summaryrefslogtreecommitdiff
path: root/absl/random/gaussian_distribution_test.cc
diff options
context:
space:
mode:
Diffstat (limited to 'absl/random/gaussian_distribution_test.cc')
-rw-r--r--absl/random/gaussian_distribution_test.cc573
1 files changed, 573 insertions, 0 deletions
diff --git a/absl/random/gaussian_distribution_test.cc b/absl/random/gaussian_distribution_test.cc
new file mode 100644
index 00000000..47c2989d
--- /dev/null
+++ b/absl/random/gaussian_distribution_test.cc
@@ -0,0 +1,573 @@
+// 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/gaussian_distribution.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstddef>
+#include <ios>
+#include <iterator>
+#include <random>
+#include <string>
+#include <vector>
+
+#include "gmock/gmock.h"
+#include "gtest/gtest.h"
+#include "absl/base/internal/raw_logging.h"
+#include "absl/base/macros.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"
+#include "absl/strings/str_format.h"
+#include "absl/strings/str_replace.h"
+#include "absl/strings/strip.h"
+
+namespace {
+
+using absl::random_internal::kChiSquared;
+
+template <typename RealType>
+class GaussianDistributionInterfaceTest : public ::testing::Test {};
+
+using RealTypes = ::testing::Types<float, double, long double>;
+TYPED_TEST_CASE(GaussianDistributionInterfaceTest, RealTypes);
+
+TYPED_TEST(GaussianDistributionInterfaceTest, SerializeTest) {
+ using param_type =
+ typename absl::gaussian_distribution<TypeParam>::param_type;
+
+ const TypeParam kParams[] = {
+ // Cases around 1.
+ 1, //
+ std::nextafter(TypeParam(1), TypeParam(0)), // 1 - epsilon
+ std::nextafter(TypeParam(1), TypeParam(2)), // 1 + epsilon
+ // Arbitrary values.
+ TypeParam(1e-8), TypeParam(1e-4), TypeParam(2), TypeParam(1e4),
+ TypeParam(1e8), TypeParam(1e20), TypeParam(2.5),
+ // Boundary cases.
+ std::numeric_limits<TypeParam>::infinity(),
+ std::numeric_limits<TypeParam>::max(),
+ std::numeric_limits<TypeParam>::epsilon(),
+ std::nextafter(std::numeric_limits<TypeParam>::min(),
+ TypeParam(1)), // min + epsilon
+ std::numeric_limits<TypeParam>::min(), // smallest normal
+ // There are some errors dealing with denorms on apple platforms.
+ std::numeric_limits<TypeParam>::denorm_min(), // smallest denorm
+ std::numeric_limits<TypeParam>::min() / 2,
+ std::nextafter(std::numeric_limits<TypeParam>::min(),
+ TypeParam(0)), // denorm_max
+ };
+
+ constexpr int kCount = 1000;
+ absl::InsecureBitGen gen;
+
+ // Use a loop to generate the combinations of {+/-x, +/-y}, and assign x, y to
+ // all values in kParams,
+ for (const auto mod : {0, 1, 2, 3}) {
+ for (const auto x : kParams) {
+ if (!std::isfinite(x)) continue;
+ for (const auto y : kParams) {
+ const TypeParam mean = (mod & 0x1) ? -x : x;
+ const TypeParam stddev = (mod & 0x2) ? -y : y;
+ const param_type param(mean, stddev);
+
+ absl::gaussian_distribution<TypeParam> before(mean, stddev);
+ EXPECT_EQ(before.mean(), param.mean());
+ EXPECT_EQ(before.stddev(), param.stddev());
+
+ {
+ absl::gaussian_distribution<TypeParam> via_param(param);
+ EXPECT_EQ(via_param, before);
+ EXPECT_EQ(via_param.param(), before.param());
+ }
+
+ // Smoke test.
+ auto sample_min = before.max();
+ auto sample_max = before.min();
+ for (int i = 0; i < kCount; i++) {
+ auto sample = before(gen);
+ if (sample > sample_max) sample_max = sample;
+ if (sample < sample_min) sample_min = sample;
+ EXPECT_GE(sample, before.min()) << before;
+ EXPECT_LE(sample, before.max()) << before;
+ }
+ if (!std::is_same<TypeParam, long double>::value) {
+ ABSL_INTERNAL_LOG(
+ INFO, absl::StrFormat("Range{%f, %f}: %f, %f", mean, stddev,
+ sample_min, sample_max));
+ }
+
+ std::stringstream ss;
+ ss << before;
+
+ if (!std::isfinite(mean) || !std::isfinite(stddev)) {
+ // Streams do not parse inf/nan.
+ continue;
+ }
+
+ // Validate stream serialization.
+ absl::gaussian_distribution<TypeParam> after(-0.53f, 2.3456f);
+
+ EXPECT_NE(before.mean(), after.mean());
+ EXPECT_NE(before.stddev(), after.stddev());
+ EXPECT_NE(before.param(), after.param());
+ EXPECT_NE(before, after);
+
+ ss >> after;
+
+#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \
+ defined(__ppc__) || defined(__PPC__)
+ if (std::is_same<TypeParam, long double>::value) {
+ // Roundtripping floating point values requires sufficient precision
+ // to reconstruct the exact value. It turns out that long double
+ // has some errors doing this on ppc, particularly for values
+ // near {1.0 +/- epsilon}.
+ if (mean <= std::numeric_limits<double>::max() &&
+ mean >= std::numeric_limits<double>::lowest()) {
+ EXPECT_EQ(static_cast<double>(before.mean()),
+ static_cast<double>(after.mean()))
+ << ss.str();
+ }
+ if (stddev <= std::numeric_limits<double>::max() &&
+ stddev >= std::numeric_limits<double>::lowest()) {
+ EXPECT_EQ(static_cast<double>(before.stddev()),
+ static_cast<double>(after.stddev()))
+ << ss.str();
+ }
+ continue;
+ }
+#endif
+
+ EXPECT_EQ(before.mean(), after.mean());
+ EXPECT_EQ(before.stddev(), after.stddev()) //
+ << ss.str() << " " //
+ << (ss.good() ? "good " : "") //
+ << (ss.bad() ? "bad " : "") //
+ << (ss.eof() ? "eof " : "") //
+ << (ss.fail() ? "fail " : "");
+ }
+ }
+ }
+}
+
+// http://www.itl.nist.gov/div898/handbook/eda/section3/eda3661.htm
+
+class GaussianModel {
+ public:
+ GaussianModel(double mean, double stddev) : mean_(mean), stddev_(stddev) {}
+
+ double mean() const { return mean_; }
+ double variance() const { return stddev() * stddev(); }
+ double stddev() const { return stddev_; }
+ double skew() const { return 0; }
+ double kurtosis() const { return 3.0; }
+
+ // The inverse CDF, or PercentPoint function.
+ double InverseCDF(double p) {
+ ABSL_ASSERT(p >= 0.0);
+ ABSL_ASSERT(p < 1.0);
+ return mean() + stddev() * -absl::random_internal::InverseNormalSurvival(p);
+ }
+
+ private:
+ const double mean_;
+ const double stddev_;
+};
+
+struct Param {
+ double mean;
+ double stddev;
+ double p_fail; // Z-Test probability of failure.
+ int trials; // Z-Test trials.
+};
+
+// GaussianDistributionTests implements a z-test for the gaussian
+// distribution.
+class GaussianDistributionTests : public testing::TestWithParam<Param>,
+ public GaussianModel {
+ public:
+ GaussianDistributionTests()
+ : GaussianModel(GetParam().mean, GetParam().stddev) {}
+
+ // SingleZTest provides a basic z-squared test of the mean vs. expected
+ // mean for data generated by the poisson distribution.
+ template <typename D>
+ bool SingleZTest(const double p, const size_t samples);
+
+ // SingleChiSquaredTest provides a basic chi-squared test of the normal
+ // distribution.
+ template <typename D>
+ double SingleChiSquaredTest();
+
+ absl::InsecureBitGen rng_;
+};
+
+template <typename D>
+bool GaussianDistributionTests::SingleZTest(const double p,
+ const size_t samples) {
+ D dis(mean(), stddev());
+
+ std::vector<double> data;
+ data.reserve(samples);
+ for (size_t i = 0; i < samples; i++) {
+ const double x = dis(rng_);
+ data.push_back(x);
+ }
+
+ const double max_err = absl::random_internal::MaxErrorTolerance(p);
+ const auto m = absl::random_internal::ComputeDistributionMoments(data);
+ const double z = absl::random_internal::ZScore(mean(), m);
+ const bool pass = absl::random_internal::Near("z", z, 0.0, max_err);
+
+ // NOTE: Informational statistical test:
+ //
+ // Compute the Jarque-Bera test statistic given the excess skewness
+ // and kurtosis. The statistic is drawn from a chi-square(2) distribution.
+ // https://en.wikipedia.org/wiki/Jarque%E2%80%93Bera_test
+ //
+ // The null-hypothesis (normal distribution) is rejected when
+ // (p = 0.05 => jb > 5.99)
+ // (p = 0.01 => jb > 9.21)
+ // NOTE: JB has a large type-I error rate, so it will reject the
+ // null-hypothesis even when it is true more often than the z-test.
+ //
+ const double jb =
+ static_cast<double>(m.n) / 6.0 *
+ (std::pow(m.skewness, 2.0) + std::pow(m.kurtosis - 3.0, 2.0) / 4.0);
+
+ if (!pass || jb > 9.21) {
+ ABSL_INTERNAL_LOG(
+ INFO, absl::StrFormat("p=%f max_err=%f\n"
+ " mean=%f vs. %f\n"
+ " stddev=%f vs. %f\n"
+ " skewness=%f vs. %f\n"
+ " kurtosis=%f vs. %f\n"
+ " z=%f vs. 0\n"
+ " jb=%f vs. 9.21",
+ p, max_err, m.mean, mean(), std::sqrt(m.variance),
+ stddev(), m.skewness, skew(), m.kurtosis,
+ kurtosis(), z, jb));
+ }
+ return pass;
+}
+
+template <typename D>
+double GaussianDistributionTests::SingleChiSquaredTest() {
+ const size_t kSamples = 10000;
+ const int kBuckets = 50;
+
+ // The InverseCDF is the percent point function of the
+ // distribution, and can be used to assign buckets
+ // roughly uniformly.
+ std::vector<double> cutoffs;
+ const double kInc = 1.0 / static_cast<double>(kBuckets);
+ for (double p = kInc; p < 1.0; p += kInc) {
+ cutoffs.push_back(InverseCDF(p));
+ }
+ if (cutoffs.back() != std::numeric_limits<double>::infinity()) {
+ cutoffs.push_back(std::numeric_limits<double>::infinity());
+ }
+
+ D dis(mean(), stddev());
+
+ std::vector<int32_t> counts(cutoffs.size(), 0);
+ for (int j = 0; j < kSamples; j++) {
+ const double x = dis(rng_);
+ auto it = std::upper_bound(cutoffs.begin(), cutoffs.end(), x);
+ counts[std::distance(cutoffs.begin(), it)]++;
+ }
+
+ // Null-hypothesis is that the distribution is a gaussian distribution
+ // with the provided mean and stddev (not estimated from the data).
+ const int dof = static_cast<int>(counts.size()) - 1;
+
+ // Our threshold for logging is 1-in-50.
+ const double threshold = absl::random_internal::ChiSquareValue(dof, 0.98);
+
+ const double expected =
+ static_cast<double>(kSamples) / static_cast<double>(counts.size());
+
+ double chi_square = absl::random_internal::ChiSquareWithExpected(
+ std::begin(counts), std::end(counts), expected);
+ double p = absl::random_internal::ChiSquarePValue(chi_square, dof);
+
+ // Log if the chi_square value is above the threshold.
+ if (chi_square > threshold) {
+ for (int i = 0; i < cutoffs.size(); i++) {
+ ABSL_INTERNAL_LOG(
+ INFO, absl::StrFormat("%d : (%f) = %d", i, cutoffs[i], counts[i]));
+ }
+
+ ABSL_INTERNAL_LOG(
+ INFO, absl::StrCat("mean=", mean(), " stddev=", stddev(), "\n", //
+ " expected ", expected, "\n", //
+ kChiSquared, " ", chi_square, " (", p, ")\n", //
+ kChiSquared, " @ 0.98 = ", threshold));
+ }
+ return p;
+}
+
+TEST_P(GaussianDistributionTests, ZTest) {
+ // TODO(absl-team): Run these tests against std::normal_distribution<double>
+ // to validate outcomes are similar.
+ const size_t kSamples = 10000;
+ const auto& param = GetParam();
+ const int expected_failures =
+ std::max(1, static_cast<int>(std::ceil(param.trials * param.p_fail)));
+ const double p = absl::random_internal::RequiredSuccessProbability(
+ param.p_fail, param.trials);
+
+ int failures = 0;
+ for (int i = 0; i < param.trials; i++) {
+ failures +=
+ SingleZTest<absl::gaussian_distribution<double>>(p, kSamples) ? 0 : 1;
+ }
+ EXPECT_LE(failures, expected_failures);
+}
+
+TEST_P(GaussianDistributionTests, ChiSquaredTest) {
+ const int kTrials = 20;
+ int failures = 0;
+
+ for (int i = 0; i < kTrials; i++) {
+ double p_value =
+ SingleChiSquaredTest<absl::gaussian_distribution<double>>();
+ if (p_value < 0.0025) { // 1/400
+ failures++;
+ }
+ }
+ // There is a 0.05% chance of producing at least one failure, so raise the
+ // failure threshold high enough to allow for a flake rate of less than one in
+ // 10,000.
+ EXPECT_LE(failures, 4);
+}
+
+std::vector<Param> GenParams() {
+ return {
+ // Mean around 0.
+ Param{0.0, 1.0, 0.01, 100},
+ Param{0.0, 1e2, 0.01, 100},
+ Param{0.0, 1e4, 0.01, 100},
+ Param{0.0, 1e8, 0.01, 100},
+ Param{0.0, 1e16, 0.01, 100},
+ Param{0.0, 1e-3, 0.01, 100},
+ Param{0.0, 1e-5, 0.01, 100},
+ Param{0.0, 1e-9, 0.01, 100},
+ Param{0.0, 1e-17, 0.01, 100},
+
+ // Mean around 1.
+ Param{1.0, 1.0, 0.01, 100},
+ Param{1.0, 1e2, 0.01, 100},
+ Param{1.0, 1e-2, 0.01, 100},
+
+ // Mean around 100 / -100
+ Param{1e2, 1.0, 0.01, 100},
+ Param{-1e2, 1.0, 0.01, 100},
+ Param{1e2, 1e6, 0.01, 100},
+ Param{-1e2, 1e6, 0.01, 100},
+
+ // More extreme
+ Param{1e4, 1e4, 0.01, 100},
+ Param{1e8, 1e4, 0.01, 100},
+ Param{1e12, 1e4, 0.01, 100},
+ };
+}
+
+std::string ParamName(const ::testing::TestParamInfo<Param>& info) {
+ const auto& p = info.param;
+ std::string name = absl::StrCat("mean_", absl::SixDigits(p.mean), "__stddev_",
+ absl::SixDigits(p.stddev));
+ return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}});
+}
+
+INSTANTIATE_TEST_SUITE_P(, GaussianDistributionTests,
+ ::testing::ValuesIn(GenParams()), ParamName);
+
+// NOTE: absl::gaussian_distribution is not guaranteed to be stable.
+TEST(GaussianDistributionTest, StabilityTest) {
+ // absl::gaussian_distribution stability relies on the underlying zignor
+ // data, absl::random_interna::RandU64ToDouble, std::exp, std::log, and
+ // std::abs.
+ absl::random_internal::sequence_urbg urbg(
+ {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull,
+ 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull,
+ 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull,
+ 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull});
+
+ std::vector<int> output(11);
+
+ {
+ absl::gaussian_distribution<double> dist;
+ std::generate(std::begin(output), std::end(output),
+ [&] { return static_cast<int>(10000000.0 * dist(urbg)); });
+
+ EXPECT_EQ(13, urbg.invocations());
+ EXPECT_THAT(output, //
+ testing::ElementsAre(1494, 25518841, 9991550, 1351856,
+ -20373238, 3456682, 333530, -6804981,
+ -15279580, -16459654, 1494));
+ }
+
+ urbg.reset();
+ {
+ absl::gaussian_distribution<float> dist;
+ std::generate(std::begin(output), std::end(output),
+ [&] { return static_cast<int>(1000000.0f * dist(urbg)); });
+
+ EXPECT_EQ(13, urbg.invocations());
+ EXPECT_THAT(
+ output, //
+ testing::ElementsAre(149, 2551884, 999155, 135185, -2037323, 345668,
+ 33353, -680498, -1527958, -1645965, 149));
+ }
+}
+
+// This is an implementation-specific test. If any part of the implementation
+// changes, then it is likely that this test will change as well.
+// Also, if dependencies of the distribution change, such as RandU64ToDouble,
+// then this is also likely to change.
+TEST(GaussianDistributionTest, AlgorithmBounds) {
+ absl::gaussian_distribution<double> dist;
+
+ // In ~95% of cases, a single value is used to generate the output.
+ // for all inputs where |x| < 0.750461021389 this should be the case.
+ //
+ // The exact constraints are based on the ziggurat tables, and any
+ // changes to the ziggurat tables may require adjusting these bounds.
+ //
+ // for i in range(0, len(X)-1):
+ // print i, X[i+1]/X[i], (X[i+1]/X[i] > 0.984375)
+ //
+ // 0.125 <= |values| <= 0.75
+ const uint64_t kValues[] = {
+ 0x1000000000000100ull, 0x2000000000000100ull, 0x3000000000000100ull,
+ 0x4000000000000100ull, 0x5000000000000100ull, 0x6000000000000100ull,
+ // negative values
+ 0x9000000000000100ull, 0xa000000000000100ull, 0xb000000000000100ull,
+ 0xc000000000000100ull, 0xd000000000000100ull, 0xe000000000000100ull};
+
+ // 0.875 <= |values| <= 0.984375
+ const uint64_t kExtraValues[] = {
+ 0x7000000000000100ull, 0x7800000000000100ull, //
+ 0x7c00000000000100ull, 0x7e00000000000100ull, //
+ // negative values
+ 0xf000000000000100ull, 0xf800000000000100ull, //
+ 0xfc00000000000100ull, 0xfe00000000000100ull};
+
+ auto make_box = [](uint64_t v, uint64_t box) {
+ return (v & 0xffffffffffffff80ull) | box;
+ };
+
+ // The box is the lower 7 bits of the value. When the box == 0, then
+ // the algorithm uses an escape hatch to select the result for large
+ // outputs.
+ for (uint64_t box = 0; box < 0x7f; box++) {
+ for (const uint64_t v : kValues) {
+ // Extra values are added to the sequence to attempt to avoid
+ // infinite loops from rejection sampling on bugs/errors.
+ absl::random_internal::sequence_urbg urbg(
+ {make_box(v, box), 0x0003eb76f6f7f755ull, 0x5FCEA50FDB2F953Bull});
+
+ auto a = dist(urbg);
+ EXPECT_EQ(1, urbg.invocations()) << box << " " << std::hex << v;
+ if (v & 0x8000000000000000ull) {
+ EXPECT_LT(a, 0.0) << box << " " << std::hex << v;
+ } else {
+ EXPECT_GT(a, 0.0) << box << " " << std::hex << v;
+ }
+ }
+ if (box > 10 && box < 100) {
+ // The center boxes use the fast algorithm for more
+ // than 98.4375% of values.
+ for (const uint64_t v : kExtraValues) {
+ absl::random_internal::sequence_urbg urbg(
+ {make_box(v, box), 0x0003eb76f6f7f755ull, 0x5FCEA50FDB2F953Bull});
+
+ auto a = dist(urbg);
+ EXPECT_EQ(1, urbg.invocations()) << box << " " << std::hex << v;
+ if (v & 0x8000000000000000ull) {
+ EXPECT_LT(a, 0.0) << box << " " << std::hex << v;
+ } else {
+ EXPECT_GT(a, 0.0) << box << " " << std::hex << v;
+ }
+ }
+ }
+ }
+
+ // When the box == 0, the fallback algorithm uses a ratio of uniforms,
+ // which consumes 2 additional values from the urbg.
+ // Fallback also requires that the initial value be > 0.9271586026096681.
+ auto make_fallback = [](uint64_t v) { return (v & 0xffffffffffffff80ull); };
+
+ double tail[2];
+ {
+ // 0.9375
+ absl::random_internal::sequence_urbg urbg(
+ {make_fallback(0x7800000000000000ull), 0x13CCA830EB61BD96ull,
+ 0x00000076f6f7f755ull});
+ tail[0] = dist(urbg);
+ EXPECT_EQ(3, urbg.invocations());
+ EXPECT_GT(tail[0], 0);
+ }
+ {
+ // -0.9375
+ absl::random_internal::sequence_urbg urbg(
+ {make_fallback(0xf800000000000000ull), 0x13CCA830EB61BD96ull,
+ 0x00000076f6f7f755ull});
+ tail[1] = dist(urbg);
+ EXPECT_EQ(3, urbg.invocations());
+ EXPECT_LT(tail[1], 0);
+ }
+ EXPECT_EQ(tail[0], -tail[1]);
+ EXPECT_EQ(418610, static_cast<int64_t>(tail[0] * 100000.0));
+
+ // When the box != 0, the fallback algorithm computes a wedge function.
+ // Depending on the box, the threshold for varies as high as
+ // 0.991522480228.
+ {
+ // 0.9921875, 0.875
+ absl::random_internal::sequence_urbg urbg(
+ {make_box(0x7f00000000000000ull, 120), 0xe000000000000001ull,
+ 0x13CCA830EB61BD96ull});
+ tail[0] = dist(urbg);
+ EXPECT_EQ(2, urbg.invocations());
+ EXPECT_GT(tail[0], 0);
+ }
+ {
+ // -0.9921875, 0.875
+ absl::random_internal::sequence_urbg urbg(
+ {make_box(0xff00000000000000ull, 120), 0xe000000000000001ull,
+ 0x13CCA830EB61BD96ull});
+ tail[1] = dist(urbg);
+ EXPECT_EQ(2, urbg.invocations());
+ EXPECT_LT(tail[1], 0);
+ }
+ EXPECT_EQ(tail[0], -tail[1]);
+ EXPECT_EQ(61948, static_cast<int64_t>(tail[0] * 100000.0));
+
+ // Fallback rejected, try again.
+ {
+ // -0.9921875, 0.0625
+ absl::random_internal::sequence_urbg urbg(
+ {make_box(0xff00000000000000ull, 120), 0x1000000000000001,
+ make_box(0x1000000000000100ull, 50), 0x13CCA830EB61BD96ull});
+ dist(urbg);
+ EXPECT_EQ(3, urbg.invocations());
+ }
+}
+
+} // namespace
generated by cgit v1.2.3 (git 2.39.1) at 2024-11-10 12:20:46 +0000