private void clearInputOutput(MNeuronStack neuronStack)
{
foreach (MNeuron x in neuronStack.Stack)
{
x.Inputs.Clear();
//x.Weights.Clear();
}
}
private void computeOutput(MHiddenLayerHeader header, MNeuronStack neuronStack)
{
foreach (MNeuron x in neuronStack.Stack)
{
x.Output = ActivationFunctions.computeOutput(header, x);
Console.WriteLine("test_output " + x.Output);
x.OutputTextBox.Text = x.Output.ToString();
}
}
private void computeGlobalInput(MHiddenLayerHeader header, MNeuronStack neuronStack)
{
var inputFunction = header.InputFunction;
foreach (MNeuron x in neuronStack.Stack)
{
x.GlobalInput = InputFunctions.computeGlobalInput(inputFunction, x);
Console.WriteLine("test_input " + x.GlobalInput);
Console.WriteLine(inputFunction);
}
}
private void removeInputOutput(MNeuronStack neuronStack, uint diff)
{
foreach (MNeuron x in neuronStack.Stack)
{
for (uint i = 0; i < diff; ++i)
{
x.Inputs.RemoveAt(x.Inputs.Count - 1);
x.Weights.RemoveAt(x.Weights.Count - 1);
}
}
}
// Generate Input
//private FlowLayoutPanel generateInput(String text)
//{
// var input = new FlowLayoutPanel();
// var label = new Label();
// label.Text = text;
// label.
//}
private void addInputOutput(MNeuronStack neuronStack, uint diff)
{
foreach (MNeuron x in neuronStack.Stack)
{
for (uint i = 0; i < diff; ++i)
{
var input = 0.0m;
var weight = 0.0m;
x.Inputs.Add(input);
x.Weights.Add(weight);
}
}
}
private void addInputsOutputsHiddenLayers(MNeuronStack from, MNeuronStack to)
{
var size = from.Stack.Count;
clearInputOutput(to);
addInputOutput(to, (uint)size);
var list = from.Stack.Reverse().ToList <MNeuron>();
foreach (MNeuron x in to.Stack)
{
for (int i = 0; i < list.Count; ++i)
{
x.Inputs[i] = list[i].Output;
}
}
}
