wolffd@0: function CPD = gmux_CPD(bnet, self, varargin)
wolffd@0: % GMUX_CPD Make a Gaussian multiplexer node
wolffd@0: %
wolffd@0: % CPD = gmux_CPD(bnet, node, ...) is used similarly to gaussian_CPD,
wolffd@0: % except we assume there is exactly one discrete parent (call it M)
wolffd@0: % which is used to select which cts parent to pass through to the output.
wolffd@0: % i.e., we define P(Y=y|M=m, X1, ..., XK) = N(y | W(m)*x(m) + mu(m), Sigma(m))
wolffd@0: % where Y represents this node, and the Xi's are the cts parents.
wolffd@0: % All the Xi must have the same size, and the num values for M must be K.
wolffd@0: %
wolffd@0: % Currently the params for this kind of CPD cannot be learned.
wolffd@0: %
wolffd@0: % Optional arguments [ default in brackets ]
wolffd@0: %
wolffd@0: % mean       - mu(:,i) is the mean given M=i [ zeros(Y,K) ]
wolffd@0: % cov        - Sigma(:,:,i) is the covariance given M=i [ repmat(1*eye(Y,Y), [1 1 K]) ]
wolffd@0: % weights    - W(:,:,i) is the regression matrix given M=i [ randn(Y,X,K) ]
wolffd@0: 
wolffd@0: if nargin==0
wolffd@0:   % This occurs if we are trying to load an object from a file.
wolffd@0:   CPD = init_fields;
wolffd@0:   clamp = 0;
wolffd@0:   CPD = class(CPD, 'gmux_CPD', generic_CPD(clamp));
wolffd@0:   return;
wolffd@0: elseif isa(bnet, 'gmux_CPD')
wolffd@0:   % This might occur if we are copying an object.
wolffd@0:   CPD = bnet;
wolffd@0:   return;
wolffd@0: end
wolffd@0: CPD = init_fields;
wolffd@0:  
wolffd@0: CPD = class(CPD, 'gmux_CPD', generic_CPD(1));
wolffd@0: 
wolffd@0: ns = bnet.node_sizes;
wolffd@0: ps = parents(bnet.dag, self);
wolffd@0: dps = myintersect(ps, bnet.dnodes);
wolffd@0: cps = myintersect(ps, bnet.cnodes);
wolffd@0: fam_sz = ns([ps self]);
wolffd@0: 
wolffd@0: CPD.self = self;
wolffd@0: CPD.sizes = fam_sz;
wolffd@0: 
wolffd@0: % Figure out which (if any) of the parents are discrete, and which cts, and how big they are
wolffd@0: % dps = discrete parents, cps = cts parents
wolffd@0: CPD.cps = find_equiv_posns(cps, ps); % cts parent index
wolffd@0: CPD.dps = find_equiv_posns(dps, ps);
wolffd@0: if length(CPD.dps) ~= 1
wolffd@0:   error('gmux must have exactly 1 discrete parent')
wolffd@0: end
wolffd@0: ss = fam_sz(end);
wolffd@0: cpsz = fam_sz(CPD.cps(1)); % in gaussian_CPD, cpsz = sum(fam_sz(CPD.cps))
wolffd@0: if ~all(fam_sz(CPD.cps) == cpsz)
wolffd@0:   error('all cts parents must have same size')
wolffd@0: end
wolffd@0: dpsz = fam_sz(CPD.dps);
wolffd@0: if dpsz ~= length(cps)
wolffd@0:   error(['the arity of the mux node is ' num2str(dpsz) ...
wolffd@0: 	 ' but there are ' num2str(length(cps)) ' cts parents']);
wolffd@0: end
wolffd@0: 
wolffd@0: % set default params
wolffd@0: %CPD.mean = zeros(ss, 1);
wolffd@0: %CPD.cov = eye(ss);
wolffd@0: %CPD.weights = randn(ss, cpsz);
wolffd@0: CPD.mean = zeros(ss, dpsz);
wolffd@0: CPD.cov = 1*repmat(eye(ss), [1 1 dpsz]);    
wolffd@0: CPD.weights = randn(ss, cpsz, dpsz);
wolffd@0: 
wolffd@0: args = varargin;
wolffd@0: nargs = length(args);
wolffd@0: for i=1:2:nargs
wolffd@0:   switch args{i},
wolffd@0:    case 'mean',        CPD.mean = args{i+1}; 
wolffd@0:    case 'cov',         CPD.cov = args{i+1}; 
wolffd@0:    case 'weights',    CPD.weights = args{i+1}; 
wolffd@0:    otherwise,  
wolffd@0:     error(['invalid argument name ' args{i}]);
wolffd@0:   end
wolffd@0: end
wolffd@0: 
wolffd@0: %%%%%%%%%%%
wolffd@0: 
wolffd@0: function CPD = 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: CPD.self = [];
wolffd@0: CPD.sizes = [];
wolffd@0: CPD.cps = [];
wolffd@0: CPD.dps = [];
wolffd@0: CPD.mean = [];
wolffd@0: CPD.cov = [];
wolffd@0: CPD.weights = [];
wolffd@0: