Daniel@0: function pot = convert_to_pot(CPD, pot_type, domain, evidence)
Daniel@0: % CONVERT_TO_POT Convert a softmax CPD to a potential
Daniel@0: % pots = convert_to_pot(CPD, pot_type, domain, evidence)
Daniel@0: %
Daniel@0: % pots = CPD evaluated using evidence(domain)
Daniel@0: 
Daniel@0: ncases = size(domain,2);
Daniel@0: assert(ncases==1); % not yet vectorized
Daniel@0: 
Daniel@0: sz = dom_sizes(CPD);
Daniel@0: ns = zeros(1, max(domain));
Daniel@0: ns(domain) = sz;
Daniel@0: 
Daniel@0: odom = domain(~isemptycell(evidence(domain)));
Daniel@0: T = convert_to_table(CPD, domain, evidence);
Daniel@0: 
Daniel@0: switch pot_type
Daniel@0:  case 'u',
Daniel@0:   pot = upot(domain, sz, T, 0*myones(sz));  
Daniel@0:  case 'd',
Daniel@0:   ns(odom) = 1;
Daniel@0:   pot = dpot(domain, ns(domain), T);          
Daniel@0:  
Daniel@0:  case {'c','g'},
Daniel@0:   % Since we want the output to be a Gaussian, the whole family must be observed.
Daniel@0:   % In other words, the potential is really just a constant.
Daniel@0:   p = T;
Daniel@0:   %p = prob_node(CPD, evidence(domain(end)), evidence(domain(1:end-1)));
Daniel@0:   ns(domain) = 0;
Daniel@0:   pot = cpot(domain, ns(domain), log(p));       
Daniel@0:  
Daniel@0:  case 'cg',
Daniel@0:   T = T(:);
Daniel@0:   ns(odom) = 1;
Daniel@0:   can = cell(1, length(T));
Daniel@0:   for i=1:length(T)
Daniel@0:     can{i} = cpot([], [], log(T(i)));
Daniel@0:   end
Daniel@0:   ps = domain(1:end-1);
Daniel@0:   dps = ps(CPD.dpndx);
Daniel@0:   cps = ps(CPD.cpndx);
Daniel@0:   ddom = [dps CPD.self];
Daniel@0:   cdom = cps;
Daniel@0:   pot = cgpot(ddom, cdom, ns, can);   
Daniel@0:   
Daniel@0:  case 'scg'
Daniel@0:   T = T(:);
Daniel@0:   ns(odom) = 1;
Daniel@0:   pot_array = cell(1, length(T));
Daniel@0:   for i=1:length(T)
Daniel@0:     pot_array{i} = scgcpot([], [], T(i));
Daniel@0:   end
Daniel@0:   pot = scgpot(domain, [], [], ns, pot_array);   
Daniel@0: 
Daniel@0:  otherwise,
Daniel@0:   error(['unrecognized pot type ' pot_type])
Daniel@0: end
Daniel@0: