%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 (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% 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)
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 = prdataset(a,lab,'name','Multi-Class Problem');
a = setprior(a,0); % make all classes equally probable
return