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, 45 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/set_fields.m b/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/set_fields.m
new file mode 100644
index 00000000..6c64b197
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@softmax_CPD/set_fields.m
@@ -0,0 +1,45 @@
+function CPD = set_params(CPD, varargin)
+% SET_PARAMS Set the parameters (fields) for a softmax_CPD object
+% CPD = set_params(CPD, name/value pairs)
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y), Q1=ns(dps(1)), Q2=ns(dps(2)), ...
+%   where dps are the discrete parents; if there are no discrete parents, we set Q1=1.)
+%
+% weights - (W(:,j,a,b,...) - W(:,j',a,b,...)) is ppn to dec. boundary
+%           between j,j' given Q1=a,Q2=b,... [ randn(X,Y,Q1,Q2,...) ]
+% offset  - (offset(j,a,b,...) - offset(j',a,b,...)) is the offset to dec. boundary
+%           between j,j' given Q1=a,Q2=b,... [ randn(Y,Q1,Q2,...) ]
+% clamped     - 'yes' means don't adjust params during learning ['no']
+% max_iter    - the maximum number of steps to take [10]
+% verbose     - 'yes' means print the LL at each step of IRLS ['no']
+% wthresh     - convergence threshold for weights [1e-2]
+% llthresh    - convergence threshold for log likelihood [1e-2]
+% approx_hess - 'yes' means approximate the Hessian for speed ['no']
+%
+% e.g., CPD = set_params(CPD,'offset', zeros(ns(i),1));
+
+args = varargin;
+nargs = length(args);
+glimsz = prod(CPD.sizes(CPD.dpndx));
+for i=1:2:nargs
+  switch args{i},
+   case 'discrete',     str='nothing to do';   
+   case 'clamped',      CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes'));
+   case 'max_iter',     CPD.max_iter = args{i+1};
+   case 'verbose',      CPD.verbose = strcmp(args{i+1}, 'yes');
+   case 'max_iter',     CPD.max_iter = args{i+1};
+   case 'wthresh',      CPD.wthresh = args{i+1};
+   case 'llthresh',     CPD.llthresh = args{i+1};
+   case 'approx_hess',  CPD.approx_hess = strcmp(args{i+1}, 'yes');
+   case 'weights',      for q=1:glimsz, CPD.glim{q}.w1 = args{i+1}(:,:,q); end; 
+   case 'offset',
+    if glimsz == 1
+      CPD.glim{1}.b1 = args{i+1};
+    else
+      for q=1:glimsz, CPD.glim{q}.b1 = args{i+1}(:,q); end; 
+    end
+   otherwise,  
+    error(['invalid argument name ' args{i}]);       
+  end
+end