go(algo,de): implement jDE (wip)

algo/algo.go

@@ -38,6 +38,13 @@ func getComparisonOfMeansPics() []report.Pic {
 	return comparisonOfMeansPicList.Pics
 }
 
+// saveAlgoMeans saves algo bench means safely.
+func saveAlgoMeans(sabm stats.AlgoBenchMean) {
+	mu.Lock()
+	meanStats.AlgoMeans = append(meanStats.AlgoMeans, sabm)
+	mu.Unlock()
+}
+
 // GetMeanStats returns a pointer of type stats.MeanStats to a sorted package
 // global 'meanStats'.
 func GetMeanStats() *stats.MeanStats {
@@ -338,8 +345,11 @@ func DojDE(wg *sync.WaitGroup, m *sync.Mutex) {
 	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
@@ -360,7 +370,7 @@ func DojDE(wg *sync.WaitGroup, m *sync.Mutex) {
 		//  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, bench.DimensionsGA, bench.FuncNames[i], ch)
+		jDE.Init(-1, 30, 0, 0, np, f, cr, bench.DimensionsGA, bench.FuncNames[i], ch, chAlgoMeans)
 
 		go jDE.Run()
 	}
@@ -368,8 +378,11 @@ func DojDE(wg *sync.WaitGroup, m *sync.Mutex) {
 	// get results.
 	for i := range algoStats {
 		s := <-ch
+		aM := <-chAlgoMeans
 
 		algoStats[i] = s
+
+		saveAlgoMeans(*aM)
 	}
 
 	pCh := make(chan report.PicList, funcCount*len(bench.DimensionsGA))

algo/de/jDE.go

@@ -6,10 +6,14 @@ package de
 import (
 	"log"
 	"os"
+	"sort"
 	"time"
 
+	"golang.org/x/exp/rand"
+
 	"git.dotya.ml/wanderer/math-optim/bench"
 	"git.dotya.ml/wanderer/math-optim/stats"
+	"gonum.org/v1/gonum/stat/distuv"
 )
 
 // JDE is a holder for the settings of an instance of a self-adapting
@@ -37,6 +41,8 @@ type JDE struct {
 	BenchName string
 	// ch is a channel for writing back computed results.
 	ch chan []stats.Stats
+	// chAlgoMeans is a channel for writing back algo means.
+	chAlgoMeans chan *stats.AlgoBenchMean
 	// initialised denotes the initialisation state of the struct.
 	initialised bool
 }
@@ -58,7 +64,7 @@ const (
 var jDELogger = log.New(os.Stderr, " *** δ jDE:", log.Ldate|log.Ltime|log.Lshortfile)
 
 // Init initialises the jDE algorithm, performs sanity checks on the inputs.
-func (j *JDE) Init(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats) {
+func (j *JDE) Init(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
 	if j == nil {
 		jDELogger.Fatalln("jDE needs to be initialised before calling RunjDE, exiting...")
 	}
@@ -106,18 +112,19 @@ func (j *JDE) Init(generations, benchMinIters, mutStrategy, adptScheme, np int,
 	j.Dimensions = dimensions
 	j.BenchName = bench
 	j.ch = ch
+	j.chAlgoMeans = chAlgoMeans
 
 	j.initialised = true
 }
 
 // InitAndRun initialises the jDE algorithm, performs sanity checks on the
 // inputs and calls the Run method.
-func (j *JDE) InitAndRun(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats) {
+func (j *JDE) InitAndRun(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
 	if j == nil {
 		jDELogger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
 	}
 
-	j.Init(generations, benchMinIters, mutStrategy, adptScheme, np, f, cr, dimensions, bench, ch)
+	j.Init(generations, benchMinIters, mutStrategy, adptScheme, np, f, cr, dimensions, bench, ch, chAlgoMeans)
 
 	j.Run()
 }
@@ -132,26 +139,271 @@ func (j *JDE) Run() {
 		jDELogger.Fatalln("jDE needs to be initialised before calling Run(), exiting...")
 	}
 
+	var jDEStats []stats.Stats
+
+	jDEMeans := &stats.AlgoBenchMean{
+		Algo:       "jDE",
+		BenchMeans: make([]stats.BenchMean, 0, len(j.Dimensions)),
+	}
+
 	// run evolve for for all dimensions.
 	for _, dim := range j.Dimensions {
 		maxFES := bench.GetGAMaxFES(dim)
+		fesPerIter := int(float64(maxFES / j.NP))
+		jDEStatDimX := &stats.Stats{
+			Algo:        "jDE",
+			Dimens:      dim,
+			Iterations:  j.BenchMinIters,
+			Generations: maxFES,
+		}
+		funcStats := &stats.FuncStats{BenchName: j.BenchName}
+		dimXMean := &stats.BenchMean{
+			Bench:       j.BenchName,
+			Dimens:      dim,
+			Iterations:  j.BenchMinIters,
+			Generations: maxFES,
+			Neighbours:  -1,
+		}
+		benchFuncParams := bench.FunctionParams[j.BenchName]
+		uniDist := distuv.Uniform{
+			Min: benchFuncParams.Min(),
+			Max: benchFuncParams.Max(),
+		}
 
-		// create a population with known params.
-		pop := newPopulation(j.BenchName, j.NP, dim)
+		jDELogger.Printf("running bench \"%s\" for %dD, maxFES: %d\n",
+			j.BenchName, dim, maxFES,
+		)
 
-		// set population seed.
-		pop.Seed = uint64(time.Now().UnixNano())
-		// initialise the population.
-		pop.Init()
+		funcStats.BenchResults = make([]stats.BenchRound, j.BenchMinIters)
 
-		j.evolve(maxFES, pop)
+		// rand.Seed(uint64(time.Now().UnixNano()))
+		// create and seed a source of preudo-randomness
+		src := rand.NewSource(uint64(rand.Int63()))
+
+		// track execution duration.
+		start := time.Now()
+
+		for iter := 0; iter < j.BenchMinIters; iter++ {
+			jDELogger.Printf("run: %d, bench: %s, %dD, started at %s",
+				iter, j.BenchName, dim, start,
+			)
+
+			funcStats.BenchResults[iter].Iteration = iter
+
+			uniDist.Src = src
+
+			// create a population with known params.
+			pop := newPopulation(j.BenchName, j.NP, dim)
+
+			// set population seed.
+			pop.Seed = uint64(time.Now().UnixNano())
+			// initialise the population.
+			pop.Init()
+			// jDELogger.Printf("%+v\n", pop.Population)
+
+			var bestResult float64
+
+			// the core.
+			for i := 0; i < fesPerIter; i++ {
+				r := j.evaluate(pop)
+
+				// distinguish the first or any of the subsequent iterations.
+				switch i {
+				case 0:
+					bestResult = r
+
+				default:
+					jDELogger.Printf("run: %d, bench: %s, %dD, iteration: %d/%d",
+						iter, j.BenchName, dim, i, fesPerIter-1,
+					)
+
+					// call evolve where jDE runs.
+					j.evolve(pop, &uniDist)
+
+					// take measurements of current population fitness.
+					r = j.evaluate(pop)
+
+					// save if better.
+					if r < bestResult {
+						bestResult = r
+					}
+				}
+
+				funcStats.BenchResults[iter].Results = append(
+					funcStats.BenchResults[iter].Results,
+					bestResult,
+				)
+				// this block makes sure we properly count func evaluations for
+				// the purpose of correctly comparable plot comparison. i.e.
+				// append the winning (current best) value NP-1 (the first best
+				// is already saved at this point) times to represent the fact
+				// that while evaluating (and comparing) other population
+				// individuals to the current best value is taking place in the
+				// background, the current best value itself is kept around and
+				// symbolically saved as the best of the Generation.
+				for x := 0; x < j.NP-1; x++ {
+					funcStats.BenchResults[iter].Results = append(
+						funcStats.BenchResults[iter].Results,
+						bestResult,
+					)
+				}
+			}
+		}
+
+		elapsed := time.Since(start)
+
+		jDELogger.Printf("completed: bench: %s, %dD, computing took %s\n",
+			j.BenchName, dim, elapsed,
+		)
+
+		// get mean vals.
+		dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, maxFES)
+		funcStats.MeanVals = dimXMean.MeanVals
+
+		jDEStatDimX.BenchFuncStats = append(
+			jDEStatDimX.BenchFuncStats, *funcStats,
+		)
+
+		jDEStats = append(jDEStats, *jDEStatDimX)
+
+		jDEMeans.BenchMeans = append(jDEMeans.BenchMeans, *dimXMean)
 	}
+
+	sort.Sort(jDEMeans)
+
+	j.chAlgoMeans <- jDEMeans
+	j.ch <- jDEStats
+}
+
+// evaluate evaluates the fitness of current population.
+func (j *JDE) evaluate(pop *Population) float64 {
+	f := bench.Functions[pop.Problem]
+
+	bestIndividual := pop.Population[0].CurX
+	bestSolution := f(bestIndividual)
+
+	for _, v := range pop.Population {
+		if solution := f(v.CurX); solution < bestSolution {
+			bestSolution = solution
+		}
+	}
+
+	return bestSolution
 }
 
 // evolve evolves a population by running the jDE (self-adapting Differential
 // Evolution) algorithm on the passed population until termination conditions
 // are met.
-func (j *JDE) evolve(maxFES int, pop *Population) {}
+// nolint: gocognit
+func (j *JDE) evolve(pop *Population, uniDist *distuv.Uniform) {
+	popCount := len(pop.Population)
+
+	for i, currentIndividual := range pop.Population {
+		idcs := make([]int, 0, popCount-1)
+
+		// gather indices.
+		for k := 0; k < popCount; k++ {
+			if k != i {
+				idcs = append(idcs, k)
+			}
+		}
+
+		// randomly choose 3 of those idcs.
+		selectedIdcs := make([]int, 0)
+
+		selectedA := false
+		selectedB := false
+		selectedC := false
+
+		for !selectedA {
+			candidateA := rand.Intn(len(idcs)) % len(idcs)
+
+			if candidateA != i {
+				selectedIdcs = append(selectedIdcs, candidateA)
+				selectedA = true
+			}
+		}
+
+		for !selectedB {
+			a := selectedIdcs[0]
+			candidateB := rand.Intn(len(idcs)) % len(idcs)
+
+			if candidateB != i && candidateB != a {
+				selectedIdcs = append(selectedIdcs, candidateB)
+				selectedB = true
+			}
+		}
+
+		for !selectedC {
+			a := selectedIdcs[0]
+			b := selectedIdcs[1]
+			candidateC := rand.Intn(len(idcs)) % len(idcs)
+
+			if candidateC != i && candidateC != a && candidateC != b {
+				selectedIdcs = append(selectedIdcs, candidateC)
+				selectedC = true
+			}
+		}
+
+		// selected contains the selected population individuals.
+		selected := make([]PopulationIndividual, 0)
+
+		// select individuals for rand/1/bin
+		for _, idx := range selectedIdcs {
+			for k := 0; k < popCount; k++ {
+				if k == idx {
+					selected = append(selected, pop.Population[idx])
+				}
+			}
+		}
+
+		mutant := make([]float64, pop.Dimen)
+
+		// mutate.
+		for k := 0; k < pop.Dimen; k++ {
+			// mutant = a + mut * (b - c)
+			mutant[k] = selected[0].CurX[k] + (j.F * (selected[1].CurX[k] - selected[2].CurX[k]))
+
+			// clip values to <0;1>.
+			switch {
+			case mutant[k] < 0:
+				mutant[k] = 0
+
+			case mutant[k] > 1:
+				mutant[k] = 1
+			}
+		}
+
+		crossPoints := make([]bool, pop.Dimen)
+
+		// prepare crossover points (binomial crossover).
+		for k := 0; k < pop.Dimen; k++ {
+			if v := uniDist.Rand(); v < j.CR {
+				crossPoints[k] = true
+			} else {
+				crossPoints[k] = false
+			}
+		}
+
+		trial := make([]float64, pop.Dimen)
+
+		// recombine using crossover points.
+		for k := 0; k < pop.Dimen; k++ {
+			if crossPoints[k] {
+				trial[k] = currentIndividual.CurX[k]
+			}
+		}
+
+		f := bench.Functions[pop.Problem]
+		currentFitness := f(currentIndividual.CurX)
+		trialFitness := f(trial)
+
+		// replace if better.
+		if trialFitness < currentFitness {
+			pop.Population[i].CurX = trial
+		}
+	}
+}
 
 // NewjDE returns a pointer to a new, uninitialised jDE instance.
 func NewjDE() *JDE {

algo/de/population.go

@@ -97,16 +97,29 @@ func (p *Population) SetV(n int, nuV DecisionVector) {}
 
 // Init initialises all individuals to random values.
 func (p *Population) Init() {
-	uniform := distuv.Uniform{}
+	benchFuncParams := bench.FunctionParams[p.Problem]
+
+	uniform := distuv.Uniform{
+		Min: benchFuncParams.Min(),
+		Max: benchFuncParams.Max(),
+	}
 	uniform.Src = rand.NewSource(p.Seed)
 
-	for _, v := range p.Population {
+	jDELogger.Printf("population initialisation - popCount: %d, seed: %d\n",
+		len(p.Population), p.Seed,
+	)
+
+	for i, v := range p.Population {
 		v.CurX = make([]float64, p.Dimen)
 
-		for i := 0; i < p.Dimen; i++ {
-			v.CurX[i] = uniform.Rand()
+		for j := 0; j < p.Dimen; j++ {
+			v.CurX[j] = uniform.Rand()
 		}
+
+		p.Population[i] = v
 	}
+
+	jDELogger.Println("population initialised")
 }
 
 // Reinit reinitialises all individuals.
@@ -144,7 +157,7 @@ func newPopulation(benchProblem string, np, dimen int) *Population {
 	p.Dimen = dimen
 
 	// pre-alloc.
-	p.Population = make([]PopulationIndividual, 0, np)
+	p.Population = make([]PopulationIndividual, np)
 
 	return p
 }

run.go

@@ -24,7 +24,8 @@ func run() {
 
 	if *generate {
 		// atm we're only doing Random search and SHC
-		algoCount := 2
+		// algoCount := 2
+		algoCount := 1
 
 		var wg sync.WaitGroup
 
@@ -32,15 +33,16 @@ func run() {
 
 		var m sync.Mutex
 
-		go algo.DoRandomSearch(&wg, &m)
-		go algo.DoStochasticHillClimbing(&wg, &m)
-		// go algo.DoStochasticHillClimbing100Neigh(&wg, &m)
+		// go algo.DoRandomSearch(&wg, &m)
+		// go algo.DoStochasticHillClimbing(&wg, &m)
+		// // go algo.DoStochasticHillClimbing100Neigh(&wg, &m)
+		go algo.DojDE(&wg, &m)
 
 		wg.Wait()
 
-		pL, benchCount := algo.PrepComparisonOfMeans(&wg)
+		// pL, benchCount := algo.PrepComparisonOfMeans(&wg)
 
-		report.SaveComparisonOfMeans(*pL, benchCount)
+		// report.SaveComparisonOfMeans(*pL, benchCount)
 		report.SaveTexAllPics()
 		report.SaveTexAllTables()
 	}