diff options
Diffstat (limited to '')
| -rw-r--r-- | third_party/libwebrtc/api/numerics/samples_stats_counter_unittest.cc | 221 |
1 files changed, 221 insertions, 0 deletions
diff --git a/third_party/libwebrtc/api/numerics/samples_stats_counter_unittest.cc b/third_party/libwebrtc/api/numerics/samples_stats_counter_unittest.cc new file mode 100644 index 0000000000..1f9cabfb29 --- /dev/null +++ b/third_party/libwebrtc/api/numerics/samples_stats_counter_unittest.cc @@ -0,0 +1,221 @@ +/* + * 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 "api/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_NEAR(stats.GetAverage(), 50.5, 1e-6); + 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 |