diff toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/quickscore.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/quickscore.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,76 @@
+function prob = quickscore(fpos, fneg, inhibit, prior, leak)
+% QUICKSCORE Heckerman's algorithm for BN2O networks.
+% prob = quickscore(fpos, fneg, inhibit, prior, leak)
+% 
+% Consider a BN2O (Binary Node 2-layer Noisy-or) network such as QMR with
+% dieases on the top and findings on the bottom. (We assume all findings are observed,
+% since hidden leaves can be marginalized away.)
+% This algorithm takes O(2^|fpos|) time to compute the marginal on all the diseases.
+%
+% Inputs:
+% fpos = the positive findings (a vector of numbers in {1, ..., Nfindings})
+% fneg = the negative findings (a vector of numbers in {1, ..., Nfindings})
+% inhibit(i,j) = inhibition prob. for finding i, disease j, or 1.0 if j is not a parent.
+% prior(j) = prior prob. disease j is ON. We assume prior(off) = 1-prior(on).
+% leak(i) = inhibition prob. for the leak node for finding i
+%
+% Output:
+% prob(d) = Pr(disease d = on | ev)
+%
+% For details, see
+% - Heckerman, "A tractable inference algorithm for diagnosing multiple diseases", UAI89.
+% - Rish and Dechter, "On the impact of causal independence", UCI tech report, 1998.
+%
+% Note that this algorithm is numerically unstable, since it adds a large number of positive and
+% negative terms and hopes that some of them exactly cancel.
+%
+% For matlab experts, use 'mex' to compile C_quickscore, which has identical behavior to this function.
+
+[nfindings ndiseases] = size(inhibit);
+
+% make the first disease be always on, for the leak term
+Pon = [1 prior(:)'];
+Poff = 1-Pon;
+Uon = [leak(:) inhibit]; % U(f,d) = Pr(f=0|d=1)
+Uoff = [leak(:) ones(nfindings, ndiseases)]; % Uoff(f,d) = Pr(f=0|d=0)
+ndiseases = ndiseases + 1;
+
+npos = length(fpos);
+post = zeros(ndiseases, 2);
+% post(d,1) = alpha Pr(d=off), post(d,2) = alpha Pr(d=m)
+
+FP = length(fpos);
+%allbits = logical(dec2bitv(0:(2^FP - 1), FP));
+allbits = logical(ind2subv(2*ones(1,FP), 1:(2^FP))-1);
+
+for si=1:2^FP
+  bits = allbits(si,:);
+  fprime = fpos(bits);
+  fmask = zeros(1, nfindings);
+  fmask(fneg)=1;
+  fmask(fprime)=1;
+  fmask = logical(fmask);
+  p = 1;
+  pterm = zeros(1, ndiseases);
+  ptermOff = zeros(1, ndiseases);
+  ptermOn = zeros(1, ndiseases);
+  for d=1:ndiseases
+    ptermOff(d) = prod(Uoff(fmask,d));
+    ptermOn(d) = prod(Uon(fmask,d));
+    pterm(d) = Poff(d)*ptermOff(d) + Pon(d)*ptermOn(d);
+  end
+  p = prod(pterm);
+  sign = (-1)^(length(fprime));
+  for d=1:ndiseases
+    myp = p / pterm(d);
+    post(d,1) = post(d,1) + sign*(myp * ptermOff(d));
+    post(d,2) = post(d,2) + sign*(myp * ptermOn(d));
+  end
+end
+
+post(:,1) = post(:,1) .* Poff(:);
+post(:,2) = post(:,2) .* Pon(:);
+post = mk_stochastic(post);
+prob = post(2:end,2)'; % skip the leak term
+
+