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author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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+++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/glm.htm	Tue Feb 10 15:05:51 2015 +0000
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+<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>
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