2009-02-16 12:23:29 +00:00
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%
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% Utilities for learning
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%
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:- module(clpbn_learn_utils, [run_all/1,
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clpbn_vars/2,
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normalise_counts/2,
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compute_likelihood/3,
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soften_sample/2,
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soften_sample/3]).
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:- use_module(library(clpbn),
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[clpbn_flag/2]).
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:- use_module(library('clpbn/table'),
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[clpbn_reset_tables/0]).
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:- use_module(library(matrix),
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[matrix_agg_lines/3,
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matrix_op_to_lines/4,
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matrix_agg_cols/3,
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matrix_op_to_cols/4,
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matrix_to_logs/2,
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matrix_op/4,
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matrix_sum/2,
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matrix_to_list/2,
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matrix_op_to_all/4]).
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:- meta_predicate run_all(:).
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run_all([]).
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run_all([G|Gs]) :-
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2009-10-21 00:05:23 +01:00
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run_all(user:[G:Gs]).
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2009-02-16 12:23:29 +00:00
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run_all(M:Gs) :-
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clpbn_reset_tables,
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run_all(Gs,M).
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run_all([],_).
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run_all([G|Gs],M) :-
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2009-10-21 00:05:23 +01:00
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% (G = _:ge(ybr136w,t8,23,-1) -> nb_getval(clpbn_tables, Tab), writeln(Tab) ; true ),
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( call(M:G) -> true ; writeln(bad:M:G), start_low_level_trace, M:G ; halt ),
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2009-02-16 12:23:29 +00:00
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run_all(Gs,M).
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clpbn_vars(Vs,BVars) :-
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get_clpbn_vars(Vs,CVs),
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keysort(CVs,KVs),
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merge_vars(KVs,BVars).
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get_clpbn_vars([],[]).
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get_clpbn_vars([V|GVars],[K-V|CLPBNGVars]) :-
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clpbn:get_atts(V, [key(K)]), !,
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get_clpbn_vars(GVars,CLPBNGVars).
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get_clpbn_vars([_|GVars],CLPBNGVars) :-
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get_clpbn_vars(GVars,CLPBNGVars).
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merge_vars([],[]).
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merge_vars([K-V|KVs],[V|BVars]) :-
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get_var_has_same_key(KVs,K,V,KVs0),
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merge_vars(KVs0,BVars).
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get_var_has_same_key([K-V|KVs],K,V,KVs0) :- !,
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get_var_has_same_key(KVs,K,V,KVs0).
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get_var_has_same_key(KVs,_,_,KVs).
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soften_sample(T0,T) :-
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clpbn_flag(parameter_softening, Soften),
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soften_sample(Soften, T0, T).
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soften_sample(no,T,T).
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soften_sample(m_estimate(M), T0, T) :-
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matrix_agg_cols(T0,+,Cols),
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matrix_op_to_all(Cols, *, M, R),
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matrix_op_to_cols(T0,R,+,T).
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soften_sample(auto_m, T0,T) :-
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matrix_agg_cols(T0,+,Cols),
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matrix_sum(Cols,TotM),
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M is sqrt(TotM),
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matrix_op_to_all(Cols, *, M, R),
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matrix_op_to_cols(T0,R,+,T).
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soften_sample(laplace,T0,T) :-
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matrix_op_to_all(T0, +, 1, T).
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normalise_counts(MAT,NMAT) :-
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matrix_agg_lines(MAT, +, Sum),
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matrix_op_to_lines(MAT, Sum, /, NMAT).
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compute_likelihood(Table0, NewTable, DeltaLik) :-
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matrix_to_logs(NewTable, Logs),
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matrix_to_list(Table0,L1),
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matrix_to_list(Logs,L2),
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sum_prods(L1,L2,0,DeltaLik).
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sum_prods([],[],DeltaLik,DeltaLik).
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sum_prods([0.0|L1],[_|L2],DeltaLik0,DeltaLik) :- !,
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sum_prods(L1,L2,DeltaLik0,DeltaLik).
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sum_prods([Count|L1],[Log|L2],DeltaLik0,DeltaLik) :- !,
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DeltaLik1 is DeltaLik0+Count*Log,
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sum_prods(L1,L2,DeltaLik1,DeltaLik).
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