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