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Diffstat (limited to 'sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m')
| -rw-r--r-- | sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m | 172 |
1 files changed, 172 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m new file mode 100644 index 00000000..2fa81ac6 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/learning/learn_struct_tan_EM.m @@ -0,0 +1,172 @@ +function dag = learn_struct_tan_EM(data, class, node_sizes, root, prior, nbloopmax, thresh) +% LEARN_STRUCT_TAN_EM +% dag = learn_struct_tan_EM(data, class, node_sizes, root, prior, nbloopmax, thresh) +% +% Learn TAN classifier for discrete variables from incomplete dataset +% +% Input : +% data{i,m} a cell where the node i in the case m, +% class is the number of the class node, +% node_sizes = 1 if gaussian node, (max on complete samples) +% root is the futur root-node of the tree T. (random) +% prior = 1 to use uniform Dirichlet prior (0) +% nbloopmax = max loop number (ceil(log(N*log(N)))) +% thresh = the convergence test's threshold (1e-3) +% +% Output : +% bnet = the output bayesian network +% Ebic = the espected BIC score of bnet given the data +% +% francois.olivier.c.h@gmail.com + +[N, m]=size(data); +log_m = log(m); +if nargin<7, thresh = 1e-4; end +max_iter = 10; % for learn_struct_params +if nargin<6, nbloopmax = 15, end +if nargin<5, prior = 0; end +if nargin<4, root = ceil(N*rand(1)), end +if nargin<3, + misv = -9999; + data_mat = bnt_to_mat(data,misv); + node_sizes = max(data_mat'), + clear data_mat +end + +discrete = 1:N; +nbloop = 0; +variab = mysetdiff(1:N, class); + +[Bbest1, Sbest, Obest] = learn_struct_mwst_EM4tan(data(variab, :), 1:N-1, node_sizes(variab), prior, nbloopmax, thresh); +dag = zeros(N); +dag(variab, variab) = Bbest1.dag; +dag(class, variab) = 1; + +%Bbest = mk_bnet(dag, discrete); +%for i=discrete, Bbest.CPD{i} = tabular_CPD(Bbest, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end +%engine = jtree_inf_engine(Bbest); +%Bbest = learn_params_em(engine, data, max_iter, thresh); + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function [Bbest, Sbest, Obest] = learn_struct_mwst_EM4tan(data, discrete, node_sizes, prior, nbloopmax, thresh) + +%fprintf('-- INITIALIZATION\n'); +%%%%%%%%%%%%% +[N, m]=size(data); + rand('state',sum(100*clock)) + +if nargin<6, thresh = 1e-4; end +if nargin<5, nbloopmax = 15; end +if nargin<4, prior = 0; end +if nargin<3, + misv = -9999; + data_mat = bnt_to_mat(data,misv); + node_sizes = max(data_mat'), +end +if nargin<2, discrete = ones(1,N); end + +nbloop = 1; last=0; +max_iter = 15; % for learn_struct_params + +%%%%%%%%%%%%% +% Random Chain like DAG +T = diag(ones(N-1,1),1); T=T+T'; +order=randperm(N); root=randperm(N); root=root(1); +[tmp order2]=sort(order); order2; +T2 = full(mk_rooted_tree(T,order2(root))); +torder=topological_sort(T2(order2,order2)); +[tmp torder2]=sort(torder); torder2; + +bnet0 = mk_bnet(T2(order2(torder),order2(torder)), node_sizes(torder)); +for i=1:N, bnet0.CPD{i} = tabular_CPD(bnet0, i, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); end % a priori -> change les espected counts + +%%%%%%%%%%%%% +Sbest = -Inf; Bbest = bnet0; fini=0; + +while not(fini) + %fprintf('Loop %d : learning parameters...\n',nbloop); + engine0=jtree_inf_engine(bnet0); + [bnet1, LL1, engine1] = learn_params_em(engine0, data(torder,:), max_iter, thresh); + + BIC0=0; + for i=1:N, + xxx=struct(bnet1.CPD{i}); + BIC0=BIC0+bic_score_family(xxx.counts, xxx.CPT, xxx.nsamples); + end + + if BIC0 < Sbest+ thresh*abs(Sbest), + fini=1; + else + Sbest = BIC0; + Bbest = bnet1; + Obest = torder2 ; + %%%%%%%%%%%%% + + theta_Xi=cell(N,1); + evidence = cell(1,N); + [engine2, loglik] = enter_evidence(engine1, evidence); + for j=1:N, + SS= marginal_nodes (engine2,torder2(j)); + theta_Xi{j} = SS.T; + end + + theta_Xj_given_Xi = cell(N,N); + for i=1:N + for vali = 1:node_sizes(i) + evidence = cell(1,N); evidence{torder2(i)} = vali; + [engine2, loglik] = enter_evidence(engine1, evidence); + for j=mysetdiff(1:N,i) + SS= marginal_nodes (engine2,torder2(j)); + theta_Xj_given_Xi{j,i} = [theta_Xj_given_Xi{j,i}, SS.T]; + end + end + end + + BIC_mat=zeros(N,N); + for i=1:N, + BIC_mat(i,i)=bic_score_family(theta_Xi{i}*m,theta_Xi{i},m); + for j=mysetdiff(1:N,i) + theta_XjXi=(ones(node_sizes(i),1)*theta_Xi{j}').*theta_Xj_given_Xi{i,j}; + BIC_mat(i,j)= bic_score_family(m*theta_XjXi,theta_Xj_given_Xi{i,j},m); + end + end + BIC_delta = BIC_mat-diag(BIC_mat)*ones(1,N); + + BIC1=0; + for i = 1:N + j = find(bnet0.dag(:,i)==1); + if isempty(j) + BIC1 = BIC1 + BIC_mat(torder2(i),torder2(i)); + else + BIC1 = BIC1 + BIC_mat(torder2(i),torder2(j)); + end + end + + %fprintf(' Creation of the new bnet ');tic + %%%%%%%%%%%%% + T2 = minimum_spanning_tree(-BIC_delta); + root=randperm(N); root=root(1); + T3 = full(mk_rooted_tree(T2, root)); + torder=topological_sort(T3); + [tmp torder2]=sort(torder); torder2; + + bnet0 = mk_bnet(T3(torder,torder), node_sizes(torder)); + %BIC=0; + for i = 1:N + j = find(T3(:,i)==1); + if isempty(j) + bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xi{i}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); + %BIC = BIC + BIC_mat(i,i); + else + bnet0.CPD{torder2(i)} = tabular_CPD(bnet0, torder2(i), 'CPT', theta_Xj_given_Xi{i,j}, 'prior_type', 'dirichlet', 'dirichlet_type', 'unif'); + %BIC=BIC + BIC_mat(i,j); + end + end + nbloop=nbloop+1; + fprintf('================================================================================\n'); + if nbloop>nbloopmax + fini=1; + end + end +end |