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update examples

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This commit is contained in:
Tiago Gomes 2012-05-23 17:17:50 +01:00
parent bdef49bb20
commit 3890aafc1a
8 changed files with 12 additions and 460 deletions
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42
packages/CLPBN/examples/burglary-alarm.yap
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:- use_module(library(pfl)).
%:- set_pfl_flag(solver,ve).
:- set_pfl_flag(solver,bp), clpbn_horus:set_horus_flag(inf_alg,ve).
%:- set_pfl_flag(solver,bp), clpbn_horus:set_horus_flag(inf_alg,bp).
%:- set_pfl_flag(solver,fove).
%:- set_solver(fove).
%:- set_solver(hve).
%:- set_solver(bp).
%:- set_solver(cbp).
% :- yap_flag(write_strings, off).
:- yap_flag(write_strings, off).
bayes burglary::[b1,b3] ; [0.001, 0.999] ; [].
bayes burglary::[b1,b2] ; [0.001, 0.999] ; [].
bayes earthquake::[e1,e2] ; [0.002, 0.998]; [].
bayes alarm::[a1,a2] , burglary, earthquake ; [0.95, 0.94, 0.29, 0.001, 0.05, 0.06, 0.71, 0.999] ; [].
bayes alarm::[a1,a2], burglary, earthquake ;
[0.95, 0.94, 0.29, 0.001, 0.05, 0.06, 0.71, 0.999] ;
[].
bayes john_calls::[j1,j2] , alarm ; [0.9, 0.05, 0.1, 0.95] ; [].
bayes john_calls::[j1,j2], alarm ; [0.9, 0.05, 0.1, 0.95] ; [].
bayes mary_calls::[m1,m2] , alarm ; [0.7, 0.01, 0.3, 0.99] ; [].
b_cpt([0.001, 0.999]).
e_cpt([0.002, 0.998]).
a_cpt([0.95, 0.94, 0.29, 0.001,
0.05, 0.06, 0.71, 0.999]).
jc_cpt([0.9, 0.05,
0.1, 0.95]).
mc_cpt([0.7, 0.01,
0.3, 0.99]).
% ?- alarm(A).
?- john_calls(J), mary_calls(m1).
%?- john_calls(J), mary_calls(m1), alarm(a1).
%?- john_calls(J), alarm(a1).
bayes mary_calls::[m1,m2], alarm ; [0.7, 0.01, 0.3, 0.99] ; [].
% ?- john_calls(J), mary_calls(m1).

15
packages/CLPBN/examples/cbp_example.uai
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# example in counting belief propagation paper
MARKOV
3
2 2 2
2
2 0 1
2 2 1
4
1.2 1.4 2.0 0.4
4
1.2 1.4 2.0 0.4

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packages/CLPBN/examples/smokers27.fg
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0
packages/CLPBN/examples/smokers1.yap → packages/CLPBN/examples/social_domain1.yap
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0
packages/CLPBN/examples/smokers2.yap → packages/CLPBN/examples/social_domain2.yap
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packages/CLPBN/examples/test_city.yap
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:- use_module(library(pfl)).
:- set_solver(fove).
%:- set_solver(hve).
%:- set_solver(bp).
%:- set_solver(cbp).
:- multifile people/2.
:- multifile ev/1.
:- clpbn_horus:set_horus_flag(verbosity,5).
people(joe,nyc).
people(p2, nyc).
people(p3, nyc).
people(p4, nyc).
people(p5, nyc).
people(p6, nyc).
people(p7, nyc).
people(p8, nyc).
%ev(descn(p2, t)).
%ev(descn(p3, t)).
%ev(descn(p4, t)).
%ev(descn(p5, t)).
bayes city_conservativeness(C)::[y,n] ; cons_table(C) ; [people(_,C)].
bayes gender(P)::[m,f] ; gender_table(P) ; [people(P,_)].
bayes hair_color(P)::[t,f], city_conservativeness(C) ; hair_color_table(P) ; [people(P,C)].
bayes car_color(P)::[t,f], hair_color(P) ; car_color_table(P); [people(P,_)].
bayes height(P)::[t,f], gender(P) ; height_table(P) ; [people(P,_)].
bayes shoe_size(P):[t,f], height(P) ; shoe_size_table(P); [people(P,_)].
bayes guilty(P)::[y,n] ; guilty_table(P) ; [people(P,_)].
bayes descn(P)::[t,f], car_color(P), hair_color(P), height(P), guilty(P) ; descn_table(P) ; [people(P,_)].
bayes witness(C)::[t,f], descn(Joe), descn(P2) ; wit_table ; [people(_,C), Joe=joe, P2=p2].
% FIXME
%cons_table(amsterdam, [0.2, 0.8]) :- !.
cons_table(_, [0.8, 0.2]).
gender_table(_, [0.55, 0.45]).
hair_color_table(_,
/* conservative_city */
/* y n */
[ 0.05, 0.1,
0.95, 0.9 ]).
car_color_table(_,
/* t f */
[ 0.9, 0.2,
0.1, 0.8 ]).
height_table(_,
/* m f */
[ 0.6, 0.4,
0.4, 0.6 ]).
shoe_size_table(_,
/* t f */
[ 0.9, 0.1,
0.1, 0.9 ]).
guilty_table(_, [0.23, 0.77]).
descn_table(_,
/* color, hair, height, guilt */
/* ttttt tttf ttft ttff tfttt tftf tfft tfff ttttt fttf ftft ftff ffttt fftf ffft ffff */
[ 0.99, 0.5, 0.23, 0.88, 0.41, 0.3, 0.76, 0.87, 0.44, 0.43, 0.29, 0.72, 0.23, 0.91, 0.95, 0.92,
0.01, 0.5, 0.77, 0.12, 0.59, 0.7, 0.24, 0.13, 0.56, 0.57, 0.71, 0.28, 0.77, 0.09, 0.05, 0.08]).
wit_table([0.2, 0.45, 0.24, 0.34,
0.8, 0.55, 0.76, 0.66]).
runall(G, Wrapper) :-
findall(G, Wrapper, L),
execute_all(L).
execute_all([]).
execute_all(G.L) :-
call(G),
execute_all(L).
is_joe_guilty(Guilty) :-
witness(nyc, t),
runall(X, ev(X)),
guilty(joe, Guilty).
?- is_joe_guilty(Guilty).

33
packages/CLPBN/examples/test_smokers1.yap
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:- use_module(library(pfl)).
:- set_solver(fove).
%:- set_solver(hve).
%:- set_solver(bp).
%:- set_solver(cbp).
:- yap_flag(write_strings, off).
:- multifile people/1.
:- clpbn_horus:set_horus_flag(verbosity,5).
people @ 5.
people(X,Y) :-
people(X),
people(Y),
X \== Y.
markov smokes(X)::[t,f]; [1.0, 4.0552]; [people(X)].
markov cancer(X)::[t,f]; [1.0, 9.9742]; [people(X)].
markov friends(X,Y)::[t,f] ; [1.0, 99.48432] ; [people(X,Y)].
markov smokes(X)::[t,f], cancer(X)::[t,f] ; [4.48169, 4.48169, 1.0, 4.48169] ; [people(X)].
markov friends(X,Y)::[t,f], smokes(X)::[t,f], smokes(Y)::[t,f] ;
[3.004166, 3.004166, 3.004166, 3.004166, 3.004166, 1.0, 1.0, 3.004166] ; [people(X,Y)].
?- friends(p1,p2,X).

33
packages/CLPBN/examples/test_smokers2.yap
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:- use_module(library(pfl)).
:- set_solver(fove).
%:- set_solver(hve).
%:- set_solver(bp).
%:- set_solver(cbp).
:- yap_flag(write_strings, off).
:- clpbn_horus:set_horus_flag(verbosity,5).
:- clpbn_horus:set_horus_flag(use_logarithms,true).
:- multifile people/1.
people @ 12.
people(X,Y) :-
people(X),
people(Y).
markov smokes(X)::[t,f]; [1.0, 4.0]; [people(X)].
markov asthma(X)::[t,f]; [2.5, 9.0] ; [people(X)].
markov friends(X,Y)::[t,f]; [1.0, 14.0] ; [people(X,Y)].
markov asthma(X)::[t,f], smokes(X)::[t,f]; [4.5, 4.3, 2.0, 1.45] ; [people(X)].
markov asthma(X)::[t,f], friends(X,Y)::[t,f], smokes(Y)::[t,f];
[2.1, 3.3, 7.4, 3.9, 1.6, 5.0, 4.0, 3.0] ; [people(X,Y)].
?- friends(p1,p2,X).