%Set Parameters of the simulation
gw = 0.1		%Learning rate for Winners (large)
gl = 0.01		%Learning Rate for Non-winners (small)

Inputtotal = 4		%Number of different input patterns
Htotal = 2		%Number of Hidden (non-input) Units
Itotal = 98		%Dimensionality of Input patterns (7X14 grid = 98)

Inum = 0
Wnum = 0

%the input patterns are defined below.  They consist of the four patterns
AA, AB, BA, and BB drawn on a seven by fourteen grid.
I(1, 1:14) = [0 0 0 1 0 0 0 0 0 0 1 0 0 0];
I(1,15:28) = [0 0 1 0 1 0 0 0 0 1 0 1 0 0];
I(1,29:42) = [0 1 0 0 0 1 0 0 1 0 0 0 1 0];
I(1,43:56) = [0 1 1 1 1 1 0 0 1 1 1 1 1 0];
I(1,57:70) = [0 1 0 0 0 1 0 0 1 0 0 0 1 0];
I(1,71:84) = [0 1 0 0 0 1 0 0 1 0 0 0 1 0];
I(1,85:98) = [0 1 0 0 0 1 0 0 1 0 0 0 1 0];

I(2, 1:14) = [0 0 0 1 0 0 0 0 1 1 1 1 0 0];
I(2,15:28) = [0 0 1 0 1 0 0 0 0 1 0 0 1 0];
I(2,29:42) = [0 1 0 0 0 1 0 0 0 1 0 0 1 0];
I(2,43:56) = [0 1 1 1 1 1 0 0 0 1 1 1 0 0];
I(2,57:70) = [0 1 0 0 0 1 0 0 0 1 0 0 1 0];
I(2,71:84) = [0 1 0 0 0 1 0 0 0 1 0 0 1 0];
I(2,85:98) = [0 1 0 0 0 1 0 0 1 1 1 1 0 0];

I(3, 1:14) = [0 1 1 1 1 0 0 0 0 0 1 0 0 0];
I(3,15:28) = [0 0 1 0 0 1 0 0 0 1 0 1 0 0];
I(3,29:42) = [0 0 1 0 0 1 0 0 1 0 0 0 1 0];
I(3,43:56) = [0 0 1 1 1 0 0 0 1 1 1 1 1 0];
I(3,57:70) = [0 0 1 0 0 1 0 0 1 0 0 0 1 0];
I(3,71:84) = [0 0 1 0 0 1 0 0 1 0 0 0 1 0];
I(3,85:98) = [0 1 1 1 1 0 0 0 1 0 0 0 1 0];

I(4, 1:14) = [0 1 1 1 1 0 0 0 1 1 1 1 0 0];
I(4,15:28) = [0 0 1 0 0 1 0 0 0 1 0 0 1 0];
I(4,29:42) = [0 0 1 0 0 1 0 0 0 1 0 0 1 0];
I(4,43:56) = [0 0 1 1 1 0 0 0 0 1 1 1 0 0];
I(4,57:70) = [0 0 1 0 0 1 0 0 0 1 0 0 1 0];
I(4,71:84) = [0 0 1 0 0 1 0 0 0 1 0 0 1 0];
I(4,85:98) = [0 1 1 1 1 0 0 0 1 1 1 1 0 0];


%Randomize the Weights to start with.  Then, since they must sum to 1, normalize
%them by dividing each individual weight by the sum of all the weights.
for Hcount = 1:Htotal
  NormTotal = 0;
  for Icount = 1:Itotal
    W(Hcount, Icount) = rand;
    NormTotal = W(Hcount, Icount) + NormTotal;
  end %Icount
  for Icount = 1:Itotal
    W(Hcount, Icount) = W(Hcount, Icount) / NormTotal;
  end %Icount
end %Hcount



%Now start the simulation itself.  For each of 100 training epochs, cycle
%through each of the training inputs in order
for epochs = 1:100
for Inputcount = 1:Inputtotal
  OMax = 0;

  for j = 1:Htotal			
    O(j) = 0;
  end

  %convert the weight matrix to vectors  
  for j = 1:Htotal  %j is the element of the hidden cluster
    O(j) = dot(I(Inputcount,1:Itotal),W(j,1:Itotal));

    %determine if this unit is the winner yet
    if O(j) > OMax
      winner = j;
      OMax = O(j);
    end

  end %for

  %Now that the winning units number is stored in variable Winner, set 
  %all the others to zero
  for j = 1:Htotal
    O(j) = 0;
  end %j

  O(winner) = 1  	%(Set the output/activation of the winner to 1)



%Training Portion
%Now that the activations with the current set of weights has been calculated
%for this input pattern, update the weights according the learning rule
  %update all elements, changing the amount of learning for the winner only
  for j = 1:Htotal

    %Count Number of active lines for this (j) element
    Nj = 0;
    for i = 1:Itotal
      if I(Inputcount,i) ==1
        Nj = Nj+1;
      end
    end
 
    %update weights for elements
    if j == winner
      g = gw;				%fast learning
    else 
      g = gl;				%slow learning
    end

    for i = 1:Itotal	
      if I(Inputcount,i) == 1
        W(j,i) = W(j,i) + g*(1/Nj) - g*W(j,i);		%update active lines
      else
        W(j,i) = W(j,i) - g*W(j,i);			%Update inactive lines
      end
    end
  end

end %Inputcount
end %epoch