%GENDATM Generation of multi-class 2-D data% % 	A = GENDATM(N)% % INPUT%   N   Vector of class sizes (default: 20)%% OUTPUT%   A   Dataset%% DESCRIPTION% Generation of N samples in 8 classes of 2 dimensionally distributed data% vectors. Classes have equal prior probabilities. If N is a vector of% sizes, exactly N(I) objects are generated for class I, I = 1..8.% % SEE ALSO% DATASETS, PRDATASETS% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: gendatm.m,v 1.2 2006/03/08 22:06:58 duin Exp $function a = gendatm(n)	prtrace(mfilename);  if (nargin == 0)		prwarning(3,'number of samples to generate not specified, assuming 20');		n = repmat(20,1,8); 	end;	% Set equal priors and generate a class distribution according to it.	p = repmat(1/8,1,8); n = genclass(n,p);	% Generate 8 classes...	a1 = +gendath(n(1:2));			% ...first 2 classes: Highleyman data.	a2 = +gendatc(n(3:4))./5;		% ...next 2 classes : spherical classes.	a3 = +gendatb(n(5:6))./5;		% ...next 2 classes : banana data.	a4 = +gendatl(n(7:8))./5;		% ...next 2 classes : Lithuanian data.	% Glue classes together with some proper offsets.	a = [a1; a2+5; a3+repmat([5,0],n(5)+n(6),1); a4+repmat([0 5],n(7)+n(8),1)];	lab = genlab(n,['a';'b';'c';'d';'e';'f';'g';'h']);	a = dataset(a,lab,'name','Multi-Class Problem');return