public IActivationFunction CreateActivationFunction()
{
IActivationFunction activationFunction = null;
Func <string, bool> equals = value => string.Equals(ActivationFunction, value, StringComparison.InvariantCultureIgnoreCase);
if (equals("UnipolarBinaryFunction"))
{
activationFunction = new UnipolarBinaryFunction();
}
else if (equals("BipolarBinaryFunction"))
{
activationFunction = new BipolarBinaryFunction();
}
else if (equals("SigmoidFunction"))
{
activationFunction = new SigmoidFunction();
}
else if (equals("TanhFunction"))
{
activationFunction = new TanhFunction();
}
else
{
Console.WriteLine($"Wrong activation function: {ActivationFunction}");
}
return(activationFunction);
}
private void Awake()
{
target = GetComponent <Tank>();
network = new GeneticOptimizeableNerualNetwork(5, 3);
var actvationFunction = new TanhFunction();
for (var i = 0; i < network.activateFunctions.Length; i++)
{
network.SetActivationFunctionForLayer(i, actvationFunction);
}
}
public static trainerParams BuildNet(FlowLayoutPanel layers, FileDialog LoadData_dlg, Result r, Telerik.WinControls.UI.RadDiagram radDiagram1)
{
Training.Trainer trainer = new Training.Trainer();
NeuralNetworkBuilder b = new NeuralNetworkBuilder();
L_ctrl_mat temp;
int neuronsnumber;
IActivatorFunction AF = null;
FunctionApplier functionApplier = new FunctionApplier();
foreach (var layer in layers.Controls)
{
temp = layer as L_ctrl_mat;
neuronsnumber = Convert.ToInt16(temp.NN_drpdn.Value);
if (ActivatorFunctions.FunctionName.SIGMOID.ToString() == temp.AF_drpdn.SelectedItem.Text)
{
AF = new SigmoidFunction();
imgs.Add(Resources.Layer__Sigmoid);
}
if (ActivatorFunctions.FunctionName.TANH.ToString() == temp.AF_drpdn.SelectedItem.Text)
{
AF = new TanhFunction();
imgs.Add(Resources.Layer_Tan_H);
}
functionApplier.ActivatorFunction = AF;
b.Layer(neuronsnumber, functionApplier, (double)temp.Lr_drpdn.Value);
}
NeuralNetwork.NeuralNetwork nn = b.Build();
string FileName = LoadData_dlg.FileName;
var tuples = DataReader.DataReader.Instance.ReadFromFile(FileName);
var inputs = tuples.Item1;
var outputs = tuples.Item2;
// StaticDivider Divider = new StaticDivider(.6,.3);
//var temp2 = Divider.Divide(inputs, outputs);
//ActivatorFunctions.FunctionName applier ;
//// test case (should belong to the second class = [0 1 0])
//var tt = nn.ForwardInput(Vector<double>.Build.DenseOfArray(new double[] { 5.5, 2.5, 4.0, 1.3 }));
Params = new trainerParams();
Params.nn = nn;
Params.Tuple = tuples;
NetGraph(nn, radDiagram1);
return(Params);
}
