maximize_params.m

CGridCtrl_demo for mobile robots

function CPD = maximize_params(CPD, temp)
% MAXIMIZE_PARAMS Set the params of a tabular node to their ML/MAP values.
% CPD = maximize_params(CPD, temp)

if ~adjustable_CPD(CPD), return; end

%assert(approxeq(sum(CPD.counts(:)), CPD.nsamples)); % false!
switch CPD.prior_type
 case 'none',
  counts = reshape(CPD.counts, size(CPD.CPT));
  CPD.CPT = mk_stochastic(counts);
 case 'dirichlet',
  counts = reshape(CPD.counts, size(CPD.CPT));
  CPD.CPT = mk_stochastic(counts + CPD.dirichlet);
 case 'entropic',
  % For an HMM,
  % CPT(i,j) = pr(X(t)=j | X(t-1)=i) = transprob(i,j)
  % counts(i,j) = E #(X(t-1)=i, X(t)=j) = exp_num_trans(i,j)
  Z = 1-temp;
  fam_sz = CPD.sizes;
  psz = prod(fam_sz(1:end-1));
  ssz = fam_sz(end);
  counts = reshape(CPD.counts, psz, ssz);
  CPT = zeros(psz, ssz);
  for i=CPD.entropic_pcases(:)'
    [CPT(i,:), logpost] = entropic_map_estimate(counts(i,:), Z);
  end
  non_entropic_pcases = mysetdiff(1:psz, CPD.entropic_pcases);
  for i=non_entropic_pcases(:)'
    CPT(i,:) = mk_stochastic(counts(i,:));
  end
  %for i=1:psz
  %  [CPT(i,:), logpost] = entropic_map(counts(i,:), Z);
  %end
  if CPD.trim & (temp < 2) % at high temps, we would trim everything!
    % grad(j) = d log lik / d theta(i ->j)
    % CPT(i,j) = 0 => counts(i,j) = 0
    % so we can safely replace 0s by 1s in the denominator
    denom = CPT(i,:) + (CPT(i,:)==0);
    grad = counts(i,:) ./ denom;
    trim = find(CPT(i,:) <= exp(-(1/Z)*grad)); % eqn 32
    if ~isempty(trim)
      CPT(i,trim) = 0;
      if all(CPD.trimmed_trans(i,trim)==0) % trimming for 1st time
	disp(['trimming CPT(' num2str(i) ',' num2str(trim) ')']) 
      end
      CPD.trimmed_trans(i,trim) = 1;
    end
  end
  CPD.CPT = myreshape(CPT, CPD.sizes);
end