diff toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/set_fields.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
parents
children
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/CPDs/@softmax_CPD/set_fields.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,45 @@
+function CPD = set_params(CPD, varargin)
+% SET_PARAMS Set the parameters (fields) for a softmax_CPD object
+% CPD = set_params(CPD, name/value pairs)
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% (Let ns(i) be the size of node i, X = ns(X), Y = ns(Y), Q1=ns(dps(1)), Q2=ns(dps(2)), ...
+%   where dps are the discrete parents; if there are no discrete parents, we set Q1=1.)
+%
+% weights - (W(:,j,a,b,...) - W(:,j',a,b,...)) is ppn to dec. boundary
+%           between j,j' given Q1=a,Q2=b,... [ randn(X,Y,Q1,Q2,...) ]
+% offset  - (offset(j,a,b,...) - offset(j',a,b,...)) is the offset to dec. boundary
+%           between j,j' given Q1=a,Q2=b,... [ randn(Y,Q1,Q2,...) ]
+% clamped     - 'yes' means don't adjust params during learning ['no']
+% max_iter    - the maximum number of steps to take [10]
+% verbose     - 'yes' means print the LL at each step of IRLS ['no']
+% wthresh     - convergence threshold for weights [1e-2]
+% llthresh    - convergence threshold for log likelihood [1e-2]
+% approx_hess - 'yes' means approximate the Hessian for speed ['no']
+%
+% e.g., CPD = set_params(CPD,'offset', zeros(ns(i),1));
+
+args = varargin;
+nargs = length(args);
+glimsz = prod(CPD.sizes(CPD.dpndx));
+for i=1:2:nargs
+  switch args{i},
+   case 'discrete',     str='nothing to do';   
+   case 'clamped',      CPD = set_clamped(CPD, strcmp(args{i+1}, 'yes'));
+   case 'max_iter',     CPD.max_iter = args{i+1};
+   case 'verbose',      CPD.verbose = strcmp(args{i+1}, 'yes');
+   case 'max_iter',     CPD.max_iter = args{i+1};
+   case 'wthresh',      CPD.wthresh = args{i+1};
+   case 'llthresh',     CPD.llthresh = args{i+1};
+   case 'approx_hess',  CPD.approx_hess = strcmp(args{i+1}, 'yes');
+   case 'weights',      for q=1:glimsz, CPD.glim{q}.w1 = args{i+1}(:,:,q); end; 
+   case 'offset',
+    if glimsz == 1
+      CPD.glim{1}.b1 = args{i+1};
+    else
+      for q=1:glimsz, CPD.glim{q}.b1 = args{i+1}(:,q); end; 
+    end
+   otherwise,  
+    error(['invalid argument name ' args{i}]);       
+  end
+end