// Hopfield.cpp
// (Discrete (Binary)) Hopfield Artificial Neural Network (ANN)
// For more info see:
// http://en.wikipedia.org/wiki/Hopfield_net
// http://www.scholarpedia.org/article/Hopfield_network
// Compiler used: Dev-C++ 4.9.9.2
// FB - 201011136
#include <iostream>
#include <conio.h> // getch()
using namespace std;
int main (int argc, char *argv[])
{
cout<<"Hopfield Artificial Neural Network (ANN):"<<endl<<endl;
srand(time(NULL)); // use current time to seed random number generator
int n=25; // size of each pattern = number of neurons
int i,j,k,sum;
// Create a random pattern matrix to learn.
// Each row is a separate pattern to learn (n bits each).
cout<<"Training patterns:"<<endl<<endl;
// max capacity (number of patterns it can learn) of Hopfield network
// is 0.138N (N: number of neurons)
int m=static_cast<int>(0.138f*n); // number of patterns (rows)
int* pattern=new int[m*n];
for(j=0;j<m;j++) // rows
{
for(i=0;i<n;i++) // columns
{
pattern[j*n+i]=rand()%2;
cout<<pattern[j*n+i];
}
cout<<endl;
}
cout<<endl;
// calculate the weight matrix (symmetric and square)
// w[i,j]=w[j,i] & i!=j (i==j => w=0)
int* w=new int[n*n];
for(j=0;j<n;j++)
for(i=j;i<n;i++)
if(i==j)
w[j*n+i]=0;
else
{
sum=0;
for(k=0;k<m;k++)
sum+=(pattern[k*n+i]*2-1)*(pattern[k*n+j]*2-1);
w[j*n+i]=sum;
w[i*n+j]=sum;
}
// print the weight matrix
cout<<"The weight matrix:"<<endl<<endl;
for(j=0;j<n;j++)
{
for(i=0;i<n;i++)
printf("%2d ",w[j*n+i]);
cout<<endl;
}
cout<<endl;
cout<<"Pattern-recognition Test:"<<endl<<endl;
// Select one of the training patterns randomly
int selectedPattern=rand()%m;
cout<<"Test pattern selected:"<<endl;
for(i=0;i<n;i++)
{
cout<<pattern[selectedPattern*n+i];
}
cout<<endl<<endl;
int errorPercentage=10;
cout<<"Initial network state:"<<endl;
cout<<"The test pattern w/ "<<errorPercentage<<"% error added:"<<endl;
int* neuron=new int[n]; // current state of the network
int* neuron_prev=new int[n]; // prev state of the network
for(i=0;i<n;i++)
{
neuron[i]=pattern[selectedPattern*n+i];
if(rand()%100<errorPercentage) neuron[i]=1-neuron[i];
cout<<neuron[i];
neuron_prev[i]=neuron[i]; // initially prev state=current
}
cout<<endl<<endl;
// if state of the network stays unchanged for ? steps
// that means the network is converged to an answer
// so then exit the loop and printout the last state
int ctr_unchg=0;
// loop counter to ensure a stop just in case
// if the network becomes cyclic or chaotic
int ctr=0;
while(ctr_unchg<100 && ctr<1000) // max 1000 loops allowed
{
// First choice for updating the network
for(k=0;k<n;k++) // update the whole network ?
{
// Serial-Random updating:
// Randomly select a neuron and update its value
j=rand()%n;
sum=0;
for(i=0;i<n;i++)
if(i!=j)
sum+=neuron[i]*w[j*n+i];
if(sum>=0)
neuron[j]=1;
else
neuron[j]=0;
}
/*
// Second Choice for updating the network:
// Parallel updating:
// Update all neurons simultaneously
int* neuron_temp=new int[n];
// calculate the new values of each neuron
// but do not update immediately!
for(j=0;j<n;j++)
{
sum=0;
for(i=0;i<n;i++)
if(i!=j)
sum+=neuron[i]*w[j*n+i];
if(sum>=0)
neuron_temp[j]=1;
else
neuron_temp[j]=0;
}
// update the neurons with the new values
neuron=neuron_temp; // update the array pointer
delete []neuron; // delete the old values
*/
// if state of the network unchanged
// then increase the unchanged counter
// else reset it
bool changed=false;
for(k=0;k<n;k++)
if(neuron[k]!=neuron_prev[k])
{
changed=true;
break;
}
if(changed==false)
ctr_unchg++;
else
ctr_unchg=0;
// update the previous network state
for(k=0;k<n;k++)
neuron_prev[k]=neuron[k];
ctr++;
}
// note: Hopfield net also learns inverse of each bit pattern
// so it can also end up with one of the inverse patterns!
// if the network is converged then
// printout the last state of the network
if(ctr_unchg>=100)
{
cout<<"Converged network state:"<<endl<<endl;
for(i=0;i<n;i++)
cout<<neuron[i];
cout<<endl<<endl;
// calculate the convergence error percentage
int sumDif=0; // total number of differences
for(i=0;i<n;i++)
if(neuron[i]!=pattern[selectedPattern*n+i])
sumDif++;
cout<<"Convergence error percentage:"<<100*sumDif/n<<endl;
}
else
cout<<"The network did not reach the convergence limit set!"<<endl;
// garbage collection
delete []pattern;
delete []w;
delete []neuron;
delete []neuron_prev;
getch(); // wait until any key is pressed
return 0;
}
