run,algo: tweak what runs when

algo/algo.go

@@ -9,8 +9,10 @@ import (
 	"sort"
 	"sync"
 
+	"git.dotya.ml/wanderer/math-optim/algo/cec2020"
 	"git.dotya.ml/wanderer/math-optim/algo/de"
 	"git.dotya.ml/wanderer/math-optim/bench"
+	c20 "git.dotya.ml/wanderer/math-optim/bench/cec2020"
 	"git.dotya.ml/wanderer/math-optim/report"
 	"git.dotya.ml/wanderer/math-optim/stats"
 )
@@ -403,6 +405,85 @@ func DojDE(wg *sync.WaitGroup, m *sync.Mutex) {
 		pLsMean = append(pLsMean, pLMean)
 	}
 
+	algoName := "Differential Evolution"
+
+	// protect access to shared data.
+	m.Lock()
+	report.SavePicsToFile(pLs, pLsMean, algoName)
+
+	stats.SaveTable(algoName, algoStats)
+	m.Unlock()
+}
+
+func DoCEC2020jDE(wg *sync.WaitGroup, m *sync.Mutex) {
+	defer wg.Done()
+
+	cec2020.LogPrintln("starting")
+
+	// funcCount is the number of bench functions available and tested.
+	funcCount := len(c20.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)
+	// chAlgoMeans := make(chan *stats.AlgoBenchMean, 1)
+	chAlgoMeans := make(chan *stats.AlgoBenchMean, funcCount)
+
+	defer close(ch)
+	defer close(chAlgoMeans)
+
+	// jDE params.
+	np := 50
+	f := 0.5
+	cr := 0.9
+
+	for i := range algoStats {
+		jDE := cec2020.NewjDE()
+
+		// params:
+		//  Generations, minimum bench iterations, 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,
+		//  n > 0 for minimum bench iterations
+		//  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, 30, 0, 0, np, f, cr, c20.Dimensions, c20.FuncNames[i], ch, chAlgoMeans)
+
+		go jDE.Run()
+	}
+
+	// get results.
+	for i := range algoStats {
+		s := <-ch
+		aM := <-chAlgoMeans
+
+		algoStats[i] = s
+
+		saveAlgoMeans(*aM)
+	}
+
+	pCh := make(chan report.PicList, funcCount*len(c20.Dimensions))
+	pMeanCh := make(chan report.PicList, funcCount*len(c20.Dimensions))
+
+	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.

run.go

@@ -23,8 +23,9 @@ var (
 
 	// TODO(me): add flag for plot output format: -plotout=(svg,eps,pdf).
 
-	// run jDE by default.
-	jDE = flag.Bool("jde", true, "run Differential Evolution algorithm with parameter self adaptation")
+	jDE = flag.Bool("jde", false, "run Differential Evolution algorithm with parameter self adaptation")
+	// run CEC2020 jDE by default.
+	c2jDE = flag.Bool("c2jde", true, "run CEC2020 version of the Differential Evolution algorithm with parameter self adaptation")
 )
 
 func run() {
@@ -33,7 +34,7 @@ func run() {
 	flag.Parse()
 
 	if *generate {
-		if !*jDE && !*sHC && !*rS {
+		if !*jDE && !*c2jDE && !*sHC && !*rS {
 			log.Println("at least one algo needs to be specified, exiting...")
 
 			return
@@ -43,18 +44,25 @@ func run() {
 
 		var m sync.Mutex
 
-		switch {
-		case *jDE:
+		if *jDE {
 			wg.Add(1)
 
 			go algo.DojDE(&wg, &m)
+		}
 
-		case *rS:
+		if *c2jDE {
+			wg.Add(1)
+
+			go algo.DoCEC2020jDE(&wg, &m)
+		}
+
+		if *rS {
 			wg.Add(1)
 
 			go algo.DoRandomSearch(&wg, &m)
+		}
 
-		case *sHC:
+		if *sHC {
 			wg.Add(1)
 
 			if *n100 {

run_test.go

@@ -19,7 +19,7 @@ func TestRun(t *testing.T) {
 		t.Errorf("failed to set timeout to %s", timeout)
 	}
 
-	if err := flag.Set("jde", "false"); err != nil {
+	if err := flag.Set("c2jde", "false"); err != nil {
 		t.Errorf("failed to not run jDE: %q", err)
 	}