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Diffstat (limited to 'third_party/abseil_cpp/absl/random/discrete_distribution_test.cc')
| -rw-r--r-- | third_party/abseil_cpp/absl/random/discrete_distribution_test.cc | 250 |
1 files changed, 0 insertions, 250 deletions
diff --git a/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc b/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc deleted file mode 100644 index 6d007006ef..0000000000 --- a/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc +++ /dev/null @@ -1,250 +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/discrete_distribution.h" - -#include <cmath> -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <numeric> -#include <random> -#include <sstream> -#include <string> -#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/strip.h" - -namespace { - -template <typename IntType> -class DiscreteDistributionTypeTest : 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(DiscreteDistributionTypeTest, IntTypes); - -TYPED_TEST(DiscreteDistributionTypeTest, ParamSerializeTest) { - using param_type = - typename absl::discrete_distribution<TypeParam>::param_type; - - absl::discrete_distribution<TypeParam> empty; - EXPECT_THAT(empty.probabilities(), testing::ElementsAre(1.0)); - - absl::discrete_distribution<TypeParam> before({1.0, 2.0, 1.0}); - - // Validate that the probabilities sum to 1.0. We picked values which - // can be represented exactly to avoid floating-point roundoff error. - double s = 0; - for (const auto& x : before.probabilities()) { - s += x; - } - EXPECT_EQ(s, 1.0); - EXPECT_THAT(before.probabilities(), testing::ElementsAre(0.25, 0.5, 0.25)); - - // Validate the same data via an initializer list. - { - std::vector<double> data({1.0, 2.0, 1.0}); - - absl::discrete_distribution<TypeParam> via_param{ - param_type(std::begin(data), std::end(data))}; - - EXPECT_EQ(via_param, before); - } - - std::stringstream ss; - ss << before; - absl::discrete_distribution<TypeParam> after; - - EXPECT_NE(before, after); - - ss >> after; - - EXPECT_EQ(before, after); -} - -TYPED_TEST(DiscreteDistributionTypeTest, Constructor) { - auto fn = [](double x) { return x; }; - { - absl::discrete_distribution<int> unary(0, 1.0, 9.0, fn); - EXPECT_THAT(unary.probabilities(), testing::ElementsAre(1.0)); - } - - { - absl::discrete_distribution<int> unary(2, 1.0, 9.0, fn); - // => fn(1.0 + 0 * 4 + 2) => 3 - // => fn(1.0 + 1 * 4 + 2) => 7 - EXPECT_THAT(unary.probabilities(), testing::ElementsAre(0.3, 0.7)); - } -} - -TEST(DiscreteDistributionTest, InitDiscreteDistribution) { - using testing::Pair; - - { - std::vector<double> p({1.0, 2.0, 3.0}); - std::vector<std::pair<double, size_t>> q = - absl::random_internal::InitDiscreteDistribution(&p); - - EXPECT_THAT(p, testing::ElementsAre(1 / 6.0, 2 / 6.0, 3 / 6.0)); - - // Each bucket is p=1/3, so bucket 0 will send half it's traffic - // to bucket 2, while the rest will retain all of their traffic. - EXPECT_THAT(q, testing::ElementsAre(Pair(0.5, 2), // - Pair(1.0, 1), // - Pair(1.0, 2))); - } - - { - std::vector<double> p({1.0, 2.0, 3.0, 5.0, 2.0}); - - std::vector<std::pair<double, size_t>> q = - absl::random_internal::InitDiscreteDistribution(&p); - - EXPECT_THAT(p, testing::ElementsAre(1 / 13.0, 2 / 13.0, 3 / 13.0, 5 / 13.0, - 2 / 13.0)); - - // A more complex bucketing solution: Each bucket has p=0.2 - // So buckets 0, 1, 4 will send their alternate traffic elsewhere, which - // happens to be bucket 3. - // However, summing up that alternate traffic gives bucket 3 too much - // traffic, so it will send some traffic to bucket 2. - constexpr double b0 = 1.0 / 13.0 / 0.2; - constexpr double b1 = 2.0 / 13.0 / 0.2; - constexpr double b3 = (5.0 / 13.0 / 0.2) - ((1 - b0) + (1 - b1) + (1 - b1)); - - EXPECT_THAT(q, testing::ElementsAre(Pair(b0, 3), // - Pair(b1, 3), // - Pair(1.0, 2), // - Pair(b3, 2), // - Pair(b1, 3))); - } -} - -TEST(DiscreteDistributionTest, ChiSquaredTest50) { - using absl::random_internal::kChiSquared; - - constexpr size_t kTrials = 10000; - constexpr int kBuckets = 50; // inclusive, so actally +1 - - // 1-in-100000 threshold, but remember, there are about 8 tests - // in this file. And the test could fail for other reasons. - // Empirically validated with --runs_per_test=10000. - const int kThreshold = - absl::random_internal::ChiSquareValue(kBuckets, 0.99999); - - std::vector<double> weights(kBuckets, 0); - std::iota(std::begin(weights), std::end(weights), 1); - absl::discrete_distribution<int> dist(std::begin(weights), std::end(weights)); - - // 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); - - std::vector<int32_t> counts(kBuckets, 0); - for (size_t i = 0; i < kTrials; i++) { - auto x = dist(rng); - counts[x]++; - } - - // Scale weights. - double sum = 0; - for (double x : weights) { - sum += x; - } - for (double& x : weights) { - x = kTrials * (x / sum); - } - - double chi_square = - absl::random_internal::ChiSquare(std::begin(counts), std::end(counts), - std::begin(weights), std::end(weights)); - - 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 (size_t i = 0; i < counts.size(); i++) { - absl::StrAppend(&msg, i, ": ", counts[i], " vs ", weights[i], "\n"); - } - absl::StrAppend(&msg, kChiSquared, " p-value ", p_value, "\n"); - absl::StrAppend(&msg, "High ", kChiSquared, " value: ", chi_square, " > ", - kThreshold); - ABSL_RAW_LOG(INFO, "%s", msg.c_str()); - FAIL() << msg; - } -} - -TEST(DiscreteDistributionTest, StabilityTest) { - // absl::discrete_distribution stabilitiy relies on - // absl::uniform_int_distribution and absl::bernoulli_distribution. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(6); - - { - absl::discrete_distribution<int32_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); - EXPECT_EQ(0, dist.min()); - EXPECT_EQ(4, dist.max()); - for (auto& v : output) { - v = dist(urbg); - } - EXPECT_EQ(12, urbg.invocations()); - } - - // With 12 calls to urbg, each call into discrete_distribution consumes - // precisely 2 values: one for the uniform call, and a second for the - // bernoulli. - // - // Given the alt mapping: 0=>3, 1=>3, 2=>2, 3=>2, 4=>3, we can - // - // uniform: 443210143131 - // bernoulli: b0 000011100101 - // bernoulli: b1 001111101101 - // bernoulli: b2 111111111111 - // bernoulli: b3 001111101111 - // bernoulli: b4 001111101101 - // ... - EXPECT_THAT(output, testing::ElementsAre(3, 3, 1, 3, 3, 3)); - - { - urbg.reset(); - absl::discrete_distribution<int64_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); - EXPECT_EQ(0, dist.min()); - EXPECT_EQ(4, dist.max()); - for (auto& v : output) { - v = dist(urbg); - } - EXPECT_EQ(12, urbg.invocations()); - } - EXPECT_THAT(output, testing::ElementsAre(3, 3, 0, 3, 0, 4)); -} - -} // namespace |