class NNC
{
public:		// constructor / destructor

	NNC();
	~NNC();

public:		// root methods

	void	Param(float, int);						// change nnc structure

	int		LoadWts(char *);						// file -> memory
	int		SaveWts(char *);						// memory -> file
	int		MakeWts();								// random generation

	void	FwdProp();								// forward propagation
	void	BckProp();								// find each node's errors
	void	AdjWts();								// back propagation

private:	// sub: methods

	double	absol(double);							// magnitude
	double	Lin_func(double);						// linear filter
	double  Sig_func(double);						// sigmoid filter

public:		// variables from NNCview.cpp

	int		nWts;									// number of weigths
	int		nofN;									// number of nodes
	int		func;									// filter type for each layer's output

	double	Er, Eg, Eb;								// mean error (P - out)
	double	mEr, mEg, mEb;							// maximum magnitude error
	double	rt;										// learning rate per layer

	BYTE	*in_img, *de_img;						// pointer to input and desire images

private:	// internal variables

	int		iErr;									// error value
	DWORD	len;									// length of wts file
	
	double	*in, *out;								// input & output layer
	double	*hid;									// hidden layers
	double	*P;										// desire output
	double	*E;										// derivative of error at layer
	
	double	*Wts;									// weights
	double	e;										// natural log value
};