diff toolboxes/FullBNT-1.0.7/bnt/examples/static/sprinkler1.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/examples/static/sprinkler1.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,112 @@
+% Lawn sprinker example from Russell and Norvig p454
+% For a picture, see http://www.cs.berkeley.edu/~murphyk/Bayes/usage.html#basics
+
+N = 4; 
+dag = zeros(N,N);
+C = 1; S = 2; R = 3; W = 4;
+dag(C,[R S]) = 1;
+dag(R,W) = 1;
+dag(S,W)=1;
+
+false = 1; true = 2;
+ns = 2*ones(1,N); % binary nodes
+
+%bnet = mk_bnet(dag, ns);
+bnet = mk_bnet(dag, ns, 'names', {'cloudy','S','R','W'}, 'discrete', 1:4);
+names = bnet.names;
+%C = names{'cloudy'};
+bnet.CPD{C} = tabular_CPD(bnet, C, [0.5 0.5]);
+bnet.CPD{R} = tabular_CPD(bnet, R, [0.8 0.2 0.2 0.8]);
+bnet.CPD{S} = tabular_CPD(bnet, S, [0.5 0.9 0.5 0.1]);
+bnet.CPD{W} = tabular_CPD(bnet, W, [1 0.1 0.1 0.01 0 0.9 0.9 0.99]);
+
+
+CPD{C} = reshape([0.5 0.5], 2, 1);
+CPD{R} = reshape([0.8 0.2 0.2 0.8], 2, 2);
+CPD{S} = reshape([0.5 0.9 0.5 0.1], 2, 2);
+CPD{W} = reshape([1 0.1 0.1 0.01 0 0.9 0.9 0.99], 2, 2, 2);
+joint = zeros(2,2,2,2);
+for c=1:2
+  for r=1:2
+    for s=1:2
+      for w=1:2
+	joint(c,s,r,w) = CPD{C}(c) * CPD{S}(c,s) * CPD{R}(c,r) * ...
+	    CPD{W}(s,r,w);
+      end
+    end
+  end
+end
+
+joint2 = repmat(reshape(CPD{C}, [2 1 1 1]), [1 2 2 2]) .* ...
+	 repmat(reshape(CPD{S}, [2 2 1 1]), [1 1 2 2]) .* ...
+	 repmat(reshape(CPD{R}, [2 1 2 1]), [1 2 1 2]) .* ...
+	 repmat(reshape(CPD{W}, [1 2 2 2]), [2 1 1 1]);
+
+assert(approxeq(joint, joint2));
+
+
+engine = jtree_inf_engine(bnet);
+
+evidence = cell(1,N);
+evidence{W} = true;
+
+[engine, ll] = enter_evidence(engine, evidence);
+
+m = marginal_nodes(engine, S);
+p1 = m.T(true) % P(S=true|W=true) = 0.4298
+lik1 = exp(ll); % P(W=true) = 0.6471
+assert(approxeq(p1, 0.4298));
+assert(approxeq(lik1, 0.6471));
+
+pSandW = sumv(joint(:,true,:,true), [C R]); % P(S,W) = sum_cr P(CSRW)
+pW = sumv(joint(:,:,:,true), [C S R]);
+pSgivenW = pSandW / pW; % P(S=t|W=t) = P(S=t,W=t)/P(W=t)
+assert(approxeq(pW, lik1))
+assert(approxeq(pSgivenW, p1))
+
+
+m = marginal_nodes(engine, R);
+p2 = m.T(true)  % P(R=true|W=true) =  0.7079     
+
+pRandW = sumv(joint(:,:,true,true), [C S]); % P(R,W) = sum_cr P(CSRW)
+pRgivenW = pRandW / pW; % P(R=t|W=t) = P(R=t,W=t)/P(W=t)
+assert(approxeq(pRgivenW, p2))
+
+
+% Add extra evidence that R=true
+evidence{R} = true;
+
+[engine, ll] = enter_evidence(engine, evidence);
+
+m = marginal_nodes(engine, S);
+p3 = m.T(true) % P(S=true|W=true,R=true) = 0.1945 
+assert(approxeq(p3, 0.1945))
+
+
+pSandRandW = sumv(joint(:,true,true,true), [C]); % P(S,R,W) = sum_c P(cSRW)
+pRandW = sumv(joint(:,:,true,true), [C S]); % P(R,W) = sum_cs P(cSRW)
+pSgivenWR = pSandRandW / pRandW; % P(S=t|W=t,R=t) = P(S=t,R=t,W=t)/P(W=t,R=t)
+assert(approxeq(pSgivenWR, p3))
+
+% So the sprinkler is less likely to be on if we know that
+% it is raining, since the rain can "explain away" the fact
+% that the grass is wet.
+
+lik3 = exp(ll); % P(W=true, R=true) = 0.4581
+% So the combined evidence is less likely (of course)
+
+
+
+
+% Joint distributions
+
+evidence = cell(1,N);
+[engine, ll] = enter_evidence(engine, evidence);
+m = marginal_nodes(engine, [S R W]);
+
+evidence{R} = 2;
+[engine, ll] = enter_evidence(engine, evidence);
+m = marginal_nodes(engine, [S R W]);
+
+
+