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