Move webrtc/{base => rtc_base}

webrtc/base/rollingaccumulator.h

 /*
  *  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 WEBRTC_BASE_ROLLINGACCUMULATOR_H_
 #define WEBRTC_BASE_ROLLINGACCUMULATOR_H_
 
-#include <algorithm>
-#include <vector>
 
-#include "webrtc/base/checks.h"
-#include "webrtc/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
+// This header is deprecated and is just left here temporarily during
+// refactoring. See https://bugs.webrtc.org/7634 for more details.
+#include "webrtc/rtc_base/rollingaccumulator.h"
 
 #endif  // WEBRTC_BASE_ROLLINGACCUMULATOR_H_