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ee781b71b4
Vendors the [blink perf tests](https://chromium.googlesource.com/chromium/src/+/HEAD/third_party/blink/perf_tests/). These perf tests are useful to evaluate the performance of servo. The license that governs the perf tests is included in the folder. Running benchmark cases automatically is left to future work. The update.py script is taken from mozjs and slightly adapted, so we can easily filter (and patch if this should be necessary in the future. Testing: This PR just adds the perf_tests, but does not use or modify them in any way. --------- Signed-off-by: Jonathan Schwender <schwenderjonathan@gmail.com>
189 lines
11 KiB
JavaScript
Vendored
189 lines
11 KiB
JavaScript
Vendored
/*
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* Copyright (C) 2012, 2013 Apple Inc. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY APPLE INC. AND ITS CONTRIBUTORS ``AS IS''
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* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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* THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL APPLE INC. OR ITS CONTRIBUTORS
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* BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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* SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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* INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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* CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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* ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
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* THE POSSIBILITY OF SUCH DAMAGE.
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*/
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var Statistics = new (function () {
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this.max = function (values) {
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var maxVal = values[0];
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for (var i = 1; i < values.length; i++) {
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maxVal = Math.max(maxVal, values[i]);
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}
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return maxVal;
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}
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this.min = function (values) {
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var minVal = values[0];
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for (var i = 1; i < values.length; i++) {
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minVal = Math.min(minVal, values[i]);
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}
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return minVal;
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}
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this.sum = function (values) {
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return values.reduce(function (a, b) { return a + b; }, 0);
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}
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this.squareSum = function (values) {
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return values.reduce(function (sum, value) { return sum + value * value;}, 0);
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}
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// With sum and sum of squares, we can compute the sample standard deviation in O(1).
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// See https://rniwa.com/2012-11-10/sample-standard-deviation-in-terms-of-sum-and-square-sum-of-samples/
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this.sampleStandardDeviation = function (numberOfSamples, sum, squareSum) {
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if (numberOfSamples < 2)
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return 0;
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return Math.sqrt(squareSum / (numberOfSamples - 1)
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- sum * sum / (numberOfSamples - 1) / numberOfSamples);
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}
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this.supportedConfidenceLevels = function () {
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var supportedLevels = [];
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for (var quantile in tDistributionInverseCDF)
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supportedLevels.push((1 - (1 - quantile) * 2).toFixed(2));
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return supportedLevels;
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}
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// Computes the delta d s.t. (mean - d, mean + d) is the confidence interval with the specified confidence level in O(1).
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this.confidenceIntervalDelta = function (confidenceLevel, numberOfSamples, sum, squareSum) {
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var probability = (1 - (1 - confidenceLevel) / 2);
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if (!(probability in tDistributionInverseCDF)) {
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console.warn('We only support ' + this.supportedConfidenceLevels().map(
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function (level) { return level * 100 + '%'; } ).join(', ') + ' confidence intervals.');
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return NaN;
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}
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if (numberOfSamples < 2)
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return Number.POSITIVE_INFINITY;
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var cdfForProbability = tDistributionInverseCDF[probability];
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var degreesOfFreedom = numberOfSamples - 1;
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// tDistributionQuantile(degreesOfFreedom, confidenceLevel) * sampleStandardDeviation / sqrt(numberOfSamples) * S/sqrt(numberOfSamples)
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if (degreesOfFreedom <= 100)
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var quantile = cdfForProbability[degreesOfFreedom - 1]; // The first entry is for the one degree of freedom.
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else if (degreesOfFreedom <= 300)
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var quantile = cdfForProbability[Math.round(degreesOfFreedom / 10) + 100 - 10 - 1];
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else if (degreesOfFreedom <= 1300)
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var quantile = cdfForProbability[Math.round(degreesOfFreedom / 100) + 120 - 3 - 1];
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else
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var quantile = cdfForProbability[cdfForProbability.length - 1];
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return quantile * this.sampleStandardDeviation(numberOfSamples, sum, squareSum) / Math.sqrt(numberOfSamples);
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}
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this.confidenceInterval = function (values, probability) {
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var sum = this.sum(values);
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var mean = sum / values.length;
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var delta = this.confidenceIntervalDelta(probability || 0.95, values.length, sum, this.squareSum(values));
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return [mean - delta, mean + delta];
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}
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// See http://en.wikipedia.org/wiki/Student's_t-distribution#Table_of_selected_values
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// This table contains one sided (a.k.a. tail) values.
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// Use TINV((1 - probability) * 2, df) in your favorite spreadsheet software to compute these.
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// The spacing of the values with df greater than 100 maintains error less than 0.8%.
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var tDistributionInverseCDF = {
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0.9: [
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// 1 - 100 step 1
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3.077684, 1.885618, 1.637744, 1.533206, 1.475884, 1.439756, 1.414924, 1.396815, 1.383029, 1.372184,
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1.363430, 1.356217, 1.350171, 1.345030, 1.340606, 1.336757, 1.333379, 1.330391, 1.327728, 1.325341,
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1.323188, 1.321237, 1.319460, 1.317836, 1.316345, 1.314972, 1.313703, 1.312527, 1.311434, 1.310415,
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1.309464, 1.308573, 1.307737, 1.306952, 1.306212, 1.305514, 1.304854, 1.304230, 1.303639, 1.303077,
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1.302543, 1.302035, 1.301552, 1.301090, 1.300649, 1.300228, 1.299825, 1.299439, 1.299069, 1.298714,
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1.298373, 1.298045, 1.297730, 1.297426, 1.297134, 1.296853, 1.296581, 1.296319, 1.296066, 1.295821,
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1.295585, 1.295356, 1.295134, 1.294920, 1.294712, 1.294511, 1.294315, 1.294126, 1.293942, 1.293763,
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1.293589, 1.293421, 1.293256, 1.293097, 1.292941, 1.292790, 1.292643, 1.292500, 1.292360, 1.292224,
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1.292091, 1.291961, 1.291835, 1.291711, 1.291591, 1.291473, 1.291358, 1.291246, 1.291136, 1.291029,
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1.290924, 1.290821, 1.290721, 1.290623, 1.290527, 1.290432, 1.290340, 1.290250, 1.290161, 1.290075,
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// 110 - 300 step 10
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1.289295, 1.288646, 1.288098, 1.287628, 1.287221, 1.286865, 1.286551, 1.286272, 1.286023, 1.285799,
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1.285596, 1.285411, 1.285243, 1.285089, 1.284947, 1.284816, 1.284695, 1.284582, 1.284478, 1.284380,
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// 400 - 1300 step 100
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1.283672, 1.283247, 1.282964, 1.282762, 1.282611, 1.282493, 1.282399, 1.282322, 1.282257, 1.282203,
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// Infinity
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1.281548],
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0.95: [
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// 1 - 100 step 1
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6.313752, 2.919986, 2.353363, 2.131847, 2.015048, 1.943180, 1.894579, 1.859548, 1.833113, 1.812461,
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1.795885, 1.782288, 1.770933, 1.761310, 1.753050, 1.745884, 1.739607, 1.734064, 1.729133, 1.724718,
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1.720743, 1.717144, 1.713872, 1.710882, 1.708141, 1.705618, 1.703288, 1.701131, 1.699127, 1.697261,
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1.695519, 1.693889, 1.692360, 1.690924, 1.689572, 1.688298, 1.687094, 1.685954, 1.684875, 1.683851,
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1.682878, 1.681952, 1.681071, 1.680230, 1.679427, 1.678660, 1.677927, 1.677224, 1.676551, 1.675905,
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1.675285, 1.674689, 1.674116, 1.673565, 1.673034, 1.672522, 1.672029, 1.671553, 1.671093, 1.670649,
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1.670219, 1.669804, 1.669402, 1.669013, 1.668636, 1.668271, 1.667916, 1.667572, 1.667239, 1.666914,
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1.666600, 1.666294, 1.665996, 1.665707, 1.665425, 1.665151, 1.664885, 1.664625, 1.664371, 1.664125,
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1.663884, 1.663649, 1.663420, 1.663197, 1.662978, 1.662765, 1.662557, 1.662354, 1.662155, 1.661961,
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1.661771, 1.661585, 1.661404, 1.661226, 1.661052, 1.660881, 1.660715, 1.660551, 1.660391, 1.660234,
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// 110 - 300 step 10
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1.658824, 1.657651, 1.656659, 1.655811, 1.655076, 1.654433, 1.653866, 1.653363, 1.652913, 1.652508,
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1.652142, 1.651809, 1.651506, 1.651227, 1.650971, 1.650735, 1.650517, 1.650314, 1.650125, 1.649949,
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// 400 - 1300 step 100
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1.648672, 1.647907, 1.647397, 1.647033, 1.646761, 1.646548, 1.646379, 1.646240, 1.646124, 1.646027,
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// Infinity
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1.644847],
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0.975: [
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// 1 - 100 step 1
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12.706205, 4.302653, 3.182446, 2.776445, 2.570582, 2.446912, 2.364624, 2.306004, 2.262157, 2.228139,
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2.200985, 2.178813, 2.160369, 2.144787, 2.131450, 2.119905, 2.109816, 2.100922, 2.093024, 2.085963,
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2.079614, 2.073873, 2.068658, 2.063899, 2.059539, 2.055529, 2.051831, 2.048407, 2.045230, 2.042272,
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2.039513, 2.036933, 2.034515, 2.032245, 2.030108, 2.028094, 2.026192, 2.024394, 2.022691, 2.021075,
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2.019541, 2.018082, 2.016692, 2.015368, 2.014103, 2.012896, 2.011741, 2.010635, 2.009575, 2.008559,
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2.007584, 2.006647, 2.005746, 2.004879, 2.004045, 2.003241, 2.002465, 2.001717, 2.000995, 2.000298,
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1.999624, 1.998972, 1.998341, 1.997730, 1.997138, 1.996564, 1.996008, 1.995469, 1.994945, 1.994437,
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1.993943, 1.993464, 1.992997, 1.992543, 1.992102, 1.991673, 1.991254, 1.990847, 1.990450, 1.990063,
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1.989686, 1.989319, 1.988960, 1.988610, 1.988268, 1.987934, 1.987608, 1.987290, 1.986979, 1.986675,
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1.986377, 1.986086, 1.985802, 1.985523, 1.985251, 1.984984, 1.984723, 1.984467, 1.984217, 1.983972,
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// 110 - 300 step 10
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1.981765, 1.979930, 1.978380, 1.977054, 1.975905, 1.974902, 1.974017, 1.973231, 1.972528, 1.971896,
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1.971325, 1.970806, 1.970332, 1.969898, 1.969498, 1.969130, 1.968789, 1.968472, 1.968178, 1.967903,
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// 400 - 1300 step 100
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1.965912, 1.964720, 1.963926, 1.963359, 1.962934, 1.962603, 1.962339, 1.962123, 1.961943, 1.961790,
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// Infinity
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1.959964],
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0.99: [
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// 1 - 100 step 1
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31.820516, 6.964557, 4.540703, 3.746947, 3.364930, 3.142668, 2.997952, 2.896459, 2.821438, 2.763769,
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2.718079, 2.680998, 2.650309, 2.624494, 2.602480, 2.583487, 2.566934, 2.552380, 2.539483, 2.527977,
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2.517648, 2.508325, 2.499867, 2.492159, 2.485107, 2.478630, 2.472660, 2.467140, 2.462021, 2.457262,
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2.452824, 2.448678, 2.444794, 2.441150, 2.437723, 2.434494, 2.431447, 2.428568, 2.425841, 2.423257,
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2.420803, 2.418470, 2.416250, 2.414134, 2.412116, 2.410188, 2.408345, 2.406581, 2.404892, 2.403272,
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2.401718, 2.400225, 2.398790, 2.397410, 2.396081, 2.394801, 2.393568, 2.392377, 2.391229, 2.390119,
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2.389047, 2.388011, 2.387008, 2.386037, 2.385097, 2.384186, 2.383302, 2.382446, 2.381615, 2.380807,
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2.380024, 2.379262, 2.378522, 2.377802, 2.377102, 2.376420, 2.375757, 2.375111, 2.374482, 2.373868,
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2.373270, 2.372687, 2.372119, 2.371564, 2.371022, 2.370493, 2.369977, 2.369472, 2.368979, 2.368497,
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2.368026, 2.367566, 2.367115, 2.366674, 2.366243, 2.365821, 2.365407, 2.365002, 2.364606, 2.364217,
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// 110 - 300 step 10
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2.360726, 2.357825, 2.355375, 2.353278, 2.351465, 2.349880, 2.348483, 2.347243, 2.346134, 2.345137,
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2.344236, 2.343417, 2.342670, 2.341985, 2.341356, 2.340775, 2.340238, 2.339739, 2.339275, 2.338842,
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// 400 - 1300 step 100
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2.335706, 2.333829, 2.332579, 2.331687, 2.331018, 2.330498, 2.330083, 2.329743, 2.329459, 2.329220,
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// Infinity
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2.326348],
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};
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})();
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if (typeof module != 'undefined') {
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for (var key in Statistics)
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module.exports[key] = Statistics[key];
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}
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