Simple Hebbian Network

simpleNeuron.m

% train neurons

nr_class=3;

A=im2bin(imread('A.bmp'));
B=im2bin(imread('B.bmp'));
C=im2bin(imread('C.bmp'));

trainIms=cat(3,A,B,C);

[nr_row,nr_col]=size(A);
neurons=zeros(nr_row*nr_col,nr_row*nr_col);
for i=1:nr_row*nr_col
    for j=i+1:nr_row*nr_col
        ir=int16(floor((i-1)/nr_col)+1);
        ic=int16(mod((i-1),nr_col)+1);
        jr=int16(floor((j-1)/nr_col)+1);
        jc=int16(mod((j-1),nr_col)+1);
        
        neurons(i,j)=sum(trainIms(ir,ic,:).*trainIms(jr,jc,:));
        neurons(j,i)=neurons(i,j);
    end
end


% run test neuron

t1=im2bin(imread('test/1.bmp'));
showMatAsIm(t1);
oldt1=t1;

stopCondition=false;
while ~stopCondition
    tmpt1=int8(zeros(size(t1)));
    for i=1:nr_row*nr_col
        for j=1:nr_row*nr_col
            ir=int16(floor((i-1)/nr_col)+1);
            ic=int16(mod((i-1),nr_col)+1);
            jr=int16(floor((j-1)/nr_col)+1);
            jc=int16(mod((j-1),nr_col)+1);

            tmpt1(ir,ic)=tmpt1(ir,ic)+neurons(i,j)*t1(jr,jc);
        end
    end
    t1=sign(tmpt1);
    stopCondition=isequal(t1,oldt1);
    oldt1=t1;
end

showMatAsIm(t1);

function binary = im2bin(im)
%change unit8 to +1/-1
binary=double(im./255.*2)-1;
binary=binary(:,:,1);
end

function showMatAsIm(matrix)
matrix=uint8(matrix);
matrix(matrix==-1)=0;
matrix(matrix==1)=255;
imshow(matrix,'InitialMagnification',2000)
end