diff options
author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-19 00:47:55 +0000 |
---|---|---|
committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-04-19 00:47:55 +0000 |
commit | 26a029d407be480d791972afb5975cf62c9360a6 (patch) | |
tree | f435a8308119effd964b339f76abb83a57c29483 /third_party/libwebrtc/rtc_base/rolling_accumulator_unittest.cc | |
parent | Initial commit. (diff) | |
download | firefox-26a029d407be480d791972afb5975cf62c9360a6.tar.xz firefox-26a029d407be480d791972afb5975cf62c9360a6.zip |
Adding upstream version 124.0.1.upstream/124.0.1
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'third_party/libwebrtc/rtc_base/rolling_accumulator_unittest.cc')
-rw-r--r-- | third_party/libwebrtc/rtc_base/rolling_accumulator_unittest.cc | 150 |
1 files changed, 150 insertions, 0 deletions
diff --git a/third_party/libwebrtc/rtc_base/rolling_accumulator_unittest.cc b/third_party/libwebrtc/rtc_base/rolling_accumulator_unittest.cc new file mode 100644 index 0000000000..3a26f9e42f --- /dev/null +++ b/third_party/libwebrtc/rtc_base/rolling_accumulator_unittest.cc @@ -0,0 +1,150 @@ +/* + * Copyright 2011 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/rolling_accumulator.h" + +#include <random> + +#include "test/gtest.h" + +namespace rtc { + +namespace { + +const double kLearningRate = 0.5; + +// Add `n` samples drawn from uniform distribution in [a;b]. +void FillStatsFromUniformDistribution(RollingAccumulator<double>& stats, + int n, + double a, + double b) { + std::mt19937 gen{std::random_device()()}; + std::uniform_real_distribution<> dis(a, b); + + for (int i = 1; i <= n; i++) { + stats.AddSample(dis(gen)); + } +} +} // namespace + +TEST(RollingAccumulatorTest, ZeroSamples) { + RollingAccumulator<int> accum(10); + + EXPECT_EQ(0U, accum.count()); + EXPECT_DOUBLE_EQ(0.0, accum.ComputeMean()); + EXPECT_DOUBLE_EQ(0.0, accum.ComputeVariance()); + EXPECT_EQ(0, accum.ComputeMin()); + EXPECT_EQ(0, accum.ComputeMax()); +} + +TEST(RollingAccumulatorTest, SomeSamples) { + RollingAccumulator<int> accum(10); + for (int i = 0; i < 4; ++i) { + accum.AddSample(i); + } + + EXPECT_EQ(4U, accum.count()); + EXPECT_DOUBLE_EQ(1.5, accum.ComputeMean()); + EXPECT_NEAR(2.26666, accum.ComputeWeightedMean(kLearningRate), 0.01); + EXPECT_DOUBLE_EQ(1.25, accum.ComputeVariance()); + EXPECT_EQ(0, accum.ComputeMin()); + EXPECT_EQ(3, accum.ComputeMax()); +} + +TEST(RollingAccumulatorTest, RollingSamples) { + RollingAccumulator<int> accum(10); + for (int i = 0; i < 12; ++i) { + accum.AddSample(i); + } + + EXPECT_EQ(10U, accum.count()); + EXPECT_DOUBLE_EQ(6.5, accum.ComputeMean()); + EXPECT_NEAR(10.0, accum.ComputeWeightedMean(kLearningRate), 0.01); + EXPECT_NEAR(9.0, accum.ComputeVariance(), 1.0); + EXPECT_EQ(2, accum.ComputeMin()); + EXPECT_EQ(11, accum.ComputeMax()); +} + +TEST(RollingAccumulatorTest, ResetSamples) { + RollingAccumulator<int> accum(10); + + for (int i = 0; i < 10; ++i) { + accum.AddSample(100); + } + EXPECT_EQ(10U, accum.count()); + EXPECT_DOUBLE_EQ(100.0, accum.ComputeMean()); + EXPECT_EQ(100, accum.ComputeMin()); + EXPECT_EQ(100, accum.ComputeMax()); + + accum.Reset(); + EXPECT_EQ(0U, accum.count()); + + for (int i = 0; i < 5; ++i) { + accum.AddSample(i); + } + + EXPECT_EQ(5U, accum.count()); + EXPECT_DOUBLE_EQ(2.0, accum.ComputeMean()); + EXPECT_EQ(0, accum.ComputeMin()); + EXPECT_EQ(4, accum.ComputeMax()); +} + +TEST(RollingAccumulatorTest, RollingSamplesDouble) { + RollingAccumulator<double> accum(10); + for (int i = 0; i < 23; ++i) { + accum.AddSample(5 * i); + } + + EXPECT_EQ(10u, accum.count()); + EXPECT_DOUBLE_EQ(87.5, accum.ComputeMean()); + EXPECT_NEAR(105.049, accum.ComputeWeightedMean(kLearningRate), 0.1); + EXPECT_NEAR(229.166667, accum.ComputeVariance(), 25); + EXPECT_DOUBLE_EQ(65.0, accum.ComputeMin()); + EXPECT_DOUBLE_EQ(110.0, accum.ComputeMax()); +} + +TEST(RollingAccumulatorTest, ComputeWeightedMeanCornerCases) { + RollingAccumulator<int> accum(10); + EXPECT_DOUBLE_EQ(0.0, accum.ComputeWeightedMean(kLearningRate)); + EXPECT_DOUBLE_EQ(0.0, accum.ComputeWeightedMean(0.0)); + EXPECT_DOUBLE_EQ(0.0, accum.ComputeWeightedMean(1.1)); + + for (int i = 0; i < 8; ++i) { + accum.AddSample(i); + } + + EXPECT_DOUBLE_EQ(3.5, accum.ComputeMean()); + EXPECT_DOUBLE_EQ(3.5, accum.ComputeWeightedMean(0)); + EXPECT_DOUBLE_EQ(3.5, accum.ComputeWeightedMean(1.1)); + EXPECT_NEAR(6.0, accum.ComputeWeightedMean(kLearningRate), 0.1); +} + +TEST(RollingAccumulatorTest, VarianceFromUniformDistribution) { + // Check variance converge to 1/12 for [0;1) uniform distribution. + // Acts as a sanity check for NumericStabilityForVariance test. + RollingAccumulator<double> stats(/*max_count=*/0.5e6); + FillStatsFromUniformDistribution(stats, 1e6, 0, 1); + + EXPECT_NEAR(stats.ComputeVariance(), 1. / 12, 1e-3); +} + +TEST(RollingAccumulatorTest, 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. + RollingAccumulator<double> stats(/*max_count=*/0.5e6); + FillStatsFromUniformDistribution(stats, 1e6, 1e9, 1e9 + 1); + + EXPECT_NEAR(stats.ComputeVariance(), 1. / 12, 1e-3); +} +} // namespace rtc |