wolffd@0: % Here the training data is adapted from UCI ML repository, 'zoo' data
wolffd@0: 
wolffd@0: dtreeCPD=tree_CPD;
wolffd@0: 
wolffd@0: % load data
wolffd@0: fname = fullfile(BNT_HOME, 'examples', 'static', 'uci_data', 'zoo', 'zoo1.data')
wolffd@0: data=load(fname);
wolffd@0: data=data';
wolffd@0: 
wolffd@0: data=transform_data_into_bnt_format(data, []);
wolffd@0: 
wolffd@0: % learn decision tree from data 
wolffd@0: ns=2*ones(1,17);
wolffd@0: ns(13)=6;
wolffd@0: ns(17)=7;
wolffd@0: dtreeCPD1=learn_params(dtreeCPD,1:17,data,ns,[],'stop_cases',5); % a node with less than 5 cases will not be splitted
wolffd@0: 
wolffd@0: % evaluate on data
wolffd@0: [score,outputs]=evaluate_tree_performance(dtreeCPD1,1:17,data,ns,[]);
wolffd@0: fprintf('Accuracy in old training data %6.3f\n',score);
wolffd@0: