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