%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 (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% DATASETS, PRDATASETS
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands
% $Id: gendats.m,v 1.3 2009/01/27 13:01:42 duin Exp $
function A = gendats (N,k,d,labtype)
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 = prdataset([ma;mb],[1 2]');
U = setprior(U,p);
% Create dataset.
A = gendatgauss(N,U,cat(3,GA,GB),labtype);
A = setname(A,'Simple Problem');
return