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Diffstat (limited to 'third_party/abseil_cpp/absl/random/beta_distribution_test.cc')
| -rw-r--r-- | third_party/abseil_cpp/absl/random/beta_distribution_test.cc | 619 |
1 files changed, 0 insertions, 619 deletions
diff --git a/third_party/abseil_cpp/absl/random/beta_distribution_test.cc b/third_party/abseil_cpp/absl/random/beta_distribution_test.cc deleted file mode 100644 index 277e4dc6ee..0000000000 --- a/third_party/abseil_cpp/absl/random/beta_distribution_test.cc +++ /dev/null @@ -1,619 +0,0 @@ -// 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/beta_distribution.h" - -#include <algorithm> -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <random> -#include <sstream> -#include <string> -#include <unordered_map> -#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" -#include "absl/strings/str_format.h" -#include "absl/strings/str_replace.h" -#include "absl/strings/strip.h" - -namespace { - -template <typename IntType> -class BetaDistributionInterfaceTest : public ::testing::Test {}; - -using RealTypes = ::testing::Types<float, double, long double>; -TYPED_TEST_CASE(BetaDistributionInterfaceTest, RealTypes); - -TYPED_TEST(BetaDistributionInterfaceTest, SerializeTest) { - // The threshold for whether std::exp(1/a) is finite. - const TypeParam kSmallA = - 1.0f / std::log((std::numeric_limits<TypeParam>::max)()); - // The threshold for whether a * std::log(a) is finite. - const TypeParam kLargeA = - std::exp(std::log((std::numeric_limits<TypeParam>::max)()) - - std::log(std::log((std::numeric_limits<TypeParam>::max)()))); - const TypeParam kLargeAPPC = std::exp( - std::log((std::numeric_limits<TypeParam>::max)()) - - std::log(std::log((std::numeric_limits<TypeParam>::max)())) - 10.0f); - using param_type = typename absl::beta_distribution<TypeParam>::param_type; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - const TypeParam kValues[] = { - TypeParam(1e-20), TypeParam(1e-12), TypeParam(1e-8), TypeParam(1e-4), - TypeParam(1e-3), TypeParam(0.1), TypeParam(0.25), - std::nextafter(TypeParam(0.5), TypeParam(0)), // 0.5 - epsilon - std::nextafter(TypeParam(0.5), TypeParam(1)), // 0.5 + epsilon - TypeParam(0.5), TypeParam(1.0), // - std::nextafter(TypeParam(1), TypeParam(0)), // 1 - epsilon - std::nextafter(TypeParam(1), TypeParam(2)), // 1 + epsilon - TypeParam(12.5), TypeParam(1e2), TypeParam(1e8), TypeParam(1e12), - TypeParam(1e20), // - kSmallA, // - std::nextafter(kSmallA, TypeParam(0)), // - std::nextafter(kSmallA, TypeParam(1)), // - kLargeA, // - std::nextafter(kLargeA, TypeParam(0)), // - std::nextafter(kLargeA, std::numeric_limits<TypeParam>::max()), - kLargeAPPC, // - std::nextafter(kLargeAPPC, TypeParam(0)), - std::nextafter(kLargeAPPC, std::numeric_limits<TypeParam>::max()), - // Boundary cases. - std::numeric_limits<TypeParam>::max(), - std::numeric_limits<TypeParam>::epsilon(), - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(1)), // min + epsilon - std::numeric_limits<TypeParam>::min(), // smallest normal - std::numeric_limits<TypeParam>::denorm_min(), // smallest denorm - std::numeric_limits<TypeParam>::min() / 2, // denorm - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(0)), // denorm_max - }; - for (TypeParam alpha : kValues) { - for (TypeParam beta : kValues) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("Smoke test for Beta(%a, %a)", alpha, beta)); - - param_type param(alpha, beta); - absl::beta_distribution<TypeParam> before(alpha, beta); - EXPECT_EQ(before.alpha(), param.alpha()); - EXPECT_EQ(before.beta(), param.beta()); - - { - absl::beta_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - EXPECT_EQ(via_param.param(), before.param()); - } - - // Smoke test. - for (int i = 0; i < kCount; ++i) { - auto sample = before(gen); - EXPECT_TRUE(std::isfinite(sample)); - EXPECT_GE(sample, before.min()); - EXPECT_LE(sample, before.max()); - } - - // Validate stream serialization. - std::stringstream ss; - ss << before; - absl::beta_distribution<TypeParam> after(3.8f, 1.43f); - EXPECT_NE(before.alpha(), after.alpha()); - EXPECT_NE(before.beta(), after.beta()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - -#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \ - defined(__ppc__) || defined(__PPC__) - if (std::is_same<TypeParam, long double>::value) { - // Roundtripping floating point values requires sufficient precision - // to reconstruct the exact value. It turns out that long double - // has some errors doing this on ppc. - if (alpha <= std::numeric_limits<double>::max() && - alpha >= std::numeric_limits<double>::lowest()) { - EXPECT_EQ(static_cast<double>(before.alpha()), - static_cast<double>(after.alpha())) - << ss.str(); - } - if (beta <= std::numeric_limits<double>::max() && - beta >= std::numeric_limits<double>::lowest()) { - EXPECT_EQ(static_cast<double>(before.beta()), - static_cast<double>(after.beta())) - << ss.str(); - } - continue; - } -#endif - - EXPECT_EQ(before.alpha(), after.alpha()); - EXPECT_EQ(before.beta(), after.beta()); - EXPECT_EQ(before, after) // - << ss.str() << " " // - << (ss.good() ? "good " : "") // - << (ss.bad() ? "bad " : "") // - << (ss.eof() ? "eof " : "") // - << (ss.fail() ? "fail " : ""); - } - } -} - -TYPED_TEST(BetaDistributionInterfaceTest, DegenerateCases) { - // 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); - - // Extreme cases when the params are abnormal. - constexpr int kCount = 1000; - const TypeParam kSmallValues[] = { - std::numeric_limits<TypeParam>::min(), - std::numeric_limits<TypeParam>::denorm_min(), - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(0)), // denorm_max - std::numeric_limits<TypeParam>::epsilon(), - }; - const TypeParam kLargeValues[] = { - std::numeric_limits<TypeParam>::max() * static_cast<TypeParam>(0.9999), - std::numeric_limits<TypeParam>::max() - 1, - std::numeric_limits<TypeParam>::max(), - }; - { - // Small alpha and beta. - // Useful WolframAlpha plots: - // * plot InverseBetaRegularized[x, 0.0001, 0.0001] from 0.495 to 0.505 - // * Beta[1.0, 0.0000001, 0.0000001] - // * Beta[0.9999, 0.0000001, 0.0000001] - for (TypeParam alpha : kSmallValues) { - for (TypeParam beta : kSmallValues) { - int zeros = 0; - int ones = 0; - absl::beta_distribution<TypeParam> d(alpha, beta); - for (int i = 0; i < kCount; ++i) { - TypeParam x = d(rng); - if (x == 0.0) { - zeros++; - } else if (x == 1.0) { - ones++; - } - } - EXPECT_EQ(ones + zeros, kCount); - if (alpha == beta) { - EXPECT_NE(ones, 0); - EXPECT_NE(zeros, 0); - } - } - } - } - { - // Small alpha, large beta. - // Useful WolframAlpha plots: - // * plot InverseBetaRegularized[x, 0.0001, 10000] from 0.995 to 1 - // * Beta[0, 0.0000001, 1000000] - // * Beta[0.001, 0.0000001, 1000000] - // * Beta[1, 0.0000001, 1000000] - for (TypeParam alpha : kSmallValues) { - for (TypeParam beta : kLargeValues) { - absl::beta_distribution<TypeParam> d(alpha, beta); - for (int i = 0; i < kCount; ++i) { - EXPECT_EQ(d(rng), 0.0); - } - } - } - } - { - // Large alpha, small beta. - // Useful WolframAlpha plots: - // * plot InverseBetaRegularized[x, 10000, 0.0001] from 0 to 0.001 - // * Beta[0.99, 1000000, 0.0000001] - // * Beta[1, 1000000, 0.0000001] - for (TypeParam alpha : kLargeValues) { - for (TypeParam beta : kSmallValues) { - absl::beta_distribution<TypeParam> d(alpha, beta); - for (int i = 0; i < kCount; ++i) { - EXPECT_EQ(d(rng), 1.0); - } - } - } - } - { - // Large alpha and beta. - absl::beta_distribution<TypeParam> d(std::numeric_limits<TypeParam>::max(), - std::numeric_limits<TypeParam>::max()); - for (int i = 0; i < kCount; ++i) { - EXPECT_EQ(d(rng), 0.5); - } - } - { - // Large alpha and beta but unequal. - absl::beta_distribution<TypeParam> d( - std::numeric_limits<TypeParam>::max(), - std::numeric_limits<TypeParam>::max() * 0.9999); - for (int i = 0; i < kCount; ++i) { - TypeParam x = d(rng); - EXPECT_NE(x, 0.5f); - EXPECT_FLOAT_EQ(x, 0.500025f); - } - } -} - -class BetaDistributionModel { - public: - explicit BetaDistributionModel(::testing::tuple<double, double> p) - : alpha_(::testing::get<0>(p)), beta_(::testing::get<1>(p)) {} - - double Mean() const { return alpha_ / (alpha_ + beta_); } - - double Variance() const { - return alpha_ * beta_ / (alpha_ + beta_ + 1) / (alpha_ + beta_) / - (alpha_ + beta_); - } - - double Kurtosis() const { - return 3 + 6 * - ((alpha_ - beta_) * (alpha_ - beta_) * (alpha_ + beta_ + 1) - - alpha_ * beta_ * (2 + alpha_ + beta_)) / - alpha_ / beta_ / (alpha_ + beta_ + 2) / (alpha_ + beta_ + 3); - } - - protected: - const double alpha_; - const double beta_; -}; - -class BetaDistributionTest - : public ::testing::TestWithParam<::testing::tuple<double, double>>, - public BetaDistributionModel { - public: - BetaDistributionTest() : BetaDistributionModel(GetParam()) {} - - protected: - template <class D> - bool SingleZTestOnMeanAndVariance(double p, size_t samples); - - template <class D> - bool SingleChiSquaredTest(double p, size_t samples, size_t buckets); - - absl::InsecureBitGen rng_; -}; - -template <class D> -bool BetaDistributionTest::SingleZTestOnMeanAndVariance(double p, - size_t samples) { - D dis(alpha_, beta_); - - std::vector<double> data; - data.reserve(samples); - for (size_t i = 0; i < samples; i++) { - const double variate = dis(rng_); - EXPECT_FALSE(std::isnan(variate)); - // Note that equality is allowed on both sides. - EXPECT_GE(variate, 0.0); - EXPECT_LE(variate, 1.0); - data.push_back(variate); - } - - // We validate that the sample mean and sample variance are indeed from a - // Beta distribution with the given shape parameters. - const auto m = absl::random_internal::ComputeDistributionMoments(data); - - // The variance of the sample mean is variance / n. - const double mean_stddev = std::sqrt(Variance() / static_cast<double>(m.n)); - - // The variance of the sample variance is (approximately): - // (kurtosis - 1) * variance^2 / n - const double variance_stddev = std::sqrt( - (Kurtosis() - 1) * Variance() * Variance() / static_cast<double>(m.n)); - // z score for the sample variance. - const double z_variance = (m.variance - Variance()) / variance_stddev; - - const double max_err = absl::random_internal::MaxErrorTolerance(p); - const double z_mean = absl::random_internal::ZScore(Mean(), m); - const bool pass = - absl::random_internal::Near("z", z_mean, 0.0, max_err) && - absl::random_internal::Near("z_variance", z_variance, 0.0, max_err); - if (!pass) { - ABSL_INTERNAL_LOG( - INFO, - absl::StrFormat( - "Beta(%f, %f), " - "mean: sample %f, expect %f, which is %f stddevs away, " - "variance: sample %f, expect %f, which is %f stddevs away.", - alpha_, beta_, m.mean, Mean(), - std::abs(m.mean - Mean()) / mean_stddev, m.variance, Variance(), - std::abs(m.variance - Variance()) / variance_stddev)); - } - return pass; -} - -template <class D> -bool BetaDistributionTest::SingleChiSquaredTest(double p, size_t samples, - size_t buckets) { - constexpr double kErr = 1e-7; - std::vector<double> cutoffs, expected; - const double bucket_width = 1.0 / static_cast<double>(buckets); - int i = 1; - int unmerged_buckets = 0; - for (; i < buckets; ++i) { - const double p = bucket_width * static_cast<double>(i); - const double boundary = - absl::random_internal::BetaIncompleteInv(alpha_, beta_, p); - // The intention is to add `boundary` to the list of `cutoffs`. It becomes - // problematic, however, when the boundary values are not monotone, due to - // numerical issues when computing the inverse regularized incomplete - // Beta function. In these cases, we merge that bucket with its previous - // neighbor and merge their expected counts. - if ((cutoffs.empty() && boundary < kErr) || - (!cutoffs.empty() && boundary <= cutoffs.back())) { - unmerged_buckets++; - continue; - } - if (boundary >= 1.0 - 1e-10) { - break; - } - cutoffs.push_back(boundary); - expected.push_back(static_cast<double>(1 + unmerged_buckets) * - bucket_width * static_cast<double>(samples)); - unmerged_buckets = 0; - } - cutoffs.push_back(std::numeric_limits<double>::infinity()); - // Merge all remaining buckets. - expected.push_back(static_cast<double>(buckets - i + 1) * bucket_width * - static_cast<double>(samples)); - // Make sure that we don't merge all the buckets, making this test - // meaningless. - EXPECT_GE(cutoffs.size(), 3) << alpha_ << ", " << beta_; - - D dis(alpha_, beta_); - - std::vector<int32_t> counts(cutoffs.size(), 0); - for (int i = 0; i < samples; i++) { - const double x = dis(rng_); - auto it = std::upper_bound(cutoffs.begin(), cutoffs.end(), x); - counts[std::distance(cutoffs.begin(), it)]++; - } - - // Null-hypothesis is that the distribution is beta distributed with the - // provided alpha, beta params (not estimated from the data). - const int dof = cutoffs.size() - 1; - - const double chi_square = absl::random_internal::ChiSquare( - counts.begin(), counts.end(), expected.begin(), expected.end()); - const bool pass = - (absl::random_internal::ChiSquarePValue(chi_square, dof) >= p); - if (!pass) { - for (int i = 0; i < cutoffs.size(); i++) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("cutoff[%d] = %f, actual count %d, expected %d", - i, cutoffs[i], counts[i], - static_cast<int>(expected[i]))); - } - - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat( - "Beta(%f, %f) %s %f, p = %f", alpha_, beta_, - absl::random_internal::kChiSquared, chi_square, - absl::random_internal::ChiSquarePValue(chi_square, dof))); - } - return pass; -} - -TEST_P(BetaDistributionTest, TestSampleStatistics) { - static constexpr int kRuns = 20; - static constexpr double kPFail = 0.02; - const double p = - absl::random_internal::RequiredSuccessProbability(kPFail, kRuns); - static constexpr int kSampleCount = 10000; - static constexpr int kBucketCount = 100; - int failed = 0; - for (int i = 0; i < kRuns; ++i) { - if (!SingleZTestOnMeanAndVariance<absl::beta_distribution<double>>( - p, kSampleCount)) { - failed++; - } - if (!SingleChiSquaredTest<absl::beta_distribution<double>>( - 0.005, kSampleCount, kBucketCount)) { - failed++; - } - } - // Set so that the test is not flaky at --runs_per_test=10000 - EXPECT_LE(failed, 5); -} - -std::string ParamName( - const ::testing::TestParamInfo<::testing::tuple<double, double>>& info) { - std::string name = absl::StrCat("alpha_", ::testing::get<0>(info.param), - "__beta_", ::testing::get<1>(info.param)); - return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}}); -} - -INSTANTIATE_TEST_CASE_P( - TestSampleStatisticsCombinations, BetaDistributionTest, - ::testing::Combine(::testing::Values(0.1, 0.2, 0.9, 1.1, 2.5, 10.0, 123.4), - ::testing::Values(0.1, 0.2, 0.9, 1.1, 2.5, 10.0, 123.4)), - ParamName); - -INSTANTIATE_TEST_CASE_P( - TestSampleStatistics_SelectedPairs, BetaDistributionTest, - ::testing::Values(std::make_pair(0.5, 1000), std::make_pair(1000, 0.5), - std::make_pair(900, 1000), std::make_pair(10000, 20000), - std::make_pair(4e5, 2e7), std::make_pair(1e7, 1e5)), - ParamName); - -// NOTE: absl::beta_distribution is not guaranteed to be stable. -TEST(BetaDistributionTest, StabilityTest) { - // absl::beta_distribution stability relies on the stability of - // absl::random_interna::RandU64ToDouble, std::exp, std::log, std::pow, - // and std::sqrt. - // - // This test also depends on the stability of std::frexp. - using testing::ElementsAre; - absl::random_internal::sequence_urbg urbg({ - 0xffff00000000e6c8ull, 0xffff0000000006c8ull, 0x800003766295CFA9ull, - 0x11C819684E734A41ull, 0x832603766295CFA9ull, 0x7fbe76c8b4395800ull, - 0xB3472DCA7B14A94Aull, 0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, - 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, 0x00035C904C70A239ull, - 0x00009E0BCBAADE14ull, 0x0000000000622CA7ull, 0x4864f22c059bf29eull, - 0x247856d8b862665cull, 0xe46e86e9a1337e10ull, 0xd8c8541f3519b133ull, - 0xffe75b52c567b9e4ull, 0xfffff732e5709c5bull, 0xff1f7f0b983532acull, - 0x1ec2e8986d2362caull, 0xC332DDEFBE6C5AA5ull, 0x6558218568AB9702ull, - 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, 0xECDD4775619F1510ull, - 0x814c8e35fe9a961aull, 0x0c3cd59c9b638a02ull, 0xcb3bb6478a07715cull, - 0x1224e62c978bbc7full, 0x671ef2cb04e81f6eull, 0x3c1cbd811eaf1808ull, - 0x1bbc23cfa8fac721ull, 0xa4c2cda65e596a51ull, 0xb77216fad37adf91ull, - 0x836d794457c08849ull, 0xe083df03475f49d7ull, 0xbc9feb512e6b0d6cull, - 0xb12d74fdd718c8c5ull, 0x12ff09653bfbe4caull, 0x8dd03a105bc4ee7eull, - 0x5738341045ba0d85ull, 0xf3fd722dc65ad09eull, 0xfa14fd21ea2a5705ull, - 0xffe6ea4d6edb0c73ull, 0xD07E9EFE2BF11FB4ull, 0x95DBDA4DAE909198ull, - 0xEAAD8E716B93D5A0ull, 0xD08ED1D0AFC725E0ull, 0x8E3C5B2F8E7594B7ull, - 0x8FF6E2FBF2122B64ull, 0x8888B812900DF01Cull, 0x4FAD5EA0688FC31Cull, - 0xD1CFF191B3A8C1ADull, 0x2F2F2218BE0E1777ull, 0xEA752DFE8B021FA1ull, - }); - - // Convert the real-valued result into a unit64 where we compare - // 5 (float) or 10 (double) decimal digits plus the base-2 exponent. - auto float_to_u64 = [](float d) { - int exp = 0; - auto f = std::frexp(d, &exp); - return (static_cast<uint64_t>(1e5 * f) * 10000) + std::abs(exp); - }; - auto double_to_u64 = [](double d) { - int exp = 0; - auto f = std::frexp(d, &exp); - return (static_cast<uint64_t>(1e10 * f) * 10000) + std::abs(exp); - }; - - std::vector<uint64_t> output(20); - { - // Algorithm Joehnk (float) - absl::beta_distribution<float> dist(0.1f, 0.2f); - std::generate(std::begin(output), std::end(output), - [&] { return float_to_u64(dist(urbg)); }); - EXPECT_EQ(44, urbg.invocations()); - EXPECT_THAT(output, // - testing::ElementsAre( - 998340000, 619030004, 500000001, 999990000, 996280000, - 500000001, 844740004, 847210001, 999970000, 872320000, - 585480007, 933280000, 869080042, 647670031, 528240004, - 969980004, 626050008, 915930002, 833440033, 878040015)); - } - - urbg.reset(); - { - // Algorithm Joehnk (double) - absl::beta_distribution<double> dist(0.1, 0.2); - std::generate(std::begin(output), std::end(output), - [&] { return double_to_u64(dist(urbg)); }); - EXPECT_EQ(44, urbg.invocations()); - EXPECT_THAT( - output, // - testing::ElementsAre( - 99834713000000, 61903356870004, 50000000000001, 99999721170000, - 99628374770000, 99999999990000, 84474397860004, 84721276240001, - 99997407490000, 87232528120000, 58548364780007, 93328932910000, - 86908237770042, 64767917930031, 52824581970004, 96998544140004, - 62605946270008, 91593604380002, 83345031740033, 87804397230015)); - } - - urbg.reset(); - { - // Algorithm Cheng 1 - absl::beta_distribution<double> dist(0.9, 2.0); - std::generate(std::begin(output), std::end(output), - [&] { return double_to_u64(dist(urbg)); }); - EXPECT_EQ(62, urbg.invocations()); - EXPECT_THAT( - output, // - testing::ElementsAre( - 62069004780001, 64433204450001, 53607416560000, 89644295430008, - 61434586310019, 55172615890002, 62187161490000, 56433684810003, - 80454622050005, 86418558710003, 92920514700001, 64645184680001, - 58549183380000, 84881283650005, 71078728590002, 69949694970000, - 73157461710001, 68592191300001, 70747623900000, 78584696930005)); - } - - urbg.reset(); - { - // Algorithm Cheng 2 - absl::beta_distribution<double> dist(1.5, 2.5); - std::generate(std::begin(output), std::end(output), - [&] { return double_to_u64(dist(urbg)); }); - EXPECT_EQ(54, urbg.invocations()); - EXPECT_THAT( - output, // - testing::ElementsAre( - 75000029250001, 76751482860001, 53264575220000, 69193133650005, - 78028324470013, 91573587560002, 59167523770000, 60658618560002, - 80075870540000, 94141320460004, 63196592770003, 78883906300002, - 96797992590001, 76907587800001, 56645167560000, 65408302280003, - 53401156320001, 64731238570000, 83065573750001, 79788333820001)); - } -} - -// This is an implementation-specific test. If any part of the implementation -// changes, then it is likely that this test will change as well. Also, if -// dependencies of the distribution change, such as RandU64ToDouble, then this -// is also likely to change. -TEST(BetaDistributionTest, AlgorithmBounds) { - { - absl::random_internal::sequence_urbg urbg( - {0x7fbe76c8b4395800ull, 0x8000000000000000ull}); - // u=0.499, v=0.5 - absl::beta_distribution<double> dist(1e-4, 1e-4); - double a = dist(urbg); - EXPECT_EQ(a, 2.0202860861567108529e-09); - EXPECT_EQ(2, urbg.invocations()); - } - - // Test that both the float & double algorithms appropriately reject the - // initial draw. - { - // 1/alpha = 1/beta = 2. - absl::beta_distribution<float> dist(0.5, 0.5); - - // first two outputs are close to 1.0 - epsilon, - // thus: (u ^ 2 + v ^ 2) > 1.0 - absl::random_internal::sequence_urbg urbg( - {0xffff00000006e6c8ull, 0xffff00000007c7c8ull, 0x800003766295CFA9ull, - 0x11C819684E734A41ull}); - { - double y = absl::beta_distribution<double>(0.5, 0.5)(urbg); - EXPECT_EQ(4, urbg.invocations()); - EXPECT_EQ(y, 0.9810668952633862) << y; - } - - // ...and: log(u) * a ~= log(v) * b ~= -0.02 - // thus z ~= -0.02 + log(1 + e(~0)) - // ~= -0.02 + 0.69 - // thus z > 0 - urbg.reset(); - { - float x = absl::beta_distribution<float>(0.5, 0.5)(urbg); - EXPECT_EQ(4, urbg.invocations()); - EXPECT_NEAR(0.98106688261032104, x, 0.0000005) << x << "f"; - } - } -} - -} // namespace |