% This example illustrates how we can build a naive Bayes classifier% with different kinds of observed leaves.N = 3;dag = zeros(N,N);dag(1,2:N)=1;dnodes = [1 2];nclasses = 2;ns = [nclasses 2 3];bnet = make_bnet(dag, ns, dnodes);rand('state', 0);randn('state', 0);bnet.CPD{1} = tabular_CPD(bnet, 1);bnet.CPD{2} = tabular_CPD(bnet, 2);bnet.CPD{3} = gaussian_CPD(bnet, 3);%bnet = randomize_params(bnet, 0);engine = var_elim_inf_engine(bnet);evidence = cell(1,N);evidence{2} = 2;evidence{3} = [-0.1 0.1 0.2]';query = 1;m1 = infer(engine, evidence, query);% Compare with BNTbnet2 = mk_bnet(dag, ns, dnodes);rand('state', 0);randn('state', 0);bnet2.bnodes{1} = mk_tabular_node(bnet2, 1);bnet2.bnodes{2} = mk_tabular_node(bnet2, 2);bnet2.bnodes{3} = mk_gaussian_node(bnet2, 3);%bnet2 = randomize_params(bnet2, 0);vals = [2 -0.1 0.1 0.2];onodes = [2 3];[bnet2, ll2] = enter_evidence(bnet2, vals, onodes);m2 = marginal_nodes(bnet2, query);approxeq(m2.T(:), m1(:))