128 lines
2.9 KiB
Go
128 lines
2.9 KiB
Go
// Copyright 2022 wanderer <a_mirre at utb dot cz>
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// SPDX-License-Identifier: GPL-3.0-or-later
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package algo
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import (
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"sync"
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"git.dotya.ml/wanderer/math-optim/bench"
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"git.dotya.ml/wanderer/math-optim/report"
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"git.dotya.ml/wanderer/math-optim/stats"
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)
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// Values type is just a fancy named []float64 that will allow us to define
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// methods over it.
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type Values []float64
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// DoRandomSearch executes a search using the 'Random search' method.
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func DoRandomSearch(wg *sync.WaitGroup, m *sync.Mutex) {
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defer wg.Done()
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printRandomSearch("starting...")
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// funcCount is the number of bench functions available.
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funcCount := len(bench.Functions)
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// stats for the current algo (RandomSearch).
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algoStats := make([][]stats.Stats, funcCount)
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// ch serves as a way to get the actual computed output.
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ch := make(chan []stats.Stats, funcCount)
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for i := range algoStats {
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// ng y'all.
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go RandomSearchNG(10000, 30, bench.Dimensions, bench.FuncNames[i], ch)
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}
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// get results.
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for i := range algoStats {
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s := <-ch
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algoStats[i] = s
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}
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pCh := make(chan report.PicList, funcCount*len(bench.Dimensions))
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pMeanCh := make(chan report.PicList, funcCount*len(bench.Dimensions))
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for i := range algoStats {
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go plotAllDims(algoStats[i], "plot", ".svg", pCh, pMeanCh)
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}
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pLs := []report.PicList{}
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pLsMean := []report.PicList{}
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for range algoStats {
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pL := <-pCh
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pLMean := <-pMeanCh
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pLs = append(pLs, pL)
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pLsMean = append(pLsMean, pLMean)
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}
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algoName := "Random Search"
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// protect access to shared data.
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m.Lock()
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report.SavePicsToFile(pLs, pLsMean, algoName)
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stats.SaveTable(algoName, algoStats)
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m.Unlock()
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}
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// DoStochasticHillClimbing performs a search using the 'Stochastic Hill
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// Climbing' method.
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func DoStochasticHillClimbing(wg *sync.WaitGroup, m *sync.Mutex) {
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defer wg.Done()
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printSHC("starting...")
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// funcCount is the number of bench functions available.
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funcCount := len(bench.Functions)
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// stats for the current algo (StochasticHillClimber).
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algoStats := make([][]stats.Stats, funcCount)
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// ch serves as a way to get the actual computed output.
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ch := make(chan []stats.Stats, funcCount)
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for i := range algoStats {
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go HillClimb(10000, 30, bench.Dimensions, bench.FuncNames[i], ch)
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}
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// get results.
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for i := range algoStats {
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s := <-ch
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algoStats[i] = s
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}
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pCh := make(chan report.PicList, funcCount*len(bench.Dimensions))
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pMeanCh := make(chan report.PicList, funcCount*len(bench.Dimensions))
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for _, algoStat := range algoStats {
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go plotAllDims(algoStat, "plot", ".svg", pCh, pMeanCh)
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}
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pLs := []report.PicList{}
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pLsMean := []report.PicList{}
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for range algoStats {
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pL := <-pCh
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pLMean := <-pMeanCh
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pLs = append(pLs, pL)
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pLsMean = append(pLsMean, pLMean)
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}
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algoName := "Stochastic Hill Climbing"
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// protect access to shared data.
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m.Lock()
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report.SavePicsToFile(pLs, pLsMean, algoName)
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stats.SaveTable(algoName, algoStats)
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m.Unlock()
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}
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func newValues() *Values {
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var v Values
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return &v
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}
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