|  | /* | 
|  | *  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/constructor_magic.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() {} | 
|  |  | 
|  | 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_; | 
|  |  | 
|  | RTC_DISALLOW_COPY_AND_ASSIGN(RollingAccumulator); | 
|  | }; | 
|  |  | 
|  | }  // namespace rtc | 
|  |  | 
|  | #endif  // RTC_BASE_ROLLING_ACCUMULATOR_H_ |