaving the definition of InferredUniformReturnT<A, B> to |
| 65 | // resolve (via SFINAE) to the overload which returns type "Invalid". This |
| 66 | // allows tests to assert that an invocation such as |
| 67 | // |
| 68 | // absl::Uniform(gen, 1.23f, std::numeric_limits<int>::max() - 1) |
| 69 | // |
| 70 | // should not compile, since neither type, float nor int, can precisely |
| 71 | // represent both endpoint-values. Writing: |
| 72 | // |
| 73 | // CheckArgsInferType<float, int, Invalid>() |
| 74 | // |
| 75 | // will assert that this overload does not exist. |
| 76 | template <typename A, typename B, typename Expect> |
| 77 | void CheckArgsInferType() { |
| 78 | static_assert( |
| 79 | absl::conjunction< |
| 80 | std::is_same<Expect, decltype(InferredUniformReturnT<A, B>(0))>, |
| 81 | std::is_same<Expect, |
| 82 | decltype(InferredUniformReturnT<B, A>(0))>>::value, |
| 83 | ""); |
| 84 | static_assert( |
| 85 | absl::conjunction< |
| 86 | std::is_same<Expect, decltype(InferredTaggedUniformReturnT< |
| 87 | absl::IntervalOpenOpenTag, A, B>(0))>, |
| 88 | std::is_same<Expect, |
| 89 | decltype(InferredTaggedUniformReturnT< |
| 90 | absl::IntervalOpenOpenTag, B, A>(0))>>::value, |
| 91 | ""); |
| 92 | } |
| 93 | |
| 94 | template <typename A, typename B, typename ExplicitRet> |
| 95 | auto ExplicitUniformReturnT(int) -> decltype( |
| 96 | absl::Uniform<ExplicitRet>(*std::declval<absl::InsecureBitGen*>(), |
| 97 | std::declval<A>(), std::declval<B>())); |
| 98 | |
| 99 | template <typename, typename, typename ExplicitRet> |
| 100 | Invalid ExplicitUniformReturnT(...); |
| 101 | |
| 102 | template <typename TagType, typename A, typename B, typename ExplicitRet> |
| 103 | auto ExplicitTaggedUniformReturnT(int) -> decltype(absl::Uniform<ExplicitRet>( |
| 104 | std::declval<TagType>(), *std::declval<absl::InsecureBitGen*>(), |
| 105 | std::declval<A>(), std::declval<B>())); |
| 106 | |
| 107 | template <typename, typename, typename, typename ExplicitRet> |
| 108 | Invalid ExplicitTaggedUniformReturnT(...); |
| 109 | |
| 110 | // Given types <A, B, Expect>, CheckArgsReturnExpectedType() verifies that |
| 111 | // |
| 112 | // absl::Uniform<Expect>(gen, A{}, B{}) |
| 113 | // |
| 114 | // returns the type "Expect", and that the function-overload has the signature |
| 115 | // |
| 116 | // Expect(URBG&, Expect, Expect) |
| 117 | template <typename A, typename B, typename Expect> |
| 118 | void CheckArgsReturnExpectedType() { |
| 119 | static_assert( |
| 120 | absl::conjunction< |
| 121 | std::is_same<Expect, |
| 122 | decltype(ExplicitUniformReturnT<A, B, Expect>(0))>, |
| 123 | std::is_same<Expect, decltype(ExplicitUniformReturnT<B, A, Expect>( |
| 124 | 0))>>::value, |
| 125 | ""); |
| 126 | static_assert( |
| 127 | absl::conjunction< |
| 128 | std::is_same<Expect, |
| 129 | decltype(ExplicitTaggedUniformReturnT< |
| 130 | absl::IntervalOpenOpenTag, A, B, Expect>(0))>, |
| 131 | std::is_same<Expect, decltype(ExplicitTaggedUniformReturnT< |
| 132 | absl::IntervalOpenOpenTag, B, A, |
| 133 | Expect>(0))>>::value, |
| 134 | ""); |
| 135 | } |
| 136 | |
| 137 | TEST_F(RandomDistributionsTest, UniformTypeInference) { |
| 138 | // Infers common types. |
| 139 | CheckArgsInferType<uint16_t, uint16_t, uint16_t>(); |
| 140 | CheckArgsInferType<uint32_t, uint32_t, uint32_t>(); |
| 141 | CheckArgsInferType<uint64_t, uint64_t, uint64_t>(); |
| 142 | CheckArgsInferType<int16_t, int16_t, int16_t>(); |
| 143 | CheckArgsInferType<int32_t, int32_t, int32_t>(); |
| 144 | CheckArgsInferType<int64_t, int64_t, int64_t>(); |
| 145 | CheckArgsInferType<float, float, float>(); |
| 146 | CheckArgsInferType<double, double, double>(); |
| 147 | |
| 148 | // Explicitly-specified return-values override inferences. |
| 149 | CheckArgsReturnExpectedType<int16_t, int16_t, int32_t>(); |
| 150 | CheckArgsReturnExpectedType<uint16_t, uint16_t, int32_t>(); |
| 151 | CheckArgsReturnExpectedType<int16_t, int16_t, int64_t>(); |
| 152 | CheckArgsReturnExpectedType<int16_t, int32_t, int64_t>(); |
| 153 | CheckArgsReturnExpectedType<int16_t, int32_t, double>(); |
| 154 | CheckArgsReturnExpectedType<float, float, double>(); |
| 155 | CheckArgsReturnExpectedType<int, int, int16_t>(); |
| 156 | |
| 157 | // Properly promotes uint16_t. |
| 158 | CheckArgsInferType<uint16_t, uint32_t, uint32_t>(); |
| 159 | CheckArgsInferType<uint16_t, uint64_t, uint64_t>(); |
| 160 | CheckArgsInferType<uint16_t, int32_t, int32_t>(); |
| 161 | CheckArgsInferType<uint16_t, int64_t, int64_t>(); |
| 162 | CheckArgsInferType<uint16_t, float, float>(); |
| 163 | CheckArgsInferType<uint16_t, double, double>(); |
| 164 | |
| 165 | // Properly promotes int16_t. |
| 166 | CheckArgsInferType<int16_t, int32_t, int32_t>(); |
| 167 | CheckArgsInferType<int16_t, int64_t, int64_t>(); |
| 168 | CheckArgsInferType<int16_t, float, float>(); |
| 169 | CheckArgsInferType<int16_t, double, double>(); |
| 170 | |
| 171 | // Invalid (u)int16_t-pairings do not compile. |
| 172 | // See "CheckArgsInferType" comments above, for how this is achieved. |
| 173 | CheckArgsInferType<uint16_t, int16_t, Invalid>(); |
| 174 | CheckArgsInferType<int16_t, uint32_t, Invalid>(); |
| 175 | CheckArgsInferType<int16_t, uint64_t, Invalid>(); |
| 176 | |
| 177 | // Properly promotes uint32_t. |
| 178 | CheckArgsInferType<uint32_t, uint64_t, uint64_t>(); |
| 179 | CheckArgsInferType<uint32_t, int64_t, int64_t>(); |
| 180 | CheckArgsInferType<uint32_t, double, double>(); |
| 181 | |
| 182 | // Properly promotes int32_t. |
| 183 | CheckArgsInferType<int32_t, int64_t, int64_t>(); |
| 184 | CheckArgsInferType<int32_t, double, double>(); |
| 185 | |
| 186 | // Invalid (u)int32_t-pairings do not compile. |
| 187 | CheckArgsInferType<uint32_t, int32_t, Invalid>(); |
| 188 | CheckArgsInferType<int32_t, uint64_t, Invalid>(); |
| 189 | CheckArgsInferType<int32_t, float, Invalid>(); |
| 190 | CheckArgsInferType<uint32_t, float, Invalid>(); |
| 191 | |
| 192 | // Invalid (u)int64_t-pairings do not compile. |
| 193 | CheckArgsInferType<uint64_t, int64_t, Invalid>(); |
| 194 | CheckArgsInferType<int64_t, float, Invalid>(); |
| 195 | CheckArgsInferType<int64_t, double, Invalid>(); |
| 196 | |
| 197 | // Properly promotes float. |
| 198 | CheckArgsInferType<float, double, double>(); |
Austin Schuh | b4691e9 | 2020-12-31 12:37:18 -0800 | [diff] [blame^] | 199 | } |
Austin Schuh | 36244a1 | 2019-09-21 17:52:38 -0700 | [diff] [blame] | 200 | |
Austin Schuh | b4691e9 | 2020-12-31 12:37:18 -0800 | [diff] [blame^] | 201 | TEST_F(RandomDistributionsTest, UniformExamples) { |
Austin Schuh | 36244a1 | 2019-09-21 17:52:38 -0700 | [diff] [blame] | 202 | // Examples. |
| 203 | absl::InsecureBitGen gen; |
| 204 | EXPECT_NE(1, absl::Uniform(gen, static_cast<uint16_t>(0), 1.0f)); |
| 205 | EXPECT_NE(1, absl::Uniform(gen, 0, 1.0)); |
| 206 | EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, |
| 207 | static_cast<uint16_t>(0), 1.0f)); |
| 208 | EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, 0, 1.0)); |
| 209 | EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, -1, 1.0)); |
| 210 | EXPECT_NE(1, absl::Uniform<double>(absl::IntervalOpenOpen, gen, -1, 1)); |
| 211 | EXPECT_NE(1, absl::Uniform<float>(absl::IntervalOpenOpen, gen, 0, 1)); |
| 212 | EXPECT_NE(1, absl::Uniform<float>(gen, 0, 1)); |
| 213 | } |
| 214 | |
| 215 | TEST_F(RandomDistributionsTest, UniformNoBounds) { |
| 216 | absl::InsecureBitGen gen; |
| 217 | |
| 218 | absl::Uniform<uint8_t>(gen); |
| 219 | absl::Uniform<uint16_t>(gen); |
| 220 | absl::Uniform<uint32_t>(gen); |
| 221 | absl::Uniform<uint64_t>(gen); |
| 222 | } |
| 223 | |
Austin Schuh | b4691e9 | 2020-12-31 12:37:18 -0800 | [diff] [blame^] | 224 | TEST_F(RandomDistributionsTest, UniformNonsenseRanges) { |
| 225 | // The ranges used in this test are undefined behavior. |
| 226 | // The results are arbitrary and subject to future changes. |
| 227 | absl::InsecureBitGen gen; |
| 228 | |
| 229 | // <uint> |
| 230 | EXPECT_EQ(0, absl::Uniform<uint64_t>(gen, 0, 0)); |
| 231 | EXPECT_EQ(1, absl::Uniform<uint64_t>(gen, 1, 0)); |
| 232 | EXPECT_EQ(0, absl::Uniform<uint64_t>(absl::IntervalOpenOpen, gen, 0, 0)); |
| 233 | EXPECT_EQ(1, absl::Uniform<uint64_t>(absl::IntervalOpenOpen, gen, 1, 0)); |
| 234 | |
| 235 | constexpr auto m = (std::numeric_limits<uint64_t>::max)(); |
| 236 | |
| 237 | EXPECT_EQ(m, absl::Uniform(gen, m, m)); |
| 238 | EXPECT_EQ(m, absl::Uniform(gen, m, m - 1)); |
| 239 | EXPECT_EQ(m - 1, absl::Uniform(gen, m - 1, m)); |
| 240 | EXPECT_EQ(m, absl::Uniform(absl::IntervalOpenOpen, gen, m, m)); |
| 241 | EXPECT_EQ(m, absl::Uniform(absl::IntervalOpenOpen, gen, m, m - 1)); |
| 242 | EXPECT_EQ(m - 1, absl::Uniform(absl::IntervalOpenOpen, gen, m - 1, m)); |
| 243 | |
| 244 | // <int> |
| 245 | EXPECT_EQ(0, absl::Uniform<int64_t>(gen, 0, 0)); |
| 246 | EXPECT_EQ(1, absl::Uniform<int64_t>(gen, 1, 0)); |
| 247 | EXPECT_EQ(0, absl::Uniform<int64_t>(absl::IntervalOpenOpen, gen, 0, 0)); |
| 248 | EXPECT_EQ(1, absl::Uniform<int64_t>(absl::IntervalOpenOpen, gen, 1, 0)); |
| 249 | |
| 250 | constexpr auto l = (std::numeric_limits<int64_t>::min)(); |
| 251 | constexpr auto r = (std::numeric_limits<int64_t>::max)(); |
| 252 | |
| 253 | EXPECT_EQ(l, absl::Uniform(gen, l, l)); |
| 254 | EXPECT_EQ(r, absl::Uniform(gen, r, r)); |
| 255 | EXPECT_EQ(r, absl::Uniform(gen, r, r - 1)); |
| 256 | EXPECT_EQ(r - 1, absl::Uniform(gen, r - 1, r)); |
| 257 | EXPECT_EQ(l, absl::Uniform(absl::IntervalOpenOpen, gen, l, l)); |
| 258 | EXPECT_EQ(r, absl::Uniform(absl::IntervalOpenOpen, gen, r, r)); |
| 259 | EXPECT_EQ(r, absl::Uniform(absl::IntervalOpenOpen, gen, r, r - 1)); |
| 260 | EXPECT_EQ(r - 1, absl::Uniform(absl::IntervalOpenOpen, gen, r - 1, r)); |
| 261 | |
| 262 | // <double> |
| 263 | const double e = std::nextafter(1.0, 2.0); // 1 + epsilon |
| 264 | const double f = std::nextafter(1.0, 0.0); // 1 - epsilon |
| 265 | const double g = std::numeric_limits<double>::denorm_min(); |
| 266 | |
| 267 | EXPECT_EQ(1.0, absl::Uniform(gen, 1.0, e)); |
| 268 | EXPECT_EQ(1.0, absl::Uniform(gen, 1.0, f)); |
| 269 | EXPECT_EQ(0.0, absl::Uniform(gen, 0.0, g)); |
| 270 | |
| 271 | EXPECT_EQ(e, absl::Uniform(absl::IntervalOpenOpen, gen, 1.0, e)); |
| 272 | EXPECT_EQ(f, absl::Uniform(absl::IntervalOpenOpen, gen, 1.0, f)); |
| 273 | EXPECT_EQ(g, absl::Uniform(absl::IntervalOpenOpen, gen, 0.0, g)); |
| 274 | } |
| 275 | |
Austin Schuh | 36244a1 | 2019-09-21 17:52:38 -0700 | [diff] [blame] | 276 | // TODO(lar): Validate properties of non-default interval-semantics. |
| 277 | TEST_F(RandomDistributionsTest, UniformReal) { |
| 278 | std::vector<double> values(kSize); |
| 279 | |
| 280 | absl::InsecureBitGen gen; |
| 281 | for (int i = 0; i < kSize; i++) { |
| 282 | values[i] = absl::Uniform(gen, 0, 1.0); |
| 283 | } |
| 284 | |
| 285 | const auto moments = |
| 286 | absl::random_internal::ComputeDistributionMoments(values); |
| 287 | EXPECT_NEAR(0.5, moments.mean, 0.02); |
| 288 | EXPECT_NEAR(1 / 12.0, moments.variance, 0.02); |
| 289 | EXPECT_NEAR(0.0, moments.skewness, 0.02); |
| 290 | EXPECT_NEAR(9 / 5.0, moments.kurtosis, 0.02); |
| 291 | } |
| 292 | |
| 293 | TEST_F(RandomDistributionsTest, UniformInt) { |
| 294 | std::vector<double> values(kSize); |
| 295 | |
| 296 | absl::InsecureBitGen gen; |
| 297 | for (int i = 0; i < kSize; i++) { |
| 298 | const int64_t kMax = 1000000000000ll; |
| 299 | int64_t j = absl::Uniform(absl::IntervalClosedClosed, gen, 0, kMax); |
| 300 | // convert to double. |
| 301 | values[i] = static_cast<double>(j) / static_cast<double>(kMax); |
| 302 | } |
| 303 | |
| 304 | const auto moments = |
| 305 | absl::random_internal::ComputeDistributionMoments(values); |
| 306 | EXPECT_NEAR(0.5, moments.mean, 0.02); |
| 307 | EXPECT_NEAR(1 / 12.0, moments.variance, 0.02); |
| 308 | EXPECT_NEAR(0.0, moments.skewness, 0.02); |
| 309 | EXPECT_NEAR(9 / 5.0, moments.kurtosis, 0.02); |
| 310 | |
| 311 | /* |
| 312 | // NOTE: These are not supported by absl::Uniform, which is specialized |
| 313 | // on integer and real valued types. |
| 314 | |
| 315 | enum E { E0, E1 }; // enum |
| 316 | enum S : int { S0, S1 }; // signed enum |
| 317 | enum U : unsigned int { U0, U1 }; // unsigned enum |
| 318 | |
| 319 | absl::Uniform(gen, E0, E1); |
| 320 | absl::Uniform(gen, S0, S1); |
| 321 | absl::Uniform(gen, U0, U1); |
| 322 | */ |
| 323 | } |
| 324 | |
| 325 | TEST_F(RandomDistributionsTest, Exponential) { |
| 326 | std::vector<double> values(kSize); |
| 327 | |
| 328 | absl::InsecureBitGen gen; |
| 329 | for (int i = 0; i < kSize; i++) { |
| 330 | values[i] = absl::Exponential<double>(gen); |
| 331 | } |
| 332 | |
| 333 | const auto moments = |
| 334 | absl::random_internal::ComputeDistributionMoments(values); |
| 335 | EXPECT_NEAR(1.0, moments.mean, 0.02); |
| 336 | EXPECT_NEAR(1.0, moments.variance, 0.025); |
| 337 | EXPECT_NEAR(2.0, moments.skewness, 0.1); |
| 338 | EXPECT_LT(5.0, moments.kurtosis); |
| 339 | } |
| 340 | |
| 341 | TEST_F(RandomDistributionsTest, PoissonDefault) { |
| 342 | std::vector<double> values(kSize); |
| 343 | |
| 344 | absl::InsecureBitGen gen; |
| 345 | for (int i = 0; i < kSize; i++) { |
| 346 | values[i] = absl::Poisson<int64_t>(gen); |
| 347 | } |
| 348 | |
| 349 | const auto moments = |
| 350 | absl::random_internal::ComputeDistributionMoments(values); |
| 351 | EXPECT_NEAR(1.0, moments.mean, 0.02); |
| 352 | EXPECT_NEAR(1.0, moments.variance, 0.02); |
| 353 | EXPECT_NEAR(1.0, moments.skewness, 0.025); |
| 354 | EXPECT_LT(2.0, moments.kurtosis); |
| 355 | } |
| 356 | |
| 357 | TEST_F(RandomDistributionsTest, PoissonLarge) { |
| 358 | constexpr double kMean = 100000000.0; |
| 359 | std::vector<double> values(kSize); |
| 360 | |
| 361 | absl::InsecureBitGen gen; |
| 362 | for (int i = 0; i < kSize; i++) { |
| 363 | values[i] = absl::Poisson<int64_t>(gen, kMean); |
| 364 | } |
| 365 | |
| 366 | const auto moments = |
| 367 | absl::random_internal::ComputeDistributionMoments(values); |
| 368 | EXPECT_NEAR(kMean, moments.mean, kMean * 0.015); |
| 369 | EXPECT_NEAR(kMean, moments.variance, kMean * 0.015); |
| 370 | EXPECT_NEAR(std::sqrt(kMean), moments.skewness, kMean * 0.02); |
| 371 | EXPECT_LT(2.0, moments.kurtosis); |
| 372 | } |
| 373 | |
| 374 | TEST_F(RandomDistributionsTest, Bernoulli) { |
| 375 | constexpr double kP = 0.5151515151; |
| 376 | std::vector<double> values(kSize); |
| 377 | |
| 378 | absl::InsecureBitGen gen; |
| 379 | for (int i = 0; i < kSize; i++) { |
| 380 | values[i] = absl::Bernoulli(gen, kP); |
| 381 | } |
| 382 | |
| 383 | const auto moments = |
| 384 | absl::random_internal::ComputeDistributionMoments(values); |
| 385 | EXPECT_NEAR(kP, moments.mean, 0.01); |
| 386 | } |
| 387 | |
| 388 | TEST_F(RandomDistributionsTest, Beta) { |
| 389 | constexpr double kAlpha = 2.0; |
| 390 | constexpr double kBeta = 3.0; |
| 391 | std::vector<double> values(kSize); |
| 392 | |
| 393 | absl::InsecureBitGen gen; |
| 394 | for (int i = 0; i < kSize; i++) { |
| 395 | values[i] = absl::Beta(gen, kAlpha, kBeta); |
| 396 | } |
| 397 | |
| 398 | const auto moments = |
| 399 | absl::random_internal::ComputeDistributionMoments(values); |
| 400 | EXPECT_NEAR(0.4, moments.mean, 0.01); |
| 401 | } |
| 402 | |
| 403 | TEST_F(RandomDistributionsTest, Zipf) { |
| 404 | std::vector<double> values(kSize); |
| 405 | |
| 406 | absl::InsecureBitGen gen; |
| 407 | for (int i = 0; i < kSize; i++) { |
| 408 | values[i] = absl::Zipf<int64_t>(gen, 100); |
| 409 | } |
| 410 | |
| 411 | // The mean of a zipf distribution is: H(N, s-1) / H(N,s). |
| 412 | // Given the parameter v = 1, this gives the following function: |
| 413 | // (Hn(100, 1) - Hn(1,1)) / (Hn(100,2) - Hn(1,2)) = 6.5944 |
| 414 | const auto moments = |
| 415 | absl::random_internal::ComputeDistributionMoments(values); |
| 416 | EXPECT_NEAR(6.5944, moments.mean, 2000) << moments; |
| 417 | } |
| 418 | |
| 419 | TEST_F(RandomDistributionsTest, Gaussian) { |
| 420 | std::vector<double> values(kSize); |
| 421 | |
| 422 | absl::InsecureBitGen gen; |
| 423 | for (int i = 0; i < kSize; i++) { |
| 424 | values[i] = absl::Gaussian<double>(gen); |
| 425 | } |
| 426 | |
| 427 | const auto moments = |
| 428 | absl::random_internal::ComputeDistributionMoments(values); |
| 429 | EXPECT_NEAR(0.0, moments.mean, 0.02); |
| 430 | EXPECT_NEAR(1.0, moments.variance, 0.04); |
| 431 | EXPECT_NEAR(0, moments.skewness, 0.2); |
| 432 | EXPECT_NEAR(3.0, moments.kurtosis, 0.5); |
| 433 | } |
| 434 | |
| 435 | TEST_F(RandomDistributionsTest, LogUniform) { |
| 436 | std::vector<double> values(kSize); |
| 437 | |
| 438 | absl::InsecureBitGen gen; |
| 439 | for (int i = 0; i < kSize; i++) { |
| 440 | values[i] = absl::LogUniform<int64_t>(gen, 0, (1 << 10) - 1); |
| 441 | } |
| 442 | |
| 443 | // The mean is the sum of the fractional means of the uniform distributions: |
| 444 | // [0..0][1..1][2..3][4..7][8..15][16..31][32..63] |
| 445 | // [64..127][128..255][256..511][512..1023] |
| 446 | const double mean = (0 + 1 + 1 + 2 + 3 + 4 + 7 + 8 + 15 + 16 + 31 + 32 + 63 + |
| 447 | 64 + 127 + 128 + 255 + 256 + 511 + 512 + 1023) / |
| 448 | (2.0 * 11.0); |
| 449 | |
| 450 | const auto moments = |
| 451 | absl::random_internal::ComputeDistributionMoments(values); |
| 452 | EXPECT_NEAR(mean, moments.mean, 2) << moments; |
| 453 | } |
| 454 | |
| 455 | } // namespace |