public extern static IntPtr MlpInstantiate(
uint numberOfInputNeurons,
uint numberOfHiddenLayers,
[MarshalAs(UnmanagedType.LPArray)] uint[] numberOfUnitsForHiddenLayers,
uint numberOfUnitsOfOutputLayer,
bool useBias,
ActivationNeuroMode mode);
/// <summary>
/// Build a new M-Adeline network
/// </summary>
/// <param name="inputUnits">Number of input neurons</param>
/// <param name="outputUnits">Number of output neurons</param>
/// <param name="bias">Bias flag (true raccomanded)</param>
/// <param name="mode">Neurons activation mode</param>
public MAdeline(uint inputUnits, uint outputUnits = 1, bool bias = true, ActivationNeuroMode mode = ActivationNeuroMode.SIGMOIDAL)
{
_inputSize = inputUnits;
_outputSize = outputUnits;
_transferFunction = mode;
useBias = bias;
}
/// <summary>
/// Build a new multi layered Elman feedforward network (mlp with memory)
/// </summary>
/// <param name="inputUnits">Number of input neurons</param>
/// <param name="hiddenUnits">Number of hidden neurons for each hidden layers</param>
/// <param name="hiddenLayers">Number of hidden layers</param>
/// <param name="outputUnits">Number of output neurons</param>
/// <param name="bias">Bias flag (true raccomanded)</param>
/// <param name="mode">Neurons activation mode</param>
public ElmanMlp(uint inputUnits, uint[] hiddenUnits, uint hiddenLayers, uint outputUnits, bool bias = true, ActivationNeuroMode mode = ActivationNeuroMode.SIGMOIDAL)
{
if (hiddenUnits.Count() != hiddenLayers)
{
throw (new ArgumentException("HiddenUnits inadequate for hiddenLayers."));
}
else
{
_outputSize = outputUnits;
_inputSize = inputUnits;
_hiddenUnits = hiddenUnits;
_hiddenLayers = hiddenLayers;
_transferFunction = mode;
useBias = bias;
}
}
public extern static IntPtr mAdelineInstantiate(
uint numberOfInputNeurons,
uint numberOfOutputNeurons,
bool useBias,
ActivationNeuroMode mode);
