Move SampleStatsCounter to public API
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}
diff --git a/api/numerics/samples_stats_counter_unittest.cc b/api/numerics/samples_stats_counter_unittest.cc
new file mode 100644
index 0000000..1f9cabf
--- /dev/null
+++ b/api/numerics/samples_stats_counter_unittest.cc
@@ -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
