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/gpfwd.htm | 73 +++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/gpfwd.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/gpfwd.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/gpfwd.htm b/sourcecodes/bnt-master/nethelp3.3/gpfwd.htm new file mode 100644 index 00000000..51fc239c --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gpfwd.htm @@ -0,0 +1,73 @@ + + + +Netlab Reference Manual gpfwd + + + +

gpfwd +

+Purpose +

+Forward propagation through Gaussian Process. + +

+Synopsis +

+y = gpfwd(net, x)
+[y, sigsq] = gpfwd(net, x)
+[y, sigsq] = gpfwd(net, x, cninv)
+

+ + +

+Description +

+y = gpfwd(net, x) takes a Gaussian Process data structure net +together +with a matrix x of input vectors, and forward propagates the inputs +through the model to generate a matrix y of output +vectors. Each row of x corresponds to one input vector and each +row of y corresponds to one output vector. This assumes that the +training data (both inputs and targets) has been stored in net by +a call to gpinit; these are needed to compute the training +data covariance matrix. + +

[y, sigsq] = gpfwd(net, x) also generates a column vector sigsq of +conditional variances (or squared error bars) where each value corresponds to a pattern. + +

[y, sigsq] = gpfwd(net, x, cninv) uses the pre-computed inverse covariance +matrix cninv in the forward propagation. This increases efficiency if +several calls to gpfwd are made. + +

+Example +

+The following code creates a Gaussian Process, trains it, and then plots the +predictions on a test set with one standard deviation error bars: +

+
+net = gp(1, 'sqexp');
+net = gpinit(net, x, t);
+net = netopt(net, options, x, t, 'scg');
+[pred, sigsq] = gpfwd(net, xtest);
+plot(xtest, pred, '-k');
+hold on
+plot(xtest, pred+sqrt(sigsq), '-b', xtest, pred-sqrt(sigsq), '-b');
+

+ + +

+See Also +

+gp, demgp, gpinit

+Pages: +Index +