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Diffstat (limited to 'third_party/libwebrtc/webrtc/rtc_base/rollingaccumulator.h')
| -rw-r--r-- | third_party/libwebrtc/webrtc/rtc_base/rollingaccumulator.h | 174 |
1 files changed, 174 insertions, 0 deletions
diff --git a/third_party/libwebrtc/webrtc/rtc_base/rollingaccumulator.h b/third_party/libwebrtc/webrtc/rtc_base/rollingaccumulator.h new file mode 100644 index 0000000000..e7d5b06226 --- /dev/null +++ b/third_party/libwebrtc/webrtc/rtc_base/rollingaccumulator.h @@ -0,0 +1,174 @@ +/* + * 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_ROLLINGACCUMULATOR_H_ +#define RTC_BASE_ROLLINGACCUMULATOR_H_ + +#include <algorithm> +#include <vector> + +#include "rtc_base/checks.h" +#include "rtc_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_) { + 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 { + 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 // RTC_BASE_ROLLINGACCUMULATOR_H_ |