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/rbffwd.htm | 67 ++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/rbffwd.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/rbffwd.htm') 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 @@ + + + +Netlab Reference Manual rbffwd + + + +

rbffwd +

+Purpose +

+Forward propagation through RBF network with linear outputs. + +

+Synopsis +

+a = rbffwd(net, x)
+function [a, z, n2] = rbffwd(net, x)
+

+ + +

+Description +

+a = rbffwd(net, x) takes a network data structure +net and a matrix x of input +vectors and forward propagates the inputs through the network to generate +a matrix a of output vectors. Each row of x corresponds to one +input vector and each row of a contains the corresponding output vector. +The activation function that is used is determined by net.actfn. + +

[a, z, n2] = rbffwd(net, x) also generates a matrix z of +the hidden unit activations where each row corresponds to one pattern. +These hidden unit activations represent the design matrix for +the RBF. The matrix n2 is the squared distances between each +basis function centre and each pattern in which each row corresponds +to a data point. + +

+Examples +

+
+[a, z] = rbffwd(net, x);
+
+temp = pinv([z ones(size(x, 1), 1)]) * t;
+net.w2 = temp(1: nd(2), :);
+net.b2 = temp(size(x, nd(2)) + 1, :);
+

+ +Here x is the input data, t are the target values, and we use the +pseudo-inverse to find the output weights and biases. + +

+See Also +

+rbf, rbferr, rbfgrad, rbfpak, rbftrain, rbfunpak

+Pages: +Index +