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/*
* 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 webrtc {
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 webrtc
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