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/gp.htm | 80 ++++++++++++++++++++++++++++++++ 1 file changed, 80 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/gp.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/gp.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/gp.htm b/sourcecodes/bnt-master/nethelp3.3/gp.htm new file mode 100644 index 00000000..77d6ab1a --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gp.htm @@ -0,0 +1,80 @@ + + + +Netlab Reference Manual gp + + + +

gp +

+Purpose +

+Create a Gaussian Process. + +

+Synopsis +

+net = gp(nin, covarfn)
+net = gp(nin, covarfn, prior)
+

+ + +

+Description +

+ +

net = gp(nin, covarfn) takes the number of inputs nin +for a Gaussian Process model with a single output, together +with a string covarfn which specifies the type of the covariance function, +and returns a data structure net. The parameters are set to zero. + +

The fields in net are +

+  type = 'gp'
+  nin = number of inputs
+  nout = number of outputs: always 1
+  nwts = total number of weights and covariance function parameters
+  bias = logarithm of constant offset in covariance function
+  noise = logarithm of output noise variance
+  inweights = logarithm of inverse length scale for each input 
+  covarfn = string describing the covariance function:
+      'sqexp'
+      'ratquad'
+  fpar = covariance function specific parameters (1 for squared exponential,
+   2 for rational quadratic)
+  trin = training input data (initially empty)
+  trtargets = training target data (initially empty)
+

+ + +

net = gp(nin, covarfn, prior) sets a Gaussian prior on the +parameters of the model. prior must contain the fields +pr_mean and pr_variance. If pr_mean is a scalar, +then the Gaussian is assumed to be isotropic and the additional fields +net.pr_mean and pr_variance are set. Otherwise, +the Gaussian prior has a mean +defined by a column vector of parameters prior.pr_mean and +covariance defined by a column vector of parameters prior.pr_variance. +Each element of prmean corresponds to a separate group of parameters, which +need not be mutually exclusive. The membership of the groups is defined +by the matrix prior.index in which the columns correspond to the elements of +prmean. Each column has one element for each weight in the matrix, +in the order defined by the function gppak, and each element +is 1 or 0 according to whether the parameter is a member of the +corresponding group or not. The additional field net.index is set +in this case. + +

+See Also +

+gppak, gpunpak, gpfwd, gperr, gpcovar, gpgrad

+Pages: +Index +