riverarodrigoa / statis-matlab Goto Github PK

%% Validation de l'image euclidienne compromis
alpha = sqrt(alpha_t);
B_val = alpha(1)*Xc(:,:,1);
for i=1:T
    B_val =[B_val [alpha(i)*Xc(:,:,i)]];
end
% ACP du B_val
[XU_v, VaP_v, VeP_v] = ACP(B_val);

B_val_c = XU_v(:,1:j);

Decision = isequal(B,B_val_c);
if Decision
    disp('[Validation test] Image euclidienne compromis correcte');
else
    disp('[Validation test] Image euclidienne compromis incorrecte');
    disp('Image euclidienne');
    disp(B);
    disp('Validation');
    disp(B_val_c);
end

function [Acr] = centrer(A)
%----------------------------------
% Centrage et reduction des donnees
%----------------------------------
[n,p]=size(A);
Ac= A - repmat(mean(A),n,1);
Acr=Ac.*repmat(std(A),n,1);

function [Acr] = centrer(A)
%----------------------------------
% Centrage et reduction des donnees
%----------------------------------
UN = ones(size(A));
Me = UN * diag(mean(mean(A)));
Ecart_type = UN * diag(std(std(A)));
ec  = 1./Ecart_type;
Acr  = (A - Me).*ec;
end