Merge tag '20220623.1-1' into bullseye-backports-staging

1 files changed, 0 insertions, 199 deletions

diff --git a/absl/base/internal/exponential_biased_test.cc b/absl/base/internal/exponential_biased_test.cc
deleted file mode 100644
index 075583ca..00000000
--- a/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 <stddef.h>
-
-#include <cmath>
-#include <cstdint>
-#include <vector>
-
-#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<double>& 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<double>(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<double>& 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<int>({
-           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<uint64_t>(1) << 48;
-    // Initialize.
-    for (int i = 1; i <= 20; i++) {
-      x = ExponentialBiased::NextRandom(x);
-    }
-    std::vector<uint64_t> 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<double> 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<double>(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