public void Test(FCM fcm, int generations, string[] args)
{
var startTime = DateTime.Now;
for (int generation = 0; generation < generations; generation++)
{
Console.WriteLine("\nTest generation: {0} of {1}", generation, generations);
LoggerFactory.SetLogLevel(LogLevel.Warning);
LoggerFactory.DeactivateConsoleLogging();
SimulationStarter task = SimulationStarter.Start(this.modelDescription, args);
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
SimulationWorkflowState loopResults = task.Run();
if (loopResults.IsFinished)
{
stopWatch.Stop();
// Console.WriteLine($"Simulation execution finished in {stopWatch.ElapsedMilliseconds / 1000:N2} seconds");
stopWatch.Restart();
fcm.Run(false);
stopWatch.Stop();
// Console.WriteLine($"FCM finished in {stopWatch.ElapsedMilliseconds / 100:N2} seconds");
GC.Collect();
}
}
}
public async Task <BaseDto <NotifInput> > Handle(CreateNotificationCommand request, CancellationToken cancellationToken)
{
var recieved = BackgroundJob.Enqueue(() => Subscriber.Recieved());
var Data = request.data.attributes;
// Add Notification Data
var notificationData = new Notification_Model
{
title = Data.title,
message = Data.message
};
_context.notifications.Add(notificationData);
await _context.SaveChangesAsync(cancellationToken);
// Get Id for notification_id in notification logs
var Id = await _context.notifications.ToListAsync();
var userData = await _context.users.FindAsync(Data.from);
// Add Notification Log Data
foreach (var logs in Data.targets)
{
_context.notificationLogs.Add(new NotificationLogs
{
notification_id = Id.Last().id,
type = Data.type,
from = Data.from,
target = logs.id,
email_destination = logs.email_destination
});
await _context.SaveChangesAsync();
if (logs.email_destination != null)
{
BackgroundJob.Enqueue(() => EmailSender.Send(notificationData.title, notificationData.message, userData.address, userData.name, logs.email_destination));
}
BackgroundJob.Enqueue(() => FCM.SendAsync(notificationData.title, notificationData.message));
}
return(new BaseDto <NotifInput>
{
message = "Success add Notification Data",
success = true,
data = Data
});
}
public IHttpActionResult kiemtra(string device, string token)
{
try
{
FCM fcm = db.FCMs.FirstOrDefault(e => e.device.Equals(device) && e.token.Equals(token));
if (fcm == null)
{
return(StatusCode(HttpStatusCode.NotFound));
}
return(Ok(fcm));
}
catch (Exception ex)
{
return(BadRequest(ex.Message));
}
}
public IHttpActionResult Xoa(int id)
{
try
{
FCM fcm = db.FCMs.FirstOrDefault(e => e.id == id);
if (fcm == null)
{
return(StatusCode(HttpStatusCode.NoContent));
}
db.FCMs.DeleteOnSubmit(fcm);
db.SubmitChanges();
return(Ok());
}
catch (Exception ex)
{
return(BadRequest(ex.Message));
}
}
public void Train(FCM fcm, int generations, float targetFitness, Boolean saveGenomes, string[] args)
{
var startTime = DateTime.Now;
for (int generation = 0; generation < generations; generation++)
{
Console.WriteLine("\nGeneration: {0} of {1}", generation, generations);
LoggerFactory.SetLogLevel(LogLevel.Warning);
LoggerFactory.DeactivateConsoleLogging();
SimulationStarter task = SimulationStarter.Start(this.modelDescription, args);
Stopwatch stopWatch = new Stopwatch();
stopWatch.Start();
SimulationWorkflowState loopResults = task.Run();
if (loopResults.IsFinished)
{
stopWatch.Stop();
Console.WriteLine($"Simulation execution finished in {stopWatch.ElapsedMilliseconds / 1000:N2} seconds");
stopWatch.Restart();
fcm.Run(true, targetFitness, saveGenomes);
stopWatch.Stop();
Console.WriteLine($"FCM finished in {stopWatch.ElapsedMilliseconds / 100:N2} seconds");
GC.Collect();
}
}
string filename = FileUtils.CreateTimestampedFilename("Genomes", DateTime.Now, ".csv");
fcm.WriteGenomes(filename);
fcm.WriteGenomes("genomes.csv");
}
private void backgroundWorker1_DoWork(object sender, DoWorkEventArgs e)
{
backgroundWorker.ReportProgress(0, "working...");
filteredImage = (Bitmap)OriImage.Image.Clone();
int numCluster = (int)numericUpDown1.Value;
int maxIterations = (int)numericUpDown2.Value;
double accuracy = (double)numericUpDown3.Value;
List <ClusterPoint> points = new List <ClusterPoint>();
for (int row = 0; row < originalImage.Width; ++row)
{
for (int col = 0; col < originalImage.Height; ++col)
{
Color c2 = originalImage.GetPixel(row, col);
points.Add(new ClusterPoint(row, col, c2));
}
}
List <ClusterCentroid> centroids = new List <ClusterCentroid>();
//Create random points to use a the cluster centroids
Random random = new Random();
for (int i = 0; i < numCluster; i++)
{
int randomNumber1 = random.Next(sourceImage.Width);
int randomNumber2 = random.Next(sourceImage.Height);
centroids.Add(new ClusterCentroid(randomNumber1, randomNumber2, filteredImage.GetPixel(randomNumber1, randomNumber2)));
}
FCM alg = new FCM(points, centroids, 2, filteredImage, (int)numericUpDown1.Value);
int k = 0;
do
{
if ((backgroundWorker.CancellationPending == true))
{
e.Cancel = true;
break;
}
else
{
k++;
alg.J = alg.CalculateObjectiveFunction();
alg.CalculateClusterCentroids();
alg.Step();
double Jnew = alg.CalculateObjectiveFunction();
Console.WriteLine("Run method i={0} accuracy = {1} delta = {2}", k, alg.J, Math.Abs(alg.J - Jnew));
precision.Text = "Precision " + Math.Abs(alg.J - Jnew);
//Format and Display the TimeSpan Value;
string elapsedTime = String.Format("{0:00}:{1:00}:{2:00}.{3:00}", stopWatch.Elapsed.Hours, stopWatch.Elapsed.Minutes, stopWatch.Elapsed.Seconds, stopWatch.Elapsed.Milliseconds / 10);
duration.Text = "Duration: " + elapsedTime;
SegmentedImage.Image = (Bitmap)alg.getProcessedImage;
backgroundWorker.ReportProgress((100 * k) / maxIterations, "Iteration " + k);
if (Math.Abs(alg.J - Jnew) < accuracy)
{
break;
}
}
} while (maxIterations > k);
Console.WriteLine("Done..");
//Save the Segmented Image
SegmentedImage.Image = (Bitmap)alg.getProcessedImage.Clone();
alg.getProcessedImage.Save("SegmentedImage.png");
//Create a new image for each cluster in order to extratc the feature
double[,] Matrix = alg.U;
Bitmap[] bmapArray = new Bitmap[centroids.Count];
for (int i = 0; i < centroids.Count; i++)
{
bmapArray[i] = new Bitmap(sourceImage.Width, sourceImage.Height, PixelFormat.Format32bppRgb);
}
for (int j = 0; j < points.Count; j++)
{
for (int i = 0; i < centroids.Count; i++)
{
ClusterPoint p = points[j];
if (Matrix[j, i] == p.ClusterIndex)
{
bmapArray[i].SetPixel((int)p.X, (int)p.Y, p.OriginalPixelColor);
}
}
}
//Save the image for each segmented Cluster
for (int i = 0; i < centroids.Count; i++)
{
bmapArray[i].Save("Cluster" + i + ".png");
}
imgCluster1 = (Bitmap)bmapArray[0].Clone();
imgCluster3 = (Bitmap)bmapArray[2].Clone();
imgCluster2 = (Bitmap)bmapArray[1].Clone();
pictClus1.Image = imgCluster1;
pictClus2.Image = imgCluster2;
pictClus3.Image = imgCluster3;
// Resource Cleanup..
backgroundWorker.ReportProgress(100, "Done in " + k + "iteration.");
//getDataCluster();
for (int i = 0; i < points.Count; i++)
{
points[i] = null;
}
for (int i = 0; i < centroids.Count; i++)
{
centroids[i] = null;
}
alg = null;
}
