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

1 files changed, 139 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/skf_data_assoc_gmux.m b/sourcecodes/bnt-master/BNT/examples/dynamic/skf_data_assoc_gmux.m
new file mode 100644
index 00000000..31fdeda9
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/skf_data_assoc_gmux.m
@@ -0,0 +1,139 @@
+% We consider a switching Kalman filter of the kind studied
+% by Zoubin Ghahramani, i.e., where the switch node determines
+% which of the hidden chains we get to observe (data association).
+% e.g., for n=2 chains
+% 
+% X1 -> X1
+% | X2 -> X2
+% \ |
+%  v
+%  Y
+%  ^
+%  |
+%  S
+%
+% Y is a gmux (multiplexer) node, where S switches in one of the parents.
+% We differ from Zoubin by not connecting the S nodes over time (which
+% doesn't make sense for data association).
+% Indeed, we assume the S nodes are always observed.
+% 
+%
+% We will track 2 objects (points) moving in the plane, as in BNT/Kalman/tracking_demo.
+% We will alternate between observing them.
+
+nobj = 2;
+N = nobj+2;
+Xs = 1:nobj;
+S = nobj+1;
+Y = nobj+2;
+
+intra = zeros(N,N);
+inter = zeros(N,N);
+intra([Xs S], Y) =1;
+for i=1:nobj
+  inter(Xs(i), Xs(i))=1;
+end
+
+Xsz = 4; % state space = (x y xdot ydot)
+Ysz = 2;
+ns = zeros(1,N);
+ns(Xs) = Xsz;
+ns(Y) = Ysz;
+ns(S) = n;
+
+bnet = mk_dbn(intra, inter, ns, 'discrete', S, 'observed', [S Y]);
+
+% For each object, we have
+% X(t+1) = F X(t) + noise(Q)
+% Y(t) = H X(t) + noise(R)
+F = [1 0 1 0; 0 1 0 1; 0 0 1 0; 0 0 0 1];
+H = [1 0 0 0; 0 1 0 0];
+Q = 1e-3*eye(Xsz);
+%R = 1e-3*eye(Ysz);
+R = eye(Ysz);
+
+% We initialise object 1 moving to the right, and object 2 moving to the left
+% (Here, we assume nobj=2)
+init_state{1} = [10 10 1 0]';
+init_state{2} = [10 -10 -1 0]';
+
+for i=1:nobj
+  bnet.CPD{Xs(i)} = gaussian_CPD(bnet, Xs(i), 'mean', init_state{i}, 'cov', 1e-4*eye(Xsz));
+end
+bnet.CPD{S} = root_CPD(bnet, S); % always observed
+bnet.CPD{Y} = gmux_CPD(bnet, Y, 'cov', repmat(R, [1 1 nobj]), 'weights', repmat(H, [1 1 nobj]));
+% slice 2
+eclass = bnet.equiv_class;
+for i=1:nobj
+  bnet.CPD{eclass(Xs(i), 2)} = gaussian_CPD(bnet, Xs(i)+N, 'mean', zeros(Xsz,1), 'cov', Q, 'weights', F);
+end
+
+% Observe objects at random
+T = 10;
+evidence = cell(N, T);
+data_assoc = sample_discrete(normalise(ones(1,nobj)), 1, T);
+evidence(S,:) = num2cell(data_assoc);
+evidence = sample_dbn(bnet, 'evidence', evidence);
+
+% plot the data
+true_state = cell(1,nobj);
+for i=1:nobj
+  true_state{i} = cell2num(evidence(Xs(i), :)); % true_state{i}(:,t) = [x y xdot ydot]'
+end
+obs_pos = cell2num(evidence(Y,:));
+figure(1)
+clf
+hold on
+styles = {'rx', 'go', 'b+', 'k*'};
+for i=1:nobj
+  plot(true_state{i}(1,:), true_state{i}(2,:), styles{i});
+end
+for t=1:T
+  text(obs_pos(1,t), obs_pos(2,t), sprintf('%d', t));
+end
+hold off
+relax_axes(0.1)
+
+
+% Inference
+ev = cell(N,T);
+ev(bnet.observed,:) = evidence(bnet.observed, :);
+
+engines = {};
+engines{end+1} = jtree_dbn_inf_engine(bnet);
+%engines{end+1} = scg_unrolled_dbn_inf_engine(bnet, T);
+engines{end+1} = pearl_unrolled_dbn_inf_engine(bnet);
+E = length(engines);
+
+inferred_state = cell(nobj,E); % inferred_state{i,e}(:,t)
+for e=1:E
+  engines{e} = enter_evidence(engines{e}, ev);
+  for i=1:nobj
+    inferred_state{i,e} = zeros(4, T);
+    for t=1:T
+      m = marginal_nodes(engines{e}, Xs(i), t);
+      inferred_state{i,e}(:,t) = m.mu;
+    end
+  end
+end
+inferred_state{1,1}
+inferred_state{1,2}
+
+% Plot results
+figure(2)
+clf
+hold on
+styles = {'rx', 'go', 'b+', 'k*'};
+nstyles = length(styles);
+c = 1;
+for e=1:E
+  for i=1:nobj
+    plot(inferred_state{i,e}(1,:), inferred_state{i,e}(2,:), styles{mod(c-1,nstyles)+1});
+    c = c + 1;
+  end
+end
+for t=1:T
+  text(obs_pos(1,t), obs_pos(2,t), sprintf('%d', t));
+end
+hold off
+relax_axes(0.1)