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

12 files changed, 516 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/CVS/Entries
new file mode 100644
index 00000000..dc32f52b
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/CVS/Entries
@@ -0,0 +1,6 @@
+/disp_map_hhmm.m/1.1.1.1/Tue Sep 24 22:45:56 2002//
+/learn_map.m/1.1.1.1/Sat Jan 11 18:48:46 2003//
+/mk_map_hhmm.m/1.1.1.1/Tue Sep 24 10:49:52 2002//
+/mk_rnd_map_hhmm.m/1.1.1.1/Tue Sep 24 22:13:48 2002//
+/sample_from_map.m/1.1.1.1/Tue Sep 24 13:02:30 2002//
+D/Old////
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/CVS/Repository
new file mode 100644
index 00000000..66b47bbc
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/examples/dynamic/HHMM/Map
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/CVS/Root b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/CVS/Entries b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/CVS/Entries
new file mode 100644
index 00000000..6079d451
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/CVS/Entries
@@ -0,0 +1,2 @@
+/mk_map_hhmm.m/1.1.1.1/Tue Sep 24 07:02:44 2002//
+D
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/CVS/Repository b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/CVS/Repository
new file mode 100644
index 00000000..354057a9
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/CVS/Repository
@@ -0,0 +1 @@
+FullBNT/BNT/examples/dynamic/HHMM/Map/Old
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/CVS/Root b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/CVS/Root
new file mode 100644
index 00000000..f3bd14a6
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/CVS/Root
@@ -0,0 +1 @@
+:ext:nsaunier@bnt.cvs.sourceforge.net:/cvsroot/bnt
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m
new file mode 100644
index 00000000..7b646745
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/Old/mk_map_hhmm.m
@@ -0,0 +1,156 @@
+function bnet = mk_map_hhmm(varargin)
+
+% p is the prob of a successful move (defines the reliability of motors)
+p = 1;
+num_obs_nodes = 1;
+
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'p', p = varargin{i+1};
+   case 'numobs', num_obs_node = varargin{i+1};
+  end
+end
+
+
+q = 1-p;
+
+% assign numbers to the nodes in topological order
+U = 1; A = 2; C = 3; F = 4; O = 5;
+
+% create graph structure
+
+ss = 5; % slice size
+intra = zeros(ss,ss);
+intra(U,F)=1;
+intra(A,[C F O])=1;
+intra(C,[F O])=1;
+
+inter = zeros(ss,ss);
+inter(U,[A C])=1;
+inter(A,[A C])=1;
+inter(F,[A C])=1;
+inter(C,C)=1;
+
+% node sizes
+ns = zeros(1,ss);
+ns(U) = 2; % left/right
+ns(A) = 2;
+ns(C) = 3;
+ns(F) = 2;
+ns(O) = 5; % we will assign each state a unique symbol
+l = 1; r = 2; % left/right
+L = 1; R = 2;
+
+% Make the DBN
+bnet = mk_dbn(intra, inter, ns, 'observed', O);
+eclass = bnet.equiv_class;
+
+
+
+% Define CPDs for slice 1
+% We clamp all of them, i.e., do not try to learn them.
+
+% uniform probs over actions (the input could be chosen from a policy)
+bnet.CPD{eclass(U,1)} = tabular_CPD(bnet, U, 'CPT', mk_stochastic(ones(ns(U),1)), ...
+				    'adjustable', 0);
+
+% uniform probs over starting abstract state
+bnet.CPD{eclass(A,1)} = tabular_CPD(bnet, A, 'CPT', mk_stochastic(ones(ns(A),1)), ...
+				    'adjustable', 0);
+
+% Uniform probs over starting concrete state, modulo the fact
+% that corridor 2 is only of length 2.
+CPT = zeros(ns(A), ns(C)); % CPT(i,j) = P(C starts in j | A=i)
+CPT(1, :) = [1/3 1/3 1/3];
+CPT(2, :) = [1/2 1/2 0];
+bnet.CPD{eclass(C,1)} = tabular_CPD(bnet, C, 'CPT', CPT, 'adjustable', 0);
+
+% Termination probs
+CPT = zeros(ns(U), ns(A), ns(C), ns(F));
+CPT(r,1,1,:) = [1 0];
+CPT(r,1,2,:) = [1 0];
+CPT(r,1,3,:) = [q p];
+CPT(r,2,1,:) = [1 0];
+CPT(r,2,2,:) = [q p];
+CPT(l,1,1,:) = [q p];
+CPT(l,1,2,:) = [1 0];
+CPT(l,1,3,:) = [1 0];
+CPT(l,2,1,:) = [q p];
+CPT(l,2,2,:) = [1 0];
+
+bnet.CPD{eclass(F,1)} = tabular_CPD(bnet, F, 'CPT', CPT);
+
+
+% Assign each state a unique observation
+CPT = zeros(ns(A), ns(C), ns(O));
+CPT(1,1,1)=1; 
+CPT(1,2,2)=1;
+CPT(1,3,3)=1;
+CPT(2,1,4)=1;
+CPT(2,2,5)=1;
+%CPT(2,3,:) undefined
+
+bnet.CPD{eclass(O,1)} = tabular_CPD(bnet, O, 'CPT', CPT);
+
+
+% Define the CPDs for slice 2
+
+% Abstract
+
+% Since the top level never resets, the starting distribution is irrelevant:
+% A2 will be determined by sampling from transmat(A1,:).
+% But the code requires we specify it anyway; we make it all 0s, a dummy value.
+startprob = zeros(ns(U), ns(A));
+
+transmat = zeros(ns(U), ns(A), ns(A));
+transmat(R,1,:) = [q p];
+transmat(R,2,:) = [0 1];
+transmat(L,1,:) = [1 0];
+transmat(L,2,:) = [p q];
+
+% Qps are the parents we condition the parameters on, in this case just
+% the past action.
+bnet.CPD{eclass(A,2)} = hhmm2Q_CPD(bnet, A+ss, 'Fbelow', F, ...
+				  'startprob', startprob, 'transprob', transmat);
+
+
+
+% Concrete
+
+transmat = zeros(ns(C), ns(U), ns(A), ns(C));
+transmat(1,r,1,:) = [q p 0.0];
+transmat(2,r,1,:) = [0.0 q p];
+transmat(3,r,1,:) = [0.0 0.0 1.0];
+transmat(1,r,2,:) = [q p 0.0];
+transmat(2,r,2,:) = [0.0 1.0 0.0];
+%
+transmat(1,l,1,:) = [1.0 0.0 0.0];
+transmat(2,l,1,:) = [p q 0.0];
+transmat(3,l,1,:) = [0.0 p q];
+transmat(1,l,2,:) = [1.0 0.0 0.0];
+transmat(2,l,2,:) = [p q 0.0];
+
+% Add a new dimension for A(t-1), by copying old vals,
+% so the matrix is the same size as startprob
+
+
+transmat = reshape(transmat, [ns(C) ns(U) ns(A) 1 ns(C)]);
+transmat = repmat(transmat, [1 1 1 ns(A) 1]);
+
+% startprob(C(t-1), U(t-1), A(t-1), A(t), C(t))
+startprob = zeros(ns(C), ns(U), ns(A), ns(A), ns(C));
+startprob(1,L,1,1,:) = [1.0 0.0 0.0];
+startprob(3,R,1,2,:) = [1.0 0.0 0.0];
+startprob(3,R,1,1,:) = [0.0 0.0 1.0];
+% 
+startprob(1,L,2,1,:) = [0.0 0.0 010];
+startprob(2,L,2,1,:) = [1.0 0.0 0.0];
+startprob(2,R,2,2,:) = [0.0 1.0 0.0];
+
+% want transmat(U,A,C,At,Ct), ie. in topo order
+transmat = permute(transmat, [2 3 1 4 5]);
+startprob  = permute(startprob, [2 3 1 4 5]);
+bnet.CPD{eclass(C,2)} = hhmm2Q_CPD(bnet, C+ss, 'Fself', F, ...
+				  'startprob', startprob, 'transprob', transmat);
+
+
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/disp_map_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/disp_map_hhmm.m
new file mode 100644
index 00000000..0aadf2bb
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/disp_map_hhmm.m
@@ -0,0 +1,13 @@
+function disp_map_hhmm(bnet)
+
+eclass = bnet.equiv_class;
+U = 1; A = 2; C = 3; F = 4;
+
+S = struct(bnet.CPD{eclass(A,2)});
+disp('abstract trans')
+dispcpt(S.transprob)
+
+S = struct(bnet.CPD{eclass(C,2)});
+disp('concrete trans for go left') % UAC AC
+dispcpt(squeeze(S.transprob(1,:,:,:,:)))
+
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/learn_map.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/learn_map.m
new file mode 100644
index 00000000..ac36586a
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/learn_map.m
@@ -0,0 +1,40 @@
+seed = 1;
+rand('state', seed);
+randn('state', seed);
+
+obs_model = 'unique';  % each cell has a unique label (essentially fully observable)
+%obs_model = 'four'; % each cell generates 4 observations, NESW
+
+% Generate the true network, and a randomization of it
+realnet = mk_map_hhmm('p', 0.9, 'obs_model', obs_model);
+rndnet = mk_rnd_map_hhmm('obs_model', obs_model);
+eclass = realnet.equiv_class;
+U = 1; A = 2; C = 3; F = 4; onodes = 5;
+
+ss = realnet.nnodes_per_slice;
+T = 100;
+evidence = sample_dbn(realnet, 'length', T);
+ev = cell(ss,T);
+ev(onodes,:) = evidence(onodes,:);
+
+infeng = jtree_dbn_inf_engine(rndnet);
+
+if 0
+% suppose we do not observe the final finish node, but only know 
+% it is more likely to be on that off
+ev2 = ev;
+infeng = enter_evidence(infeng, ev2, 'soft_evidence_nodes', [F T], 'soft_evidence',  {[0.3 0.7]'});
+end
+
+
+learnednet = learn_params_dbn_em(infeng, {evidence}, 'max_iter', 5);
+
+disp('real model')
+disp_map_hhmm(realnet)
+
+disp('learned model')
+disp_map_hhmm(learnednet)
+
+disp('rnd model')
+disp_map_hhmm(rndnet)
+
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/mk_map_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/mk_map_hhmm.m
new file mode 100644
index 00000000..7b077ddb
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/mk_map_hhmm.m
@@ -0,0 +1,181 @@
+function bnet = mk_map_hhmm(varargin)
+
+% p is the prob of a successful move (defines the reliability of motors)
+p = 1;
+obs_model = 'unique';
+
+for i=1:2:length(varargin)
+  switch varargin{i},
+   case 'p', p = varargin{i+1};
+   case 'obs_model', obs_model = varargin{i+1};
+  end
+end
+
+
+q = 1-p;
+unique_obs = strcmp(obs_model, 'unique');
+
+% assign numbers to the nodes in topological order
+U = 1; A = 2; C = 3; F = 4;
+if unique_obs
+  onodes = 5;
+else
+  N = 5; E = 6; S = 7; W = 8; % north, east, south, west
+  onodes = [N E S W];
+end
+
+% create graph structure
+
+ss = 4 + length(onodes); % slice size
+intra = zeros(ss,ss);
+intra(U,F)=1;
+intra(A,[C F onodes])=1;
+intra(C,[F onodes])=1;
+
+inter = zeros(ss,ss);
+inter(U,[A C])=1;
+inter(A,[A C])=1;
+inter(F,[A C])=1;
+inter(C,C)=1;
+
+% node sizes
+ns = zeros(1,ss);
+ns(U) = 2; % left/right
+ns(A) = 2;
+ns(C) = 3;
+ns(F) = 2;
+if unique_obs
+  ns(onodes) = 5; % we will assign each state a unique symbol
+else
+  ns(onodes) = 2;
+end
+l = 1; r = 2; % left/right
+L = 1; R = 2;
+
+% Make the DBN
+bnet = mk_dbn(intra, inter, ns, 'observed', onodes);
+eclass = bnet.equiv_class;
+
+
+
+% Define CPDs for slice 1
+% We clamp all the CPDs that are not tied,
+% since we cannot learn them from a single sequence.
+
+% uniform probs over actions (the input could be chosen from a policy)
+bnet.CPD{eclass(U,1)} = tabular_CPD(bnet, U, 'CPT', mk_stochastic(ones(ns(U),1)), ...
+				    'adjustable', 0);
+
+% uniform probs over starting abstract state
+bnet.CPD{eclass(A,1)} = tabular_CPD(bnet, A, 'CPT', mk_stochastic(ones(ns(A),1)), ...
+				    'adjustable', 0);
+
+% Uniform probs over starting concrete state, modulo the fact
+% that corridor 2 is only of length 2.
+CPT = zeros(ns(A), ns(C)); % CPT(i,j) = P(C starts in j | A=i)
+CPT(1, :) = [1/3 1/3 1/3];
+CPT(2, :) = [1/2 1/2 0];
+bnet.CPD{eclass(C,1)} = tabular_CPD(bnet, C, 'CPT', CPT, 'adjustable', 0);
+
+% Termination probs
+CPT = zeros(ns(U), ns(A), ns(C), ns(F));
+CPT(r,1,1,:) = [1 0];
+CPT(r,1,2,:) = [1 0];
+CPT(r,1,3,:) = [q p];
+CPT(r,2,1,:) = [1 0];
+CPT(r,2,2,:) = [q p];
+CPT(l,1,1,:) = [q p];
+CPT(l,1,2,:) = [1 0];
+CPT(l,1,3,:) = [1 0];
+CPT(l,2,1,:) = [q p];
+CPT(l,2,2,:) = [1 0];
+
+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
+  % north/east/south/west can see wall (1) or opening (2)
+  CPT = zeros(ns(A), ns(C), 2);
+  CPT(:,:,1) = q;
+  CPT(:,:,2) = p;
+  bnet.CPD{eclass(W,1)} = tabular_CPD(bnet, W, 'CPT', CPT);
+  bnet.CPD{eclass(E,1)} = tabular_CPD(bnet, E, 'CPT', CPT);
+  CPT = zeros(ns(A), ns(C), 2);
+  CPT(:,:,1) = p;
+  CPT(:,:,2) = q;
+  bnet.CPD{eclass(S,1)} = tabular_CPD(bnet, S, 'CPT', CPT);
+  bnet.CPD{eclass(N,1)} = tabular_CPD(bnet, N, 'CPT', CPT);
+end
+
+% Define the CPDs for slice 2
+
+% Abstract
+
+% Since the top level never resets, the starting distribution is irrelevant:
+% A2 will be determined by sampling from transmat(A1,:).
+% But the code requires we specify it anyway; we make it all 0s, a dummy value.
+startprob = zeros(ns(U), ns(A));
+
+transmat = zeros(ns(U), ns(A), ns(A));
+transmat(R,1,:) = [q p];
+transmat(R,2,:) = [0 1];
+transmat(L,1,:) = [1 0];
+transmat(L,2,:) = [p q];
+
+% Qps are the parents we condition the parameters on, in this case just
+% the past action.
+bnet.CPD{eclass(A,2)} = hhmm2Q_CPD(bnet, A+ss, 'Fbelow', F, ...
+				  'startprob', startprob, 'transprob', transmat);
+
+
+
+% Concrete
+
+transmat = zeros(ns(C), ns(U), ns(A), ns(C));
+transmat(1,r,1,:) = [q p 0.0];
+transmat(2,r,1,:) = [0.0 q p];
+transmat(3,r,1,:) = [0.0 0.0 1.0];
+transmat(1,r,2,:) = [q p 0.0];
+transmat(2,r,2,:) = [0.0 1.0 0.0];
+%
+transmat(1,l,1,:) = [1.0 0.0 0.0];
+transmat(2,l,1,:) = [p q 0.0];
+transmat(3,l,1,:) = [0.0 p q];
+transmat(1,l,2,:) = [1.0 0.0 0.0];
+transmat(2,l,2,:) = [p q 0.0];
+
+% Add a new dimension for A(t-1), by copying old vals,
+% so the matrix is the same size as startprob
+
+
+transmat = reshape(transmat, [ns(C) ns(U) ns(A) 1 ns(C)]);
+transmat = repmat(transmat, [1 1 1 ns(A) 1]);
+
+% startprob(C(t-1), U(t-1), A(t-1), A(t), C(t))
+startprob = zeros(ns(C), ns(U), ns(A), ns(A), ns(C));
+startprob(1,L,1,1,:) = [1.0 0.0 0.0];
+startprob(3,R,1,2,:) = [1.0 0.0 0.0];
+startprob(3,R,1,1,:) = [0.0 0.0 1.0];
+% 
+startprob(1,L,2,1,:) = [0.0 0.0 010];
+startprob(2,L,2,1,:) = [1.0 0.0 0.0];
+startprob(2,R,2,2,:) = [0.0 1.0 0.0];
+
+% want transmat(U,A,C,At,Ct), ie. in topo order
+transmat = permute(transmat, [2 3 1 4 5]);
+startprob  = permute(startprob, [2 3 1 4 5]);
+bnet.CPD{eclass(C,2)} = hhmm2Q_CPD(bnet, C+ss, 'Fself', F, ...
+				  'startprob', startprob, 'transprob', transmat);
+
+
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/mk_rnd_map_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/mk_rnd_map_hhmm.m
new file mode 100644
index 00000000..76b06fc7
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/mk_rnd_map_hhmm.m
@@ -0,0 +1,73 @@
+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);
+
+
diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/sample_from_map.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/sample_from_map.m
new file mode 100644
index 00000000..816b741e
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/Map/sample_from_map.m
@@ -0,0 +1,41 @@
+if 0
+% Generate some sample paths
+
+bnet = mk_map_hhmm('p', 1);
+% assign numbers to the nodes in topological order
+U = 1; A = 2; C = 3; F = 4; O = 5;
+
+
+seed = 0;
+rand('state', seed);
+randn('state', seed);
+
+% control policy = sweep right then left
+T = 10;
+ss = 5;
+ev = cell(ss, T);
+ev(U,:) = num2cell([R*ones(1,5) L*ones(1,5)]);
+
+% fix initial conditions to be in left most state
+ev{A,1} = 1; 
+ev{C,1} = 1; 
+evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)
+
+
+% Now do same but with noisy actuators
+
+bnet = mk_map_hhmm('p', 0.8);
+evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)
+
+end
+
+% Now do same but with 4 observations per slice
+
+bnet = mk_map_hhmm('p', 0.8, 'obs_model', 'four');
+ss = bnet.nnodes_per_slice;
+
+ev = cell(ss, T);
+ev(U,:) = num2cell([R*ones(1,5) L*ones(1,5)]);
+ev{A,1} = 1; 
+ev{C,1} = 1; 
+evidence = sample_dbn(bnet, 'length', T, 'evidence', ev)