From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 28 Sep 2017 15:04:40 -0500 Subject: 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 --- sourcecodes/bnt-master/nethelp3.3/glm.htm | 85 +++++++++++++++++++++++++++++++ 1 file changed, 85 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/glm.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/glm.htm') 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 @@ + + + +Netlab Reference Manual glm + + + +

glm +

+Purpose +

+Create a generalized linear model. + +

+Synopsis +

+net = glm(nin, nout, func)
+net = glm(nin, nout, func, prior)
+net = glm(nin, nout, func, prior, beta)
+

+ + +

+Description +

+ +

net = glm(nin, nout, func) takes the number of inputs +and outputs for a generalized linear model, together +with a string func which specifies the output unit activation function, +and returns a data structure net. 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 +randn and so the seed for the random weight initialization can be +set using randn('state', s) where s is the seed value. The optional +argument alpha sets the inverse variance for the weight +initialization. + +

The fields in net are +

+  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
+

+ + +

net = glm(nin, nout, func, prior), in which prior is +a scalar, allows the field +net.alpha in the data structure net to be set, corresponding +to a zero-mean isotropic Gaussian prior with inverse variance with +value prior. Alternatively, prior can consist of a data +structure with fields alpha and index, allowing individual +Gaussian priors to be set over groups of weights in the network. Here +alpha 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 index in which +the columns correspond to the elements of alpha. Each column has +one element for each weight in the matrix, in the order defined by the +function glmpak, and each element is 1 or 0 according to whether +the weight is a member of the corresponding group or not. + +

net = glm(nin, nout, func, prior, beta) also sets the +additional field net.beta in the data structure net, where +beta corresponds to the inverse noise variance. + +

+See Also +

+glmpak, glmunpak, glmfwd, glmerr, glmgrad, glmtrain

+Pages: +Index +