%GENDATD Generation of 'difficult' normally distributed classes% %   A = GENDATD(N,K,D1,D2,LABTYPE)%% INPUT%   N        Number of objects in each of the classes (default: [50 50])%   K        Dimensionality of the dataset (default: 2)%   D1       Difference in mean in feature 1 (default: 3)%   D2       Difference in mean in feature 2 (default: 3)%   LABTYPE  'crisp' or 'soft' labels (default: 'crisp').%% OUTPUT%   A        Generated dataset%% DESCRIPTION% Generation of a K-dimensional 2-class dataset A of N objects.% Class variances are very different for the first two dimensions.% Separation is thereby, for small sample sizes, 'difficult'. % % D1 is the difference between the means for the first feature, D2% is the difference between the means for the second feature. In all% other directions the means are equal. The two covariance matrices% are equal with a variance of 1 in all directions except for the% second feature, which has a variance of 40. The first two feature% are rotated over 45 degrees to construct a strong correlation.% 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, duin@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: gendatd.m,v 1.3 2003/08/18 12:19:40 pavel Exp $function A = gendatd(N,k,d1,d2,labtype)	prtrace(mfilename);		if nargin < 5, labtype = 'crisp'; end	if nargin < 4, d2 = 3; end	if nargin < 3, d1 = 3; end	if nargin < 2,  k = 2; end	if nargin < 1, N = [50 50]; end	if k < 2,		error('Number of features should be larger than 1'),	end	V = ones(1,k); V(2) = 40; V = sqrt(V);	p = [0.5 0.5];	N = genclass(N,p);		ma = zeros(1,k);	mb = zeros(1,k); mb(1:2) = [d1, d2];	A = [randn(N(1),k).*V(ones(1,N(1)),:) + ma(ones(1,N(1)),:); ...	     randn(N(2),k).*V(ones(1,N(2)),:) + mb(ones(1,N(2)),:)];	A(:,1:2) = A(:,1:2)*[1 -1; 1 1]./sqrt(2);	lab = genlab(N);	A = dataset(A,lab,'name','Difficult Dataset','prior',p);	switch labtype	 case 'crisp'	  ;	 case 'soft'	  U = dataset([ma(1:2);mb(1:2)],getlablist(A));	  G = diag(V(1:2));	  W = nbayesc(U,G);	  targets = A(:,1:2)*W*classc;	  A = setlabtype(A,'soft',targets);	 otherwise	  error(['Label type ' labtype ' not supported'])	end	return