function net = mlp(nin, nhidden, nout, outfunc, prior, beta)%MLP	Create a 2-layer feedforward network.%%	Description%	NET = MLP(NIN, NHIDDEN, NOUT, FUNC) takes the number of inputs,%	hidden units and output units for a 2-layer feed-forward network,%	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, unit variance isotropic Gaussian, with%	varianced scaled by the fan-in of the hidden or output units as%	appropriate. 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 hidden units use%	the TANH activation function.%%	The fields in NET are%	  type = 'mlp'%	  nin = number of inputs%	  nhidden = number of hidden units%	  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%	  w2 = second-layer weight matrix%	  b2 = second-layer bias vector%	 Here W1 has dimensions NIN times NHIDDEN, B1 has dimensions 1 times%	NHIDDEN, W2 has dimensions NHIDDEN times NOUT, and B2 has dimensions%	1 times NOUT.%%	NET = MLP(NIN, NHIDDEN, 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 INDX 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 MLPPAK, and each element is 1 or 0 according to whether the%	weight is a member of the corresponding group or not. A utility%	function MLPPRIOR is provided to help in setting up the PRIOR data%	structure.%%	NET = MLP(NIN, NHIDDEN, 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%	MLPPRIOR, MLPPAK, MLPUNPAK, MLPFWD, MLPERR, MLPBKP, MLPGRAD%%	Copyright (c) Ian T Nabney (1996-2001)net.type = 'mlp';net.nin = nin;net.nhidden = nhidden;net.nout = nout;net.nwts = (nin + 1)*nhidden + (nhidden + 1)*nout;outfns = {'linear', 'logistic', 'softmax'};if sum(strcmp(outfunc, outfns)) == 0  error('Undefined output function. Exiting.');else  net.outfn = outfunc;endif nargin > 4  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 a structure');  end  endnet.w1 = randn(nin, nhidden)/sqrt(nin + 1);net.b1 = randn(1, nhidden)/sqrt(nin + 1);net.w2 = randn(nhidden, nout)/sqrt(nhidden + 1);net.b2 = randn(1, nout)/sqrt(nhidden + 1);if nargin == 6  net.beta = beta;end