zqz/platform-external-webrtc
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Move SampleStatsCounter to public API

Browse Source 
Bug: None
Change-Id: I8956f6febbb1caf71e951d212d57746fe1ec5eb2
Reviewed-on: https://webrtc-review.googlesource.com/c/src/+/184506
Commit-Queue: Artem Titov <titovartem@webrtc.org>
Reviewed-by: Karl Wiberg <kwiberg@webrtc.org>
Cr-Commit-Position: refs/heads/master@{#32142}
This commit is contained in:
Artem Titov
2020-09-18 18:23:08 +02:00
committed by Commit Bot
parent 979c13f565
commit 9d77762023
25 changed files with 126 additions and 56 deletions
Download Patch File Download Diff File Expand all files Collapse all files

221
api/numerics/samples_stats_counter_unittest.cc Normal file
View File

@ -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