Adding upstream version 124.0.1.upstream/124.0.1

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

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