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wolffd@0 1 <html>
wolffd@0 2 <head>
wolffd@0 3 <title>
wolffd@0 4 Netlab Reference Manual glminit
wolffd@0 5 </title>
wolffd@0 6 </head>
wolffd@0 7 <body>
wolffd@0 8 <H1> glminit
wolffd@0 9 </H1>
wolffd@0 10 <h2>
wolffd@0 11 Purpose
wolffd@0 12 </h2>
wolffd@0 13 Initialise the weights in a generalized linear model.
wolffd@0 14
wolffd@0 15 <p><h2>
wolffd@0 16 Synopsis
wolffd@0 17 </h2>
wolffd@0 18 <PRE>
wolffd@0 19 net = glminit(net, prior)
wolffd@0 20 </PRE>
wolffd@0 21
wolffd@0 22
wolffd@0 23 <p><h2>
wolffd@0 24 Description
wolffd@0 25 </h2>
wolffd@0 26
wolffd@0 27 <p><CODE>net = glminit(net, prior)</CODE> takes a generalized linear model
wolffd@0 28 <CODE>net</CODE> and sets the weights and biases by sampling from a Gaussian
wolffd@0 29 distribution. If <CODE>prior</CODE> is a scalar, then all of the parameters
wolffd@0 30 (weights and biases) are sampled from a single isotropic Gaussian with
wolffd@0 31 inverse variance equal to <CODE>prior</CODE>. If <CODE>prior</CODE> is a data
wolffd@0 32 structure similar to that in <CODE>mlpprior</CODE> but for a single layer of
wolffd@0 33 weights, then the parameters
wolffd@0 34 are sampled from multiple Gaussians according to their groupings
wolffd@0 35 (defined by the <CODE>index</CODE> field) with corresponding variances
wolffd@0 36 (defined by the <CODE>alpha</CODE> field).
wolffd@0 37
wolffd@0 38 <p><h2>
wolffd@0 39 See Also
wolffd@0 40 </h2>
wolffd@0 41 <CODE><a href="glm.htm">glm</a></CODE>, <CODE><a href="glmpak.htm">glmpak</a></CODE>, <CODE><a href="glmunpak.htm">glmunpak</a></CODE>, <CODE><a href="mlpinit.htm">mlpinit</a></CODE>, <CODE><a href="mlpprior.htm">mlpprior</a></CODE><hr>
wolffd@0 42 <b>Pages:</b>
wolffd@0 43 <a href="index.htm">Index</a>
wolffd@0 44 <hr>
wolffd@0 45 <p>Copyright (c) Ian T Nabney (1996-9)
wolffd@0 46
wolffd@0 47
wolffd@0 48 </body>
wolffd@0 49 </html>