BNW using Octave instead of Matlab.

This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning.

I am calling this BNW_1.02. It can be accessed at:
compbio.uthsc.edu/BNW_1.02

1 files changed, 112 insertions, 0 deletions

diff --git a/sourcecodes/bnt-master/BNT/examples/static/sprinkler1.m b/sourcecodes/bnt-master/BNT/examples/static/sprinkler1.m
new file mode 100644
index 00000000..f021cf46
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+++ b/sourcecodes/bnt-master/BNT/examples/static/sprinkler1.m
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+% 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]);
+
+
+