%GENDATS Generation of a simple classification problem of 2 Gaussian classes% %   A = GENDATS (N,K,D,LABTYPE)% % INPUT%   N       Dataset size, or 2-element array of class sizes (default: [50 50]).%   K       Dimensionality of the dataset to be generated (default: 2).%   D       Distance between class means in the first dimension (default: 1).%   LABTYPE Label type to generate, 'crisp' or 'soft' (default: 'crisp').%% OUTPUT%   A       Dataset.%% DESCRIPTION% Generation of a K-dimensional 2-class dataset A of N objects. Both classes % are Gaussian distributed with identity matrix as covariance matrix. Their % means are on a distance D. 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.%% SEE ALSO% DATASETS, PRDATASETS% Copyright: R.P.W. Duin, r.p.w.duin@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlands% $Id: gendats.m,v 1.2 2006/03/08 22:06:58 duin Exp $function A = gendats (N,k,d,labtype)	prtrace(mfilename);	if (nargin < 1), 		prwarning(2,'class sizes not specified, assuming [50 50]');		N = [50 50]; 	end	if (nargin < 2), 		prwarning(2,'dimensionality not specified, assuming 2');		k = 2; 	end	if (nargin < 3),		prwarning(2,'class mean distance not specified, assuming 1');		d = 2; 	end	if (nargin < 4), 		prwarning(2,'label type not specified, assuming "crisp"');		labtype = 'crisp'; 	end	% Set equal priors and generate random class sizes according to these.	p = [0.5 0.5]; N = genclass(N,p);		% Unit covariance matrices, zero mean except for distance D in first dim.	GA = eye(k); GB = eye(k);	ma = zeros(1,k); mb = zeros(1,k); mb(1) = d;	U = dataset([ma;mb],[1 2]');	U = setprior(U,p);	% Create dataset.	A = gauss(N,U,cat(3,GA,GB),labtype);	A = setname(A,'Simple Problem');return