| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250 | // 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
 |