go: add a way to print statistic table

algo/algo.go

@@ -5,6 +5,9 @@
 
 import (
 	"sync"
+
+	"git.dotya.ml/wanderer/math-optim/bench"
+	"git.dotya.ml/wanderer/math-optim/stats"
 )
 
 // Values type is just a fancy named []float64 that will allow us to define
@@ -19,14 +22,21 @@ func DoRandomSearch(wg *sync.WaitGroup) {
 
 	printRandomSearch("starting...")
 
-	// ng y'all.
-	schw := RandomSearchNG(10000, 30, []int{5, 10, 20}, "Schwefel")
-	djg1 := RandomSearchNG(10000, 30, []int{5, 10, 20}, "De Jong 1st")
-	djg2 := RandomSearchNG(10000, 30, []int{5, 10, 20}, "De Jong 2nd")
+	// funcCount is the number of bench functions available.
+	funcCount := len(bench.Functions)
+	// stats for the current algo (RandomSearch).
+	algoStats := make([][]stats.Stats, funcCount)
 
-	plotAllDims(schw, "plot", ".svg")
-	plotAllDims(djg1, "plot", ".svg")
-	plotAllDims(djg2, "plot", ".svg")
+	// ng y'all.
+	algoStats[0] = RandomSearchNG(10000, 30, []int{5, 10, 20}, "Schwefel")
+	algoStats[1] = RandomSearchNG(10000, 30, []int{5, 10, 20}, "De Jong 1st")
+	algoStats[2] = RandomSearchNG(10000, 30, []int{5, 10, 20}, "De Jong 2nd")
+
+	for i := range algoStats {
+		plotAllDims(algoStats[i], "plot", ".svg")
+	}
+
+	stats.PrintStatisticTable(algoStats)
 }
 
 // DoStochasticHillClimbing performs a search using the 'Stochastic Hill

stats/table.go

@@ -0,0 +1,96 @@
+// Copyright 2022 wanderer <a_mirre at utb dot cz>
+// SPDX-License-Identifier: GPL-3.0-or-later
+
+package stats
+
+import (
+	"fmt"
+	"os"
+
+	"gonum.org/v1/gonum/floats"
+	"gonum.org/v1/gonum/stat"
+)
+
+type statsHdr struct {
+	Algo          string
+	BenchFuncName string
+	Dimens        int
+	Generations   int
+	Iterations    int
+}
+
+type statsRow struct {
+	Min    float64
+	Max    float64
+	Mean   float64
+	Median float64
+	StdDev float64
+}
+
+// PrintStatisticTable prints to console computed statistics for current algo.
+func PrintStatisticTable(algoStats [][]Stats) {
+	fmt.Fprintln(os.Stderr, "printing statistic table data (min, max, mean, median, stddev)")
+
+	for _, singleFunc := range algoStats {
+		statsSingleFunc(singleFunc)
+	}
+}
+
+// statsSingleFunc computes statistics out of results of  a single bench func.
+func statsSingleFunc(singleFuncStats []Stats) {
+	var (
+		// hdr is the table header as determined based on the data being dealt with.
+		hdr statsHdr
+		// row contains the data of the statistic properties being tracked.
+		row statsRow
+	)
+
+	for _, s := range singleFuncStats {
+		for _, dim := range s.BenchFuncStats {
+			hdr = makeTableHdr(
+				s.Algo,
+				s.BenchFuncStats[0].BenchName,
+				s.Dimens,
+				s.Generations,
+				s.Iterations,
+			)
+			fmt.Fprintln(os.Stderr, hdr)
+
+			// collect the best.
+			var best []float64
+
+			for _, iter := range dim.Solution {
+				last := s.Generations - 1
+
+				best = append(best, iter.Results[last])
+			}
+
+			row.Min = floats.Min(best)
+			row.Max = floats.Max(best)
+			row.Mean = stat.Mean(best, nil)
+			row.Median = stat.Mean(best, nil)
+			row.StdDev = stat.StdDev(best, nil)
+
+			fmt.Fprintln(os.Stderr, row)
+		}
+	}
+}
+
+func makeTableHdr(
+	algo, benchFuncName string,
+	dimens, generations, iterations int,
+) statsHdr {
+	hdr := newStatsHdr()
+
+	hdr.Algo = algo
+	hdr.BenchFuncName = benchFuncName
+	hdr.Dimens = dimens
+	hdr.Generations = generations
+	hdr.Iterations = iterations
+
+	return *hdr
+}
+
+func newStatsHdr() *statsHdr {
+	return &statsHdr{}
+}