Adding upstream version 1:115.7.0.upstream/1%115.7.0upstream

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 397 insertions, 0 deletions

diff --git a/third_party/webkit/PerformanceTests/MotionMark/resources/statistics.js b/third_party/webkit/PerformanceTests/MotionMark/resources/statistics.js
new file mode 100644
index 0000000000..9322a797b6
--- /dev/null
+++ b/third_party/webkit/PerformanceTests/MotionMark/resources/statistics.js
@@ -0,0 +1,397 @@
+Pseudo =
+{
+    initialRandomSeed: 49734321,
+    randomSeed: 49734321,
+
+    resetRandomSeed: function()
+    {
+        Pseudo.randomSeed = Pseudo.initialRandomSeed;
+    },
+
+    random: function()
+    {
+        var randomSeed = Pseudo.randomSeed;
+        randomSeed = ((randomSeed + 0x7ed55d16) + (randomSeed << 12))  & 0xffffffff;
+        randomSeed = ((randomSeed ^ 0xc761c23c) ^ (randomSeed >>> 19)) & 0xffffffff;
+        randomSeed = ((randomSeed + 0x165667b1) + (randomSeed << 5))   & 0xffffffff;
+        randomSeed = ((randomSeed + 0xd3a2646c) ^ (randomSeed << 9))   & 0xffffffff;
+        randomSeed = ((randomSeed + 0xfd7046c5) + (randomSeed << 3))   & 0xffffffff;
+        randomSeed = ((randomSeed ^ 0xb55a4f09) ^ (randomSeed >>> 16)) & 0xffffffff;
+        Pseudo.randomSeed = randomSeed;
+        return (randomSeed & 0xfffffff) / 0x10000000;
+    }
+};
+
+Statistics =
+{
+    sampleMean: function(numberOfSamples, sum)
+    {
+        if (numberOfSamples < 1)
+            return 0;
+        return sum / numberOfSamples;
+    },
+
+    // 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/
+    unbiasedSampleStandardDeviation: function(numberOfSamples, sum, squareSum)
+    {
+        if (numberOfSamples < 2)
+            return 0;
+        return Math.sqrt((squareSum - sum * sum / numberOfSamples) / (numberOfSamples - 1));
+    },
+
+    geometricMean: function(values)
+    {
+        if (!values.length)
+            return 0;
+        var roots = values.map(function(value) { return Math.pow(value, 1 / values.length); })
+        return roots.reduce(function(a, b) { return a * b; });
+    },
+
+    // Cumulative distribution function
+    cdf: function(value, mean, standardDeviation)
+    {
+        return 0.5 * (1 + Statistics.erf((value - mean) / (Math.sqrt(2 * standardDeviation * standardDeviation))));
+    },
+
+    // Approximation of Gauss error function, Abramowitz and Stegun 7.1.26
+    erf: function(value)
+    {
+          var sign = (value >= 0) ? 1 : -1;
+          value = Math.abs(value);
+
+          var a1 = 0.254829592;
+          var a2 = -0.284496736;
+          var a3 = 1.421413741;
+          var a4 = -1.453152027;
+          var a5 = 1.061405429;
+          var p = 0.3275911;
+
+          var t = 1.0 / (1.0 + p * value);
+          var y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * Math.exp(-value * value);
+          return sign * y;
+    },
+
+    largestDeviationPercentage: function(low, mean, high)
+    {
+        return Math.max(Math.abs(low / mean - 1), (high / mean - 1));
+    }
+};
+
+Experiment = Utilities.createClass(
+    function(includeConcern)
+    {
+        if (includeConcern)
+            this._maxHeap = Heap.createMaxHeap(Experiment.defaults.CONCERN_SIZE);
+        this.reset();
+    }, {
+
+    reset: function()
+    {
+        this._sum = 0;
+        this._squareSum = 0;
+        this._numberOfSamples = 0;
+        if (this._maxHeap)
+            this._maxHeap.init();
+    },
+
+    get sampleCount()
+    {
+        return this._numberOfSamples;
+    },
+
+    sample: function(value)
+    {
+        this._sum += value;
+        this._squareSum += value * value;
+        if (this._maxHeap)
+            this._maxHeap.push(value);
+        ++this._numberOfSamples;
+    },
+
+    mean: function()
+    {
+        return Statistics.sampleMean(this._numberOfSamples, this._sum);
+    },
+
+    standardDeviation: function()
+    {
+        return Statistics.unbiasedSampleStandardDeviation(this._numberOfSamples, this._sum, this._squareSum);
+    },
+
+    cdf: function(value)
+    {
+        return Statistics.cdf(value, this.mean(), this.standardDeviation());
+    },
+
+    percentage: function()
+    {
+        var mean = this.mean();
+        return mean ? this.standardDeviation() * 100 / mean : 0;
+    },
+
+    concern: function(percentage)
+    {
+        if (!this._maxHeap)
+            return this.mean();
+
+        var size = Math.ceil(this._numberOfSamples * percentage / 100);
+        var values = this._maxHeap.values(size);
+        return values.length ? values.reduce(function(a, b) { return a + b; }) / values.length : 0;
+    },
+
+    score: function(percentage)
+    {
+        return Statistics.geometricMean([this.mean(), Math.max(this.concern(percentage), 1)]);
+    }
+});
+
+Experiment.defaults =
+{
+    CONCERN: 5,
+    CONCERN_SIZE: 100,
+};
+
+Regression = Utilities.createClass(
+    function(samples, getComplexity, getFrameLength, startIndex, endIndex, options)
+    {
+        var desiredFrameLength = options.desiredFrameLength || 1000/60;
+        var bestProfile;
+
+        if (!options.preferredProfile || options.preferredProfile == Strings.json.profiles.slope) {
+            var slope = this._calculateRegression(samples, getComplexity, getFrameLength, startIndex, endIndex, {
+                shouldClip: true,
+                s1: desiredFrameLength,
+                t1: 0
+            });
+            if (!bestProfile || slope.error < bestProfile.error) {
+                bestProfile = slope;
+                this.profile = Strings.json.profiles.slope;
+            }
+        }
+        if (!options.preferredProfile || options.preferredProfile == Strings.json.profiles.flat) {
+            var flat = this._calculateRegression(samples, getComplexity, getFrameLength, startIndex, endIndex, {
+                shouldClip: true,
+                s1: desiredFrameLength,
+                t1: 0,
+                t2: 0
+            });
+
+            if (!bestProfile || flat.error < bestProfile.error) {
+                bestProfile = flat;
+                this.profile = Strings.json.profiles.flat;
+            }
+        }
+
+        this.startIndex = Math.min(startIndex, endIndex);
+        this.endIndex = Math.max(startIndex, endIndex);
+
+        this.complexity = bestProfile.complexity;
+        this.s1 = bestProfile.s1;
+        this.t1 = bestProfile.t1;
+        this.s2 = bestProfile.s2;
+        this.t2 = bestProfile.t2;
+        this.stdev1 = bestProfile.stdev1;
+        this.stdev2 = bestProfile.stdev2;
+        this.n1 = bestProfile.n1;
+        this.n2 = bestProfile.n2;
+        this.error = bestProfile.error;
+    }, {
+
+    valueAt: function(complexity)
+    {
+        if (this.n1 == 1 || complexity > this.complexity)
+            return this.s2 + this.t2 * complexity;
+        return this.s1 + this.t1 * complexity;
+    },
+
+    // A generic two-segment piecewise regression calculator. Based on Kundu/Ubhaya
+    //
+    // Minimize sum of (y - y')^2
+    // where                        y = s1 + t1*x
+    //                              y = s2 + t2*x
+    //                y' = s1 + t1*x' = s2 + t2*x'   if x_0 <= x' <= x_n
+    //
+    // Allows for fixing s1, t1, s2, t2
+    //
+    // x is assumed to be complexity, y is frame length. Can be used for pure complexity-FPS
+    // analysis or for ramp controllers since complexity monotonically decreases with time.
+    _calculateRegression: function(samples, getComplexity, getFrameLength, startIndex, endIndex, options)
+    {
+        if (startIndex == endIndex) {
+            // Only one sample point; we can't calculate any regression.
+            var x = getComplexity(samples, startIndex);
+            return {
+                complexity: x,
+                s1: x,
+                t1: 0,
+                s2: x,
+                t2: 0,
+                error1: 0,
+                error2: 0
+            };
+        }
+
+        // x is expected to increase in complexity
+        var iterationDirection = endIndex > startIndex ? 1 : -1;
+        var lowComplexity = getComplexity(samples, startIndex);
+        var highComplexity = getComplexity(samples, endIndex);
+        var a1 = 0, b1 = 0, c1 = 0, d1 = 0, h1 = 0, k1 = 0;
+        var a2 = 0, b2 = 0, c2 = 0, d2 = 0, h2 = 0, k2 = 0;
+
+        // Iterate from low to high complexity
+        for (var i = startIndex; iterationDirection * (endIndex - i) > -1; i += iterationDirection) {
+            var x = getComplexity(samples, i);
+            var y = getFrameLength(samples, i);
+            a2 += 1;
+            b2 += x;
+            c2 += x * x;
+            d2 += y;
+            h2 += y * x;
+            k2 += y * y;
+        }
+
+        var s1_best, t1_best, s2_best, t2_best, n1_best, n2_best, error1_best, error2_best, x_best, x_prime;
+
+        function setBest(s1, t1, error1, s2, t2, error2, splitIndex, x_prime, x)
+        {
+            s1_best = s1;
+            t1_best = t1;
+            error1_best = error1;
+            s2_best = s2;
+            t2_best = t2;
+            error2_best = error2;
+            // Number of samples included in the first segment, inclusive of splitIndex
+            n1_best = iterationDirection * (splitIndex - startIndex) + 1;
+            // Number of samples included in the second segment
+            n2_best = iterationDirection * (endIndex - splitIndex);
+            if (!options.shouldClip || (x_prime >= lowComplexity && x_prime <= highComplexity))
+                x_best = x_prime;
+            else {
+                // Discontinuous piecewise regression
+                x_best = x;
+            }
+        }
+
+        // Iterate from startIndex to endIndex - 1, inclusive
+        for (var i = startIndex; iterationDirection * (endIndex - i) > 0; i += iterationDirection) {
+            var x = getComplexity(samples, i);
+            var y = getFrameLength(samples, i);
+            var xx = x * x;
+            var yx = y * x;
+            var yy = y * y;
+            // a1, b1, etc. is sum from startIndex to i, inclusive
+            a1 += 1;
+            b1 += x;
+            c1 += xx;
+            d1 += y;
+            h1 += yx;
+            k1 += yy;
+            // a2, b2, etc. is sum from i+1 to endIndex, inclusive
+            a2 -= 1;
+            b2 -= x;
+            c2 -= xx;
+            d2 -= y;
+            h2 -= yx;
+            k2 -= yy;
+
+            var A = c1*d1 - b1*h1;
+            var B = a1*h1 - b1*d1;
+            var C = a1*c1 - b1*b1;
+            var D = c2*d2 - b2*h2;
+            var E = a2*h2 - b2*d2;
+            var F = a2*c2 - b2*b2;
+            var s1 = options.s1 !== undefined ? options.s1 : (A / C);
+            var t1 = options.t1 !== undefined ? options.t1 : (B / C);
+            var s2 = options.s2 !== undefined ? options.s2 : (D / F);
+            var t2 = options.t2 !== undefined ? options.t2 : (E / F);
+            // Assumes that the two segments meet
+            var x_prime = (s1 - s2) / (t2 - t1);
+
+            var error1 = (k1 + a1*s1*s1 + c1*t1*t1 - 2*d1*s1 - 2*h1*t1 + 2*b1*s1*t1) || Number.MAX_VALUE;
+            var error2 = (k2 + a2*s2*s2 + c2*t2*t2 - 2*d2*s2 - 2*h2*t2 + 2*b2*s2*t2) || Number.MAX_VALUE;
+
+            if (i == startIndex) {
+                setBest(s1, t1, error1, s2, t2, error2, i, x_prime, x);
+                continue;
+            }
+
+            if (C == 0 || F == 0)
+                continue;
+
+            // Projected point is not between this and the next sample
+            if (x_prime > getComplexity(samples, i + iterationDirection) || x_prime < x) {
+                // Calculate lambda, which divides the weight of this sample between the two lines
+
+                // These values remove the influence of this sample
+                var I = c1 - 2*b1*x + a1*xx;
+                var H = C - I;
+                var G = A + B*x - C*y;
+
+                var J = D + E*x - F*y;
+                var K = c2 - 2*b2*x + a2*xx;
+
+                var lambda = (G*F + G*K - H*J) / (I*J + G*K);
+                if (lambda > 0 && lambda < 1) {
+                    var lambda1 = 1 - lambda;
+                    s1 = options.s1 !== undefined ? options.s1 : ((A - lambda1*(-h1*x + d1*xx + c1*y - b1*yx)) / (C - lambda1*I));
+                    t1 = options.t1 !== undefined ? options.t1 : ((B - lambda1*(h1 - d1*x - b1*y + a1*yx)) / (C - lambda1*I));
+                    s2 = options.s2 !== undefined ? options.s2 : ((D + lambda1*(-h2*x + d2*xx + c2*y - b2*yx)) / (F + lambda1*K));
+                    t2 = options.t2 !== undefined ? options.t2 : ((E + lambda1*(h2 - d2*x - b2*y + a2*yx)) / (F + lambda1*K));
+                    x_prime = (s1 - s2) / (t2 - t1);
+
+                    error1 = ((k1 + a1*s1*s1 + c1*t1*t1 - 2*d1*s1 - 2*h1*t1 + 2*b1*s1*t1) - lambda1 * Math.pow(y - (s1 + t1*x), 2)) || Number.MAX_VALUE;
+                    error2 = ((k2 + a2*s2*s2 + c2*t2*t2 - 2*d2*s2 - 2*h2*t2 + 2*b2*s2*t2) + lambda1 * Math.pow(y - (s2 + t2*x), 2)) || Number.MAX_VALUE;
+                } else if (t1 != t2)
+                    continue;
+            }
+
+            if (error1 + error2 < error1_best + error2_best)
+                setBest(s1, t1, error1, s2, t2, error2, i, x_prime, x);
+        }
+
+        return {
+            complexity: x_best,
+            s1: s1_best,
+            t1: t1_best,
+            stdev1: Math.sqrt(error1_best / n1_best),
+            s2: s2_best,
+            t2: t2_best,
+            stdev2: Math.sqrt(error2_best / n2_best),
+            error: error1_best + error2_best,
+            n1: n1_best,
+            n2: n2_best
+        };
+    }
+});
+
+Utilities.extendObject(Regression, {
+    bootstrap: function(samples, iterationCount, processResample, confidencePercentage)
+    {
+        var sampleLength = samples.length;
+        var resample = new Array(sampleLength);
+
+        var bootstrapEstimator = new Experiment;
+        var bootstrapData = new Array(iterationCount);
+
+        Pseudo.resetRandomSeed();
+        for (var i = 0; i < iterationCount; ++i) {
+            for (var j = 0; j < sampleLength; ++j)
+                resample[j] = samples[Math.floor(Pseudo.random() * sampleLength)];
+
+            var resampleResult = processResample(resample);
+            bootstrapEstimator.sample(resampleResult);
+            bootstrapData[i] = resampleResult;
+        }
+
+        bootstrapData.sort(function(a, b) { return a - b; });
+        return {
+            confidenceLow: bootstrapData[Math.round((iterationCount - 1) * (1 - confidencePercentage) / 2)],
+            confidenceHigh: bootstrapData[Math.round((iterationCount - 1) * (1 + confidencePercentage) / 2)],
+            median: bootstrapData[Math.round(iterationCount / 2)],
+            mean: bootstrapEstimator.mean(),
+            data: bootstrapData,
+            confidencePercentage: confidencePercentage
+        };
+    }
+});