public void ConnectForward(Can[] _dL_dnextX, Can[] _dnextX_dOutput)
{
int out_dim = dL_dnextX.Length;
if (_dL_dnextX.Length != out_dim || _dnextX_dOutput.Length != out_dim)
throw new Exception("Neural.CellSet.Cell.ConnectForward: wrong output dimension");
for (int i = 0; i < out_dim; i++)
{
dL_dnextX[i] = _dL_dnextX[i];
dnextX_dOutput[i] = _dnextX_dOutput[i];
}
}
public void Init(int input_dim, CellType typ, int out_dim)
{
if (input_dim < 1 || out_dim < 1)
throw (new System.Exception("Neural.CellSet.Cell.Init: wrong dimension"));
switch (typ)
{
case CellType.Linear:
Function = new Delegate11(FunctionLinear);
Derivative = new Delegate12(DerivativeLinear);
break;
case CellType.Expotential:
Function = new Delegate11(FunctionExpotential);
Derivative = new Delegate12(DerivativeExpotential);
break;
case CellType.Arcustangent:
Function = new Delegate11(FunctionArcustangent);
Derivative = new Delegate12(DerivativeArcustangent);
break;
default:
throw new System.Exception("Neural.CellSet.Cell.Init: wrong cell type");
}
Type = typ;
Input = new Can[input_dim];
Output = new Can();
Weight = new Can[input_dim];
dL_dX = new Can();
dL_dWeight = new Can[input_dim];
for (int i = 0; i < input_dim; i++)
{
Weight[i] = new Can();
dL_dWeight[i] = new Can();
}
dL_dnextX = new Can[out_dim];
dnextX_dOutput = new Can[out_dim];
}
public void ConnectBackward(Can[] inputs)
{
int in_dim = Input.Length;
if (in_dim != inputs.Length)
throw new Exception("Neural.CellSet.Cell.ConnectBackward: wrong input dimension");
for (int i = 0; i < in_dim; i++)
Input[i] = inputs[i];
}
