static void Main(string[] args)
{
int[] layout = { 1, 6, 1 };
ActivationFunction[] activationFunctions = { Sigmoid(), Sigmoid() };
FFANN ffann = new FFANN(layout, activationFunctions);
int n = ffann.WeightCount();
double[] weights = new double[n];
Random rnd = new Random();
for (int i = 0; i < n; ++i)
{
weights[i] = rnd.NextDouble();
}
ffann.SetWeights(weights);
Dataset dataset = new Dataset();
dataset.Read("dummyData.txt");
Console.WriteLine(ffann.CalculateError(dataset));
Backpropagation train = new Backpropagation(ffann, 0.1, dataset);
train.MaxIteration = 100000;
train.MaxError = 1e-6;
train.Train(1);
for (int i = 0; i < dataset.Size; ++i)
{
Console.WriteLine(ffann.GetOutput(dataset.GetInput(i))[0].ToString("0.0000") + " " + dataset.GetOutput(i)[0]);
}
}
private void canvas_MouseUp(object sender, MouseEventArgs e)
{
List <PointF> points = canvas.GetPoints();
int n = _ffann.InputSize() / 2;
var input = PointFListToDoubleArray(ScalePoints(points, n));
double[] output = _ffann.GetOutput(input);
Label label = Encoder.Decode(output);
lbl_label.Text = label.ToString();
}
public double Evaluate(Chromosome c)
{
double error = 0;
_ffann.SetWeights(c._values);
foreach (var data in _data)
{
double x = data.X;
double y = data.Y;
double[] input = { x, y };
double[] output = _ffann.GetOutput(input);
error += Math.Pow(output[0] - data.Z1, 2);
error += Math.Pow(output[1] - data.Z2, 2);
error += Math.Pow(output[2] - data.Z3, 2);
}
error /= 3;
error /= _data.Count;
c.Cost = error;
return(error);
}