146 lines
4.0 KiB
C++
146 lines
4.0 KiB
C++
/*
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* Copyright 2011 The WebRTC Project Authors. All rights reserved.
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*
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* Use of this source code is governed by a BSD-style license
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* that can be found in the LICENSE file in the root of the source
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* tree. An additional intellectual property rights grant can be found
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* in the file PATENTS. All contributing project authors may
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* be found in the AUTHORS file in the root of the source tree.
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*/
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#ifndef RTC_BASE_ROLLING_ACCUMULATOR_H_
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#define RTC_BASE_ROLLING_ACCUMULATOR_H_
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#include <stddef.h>
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#include <algorithm>
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#include <vector>
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#include "rtc_base/checks.h"
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#include "rtc_base/constructor_magic.h"
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#include "rtc_base/numerics/running_statistics.h"
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namespace rtc {
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// RollingAccumulator stores and reports statistics
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// over N most recent samples.
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//
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// T is assumed to be an int, long, double or float.
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template <typename T>
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class RollingAccumulator {
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public:
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explicit RollingAccumulator(size_t max_count) : samples_(max_count) {
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RTC_DCHECK(max_count > 0);
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Reset();
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}
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~RollingAccumulator() {}
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size_t max_count() const { return samples_.size(); }
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size_t count() const { return static_cast<size_t>(stats_.Size()); }
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void Reset() {
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stats_ = webrtc::RunningStatistics<T>();
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next_index_ = 0U;
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max_ = T();
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max_stale_ = false;
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min_ = T();
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min_stale_ = false;
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}
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void AddSample(T sample) {
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if (count() == max_count()) {
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// Remove oldest sample.
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T sample_to_remove = samples_[next_index_];
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stats_.RemoveSample(sample_to_remove);
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if (sample_to_remove >= max_) {
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max_stale_ = true;
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}
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if (sample_to_remove <= min_) {
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min_stale_ = true;
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}
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}
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// Add new sample.
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samples_[next_index_] = sample;
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if (count() == 0 || sample >= max_) {
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max_ = sample;
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max_stale_ = false;
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}
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if (count() == 0 || sample <= min_) {
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min_ = sample;
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min_stale_ = false;
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}
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stats_.AddSample(sample);
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// Update next_index_.
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next_index_ = (next_index_ + 1) % max_count();
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}
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double ComputeMean() const { return stats_.GetMean().value_or(0); }
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T ComputeMax() const {
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if (max_stale_) {
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RTC_DCHECK(count() > 0)
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<< "It shouldn't be possible for max_stale_ && count() == 0";
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max_ = samples_[next_index_];
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for (size_t i = 1u; i < count(); i++) {
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max_ = std::max(max_, samples_[(next_index_ + i) % max_count()]);
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}
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max_stale_ = false;
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}
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return max_;
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}
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T ComputeMin() const {
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if (min_stale_) {
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RTC_DCHECK(count() > 0)
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<< "It shouldn't be possible for min_stale_ && count() == 0";
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min_ = samples_[next_index_];
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for (size_t i = 1u; i < count(); i++) {
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min_ = std::min(min_, samples_[(next_index_ + i) % max_count()]);
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}
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min_stale_ = false;
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}
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return min_;
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}
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// O(n) time complexity.
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// Weights nth sample with weight (learning_rate)^n. Learning_rate should be
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// between (0.0, 1.0], otherwise the non-weighted mean is returned.
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double ComputeWeightedMean(double learning_rate) const {
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if (count() < 1 || learning_rate <= 0.0 || learning_rate >= 1.0) {
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return ComputeMean();
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}
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double weighted_mean = 0.0;
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double current_weight = 1.0;
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double weight_sum = 0.0;
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const size_t max_size = max_count();
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for (size_t i = 0; i < count(); ++i) {
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current_weight *= learning_rate;
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weight_sum += current_weight;
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// Add max_size to prevent underflow.
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size_t index = (next_index_ + max_size - i - 1) % max_size;
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weighted_mean += current_weight * samples_[index];
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}
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return weighted_mean / weight_sum;
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}
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// Compute estimated variance. Estimation is more accurate
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// as the number of samples grows.
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double ComputeVariance() const { return stats_.GetVariance().value_or(0); }
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private:
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webrtc::RunningStatistics<T> stats_;
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size_t next_index_;
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mutable T max_;
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mutable bool max_stale_;
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mutable T min_;
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mutable bool min_stale_;
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std::vector<T> samples_;
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RTC_DISALLOW_COPY_AND_ASSIGN(RollingAccumulator);
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};
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} // namespace rtc
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#endif // RTC_BASE_ROLLING_ACCUMULATOR_H_
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