| Index: tracing/tracing/base/running_statistics.html
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| diff --git a/tracing/tracing/base/running_statistics.html b/tracing/tracing/base/running_statistics.html
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| deleted file mode 100644
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| index 6ebc3d06c9f1767c2326017a7c59d1b86f96b214..0000000000000000000000000000000000000000
|
| --- a/tracing/tracing/base/running_statistics.html
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| +++ /dev/null
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| @@ -1,180 +0,0 @@
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| -<!DOCTYPE html>
|
| -<!--
|
| -Copyright 2016 The Chromium Authors. All rights reserved.
|
| -Use of this source code is governed by a BSD-style license that can be
|
| -found in the LICENSE file.
|
| --->
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| -
|
| -<link rel="import" href="/tracing/base/base.html">
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| -
|
| -<script>
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| -'use strict';
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| -
|
| -tr.exportTo('tr.b', function() {
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| - /**
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| - * An object of this class computes basic statistics online in O(1).
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| - * Usage:
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| - * 1. Create an instance.
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| - * 2. Add numbers using the |add| method.
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| - * 3. Query statistics.
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| - * 4. Repeat from step 2.
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| - */
|
| - class RunningStatistics {
|
| - constructor() {
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| - this.mean_ = 0;
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| - this.count_ = 0;
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| - this.max_ = -Infinity;
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| - this.min_ = Infinity;
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| - this.sum_ = 0;
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| - this.variance_ = 0;
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| -
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| - // Mean of logarithms of absolute values of samples, or undefined if any
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| - // samples were <= 0.
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| - this.meanlogs_ = 0;
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| - }
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| -
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| - get count() {
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| - return this.count_;
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| - }
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| -
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| - get geometricMean() {
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| - if (this.meanlogs_ === undefined)
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| - return 0;
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| - return Math.exp(this.meanlogs_);
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| - }
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| -
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| - get mean() {
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| - if (this.count_ === 0)
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| - return undefined;
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| - return this.mean_;
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| - }
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| -
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| - get max() {
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| - return this.max_;
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| - }
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| -
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| - get min() {
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| - return this.min_;
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| - }
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| -
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| - get sum() {
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| - return this.sum_;
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| - }
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| -
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| - get variance() {
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| - if (this.count_ === 0)
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| - return undefined;
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| - if (this.count_ === 1)
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| - return 0;
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| - return this.variance_ / (this.count_ - 1);
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| - }
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| -
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| - get stddev() {
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| - if (this.count_ === 0)
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| - return undefined;
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| - return Math.sqrt(this.variance);
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| - }
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| -
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| - add(x) {
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| - this.count_++;
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| - this.max_ = Math.max(this.max_, x);
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| - this.min_ = Math.min(this.min_, x);
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| - this.sum_ += x;
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| -
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| - // The geometric mean is computed using the arithmetic mean of logarithms.
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| - if (x <= 0)
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| - this.meanlogs_ = undefined;
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| - else if (this.meanlogs_ !== undefined)
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| - this.meanlogs_ += (Math.log(Math.abs(x)) - this.meanlogs_) / this.count;
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| -
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| - // The following uses Welford's algorithm for computing running mean
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| - // and variance. See http://www.johndcook.com/blog/standard_deviation.
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| - if (this.count_ === 1) {
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| - this.mean_ = x;
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| - this.variance_ = 0;
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| - } else {
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| - var oldMean = this.mean_;
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| - var oldVariance = this.variance_;
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| - // Using the 2nd formula for updating the mean yields better precision
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| - // but it doesn't work for the case oldMean is Infinity. Hence we handle
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| - // that case separately.
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| - if (oldMean === Infinity || oldMean === -Infinity) {
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| - this.mean_ = this.sum_ / this.count_;
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| - } else {
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| - this.mean_ = oldMean + (x - oldMean) / this.count_;
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| - }
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| - this.variance_ = oldVariance + (x - oldMean) * (x - this.mean_);
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| - }
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| - }
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| -
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| - merge(other) {
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| - var result = new RunningStatistics();
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| - result.count_ = this.count_ + other.count_;
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| - result.sum_ = this.sum_ + other.sum_;
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| - result.min_ = Math.min(this.min_, other.min_);
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| - result.max_ = Math.max(this.max_, other.max_);
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| - if (result.count === 0) {
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| - result.mean_ = 0;
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| - result.variance_ = 0;
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| - result.meanlogs_ = 0;
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| - } else {
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| - // Combine the mean and the variance using the formulas from
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| - // https://goo.gl/ddcAep.
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| - result.mean_ = result.sum / result.count;
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| - var deltaMean = (this.mean || 0) - (other.mean || 0);
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| - result.variance_ = this.variance_ + other.variance_ +
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| - (this.count * other.count * deltaMean * deltaMean / result.count);
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| -
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| - // Merge the arithmetic means of logarithms of absolute values of
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| - // samples, weighted by counts.
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| - if (this.meanlogs_ === undefined || other.meanlogs_ === undefined) {
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| - result.meanlogs_ = undefined;
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| - } else {
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| - result.meanlogs_ = (this.count * this.meanlogs_ +
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| - other.count * other.meanlogs_) / result.count;
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| - }
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| - }
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| - return result;
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| - }
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| -
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| - asDict() {
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| - if (!this.count) {
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| - return [];
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| - }
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| - // It's more efficient to serialize these fields in an array. If you
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| - // add any other fields, you should re-evaluate whether it would be more
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| - // efficient to serialize as a dict.
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| - return [
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| - this.count_,
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| - this.max_,
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| - this.meanlogs_,
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| - this.mean_,
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| - this.min_,
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| - this.sum_,
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| - this.variance_,
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| - ];
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| - }
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| -
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| - static fromDict(dict) {
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| - var result = new RunningStatistics();
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| - if (dict.length !== 7) {
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| - return result;
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| - }
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| - [
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| - result.count_,
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| - result.max_,
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| - result.meanlogs_,
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| - result.mean_,
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| - result.min_,
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| - result.sum_,
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| - result.variance_,
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| - ] = dict;
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| - return result;
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| - }
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| - }
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| -
|
| - return {
|
| - RunningStatistics,
|
| - };
|
| -});
|
| -</script>
|
|
|
Chromium Code Reviews
Issue 2771723003:
[tracing] Move math utilities from base into their own subdirectory (attempt 2) (Closed)
