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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/gpinit.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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diff --git a/sourcecodes/bnt-master/nethelp3.3/gpinit.htm b/sourcecodes/bnt-master/nethelp3.3/gpinit.htm new file mode 100644 index 00000000..1874dde7 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gpinit.htm @@ -0,0 +1,76 @@ +<html> +<head> +<title> +Netlab Reference Manual gpinit +</title> +</head> +<body> +<H1> gpinit +</H1> +<h2> +Purpose +</h2> +Initialise Gaussian Process model. + +<p><h2> +Synopsis +</h2> +<PRE> +net = gpinit(net, trin, trtargets, prior) +net = gpinit(net, trin, trtargets, prior) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = gpinit(net, trin, trtargets)</CODE> takes a Gaussian Process data structure <CODE>net</CODE> +together +with a matrix <CODE>trin</CODE> of training input vectors and a matrix <CODE>trtargets</CODE> of +training target +vectors, and stores them in <CODE>net</CODE>. These datasets are required if +the corresponding inverse covariance matrix is not supplied to <CODE>gpfwd</CODE>. +This is important if the data structure is saved and then reloaded before +calling <CODE>gpfwd</CODE>. +Each row +of <CODE>trin</CODE> corresponds to one input vector and each row of <CODE>trtargets</CODE> +corresponds to one target vector. + +<p><CODE>net = gpinit(net, trin, trtargets, prior)</CODE> additionally initialises the +parameters in <CODE>net</CODE> from the <CODE>prior</CODE> data structure which contains the +mean and variance of the Gaussian distribution which is sampled from. + +<p><h2> +Example +</h2> +Suppose that a Gaussian Process model is created and trained with input data <CODE>x</CODE> +and targets <CODE>t</CODE>: +<PRE> + +net = gp(2, 'sqexp'); +net = gpinit(net, x, t); +% Train the network +save 'gp.net' net; +</PRE> + +Another Matlab program can now read in the network and make predictions on a data set +<CODE>testin</CODE>: +<PRE> + +load 'gp.net'; +pred = gpfwd(net, testin); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</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 |