verbose vs verbosity
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@@ -826,17 +826,17 @@ gradient_descent :-
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format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
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format(Handle,"% Iteration, train/test, QueryID, Query, GroundTruth, Prediction %~n",[]),
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format(Handle,"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%~n",[]),
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format_learning(2,'Gradient ',[]),
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findall(FactID,tunable_fact(FactID,GroundTruth),L), length(L,N),
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% leash(0),trace,
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lbfgs_initialize(N,X,0,Solver),
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forall(tunable_fact(FactID,GroundTruth),
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(XZ is 0.5, X[FactID] <== XZ,set_fact_probability(FactID,XZ))),
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problog_flag(sigmoid_slope,Slope),
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lbfgs_set_parameter(min_step, Solver, 0.0),
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lbfgs_set_parameter(min_step, 0.0, Solver),
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lbfgs_run(Solver,BestF),
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format('~2nOptimization done~nWe found a minimum ~4f.~n',[BestF]),
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forall(tunable_fact(FactID,GroundTruth), set_tunable(FactID,X)),
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set_problog_flag(mse_trainset, BestF),
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lbfgs_finalize(Solver).
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set_tunable(I,P) :-
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