diff toolboxes/FullBNT-1.0.7/netlab3.3/glm.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/netlab3.3/glm.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,82 @@
+function net = glm(nin, nout, outfunc, prior, beta)
+%GLM	Create a generalized linear model.
+%
+%	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
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+net.type = 'glm';
+net.nin = nin;
+net.nout = nout;
+net.nwts = (nin + 1)*nout;
+
+outtfns = {'linear', 'logistic', 'softmax'};
+
+if sum(strcmp(outfunc, outtfns)) == 0
+  error('Undefined activation function. Exiting.');
+else
+  net.outfn = outfunc;
+end
+
+if nargin > 3
+  if isstruct(prior)
+    net.alpha = prior.alpha;
+    net.index = prior.index;
+  elseif size(prior) == [1 1]
+    net.alpha = prior;
+  else
+    error('prior must be a scalar or structure');
+  end
+end
+  
+net.w1 = randn(nin, nout)/sqrt(nin + 1);
+net.b1 = randn(1, nout)/sqrt(nin + 1);
+
+if nargin == 5
+  net.beta = beta;
+end
+