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, 173 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/tabular_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/tabular_CPD.m
new file mode 100644
index 00000000..728302d4
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@tabular_CPD/tabular_CPD.m
@@ -0,0 +1,173 @@
+function CPD = tabular_CPD(bnet, self, varargin)
+% TABULAR_CPD Make a multinomial conditional prob. distrib. (CPT)
+%
+% CPD = tabular_CPD(bnet, node) creates a random CPT.
+%
+% The following arguments can be specified [default in brackets]
+%
+% CPT - specifies the params ['rnd']
+%   - T means use table T; it will be reshaped to the size of node's family.
+%   - 'rnd' creates rnd params (drawn from uniform)
+%   - 'unif' creates a uniform distribution
+% adjustable - 0 means don't adjust the parameters during learning [1]
+% prior_type - defines type of prior ['none']
+%  - 'none' means do ML estimation
+%  - 'dirichlet' means add pseudo-counts to every cell
+%  - 'entropic' means use a prior P(theta) propto exp(-H(theta)) (see Brand)
+% dirichlet_weight - equivalent sample size (ess) of the dirichlet prior [1]
+% dirichlet_type - defines the type of Dirichlet prior ['BDeu']
+%  - 'unif' means put dirichlet_weight in every cell
+%  - 'BDeu' means we put 'dirichlet_weight/(r q)' in every cell
+%    where r = self_sz and q = prod(parent_sz) (see Heckerman)
+% trim - 1 means trim redundant params (rows in CPT) when using entropic prior [0]
+% entropic_pcases - list of assignments to the parents nodes when we should use 
+%      the entropic prior; all other cases will be estimated using ML [1:psz]
+% sparse - 1 means use 1D sparse array to represent CPT [0]
+%
+% e.g., tabular_CPD(bnet, i, 'CPT', T)
+% e.g., tabular_CPD(bnet, i, 'CPT', 'unif', 'dirichlet_weight', 2, 'dirichlet_type', 'unif')
+%
+% REFERENCES
+% M. Brand - "Structure learning in conditional probability models via an entropic  prior
+%   and parameter extinction", Neural Computation 11 (1999): 1155--1182
+% M. Brand - "Pattern discovery via entropy minimization" [covers annealing]
+%   AI & Statistics 1999. Equation numbers refer to this paper, which is available from
+%   www.merl.com/reports/docs/TR98-21.pdf
+% D. Heckerman, D. Geiger and M. Chickering, 
+%   "Learning Bayesian networks: the combination of knowledge and statistical data",
+%   Microsoft Research Tech Report, 1994
+
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = init_fields;
+  CPD = class(CPD, 'tabular_CPD', discrete_CPD(0, []));
+  return;
+elseif isa(bnet, 'tabular_CPD')
+  % This might occur if we are copying an object.
+  CPD = bnet;
+  return;
+end
+CPD = init_fields;
+
+ns = bnet.node_sizes;
+ps = parents(bnet.dag, self);
+fam_sz = ns([ps self]);
+psz = prod(ns(ps));
+CPD.sizes = fam_sz;
+CPD.leftright = 0;
+CPD.sparse = 0;
+
+% set defaults
+CPD.CPT = mk_stochastic(myrand(fam_sz));
+CPD.adjustable = 1;
+CPD.prior_type = 'none';
+dirichlet_type = 'BDeu';
+dirichlet_weight = 1;
+CPD.trim = 0;
+selfprob = 0.1;
+CPD.entropic_pcases = 1:psz;
+
+% extract optional args
+args = varargin;
+% check for old syntax CPD(bnet, i, CPT) as opposed to CPD(bnet, i, 'CPT', CPT)
+if ~isempty(args) && ~ischar(args{1})
+  CPD.CPT = myreshape(args{1}, fam_sz);
+  args = [];
+end
+
+for i=1:2:length(args)
+  switch args{i},
+   case 'CPT',
+    T = args{i+1};
+    if ischar(T)
+      switch T
+       case 'unif', CPD.CPT = mk_stochastic(myones(fam_sz));
+       case 'rnd',  CPD.CPT = mk_stochastic(myrand(fam_sz));
+       otherwise,   error(['invalid CPT ' T]);       
+      end
+    else
+      CPD.CPT = myreshape(T, fam_sz);
+    end
+   case 'prior_type', CPD.prior_type = args{i+1};
+   case 'dirichlet_type', dirichlet_type = args{i+1};
+   case 'dirichlet_weight', dirichlet_weight = args{i+1};
+   case 'adjustable', CPD.adjustable = args{i+1};
+   case 'clamped', CPD.adjustable = ~args{i+1};
+   case 'trim', CPD.trim = args{i+1};
+   case 'entropic_pcases', CPD.entropic_pcases = args{i+1};
+   case 'sparse', CPD.sparse = args{i+1};
+   otherwise, error(['invalid argument name: ' args{i}]);       
+  end
+end
+
+switch CPD.prior_type
+ case 'dirichlet',
+  switch dirichlet_type
+   case 'unif', CPD.dirichlet = dirichlet_weight * myones(fam_sz);
+   case 'BDeu',  CPD.dirichlet = (dirichlet_weight/psz) * mk_stochastic(myones(fam_sz));
+   otherwise, error(['invalid dirichlet_type ' dirichlet_type])
+  end
+ case {'entropic', 'none'}
+  CPD.dirichlet = [];
+ otherwise, error(['invalid prior_type ' prior_type])
+end
+
+  
+
+% fields to do with learning
+if ~CPD.adjustable
+  CPD.counts = [];
+  CPD.nparams = 0;
+  CPD.nsamples = [];
+else
+  %CPD.counts = zeros(size(CPD.CPT));
+  CPD.counts = zeros(prod(size(CPD.CPT)), 1);
+  psz = fam_sz(1:end-1);
+  ss = fam_sz(end);
+  if CPD.leftright
+    % For each of the Qps contexts, we specify Q elements on the diagoanl
+    CPD.nparams = Qps * Q;
+  else
+    % sum-to-1 constraint reduces the effective arity of the node by 1
+    CPD.nparams = prod([psz ss-1]);
+  end
+  CPD.nsamples = 0;
+end
+
+CPD.trimmed_trans = [];
+fam_sz = CPD.sizes;
+
+%psz = prod(fam_sz(1:end-1));
+%ssz = fam_sz(end);
+%CPD.trimmed_trans = zeros(psz, ssz); % must declare before reading
+
+%sparse CPT
+if CPD.sparse
+   CPD.CPT = sparse(CPD.CPT(:));
+end
+
+CPD = class(CPD, 'tabular_CPD', discrete_CPD(~CPD.adjustable, fam_sz));
+
+
+%%%%%%%%%%%
+
+function CPD = init_fields()
+% This ensures we define the fields in the same order 
+% no matter whether we load an object from a file,
+% or create it from scratch. (Matlab requires this.)
+
+CPD.CPT = [];
+CPD.sizes = [];
+CPD.prior_type = [];
+CPD.dirichlet = [];
+CPD.adjustable = [];
+CPD.counts = [];
+CPD.nparams = [];
+CPD.nsamples = [];
+CPD.trim = [];
+CPD.trimmed_trans = [];
+CPD.leftright = [];
+CPD.entropic_pcases = [];
+CPD.sparse = [];
+