// 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/uniform_int_distribution.h"
#include <cmath>
#include <cstdint>
#include <iterator>
#include <random>
#include <sstream>
#include <string>
#include <vector>
#include "gmock/gmock.h"
#include "gtest/gtest.h"
#include "absl/log/log.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"
namespace {
template <typename IntType>
class UniformIntDistributionTest : 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(UniformIntDistributionTest, IntTypes);
TYPED_TEST(UniformIntDistributionTest, ParamSerializeTest) {
// This test essentially ensures that the parameters serialize,
// not that the values generated cover the full range.
using Limits = std::numeric_limits<TypeParam>;
using param_type =
typename absl::uniform_int_distribution<TypeParam>::param_type;
const TypeParam kMin = std::is_unsigned<TypeParam>::value ? 37 : -105;
const TypeParam kNegOneOrZero = std::is_unsigned<TypeParam>::value ? 0 : -1;
constexpr int kCount = 1000;
absl::InsecureBitGen gen;
for (const auto& param : {
param_type(),
param_type(2, 2), // Same
param_type(9, 32),
param_type(kMin, 115),
param_type(kNegOneOrZero, Limits::max()),
param_type(Limits::min(), Limits::max()),
param_type(Limits::lowest(), Limits::max()),
param_type(Limits::min() + 1, Limits::max() - 1),
}) {
const auto a = param.a();
const auto b = param.b();
absl::uniform_int_distribution<TypeParam> before(a, b);
EXPECT_EQ(before.a(), param.a());
EXPECT_EQ(before.b(), param.b());
{
// Initialize via param_type
absl::uniform_int_distribution<TypeParam> via_param(param);
EXPECT_EQ(via_param, before);
}
// Initialize via iostreams
std::stringstream ss;
ss << before;
absl::uniform_int_distribution<TypeParam> after(Limits::min() + 3,
Limits::max() - 5);
EXPECT_NE(before.a(), after.a());
EXPECT_NE(before.b(), after.b());
EXPECT_NE(before.param(), after.param());
EXPECT_NE(before, after);
ss >> after;
EXPECT_EQ(before.a(), after.a());
EXPECT_EQ(before.b(), after.b());
EXPECT_EQ(before.param(), after.param());
EXPECT_EQ(before, after);
// Smoke test.
auto sample_min = after.max();
auto sample_max = after.min();
for (int i = 0; i < kCount; i++) {
auto sample = after(gen);
EXPECT_GE(sample, after.min());
EXPECT_LE(sample, after.max());
if (sample > sample_max) {
sample_max = sample;
}
if (sample < sample_min) {
sample_min = sample;
}
}
LOG(INFO) << "Range: " << sample_min << ", " << sample_max;
}
}
TYPED_TEST(UniformIntDistributionTest, ViolatesPreconditionsDeathTest) {
#if GTEST_HAS_DEATH_TEST
// Hi < Lo
EXPECT_DEBUG_DEATH({ absl::uniform_int_distribution<TypeParam> dist(10, 1); },
"");
#endif // GTEST_HAS_DEATH_TEST
#if defined(NDEBUG)
// opt-mode, for invalid parameters, will generate a garbage value,
// but should not enter an infinite loop.
absl::InsecureBitGen gen;
absl::uniform_int_distribution<TypeParam> dist(10, 1);
auto x = dist(gen);
// Any value will generate a non-empty string.
EXPECT_FALSE(absl::StrCat(+x).empty()) << x;
#endif // NDEBUG
}
TYPED_TEST(UniformIntDistributionTest, TestMoments) {
constexpr int kSize = 100000;
using Limits = std::numeric_limits<TypeParam>;
using param_type =
typename absl::uniform_int_distribution<TypeParam>::param_type;
// We use a fixed bit generator for distribution accuracy tests. This allows
// these tests to be deterministic, while still testing the quality of the
// implementation.
absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6};
std::vector<double> values(kSize);
for (const auto& param :
{param_type(0, Limits::max()), param_type(13, 127)}) {
absl::uniform_int_distribution<TypeParam> dist(param);
for (int i = 0; i < kSize; i++) {
const auto sample = dist(rng);
ASSERT_LE(dist.param().a(), sample);
ASSERT_GE(dist.param().b(), sample);
values[i] = sample;
}
auto moments = absl::random_internal::ComputeDistributionMoments(values);
const double a = dist.param().a();
const double b = dist.param().b();
const double n = (b - a + 1);
const double mean = (a + b) / 2;
const double var = ((b - a + 1) * (b - a + 1) - 1) / 12;
const double kurtosis = 3 - 6 * (n * n + 1) / (5 * (n * n - 1));
// TODO(ahh): this is not the right bound
// empirically validated with --runs_per_test=10000.
EXPECT_NEAR(mean, moments.mean, 0.01 * var);
EXPECT_NEAR(var, moments.variance, 0.015 * var);
EXPECT_NEAR(0.0, moments.skewness, 0.025);
EXPECT_NEAR(kurtosis, moments.kurtosis, 0.02 * kurtosis);
}
}
TYPED_TEST(UniformIntDistributionTest, ChiSquaredTest50) {
using absl::random_internal::kChiSquared;
constexpr size_t kTrials = 1000;
constexpr int kBuckets = 50; // inclusive, so actually +1
constexpr double kExpected =
static_cast<double>(kTrials) / static_cast<double>(kBuckets);
// Empirically validated with --runs_per_test=10000.
const int kThreshold =
absl::random_internal::ChiSquareValue(kBuckets, 0.999999);
const TypeParam min = std::is_unsigned<TypeParam>::value ? 37 : -37;
const TypeParam max = min + kBuckets;
// We use a fixed bit generator for distribution accuracy tests. This allows
// these tests to be deterministic, while still testing the quality of the
// implementation.
absl::random_internal::pcg64_2018_engine rng{0x2B7E151628AED2A6};
absl::uniform_int_distribution<TypeParam> dist(min, max);
std::vector<int32_t> counts(kBuckets + 1, 0);
for (size_t i = 0; i < kTrials; i++) {
auto x = dist(rng);
counts[x - min]++;
}
double chi_square = absl::random_internal::ChiSquareWithExpected(
std::begin(counts), std::end(counts), kExpected);
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 (const auto& a : counts) {
absl::StrAppend(&msg, a, "\n");
}
absl::StrAppend(&msg, kChiSquared, " p-value ", p_value, "\n");
absl::StrAppend(&msg, "High ", kChiSquared, " value: ", chi_square, " > ",
kThreshold);
LOG(INFO) << msg;
FAIL() << msg;
}
}
TEST(UniformIntDistributionTest, StabilityTest) {
// absl::uniform_int_distribution stability relies only on integer operations.
absl::random_internal::sequence_urbg urbg(
{0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull,
0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull,
0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull,
0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull});
std::vector<int> output(12);
{
absl::uniform_int_distribution<int32_t> dist(0, 4);
for (auto& v : output) {
v = dist(urbg);
}
}
EXPECT_EQ(12, urbg.invocations());
EXPECT_THAT(output, testing::ElementsAre(4, 4, 3, 2, 1, 0, 1, 4, 3, 1, 3, 1));
{
urbg.reset();
absl::uniform_int_distribution<int32_t> dist(0, 100);
for (auto& v : output) {
v = dist(urbg);
}
}
EXPECT_EQ(12, urbg.invocations());
EXPECT_THAT(output, testing::ElementsAre(97, 86, 75, 41, 36, 16, 38, 92, 67,
30, 80, 38));
{
urbg.reset();
absl::uniform_int_distribution<int32_t> dist(0, 10000);
for (auto& v : output) {
v = dist(urbg);
}
}
EXPECT_EQ(12, urbg.invocations());
EXPECT_THAT(output, testing::ElementsAre(9648, 8562, 7439, 4089, 3571, 1602,
3813, 9195, 6641, 2986, 7956, 3765));
}
} // namespace
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