nFold_C45.m

function stats = nFold_C45(data_set,data_labels,shuffle,n,nIter,Subsets)
% Using n-fold subset selection and C4.5 decision tree classifier
% Input:
%   data_set: data
%   data_labels: labels
%   shuffle: 1 for random, 0 for non-random partition (Default: 1)
%   n: Number of folds to your cross-validation (Default: 3)
%   nIter: Number of cross-validation iterations (Default: 1)
%   Subsets: pass your own training and testing subsets & labels (Default:
%   computer will generate using 'nFold')
%
% Output:
%   stats: struct containing TP, FP, TN, FN, etc.

% data_set = eigVec_graphfeats;
% data_labels = labels(:);
% classifier = 'SVM';
% shuffle = 1;
% n = 3;
if nargin < 6
    Subsets = {};
end
if nargin < 5
    nIter = 1;
end
if nargin < 4
    n = 3; % 3-fold cross-validation
end
if nargin < 3
    shuffle = 1; % randomized
end

if any(~xor(data_labels == 1, data_labels == -1)), error('Labels must be 1 and -1'); end

stats = struct; %cell(1,nIter);
for j=1:nIter
    fprintf('Iteration: %i\n',j);
    
    % reset total statistics
    Ttp = 0; Ttn = 0; Tfp = 0; Tfn = 0;
    
    if isempty(Subsets)
        [tra tes]=GenerateSubsets('nFold',data_set,data_labels,shuffle,n);
    else
        tra = Subsets{j}.training;
        tes = Subsets{j}.testing;
    end
    
    for i=1:n
        fprintf(['Fold # ' num2str(i) '\n']);

        training_set = data_set(tra{i},:);
        testing_set = data_set(tes{i},:);
        training_labels = data_labels(tra{i});
        testing_labels = data_labels(tes{i});

        [temp_stats,methodstring] = Classify( 'C45', training_set , testing_set, training_labels, testing_labels);
        Ttp = Ttp + temp_stats.tp;
        Ttn = Ttn + temp_stats.tn;
        Tfp = Tfp + temp_stats.fp;
        Tfn = Tfn + temp_stats.fn;

    end
    
    % output statistics
    stats(j).tp = Ttp;
    stats(j).tn = Ttn;
    stats(j).fp = Tfp;
    stats(j).fn = Tfn;
    stats(j).acc = (Ttp+Ttn)/(Ttp+Ttn+Tfp+Tfn);
    stats(j).ppv = Ttp/(Ttp+Tfp);
    stats(j).sens = Ttp/(Ttp+Tfn);
    stats(j).spec = Ttn/(Tfp+Ttn);
    stats(j).subsets.training = tra;
    stats(j).subsets.testing = tes;
%     stats{j}.prediction = prediction;
end