math-optim/algo/randomSearch.go

// Copyright 2022 wanderer <a_mirre at utb dot cz>
// SPDX-License-Identifier: GPL-3.0-or-later

package algo

import (
	"fmt"
	"log"
	"os"

	"git.dotya.ml/wanderer/math-optim/bench"
	"gonum.org/v1/gonum/stat/distuv"
)

func getRandomSearchLogPrefix() string {
	return " ***  random search:"
}

func fmtRandomSearchOut(input string) string {
	return getRandomSearchLogPrefix() + " " + input
}

func printRandomSearch(input string) {
	if _, err := fmt.Fprintln(os.Stderr, fmtRandomSearchOut(input)); err != nil {
		fmt.Fprintf(
			os.Stdout,
			getRandomSearchLogPrefix(),
			"error while printing to stderr: %q\n * original message was: %q",
			err, input,
		)
	}
}

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.
func singleRandomSearch(dimens uint, f string, min, max float64) ([]float64, float64) {
	vals := make([]float64, dimens)

	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
}

// TODO(me): split this up
// nolint: gocognit
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

	var bestResultsSchwefel []float64

	var bestResultsDeJong1st []float64

	var bestResultsDeJong2nd []float64

	// iterations needed to establish a minimal viable statistical baseline
	minIters := 30

	for _, dimens := range bench.Dimensions {
		for i := 0; i < minIters; i++ {
			var bestResult float64

			for j := 0; j < int(fes); j++ {
				// run Schwefel.
				v, r := singleRandomSearch(
					dimens,
					bench.Functions[0],
					bench.SchwefelParams.Min(),
					bench.SchwefelParams.Max(),
				)
				valsSchwefel = append(valsSchwefel, v)
				resultsSchwefel = append(resultsSchwefel, r)

				switch j {
				// first iteration
				case 0:
					bestResult = r
				default:
					// any other than the first iteration and a better solution
					if r < bestResult {
						bestResult = r
					}
				}

				bestResultsSchwefel = append(bestResultsSchwefel, bestResult)
			}

			for k := 0; k < int(fes); k++ {
				// run De Jong 1st.
				v, r := singleRandomSearch(
					dimens,
					bench.Functions[1],
					bench.DeJong1Params.Min(),
					bench.DeJong1Params.Max(),
				)
				valsDeJong1st = append(valsDeJong1st, v)
				resultsDeJong1st = append(resultsDeJong1st, r)

				// first iteration or better solution
				if k == 0 || r < bestResult {
					bestResult = r
				}

				bestResultsDeJong1st = append(bestResultsDeJong1st, bestResult)
			}

			for l := 0; l < int(fes); l++ {
				// run De Jong 2nd.
				v, r := singleRandomSearch(
					dimens,
					bench.Functions[2],
					bench.DeJong2Params.Min(),
					bench.DeJong2Params.Max(),
				)
				valsDeJong2nd = append(valsDeJong2nd, v)
				resultsDeJong2nd = append(resultsDeJong2nd, r)

				// first iteration or better solution
				if l == 0 || r < bestResult {
					bestResult = r
				}

				bestResultsDeJong2nd = append(bestResultsDeJong2nd, bestResult)
			}
		}
	}

	fmt.Fprintln(os.Stderr, "vals schw len", len(valsSchwefel))
	fmt.Fprintln(os.Stderr, "vals dj1 len", len(valsDeJong1st))
	fmt.Fprintln(os.Stderr, "vals dj2 len", len(valsDeJong2nd))

	fmt.Fprintln(os.Stderr, "res schw len", len(resultsSchwefel))
	fmt.Fprintln(os.Stderr, "res dj1 len", len(resultsDeJong1st))
	fmt.Fprintln(os.Stderr, "res dj2 len", len(resultsDeJong2nd))

	fmt.Fprintln(os.Stderr, "best res schw len", len(bestResultsSchwefel))
	fmt.Fprintln(os.Stderr, "best res dj1 len", len(bestResultsDeJong1st))
	fmt.Fprintln(os.Stderr, "best res dj2 len", len(bestResultsDeJong2nd))
}