Imported Upstream version 5.18.0.207

Former-commit-id: 3b152f462918d427ce18620a2cbe4f8b79650449

external/llvm/unittests/FuzzMutate/ReservoirSamplerTest.cpp

@@ -1,69 +0,0 @@
-//===- ReservoirSampler.cpp - Tests for the ReservoirSampler --------------===//
-//
-//                     The LLVM Compiler Infrastructure
-//
-// This file is distributed under the University of Illinois Open Source
-// License. See LICENSE.TXT for details.
-//
-//===----------------------------------------------------------------------===//
-
-#include "llvm/FuzzMutate/Random.h"
-#include "gtest/gtest.h"
-#include <random>
-
-using namespace llvm;
-
-TEST(ReservoirSamplerTest, OneItem) {
-  std::mt19937 Rand;
-  auto Sampler = makeSampler(Rand, 7, 1);
-  ASSERT_FALSE(Sampler.isEmpty());
-  ASSERT_EQ(7, Sampler.getSelection());
-}
-
-TEST(ReservoirSamplerTest, NoWeight) {
-  std::mt19937 Rand;
-  auto Sampler = makeSampler(Rand, 7, 0);
-  ASSERT_TRUE(Sampler.isEmpty());
-}
-
-TEST(ReservoirSamplerTest, Uniform) {
-  std::mt19937 Rand;
-
-  // Run three chi-squared tests to check that the distribution is reasonably
-  // uniform.
-  std::vector<int> Items = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
-
-  int Failures = 0;
-  for (int Run = 0; Run < 3; ++Run) {
-    std::vector<int> Counts(Items.size(), 0);
-
-    // We need $np_s > 5$ at minimum, but we're better off going a couple of
-    // orders of magnitude larger.
-    int N = Items.size() * 5 * 100;
-    for (int I = 0; I < N; ++I) {
-      auto Sampler = makeSampler(Rand, Items);
-      Counts[Sampler.getSelection()] += 1;
-    }
-
-    // Knuth. TAOCP Vol. 2, 3.3.1 (8):
-    // $V = \frac{1}{n} \sum_{s=1}^{k} \left(\frac{Y_s^2}{p_s}\right) - n$
-    double Ps = 1.0 / Items.size();
-    double Sum = 0.0;
-    for (int Ys : Counts)
-      Sum += Ys * Ys / Ps;
-    double V = (Sum / N) - N;
-
-    assert(Items.size() == 10 && "Our chi-squared values assume 10 items");
-    // Since we have 10 items, there are 9 degrees of freedom and the table of
-    // chi-squared values is as follows:
-    //
-    //     | p=1%  |   5%  |  25%  |  50%  |  75%  |  95%  |  99%  |
-    // v=9 | 2.088 | 3.325 | 5.899 | 8.343 | 11.39 | 16.92 | 21.67 |
-    //
-    // Check that we're in the likely range of results.
-    //if (V < 2.088 || V > 21.67)
-    if (V < 2.088 || V > 21.67)
-      ++Failures;
-  }
-  EXPECT_LT(Failures, 3) << "Non-uniform distribution?";
-}