Generation of two spherical classes with different variances
A = GENDATC(N,K,U,LABTYPE)
Generation of a K-dimensional 2-class dataset A of N objects. Both classes are spherically Gaussian distributed.
Class 1 has the identity matrix as covariance matrix and mean U. If U is a scalar then [U,0,0,..] is used as class mean. Class 2 has also the identity matrix as covariance matrix, except for a variance of 4 for the first two features. Its mean is 0. 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.
The default means result in a class overlap of 0.16.
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], K = 2, U = 0, LABTYPE = 'crisp'.