go: implement RandomSearch (wip)

algo/randomSearch.go

@@ -5,7 +5,11 @@ package algo
 
 import (
 	"fmt"
+	"log"
 	"os"
+
+	"git.dotya.ml/wanderer/math-optim/bench"
+	"gonum.org/v1/gonum/stat/distuv"
 )
 
 func getRandomSearchLogPrefix() string {
@@ -21,3 +25,92 @@ func printRandomSearch(input string) error {
 
 	return err
 }
+
+func genValsRandomSearch(dimens uint, vals []float64, uniform *distuv.Uniform) {
+	for i := uint(0); i < dimens; i++ {
+		// using Uniform.Rand from gonum's stat/distuv package.
+		// https://pkg.go.dev/gonum.org/v1/gonum/stat/distuv#Uniform.Rand
+		// boundaries are already set at this point.
+		vals[i] = uniform.Rand()
+	}
+}
+
+// singleRandomSearch performs a single iteration of the 'RandomSearch' algorithm.
+// it takes a couple of arguments:
+// * dimens uint: number of dimensions of the objective function
+// * f string: name of the bench func to optimise (see bench/functions)
+// * min/max float64: the upper/lower limit of the uniform distribution span,
+// which is relevant to the objective function.
+// nolint
+func singleRandomSearch(dimens uint, f string, min, max float64) ([]float64, float64) {
+	var vals []float64
+
+	var res float64
+
+	// create a continuous uniform distribution representation within min/max bounds
+	uniformDist := distuv.Uniform{Min: min, Max: max}
+
+	genValsRandomSearch(dimens, vals, &uniformDist)
+
+	// result of the benchmarking function computation over vals
+	switch f {
+	// Schwefel
+	case bench.Functions[0]:
+		res = bench.Schwefel(vals)
+	// "DeJong1st"
+	case bench.Functions[1]:
+		res = bench.DeJong1st(vals)
+	// "DeJong2nd"
+	case bench.Functions[2]:
+		res = bench.DeJong2nd(vals)
+	}
+
+	return vals, res
+}
+
+func RandomSearch(fes uint) {
+	if fes == 0 {
+		log.Fatalln(" random search: fes set to 0, bailing")
+	}
+
+	var valsSchwefel []Values
+
+	var valsDeJong1st []Values
+
+	var valsDeJong2nd []Values
+
+	var resultsSchwefel Values
+
+	var resultsDeJong1st Values
+
+	var resultsDeJong2nd Values
+
+	// iterations needed to establish a minimal viable statistical baseline
+	minIters := 30
+
+	for _, dimens := range bench.Dimensions {
+		for j := 0; j < minIters; j++ {
+			// run Schwefel.
+			v, r := singleRandomSearch(dimens+1, "Schwefel", bench.SchwefelParams.Min(), bench.SchwefelParams.Max())
+			valsSchwefel = append(valsSchwefel, v)
+			resultsSchwefel = append(resultsSchwefel, r)
+
+			// run De Jong 1st.
+			vDJ1, rDJ1 := singleRandomSearch(dimens+1, "DeJong1st", bench.DeJong1Params.Min(), bench.DeJong1Params.Max())
+			valsDeJong1st = append(valsDeJong1st, vDJ1)
+			resultsDeJong1st = append(resultsDeJong1st, rDJ1)
+			// run De Jong 2nd.
+			vDJ2, rDJ2 := singleRandomSearch(dimens+1, "DeJong2nd", bench.DeJong2Params.Min(), bench.DeJong2Params.Max())
+			valsDeJong2nd = append(valsDeJong2nd, vDJ2)
+			resultsDeJong2nd = append(resultsDeJong2nd, rDJ2)
+		}
+	}
+
+	fmt.Fprintln(os.Stderr, "vals schw:", valsSchwefel)
+	fmt.Fprintln(os.Stderr, "vals dj1", valsDeJong1st)
+	fmt.Fprintln(os.Stderr, "vals dj2", valsDeJong2nd)
+
+	fmt.Fprintln(os.Stderr, "res schw:", resultsSchwefel)
+	fmt.Fprintln(os.Stderr, "res dj1", resultsDeJong1st)
+	fmt.Fprintln(os.Stderr, "res dj2", resultsDeJong2nd)
+}

bench/bench.go

@@ -16,7 +16,7 @@ const (
 
 var (
 	// Dimensions to compute for (spatial complexity..?).
-	Dimensions = []int{5, 10, 20}
+	Dimensions = []uint{5, 10, 20}
 
 	SchwefelParams = funcParams{min: -500.0, max: 500.0}
 	DeJong1Params  = funcParams{min: -5.0, max: 5.0}
@@ -24,11 +24,11 @@ var (
 )
 
 // Min returns the non-exported "min" field of a funcParams struct.
-func (*funcParams) Min(self *funcParams) float64 {
+func (f *funcParams) Min() float64 {
-	return self.max
+	return f.min
 }
 
 // Max returns the non-exported 'max' field of a funcParams struct.
-func (*funcParams) Max(self *funcParams) float64 {
+func (f *funcParams) Max() float64 {
-	return self.max
+	return f.max
 }

bench/functions.go

@@ -5,6 +5,8 @@ package bench
 
 import "math"
 
+var Functions = []string{"Schwefel", "De Jong 1st", "De Jong 2nd"}
+
 // Schwefel computes a value of the Schwefel function for x.
 func Schwefel(x []float64) float64 {
 	var res float64

flake.nix

@@ -80,7 +80,7 @@
 
           modSha256 = lib.fakeSha256;
           # dont't forget to update vendorSha256 whenever go.mod or go.sum change
-          vendorSha256 = "sha256-pQpattmS9VmO3ZIQUFn66az8GSmB4IvYhTTCFn6SUmo=";
+          vendorSha256 = "sha256-NpZWn2yASg7HXr4sG849QSgUetouj8qjwS+p5jhlum0=";
 
           # In 'nix develop', we don't need a copy of the source tree
           # in the Nix store.

go.mod

@@ -1,3 +1,7 @@
 module git.dotya.ml/wanderer/math-optim
 
 go 1.18
+
+require gonum.org/v1/gonum v0.11.0
+
+require golang.org/x/exp v0.0.0-20191002040644-a1355ae1e2c3 // indirect

go.sum

@@ -0,0 +1,23 @@
+dmitri.shuralyov.com/gpu/mtl v0.0.0-20190408044501-666a987793e9/go.mod h1:H6x//7gZCb22OMCxBHrMx7a5I7Hp++hsVxbQ4BYO7hU=
+github.com/BurntSushi/xgb v0.0.0-20160522181843-27f122750802/go.mod h1:IVnqGOEym/WlBOVXweHU+Q+/VP0lqqI8lqeDx9IjBqo=
+github.com/go-gl/glfw v0.0.0-20190409004039-e6da0acd62b1/go.mod h1:vR7hzQXu2zJy9AVAgeJqvqgH9Q5CA+iKCZ2gyEVpxRU=
+golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACkg1iLfiJU5Ep61QUkGW8qpdssI0+w=
+golang.org/x/crypto v0.0.0-20190510104115-cbcb75029529/go.mod h1:yigFU9vqHzYiE8UmvKecakEJjdnWj3jj499lnFckfCI=
+golang.org/x/exp v0.0.0-20190306152737-a1d7652674e8/go.mod h1:CJ0aWSM057203Lf6IL+f9T1iT9GByDxfZKAQTCR3kQA=
+golang.org/x/exp v0.0.0-20191002040644-a1355ae1e2c3 h1:n9HxLrNxWWtEb1cA950nuEEj3QnKbtsCJ6KjcgisNUs=
+golang.org/x/exp v0.0.0-20191002040644-a1355ae1e2c3/go.mod h1:NOZ3BPKG0ec/BKJQgnvsSFpcKLM5xXVWnvZS97DWHgE=
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