comparison toolboxes/FullBNT-1.0.7/bnt/examples/dynamic/HHMM/Map/mk_rnd_map_hhmm.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function bnet = mk_rnd_map_hhmm(varargin)
+
+% We copy the deterministic structure of the real HHMM,
+% but randomize the probabilities of the adjustable CPDs.
+% The key trick is that 0s in the real HHMM remain 0
+% even when multiplied by a randon number.
+
+obs_model = 'unique';
+
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'obs_model', obs_model = varargin{i+1};
+  end
+end
+
+
+unique_obs = strcmp(obs_model, 'unique');
+
+psuccess = 0.9;
+% must be less than 1, so that pfail > 0
+% otherwise we copy too many 0s
+bnet = mk_map_hhmm('p', psuccess, 'obs_model', obs_model);
+ns = bnet.node_sizes;
+ss = bnet.nnodes_per_slice;
+
+U = 1; A = 2; C = 3; F = 4;
+%unique_obs = (bnet.nnodes_per_slice == 5);
+if unique_obs
+  onodes = 5;
+else
+  north = 5; east = 6; south = 7; west = 8;
+  onodes = [north east south west];
+end
+
+eclass = bnet.equiv_class;
+S=struct(bnet.CPD{eclass(F,1)});
+CPT = mk_stochastic(rand(size(S.CPT)) .* S.CPT);
+bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', CPT);
+
+
+% Observation model
+if unique_obs
+  CPT = zeros(ns(A), ns(C), 5);
+  CPT(1,1,1)=1;  % Theo state 4
+  CPT(1,2,2)=1;  % Theo state 5
+  CPT(1,3,3)=1; % Theo state 6
+  CPT(2,1,4)=1; % Theo state 9
+  CPT(2,2,5)=1; % Theo state 10
+  %CPT(2,3,:) undefined
+  O = onodes(1);
+  bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', CPT);
+else
+  for i=[north east south west]
+    CPT = mk_stochastic(rand(ns(A), ns(C), 2));
+    bnet.CPD{eclass(i,1)} = tabular_CPD(bnet, i, 'CPT', CPT);
+  end
+end
+
+% Define the CPDs for slice 2
+
+startprob = zeros(ns(U), ns(A));
+S = struct(bnet.CPD{eclass(A,2)});
+transprob = mk_stochastic(rand(size(S.transprob)) .* S.transprob);
+bnet.CPD{eclass(A,2)} = hhmm2Q_CPD(bnet, A+ss, 'Fbelow', F, ...
+				  'startprob', startprob, 'transprob', transprob);
+
+S = struct(bnet.CPD{eclass(C,2)});
+transprob = mk_stochastic(rand(size(S.transprob)) .* S.transprob);
+startprob = mk_stochastic(rand(size(S.startprob)) .* S.startprob);
+bnet.CPD{eclass(C,2)} = hhmm2Q_CPD(bnet, C+ss, 'Fself', F, ...
+				  'startprob', startprob, 'transprob', transprob);
+
+