protected async Task BeginTrainNetwork()
{
await Task.Factory.StartNew(() => {
lock (locker) {
ReadyToTrain = false;
Training = true;
IsRunning = true;
Status = "Training";
var localIteration = 0;
var rand = new Random();
var width = (int)Math.Sqrt(network.Layers[0].Neurons.Length);
var trainer = new SOMTrainer(width, width, network);
var fixedLearningRate = LearningRate / 10.0;
var driftingLearningRate = fixedLearningRate * 9.0;
var maxIterations = Iterations + CurrentIteration;
var input = new double[3];
while (CurrentIteration < maxIterations && IsRunning)
{
trainer.LearningRate = driftingLearningRate * (Iterations - localIteration) / Iterations + this.LearningRate;
trainer.LearningRadius = (double)ClusterRadius * (Iterations - localIteration) / Iterations;
for (var i = 0; i < input.Length; i++)
{
input[i] = rand.NextDouble();
}
trainer.Run(input);
if (CurrentIteration % RefreshRate == 0)
{
try {
BitMap.Dispatcher.Invoke(UpdateMap);
}
catch (TaskCanceledException) { }
}
CurrentIteration++;
localIteration++;
}
BitMap.Dispatcher.Invoke(UpdateMap);
Status = "Ready";
Training = false;
ReadyToTrain = true;
}
});
}
public static void Demo()
{
SOMNetwork network = SOMNetwork.Create(9, 3);
network.Display();
var dataSet = DataGenerator.GenerateDataSet3();
var som = new SOM(network);
List <SOMSetting> setting = new List <SOMSetting>();
setting.Add(new SOMSetting()
{
FromEpoch = 0, ToEpoch = 500, LearningRate = 0.8, Radius = 2
});
setting.Add(new SOMSetting()
{
FromEpoch = 501, ToEpoch = 1000, LearningRate = 0.4, Radius = 1
});
SOMTrainer trainer = new SOMTrainer(network, 1000, som, setting);
//Normalizer2 normalizer = new Normalizer2();
Normalizer2 normalizer = new Normalizer2();
normalizer.Fit(dataSet.XList);
var normalizedX = normalizer.Normalize(dataSet.XList);
trainer.Fit(normalizedX);
List <List <double> > convertedX = trainer.GetOutputs(normalizedX);
List <List <double> > convertedX2 = trainer.GetOutputs2(normalizedX);
network.Display();
Console.WriteLine("OLD VECTORS===>");
Utils.DisplayListList(dataSet.XList);
Console.WriteLine("OLD VECTORS(NORMALIZED)===>");
Utils.DisplayListList(normalizedX);
Console.WriteLine("NEW CLUSTERED VECTORS===>");
Utils.DisplayListList(convertedX);
Console.WriteLine("NEW CLUSTERED VECTORS(DISTANCE)===>");
Utils.DisplayListList(convertedX2);
StringBuilder sb_x = new StringBuilder();
StringBuilder sb_y = new StringBuilder();
foreach (var l in convertedX2)
{
var maxIndex = l.IndexOf(l.Max());
for (var x = 0; x < network.OutputNeuronCountPerDim; x++)
{
for (var y = 0; y < network.OutputNeuronCountPerDim; y++)
{
if (x * network.OutputNeuronCountPerDim + y == maxIndex)
{
sb_x.Append(x + ",");
sb_y.Append(y + ",");
Console.Write("(" + x + "," + y + "),");
}
}
}
Console.WriteLine();
}
StringBuilder sb = new StringBuilder();
sb.AppendLine("import matplotlib.pyplot as plt");
sb.AppendLine("x1=[" + sb_x.ToString().TrimEnd(",".ToCharArray()) + "]");
sb.AppendLine("y1=[" + sb_y.ToString().TrimEnd(",".ToCharArray()) + "]");
sb.AppendLine("plt.plot(x1,y1,'b^')");
sb.AppendLine("plt.show()");
var file = System.IO.Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "display.py");
File.WriteAllText(file, sb.ToString());
Console.WriteLine("saved to path: " + file);
System.Diagnostics.Process.Start("C:\\ProgramData\\Anaconda3\\envs\\keras\\python.exe", "\"" + file + "\"");
}
