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, 104 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/learning/learn_struct_K2.m b/sourcecodes/bnt-master/BNT/learning/learn_struct_K2.m
new file mode 100644
index 00000000..8a01a2e9
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+++ b/sourcecodes/bnt-master/BNT/learning/learn_struct_K2.m
@@ -0,0 +1,104 @@
+function dag = learn_struct_K2(data, ns, order, varargin)
+% LEARN_STRUCT_K2 Greedily learn the best structure compatible with a fixed node ordering
+% best_dag = learn_struct_K2(data, node_sizes, order, ...)
+%
+% data(i,m) = value of node i in case m (can be a cell array).
+% node_sizes(i) is the size of node i.
+% order(i) is the i'th node in the topological ordering.
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% [default value in brackets]
+%
+% max_fan_in - this the largest number of parents we allow per node [N]
+% scoring_fn - 'bayesian' or 'bic' [ 'bayesian' ]
+%              Currently, only networks with all tabular nodes support Bayesian scoring.
+% type       - type{i} is the type of CPD to use for node i, where the type is a string
+%              of the form 'tabular', 'noisy_or', 'gaussian', etc. [ all cells contain 'tabular' ]
+% params     - params{i} contains optional arguments passed to the CPD constructor for node i,
+%              or [] if none.  [ all cells contain {'prior', 1}, meaning use uniform Dirichlet priors ]
+% discrete   - the list of discrete nodes [ 1:N ]
+% clamped    - clamped(i,m) = 1 if node i is clamped in case m [ zeros(N, ncases) ]
+% verbose    - 'yes' means display output while running [ 'no' ]
+%
+% e.g., dag = learn_struct_K2(data, ns, order, 'scoring_fn', 'bic', 'params', [])
+%
+% To be backwards compatible with BNT2, you can also specify arguments as follows
+%   dag = learn_struct_K2(data, node_sizes, order, max_fan_in)    
+%
+% This algorithm is described in
+% - Cooper and Herskovits,  "A Bayesian method for the induction of probabilistic
+%      networks from data", Machine Learning Journal 9:308--347, 1992
+
+[n ncases] = size(data);
+
+% set default params
+type = cell(1,n);
+params = cell(1,n);
+for i=1:n
+  type{i} = 'tabular';
+  %params{i} = { 'prior', 1 };
+  params{i} = { 'prior_type', 'dirichlet', 'dirichlet_weight', 1 };
+end
+scoring_fn = 'bayesian';
+discrete = 1:n;
+clamped = zeros(n, ncases);
+
+max_fan_in = n;
+verbose = 0;
+
+args = varargin;
+nargs = length(args);
+if length(args) > 0 
+  if isstr(args{1})
+    for i=1:2:nargs
+      switch args{i},
+       case 'verbose',    verbose = strcmp(args{i+1}, 'yes');
+       case 'max_fan_in', max_fan_in = args{i+1}; 
+       case 'scoring_fn', scoring_fn = args{i+1};
+       case 'type',       type = args{i+1}; 
+       case 'discrete',   discrete = args{i+1}; 
+       case 'clamped',    clamped = args{i+1}; 
+       case 'params',     if isempty(args{i+1}), params = cell(1,n); else params = args{i+1};  end
+      end
+    end
+  else
+    max_fan_in = args{1};
+  end
+end
+
+dag = zeros(n,n);
+
+for i=1:n
+  ps = [];
+  j = order(i);
+  u = find(clamped(j,:)==0);    
+  score = score_family(j, ps, type{j}, scoring_fn, ns, discrete, data(:,u), params{j});
+  if verbose, fprintf('\nnode %d, empty score %6.4f\n', j, score); end
+  done = 0;
+  while ~done & (length(ps) <= max_fan_in)
+    pps = mysetdiff(order(1:i-1), ps); % potential parents
+    nps = length(pps);
+    pscore = zeros(1, nps);
+    for pi=1:nps
+      p = pps(pi);
+      pscore(pi) = score_family(j, [ps p], type{j}, scoring_fn, ns, discrete, data(:,u), params{j});
+      if verbose, fprintf('considering adding %d to %d, score %6.4f\n', p, j, pscore(pi)); end
+    end
+    [best_pscore, best_p] = max(pscore);
+    best_p = pps(best_p);
+    if best_pscore > score
+      score = best_pscore;
+      ps = [ps best_p];
+      if verbose, fprintf('* adding %d to %d, score %6.4f\n', best_p, j, best_pscore); end
+    else
+      done = 1;
+    end
+  end
+  if ~isempty(ps) % need this check for matlab 5.2
+    dag(ps, j) = 1;
+  end
+end
+
+
+
+