Daniel@0: Daniel@0: Daniel@0: Daniel@0: Netlab Reference Manual gtm Daniel@0: Daniel@0: Daniel@0: Daniel@0:

gtm Daniel@0:

Daniel@0:

Daniel@0: Purpose Daniel@0:

Daniel@0: Create a Generative Topographic Map. Daniel@0: Daniel@0:

Daniel@0: Synopsis Daniel@0:

Daniel@0:

Daniel@0: net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc)
Daniel@0: net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)
Daniel@0:

Daniel@0: Daniel@0: Daniel@0:

Daniel@0: Description Daniel@0:

Daniel@0: Daniel@0:

net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc), Daniel@0: takes the dimension of the latent space dimlatent, the Daniel@0: number of data points sampled in the latent space nlatent, the Daniel@0: dimension of the data space dimdata, the number of centres in the Daniel@0: RBF model ncentres, the activation function for the RBF Daniel@0: rbfunc Daniel@0: and returns a data structure net. The parameters in the Daniel@0: RBF and GMM sub-models are set by calls to the corresponding creation routines Daniel@0: rbf and gmm. Daniel@0: Daniel@0:

The fields in net are Daniel@0:

Daniel@0:   type = 'gtm'
Daniel@0:   nin = dimension of data space
Daniel@0:   dimlatent = dimension of latent space
Daniel@0:   rbfnet = RBF network data structure
Daniel@0:   gmmnet = GMM data structure
Daniel@0:   X = sample of latent points
Daniel@0:

Daniel@0: Daniel@0: Daniel@0:

net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior), Daniel@0: sets a Gaussian zero mean prior on the Daniel@0: parameters of the RBF model. prior must be a scalar and represents Daniel@0: the inverse variance of the prior distribution. This gives rise to Daniel@0: a weight decay term in the error function. Daniel@0: Daniel@0:

Daniel@0: See Also Daniel@0:

Daniel@0: gtmfwd, gtmpost, rbf, gmm

Daniel@0: Pages: Daniel@0: Index Daniel@0:

Daniel@0: