hopefield.cpp

BP神经网络算法的VC＋源码

/*
    nn-utility (Provides neural networking utilities for c++ programmers)
    Copyright (C) 2003 Panayiotis Thomakos

    This library is free software; you can redistribute it and/or
    modify it under the terms of the GNU Lesser General Public
    License as published by the Free Software Foundation; either
    version 2.1 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
    Lesser General Public License for more details.
*/
//To contact the author send an email to [e-mail]

/*Demonstrates a hopefiled network that remembers 5x5 bitmap letters A, B, C and D.*/

#include <nn-utility.h>
using namespace nn_utility;
nn_utility_functions<int> derived;

//create a bitmap and a vector to store the 4 letters letters[0...3]
BITMAP<int> *bit = new BITMAP<int>( "bit.txt" );
nn_utility_functions<int>::VECTOR letters[4];

int main(){
	//create an SGN network
	//SGN networks are based on HOPEFIELD, but allow autoasociative backpropagation
	SGN *hopefield = new SGN( );
	//define it's size
	hopefield->define( 25,4 );

	//read the bitmap and convert it into a hopefield bitmap
	//( 0's and 1's converted to -1's and 1's )
	for ( int i = 0; i < 4; i++ ){
		bit->readbitmap( letters[i], i );
		hopefield->Hopefield( letters[i], 25 ); }
	
	//create autoasociative vectors "a", "b", "c", and "d" respectively
	nn_utility_functions<int>::VECTOR a, b, c, d;
	//load the values into them
	derived.LoadVectori( a, 4,  1, -1, -1, -1 );
	derived.LoadVectori( b, 4, -1,  1, -1, -1 );
	derived.LoadVectori( c, 4, -1, -1,  1, -1 );
	derived.LoadVectori( d, 4, -1, -1, -1,  1 );

	//add onto the hopefield matrix by doing the following:
	hopefield->HopefieldMultiply( letters[0], a ); //associate letters[0] with a
	hopefield->HopefieldAddTo( letters[1], b );    //associate letters[1] with b
	hopefield->HopefieldAddTo( letters[2], c );    //associate letters[2] with c
	hopefield->HopefieldAddTo( letters[3], d );    //associate letters[3] with d

	//final VECTOR
	nn_utility_functions<int>::VECTOR FINAL;
	
	//verify that the output is correct
	cout << '\n';
	for ( int go = 0; go < 4; go++ ){
		cout << "Given noisy input pattern: >= 0 means an activation at that weight\n";
		bit->noise( letters[go], 25, 2 ); //add some noise or disturbance to the bitmap
						  //to check if the network is still learning
		hopefield->FeedForward( letters[go], FINAL );  //feed the bitmap to the network
		derived.PrintVector( FINAL, 4 );  //print the result
	}
	return 0;
}