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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual glmerr
5 </title>
6 </head>
7 <body>
8 <H1> glmerr
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Evaluate error function for generalized linear model.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 e = glmerr(net, x, t)
20 [e, edata, eprior] = glmerr(net, x, t)
21 [e, edata, eprior, y, a] = glmerr(net, x, t)
22 </PRE>
23
24
25 <p><h2>
26 Description
27 </h2>
28
29 <CODE>e = glmerr(net, x, t)</CODE> takes a generalized
30 linear model data structure <CODE>net</CODE> together with a matrix <CODE>x</CODE>
31 of input vectors and a matrix <CODE>t</CODE> of target vectors, and evaluates
32 the error function <CODE>e</CODE>. The choice of error function corresponds
33 to the output unit activation function. Each row of <CODE>x</CODE>
34 corresponds to one input vector and each row of <CODE>t</CODE> corresponds to
35 one target vector.
36
37 <p><CODE>[e, edata, eprior, y, a] = glmerr(net, x, t)</CODE> also returns
38 the data and prior components of the total error.
39
40 <p><CODE>[e, edata, eprior, y, a] = glmerr(net, x)</CODE> also returns a matrix <CODE>y</CODE>
41 giving the outputs of the models and a matrix <CODE>a</CODE>
42 giving the summed inputs to each output unit, where each row
43 corresponds to one pattern.
44
45 <p><h2>
46 See Also
47 </h2>
48 <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="glmfwd.htm">glmfwd</a></CODE>, <CODE><a href="glmgrad.htm">glmgrad</a></CODE>, <CODE><a href="glmtrain.htm">glmtrain</a></CODE><hr>
49 <b>Pages:</b>
50 <a href="index.htm">Index</a>
51 <hr>
52 <p>Copyright (c) Ian T Nabney (1996-9)
53
54
55 </body>
56 </html>