diff toolboxes/FullBNT-1.0.7/netlabKPM/glmtrain_weighted.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/netlabKPM/glmtrain_weighted.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,141 @@
+function [net, options] = glmtrain_weighted(net, options, x, t, eso_w, alfa)
+%GLMTRAIN Specialised training of generalized linear model
+%
+%	Description
+%	NET = GLMTRAIN(NET, OPTIONS, X, T) uses the iterative reweighted
+%	least squares (IRLS) algorithm to set the weights in the generalized
+%	linear model structure NET.  This is a more efficient alternative to
+%	using GLMERR and GLMGRAD and a non-linear optimisation routine
+%	through NETOPT. Note that for linear outputs, a single pass through
+%	the  algorithm is all that is required, since the error function is
+%	quadratic in the weights.  The error function value at the final set
+%	of weights is returned in OPTIONS(8). Each row of X corresponds to
+%	one input vector and each row of T corresponds to one target vector.
+%
+%	The optional parameters have the following interpretations.
+%
+%	OPTIONS(1) is set to 1 to display error values during training. If
+%	OPTIONS(1) is set to 0, then only warning messages are displayed.  If
+%	OPTIONS(1) is -1, then nothing is displayed.
+%
+%	OPTIONS(2) is a measure of the precision required for the value of
+%	the weights W at the solution.
+%
+%	OPTIONS(3) is a measure of the precision required of the objective
+%	function at the solution.  Both this and the previous condition must
+%	be satisfied for termination.
+%
+%	OPTIONS(5) is set to 1 if an approximation to the Hessian (which
+%	assumes that all outputs are independent) is used for softmax
+%	outputs. With the default value of 0 the exact Hessian (which is more
+%	expensive to compute) is used.
+%
+%	OPTIONS(14) is the maximum number of iterations for the IRLS
+%	algorithm;  default 100.
+%
+%	See also
+%	GLM, GLMERR, GLMGRAD
+%
+
+%	Copyright (c) Christopher M Bishop, Ian T Nabney (1996, 1997)
+
+% Check arguments for consistency
+errstring = consist(net, 'glm', x, t);
+if ~errstring
+  error(errstring);
+end
+
+if(~options(14))
+  options(14) = 100;
+end
+
+display = options(1);
+
+test = (options(2) | options(3));           % Do we need to test for termination?
+
+ndata = size(x, 1);
+
+inputs = [x ones(ndata, 1)];                % Add a column of ones for the bias 
+
+% Use weighted iterative reweighted least squares (WIRLS)
+e = ones(1, net.nin+1);
+for n = 1:options(14)
+    
+    %switch net.actfn
+      switch net.outfn
+    case 'softmax'
+      if n == 1        
+        p = (t + (1/size(t, 2)))/2;    % Initialise model: ensure that row sum of p is one no matter
+	    act = log(p./(1-p));           % how many classes there are
+      end
+      if options(5) == 1 | n == 1
+        link_deriv = p.*(1-p);
+        weights = sqrt(link_deriv);          % sqrt of weights
+        if (min(min(weights)) < eps)
+          fprintf(1, 'Warning: ill-conditioned weights in glmtrain\n')
+          return
+        end
+        z = act + (t-p)./link_deriv;
+        % Treat each output independently with relevant set of weights
+        for j = 1:net.nout
+          indep = inputs.*(weights(:,j)*e);
+	      dep   = z(:,j).*weights(:,j);
+	      temp  = indep\dep;
+	      net.w1(:,j) = temp(1:net.nin);
+	      net.b1(j)   = temp(net.nin+1);
+        end
+        [err, edata, eprior, p, act] = glmerr_weighted(net, x, t, eso_w);
+        if n == 1
+          errold = err;
+          wold = netpak(net);
+        else
+          w = netpak(net);
+        end
+      else
+	    % Exact method of calculation after w first initialised
+	    % Start by working out Hessian
+	    Hessian = glmhess_weighted(net, x, t, eso_w);
+	    temp = p-t;
+	    for m=1:ndata,
+	       temp(m,:)=eso_w(m,1)*temp(m,:);
+	    end
+	    gw1 = x'*(temp);
+	    gb1 = sum(temp, 1);
+	    gradient = [gw1(:)', gb1];
+	    % Now compute modification to weights
+	    deltaw = -gradient*pinv(Hessian);
+	    w = wold + alfa*deltaw;
+	    net = glmunpak(net, w);
+	    [err, edata, eprior, p] = glmerr_weighted(net, x, t, eso_w);
+      end
+    otherwise
+      error(['Unknown activation function ', net.actfn]);
+    end    % switch' end
+    
+   if options(1)==1
+     fprintf(1, 'Cycle %4d Error %11.6f\n', n, err)
+   end
+   % Test for termination
+   % Terminate if error increases
+   if err >  errold
+     errold = err;
+     w = wold;
+     options(8) = err;
+     fprintf(1, 'Error has increased: terminating\n')
+     return;
+   end
+   if test & n > 1
+     if (max(abs(w - wold)) < options(2) & abs(err-errold) < options(3))
+       options(8) = err;
+       return;
+     else
+       errold = err;
+       wold = w;
+     end
+   end
+end
+
+options(8) = err;
+if (options(1) > 0)
+  disp('Warning: Maximum number of iterations has been exceeded');
+end