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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=[];
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