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