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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/rbf.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/rbf.htm b/sourcecodes/bnt-master/nethelp3.3/rbf.htm new file mode 100644 index 00000000..97a7f925 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/rbf.htm @@ -0,0 +1,114 @@ +<html> +<head> +<title> +Netlab Reference Manual rbf +</title> +</head> +<body> +<H1> rbf +</H1> +<h2> +Purpose +</h2> +Creates an RBF network with specified architecture + +<p><h2> +Synopsis +</h2> +<PRE> + +net = rbf(nin, nhidden, nout, rbfunc) +net = rbf(nin, nhidden, nout, rbfunc, outfunc) +net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = rbf(nin, nhidden, nout, rbfunc)</CODE> constructs and initialises +a radial basis function network returning a data structure <CODE>net</CODE>. +The weights are all initialised with a zero mean, unit variance normal +distribution, with the exception of the variances, which are set to one. +This makes use of the Matlab function +<CODE>randn</CODE> and so the seed for the random weight initialization can be +set using <CODE>randn('state', s)</CODE> where <CODE>s</CODE> is the seed value. The +activation functions are defined in terms of the distance between +the data point and the corresponding centre. Note that the functions are +computed to a convenient constant multiple: for example, the Gaussian +is not normalised. (Normalisation is not needed as the function outputs +are linearly combined in the next layer.) + +<p>The fields in <CODE>net</CODE> are +<PRE> + + type = 'rbf' + nin = number of inputs + nhidden = number of hidden units + nout = number of outputs + nwts = total number of weights and biases + actfn = string defining hidden unit activation function: + 'gaussian' for a radially symmetric Gaussian function. + 'tps' for r^2 log r, the thin plate spline function. + 'r4logr' for r^4 log r. + outfn = string defining output error function: + 'linear' for linear outputs (default) and SoS error. + 'neuroscale' for Sammon stress measure. + c = centres + wi = squared widths (null for rlogr and tps) + w2 = second layer weight matrix + b2 = second layer bias vector +</PRE> + + +<p><CODE>net = rbf(nin, nhidden, nout, rbfund, outfunc)</CODE> allows the user to +specify the type of error function to be used. The field <CODE>outfn</CODE> +is set to the value of this string. Linear outputs (for regression problems) +and Neuroscale outputs (for topographic mappings) are supported. + +<p><CODE>net = rbf(nin, nhidden, nout, rbfunc, outfunc, prior, beta)</CODE>, +in which <CODE>prior</CODE> is +a scalar, allows the field <CODE>net.alpha</CODE> in the data structure +<CODE>net</CODE> to be set, corresponding to a zero-mean isotropic Gaussian +prior with inverse variance with value <CODE>prior</CODE>. Alternatively, +<CODE>prior</CODE> can consist of a data structure with fields <CODE>alpha</CODE> +and <CODE>index</CODE>, allowing individual Gaussian priors to be set over +groups of weights in the network. Here <CODE>alpha</CODE> is a column vector +in which each element corresponds to a separate group of weights, +which need not be mutually exclusive. The membership of the groups is +defined by the matrix <CODE>indx</CODE> in which the columns correspond to +the elements of <CODE>alpha</CODE>. Each column has one element for each +weight in the matrix, in the order defined by the function +<CODE>rbfpak</CODE>, and each element is 1 or 0 according to whether the +weight is a member of the corresponding group or not. A utility +function <CODE>rbfprior</CODE> is provided to help in setting up the +<CODE>prior</CODE> data structure. + +<p><CODE>net = rbf(nin, nhidden, nout, func, prior, beta)</CODE> also sets the +additional field <CODE>net.beta</CODE> in the data structure <CODE>net</CODE>, where +beta corresponds to the inverse noise variance. + +<p><h2> +Example +</h2> +The following code constructs an RBF network with 1 input and output node +and 5 hidden nodes and then propagates some data <CODE>x</CODE> through it. +<PRE> + +net = rbf(1, 5, 1, 'tps'); +[y, act] = rbffwd(net, x); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</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 |