115 lines
3.1 KiB
Prolog
115 lines
3.1 KiB
Prolog
%
|
|
% The world famous EM algorithm, in a nutshell
|
|
%
|
|
|
|
:- module(clpbn_em, [em/6]).
|
|
|
|
:- use_module(library(lists),
|
|
[append/3]).
|
|
|
|
:- use_module(library('clpbn/learning/learn_utils'),
|
|
[run_all/1,
|
|
clpbn_vars/2,
|
|
normalise_counts/2]).
|
|
|
|
em(Items, MaxError, MaxIts, Tables, Likelihood) :-
|
|
init_em(Items, State),
|
|
em_loop(0, 0.0, state(AllVars,AllDists), MaxError, MaxIts, Likelihood),
|
|
get_tables(State, Tables).
|
|
|
|
% This gets you an initial configuration. If there is a lot of evidence
|
|
% tables may be filled in close to optimal, otherwise they may be
|
|
% close to uniform.
|
|
% it also gets you a run for random variables
|
|
init_em(Items, state(AllVars, AllDists, AllDistInstances)) :-
|
|
run_all(Items),
|
|
different_dists(AllVars, AllDists, AllDistInstances).
|
|
|
|
% loop for as long as you want.
|
|
em_loop(MaxIts, Likelihood, State, _, _, MaxIts, Likelihood) :- !.
|
|
em_loop(Its, Likelihood0, State, MaxError, MaxIts, LikelihoodF) :-
|
|
estimate(State),
|
|
maximise(State, Likelihood),
|
|
(
|
|
(
|
|
(Likelihood - Likelihood0)/Likelihood < MaxError
|
|
;
|
|
Its == MaxIts
|
|
)
|
|
->
|
|
LikelihoodF = Likelihood
|
|
;
|
|
Its1 is Its+1,
|
|
em_loop(Its1, Likelihood, State, MaxError, MaxIts, LikelihoodF)
|
|
).
|
|
|
|
% collect the different dists we are going to learn next.
|
|
different_dists(AllVars, AllDists, AllInfo) :-
|
|
all_dists(AllVars, Dists0, AllInfo),
|
|
sort(Dists0, Dists1),
|
|
group(Dists1, AllInfo).
|
|
|
|
group([], []).
|
|
group([i(Id,V,Ps)|Dists1], [Id-[[V|Ps]|Extra]|AllInfo]) :-
|
|
same_id(Dists1, Id, Extra, Rest),
|
|
group(Rest, AllInfo).
|
|
|
|
same_id([i(Id,V,Ps)|Dists1], Id, [[V|Ps]|Extra], Rest) :- !,
|
|
same_id(Dists1, Id, Extra, Rest).
|
|
same_id(Dists, _, [], Dists).
|
|
|
|
all_dists([], [], []).
|
|
all_dists([V|AllVars], Dists, [i(Id, AllInfo, Parents)|AllInfo]) :-
|
|
clpbn:get_atts(V, [dist(Id,_)]),
|
|
with_evidence(V, Id, Dists, Dists0), !,
|
|
all_dists(AllVars, Dists0, AllInfo).
|
|
|
|
with_evidence(V, Id) -->
|
|
{clpbn:get_atts(V, [evidence(Pos)]) }, !,
|
|
{ dist_pos2bin(Pos, Id, Bin) }.
|
|
with_evidence(V, Id) -->
|
|
[d(V,Id)].
|
|
|
|
estimate(state(Vars,Info,_)) :-
|
|
clpbn_solve_graph(Vars, OVars),
|
|
marg_vars(Info, Vars).
|
|
|
|
marg_vars([], _).
|
|
marg_vars([d(V,Id)|Vars], AllVs) :-
|
|
clpbn_marginalise_in_vars(V, AllVs),
|
|
marg_vars(Vars, AllVs).
|
|
|
|
maximise(state(_,_,DistInstances), Tables, Likelihood) :-
|
|
compute_parameters(DistInstances, Tables, 0.0, Likelihood).
|
|
|
|
compute_parameters([], [], Lik, Lik).
|
|
compute_parameters([Id-Samples|Dists], [Tab|Tables], Lik0, Lik) :-
|
|
empty_dist(Id, NewTable),
|
|
add_samples(Samples, NewTable),
|
|
normalise_table(Id, NewTable),
|
|
compute_parameters(Dists, Tables, Lik0, Lik).
|
|
|
|
add_samples([], _).
|
|
add_samples([S|Samples], Table) :-
|
|
run_sample(S, 1.0, Pos, Tot),
|
|
matrix_add(Table, Pos, Tot),
|
|
fail.
|
|
add_samples([_|Samples], Table) :-
|
|
add_samples(Samples, Table).
|
|
|
|
run_sample([], Tot, [], Tot).
|
|
run_sample([V|S], W0, [P|Pos], Tot) :-
|
|
{clpbn:get_atts(V, [evidence(P)]) }, !,
|
|
run_sample(S, W0, Pos, Tot).
|
|
run_sample([V|S], W0, [P|Pos], Tot) :-
|
|
{clpbn_display:get_atts(V, [posterior,(_,_,Ps,_)]) },
|
|
count_cases(Ps, 0, D0, P),
|
|
W1 is D0*W0,
|
|
run_sample(S, W1, Pos, Tot).
|
|
|
|
count_cases([D0|Ps], I0, D0, I0).
|
|
count_cases([_|Ps], I0, P, W1) :-
|
|
I is I0+1,
|
|
count_cases(Ps, I, P, W1).
|
|
|