// 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/distributions.h" #include #include #include #include #include #include #include #include "gtest/gtest.h" #include "absl/meta/type_traits.h" #include "absl/numeric/int128.h" #include "absl/random/internal/distribution_test_util.h" #include "absl/random/random.h" namespace { constexpr int kSize = 400000; class RandomDistributionsTest : public testing::Test {}; struct Invalid {}; template auto InferredUniformReturnT(int) -> decltype(absl::Uniform(std::declval(), std::declval(), std::declval())); template Invalid InferredUniformReturnT(...); template auto InferredTaggedUniformReturnT(int) -> decltype(absl::Uniform(std::declval(), std::declval(), std::declval(), std::declval())); template Invalid InferredTaggedUniformReturnT(...); // Given types , CheckArgsInferType() verifies that // // absl::Uniform(gen, A{}, B{}) // // returns the type "Expect". // // This interface can also be used to assert that a given absl::Uniform() // overload does not exist / will not compile. Given types , the // expression // // decltype(absl::Uniform(..., std::declval(), std::declval())) // // will not compile, leaving the definition of InferredUniformReturnT to // resolve (via SFINAE) to the overload which returns type "Invalid". This // allows tests to assert that an invocation such as // // absl::Uniform(gen, 1.23f, std::numeric_limits::max() - 1) // // should not compile, since neither type, float nor int, can precisely // represent both endpoint-values. Writing: // // CheckArgsInferType() // // will assert that this overload does not exist. template void CheckArgsInferType() { static_assert( absl::conjunction< std::is_same(0))>, std::is_same(0))>>::value, ""); static_assert( absl::conjunction< std::is_same(0))>, std::is_same(0))>>::value, ""); } template auto ExplicitUniformReturnT(int) -> decltype(absl::Uniform( std::declval(), std::declval(), std::declval())); template Invalid ExplicitUniformReturnT(...); template auto ExplicitTaggedUniformReturnT(int) -> decltype(absl::Uniform( std::declval(), std::declval(), std::declval(), std::declval())); template Invalid ExplicitTaggedUniformReturnT(...); // Given types , CheckArgsReturnExpectedType() verifies that // // absl::Uniform(gen, A{}, B{}) // // returns the type "Expect", and that the function-overload has the signature // // Expect(URBG&, Expect, Expect) template void CheckArgsReturnExpectedType() { static_assert( absl::conjunction< std::is_same(0))>, std::is_same( 0))>>::value, ""); static_assert( absl::conjunction< std::is_same(0))>, std::is_same(0))>>::value, ""); } // Takes the type of `absl::Uniform(gen)` if valid or `Invalid` otherwise. template auto UniformNoBoundsReturnT(int) -> decltype(absl::Uniform(std::declval())); template Invalid UniformNoBoundsReturnT(...); TEST_F(RandomDistributionsTest, UniformTypeInference) { // Infers common types. CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); // Explicitly-specified return-values override inferences. CheckArgsReturnExpectedType(); CheckArgsReturnExpectedType(); CheckArgsReturnExpectedType(); CheckArgsReturnExpectedType(); CheckArgsReturnExpectedType(); CheckArgsReturnExpectedType(); CheckArgsReturnExpectedType(); // Properly promotes uint16_t. CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); // Properly promotes int16_t. CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); // Invalid (u)int16_t-pairings do not compile. // See "CheckArgsInferType" comments above, for how this is achieved. CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); // Properly promotes uint32_t. CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); // Properly promotes int32_t. CheckArgsInferType(); CheckArgsInferType(); // Invalid (u)int32_t-pairings do not compile. CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); // Invalid (u)int64_t-pairings do not compile. CheckArgsInferType(); CheckArgsInferType(); CheckArgsInferType(); // Properly promotes float. CheckArgsInferType(); } TEST_F(RandomDistributionsTest, UniformExamples) { // Examples. absl::InsecureBitGen gen; EXPECT_NE(1, absl::Uniform(gen, static_cast(0), 1.0f)); EXPECT_NE(1, absl::Uniform(gen, 0, 1.0)); EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, static_cast(0), 1.0f)); EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, 0, 1.0)); EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, -1, 1.0)); EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, -1, 1)); EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, 0, 1)); EXPECT_NE(1, absl::Uniform(gen, 0, 1)); } TEST_F(RandomDistributionsTest, UniformNoBounds) { absl::InsecureBitGen gen; absl::Uniform(gen); absl::Uniform(gen); absl::Uniform(gen); absl::Uniform(gen); absl::Uniform(gen); // Compile-time validity tests. // Allows unsigned ints. testing::StaticAssertTypeEq(0))>(); testing::StaticAssertTypeEq(0))>(); testing::StaticAssertTypeEq(0))>(); testing::StaticAssertTypeEq(0))>(); testing::StaticAssertTypeEq< absl::uint128, decltype(UniformNoBoundsReturnT(0))>(); // Disallows signed ints. testing::StaticAssertTypeEq(0))>(); testing::StaticAssertTypeEq(0))>(); testing::StaticAssertTypeEq(0))>(); testing::StaticAssertTypeEq(0))>(); testing::StaticAssertTypeEq< Invalid, decltype(UniformNoBoundsReturnT(0))>(); // Disallows float types. testing::StaticAssertTypeEq(0))>(); testing::StaticAssertTypeEq(0))>(); } TEST_F(RandomDistributionsTest, UniformNonsenseRanges) { // The ranges used in this test are undefined behavior. // The results are arbitrary and subject to future changes. #if (defined(__i386__) || defined(_M_IX86)) && FLT_EVAL_METHOD != 0 // We're using an x87-compatible FPU, and intermediate operations can be // performed with 80-bit floats. This produces slightly different results from // what we expect below. GTEST_SKIP() << "Skipping the test because we detected x87 floating-point semantics"; #endif absl::InsecureBitGen gen; // EXPECT_EQ(0, absl::Uniform(gen, 0, 0)); EXPECT_EQ(1, absl::Uniform(gen, 1, 0)); EXPECT_EQ(0, absl::Uniform(absl::IntervalOpenOpen, gen, 0, 0)); EXPECT_EQ(1, absl::Uniform(absl::IntervalOpenOpen, gen, 1, 0)); constexpr auto m = (std::numeric_limits::max)(); EXPECT_EQ(m, absl::Uniform(gen, m, m)); EXPECT_EQ(m, absl::Uniform(gen, m, m - 1)); EXPECT_EQ(m - 1, absl::Uniform(gen, m - 1, m)); EXPECT_EQ(m, absl::Uniform(absl::IntervalOpenOpen, gen, m, m)); EXPECT_EQ(m, absl::Uniform(absl::IntervalOpenOpen, gen, m, m - 1)); EXPECT_EQ(m - 1, absl::Uniform(absl::IntervalOpenOpen, gen, m - 1, m)); // EXPECT_EQ(0, absl::Uniform(gen, 0, 0)); EXPECT_EQ(1, absl::Uniform(gen, 1, 0)); EXPECT_EQ(0, absl::Uniform(absl::IntervalOpenOpen, gen, 0, 0)); EXPECT_EQ(1, absl::Uniform(absl::IntervalOpenOpen, gen, 1, 0)); constexpr auto l = (std::numeric_limits::min)(); constexpr auto r = (std::numeric_limits::max)(); EXPECT_EQ(l, absl::Uniform(gen, l, l)); EXPECT_EQ(r, absl::Uniform(gen, r, r)); EXPECT_EQ(r, absl::Uniform(gen, r, r - 1)); EXPECT_EQ(r - 1, absl::Uniform(gen, r - 1, r)); EXPECT_EQ(l, absl::Uniform(absl::IntervalOpenOpen, gen, l, l)); EXPECT_EQ(r, absl::Uniform(absl::IntervalOpenOpen, gen, r, r)); EXPECT_EQ(r, absl::Uniform(absl::IntervalOpenOpen, gen, r, r - 1)); EXPECT_EQ(r - 1, absl::Uniform(absl::IntervalOpenOpen, gen, r - 1, r)); // const double e = std::nextafter(1.0, 2.0); // 1 + epsilon const double f = std::nextafter(1.0, 0.0); // 1 - epsilon const double g = std::numeric_limits::denorm_min(); EXPECT_EQ(1.0, absl::Uniform(gen, 1.0, e)); EXPECT_EQ(1.0, absl::Uniform(gen, 1.0, f)); EXPECT_EQ(0.0, absl::Uniform(gen, 0.0, g)); EXPECT_EQ(e, absl::Uniform(absl::IntervalOpenOpen, gen, 1.0, e)); EXPECT_EQ(f, absl::Uniform(absl::IntervalOpenOpen, gen, 1.0, f)); EXPECT_EQ(g, absl::Uniform(absl::IntervalOpenOpen, gen, 0.0, g)); } // TODO(lar): Validate properties of non-default interval-semantics. TEST_F(RandomDistributionsTest, UniformReal) { std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { values[i] = absl::Uniform(gen, 0, 1.0); } const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(0.5, moments.mean, 0.02); EXPECT_NEAR(1 / 12.0, moments.variance, 0.02); EXPECT_NEAR(0.0, moments.skewness, 0.02); EXPECT_NEAR(9 / 5.0, moments.kurtosis, 0.02); } TEST_F(RandomDistributionsTest, UniformInt) { std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { const int64_t kMax = 1000000000000ll; int64_t j = absl::Uniform(absl::IntervalClosedClosed, gen, 0, kMax); // convert to double. values[i] = static_cast(j) / static_cast(kMax); } const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(0.5, moments.mean, 0.02); EXPECT_NEAR(1 / 12.0, moments.variance, 0.02); EXPECT_NEAR(0.0, moments.skewness, 0.02); EXPECT_NEAR(9 / 5.0, moments.kurtosis, 0.02); /* // NOTE: These are not supported by absl::Uniform, which is specialized // on integer and real valued types. enum E { E0, E1 }; // enum enum S : int { S0, S1 }; // signed enum enum U : unsigned int { U0, U1 }; // unsigned enum absl::Uniform(gen, E0, E1); absl::Uniform(gen, S0, S1); absl::Uniform(gen, U0, U1); */ } TEST_F(RandomDistributionsTest, Exponential) { std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { values[i] = absl::Exponential(gen); } const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(1.0, moments.mean, 0.02); EXPECT_NEAR(1.0, moments.variance, 0.025); EXPECT_NEAR(2.0, moments.skewness, 0.1); EXPECT_LT(5.0, moments.kurtosis); } TEST_F(RandomDistributionsTest, PoissonDefault) { std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { values[i] = absl::Poisson(gen); } const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(1.0, moments.mean, 0.02); EXPECT_NEAR(1.0, moments.variance, 0.02); EXPECT_NEAR(1.0, moments.skewness, 0.025); EXPECT_LT(2.0, moments.kurtosis); } TEST_F(RandomDistributionsTest, PoissonLarge) { constexpr double kMean = 100000000.0; std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { values[i] = absl::Poisson(gen, kMean); } const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(kMean, moments.mean, kMean * 0.015); EXPECT_NEAR(kMean, moments.variance, kMean * 0.015); EXPECT_NEAR(std::sqrt(kMean), moments.skewness, kMean * 0.02); EXPECT_LT(2.0, moments.kurtosis); } TEST_F(RandomDistributionsTest, Bernoulli) { constexpr double kP = 0.5151515151; std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { values[i] = absl::Bernoulli(gen, kP); } const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(kP, moments.mean, 0.01); } TEST_F(RandomDistributionsTest, Beta) { constexpr double kAlpha = 2.0; constexpr double kBeta = 3.0; std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { values[i] = absl::Beta(gen, kAlpha, kBeta); } const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(0.4, moments.mean, 0.01); } TEST_F(RandomDistributionsTest, Zipf) { std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { values[i] = absl::Zipf(gen, 100); } // The mean of a zipf distribution is: H(N, s-1) / H(N,s). // Given the parameter v = 1, this gives the following function: // (Hn(100, 1) - Hn(1,1)) / (Hn(100,2) - Hn(1,2)) = 6.5944 const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(6.5944, moments.mean, 2000) << moments; } TEST_F(RandomDistributionsTest, Gaussian) { std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { values[i] = absl::Gaussian(gen); } const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(0.0, moments.mean, 0.02); EXPECT_NEAR(1.0, moments.variance, 0.04); EXPECT_NEAR(0, moments.skewness, 0.2); EXPECT_NEAR(3.0, moments.kurtosis, 0.5); } TEST_F(RandomDistributionsTest, LogUniform) { std::vector values(kSize); absl::InsecureBitGen gen; for (int i = 0; i < kSize; i++) { values[i] = absl::LogUniform(gen, 0, (1 << 10) - 1); } // The mean is the sum of the fractional means of the uniform distributions: // [0..0][1..1][2..3][4..7][8..15][16..31][32..63] // [64..127][128..255][256..511][512..1023] const double mean = (0 + 1 + 1 + 2 + 3 + 4 + 7 + 8 + 15 + 16 + 31 + 32 + 63 + 64 + 127 + 128 + 255 + 256 + 511 + 512 + 1023) / (2.0 * 11.0); const auto moments = absl::random_internal::ComputeDistributionMoments(values); EXPECT_NEAR(mean, moments.mean, 2) << moments; } } // namespace