wolffd@0: function CPD = tree_CPD(varargin)
wolffd@0: %DTREE_CPD Make a conditional prob. distrib. which is a decision/regression tree.
wolffd@0: %
wolffd@0: % CPD =dtree_CPD() will create an empty tree.
wolffd@0: 
wolffd@0: if nargin==0
wolffd@0:   % This occurs if we are trying to load an object from a file.
wolffd@0:   CPD = init_fields;
wolffd@0:   clamp = 0;
wolffd@0:   CPD = class(CPD, 'tree_CPD', discrete_CPD(clamp, []));
wolffd@0:   return;
wolffd@0: elseif isa(varargin{1}, 'tree_CPD')
wolffd@0:   % This might occur if we are copying an object.
wolffd@0:   CPD = varargin{1};
wolffd@0:   return;
wolffd@0: end
wolffd@0: 
wolffd@0: CPD = init_fields;
wolffd@0: 
wolffd@0: 
wolffd@0: clamped = 0;
wolffd@0: fam_sz = [];
wolffd@0: CPD = class(CPD, 'tree_CPD', discrete_CPD(clamped, fam_sz));
wolffd@0: 
wolffd@0: 
wolffd@0: %%%%%%%%%%%
wolffd@0: 
wolffd@0: function CPD = init_fields()
wolffd@0: % This ensures we define the fields in the same order 
wolffd@0: % no matter whether we load an object from a file,
wolffd@0: % or create it from scratch. (Matlab requires this.)
wolffd@0: 
wolffd@0: %init the decision tree set the root to null
wolffd@0: CPD.tree.num_node = 0;
wolffd@0: CPD.tree.root=1;
wolffd@0: CPD.tree.nodes=[];
wolffd@0: