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Diffstat (limited to 'sourcecodes/bnt-master/SLP/scoring/kl_divergence.m')
| -rw-r--r-- | sourcecodes/bnt-master/SLP/scoring/kl_divergence.m | 48 |
1 files changed, 48 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/SLP/scoring/kl_divergence.m b/sourcecodes/bnt-master/SLP/scoring/kl_divergence.m new file mode 100644 index 00000000..f4e18147 --- /dev/null +++ b/sourcecodes/bnt-master/SLP/scoring/kl_divergence.m @@ -0,0 +1,48 @@ +function KLdiv = KL_divergence(bnetP, bnetQ) +% KL_DIVERGENCE computes the Kullback-Leibler divergence between two BNET distributions +% KLdiv = KL_divergence(bnetP, bnetQ) +% +% Output : +% div = sum_x P(x).log(P(x)/Q(x)) +% +% Rem : +% This version is optimized for speed, but can use too many memory +% ==> if you have a memory problem, use kl_divergence2 instead +% + +% ONLY FOR TABULAR NODES +% Make sure that you have done the params learning. +% +% V1.1 : 8 oct 2004 (Ph. Leray - philippe.leray@univ-nantes.fr) + +N = size(bnetP.dag,1); +N2 = size(bnetQ.dag,1); +ns= bnetP.node_sizes; +ns2= bnetQ.node_sizes; +if N~=N2, error('size of dags must be the same'), end +if ns~=ns2, error('node sizes of dags must be the same'), end +tiny = exp(-700); +KLdiv=0; + +inst = ind2subv(ns, 1:prod(ns)); + %Px=1; Qx=1; + for i=1:N, + ps = parents(bnetP.dag, i); + %e = bnetP.equiv_class(i); + %[tmp Px(:,i)] = prob_node(bnetP.CPD{e}, inst(:,i)', inst(:,ps)'); + [tmp Px(:,i)] = prob_node(bnetP.CPD{i}, inst(:,i)', inst(:,ps)'); + + ps = parents(bnetQ.dag, i); + %e = bnetQ.equiv_class(i); + %[tmp Qx(:,i)] = prob_node(bnetQ.CPD{e}, inst(:,i)', inst(:,ps)'); + [tmp Qx(:,i)] = prob_node(bnetQ.CPD{i}, inst(:,i)', inst(:,ps)'); + end + Px=prod(Px,2); + Px = Px + (Px==0)*tiny; % replace 0s by tiny + Qx=prod(Qx,2); + Qx = Qx + (Qx==0)*tiny; % replace 0s by tiny + + %%%%% Faut-il diviser par le nb de configurations possibles ? (sum => mean) + KLdiv = sum(Px.*log(Px./Qx)); + %end + |