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function [eigvectors, eigvalues, meanData, newTrainData, newTestData] = TDPCA(trainData, testData, height, width, numvecs)
%2DPCA Two Dimensional Principal component analysis
% Usage:
% [eigvectors, eigvalues, meanData, newTrainData, newTestData] = TDPCA(trainData, testData, height, width, numvecs)
%
% trainData: Rows of vectors of training data points
% testData: Rows of vectors of testing data points
% height: height of the image matrix
% width: width of the image matrix
% numvecs: the needed number of eigenvectors
%
% meanData: Mean of all the data.
% newTrainData: The data after projection (mean removed)
% newTestData: The data after projection (mean removed)
% eigvectors: Each column of this matrix is a eigenvector of the convariance
% matrix defined in 2DPCA
% eigvalues: Eigenvalues of the convariance matrix
%
%
% Reference paper: J.Yang,D.Zhang,A.F.Frangi,and J.Yang.Two-dimensional
% pca:A new approach to a appearance-based face
% represenation and recognition. IEEE Trans.on
% PAMI,2004
% Written by Zhonghua Shen (cnjsnt_s@yahoo.com.cn), 2006.07
% Check arguments
if nargin ~= 5
error('usage: [eigvectors, eigvalues, meanData, newTrainData, newTestData] = TDPCA(trainData, testData, height, width, numvecs)');
end;
[nSam,nFea] = size(trainData);
fprintf(1,'Computing average matrix...\n');
meanDataVector = mean(trainData);
meanData = reshape(meanDataVector,height,width);
fprintf(1,'Calculating matrix differences from avg and 2DPCA covariance matrix L...\n');
L = zeros(width,width);
ddata = zeros(nSam,nFea);
for i = 1:nSam
ddata(i,:) = trainData(i,:)-meanDataVector;
dummyMat = reshape(ddata(i,:),height,width);
L = L + dummyMat'*dummyMat;
end;
L = L/nSam;
L = (L + L')/2;
fprintf(1,'Calculating eigenvectors of L...\n');
[eigvectors,eigvalues] = eig(L);
fprintf(1,'Sorting eigenvectors according to eigenvalues...\n');
[eigvectors,eigvalues] = sortem(eigvectors,eigvalues);
eigvalues = diag(eigvalues);
fprintf(1,'Normalize Vectors to unit length, kill vectors corr. to tiny evalues...\n');
num_good = 0;
for i = 1:size(eigvectors,2)
eigvectors(:,i) = eigvectors(:,i)/norm(eigvectors(:,i));
if eigvalues(i) < 0.00001
% Set the vector to the 0 vector; set the value to 0.
eigvalues(i) = 0;
eigvectors(:,i) = zeros(size(eigvectors,1),1);
else
num_good = num_good + 1;
end;
end;
if (numvecs > num_good)
fprintf(1,'Warning: numvecs is %d; only %d exist.\n',numvecs,num_good);
numvecs = num_good;
end;
eigvectors = eigvectors(:,1:numvecs);
if nargout == 5
fprintf(1,'Feature extraction and calculating new training and testing data...\n');
newTrainData = zeros(nSam,height*numvecs);
for i = 1:nSam
dummyMat = reshape(ddata(i,:),height,width);
newTrainData(i,:) = reshape(dummyMat*eigvectors,1,height*numvecs);
end
%testData
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