diff toolboxes/FullBNT-1.0.7/bnt/learning/learn_params_em.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/learning/learn_params_em.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,88 @@
+function [bnet, LL, engine] = learn_params_em(engine, evidence, max_iter, thresh)
+% LEARN_PARAMS_EM Set the parameters of each adjustable node to their ML/MAP values using batch EM.
+% [bnet, LLtrace, engine] = learn_params_em(engine, data, max_iter, thresh)
+%
+% data{i,l} is the value of node i in case l, or [] if hidden.
+%   Suppose you have L training cases in an O*L array, D, where O is the num observed
+%   scalar nodes, and N is the total num nodes.
+%   Then you can create 'data' as follows, where onodes is the index of the observable nodes:
+%      data = cell(N, L);
+%      data(onodes,:) = num2cell(D);
+%   Of course it is possible for different sets of nodes to be observed in each case.
+%
+% We return the modified bnet and engine.
+% To see the learned parameters for node i, use the construct
+%   s = struct(bnet.CPD{i}); % violate object privacy
+% LLtrace is the learning curve: the vector of log-likelihood scores at each iteration.
+%
+% max_iter specifies the maximum number of iterations. Default: 10.
+%
+% thresh specifies the thresold for stopping EM. Default: 1e-3.
+% We stop when |f(t) - f(t-1)| / avg < threshold,
+% where avg = (|f(t)| + |f(t-1)|)/2 and f is log lik.  
+
+if nargin < 3, max_iter = 10; end
+if nargin < 4, thresh = 1e-3; end
+
+verbose = 1;
+
+loglik = 0;
+previous_loglik = -inf;
+converged = 0;
+num_iter = 1;
+LL = [];
+
+while ~converged & (num_iter <= max_iter)
+  [engine, loglik] = EM_step(engine, evidence);
+  if verbose, fprintf('EM iteration %d, ll = %8.4f\n', num_iter, loglik); end
+  num_iter = num_iter + 1;
+  converged = em_converged(loglik, previous_loglik, thresh);
+  previous_loglik = loglik;
+  LL = [LL loglik];
+end
+if verbose, fprintf('\n'); end
+
+bnet = bnet_from_engine(engine);
+
+%%%%%%%%%
+
+function [engine, loglik] = EM_step(engine, cases)
+
+bnet = bnet_from_engine(engine); % engine contains the old params that are used for the E step
+CPDs = bnet.CPD; % these are the new params that get maximized
+num_CPDs = length(CPDs);
+adjustable = zeros(1,num_CPDs);
+for e=1:num_CPDs
+  adjustable(e) = adjustable_CPD(CPDs{e});
+end
+adj = find(adjustable);
+n = length(bnet.dag);
+
+for e=adj(:)'
+  CPDs{e} = reset_ess(CPDs{e});
+end
+
+loglik = 0;
+ncases = size(cases, 2);
+for l=1:ncases
+  evidence = cases(:,l);
+  [engine, ll] = enter_evidence(engine, evidence);
+  loglik = loglik + ll;
+  hidden_bitv = zeros(1,n);
+  hidden_bitv(isemptycell(evidence))=1;
+  for i=1:n
+    e = bnet.equiv_class(i);
+    if adjustable(e)
+      fmarg = marginal_family(engine, i);
+      CPDs{e} = update_ess(CPDs{e}, fmarg, evidence, bnet.node_sizes, bnet.cnodes, hidden_bitv);
+    end
+  end
+end
+
+for e=adj(:)'
+  CPDs{e} = maximize_params(CPDs{e});
+end
+
+engine = update_engine(engine, CPDs);
+
+