// 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"
	"git.dotya.ml/wanderer/math-optim/stats"
	"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 func([]float64) float64: bench func to execute (see Functions map in
//   bench/functions.go)
// * min/max float64: the upper/lower limit of the uniform distribution span,
// which is relevant to the objective function.
func singleRandomSearch(dimens uint, f func([]float64) float64, min, max float64) ([]float64, float64) {
	vals := make([]float64, dimens)

	// create a continuous uniform distribution representation within min/max bounds
	uniformDist := distuv.Uniform{Min: min, Max: max}

	genValsRandomSearch(dimens, vals, &uniformDist)

	// result of the bench function.
	res := f(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")
	}

	picPrefix := "res/"

	var valsSchwefel []Values

	var valsDeJong1st []Values

	var valsDeJong2nd []Values

	var bestResultsSchwefel []float64

	var bestResultsDeJong1st []float64

	var bestResultsDeJong2nd []float64

	// randomSearchStats stores Stats for all the function/dimension
	// combinations for this algo.
	var randomSearchStats []stats.Stats

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

	//nolint: staticcheck
	for _, dimens := range bench.Dimensions {
		randomSearchStatXD := &stats.Stats{
			Algo:        "Random Search",
			Dimens:      int(dimens),
			Iterations:  minIters,
			Generations: bench.MaxFES,
		}
		schwefelStats := &stats.FuncStats{BenchName: "Schwefel"}
		deJong1stStats := &stats.FuncStats{BenchName: "De Jong 1st"}
		deJong2ndStats := &stats.FuncStats{BenchName: "De Jong 2nd"}

		randomSearchStatXD.Dimens = int(dimens)

		var resultsSchwefel Values

		var resultsDeJong1st Values

		var resultsDeJong2nd Values

		log.Println("dimens:", dimens)

		// current iteration is named iter
		for iter := 0; iter < minIters; iter++ {
			if iter == 29 {
				log.Println("iter:", iter, "(last for dimen)")
			}

			var bestResult float64

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

				// collect results after each call to bench function.
				schwefelStats.Solution = append(
					schwefelStats.Solution,
					stats.BenchRound{Iteration: iter, Results: resultsSchwefel},
				)

				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)
			}

			// add schwefel results (for the current dimension) to the stats
			// slice.
			randomSearchStatXD.BenchFuncStats = append(
				randomSearchStatXD.BenchFuncStats,
				*schwefelStats,
			)

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

				// collect results after each call to bench function.
				deJong1stStats.Solution = append(
					deJong1stStats.Solution,
					stats.BenchRound{Iteration: iter, Results: resultsDeJong1st},
				)

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

				bestResultsDeJong1st = append(bestResultsDeJong1st, bestResult)
			}

			// add De Jong 1st results (for the current dimension) to the stats
			// slice.
			randomSearchStatXD.BenchFuncStats = append(
				randomSearchStatXD.BenchFuncStats,
				*deJong1stStats,
			)

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

				// collect results after each call to bench function.
				deJong2ndStats.Solution = append(
					deJong2ndStats.Solution,
					stats.BenchRound{Iteration: iter, Results: resultsDeJong2nd},
				)

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

				bestResultsDeJong2nd = append(bestResultsDeJong2nd, bestResult)
			}

			// add De Jong 2nd results (for the current dimension) to the stats
			// slice.
			randomSearchStatXD.BenchFuncStats = append(
				randomSearchStatXD.BenchFuncStats,
				*deJong2ndStats,
			)

			// append the stats slice for the current dimension to the (per
			// algo) stats slice.
			randomSearchStats = append(randomSearchStats, *randomSearchStatXD)

			if iter == 29 {
				log.Println("do plot")
				doCombined30Plot(
					schwefelStats,
					dimens,
					picPrefix+"schw-"+fmt.Sprint(dimens)+"D.png",
				)
				doCombined30Plot(deJong1stStats,
					dimens,
					picPrefix+"dj1-"+fmt.Sprint(dimens)+"D.png",
				)
				doCombined30Plot(deJong2ndStats,
					dimens,
					picPrefix+"dj2-"+fmt.Sprint(dimens)+"D.png",
				)
			}
		}
	}
}