comparison toolboxes/FullBNT-1.0.7/bnt/examples/limids/oil1.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 % oil wildcatter influence diagram in Cowell et al p172
2
3 % T = test for oil?
4 % UT = utility (negative cost) of testing
5 % O = amount of oil = Dry, Wet or Soaking
6 % R = results of test = NoStrucure, OpenStructure, ClosedStructure or NoResult
7 % D = drill?
8 % UD = utility of drilling
9
10 % Decision sequence = T R D O
11
12 T = 1; UT = 2; O = 3; R = 4; D = 5; UD = 6;
13 N = 6;
14 dag = zeros(N);
15 dag(T, [UT R D]) = 1;
16 dag(O, [R UD]) = 1;
17 dag(R, D) = 1;
18 dag(D, UD) = 1;
19
20 ns = zeros(1,N);
21 ns(O) = 3; ns(R) = 4; ns(T) = 2; ns(D) = 2; ns(UT) = 1; ns(UD) = 1;
22
23 limid = mk_limid(dag, ns, 'chance', [O R], 'decision', [T D], 'utility', [UT UD]);
24
25 limid.CPD{O} = tabular_CPD(limid, O, [0.5 0.3 0.2]);
26 tbl = [0.6 0 0.3 0 0.1 0 0.3 0 0.4 0 0.4 0 0.1 0 0.3 0 0.5 0 0 1 0 1 0 1];
27 limid.CPD{R} = tabular_CPD(limid, R, tbl);
28
29 limid.CPD{UT} = tabular_utility_node(limid, UT, [-10 0]);
30 limid.CPD{UD} = tabular_utility_node(limid, UD, [-70 50 200 0 0 0]);
31
32 if 1
33 % start with uniform policies
34 limid.CPD{T} = tabular_decision_node(limid, T);
35 limid.CPD{D} = tabular_decision_node(limid, D);
36 else
37 % hard code optimal policies
38 limid.CPD{T} = tabular_decision_node(limid, T, [1.0 0.0]);
39 a = 0.5; b = 1-a; % arbitrary value
40 tbl = myreshape([0 a 1 a 1 a a a 1 b 0 b 0 b b b], ns([T R D]));
41 limid.CPD{D} = tabular_decision_node(limid, D, tbl);
42 end
43
44 %fname = '/home/cs/murphyk/matlab/Misc/loopybel.txt';
45
46 engines = {};
47 engines{end+1} = global_joint_inf_engine(limid);
48 engines{end+1} = jtree_limid_inf_engine(limid);
49 %engines{end+1} = belprop_inf_engine(limid, 'max_iter', 3*N, 'filename', fname);
50
51 exact = [1 2];
52 %approx = 3;
53 approx = [];
54
55 E = length(engines);
56 strategy = cell(1, E);
57 MEU = zeros(1, E);
58 for e=1:E
59 [strategy{e}, MEU(e)] = solve_limid(engines{e});
60 MEU
61 end
62 MEU
63
64 for e=exact(:)'
65 assert(approxeq(MEU(e), 22.5))
66 % U(T=yes) U(T=no)
67 % 1 0
68 assert(argmax(strategy{e}{T}) == 1); % test = yes
69 t = 1; % test = yes
70 % strategy{D} T R U(D=yes=1) U(D=no=2)
71 % 1=yes 1=noS 0 1 Don't drill
72 % 2=no 1=noS 1 0
73 % 1=yes 2=opS 1 0
74 % 2=no 2=opS 1 0
75 % 1=yes 3=clS 1 0
76 % 2=no 3=clS 1 0
77 % 1=yes 4=unk 1 0
78 % 2=no 4=unk 1 0
79
80 for r=[2 3] % OpS, ClS
81 assert(argmax(squeeze(strategy{e}{D}(t,r,:))) == 1); % drill = yes
82 end
83 r = 1; % noS
84 assert(argmax(squeeze(strategy{e}{D}(t,r,:))) == 2); % drill = no
85 end
86
87
88 for e=approx(:)'
89 approxeq(strategy{exact(1)}{T}, strategy{e}{T})
90 approxeq(strategy{exact(1)}{D}, strategy{e}{D})
91 end