private static void SolveHC()
{
HillClimbing hc = new HillClimbing();
Console.WriteLine("FA HC Solver Started");
Console.WriteLine("Press ESC to stop");
hc.Solve(3);
Console.WriteLine("FA HC Solver Finished");
}
private void FindOptimalDecision()
{
double optimal = 1000;
for (int i = 0; i < AmountOfTests; i++)
{
var temp = new HillClimbing(Elements, 5);
if (temp.OptimalValue.Value < optimal)
{
Algorithm = temp;
optimal = temp.OptimalValue.Value;
}
}
}
private void ButtonHillStart_Click(object sender, RoutedEventArgs e)
{
DisableAllControls();
_infoBacklog = new List <Tuple <int, ulong, double, ulong> >(_viewModel.MaxGenerationCount);
var hill = new HillClimbing(_viewModel, _geneticAlgorithmController.CityModels);
hill.OnAlgorithmStateHasChangedEvent += OnAlgorithmStateHasChangedEvent;
hill.OnAlgorithmFinishedEvent += OnAlgorithmFinishedEvent;
hill.OnLogChangedEvent += OnLogChangedEvent;
_algorithmCancellationToken = new CancellationTokenSource();
Task.Factory.StartNew(() => hill.DoAlgorithm(_algorithmCancellationToken.Token),
_algorithmCancellationToken.Token);
}
private void SameInitValuebutton_Click(object sender, EventArgs e)
{
double alpha = Convert.ToDouble(textBox11.Text);
File.Delete(@"C:\Users\User\Downloads\ResultFAHillClimbing.txt");
var hill = new HillClimbing(knapsackModel);
File.Delete(@"C:\Users\User\Downloads\ResultHillClimbing.txt");
var resultModel = hill.HillClimbingMethod(knapsackModel, alpha);
textBoxResultC2.Text = Convert.ToString(resultModel.ResultC);
textBoxResultW2.Text = Convert.ToString(resultModel.ResultW);
listBoxSelectedItems2.Items.Clear();
if (checkBoxShowFull2.Checked)
{
listBoxSelectedItems2.Items.Add("cost\t\tweight");
for (int i = 0; i < resultModel.X.Length; i++)
{
if (resultModel.X[i] == true)
{
listBoxSelectedItems2.Items.Add(knapsackModel.Items[i].Item1 + "\t" + knapsackModel.Items[i].Item2);
}
}
}
resultModel = hill.NewFirstAscendHillClimbingMethod(knapsackModel, alpha);
textBoxResultC.Text = Convert.ToString(resultModel.ResultC);
textBoxResultW.Text = Convert.ToString(resultModel.ResultW);
listBoxSelectedItems.Items.Clear();
if (checkBoxShowFull.Checked)
{
listBoxSelectedItems.Items.Add("cost\t\tweight");
for (int i = 0; i < resultModel.X.Length; i++)
{
if (resultModel.X[i] == true)
{
listBoxSelectedItems.Items.Add(knapsackModel.Items[i].Item1 + "\t" + knapsackModel.Items[i].Item2);
}
}
}
}
static void Main(string[] args)
{
//MonteCarlo mc = new MonteCarlo(15, short.MaxValue, 100, LandscapeFilling.Random);
//var result = mc.DoAlgo();
HillClimbing hc = new HillClimbing(5, 32, LandscapeFilling.ValueOfBinary, HillClimbingType.Width);
hc.DoAlgo();
// var values = new List<List<double>>
// {
//new List<double>{0.00, 10.44, 11.18, 4.24, 13.60, 7.00, 12.21, 13.45, 13.45, 9.49, 11.05, 3.16, 1.41, 7.21, 10.20},
//new List<double>{10.44, 0.00, 2.00, 13.00, 8.25, 4.24, 4.00, 7.07, 6.00, 14.32, 13.60, 8.06, 11.18, 14.32, 13.89},
//new List<double>{11.18, 2.00, 0.00, 13.15, 6.32, 5.83, 2.00, 5.10, 4.00, 13.60, 12.53, 9.22, 11.70, 14.04, 13.00},
//new List<double>{4.24, 13.00, 13.15, 0.00, 13.60, 10.44, 13.60, 13.89, 14.32, 6.00, 8.25, 7.21, 2.83, 3.16, 7.07},
//new List<double>{13.60, 8.25, 6.32, 13.60, 0.00, 11.05, 4.47, 1.41, 2.83, 11.18, 9.00, 13.00, 13.45, 13.00, 10.05},
//new List<double>{7.00, 4.24, 5.83, 10.44, 11.05, 0.00, 7.62, 10.20, 9.49, 13.45, 13.60, 4.12, 8.06, 12.53, 13.45},
//new List<double>{12.21, 4.00, 2.00, 13.60, 4.47, 7.62, 0.00, 3.16, 2.00, 13.15, 11.70, 10.63, 12.53, 14.04, 12.37},
//new List<double>{13.45, 7.07, 5.10, 13.89, 1.41, 10.20, 3.16, 0.00, 1.41, 12.04, 10.05, 12.53, 13.45, 13.60, 11.00},
//new List<double>{13.45, 6.00, 4.00, 14.32, 2.83, 9.49, 2.00, 1.41, 0.00, 13.00, 11.18, 12.21, 13.60, 14.32, 12.04},
//new List<double>{9.49, 14.32, 13.60, 6.00, 11.18, 13.45, 13.15, 12.04, 13.00, 0.00, 2.83, 11.66, 8.25, 3.16, 1.41},
//new List<double>{11.05, 13.60, 12.53, 8.25, 9.00, 13.60, 11.70, 10.05, 11.18, 2.83, 0.00, 12.65, 10.00, 5.83, 1.41},
//new List<double>{3.16, 8.06, 9.22, 7.21, 13.00, 4.12, 10.63, 12.53, 12.21, 11.66, 12.65, 0.00, 4.47, 9.90, 12.08},
//new List<double>{1.41, 11.18, 11.70, 2.83, 13.45, 8.06, 12.53, 13.45, 13.60, 8.25, 10.00, 4.47, 0.00, 5.83, 9.06},
//new List<double>{7.21, 14.32, 14.04, 3.16, 13.00, 12.53, 14.04, 13.60, 14.32, 3.16, 5.83, 9.90, 5.83, 0.00, 4.47},
//new List<double>{10.20, 13.89, 13.00, 7.07, 10.05, 13.45, 12.37, 11.00, 12.04, 1.41, 1.41, 12.08, 9.06, 4.47, 0.00},
// };
// EGA ega = new EGA(values);
// ega.SetupInitialPopulation(Encoding.ClosestTown)
// .SetupParents(Pairs.Quality)
// .SetupCrossover(Crossover.Order)
// .SetupMutation(0.1, Mutation.Inversion)
// .SetupBreeding(Breeding.Roulette, 0.15)
// .EnableElitary()
// .DoAlgo(10);
// ega.PrintBestResult();
}
private void MultiStartHillClimbingButton_Click(object sender, EventArgs e)
{
var maxAttemt = Convert.ToInt32(textBoxMaxAttemps.Text);
var objDetails = checkBoxObjDetails.Checked;
double alpha = Convert.ToDouble(textBox11.Text);
var resultModel = new HillClimbing().MultiStartHillClimbingMethod(knapsackModel, maxAttemt, objDetails, alpha);
Series ser1 = new Series("Attempts statistics");
chart1.Series.Clear();
foreach (var pair in resultModel.AttemptStatistics)
{
ser1.Points.AddXY(pair.Key.ToString(), pair.Value.ToString());
}
chart1.Series.Add(ser1);
chart1.ChartAreas[0].AxisX.Interval = 1;
textBoxDelta.Text = Convert.ToString(resultModel.Delta);
if (objDetails)
{
chart2.Series.Clear();
Series ser2 = new Series("Objective");
ser2.ChartType = SeriesChartType.Line;
chart2.ChartAreas["Area1"].AxisX.ScaleView.Zoom(0, 50);
chart2.ChartAreas["Area1"].AxisX.Interval = 1;
for (int i = 0; i < resultModel.ObjectivesDetails.Count; i++)
{
for (int j = 0; j < resultModel.ObjectivesDetails[i].Count; j++)
{
ser2.Points.AddXY(j.ToString(), resultModel.ObjectivesDetails[i][j]);
}
}
chart2.Series.Add(ser2);
}
}
public async Task <IActionResult> Index(ICollection <IFormFile> files, UserInput u)
{
/* Uploader File */
user_input = u;
var uploads = Path.Combine(_environment.WebRootPath, "uploads");
foreach (var file in files)
{
if (file.Length > 0)
{
uploads = Path.Combine(uploads, file.FileName);
using (var fileStream = new FileStream(uploads, FileMode.Create))
{
await file.CopyToAsync(fileStream);
}
}
}
var openFile = new FileStream(uploads, FileMode.Open, FileAccess.Read);
using (var readStream = new StreamReader(openFile))
{
int conflict = 0;
string line;
Boolean isRuangan = false;
Boolean isKelas = false;
string temp = "";
char b;
RuanganManagement listOfRuangan = new RuanganManagement();
KelasManagement listOfKelas = new KelasManagement();
/* ADT RUANGAN */
string nama = ""; // Contoh: "7602"
int jam_buka = 0; // Contoh: 7 (buka mulai jam 7)
int jam_tutup = 0; // Contoh: 14 (tutup jam 14)
List <int> hari_buka = new List <int>();
/* ADT KELAS */
// Domain value yang dimiliki 'Kelas'
string ruangan = "";
List <string> domainRuangan = new List <string>();
List <int> domainMulai = new List <int>(); // Contoh: [7, 8, 9] (Kelas hanya bisa mulai jam 7, 8, atau 9)
List <int> domainHari = new List <int>(); // Contoh: [1, 3, 5] (Kelas hanya bisa dilakukan hari Senin, Rabu, atau Jumat)
int durasi = 0; // Contoh: 4 (4 jam)
while ((line = readStream.ReadLine()) != null)
{
if (line.Equals(""))
{
// Do nothing
}
else if (line.Equals("Ruangan"))
{
isRuangan = true;
isKelas = false;
}
else if (line.Equals("Jadwal"))
{
isRuangan = false;
isKelas = true;
}
/* Membaca data ruangan */
else if (isRuangan)
{
var sr = new StringReader(line);
int point = 0;
temp = "";
hari_buka = new List <int>();
domainMulai = new List <int>();
for (int k = 0; k < line.Length; k++)
{
b = (char)sr.Read();
if (b == ';')
{
if (point == 0)
{
nama = temp;
}
else if (point == 1)
{
jam_buka = timeToInt(temp);
}
else if (point == 2)
{
jam_tutup = timeToInt(temp);
}
point++;
temp = "";
}
else
{
if (b != ',')
{
temp += b;
}
}
}
var parse = new StringReader(temp);
for (int j = 0; j < temp.Length; j++)
{
char val = (char)parse.Read();
hari_buka.Add(Int32.Parse(val.ToString()));
}
listOfRuangan.addRuangan(new Ruangan(nama, jam_buka, jam_tutup, hari_buka));
}
/* Menambah info kelas */
else if (isKelas)
{
var sr = new StringReader(line);
int point = 0;
temp = "";
hari_buka = new List <int>();
domainRuangan = new List <string>();
for (int k = 0; k < line.Length; k++)
{
b = (char)sr.Read();
if ((point == 1) && (b != ';'))
{
if (b == ',')
{
domainRuangan.Add(temp);
temp = "";
}
else
{
temp += b;
}
}
else if (b == ';')
{
if (point == 0)
{
nama = temp;
}
else if (point == 1)
{
domainRuangan.Add(temp);
}
else if (point == 2)
{
jam_buka = timeToInt(temp);
}
else if (point == 3)
{
jam_tutup = timeToInt(temp);
}
else if (point == 4)
{
durasi = Int32.Parse(temp);
}
point++;
temp = "";
}
else
{
if (b != ',')
{
temp += b;
}
}
}
var parse = new StringReader(temp);
for (int j = 0; j < temp.Length; j++)
{
char val = (char)parse.Read();
hari_buka.Add(Int32.Parse(val.ToString()));
}
Kelas c = new Kelas(nama, domainRuangan, jam_buka, jam_tutup, durasi, hari_buka, listOfRuangan);
listOfKelas.addKelas(c);
}
}
KelasManagement ans = new KelasManagement();
if (user_input.choice == 0)
{
HillClimbing hc = new HillClimbing(listOfKelas, listOfRuangan);
ans = hc.getSol();
ViewData["Choice"] = "Hill Climbing";
}
else if (user_input.choice == 1)
{
SimulatedAnnealing sa = new SimulatedAnnealing(listOfKelas, listOfRuangan);
sa.execute(1000, 0.9f);
ans = sa.getSol();
ViewData["Choice"] = "Simulated Annealing";
}
else if (user_input.choice == 2)
{
Population p = new Population();
p.generatePopulation(200, listOfKelas);
GeneticAlgorithm ga = new GeneticAlgorithm(p);
for (int ax = 0; ax < 100000; ax++)
{
if (ga.getSolution().getConflict() == 0)
{
break;
}
else
{
ga.crossover();
ga.mutation();
}
}
ans = ga.getSolution();
ViewData["Choice"] = "Genetic Algorithm";
}
ViewData["tes"] = ans.getConflict();
int i = 0;
// Inisialisasi
for (int j = 7; j < 18; j++)
{
for (int k = 1; k < 6; k++)
{
ViewData["marker" + j.ToString() + k.ToString()] = 5;
ViewData["nama" + j.ToString() + k.ToString()] = "";
ViewData["ruangan" + j.ToString() + k.ToString()] = "";
}
}
// Pengisian kelas di tabel
foreach (Kelas k in ans.getArrayKelas())
{
ViewData["nama" + i.ToString()] = k.getNama();
ViewData["ruangan" + i.ToString()] = k.getNamaRuangan();
ViewData["durasi" + i.ToString()] = k.getDurasi();
ViewData["hari" + i.ToString()] = k.getHari();
ViewData["jam" + i.ToString()] = k.getMulai();
for (int z = k.getMulai(); z < (k.getMulai() + k.getDurasi()); z++)
{
if (ViewData["ruangan" + z.ToString() + k.getHari().ToString()].Equals(""))
{
ViewData["kelas" + z.ToString() + k.getHari().ToString()] = i.ToString();
ViewData["marker" + z.ToString() + k.getHari().ToString()] = 0;
ViewData["nama" + z.ToString() + k.getHari().ToString()] = k.getNama();
ViewData["ruangan" + z.ToString() + k.getHari().ToString()] = k.getNamaRuangan();
//Kurangi waktu available dan tambahkan waktu terpakai;
listOfRuangan.getRuangan(k.getNamaRuangan()).decrement_waktu_available();
}
else if (cekRuanganSama(k.getNamaRuangan(), ViewData["ruangan" + z.ToString() + k.getHari().ToString()].ToString()))
{
conflict++;
ViewData["kelas" + z.ToString() + k.getHari().ToString()] = i.ToString();
ViewData["marker" + z.ToString() + k.getHari().ToString()] = 1;
ViewData["nama" + z.ToString() + k.getHari().ToString()] += " " + k.getNama();
ViewData["ruangan" + z.ToString() + k.getHari().ToString()] += " " + k.getNamaRuangan();
}
else
{
ViewData["kelas" + z.ToString() + k.getHari().ToString()] += " " + i.ToString();
ViewData["marker" + z.ToString() + k.getHari().ToString()] = 2;
ViewData["nama" + z.ToString() + k.getHari().ToString()] += " " + k.getNama();
ViewData["ruangan" + z.ToString() + k.getHari().ToString()] += " " + k.getNamaRuangan();
/* Kurangi waktu available dan tambahkan waktu terpakai*/
listOfRuangan.getRuangan(k.getNamaRuangan()).decrement_waktu_available();
}
}
i++;
}
ViewData["tes"] = conflict;
ViewData["Length"] = i;
/* Memasukkan data keefektifan untuk ditampilkan di web */
int nRuangan = 0;
foreach (Ruangan ruang_ruang in listOfRuangan.getAllRuangan())
{
ViewData["namaruangan" + nRuangan.ToString()] = ruang_ruang.getName();
ViewData["efektifitas" + nRuangan.ToString()] = ruang_ruang.getEfektifitasRuangan();
nRuangan++;
}
ViewData["banyakruangan"] = nRuangan;
}
ViewData["color0"] = "green";
ViewData["color1"] = "yellow";
ViewData["color2"] = "brown";
ViewData["color3"] = "blue";
ViewData["color4"] = "black";
ViewData["color5"] = "aqua";
ViewData["color6"] = "purple";
ViewData["color7"] = "wood";
ViewData["color8"] = "pink";
ViewData["color9"] = "orange";
ViewData["Start"] = 1;
return(View());
}
static void TestRun(bool randomWorkloadJumps, string fileName)
{
// Ported from http://mattwarren.org/2017/04/13/The-CLR-Thread-Pool-Thread-Injection-Algorithm/
Console.WriteLine("Running Hill Climbing algorithm ({0})", fileName);
var options = new HillClimbingOptions();
options.MinThreads = () => 2;
options.MaxThreads = () => 1000;
var hc = new HillClimbing(options);
int newMax = 0, threadAdjustmentInterval = 0;
long totalCompletions = 0, priorCompletionCount = 0;
int timer = 0, lastSampleTimer = 0;
int currentThreadCount = options.MinThreads();
hc.ForceChange(currentThreadCount, HillClimbingStateTransition.Initializing);
var randomGenerator = new Random();
using (var fp = new StreamWriter(fileName))
{
fp.WriteLine("Time,Throughput,Threads");
for (int mode = 1; mode <= 5; mode++)
{
int currentWorkLoad = 0;
switch (mode)
{
case 1:
case 5:
currentWorkLoad = 3;
break;
case 2:
case 4:
currentWorkLoad = 7;
break;
case 3:
currentWorkLoad = 10;
break;
default:
currentWorkLoad = 1;
break;
}
bool reportedMsgInWorkload = false;
int workLoadForSection = currentWorkLoad * 500;
while (workLoadForSection > 0)
{
if (randomWorkloadJumps)
{
int randomValue = randomGenerator.Next(21); // 0 to 20
if (randomValue >= 19)
{
int randomChange = randomGenerator.Next(-2, 3); // i.e. -2, -1, 0, 1, 2 (not 3)
if (randomChange != 0)
{
Console.WriteLine("Changing workload from {0} -> {1}\n", currentWorkLoad, currentWorkLoad + randomChange);
currentWorkLoad += randomChange;
}
}
}
timer += 1; //tick-tock, each iteration of the loop is 1 second
totalCompletions += currentThreadCount;
workLoadForSection -= currentThreadCount;
//fprintf(fp, "%d,%d\n", min(currentWorkLoad, currentThreadCount), currentThreadCount);
double randomNoise = randomGenerator.NextDouble() / 100.0 * 5; // [0..1) -> [0..0.01) -> [0..0.05)
fp.WriteLine("{0},{1},{2}", timer, (Math.Min(currentWorkLoad, currentThreadCount) * (0.95 + randomNoise)).ToString(CultureInfo.InvariantCulture), currentThreadCount);
// Calling HillClimbingInstance.Update(..) should ONLY happen when we need more threads, not all the time!!
if (currentThreadCount != currentWorkLoad)
{
// We naively assume that each work items takes 1 second (which is also our loop/timer length)
// So in every loop we complete 'currentThreadCount' pieces of work
int numCompletions = currentThreadCount;
// In win32threadpool.cpp it does the following check before calling Update(..)
// if (elapsed*1000.0 >= (ThreadAdjustmentInterval/2)) //
// Also 'ThreadAdjustmentInterval' is initially set like so ('INTERNAL_HillClimbing_SampleIntervalLow' = 10):
// ThreadAdjustmentInterval = CLRConfig::GetConfigValue(CLRConfig::INTERNAL_HillClimbing_SampleIntervalLow);
double sampleDuration = (double)(timer - lastSampleTimer);
if (sampleDuration * 1000.0 >= (threadAdjustmentInterval / 2))
{
newMax = hc.Update(currentThreadCount, sampleDuration, numCompletions, out threadAdjustmentInterval);
Console.WriteLine("Mode = {0}, Num Completions = {1} ({2}), New Max = {3} (Old = {4}), threadAdjustmentInterval = {5}",
mode, numCompletions, totalCompletions, newMax, currentThreadCount, threadAdjustmentInterval);
if (newMax > currentThreadCount)
{
// Never go beyound what we actually need (plus 1)
int newThreadCount = Math.Min(newMax, currentWorkLoad + 1); // + 1
if (newThreadCount != 0 && newThreadCount > currentThreadCount)
{
// We only ever increase by 1 at a time!
Console.WriteLine("*** INCREASING thread count, from {0} -> {1} (CurrentWorkLoad = {2}, Hill-Climbing New Max = {3})***",
currentThreadCount, currentThreadCount + 1, currentWorkLoad, newMax);
currentThreadCount += 1;
}
else
{
Console.WriteLine("*** SHOULD HAVE INCREASED thread count, but didn't, newMax = {0}, currentThreadCount = {1}, currentWorkLoad = {2}",
newMax, currentThreadCount, currentWorkLoad);
}
}
else if (newMax < (currentThreadCount - 1) && newMax != 0)
{
Console.WriteLine("*** DECREASING thread count, from {0} -> {1} (CurrentWorkLoad = {2}, Hill-Climbing New Max = {3})***",
currentThreadCount, currentThreadCount - 1, currentWorkLoad, newMax);
currentThreadCount -= 1;
}
priorCompletionCount = totalCompletions;
lastSampleTimer = timer;
}
else
{
Console.WriteLine("Sample Duration is too small, current = {0}, needed = {1} (threadAdjustmentInterval = {2})",
sampleDuration, (threadAdjustmentInterval / 2) / 1000.0, threadAdjustmentInterval);
}
}
else
{
if (reportedMsgInWorkload == false)
{
Console.WriteLine("Enough threads to carry out current workload, currentThreadCount = {0}, currentWorkLoad= {1}\n",
currentThreadCount, currentWorkLoad);
}
reportedMsgInWorkload = true;
}
}
fp.Flush();
}
}
}
private static void Main()
{
Parameters.Verbosity = VerbosityLevels.Normal;
// this next line is to set the Debug statements from OOOT to the Console.
Trace.Listeners.Add(new TextWriterTraceListener(Console.Out));
/* In this example, we first present how the details of an optimzation
* problem can be saved to an XML-file so that it can be read in
* and solved as opposed to defining all the details in an imperative
* (code line by code line) way. In the first function, the xml file
* name "test1.xml" is created. */
makeAndSaveProblemDefinition();
/* now we create a series of different optimization methods and test
* them on the problem. The problem is now opened from the file and
* the details are stored in an object of class "Problem Definition".*/
var stream = new FileStream(filename, FileMode.Open);
double[] xStar;
ProblemDefinition probTest1 = ProblemDefinition.OpenprobFromXml(stream);
abstractOptMethod opty;
/******************Exhaustive Search ***********************/
//SearchIO.output("******************Exhaustive Search ***********************");
//Console.ReadKey();
//opty = new ExhaustiveSearch(probTest1.SpaceDescriptor, optimize.minimize);
//opty.Add(probTest1);
///* No convergence criteria is needed as the process concludes when all
// * states have been visited but for this problem that is 4 trillion states.*/
//opty.ConvergenceMethods.Clear();
///* if you DID KNOW the best, you can include a criteria like...*/
//opty.ConvergenceMethods.Add(new ToKnownBestXConvergence(new[] { 3.0, 3.0 }, 0.0000001));
//var timer = Stopwatch.StartNew();
//var fStar = opty.Run(out xStar);
///* you probably will never see this process complete. Even with the added
// * convergence criteria (which is not factored into the estimated time of
// * completion), you are probably looking at 1 to 2 years. */
//printResults(opty, xStar, fStar, timer);
/***********Gradient Based Optimization with Steepest Descent****************/
//SearchIO.output("***********Gradient Based Optimization with Steepest Descent****************");
//Console.ReadKey();
//opty = new GradientBasedOptimization();
//opty.Add(probTest1);
//abstractSearchDirection searchDirMethod = new SteepestDescent();
//opty.Add(searchDirMethod);
////abstractLineSearch lineSearchMethod = new ArithmeticMean(0.0001, 1, 100);
////abstractLineSearch lineSearchMethod = new DSCPowell(0.0001, 1, 100);
//abstractLineSearch lineSearchMethod = new GoldenSection(0.0001, 1);
//opty.Add(lineSearchMethod);
//opty.Add(new squaredExteriorPenalty(opty, 10));
///* since this is not a population-based optimization method, we need to remove the MaxSpan criteria. */
//opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
//timer = Stopwatch.StartNew();
//fStar = opty.Run(out xStar);
//printResults(opty, xStar, fStar, timer);
///***********Gradient Based Optimization with Fletcher-Reeves****************/
//SearchIO.output("***********Gradient Based Optimization with Fletcher-Reeves****************");
//Console.ReadKey();
///* we don't need to reset (invoke the constructor) for GradientBasedOptimization since we are only
// * change the seaach direction method. */
//searchDirMethod = new FletcherReevesDirection();
///* you could also try the remaining 3 search direction methods. */
////searchDirMethod = new CyclicCoordinates();
////searchDirMethod = new BFGSDirection();
////searchDirMethod = new PowellMethod(0.001, 6);
//opty.Add(searchDirMethod);
//timer = Stopwatch.StartNew();
//opty.ResetFunctionEvaluationDatabase();
//fStar = opty.Run(out xStar);
//printResults(opty, xStar, fStar, timer);
///******************Generalized Reduced Gradient***********************/
//SearchIO.output("******************Generalized Reduced Gradient***********************");
//Console.ReadKey();
//opty = new GeneralizedReducedGradientActiveSet();
//opty.Add(probTest1);
//opty.Add(new squaredExteriorPenalty(opty, 10));
//opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
//timer = Stopwatch.StartNew();
//fStar = opty.Run(out xStar);
//printResults(opty, xStar, fStar, timer);
/* GRG is the ONLY one here that handles constraints explicity. It find the
* optimal very quickly and accurately. However, many of the other show a
* better value of f*, this is because they are using an imperfect penalty
* function (new squaredExteriorPenalty(opty, 10)). While it seems that GRG
* includes it as well, it is only used in the the line search method. */
/******************Random Hill Climbing ***********************/
probTest1.SpaceDescriptor = new DesignSpaceDescription(new[] { new VariableDescriptor(-5000, 5000, 0.1),
new VariableDescriptor(-5000, 5000, 0.1) });
SearchIO.output("******************Random Hill Climbing ***********************");
Console.ReadKey();
opty = new HillClimbing();
opty.Add(probTest1);
opty.Add(new squaredExteriorPenalty(opty, 8));
opty.Add(new RandomNeighborGenerator(probTest1.SpaceDescriptor));
opty.Add(new KeepSingleBest(optimize.minimize));
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
/* the deltaX convergence needs to be removed as well, since RHC will end many iterations
* at the same point it started. */
opty.ConvergenceMethods.RemoveAll(a => a is DeltaXConvergence);
var timer = Stopwatch.StartNew();
var fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Exhaustive Hill Climbing ***********************/
SearchIO.output("******************Exhaustive Hill Climbing ***********************");
Console.ReadKey();
/* Everything else about the Random Hill Climbing stays the same. */
opty.Add(new ExhaustiveNeighborGenerator(probTest1.SpaceDescriptor));
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
/******************Simulated Annealing***********************/
SearchIO.output("******************Simulated Annealing***********************");
Console.ReadKey();
opty = new SimulatedAnnealing(optimize.minimize);
opty.Add(probTest1);
opty.Add(new squaredExteriorPenalty(opty, 10));
opty.Add(new RandomNeighborGenerator(probTest1.SpaceDescriptor, 100));
opty.Add(new SACoolingSangiovanniVincentelli(100));
opty.ConvergenceMethods.RemoveAll(a => a is MaxSpanInPopulationConvergence);
/* the deltaX convergence needs to be removed as well, since RHC will end many iterations
* at the same point it started. */
opty.ConvergenceMethods.RemoveAll(a => a is DeltaXConvergence);
timer = Stopwatch.StartNew();
fStar = opty.Run(out xStar);
printResults(opty, xStar, fStar, timer);
}
/// <summary>
/// This constructor creates a default network to work with.
/// </summary>
/// <param name="aoe2Directory">Directory of your age of empires game.</param>
/// <param name="aiScript">Name of your ai script that you want to generate.</param>
public AITrainingModule(string aoe2Directory, string aiScript)
{
_logger = Program.Logger;
_logger.Info("Initializing Training module.");
_aoe2Directory = aoe2Directory;
_aiScript = aiScript;
_numberOfInitialCycles = 100000;
_numberOfContinuousCycles = 10000;
_numberOfNeuronRefreshes = 0;
// Keep track of random number of neurons here.
int numberOfInputNeurons = 10;
int numberOfHiddenNeurons = 10;
int numberOfOutputNeurons = 8;
double learningRate = 0.25;
_errorList = new LinkedList <double>();
LinearLayer inputLayer = new LinearLayer(numberOfInputNeurons);
SigmoidLayer hiddenLayer = new SigmoidLayer(numberOfHiddenNeurons);
SigmoidLayer outputLayer = new SigmoidLayer(numberOfOutputNeurons);
// Wow, how hidden is really hidden. So that I think these connectors do is
// insert themselves as part of the various layers. This really hides the hidden
// layer from the network, as only the connectors then modify the hidden layer.
// In other words "trained".
var conn1 = new BackpropagationConnector(inputLayer, hiddenLayer);
var conn2 = new BackpropagationConnector(hiddenLayer, outputLayer);
_nueralNetwork = new BackpropagationNetwork(inputLayer, outputLayer);
_nueralNetwork.SetLearningRate(learningRate);
_nueralNetwork.EndEpochEvent += BackgroundTasks; // hehe call back methods.
// Needs to make initial configuration of AI.
_logger.Warn("Begining initial training cycle...");
// If this module is being instantiated for the first time, create a comprehensive
// knowledge base/ network so it can continue where it last left off. Tweak the
// query to filter outliers.
_rawMgxStats = StreamUtilities.GetAiDataSet();
_nueralNetwork.EndEpochEvent +=
(object networkInput, TrainingEpochEventArgs args) =>
{
if (_percent % (_numberOfInitialCycles / 100) == 0 && _percent > 0)
{
_logger.Info(string.Format("Precent completed {0}%", _percent / (_numberOfInitialCycles / 100)));
}
_percent++;
};
_nueralNetwork.Learn(CompileTrainingSet(_rawMgxStats), _numberOfInitialCycles);
_logger.Warn("Finished initial training cycle.");
// Get the latest dataset so we can generate some kind of graph and push the data set to database.
var knowledgeBase = StreamUtilities.GetLatestAiEntry().ToList();
var aiTrainingSet = CompileTrainingSet(knowledgeBase);
_currentStats = knowledgeBase[knowledgeBase.Count - 1];
// push data, hacked to show simple output
//double[] veryFirstInput
// =
//{
// 0.2,0.2,0.2,0.2,0.2,
// 0.2,0.2,0.2,0.2,0.2
//};
_climber = new HillClimbing(aiTrainingSet[0].InputVector, _nueralNetwork);
// _climber = new HillClimbing(veryFirstInput, _nueralNetwork);
// Hardcoding these dimensions
int ordinalTracker = 1;
for (int i = 0; i < 4; i++)
{
for (int j = i + 1; j < 5; j++)
{
//write normalized data
StreamUtilities.SubmitPlotableData(_climber.GenerateTopologyData(i, j), ordinalTracker);
//write unnormalized data.
StreamUtilities.SubmitPlotableUnnormailizedData(_climber.GenerateUnormalizedTopologyData(i, j), ordinalTracker);
_logger.Debug(string.Format("Writing Axis{0} and Axis{1} with ordinal {2}.", i, j, ordinalTracker));
ordinalTracker++;
}
}
// If input table == output, then a new game is needed
if (StreamUtilities.CheckIfNewGameIsNeeded())
{
TriggerNewGame();
}
}
private void ManipulatorProperties(Manipulator manipulator)
{
ImGui.Checkbox($"Show collider", ref manipulator.ShowCollider);
ImGui.InputFloat3("Goal", ref manipulator.Goal);
ImGui.Separator();
if (ImGui.BeginTabBar("ManipulatorTabs"))
{
if (ImGui.BeginTabItem("Solver"))
{
int currType = (int)manipulator.Controller.InverseKinematicsSolver.Type; // TODO: refactor?
int newType = currType;
ImGui.Combo("Type", ref newType, InverseKinematicsSolver.Types, InverseKinematicsSolver.Types.Length);
// change inverse kinematics solver if queried
if (newType != currType)
{
switch ((InverseKinematicsSolverType)newType)
{
case InverseKinematicsSolverType.JacobianTranspose:
manipulator.Controller.InverseKinematicsSolver = JacobianTranspose.Default();
break;
case InverseKinematicsSolverType.JacobianPseudoinverse:
manipulator.Controller.InverseKinematicsSolver = JacobianPseudoinverse.Default();
break;
case InverseKinematicsSolverType.DampedLeastSquares:
manipulator.Controller.InverseKinematicsSolver = DampedLeastSquares.Default();
break;
case InverseKinematicsSolverType.HillClimbing:
manipulator.Controller.InverseKinematicsSolver = HillClimbing.Default();
break;
}
}
ImGui.Separator();
// inverse kinematics solver properties
ImGui.InputFloat("Threshold", ref manipulator.Controller.InverseKinematicsSolver.Threshold);
ImGui.InputInt("Max iterations", ref manipulator.Controller.InverseKinematicsSolver.MaxIterations);
if (manipulator.Controller.InverseKinematicsSolver is JacobianTranspose jacobianTranspose)
{
ImGui.InputFloat("Base damping coefficient", ref jacobianTranspose.Damping);
}
else if (manipulator.Controller.InverseKinematicsSolver is JacobianPseudoinverse jacobianInverse)
{
// TODO: input something here?
}
else if (manipulator.Controller.InverseKinematicsSolver is DampedLeastSquares dampedLeastSquares)
{
ImGui.InputFloat("Damping coefficient", ref dampedLeastSquares.Damping);
}
else if (manipulator.Controller.InverseKinematicsSolver is HillClimbing hillClimbing)
{
ImGui.InputFloat("Max step size", ref hillClimbing.MaxStepSize);
}
ImGui.EndTabItem();
}
if (ImGui.BeginTabItem("Planner"))
{
int currType = (int)manipulator.Controller.PathPlanner.Type;
int newType = currType;
ImGui.Combo("Type", ref newType, PathPlanner.Types, PathPlanner.Types.Length);
// change path planner if queried
if (newType != currType)
{
switch ((PathPlannerType)newType)
{
case PathPlannerType.RRT:
manipulator.Controller.PathPlanner = RRT.Default();
break;
case PathPlannerType.ARRT:
manipulator.Controller.PathPlanner = ARRT.Default(manipulator);
break;
case PathPlannerType.GeneticAlgorithm:
manipulator.Controller.PathPlanner = GeneticAlgorithm.Default();
break;
}
}
ImGui.Separator();
// path planner properties
ImGui.Checkbox("Collision check", ref manipulator.Controller.PathPlanner.CollisionCheck);
ImGui.InputInt("Max iterations", ref manipulator.Controller.PathPlanner.MaxIterations);
ImGui.InputFloat("Threshold", ref manipulator.Controller.PathPlanner.Threshold);
if (manipulator.Controller.PathPlanner is RRT rrt)
{
ImGui.Checkbox($"Show tree", ref rrt.ShowTree);
ImGui.InputFloat("Step", ref rrt.Step);
ImGui.Checkbox("Enable trimming", ref rrt.EnableTrimming);
ImGui.InputInt("Trim period", ref rrt.TrimPeriod);
if (rrt is ARRT arrt)
{
ImGui.InputInt("Attractors count", ref arrt.AttractorsCount);
}
else
{
ImGui.InputInt("Goal bias period", ref rrt.GoalBiasPeriod);
}
}
else if (manipulator.Controller.PathPlanner is GeneticAlgorithm geneticAlgorithm)
{
// TODO: add genetic algorithm related properties
ImGui.InputInt("Offspring size", ref geneticAlgorithm.OffspringSize);
ImGui.InputInt("Survival size", ref geneticAlgorithm.SurvivalSize);
ImGui.InputInt("Bezier control points", ref geneticAlgorithm.BezierControlPointsCount);
ImGui.InputFloat("Bezier step", ref geneticAlgorithm.BezierStep);
}
ImGui.EndTabItem();
}
if (ImGui.BeginTabItem("Controller"))
{
// TODO: add motion control related properties
ImGui.EndTabItem();
}
ImGui.EndTabBar();
}
}
static void Main(string[] args)
{
//State state = new State();
//Stack<string> inp = new Stack<string>();
//inp.Push("C");
//inp.Push("B");
//inp.Push("D");
//inp.Push("A");
//inp.Push("E");
//Stack<string> goal = new Stack<string>();
//goal.Push("E");
//goal.Push("D");
//goal.Push("C");
//goal.Push("B");
//goal.Push("A");
//var result = state.GetRouteWithHillClimbng(inp, goal);
List <WorkToDo> workToDos = new List <WorkToDo>()
{
new WorkToDo()
{
Name = "A", Complexity = 9
},
new WorkToDo()
{
Name = "B", Complexity = 7
},
new WorkToDo()
{
Name = "C", Complexity = 16
},
new WorkToDo()
{
Name = "D", Complexity = 21
},
new WorkToDo()
{
Name = "E", Complexity = 15
}
};
List <WorkerMan> workerMens = new List <WorkerMan>()
{
new WorkerMan()
{
Name = "1", Quality = 2, WorkingMinutes = 50
},
new WorkerMan()
{
Name = "2", Quality = 1, WorkingMinutes = 30
},
new WorkerMan()
{
Name = "3", Quality = 2, WorkingMinutes = 70
},
new WorkerMan()
{
Name = "4", Quality = 2, WorkingMinutes = 50
},
new WorkerMan()
{
Name = "5", Quality = 3, WorkingMinutes = 70
},
new WorkerMan()
{
Name = "6", Quality = 2, WorkingMinutes = 32
},
new WorkerMan()
{
Name = "7", Quality = 1, WorkingMinutes = 44
},
new WorkerMan()
{
Name = "8", Quality = 1, WorkingMinutes = 90
}
};
// OptimalizationTransform optimalizationTransform = new OptimalizationTransform(workToDos, workerMens, 1000000, 155);
// var solution = optimalizationTransform.HillClimbing();
IHillClimbProblem problem = new HillClimbingProblem(workToDos, workerMens, 100);
ISolver solver = new HillClimbing(problem, 155);
ISolution solution = solver.Start();
IGeneticAlgorithmProblem geneticAlgorithmProblem = new GeneticAlgorithmProblem(workToDos, workerMens, 100);
ISolver genSolver = new GeneticAlgorithm.GeneticAlgorithm(geneticAlgorithmProblem);
ISolution genSolution = genSolver.Start();
IParticleSwarmProblem particleSwarmProblem = new ParticleSwarmProblem(workToDos, workerMens, 100, 150);
ISolver psSolver = new ParticleSwarmOptimaziation(particleSwarmProblem);
ISolution psSol = psSolver.Start();
Console.ReadLine();
}