//private void BTN_CLASSIFY_Click(object sender, EventArgs e)
//{
// ClassifySpace();
//}
private void ClassifySpace()
{
double value;
for (int x = -35; x < 35; x++)
{
for (int y = -35; y < 35; y++)
{
if (x == 1 && y == 1)
{
Console.Write("here");
}
if (x == -1 && y == 1)
{
Console.Write("here");
}
value = net.ComputeNetworkOutput(new Input(new double[] { x, y }, new double[] { 1 }))[0];
PaintPoint(x, y, value);
}
}
PaintInput();
MyDelegates.SetBMPValue(PCT_CANVAS, canvas);
}
private void BuildNet()
{
List <Layer> layers;
int hiddenNuerons, it;
double rate, lower;
double error;
FunctionType Ftype;
layers = new List <Layer>();
hiddenNuerons = 7;
rate = 0.5;
Ftype = FunctionType.SIGMOID;
layers.Add(new Layer(inputs[0].Features.Length, inputs[0].Features.Length, rate, Ftype));
layers.Add(new Layer(hiddenNuerons, layers[layers.Count - 1].Neurons.Length, rate, Ftype));
layers.Add(new Layer(inputs[0].DesiredValue.Length, layers[layers.Count - 1].Neurons.Length, rate, Ftype));
net = new NeuralNetwork(layers.ToArray());
net.SetTrainSet(inputs.ToArray());
error = double.MaxValue;
lower = double.MaxValue;
it = 0;
while (error > 0.001 && it++ < 10000000)
{
error = net.Train();
if (error < lower)
{
lower = error;
}
if (it % 50 == 0)
{
MyDelegates.SetTextValue(LBL_IT, "epochs : " + it);
MyDelegates.SetTextValue(LBL_ERROR, "Error : " + Math.Round(error, 5));
MyDelegates.SetTextValue(LBL_LOWEST, "Lowest : " + Math.Round(lower, 9));
}
if (it % 1000 == 0)
{
try
{
ClassifySpace();
}catch (Exception) {}
}
}
}
