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96e3b991da
Bug: webrtc:12338 Change-Id: I72fcb505a92f03b2ace7160ee33d555a977eddfd Reviewed-on: https://webrtc-review.googlesource.com/c/src/+/226955 Reviewed-by: Harald Alvestrand <hta@webrtc.org> Commit-Queue: Artem Titov <titovartem@webrtc.org> Cr-Commit-Position: refs/heads/master@{#34587}
82 lines
2.7 KiB
C++
82 lines
2.7 KiB
C++
/*
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* Copyright 2019 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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#include "rtc_base/numerics/event_based_exponential_moving_average.h"
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#include <cmath>
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#include "rtc_base/checks.h"
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namespace {
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// For a normal distributed value, the 95% double sided confidence interval is
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// is 1.96 * stddev.
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constexpr double ninetyfive_percent_confidence = 1.96;
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} // namespace
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namespace rtc {
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// `half_time` specifies how much weight will be given to old samples,
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// a sample gets exponentially less weight so that it's 50%
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// after `half_time` time units has passed.
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EventBasedExponentialMovingAverage::EventBasedExponentialMovingAverage(
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int half_time) {
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SetHalfTime(half_time);
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}
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void EventBasedExponentialMovingAverage::SetHalfTime(int half_time) {
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tau_ = static_cast<double>(half_time) / log(2);
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Reset();
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}
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void EventBasedExponentialMovingAverage::Reset() {
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value_ = std::nan("uninit");
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sample_variance_ = std::numeric_limits<double>::infinity();
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estimator_variance_ = 1;
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last_observation_timestamp_.reset();
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}
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void EventBasedExponentialMovingAverage::AddSample(int64_t now, int sample) {
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if (!last_observation_timestamp_.has_value()) {
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value_ = sample;
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} else {
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// TODO(webrtc:11140): This should really be > (e.g not >=)
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// but some pesky tests run with simulated clock and let
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// samples arrive simultaneously!
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RTC_DCHECK(now >= *last_observation_timestamp_);
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// Variance gets computed after second sample.
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int64_t age = now - *last_observation_timestamp_;
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double e = exp(-age / tau_);
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double alpha = e / (1 + e);
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double one_minus_alpha = 1 - alpha;
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double sample_diff = sample - value_;
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value_ = one_minus_alpha * value_ + alpha * sample;
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estimator_variance_ =
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(one_minus_alpha * one_minus_alpha) * estimator_variance_ +
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(alpha * alpha);
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if (sample_variance_ == std::numeric_limits<double>::infinity()) {
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// First variance.
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sample_variance_ = sample_diff * sample_diff;
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} else {
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double new_variance = one_minus_alpha * sample_variance_ +
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alpha * sample_diff * sample_diff;
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sample_variance_ = new_variance;
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}
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}
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last_observation_timestamp_ = now;
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}
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double EventBasedExponentialMovingAverage::GetConfidenceInterval() const {
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return ninetyfive_percent_confidence *
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sqrt(sample_variance_ * estimator_variance_);
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}
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} // namespace rtc
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