comparison toolboxes/FullBNT-1.0.7/bnt/CPDs/@tree_CPD/tree_CPD.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function CPD = tree_CPD(varargin)
+%DTREE_CPD Make a conditional prob. distrib. which is a decision/regression tree.
+%
+% CPD =dtree_CPD() will create an empty tree.
+
+if nargin==0
+  % This occurs if we are trying to load an object from a file.
+  CPD = init_fields;
+  clamp = 0;
+  CPD = class(CPD, 'tree_CPD', discrete_CPD(clamp, []));
+  return;
+elseif isa(varargin{1}, 'tree_CPD')
+  % This might occur if we are copying an object.
+  CPD = varargin{1};
+  return;
+end
+
+CPD = init_fields;
+
+
+clamped = 0;
+fam_sz = [];
+CPD = class(CPD, 'tree_CPD', discrete_CPD(clamped, 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.)
+
+%init the decision tree set the root to null
+CPD.tree.num_node = 0;
+CPD.tree.root=1;
+CPD.tree.nodes=[];
+