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/rbfprior.htm | 59 ++++++++++++++++++++++++++ 1 file changed, 59 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/rbfprior.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/rbfprior.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/rbfprior.htm b/sourcecodes/bnt-master/nethelp3.3/rbfprior.htm new file mode 100644 index 00000000..202bdde7 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/rbfprior.htm @@ -0,0 +1,59 @@ + + + +Netlab Reference Manual rbfprior + + + +

rbfprior +

+Purpose +

+Create Gaussian prior and output layer mask for RBF. + +

+Synopsis +

+[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2)

+ + +

+Description +

+[mask, prior] = rbfprior(rbfunc, nin, nhidden, nout, aw2, ab2) +generates a vector +mask that selects only the output +layer weights. This is because most uses of RBF networks in a Bayesian +context have fixed basis functions with the output layer as the only +adjustable parameters. In particular, the Neuroscale output error function +is designed to work only with this mask. + +

The return value +prior is a data structure, +with fields prior.alpha and prior.index, which +specifies a Gaussian prior distribution for the network weights in an +RBF network. The parameters aw2 and ab2 are all +scalars and represent the regularization coefficients for two groups +of parameters in the network corresponding to + second-layer weights, and second-layer biases +respectively. Then prior.alpha represents a column vector of +length 2 containing the parameters, and prior.index is a matrix +specifying which weights belong in each group. Each column has one +element for each weight in the matrix, using the standard ordering as +defined in rbfpak, and each element is 1 or 0 according to +whether the weight is a member of the corresponding group or not. + +

+See Also +

+rbf, rbferr, rbfgrad, evidence

+Pages: +Index +