martin.m

% Make SVMlib available
addpath svm/

%trainDat = load('mnistTrain.mat');
%data   = trainDat.images;
%labels= trainDat.labels;

% Load AU database files
trainDat    = load('auTrain.mat');
data   = trainDat.au_train_digits;
labels = trainDat.au_train_labels;

% Generate more data
[data,labels] = genData(data, labels, ceil(length(data)*1.3) );
disp('done generating data');
% Reduce dimensions
data  = dimReduce(data,25*25);
disp('done reducing data');
% Scramble data
idx = randperm(numel(labels))';

% Split into a train and test set
numTrain = round(3*numel(labels)/4);
 
trainData   = data(idx(1:numTrain),:);
trainLabels = labels(idx(1:numTrain)); % Shift Labels to the Range 1-5

testData   = data(idx(numTrain+1:end),:);
testLabels = labels(idx(numTrain+1:end));   % Shift Labels to the Range 1-5
disp('Ready to train...');

% Train
tic;
model = bestRBF(trainData, trainLabels);
toc;
% Test
[predicted_label, accuracy, dp] = svmpredict(testLabels, testData, model);

% Classification Score
fprintf('Test Accuracy: %f%%\n', accuracy);

% Print the misclassified
%diff = find((predicted_label - testLabels) ~= 0);
%misDat = testData(diff, :);
%misLab = testLabels(diff);
%preLab = predicted_label(diff);

%numShow = min(16, numel(misLab));

%imgs = cell(16,1);
%for i=1:16
%    imgs{i} = reshape(misDat(i, :), 28, 28);
%end

%subsize = ceil(sqrt(length(misLab)));

%# show them in subplots
%figure(1)
%for i=1:16
%    subplot(4,4,i);
%    h = imshow(imgs{i}, 'InitialMag',100, 'Border','tight');
%    title(sprintf('Lab.: %d Pre.: %d', misLab(i), preLab(i)))
%end