% Training of linear PCA model to tuned for image restoration.%% Modifications:% 5-may-2004, VF% 14-apr-2004, VF% 13-apr-2004, VF% 17-mar-2004, VF% == Setting ==========================================Num_Folds = 1;%New_Dim_Range = [5 10 20 30 40 60 80 100];New_Dim_Range = [1 2];input_data_file = 'circle_data';output_data_file = [];%input_data_file = '/home.dokt/xfrancv/data/usps_noisy';%output_data_file = 'LinearPCAModelUSPS';% ========================================================% Loads training and testing data.load(input_data_file);[Dim,Num_Data] = size( trn.X );% Data partitioning[itrn,itst]=crossval(Num_Data,Num_Folds);% Tuning linear PCA modelMse = [];for new_dim = New_Dim_Range,     fprintf('\nnew_dim = %d\n', new_dim);    cv_mse = 0;   for i=1:Num_Folds,       fprintf('\n');       trn_X = trn.gnd_X(:,itrn{i});   val_gnd_X = trn.gnd_X(:,itst{i});   val_corr_X = trn.X(:,itst{i});      fprintf('Computing Linear PCA...');   lin_model = pca(trn_X, new_dim);   fprintf('done\n');   fprintf('Projecting validation data...');   val_reconst_X = lpimage( val_corr_X, lin_model );   fprintf('done.\n');   dummy = (val_reconst_X - val_gnd_X).^2;     mse = sum(dummy(:))/size(val_gnd_X,2);     fprintf('folder %d/%d: validation errors mse=%f\n', ...      i, Num_Folds, mse);      cv_mse = cv_mse + mse; end  cv_mse = cv_mse/Num_Folds;  Mse(find(new_dim==New_Dim_Range)) = cv_mse;  fprintf('new_dim = %d: mse = %f\n', new_dim, cv_mse);end   [dummy,inx] = min(Mse);fprintf('\nMin(mse) = %f, dim = %f\n', ...   Mse(inx), New_Dim_Range(inx) );fprintf('Computing optimal Kernel PCA...');lpca_model = pca( trn.X, New_Dim_Range(inx) );fprintf('done.\n');if isempty(output_data_file),  figure; hold on;  xlabel('dim'); ylabel('mse');  plot(New_Dim_Range,Mse);else   save(output_data_file,'New_Dim_Range',...      'Mse','Num_Folds','input_data_file',...      'output_data_file','lpca_model');end% plots 2D dataif Dim == 2 & isempty(output_data_file),  X = lpimage(tst.X, lin_model );  mse = sum(sum((X-tst.gnd_X).^2 ));  fprintf('\ntest mse=%f\n', mse);    figure; hold on;  h0=ppatterns(tst.gnd_X,'r+');  h1=ppatterns(tst.X,'gx');  h2=ppatterns(X,'bo');  legend([h0.points h1.points h2.points],'Ground truth','Noisy','Reconst');end% EOF