Creation of LTS branch "lts_2019_08_08"20190808

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GitOrigin-RevId: 9ee91d3e430fb33a4590486573792eb0fa146c2d
Change-Id: Ia06e548bc106cc9d136f6c65714be6645317aced

1 files changed, 322 insertions, 0 deletions

diff --git a/absl/random/uniform_real_distribution_test.cc b/absl/random/uniform_real_distribution_test.cc
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+++ b/absl/random/uniform_real_distribution_test.cc
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+// 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_real_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/base/internal/raw_logging.h"
+#include "absl/random/internal/chi_square.h"
+#include "absl/random/internal/distribution_test_util.h"
+#include "absl/random/internal/sequence_urbg.h"
+#include "absl/random/random.h"
+#include "absl/strings/str_cat.h"
+
+// NOTES:
+// * Some documentation on generating random real values suggests that
+//   it is possible to use std::nextafter(b, DBL_MAX) to generate a value on
+//   the closed range [a, b]. Unfortunately, that technique is not universally
+//   reliable due to floating point quantization.
+//
+// * absl::uniform_real_distribution<float> generates between 2^28 and 2^29
+//   distinct floating point values in the range [0, 1).
+//
+// * absl::uniform_real_distribution<float> generates at least 2^23 distinct
+//   floating point values in the range [1, 2). This should be the same as
+//   any other range covered by a single exponent in IEEE 754.
+//
+// * absl::uniform_real_distribution<double> generates more than 2^52 distinct
+//   values in the range [0, 1), and should generate at least 2^52 distinct
+//   values in the range of [1, 2).
+//
+
+namespace {
+
+template <typename RealType>
+class UniformRealDistributionTest : public ::testing::Test {};
+
+using RealTypes = ::testing::Types<float, double, long double>;
+TYPED_TEST_SUITE(UniformRealDistributionTest, RealTypes);
+
+TYPED_TEST(UniformRealDistributionTest, ParamSerializeTest) {
+  using param_type =
+      typename absl::uniform_real_distribution<TypeParam>::param_type;
+
+  constexpr const TypeParam a{1152921504606846976};
+
+  constexpr int kCount = 1000;
+  absl::InsecureBitGen gen;
+  for (const auto& param : {
+           param_type(),
+           param_type(TypeParam(2.0), TypeParam(2.0)),  // Same
+           param_type(TypeParam(-0.1), TypeParam(0.1)),
+           param_type(TypeParam(0.05), TypeParam(0.12)),
+           param_type(TypeParam(-0.05), TypeParam(0.13)),
+           param_type(TypeParam(-0.05), TypeParam(-0.02)),
+           // double range = 0
+           // 2^60 , 2^60 + 2^6
+           param_type(a, TypeParam(1152921504606847040)),
+           // 2^60 , 2^60 + 2^7
+           param_type(a, TypeParam(1152921504606847104)),
+           // double range = 2^8
+           // 2^60 , 2^60 + 2^8
+           param_type(a, TypeParam(1152921504606847232)),
+           // float range = 0
+           // 2^60 , 2^60 + 2^36
+           param_type(a, TypeParam(1152921573326323712)),
+           // 2^60 , 2^60 + 2^37
+           param_type(a, TypeParam(1152921642045800448)),
+           // float range = 2^38
+           // 2^60 , 2^60 + 2^38
+           param_type(a, TypeParam(1152921779484753920)),
+           // Limits
+           param_type(0, std::numeric_limits<TypeParam>::max()),
+           param_type(std::numeric_limits<TypeParam>::lowest(), 0),
+           param_type(0, std::numeric_limits<TypeParam>::epsilon()),
+           param_type(-std::numeric_limits<TypeParam>::epsilon(),
+                      std::numeric_limits<TypeParam>::epsilon()),
+           param_type(std::numeric_limits<TypeParam>::epsilon(),
+                      2 * std::numeric_limits<TypeParam>::epsilon()),
+       }) {
+    // Validate parameters.
+    const auto a = param.a();
+    const auto b = param.b();
+    absl::uniform_real_distribution<TypeParam> before(a, b);
+    EXPECT_EQ(before.a(), param.a());
+    EXPECT_EQ(before.b(), param.b());
+
+    {
+      absl::uniform_real_distribution<TypeParam> via_param(param);
+      EXPECT_EQ(via_param, before);
+    }
+
+    std::stringstream ss;
+    ss << before;
+    absl::uniform_real_distribution<TypeParam> after(TypeParam(1.0),
+                                                     TypeParam(3.1));
+
+    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);
+      // Failure here indicates a bug in uniform_real_distribution::operator(),
+      // or bad parameters--range too large, etc.
+      if (after.min() == after.max()) {
+        EXPECT_EQ(sample, after.min());
+      } else {
+        EXPECT_GE(sample, after.min());
+        EXPECT_LT(sample, after.max());
+      }
+      if (sample > sample_max) {
+        sample_max = sample;
+      }
+      if (sample < sample_min) {
+        sample_min = sample;
+      }
+    }
+
+    if (!std::is_same<TypeParam, long double>::value) {
+      // static_cast<double>(long double) can overflow.
+      std::string msg = absl::StrCat("Range: ", static_cast<double>(sample_min),
+                                     ", ", static_cast<double>(sample_max));
+      ABSL_RAW_LOG(INFO, "%s", msg.c_str());
+    }
+  }
+}
+
+TYPED_TEST(UniformRealDistributionTest, ViolatesPreconditionsDeathTest) {
+#if GTEST_HAS_DEATH_TEST
+  // Hi < Lo
+  EXPECT_DEBUG_DEATH(
+      { absl::uniform_real_distribution<TypeParam> dist(10.0, 1.0); }, "");
+
+  // Hi - Lo > numeric_limits<>::max()
+  EXPECT_DEBUG_DEATH(
+      {
+        absl::uniform_real_distribution<TypeParam> dist(
+            std::numeric_limits<TypeParam>::lowest(),
+            std::numeric_limits<TypeParam>::max());
+      },
+      "");
+#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_real_distribution<TypeParam> dist(10.0, 1.0);
+    auto x = dist(gen);
+    EXPECT_FALSE(std::isnan(x)) << x;
+  }
+  {
+    absl::uniform_real_distribution<TypeParam> dist(
+        std::numeric_limits<TypeParam>::lowest(),
+        std::numeric_limits<TypeParam>::max());
+    auto x = dist(gen);
+    // Infinite result.
+    EXPECT_FALSE(std::isfinite(x)) << x;
+  }
+#endif  // NDEBUG
+}
+
+TYPED_TEST(UniformRealDistributionTest, TestMoments) {
+  constexpr int kSize = 1000000;
+  std::vector<double> values(kSize);
+
+  absl::InsecureBitGen rng;
+  absl::uniform_real_distribution<TypeParam> dist;
+  for (int i = 0; i < kSize; i++) {
+    values[i] = dist(rng);
+  }
+
+  const auto moments =
+      absl::random_internal::ComputeDistributionMoments(values);
+  EXPECT_NEAR(0.5, moments.mean, 0.01);
+  EXPECT_NEAR(1 / 12.0, moments.variance, 0.015);
+  EXPECT_NEAR(0.0, moments.skewness, 0.02);
+  EXPECT_NEAR(9 / 5.0, moments.kurtosis, 0.015);
+}
+
+TYPED_TEST(UniformRealDistributionTest, ChiSquaredTest50) {
+  using absl::random_internal::kChiSquared;
+  using param_type =
+      typename absl::uniform_real_distribution<TypeParam>::param_type;
+
+  constexpr size_t kTrials = 100000;
+  constexpr int kBuckets = 50;
+  constexpr double kExpected =
+      static_cast<double>(kTrials) / static_cast<double>(kBuckets);
+
+  // 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 - 1, 0.999999);
+
+  absl::InsecureBitGen rng;
+  for (const auto& param : {param_type(0, 1), param_type(5, 12),
+                            param_type(-5, 13), param_type(-5, -2)}) {
+    const double min_val = param.a();
+    const double max_val = param.b();
+    const double factor = kBuckets / (max_val - min_val);
+
+    std::vector<int32_t> counts(kBuckets, 0);
+    absl::uniform_real_distribution<TypeParam> dist(param);
+    for (size_t i = 0; i < kTrials; i++) {
+      auto x = dist(rng);
+      auto bucket = static_cast<size_t>((x - min_val) * factor);
+      counts[bucket]++;
+    }
+
+    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);
+      ABSL_RAW_LOG(INFO, "%s", msg.c_str());
+      FAIL() << msg;
+    }
+  }
+}
+
+TYPED_TEST(UniformRealDistributionTest, StabilityTest) {
+  // absl::uniform_real_distribution stability relies only on
+  // random_internal::RandU64ToDouble and random_internal::RandU64ToFloat.
+  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_real_distribution<TypeParam> dist;
+  std::generate(std::begin(output), std::end(output), [&] {
+    return static_cast<int>(TypeParam(1000000) * dist(urbg));
+  });
+
+  EXPECT_THAT(
+      output,  //
+      testing::ElementsAre(59, 999246, 762494, 395876, 167716, 82545, 925251,
+                           77341, 12527, 708791, 834451, 932808));
+}
+
+TEST(UniformRealDistributionTest, AlgorithmBounds) {
+  absl::uniform_real_distribution<double> dist;
+
+  {
+    // This returns the smallest value >0 from absl::uniform_real_distribution.
+    absl::random_internal::sequence_urbg urbg({0x0000000000000001ull});
+    double a = dist(urbg);
+    EXPECT_EQ(a, 5.42101086242752217004e-20);
+  }
+
+  {
+    // This returns a value very near 0.5 from absl::uniform_real_distribution.
+    absl::random_internal::sequence_urbg urbg({0x7fffffffffffffefull});
+    double a = dist(urbg);
+    EXPECT_EQ(a, 0.499999999999999944489);
+  }
+  {
+    // This returns a value very near 0.5 from absl::uniform_real_distribution.
+    absl::random_internal::sequence_urbg urbg({0x8000000000000000ull});
+    double a = dist(urbg);
+    EXPECT_EQ(a, 0.5);
+  }
+
+  {
+    // This returns the largest value <1 from absl::uniform_real_distribution.
+    absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFEFull});
+    double a = dist(urbg);
+    EXPECT_EQ(a, 0.999999999999999888978);
+  }
+  {
+    // This *ALSO* returns the largest value <1.
+    absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFFFull});
+    double a = dist(urbg);
+    EXPECT_EQ(a, 0.999999999999999888978);
+  }
+}
+
+}  // namespace