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

package algo

import (
	"sync"

	"git.dotya.ml/wanderer/math-optim/bench"
	"git.dotya.ml/wanderer/math-optim/report"
	"git.dotya.ml/wanderer/math-optim/stats"
)

// Values type is just a fancy named []float64 that will allow us to define
// methods over it.
type Values []float64

// mu protects access to meanStats.
var mu sync.Mutex

var meanStats = &stats.MeanStats{}

// DoRandomSearch executes a search using the 'Random search' method.
func DoRandomSearch(wg *sync.WaitGroup, m *sync.Mutex) {
	defer wg.Done()

	printRandomSearch("starting...")

	// funcCount is the number of bench functions available.
	funcCount := len(bench.Functions)
	// stats for the current algo (RandomSearch).
	algoStats := make([][]stats.Stats, funcCount)
	// ch serves as a way to get the actual computed output.
	ch := make(chan []stats.Stats, funcCount)

	for i := range algoStats {
		// ng y'all.
		go RandomSearchNG(10000, 30, bench.Dimensions, bench.FuncNames[i], ch)
	}

	// get results.
	for i := range algoStats {
		s := <-ch

		algoStats[i] = s
	}

	pCh := make(chan report.PicList, funcCount*len(bench.Dimensions))
	pMeanCh := make(chan report.PicList, funcCount*len(bench.Dimensions))

	for i := range algoStats {
		go plotAllDims(algoStats[i], "plot", ".svg", pCh, pMeanCh)
	}

	pLs := []report.PicList{}
	pLsMean := []report.PicList{}

	for range algoStats {
		pL := <-pCh
		pLMean := <-pMeanCh

		pLs = append(pLs, pL)
		pLsMean = append(pLsMean, pLMean)
	}

	algoName := "Random Search"

	// protect access to shared data.
	m.Lock()
	report.SavePicsToFile(pLs, pLsMean, algoName)

	stats.SaveTable(algoName, algoStats)
	m.Unlock()
}

// DoStochasticHillClimbing performs a search using the 'Stochastic Hill
// Climbing' method.
func DoStochasticHillClimbing(wg *sync.WaitGroup, m *sync.Mutex) {
	defer wg.Done()

	printSHC("starting...")

	// funcCount is the number of bench functions available.
	funcCount := len(bench.Functions)
	// stats for the current algo (StochasticHillClimber).
	algoStats := make([][]stats.Stats, funcCount)
	// ch serves as a way to get the actual computed output.
	ch := make(chan []stats.Stats, funcCount)

	for i := range algoStats {
		go HillClimb(10000, 30, bench.Dimensions, bench.FuncNames[i], ch)
	}

	// get results.
	for i := range algoStats {
		s := <-ch

		algoStats[i] = s
	}

	pCh := make(chan report.PicList, funcCount*len(bench.Dimensions))
	pMeanCh := make(chan report.PicList, funcCount*len(bench.Dimensions))

	for _, algoStat := range algoStats {
		go plotAllDims(algoStat, "plot", ".svg", pCh, pMeanCh)
	}

	pLs := []report.PicList{}
	pLsMean := []report.PicList{}

	for range algoStats {
		pL := <-pCh
		pLMean := <-pMeanCh

		pLs = append(pLs, pL)
		pLsMean = append(pLsMean, pLMean)
	}

	algoName := "Stochastic Hill Climbing"

	// protect access to shared data.
	m.Lock()
	report.SavePicsToFile(pLs, pLsMean, algoName)

	stats.SaveTable(algoName, algoStats)
	m.Unlock()
}

func newValues() *Values {
	var v Values
	return &v
}