From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 28 Sep 2017 15:04:40 -0500 Subject: BNW using Octave instead of Matlab. This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02 --- sourcecodes/bnt-master/nethelp3.3/gtminit.htm | 66 +++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/gtminit.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/gtminit.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/gtminit.htm b/sourcecodes/bnt-master/nethelp3.3/gtminit.htm new file mode 100644 index 00000000..8ac58331 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gtminit.htm @@ -0,0 +1,66 @@ + + + +Netlab Reference Manual gtminit + + + +

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 +