diogo/yap-6.3
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more support for learning

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This commit is contained in:
Vítor Santos Costa 2008-11-26 09:56:55 +00:00
parent 58d4442883
commit 902dafa906
6 changed files with 14 additions and 13 deletions
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2
CLPBN/clpbn.yap
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@ -91,7 +91,7 @@ em_solver(vel).
%output(gviz(user_error)). %output(gviz(user_error)).
output(no). output(no).
suppress_attribute_display(false). suppress_attribute_display(false).
parameter_softening(laplace). parameter_softening(m_estimate(10)).
clpbn_flag(Flag,Option) :- clpbn_flag(Flag,Option) :-
clpbn_flag(Flag, Option, Option). clpbn_flag(Flag, Option, Option).

1
CLPBN/clpbn/dists.yap
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@ -12,7 +12,6 @@
get_dist_matrix/5, get_dist_matrix/5,
get_possibly_deterministic_dist_matrix/5, get_possibly_deterministic_dist_matrix/5,
get_dist_domain/2, get_dist_domain/2,
get_dist_params/2,
get_dist_domain_size/2, get_dist_domain_size/2,
get_dist_params/2, get_dist_params/2,
get_dist_key/2, get_dist_key/2,

4
CLPBN/clpbn/table.yap
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@ -100,10 +100,10 @@ clpbn_tabled_clause(Head, Body) :-
clpbn_tabled_clause(Head, M, Body). clpbn_tabled_clause(Head, M, Body).
clpbn_tabled_clause(M:Head, _, Body) :- !, clpbn_tabled_clause(M:Head, _, Body) :- !,
clpbn_table(Head, M, Body). clpbn_tabled_clause(Head, M, Body).
clpbn_tabled_clause(Head, M, Body) :- clpbn_tabled_clause(Head, M, Body) :-
clpbn_table(Head, M, THead), clpbn_table(Head, M, THead),
clause(THead, Body). clause(M:THead, Body).
clpbn_tabled_assertz(M:Clause) :- !, clpbn_tabled_assertz(M:Clause) :- !,

8
CLPBN/learning/aleph_params.yap
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@ -65,7 +65,7 @@
:- user:set(record_testclause_hook, clpbn_aleph:do_nothing). :- user:set(record_testclause_hook, clpbn_aleph:do_nothing).
%:- user:set(newbest_hook, clpbn_aleph:store_theory). :- user:set(newbest_hook, clpbn_aleph:store_theory).
disable_solver(_) :- disable_solver(_) :-
clpbn_flag(solver, Old, none), clpbn_flag(solver, Old, none),
@ -109,15 +109,17 @@ store_theory(_).
add_correct_cpt((G,B),(G,NB)) :- !, add_correct_cpt((G,B),(G,NB)) :- !,
add_correct_cpt(B,NB). add_correct_cpt(B,NB).
add_correct_cpt((clpbn:{V = K with Tab }), ({V = K with NTab})) :-
correct_tab(Tab,K,NTab).
add_correct_cpt(({V = K with Tab }), ({V = K with NTab})) :- add_correct_cpt(({V = K with Tab }), ({V = K with NTab})) :-
correct_tab(Tab,K,NTab). correct_tab(Tab,K,NTab).
correct_tab(p(Vs,_),K,p(Vs,TDist)) :- correct_tab(p(Vs,_),K,p(Vs,TDist)) :-
get_dist_key(Id, K), get_dist_key(Id, K),
get_dist_parms(Id, TDist). get_dist_params(Id, TDist).
correct_tab(p(Vs,_,Ps),K,p(Vs,TDist,Ps)) :- correct_tab(p(Vs,_,Ps),K,p(Vs,TDist,Ps)) :-
get_dist_key(Id, K), get_dist_key(Id, K),
get_dist_parms(Id, TDist). get_dist_params(Id, TDist).
store_cl(Cl) :- store_cl(Cl) :-
recordz(best_theory, Cl, _). recordz(best_theory, Cl, _).

4
CLPBN/learning/em.yap
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@ -90,7 +90,7 @@ init_em(Items, state( AllDists, AllDistInstances, MargVars, SolverVars)) :-
em_loop(Its, Likelihood0, State, MaxError, MaxIts, LikelihoodF, FTables) :- em_loop(Its, Likelihood0, State, MaxError, MaxIts, LikelihoodF, FTables) :-
estimate(State, LPs), estimate(State, LPs),
maximise(State, Tables, LPs, Likelihood), maximise(State, Tables, LPs, Likelihood),
writeln(Likelihood:Its:Likelihood0:Tables), % writeln(Likelihood:Its:Likelihood0:Tables),
( (
( (
abs((Likelihood - Likelihood0)/Likelihood) < MaxError abs((Likelihood - Likelihood0)/Likelihood) < MaxError
@ -205,7 +205,7 @@ compute_parameters([Id-Samples|Dists], [Id-NewTable|Tables], MDistTable, Lik0,
empty_dist(Id, Table0), empty_dist(Id, Table0),
add_samples(Samples, Table0, MDistTable), add_samples(Samples, Table0, MDistTable),
soften_sample(Table0, SoftenedTable), soften_sample(Table0, SoftenedTable),
matrix:matrix_sum(Table0,TotM),writeln(Id-TotM), matrix:matrix_sum(Table0,TotM),
normalise_counts(SoftenedTable, NewTable), normalise_counts(SoftenedTable, NewTable),
compute_likelihood(Table0, NewTable, DeltaLik), compute_likelihood(Table0, NewTable, DeltaLik),
dist_new_table(Id, NewTable), dist_new_table(Id, NewTable),

8
CLPBN/learning/learn_utils.yap
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@ -68,15 +68,15 @@ soften_sample(T0,T) :-
soften_sample(no,T,T). soften_sample(no,T,T).
soften_sample(m_estimate(M), T0, T) :- soften_sample(m_estimate(M), T0, T) :-
matrix_agg_cols(T0,+,Cols),matrix:matrix_to_list(Cols), writeln(Cols), matrix_agg_cols(T0,+,Cols),
matrix_op_to_all(Cols, *, M, R), matrix_op_to_all(Cols, *, M, R),
matrix_op_to_cols(T0,+,R,T). matrix_op_to_cols(T0,R,+,T).
soften_sample(auto_m, T0,T) :- soften_sample(auto_m, T0,T) :-
matrix_agg_cols(T0,+,Cols),matrix:matrix_to_list(Cols), writeln(Cols), matrix_agg_cols(T0,+,Cols),
matrix_sum(Cols,TotM), matrix_sum(Cols,TotM),
M is sqrt(TotM), M is sqrt(TotM),
matrix_op_to_all(Cols, *, M, R), matrix_op_to_all(Cols, *, M, R),
matrix_op_to_cols(T0,+,R,T). matrix_op_to_cols(T0,R,+,T).
soften_sample(laplace,T0,T) :- soften_sample(laplace,T0,T) :-
matrix_op_to_all(T0, +, 1, T). matrix_op_to_all(T0, +, 1, T).