fix(go): collect and plot algo stats comparably

...i.e. based on FES, not Generations.

algo/plot.go

@@ -55,7 +55,7 @@ func PlotMeanValsMulti(
 		iterations, bench, dimens,
 	)
 
-	p.X.Label.Text = xAxisLabel
+	p.X.Label.Text = "Objective func. evaluations"
 	p.X.Label.TextStyle.Font.Variant = preferredFont
 	p.X.Label.TextStyle.Font.Weight = 1 // Medium
 	p.X.Tick.Label.Font.Variant = preferredFont

algo/stochasticHillClimbing.go

@@ -157,6 +157,7 @@ func singleHillClimb(
 // HillClimb performs 30 iterations of SHC (30 singleHillClimb func calls
 // internally) to establish a semi-relevant statistical baseline, and reports
 // the results of the computation.
+// nolint: gocognit
 func HillClimb(
 	maxFES, benchMinIters, neighbours int,
 	theD []int,
@@ -207,10 +208,9 @@ func HillClimb(
 			Algo:       "Stochastic Hill Climbing",
 			Dimens:     dimens,
 			Iterations: minIters,
-			// Generations for Stochastic Hill Climbing may vary based on both
+			// this is subject to change, see note on Stats struct in pkg
-			// the maxFES and bench.Neighbourhood, since that is how we arrive
+			// stats.
-			// at the value of fesPerIter.
+			Generations: fes,
-			Generations: fesPerIter,
 		}
 		funcStats := &stats.FuncStats{BenchName: benchFunc}
 		benchFuncParams := bench.FunctionParams[benchFunc]
@@ -218,7 +218,7 @@ func HillClimb(
 			Bench:       benchFunc,
 			Dimens:      dimens,
 			Iterations:  minIters,
-			Generations: fesPerIter,
+			Generations: fes,
 			Neighbours:  neighbours,
 		}
 		uniDist := distuv.Uniform{
@@ -293,6 +293,21 @@ func HillClimb(
 					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 neighbours-1 (the
+				// first best is already saved at this point) times to
+				// represent the fact that while evaluating the neighbours 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 < neighbours-1; x++ {
+					funcStats.BenchResults[iter].Results = append(
+						funcStats.BenchResults[iter].Results,
+						bestResult,
+					)
+				}
 			}
 		}
 
@@ -303,7 +318,7 @@ func HillClimb(
 		)
 
 		// get mean vals.
-		dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, fesPerIter)
+		dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, fes)
 		// save to funcStats, too.
 		funcStats.MeanVals = dimXMean.MeanVals
 

stats/stats.go

@@ -63,7 +63,10 @@ type Stats struct {
 	Dimens         int
 	BenchFuncStats []FuncStats
 	Iterations     int
-	Generations    int
+	// this should perhaps be named FES as that is a smarter thing to be
+	// comparing than algo generations, which can vary based on the type of
+	// algo, number of neighbours, etc..
+	Generations int
 }
 
 // Len implements the sort.Interface.