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Merge branch 'master' of https://github.com/vscosta/yap-6.3

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This commit is contained in:
Vitor Santos Costa
2018-10-13 08:51:54 +01:00
parent c386e9d9f6 a33f2be18b
commit 9dac6e9612
16 changed files with 84 additions and 66 deletions
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30
packages/ProbLog/problog_lbfgs.yap
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@@ -826,20 +826,21 @@ gradient_descent :-
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %~n",[]),
format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
format_learning(2,'Gradient ',[]),
findall(FactID,tunable_fact(FactID,GroundTruth),L), length(L,N),
% leash(0),trace,
lbfgs_initialize(N,X,0,Solver),
forall(tunable_fact(FactID,GroundTruth),
(XZ is 0.5, X[FactID] <== XZ,set_fact_probability(FactID,XZ))),
(XZ is 0.0, X[FactID] <== XZ,sigmoid(XZ,Slope,Pr),set_fact_probability(FactID,Pr))),
problog_flag(sigmoid_slope,Slope),
lbfgs_run(Solver,BestF),
format('~2nOptimization done~nWe found a minimum ~4f.~n',[BestF]),
forall(tunable_fact(FactID,GroundTruth), set_tunable(FactID,X)),
forall(tunable_fact(FactID,GroundTruth), set_tunable(FactID,Slope,X)),
set_problog_flag(mse_trainset, BestF),
lbfgs_finalize(Solver).
set_tunable(I,P) :-
Pr <== P[I],
set_tunable(I,Slope,P) :-
X <== P[I],
sigmoid(X,Slope,Pr),
set_fact_probability(I,Pr).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@@ -848,12 +849,12 @@ set_tunable(I,P) :-
user:evaluate(LLH_Training_Queries, X,Grad,N,_,_) :-
%Handle = user_error,
GradCount <== array[N] of ints,
Probs <== array[N] of floats,
problog_flag(sigmoid_slope,Slope),
Probs = X,
N1 is N-1,
forall(between(0,N1,I),
(Grad[I] <== 0.0) %, sigmoid(X[I],Slope,Probs[I]) )
(Grad[I] <== 0.0, S <== X[I], sigmoid(S,Slope, P), Probs[I] <== P)
),
findall(LL,
compute_grad(Grad, GradCount, Probs, Slope, LL),
@@ -875,15 +876,16 @@ compute_grad(Grad, GradCount, Probs, Slope, LL) :-
%writeln( qprobability(BDD,Slope,BDDProb) ),
forall(
member(I-_, MapList),
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, GradCount)
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, Probs, GradCount)
).
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, GradCount) :-
gradientpair(I, BDD,Slope,BDDProb, QueryProb, Grad, Probs, GradCount) :-
qgradient(I, BDD, Slope, FactID, GradValue),
% writeln(FactID),
G0 <== Grad[FactID],
Prob <== Probs[FactID],
%writeln( GN is G0-GradValue*(QueryProb-BDDProb)),
GN is G0-GradValue*2*(QueryProb-BDDProb),
GN is G0-GradValue*2*Prob*(1-Prob)*(QueryProb-BDDProb),
%writeln(FactID:(G0->GN)),
GC <== GradCount[FactID],
GC1 is GC+1,
@@ -977,10 +979,10 @@ bind_maplist([Node-Pr|MapList], Slope, X) :-
% stop calculate gradient
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
user:progress(FX,X,_G,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
% problog_flag(sigmoid_slope,Slope),
X0 <== X[0],
X1 <== X[1],
format('~d. Iteration : (x0,x1)=(~4f,~4f) f(X)=~4f |X|=~4f |X\'|=~4f Step=~4f Ls=~4f~n',[Iteration,X0 ,X1,FX,X_Norm,G_Norm,Step,Ls]).
problog_flag(sigmoid_slope,Slope),
X0 <== X[0], sigmoid(X0,Slope,P0),
X1 <== X[1], sigmoid(X1,Slope,P1),
format('~d. Iteration : (x0,x1)=(~4f,~4f) f(X)=~4f |X|=~4f |X\'|=~4f Step=~4f Ls=~4f~n',[Iteration,P0 ,P1,FX,X_Norm,G_Norm,Step,Ls]).
%========================================================================