252 lines
8.1 KiB
C++
252 lines
8.1 KiB
C++
// 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::_;
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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, _), //
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Pair(1.0, _)));
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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, _), //
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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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