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