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
|
/*
* libjingle
* Copyright 2011, Google Inc.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice,
* this list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
* 3. The name of the author may not be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR IMPLIED
* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
* MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
* EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
* SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
* OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
* WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
* OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
* ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
#ifndef TALK_BASE_ROLLINGACCUMULATOR_H_
#define TALK_BASE_ROLLINGACCUMULATOR_H_
#include <vector>
#include "talk/base/common.h"
namespace talk_base {
// 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)
: count_(0),
next_index_(0),
sum_(0.0),
sum_2_(0.0),
samples_(max_count) {
}
~RollingAccumulator() {
}
size_t max_count() const {
return samples_.size();
}
size_t count() const {
return count_;
}
void AddSample(T sample) {
if (count_ == max_count()) {
// Remove oldest sample.
T sample_to_remove = samples_[next_index_];
sum_ -= sample_to_remove;
sum_2_ -= sample_to_remove * sample_to_remove;
} else {
// Increase count of samples.
++count_;
}
// Add new sample.
samples_[next_index_] = sample;
sum_ += sample;
sum_2_ += sample * sample;
// Update next_index_.
next_index_ = (next_index_ + 1) % max_count();
}
T ComputeSum() const {
return static_cast<T>(sum_);
}
T ComputeMean() const {
if (count_ == 0) {
return static_cast<T>(0);
}
return static_cast<T>(sum_ / count_);
}
// 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.
T 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 static_cast<T>(weighted_mean / weight_sum);
}
// Compute estimated variance. Estimation is more accurate
// as the number of samples grows.
T ComputeVariance() const {
if (count_ == 0) {
return static_cast<T>(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 static_cast<T>(mean_2 - (mean * mean));
}
private:
size_t count_;
size_t next_index_;
double sum_; // Sum(x)
double sum_2_; // Sum(x*x)
std::vector<T> samples_;
DISALLOW_COPY_AND_ASSIGN(RollingAccumulator);
};
} // namespace talk_base
#endif // TALK_BASE_ROLLINGACCUMULATOR_H_
|
