rhubarb-lip-sync/lib/webrtc-8d2248ff/webrtc/base/rollingaccumulator.h

175 lines
4.5 KiB
C++

/*
* 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 WEBRTC_BASE_ROLLINGACCUMULATOR_H_
#define WEBRTC_BASE_ROLLINGACCUMULATOR_H_
#include <algorithm>
#include <vector>
#include "webrtc/base/common.h"
#include "webrtc/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_) {
ASSERT(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_) {
ASSERT(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 // WEBRTC_BASE_ROLLINGACCUMULATOR_H_