function Z=pca_HMM(X)
[X_norm, ~, ~]=featureNormalize(X);
% sigma mat~rix computation
cov_mat=1/size(X_norm,1)*(X_norm')*X_norm;
% SVD
[U, ~, ~]=svd(cov_mat);
% creer data
k=20;
Ured=U(:, 1:k);
Z=Ured'*X_norm';
% Z is the final data we have 
% FINAL 20 dimensions is enough to keep 99 variance 
%     0.962 -> 20 dimensions
end