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