clusterv (version 1.1.1)

generate.sample5: Sample5 generator of synthetic data

Description

Multivariate normally distributed data synthetic generator. Data sets with 4 clusters are randomly generated. n examples for each class are generated. All classes (each one with n examples) has (1-ratio.noisy)*dim of no-noisy features and ratio.noisy*dim of noisy features. For "noisy" feature we mean features that are equally distributed in all the classes (these variables are centered in 0), while for "no-noisy" we mean features that are centered in different points in the different classes. Note that if the number on no-noisy feature is less than 2 the generation is aborted. A full covariance matrix (equal for all classes) is used. The first class (first n examples) has its no-noisy features centered in 0. The second class (second n examples) has its no-noisy features centered in m The third class (third n examples) has its no-noisy features centered in -m A fourth cluster (third n examples) has its no-noisy features centered in (m,-m) alternatively Covariance matrix Sigma = (B, Zero; Zero', I) where B is a (dim*(1-ratio.noisy))X(dim*(1-ratio.noisy)) matrix s.t. B[i,i]=1, B[i,i+1]=B[i,i-1]=0.5 and B[i,j]=0.1 if j!=i-1,i,i+1; Zero is a (dim*(1-ratio.noisy))X(dim*ratio.noisy) zero matrix and Zero' its transpose; I is a (dim*ratio.noisy)X(dim*ratio.noisy) identity matrix

Usage

generate.sample5(n = 10, dim = 10, ratio.noisy = 0.8, m = 2)

Value

a matrix with dim rows (variables) and n*4 columns (examples)

Arguments

n

number of examples for each class

dim

dimension of the examples

ratio.noisy

ratio of the noisy variables. The number of "noisy" features is ratio.noisy * dim

m

center of the II cluster (the third has center -m)

Author

Giorgio Valentini

Examples

# \donttest{
generate.sample5()
# Generation of a data set with 80 1000-dimensional examples, with the 200 no-noisy 
# features of the examples of the first class  centered in 0, the 200 no-noisy features 
# of the examples of the second class  centered in 2, the 200 no-noisy features of 
# the examples of the third class  centered in -2, and the 200 no-noisy features of the 
# examples of the fourth class  centered in alternatively in (2,-2).
generate.sample5(n = 20, m = 2, ratio.noisy = 0.8, dim = 1000)
# }