BaggedC45_multiclass.asv

function [methodstring,stats] = BaggedC45(training_set , testing_set, training_labels, testing_labels)

% format labels from (-1,1) to (0,1)
if training_labels < 0
    training_labels(training_labels==-1) = 0;
    testing_labels(testing_labels==-1) = 0;
end

methodstring = 'BaggedC45';
unique_testing_labels = unique(testing_labels);

% [final, labels, error] = baggingdtrees(training_set, training_labels, testing_set, testing_labels);
% 
% 
% tp=0; tn=0; fp=0; fn=0; prediction = zeros(size(final));
% for i=1:size(labels,1)
%     prediction(i) = round(mean(labels(i,:)));
%     
%     if testing_labels(i)==1
%         if prediction(i) == testing_labels(i)
%             tp = tp + 1;
%         else
%             fp = fp + 1;
%         end
%     elseif testing_labels(i)==2
%         if prediction(i) == testing_labels(i)
%             tn = tn + 1;
%         else
%             fn = fn + 1;
%         end
%     end
% end

% Check for matlabpool
try
if matlabpool('size')==0, matlabpool open; end
catch e
    fprintf('Could not open matlabpool...skipping\n');
end

% Options for TreeBagger
options = statset('UseParallel','always','UseSubstreams','never');

B = TreeBagger(50,training_set,training_labels,'FBoot',0.667,'oobpred','on','Method','classification','NVarToSample','all','NPrint',4,'Options',options);    % create bagged d-tree classifiers from training
[Yfit,Scores] = predict(B,testing_set);   % use to classify testing
% probs = Scores(:,1); % Select probabilities -- check manual entry for 'predict', look at 'B' to make sure your reqd class is the second column
% if numel(unique(testing_labels))>1
%     [FPR,TPR,T,AUC,OPTROCPT,~,~] = perfcurve(testing_labels,probs,min(unique(testing_labels)));  % calculate AUC. 'perfcurve' can also calculate sens, spec etc. to plot the ROC curve.
%     [TP FN] = perfcurve(testing_labels,probs,1,'xCrit','TP','yCrit','FN');
%     [FP TN] = perfcurve(testing_labels,probs,1,'xCrit','FP','yCrit','TN');
%     [~,ACC] = perfcurve(testing_labels,probs,1,'xCrit','TP','yCrit','accu');
%     [~,PPV] = perfcurve(testing_labels,probs,1,'xCrit','TP','yCrit','PPV');
%     
%     optim_idx = find(FPR == OPTROCPT(1) & TPR == OPTROCPT(2));
%     stats.tp = TP(optim_idx);
%     stats.fn = FN(optim_idx);
%     stats.fp = FP(optim_idx);
%     stats.tn = TN(optim_idx);
%     stats.auc = AUC;
%     stats.spec = 1-FPR(optim_idx);
%     stats.sens = TPR(optim_idx);
%     stats.acc = ACC(optim_idx);
%     stats.ppv = PPV(optim_idx);
    stats.prediction = Scores;
    [C, I] = max(Scores,[],2);
    stats.decision = unique_testing_labels(I);
    stats.tp = nnz(testing_labels == stats.decision);
    stats.tn = nnz(~(testing_labels==

%     stats.threshold = T(optim_idx);
%     stats.decision = stats.prediction >= stats.threshold;
% 	stats.trained_classifier = B;
% end