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/*
* 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/running_statistics.h"
#include <math.h>
#include <random>
#include <vector>
#include "absl/algorithm/container.h"
#include "test/gtest.h"
// Tests were copied from samples_stats_counter_unittest.cc.
namespace webrtc {
namespace webrtc_impl {
namespace {
RunningStatistics<double> 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()()));
RunningStatistics<double> stats;
for (double v : data) {
stats.AddSample(v);
}
return stats;
}
// Add n samples drawn from uniform distribution in [a;b].
RunningStatistics<double> CreateStatsFromUniformDistribution(int n,
double a,
double b) {
std::mt19937 gen{std::random_device()()};
std::uniform_real_distribution<> dis(a, b);
RunningStatistics<double> stats;
for (int i = 1; i <= n; i++) {
stats.AddSample(dis(gen));
}
return stats;
}
class RunningStatisticsTest : public ::testing::TestWithParam<int> {};
constexpr int SIZE_FOR_MERGE = 5;
TEST(RunningStatistics, FullSimpleTest) {
auto stats = CreateStatsFilledWithIntsFrom1ToN(100);
EXPECT_DOUBLE_EQ(*stats.GetMin(), 1.0);
EXPECT_DOUBLE_EQ(*stats.GetMax(), 100.0);
// EXPECT_DOUBLE_EQ is too strict (max 4 ULP) for this one.
ASSERT_NEAR(*stats.GetMean(), 50.5, 1e-10);
}
TEST(RunningStatistics, VarianceAndDeviation) {
RunningStatistics<int> stats;
stats.AddSample(2);
stats.AddSample(2);
stats.AddSample(-1);
stats.AddSample(5);
EXPECT_DOUBLE_EQ(*stats.GetMean(), 2.0);
EXPECT_DOUBLE_EQ(*stats.GetVariance(), 4.5);
EXPECT_DOUBLE_EQ(*stats.GetStandardDeviation(), sqrt(4.5));
}
TEST(RunningStatistics, RemoveSample) {
// We check that adding then removing sample is no-op,
// or so (due to loss of precision).
RunningStatistics<int> stats;
stats.AddSample(2);
stats.AddSample(2);
stats.AddSample(-1);
stats.AddSample(5);
constexpr int iterations = 1e5;
for (int i = 0; i < iterations; ++i) {
stats.AddSample(i);
stats.RemoveSample(i);
EXPECT_NEAR(*stats.GetMean(), 2.0, 1e-8);
EXPECT_NEAR(*stats.GetVariance(), 4.5, 1e-3);
EXPECT_NEAR(*stats.GetStandardDeviation(), sqrt(4.5), 1e-4);
}
}
TEST(RunningStatistics, RemoveSamplesSequence) {
// We check that adding then removing a sequence of samples is no-op,
// or so (due to loss of precision).
RunningStatistics<int> stats;
stats.AddSample(2);
stats.AddSample(2);
stats.AddSample(-1);
stats.AddSample(5);
constexpr int iterations = 1e4;
for (int i = 0; i < iterations; ++i) {
stats.AddSample(i);
}
for (int i = 0; i < iterations; ++i) {
stats.RemoveSample(i);
}
EXPECT_NEAR(*stats.GetMean(), 2.0, 1e-7);
EXPECT_NEAR(*stats.GetVariance(), 4.5, 1e-3);
EXPECT_NEAR(*stats.GetStandardDeviation(), sqrt(4.5), 1e-4);
}
TEST(RunningStatistics, VarianceFromUniformDistribution) {
// Check variance converge to 1/12 for [0;1) uniform distribution.
// Acts as a sanity check for NumericStabilityForVariance test.
auto stats = CreateStatsFromUniformDistribution(1e6, 0, 1);
EXPECT_NEAR(*stats.GetVariance(), 1. / 12, 1e-3);
}
TEST(RunningStatistics, 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.
auto stats = CreateStatsFromUniformDistribution(1e6, 1e9, 1e9 + 1);
EXPECT_NEAR(*stats.GetVariance(), 1. / 12, 1e-3);
}
TEST(RunningStatistics, MinRemainsUnchangedAfterRemove) {
// We don't want to recompute min (that's RollingAccumulator's role),
// check we get the overall min.
RunningStatistics<int> stats;
stats.AddSample(1);
stats.AddSample(2);
stats.RemoveSample(1);
EXPECT_EQ(stats.GetMin(), 1);
}
TEST(RunningStatistics, MaxRemainsUnchangedAfterRemove) {
// We don't want to recompute max (that's RollingAccumulator's role),
// check we get the overall max.
RunningStatistics<int> stats;
stats.AddSample(1);
stats.AddSample(2);
stats.RemoveSample(2);
EXPECT_EQ(stats.GetMax(), 2);
}
TEST_P(RunningStatisticsTest, MergeStatistics) {
int data[SIZE_FOR_MERGE] = {2, 2, -1, 5, 10};
// Split the data in different partitions.
// We have 6 distinct tests:
// * Empty merged with full sequence.
// * 1 sample merged with 4 last.
// * 2 samples merged with 3 last.
// [...]
// * Full merged with empty sequence.
// All must lead to the same result.
// I miss QuickCheck so much.
RunningStatistics<int> 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.MergeStatistics(stats1);
EXPECT_EQ(stats0.Size(), SIZE_FOR_MERGE);
EXPECT_DOUBLE_EQ(*stats0.GetMin(), -1);
EXPECT_DOUBLE_EQ(*stats0.GetMax(), 10);
EXPECT_DOUBLE_EQ(*stats0.GetMean(), 3.6);
EXPECT_DOUBLE_EQ(*stats0.GetVariance(), 13.84);
EXPECT_DOUBLE_EQ(*stats0.GetStandardDeviation(), sqrt(13.84));
}
INSTANTIATE_TEST_SUITE_P(RunningStatisticsTests,
RunningStatisticsTest,
::testing::Range(0, SIZE_FOR_MERGE + 1));
} // namespace
} // namespace webrtc_impl
} // namespace webrtc
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