PerformanceTests/Animometer/resources/sampler.js

   1 var Statistics =
   2 {
   3     sampleMean: function(numberOfSamples, sum)
   4     {
   5         if (numberOfSamples < 1)
   6             return 0;
   7         return sum / numberOfSamples;
   8     },
   9
  10     // With sum and sum of squares, we can compute the sample standard deviation in O(1).
  11     // See https://rniwa.com/2012-11-10/sample-standard-deviation-in-terms-of-sum-and-square-sum-of-samples/
  12     unbiasedSampleStandardDeviation: function(numberOfSamples, sum, squareSum)
  13     {
  14         if (numberOfSamples < 2)
  15             return 0;
  16         return Math.sqrt((squareSum - sum * sum / numberOfSamples) / (numberOfSamples - 1));
  17     },
  18
  19     geometricMean: function(values)
  20     {
  21         if (!values.length)
  22             return 0;
  23         var roots = values.map(function(value) { return  Math.pow(value, 1 / values.length); })
  24         return roots.reduce(function(a, b) { return a * b; });
  25     }
  26 }
  27
  28 function Experiment()
  29 {
  30     this._init();
  31     this._maxHeap = Algorithm.createMaxHeap(Experiment.defaults.CONCERN_SIZE);
  32 }
  33
  34 Experiment.defaults =
  35 {
  36     CONCERN: 5,
  37     CONCERN_SIZE: 100,
  38 }
  39
  40 Experiment.prototype =
  41 {
  42     _init: function()
  43     {
  44         this._sum = 0;
  45         this._squareSum = 0;
  46         this._numberOfSamples = 0;
  47     },
  48
  49     // Called after a warm-up period
  50     startSampling: function()
  51     {
  52         var mean = this.mean();
  53         this._init();
  54         this._maxHeap.init();
  55         this.sample(mean);
  56     },
  57
  58     sample: function(value)
  59     {
  60         this._sum += value;
  61         this._squareSum += value * value;
  62         this._maxHeap.push(value);
  63         ++this._numberOfSamples;
  64     },
  65
  66     mean: function()
  67     {
  68         return Statistics.sampleMean(this._numberOfSamples, this._sum);
  69     },
  70
  71     standardDeviation: function()
  72     {
  73         return Statistics.unbiasedSampleStandardDeviation(this._numberOfSamples, this._sum, this._squareSum);
  74     },
  75
  76     percentage: function()
  77     {
  78         var mean = this.mean();
  79         return mean ? this.standardDeviation() * 100 / mean : 0;
  80     },
  81
  82     concern: function(percentage)
  83     {
  84         var size = Math.ceil(this._numberOfSamples * percentage / 100);
  85         var values = this._maxHeap.values(size);
  86         return values.length ? values.reduce(function(a, b) { return a + b; }) / values.length : 0;
  87     },
  88
  89     score: function(percentage)
  90     {
  91         return Statistics.geometricMean([this.mean(), Math.max(this.concern(percentage), 1)]);
  92     }
  93 }
  94
  95 function Sampler(count)
  96 {
  97     this.experiments = [];
  98     while (count--)
  99         this.experiments.push(new Experiment());
 100     this.samples = [];
 101     this.samplingTimeOffset = 0;
 102 }
 103
 104 Sampler.prototype =
 105 {
 106     startSampling: function(samplingTimeOffset)
 107     {
 108         this.experiments.forEach(function(experiment) {
 109             experiment.startSampling();
 110         });
 111
 112         this.samplingTimeOffset = samplingTimeOffset / 1000;
 113     },
 114
 115     sample: function(timeOffset, values)
 116     {
 117         if (values.length < this.experiments.length)
 118             throw "Not enough sample points";
 119
 120         this.experiments.forEach(function(experiment, index) {
 121             experiment.sample(values[index]);
 122         });
 123
 124         this.samples.push({ timeOffset: timeOffset / 1000, values: values });
 125     },
 126
 127     toJSON: function(statistics, graph)
 128     {
 129         var results = {};
 130
 131         results[Strings["JSON_SCORE"]] = this.experiments[0].score(Experiment.defaults.CONCERN);
 132
 133         if (statistics) {
 134             this.experiments.forEach(function(experiment, index) {
 135                 results[Strings["JSON_EXPERIMENTS"][index]] = {};
 136                 results[Strings["JSON_EXPERIMENTS"][index]][Strings["JSON_MEASUREMENTS"][0]] = experiment.mean();
 137                 results[Strings["JSON_EXPERIMENTS"][index]][Strings["JSON_MEASUREMENTS"][1]] = experiment.concern(Experiment.defaults.CONCERN);
 138                 results[Strings["JSON_EXPERIMENTS"][index]][Strings["JSON_MEASUREMENTS"][2]] = experiment.standardDeviation();
 139                 results[Strings["JSON_EXPERIMENTS"][index]][Strings["JSON_MEASUREMENTS"][3]] = experiment.percentage();
 140             });
 141         }
 142
 143         if (graph) {
 144             results[Strings["JSON_SAMPLES"][0]] = {};
 145             results[Strings["JSON_SAMPLES"][0]][Strings["JSON_GRAPH"][0]] = this.samples;
 146             results[Strings["JSON_SAMPLES"][0]][Strings["JSON_GRAPH"][1]] = this.samplingTimeOffset;
 147         }
 148
 149         return results;
 150     }
 151 }