Daniel@0: function [y, extra, invhess] = mlpevfwd(net, x, t, x_test, invhess)
Daniel@0: %MLPEVFWD Forward propagation with evidence for MLP
Daniel@0: %
Daniel@0: %	Description
Daniel@0: %	Y = MLPEVFWD(NET, X, T, X_TEST) takes a network data structure  NET
Daniel@0: %	together with the input X and target T training data and input test
Daniel@0: %	data X_TEST. It returns the normal forward propagation through the
Daniel@0: %	network Y together with a matrix EXTRA which consists of error bars
Daniel@0: %	(variance) for a regression problem or moderated outputs for a
Daniel@0: %	classification problem. The optional argument (and return value)
Daniel@0: %	INVHESS is the inverse of the network Hessian computed on the
Daniel@0: %	training data inputs and targets.  Passing it in avoids recomputing
Daniel@0: %	it, which can be a significant saving for large training sets.
Daniel@0: %
Daniel@0: %	See also
Daniel@0: %	FEVBAYES
Daniel@0: %
Daniel@0: 
Daniel@0: %	Copyright (c) Ian T Nabney (1996-2001)
Daniel@0: 
Daniel@0: [y, z, a] = mlpfwd(net, x_test);
Daniel@0: if nargin == 4
Daniel@0:   [extra, invhess] = fevbayes(net, y, a, x, t, x_test);
Daniel@0: else
Daniel@0:   [extra, invhess] = fevbayes(net, y, a, x, t, x_test, invhess);
Daniel@0: end