clrssimulation.m

matlab实现主成分分析PAC神经网络的程序

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%% Copy Right:  the Computational Intelligence Laboratory 
%% System name: PCA
%% File name:   CLRSSimulation.m
%% Description: This is a simulation of the CRLS neural network.The first 4 principal
%%              omponents will be extracted, and then the original image is reconstructed.
%%              
%%
%% Author: 
%% Date:         4/14/2004
%% 
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clear all;
originalImage = imread('lina.bmp');
preserveOrigianl = originalImage;
[rr, cc] = size(originalImage);



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%%  the image is digitized into the 64x4096 matrix.
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Img = originalImage;
mc=256;

r = 8; c = 8 ; p = r*c ;
originalImage = blkM2vc(Img, [r c]) ;
[p N] = size(originalImage) ; 

Xm = mean(originalImage')' ;
Xmax=max(max(abs(originalImage)));
originalImage = originalImage - Xm(:, ones(1, N)) ;
originalImage = originalImage/max(max(abs(originalImage))) ;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% the original image is showed.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
XX=originalImage*Xmax;
XX=XX+Xm(:, ones(1, N)) ;
Imr = vc2blkM(XX, r, rr) ;
Imr = round(mc*Imr/max(max(Imr))) ;
figure(1), set(1,'Name','the original picture'), image(Imr), colormap(gray(mc))


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% the 4 neurons network is trained until it converge. 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

m=4;
time(m)=rem(Now,1);
W = 0.6*(rand(m, p)-0.5) ; % weight initialisation
er = .0001 ; % the length convergence error
e0=originalImage;


for j=1:m


    for s=1:16
    [rs rn] = sort(rand(1, N)) ;
    for n=1:N
        if n==1
            eta(j)=sum(sum(e0.^2))/N;    
        end
        x = e0(:, rn(n)) ; % randomised selection of patterns
        y = W(j,:)*x ;
        eta(j)=y^2+eta(j);
        dW(j,:)=(y/eta(j))*(x' - W(j,:)*y);
        W(j,:)=W(j,:)+dW(j,:);
        if  sqrt(sum(dW(j,:).^2))<er 
            break; 
        end
    end
    if  sqrt(sum(dW(j,:).^2))<er 
            break; 
    end
    end
    y=W(j,:)*e0;
    e0=e0-W(j,:)'*y;
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% extracting the first 4 principal component.
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yy = W*originalImage ;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% the principal component is showed. 
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[rTem,cTem] = size(yy);
tV = rr/8;
for j = 1:rTem
  rVc = 256*yy(j,:);
  Imr = eigvcToMask(rVc,tV);
  Imr = round(mc*Imr/max(max(Imr))) ;
  Imr = round(Imr) ;
  tempPicNo = 1+j;
  strTitle = strcat('the extracted first ', int2str(j), ' Principal component');
  figure(tempPicNo),set(tempPicNo,'Name',strTitle), image(Imr), colormap(gray(mc))

end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% the wiegh vector mask is showed.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[rTem,cTem] = size(W);
tV = 64/8;
for j = 1:rTem
  rVc = 256*W(j,:);
  Imr = eigvcToMask(rVc,tV);
  Imr = round(mc*Imr/max(max(Imr))) ;
  Imr = round(Imr) ;
  tempPicNo = 5+j;
  strTitle = strcat('the first ', int2str(j),' weight vector mask');
  figure(tempPicNo),set(tempPicNo,'Name',strTitle), image(Imr), colormap(gray(mc))

end




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% the image will be reconstructed.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Xr = W'*yy;

Xr=Xr*Xmax;
Xr = Xr + Xm(:, ones(1, N)) ;
Imr = vc2blkM(Xr, r, rr) ;
Imr = round(mc*Imr/max(max(Imr))) ;
Imr = round(Imr) ;

figure(10), set(10,'Name','the reconstructed picture'),image(Imr), colormap(gray(mc))

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%% this is end.