Abseil Common Libraries (C++) (grcp 依赖)
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250 lines
8.1 KiB
250 lines
8.1 KiB
// 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/discrete_distribution.h" |
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#include <cmath> |
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#include <cstddef> |
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#include <cstdint> |
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#include <iterator> |
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#include <numeric> |
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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/pcg_engine.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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#include "absl/strings/strip.h" |
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namespace { |
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template <typename IntType> |
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class DiscreteDistributionTypeTest : public ::testing::Test {}; |
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using IntTypes = ::testing::Types<int8_t, uint8_t, int16_t, uint16_t, int32_t, |
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uint32_t, int64_t, uint64_t>; |
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TYPED_TEST_SUITE(DiscreteDistributionTypeTest, IntTypes); |
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TYPED_TEST(DiscreteDistributionTypeTest, ParamSerializeTest) { |
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using param_type = |
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typename absl::discrete_distribution<TypeParam>::param_type; |
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absl::discrete_distribution<TypeParam> empty; |
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EXPECT_THAT(empty.probabilities(), testing::ElementsAre(1.0)); |
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absl::discrete_distribution<TypeParam> before({1.0, 2.0, 1.0}); |
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// Validate that the probabilities sum to 1.0. We picked values which |
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// can be represented exactly to avoid floating-point roundoff error. |
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double s = 0; |
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for (const auto& x : before.probabilities()) { |
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s += x; |
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} |
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EXPECT_EQ(s, 1.0); |
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EXPECT_THAT(before.probabilities(), testing::ElementsAre(0.25, 0.5, 0.25)); |
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// Validate the same data via an initializer list. |
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{ |
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std::vector<double> data({1.0, 2.0, 1.0}); |
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absl::discrete_distribution<TypeParam> via_param{ |
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param_type(std::begin(data), std::end(data))}; |
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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::discrete_distribution<TypeParam> after; |
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EXPECT_NE(before, after); |
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ss >> after; |
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EXPECT_EQ(before, after); |
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} |
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TYPED_TEST(DiscreteDistributionTypeTest, Constructor) { |
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auto fn = [](double x) { return x; }; |
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{ |
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absl::discrete_distribution<int> unary(0, 1.0, 9.0, fn); |
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EXPECT_THAT(unary.probabilities(), testing::ElementsAre(1.0)); |
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} |
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{ |
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absl::discrete_distribution<int> unary(2, 1.0, 9.0, fn); |
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// => fn(1.0 + 0 * 4 + 2) => 3 |
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// => fn(1.0 + 1 * 4 + 2) => 7 |
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EXPECT_THAT(unary.probabilities(), testing::ElementsAre(0.3, 0.7)); |
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} |
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} |
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TEST(DiscreteDistributionTest, InitDiscreteDistribution) { |
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using testing::Pair; |
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{ |
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std::vector<double> p({1.0, 2.0, 3.0}); |
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std::vector<std::pair<double, size_t>> q = |
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absl::random_internal::InitDiscreteDistribution(&p); |
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EXPECT_THAT(p, testing::ElementsAre(1 / 6.0, 2 / 6.0, 3 / 6.0)); |
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// Each bucket is p=1/3, so bucket 0 will send half it's traffic |
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// to bucket 2, while the rest will retain all of their traffic. |
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EXPECT_THAT(q, testing::ElementsAre(Pair(0.5, 2), // |
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Pair(1.0, 1), // |
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Pair(1.0, 2))); |
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} |
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{ |
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std::vector<double> p({1.0, 2.0, 3.0, 5.0, 2.0}); |
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std::vector<std::pair<double, size_t>> q = |
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absl::random_internal::InitDiscreteDistribution(&p); |
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EXPECT_THAT(p, testing::ElementsAre(1 / 13.0, 2 / 13.0, 3 / 13.0, 5 / 13.0, |
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2 / 13.0)); |
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// A more complex bucketing solution: Each bucket has p=0.2 |
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// So buckets 0, 1, 4 will send their alternate traffic elsewhere, which |
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// happens to be bucket 3. |
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// However, summing up that alternate traffic gives bucket 3 too much |
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// traffic, so it will send some traffic to bucket 2. |
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constexpr double b0 = 1.0 / 13.0 / 0.2; |
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constexpr double b1 = 2.0 / 13.0 / 0.2; |
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constexpr double b3 = (5.0 / 13.0 / 0.2) - ((1 - b0) + (1 - b1) + (1 - b1)); |
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EXPECT_THAT(q, testing::ElementsAre(Pair(b0, 3), // |
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Pair(b1, 3), // |
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Pair(1.0, 2), // |
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Pair(b3, 2), // |
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Pair(b1, 3))); |
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} |
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} |
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TEST(DiscreteDistributionTest, ChiSquaredTest50) { |
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using absl::random_internal::kChiSquared; |
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constexpr size_t kTrials = 10000; |
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constexpr int kBuckets = 50; // inclusive, so actally +1 |
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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, 0.99999); |
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std::vector<double> weights(kBuckets, 0); |
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std::iota(std::begin(weights), std::end(weights), 1); |
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absl::discrete_distribution<int> dist(std::begin(weights), std::end(weights)); |
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// We use a fixed bit generator for distribution accuracy tests. This allows |
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// these tests to be deterministic, while still testing the qualify of the |
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// implementation. |
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absl::random_internal::pcg64_2018_engine rng(0x2B7E151628AED2A6); |
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std::vector<int32_t> counts(kBuckets, 0); |
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for (size_t i = 0; i < kTrials; i++) { |
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auto x = dist(rng); |
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counts[x]++; |
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} |
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// Scale weights. |
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double sum = 0; |
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for (double x : weights) { |
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sum += x; |
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} |
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for (double& x : weights) { |
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x = kTrials * (x / sum); |
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} |
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double chi_square = |
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absl::random_internal::ChiSquare(std::begin(counts), std::end(counts), |
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std::begin(weights), std::end(weights)); |
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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 (size_t i = 0; i < counts.size(); i++) { |
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absl::StrAppend(&msg, i, ": ", counts[i], " vs ", weights[i], "\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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TEST(DiscreteDistributionTest, StabilityTest) { |
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// absl::discrete_distribution stabilitiy relies on |
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// absl::uniform_int_distribution and absl::bernoulli_distribution. |
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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(6); |
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{ |
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absl::discrete_distribution<int32_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); |
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EXPECT_EQ(0, dist.min()); |
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EXPECT_EQ(4, dist.max()); |
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for (auto& v : output) { |
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v = dist(urbg); |
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} |
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EXPECT_EQ(12, urbg.invocations()); |
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} |
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// With 12 calls to urbg, each call into discrete_distribution consumes |
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// precisely 2 values: one for the uniform call, and a second for the |
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// bernoulli. |
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// |
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// Given the alt mapping: 0=>3, 1=>3, 2=>2, 3=>2, 4=>3, we can |
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// |
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// uniform: 443210143131 |
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// bernoulli: b0 000011100101 |
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// bernoulli: b1 001111101101 |
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// bernoulli: b2 111111111111 |
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// bernoulli: b3 001111101111 |
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// bernoulli: b4 001111101101 |
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// ... |
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EXPECT_THAT(output, testing::ElementsAre(3, 3, 1, 3, 3, 3)); |
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{ |
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urbg.reset(); |
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absl::discrete_distribution<int64_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); |
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EXPECT_EQ(0, dist.min()); |
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EXPECT_EQ(4, dist.max()); |
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for (auto& v : output) { |
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v = dist(urbg); |
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} |
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EXPECT_EQ(12, urbg.invocations()); |
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} |
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EXPECT_THAT(output, testing::ElementsAre(3, 3, 0, 3, 0, 4)); |
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} |
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} // namespace
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