1 //===- ReservoirSampler.cpp - Tests for the ReservoirSampler --------------===//
2 //
3 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
4 // See https://llvm.org/LICENSE.txt for license information.
5 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
6 //
7 //===----------------------------------------------------------------------===//
8
9 #include "llvm/FuzzMutate/Random.h"
10 #include "gtest/gtest.h"
11 #include <random>
12
13 using namespace llvm;
14
TEST(ReservoirSamplerTest,OneItem)15 TEST(ReservoirSamplerTest, OneItem) {
16 std::mt19937 Rand;
17 auto Sampler = makeSampler(Rand, 7, 1);
18 ASSERT_FALSE(Sampler.isEmpty());
19 ASSERT_EQ(7, Sampler.getSelection());
20 }
21
TEST(ReservoirSamplerTest,NoWeight)22 TEST(ReservoirSamplerTest, NoWeight) {
23 std::mt19937 Rand;
24 auto Sampler = makeSampler(Rand, 7, 0);
25 ASSERT_TRUE(Sampler.isEmpty());
26 }
27
TEST(ReservoirSamplerTest,Uniform)28 TEST(ReservoirSamplerTest, Uniform) {
29 std::mt19937 Rand;
30
31 // Run three chi-squared tests to check that the distribution is reasonably
32 // uniform.
33 std::vector<int> Items = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
34
35 int Failures = 0;
36 for (int Run = 0; Run < 3; ++Run) {
37 std::vector<int> Counts(Items.size(), 0);
38
39 // We need $np_s > 5$ at minimum, but we're better off going a couple of
40 // orders of magnitude larger.
41 int N = Items.size() * 5 * 100;
42 for (int I = 0; I < N; ++I) {
43 auto Sampler = makeSampler(Rand, Items);
44 Counts[Sampler.getSelection()] += 1;
45 }
46
47 // Knuth. TAOCP Vol. 2, 3.3.1 (8):
48 // $V = \frac{1}{n} \sum_{s=1}^{k} \left(\frac{Y_s^2}{p_s}\right) - n$
49 double Ps = 1.0 / Items.size();
50 double Sum = 0.0;
51 for (int Ys : Counts)
52 Sum += Ys * Ys / Ps;
53 double V = (Sum / N) - N;
54
55 assert(Items.size() == 10 && "Our chi-squared values assume 10 items");
56 // Since we have 10 items, there are 9 degrees of freedom and the table of
57 // chi-squared values is as follows:
58 //
59 // | p=1% | 5% | 25% | 50% | 75% | 95% | 99% |
60 // v=9 | 2.088 | 3.325 | 5.899 | 8.343 | 11.39 | 16.92 | 21.67 |
61 //
62 // Check that we're in the likely range of results.
63 //if (V < 2.088 || V > 21.67)
64 if (V < 2.088 || V > 21.67)
65 ++Failures;
66 }
67 EXPECT_LT(Failures, 3) << "Non-uniform distribution?";
68 }
69