%RANDOM_DD Random one-class classifier% %       W = RANDOM_DD(A,FRACREJ)% % This is the trivial one-class classifier, randomly assigning labels% and rejecting FRACREJ of the data objects. This procedure is just to% show the basic setup of a Prtools classifier, and what is required% to define a one-class classifier for dd_tools.% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlandsfunction W = random_dd(a,fracrej)% Take care of empty/not-defined arguments:if nargin < 2 fracrej = 0.05; endif nargin < 1 | isempty(a) 	% When no inputs are given, we are expected to return an empty	% mapping:	W = mapping(mfilename,{fracrej});	% And give a suitable name:	W = setname(W,'Random one-class classifier');	returnendif ~ismapping(fracrej)           %training	a = target_class(a);     % only use the target class	[m,k] = size(a);	% train it:	% This trivial classifier cannot be trained. For each object we will	% output a random value between 0 and 1, indicating the probability	% that an object belongs to class 'target'	% If you would like to train something, you should do it here.	%and save all useful data in a structure:	W.threshold = fracrej;  % a threshold should *always* be defined	W = mapping(mfilename,'trained',W,str2mat('target','outlier'),k,2);	W = setname(W,'Random one-class classifier');else                               %testing	% Unpack the mapping and dataset:	W = getdata(fracrej);	[m,k] = size(a); 	% This classifier only contains the threshold, nothing more.	% Output should consist of two numbers: the first indicating the	% 'probability' that it belongs to the target, the second indicating	% the 'probability' that it belongs to the outlier class. The latter	% is often the constant threshold. Note that the object will be	% classified to the class with the highest output. In the definition	% above, the first column was for the target, the second column for	% the outlier class:	newout = [rand(m,1) repmat(W.threshold,m,1)];	% Fill in the data, keeping all other fields in the dataset intact:	W = setdat(a,newout,fracrej);	W = setfeatdom(W,{[0 inf] [0 inf]});endreturn