%GENDATH Generation of Highleyman classes
%
% A = GENDATH(N,LABTYPE)
%
% INPUT
% N Number of objects (optional; default: [50,50])
% LABTYPE Label type (optional; default: 'crisp')
%
% OUTPUT
% A Generated dataset
%
% DESCRIPTION
% Generation of a 2-dimensional 2-class dataset A of N objects
% according to Highleyman.
%
% The two Highleyman classes are defined by
% 1: Gauss([1 1],[1 0; 0 0.25]).
% 2: Gauss([2 0],[0.01 0; 0 4]).
% Class priors are P(1) = P(2) = 0.5
%
% If N is a vector of sizes, exactly N(I) objects are generated
% for class I, I = 1,2.
%
% LABTYPE defines the desired label type: 'crisp' or 'soft'. In the
% latter case true posterior probabilities are set for the labels.
%
% Defaults: N = [50,50], LABTYPE = 'crisp'.
%
% EXAMPLES
% PREX_PLOTC, PREX_CLEVAL
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, GAUSS, 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: gendath.m,v 1.5 2009/01/27 13:01:42 duin Exp $
function A = gendath(N,labtype)
if nargin < 1, N = [50, 50]; end
if nargin < 2, labtype = 'crisp'; end
GA = [1 0; 0 0.25];
GB = [0.01 0; 0 4];
G = cat(3,GA,GB);
p = [0.5 0.5];
N = genclass(N,p);
U = prdataset([1 1; 2 0],[1 2]','prior',p);
U = setprior(U,p);
A = gendatgauss(N,U,G);
A = setname(A,'Highleyman Dataset');
switch labtype
case 'crisp'
;
case 'soft'
W = nbayesc(U,cat(3,GA,GB));
targets = A*W*classc;
A = setlabtype(A,'soft',targets);
otherwise
error(['Label type ' labtype ' not supported'])
end
return