From 5aa5d282eac56a21e74611c1cdbaa97bb5db2dca Mon Sep 17 00:00:00 2001 From: Vincent Ambo Date: Tue, 8 Feb 2022 02:05:36 +0300 Subject: chore(3p/abseil_cpp): unvendor abseil_cpp we weren't actually using these sources anymore, okay? Change-Id: If701571d9716de308d3512e1eb22c35db0877a66 Reviewed-on: https://cl.tvl.fyi/c/depot/+/5248 Tested-by: BuildkiteCI Reviewed-by: grfn Autosubmit: tazjin --- .../absl/base/internal/exponential_biased_test.cc | 199 --------------------- 1 file changed, 199 deletions(-) delete mode 100644 third_party/abseil_cpp/absl/base/internal/exponential_biased_test.cc (limited to 'third_party/abseil_cpp/absl/base/internal/exponential_biased_test.cc') diff --git a/third_party/abseil_cpp/absl/base/internal/exponential_biased_test.cc b/third_party/abseil_cpp/absl/base/internal/exponential_biased_test.cc deleted file mode 100644 index 075583ca6fd4..000000000000 --- a/third_party/abseil_cpp/absl/base/internal/exponential_biased_test.cc +++ /dev/null @@ -1,199 +0,0 @@ -// Copyright 2019 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/base/internal/exponential_biased.h" - -#include - -#include -#include -#include - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/strings/str_cat.h" - -using ::testing::Ge; - -namespace absl { -ABSL_NAMESPACE_BEGIN -namespace base_internal { - -MATCHER_P2(IsBetween, a, b, - absl::StrCat(std::string(negation ? "isn't" : "is"), " between ", a, - " and ", b)) { - return a <= arg && arg <= b; -} - -// Tests of the quality of the random numbers generated -// This uses the Anderson Darling test for uniformity. -// See "Evaluating the Anderson-Darling Distribution" by Marsaglia -// for details. - -// Short cut version of ADinf(z), z>0 (from Marsaglia) -// This returns the p-value for Anderson Darling statistic in -// the limit as n-> infinity. For finite n, apply the error fix below. -double AndersonDarlingInf(double z) { - if (z < 2) { - return exp(-1.2337141 / z) / sqrt(z) * - (2.00012 + - (0.247105 - - (0.0649821 - (0.0347962 - (0.011672 - 0.00168691 * z) * z) * z) * - z) * - z); - } - return exp( - -exp(1.0776 - - (2.30695 - - (0.43424 - (0.082433 - (0.008056 - 0.0003146 * z) * z) * z) * z) * - z)); -} - -// Corrects the approximation error in AndersonDarlingInf for small values of n -// Add this to AndersonDarlingInf to get a better approximation -// (from Marsaglia) -double AndersonDarlingErrFix(int n, double x) { - if (x > 0.8) { - return (-130.2137 + - (745.2337 - - (1705.091 - (1950.646 - (1116.360 - 255.7844 * x) * x) * x) * x) * - x) / - n; - } - double cutoff = 0.01265 + 0.1757 / n; - if (x < cutoff) { - double t = x / cutoff; - t = sqrt(t) * (1 - t) * (49 * t - 102); - return t * (0.0037 / (n * n) + 0.00078 / n + 0.00006) / n; - } else { - double t = (x - cutoff) / (0.8 - cutoff); - t = -0.00022633 + - (6.54034 - (14.6538 - (14.458 - (8.259 - 1.91864 * t) * t) * t) * t) * - t; - return t * (0.04213 + 0.01365 / n) / n; - } -} - -// Returns the AndersonDarling p-value given n and the value of the statistic -double AndersonDarlingPValue(int n, double z) { - double ad = AndersonDarlingInf(z); - double errfix = AndersonDarlingErrFix(n, ad); - return ad + errfix; -} - -double AndersonDarlingStatistic(const std::vector& random_sample) { - int n = random_sample.size(); - double ad_sum = 0; - for (int i = 0; i < n; i++) { - ad_sum += (2 * i + 1) * - std::log(random_sample[i] * (1 - random_sample[n - 1 - i])); - } - double ad_statistic = -n - 1 / static_cast(n) * ad_sum; - return ad_statistic; -} - -// Tests if the array of doubles is uniformly distributed. -// Returns the p-value of the Anderson Darling Statistic -// for the given set of sorted random doubles -// See "Evaluating the Anderson-Darling Distribution" by -// Marsaglia and Marsaglia for details. -double AndersonDarlingTest(const std::vector& random_sample) { - double ad_statistic = AndersonDarlingStatistic(random_sample); - double p = AndersonDarlingPValue(random_sample.size(), ad_statistic); - return p; -} - -TEST(ExponentialBiasedTest, CoinTossDemoWithGetSkipCount) { - ExponentialBiased eb; - for (int runs = 0; runs < 10; ++runs) { - for (int flips = eb.GetSkipCount(1); flips > 0; --flips) { - printf("head..."); - } - printf("tail\n"); - } - int heads = 0; - for (int i = 0; i < 10000000; i += 1 + eb.GetSkipCount(1)) { - ++heads; - } - printf("Heads = %d (%f%%)\n", heads, 100.0 * heads / 10000000); -} - -TEST(ExponentialBiasedTest, SampleDemoWithStride) { - ExponentialBiased eb; - int stride = eb.GetStride(10); - int samples = 0; - for (int i = 0; i < 10000000; ++i) { - if (--stride == 0) { - ++samples; - stride = eb.GetStride(10); - } - } - printf("Samples = %d (%f%%)\n", samples, 100.0 * samples / 10000000); -} - - -// Testing that NextRandom generates uniform random numbers. Applies the -// Anderson-Darling test for uniformity -TEST(ExponentialBiasedTest, TestNextRandom) { - for (auto n : std::vector({ - 10, // Check short-range correlation - 100, 1000, - 10000 // Make sure there's no systemic error - })) { - uint64_t x = 1; - // This assumes that the prng returns 48 bit numbers - uint64_t max_prng_value = static_cast(1) << 48; - // Initialize. - for (int i = 1; i <= 20; i++) { - x = ExponentialBiased::NextRandom(x); - } - std::vector int_random_sample(n); - // Collect samples - for (int i = 0; i < n; i++) { - int_random_sample[i] = x; - x = ExponentialBiased::NextRandom(x); - } - // First sort them... - std::sort(int_random_sample.begin(), int_random_sample.end()); - std::vector random_sample(n); - // Convert them to uniform randoms (in the range [0,1]) - for (int i = 0; i < n; i++) { - random_sample[i] = - static_cast(int_random_sample[i]) / max_prng_value; - } - // Now compute the Anderson-Darling statistic - double ad_pvalue = AndersonDarlingTest(random_sample); - EXPECT_GT(std::min(ad_pvalue, 1 - ad_pvalue), 0.0001) - << "prng is not uniform: n = " << n << " p = " << ad_pvalue; - } -} - -// The generator needs to be available as a thread_local and as a static -// variable. -TEST(ExponentialBiasedTest, InitializationModes) { - ABSL_CONST_INIT static ExponentialBiased eb_static; - EXPECT_THAT(eb_static.GetSkipCount(2), Ge(0)); - -#ifdef ABSL_HAVE_THREAD_LOCAL - thread_local ExponentialBiased eb_thread; - EXPECT_THAT(eb_thread.GetSkipCount(2), Ge(0)); -#endif - - ExponentialBiased eb_stack; - EXPECT_THAT(eb_stack.GetSkipCount(2), Ge(0)); -} - -} // namespace base_internal -ABSL_NAMESPACE_END -} // namespace absl -- cgit 1.4.1