From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001
From: ziejd2
Date: Thu, 28 Sep 2017 15:04:40 -0500
Subject: BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02
---
 .../bnt-master/BNT/examples/static/softmax1.m      | 109 +++++++++++++++++++++
 1 file changed, 109 insertions(+)
 create mode 100644 sourcecodes/bnt-master/BNT/examples/static/softmax1.m

(limited to 'sourcecodes/bnt-master/BNT/examples/static/softmax1.m')

diff --git a/sourcecodes/bnt-master/BNT/examples/static/softmax1.m b/sourcecodes/bnt-master/BNT/examples/static/softmax1.m
new file mode 100644
index 00000000..06434549
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/static/softmax1.m
@@ -0,0 +1,109 @@
+% Check that softmax works with a simple classification demo.
+% Based on netlab's demglm2
+% X -> Q where X is an input node, and Q is a softmax
+
+rand('state', 0);
+randn('state', 0);
+  
+% Check inference
+
+input_dim = 2;
+num_classes = 3;
+IRLS_iter = 3;
+
+net = glm(input_dim, num_classes, 'softmax');
+
+dag = zeros(2);
+dag(1,2) = 1;
+discrete_nodes = [2];
+bnet = mk_bnet(dag, [input_dim num_classes], 'discrete', discrete_nodes, 'observed', 1);
+bnet.CPD{1} = root_CPD(bnet, 1);
+clamped = 0;
+bnet.CPD{2} = softmax_CPD(bnet, 2, net.w1, net.b1, clamped, IRLS_iter);
+
+engine = jtree_inf_engine(bnet);
+
+x = rand(1, input_dim);
+q = glmfwd(net, x);
+
+[engine, ll] = enter_evidence(engine, {x, []});
+m = marginal_nodes(engine, 2);
+assert(approxeq(m.T(:), q(:)));
+
+
+% Check learning
+% We use EM, but in fact there is no hidden data.
+% The M step will call IRLS on the softmax node.
+
+% Generate data from three classes in 2d
+input_dim = 2;
+num_classes = 3;
+
+% Fix seeds for reproducible results
+randn('state', 42);
+rand('state', 42);
+
+ndata = 10;
+% Generate mixture of three Gaussians in two dimensional space
+data = randn(ndata, input_dim);
+targets = zeros(ndata, 3);
+
+% Priors for the clusters
+prior(1) = 0.4;
+prior(2) = 0.3;
+prior(3) = 0.3;
+
+% Cluster centres
+c = [2.0, 2.0; 0.0, 0.0; 1, -1];
+
+ndata1 = prior(1)*ndata;
+ndata2 = (prior(1) + prior(2))*ndata;
+% Put first cluster at (2, 2)
+data(1:ndata1, 1) = data(1:ndata1, 1) * 0.5 + c(1,1);
+data(1:ndata1, 2) = data(1:ndata1, 2) * 0.5 + c(1,2);
+targets(1:ndata1, 1) = 1;
+
+% Leave second cluster at (0,0)
+data((ndata1 + 1):ndata2, :) = ...
+  data((ndata1 + 1):ndata2, :);
+targets((ndata1+1):ndata2, 2) = 1;
+
+data((ndata2+1):ndata, 1) = data((ndata2+1):ndata,1) *0.6 + c(3, 1);
+data((ndata2+1):ndata, 2) = data((ndata2+1):ndata,2) *0.6 + c(3, 2);
+targets((ndata2+1):ndata, 3) = 1;
+
+
+if 0
+  ndata = 1;
+  data = x;
+  targets = [1 0 0];
+end
+
+options = foptions;
+options(1) = -1; % verbose
+options(14) = IRLS_iter;
+[net2, options2] = glmtrain(net, options, data, targets);
+net2.ll = options2(8); % type 'help foptions' for details
+
+cases = cell(2, ndata);
+for l=1:ndata
+  q = find(targets(l,:)==1);
+  x = data(l,:);
+  cases{1,l} = x(:);
+  cases{2,l} = q;
+end
+
+max_iter = 2; % we have complete observability, so 1 iter is enough
+[bnet2, ll2] = learn_params_em(engine, cases, max_iter);
+
+w = get_field(bnet2.CPD{2},'weights');
+b = get_field(bnet2.CPD{2},'offset')';
+
+w
+net2.w1
+
+b
+net2.b1
+
+% assert(approxeq(net2.ll, ll2)); % glmtrain returns ll after final M step, learn_params before
+
-- 
cgit 1.4.1