GeneticAlgorithm.ps1
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<#
script for learning genetics algorithms # locus [łac.], genet. pozycja w chromosomie zajmowana przez dany gen; GA tutorial - https://www.tutorialspoint.com/genetic_algorithms/index.htm PowerShell Multithreading: A Deep Dive - https://adamtheautomator.com/powershell-multithreading/ MathNet - https://www.sans.org/blog/truerng-random-numbers-with-powershell-and-math-net-numerics/ charts in Powershell - https://docs.microsoft.com/en-us/archive/blogs/richard_macdonald/charting-with-powershell Trace-Command -Name ParameterBinding, TypeConversion -Expression {.\start-genalg.ps1} -PSHost PS2EXE: Invoke-ps2exe -inputFile .\start-genalg.ps1 -outputFile ga_x64.exe -x64 -noConsole -MTA , - https://devblogs.microsoft.com/powershell/array-literals-in-powershell/ posortowanie tabeli hash po value i pobranie z pierwszego rekordu wartsci value: ($b.GetEnumerator() | Sort-Object -Descending -Property value |Select-Object -First 1).value convert array to hash table: $d=@{} $i=0 ($population).foreach{$d[$i]=$_;$i++} Get-Random -Minimum 0.0 -maximum 1.0 #> #8 function Start-GA { [CmdletBinding()] param ( [int]$Generations = 20, [ValidateScript( { if ($_ -eq 0) { throw "Population size can not be [$_]!" } elseif ($_ -gt 0 -and ($_ % 2) -ne 0 ) { throw "Population size [$_] is not even!" } else { $true } })] [int]$PopulationSize = 30, [int]$ChromosomeSize = 20, [double]$CrossOverProbability = 0.6, [double]$MutationProbability = 0.001, [Validateset("Roulette", "Tournament")] $Selection = "Roulette", [switch]$Log, [switch]$Zeros, [switch]$ShowGraph, [switch]$ShowChart, [switch]$ReturnAllGenerations ) $MeasureScript = [system.diagnostics.stopwatch]::startnew() #7 #25 if ($exportExcel) { if (Get-Module -ListAvailable -Name importexcel) { try { import-module importexcel } catch { Write-Error "Error importing module 'importexcel'" } } else { write-warning "Module 'ImportExcel' wasn't found. Invoke 'install-module importexcel'." } } if ($ShowGraph) { if (Get-Module -ListAvailable -Name Graphical) { try { import-module Graphical } catch { Write-Error "Error importing module 'Graphical'" } } else { #20 write-warning "Module 'Graphical' wasn't found. Invoke 'install-module Graphical'." } } #7 if (!(get-module importexcel)) { write-warning "Module 'ImportExcel wasn't found. Invoke 'install-module importexcel'." } if ($Log) { Write-Log "$(Get-Date): [Initialize GA]" } if (Get-Variable m -ErrorAction SilentlyContinue) { Remove-Variable m -Scope script } if (Get-Variable _crossover -ErrorAction SilentlyContinue) { Remove-Variable _crossover -Scope script } if (Get-Variable _functionExecutionTime -ErrorAction SilentlyContinue) { Remove-Variable _functionExecutionTime -Scope script } #4 new-variable -scope script -name m -Value 0 #9 new-variable -scope script -name _crossover -Value 0 #5 New-Variable -Scope script -Name _functionExecutionTime -Value 0 $_crossoverGlobalCount = 0 #9 $_mutations = 0 #4 $_SelectionGlobalExecutionTime = 0 $_CrossoverGlobalExecutionTime = 0 $_MutationGlobalExecutionTime = 0 if ($Log) { Write-Log "$(Get-Date): Number of iterations/generations: [$($generations)]" Write-Log "$(Get-Date): Population size (chromosomes): [$($populationSize)]" Write-Log "$(Get-Date): Chromosome Size (genes): [$($ChromosomeSize)]" Write-Log "$(Get-Date): Crossover probability: [$($CrossOverProbability)]" Write-Log "$(Get-Date): Mutation probability: [$($MutationProbability)]" } if ($zeros) { [array]$population = generatePopulation -zeros -chromosomeCount $PopulationSize -geneCount $ChromosomeSize } else { [array]$population = generatePopulation -chromosomeCount $PopulationSize -geneCount $ChromosomeSize } if ($Log) { Write-Log "$(Get-Date): Population was generated." } #11 if ($zeros -and $log) { Write-Log "$(Get-Date): Used param '-zeros'. Population with all genes = 0." } if ($Log) { Write-Log "$(Get-Date): Generation/Iteration: [0]" } if ($zeros) { $populationFitnessValue = GenerateFitnessValue_Population -population $population $fitnessPopulation_max = 0 $fitnessPopulation_avg = 0 $fitnessPopulationZero = $fitnessPopulation = 0 } else { $populationFitnessValue = GenerateFitnessValue_Population -population $population $fitnessPopulation_max = ($populationFitnessValue | Measure-Object -Maximum).Maximum $fitnessPopulation_avg = ($populationFitnessValue | Measure-Object -Average).Average $fitnessPopulationZero = $fitnessPopulation = PopulationStatictics -population $population -fitness } if ($Log) { Write-Log "$(Get-Date): Value of the fitness function of population: [$($fitnessPopulation)]" } if ($Log) { Write-Log "$(Get-Date): Maximum value of the fitness function for a chromosome in the population: [$($fitnessPopulation_max)]" } if ($Log) { Write-Log "$(Get-Date): Average value of the fitness function for the population: [$($fitnessPopulation_avg)]" } $IndexBestGeneration_2 = 0 $fitnessPopulationZero_2 = $fitnessPopulationZero [array[]]$allGenerations += , @(0, $fitnessPopulation, $population) # Main genetic algorithm iteration region for ($i = 1; $i -le $generations; $i++) { $fitnessPopulation = 0 if ($Log) { Write-Log "$(Get-Date): No. Generation/Iteration: [$($i)]" } if ($Log) { Write-Log "$(Get-Date): Selection." } switch ($selection) { "roulette" { $_ReproductionItems = Roulette -population $population -fitness $populationFitnessValue -Population_Size $populationSize -_ChromosomeSize $ChromosomeSize } "tournament" { $_ReproductionItems = Tournament -population $population -fitness $populationFitnessValue -Population_Size $populationSize } Default {} } $_SelectionGlobalExecutionTime = $_SelectionGlobalExecutionTime + $script:_functionExecutionTime $script:_functionExecutionTime = 0 if ($Log) { Write-Log "$(Get-Date): Crossover." } $CrossovertPopulation = Crossover -population $_ReproductionItems -ChromosomeSize $ChromosomeSize -crossoverProb $CrossOverProbability -Population_Size $populationSize $_CrossoverGlobalExecutionTime = $_CrossoverGlobalExecutionTime + $script:_functionExecutionTime $script:_functionExecutionTime = 0 #9 $_crossoverGlobalCount = $_crossoverGlobalCount + $script:_crossover if ($Log) { Write-Log "$(Get-Date): Mutating." } $mutedPopulation = Mutation -population $CrossovertPopulation -mutationProb $MutationProbability $_MutationGlobalExecutionTime = $_MutationGlobalExecutionTime + $script:_functionExecutionTime $script:_functionExecutionTime = 0 #4 $_mutations = $_mutations + $script:m $populationFitnessValue = GenerateFitnessValue_Population -population $mutedPopulation $fitnessPopulation_max = ($populationFitnessValue | Measure-Object -Maximum).Maximum $fitnessPopulation_avg = ($populationFitnessValue | Measure-Object -Average).Average $fitnessPopulation = PopulationStatictics -population $mutedPopulation -fitness if ($Log) { Write-Log "$(Get-Date): Value of the fitness function of population: [$($fitnessPopulation)]" } if ($Log) { Write-Log "$(Get-Date): Maximum value of the fitness function for a chromosome in the population: [$($fitnessPopulation_max)]" } if ($Log) { Write-Log "$(Get-Date): Average value of the fitness function for the population: [$($fitnessPopulation_avg)]" } if ($fitnessPopulationZero_2 -lt $fitnessPopulation) { # first generation index with max $IndexBestGeneration_2 = $i $fitnessPopulationZero_2 = $fitnessPopulation } # building generations data [array[]]$allGenerations += , @($i, $fitnessPopulation, $mutedPopulation) $population = $mutedPopulation if ($Log) { Write-Log "$(Get-Date): End generation/iteration (index): [$($i)]" } Write-Progress -Activity "Reproduction" -Status "Progress:" -PercentComplete ($i / $generations * 100) } if ($Log) { Write-Log "$(Get-Date): End of all generations/Iterations." } #9 if ($Log) { Write-Log "$(Get-Date): Number of all crossovers: [$_crossoverGlobalCount]" } #4 if ($Log) { Write-Log "$(Get-Date): Number of all mutations: [$_mutations]" } #5 if ($Log) { Write-Log "$(Get-Date): Global selection execution time: [$_SelectionGlobalExecutionTime ms]" } if ($Log) { Write-Log "$(Get-Date): Global crossover execution time: [$_CrossoverGlobalExecutionTime ms]" } if ($Log) { Write-Log "$(Get-Date): Global mutation execution time: [$_MutationGlobalExecutionTime ms]" } $IndexBestGeneration = ($allGenerations | sort-object @{Expression = { $_[1] }; Ascending = $false } | Select-Object @{expression = { $_[0] }; Label = "Generation" }, @{expression = { $_[1] }; Label = "Fitness" } -First 1).Generation if ($Log) { Write-Log "$(Get-Date): Index of generation with highest value of fitness function: [$IndexBestGeneration]" } if ($Log) { Write-Log "$(Get-Date): Index of generation with highest value of fitness function: [$IndexBestGeneration_2]" } if ($Log) { Write-Log "$(Get-Date): Highest value of fitness function: [$($allGenerations[$IndexBestGeneration][1])]" } if ($zeros) { $FitnessGain = (($allGenerations[$IndexBestGeneration_2][1] - $fitnessPopulationZero) * 100) } else { $FitnessGain = (($allGenerations[$IndexBestGeneration_2][1] - $fitnessPopulationZero) / $fitnessPopulationZero) * 100 } $FitnessGain = "{0:n2}" -f $FitnessGain if ($Log) { Write-Log "$(Get-Date): Fitness gain (((f(max)-f(0))/f(0))*100): [$FitnessGain %]" } write-information -MessageData "Best generation: [$IndexBestGeneration_2]" -InformationAction Continue write-information -MessageData "Best fitness: [$($allGenerations[$IndexBestGeneration_2][1])]" -InformationAction Continue write-information -MessageData "Fitness gain: [$FitnessGain %]" -InformationAction Continue $AllGenerationFitness = $allGenerations.foreach{ $psitem[1] } if ($showgraph) { Show-Graph $AllGenerationFitness -XAxisTitle "Generations" -YAxisTitle "Fitness" -GraphTitle "GA" } if ($Log) { Write-Log "$(Get-Date): Script execution time: [$($MeasureScript.ElapsedMilliseconds) ms]" } if ($Log) { Write-Log "$(Get-Date): [End of GA]" } if ($Log) { Write-information -MessageData "LOG: $env:TEMP\GA.log" -InformationAction Continue } #10 $allGenerations | ConvertTo-Json | Out-File "$env:TEMP\allGenerations.json" write-information -MessageData "OUT DATA: $env:TEMP\allGenerations.json" -InformationAction Continue #19 if ($ShowChart) { # show and save ShowChart -AllGenerationFitness $AllGenerationFitness -ShowChart -SaveChart } else { # save only ShowChart -AllGenerationFitness $AllGenerationFitness -SaveChart } if ($ReturnAllGenerations) { return $allGenerations } <# .SYNOPSIS Genetic Algorithm in Powershell. .DESCRIPTION Powershell module with implementation of genetic algorithm (GA). .PARAMETER Generations The parameter defines the number of recalculation iterations for the population before we complete the algorithm. The parameter has a default value and it is 20 generations. .PARAMETER PopulationSize We define the size of the population used in the GA. Size is understood as the number of genomes - in this abbreviation a genome is equal to a chromosome. The size of the population is constant for the duration of the algorithm's operation and must be even. The parameter has a default value and it is 30 genomes. .PARAMETER ChromosomeSize The parameter determines the number of genes in the chromosome. The parameter has a default value and it is 20 chromosomes. .PARAMETER CrossOverProbability Determines the probability of crossing two chromosomes at a crossing point. The crossing point is random and is not a parameter. The parameter has a default value and it is 0.6. .PARAMETER MutationProbability The parameter determines the probability of a gene mutation in the chromosome. A mutation probability is generated for each gene. The parameter has a default value and it is 0.001. .PARAMETER Selection The value of this parameter specifies the type of selection that will be used in the iteration of the genetic algorithm. The parameter has a defined list of values, they are: 1. "Roulette" 2. "Tournament" "Roulette" is default one. The default value has been chosen because of its better performance. .PARAMETER Log The switch determines whether a log file from the algorithm's operation is to be generated. If there is a log file, new data will be added to it. It is not possible to specify the path and file name. The default value is $env:TEMP\GA.log .PARAMETER Zeros The switch specifies that the initial population consists of chromosomes, where all genes are 0. By default, the initial population is randomly generated. .PARAMETER ShowGraph After the algorithm is completed, an ASCII chart is generated. Draws graph in the Powershell console. The graph is the value of the objective function for the initial population and population from all iterations of the algorithm. The Graphical module is required. .PARAMETER ShowChart After the algorithm is completed, an PNG chart is generated. The graph is the value of the objective function for the initial population and population from all iterations of the algorithm. The [System.Windows.Forms] and [System.Windows.Forms.DataVisualizationmodule] namespaces are used. Regardless of whether the switch is turned on, a PNG image is generated and saved in $env:TEMP\GA.png .PARAMETER ReturnAllGenerations Enabled parameter causes the function to return result array of all generations. The first element is the initial generation. .EXAMPLE Start-GA .EXAMPLE Start-GA -Log -ShowGraph .EXAMPLE Start-GA -ShowChart .EXAMPLE Start-GA -Generations 100 -PopulationSize 40 -MutationProbability 0.009 -zeros -Log -ShowGraph .EXAMPLE [array[]]$GAOutput=Start-GA -Generations 80 -ChromosomeSize 60 To display populations from 30 iterations: $GAOutput[30][2].foreach{"$_"} .LINK https://github.com/voytas75/genetic-algorithm .NOTES My post on reddit to request for comments: https://www.reddit.com/r/PowerShell/comments/i5csrc/genetic_algorithm_in_powershell/ #> } function generateChromosome { param ( [ValidateNotNullorEmpty()] [int]$geneCount, #18 [switch]$zeros ) <# function generate vale of gene powershell statistics check .net statistics #> $_chromosome = @() if ($zeros) { return [array]$_chromosome = (1..$genecount).foreach{ 0 } } else { return [array]$_chromosome = (1..$genecount).foreach{ 0..1 | get-random } } } function generatePopulation { [CmdletBinding()] param ( [ValidateNotNullorEmpty()] [int]$chromosomeCount, #18 [ValidateNotNullorEmpty()] [int]$geneCount, #18 [switch]$zeros ) <# function generates chromosome one or more. default values are definied. #> $_population = @() if ($zeros) { (1..$chromosomeCount).foreach{ $_population += , [array](generateChromosome -zeros -geneCount $geneCount) } } else { (1..$chromosomeCount).foreach{ $_population += , [array](generateChromosome -geneCount $geneCount) } } return $_population } function PopulationStatictics { param ( [ValidateNotNullorEmpty()] [array[]]$population, [switch]$Count, [switch]$fitness, [switch]$Maximum, [switch]$Average, [switch]$display ) <# param options - https://learn-powershell.net/2014/02/04/using-powershell-parameter-validation-to-make-your-day-easier/ #> $_FitnessSum = 0 if ($fitness) { #$_fitness = GenerateFitnessValue_Population -population $population (GenerateFitnessValue_Population -population $population).foreach{ $_FitnessSum += $PSItem } return $_FitnessSum } elseif ($Count) { return $population.count } elseif ($Maximum) { return $population.count } elseif ($Average) { return $population.count } elseif ($display) { $population.foreach{ "[$_]" } } else { return $null } } function GenerateFitnessValue_Population { param ( [ValidateNotNullorEmpty()] [array[]]$population ) <# example fitness function sum of genes is odd #> $_FitnessPopulationItems = @() $_GenerateSumGenes = $population.ForEach{ ($_ -match 1).count } # array of sums 1 in genes return [array]$_FitnessPopulationItems = $_GenerateSumGenes.foreach{ if ([bool]($psitem % 2)) { $PSItem }else { 0 } } } function Roulette { param ( [ValidateNotNullorEmpty()] [array[]]$population, [ValidateNotNullorEmpty()] [array]$fitness, $Population_Size, $_ChromosomeSize ) #5 $MeasureFunction = [system.diagnostics.stopwatch]::startnew() $script:_functionExecutionTime = 0 $_FitnessSum = 0 $_NormalizeItem = @() $_aggregatesum = 0 $fitness.foreach{ $_FitnessSum += $PSItem } #13 #15 if ($_FitnessSum -eq 0 -and -not $zeros) { #$_FitnessSum.foreach{ "Fitness sum: [$PSItem]" } #$population.foreach{ "Population item: [$PSItem]" } #"[STOP]" #14 if ($Log) { Write-Log "$(Get-Date): [EXIT] Fitness is 0. Terminating." } if ($Log) { Write-Log "$(Get-Date): [End of GA]" } Write-output "[EXIT] Fitness is 0. Terminating." exit } elseif ($_FitnessSum -gt 0) { $_NormalizeItem = $fitness.foreach{ $Psitem / $_FitnessSum } } elseif ($_FitnessSum -eq 0 -and $zeros) { #$_NormalizeItem = $fitness.foreach{ 0 } #16 return (generatePopulation -zeros -chromosomeCount $Population_Size -geneCount $_ChromosomeSize) } [array]$AgregateSum = $_NormalizeItem.foreach{ $_aggregatesum += $PSItem; $_aggregatesum } [Object]$Random = New-Object System.Random #17 #[int]$_popcount = PopulationStatictics -population $population -count [int]$_popcount = $Population_Size [array]$_randomvalue = (1..$_popcount).foreach{ $Random.NextDouble() } $i = $j = 0 $_reproduceItems = @() do { $j = 0 if ($_Normalizeitem[0] -lt 1) { do { if ($_randomvalue[0] -le $AgregateSum[0] -or $_randomvalue[$i] -lt $AgregateSum[0]) { break } $j++ } until (($_randomvalue[$i] -gt $AgregateSum[$j - 1] -and $_randomvalue[$i] -le $AgregateSum[$j]) -or $AgregateSum[$j - 1] -eq 1 -or $j -gt $_popcount) } [array]$_reproduceItems += , @($population[($j)]) $i++ } until ($i -ge $_popcount) #5 $script:_functionExecutionTime = $MeasureFunction.ElapsedMilliseconds if ($Log) { Write-Log "$(Get-Date): Selection 'Roulette' execution time: ($script:_functionExecutionTime ms)" } return $_reproduceItems } function Tournament { param ( [ValidateNotNullorEmpty()] [array[]]$population, [ValidateNotNullorEmpty()] [array]$fitness, $Population_Size ) #5 $MeasureFunction = [system.diagnostics.stopwatch]::startnew() $_Kindividuals = 4 #17 $_PopulationWinners = @() $_PopulationSize = $Population_Size for ($ii = 0; $ii -lt $_PopulationSize; $ii++) { $_population_hashtable_temp = @{} $_populationHashTable = @{} $i = 0 # convert array to hash table: $_populationHashTable = ($population).foreach{ $_population_hashtable_temp + @{item = $i; genome = $_; fitness = $fitness[$i] }; $i++ } # get items to tournament $_TournamentPlayers = get-random -InputObject $_populationHashTable -count $_Kindividuals # sort items by fitness $_TournamentPlayers_SortFitness = $_TournamentPlayers.GetEnumerator() | Sort-Object -property { $_.fitness } -descending $_TurnamentWinner = @{} # sorting items by fitness and take first one $_TurnamentWinner = $_TournamentPlayers_SortFitness | Select-Object -first 1 # building Tournament winner as population to mutate $_PopulationWinners += , ($population[$_TurnamentWinner.item]) } #5 $script:_functionExecutionTime = $MeasureFunction.ElapsedMilliseconds if ($Log) { Write-Log "$(Get-Date): Selection 'Tournament' execution time: ($script:_functionExecutionTime ms)" } return $_PopulationWinners } function Crossover { param ( [ValidateNotNullorEmpty()] [array[]]$population, [ValidateNotNullorEmpty()] $ChromosomeSize, [ValidateNotNullorEmpty()] $crossoverProb, $Population_Size ) $MeasureFunction = [system.diagnostics.stopwatch]::startnew() $script:_crossover = 0 #9 [Object]$Random = New-Object System.Random #17 $_PopulationSize = $Population_Size for ($i = 0; $i -lt $_PopulationSize; $i += 2) { if (($Random.NextDouble()) -le $crossoverProb) { $script:_crossover++ #9 $_crossoverPoint = 1..($ChromosomeSize - 2) | get-random [array[]]$_crossoverpopulation += , ($population[$i][0..$_crossoverPoint] + $population[$i + 1][($_crossoverPoint + 1)..($ChromosomeSize)]) [array[]]$_crossoverpopulation += , ($population[$i + 1][0..$_crossoverPoint] + $population[$i][($_crossoverPoint + 1)..($ChromosomeSize)]) } else { [array[]]$_crossoverpopulation += , ($population[$i]) [array[]]$_crossoverpopulation += , ($population[$i + 1]) } } if ($Log) { Write-Log "$(Get-Date): Number of all crossovers in population: [$script:_crossover]" } #9 $script:_functionExecutionTime = $MeasureFunction.ElapsedMilliseconds if ($Log) { Write-Log "$(Get-Date): Crossover execution time: ($script:_functionExecutionTime ms)" } return $_CrossoverPopulation } function Mutation { param ( [ValidateNotNullorEmpty()] [array[]]$population, [ValidateNotNullorEmpty()] $mutationProb ) $MeasureFunction = [system.diagnostics.stopwatch]::startnew() [Object]$Random = New-Object System.Random $i = 0 $script:m = 0 foreach ($items in $population) { $j = 0 foreach ($item in $items) { if (($Random.NextDouble()) -le $mutationProb) { # we are in! mutation time! Lets change some genes! #4 $script:m++ if ($population[$i][$j] -eq 1) { $population[$i][$j] = 0 if ($Log) { Write-Log "$(Get-Date): Mutation! Item [$i], Gene [$j] 1 -> 0" } } else { $population[$i][$j] = 1 if ($Log) { Write-Log "$(Get-Date): Mutation! Item [$i], Gene [$j] 0 -> 1" } } } $j++ } $i++ } if ($Log) { Write-Log "$(Get-Date): Number of all mutations in population: [$script:m]" } #5 $script:_functionExecutionTime = $MeasureFunction.ElapsedMilliseconds if ($Log) { Write-Log "$(Get-Date): Mutation execution time: ($script:_functionExecutionTime ms)" } return $population } #19 function ShowChart { param ( $AllGenerationFitness, [switch]$SaveChart, [switch]$ShowChart ) # CHART # load the appropriate assemblies [void][Reflection.Assembly]::LoadWithPartialName("System.Windows.Forms") [void][Reflection.Assembly]::LoadWithPartialName("System.Windows.Forms.DataVisualization") # create chart object $Chart = New-object System.Windows.Forms.DataVisualization.Charting.Chart $Chart.Width = 1000 $Chart.Height = 400 $Chart.Left = 40 $Chart.Top = 30 # create a chartarea to draw on and add to chart $ChartArea = New-Object System.Windows.Forms.DataVisualization.Charting.ChartArea $Chart.ChartAreas.Add($ChartArea) # add data to chart #$Cities = @{London = 7556900; Berlin = 3429900; Madrid = 3213271; Rome = 2726539; Paris = 2188500 } [void]$Chart.Series.Add("Data") $gg = 0 $Chart.Series["Data"].Points.DataBindXY([int[]]$AllGenerationFitness.foreach{ , ($gg++) }, [int[]]$AllGenerationFitness ) # Find point with max/min values and change their colour $maxValuePoint = $Chart.Series["Data"].Points.FindMaxByValue() $maxValuePoint.Color = [System.Drawing.Color]::Red $minValuePoint = $Chart.Series["Data"].Points.FindMinByValue() $minValuePoint.Color = [System.Drawing.Color]::Green # change chart area colour #$Chart.BackColor = [System.Drawing.Color]::Transparent # add a save button $SaveButton = New-Object Windows.Forms.Button $SaveButton.Text = "Save" $SaveButton.Top = 500 $SaveButton.Left = 450 $SaveButton.Anchor = [System.Windows.Forms.AnchorStyles]::Bottom -bor [System.Windows.Forms.AnchorStyles]::Right $SaveButton.add_click( { $Chart.SaveImage($env:TEMP + "\GA.png", "PNG") }) if ($SaveChart) { $Chart.SaveImage($env:TEMP + "\GA.png", "PNG") write-information -MessageData "PNG: $env:TEMP\GA.png" -InformationAction Continue } if ($ShowChart) { # display the chart on a form $Chart.Anchor = [System.Windows.Forms.AnchorStyles]::Bottom -bor [System.Windows.Forms.AnchorStyles]::Right -bor [System.Windows.Forms.AnchorStyles]::Top -bor [System.Windows.Forms.AnchorStyles]::Left $Form = New-Object Windows.Forms.Form $Form.Text = "PowerShell Chart" $Form.Width = 1100 $Form.Height = 600 $Form.controls.add($Chart) $Form.Add_Shown( { $Form.Activate() }) #$Form.controls.add($SaveButton) $Form.ShowDialog() } } function Write-Log { param( [string]$logstring ) [string]$Logfile = "$env:TEMP\GA.log" Add-Content $logfile -Value $logstring -Force } |