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/*
* 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_ROLLING_ACCUMULATOR_H_
#define RTC_BASE_ROLLING_ACCUMULATOR_H_
#include <stddef.h>
#include <algorithm>
#include <vector>
#include "rtc_base/checks.h"
#include "rtc_base/numerics/running_statistics.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) {
RTC_DCHECK(max_count > 0);
Reset();
}
~RollingAccumulator() {}
RollingAccumulator(const RollingAccumulator&) = delete;
RollingAccumulator& operator=(const RollingAccumulator&) = delete;
size_t max_count() const { return samples_.size(); }
size_t count() const { return static_cast<size_t>(stats_.Size()); }
void Reset() {
stats_ = webrtc::webrtc_impl::RunningStatistics<T>();
next_index_ = 0U;
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_];
stats_.RemoveSample(sample_to_remove);
if (sample_to_remove >= max_) {
max_stale_ = true;
}
if (sample_to_remove <= min_) {
min_stale_ = true;
}
}
// Add new sample.
samples_[next_index_] = sample;
if (count() == 0 || sample >= max_) {
max_ = sample;
max_stale_ = false;
}
if (count() == 0 || sample <= min_) {
min_ = sample;
min_stale_ = false;
}
stats_.AddSample(sample);
// Update next_index_.
next_index_ = (next_index_ + 1) % max_count();
}
double ComputeMean() const { return stats_.GetMean().value_or(0); }
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 { return stats_.GetVariance().value_or(0); }
private:
webrtc::webrtc_impl::RunningStatistics<T> stats_;
size_t next_index_;
mutable T max_;
mutable bool max_stale_;
mutable T min_;
mutable bool min_stale_;
std::vector<T> samples_;
};
} // namespace rtc
#endif // RTC_BASE_ROLLING_ACCUMULATOR_H_
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