#ifndef NEURAL_COLUMN_H
#define NEURAL_COLUMN_H

#include "neural/container.h"

namespace Neural
{
	template<typename sigtype>
	class Column : public Container<sigtype>
	{
	public:
		Column()
		{
		}

		virtual ~Column()
		{
		}

		virtual void finalize( int iNumInputs )
		{
			iInputs = iNumInputs;
			iWeights = 0;
			iBiases = 0;

			int iNextInputs = iInputs;
			for( typename Container<sigtype>::NodeList::iterator i =
				Container<sigtype>::getNodeList().begin(); i; i++ )
			{
				(*i)->finalize( iNextInputs );
				iNextInputs = (*i)->getNumOutputs();
				if( (i+1) )
				{
					lBuffer.append( new sigtype[iNextInputs] );
				}
				iWeights += (*i)->getNumWeights();
				iBiases += (*i)->getNumBiases();
			}
		}

		virtual void process( sigtype *aInput, sigtype *aOutput )
		{
			typename BufferList::iterator iBuf = lBuffer.begin();
			sigtype *pBuffer = aInput;
			sigtype *pNextBuffer = *iBuf;
			for( typename Container<sigtype>::NodeList::iterator i =
				Container<sigtype>::getNodeList().begin(); i; i++ )
			{
				(*i)->process( pBuffer, pNextBuffer );
				pBuffer = pNextBuffer;
				if( iBuf )
					iBuf++;
				if( iBuf )
					pNextBuffer = *iBuf;
				else
					pNextBuffer = aOutput;
			}
		}

		virtual int getNumInputs() const
		{
			return iInputs;
		}

		virtual int getNumOutputs() const
		{
			return Container<sigtype>::getNodeList().last()->getNumOutputs();
		}

		virtual int getNumWeights() const
		{
			return iWeights;
		}

		virtual int getNumBiases() const
		{
			return iBiases;
		}

	private:
		int iInputs;
		int iWeights;
		int iBiases;
		typedef Bu::List<sigtype *> BufferList;
		BufferList lBuffer;
	};
};

#endif