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, 258 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/mk_hhmm.m b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/mk_hhmm.m
new file mode 100644
index 00000000..85ff7f6a
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+++ b/sourcecodes/bnt-master/BNT/examples/dynamic/HHMM/mk_hhmm.m
@@ -0,0 +1,258 @@
+function [bnet, Qnodes, Fnodes, Onode] = mk_hhmm(varargin)
+% MK_HHMM Make a Hierarchical HMM
+% function [bnet, Qnodes, Fnodes, Onode] = mk_hhmm(...)
+%
+% e.g. 3-layer hierarchical HMM where level 1 only connects to level 2
+% and the parents of the observed node are levels 2 and 3.
+% (This DBN is the same as Fig 10 in my tech report.)
+%
+%   Q1 ---------->   Q1
+%   |  \            ^ |
+%   |   v          /  |
+%   |    F2 ------/   |
+%   |   ^ ^        \  |
+%   |  /  |         \ |
+%   | /   |          ||
+%   v     |          vv
+%   Q2----| --------> Q2
+%  /| \   |          ^|
+% / |  v  |         / |
+% | |   F3 --------/  |
+% | |   ^          \  |
+% | v  /            v v
+% | Q3 ----------->  Q3
+% |  |    
+% \  | 
+%  v v    
+%   O
+%
+%
+% Optional arguments in name/value format [default value in brackets]
+%
+% Qsizes      - sizes at each level [ none ]
+% allQ       - 1 means level i connects to all Q levels below, 0 means just to i+1 [0]
+% transprob  - transprob{d}(i,k,j) = P(Q(d,t)=j|Q(d,t-1)=i,Q(1:d-1,t)=k)  ['leftright']
+% startprob  - startprob{d}(k,j) = P(Q(d,t)=j|Q(1:d-1,t)=k)  ['leftstart']
+% termprob   - termprob{d}(k,j) = P(F(d,t)=2|Q(1:d-1,t)=k,Q(d,t)=j) for d>1 ['rightstop']
+% selfprop   - prob of a self transition (termprob default = 1-selfprop) [0.8]
+% Osize       - size of O node
+% discrete_obs - 1 means O is tabular_CPD, 0 means gaussian_CPD [0]
+% Oargs       - cell array of args to pass to the O CPD  [ {} ]
+% Ops         - Q parents of O [Qnodes(end)]
+% F1          - 1 means level 1 can finish (restart), else there is no F1->Q1 arc [0]
+% clamp1     - 1 means we clamp the params of the Q nodes in slice 1 (Qt1params) [1]
+%   Note: the Qt1params are startprob, which should be shared with other slices.
+%   However, in the current implementation, the Qt1params will only be estimated
+%   from the initial state of each sequence.
+%
+% For d=1, startprob{1}(1,j) is only used in the first slice and
+% termprob{1} is ignored, since we assume the top level never resets.
+% Also, transprob{1}(i,j) can be used instead of transprob{1}(i,1,j).
+%
+% leftstart means the model always starts in state 1.
+% rightstop means the model can only finish in its last state (Qsize(d)).
+% unif means each state is equally like to reach any other
+% rnd means the transition/starting probs are random (drawn from rand)
+%
+% Q1:QD in slice 1 are of type tabular_CPD
+% Q1:QD in slice 2 are of type hhmmQ_CPD.
+% F(2:D-1) is of type hhmmF_CPD, FD is of type tabular_CPD.
+
+args = varargin;
+nargs = length(args);
+
+% get sizes of nodes and topology
+Qsizes = [];
+Osize = [];
+allQ = 0;
+Ops = [];
+F1 = 0;
+for i=1:2:nargs
+  switch args{i},
+   case 'Qsizes', Qsizes = args{i+1}; 
+   case 'Osize',  Osize = args{i+1}; 
+   case 'allQ',   allQ = args{i+1}; 
+   case 'Ops',    Ops = args{i+1}; 
+   case 'F1',     F1 = args{i+1}; 
+  end
+end
+if isempty(Qsizes), error('must specify Qsizes'); end
+if Osize==0, error('must specify Osize'); end
+D = length(Qsizes);
+Qnodes = 1:D;
+
+if isempty(Ops), Ops = Qnodes(end); end
+
+
+[intra, inter, Qnodes, Fnodes, Onode] = mk_hhmm_topo(D, allQ, Ops, F1);
+ss = length(intra);
+names = {};
+
+if F1
+  Fnodes_ndx = Fnodes;
+else
+  Fnodes_ndx = [-1 Fnodes]; % Fnodes(1) is a dummy index
+end
+		
+% set default params
+discrete_obs = 0;
+Oargs = {};
+startprob = cell(1,D);
+startprob{1} = 'unif';
+for d=2:D
+  startprob{d} = 'leftstart';
+end
+transprob = cell(1,D);
+transprob{1} = 'unif';
+for d=2:D
+  transprob{d} = 'leftright';
+end
+termprob = cell(1,D);
+for d=2:D
+  termprob{d} = 'rightstop';
+end
+selfprob = 0.8;
+clamp1 = 1;
+
+for i=1:2:nargs
+  switch args{i},
+   case 'discrete_obs', discrete_obs = args{i+1}; 
+   case 'Oargs',        Oargs = args{i+1};
+   case 'startprob',    startprob = args{i+1};
+   case 'transprob',    transprob = args{i+1};
+   case 'termprob',     termprob = args{i+1};
+   case 'selfprob',     selfprob = args{i+1};
+   case 'clamp1',       clamp1 = args{i+1};
+  end
+end
+
+ns = zeros(1,ss);
+ns(Qnodes) = Qsizes;
+ns(Onode) = Osize;
+ns(Fnodes) = 2;
+
+dnodes = [Qnodes Fnodes];
+if discrete_obs
+  dnodes = [dnodes Onode];
+end
+onodes = [Onode];
+
+bnet = mk_dbn(intra, inter, ns, 'observed', onodes, 'discrete', dnodes, 'names', names);
+eclass = bnet.equiv_class;
+
+for d=1:D
+  if d==1
+    Qps = [];
+  elseif allQ
+    Qps = Qnodes(1:d-1);
+  else
+    Qps = Qnodes(d-1);
+  end
+  Qpsz = prod(ns(Qps));
+  Qsz = ns(Qnodes(d));
+  if isstr(startprob{d})
+    switch startprob{d}
+     case 'unif', startprob{d} = mk_stochastic(ones(Qpsz, Qsz));
+     case 'rnd', startprob{d} = mk_stochastic(rand(Qpsz, Qsz));
+     case 'leftstart', startprob{d} = zeros(Qpsz, Qsz); startprob{d}(:,1) = 1;
+    end
+  end
+  if isstr(transprob{d})
+    switch transprob{d}
+     case 'unif', transprob{d} = mk_stochastic(ones(Qsz, Qpsz, Qsz));
+     case 'rnd', transprob{d} = mk_stochastic(rand(Qsz, Qpsz, Qsz));
+     case 'leftright',
+      LR = mk_leftright_transmat(Qsz, selfprob);
+      temp = repmat(reshape(LR, [1 Qsz Qsz]), [Qpsz 1 1]); % transprob(k,i,j)
+      transprob{d} = permute(temp, [2 1 3]); % now transprob(i,k,j)
+    end
+  end
+  if isstr(termprob{d})
+    switch termprob{d}
+     case 'unif', termprob{d} = mk_stochastic(ones(Qpsz, Qsz, 2));
+     case 'rnd', termprob{d} = mk_stochastic(rand(Qpsz, Qsz, 2));
+     case 'rightstop',
+      %termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsz
+      stopprob = 1-selfprob;
+      termprob{d} = zeros(Qpsz, Qsz, 2);
+      termprob{d}(:,Qsz,2) = stopprob;
+      termprob{d}(:,Qsz,1) = 1-stopprob;
+      termprob{d}(:,1:(Qsz-1),1) = 1;
+     otherwise, error(['unrecognized termprob ' termprob{d}])
+    end
+  elseif d>1 % passed in termprob{d}(k,j)
+    temp = termprob{d};
+    termprob{d} = zeros(Qpsz, Qsz, 2);
+    termprob{d}(:,:,2) = temp;
+    termprob{d}(:,:,1) = ones(Qpsz,Qsz) - temp;
+  end
+end
+
+
+% SLICE 1
+
+for d=1:D
+  bnet.CPD{eclass(Qnodes(d),1)} = tabular_CPD(bnet, Qnodes(d), 'CPT', startprob{d}, 'adjustable', clamp1);
+end
+
+if F1
+  d = 1;
+  bnet.CPD{eclass(Fnodes_ndx(d),1)}  = hhmmF_CPD(bnet, Fnodes_ndx(d), Qnodes(d), Fnodes_ndx(d+1), ...
+						 'termprob', termprob{d});
+end
+for d=2:D-1
+  if allQ
+    Qps = Qnodes(1:d-1);
+  else
+    Qps = Qnodes(d-1);
+  end
+  bnet.CPD{eclass(Fnodes_ndx(d),1)}  = hhmmF_CPD(bnet, Fnodes_ndx(d), Qnodes(d), Fnodes_ndx(d+1), ...
+						 'Qps', Qps, 'termprob', termprob{d});
+end
+bnet.CPD{eclass(Fnodes_ndx(D),1)}  = tabular_CPD(bnet, Fnodes_ndx(D), 'CPT', termprob{D});
+
+if discrete_obs
+  bnet.CPD{eclass(Onode,1)} = tabular_CPD(bnet, Onode, Oargs{:});
+else
+  bnet.CPD{eclass(Onode,1)} = gaussian_CPD(bnet, Onode, Oargs{:});
+end
+
+% SLICE 2
+
+%for d=1:D
+%  bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, Qnodes, d, D, ...
+%					    'startprob', startprob{d}, 'transprob', transprob{d}, ...
+%					    'allQ', allQ);
+%end
+
+d = 1;
+if F1
+  bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, 'Fself', Fnodes_ndx(d), ...
+					    'Fbelow', Fnodes_ndx(d+1), ...
+					    'startprob', startprob{d}, 'transprob', transprob{d});
+else
+    bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, ...
+					    'Fbelow', Fnodes_ndx(d+1), ...
+					    'startprob', startprob{d}, 'transprob', transprob{d});
+end
+for d=2:D-1
+  if allQ
+    Qps = Qnodes(1:d-1);
+  else
+    Qps = Qnodes(d-1);
+  end
+  Qps = Qps + ss; % since all in slice 2
+  bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, 'Fself', Fnodes_ndx(d), ...
+					    'Fbelow', Fnodes_ndx(d+1), 'Qps', Qps, ...
+					    'startprob', startprob{d}, 'transprob', transprob{d});
+end
+d = D;
+if allQ
+  Qps = Qnodes(1:d-1);
+else
+  Qps = Qnodes(d-1);
+end
+Qps = Qps + ss; % since all in slice 2
+bnet.CPD{eclass(Qnodes(d),2)} = hhmmQ_CPD(bnet, Qnodes(d)+ss, 'Fself', Fnodes_ndx(d), ...
+					  'Qps', Qps, ...
+					  'startprob', startprob{d}, 'transprob', transprob{d});