Daniel@0: N = 4;
Daniel@0: dag = zeros(N,N);
Daniel@0: %C = 1; S = 2; R = 3; W = 4;
Daniel@0: C = 4; S = 2; R = 3; W = 1; % arbitrary order
Daniel@0: dag(C,[R S]) = 1;
Daniel@0: dag(R,W) = 1;
Daniel@0: dag(S,W)=1;
Daniel@0: 
Daniel@0: false = 1; true = 2;
Daniel@0: ns = 2*ones(1,N); % binary nodes
Daniel@0: 
Daniel@0: bnet = mk_bnet(dag, ns);
Daniel@0: bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]);
Daniel@0: bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]);
Daniel@0: bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]);
Daniel@0: bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]);
Daniel@0: 
Daniel@0: seed = 0;
Daniel@0: rand('state', seed);
Daniel@0: randn('state', seed);
Daniel@0: ncases = 100;
Daniel@0: data = zeros(N, ncases);
Daniel@0: for m=1:ncases
Daniel@0:   data(:,m) = cell2num(sample_bnet(bnet));
Daniel@0: end
Daniel@0: 
Daniel@0: order = [C S R W];
Daniel@0: max_fan_in = 2;
Daniel@0: 
Daniel@0: %dag2 = learn_struct_K2(data, ns, order, 'max_fan_in', max_fan_in, 'verbose', 'yes');
Daniel@0:   
Daniel@0: sz = 5:5:50;
Daniel@0: for i=1:length(sz)
Daniel@0:   dag2 = learn_struct_K2(data(:,1:sz(i)), ns, order, 'max_fan_in', max_fan_in);
Daniel@0:   correct(i) = isequal(dag, dag2);
Daniel@0: end
Daniel@0: correct
Daniel@0: 
Daniel@0: for i=1:length(sz)
Daniel@0:   dag3 = learn_struct_K2(data(:,1:sz(i)), ns, order, 'max_fan_in', max_fan_in, 'scoring_fn', 'bic', 'params', []);
Daniel@0:   correct(i) = isequal(dag, dag3);
Daniel@0: end
Daniel@0: correct
Daniel@0: 
Daniel@0: