wolffd@0: function K = tabular_kernel(fg, self)
wolffd@0: % TABULAR_KERNEL Make a table-based local kernel (discrete potential)
wolffd@0: % K = tabular_kernel(fg, self)
wolffd@0: %
wolffd@0: % fg is a factor graph
wolffd@0: % self is the number of a representative domain
wolffd@0: %
wolffd@0: % Use 'set_params_kernel' to adjust the following fields
wolffd@0: %   table - a q[1]xq[2]x... array, where q[i] is the number of values for i'th node
wolffd@0: %       in this domain [default: random values from [0,1], which need not sum to 1]
wolffd@0: 
wolffd@0: 
wolffd@0: if nargin==0
wolffd@0:   % This occurs if we are trying to load an object from a file.
wolffd@0:   K = init_fields;
wolffd@0:   K = class(K, 'tabular_kernel');
wolffd@0:   return;
wolffd@0: elseif isa(fg, 'tabular_kernel')
wolffd@0:   % This might occur if we are copying an object.
wolffd@0:   K = fg;
wolffd@0:   return;
wolffd@0: end
wolffd@0: K = init_fields;
wolffd@0: 
wolffd@0: ns = fg.node_sizes;
wolffd@0: dom = fg.doms{self};
wolffd@0: % we don't store the actual domain since it may vary due to parameter tieing
wolffd@0: K.sz = ns(dom);
wolffd@0: K.table = myrand(K.sz);
wolffd@0: 
wolffd@0: K = class(K, 'tabular_kernel');
wolffd@0: 
wolffd@0: 
wolffd@0: %%%%%%%
wolffd@0: 
wolffd@0: 
wolffd@0: function K = init_fields()
wolffd@0: % This ensures we define the fields in the same order 
wolffd@0: % no matter whether we load an object from a file,
wolffd@0: % or create it from scratch. (Matlab requires this.)
wolffd@0: 
wolffd@0: K.table = [];
wolffd@0: K.sz = [];
wolffd@0: 
wolffd@0: