a go package that implements the efficient, accurate, and stable calculation of online statistical quantities.
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 ```package ostat ``` ``` ``` ```import ( ``` ``` "math" ``` ``` "testing" ``` ```) ``` ``` ``` ```const tolerance = 1e-7 ``` ``` ``` ```func TestInsert(t *testing.T) { ``` ``` tests := []struct { ``` ``` samples []float64 ``` ``` min float64 ``` ``` max float64 ``` ``` mean float64 ``` ``` pvariance, svariance float64 ``` ``` pstdev, sstdev float64 ``` ``` }{ ``` ``` { ``` ``` min: math.Inf(1), ``` ``` max: math.Inf(-1), ``` ``` }, ``` ``` { ``` ``` samples: []float64{4, 7, 13, 16}, ``` ``` min: 4, ``` ``` max: 16, ``` ``` mean: 10.0, ``` ``` pvariance: 22.5, ``` ``` pstdev: math.Sqrt(22.5), ``` ``` svariance: 30, ``` ``` sstdev: math.Sqrt(30.0), ``` ``` }, ``` ``` { ``` ``` samples: []float64{10e8 + 4, 10e8 + 7, 10e8 + 13, 10e8 + 16}, ``` ``` min: 10e8 + 4, ``` ``` max: 10e8 + 16, ``` ``` mean: 10e8 + 10.0, ``` ``` pvariance: 22.5, ``` ``` pstdev: math.Sqrt(22.5), ``` ``` svariance: 30, ``` ``` sstdev: math.Sqrt(30.0), ``` ``` }, ``` ``` { ``` ``` samples: []float64{10e9 + 4, 10e9 + 7, 10e9 + 13, 10e9 + 16}, ``` ``` min: 10e9 + 4, ``` ``` max: 10e9 + 16, ``` ``` mean: 10e9 + 10.0, ``` ``` pvariance: 22.5, ``` ``` pstdev: math.Sqrt(22.5), ``` ``` svariance: 30, ``` ``` sstdev: math.Sqrt(30.0), ``` ``` }, ``` ``` { ``` ``` ``` ``` samples: []float64{119., 480., 900., -561., 664., -652., 549., -342., -754., 983., -485., 6., 572., 683., -111., 400., -179., 60., 142., 253., -330., -886., -120., 590., 465., -374., 299., -32., -794., -107., -531., -649., -877., 114., 179., 704., 508., -210., -128., 147., 654., -251., -337., 643., 865., 530., 535., 534., 528., -115., -645., 55., -584., 104., -556., 496., -863., 483., 145., 578., 318., -611., 290., 178., -25., -792., -45., 221., -172., 491., 911., 904., 523., 778., -484., 230., -897., -97., 316., -255., -749., -737., 709., -74., 48., 839., 428., -560., -613., -639., 371., 948., -966., -802., -618., -753., 835., -372., -492., 89.}, ``` ``` min: -966.0, ``` ``` max: 983.0, ``` ``` mean: 21.68, ``` ``` pvariance: 293082.29760000011, ``` ``` pstdev: 541.3707579838424, ``` ``` svariance: 296042.72484848, ``` ``` sstdev: 544.0980838493, ``` ``` }, ``` ``` { ``` ``` samples: []float64{36.22727273, -8., -0.79775281, -1.14772727, 29.03333333, -19.53571429, 0.49090909, -0.41666667, -25.92, -10.77464789, 67.71428571, -39.28571429, 10.05154639, -46., 7.91891892, -9.92, -10.30769231, -11.20634921, 13.85, -9.19565217, -16.9, -2.725, 14.32142857, -18.64285714, 9.70238095, 5.92307692, 15.15789474, 8.22368421, 3.56179775, 5.87368421, -16.68, -104.57142857, 6.42352941, -5.14893617, -4.925, -13.31818182, 7.81538462, -5.01492537, -15.35483871, 7.08421053, -4.47777778, 7.97727273, 10.09574468, 10.56521739, 7.67777778, -57.5, 9.03773585, -2.0989011, 12.34482759, -85.5, -107., -4.89473684, 0.90217391, 13.2, 7.31111111, -1.98611111, -7.3375, 9.80722892, -5.88043478, -404., 47.33333333, 16.85416667, -3.06410256, -72.85714286, 29.08333333, 13.58333333, -11.84615385, -14.36363636, 10.25373134, 3.92207792, 11.14634146, -16.15151515, -10.16363636, 13.13513514, -4.55223881, 14.45, 44.88888889, 5.36781609, 1.29591837, 176.5, 18.95652174, 1.85416667, 8.4125, -168.33333333, 2.97590361, 5.45555556, 5.20930233, -7.11764706, 26.53846154, -16.94285714, 17.18181818, 32.86666667, -22.375, 10.14285714, -51.625, -20.72916667, 0.4516129, -5.47058824, -17.81818182, 9.}, ``` ``` min: -404.0, ``` ``` max: 176.5, ``` ``` mean: -6.5472287624739636, ``` ``` pvariance: 2864.1893930503825, ``` ``` pstdev: 53.518122099438266, ``` ``` svariance: 2893.1205990296, ``` ``` sstdev: 53.787736511491, ``` ``` }, ``` ``` } ``` ``` ``` ``` for _, test := range tests { ``` ``` ps := NewPopulationStat() ``` ``` ss := NewSampleStat() ``` ``` for _, i := range test.samples { ``` ``` ps.Push(i) ``` ``` ss.Push(i) ``` ``` } ``` ``` if ps.Min != ss.Min { ``` ``` t.Errorf("Mins don't match") ``` ``` } ``` ``` if ps.Max != ss.Max { ``` ``` t.Errorf("Maxs don't match") ``` ``` } ``` ``` if ps.Min != test.min { ``` ``` t.Errorf("incorrectly calculated min: %f != %f", ps.Min, test.min) ``` ``` } ``` ``` if ps.Max != test.max { ``` ``` t.Errorf("incorrectly calculated max: %f != %f", ps.Max, test.max) ``` ``` } ``` ``` ``` ``` pmean, _ := ps.Mean() ``` ``` smean, _ := ss.Mean() ``` ``` if pmean != smean { ``` ``` t.Errorf("Means don't match") ``` ``` } ``` ``` if m, _ := ps.Mean(); math.Abs(m-test.mean) > tolerance { ``` ``` t.Errorf("incorrect mean: %f != %f", m, test.mean) ``` ``` } ``` ``` ``` ``` if variance, _ := ps.Variance(); math.Abs(variance-test.pvariance) > tolerance { ``` ``` t.Errorf("incorrect variance: %f != %f", variance, test.pvariance) ``` ``` } ``` ``` if stdev, _ := ps.StdDev(); math.Abs(stdev-test.pstdev) > tolerance { ``` ``` t.Errorf("incorrect stdev: %f != %f", stdev, test.pstdev) ``` ``` } ``` ``` ``` ``` if variance, _ := ss.Variance(); math.Abs(variance-test.svariance) > tolerance { ``` ``` t.Errorf("incorrect variance: %f != %f", variance, test.svariance) ``` ``` } ``` ``` if stdev, _ := ss.StdDev(); math.Abs(stdev-test.sstdev) > tolerance { ``` ``` t.Errorf("incorrect stdev: %f != %f", stdev, test.sstdev) ``` ``` } ``` ``` } ``` ```} ``` ``` ``` ```func TestEmpty(t *testing.T) { ``` ``` ps := NewSampleStat() ``` ``` _, err := ps.Mean() ``` ``` if err == nil { ``` ``` t.Errorf("failure to notify the running stat was empty") ``` ``` } ``` ``` _, err = ps.Variance() ``` ``` if err == nil { ``` ``` t.Errorf("failure to notify the running stat was empty") ``` ``` } ``` ``` _, err = ps.StdDev() ``` ``` if err == nil { ``` ``` t.Errorf("failure to notify the running stat was empty") ``` ``` } ``` ```} ``` ``` ```