diff toolboxes/FullBNT-1.0.7/bnt/examples/static/mixexp2.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/examples/static/mixexp2.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,104 @@
+% Fit a piece-wise linear regression model.
+% Here is the model
+%
+%  X \
+%  | |
+%  Q |
+%  | /
+%  Y
+%
+% where all arcs point down.
+% We condition everything on X, so X is a root node. Q is a softmax, and Y is a linear Gaussian.
+% Q is hidden, X and Y are observed.
+
+X = 1;
+Q = 2;
+Y = 3;
+dag = zeros(3,3);
+dag(X,[Q Y]) = 1;
+dag(Q,Y) = 1;
+ns = [1 2 1]; % make X and Y scalars, and have 2 experts
+dnodes = [2];
+onodes = [1 3];
+bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', onodes);
+
+IRLS_iter = 10;
+clamped = 0;
+
+bnet.CPD{1} = root_CPD(bnet, 1);
+
+if 0
+  % start with good initial params
+  w = [-5 5];  % w(:,i) is the normal vector to the i'th decisions boundary
+  b = [0 0];  % b(i) is the offset (bias) to the i'th decisions boundary
+  
+  mu = [0 0];
+  sigma = 1;
+  Sigma = repmat(sigma*eye(ns(Y)), [ns(Y) ns(Y) ns(Q)]);
+  W = [-1 1];
+  W2 = reshape(W, [ns(Y) ns(X) ns(Q)]);
+
+  bnet.CPD{2} = softmax_CPD(bnet, 2, w, b,  clamped, IRLS_iter);
+  bnet.CPD{3} = gaussian_CPD(bnet, 3, mu, Sigma, W2);
+else
+  % start with rnd initial params
+  rand('state', 0);
+  randn('state', 0);
+  bnet.CPD{2} = softmax_CPD(bnet, 2, 'clamped', clamped, 'max_iter', IRLS_iter);
+  bnet.CPD{3} = gaussian_CPD(bnet, 3);
+end
+
+
+
+load('/examples/static/Misc/mixexp_data.txt', '-ascii');        
+% Just use 1/10th of the data, to speed things up
+data = mixexp_data(1:10:end, :);
+%data = mixexp_data;
+ 
+%plot(data(:,1), data(:,2), '.')
+
+
+s = struct(bnet.CPD{2}); % violate object privacy
+%eta0 = [s.glim.b1; s.glim.w1]';
+eta0 = [s.glim{1}.b1; s.glim{1}.w1]';
+s = struct(bnet.CPD{3}); % violate object privacy
+W = reshape(s.weights, [1 2]);
+theta0 = [s.mean; W]';
+
+%figure(1)
+%mixexp_plot(theta0, eta0, data);
+%suptitle('before learning')
+
+ncases = size(data, 1);
+cases = cell(3, ncases);
+cases([1 3], :) = num2cell(data');
+
+engine = jtree_inf_engine(bnet);
+
+% log lik before learning
+ll = 0;
+for l=1:ncases
+  ev = cases(:,l);
+  [engine, loglik] = enter_evidence(engine, ev);
+  ll = ll + loglik;
+end
+
+% do learning
+max_iter = 5;
+[bnet2, LL2] = learn_params_em(engine, cases, max_iter);
+
+s = struct(bnet2.CPD{2});
+%eta2 = [s.glim.b1; s.glim.w1]';
+eta2 = [s.glim{1}.b1; s.glim{1}.w1]';
+s = struct(bnet2.CPD{3});
+W = reshape(s.weights, [1 2]);
+theta2 = [s.mean; W]';
+
+%figure(2)
+%mixexp_plot(theta2, eta2, data);
+%suptitle('after learning')
+
+fprintf('mixexp2: loglik before learning %f, after %d iters %f\n', ll, length(LL2),  LL2(end));
+
+
+