Reland "Move webrtc/{base => rtc_base}" (https://codereview.webrtc.org/2877023002)
Reland the base->rtc_base without adding stub headers (will be done in follow-up CL). This preserves git blame history of all files. BUG=webrtc:7634 NOTRY=True TBR=kwiberg@webrtc.org Change-Id: Iea3bb6f3f67b8374c96337b63e8f5aa3e6181012 Reviewed-on: https://chromium-review.googlesource.com/554611 Reviewed-by: Henrik Kjellander <kjellander@webrtc.org> Cr-Commit-Position: refs/heads/master@{#18821}
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Henrik Kjellander
@ -1,174 +0,0 @@
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/*
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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 WEBRTC_BASE_ROLLINGACCUMULATOR_H_
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#define WEBRTC_BASE_ROLLINGACCUMULATOR_H_
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#include <algorithm>
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#include <vector>
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#include "webrtc/base/checks.h"
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#include "webrtc/base/constructormagic.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)
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: samples_(max_count) {
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Reset();
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}
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~RollingAccumulator() {
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}
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size_t max_count() const {
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return samples_.size();
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}
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size_t count() const {
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return count_;
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}
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void Reset() {
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count_ = 0U;
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next_index_ = 0U;
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sum_ = 0.0;
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sum_2_ = 0.0;
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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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sum_ -= sample_to_remove;
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sum_2_ -= static_cast<double>(sample_to_remove) * 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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} else {
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// Increase count of samples.
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++count_;
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}
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// Add new sample.
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samples_[next_index_] = sample;
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sum_ += sample;
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sum_2_ += static_cast<double>(sample) * sample;
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if (count_ == 1 || 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_ == 1 || sample <= min_) {
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min_ = sample;
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min_stale_ = false;
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}
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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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T ComputeSum() const {
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return static_cast<T>(sum_);
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}
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double ComputeMean() const {
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if (count_ == 0) {
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return 0.0;
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}
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return sum_ / count_;
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}
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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 {
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if (count_ == 0) {
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return 0.0;
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}
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// Var = E[x^2] - (E[x])^2
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double count_inv = 1.0 / count_;
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double mean_2 = sum_2_ * count_inv;
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double mean = sum_ * count_inv;
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return mean_2 - (mean * mean);
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}
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private:
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size_t count_;
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size_t next_index_;
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double sum_; // Sum(x) - double to avoid overflow
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double sum_2_; // Sum(x*x) - double to avoid overflow
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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 // WEBRTC_BASE_ROLLINGACCUMULATOR_H_
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