function net = glm(nin, nout, func, 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'%	      'sigmoid'%	      '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) Christopher M Bishop, Ian T Nabney (1996, 1997)net.type = 'glm';net.nin = nin;net.nout = nout;net.nwts = (nin + 1)*nout;actfns = {'linear', 'logistic', 'softmax'};if sum(strcmp(func, actfns)) == 0  error('Undefined activation function. Exiting.');else  net.actfn = func;endif 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');  endend  net.w1 = randn(nin, nout)/sqrt(nin + 1);net.b1 = randn(1, nout)/sqrt(nin + 1);if nargin == 5  net.beta = beta;end