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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
|---|---|---|
| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/gp.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
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
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| -rw-r--r-- | sourcecodes/bnt-master/nethelp3.3/gp.htm | 80 |
1 files changed, 80 insertions, 0 deletions
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 @@ +<html> +<head> +<title> +Netlab Reference Manual gp +</title> +</head> +<body> +<H1> gp +</H1> +<h2> +Purpose +</h2> +Create a Gaussian Process. + +<p><h2> +Synopsis +</h2> +<PRE> +net = gp(nin, covarfn) +net = gp(nin, covarfn, prior) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = gp(nin, covarfn)</CODE> takes the number of inputs <CODE>nin</CODE> +for a Gaussian Process model with a single output, together +with a string <CODE>covarfn</CODE> which specifies the type of the covariance function, +and returns a data structure <CODE>net</CODE>. The parameters are set to zero. + +<p>The fields in <CODE>net</CODE> are +<PRE> + 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) +</PRE> + + +<p><CODE>net = gp(nin, covarfn, prior)</CODE> sets a Gaussian prior on the +parameters of the model. <CODE>prior</CODE> must contain the fields +<CODE>pr_mean</CODE> and <CODE>pr_variance</CODE>. If <CODE>pr_mean</CODE> is a scalar, +then the Gaussian is assumed to be isotropic and the additional fields +<CODE>net.pr_mean</CODE> and <CODE>pr_variance</CODE> are set. Otherwise, +the Gaussian prior has a mean +defined by a column vector of parameters <CODE>prior.pr_mean</CODE> and +covariance defined by a column vector of parameters <CODE>prior.pr_variance</CODE>. +Each element of <CODE>prmean</CODE> corresponds to a separate group of parameters, which +need not be mutually exclusive. The membership of the groups is defined +by the matrix <CODE>prior.index</CODE> in which the columns correspond to the elements of +<CODE>prmean</CODE>. Each column has one element for each weight in the matrix, +in the order defined by the function <CODE>gppak</CODE>, and each element +is 1 or 0 according to whether the parameter is a member of the +corresponding group or not. The additional field <CODE>net.index</CODE> is set +in this case. + +<p><h2> +See Also +</h2> +<CODE><a href="gppak.htm">gppak</a></CODE>, <CODE><a href="gpunpak.htm">gpunpak</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpcovar.htm">gpcovar</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file |