clearvars
close all
clc
load data_pca.mat
%%
X=data(:,2:end);
X_zscore=zscore(X);
[coeff,score,latent,tsquared,explained] = pca(X_zscore);

% score = componente principale
% coeff = coefficienti di caratterizzazione

Xcentered = score*coeff';
figure, subplot(211), imagesc(X_zscore), subplot(212), imagesc(Xcentered)


figure, subplot(211), plot(latent,'o'), title('autovalori'), subplot(212), plot(explained,'ro'), title('autovalori %')
%%
figure()
pareto(explained)
xlabel('Principal Component')
ylabel('Variance Explained (%)')
%%
figure, imagesc(score), colormap 'jet'
figure, imagesc(abs(coeff)), colormap 'jet'