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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/glm.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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| -rw-r--r-- | sourcecodes/bnt-master/nethelp3.3/glm.htm | 85 |
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diff --git a/sourcecodes/bnt-master/nethelp3.3/glm.htm b/sourcecodes/bnt-master/nethelp3.3/glm.htm new file mode 100644 index 00000000..a860372f --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/glm.htm @@ -0,0 +1,85 @@ +<html> +<head> +<title> +Netlab Reference Manual glm +</title> +</head> +<body> +<H1> glm +</H1> +<h2> +Purpose +</h2> +Create a generalized linear model. + +<p><h2> +Synopsis +</h2> +<PRE> +net = glm(nin, nout, func) +net = glm(nin, nout, func, prior) +net = glm(nin, nout, func, prior, beta) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = glm(nin, nout, func)</CODE> takes the number of inputs +and outputs for a generalized linear model, together +with a string <CODE>func</CODE> which specifies the output unit activation function, +and returns a data structure <CODE>net</CODE>. The weights are drawn from a zero mean, +isotropic Gaussian, with variance scaled by the fan-in of the +output units. 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 optional +argument <CODE>alpha</CODE> sets the inverse variance for the weight +initialization. + +<p>The fields in <CODE>net</CODE> are +<PRE> + type = 'glm' + nin = number of inputs + nout = number of outputs + nwts = total number of weights and biases + actfn = string describing the output unit activation function: + 'linear' + 'logistic' + 'softmax' + w1 = first-layer weight matrix + b1 = first-layer bias vector +</PRE> + + +<p><CODE>net = glm(nin, nout, func, prior)</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>index</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>glmpak</CODE>, and each element is 1 or 0 according to whether +the weight is a member of the corresponding group or not. + +<p><CODE>net = glm(nin, 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> +See Also +</h2> +<CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmerr.htm">glmerr</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</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 |