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);