Generates the components of a GTM with a 2D latent space.
[X, MU, FI, W, beta] = gtm_stp2(T, noLatVarSmpl, noBasFn, s)
T - target data, to be modelled by the GTM.
noLatVarSmpl - number of samples in the latent variable space; must be an integer^2, e.g. 1, 4,
9, 16, 25, 36, 49, ...
noBasFn - number of basis functions in the; must be an integer^2
s - the width of basis functions relative to the distance between two neighbouring basis function
centres, i.e. if s = 1, the basis functions will have widths (std.dev) equal to (1 times) the distance between
two neighbouring basis function centres.
X - the grid of data points making up the latent variable sample; a matrix of size noLatVarSmpl-by-2,
in which each row is a data point
MU - a noBasFn-by-2 matrix holding the coordinates of the centres of the basis functions
FI - the activations of the basis functions when fed the latent variable sample X, and a bias unit
fixed to 1.0; a matrix with the same number of rows as X and noBasFn+1 columns (+1 for the bias).
W - the initial matrix of weights, mapping the latent variable sample X linearly onto the 2 first
principal components of the target data (T)
The latent variable sample is constructed as a uniform grid in the square [-1 -1; -1 1; 1 1; 1 -1]. Similarly the centres of the basis function are gridded uniformly over the latent variable sample, with equal standard deviation, set relative to the distance between neigh- bouring centres.The initial linear mapping maps the std.devs. 1:1 from the latent to the target sample