Mercurial > hg > camir-aes2014
annotate toolboxes/FullBNT-1.0.7/bnt/examples/static/mixexp3.m @ 0:e9a9cd732c1e tip
first hg version after svn
| author | wolffd |
|---|---|
| date | Tue, 10 Feb 2015 15:05:51 +0000 |
| parents | |
| children |
| rev | line source |
|---|---|
| wolffd@0 | 1 % Fit a piece-wise linear regression model. |
| wolffd@0 | 2 % Here is the model |
| wolffd@0 | 3 % |
| wolffd@0 | 4 % X \ |
| wolffd@0 | 5 % | | |
| wolffd@0 | 6 % Q | |
| wolffd@0 | 7 % | / |
| wolffd@0 | 8 % Y |
| wolffd@0 | 9 % |
| wolffd@0 | 10 % where all arcs point down. |
| wolffd@0 | 11 % We condition everything on X, so X is a root node. Q is a softmax, and Y is a linear Gaussian. |
| wolffd@0 | 12 % Q is hidden, X and Y are observed. |
| wolffd@0 | 13 |
| wolffd@0 | 14 X = 1; |
| wolffd@0 | 15 Q = 2; |
| wolffd@0 | 16 Y = 3; |
| wolffd@0 | 17 dag = zeros(3,3); |
| wolffd@0 | 18 dag(X,[Q Y]) = 1; |
| wolffd@0 | 19 dag(Q,Y) = 1; |
| wolffd@0 | 20 ns = [1 2 1]; % make X and Y scalars, and have 2 experts |
| wolffd@0 | 21 dnodes = [2]; |
| wolffd@0 | 22 onodes = [1 3]; |
| wolffd@0 | 23 bnet = mk_bnet(dag, ns, 'discrete', dnodes, 'observed', onodes); |
| wolffd@0 | 24 |
| wolffd@0 | 25 IRLS_iter = 10; |
| wolffd@0 | 26 clamped = 0; |
| wolffd@0 | 27 |
| wolffd@0 | 28 bnet.CPD{1} = root_CPD(bnet, 1); |
| wolffd@0 | 29 |
| wolffd@0 | 30 % start with good initial params |
| wolffd@0 | 31 w = [-5 5]; % w(:,i) is the normal vector to the i'th decisions boundary |
| wolffd@0 | 32 b = [0 0]; % b(i) is the offset (bias) to the i'th decisions boundary |
| wolffd@0 | 33 |
| wolffd@0 | 34 mu = [0 0]; |
| wolffd@0 | 35 sigma = 1; |
| wolffd@0 | 36 Sigma = repmat(sigma*eye(ns(Y)), [ns(Y) ns(Y) ns(Q)]); |
| wolffd@0 | 37 W = [-1 1]; |
| wolffd@0 | 38 W2 = reshape(W, [ns(Y) ns(X) ns(Q)]); |
| wolffd@0 | 39 |
| wolffd@0 | 40 bnet.CPD{2} = softmax_CPD(bnet, 2, w, b, clamped, IRLS_iter); |
| wolffd@0 | 41 bnet.CPD{3} = gaussian_CPD(bnet, 3, 'mean', mu, 'cov', Sigma, 'weights', W2); |
| wolffd@0 | 42 |
| wolffd@0 | 43 |
| wolffd@0 | 44 engine = jtree_inf_engine(bnet); |
| wolffd@0 | 45 |
| wolffd@0 | 46 evidence = cell(1,3); |
| wolffd@0 | 47 evidence{X} = 0.68; |
| wolffd@0 | 48 |
| wolffd@0 | 49 engine = enter_evidence(engine, evidence); |
| wolffd@0 | 50 |
| wolffd@0 | 51 m = marginal_nodes(engine, Y); |
| wolffd@0 | 52 m.mu |