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, 43 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/set_fields.m b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/set_fields.m
new file mode 100644
index 00000000..4c1aef22
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@gaussian_CPD/set_fields.m
@@ -0,0 +1,43 @@
+function CPD = set_fields(CPD, varargin)
+% SET_PARAMS Set the parameters (fields) for a gaussian_CPD object
+% CPD = set_params(CPD, name/value pairs)
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+%
+% mean       - mu(:,i) is the mean given Q=i
+% cov        - Sigma(:,:,i) is the covariance given Q=i 
+% weights    - W(:,:,i) is the regression matrix given Q=i 
+% cov_type   - if 'diag', Sigma(:,:,i) is diagonal 
+% tied_cov   - if 1, we constrain Sigma(:,:,i) to be the same for all i
+% clamp_mean - if 1, we do not adjust mu(:,i) during learning 
+% clamp_cov  - if 1, we do not adjust Sigma(:,:,i) during learning 
+% clamp_weights - if 1, we do not adjust W(:,:,i) during learning
+% clamp      - if 1, we do not adjust any params
+% cov_prior_weight - weight given to I prior for estimating Sigma
+% cov_prior_entropic - if 1, we also use an entropic prior for Sigma [0]
+%
+% e.g., CPD = set_params(CPD, 'mean', [0;0])
+
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'mean',        CPD.mean = args{i+1}; 
+   case 'cov',         CPD.cov = args{i+1}; 
+   case 'weights',     CPD.weights = args{i+1}; 
+   case 'cov_type',    CPD.cov_type = args{i+1}; 
+   %case 'tied_cov',    CPD.tied_cov = strcmp(args{i+1}, 'yes');
+   case 'tied_cov',    CPD.tied_cov = args{i+1};
+   case 'clamp_mean',  CPD.clamped_mean = args{i+1};
+   case 'clamp_cov',   CPD.clamped_cov = args{i+1};
+   case 'clamp_weights',  CPD.clamped_weights = args{i+1};
+   case 'clamp',  clamp = args{i+1};
+    CPD.clamped_mean = clamp;
+    CPD.clamped_cov = clamp;
+    CPD.clamped_weights = clamp;
+   case 'cov_prior_weight',  CPD.cov_prior_weight = args{i+1};
+   case 'cov_prior_entropic',  CPD.cov_prior_entropic = args{i+1};
+   otherwise,  
+    error(['invalid argument name ' args{i}]);
+  end
+end