go(algo): add DojDE()

the implementation is a WIP.

adjust linter ignoring in algo/plot.go: unparam does not like that
fPrefix is "plot" each time the func is called. this might get reworked
later.

also add field from bench/bench.go - DimensionsGA - Genetic Algorithm
specific dimensions.

algo/algo.go

@@ -9,6 +9,7 @@ import (
 	"sort"
 	"sync"
 
+	"git.dotya.ml/wanderer/math-optim/algo/de"
 	"git.dotya.ml/wanderer/math-optim/bench"
 	"git.dotya.ml/wanderer/math-optim/report"
 	"git.dotya.ml/wanderer/math-optim/stats"
@@ -325,3 +326,74 @@ func DoStochasticHillClimbing100Neigh(wg *sync.WaitGroup, m *sync.Mutex) {
 	stats.SaveTable(algoName, algoStats)
 	m.Unlock()
 }
+
+func DojDE(wg *sync.WaitGroup, m *sync.Mutex) {
+	defer wg.Done()
+
+	de.LogPrintln("starting")
+
+	// funcCount is the number of bench functions available and tested.
+	funcCount := len(bench.Functions)
+	// stats for the current algo.
+	algoStats := make([][]stats.Stats, funcCount)
+	// ch serves as a way to get the actual computed output.
+	ch := make(chan []stats.Stats, funcCount)
+
+	defer close(ch)
+
+	// jDE params.
+	np := 50
+	f := 0.5
+	cr := 0.9
+
+	for i := range algoStats {
+		jDE := de.NewjDE()
+
+		// params:
+		//  Generations, mutation strategy, parameter self-adaptation scheme,
+		//  initial population size, differential weight, mutation constant,
+		//  dimensions, bench name and a synchronisation channel.
+		//
+		//  -1 to disable generation limits,
+		//  0..17 to choose a mutation strategy,
+		//  0..1 to select a parameter self-adaptation scheme,
+		//  np >= 4 as initial population size.
+		jDE.Init(-1, 0, 0, np, f, cr, bench.DimensionsGA, bench.FuncNames[i], ch)
+
+		go jDE.Run()
+	}
+
+	// get results.
+	for i := range algoStats {
+		s := <-ch
+
+		algoStats[i] = s
+	}
+
+	pCh := make(chan report.PicList, funcCount*len(bench.DimensionsGA))
+	pMeanCh := make(chan report.PicList, funcCount*len(bench.DimensionsGA))
+
+	for _, algoStat := range algoStats {
+		go plotAllDims(algoStat, "plot", ".pdf", pCh, pMeanCh)
+	}
+
+	pLs := []report.PicList{}
+	pLsMean := []report.PicList{}
+
+	for range algoStats {
+		pL := <-pCh
+		pLMean := <-pMeanCh
+
+		pLs = append(pLs, pL)
+		pLsMean = append(pLsMean, pLMean)
+	}
+
+	algoName := "Self-adapting Differential Evolution"
+
+	// protect access to shared data.
+	m.Lock()
+	report.SavePicsToFile(pLs, pLsMean, algoName)
+
+	stats.SaveTable(algoName, algoStats)
+	m.Unlock()
+}

algo/plot.go

@@ -208,8 +208,8 @@ func plotMeanVals(meanVals []float64, title string, fes int) *plot.Plot {
 	return p
 }
 
-// violating gocognit 30, TODO(me): still split this up.
-// nolint: gocognit
+// violating gocognit 30, TODO(me): still split this up. fPrefix is always "plot" according to unparam.
+// nolint: gocognit,unparam
 func plotAllDims(algoStats []stats.Stats, fPrefix, fExt string, ch chan report.PicList, chMean chan report.PicList) {
 	start := time.Now()
 

bench/bench.go

@@ -25,6 +25,9 @@ var (
 	// Dimensions to compute for (spatial complexity..?).
 	Dimensions = []int{5, 10, 20}
 
+	// DimensionsGA are used with Genetic Algorithms (such as DE, see algo/de).
+	DimensionsGA = []int{10, 30}
+
 	// SchwefelParams is a struct holding the min, max allowed value of inputs
 	// passed to the Schwefel function.
 	SchwefelParams = funcParams{min: -500.0, max: 500.0}