| // 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_int_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/log/log.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" |
| |
| namespace { |
| |
| template <typename IntType> |
| class UniformIntDistributionTest : public ::testing::Test {}; |
| |
| using IntTypes = ::testing::Types<int8_t, uint8_t, int16_t, uint16_t, int32_t, |
| uint32_t, int64_t, uint64_t>; |
| TYPED_TEST_SUITE(UniformIntDistributionTest, IntTypes); |
| |
| TYPED_TEST(UniformIntDistributionTest, ParamSerializeTest) { |
| // This test essentially ensures that the parameters serialize, |
| // not that the values generated cover the full range. |
| using Limits = std::numeric_limits<TypeParam>; |
| using param_type = |
| typename absl::uniform_int_distribution<TypeParam>::param_type; |
| const TypeParam kMin = std::is_unsigned<TypeParam>::value ? 37 : -105; |
| const TypeParam kNegOneOrZero = std::is_unsigned<TypeParam>::value ? 0 : -1; |
| |
| constexpr int kCount = 1000; |
| absl::InsecureBitGen gen; |
| for (const auto& param : { |
| param_type(), |
| param_type(2, 2), // Same |
| param_type(9, 32), |
| param_type(kMin, 115), |
| param_type(kNegOneOrZero, Limits::max()), |
| param_type(Limits::min(), Limits::max()), |
| param_type(Limits::lowest(), Limits::max()), |
| param_type(Limits::min() + 1, Limits::max() - 1), |
| }) { |
| const auto a = param.a(); |
| const auto b = param.b(); |
| absl::uniform_int_distribution<TypeParam> before(a, b); |
| EXPECT_EQ(before.a(), param.a()); |
| EXPECT_EQ(before.b(), param.b()); |
| |
| { |
| // Initialize via param_type |
| absl::uniform_int_distribution<TypeParam> via_param(param); |
| EXPECT_EQ(via_param, before); |
| } |
| |
| // Initialize via iostreams |
| std::stringstream ss; |
| ss << before; |
| |
| absl::uniform_int_distribution<TypeParam> after(Limits::min() + 3, |
| Limits::max() - 5); |
| |
| 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); |
| EXPECT_GE(sample, after.min()); |
| EXPECT_LE(sample, after.max()); |
| if (sample > sample_max) { |
| sample_max = sample; |
| } |
| if (sample < sample_min) { |
| sample_min = sample; |
| } |
| } |
| LOG(INFO) << "Range: " << sample_min << ", " << sample_max; |
| } |
| } |
| |
| TYPED_TEST(UniformIntDistributionTest, ViolatesPreconditionsDeathTest) { |
| #if GTEST_HAS_DEATH_TEST |
| // Hi < Lo |
| EXPECT_DEBUG_DEATH({ absl::uniform_int_distribution<TypeParam> dist(10, 1); }, |
| ""); |
| #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_int_distribution<TypeParam> dist(10, 1); |
| auto x = dist(gen); |
| |
| // Any value will generate a non-empty string. |
| EXPECT_FALSE(absl::StrCat(+x).empty()) << x; |
| #endif // NDEBUG |
| } |
| |
| TYPED_TEST(UniformIntDistributionTest, TestMoments) { |
| constexpr int kSize = 100000; |
| using Limits = std::numeric_limits<TypeParam>; |
| using param_type = |
| typename absl::uniform_int_distribution<TypeParam>::param_type; |
| |
| // We use a fixed bit generator for distribution accuracy tests. This allows |
| // these tests to be deterministic, while still testing the quality of the |
| // implementation. |
| absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6}; |
| |
| std::vector<double> values(kSize); |
| for (const auto& param : |
| {param_type(0, Limits::max()), param_type(13, 127)}) { |
| absl::uniform_int_distribution<TypeParam> dist(param); |
| for (int i = 0; i < kSize; i++) { |
| const auto sample = dist(rng); |
| ASSERT_LE(dist.param().a(), sample); |
| ASSERT_GE(dist.param().b(), sample); |
| values[i] = sample; |
| } |
| |
| auto moments = absl::random_internal::ComputeDistributionMoments(values); |
| const double a = dist.param().a(); |
| const double b = dist.param().b(); |
| const double n = (b - a + 1); |
| const double mean = (a + b) / 2; |
| const double var = ((b - a + 1) * (b - a + 1) - 1) / 12; |
| const double kurtosis = 3 - 6 * (n * n + 1) / (5 * (n * n - 1)); |
| |
| // TODO(ahh): this is not the right bound |
| // empirically validated with --runs_per_test=10000. |
| EXPECT_NEAR(mean, moments.mean, 0.01 * var); |
| EXPECT_NEAR(var, moments.variance, 0.015 * var); |
| EXPECT_NEAR(0.0, moments.skewness, 0.025); |
| EXPECT_NEAR(kurtosis, moments.kurtosis, 0.02 * kurtosis); |
| } |
| } |
| |
| TYPED_TEST(UniformIntDistributionTest, ChiSquaredTest50) { |
| using absl::random_internal::kChiSquared; |
| |
| constexpr size_t kTrials = 1000; |
| constexpr int kBuckets = 50; // inclusive, so actually +1 |
| constexpr double kExpected = |
| static_cast<double>(kTrials) / static_cast<double>(kBuckets); |
| |
| // Empirically validated with --runs_per_test=10000. |
| const int kThreshold = |
| absl::random_internal::ChiSquareValue(kBuckets, 0.999999); |
| |
| const TypeParam min = std::is_unsigned<TypeParam>::value ? 37 : -37; |
| const TypeParam max = min + kBuckets; |
| |
| // We use a fixed bit generator for distribution accuracy tests. This allows |
| // these tests to be deterministic, while still testing the quality of the |
| // implementation. |
| absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6}; |
| |
| absl::uniform_int_distribution<TypeParam> dist(min, max); |
| |
| std::vector<int32_t> counts(kBuckets + 1, 0); |
| for (size_t i = 0; i < kTrials; i++) { |
| auto x = dist(rng); |
| counts[x - min]++; |
| } |
| 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); |
| LOG(INFO) << msg; |
| FAIL() << msg; |
| } |
| } |
| |
| TEST(UniformIntDistributionTest, StabilityTest) { |
| // absl::uniform_int_distribution stability relies only on integer operations. |
| 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_int_distribution<int32_t> dist(0, 4); |
| for (auto& v : output) { |
| v = dist(urbg); |
| } |
| } |
| EXPECT_EQ(12, urbg.invocations()); |
| EXPECT_THAT(output, testing::ElementsAre(4, 4, 3, 2, 1, 0, 1, 4, 3, 1, 3, 1)); |
| |
| { |
| urbg.reset(); |
| absl::uniform_int_distribution<int32_t> dist(0, 100); |
| for (auto& v : output) { |
| v = dist(urbg); |
| } |
| } |
| EXPECT_EQ(12, urbg.invocations()); |
| EXPECT_THAT(output, testing::ElementsAre(97, 86, 75, 41, 36, 16, 38, 92, 67, |
| 30, 80, 38)); |
| |
| { |
| urbg.reset(); |
| absl::uniform_int_distribution<int32_t> dist(0, 10000); |
| for (auto& v : output) { |
| v = dist(urbg); |
| } |
| } |
| EXPECT_EQ(12, urbg.invocations()); |
| EXPECT_THAT(output, testing::ElementsAre(9648, 8562, 7439, 4089, 3571, 1602, |
| 3813, 9195, 6641, 2986, 7956, 3765)); |
| } |
| |
| } // namespace |