diff toolboxes/FullBNT-1.0.7/netlab3.3/mlpderiv.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/mlpderiv.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,47 @@
+function g = mlpderiv(net, x)
+%MLPDERIV Evaluate derivatives of network outputs with respect to weights.
+%
+%	Description
+%	G = MLPDERIV(NET, X) takes a network data structure NET and a matrix
+%	of input vectors X and returns a three-index matrix G whose I, J, K
+%	element contains the derivative of network output K with respect to
+%	weight or bias parameter J for input pattern I. The ordering of the
+%	weight and bias parameters is defined by MLPUNPAK.
+%
+%	See also
+%	MLP, MLPPAK, MLPGRAD, MLPBKP
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check arguments for consistency
+errstring = consist(net, 'mlp', x);
+if ~isempty(errstring);
+  error(errstring);
+end
+
+[y, z] = mlpfwd(net, x);
+
+ndata = size(x, 1);
+
+if isfield(net, 'mask')
+  nwts = size(find(net.mask), 1);
+  temp = zeros(1, net.nwts);
+else
+  nwts = net.nwts;
+end
+
+g = zeros(ndata, nwts, net.nout);
+for k = 1 : net.nout
+  delta = zeros(1, net.nout);
+  delta(1, k) = 1;
+  for n = 1 : ndata
+    if isfield(net, 'mask')
+      temp = mlpbkp(net, x(n, :), z(n, :), delta);
+      g(n, :, k) = temp(logical(net.mask));
+    else
+      g(n, :, k) = mlpbkp(net, x(n, :), z(n, :),...
+	delta);
+    end
+  end
+end