%STATSDTC Stats Decision tree Classifier (Matlab Stats Toolbox)
%
% W = STATSDTC(A,'PARAM1',val1,'PARAM2',val2,...)
% W = A*STATSDTC([],'PARAM1',val1,'PARAM2',val2,...)
% D = B*W
%
% INPUT
% A Dataset used for training
% PARAM1 Optional parameter, see CLASSIFICATIONTREE.FIT
% B Dataset used for evaluation
%
% OUTPUT
% W Decision tree classifier
% D Classification matrix, dataset with posteriors
%
% DESCRIPTION
% This is the PRTools interface to the CLASSIFICATIONTREE of the Matlab
% Stats toolbox. See there for more information. It is assumed that objects
% labels, feature labels and class priors are included in the dataset A.
%
% The decision tree is stored in W and can be retrieved by T = +W or by
% T = getdata(W). The Stats toolbox command VIEW can be used to visualize
% it, either in the command window (default) or graphically setting the
% 'mode' options to 'graph'.
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, MAPPINGS, DTC, TREEC, CLASSIFICATIONTREE, VIEW
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
function W = statsdtc(varargin)
name = 'Stats DecTree';
if mapping_task(varargin,'definition')
W = define_mapping(varargin,[],name);
elseif mapping_task(varargin,'training')
A = varargin{1};
data = +A;
labels = getlabels(A);
prior = getprior(A);
featlab = getfeatlab(A);
if ischar(featlab)
featlab = cellstr(featlab);
end
tree = ClassificationTree.fit(data,labels,'prior',prior, ...
'PredictorNames',featlab,varargin{2:end});
W = trained_mapping(A,tree);
else % evaluation
[A,W] = deal(varargin{:});
tree = getdata(W);
[dummy,post] = predict(tree,+A);
W = setdat(A,post,W);
end
return