// Filter out a serie of values func (df *DataFilter) Filter(measure string, values []float64) []float64 { // do we have a filter for this measure ? if _, ok := df.percentiles[measure]; !ok { return values } // Compute the percentile value max, err := stats.PercentileNearestRank(values, df.percentiles[measure]) if err != nil { log.Lvl2("Monitor: Error filtering data(", values, "):", err) return values } // Find the index from where to filter maxIndex := -1 for i, v := range values { if v > max { maxIndex = i } } // check if we foud something to filter out if maxIndex == -1 { log.Lvl3("Filtering: nothing to filter for", measure) return values } // return the values below the percentile log.Lvl3("Filtering: filters out", measure, ":", maxIndex, "/", len(values)) return values[:maxIndex] }
func main() { d := stats.LoadRawData([]interface{}{1.1, "2", 3.0, 4, "5"}) a, _ := stats.Min(d) fmt.Println(a) // 1.1 a, _ = stats.Max(d) fmt.Println(a) // 5 a, _ = stats.Sum([]float64{1.1, 2.2, 3.3}) fmt.Println(a) // 6.6 a, _ = stats.Mean([]float64{1, 2, 3, 4, 5}) fmt.Println(a) // 3 a, _ = stats.Median([]float64{1, 2, 3, 4, 5, 6, 7}) fmt.Println(a) // 4 m, _ := stats.Mode([]float64{5, 5, 3, 3, 4, 2, 1}) fmt.Println(m) // [5 3] a, _ = stats.PopulationVariance([]float64{1, 2, 3, 4, 5}) fmt.Println(a) // 2 a, _ = stats.SampleVariance([]float64{1, 2, 3, 4, 5}) fmt.Println(a) // 2.5 a, _ = stats.MedianAbsoluteDeviationPopulation([]float64{1, 2, 3}) fmt.Println(a) // 1 a, _ = stats.StandardDeviationPopulation([]float64{1, 2, 3}) fmt.Println(a) // 0.816496580927726 a, _ = stats.StandardDeviationSample([]float64{1, 2, 3}) fmt.Println(a) // 1 a, _ = stats.Percentile([]float64{1, 2, 3, 4, 5}, 75) fmt.Println(a) // 4 a, _ = stats.PercentileNearestRank([]float64{35, 20, 15, 40, 50}, 75) fmt.Println(a) // 40 c := []stats.Coordinate{ {1, 2.3}, {2, 3.3}, {3, 3.7}, {4, 4.3}, {5, 5.3}, } r, _ := stats.LinearRegression(c) fmt.Println(r) // [{1 2.3800000000000026} {2 3.0800000000000014} {3 3.7800000000000002} {4 4.479999999999999} {5 5.179999999999998}] r, _ = stats.ExponentialRegression(c) fmt.Println(r) // [{1 2.5150181024736638} {2 3.032084111136781} {3 3.6554544271334493} {4 4.406984298281804} {5 5.313022222665875}] r, _ = stats.LogarithmicRegression(c) fmt.Println(r) // [{1 2.1520822363811702} {2 3.3305559222492214} {3 4.019918836568674} {4 4.509029608117273} {5 4.888413396683663}] s, _ := stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 3, false) fmt.Println(s) // [0.2,0.4,0.3] s, _ = stats.Sample([]float64{0.1, 0.2, 0.3, 0.4}, 10, true) fmt.Println(s) // [0.2,0.2,0.4,0.1,0.2,0.4,0.3,0.2,0.2,0.1] q, _ := stats.Quartile([]float64{7, 15, 36, 39, 40, 41}) fmt.Println(q) // {15 37.5 40} iqr, _ := stats.InterQuartileRange([]float64{102, 104, 105, 107, 108, 109, 110, 112, 115, 116, 118}) fmt.Println(iqr) // 10 mh, _ := stats.Midhinge([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13}) fmt.Println(mh) // 7.5 tr, _ := stats.Trimean([]float64{1, 3, 4, 4, 6, 6, 6, 6, 7, 7, 7, 8, 8, 9, 9, 10, 11, 12, 13}) fmt.Println(tr) // 7.25 o, _ := stats.QuartileOutliers([]float64{-1000, 1, 3, 4, 4, 6, 6, 6, 6, 7, 8, 15, 18, 100}) fmt.Printf("%+v\n", o) // {Mild:[15 18] Extreme:[-1000 100]} gm, _ := stats.GeometricMean([]float64{10, 51.2, 8}) fmt.Println(gm) // 15.999999999999991 hm, _ := stats.HarmonicMean([]float64{1, 2, 3, 4, 5}) fmt.Println(hm) // 2.18978102189781 a, _ = stats.Round(2.18978102189781, 3) fmt.Println(a) // 2.189 }