Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/bnt/inference/static/@quickscore_inf_engine/private/quickscore.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
---|---|
date | Tue, 10 Feb 2015 15:05:51 +0000 |
parents | |
children |
line wrap: on
line diff
--- /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 + +