Optimize (train) the parameters of a GTM model, using an EM algorithm.
[W, beta, llhLog] = gtm_trn(T, FI, W, l, cycles, beta, m, q)
[W, beta] = gtm_trn(T, FI, W, l, cycles, beta)
T - matrix containing a sample of the distribution to be modeled; N-by-D
FI - matrix containing the output values from the basis functions, when fed the latent variable
sample; K-by-(M+1)
W - an initial weight matrix; (M+1)-by-D
l - weight regularisation factor
cycles - no of training cycles
beta - an initial value for beta, the inverse variance of the Gaussian mixture generated in the
data space
m - mode of calculation; it can be set to 0, 1 or 2 corresponding to increasingly elaborate measure
taken to reduce the amount of numerical errors; mode = 0 will be fast but less accurate, mode = 2 will be slow
but more accurate; the default mode is 1
q - quiet execution; if q equals the string 'quiet', the plotting and echoing of the values
of log- likelihood and beta during traaining is supressed. This argument is optional; if omitted the training is
run non-quiet.
W, beta - the corresponding weight matrix and inverse variance after training
llhLog - the log-likelihood after each cycle of training; optional output argument