/****************************************************************************

    Neural Network Library
    Copyright (C) 1998 Daniel Franklin

    This library is free software; you can redistribute it and/or
    modify it under the terms of the GNU Library General Public
    License as published by the Free Software Foundation; either
    version 2 of the License, or (at your option) any later version.

    This library is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
    Library General Public License for more details.

    You should have received a copy of the GNU Library General Public
    License along with this library; if not, write to the Free Software
    Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA
    02111-1307, USA.

 ****************************************************************************/

#ifndef _NNWORK_H
#define _NNWORK_H
#include "neuron.h"

#define ALL 0
#define INPUT 1
#define HIDDEN 2
#define OUTPUT 3

// Sigmoid function. Basically a differentiable threshold function.
double sigmoid (double);

// This class implements a simple three-layer backpropagation network.
class nnwork {
// Initialise with the dimensions of the network (input, hidden, output)
public:
	nnwork (int, int, int);
	nnwork ();
	nnwork (char*);
	~nnwork ();
	
	
	// returns dims of network - argument is either ALL, INPUT, HIDDEN or OUTPUT
	// (see above). ALL gives total nodes (useful to see if network is empty).
	int get_layersize (int);

	// Training args are input, desired output, minimum error, learning rate
	void train (double [], double [], double, double, int);

	// Run args are input data, output
	void run (double [], double []);
	
private:
	nnlayer *output_nodes;
	nnlayer *hidden_nodes;
	int input_size;
	int output_size;
	int hidden_size;
};

#endif