singles
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@ -77,7 +77,7 @@ aux_args([Arg|Args], MVars, [Arg|PArgs], PVars, [Arg|ProtoArgs]) :-
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aux_args([Arg|Args], [Arg|MVars], [PVar|PArgs], [PVar|PVars], ['_'|ProtoArgs]) :-
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aux_args(Args, MVars, PArgs, PVars, ProtoArgs).
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pred_name(Macro, Arity, _ , Name) :-
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pred_name(Macro, Arity, P , Name) :-
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prolog_load_context(file, FullFileName),
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file_base_name( FullFileName, File ),
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prolog_load_context(term_position, Pos),
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@ -263,7 +263,7 @@
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% this is a test to determine whether YAP provides the needed trie library
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:- initialization(
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( predicate_property(trie_disable_hash, imported_from(M)) ->
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( predicate_property(trie_disable_hash, imported_from(_M)) ->
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trie_disable_hash
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; print_message(warning,'The predicate tries:trie_disable_hash/0 does not exist. Please update trie library.')
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)
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@ -649,64 +649,6 @@ init_one_query(QueryID,Query,Type) :-
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recordz(QueryID,bdd(Dir, Grad, MapList),_)
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).
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qprobability(bdd(Dir, Tree, MapList), Slope, Prob) :-
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/* query_probability(21,6.775948e-01). */
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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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qgradient(bdd(Dir, Tree, MapList), Slope, I, Grad) :-
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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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% writeln(grad(QueryID:I:Grad)),
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% assert(query_gradient_intern(QueryID,I,p,Grad)),
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% fail.
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%gradient(QueryID, g, Slope) :-
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% gradient(QueryID, l, Slope).
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maplist_to_hash([], H0, H0).
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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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tree_to_grad([], _, Grad, Grad).
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tree_to_grad([Node|Tree], H, Grad0, Grad) :-
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node_to_gradient_node(Node, H, GNode),
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tree_to_grad(Tree, H, [GNode|Grad0], Grad).
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node_to_gradient_node(pp(P-G,X,L,R), H, gnodep(P,G,X,Id,PL,GL,PR,GR)) :-
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rb_lookup(X,Id,H),
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(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
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(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
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node_to_gradient_node(pn(P-G,X,L,R), H, gnoden(P,G,X,Id,PL,GL,PR,GR)) :-
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rb_lookup(X,Id,H),
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(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
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(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
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run_sp([], _, P0, P0).
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run_sp(gnodep(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
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P is EP*PL+ (1.0-EP)*PR,
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run_sp(Tree, Slope, P, PF).
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run_sp(gnoden(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
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P is EP*PL + (1.0-EP)*(1.0 - PR),
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run_sp(Tree, Slope, P, PF).
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run_grad([], _I, _, G0, G0).
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run_grad([gnodep(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
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P is EP*PL+ (1.0-EP)*PR,
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G0 is EP*GL + (1.0-EP)*GR,
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% don' t forget the -X
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( I == Id -> G is G0+(PL-PR)* EP*(1-EP)*Slope ; G = G0 ),
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run_grad(Tree, I, Slope, G, GF).
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run_grad([gnoden(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
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P is EP*PL + (1.0-EP)*(1.0 - PR),
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G0 is EP*GL - (1.0 - EP) * GR,
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( I == Id -> G is G0+(PL+PR-1)*EP*(1-EP)*Slope ; G = G0 ),
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run_grad(Tree, I, Slope, G, GF).
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@ -901,19 +843,6 @@ set_tunable(I, GroundTruth,P) :-
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format('fact(~d, ~q, ~4f, ~4f).~n',[I,Source,GroundTruth,Pr]),
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set_fact_probability(I,Pr).
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prob2log(X,Slope,FactID) :-
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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) :-
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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-Theta|MapList], Slope, X) :-
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Theta <== X[Node],
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bind_maplist(MapList, Slope, X).
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% start calculate gradient
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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@ -933,10 +862,13 @@ user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :-
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forall(tunable_fact(FactID,GroundTruth), (Z<==X[FactID],W<==Grad[FactID],writeln(FactID:(W->Z)))).
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compute_grad(N, X, Grad, Probs, Slope, Handle, LL) :-
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user:example(QueryID,Query,QueryProb,Type),
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BDD = bdd(Dir, GradTree, MapList),
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user:example(QueryID,_Query,QueryProb,_),
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recorded(QueryID,BDD,_),
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BDD = bdd(_Dir, _GradTree, MapList),
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bind_maplist(MapList, Slope, Probs),
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%writeln( qprobability(BDD,Slope,BDDProb) ),
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qprobability(BDD,Slope,BDDProb),
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%writeln( gradientpair(BDD,Slope,BDDProb, QueryProb, Grad) ),
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gradientpair(BDD,Slope,BDDProb, QueryProb, Grad),
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LL is (((BDDProb)-(QueryProb))**2).
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@ -944,11 +876,85 @@ gradientpair(BDD,Slope,BDDProb, QueryProb, Grad) :-
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qgradient(BDD, Slope, FactID, GradValue),
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% writeln(FactID),
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G0 <== Grad[FactID],
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writeln( GN is G0-GradValue*(QueryProb-BDDProb)), GN is G0-GradValue*(QueryProb-BDDProb),
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writeln(FactID:(G0->GN)),
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%writeln( GN is G0-GradValue*(QueryProb-BDDProb)),
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GN is G0-GradValue*(QueryProb-BDDProb),
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%writeln(FactID:(G0->GN)),
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Grad[FactID] <== GN.
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gradientpair(_BDD,_Slope,_BDDProb, _Grad).
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qprobability(bdd(Dir, Tree, MapList), Slope, Prob) :-
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/* query_probability(21,6.775948e-01). */
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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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qgradient(bdd(Dir, Tree, MapList), Slope, I, Grad) :-
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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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% writeln(grad(QueryID:I:Grad)),
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% assert(query_gradient_intern(QueryID,I,p,Grad)),
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% fail.
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%gradient(QueryID, g, Slope) :-
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% gradient(QueryID, l, Slope).
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maplist_to_hash([], H0, H0).
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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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tree_to_grad([], _, Grad, Grad).
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tree_to_grad([Node|Tree], H, Grad0, Grad) :-
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node_to_gradient_node(Node, H, GNode),
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tree_to_grad(Tree, H, [GNode|Grad0], Grad).
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node_to_gradient_node(pp(P-G,X,L,R), H, gnodep(P,G,X,Id,PL,GL,PR,GR)) :-
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rb_lookup(X,Id,H),
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(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
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(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
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node_to_gradient_node(pn(P-G,X,L,R), H, gnoden(P,G,X,Id,PL,GL,PR,GR)) :-
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rb_lookup(X,Id,H),
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(L == 1 -> GL=0, PL=1 ; L == 0 -> GL = 0, PL=0 ; L = PL-GL),
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(R == 1 -> GR=0, PR=1 ; R == 0 -> GR = 0, PR=0 ; R = PR-GR).
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run_sp([], _, P0, P0).
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run_sp(gnodep(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
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P is EP*PL+ (1.0-EP)*PR,
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run_sp(Tree, Slope, P, PF).
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run_sp(gnoden(P,_G, EP, _Id, PL, _GL, PR, _GR).Tree, Slope, _, PF) :-
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P is EP*PL + (1.0-EP)*(1.0 - PR),
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run_sp(Tree, Slope, P, PF).
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run_grad([], _I, _, G0, G0).
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run_grad([gnodep(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
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P is EP*PL+ (1.0-EP)*PR,
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G0 is EP*GL + (1.0-EP)*GR,
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% don' t forget the -X
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( I == Id -> G is G0+(PL-PR)* EP*(1-EP)*Slope ; G = G0 ),
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run_grad(Tree, I, Slope, G, GF).
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run_grad([gnoden(P,G, EP, Id, PL, GL, PR, GR)|Tree], I, Slope, _, GF) :-
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P is EP*PL + (1.0-EP)*(1.0 - PR),
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G0 is EP*GL - (1.0 - EP) * GR,
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( I == Id -> G is G0+(PL+PR-1)*EP*(1-EP)*Slope ; G = G0 ),
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run_grad(Tree, I, Slope, G, GF).
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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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V0 <== X[Node],
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sigmoid(V0, Slope, V),
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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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@ -179,7 +179,7 @@ lbfgs_initialize(N,X,U,t(N,X,U,Params)) :-
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Clean up the memory.
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*/
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lbfgs_finalize(t(N,X,U,Params)) :-
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lbfgs_finalize(t(_N,X,_U,Params)) :-
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lbfgs_release(X) ,
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lbfgs_release_parameters(Params) .
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@ -66,7 +66,11 @@ static lbfgsfloatval_t evaluate(void *instance, const lbfgsfloatval_t *x,
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// Goal did not succeed
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return FALSE;
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}
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rc = YAP_FloatOfTerm(YAP_GetFromSlot(sl));
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YAP_Term o;
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if (YAP_IsIntTerm((o = YAP_GetFromSlot(sl))))
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rc = YAP_IntOfTerm(o);
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else
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rc = YAP_FloatOfTerm(o);
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YAP_RecoverSlots(1, sl);
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return rc;
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}
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