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Diffstat (limited to 'third_party/abseil_cpp/absl/random/uniform_real_distribution_test.cc')
| -rw-r--r-- | third_party/abseil_cpp/absl/random/uniform_real_distribution_test.cc | 343 |
1 files changed, 343 insertions, 0 deletions
diff --git a/third_party/abseil_cpp/absl/random/uniform_real_distribution_test.cc b/third_party/abseil_cpp/absl/random/uniform_real_distribution_test.cc new file mode 100644 index 0000000000..be107cdde4 --- /dev/null +++ b/third_party/abseil_cpp/absl/random/uniform_real_distribution_test.cc @@ -0,0 +1,343 @@ +// Copyright 2017 The Abseil Authors. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// https://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "absl/random/uniform_real_distribution.h" + +#include <cmath> +#include <cstdint> +#include <iterator> +#include <random> +#include <sstream> +#include <string> +#include <vector> + +#include "gmock/gmock.h" +#include "gtest/gtest.h" +#include "absl/base/internal/raw_logging.h" +#include "absl/random/internal/chi_square.h" +#include "absl/random/internal/distribution_test_util.h" +#include "absl/random/internal/pcg_engine.h" +#include "absl/random/internal/sequence_urbg.h" +#include "absl/random/random.h" +#include "absl/strings/str_cat.h" + +// NOTES: +// * Some documentation on generating random real values suggests that +// it is possible to use std::nextafter(b, DBL_MAX) to generate a value on +// the closed range [a, b]. Unfortunately, that technique is not universally +// reliable due to floating point quantization. +// +// * absl::uniform_real_distribution<float> generates between 2^28 and 2^29 +// distinct floating point values in the range [0, 1). +// +// * absl::uniform_real_distribution<float> generates at least 2^23 distinct +// floating point values in the range [1, 2). This should be the same as +// any other range covered by a single exponent in IEEE 754. +// +// * absl::uniform_real_distribution<double> generates more than 2^52 distinct +// values in the range [0, 1), and should generate at least 2^52 distinct +// values in the range of [1, 2). +// + +namespace { + +template <typename RealType> +class UniformRealDistributionTest : public ::testing::Test {}; + +#if defined(__EMSCRIPTEN__) +using RealTypes = ::testing::Types<float, double>; +#else +using RealTypes = ::testing::Types<float, double, long double>; +#endif // defined(__EMSCRIPTEN__) + +TYPED_TEST_SUITE(UniformRealDistributionTest, RealTypes); + +TYPED_TEST(UniformRealDistributionTest, ParamSerializeTest) { + using param_type = + typename absl::uniform_real_distribution<TypeParam>::param_type; + + constexpr const TypeParam a{1152921504606846976}; + + constexpr int kCount = 1000; + absl::InsecureBitGen gen; + for (const auto& param : { + param_type(), + param_type(TypeParam(2.0), TypeParam(2.0)), // Same + param_type(TypeParam(-0.1), TypeParam(0.1)), + param_type(TypeParam(0.05), TypeParam(0.12)), + param_type(TypeParam(-0.05), TypeParam(0.13)), + param_type(TypeParam(-0.05), TypeParam(-0.02)), + // double range = 0 + // 2^60 , 2^60 + 2^6 + param_type(a, TypeParam(1152921504606847040)), + // 2^60 , 2^60 + 2^7 + param_type(a, TypeParam(1152921504606847104)), + // double range = 2^8 + // 2^60 , 2^60 + 2^8 + param_type(a, TypeParam(1152921504606847232)), + // float range = 0 + // 2^60 , 2^60 + 2^36 + param_type(a, TypeParam(1152921573326323712)), + // 2^60 , 2^60 + 2^37 + param_type(a, TypeParam(1152921642045800448)), + // float range = 2^38 + // 2^60 , 2^60 + 2^38 + param_type(a, TypeParam(1152921779484753920)), + // Limits + param_type(0, std::numeric_limits<TypeParam>::max()), + param_type(std::numeric_limits<TypeParam>::lowest(), 0), + param_type(0, std::numeric_limits<TypeParam>::epsilon()), + param_type(-std::numeric_limits<TypeParam>::epsilon(), + std::numeric_limits<TypeParam>::epsilon()), + param_type(std::numeric_limits<TypeParam>::epsilon(), + 2 * std::numeric_limits<TypeParam>::epsilon()), + }) { + // Validate parameters. + const auto a = param.a(); + const auto b = param.b(); + absl::uniform_real_distribution<TypeParam> before(a, b); + EXPECT_EQ(before.a(), param.a()); + EXPECT_EQ(before.b(), param.b()); + + { + absl::uniform_real_distribution<TypeParam> via_param(param); + EXPECT_EQ(via_param, before); + } + + std::stringstream ss; + ss << before; + absl::uniform_real_distribution<TypeParam> after(TypeParam(1.0), + TypeParam(3.1)); + + EXPECT_NE(before.a(), after.a()); + EXPECT_NE(before.b(), after.b()); + EXPECT_NE(before.param(), after.param()); + EXPECT_NE(before, after); + + ss >> after; + + EXPECT_EQ(before.a(), after.a()); + EXPECT_EQ(before.b(), after.b()); + EXPECT_EQ(before.param(), after.param()); + EXPECT_EQ(before, after); + + // Smoke test. + auto sample_min = after.max(); + auto sample_max = after.min(); + for (int i = 0; i < kCount; i++) { + auto sample = after(gen); + // Failure here indicates a bug in uniform_real_distribution::operator(), + // or bad parameters--range too large, etc. + if (after.min() == after.max()) { + EXPECT_EQ(sample, after.min()); + } else { + EXPECT_GE(sample, after.min()); + EXPECT_LT(sample, after.max()); + } + if (sample > sample_max) { + sample_max = sample; + } + if (sample < sample_min) { + sample_min = sample; + } + } + + if (!std::is_same<TypeParam, long double>::value) { + // static_cast<double>(long double) can overflow. + std::string msg = absl::StrCat("Range: ", static_cast<double>(sample_min), + ", ", static_cast<double>(sample_max)); + ABSL_RAW_LOG(INFO, "%s", msg.c_str()); + } + } +} + +#ifdef _MSC_VER +#pragma warning(push) +#pragma warning(disable:4756) // Constant arithmetic overflow. +#endif +TYPED_TEST(UniformRealDistributionTest, ViolatesPreconditionsDeathTest) { +#if GTEST_HAS_DEATH_TEST + // Hi < Lo + EXPECT_DEBUG_DEATH( + { absl::uniform_real_distribution<TypeParam> dist(10.0, 1.0); }, ""); + + // Hi - Lo > numeric_limits<>::max() + EXPECT_DEBUG_DEATH( + { + absl::uniform_real_distribution<TypeParam> dist( + std::numeric_limits<TypeParam>::lowest(), + std::numeric_limits<TypeParam>::max()); + }, + ""); +#endif // GTEST_HAS_DEATH_TEST +#if defined(NDEBUG) + // opt-mode, for invalid parameters, will generate a garbage value, + // but should not enter an infinite loop. + absl::InsecureBitGen gen; + { + absl::uniform_real_distribution<TypeParam> dist(10.0, 1.0); + auto x = dist(gen); + EXPECT_FALSE(std::isnan(x)) << x; + } + { + absl::uniform_real_distribution<TypeParam> dist( + std::numeric_limits<TypeParam>::lowest(), + std::numeric_limits<TypeParam>::max()); + auto x = dist(gen); + // Infinite result. + EXPECT_FALSE(std::isfinite(x)) << x; + } +#endif // NDEBUG +} +#ifdef _MSC_VER +#pragma warning(pop) // warning(disable:4756) +#endif + +TYPED_TEST(UniformRealDistributionTest, TestMoments) { + constexpr int kSize = 1000000; + std::vector<double> values(kSize); + + // We use a fixed bit generator for distribution accuracy tests. This allows + // these tests to be deterministic, while still testing the qualify of the + // implementation. + absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6}; + + absl::uniform_real_distribution<TypeParam> dist; + for (int i = 0; i < kSize; i++) { + values[i] = dist(rng); + } + + const auto moments = + absl::random_internal::ComputeDistributionMoments(values); + EXPECT_NEAR(0.5, moments.mean, 0.01); + EXPECT_NEAR(1 / 12.0, moments.variance, 0.015); + EXPECT_NEAR(0.0, moments.skewness, 0.02); + EXPECT_NEAR(9 / 5.0, moments.kurtosis, 0.015); +} + +TYPED_TEST(UniformRealDistributionTest, ChiSquaredTest50) { + using absl::random_internal::kChiSquared; + using param_type = + typename absl::uniform_real_distribution<TypeParam>::param_type; + + constexpr size_t kTrials = 100000; + constexpr int kBuckets = 50; + constexpr double kExpected = + static_cast<double>(kTrials) / static_cast<double>(kBuckets); + + // 1-in-100000 threshold, but remember, there are about 8 tests + // in this file. And the test could fail for other reasons. + // Empirically validated with --runs_per_test=10000. + const int kThreshold = + absl::random_internal::ChiSquareValue(kBuckets - 1, 0.999999); + + // We use a fixed bit generator for distribution accuracy tests. This allows + // these tests to be deterministic, while still testing the qualify of the + // implementation. + absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6}; + + for (const auto& param : {param_type(0, 1), param_type(5, 12), + param_type(-5, 13), param_type(-5, -2)}) { + const double min_val = param.a(); + const double max_val = param.b(); + const double factor = kBuckets / (max_val - min_val); + + std::vector<int32_t> counts(kBuckets, 0); + absl::uniform_real_distribution<TypeParam> dist(param); + for (size_t i = 0; i < kTrials; i++) { + auto x = dist(rng); + auto bucket = static_cast<size_t>((x - min_val) * factor); + counts[bucket]++; + } + + double chi_square = absl::random_internal::ChiSquareWithExpected( + std::begin(counts), std::end(counts), kExpected); + if (chi_square > kThreshold) { + double p_value = + absl::random_internal::ChiSquarePValue(chi_square, kBuckets); + + // Chi-squared test failed. Output does not appear to be uniform. + std::string msg; + for (const auto& a : counts) { + absl::StrAppend(&msg, a, "\n"); + } + absl::StrAppend(&msg, kChiSquared, " p-value ", p_value, "\n"); + absl::StrAppend(&msg, "High ", kChiSquared, " value: ", chi_square, " > ", + kThreshold); + ABSL_RAW_LOG(INFO, "%s", msg.c_str()); + FAIL() << msg; + } + } +} + +TYPED_TEST(UniformRealDistributionTest, StabilityTest) { + // absl::uniform_real_distribution stability relies only on + // random_internal::RandU64ToDouble and random_internal::RandU64ToFloat. + absl::random_internal::sequence_urbg urbg( + {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, + 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, + 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, + 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); + + std::vector<int> output(12); + + absl::uniform_real_distribution<TypeParam> dist; + std::generate(std::begin(output), std::end(output), [&] { + return static_cast<int>(TypeParam(1000000) * dist(urbg)); + }); + + EXPECT_THAT( + output, // + testing::ElementsAre(59, 999246, 762494, 395876, 167716, 82545, 925251, + 77341, 12527, 708791, 834451, 932808)); +} + +TEST(UniformRealDistributionTest, AlgorithmBounds) { + absl::uniform_real_distribution<double> dist; + + { + // This returns the smallest value >0 from absl::uniform_real_distribution. + absl::random_internal::sequence_urbg urbg({0x0000000000000001ull}); + double a = dist(urbg); + EXPECT_EQ(a, 5.42101086242752217004e-20); + } + + { + // This returns a value very near 0.5 from absl::uniform_real_distribution. + absl::random_internal::sequence_urbg urbg({0x7fffffffffffffefull}); + double a = dist(urbg); + EXPECT_EQ(a, 0.499999999999999944489); + } + { + // This returns a value very near 0.5 from absl::uniform_real_distribution. + absl::random_internal::sequence_urbg urbg({0x8000000000000000ull}); + double a = dist(urbg); + EXPECT_EQ(a, 0.5); + } + + { + // This returns the largest value <1 from absl::uniform_real_distribution. + absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFEFull}); + double a = dist(urbg); + EXPECT_EQ(a, 0.999999999999999888978); + } + { + // This *ALSO* returns the largest value <1. + absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFFFull}); + double a = dist(urbg); + EXPECT_EQ(a, 0.999999999999999888978); + } +} + +} // namespace |