net = gtminit(net, options, data, samptype) net = gtminit(net, options, data, samptype, lsampsize, rbfsampsize)
net = gtminit(net, options, data, samptype) takes a GTM net
and generates a sample of latent data points and sets the centres (and
widths if appropriate) of
net.rbfnet.
If the samptype is 'regular', then regular grids of latent
data points and RBF centres are created. The dimension of the latent data
space must be
1 or 2. For one-dimensional latent space, the lsampsize parameter
gives the number of latent points and the rbfsampsize parameter
gives the number of RBF centres. For a two-dimensional latent space,
these parameters must be vectors of length 2 with the number of points
in each of the x and y directions to create a rectangular grid. The
widths of the RBF basis functions are set by a call to rbfsetfw
passing options(7) as the scaling parameter.
If the samptype is 'uniform' or 'gaussian' then the
latent data is found by sampling from a uniform or
Gaussian distribution correspondingly. The RBF basis function parameters
are set
by a call to rbfsetbf with the data parameter
as dataset and the options vector.
Finally, the output layer weights of the RBF are initialised by mapping the mean of the latent variable to the mean of the target variable, and the L-dimensional latent variale variance to the variance of the targets along the first L principal components.
gtm, gtmem, pca, rbfsetbf, rbfsetfwCopyright (c) Ian T Nabney (1996-9)