more EM stuff
This commit is contained in:
Vítor Santos Costa
@@ -8,10 +8,17 @@
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[append/3,
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delete/3]).
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:- reexport(library(clpbn),
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[
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clpbn_flag/2,
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clpbn_flag/3]).
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:- use_module(library(clpbn),
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[clpbn_init_graph/1,
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clpbn_init_solver/5,
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clpbn_run_solver/4,
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pfl_init_solver/6,
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pfl_run_solver/4,
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clpbn_finalize_solver/1,
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conditional_probability/3,
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clpbn_flag/2]).
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@@ -43,6 +50,8 @@
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:- use_module(library(lists),
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[member/2]).
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:- use_module(library(maplist)).
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:- use_module(library(matrix),
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[matrix_add/3,
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matrix_to_list/2]).
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@@ -89,27 +98,22 @@ init_em(Items, State) :-
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clpbn_flag(em_solver, Solver),
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% only used for PCGs
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clpbn_init_graph(Solver),
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% create the ground network
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call_run_all(Items),
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% randomise_all_dists,
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% set initial values for distributions
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uniformise_all_dists,
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setup_em_network(Solver, State).
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setup_em_network(Items, Solver, State).
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setup_em_network(Solver, state( AllDists, AllDistInstances, MargVars, SolverState)) :-
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setup_em_network(Items, Solver, state( AllDists, AllDistInstances, MargKeys, SolverState)) :-
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clpbn:use_parfactors(on), !,
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% get all variables to marginalise
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attributes:all_attvars(AllVars0),
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% and order them
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sort_vars_by_key(AllVars0,AllVars,[]),
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% no, we are in trouble because we don't know the network yet.
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% get the ground network
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generate_network(AllVars, _, Keys, Factors, EList),
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run_examples(Items, Keys, Factors, EList),
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% get the EM CPT connections info from the factors
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generate_dists(Factors, EList, AllDists, AllDistInstances, MargVars),
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generate_dists(Factors, EList, AllDists, AllDistInstances, MargKeys),
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% setup solver, if necessary
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clpbn_init_solver(Solver, MargVars, _AllVars, ground(MargVars, Keys, Factors, EList), SolverState).
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setup_em_network(Solver, state( AllDists, AllDistInstances, MargVars, SolverVars)) :-
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pfl_init_solver(MargKeys, Keys, Factors, EList, SolverState, Solver).
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setup_em_network(Items, Solver, state( AllDists, AllDistInstances, MargVars, SolverVars)) :-
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% create the ground network
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call_run_all(Items),
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% get all variables to marginalise
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attributes:all_attvars(AllVars0),
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% and order them
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@@ -119,6 +123,45 @@ setup_em_network(Solver, state( AllDists, AllDistInstances, MargVars, SolverVars
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% setup solver by doing parameter independent work.
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clpbn_init_solver(Solver, MargVars, AllVars, _, SolverVars).
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run_examples(user:Exs, Keys, Factors, EList) :-
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Exs = [_:_|_], !,
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trace,
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findall(ex(EKs, EFs, EEs), run_example(Exs, EKs, EFs, EEs),
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VExs),
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foldl4(join_example, VExs, [], Keys, [], Factors, [], EList, 0, _).
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run_examples(Items, Keys, Factors, EList) :-
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run_ex(Items, Keys, Factors, EList).
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join_example( ex(EKs, EFs, EEs), Keys0, Keys, Factors0, Factors, EList0, EList, I0, I) :-
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I is I0+1,
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foldl(process_key(I0), EKs, Keys0, Keys),
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foldl(process_factor(I0), EFs, Factors0, Factors),
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foldl(process_ev(I0), EEs, EList0, EList).
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process_key(I0, K, Keys0, [I0:K|Keys0]).
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process_factor(I0, f(Type, Id, Keys), Keys0, [f(Type, Id, NKeys)|Keys0]) :-
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maplist(update_key(I0), Keys, NKeys).
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update_key(I0, K, I0:K).
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process_ev(I0, K=V, Es0, [(I0:K)=V|Es0]).
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run_example([_:Items|_], Keys, Factors, EList) :-
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run_ex(user:Items, Keys, Factors, EList).
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run_example([_|LItems], Keys, Factors, EList) :-
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run_example(LItems, Keys, Factors, EList).
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run_ex(Items, Keys, Factors, EList) :-
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% create the ground network
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call_run_all(Items),
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attributes:all_attvars(AllVars0),
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% and order them
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sort_vars_by_key(AllVars0,AllVars,[]),
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% no, we are in trouble because we don't know the network yet.
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% get the ground network
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generate_network(AllVars, _, Keys, Factors, EList).
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% loop for as long as you want.
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em_loop(Its, Likelihood0, State, MaxError, MaxIts, LikelihoodF, FTables) :-
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estimate(State, LPs),
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@@ -147,37 +190,31 @@ ltables([Id-T|Tables], [Key-LTable|FTables]) :-
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generate_dists(Factors, EList, AllDists, AllInfo, MargVars) :-
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b_hash_new(Ev0),
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elist_to_hash(EList, Ev0, Ev),
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process_factors(Factors, Ev, Dists0),
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sort(Dists0, Dists1),
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group(Dists1, AllDists, AllInfo, MargVars0, []),
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sort(MargVars0, MargVars).
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b_hash_new(Ev0),
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foldl(elist_to_hash, EList, Ev0, Ev),
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maplist(process_factor(Ev), Factors, Dists0),
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sort(Dists0, Dists1),
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group(Dists1, AllDists, AllInfo, MargVars0, []),
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sort(MargVars0, MargVars).
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elist_to_hash([], Ev, Ev).
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elist_to_hash([K=V|EList], Ev0, Ev) :-
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b_hash_insert(Ev0, K, V, Evi),
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elist_to_hash(EList, Evi, Ev).
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elist_to_hash(K=V, Ev0, Ev) :-
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b_hash_insert(Ev0, K, V, Ev).
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process_factors([], _Ev, []).
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process_factors([f(bayes,Id,Ks)|Factors], Ev, [i(Id, Ks, Cases, NonEvs)|AllDistInstances]) :-
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fetch_evidence(Ks, Ev, CompactCases, NonEvs),
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uncompact_cases(CompactCases, Cases),
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process_factors(Factors, Ev, AllDistInstances).
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process_factor(Ev, f(bayes,Id,Ks), i(Id, Ks, Cases, NonEvs)) :-
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foldl( fetch_evidence(Ev), Ks, CompactCases, [], NonEvs),
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uncompact_cases(CompactCases, Cases).
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fetch_evidence([], _Ev, [], []).
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fetch_evidence([K|Ks], Ev, [E|CompactCases], NonEvs) :-
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b_hash_lookup(K, E, Ev), !,
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fetch_evidence(Ks, Ev, CompactCases, NonEvs).
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fetch_evidence([K|Ks], Ev, [Ns|CompactCases], [K|NonEvs]) :-
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fetch_evidence(Ev, K, E, NonEvs, NonEvs) :-
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b_hash_lookup(K, E, Ev), !.
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fetch_evidence(_Ev, _Id:K, Ns, NonEvs, [K|NonEvs]) :-
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pfl:skolem(K,D), !,
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foldl(domain_to_number, D, Ns, 0, _).
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fetch_evidence(_Ev, K, Ns, NonEvs, [K|NonEvs]) :-
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pfl:skolem(K,D),
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domain_to_numbers(D,0,Ns),
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fetch_evidence(Ks, Ev, CompactCases, NonEvs).
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foldl(domain_to_number, D, Ns, 0, _).
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domain_to_numbers([],_,[]).
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domain_to_numbers([_|D],I0,[I0|Ns]) :-
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I is I0+1,
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domain_to_numbers(D,I,Ns).
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domain_to_number(_, I0, I0, I) :-
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I is I0+1.
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% collect the different dists we are going to learn next.
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@@ -213,24 +250,6 @@ all_dists([V|AllVars], AllVars0, [i(Id, [V|Parents], Cases, Hiddens)|Dists]) :-
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uncompact_cases(CompactCases, Cases),
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all_dists(AllVars, AllVars0, Dists).
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find_variables([], _AllVars0, []).
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find_variables([K|PKeys], AllVars0, [Parent|Parents]) :-
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find_variable(K, AllVars0, Parent),
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find_variables(PKeys, AllVars0, Parents).
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%
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% in clp(bn) the whole network is constructed when you evaluate EM. In
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% pfl, we want to delay execution until as late as possible.
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% we just create a new variable and hope for the best.
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%
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%
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find_variable(K, [], Parent) :-
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clpbn:put_atts(Parent, [key(K)]).
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find_variable(K, [Parent|_AllVars0], Parent) :-
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clpbn:get_atts(Parent, [key(K0)]), K0 =@= K, !.
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find_variable(K, [_|AllVars0], Parent) :-
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find_variable(K, AllVars0, Parent).
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generate_hidden_cases([], [], []).
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generate_hidden_cases([V|Parents], [P|Cases], Hiddens) :-
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clpbn:get_atts(V, [evidence(P)]), !,
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@@ -280,19 +299,21 @@ compact_mvars([X1,X2|MargVars], CMVars) :- X1 == X2, !,
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compact_mvars([X|MargVars], [X|CMVars]) :- !,
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compact_mvars(MargVars, CMVars).
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estimate(state(_, _, Margs, SolverState), LPs) :-
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clpbn:use_parfactors(on), !,
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clpbn_flag(em_solver, Solver),
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pfl_run_solver(Margs, LPs, SolverState, Solver).
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estimate(state(_, _, Margs, SolverState), LPs) :-
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clpbn_flag(em_solver, Solver),
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clpbn_run_solver(Solver, Margs, LPs, SolverState).
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maximise(state(_,DistInstances,MargVars,_), Tables, LPs, Likelihood) :-
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rb_new(MDistTable0),
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create_mdist_table(MargVars, LPs, MDistTable0, MDistTable),
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foldl(create_mdist_table, MargVars, LPs, MDistTable0, MDistTable),
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compute_parameters(DistInstances, Tables, MDistTable, 0.0, Likelihood, LPs:MargVars).
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create_mdist_table([],[],MDistTable,MDistTable).
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create_mdist_table([Vs|MargVars],[Ps|LPs],MDistTable0,MDistTable) :-
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rb_insert(MDistTable0, Vs, Ps, MDistTableI),
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create_mdist_table(MargVars, LPs, MDistTableI ,MDistTable).
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create_mdist_table(Vs, Ps, MDistTable0, MDistTable) :-
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rb_insert(MDistTable0, Vs, Ps, MDistTable).
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compute_parameters([], [], _, Lik, Lik, _).
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compute_parameters([Id-Samples|Dists], [Id-NewTable|Tables], MDistTable, Lik0, Lik, LPs:MargVars) :-
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