1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
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
|
