Given training data X with true labels REALCLASS, add new records to X and REALCLASS, which are noisy copies of the training data.
addNoisyCopies(realclass, x, pars)true class of training data (can be vector, numerics, integers, factors)
a matrix containing the training data
list of parameters:
pars$ncopies: Number of new records to add
pars$ncsort: Defines if training data should be sorted by class. Default is FALSE
pars$ncsigma: The noise in each column of x has the std.dev. pars$ncsigma*(standard deviation of column). Default Value: 0.8
pars$ncmethod: =1: each 'old' record from X in turn is the centroid for a new pattern;
=2: the centroid is the average of all records from the same class, the std.dev. is the same for all classes;
=3: centroid as in '2', the std.dev. is the std.dev. of all records from the same class (*recommended*)
list res
- res contains two list entries: realclass and x (including added copies)