/* * 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 #include #include #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 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(stats_.Size()); } void Reset() { stats_ = webrtc::webrtc_impl::RunningStatistics(); 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 stats_; size_t next_index_; mutable T max_; mutable bool max_stale_; mutable T min_; mutable bool min_stale_; std::vector samples_; }; } // namespace rtc #endif // RTC_BASE_ROLLING_ACCUMULATOR_H_