lbfgs
This commit is contained in:
Vitor Santos Costa
parent
d10589d60f
commit
e8d9e71a4e
@ -654,40 +654,44 @@ Unify _NElems_ with the type of the elements in _Matrix_.
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:- use_module(library(mapargs)).
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:- use_module(library(lists)).
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( X <== '[]'(Dims0, array) of V ) :-
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var(V), !,
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foldl( norm_dim, Dims0, Dims, Bases, 1, Size ),
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length( L, Size ),
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X <== matrix( L, [dim=Dims,base=Bases] ).
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( X <== '[]'(Dims0, array) of ints ) :- !,
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foldl( norm_dim, Dims0, Dims, Bases, 1, _Size ),
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matrix_new( ints , Dims, _, X ),
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matrix_base(X, Bases).
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( X <== '[]'(Dims0, array) of floats ) :-
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atom(X), !,
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foldl( norm_dim, Dims0, _Dims, _Bases, 1, Size ),
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static_array( X, Size, [float] ).
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( X <== '[]'(Dims0, array) of floats ) :- !,
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foldl( norm_dim, Dims0, Dims, Bases, 1, _Size ),
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matrix_new( floats , Dims,_, X ),
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matrix_base(X, Bases).
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( X <== '[]'(Dims0, array) of (I:J) ) :- !,
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foldl( norm_dim, Dims0, Dims, Bases, 1, Size ),
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matrix_seq(I, J, Dims, X),
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matrixn_size(X, Size),
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matrix_base(X, Bases).
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( X <== '[]'(Dims0, array) of T ) :-
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var(X),
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( T== ints -> true ; T== floats),
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!,
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foldl( norm_dim, Dims0, Dims, Bases, 1, _Size ),
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matrix_new( T , Dims, _, X ),
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matrix_base(X, Bases).
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( X <== '[]'(Dims0, array) of T ) :-
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atom(X),
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( T== ints -> true ; T== floats),
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!,
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foldl( norm_dim, Dims0, _Dims, _Bases, 1, Size ),
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static_array( X, Size, [float] ).
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( X <== '[]'(Dims0, array) of (I:J) ) :-
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var(X),
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integer(I),
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integer(J),
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!,
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foldl( norm_dim, Dims0, Dims, Bases, 1, Size ),
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matrix_seq(I, J, Dims, X),
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matrixn_size(X, Size),
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matrix_base(X, Bases).
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( X <== '[]'(Dims0, array) of L ) :-
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length( L, Size ), !,
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is_list(L),
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!,
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length( L, Size ), !,
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foldl( norm_dim, Dims0, Dims, Bases, 1, Size ),
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X <== matrix( L, [dim=Dims,base=Bases] ).
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( X <== '[]'(Dims0, array) of Pattern ) :- !,
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array_extension(Pattern, Goal),
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foldl( norm_dim, Dims0, Dims, Bases, 1, Size ),
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call(Goal, Pattern, Dims, Size, L),
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X <== matrix( L, [dim=Dims,base=Bases] ).
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( X <== '[]'(Dims0, array) of Pattern ) :-
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array_extension(Pattern, Goal),
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!,
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foldl( norm_dim, Dims0, Dims, Bases, 1, Size ),
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call(Goal, Pattern, Dims, Size, L),
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X <== matrix( L, [dim=Dims,base=Bases] ).
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( LHS <== RHS ) :-
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rhs(RHS, R),
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set_lhs( LHS, R).
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rhs(RHS, R),
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set_lhs( LHS, R).
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@ -63,11 +63,19 @@ maplist_to_hash([I-V|MapList], H0, Hash) :-
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rb_insert(H0, V, I, H1),
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maplist_to_hash(MapList, H1, Hash).
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bind_maplist([]).
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bind_maplist([Node-Theta|MapList]) :-
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get_prob(Node, ProbFact),
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inv_sigmoid(ProbFact, Theta),
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bind_maplist(MapList).
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prob2log(_X,Slope,FactID,V) :-
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get_fact_probability(FactID, V0),
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inv_sigmoid(V0, Slope, V).
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log2prob(X,Slope,FactID,V) :-
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V0 <== X[FactID],
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sigmoid(V0, Slope, V).
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bind_maplist([], _Slope, _X).
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bind_maplist([Node-Pr|MapList], Slope, X) :-
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Pr <== X[Node],
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bind_maplist(MapList, Slope, X).
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tree_to_grad([], _, Grad, Grad).
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tree_to_grad([Node|Tree], H, Grad0, Grad) :-
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@ -95,15 +103,15 @@ gradient(QueryID, g, Slope) :-
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gradient(QueryID, g, Slope) :-
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gradient(QueryID, l, Slope).
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query_probability( DBDD, Slope, Prob) :-
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query_probability( DBDD, Slope, X, Prob) :-
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DBDD = bdd(Dir, Tree, MapList),
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bind_maplist(MapList),
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bind_maplist(MapList, Slope, X),
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run_sp(Tree, Slope, 1.0, Prob0),
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(Dir == 1 -> Prob0 = Prob ; Prob is 1.0-Prob0).
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query_gradients(bdd(Dir, Tree, MapList),Slope,I,Grad) :-
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bind_maplist(MapList),
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query_gradients(bdd(Dir, Tree, MapList),Slope,X,I,Grad) :-
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bind_maplist(MapList, Slope, X),
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member(I-_, MapList),
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run_grad(Tree, I, Slope, 0.0, Grad0),
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( Dir = 1 -> Grad = Grad0 ; Grad is -Grad0).
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@ -26,9 +26,6 @@ graph2bdd(Query,1,bdd(D,T,Vs)) :-
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bdd_new(TrieList, C),
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bdd_tree(C, BDD),
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BDD = bdd(D,T,_Vs0).
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BDD = bdd(D,T,_Vs0),
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writeln(BDD).
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:- set_problog_flag(init_method,(Q,N,Bdd,user:graph2bdd(Q,N,Bdd))).
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@ -797,8 +797,9 @@ gradient_descent :-
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lbfgs_run(Solver,_BestF),
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lbfgs_finalize(Solver).
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set_fact(FactID, Slope, X ) :-
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get_fact_probability(FactID,Pr),
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set_fact(FactID, Slope, P ) :-
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X <== P[FactID],
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sigmoid(X, Slope, Pr),
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(Pr > 0.99
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->
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NPr = 0.99
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@ -806,9 +807,8 @@ set_fact(FactID, Slope, X ) :-
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Pr < 0.01
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->
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NPr = 0.01 ;
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Pr = NPr ),
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inv_sigmoid(NPr, Slope, XZ),
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X[FactID] <== XZ.
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Pr = NPr ),
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set_fact_probability(FactID, NPr).
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set_tunable(I,Slope,P) :-
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@ -823,49 +823,33 @@ set_tunable(I,Slope,P) :-
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :-
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%Handle = user_error,
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example_count(TrainingExampleCount),
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ExProbs <== array[TrainingExampleCount] of floats,
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LLs <== array[N] of floats,
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Probs <== array[N] of floats,
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go( X,Grad, LLs),
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sum_list( LLs, LLH_Training_Queries).
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go( X,Grad, LLs) :-
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problog_flag(sigmoid_slope,Slope),
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N1 is N-1,
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forall(between(0,N1,I),
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(Grad[I] <== 0.0, S <== X[I], sigmoid(S,Slope, P), Probs[I] <== P)
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),
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forall(
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findall(
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LL,
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compute_gradient(Grad, X, Slope,LL),
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LLs
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).
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compute_gradient( Grad, X, Slope, LL) :-
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user:example(QueryID,_Query,QueryProb),
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recorded(QueryID,BDD,_),
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compute_probability(BDD,Slope,QueryID,ExProbs)
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),
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forall(
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user:example(QueryID,_Query,QueryProb),
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compute_gradient(QueryID, QueryProb,Grad, Probs, Slope,LLs)
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),
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trace,
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LLH_Training_Queries <== sum(LLs).
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compute_probability( BDD, Slope, Id, Probs) :-
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query_probability( BDD, Slope, Prob),
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Probs[Id] <== Prob.
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compute_gradient(QueryID,QueryProb, Grad, Probs, ExProbs, Slope, LLs) :-
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recorded(QueryID,BDD,_),
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BDDProb <== ExProbs[QueryID],
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forall(
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query_gradients(BDD,Slope,I,GradValue),
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gradient_pair(BDDProb, QueryProb, Grad, GradValue, I, Probs)
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),
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query_probability( BDD, Slope, X, BDDProb),
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LL is (BDDProb-QueryProb)*(BDDProb-QueryProb),
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writeln(LL),
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LLs[QueryID] <== LL.
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forall(
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query_gradients(BDD,Slope,X,I,GradValue),
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gradient_pair(BDDProb, QueryProb, Grad, GradValue, Slope, X, I)
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).
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gradient_pair(BDDProb, QueryProb, Grad, GradValue, I, Probs) :-
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gradient_pair(BDDProb, QueryProb, Grad, GradValue, Slope, X, I) :-
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G0 <== Grad[I],
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Prob <== Probs[I],
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log2prob(X,Slope,I,Prob),
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GN is G0-GradValue*2*Prob*(1-Prob)*(QueryProb-BDDProb),
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writeln(G0),
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Grad[I] <== GN.
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Grad[I] <== GN.
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wrap( X, Grad, GradCount) :-
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tunable_fact(FactID,GroundTruth),
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@ -878,22 +862,6 @@ wrap( X, Grad, GradCount) :-
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fail.
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wrap( _X, _Grad, _GradCount).
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prob2log(_X,Slope,FactID,V) :-
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get_fact_probability(FactID, V0),
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inv_sigmoid(V0, Slope, V).
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log2prob(X,Slope,FactID,V) :-
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V0 <== X[FactID],
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sigmoid(V0, Slope, V).
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bind_maplist([], _Slope, _X).
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bind_maplist([Node-Pr|MapList], Slope, X) :-
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Pr <== X[Node],
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bind_maplist(MapList, Slope, X).
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% stop calculate gradient
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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@ -901,7 +869,7 @@ user:progress(FX,_X,_G, _X_Norm,_G_Norm,_Step,_N,_Iteration,_Ls,-1) :-
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FX < 0, !,
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format('stopped on bad FX=~4f~n',[FX]).
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user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N,_Iteration,Ls,0) :-
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roblog_flag(sigmoid_slope,Slope),
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problog_flag(sigmoid_slope,Slope),
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forall(tunable_fact(FactID,_GroundTruth), set_tunable(FactID,Slope,X)),
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current_iteration(CurrentIteration),
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retractall(current_iteration(_)),
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