/**
* @license
* Copyright 2016 Google Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
goog.provide('shaka.abr.Ewma');
goog.require('goog.asserts');
/**
* Computes an exponentionally-weighted moving average.
*
* @param {number} halfLife The quantity of prior samples (by weight) used
* when creating a new estimate. Those prior samples make up half of the new
* estimate.
* @struct
* @constructor
*/
shaka.abr.Ewma = function(halfLife) {
goog.asserts.assert(halfLife > 0, 'expected halfLife to be positive');
/**
* Larger values of alpha expire historical data more slowly.
* @private {number}
*/
this.alpha_ = Math.exp(Math.log(0.5) / halfLife);
/** @private {number} */
this.estimate_ = 0;
/** @private {number} */
this.totalWeight_ = 0;
};
/**
* Takes a sample.
*
* @param {number} weight
* @param {number} value
*/
shaka.abr.Ewma.prototype.sample = function(weight, value) {
let adjAlpha = Math.pow(this.alpha_, weight);
let newEstimate = value * (1 - adjAlpha) + adjAlpha * this.estimate_;
if (!isNaN(newEstimate)) {
this.estimate_ = newEstimate;
this.totalWeight_ += weight;
}
};
/**
* @return {number}
*/
shaka.abr.Ewma.prototype.getEstimate = function() {
let zeroFactor = 1 - Math.pow(this.alpha_, this.totalWeight_);
return this.estimate_ / zeroFactor;
};