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, 95 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/gmux_CPD.m b/sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/gmux_CPD.m
new file mode 100644
index 00000000..4cef195c
--- /dev/null
+++ b/sourcecodes/bnt-master/BNT/CPDs/@gmux_CPD/gmux_CPD.m
@@ -0,0 +1,95 @@
+function CPD = gmux_CPD(bnet, self, varargin)
+% GMUX_CPD Make a Gaussian multiplexer node
+%
+% CPD = gmux_CPD(bnet, node, ...) is used similarly to gaussian_CPD,
+% except we assume there is exactly one discrete parent (call it M)
+% which is used to select which cts parent to pass through to the output.
+% i.e., we define P(Y=y|M=m, X1, ..., XK) = N(y | W(m)*x(m) + mu(m), Sigma(m))
+% where Y represents this node, and the Xi's are the cts parents.
+% All the Xi must have the same size, and the num values for M must be K.
+%
+% Currently the params for this kind of CPD cannot be learned.
+%
+% Optional arguments [ default in brackets ]
+%
+% mean       - mu(:,i) is the mean given M=i [ zeros(Y,K) ]
+% cov        - Sigma(:,:,i) is the covariance given M=i [ repmat(1*eye(Y,Y), [1 1 K]) ]
+% weights    - W(:,:,i) is the regression matrix given M=i [ randn(Y,X,K) ]
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = init_fields;
+  clamp = 0;
+  CPD = class(CPD, 'gmux_CPD', generic_CPD(clamp));
+  return;
+elseif isa(bnet, 'gmux_CPD')
+  % This might occur if we are copying an object.
+  CPD = bnet;
+  return;
+end
+CPD = init_fields;
+ 
+CPD = class(CPD, 'gmux_CPD', generic_CPD(1));
+
+ns = bnet.node_sizes;
+ps = parents(bnet.dag, self);
+dps = myintersect(ps, bnet.dnodes);
+cps = myintersect(ps, bnet.cnodes);
+fam_sz = ns([ps self]);
+
+CPD.self = self;
+CPD.sizes = fam_sz;
+
+% Figure out which (if any) of the parents are discrete, and which cts, and how big they are
+% dps = discrete parents, cps = cts parents
+CPD.cps = find_equiv_posns(cps, ps); % cts parent index
+CPD.dps = find_equiv_posns(dps, ps);
+if length(CPD.dps) ~= 1
+  error('gmux must have exactly 1 discrete parent')
+end
+ss = fam_sz(end);
+cpsz = fam_sz(CPD.cps(1)); % in gaussian_CPD, cpsz = sum(fam_sz(CPD.cps))
+if ~all(fam_sz(CPD.cps) == cpsz)
+  error('all cts parents must have same size')
+end
+dpsz = fam_sz(CPD.dps);
+if dpsz ~= length(cps)
+  error(['the arity of the mux node is ' num2str(dpsz) ...
+	 ' but there are ' num2str(length(cps)) ' cts parents']);
+end
+
+% set default params
+%CPD.mean = zeros(ss, 1);
+%CPD.cov = eye(ss);
+%CPD.weights = randn(ss, cpsz);
+CPD.mean = zeros(ss, dpsz);
+CPD.cov = 1*repmat(eye(ss), [1 1 dpsz]);    
+CPD.weights = randn(ss, cpsz, dpsz);
+
+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}; 
+   otherwise,  
+    error(['invalid argument name ' args{i}]);
+  end
+end
+
+%%%%%%%%%%%
+
+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.self = [];
+CPD.sizes = [];
+CPD.cps = [];
+CPD.dps = [];
+CPD.mean = [];
+CPD.cov = [];
+CPD.weights = [];
+