go: impl. Get{Best,Worst}Idx Population methods

algo/de/population.go

@@ -4,6 +4,7 @@
 package de
 
 import (
+	"git.dotya.ml/wanderer/math-optim/bench"
 	"golang.org/x/exp/rand"
 
 	"gonum.org/v1/gonum/stat/distuv"
@@ -52,10 +53,47 @@ type Population struct {
 
 // GetIndividal returns a reference to individual at position n.
 func (p *Population) GetIndividual(n uint) *PopulationIndividual { return &PopulationIndividual{} }
-func (p *Population) GetBestIdx() int                            { return 0 }
-func (p *Population) GetWorstIdx() int                           { return 0 }
-func (p *Population) SetX(n int, nuX DecisionVector)             {}
-func (p *Population) SetV(n int, nuV DecisionVector)             {}
+
+// GetBestIdx returns the index of the best population individual.
+func (p *Population) GetBestIdx() int {
+	f := bench.Functions[p.Problem]
+
+	bestIndividual := 0
+	// the first one is the best one.
+	bestVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current < bestVal {
+			bestIndividual = i
+		}
+	}
+
+	return bestIndividual
+}
+
+// GetWorstIdx returns the index of the worst population individual.
+func (p *Population) GetWorstIdx() int {
+	f := bench.Functions[p.Problem]
+
+	worstIndividual := 0
+	// the first one is the worst one.
+	worstVal := f(p.Population[0].CurX)
+
+	for i, v := range p.Population {
+		current := f(v.CurX)
+
+		if current > worstVal {
+			worstIndividual = i
+		}
+	}
+
+	return worstIndividual
+}
+
+func (p *Population) SetX(n int, nuX DecisionVector) {}
+func (p *Population) SetV(n int, nuV DecisionVector) {}
 
 // Init initialises all individuals to random values.
 func (p *Population) Init() {