henrike@webrtc.org | f7795df | 2014-05-13 18:00:26 +0000 | [diff] [blame^] | 1 | /* |
| 2 | * Copyright 2011 The WebRTC Project Authors. All rights reserved. |
| 3 | * |
| 4 | * Use of this source code is governed by a BSD-style license |
| 5 | * that can be found in the LICENSE file in the root of the source |
| 6 | * tree. An additional intellectual property rights grant can be found |
| 7 | * in the file PATENTS. All contributing project authors may |
| 8 | * be found in the AUTHORS file in the root of the source tree. |
| 9 | */ |
| 10 | |
| 11 | #ifndef WEBRTC_BASE_ROLLINGACCUMULATOR_H_ |
| 12 | #define WEBRTC_BASE_ROLLINGACCUMULATOR_H_ |
| 13 | |
| 14 | #include <vector> |
| 15 | |
| 16 | #include "webrtc/base/common.h" |
| 17 | |
| 18 | namespace rtc { |
| 19 | |
| 20 | // RollingAccumulator stores and reports statistics |
| 21 | // over N most recent samples. |
| 22 | // |
| 23 | // T is assumed to be an int, long, double or float. |
| 24 | template<typename T> |
| 25 | class RollingAccumulator { |
| 26 | public: |
| 27 | explicit RollingAccumulator(size_t max_count) |
| 28 | : samples_(max_count) { |
| 29 | Reset(); |
| 30 | } |
| 31 | ~RollingAccumulator() { |
| 32 | } |
| 33 | |
| 34 | size_t max_count() const { |
| 35 | return samples_.size(); |
| 36 | } |
| 37 | |
| 38 | size_t count() const { |
| 39 | return count_; |
| 40 | } |
| 41 | |
| 42 | void Reset() { |
| 43 | count_ = 0U; |
| 44 | next_index_ = 0U; |
| 45 | sum_ = 0.0; |
| 46 | sum_2_ = 0.0; |
| 47 | max_ = T(); |
| 48 | max_stale_ = false; |
| 49 | min_ = T(); |
| 50 | min_stale_ = false; |
| 51 | } |
| 52 | |
| 53 | void AddSample(T sample) { |
| 54 | if (count_ == max_count()) { |
| 55 | // Remove oldest sample. |
| 56 | T sample_to_remove = samples_[next_index_]; |
| 57 | sum_ -= sample_to_remove; |
| 58 | sum_2_ -= sample_to_remove * sample_to_remove; |
| 59 | if (sample_to_remove >= max_) { |
| 60 | max_stale_ = true; |
| 61 | } |
| 62 | if (sample_to_remove <= min_) { |
| 63 | min_stale_ = true; |
| 64 | } |
| 65 | } else { |
| 66 | // Increase count of samples. |
| 67 | ++count_; |
| 68 | } |
| 69 | // Add new sample. |
| 70 | samples_[next_index_] = sample; |
| 71 | sum_ += sample; |
| 72 | sum_2_ += sample * sample; |
| 73 | if (count_ == 1 || sample >= max_) { |
| 74 | max_ = sample; |
| 75 | max_stale_ = false; |
| 76 | } |
| 77 | if (count_ == 1 || sample <= min_) { |
| 78 | min_ = sample; |
| 79 | min_stale_ = false; |
| 80 | } |
| 81 | // Update next_index_. |
| 82 | next_index_ = (next_index_ + 1) % max_count(); |
| 83 | } |
| 84 | |
| 85 | T ComputeSum() const { |
| 86 | return static_cast<T>(sum_); |
| 87 | } |
| 88 | |
| 89 | double ComputeMean() const { |
| 90 | if (count_ == 0) { |
| 91 | return 0.0; |
| 92 | } |
| 93 | return sum_ / count_; |
| 94 | } |
| 95 | |
| 96 | T ComputeMax() const { |
| 97 | if (max_stale_) { |
| 98 | ASSERT(count_ > 0 && |
| 99 | "It shouldn't be possible for max_stale_ && count_ == 0"); |
| 100 | max_ = samples_[next_index_]; |
| 101 | for (size_t i = 1u; i < count_; i++) { |
| 102 | max_ = _max(max_, samples_[(next_index_ + i) % max_count()]); |
| 103 | } |
| 104 | max_stale_ = false; |
| 105 | } |
| 106 | return max_; |
| 107 | } |
| 108 | |
| 109 | T ComputeMin() const { |
| 110 | if (min_stale_) { |
| 111 | ASSERT(count_ > 0 && |
| 112 | "It shouldn't be possible for min_stale_ && count_ == 0"); |
| 113 | min_ = samples_[next_index_]; |
| 114 | for (size_t i = 1u; i < count_; i++) { |
| 115 | min_ = _min(min_, samples_[(next_index_ + i) % max_count()]); |
| 116 | } |
| 117 | min_stale_ = false; |
| 118 | } |
| 119 | return min_; |
| 120 | } |
| 121 | |
| 122 | // O(n) time complexity. |
| 123 | // Weights nth sample with weight (learning_rate)^n. Learning_rate should be |
| 124 | // between (0.0, 1.0], otherwise the non-weighted mean is returned. |
| 125 | double ComputeWeightedMean(double learning_rate) const { |
| 126 | if (count_ < 1 || learning_rate <= 0.0 || learning_rate >= 1.0) { |
| 127 | return ComputeMean(); |
| 128 | } |
| 129 | double weighted_mean = 0.0; |
| 130 | double current_weight = 1.0; |
| 131 | double weight_sum = 0.0; |
| 132 | const size_t max_size = max_count(); |
| 133 | for (size_t i = 0; i < count_; ++i) { |
| 134 | current_weight *= learning_rate; |
| 135 | weight_sum += current_weight; |
| 136 | // Add max_size to prevent underflow. |
| 137 | size_t index = (next_index_ + max_size - i - 1) % max_size; |
| 138 | weighted_mean += current_weight * samples_[index]; |
| 139 | } |
| 140 | return weighted_mean / weight_sum; |
| 141 | } |
| 142 | |
| 143 | // Compute estimated variance. Estimation is more accurate |
| 144 | // as the number of samples grows. |
| 145 | double ComputeVariance() const { |
| 146 | if (count_ == 0) { |
| 147 | return 0.0; |
| 148 | } |
| 149 | // Var = E[x^2] - (E[x])^2 |
| 150 | double count_inv = 1.0 / count_; |
| 151 | double mean_2 = sum_2_ * count_inv; |
| 152 | double mean = sum_ * count_inv; |
| 153 | return mean_2 - (mean * mean); |
| 154 | } |
| 155 | |
| 156 | private: |
| 157 | size_t count_; |
| 158 | size_t next_index_; |
| 159 | double sum_; // Sum(x) - double to avoid overflow |
| 160 | double sum_2_; // Sum(x*x) - double to avoid overflow |
| 161 | mutable T max_; |
| 162 | mutable bool max_stale_; |
| 163 | mutable T min_; |
| 164 | mutable bool min_stale_; |
| 165 | std::vector<T> samples_; |
| 166 | |
| 167 | DISALLOW_COPY_AND_ASSIGN(RollingAccumulator); |
| 168 | }; |
| 169 | |
| 170 | } // namespace rtc |
| 171 | |
| 172 | #endif // WEBRTC_BASE_ROLLINGACCUMULATOR_H_ |