blob: e7d5b06226c79c7fa46dd1aa2893c1ca00649295 (
plain)
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
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
|
/*
* 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.
*/
#ifndef RTC_BASE_ROLLINGACCUMULATOR_H_
#define RTC_BASE_ROLLINGACCUMULATOR_H_
#include <algorithm>
#include <vector>
#include "rtc_base/checks.h"
#include "rtc_base/constructormagic.h"
namespace rtc {
// RollingAccumulator stores and reports statistics
// over N most recent samples.
//
// T is assumed to be an int, long, double or float.
template<typename T>
class RollingAccumulator {
public:
explicit RollingAccumulator(size_t max_count)
: samples_(max_count) {
Reset();
}
~RollingAccumulator() {
}
size_t max_count() const {
return samples_.size();
}
size_t count() const {
return count_;
}
void Reset() {
count_ = 0U;
next_index_ = 0U;
sum_ = 0.0;
sum_2_ = 0.0;
max_ = T();
max_stale_ = false;
min_ = T();
min_stale_ = false;
}
void AddSample(T sample) {
if (count_ == max_count()) {
// Remove oldest sample.
T sample_to_remove = samples_[next_index_];
sum_ -= sample_to_remove;
sum_2_ -= static_cast<double>(sample_to_remove) * sample_to_remove;
if (sample_to_remove >= max_) {
max_stale_ = true;
}
if (sample_to_remove <= min_) {
min_stale_ = true;
}
} else {
// Increase count of samples.
++count_;
}
// Add new sample.
samples_[next_index_] = sample;
sum_ += sample;
sum_2_ += static_cast<double>(sample) * sample;
if (count_ == 1 || sample >= max_) {
max_ = sample;
max_stale_ = false;
}
if (count_ == 1 || sample <= min_) {
min_ = sample;
min_stale_ = false;
}
// Update next_index_.
next_index_ = (next_index_ + 1) % max_count();
}
T ComputeSum() const {
return static_cast<T>(sum_);
}
double ComputeMean() const {
if (count_ == 0) {
return 0.0;
}
return sum_ / count_;
}
T ComputeMax() const {
if (max_stale_) {
RTC_DCHECK(count_ > 0) <<
"It shouldn't be possible for max_stale_ && count_ == 0";
max_ = samples_[next_index_];
for (size_t i = 1u; i < count_; i++) {
max_ = std::max(max_, samples_[(next_index_ + i) % max_count()]);
}
max_stale_ = false;
}
return max_;
}
T ComputeMin() const {
if (min_stale_) {
RTC_DCHECK(count_ > 0) <<
"It shouldn't be possible for min_stale_ && count_ == 0";
min_ = samples_[next_index_];
for (size_t i = 1u; i < count_; i++) {
min_ = std::min(min_, samples_[(next_index_ + i) % max_count()]);
}
min_stale_ = false;
}
return min_;
}
// O(n) time complexity.
// Weights nth sample with weight (learning_rate)^n. Learning_rate should be
// between (0.0, 1.0], otherwise the non-weighted mean is returned.
double ComputeWeightedMean(double learning_rate) const {
if (count_ < 1 || learning_rate <= 0.0 || learning_rate >= 1.0) {
return ComputeMean();
}
double weighted_mean = 0.0;
double current_weight = 1.0;
double weight_sum = 0.0;
const size_t max_size = max_count();
for (size_t i = 0; i < count_; ++i) {
current_weight *= learning_rate;
weight_sum += current_weight;
// Add max_size to prevent underflow.
size_t index = (next_index_ + max_size - i - 1) % max_size;
weighted_mean += current_weight * samples_[index];
}
return weighted_mean / weight_sum;
}
// Compute estimated variance. Estimation is more accurate
// as the number of samples grows.
double ComputeVariance() const {
if (count_ == 0) {
return 0.0;
}
// Var = E[x^2] - (E[x])^2
double count_inv = 1.0 / count_;
double mean_2 = sum_2_ * count_inv;
double mean = sum_ * count_inv;
return mean_2 - (mean * mean);
}
private:
size_t count_;
size_t next_index_;
double sum_; // Sum(x) - double to avoid overflow
double sum_2_; // Sum(x*x) - double to avoid overflow
mutable T max_;
mutable bool max_stale_;
mutable T min_;
mutable bool min_stale_;
std::vector<T> samples_;
RTC_DISALLOW_COPY_AND_ASSIGN(RollingAccumulator);
};
} // namespace rtc
#endif // RTC_BASE_ROLLINGACCUMULATOR_H_
|
