Mauch MIREX 2010

function L = log_nextcase_prob_node(CPD, self_ev, pev, test_self_ev, test_pev)

% LOG_NEXTCASE_PROB_NODE compute the joint distribution of a node (tabular) of a new case given

% completely observed data.

%

% The input arguments are mainly similar with log_marg_prob_node(CPD, self_ev, pev, usecell),

% but add test_self_ev, test_pev, and without usecell

% test_self_ev(m) is the evidence on this node in a test case.

% test_pev(i) is the evidence on the i'th parent in the test case (if there are any parents).

%

% Written by qian.diao@intel.com

ncases = length(self_ev);

sz = CPD.sizes;

nparents = length(sz)-1;

assert(ncases == size(pev, 2)); 

if nargin < 6

  %usecell = 0;

  if iscell(self_ev)

    usecell = 1;

  else

    usecell = 0;

  end

end

if ncases==0

  L = 0;

  return;

elseif ncases==1  % speedup the sequential learning case; here need correction!!!

  CPT = CPD.CPT;

  % We assume the CPTs are already set to the mean of the posterior (due to bayes_update_params)

  if usecell

    x = cat(1, pev{:})';

    y = self_ev{1};

  else

    %x = pev(:)';

    x = pev;

    y = self_ev;

  end

  switch nparents

   case 0, p = CPT(y);

   case 1, p = CPT(x(1), y);

   case 2, p = CPT(x(1), x(2), y);

   case 3, p = CPT(x(1), x(2), x(3), y);

   otherwise,

    ind = subv2ind(sz, [x y]);

    p = CPT(ind);

  end

  L = log(p);

else

  % We ignore the CPTs here and assume the prior has not been changed

  % We arrange the data as in the following example.

  % Let there be 2 parents and 3 cases. Let p(i,m) be parent i in case m,

  % and y(m) be the child in case m. Then we create the data matrix

  % 

  % p(1,1) p(1,2) p(1,3)

  % p(2,1) p(2,2) p(2,3)

  % y(1)   y(2)   y(3)

  if usecell

    data = [cell2num(pev); cell2num(self_ev)]; 

  else

    data = [pev; self_ev];

  end

  counts = compute_counts(data, sz);

  % compute the (N_ijk'+ N_ijk)/(N_ij' + N_ij) under the condition of 1_m+1,ijk = 1 

  L = predict_family(counts, CPD.prior, test_self_ev, test_pev);

end