bnt_example.yap

/*
cd ~/Yap/CLPBN/FullBNT-1.0.4
addpath(genpathKPM(pwd))
N = 4; 
dag = false(N,N);
C = 1; S = 2; R = 3; W = 4;
dag(C,[R S]) = 1;
dag(R,W) = 1;
dag(S,W)=1;
discrete_nodes = 1:N;
node_sizes = 2*ones(1,N); 
bnet = mk_bnet(dag, node_sizes, 'discrete', discrete_nodes);
bnet.CPD{W} = tabular_CPD(bnet, W, 'CPT', [1 0.1 0.1 0.01 0 0.9 0.9 0.99]);
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]);
engine = jtree_inf_engine(bnet);
evidence = cell(1,N);
evidence{W} = 2;
[engine, loglik] = enter_evidence(engine, evidence);
marg = marginal_nodes(engine, S);
marg.T
*/
 
:- ensure_loaded(library(matlab)).
 
:- yap_flag(write_strings, on).
 
% syntactic sugar for matlab_call.
:- op(800,yfx,<--).
 
G op Y :-
    matlab_call(Y,G).
 
do(Out,Out2) :-
    do,
    N = 4, 
    C = 1, S = 2, R = 3, W = 4,
    mkdag(N,[C-R,C-S,R-W,S-W]),
    matlab_sequence(1,N,discrete_nodes),
    mk2s(N,L), % domain has size 2
    matlab_matrix(1,N,L,node_sizes),
    matlab_matrix matlab_matrix mk_bnet(dag, node_sizes, \discrete, discrete_nodes),
    mkcpt(bnet,W,[1, 0.1, 0.1, 0.01, 0, 0.9, 0.9, 0.99]),
    mkcpt(bnet,C,[0.5, 0.5]),
    mkcpt(bnet,R,[0.8, 0.2, 0.2, 0.8]),
    mkcpt(bnet,S,[0.5, 0.9, 0.5, 0.1]),
    mkcpt mkcpt jtree_inf_engine(bnet),
    mkevidence(N,[W-2]),
    mkevidence mkevidence marginal_nodes(engine, S),
    matlab_get_variable( marg.'T', Out),
matlab_get_variable add_evidence([R-2]),
    add_evidence add_evidence marginal_nodes(engine, S),
    matlab_get_variable( marg.'T', Out2).
 
matlab_get_variable :-
    matlab_get_variable, matlab_get_variable.
matlab_get_variable :-
    getcwd(D),
    cd('~/Yap/CLPBN/FullBNT/BNT'),
    start_matlab('matlab -nojvm -nosplash'),
    matlab_eval_string("add_BNT_to_path",_),
    cd(D).
    
mk2s(0, []) :- mk2s.
mk2s(I, [2|L]) :-
    I0 is I-1,
    mk2s(I0, L).
 
mkdag(N,Els) :-
    Tot is N*N,
    functor(Dag,dag,Tot),
    add_els(Els,N,Dag),
    Dag=..[_|L],
    addzeros(L),
    matlab_matrix(N,N,L,dag).
 
add_els([],_,_).
add_els([X-Y|Els],N,Dag) :-
    Pos is (X-1)*N+Y,
    arg(Pos,Dag,1),
    add_els(Els,N,Dag).
 
addzeros([]).
addzeros([0|L]) :- addzeros,
    addzeros(L).
addzeros([1|L]) :-
    addzeros(L).
 
mkcpt(BayesNet, V, Tab) :-
    (BayesNet.'CPD'({V})) <-- tabular_CPD(BayesNet,V,Tab).
 
mkevidence(N,L) :-
    mkeventries(L,LN),
    matlab_initialized_cells( 1, N, LN, evidence),
    [engine, loglik]  enter_evidence(engine, evidence).
 
mkeventries([],[]).
mkeventries([A-V|L],[ev(1,A,V)|LN]) :-
    mkeventries(L,LN).
 
add_evidence(L) :-
    add_to_evidence(L),
    [engine, loglik]  enter_evidence(engine, evidence).
 
add_to_evidence([]).
add_to_evidence([Pos-Val|L]) :-
    matlab_item1(evidence,Pos,Val),
    add_to_evidence(L).