go(stats,algo): get,plot mean vals

algo/plot.go

@@ -20,7 +20,60 @@ import (
 
 const preferredFontStyle = "Mono"
 
-// edit: now not violating gocognit limit of 30, TODO(me): still split this up.
+func plotMeanVals(vals []stats.BenchRound, fes int) *plot.Plot {
+	plotter.DefaultFont.Typeface = preferredFontStyle
+	plotter.DefaultLineStyle.Width = vg.Points(1.5)
+
+	p := plot.New()
+
+	p.Title.Text = "Mean"
+
+	p.X.Label.Text = "Generations"
+	p.X.Label.TextStyle.Font.Variant = preferredFontStyle
+	p.X.Label.TextStyle.Font.Weight = 1 // Medium
+	p.X.Tick.Label.Font.Variant = preferredFontStyle
+	p.Y.Label.Text = "CF value"
+	p.Y.Label.TextStyle.Font.Variant = preferredFontStyle
+	p.Y.Label.TextStyle.Font.Weight = 1 // Medium
+	p.Y.Tick.Label.Font.Variant = preferredFontStyle
+
+	p.Title.TextStyle.Font.Size = 11.5
+	p.Title.TextStyle.Font.Variant = "Sans"
+	p.Title.TextStyle.Font.Weight = 2 // SemiBold
+	p.Title.Padding = 5 * vg.Millimeter
+
+	// get mean vals.
+	meanVals := stats.GetMeanVals(vals, fes)
+
+	// mark the end of the X axis with len(meanVals).
+	p.X.Max = float64(len(meanVals))
+	p.Y.Min = floats.Min(meanVals)
+	p.Y.Max = floats.Max(meanVals)
+
+	pts := make(plotter.XYs, len(meanVals))
+
+	// fill the plotter with datapoints.
+	for k, res := range meanVals {
+		pts[k].X = float64(k)
+		pts[k].Y = res
+	}
+
+	lines := make([]interface{}, 0)
+	lines = append(lines, pts)
+
+	err := plotutil.AddLines(
+		p,
+		lines...,
+	)
+	if err != nil {
+		log.Panic(err)
+	}
+
+	return p
+}
+
+// violating gocognit 30, TODO(me): still split this up.
+// nolint: gocognit
 func plotAllDims(algoStats []stats.Stats, fPrefix, fExt string, ch chan report.PicList) {
 	start := time.Now()
 
@@ -117,12 +170,21 @@ func plotAllDims(algoStats []stats.Stats, fPrefix, fExt string, ch chan report.P
 				fmt.Sprint(s.Dimens) + "D-" +
 				fmt.Sprint(s.Generations) + "G-" +
 				fmt.Sprint(len(dim.Solution)) + "I"
+			filenameMean := filename + util.SanitiseFName(" Mean")
+
+			// NEVER EVER ATTEMPT TO INITIALISE THIS WITH `pic`!
+			picMean := report.NewPic()
+			picMean.Caption = "D:~" + fmt.Sprint(s.Dimens) + ", G:~" +
+				fmt.Sprint(s.Generations) + ", I:~" + fmt.Sprint(s.Iterations)
 
 			// set pic file path (later used in tmpl generation)
 			pic.FilePath = filename
+			picMean.FilePath = filenameMean
+
+			pMean := plotMeanVals(dim.Solution, s.Generations)
 
 			elapsed := time.Since(start)
-			info := "saving img to file: " + filename + fExt +
+			info := "saving img to file: " + filename + "(-Mean)" + fExt +
 				" [generated in " + fmt.Sprint(elapsed) + "]"
 
 			if s.Algo == "Random Search" {
@@ -142,6 +204,15 @@ func plotAllDims(algoStats []stats.Stats, fPrefix, fExt string, ch chan report.P
 
 			// save pic.
 			pics = append(pics, *pic)
+
+			// save plot of pMean.
+			if err := pMean.Save(
+				pWidth,
+				pHeight,
+				filenameMean+fExt,
+			); err != nil {
+				panic(err)
+			}
 		}
 	}
 

stats/stats.go

@@ -9,6 +9,7 @@ import (
 	"log"
 
 	"git.dotya.ml/wanderer/math-optim/util"
+	"gonum.org/v1/gonum/stat"
 )
 
 // BenchRound holds the iteration couter value and Results of size 'maxFES'.
@@ -70,3 +71,19 @@ func SaveStats(stats []Stats, fName string) {
 		}
 	}
 }
+
+func GetMeanVals(values []BenchRound, fes int) []float64 {
+	res := make([]float64, fes)
+
+	for i := 0; i < fes; i++ {
+		iterVals := make([]float64, len(values))
+
+		for j, v := range values {
+			iterVals[j] = v.Results[i]
+		}
+
+		res[i] = stat.Mean(iterVals, nil)
+	}
+
+	return res
+}