clear all; close all;
%same as last example, but p=0.6, same until p3

%input vectors
p1=[0;1;0];
p2=[1;0;0];
p3=[1;1;0];

%initial weights
w21=ones(3,3); 
w12=0.5*w21;  

%1.	Compute the Layer 1 response:
a1=p1  

%2.	Next, compute the input to Layer 2:
w12*a1  

%Since all neurons have the same input, pick the first neuron as the
%winner. (In case of a tie, pick the neuron with the smallest index.)
a2=[1;0;0]  

%3.	Now compute the L2-L1 expectation:
w211=w21*a2  

%4.	Ajust the Layer 1 output to include the L2-L1 expectation:
a1=p1&w211  
a1=[0;1;0];

%5.	Next, the Orienting Subsystem determines the degree of match between
%the expectation and the input pattern:
norm(a1)^2/norm(p1)^2
%  ans = 1	> p=0.4
%Therefore no reset
a0=0;  

%6.	Since a0=0, continue with step 7.

%7.	Resonance has occurred, therefore update row 1 of w12:
w121=2*a1/(2+norm(a1).^2-1)  
w12(1,:)=w121'  
%8.	Update column 1 of w21:
w21(:,1)=a1  

%9.	Remove p1, and return to step 1 with input pattern p2.
a1=p2  

%2.	Compute the input to Layer 2:
w12*a1  
%Since neurons 2 and 3 have the same input, pick the second neuron as the winner:
a2=[0;1;0]  

%3.	Now compute the L2-L1 expectation:
w212=w21*a2  

%4.	Adjust the Layer 1 output to include the L2-L1 expectation:
a1=p2&w212  
a1=[1;0;0]
%5.	Next, the Orienting Subsystem determines the degree of match between
%the expectation and the input pattern:

norm(a1)^2/norm(p2)^2  
%ans = 1  >p=0.4

%Therefore no reset
a0=0;  

%6.	Since a0=0, continue with step 7.
%7.	Resonance has occurred, therefore update row 2 of w12:
w122=2*a1/(2+norm(a1).^2-1)  

w12(2,:)=w122'  

%8. Update column 2 of w21:
w21(:,2)=a1  

%9.Remove p2, and return to step 1 with input pattern p3.

%1.	Compute the new Layer 1 response:
a1=p3  

%2.	Compute the input to Layer 2:
w12*a1  

%Since all neurons have the same input, pick the first neuron as the winner:
a2=[1;0;0]  

%3. Now compute the L2-L1 expectation:
w211=w21*a2  

%4. Ajust the Layer 1 output to include the L2-L1 expectation:
a1=p3&w211  
a1=[0;1;0]

%5. Next, the Orienting Subsystem determines the degree of match between the expectation and the input pattern:
norm(a1)^2/norm(p3)^2  
%ans = 0.5000  < p=0.6
%Therefore a0=1 (reset)

%6. Since a0=1, set a21=0,.inhibit it until an adquate match occurs (resonance), and return to step 1.

%1. Recompute the Layer 1 response (Layer 2 inactive):
a1=p3  

%2. Next, compute the input to Layer 2:
w12*a1  

%Since neuron 1 is inhibited, neuron 2 is the winner:
a2=[0; 1; 0]  

%3. Now compute the L2-L1 expectation:
w212=w21*a2  

%4. Adjust the Layer 1 output to include the L2-L1 expectation:
a1=p3&w212  
a1=[1;0;0]

%5. Next, the Orienting Subsystem determines the degree of match between
%the expectation and the input pattern:
norm(a1)^2/norm(p3)^2  

%ans = 0.5000  < p=0.6 
%Therefore a0=1 (reset)
%6. Since a0=1, set a21=0,.inhibit it until an adquate match occurs (resonance), and return to step 1.
a1=p3  

%2. Next, compute the input to Layer 2:
w12*a1  

%Since neuron 1 and 2 are inhibited, neuron 3 is the winner:
a2=[0; 0; 1]  

%3. Now compute the L2-L1 expectation:
w213=w21*a2  

%4. Adjust the Layer 1 output to include the L2-L1 expectation:
a1=p3&w213  
a1=[1;1;0]
%5. Next, the Orienting Subsystem determines the degree of match between the expectation and the input pattern:
norm(a1)^2/norm(p3)^2

% ans = 1  >  p=0.6
% Therefore a0=0 (no reset)

%6. Since a0=0, continue with step 7.
%7. Resonance has occurred, therefore update row 3 of w12:
w123=2*a1/(2+norm(a1).^2-1)  
w12(3,:)=w123'  

%8. Update column 3 of w21:
w21(:,3)=a1  

%This completes the training, since if you apply any of the three patterns again they will not change the weights. These patterns have been successfully clustered. 
%The closer the vigilance is to 1, the more catagorites will be used. This is because an input pattern must be closer to a prototype in order to be incorporated into that prototype.
%When the vigilance is close to zero, many different input patterns can be incorporated into one prototype. The vigilance parameter adjusts the coarseness of the categorization.