blob: 352b55f5ae7c3dfceddc50b0450d496487e795e9 [file] [log] [blame]
* Copyright 2019 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.
#include <cmath>
#include <cstdint>
#include <limits>
#include "absl/types/optional.h"
namespace rtc {
* This class implements exponential moving average for time series
* estimating both value, variance and variance of estimator based on
* with the additions from nisse@ added to
* A sample gets exponentially less weight so that it's 50%
* after |half_time| time units.
class EventBasedExponentialMovingAverage {
// |half_time| specifies how much weight will be given to old samples,
// see example above.
explicit EventBasedExponentialMovingAverage(int half_time);
void AddSample(int64_t now, int value);
double GetAverage() const { return value_; }
double GetVariance() const { return sample_variance_; }
// Compute 95% confidence interval assuming that
// - variance of samples are normal distributed.
// - variance of estimator is normal distributed.
// The returned values specifies the distance from the average,
// i.e if X = GetAverage(), m = GetConfidenceInterval()
// then a there is 95% likelihood that the observed variables is inside
// [ X +/- m ].
double GetConfidenceInterval() const;
// Reset
void Reset();
// Update the half_time.
// NOTE: resets estimate too.
void SetHalfTime(int half_time);
double tau_;
double value_ = std::nan("uninit");
double sample_variance_ = std::numeric_limits<double>::infinity();
// This is the ratio between variance of the estimate and variance of samples.
double estimator_variance_ = 1;
absl::optional<int64_t> last_observation_timestamp_;
} // namespace rtc