comparison toolboxes/FullBNT-1.0.7/netlab3.3/netinit.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function net = netinit(net, prior)
+%NETINIT Initialise the weights in a network.
+%
+%	Description
+%
+%	NET = NETINIT(NET, PRIOR) takes a network data structure NET and sets
+%	the weights and biases by sampling from a Gaussian distribution. If
+%	PRIOR is a scalar, then all of the parameters (weights and biases)
+%	are sampled from a single isotropic Gaussian with inverse variance
+%	equal to PRIOR. If PRIOR is a data structure of the kind generated by
+%	MLPPRIOR, then the parameters are sampled from multiple Gaussians
+%	according to their groupings (defined by the INDEX field) with
+%	corresponding variances (defined by the ALPHA field).
+%
+%	See also
+%	MLPPRIOR, NETUNPAK, RBFPRIOR
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+if isstruct(prior)
+    if (isfield(net, 'mask'))
+	if find(sum(prior.index, 2)) ~= find(net.mask)
+	    error('Index does not match mask');
+	end
+	sig = sqrt(prior.index*prior.alpha);
+	% Weights corresponding to zeros in mask will not be used anyway
+	% Set their priors to one to avoid division by zero
+	sig = sig + (sig == 0);  
+	sig = 1./sqrt(sig);
+    else
+	sig = 1./sqrt(prior.index*prior.alpha);
+    end
+    w = sig'.*randn(1, net.nwts); 
+elseif size(prior) == [1 1]
+  w = randn(1, net.nwts).*sqrt(1/prior);
+else
+  error('prior must be a scalar or a structure');
+end  
+
+if (isfield(net, 'mask'))
+    w = w(logical(net.mask));
+end
+net = netunpak(net, w);
+