222 lines
7.1 KiB
C++
222 lines
7.1 KiB
C++
/*
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* Copyright (c) 2016 The WebRTC project authors. All Rights Reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#include "rtc_base/numerics/samples_stats_counter.h"
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#include <math.h>
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#include <random>
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#include <vector>
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#include "absl/algorithm/container.h"
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#include "test/gtest.h"
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namespace webrtc {
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namespace {
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SamplesStatsCounter CreateStatsFilledWithIntsFrom1ToN(int n) {
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std::vector<double> data;
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for (int i = 1; i <= n; i++) {
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data.push_back(i);
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}
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absl::c_shuffle(data, std::mt19937(std::random_device()()));
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SamplesStatsCounter stats;
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for (double v : data) {
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stats.AddSample(v);
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}
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return stats;
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}
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// Add n samples drawn from uniform distribution in [a;b].
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SamplesStatsCounter CreateStatsFromUniformDistribution(int n,
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double a,
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double b) {
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std::mt19937 gen{std::random_device()()};
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std::uniform_real_distribution<> dis(a, b);
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SamplesStatsCounter stats;
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for (int i = 1; i <= n; i++) {
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stats.AddSample(dis(gen));
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}
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return stats;
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}
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class SamplesStatsCounterTest : public ::testing::TestWithParam<int> {};
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constexpr int SIZE_FOR_MERGE = 10;
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} // namespace
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TEST(SamplesStatsCounterTest, FullSimpleTest) {
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SamplesStatsCounter stats = CreateStatsFilledWithIntsFrom1ToN(100);
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EXPECT_TRUE(!stats.IsEmpty());
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EXPECT_DOUBLE_EQ(stats.GetMin(), 1.0);
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EXPECT_DOUBLE_EQ(stats.GetMax(), 100.0);
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EXPECT_NEAR(stats.GetAverage(), 50.5, 1e-6);
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for (int i = 1; i <= 100; i++) {
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double p = i / 100.0;
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EXPECT_GE(stats.GetPercentile(p), i);
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EXPECT_LT(stats.GetPercentile(p), i + 1);
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}
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}
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TEST(SamplesStatsCounterTest, VarianceAndDeviation) {
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SamplesStatsCounter stats;
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stats.AddSample(2);
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stats.AddSample(2);
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stats.AddSample(-1);
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stats.AddSample(5);
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EXPECT_DOUBLE_EQ(stats.GetAverage(), 2.0);
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EXPECT_DOUBLE_EQ(stats.GetVariance(), 4.5);
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EXPECT_DOUBLE_EQ(stats.GetStandardDeviation(), sqrt(4.5));
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}
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TEST(SamplesStatsCounterTest, FractionPercentile) {
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SamplesStatsCounter stats = CreateStatsFilledWithIntsFrom1ToN(5);
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EXPECT_DOUBLE_EQ(stats.GetPercentile(0.5), 3);
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}
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TEST(SamplesStatsCounterTest, TestBorderValues) {
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SamplesStatsCounter stats = CreateStatsFilledWithIntsFrom1ToN(5);
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EXPECT_GE(stats.GetPercentile(0.01), 1);
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EXPECT_LT(stats.GetPercentile(0.01), 2);
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EXPECT_DOUBLE_EQ(stats.GetPercentile(1.0), 5);
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}
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TEST(SamplesStatsCounterTest, VarianceFromUniformDistribution) {
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// Check variance converge to 1/12 for [0;1) uniform distribution.
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// Acts as a sanity check for NumericStabilityForVariance test.
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SamplesStatsCounter stats = CreateStatsFromUniformDistribution(1e6, 0, 1);
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EXPECT_NEAR(stats.GetVariance(), 1. / 12, 1e-3);
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}
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TEST(SamplesStatsCounterTest, NumericStabilityForVariance) {
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// Same test as VarianceFromUniformDistribution,
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// except the range is shifted to [1e9;1e9+1).
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// Variance should also converge to 1/12.
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// NB: Although we lose precision for the samples themselves, the fractional
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// part still enjoys 22 bits of mantissa and errors should even out,
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// so that couldn't explain a mismatch.
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SamplesStatsCounter stats =
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CreateStatsFromUniformDistribution(1e6, 1e9, 1e9 + 1);
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EXPECT_NEAR(stats.GetVariance(), 1. / 12, 1e-3);
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}
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TEST_P(SamplesStatsCounterTest, AddSamples) {
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int data[SIZE_FOR_MERGE] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9};
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// Split the data in different partitions.
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// We have 11 distinct tests:
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// * Empty merged with full sequence.
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// * 1 sample merged with 9 last.
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// * 2 samples merged with 8 last.
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// [...]
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// * Full merged with empty sequence.
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// All must lead to the same result.
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SamplesStatsCounter stats0, stats1;
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for (int i = 0; i < GetParam(); ++i) {
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stats0.AddSample(data[i]);
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}
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for (int i = GetParam(); i < SIZE_FOR_MERGE; ++i) {
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stats1.AddSample(data[i]);
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}
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stats0.AddSamples(stats1);
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EXPECT_EQ(stats0.GetMin(), 0);
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EXPECT_EQ(stats0.GetMax(), 9);
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EXPECT_DOUBLE_EQ(stats0.GetAverage(), 4.5);
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EXPECT_DOUBLE_EQ(stats0.GetVariance(), 8.25);
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EXPECT_DOUBLE_EQ(stats0.GetStandardDeviation(), sqrt(8.25));
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EXPECT_DOUBLE_EQ(stats0.GetPercentile(0.1), 0.9);
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EXPECT_DOUBLE_EQ(stats0.GetPercentile(0.5), 4.5);
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EXPECT_DOUBLE_EQ(stats0.GetPercentile(0.9), 8.1);
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}
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TEST(SamplesStatsCounterTest, MultiplyRight) {
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SamplesStatsCounter stats = CreateStatsFilledWithIntsFrom1ToN(10);
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EXPECT_TRUE(!stats.IsEmpty());
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EXPECT_DOUBLE_EQ(stats.GetMin(), 1.0);
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EXPECT_DOUBLE_EQ(stats.GetMax(), 10.0);
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EXPECT_DOUBLE_EQ(stats.GetAverage(), 5.5);
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SamplesStatsCounter multiplied_stats = stats * 10;
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EXPECT_TRUE(!multiplied_stats.IsEmpty());
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EXPECT_DOUBLE_EQ(multiplied_stats.GetMin(), 10.0);
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EXPECT_DOUBLE_EQ(multiplied_stats.GetMax(), 100.0);
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EXPECT_DOUBLE_EQ(multiplied_stats.GetAverage(), 55.0);
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EXPECT_EQ(multiplied_stats.GetSamples().size(), stats.GetSamples().size());
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// Check that origin stats were not modified.
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EXPECT_TRUE(!stats.IsEmpty());
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EXPECT_DOUBLE_EQ(stats.GetMin(), 1.0);
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EXPECT_DOUBLE_EQ(stats.GetMax(), 10.0);
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EXPECT_DOUBLE_EQ(stats.GetAverage(), 5.5);
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}
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TEST(SamplesStatsCounterTest, MultiplyLeft) {
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SamplesStatsCounter stats = CreateStatsFilledWithIntsFrom1ToN(10);
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EXPECT_TRUE(!stats.IsEmpty());
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EXPECT_DOUBLE_EQ(stats.GetMin(), 1.0);
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EXPECT_DOUBLE_EQ(stats.GetMax(), 10.0);
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EXPECT_DOUBLE_EQ(stats.GetAverage(), 5.5);
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SamplesStatsCounter multiplied_stats = 10 * stats;
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EXPECT_TRUE(!multiplied_stats.IsEmpty());
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EXPECT_DOUBLE_EQ(multiplied_stats.GetMin(), 10.0);
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EXPECT_DOUBLE_EQ(multiplied_stats.GetMax(), 100.0);
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EXPECT_DOUBLE_EQ(multiplied_stats.GetAverage(), 55.0);
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EXPECT_EQ(multiplied_stats.GetSamples().size(), stats.GetSamples().size());
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// Check that origin stats were not modified.
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EXPECT_TRUE(!stats.IsEmpty());
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EXPECT_DOUBLE_EQ(stats.GetMin(), 1.0);
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EXPECT_DOUBLE_EQ(stats.GetMax(), 10.0);
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EXPECT_DOUBLE_EQ(stats.GetAverage(), 5.5);
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}
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TEST(SamplesStatsCounterTest, Divide) {
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SamplesStatsCounter stats;
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for (int i = 1; i <= 10; i++) {
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stats.AddSample(i * 10);
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}
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EXPECT_TRUE(!stats.IsEmpty());
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EXPECT_DOUBLE_EQ(stats.GetMin(), 10.0);
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EXPECT_DOUBLE_EQ(stats.GetMax(), 100.0);
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EXPECT_DOUBLE_EQ(stats.GetAverage(), 55.0);
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SamplesStatsCounter divided_stats = stats / 10;
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EXPECT_TRUE(!divided_stats.IsEmpty());
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EXPECT_DOUBLE_EQ(divided_stats.GetMin(), 1.0);
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EXPECT_DOUBLE_EQ(divided_stats.GetMax(), 10.0);
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EXPECT_DOUBLE_EQ(divided_stats.GetAverage(), 5.5);
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EXPECT_EQ(divided_stats.GetSamples().size(), stats.GetSamples().size());
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// Check that origin stats were not modified.
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EXPECT_TRUE(!stats.IsEmpty());
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EXPECT_DOUBLE_EQ(stats.GetMin(), 10.0);
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EXPECT_DOUBLE_EQ(stats.GetMax(), 100.0);
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EXPECT_DOUBLE_EQ(stats.GetAverage(), 55.0);
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}
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INSTANTIATE_TEST_SUITE_P(SamplesStatsCounterTests,
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SamplesStatsCounterTest,
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::testing::Range(0, SIZE_FOR_MERGE + 1));
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} // namespace webrtc
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