# gtminit

## Purpose

Initialise the weights and latent sample in a GTM.

## Synopsis

    net = gtminit(net, options, data, samptype)
    net = gtminit(net, options, data, samptype, lsampsize, rbfsampsize)

## Description

`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.

## See Also

`gtm`, `gtmem`, `pca`, `rbfsetbf`, `rbfsetfw`

Pages: Index

Copyright (c) Ian T Nabney (1996-9)