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<title>[196605] trunk/Websites/perf.webkit.org</title>
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<dt>Revision</dt> <dd><a href="http://trac.webkit.org/projects/webkit/changeset/196605">196605</a></dd>
<dt>Author</dt> <dd>rniwa@webkit.org</dd>
<dt>Date</dt> <dd>2016-02-15 15:57:22 -0800 (Mon, 15 Feb 2016)</dd>
</dl>
<h3>Log Message</h3>
<pre>v1 UI and v2 UI should share statistics.js
https://bugs.webkit.org/show_bug.cgi?id=154262
Reviewed by Chris Dumez.
Share statistics.js between v1 and v2 UI.
* public/index.html:
* public/js/shared.js: Deleted.
* public/js/statistics.js: Removed.
* public/shared: Added.
* public/shared/statistics.js: Moved from Websites/perf.webkit.org/public/v2/js/statistics.js.
* public/v2/index.html:
* public/v2/js/statistics.js: Removed.
* public/v3/index.html:
* tools/detect-changes.js:</pre>
<h3>Modified Paths</h3>
<ul>
<li><a href="#trunkWebsitesperfwebkitorgChangeLog">trunk/Websites/perf.webkit.org/ChangeLog</a></li>
<li><a href="#trunkWebsitesperfwebkitorgpublicindexhtml">trunk/Websites/perf.webkit.org/public/index.html</a></li>
<li><a href="#trunkWebsitesperfwebkitorgpublicv2indexhtml">trunk/Websites/perf.webkit.org/public/v2/index.html</a></li>
<li><a href="#trunkWebsitesperfwebkitorgpublicv3indexhtml">trunk/Websites/perf.webkit.org/public/v3/index.html</a></li>
<li><a href="#trunkWebsitesperfwebkitorgtoolsdetectchangesjs">trunk/Websites/perf.webkit.org/tools/detect-changes.js</a></li>
</ul>
<h3>Added Paths</h3>
<ul>
<li>trunk/Websites/perf.webkit.org/public/shared/</li>
<li><a href="#trunkWebsitesperfwebkitorgpublicsharedstatisticsjs">trunk/Websites/perf.webkit.org/public/shared/statistics.js</a></li>
</ul>
<h3>Removed Paths</h3>
<ul>
<li><a href="#trunkWebsitesperfwebkitorgpublicjssharedjs">trunk/Websites/perf.webkit.org/public/js/shared.js</a></li>
<li><a href="#trunkWebsitesperfwebkitorgpublicjsstatisticsjs">trunk/Websites/perf.webkit.org/public/js/statistics.js</a></li>
<li><a href="#trunkWebsitesperfwebkitorgpublicv2jsstatisticsjs">trunk/Websites/perf.webkit.org/public/v2/js/statistics.js</a></li>
</ul>
</div>
<div id="patch">
<h3>Diff</h3>
<a id="trunkWebsitesperfwebkitorgChangeLog"></a>
<div class="modfile"><h4>Modified: trunk/Websites/perf.webkit.org/ChangeLog (196604 => 196605)</h4>
<pre class="diff"><span>
<span class="info">--- trunk/Websites/perf.webkit.org/ChangeLog 2016-02-15 23:39:02 UTC (rev 196604)
+++ trunk/Websites/perf.webkit.org/ChangeLog 2016-02-15 23:57:22 UTC (rev 196605)
</span><span class="lines">@@ -1,3 +1,22 @@
</span><ins>+2016-02-15 Ryosuke Niwa <rniwa@webkit.org>
+
+ v1 UI and v2 UI should share statistics.js
+ https://bugs.webkit.org/show_bug.cgi?id=154262
+
+ Reviewed by Chris Dumez.
+
+ Share statistics.js between v1 and v2 UI.
+
+ * public/index.html:
+ * public/js/shared.js: Deleted.
+ * public/js/statistics.js: Removed.
+ * public/shared: Added.
+ * public/shared/statistics.js: Moved from Websites/perf.webkit.org/public/v2/js/statistics.js.
+ * public/v2/index.html:
+ * public/v2/js/statistics.js: Removed.
+ * public/v3/index.html:
+ * tools/detect-changes.js:
+
</ins><span class="cx"> 2016-02-13 Ryosuke Niwa <rniwa@webkit.org>
</span><span class="cx">
</span><span class="cx"> v3 UI sometimes shows same dates twice on the x-axis of time series charts
</span></span></pre></div>
<a id="trunkWebsitesperfwebkitorgpublicindexhtml"></a>
<div class="modfile"><h4>Modified: trunk/Websites/perf.webkit.org/public/index.html (196604 => 196605)</h4>
<pre class="diff"><span>
<span class="info">--- trunk/Websites/perf.webkit.org/public/index.html 2016-02-15 23:39:02 UTC (rev 196604)
+++ trunk/Websites/perf.webkit.org/public/index.html 2016-02-15 23:57:22 UTC (rev 196605)
</span><span class="lines">@@ -10,8 +10,7 @@
</span><span class="cx"> <script src="js/jquery.flot.selection.js" defer></script>
</span><span class="cx"> <script src="js/jquery.flot.time.js" defer></script>
</span><span class="cx"> <script src="js/helper-classes.js" defer></script>
</span><del>-<script src="js/shared.js" defer></script>
-<script src="js/statistics.js" defer></script>
</del><ins>+<script src="shared/statistics.js" defer></script>
</ins><span class="cx"> <link rel="stylesheet" href="common.css">
</span><span class="cx"> <style type="text/css">
</span><span class="cx">
</span><span class="lines">@@ -832,6 +831,10 @@
</span><span class="cx"> });
</span><span class="cx"> }
</span><span class="cx">
</span><ins>+function fileNameFromPlatformAndTest(platformId, testId) {
+ return platformId + '-' + testId + '.json';
+}
+
</ins><span class="cx"> function init() {
</span><span class="cx"> var allPlatforms;
</span><span class="cx"> var tests = [];
</span></span></pre></div>
<a id="trunkWebsitesperfwebkitorgpublicjssharedjs"></a>
<div class="delfile"><h4>Deleted: trunk/Websites/perf.webkit.org/public/js/shared.js (196604 => 196605)</h4>
<pre class="diff"><span>
<span class="info">--- trunk/Websites/perf.webkit.org/public/js/shared.js 2016-02-15 23:39:02 UTC (rev 196604)
+++ trunk/Websites/perf.webkit.org/public/js/shared.js 2016-02-15 23:57:22 UTC (rev 196605)
</span><span class="lines">@@ -1,8 +0,0 @@
</span><del>-
-function fileNameFromPlatformAndTest(platformId, testId) {
- return platformId + '-' + testId + '.json';
-}
-
-if (typeof module != 'undefined') {
- module.exports.fileNameFromPlatformAndTest = fileNameFromPlatformAndTest;
-}
</del></span></pre></div>
<a id="trunkWebsitesperfwebkitorgpublicjsstatisticsjs"></a>
<div class="delfile"><h4>Deleted: trunk/Websites/perf.webkit.org/public/js/statistics.js (196604 => 196605)</h4>
<pre class="diff"><span>
<span class="info">--- trunk/Websites/perf.webkit.org/public/js/statistics.js 2016-02-15 23:39:02 UTC (rev 196604)
+++ trunk/Websites/perf.webkit.org/public/js/statistics.js 2016-02-15 23:57:22 UTC (rev 196605)
</span><span class="lines">@@ -1,107 +0,0 @@
</span><del>-var Statistics = new (function () {
-
- this.sum = function (values) {
- return values.length ? values.reduce(function (a, b) { return a + b; }) : 0;
- }
-
- this.squareSum = function (values) {
- return values.length ? values.reduce(function (sum, value) { return sum + value * value;}, 0) : 0;
- }
-
- // With sum and sum of squares, we can compute the sample standard deviation in O(1).
- // See https://rniwa.com/2012-11-10/sample-standard-deviation-in-terms-of-sum-and-square-sum-of-samples/
- this.sampleStandardDeviation = function (numberOfSamples, sum, squareSum) {
- if (numberOfSamples < 2)
- return 0;
- return Math.sqrt(squareSum / (numberOfSamples - 1) - sum * sum / (numberOfSamples - 1) / numberOfSamples);
- }
-
- this.supportedConfidenceIntervalProbabilities = function () {
- var supportedProbabilities = [];
- for (var quantile in tDistributionQuantiles)
- supportedProbabilities.push((1 - (1 - quantile) * 2).toFixed(2));
- return supportedProbabilities
- }
-
- // Computes the delta d s.t. (mean - d, mean + d) is the confidence interval with the specified probability in O(1).
- this.confidenceIntervalDelta = function (probability, numberOfSamples, sum, squareSum) {
- var quantile = (1 - (1 - probability) / 2);
- if (!(quantile in tDistributionQuantiles)) {
- throw 'We only support ' + this.supportedConfidenceIntervalProbabilities().map(function (probability)
- { return probability * 100 + '%'; } ).join(', ') + ' confidence intervals.';
- }
- if (numberOfSamples - 2 < 0)
- return NaN;
- var deltas = tDistributionQuantiles[quantile];
- var degreesOfFreedom = numberOfSamples - 1;
- if (degreesOfFreedom > deltas.length)
- throw 'We only support up to ' + deltas.length + ' degrees of freedom';
-
- // d = c * S/sqrt(numberOfSamples) where c ~ t-distribution(degreesOfFreedom) and S is the sample standard deviation.
- return deltas[degreesOfFreedom - 1] * this.sampleStandardDeviation(numberOfSamples, sum, squareSum) / Math.sqrt(numberOfSamples);
- }
-
- this.confidenceInterval = function (values, probability) {
- var sum = this.sum(values);
- var mean = sum / values.length;
- var delta = this.confidenceIntervalDelta(probability || 0.95, values.length, sum, this.squareSum(values));
- return [mean - delta, mean + delta];
- }
-
- var tDistributionQuantiles = {
- 0.9: [
- 3.077684, 1.885618, 1.637744, 1.533206, 1.475884, 1.439756, 1.414924, 1.396815, 1.383029, 1.372184,
- 1.363430, 1.356217, 1.350171, 1.345030, 1.340606, 1.336757, 1.333379, 1.330391, 1.327728, 1.325341,
- 1.323188, 1.321237, 1.319460, 1.317836, 1.316345, 1.314972, 1.313703, 1.312527, 1.311434, 1.310415,
- 1.309464, 1.308573, 1.307737, 1.306952, 1.306212, 1.305514, 1.304854, 1.304230, 1.303639, 1.303077,
- 1.302543, 1.302035, 1.301552, 1.301090, 1.300649, 1.300228, 1.299825, 1.299439, 1.299069, 1.298714,
-
- 1.298373, 1.298045, 1.297730, 1.297426, 1.297134, 1.296853, 1.296581, 1.296319, 1.296066, 1.295821,
- 1.295585, 1.295356, 1.295134, 1.294920, 1.294712, 1.294511, 1.294315, 1.294126, 1.293942, 1.293763,
- 1.293589, 1.293421, 1.293256, 1.293097, 1.292941, 1.292790, 1.292643, 1.292500, 1.292360, 1.292224,
- 1.292091, 1.291961, 1.291835, 1.291711, 1.291591, 1.291473, 1.291358, 1.291246, 1.291136, 1.291029,
- 1.290924, 1.290821, 1.290721, 1.290623, 1.290527, 1.290432, 1.290340, 1.290250, 1.290161, 1.290075],
- 0.95: [
- 6.313752, 2.919986, 2.353363, 2.131847, 2.015048, 1.943180, 1.894579, 1.859548, 1.833113, 1.812461,
- 1.795885, 1.782288, 1.770933, 1.761310, 1.753050, 1.745884, 1.739607, 1.734064, 1.729133, 1.724718,
- 1.720743, 1.717144, 1.713872, 1.710882, 1.708141, 1.705618, 1.703288, 1.701131, 1.699127, 1.697261,
- 1.695519, 1.693889, 1.692360, 1.690924, 1.689572, 1.688298, 1.687094, 1.685954, 1.684875, 1.683851,
- 1.682878, 1.681952, 1.681071, 1.680230, 1.679427, 1.678660, 1.677927, 1.677224, 1.676551, 1.675905,
-
- 1.675285, 1.674689, 1.674116, 1.673565, 1.673034, 1.672522, 1.672029, 1.671553, 1.671093, 1.670649,
- 1.670219, 1.669804, 1.669402, 1.669013, 1.668636, 1.668271, 1.667916, 1.667572, 1.667239, 1.666914,
- 1.666600, 1.666294, 1.665996, 1.665707, 1.665425, 1.665151, 1.664885, 1.664625, 1.664371, 1.664125,
- 1.663884, 1.663649, 1.663420, 1.663197, 1.662978, 1.662765, 1.662557, 1.662354, 1.662155, 1.661961,
- 1.661771, 1.661585, 1.661404, 1.661226, 1.661052, 1.660881, 1.660715, 1.660551, 1.660391, 1.660234],
- 0.975: [
- 12.706205, 4.302653, 3.182446, 2.776445, 2.570582, 2.446912, 2.364624, 2.306004, 2.262157, 2.228139,
- 2.200985, 2.178813, 2.160369, 2.144787, 2.131450, 2.119905, 2.109816, 2.100922, 2.093024, 2.085963,
- 2.079614, 2.073873, 2.068658, 2.063899, 2.059539, 2.055529, 2.051831, 2.048407, 2.045230, 2.042272,
- 2.039513, 2.036933, 2.034515, 2.032245, 2.030108, 2.028094, 2.026192, 2.024394, 2.022691, 2.021075,
- 2.019541, 2.018082, 2.016692, 2.015368, 2.014103, 2.012896, 2.011741, 2.010635, 2.009575, 2.008559,
-
- 2.007584, 2.006647, 2.005746, 2.004879, 2.004045, 2.003241, 2.002465, 2.001717, 2.000995, 2.000298,
- 1.999624, 1.998972, 1.998341, 1.997730, 1.997138, 1.996564, 1.996008, 1.995469, 1.994945, 1.994437,
- 1.993943, 1.993464, 1.992997, 1.992543, 1.992102, 1.991673, 1.991254, 1.990847, 1.990450, 1.990063,
- 1.989686, 1.989319, 1.988960, 1.988610, 1.988268, 1.987934, 1.987608, 1.987290, 1.986979, 1.986675,
- 1.986377, 1.986086, 1.985802, 1.985523, 1.985251, 1.984984, 1.984723, 1.984467, 1.984217, 1.983972],
- 0.99: [
- 31.820516, 6.964557, 4.540703, 3.746947, 3.364930, 3.142668, 2.997952, 2.896459, 2.821438, 2.763769,
- 2.718079, 2.680998, 2.650309, 2.624494, 2.602480, 2.583487, 2.566934, 2.552380, 2.539483, 2.527977,
- 2.517648, 2.508325, 2.499867, 2.492159, 2.485107, 2.478630, 2.472660, 2.467140, 2.462021, 2.457262,
- 2.452824, 2.448678, 2.444794, 2.441150, 2.437723, 2.434494, 2.431447, 2.428568, 2.425841, 2.423257,
- 2.420803, 2.418470, 2.416250, 2.414134, 2.412116, 2.410188, 2.408345, 2.406581, 2.404892, 2.403272,
-
- 2.401718, 2.400225, 2.398790, 2.397410, 2.396081, 2.394801, 2.393568, 2.392377, 2.391229, 2.390119,
- 2.389047, 2.388011, 2.387008, 2.386037, 2.385097, 2.384186, 2.383302, 2.382446, 2.381615, 2.380807,
- 2.380024, 2.379262, 2.378522, 2.377802, 2.377102, 2.376420, 2.375757, 2.375111, 2.374482, 2.373868,
- 2.373270, 2.372687, 2.372119, 2.371564, 2.371022, 2.370493, 2.369977, 2.369472, 2.368979, 2.368497,
- 2.368026, 2.367566, 2.367115, 2.366674, 2.366243, 2.365821, 2.365407, 2.365002, 2.364606, 2.364217]
- };
-
-})();
-
-if (typeof module != 'undefined') {
- for (var key in Statistics)
- module.exports[key] = Statistics[key];
-}
</del></span></pre></div>
<a id="trunkWebsitesperfwebkitorgpublicsharedstatisticsjsfromrev196603trunkWebsitesperfwebkitorgpublicv2jsstatisticsjs"></a>
<div class="copfile"><h4>Copied: trunk/Websites/perf.webkit.org/public/shared/statistics.js (from rev 196603, trunk/Websites/perf.webkit.org/public/v2/js/statistics.js) (0 => 196605)</h4>
<pre class="diff"><span>
<span class="info">--- trunk/Websites/perf.webkit.org/public/shared/statistics.js (rev 0)
+++ trunk/Websites/perf.webkit.org/public/shared/statistics.js 2016-02-15 23:57:22 UTC (rev 196605)
</span><span class="lines">@@ -0,0 +1,583 @@
</span><ins>+var Statistics = new (function () {
+
+ this.min = function (values) {
+ return Math.min.apply(Math, values);
+ }
+
+ this.max = function (values) {
+ return Math.max.apply(Math, values);
+ }
+
+ this.sum = function (values) {
+ return values.length ? values.reduce(function (a, b) { return a + b; }) : 0;
+ }
+
+ this.squareSum = function (values) {
+ return values.length ? values.reduce(function (sum, value) { return sum + value * value;}, 0) : 0;
+ }
+
+ // With sum and sum of squares, we can compute the sample standard deviation in O(1).
+ // See https://rniwa.com/2012-11-10/sample-standard-deviation-in-terms-of-sum-and-square-sum-of-samples/
+ this.sampleStandardDeviation = function (numberOfSamples, sum, squareSum) {
+ if (numberOfSamples < 2)
+ return 0;
+ return Math.sqrt(squareSum / (numberOfSamples - 1) - sum * sum / (numberOfSamples - 1) / numberOfSamples);
+ }
+
+ this.supportedConfidenceIntervalProbabilities = function () {
+ var supportedProbabilities = [];
+ for (var probability in tDistributionByOneSidedProbability)
+ supportedProbabilities.push(oneSidedToTwoSidedProbability(probability).toFixed(2));
+ return supportedProbabilities
+ }
+
+ // Computes the delta d s.t. (mean - d, mean + d) is the confidence interval with the specified probability in O(1).
+ this.confidenceIntervalDelta = function (probability, numberOfSamples, sum, squareSum) {
+ var oneSidedProbability = twoSidedToOneSidedProbability(probability);
+ if (!(oneSidedProbability in tDistributionByOneSidedProbability)) {
+ throw 'We only support ' + this.supportedConfidenceIntervalProbabilities().map(function (probability)
+ { return probability * 100 + '%'; } ).join(', ') + ' confidence intervals.';
+ }
+ if (numberOfSamples - 2 < 0)
+ return NaN;
+ var deltas = tDistributionByOneSidedProbability[oneSidedProbability];
+ var degreesOfFreedom = numberOfSamples - 1;
+ if (degreesOfFreedom > deltas.length)
+ throw 'We only support up to ' + deltas.length + ' degrees of freedom';
+
+ // d = c * S/sqrt(numberOfSamples) where c ~ t-distribution(degreesOfFreedom) and S is the sample standard deviation.
+ return deltas[degreesOfFreedom - 1] * this.sampleStandardDeviation(numberOfSamples, sum, squareSum) / Math.sqrt(numberOfSamples);
+ }
+
+ this.confidenceInterval = function (values, probability) {
+ var sum = this.sum(values);
+ var mean = sum / values.length;
+ var delta = this.confidenceIntervalDelta(probability || 0.95, values.length, sum, this.squareSum(values));
+ return [mean - delta, mean + delta];
+ }
+
+ // Welch's t-test (http://en.wikipedia.org/wiki/Welch%27s_t_test)
+ this.testWelchsT = function (values1, values2, probability) {
+ return this.computeWelchsT(values1, 0, values1.length, values2, 0, values2.length, probability).significantlyDifferent;
+ }
+
+ this.probabilityRangeForWelchsT = function (values1, values2) {
+ var result = this.computeWelchsT(values1, 0, values1.length, values2, 0, values2.length);
+ if (isNaN(result.t) || isNaN(result.degreesOfFreedom))
+ return {t: NaN, degreesOfFreedom:NaN, range: [null, null]};
+
+ var lowerBound = null;
+ var upperBound = null;
+ for (var probability in tDistributionByOneSidedProbability) {
+ var twoSidedProbability = oneSidedToTwoSidedProbability(probability);
+ if (result.t > tDistributionByOneSidedProbability[probability][Math.round(result.degreesOfFreedom - 1)])
+ lowerBound = twoSidedProbability;
+ else if (lowerBound) {
+ upperBound = twoSidedProbability;
+ break;
+ }
+ }
+ return {t: result.t, degreesOfFreedom: result.degreesOfFreedom, range: [lowerBound, upperBound]};
+ }
+
+ this.computeWelchsT = function (values1, startIndex1, length1, values2, startIndex2, length2, probability) {
+ var stat1 = sampleMeanAndVarianceForValues(values1, startIndex1, length1);
+ var stat2 = sampleMeanAndVarianceForValues(values2, startIndex2, length2);
+ var sumOfSampleVarianceOverSampleSize = stat1.variance / stat1.size + stat2.variance / stat2.size;
+ var t = Math.abs((stat1.mean - stat2.mean) / Math.sqrt(sumOfSampleVarianceOverSampleSize));
+
+ // http://en.wikipedia.org/wiki/Welch–Satterthwaite_equation
+ var degreesOfFreedom = sumOfSampleVarianceOverSampleSize * sumOfSampleVarianceOverSampleSize
+ / (stat1.variance * stat1.variance / stat1.size / stat1.size / stat1.degreesOfFreedom
+ + stat2.variance * stat2.variance / stat2.size / stat2.size / stat2.degreesOfFreedom);
+ var minT = tDistributionByOneSidedProbability[twoSidedToOneSidedProbability(probability || 0.8)][Math.round(degreesOfFreedom - 1)];
+ return {
+ t: t,
+ degreesOfFreedom: degreesOfFreedom,
+ significantlyDifferent: t > minT,
+ };
+ }
+
+ function sampleMeanAndVarianceForValues(values, startIndex, length) {
+ var sum = 0;
+ for (var i = 0; i < length; i++)
+ sum += values[startIndex + i];
+ var squareSum = 0;
+ for (var i = 0; i < length; i++)
+ squareSum += values[startIndex + i] * values[startIndex + i];
+ var sampleMean = sum / length;
+ // FIXME: Maybe we should be using the biased sample variance.
+ var unbiasedSampleVariance = (squareSum - sum * sum / length) / (length - 1);
+ return {
+ mean: sampleMean,
+ variance: unbiasedSampleVariance,
+ size: length,
+ degreesOfFreedom: length - 1,
+ }
+ }
+
+ function recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values, startIndex, length, minLength, segments) {
+ var tMax = 0;
+ var argTMax = null;
+ for (var i = 1; i < length - 1; i++) {
+ var firstLength = i;
+ var secondLength = length - i;
+ if (firstLength < minLength || secondLength < minLength)
+ continue;
+ var result = Statistics.computeWelchsT(values, startIndex, firstLength, values, startIndex + i, secondLength, 0.9);
+ if (result.significantlyDifferent && result.t > tMax) {
+ tMax = result.t;
+ argTMax = i;
+ }
+ }
+ if (!tMax) {
+ segments.push(values.slice(startIndex, startIndex + length));
+ return;
+ }
+ recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values, startIndex, argTMax, minLength, segments);
+ recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values, startIndex + argTMax, length - argTMax, minLength, segments);
+ }
+
+ var tDistributionByOneSidedProbability = {
+ 0.9: [
+ 3.077684, 1.885618, 1.637744, 1.533206, 1.475884, 1.439756, 1.414924, 1.396815, 1.383029, 1.372184,
+ 1.363430, 1.356217, 1.350171, 1.345030, 1.340606, 1.336757, 1.333379, 1.330391, 1.327728, 1.325341,
+ 1.323188, 1.321237, 1.319460, 1.317836, 1.316345, 1.314972, 1.313703, 1.312527, 1.311434, 1.310415,
+ 1.309464, 1.308573, 1.307737, 1.306952, 1.306212, 1.305514, 1.304854, 1.304230, 1.303639, 1.303077,
+ 1.302543, 1.302035, 1.301552, 1.301090, 1.300649, 1.300228, 1.299825, 1.299439, 1.299069, 1.298714,
+
+ 1.298373, 1.298045, 1.297730, 1.297426, 1.297134, 1.296853, 1.296581, 1.296319, 1.296066, 1.295821,
+ 1.295585, 1.295356, 1.295134, 1.294920, 1.294712, 1.294511, 1.294315, 1.294126, 1.293942, 1.293763,
+ 1.293589, 1.293421, 1.293256, 1.293097, 1.292941, 1.292790, 1.292643, 1.292500, 1.292360, 1.292224,
+ 1.292091, 1.291961, 1.291835, 1.291711, 1.291591, 1.291473, 1.291358, 1.291246, 1.291136, 1.291029,
+ 1.290924, 1.290821, 1.290721, 1.290623, 1.290527, 1.290432, 1.290340, 1.290250, 1.290161, 1.290075],
+ 0.95: [
+ 6.313752, 2.919986, 2.353363, 2.131847, 2.015048, 1.943180, 1.894579, 1.859548, 1.833113, 1.812461,
+ 1.795885, 1.782288, 1.770933, 1.761310, 1.753050, 1.745884, 1.739607, 1.734064, 1.729133, 1.724718,
+ 1.720743, 1.717144, 1.713872, 1.710882, 1.708141, 1.705618, 1.703288, 1.701131, 1.699127, 1.697261,
+ 1.695519, 1.693889, 1.692360, 1.690924, 1.689572, 1.688298, 1.687094, 1.685954, 1.684875, 1.683851,
+ 1.682878, 1.681952, 1.681071, 1.680230, 1.679427, 1.678660, 1.677927, 1.677224, 1.676551, 1.675905,
+
+ 1.675285, 1.674689, 1.674116, 1.673565, 1.673034, 1.672522, 1.672029, 1.671553, 1.671093, 1.670649,
+ 1.670219, 1.669804, 1.669402, 1.669013, 1.668636, 1.668271, 1.667916, 1.667572, 1.667239, 1.666914,
+ 1.666600, 1.666294, 1.665996, 1.665707, 1.665425, 1.665151, 1.664885, 1.664625, 1.664371, 1.664125,
+ 1.663884, 1.663649, 1.663420, 1.663197, 1.662978, 1.662765, 1.662557, 1.662354, 1.662155, 1.661961,
+ 1.661771, 1.661585, 1.661404, 1.661226, 1.661052, 1.660881, 1.660715, 1.660551, 1.660391, 1.660234],
+ 0.975: [
+ 12.706205, 4.302653, 3.182446, 2.776445, 2.570582, 2.446912, 2.364624, 2.306004, 2.262157, 2.228139,
+ 2.200985, 2.178813, 2.160369, 2.144787, 2.131450, 2.119905, 2.109816, 2.100922, 2.093024, 2.085963,
+ 2.079614, 2.073873, 2.068658, 2.063899, 2.059539, 2.055529, 2.051831, 2.048407, 2.045230, 2.042272,
+ 2.039513, 2.036933, 2.034515, 2.032245, 2.030108, 2.028094, 2.026192, 2.024394, 2.022691, 2.021075,
+ 2.019541, 2.018082, 2.016692, 2.015368, 2.014103, 2.012896, 2.011741, 2.010635, 2.009575, 2.008559,
+
+ 2.007584, 2.006647, 2.005746, 2.004879, 2.004045, 2.003241, 2.002465, 2.001717, 2.000995, 2.000298,
+ 1.999624, 1.998972, 1.998341, 1.997730, 1.997138, 1.996564, 1.996008, 1.995469, 1.994945, 1.994437,
+ 1.993943, 1.993464, 1.992997, 1.992543, 1.992102, 1.991673, 1.991254, 1.990847, 1.990450, 1.990063,
+ 1.989686, 1.989319, 1.988960, 1.988610, 1.988268, 1.987934, 1.987608, 1.987290, 1.986979, 1.986675,
+ 1.986377, 1.986086, 1.985802, 1.985523, 1.985251, 1.984984, 1.984723, 1.984467, 1.984217, 1.983972],
+ 0.99: [
+ 31.820516, 6.964557, 4.540703, 3.746947, 3.364930, 3.142668, 2.997952, 2.896459, 2.821438, 2.763769,
+ 2.718079, 2.680998, 2.650309, 2.624494, 2.602480, 2.583487, 2.566934, 2.552380, 2.539483, 2.527977,
+ 2.517648, 2.508325, 2.499867, 2.492159, 2.485107, 2.478630, 2.472660, 2.467140, 2.462021, 2.457262,
+ 2.452824, 2.448678, 2.444794, 2.441150, 2.437723, 2.434494, 2.431447, 2.428568, 2.425841, 2.423257,
+ 2.420803, 2.418470, 2.416250, 2.414134, 2.412116, 2.410188, 2.408345, 2.406581, 2.404892, 2.403272,
+
+ 2.401718, 2.400225, 2.398790, 2.397410, 2.396081, 2.394801, 2.393568, 2.392377, 2.391229, 2.390119,
+ 2.389047, 2.388011, 2.387008, 2.386037, 2.385097, 2.384186, 2.383302, 2.382446, 2.381615, 2.380807,
+ 2.380024, 2.379262, 2.378522, 2.377802, 2.377102, 2.376420, 2.375757, 2.375111, 2.374482, 2.373868,
+ 2.373270, 2.372687, 2.372119, 2.371564, 2.371022, 2.370493, 2.369977, 2.369472, 2.368979, 2.368497,
+ 2.368026, 2.367566, 2.367115, 2.366674, 2.366243, 2.365821, 2.365407, 2.365002, 2.364606, 2.364217]
+ };
+ function oneSidedToTwoSidedProbability(probability) { return 2 * probability - 1; }
+ function twoSidedToOneSidedProbability(probability) { return (1 - (1 - probability) / 2); }
+
+ this.MovingAverageStrategies = [
+ {
+ id: 1,
+ label: 'Simple Moving Average',
+ parameterList: [
+ {label: "Backward window size", value: 8, min: 2, step: 1},
+ {label: "Forward window size", value: 4, min: 0, step: 1}
+ ],
+ execute: function (backwardWindowSize, forwardWindowSize, values) {
+ var averages = new Array(values.length);
+ // We use naive O(n^2) algorithm for simplicy as well as to avoid accumulating round-off errors.
+ for (var i = 0; i < values.length; i++) {
+ var sum = 0;
+ var count = 0;
+ for (var j = i - backwardWindowSize; j < i + backwardWindowSize; j++) {
+ if (j >= 0 && j < values.length) {
+ sum += values[j];
+ count++;
+ }
+ }
+ averages[i] = sum / count;
+ }
+ return averages;
+ },
+
+ },
+ {
+ id: 2,
+ label: 'Cumulative Moving Average',
+ execute: function (values) {
+ var averages = new Array(values.length);
+ var sum = 0;
+ for (var i = 0; i < values.length; i++) {
+ sum += values[i];
+ averages[i] = sum / (i + 1);
+ }
+ return averages;
+ }
+ },
+ {
+ id: 3,
+ label: 'Exponential Moving Average',
+ parameterList: [{label: "Smoothing factor", value: 0.1, min: 0.001, max: 0.9}],
+ execute: function (smoothingFactor, values) {
+ if (!values.length || typeof(smoothingFactor) !== "number")
+ return null;
+
+ var averages = new Array(values.length);
+ var movingAverage = 0;
+ averages[0] = values[0];
+ for (var i = 1; i < values.length; i++)
+ averages[i] = smoothingFactor * values[i] + (1 - smoothingFactor) * averages[i - 1];
+ return averages;
+ }
+ },
+ {
+ id: 4,
+ isSegmentation: true,
+ label: 'Segmentation: Recursive t-test',
+ description: "Recursively split values into two segments if Welch's t-test detects a statistically significant difference.",
+ parameterList: [{label: "Minimum segment length", value: 20, min: 5}],
+ execute: function (minLength, values) {
+ if (values.length < 2)
+ return null;
+
+ var averages = new Array(values.length);
+ var segments = new Array;
+ recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values, 0, values.length, minLength, segments);
+ var averageIndex = 0;
+ for (var j = 0; j < segments.length; j++) {
+ var values = segments[j];
+ var mean = Statistics.sum(values) / values.length;
+ for (var i = 0; i < values.length; i++)
+ averages[averageIndex++] = mean;
+ }
+
+ return averages;
+ }
+ },
+ {
+ id: 5,
+ isSegmentation: true,
+ label: 'Segmentation: Schwarz criterion',
+ description: 'Adaptive algorithm that maximizes the Schwarz criterion (BIC).',
+ // Based on Detection of Multiple Change–Points in Multivariate Time Series by Marc Lavielle (July 2006).
+ execute: function (values) {
+ if (values.length < 2)
+ return null;
+
+ var averages = new Array(values.length);
+ var averageIndex = 0;
+
+ // Split the time series into grids since splitIntoSegmentsUntilGoodEnough is O(n^2).
+ var gridLength = 500;
+ var totalSegmentation = [0];
+ for (var gridCount = 0; gridCount < Math.ceil(values.length / gridLength); gridCount++) {
+ var gridValues = values.slice(gridCount * gridLength, (gridCount + 1) * gridLength);
+ var segmentation = splitIntoSegmentsUntilGoodEnough(gridValues);
+
+ if (Statistics.debuggingSegmentation)
+ console.log('grid=' + gridCount, segmentation);
+
+ for (var i = 1; i < segmentation.length - 1; i++)
+ totalSegmentation.push(gridCount * gridLength + segmentation[i]);
+ }
+
+ if (Statistics.debuggingSegmentation)
+ console.log('Final Segmentation', totalSegmentation);
+
+ totalSegmentation.push(values.length);
+
+ for (var i = 1; i < totalSegmentation.length; i++) {
+ var segment = values.slice(totalSegmentation[i - 1], totalSegmentation[i]);
+ var average = Statistics.sum(segment) / segment.length;
+ for (var j = 0; j < segment.length; j++)
+ averages[averageIndex++] = average;
+ }
+
+ return averages;
+ }
+ },
+ ];
+
+ this.debuggingSegmentation = false;
+
+ function splitIntoSegmentsUntilGoodEnough(values) {
+ if (values.length < 2)
+ return [0, values.length];
+
+ var matrix = new SampleVarianceUpperTriangularMatrix(values);
+
+ var SchwarzCriterionBeta = Math.log1p(values.length - 1) / values.length;
+
+ var BirgeAndMassartC = 2.5; // Suggested by the authors.
+ var BirgeAndMassartPenalization = function (segmentCount) {
+ return segmentCount * (1 + BirgeAndMassartC * Math.log1p(values.length / segmentCount - 1));
+ }
+
+ var segmentation;
+ var minTotalCost = Infinity;
+ var maxK = 50;
+
+ for (var k = 1; k < maxK; k++) {
+ var start = Date.now();
+ var result = findOptimalSegmentation(values, matrix, k);
+ var cost = result.cost / values.length;
+ var penalty = SchwarzCriterionBeta * BirgeAndMassartPenalization(k);
+ if (cost + penalty < minTotalCost) {
+ minTotalCost = cost + penalty;
+ segmentation = result.segmentation;
+ } else
+ maxK = Math.min(maxK, k + 3);
+ if (Statistics.debuggingSegmentation)
+ console.log('splitIntoSegmentsUntilGoodEnough', k, Date.now() - start, cost + penalty);
+ }
+
+ return segmentation;
+ }
+
+ function findOptimalSegmentation(values, costMatrix, segmentCount) {
+ // Dynamic programming. cost[i][k] = The cost to segmenting values up to i into k segments.
+ var cost = new Array(values.length);
+ for (var i = 0; i < values.length; i++) {
+ cost[i] = new Float32Array(segmentCount + 1);
+ }
+
+ var previousNode = new Array(values.length);
+ for (var i = 0; i < values.length; i++)
+ previousNode[i] = new Array(segmentCount + 1);
+
+ cost[0] = [0]; // The cost of segmenting single value is always 0.
+ previousNode[0] = [-1];
+ for (var segmentStart = 0; segmentStart < values.length; segmentStart++) {
+ var costBySegment = cost[segmentStart];
+ for (var count = 0; count < segmentCount; count++) {
+ if (previousNode[segmentStart][count] === undefined)
+ continue;
+ for (var segmentEnd = segmentStart + 1; segmentEnd < values.length; segmentEnd++) {
+ var newCost = costBySegment[count] + costMatrix.costBetween(segmentStart, segmentEnd);
+ if (previousNode[segmentEnd][count + 1] === undefined || newCost < cost[segmentEnd][count + 1]) {
+ cost[segmentEnd][count + 1] = newCost;
+ previousNode[segmentEnd][count + 1] = segmentStart;
+ }
+ }
+ }
+ }
+
+ if (Statistics.debuggingSegmentation) {
+ console.log('findOptimalSegmentation with k=', segmentCount);
+ for (var i = 0; i < cost.length; i++) {
+ var t = cost[i];
+ var s = '';
+ for (var j = 0; j < t.length; j++) {
+ var p = previousNode[i][j];
+ s += '(k=' + j;
+ if (p !== undefined)
+ s += ' c=' + t[j] + ' p=' + p
+ s += ')';
+ }
+ console.log(i, values[i], s);
+ }
+ }
+
+ var currentIndex = values.length - 1;
+ var segmentation = new Array(segmentCount);
+ segmentation[0] = values.length;
+ for (var i = 0; i < segmentCount; i++) {
+ currentIndex = previousNode[currentIndex][segmentCount - i];
+ segmentation[i + 1] = currentIndex;
+ }
+
+ return {segmentation: segmentation.reverse(), cost: cost[values.length - 1][segmentCount]};
+ }
+
+ function SampleVarianceUpperTriangularMatrix(values) {
+ // The cost of segment (i, j].
+ var costMatrix = new Array(values.length - 1);
+ for (var i = 0; i < values.length - 1; i++) {
+ var remainingValueCount = values.length - i - 1;
+ costMatrix[i] = new Float32Array(remainingValueCount);
+ var sum = values[i];
+ var squareSum = sum * sum;
+ costMatrix[i][0] = 0;
+ for (var j = i + 1; j < values.length; j++) {
+ var currentValue = values[j];
+ sum += currentValue;
+ squareSum += currentValue * currentValue;
+ var sampleSize = j - i + 1;
+ var stdev = Statistics.sampleStandardDeviation(sampleSize, sum, squareSum);
+ costMatrix[i][j - i - 1] = sampleSize * Math.log1p(stdev * stdev - 1);
+ }
+ }
+ this.costMatrix = costMatrix;
+ }
+
+ SampleVarianceUpperTriangularMatrix.prototype.costBetween = function(from, to) {
+ if (from >= this.costMatrix.length || from == to)
+ return 0; // The cost of the segment that starts at the last data point is 0.
+ return this.costMatrix[from][to - from - 1];
+ }
+
+ this.EnvelopingStrategies = [
+ {
+ id: 100,
+ label: 'Average Difference',
+ description: 'The average difference between consecutive values.',
+ execute: function (values, movingAverages) {
+ if (values.length < 1)
+ return NaN;
+
+ var diff = 0;
+ for (var i = 1; i < values.length; i++)
+ diff += Math.abs(values[i] - values[i - 1]);
+
+ return diff / values.length;
+ }
+ },
+ {
+ id: 101,
+ label: 'Moving Average Standard Deviation',
+ description: 'The square root of the average deviation from the moving average with Bessel\'s correction.',
+ execute: function (values, movingAverages) {
+ if (values.length < 1)
+ return NaN;
+
+ var diffSquareSum = 0;
+ for (var i = 1; i < values.length; i++) {
+ var diff = (values[i] - movingAverages[i]);
+ diffSquareSum += diff * diff;
+ }
+
+ return Math.sqrt(diffSquareSum / (values.length - 1));
+ }
+ },
+ ];
+
+ this.debuggingTestingRangeNomination = false;
+
+ this.TestRangeSelectionStrategies = [
+ {
+ id: 301,
+ label: "t-test 99% significance",
+ execute: function (values, segmentedValues) {
+ if (!values.length)
+ return [];
+
+ var previousMean = segmentedValues[0];
+ var selectedRanges = new Array;
+ for (var i = 1; i < segmentedValues.length; i++) {
+ var currentMean = segmentedValues[i];
+ if (currentMean == previousMean)
+ continue;
+ var found = false;
+ for (var leftEdge = i - 2, rightEdge = i + 2; leftEdge >= 0 && rightEdge <= values.length; leftEdge--, rightEdge++) {
+ if (segmentedValues[leftEdge] != previousMean || segmentedValues[rightEdge - 1] != currentMean)
+ break;
+ var result = Statistics.computeWelchsT(values, leftEdge, i - leftEdge, values, i, rightEdge - i, 0.98);
+ if (result.significantlyDifferent) {
+ selectedRanges.push([leftEdge, rightEdge - 1]);
+ found = true;
+ break;
+ }
+ }
+ if (!found && Statistics.debuggingTestingRangeNomination)
+ console.log('Failed to find a testing range at', i, 'changing from', previousMean, 'to', currentMean);
+ previousMean = currentMean;
+ }
+ return selectedRanges;
+ }
+ }
+ ];
+
+ function createWesternElectricRule(windowSize, minOutlinerCount, limitFactor) {
+ return function (values, movingAverages, deviation) {
+ var results = new Array(values.length);
+ var limit = limitFactor * deviation;
+ for (var i = 0; i < values.length; i++)
+ results[i] = countValuesOnSameSide(values, movingAverages, limit, i, windowSize) >= minOutlinerCount ? windowSize : 0;
+ return results;
+ }
+ }
+
+ function countValuesOnSameSide(values, movingAverages, limit, startIndex, windowSize) {
+ var valuesAboveLimit = 0;
+ var valuesBelowLimit = 0;
+ var center = movingAverages[startIndex];
+ for (var i = startIndex; i < startIndex + windowSize && i < values.length; i++) {
+ var diff = values[i] - center;
+ valuesAboveLimit += (diff > limit);
+ valuesBelowLimit += (diff < -limit);
+ }
+ return Math.max(valuesAboveLimit, valuesBelowLimit);
+ }
+
+ this.AnomalyDetectionStrategy = [
+ // Western Electric rules: http://en.wikipedia.org/wiki/Western_Electric_rules
+ {
+ id: 200,
+ label: 'Western Electric: any point beyond 3σ',
+ description: 'Any single point falls outside 3σ limit from the moving average',
+ execute: createWesternElectricRule(1, 1, 3),
+ },
+ {
+ id: 201,
+ label: 'Western Electric: 2/3 points beyond 2σ',
+ description: 'Two out of three consecutive points fall outside 2σ limit from the moving average on the same side',
+ execute: createWesternElectricRule(3, 2, 2),
+ },
+ {
+ id: 202,
+ label: 'Western Electric: 4/5 points beyond σ',
+ description: 'Four out of five consecutive points fall outside 2σ limit from the moving average on the same side',
+ execute: createWesternElectricRule(5, 4, 1),
+ },
+ {
+ id: 203,
+ label: 'Western Electric: 9 points on same side',
+ description: 'Nine consecutive points on the same side of the moving average',
+ execute: createWesternElectricRule(9, 9, 0),
+ },
+ {
+ id: 210,
+ label: 'Mozilla: t-test 5 vs. 20 before that',
+ description: "Use student's t-test to determine whether the mean of the last five data points differs from the mean of the twenty values before that",
+ execute: function (values, movingAverages, deviation) {
+ var results = new Array(values.length);
+ var p = false;
+ for (var i = 20; i < values.length - 5; i++)
+ results[i] = Statistics.testWelchsT(values.slice(i - 20, i), values.slice(i, i + 5), 0.98) ? 5 : 0;
+ return results;
+ }
+ },
+ ]
+
+ this.executeStrategy = function (strategy, rawValues, additionalArguments)
+ {
+ var parameters = (strategy.parameterList || []).map(function (param) {
+ var parsed = parseFloat(param.value);
+ return Math.min(param.max || Infinity, Math.max(param.min || -Infinity, isNaN(parsed) ? 0 : parsed));
+ });
+ parameters.push(rawValues);
+ return strategy.execute.apply(strategy, parameters.concat(additionalArguments));
+ };
+
+})();
+
+if (typeof module != 'undefined') {
+ for (var key in Statistics)
+ module.exports[key] = Statistics[key];
+}
</ins></span></pre></div>
<a id="trunkWebsitesperfwebkitorgpublicv2indexhtml"></a>
<div class="modfile"><h4>Modified: trunk/Websites/perf.webkit.org/public/v2/index.html (196604 => 196605)</h4>
<pre class="diff"><span>
<span class="info">--- trunk/Websites/perf.webkit.org/public/v2/index.html 2016-02-15 23:39:02 UTC (rev 196604)
+++ trunk/Websites/perf.webkit.org/public/v2/index.html 2016-02-15 23:57:22 UTC (rev 196605)
</span><span class="lines">@@ -16,7 +16,7 @@
</span><span class="cx"> <script src="js/ember.js" defer></script>
</span><span class="cx"> <script src="js/ember-data.js" defer></script>
</span><span class="cx"> <script src="js/d3/d3.min.js" defer></script>
</span><del>- <script src="js/statistics.js" defer></script>
</del><ins>+ <script src="../shared/statistics.js" defer></script>
</ins><span class="cx"> <script src="data.js" defer></script>
</span><span class="cx"> <script src="app.js" defer></script>
</span><span class="cx"> <script src="manifest.js" defer></script>
</span></span></pre></div>
<a id="trunkWebsitesperfwebkitorgpublicv2jsstatisticsjs"></a>
<div class="delfile"><h4>Deleted: trunk/Websites/perf.webkit.org/public/v2/js/statistics.js (196604 => 196605)</h4>
<pre class="diff"><span>
<span class="info">--- trunk/Websites/perf.webkit.org/public/v2/js/statistics.js 2016-02-15 23:39:02 UTC (rev 196604)
+++ trunk/Websites/perf.webkit.org/public/v2/js/statistics.js 2016-02-15 23:57:22 UTC (rev 196605)
</span><span class="lines">@@ -1,583 +0,0 @@
</span><del>-var Statistics = new (function () {
-
- this.min = function (values) {
- return Math.min.apply(Math, values);
- }
-
- this.max = function (values) {
- return Math.max.apply(Math, values);
- }
-
- this.sum = function (values) {
- return values.length ? values.reduce(function (a, b) { return a + b; }) : 0;
- }
-
- this.squareSum = function (values) {
- return values.length ? values.reduce(function (sum, value) { return sum + value * value;}, 0) : 0;
- }
-
- // With sum and sum of squares, we can compute the sample standard deviation in O(1).
- // See https://rniwa.com/2012-11-10/sample-standard-deviation-in-terms-of-sum-and-square-sum-of-samples/
- this.sampleStandardDeviation = function (numberOfSamples, sum, squareSum) {
- if (numberOfSamples < 2)
- return 0;
- return Math.sqrt(squareSum / (numberOfSamples - 1) - sum * sum / (numberOfSamples - 1) / numberOfSamples);
- }
-
- this.supportedConfidenceIntervalProbabilities = function () {
- var supportedProbabilities = [];
- for (var probability in tDistributionByOneSidedProbability)
- supportedProbabilities.push(oneSidedToTwoSidedProbability(probability).toFixed(2));
- return supportedProbabilities
- }
-
- // Computes the delta d s.t. (mean - d, mean + d) is the confidence interval with the specified probability in O(1).
- this.confidenceIntervalDelta = function (probability, numberOfSamples, sum, squareSum) {
- var oneSidedProbability = twoSidedToOneSidedProbability(probability);
- if (!(oneSidedProbability in tDistributionByOneSidedProbability)) {
- throw 'We only support ' + this.supportedConfidenceIntervalProbabilities().map(function (probability)
- { return probability * 100 + '%'; } ).join(', ') + ' confidence intervals.';
- }
- if (numberOfSamples - 2 < 0)
- return NaN;
- var deltas = tDistributionByOneSidedProbability[oneSidedProbability];
- var degreesOfFreedom = numberOfSamples - 1;
- if (degreesOfFreedom > deltas.length)
- throw 'We only support up to ' + deltas.length + ' degrees of freedom';
-
- // d = c * S/sqrt(numberOfSamples) where c ~ t-distribution(degreesOfFreedom) and S is the sample standard deviation.
- return deltas[degreesOfFreedom - 1] * this.sampleStandardDeviation(numberOfSamples, sum, squareSum) / Math.sqrt(numberOfSamples);
- }
-
- this.confidenceInterval = function (values, probability) {
- var sum = this.sum(values);
- var mean = sum / values.length;
- var delta = this.confidenceIntervalDelta(probability || 0.95, values.length, sum, this.squareSum(values));
- return [mean - delta, mean + delta];
- }
-
- // Welch's t-test (http://en.wikipedia.org/wiki/Welch%27s_t_test)
- this.testWelchsT = function (values1, values2, probability) {
- return this.computeWelchsT(values1, 0, values1.length, values2, 0, values2.length, probability).significantlyDifferent;
- }
-
- this.probabilityRangeForWelchsT = function (values1, values2) {
- var result = this.computeWelchsT(values1, 0, values1.length, values2, 0, values2.length);
- if (isNaN(result.t) || isNaN(result.degreesOfFreedom))
- return {t: NaN, degreesOfFreedom:NaN, range: [null, null]};
-
- var lowerBound = null;
- var upperBound = null;
- for (var probability in tDistributionByOneSidedProbability) {
- var twoSidedProbability = oneSidedToTwoSidedProbability(probability);
- if (result.t > tDistributionByOneSidedProbability[probability][Math.round(result.degreesOfFreedom - 1)])
- lowerBound = twoSidedProbability;
- else if (lowerBound) {
- upperBound = twoSidedProbability;
- break;
- }
- }
- return {t: result.t, degreesOfFreedom: result.degreesOfFreedom, range: [lowerBound, upperBound]};
- }
-
- this.computeWelchsT = function (values1, startIndex1, length1, values2, startIndex2, length2, probability) {
- var stat1 = sampleMeanAndVarianceForValues(values1, startIndex1, length1);
- var stat2 = sampleMeanAndVarianceForValues(values2, startIndex2, length2);
- var sumOfSampleVarianceOverSampleSize = stat1.variance / stat1.size + stat2.variance / stat2.size;
- var t = Math.abs((stat1.mean - stat2.mean) / Math.sqrt(sumOfSampleVarianceOverSampleSize));
-
- // http://en.wikipedia.org/wiki/Welch–Satterthwaite_equation
- var degreesOfFreedom = sumOfSampleVarianceOverSampleSize * sumOfSampleVarianceOverSampleSize
- / (stat1.variance * stat1.variance / stat1.size / stat1.size / stat1.degreesOfFreedom
- + stat2.variance * stat2.variance / stat2.size / stat2.size / stat2.degreesOfFreedom);
- var minT = tDistributionByOneSidedProbability[twoSidedToOneSidedProbability(probability || 0.8)][Math.round(degreesOfFreedom - 1)];
- return {
- t: t,
- degreesOfFreedom: degreesOfFreedom,
- significantlyDifferent: t > minT,
- };
- }
-
- function sampleMeanAndVarianceForValues(values, startIndex, length) {
- var sum = 0;
- for (var i = 0; i < length; i++)
- sum += values[startIndex + i];
- var squareSum = 0;
- for (var i = 0; i < length; i++)
- squareSum += values[startIndex + i] * values[startIndex + i];
- var sampleMean = sum / length;
- // FIXME: Maybe we should be using the biased sample variance.
- var unbiasedSampleVariance = (squareSum - sum * sum / length) / (length - 1);
- return {
- mean: sampleMean,
- variance: unbiasedSampleVariance,
- size: length,
- degreesOfFreedom: length - 1,
- }
- }
-
- function recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values, startIndex, length, minLength, segments) {
- var tMax = 0;
- var argTMax = null;
- for (var i = 1; i < length - 1; i++) {
- var firstLength = i;
- var secondLength = length - i;
- if (firstLength < minLength || secondLength < minLength)
- continue;
- var result = Statistics.computeWelchsT(values, startIndex, firstLength, values, startIndex + i, secondLength, 0.9);
- if (result.significantlyDifferent && result.t > tMax) {
- tMax = result.t;
- argTMax = i;
- }
- }
- if (!tMax) {
- segments.push(values.slice(startIndex, startIndex + length));
- return;
- }
- recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values, startIndex, argTMax, minLength, segments);
- recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values, startIndex + argTMax, length - argTMax, minLength, segments);
- }
-
- var tDistributionByOneSidedProbability = {
- 0.9: [
- 3.077684, 1.885618, 1.637744, 1.533206, 1.475884, 1.439756, 1.414924, 1.396815, 1.383029, 1.372184,
- 1.363430, 1.356217, 1.350171, 1.345030, 1.340606, 1.336757, 1.333379, 1.330391, 1.327728, 1.325341,
- 1.323188, 1.321237, 1.319460, 1.317836, 1.316345, 1.314972, 1.313703, 1.312527, 1.311434, 1.310415,
- 1.309464, 1.308573, 1.307737, 1.306952, 1.306212, 1.305514, 1.304854, 1.304230, 1.303639, 1.303077,
- 1.302543, 1.302035, 1.301552, 1.301090, 1.300649, 1.300228, 1.299825, 1.299439, 1.299069, 1.298714,
-
- 1.298373, 1.298045, 1.297730, 1.297426, 1.297134, 1.296853, 1.296581, 1.296319, 1.296066, 1.295821,
- 1.295585, 1.295356, 1.295134, 1.294920, 1.294712, 1.294511, 1.294315, 1.294126, 1.293942, 1.293763,
- 1.293589, 1.293421, 1.293256, 1.293097, 1.292941, 1.292790, 1.292643, 1.292500, 1.292360, 1.292224,
- 1.292091, 1.291961, 1.291835, 1.291711, 1.291591, 1.291473, 1.291358, 1.291246, 1.291136, 1.291029,
- 1.290924, 1.290821, 1.290721, 1.290623, 1.290527, 1.290432, 1.290340, 1.290250, 1.290161, 1.290075],
- 0.95: [
- 6.313752, 2.919986, 2.353363, 2.131847, 2.015048, 1.943180, 1.894579, 1.859548, 1.833113, 1.812461,
- 1.795885, 1.782288, 1.770933, 1.761310, 1.753050, 1.745884, 1.739607, 1.734064, 1.729133, 1.724718,
- 1.720743, 1.717144, 1.713872, 1.710882, 1.708141, 1.705618, 1.703288, 1.701131, 1.699127, 1.697261,
- 1.695519, 1.693889, 1.692360, 1.690924, 1.689572, 1.688298, 1.687094, 1.685954, 1.684875, 1.683851,
- 1.682878, 1.681952, 1.681071, 1.680230, 1.679427, 1.678660, 1.677927, 1.677224, 1.676551, 1.675905,
-
- 1.675285, 1.674689, 1.674116, 1.673565, 1.673034, 1.672522, 1.672029, 1.671553, 1.671093, 1.670649,
- 1.670219, 1.669804, 1.669402, 1.669013, 1.668636, 1.668271, 1.667916, 1.667572, 1.667239, 1.666914,
- 1.666600, 1.666294, 1.665996, 1.665707, 1.665425, 1.665151, 1.664885, 1.664625, 1.664371, 1.664125,
- 1.663884, 1.663649, 1.663420, 1.663197, 1.662978, 1.662765, 1.662557, 1.662354, 1.662155, 1.661961,
- 1.661771, 1.661585, 1.661404, 1.661226, 1.661052, 1.660881, 1.660715, 1.660551, 1.660391, 1.660234],
- 0.975: [
- 12.706205, 4.302653, 3.182446, 2.776445, 2.570582, 2.446912, 2.364624, 2.306004, 2.262157, 2.228139,
- 2.200985, 2.178813, 2.160369, 2.144787, 2.131450, 2.119905, 2.109816, 2.100922, 2.093024, 2.085963,
- 2.079614, 2.073873, 2.068658, 2.063899, 2.059539, 2.055529, 2.051831, 2.048407, 2.045230, 2.042272,
- 2.039513, 2.036933, 2.034515, 2.032245, 2.030108, 2.028094, 2.026192, 2.024394, 2.022691, 2.021075,
- 2.019541, 2.018082, 2.016692, 2.015368, 2.014103, 2.012896, 2.011741, 2.010635, 2.009575, 2.008559,
-
- 2.007584, 2.006647, 2.005746, 2.004879, 2.004045, 2.003241, 2.002465, 2.001717, 2.000995, 2.000298,
- 1.999624, 1.998972, 1.998341, 1.997730, 1.997138, 1.996564, 1.996008, 1.995469, 1.994945, 1.994437,
- 1.993943, 1.993464, 1.992997, 1.992543, 1.992102, 1.991673, 1.991254, 1.990847, 1.990450, 1.990063,
- 1.989686, 1.989319, 1.988960, 1.988610, 1.988268, 1.987934, 1.987608, 1.987290, 1.986979, 1.986675,
- 1.986377, 1.986086, 1.985802, 1.985523, 1.985251, 1.984984, 1.984723, 1.984467, 1.984217, 1.983972],
- 0.99: [
- 31.820516, 6.964557, 4.540703, 3.746947, 3.364930, 3.142668, 2.997952, 2.896459, 2.821438, 2.763769,
- 2.718079, 2.680998, 2.650309, 2.624494, 2.602480, 2.583487, 2.566934, 2.552380, 2.539483, 2.527977,
- 2.517648, 2.508325, 2.499867, 2.492159, 2.485107, 2.478630, 2.472660, 2.467140, 2.462021, 2.457262,
- 2.452824, 2.448678, 2.444794, 2.441150, 2.437723, 2.434494, 2.431447, 2.428568, 2.425841, 2.423257,
- 2.420803, 2.418470, 2.416250, 2.414134, 2.412116, 2.410188, 2.408345, 2.406581, 2.404892, 2.403272,
-
- 2.401718, 2.400225, 2.398790, 2.397410, 2.396081, 2.394801, 2.393568, 2.392377, 2.391229, 2.390119,
- 2.389047, 2.388011, 2.387008, 2.386037, 2.385097, 2.384186, 2.383302, 2.382446, 2.381615, 2.380807,
- 2.380024, 2.379262, 2.378522, 2.377802, 2.377102, 2.376420, 2.375757, 2.375111, 2.374482, 2.373868,
- 2.373270, 2.372687, 2.372119, 2.371564, 2.371022, 2.370493, 2.369977, 2.369472, 2.368979, 2.368497,
- 2.368026, 2.367566, 2.367115, 2.366674, 2.366243, 2.365821, 2.365407, 2.365002, 2.364606, 2.364217]
- };
- function oneSidedToTwoSidedProbability(probability) { return 2 * probability - 1; }
- function twoSidedToOneSidedProbability(probability) { return (1 - (1 - probability) / 2); }
-
- this.MovingAverageStrategies = [
- {
- id: 1,
- label: 'Simple Moving Average',
- parameterList: [
- {label: "Backward window size", value: 8, min: 2, step: 1},
- {label: "Forward window size", value: 4, min: 0, step: 1}
- ],
- execute: function (backwardWindowSize, forwardWindowSize, values) {
- var averages = new Array(values.length);
- // We use naive O(n^2) algorithm for simplicy as well as to avoid accumulating round-off errors.
- for (var i = 0; i < values.length; i++) {
- var sum = 0;
- var count = 0;
- for (var j = i - backwardWindowSize; j < i + backwardWindowSize; j++) {
- if (j >= 0 && j < values.length) {
- sum += values[j];
- count++;
- }
- }
- averages[i] = sum / count;
- }
- return averages;
- },
-
- },
- {
- id: 2,
- label: 'Cumulative Moving Average',
- execute: function (values) {
- var averages = new Array(values.length);
- var sum = 0;
- for (var i = 0; i < values.length; i++) {
- sum += values[i];
- averages[i] = sum / (i + 1);
- }
- return averages;
- }
- },
- {
- id: 3,
- label: 'Exponential Moving Average',
- parameterList: [{label: "Smoothing factor", value: 0.1, min: 0.001, max: 0.9}],
- execute: function (smoothingFactor, values) {
- if (!values.length || typeof(smoothingFactor) !== "number")
- return null;
-
- var averages = new Array(values.length);
- var movingAverage = 0;
- averages[0] = values[0];
- for (var i = 1; i < values.length; i++)
- averages[i] = smoothingFactor * values[i] + (1 - smoothingFactor) * averages[i - 1];
- return averages;
- }
- },
- {
- id: 4,
- isSegmentation: true,
- label: 'Segmentation: Recursive t-test',
- description: "Recursively split values into two segments if Welch's t-test detects a statistically significant difference.",
- parameterList: [{label: "Minimum segment length", value: 20, min: 5}],
- execute: function (minLength, values) {
- if (values.length < 2)
- return null;
-
- var averages = new Array(values.length);
- var segments = new Array;
- recursivelySplitIntoTwoSegmentsAtMaxTIfSignificantlyDifferent(values, 0, values.length, minLength, segments);
- var averageIndex = 0;
- for (var j = 0; j < segments.length; j++) {
- var values = segments[j];
- var mean = Statistics.sum(values) / values.length;
- for (var i = 0; i < values.length; i++)
- averages[averageIndex++] = mean;
- }
-
- return averages;
- }
- },
- {
- id: 5,
- isSegmentation: true,
- label: 'Segmentation: Schwarz criterion',
- description: 'Adaptive algorithm that maximizes the Schwarz criterion (BIC).',
- // Based on Detection of Multiple Change–Points in Multivariate Time Series by Marc Lavielle (July 2006).
- execute: function (values) {
- if (values.length < 2)
- return null;
-
- var averages = new Array(values.length);
- var averageIndex = 0;
-
- // Split the time series into grids since splitIntoSegmentsUntilGoodEnough is O(n^2).
- var gridLength = 500;
- var totalSegmentation = [0];
- for (var gridCount = 0; gridCount < Math.ceil(values.length / gridLength); gridCount++) {
- var gridValues = values.slice(gridCount * gridLength, (gridCount + 1) * gridLength);
- var segmentation = splitIntoSegmentsUntilGoodEnough(gridValues);
-
- if (Statistics.debuggingSegmentation)
- console.log('grid=' + gridCount, segmentation);
-
- for (var i = 1; i < segmentation.length - 1; i++)
- totalSegmentation.push(gridCount * gridLength + segmentation[i]);
- }
-
- if (Statistics.debuggingSegmentation)
- console.log('Final Segmentation', totalSegmentation);
-
- totalSegmentation.push(values.length);
-
- for (var i = 1; i < totalSegmentation.length; i++) {
- var segment = values.slice(totalSegmentation[i - 1], totalSegmentation[i]);
- var average = Statistics.sum(segment) / segment.length;
- for (var j = 0; j < segment.length; j++)
- averages[averageIndex++] = average;
- }
-
- return averages;
- }
- },
- ];
-
- this.debuggingSegmentation = false;
-
- function splitIntoSegmentsUntilGoodEnough(values) {
- if (values.length < 2)
- return [0, values.length];
-
- var matrix = new SampleVarianceUpperTriangularMatrix(values);
-
- var SchwarzCriterionBeta = Math.log1p(values.length - 1) / values.length;
-
- var BirgeAndMassartC = 2.5; // Suggested by the authors.
- var BirgeAndMassartPenalization = function (segmentCount) {
- return segmentCount * (1 + BirgeAndMassartC * Math.log1p(values.length / segmentCount - 1));
- }
-
- var segmentation;
- var minTotalCost = Infinity;
- var maxK = 50;
-
- for (var k = 1; k < maxK; k++) {
- var start = Date.now();
- var result = findOptimalSegmentation(values, matrix, k);
- var cost = result.cost / values.length;
- var penalty = SchwarzCriterionBeta * BirgeAndMassartPenalization(k);
- if (cost + penalty < minTotalCost) {
- minTotalCost = cost + penalty;
- segmentation = result.segmentation;
- } else
- maxK = Math.min(maxK, k + 3);
- if (Statistics.debuggingSegmentation)
- console.log('splitIntoSegmentsUntilGoodEnough', k, Date.now() - start, cost + penalty);
- }
-
- return segmentation;
- }
-
- function findOptimalSegmentation(values, costMatrix, segmentCount) {
- // Dynamic programming. cost[i][k] = The cost to segmenting values up to i into k segments.
- var cost = new Array(values.length);
- for (var i = 0; i < values.length; i++) {
- cost[i] = new Float32Array(segmentCount + 1);
- }
-
- var previousNode = new Array(values.length);
- for (var i = 0; i < values.length; i++)
- previousNode[i] = new Array(segmentCount + 1);
-
- cost[0] = [0]; // The cost of segmenting single value is always 0.
- previousNode[0] = [-1];
- for (var segmentStart = 0; segmentStart < values.length; segmentStart++) {
- var costBySegment = cost[segmentStart];
- for (var count = 0; count < segmentCount; count++) {
- if (previousNode[segmentStart][count] === undefined)
- continue;
- for (var segmentEnd = segmentStart + 1; segmentEnd < values.length; segmentEnd++) {
- var newCost = costBySegment[count] + costMatrix.costBetween(segmentStart, segmentEnd);
- if (previousNode[segmentEnd][count + 1] === undefined || newCost < cost[segmentEnd][count + 1]) {
- cost[segmentEnd][count + 1] = newCost;
- previousNode[segmentEnd][count + 1] = segmentStart;
- }
- }
- }
- }
-
- if (Statistics.debuggingSegmentation) {
- console.log('findOptimalSegmentation with k=', segmentCount);
- for (var i = 0; i < cost.length; i++) {
- var t = cost[i];
- var s = '';
- for (var j = 0; j < t.length; j++) {
- var p = previousNode[i][j];
- s += '(k=' + j;
- if (p !== undefined)
- s += ' c=' + t[j] + ' p=' + p
- s += ')';
- }
- console.log(i, values[i], s);
- }
- }
-
- var currentIndex = values.length - 1;
- var segmentation = new Array(segmentCount);
- segmentation[0] = values.length;
- for (var i = 0; i < segmentCount; i++) {
- currentIndex = previousNode[currentIndex][segmentCount - i];
- segmentation[i + 1] = currentIndex;
- }
-
- return {segmentation: segmentation.reverse(), cost: cost[values.length - 1][segmentCount]};
- }
-
- function SampleVarianceUpperTriangularMatrix(values) {
- // The cost of segment (i, j].
- var costMatrix = new Array(values.length - 1);
- for (var i = 0; i < values.length - 1; i++) {
- var remainingValueCount = values.length - i - 1;
- costMatrix[i] = new Float32Array(remainingValueCount);
- var sum = values[i];
- var squareSum = sum * sum;
- costMatrix[i][0] = 0;
- for (var j = i + 1; j < values.length; j++) {
- var currentValue = values[j];
- sum += currentValue;
- squareSum += currentValue * currentValue;
- var sampleSize = j - i + 1;
- var stdev = Statistics.sampleStandardDeviation(sampleSize, sum, squareSum);
- costMatrix[i][j - i - 1] = sampleSize * Math.log1p(stdev * stdev - 1);
- }
- }
- this.costMatrix = costMatrix;
- }
-
- SampleVarianceUpperTriangularMatrix.prototype.costBetween = function(from, to) {
- if (from >= this.costMatrix.length || from == to)
- return 0; // The cost of the segment that starts at the last data point is 0.
- return this.costMatrix[from][to - from - 1];
- }
-
- this.EnvelopingStrategies = [
- {
- id: 100,
- label: 'Average Difference',
- description: 'The average difference between consecutive values.',
- execute: function (values, movingAverages) {
- if (values.length < 1)
- return NaN;
-
- var diff = 0;
- for (var i = 1; i < values.length; i++)
- diff += Math.abs(values[i] - values[i - 1]);
-
- return diff / values.length;
- }
- },
- {
- id: 101,
- label: 'Moving Average Standard Deviation',
- description: 'The square root of the average deviation from the moving average with Bessel\'s correction.',
- execute: function (values, movingAverages) {
- if (values.length < 1)
- return NaN;
-
- var diffSquareSum = 0;
- for (var i = 1; i < values.length; i++) {
- var diff = (values[i] - movingAverages[i]);
- diffSquareSum += diff * diff;
- }
-
- return Math.sqrt(diffSquareSum / (values.length - 1));
- }
- },
- ];
-
- this.debuggingTestingRangeNomination = false;
-
- this.TestRangeSelectionStrategies = [
- {
- id: 301,
- label: "t-test 99% significance",
- execute: function (values, segmentedValues) {
- if (!values.length)
- return [];
-
- var previousMean = segmentedValues[0];
- var selectedRanges = new Array;
- for (var i = 1; i < segmentedValues.length; i++) {
- var currentMean = segmentedValues[i];
- if (currentMean == previousMean)
- continue;
- var found = false;
- for (var leftEdge = i - 2, rightEdge = i + 2; leftEdge >= 0 && rightEdge <= values.length; leftEdge--, rightEdge++) {
- if (segmentedValues[leftEdge] != previousMean || segmentedValues[rightEdge - 1] != currentMean)
- break;
- var result = Statistics.computeWelchsT(values, leftEdge, i - leftEdge, values, i, rightEdge - i, 0.98);
- if (result.significantlyDifferent) {
- selectedRanges.push([leftEdge, rightEdge - 1]);
- found = true;
- break;
- }
- }
- if (!found && Statistics.debuggingTestingRangeNomination)
- console.log('Failed to find a testing range at', i, 'changing from', previousMean, 'to', currentMean);
- previousMean = currentMean;
- }
- return selectedRanges;
- }
- }
- ];
-
- function createWesternElectricRule(windowSize, minOutlinerCount, limitFactor) {
- return function (values, movingAverages, deviation) {
- var results = new Array(values.length);
- var limit = limitFactor * deviation;
- for (var i = 0; i < values.length; i++)
- results[i] = countValuesOnSameSide(values, movingAverages, limit, i, windowSize) >= minOutlinerCount ? windowSize : 0;
- return results;
- }
- }
-
- function countValuesOnSameSide(values, movingAverages, limit, startIndex, windowSize) {
- var valuesAboveLimit = 0;
- var valuesBelowLimit = 0;
- var center = movingAverages[startIndex];
- for (var i = startIndex; i < startIndex + windowSize && i < values.length; i++) {
- var diff = values[i] - center;
- valuesAboveLimit += (diff > limit);
- valuesBelowLimit += (diff < -limit);
- }
- return Math.max(valuesAboveLimit, valuesBelowLimit);
- }
-
- this.AnomalyDetectionStrategy = [
- // Western Electric rules: http://en.wikipedia.org/wiki/Western_Electric_rules
- {
- id: 200,
- label: 'Western Electric: any point beyond 3σ',
- description: 'Any single point falls outside 3σ limit from the moving average',
- execute: createWesternElectricRule(1, 1, 3),
- },
- {
- id: 201,
- label: 'Western Electric: 2/3 points beyond 2σ',
- description: 'Two out of three consecutive points fall outside 2σ limit from the moving average on the same side',
- execute: createWesternElectricRule(3, 2, 2),
- },
- {
- id: 202,
- label: 'Western Electric: 4/5 points beyond σ',
- description: 'Four out of five consecutive points fall outside 2σ limit from the moving average on the same side',
- execute: createWesternElectricRule(5, 4, 1),
- },
- {
- id: 203,
- label: 'Western Electric: 9 points on same side',
- description: 'Nine consecutive points on the same side of the moving average',
- execute: createWesternElectricRule(9, 9, 0),
- },
- {
- id: 210,
- label: 'Mozilla: t-test 5 vs. 20 before that',
- description: "Use student's t-test to determine whether the mean of the last five data points differs from the mean of the twenty values before that",
- execute: function (values, movingAverages, deviation) {
- var results = new Array(values.length);
- var p = false;
- for (var i = 20; i < values.length - 5; i++)
- results[i] = Statistics.testWelchsT(values.slice(i - 20, i), values.slice(i, i + 5), 0.98) ? 5 : 0;
- return results;
- }
- },
- ]
-
- this.executeStrategy = function (strategy, rawValues, additionalArguments)
- {
- var parameters = (strategy.parameterList || []).map(function (param) {
- var parsed = parseFloat(param.value);
- return Math.min(param.max || Infinity, Math.max(param.min || -Infinity, isNaN(parsed) ? 0 : parsed));
- });
- parameters.push(rawValues);
- return strategy.execute.apply(strategy, parameters.concat(additionalArguments));
- };
-
-})();
-
-if (typeof module != 'undefined') {
- for (var key in Statistics)
- module.exports[key] = Statistics[key];
-}
</del></span></pre></div>
<a id="trunkWebsitesperfwebkitorgpublicv3indexhtml"></a>
<div class="modfile"><h4>Modified: trunk/Websites/perf.webkit.org/public/v3/index.html (196604 => 196605)</h4>
<pre class="diff"><span>
<span class="info">--- trunk/Websites/perf.webkit.org/public/v3/index.html 2016-02-15 23:39:02 UTC (rev 196604)
+++ trunk/Websites/perf.webkit.org/public/v3/index.html 2016-02-15 23:57:22 UTC (rev 196605)
</span><span class="lines">@@ -37,7 +37,7 @@
</span><span class="cx"> </script>
</span><span class="cx">
</span><span class="cx"> <template id="unbundled-scripts">
</span><del>- <script src="../v2/js/statistics.js"></script>
</del><ins>+ <script src="../shared/statistics.js"></script>
</ins><span class="cx"> <script src="../v2/data.js"></script>
</span><span class="cx">
</span><span class="cx"> <script src="instrumentation.js"></script>
</span></span></pre></div>
<a id="trunkWebsitesperfwebkitorgtoolsdetectchangesjs"></a>
<div class="modfile"><h4>Modified: trunk/Websites/perf.webkit.org/tools/detect-changes.js (196604 => 196605)</h4>
<pre class="diff"><span>
<span class="info">--- trunk/Websites/perf.webkit.org/tools/detect-changes.js 2016-02-15 23:39:02 UTC (rev 196604)
+++ trunk/Websites/perf.webkit.org/tools/detect-changes.js 2016-02-15 23:57:22 UTC (rev 196605)
</span><span class="lines">@@ -5,7 +5,7 @@
</span><span class="cx"> var https = require('https');
</span><span class="cx"> var data = require('../public/v2/data.js');
</span><span class="cx"> var RunsData = data.RunsData;
</span><del>-var Statistics = require('../public/v2/js/statistics.js');
</del><ins>+var Statistics = require('../public/shared/statistics.js');
</ins><span class="cx">
</span><span class="cx"> // FIXME: We shouldn't use a global variable like this.
</span><span class="cx"> var settings;
</span></span></pre>
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