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yap-6.3/packages/CLPBN/learning/mle.yap
2012-12-20 23:19:10 +00:00

118 lines
2.8 KiB
Prolog

%
% Maximum likelihood estimator and friends.
%
%
% This assumes we have a single big example.
%
:- module(clpbn_mle,
[learn_parameters/2,
learn_parameters/3,
parameters_from_evidence/3
]).
:- use_module(library('clpbn')).
:- use_module(library('clpbn/learning/learn_utils'),
[run_all/1,
clpbn_vars/2,
normalise_counts/2,
soften_table/2,
normalise_counts/2
]).
:- use_module(library('clpbn/dists'),
[empty_dist/2,
dist_new_table/2
]).
:- use_module(library(matrix),
[matrix_inc/2]).
learn_parameters(Items, Tables) :-
learn_parameters(Items, Tables, []).
%
% full evidence learning
%
learn_parameters(Items, Tables, Extras) :-
run_all(Items),
attributes:all_attvars(AVars),
% sort and incorporate evidence
clpbn_vars(AVars, AllVars),
mk_sample(AllVars, Sample),
compute_tables(Extras, Sample, Tables).
parameters_from_evidence(AllVars, Sample, Extras) :-
mk_sample_from_evidence(AllVars, Sample),
compute_tables(Extras, Sample, Tables).
mk_sample_from_evidence(AllVars, SortedSample) :-
add_evidence2sample(AllVars, Sample),
msort(Sample, SortedSample).
mk_sample(AllVars, SortedSample) :-
add2sample(AllVars, Sample),
msort(Sample, SortedSample).
%
% assumes we have full data, meaning evidence for every variable
%
add2sample([], []).
add2sample([V|Vs],[val(Id,[Ev|EParents])|Vals]) :-
clpbn:get_atts(V, [evidence(Ev),dist(Id,Parents)]),
get_eparents(Parents, EParents),
add2sample(Vs, Vals).
get_eparents([P|Parents], [E|EParents]) :-
clpbn:get_atts(P, [evidence(E)]),
get_eparents(Parents, EParents).
get_eparents([], []).
%
% assumes we ignore variables without evidence or without evidence
% on a parent!
%
add_evidence2sample([], []).
add_evidence2sample([V|Vs],[val(Id,[Ev|EParents])|Vals]) :-
clpbn:get_atts(V, [evidence(Ev),dist(Id,Parents)]),
get_eveparents(Parents, EParents), !,
add_evidence2sample(Vs, Vals).
add_evidence2sample([_|Vs],Vals) :-
add_evidence2sample(Vs, Vals).
get_eveparents([P|Parents], [E|EParents]) :-
clpbn:get_atts(P, [evidence(E)]),
get_eparents(Parents, EParents).
get_eveparents([], []).
compute_tables(Parameters, Sample, NewTables) :-
estimator(Sample, Tables),
add_priors(Parameters, Tables, NewTables).
estimator([], []).
estimator([val(Id,Sample)|Samples], [NewDist|Tables]) :-
empty_dist(Id, NewTable),
id_samples(Id, Samples, IdSamples, MoreSamples),
mle([Sample|IdSamples], NewTable),
soften_table(NewTable, SoftenedTable),
normalise_counts(SoftenedTable, NewDist),
% replace matrix in distribution
dist_new_table(Id, NewDist),
estimator(MoreSamples, Tables).
id_samples(_, [], [], []).
id_samples(Id, [val(Id,Sample)|Samples], [Sample|IdSamples], MoreSamples) :- !,
id_samples(Id, Samples, IdSamples, MoreSamples).
id_samples(_, Samples, [], Samples).
mle([Sample|IdSamples], Table) :-
matrix_inc(Table, Sample),
mle(IdSamples, Table).
mle([], _).