Abseil Common Libraries (C++) (grcp 依赖)
https://abseil.io/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
494 lines
18 KiB
494 lines
18 KiB
// 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 <cmath> |
|
#include <cstdint> |
|
#include <random> |
|
#include <vector> |
|
|
|
#include "gtest/gtest.h" |
|
#include "absl/random/internal/distribution_test_util.h" |
|
#include "absl/random/random.h" |
|
|
|
namespace { |
|
|
|
constexpr int kSize = 400000; |
|
|
|
class RandomDistributionsTest : public testing::Test {}; |
|
|
|
TEST_F(RandomDistributionsTest, UniformBoundFunctions) { |
|
using absl::IntervalClosedClosed; |
|
using absl::IntervalClosedOpen; |
|
using absl::IntervalOpenClosed; |
|
using absl::IntervalOpenOpen; |
|
using absl::random_internal::uniform_lower_bound; |
|
using absl::random_internal::uniform_upper_bound; |
|
|
|
// absl::uniform_int_distribution natively assumes IntervalClosedClosed |
|
// absl::uniform_real_distribution natively assumes IntervalClosedOpen |
|
|
|
EXPECT_EQ(uniform_lower_bound(IntervalOpenClosed, 0, 100), 1); |
|
EXPECT_EQ(uniform_lower_bound(IntervalOpenOpen, 0, 100), 1); |
|
EXPECT_GT(uniform_lower_bound<float>(IntervalOpenClosed, 0, 1.0), 0); |
|
EXPECT_GT(uniform_lower_bound<float>(IntervalOpenOpen, 0, 1.0), 0); |
|
EXPECT_GT(uniform_lower_bound<double>(IntervalOpenClosed, 0, 1.0), 0); |
|
EXPECT_GT(uniform_lower_bound<double>(IntervalOpenOpen, 0, 1.0), 0); |
|
|
|
EXPECT_EQ(uniform_lower_bound(IntervalClosedClosed, 0, 100), 0); |
|
EXPECT_EQ(uniform_lower_bound(IntervalClosedOpen, 0, 100), 0); |
|
EXPECT_EQ(uniform_lower_bound<float>(IntervalClosedClosed, 0, 1.0), 0); |
|
EXPECT_EQ(uniform_lower_bound<float>(IntervalClosedOpen, 0, 1.0), 0); |
|
EXPECT_EQ(uniform_lower_bound<double>(IntervalClosedClosed, 0, 1.0), 0); |
|
EXPECT_EQ(uniform_lower_bound<double>(IntervalClosedOpen, 0, 1.0), 0); |
|
|
|
EXPECT_EQ(uniform_upper_bound(IntervalOpenOpen, 0, 100), 99); |
|
EXPECT_EQ(uniform_upper_bound(IntervalClosedOpen, 0, 100), 99); |
|
EXPECT_EQ(uniform_upper_bound<float>(IntervalOpenOpen, 0, 1.0), 1.0); |
|
EXPECT_EQ(uniform_upper_bound<float>(IntervalClosedOpen, 0, 1.0), 1.0); |
|
EXPECT_EQ(uniform_upper_bound<double>(IntervalOpenOpen, 0, 1.0), 1.0); |
|
EXPECT_EQ(uniform_upper_bound<double>(IntervalClosedOpen, 0, 1.0), 1.0); |
|
|
|
EXPECT_EQ(uniform_upper_bound(IntervalOpenClosed, 0, 100), 100); |
|
EXPECT_EQ(uniform_upper_bound(IntervalClosedClosed, 0, 100), 100); |
|
EXPECT_GT(uniform_upper_bound<float>(IntervalOpenClosed, 0, 1.0), 1.0); |
|
EXPECT_GT(uniform_upper_bound<float>(IntervalClosedClosed, 0, 1.0), 1.0); |
|
EXPECT_GT(uniform_upper_bound<double>(IntervalOpenClosed, 0, 1.0), 1.0); |
|
EXPECT_GT(uniform_upper_bound<double>(IntervalClosedClosed, 0, 1.0), 1.0); |
|
|
|
// Negative value tests |
|
EXPECT_EQ(uniform_lower_bound(IntervalOpenClosed, -100, -1), -99); |
|
EXPECT_EQ(uniform_lower_bound(IntervalOpenOpen, -100, -1), -99); |
|
EXPECT_GT(uniform_lower_bound<float>(IntervalOpenClosed, -2.0, -1.0), -2.0); |
|
EXPECT_GT(uniform_lower_bound<float>(IntervalOpenOpen, -2.0, -1.0), -2.0); |
|
EXPECT_GT(uniform_lower_bound<double>(IntervalOpenClosed, -2.0, -1.0), -2.0); |
|
EXPECT_GT(uniform_lower_bound<double>(IntervalOpenOpen, -2.0, -1.0), -2.0); |
|
|
|
EXPECT_EQ(uniform_lower_bound(IntervalClosedClosed, -100, -1), -100); |
|
EXPECT_EQ(uniform_lower_bound(IntervalClosedOpen, -100, -1), -100); |
|
EXPECT_EQ(uniform_lower_bound<float>(IntervalClosedClosed, -2.0, -1.0), -2.0); |
|
EXPECT_EQ(uniform_lower_bound<float>(IntervalClosedOpen, -2.0, -1.0), -2.0); |
|
EXPECT_EQ(uniform_lower_bound<double>(IntervalClosedClosed, -2.0, -1.0), |
|
-2.0); |
|
EXPECT_EQ(uniform_lower_bound<double>(IntervalClosedOpen, -2.0, -1.0), -2.0); |
|
|
|
EXPECT_EQ(uniform_upper_bound(IntervalOpenOpen, -100, -1), -2); |
|
EXPECT_EQ(uniform_upper_bound(IntervalClosedOpen, -100, -1), -2); |
|
EXPECT_EQ(uniform_upper_bound<float>(IntervalOpenOpen, -2.0, -1.0), -1.0); |
|
EXPECT_EQ(uniform_upper_bound<float>(IntervalClosedOpen, -2.0, -1.0), -1.0); |
|
EXPECT_EQ(uniform_upper_bound<double>(IntervalOpenOpen, -2.0, -1.0), -1.0); |
|
EXPECT_EQ(uniform_upper_bound<double>(IntervalClosedOpen, -2.0, -1.0), -1.0); |
|
|
|
EXPECT_EQ(uniform_upper_bound(IntervalOpenClosed, -100, -1), -1); |
|
EXPECT_EQ(uniform_upper_bound(IntervalClosedClosed, -100, -1), -1); |
|
EXPECT_GT(uniform_upper_bound<float>(IntervalOpenClosed, -2.0, -1.0), -1.0); |
|
EXPECT_GT(uniform_upper_bound<float>(IntervalClosedClosed, -2.0, -1.0), -1.0); |
|
EXPECT_GT(uniform_upper_bound<double>(IntervalOpenClosed, -2.0, -1.0), -1.0); |
|
EXPECT_GT(uniform_upper_bound<double>(IntervalClosedClosed, -2.0, -1.0), |
|
-1.0); |
|
|
|
// Edge cases: the next value toward itself is itself. |
|
const double d = 1.0; |
|
const float f = 1.0; |
|
EXPECT_EQ(uniform_lower_bound(IntervalOpenClosed, d, d), d); |
|
EXPECT_EQ(uniform_lower_bound(IntervalOpenClosed, f, f), f); |
|
|
|
EXPECT_GT(uniform_lower_bound(IntervalOpenClosed, 1.0, 2.0), 1.0); |
|
EXPECT_LT(uniform_lower_bound(IntervalOpenClosed, 1.0, +0.0), 1.0); |
|
EXPECT_LT(uniform_lower_bound(IntervalOpenClosed, 1.0, -0.0), 1.0); |
|
EXPECT_LT(uniform_lower_bound(IntervalOpenClosed, 1.0, -1.0), 1.0); |
|
|
|
EXPECT_EQ(uniform_upper_bound(IntervalClosedClosed, 0.0f, |
|
std::numeric_limits<float>::max()), |
|
std::numeric_limits<float>::max()); |
|
EXPECT_EQ(uniform_upper_bound(IntervalClosedClosed, 0.0, |
|
std::numeric_limits<double>::max()), |
|
std::numeric_limits<double>::max()); |
|
} |
|
|
|
struct Invalid {}; |
|
|
|
template <typename A, typename B> |
|
auto InferredUniformReturnT(int) |
|
-> decltype(absl::Uniform(std::declval<absl::InsecureBitGen&>(), |
|
std::declval<A>(), std::declval<B>())); |
|
|
|
template <typename, typename> |
|
Invalid InferredUniformReturnT(...); |
|
|
|
template <typename TagType, typename A, typename B> |
|
auto InferredTaggedUniformReturnT(int) |
|
-> decltype(absl::Uniform(std::declval<TagType>(), |
|
std::declval<absl::InsecureBitGen&>(), |
|
std::declval<A>(), std::declval<B>())); |
|
|
|
template <typename, typename, typename> |
|
Invalid InferredTaggedUniformReturnT(...); |
|
|
|
// Given types <A, B, Expect>, 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 <A, B>, the |
|
// expression |
|
// |
|
// decltype(absl::Uniform(..., std::declval<A>(), std::declval<B>())) |
|
// |
|
// will not compile, leaving the definition of InferredUniformReturnT<A, B> 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<int>::max() - 1) |
|
// |
|
// should not compile, since neither type, float nor int, can precisely |
|
// represent both endpoint-values. Writing: |
|
// |
|
// CheckArgsInferType<float, int, Invalid>() |
|
// |
|
// will assert that this overload does not exist. |
|
template <typename A, typename B, typename Expect> |
|
void CheckArgsInferType() { |
|
static_assert( |
|
absl::conjunction< |
|
std::is_same<Expect, decltype(InferredUniformReturnT<A, B>(0))>, |
|
std::is_same<Expect, |
|
decltype(InferredUniformReturnT<B, A>(0))>>::value, |
|
""); |
|
static_assert( |
|
absl::conjunction< |
|
std::is_same<Expect, |
|
decltype(InferredTaggedUniformReturnT< |
|
absl::random_internal::IntervalOpenOpenT, A, B>( |
|
0))>, |
|
std::is_same<Expect, |
|
decltype(InferredTaggedUniformReturnT< |
|
absl::random_internal::IntervalOpenOpenT, B, A>( |
|
0))>>::value, |
|
""); |
|
} |
|
|
|
template <typename A, typename B, typename ExplicitRet> |
|
auto ExplicitUniformReturnT(int) -> decltype( |
|
absl::Uniform<ExplicitRet>(*std::declval<absl::InsecureBitGen*>(), |
|
std::declval<A>(), std::declval<B>())); |
|
|
|
template <typename, typename, typename ExplicitRet> |
|
Invalid ExplicitUniformReturnT(...); |
|
|
|
template <typename TagType, typename A, typename B, typename ExplicitRet> |
|
auto ExplicitTaggedUniformReturnT(int) -> decltype(absl::Uniform<ExplicitRet>( |
|
std::declval<TagType>(), *std::declval<absl::InsecureBitGen*>(), |
|
std::declval<A>(), std::declval<B>())); |
|
|
|
template <typename, typename, typename, typename ExplicitRet> |
|
Invalid ExplicitTaggedUniformReturnT(...); |
|
|
|
// Given types <A, B, Expect>, CheckArgsReturnExpectedType() verifies that |
|
// |
|
// absl::Uniform<Expect>(gen, A{}, B{}) |
|
// |
|
// returns the type "Expect", and that the function-overload has the signature |
|
// |
|
// Expect(URBG&, Expect, Expect) |
|
template <typename A, typename B, typename Expect> |
|
void CheckArgsReturnExpectedType() { |
|
static_assert( |
|
absl::conjunction< |
|
std::is_same<Expect, |
|
decltype(ExplicitUniformReturnT<A, B, Expect>(0))>, |
|
std::is_same<Expect, decltype(ExplicitUniformReturnT<B, A, Expect>( |
|
0))>>::value, |
|
""); |
|
static_assert( |
|
absl::conjunction< |
|
std::is_same<Expect, |
|
decltype(ExplicitTaggedUniformReturnT< |
|
absl::random_internal::IntervalOpenOpenT, A, B, |
|
Expect>(0))>, |
|
std::is_same<Expect, |
|
decltype(ExplicitTaggedUniformReturnT< |
|
absl::random_internal::IntervalOpenOpenT, B, A, |
|
Expect>(0))>>::value, |
|
""); |
|
} |
|
|
|
TEST_F(RandomDistributionsTest, UniformTypeInference) { |
|
// Infers common types. |
|
CheckArgsInferType<uint16_t, uint16_t, uint16_t>(); |
|
CheckArgsInferType<uint32_t, uint32_t, uint32_t>(); |
|
CheckArgsInferType<uint64_t, uint64_t, uint64_t>(); |
|
CheckArgsInferType<int16_t, int16_t, int16_t>(); |
|
CheckArgsInferType<int32_t, int32_t, int32_t>(); |
|
CheckArgsInferType<int64_t, int64_t, int64_t>(); |
|
CheckArgsInferType<float, float, float>(); |
|
CheckArgsInferType<double, double, double>(); |
|
|
|
// Explicitly-specified return-values override inferences. |
|
CheckArgsReturnExpectedType<int16_t, int16_t, int32_t>(); |
|
CheckArgsReturnExpectedType<uint16_t, uint16_t, int32_t>(); |
|
CheckArgsReturnExpectedType<int16_t, int16_t, int64_t>(); |
|
CheckArgsReturnExpectedType<int16_t, int32_t, int64_t>(); |
|
CheckArgsReturnExpectedType<int16_t, int32_t, double>(); |
|
CheckArgsReturnExpectedType<float, float, double>(); |
|
CheckArgsReturnExpectedType<int, int, int16_t>(); |
|
|
|
// Properly promotes uint16_t. |
|
CheckArgsInferType<uint16_t, uint32_t, uint32_t>(); |
|
CheckArgsInferType<uint16_t, uint64_t, uint64_t>(); |
|
CheckArgsInferType<uint16_t, int32_t, int32_t>(); |
|
CheckArgsInferType<uint16_t, int64_t, int64_t>(); |
|
CheckArgsInferType<uint16_t, float, float>(); |
|
CheckArgsInferType<uint16_t, double, double>(); |
|
|
|
// Properly promotes int16_t. |
|
CheckArgsInferType<int16_t, int32_t, int32_t>(); |
|
CheckArgsInferType<int16_t, int64_t, int64_t>(); |
|
CheckArgsInferType<int16_t, float, float>(); |
|
CheckArgsInferType<int16_t, double, double>(); |
|
|
|
// Invalid (u)int16_t-pairings do not compile. |
|
// See "CheckArgsInferType" comments above, for how this is achieved. |
|
CheckArgsInferType<uint16_t, int16_t, Invalid>(); |
|
CheckArgsInferType<int16_t, uint32_t, Invalid>(); |
|
CheckArgsInferType<int16_t, uint64_t, Invalid>(); |
|
|
|
// Properly promotes uint32_t. |
|
CheckArgsInferType<uint32_t, uint64_t, uint64_t>(); |
|
CheckArgsInferType<uint32_t, int64_t, int64_t>(); |
|
CheckArgsInferType<uint32_t, double, double>(); |
|
|
|
// Properly promotes int32_t. |
|
CheckArgsInferType<int32_t, int64_t, int64_t>(); |
|
CheckArgsInferType<int32_t, double, double>(); |
|
|
|
// Invalid (u)int32_t-pairings do not compile. |
|
CheckArgsInferType<uint32_t, int32_t, Invalid>(); |
|
CheckArgsInferType<int32_t, uint64_t, Invalid>(); |
|
CheckArgsInferType<int32_t, float, Invalid>(); |
|
CheckArgsInferType<uint32_t, float, Invalid>(); |
|
|
|
// Invalid (u)int64_t-pairings do not compile. |
|
CheckArgsInferType<uint64_t, int64_t, Invalid>(); |
|
CheckArgsInferType<int64_t, float, Invalid>(); |
|
CheckArgsInferType<int64_t, double, Invalid>(); |
|
|
|
// Properly promotes float. |
|
CheckArgsInferType<float, double, double>(); |
|
|
|
// Examples. |
|
absl::InsecureBitGen gen; |
|
EXPECT_NE(1, absl::Uniform(gen, static_cast<uint16_t>(0), 1.0f)); |
|
EXPECT_NE(1, absl::Uniform(gen, 0, 1.0)); |
|
EXPECT_NE(1, absl::Uniform(absl::IntervalOpenOpen, gen, |
|
static_cast<uint16_t>(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<double>(absl::IntervalOpenOpen, gen, -1, 1)); |
|
EXPECT_NE(1, absl::Uniform<float>(absl::IntervalOpenOpen, gen, 0, 1)); |
|
EXPECT_NE(1, absl::Uniform<float>(gen, 0, 1)); |
|
} |
|
|
|
TEST_F(RandomDistributionsTest, UniformNoBounds) { |
|
absl::InsecureBitGen gen; |
|
|
|
absl::Uniform<uint8_t>(gen); |
|
absl::Uniform<uint16_t>(gen); |
|
absl::Uniform<uint32_t>(gen); |
|
absl::Uniform<uint64_t>(gen); |
|
} |
|
|
|
// TODO(lar): Validate properties of non-default interval-semantics. |
|
TEST_F(RandomDistributionsTest, UniformReal) { |
|
std::vector<double> 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<double> 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<double>(j) / static_cast<double>(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<double> values(kSize); |
|
|
|
absl::InsecureBitGen gen; |
|
for (int i = 0; i < kSize; i++) { |
|
values[i] = absl::Exponential<double>(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<double> values(kSize); |
|
|
|
absl::InsecureBitGen gen; |
|
for (int i = 0; i < kSize; i++) { |
|
values[i] = absl::Poisson<int64_t>(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<double> values(kSize); |
|
|
|
absl::InsecureBitGen gen; |
|
for (int i = 0; i < kSize; i++) { |
|
values[i] = absl::Poisson<int64_t>(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<double> 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<double> 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<double> values(kSize); |
|
|
|
absl::InsecureBitGen gen; |
|
for (int i = 0; i < kSize; i++) { |
|
values[i] = absl::Zipf<int64_t>(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<double> values(kSize); |
|
|
|
absl::InsecureBitGen gen; |
|
for (int i = 0; i < kSize; i++) { |
|
values[i] = absl::Gaussian<double>(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<double> values(kSize); |
|
|
|
absl::InsecureBitGen gen; |
|
for (int i = 0; i < kSize; i++) { |
|
values[i] = absl::LogUniform<int64_t>(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
|
|
|