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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/rbffwd.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/rbffwd.htm b/sourcecodes/bnt-master/nethelp3.3/rbffwd.htm new file mode 100644 index 00000000..02e56931 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/rbffwd.htm @@ -0,0 +1,67 @@ +<html> +<head> +<title> +Netlab Reference Manual rbffwd +</title> +</head> +<body> +<H1> rbffwd +</H1> +<h2> +Purpose +</h2> +Forward propagation through RBF network with linear outputs. + +<p><h2> +Synopsis +</h2> +<PRE> +a = rbffwd(net, x) +function [a, z, n2] = rbffwd(net, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>a = rbffwd(net, x)</CODE> takes a network data structure +<CODE>net</CODE> and a matrix <CODE>x</CODE> of input +vectors and forward propagates the inputs through the network to generate +a matrix <CODE>a</CODE> of output vectors. Each row of <CODE>x</CODE> corresponds to one +input vector and each row of <CODE>a</CODE> contains the corresponding output vector. +The activation function that is used is determined by <CODE>net.actfn</CODE>. + +<p><CODE>[a, z, n2] = rbffwd(net, x)</CODE> also generates a matrix <CODE>z</CODE> of +the hidden unit activations where each row corresponds to one pattern. +These hidden unit activations represent the <CODE>design matrix</CODE> for +the RBF. The matrix <CODE>n2</CODE> is the squared distances between each +basis function centre and each pattern in which each row corresponds +to a data point. + +<p><h2> +Examples +</h2> +<PRE> + +[a, z] = rbffwd(net, x); + +<p>temp = pinv([z ones(size(x, 1), 1)]) * t; +net.w2 = temp(1: nd(2), :); +net.b2 = temp(size(x, nd(2)) + 1, :); +</PRE> + +Here <CODE>x</CODE> is the input data, <CODE>t</CODE> are the target values, and we use the +pseudo-inverse to find the output weights and biases. + +<p><h2> +See Also +</h2> +<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfgrad.htm">rbfgrad</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbftrain.htm">rbftrain</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</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 |