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LBFGS

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This commit is contained in:
Vitor Santos Costa 2018-10-05 10:26:34 +01:00
parent 724681dde8
commit 5ea98bcf53
7 changed files with 1346 additions and 192 deletions
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8
packages/ProbLog/problog_examples/learn_graph.pl
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@ -14,8 +14,8 @@
% will run 20 iterations of learning with default settings % will run 20 iterations of learning with default settings
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
:- use_module(library(problog)). :- use_module(library(matrix)).
:- use_module(library(problog_learning)). :- use_module(('../problog_lbfgs')).
%%%% %%%%
% background knowledge % background knowledge
@ -99,3 +99,7 @@ test_example(33,path(5,4),0.57).
test_example(34,path(6,4),0.51). test_example(34,path(6,4),0.51).
test_example(35,path(6,5),0.69). test_example(35,path(6,5),0.69).
:- set_problog_flag(init_method,(Query,_,BDD,
problog_exact_lbdd(user:Query,BDD))).

30
packages/ProbLog/problog_lbdd.yap
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@ -6,24 +6,23 @@
:- use_module(library(bdd)). :- use_module(library(bdd)).
:- use_module(library(bhash)). :- use_module(library(bhash)).
problog_exact_lbdd(Goal,Prob,Status) :- problog_exact_lbdd(Goal,BDD) :-
problog_control(on, exact), problog_low_lbdd(Goal, 0, _, _, BDD).
problog_low_lbdd(Goal,0,Prob,Status),
problog_control(off, exact).
problog_low_lbdd(Goal, Threshold, _, _) :- problog_low_lbdd(Goal, Threshold, _, _, _) :-
init_problog_low(Threshold), init_problog_low(Threshold),
problog_control(off, up), problog_control(off, up),
timer_start(sld_time), timer_start(sld_time),
problog_call(Goal), problog_call(Goal),
add_solution, add_solution,
fail. fail.
problog_low_lbdd(_, _, Prob, ok) :- problog_low_lbdd(_, _, Prob, ok, bdd(Dir, Tree, MapList)) :-
timer_stop(sld_time,SLD_Time), timer_stop(sld_time,SLD_Time),
problog_var_set(sld_time, SLD_Time), problog_var_set(sld_time, SLD_Time),
nb_getval(problog_completed_proofs, Trie_Completed_Proofs), nb_getval(problog_completed_proofs, Trie_Completed_Proofs),
tabled_trie_to_bdd(Trie_Completed_Proofs, BDD, MapList), trie_to_bdd(Trie_Completed_Proofs, BDD, MapList),
bind_maplist(MapList, BoundVars), bind_maplist(MapList, BoundVars),
bdd_tree(BDD, bdd(Dir, Tree, _Vars)),
bdd_to_probability_sum_product(BDD, BoundVars, Prob), bdd_to_probability_sum_product(BDD, BoundVars, Prob),
(problog_flag(verbose, true)-> (problog_flag(verbose, true)->
problog_statistics problog_statistics
@ -73,6 +72,23 @@ problog_fl_bdd(_,Prob) :-
(problog_flag(retain_tables, true) -> retain_tabling; true), (problog_flag(retain_tables, true) -> retain_tabling; true),
clear_tabling. clear_tabling.
problog_full_bdd(Goal,_K, _) :-
init_problog_low(0.0),
problog_control(off, up),
timer_start(sld_time),
problog_call(Goal),
add_solution,
fail.
problog_full_bdd(_,Prob) :-
timer_stop(sld_time,SLD_Time),
problog_var_set(sld_time, SLD_Time),
nb_getval(problog_completed_proofs, Trie_Completed_Proofs),
tabled_trie_to_bdd(Trie_Completed_Proofs, BDD, MapList),
bind_maplist(MapList, BoundVars),
bdd_to_probability_sum_product(BDD, BoundVars, Prob),
(problog_flag(retain_tables, true) -> retain_tabling; true),
clear_tabling.
bind_maplist([], []). bind_maplist([], []).
bind_maplist([Node-_|MapList], [ProbFact|BoundVars]) :- bind_maplist([Node-_|MapList], [ProbFact|BoundVars]) :-
get_fact_probability(Node,ProbFact), get_fact_probability(Node,ProbFact),

1012
packages/ProbLog/problog_lbfgs.yap Normal file
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27
packages/yap-lbfgs/ex1.pl
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@ -22,35 +22,36 @@
:- use_module(library(matrix)). :- use_module(library(matrix)).
% This is the call back function which evaluates F and the gradient of F % This is the call back function which evaluates F and the gradient of F
evaluate(FX,X,G,_N,_Step) :- evaluate(FX,X,G,_N,_Step,_User) :-
X0 <== X[0], X0 <== X[0],
FX is sin(X0), FX is sin(X0),
G0 is cos(X0), G0 is cos(X0),
G[0] <== G0. G[0] <== G0.
% This is the call back function which is invoked to report the progress % This is the call back function which is invoked to report the progress
% if the last argument is set to anywhting else than 0, the optimizer will % if the last argument is set to anything else than 0, the lbfgs will
% stop right now % stop right now
progress(FX,X,G,X_Norm,G_Norm,Step,_N,Iteration,Ls, 0) :- progress(FX,X,G,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
X0 <== X[0], X0 <== X[0],
format('~d. Iteration : x0=~4f f(X)=~4f |X|=~4f |X\'|=~4f Step=~4f Ls=~4f~n', format('~d. Iteration : x0=~4f f(X)=~4f |X|=~4f
[Iteration,X0,FX,X_Norm,G_Norm,Step,Ls]). |X\'|=~4f Step=~4f Ls=~4f~n',
[Iteration,X0,FX,X_Norm,G_Norm,Step,Ls]).
demo :- demo :-
format('Optimizing the function f(x0) = sin(x0)~n',[]), format('Optimizing the function f(x0) = sin(x0)~n',[]),
optimizer_initialize(1,X,Status), lbfgs_initialize(1,X,0,Solver),
StartX is random*10, StartX is random*10,
format('We start the search at the random position x0=~5f~2n',[StartX]), format('We start the search at the random position x0=~5f~2n',[StartX]),
X[0] <== StartX, X[0] <== StartX,
lbfgs_run(Solver,BestF,Status),
optimizer_run(Status, BestF, BestX0, O),
BestX0 <== X[0], BestX0 <== X[0],
optimizer_finalize(Status), lbfgs_finalize(Solver),
format('~2nOptimization done~nWe found a minimum at f(~f)=~f~2nLBFGS Status=~w~n',[BestX0,BestF,O]). format('~2nOptimization done~nWe found a minimum at
f(~f)=~f~2nLBFGS Status=~w~n',[BestX0,BestF,Status]).

24
packages/yap-lbfgs/ex2.pl
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@ -20,13 +20,14 @@
:- use_module(library(lbfgs)). :- use_module(library(lbfgs)).
:- use_module(library(matrix)). :- use_module(library(matrix)).
f(X0,X1,FX) :-
FX is (X0-2)*(X0-2) + (X1-1)*(X1-1).
% This is the call back function which evaluates F and the gradient of F % This is the call back function which evaluates F and the gradient of F
evaluate(FX,X,G,_N,_Step) :- evaluate(FX,X,G,_N,_Step,_U) :-
X0 <== X[0], X0 <== X[0],
X1 <== X[1], X1 <== X[1],
f(X0,X1,FX),
FX is (X0-2)*(X0-2) + (X1-1)*(X1-1),
G0 is 2*(X0-2), G0 is 2*(X0-2),
G1 is 2*(X1-2), G1 is 2*(X1-2),
G[0] <== G0, G[0] <== G0,
@ -38,28 +39,27 @@ evaluate(FX,X,G,_N,_Step) :-
progress(FX,X,_G,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :- progress(FX,X,_G,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
X0 <== X[0], X0 <== X[0],
X1 <== X[1], 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]). 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]).
demo :- demo :-
format('Optimizing the function f(x0,x1) = (x0-2)^2 + (x1-1)^2~n',[]), format('Optimizing the function f(x0,x1) = (x0-2)^2 + (x1-1)^2~n',[]),
optimizer_initialize(2,X,Status),
lbfgs_initialize(2,X,0,Solver),
StartX0 is random*1000-500, StartX0 is random*1000-500,
StartX1 is random*1000-500, StartX1 is random*1000-500,
format('We start the search at the random position (x0,x1)=(~5f,~5f)~2n',[StartX0,StartX1]), format('We start the search at the random position (x0,x1)=(~5f,~5f)~2n',[StartX0,StartX1]),
X[0] <== StartX0, X[0] <== StartX0,
X[1] <== StartX1, X[1] <== StartX1,
lbfgs_run(Solver,BestF,Status),
optimizer_run(Status,BestF,BestX0, O),
BestX0 <== X[0], BestX0 <== X[0],
BestX1 <== X[1], BestX1 <== X[1],
optimizer_finalize, optimizer_finalize(Solver),
format('~2nOptimization done~nWe found a minimum at f(~f,~f)=~f~2nLBFGS Status=~w~n',[BestX0,BestX1,BestF,Status]). format('~2nOptimization done~nWe found a minimum at f(~f,~f)=~f~2nLBFGS Status=~w~n',[BestX0,BestX1,BestF,Status]).

126
packages/yap-lbfgs/lbfgs.pl
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@ -20,14 +20,16 @@
:- module(lbfgs,[optimizer_initialize/3, :- module(lbfgs,[lbfgs_initialize/3,
optimizer_run/4, lbfgs_initialize/4,
lbfgs_run/2,
optimizer_finalize/1, lbfgs_finalize/1,
optimizer_set_parameter/3, lbfgs_set_parameter/3,
optimizer_get_parameter/3, lbfgs_get_parameter/3,
optimizer_parameters/1]). lbfgs_parameters/0,
lbfgs_parameters/1]).
% switch on all the checks to reduce bug searching time % switch on all the checks to reduce bug searching time
% :- yap_flag(unknown,error). % :- yap_flag(unknown,error).
@ -48,9 +50,11 @@ minimization problem:
~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~~~~~~~~~~~~
### Contact ### Contact YAP-LBFGS has been developed by Bernd Gutmann. In case you
YAP-LBFGS has been developed by Bernd Gutmann. In case you publish something using YAP-LBFGS, please give credit to me and to libLBFGS. And if you find YAP-LBFGS useful, or if you find a bug, or if you publish something using YAP-LBFGS, please give credit to me and to
port it to another system, ... please send me an email. libLBFGS. And if you find YAP-LBFGS useful, or if you find a bug, or
if you port it to another system, ... please send me an email.
### License ### License
@ -72,9 +76,9 @@ it by
:-use_module(library(lbfgs)). :-use_module(library(lbfgs)).
~~~~ ~~~~
+ use optimizer_set_paramater(Name,Value) to change parameters + use lbfgs_set_paramater(Name,Value) to change parameters
+ use optimizer_get_parameter(Name,Value) to see current parameters + use lbfgs_get_parameter(Name,Value) to see current parameters
+ use optimizer_parameters to print this overview + use lbfgs_parameters to print this overview
@ -89,17 +93,18 @@ calculates `f(x0)` and the gradient `d/dx0 f=cos(x0)`.
:- use_module(lbfgs). :- use_module(lbfgs).
% This is the call back function which evaluates F and the gradient of F % This is the call back function which evaluates F and the gradient of F
evaluate(FX,_N,_Step) :- evaluate(FX,X,G,_N,_Step,_User) :-
optimizer_get_x(0,X0), X0 <== X[0],
FX is sin(X0), F is sin(X0),
FX[0] <== F,
G0 is cos(X0), G0 is cos(X0),
optimizer_set_g(0,G0). G[0] <== G0.
% This is the call back function which is invoked to report the progress % This is the call back function which is invoked to report the progress
% if the last argument is set to anything else than 0, the optimizer will % if the last argument is set to anything else than 0, the lbfgs will
% stop right now % stop right now
progress(FX,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :- progress(FX,X,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
optimizer_get_x(0,X0), X0 <== X[0],
format('~d. Iteration : x0=~4f f(X)=~4f |X|=~4f format('~d. Iteration : x0=~4f f(X)=~4f |X|=~4f
|X\'|=~4f Step=~4f Ls=~4f~n', |X\'|=~4f Step=~4f Ls=~4f~n',
[Iteration,X0,FX,X_Norm,G_Norm,Step,Ls]). [Iteration,X0,FX,X_Norm,G_Norm,Step,Ls]).
@ -108,16 +113,16 @@ progress(FX,X_Norm,G_Norm,Step,_N,Iteration,Ls,0) :-
demo :- demo :-
format('Optimizing the function f(x0) = sin(x0)~n',[]), format('Optimizing the function f(x0) = sin(x0)~n',[]),
optimizer_initialize(1,evaluate,progress), lbfgs_initialize(1,X,0,Solver),
StartX is random*10, StartX is random*10,
format('We start the search at the random position x0=~5f~2n',[StartX]), format('We start the search at the random position x0=~5f~2n',[StartX]),
optimizer_set_x(0,StartX), X[0] <== StartX,
optimizer_run(BestF,Status), lbfgs_run(Solver,BestF,Status),
optimizer_get_x(0,BestX0), BestX0 <== X[0],
optimizer_finalize, lbfgs_finalize(Solver),
format('~2nOptimization done~nWe found a minimum at format('~2nOptimization done~nWe found a minimum at
f(~f)=~f~2nLBFGS Status=~w~n',[BestX0,BestF,Status]). f(~f)=~f~2nLBFGS Status=~w~n',[BestX0,BestF,Status]).
~~~~~ ~~~~~
@ -151,49 +156,54 @@ yes
:- load_foreign_files(['libLBFGS'],[],'init_lbfgs_predicates'). :- load_foreign_files(['libLBFGS'],[],'init_lbfgs_predicates').
/** @pred optimizer_initialize(+N,+Evaluate,+Progress) /** @pred lbfgs_initialize(+N, -SolverInfo)
The same as before, except that the user module is the default The same as before, except that the user module is the default
value. value.
Example Example
~~~~ ~~~~
optimizer_initialize(1) lbfgs_initialize(1, Block)
~~~~~ ~~~~~
*/ */
lbfgs_initialize(N,X,t(N,X,U,Params)) :-
lbfgs_initialize(N,X,0,t(N,X,U,Params)).
optimizer_initialize(N,X,t(N,X,XO,Params)) :- lbfgs_initialize(N,X,U,t(N,X,U,Params)) :-
lbfgs_defaults(Params),
integer(N), integer(N),
N>0, N>0,
% check whether there are such call back functions % check whether there are such call back functions
optimizer_reserve_memory(N,X,XO,Params). lbfgs_grab(N,X).
% install call back predicates in the user module which call % install call back predicates in the user module which call
% the predicates given by the arguments % the predicates given by the arguments
/** @pred optimizer_finalize/0
/** @pred lbfgs_finalize/0
Clean up the memory. Clean up the memory.
*/ */
optimizer_finalize(t(N,X,XO,Params)) :- lbfgs_finalize(t(N,X,U,Params)) :-
initialized, initialized,
optimizer_free_memory(X,XO,Params) , lbfgs_release(X) ,
lbfgs_release_parameters(Params) ,
retractall(initialized). retractall(initialized).
/** @pred optimizer_run/3
/** @pred lbfgs_run/2
Do the work. Do the work.
*/ */
optimizer_run(t(N,X,XO,Params),FX,XO,Status) :- lbfgs_run(t(N,X,U,Params),FX) :-
optimizer_run(N,X, FX, XO, Status, Params). lbfgs(N,X, Params, U, FX).
/** @pred optimizer_parameters/1 /** @pred lbfgs_parameters/1
Prints a table with the current parameters. See the <a href="http://www.chokkan.org/software/liblbfgs/structlbfgs__parameter__t.html#_details">documentation Prints a table with the current parameters. See the <a href="http://www.chokkan.org/software/liblbfgs/structlbfgs__parameter__t.html#_details">documentation
of libLBFGS</a> for the meaning of each parameter. of libLBFGS</a> for the meaning of each parameter.
~~~~ ~~~~
?- optimizer_parameters. ?- lbfgs_parameters.
========================================================================================== ==========================================================================================
Type Name Value Description Type Name Value Description
========================================================================================== ==========================================================================================
@ -215,22 +225,26 @@ int orthantwise_end -1 End index for computing the L1 norm
========================================================================================== ==========================================================================================
~~~~ ~~~~
*/ */
optimizer_parameterse(t(X,XO,Params)) :- lbfgs_parameters :-
optimizer_get_parameter(m,M ,Params), lbfgs_defaults(Params),
optimizer_get_parameter(epsilon,Epsilon ,Params), lbfgs_parameters(t(_X,_,_,Params)).
optimizer_get_parameter(past,Past ,Params),
optimizer_get_parameter(delta,Delta ,Params), lbfgs_parameters(t(_,_,_,Params)) :-
optimizer_get_parameter(max_iterations,Max_Iterations ,Params), lbfgs_get_parameter(m,M ,Params),
optimizer_get_parameter(linesearch,Linesearch ,Params), lbfgs_get_parameter(epsilon,Epsilon ,Params),
optimizer_get_parameter(max_linesearch,Max_Linesearch ,Params), lbfgs_get_parameter(past,Past ,Params),
optimizer_get_parameter(min_step,Min_Step ,Params), lbfgs_get_parameter(delta,Delta ,Params),
optimizer_get_parameter(max_step,Max_Step ,Params), lbfgs_get_parameter(max_iterations,Max_Iterations ,Params),
optimizer_get_parameter(ftol,Ftol ,Params), lbfgs_get_parameter(linesearch,Linesearch ,Params),
optimizer_get_parameter(gtol,Gtol ,Params), lbfgs_get_parameter(max_linesearch,Max_Linesearch ,Params),
optimizer_get_parameter(xtol,Xtol ,Params), lbfgs_get_parameter(min_step,Min_Step ,Params),
optimizer_get_parameter(orthantwise_c,Orthantwise_C ,Params), lbfgs_get_parameter(max_step,Max_Step ,Params),
optimizer_get_parameter(orthantwise_start,Orthantwise_Start ,Params), lbfgs_get_parameter(ftol,Ftol ,Params),
optimizer_get_parameter(orthantwise_end,Orthantwise_End ,Params), lbfgs_get_parameter(gtol,Gtol ,Params),
lbfgs_get_parameter(xtol,Xtol ,Params),
lbfgs_get_parameter(orthantwise_c,Orthantwise_C ,Params),
lbfgs_get_parameter(orthantwise_start,Orthantwise_Start ,Params),
lbfgs_get_parameter(orthantwise_end,Orthantwise_End ,Params),
format('/******************************************************************************************~n',[] ), format('/******************************************************************************************~n',[] ),
print_param('Name','Value','Description','Type' ,Params), print_param('Name','Value','Description','Type' ,Params),
@ -251,9 +265,9 @@ optimizer_parameterse(t(X,XO,Params)) :-
print_param(orthantwise_start,Orthantwise_Start,'Start index for computing the L1 norm of the variables.',int ,Params), print_param(orthantwise_start,Orthantwise_Start,'Start index for computing the L1 norm of the variables.',int ,Params),
print_param(orthantwise_end,Orthantwise_End,'End index for computing the L1 norm of the variables.',int ,Params), print_param(orthantwise_end,Orthantwise_End,'End index for computing the L1 norm of the variables.',int ,Params),
format('******************************************************************************************/~n',[]), format('******************************************************************************************/~n',[]),
format(' use optimizer_set_paramater(Name,Value) to change parameters~n',[]), format(' use lbfgs_set_parameter(Name,Value,Solver) to change parameters~n',[]),
format(' use optimizer_get_parameter(Name,Value) to see current parameters~n',[]), format(' use lbfgs_get_parameter(Name,Value,Solver) to see current parameters~n',[]),
format(' use optimizer_parameters to print this overview~2n',[]). format(' use lbfgs_parameters to print this overview~2n',[]).
print_param(Name,Value,Text,Dom) :- print_param(Name,Value,Text,Dom) :-

311
packages/yap-lbfgs/yap_lbfgs.c
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@ -24,11 +24,11 @@
// These constants describe the internal state // These constants describe the internal state
#define OPTIMIZER_STATUS_NONE 0 #define LBFGS_STATUS_NONE 0
#define OPTIMIZER_STATUS_INITIALIZED 1 #define LBFGS_STATUS_INITIALIZED 1
#define OPTIMIZER_STATUS_RUNNING 2 #define LBFGS_STATUS_RUNNING 2
#define OPTIMIZER_STATUS_CB_EVAL 3 #define LBFGS_STATUS_CB_EVAL 3
#define OPTIMIZER_STATUS_CB_PROGRESS 4 #define LBFGS_STATUS_CB_PROGRESS 4
X_API void init_lbfgs_predicates( void ) ; X_API void init_lbfgs_predicates( void ) ;
@ -44,53 +44,39 @@ static lbfgsfloatval_t evaluate(
) )
{ {
YAP_Term call; YAP_Term call;
YAP_Term v, a1;
YAP_Bool result; YAP_Bool result;
YAP_Int s1; lbfgsfloatval_t rc;
YAP_Term t[6], t2[2];
YAP_Term t[5], t2[2]; t[0] = YAP_MkIntTerm((YAP_Int)&rc);
t[0] = YAP_MkApplTerm(ffloats, 1, t);
t[0] = v = YAP_MkVarTerm();
t[1] = YAP_MkIntTerm((YAP_Int)x); t[1] = YAP_MkIntTerm((YAP_Int)x);
t[1] = YAP_MkApplTerm(ffloats, 1, t+1); t[1] = YAP_MkApplTerm(ffloats, 1, t+1);
t[2] = YAP_MkIntTerm((YAP_Int)g_tmp); t[2] = YAP_MkIntTerm((YAP_Int)g_tmp);
t[2] = YAP_MkApplTerm(ffloats, 1, t+2); t[2] = YAP_MkApplTerm(ffloats, 1, t+2);
t[3] = YAP_MkIntTerm(n); t[3] = YAP_MkIntTerm(n);
t[4] = YAP_MkFloatTerm(step); t[4] = YAP_MkFloatTerm(step);
t[5] = YAP_MkIntTerm((YAP_Int)instance);
t2[0] = tuser; t2[0] = tuser;
t2[1] = YAP_MkApplTerm(fevaluate, 5, t); t2[1] = YAP_MkApplTerm(fevaluate, 6, t);
call = YAP_MkApplTerm( fmodule, 2, t2 ); call = YAP_MkApplTerm( fmodule, 2, t2 );
s1 = YAP_InitSlot(v); // s1 = YAP_InitSlot(v);
//optimizer_status=OPTIMIZER_STATUS_CB_EVAL; //lbfgs_status=LBFGS_STATUS_CB_EVAL;
result=YAP_RunGoal(call); result=YAP_RunGoalOnce(call);
//optimizer_status=OPTIMIZER_STATUS_RUNNING; //lbfgs_status=LBFGS_STATUS_RUNNING;
if (result==FALSE) { if (result==FALSE) {
printf("ERROR: the evaluate call failed in YAP.\n"); printf("ERROR: the evaluate call failed in YAP.\n");
// Goal did not succeed // Goal did not succeed
YAP_ShutdownGoal( false );
return FALSE; return FALSE;
} }
a1 = YAP_GetFromSlot( s1 );
lbfgsfloatval_t rc;
if (YAP_IsFloatTerm(a1)) {
rc = (lbfgsfloatval_t) YAP_FloatOfTerm(a1);
} else if (YAP_IsIntTerm(a1)) {
rc = (lbfgsfloatval_t) YAP_IntOfTerm(a1);
} else {
fprintf(stderr, "ERROR: The evaluate call back function did not return a number as first argument.\n");
rc = false;
}
YAP_ShutdownGoal( false );
return rc; return rc;
} }
@ -132,15 +118,14 @@ static int progress(
call = YAP_MkApplTerm( fmodule, 2, t2 ); call = YAP_MkApplTerm( fmodule, 2, t2 );
s1 = YAP_InitSlot(v); s1 = YAP_InitSlot(v);
//optimizer_status=OPTIMIZER_STATUS_CB_PROGRESS; //lbfgs_status=LBFGS_STATUS_CB_PROGRESS;
result=YAP_RunGoal(call); result=YAP_RunGoalOnce(call);
//optimizer_status=OPTIMIZER_STATUS_RUNNING; //lbfgs_status=LBFGS_STATUS_RUNNING;
YAP_Term o = YAP_GetFromSlot( s1 ); YAP_Term o = YAP_GetFromSlot( s1 );
YAP_ShutdownGoal( false );
if (result==FALSE) { if (result==FALSE) {
printf("ERROR: the progress call failed in YAP.\n"); printf("ERROR: the progress call failed in YAP.\n");
// Goal did not succeed // Goal did not succeed
return -1; return -1;
} }
@ -154,7 +139,7 @@ static int progress(
return 1; return 1;
} }
/** @pred optimizer_initialize(+N,+Module,+Evaluate,+Progress) /** @pred lbfgs_initialize(+N,+Module,+Evaluate,+Progress)
Create space to optimize a function with _N_ variables (_N_ has to be Create space to optimize a function with _N_ variables (_N_ has to be
integer). integer).
@ -169,7 +154,7 @@ to evaluate the function math <span class="math">_F</span>_,
Example Example
~~~~ ~~~~
optimizer_initialize(1,user,evaluate,progress,e,g)</span> lbfgs_initialize(1,user,evaluate,progress,e,g)</span>
~~~~ ~~~~
@ -179,107 +164,227 @@ value _F_. _N_ is the
size of the parameter vector (the value which was used to initialize size of the parameter vector (the value which was used to initialize
LBFGS) and _Step_ is the current state of the LBFGS) and _Step_ is the current state of the
line search. The call back predicate can access the current values of line search. The call back predicate can access the current values of
`x[i]` by calling `optimizer_get_x(+I,-Xi)`. Finally, the call back `x[i]` by calling `lbfgs_get_x(+I,-Xi)`. Finally, the call back
predicate has to calculate the gradient of _F</span>_ predicate has to calculate the gradient of _F</span>_
and set its value by calling `optimizer_set_g(+I,+Gi)` for every `1<=I<=N`. and set its value by calling `lbfgs_set_g(+I,+Gi)` for every `1<=I<=N`.
The progress call back predicate has to be of the type The progress call back predicate has to be of the type
`progress(+F,+X_Norm,+G_Norm,+Step,+N,+Iteration,+LS,-Continue)`. It `progress(+F,+X_Norm,+G_Norm,+Step,+N,+Iteration,+LS,-Continue)`. It
is called after every iteration. The call back predicate can access is called after every iteration. The call back predicate can access
the current values of _X_ and of the gradient by calling the current values of _X_ and of the gradient by calling
`optimizer_get_x(+I,-Xi)` and `optimizer_get_g`(+I,-Gi)` `lbfgs_get_x(+I,-Xi)` and `lbfgs_get_g`(+I,-Gi)`
respectively. However, it must not call the setter predicates for <span respectively. However, it must not call the setter predicates for <span
class="code"_X_ or _G_. If it tries to do so, the optimizer will class="code"_X_ or _G_. If it tries to do so, the lbfgs will
terminate with an error. If _Continue_ is set to 0 (int) the terminate with an error. If _Continue_ is set to 0 (int) the
optimization process will continue for one more iteration, any other optimization process will continue for one more iteration, any other
value will terminate the optimization process. value will terminate the optimization process.
*/ */
static YAP_Bool optimizer_initialize(void) { /**
* @pred lbfgs( N, X, U, FX )
*
* @Arg1 N: number of variables in problem
* @Arg[X0]: input vector
* @Arg[FX]: function value,
* @Arg[FX]: parameter
* @Arg[X0]: user data
* @Arg[FX]: status
*/
static YAP_Bool p_lbfgs(void)
{
YAP_Term t1 = YAP_ARG1; YAP_Term t1 = YAP_ARG1;
int temp_n=0; int n;
lbfgsfloatval_t *temp_x, *temp_ox; lbfgsfloatval_t *x;
lbfgs_parameter_t *temp_p; lbfgsfloatval_t fx;
if (! YAP_IsIntTerm(t1)) { if (! YAP_IsIntTerm(t1)) {
return false; return false;
} }
temp_n=YAP_IntOfTerm(t1); n=YAP_IntOfTerm(t1);
if (temp_n<1) { if (n<1) {
return FALSE; return FALSE;
} }
x = ( lbfgsfloatval_t*) YAP_IntOfTerm(YAP_ArgOfTerm(1,YAP_ARG2));
lbfgs_parameter_t* param = ( lbfgs_parameter_t*) YAP_IntOfTerm(YAP_ARG3);
void* ui = ( void*) YAP_IntOfTerm(YAP_ARG4);
{
YAP_Term t = YAP_MkIntTerm((YAP_Int)&fx);
if (!YAP_Unify(YAP_ARG5,YAP_MkApplTerm(ffloats,1,&t)))
return false;
}
signal(SIGFPE, SIG_IGN);
int ret = lbfgs(n, x, &fx, evaluate, progress, ui, param);
if (ret == 0)
return true;
const char *s;
switch(ret) {
case LBFGS_CONVERGENCE:
case LBFGS_STOP:
return true;
/** The initial variables already minimize the objective function. */
case LBFGS_ALREADY_MINIMIZED:
s = "The initial variables already minimize the objective function.";
break;
case LBFGSERR_UNKNOWNERROR:
s = "Unknownerror";
break;
case LBFGSERR_LOGICERROR:
s = "logic error.";
break;
temp_n = 16*(temp_n/16+15); case LBFGSERR_OUTOFMEMORY:
lbfgs_parameter_init((temp_p=(lbfgs_parameter_t *)malloc(sizeof(lbfgs_parameter_t)))); s = "out of memory";
temp_ox = lbfgs_malloc(temp_n); break;
YAP_Term tox = YAP_MkIntTerm((YAP_Int)temp_ox); case LBFGSERR_CANCELED:
temp_x = lbfgs_malloc(temp_n); s = "canceled.";
YAP_Term tx = YAP_MkIntTerm((YAP_Int)temp_x); break;
tx = YAP_MkApplTerm(ffloats, 1, &tx); case LBFGSERR_INVALID_N:
tox = YAP_MkApplTerm(ffloats, 1, &tox); s = "Invalid number of variables specified.";
YAP_Term tp = YAP_MkIntTerm((YAP_Int)temp_p); break;
case LBFGSERR_INVALID_N_SSE:
s = "Invalid number of variables (for SSE) specified.";
break;
case LBFGSERR_INVALID_X_SSE:
s = "The array x must be aligned to 16 (for SSE).";
break;
case LBFGSERR_INVALID_EPSILON:
s = "Invalid parameter lbfgs_parameter_t::epsilon specified.";
break;
case LBFGSERR_INVALID_TESTPERIOD:
s = "Invalid parameter lbfgs_parameter_t::past specified.";
break;
case LBFGSERR_INVALID_DELTA:
s = "Invalid parameter lbfgs_parameter_t::delta specified.";
break;
case LBFGSERR_INVALID_LINESEARCH:
s = "Invalid parameter lbfgs_parameter_t::linesearch specified.";
break;
case LBFGSERR_INVALID_MINSTEP:
s = "Invalid parameter lbfgs_parameter_t::max_step specified.";
break;
case LBFGSERR_INVALID_MAXSTEP:
s = "Invalid parameter lbfgs_parameter_t::max_step specified.";
break;
case LBFGSERR_INVALID_FTOL:
s = "Invalid parameter lbfgs_parameter_t::ftol specified.";
break;
case LBFGSERR_INVALID_WOLFE:
s = "Invalid parameter lbfgs_parameter_t::wolfe specified.";
break;
case LBFGSERR_INVALID_GTOL:
s = "Invalid parameter lbfgs_parameter_t::gtol specified.";
break;
case LBFGSERR_INVALID_XTOL:
s = "Invalid parameter lbfgs_parameter_t::xtol specified.";
break;
case LBFGSERR_INVALID_MAXLINESEARCH:
s = "Invalid parameter lbfgs_parameter_t::max_linesearch specified.";
break;
case LBFGSERR_INVALID_ORTHANTWISE:
s = "Invalid parameter lbfgs_parameter_t::orthantwise_c specified.";
break;
case LBFGSERR_INVALID_ORTHANTWISE_START:
s = "Invalid parameter lbfgs_parameter_t::orthantwise_start specified.";
break;
case LBFGSERR_INVALID_ORTHANTWISE_END:
s = "Invalid parameter lbfgs_parameter_t::orthantwise_end specified.";
break;
case LBFGSERR_OUTOFINTERVAL:
s = "The line-search step went out of the interval of uncertainty.";
break;
case LBFGSERR_INCORRECT_TMINMAX:
return YAP_Unify(YAP_ARG2,tx) && YAP_Unify(YAP_ARG3,tox) && YAP_Unify(YAP_ARG4,tp) ; s = "A logic error occurred; alternatively, the interval of uncertaity became too small.";
break;
case LBFGSERR_ROUNDING_ERROR:
s = "A rounding error occurred; alternatively, no line-search s";
break;
case LBFGSERR_MINIMUMSTEP:
s = "The line-search step became smaller than lbfgs_parameter_t::min_step.";
break;
case LBFGSERR_MAXIMUMSTEP:
s = "The line-search step became larger than lbfgs_parameter_t::max_step.";
break;
case LBFGSERR_MAXIMUMLINESEARCH:
s = "The line-search routine reaches the maximum number of evaluations.";
break;
case LBFGSERR_MAXIMUMITERATION:
s = "The algorithm routine reaches the maximum number of iterations lbfgs_parameter_t::xtol.";
break;
case LBFGSERR_WIDTHTOOSMALL:
s = "Relative width of the interval of uncertainty is at m";
break;
case LBFGSERR_INVALIDPARAMETERS:
s = "A logic error (negative line-search step) occurred.";
break;
}
fprintf(stderr, "optimization terminated with code %d: %s\n", ret, s);
return true;
} }
/** @pred optimizer_run(-F,-Status) static YAP_Bool lbfgs_grab (void)
Runs the optimization, _F is the best (minimal) function value and {
Status (int) is the status code returned by libLBFGS. Anything except int n=YAP_IntOfTerm(YAP_ARG1);
0 indicates an error, see the documentation of libLBFGS for the
meaning.
*/
static YAP_Bool optimizer_run(void) {
int ret = 0;
int n = YAP_IntOfTerm(YAP_ARG1);
YAP_Int s1, s2;
lbfgsfloatval_t fx;
lbfgsfloatval_t *temp_x = ( lbfgsfloatval_t *)YAP_IntOfTerm( YAP_ArgOfTerm(1, YAP_ARG2)),
*temp_ox = ( lbfgsfloatval_t *) YAP_IntOfTerm(YAP_ArgOfTerm(1,YAP_ARG4));
lbfgs_parameter_t *temp_p = (lbfgs_parameter_t * ) YAP_IntOfTerm(YAP_ARG6);
ret = lbfgs(n, temp_x, &fx, evaluate, progress, temp_ox, temp_p);
return YAP_Unify(YAP_MkIntTerm(ret), YAP_ARG5) && if (n<1) {
YAP_Unify(YAP_MkFloatTerm(fx), YAP_ARG3); return FALSE;
} }
lbfgsfloatval_t * x = lbfgs_malloc(n);
YAP_Term t = YAP_MkIntTerm((YAP_Int)x);
return YAP_Unify(YAP_ARG2,YAP_MkApplTerm(ffloats,1,&t));
}
static YAP_Bool lbfgs_parameters( void )
{
lbfgs_parameter_t *x = malloc(sizeof(lbfgs_parameter_t));
lbfgs_parameter_init(x);
return YAP_Unify(YAP_ARG1,YAP_MkIntTerm((YAP_Int)x));
static YAP_Bool optimizer_finalize( void ) { }
/* if (optimizer_status == OPTIMIZER_STATUS_NONE) { */
/* printf("Error: Optimizer is not initialized.\n"); */ static YAP_Bool lbfgs_release_parameters( void )
{
free((void *)YAP_IntOfTerm(YAP_ARG1));
return true;
}
static YAP_Bool lbfgs_release( void ) {
/* if (lbfgs_status == LBFGS_STATUS_NONE) { */
/* printf("Error: Lbfgs is not initialized.\n"); */
/* return FALSE; */ /* return FALSE; */
/* } */ /* } */
/* if (optimizer_status == OPTIMIZER_STATUS_INITIALIZED) { */ /* if (lbfgs_status == LBFGS_STATUS_INITIALIZED) { */
lbfgs_free((void *)YAP_IntOfTerm(YAP_ArgOfTerm(1,YAP_ARG1))); lbfgs_free((lbfgsfloatval_t *)YAP_IntOfTerm(YAP_ArgOfTerm(1,(YAP_ARG1))));
lbfgs_free((void *)YAP_IntOfTerm(YAP_ArgOfTerm(1,YAP_ARG2)));
lbfgs_free((void *)YAP_IntOfTerm(YAP_ARG3));
return TRUE; return TRUE;
/* } */
/* printf("ERROR: Optimizer is running right now. Please wait till it is finished.\n"); */
/* return FALSE; */ /* return FALSE; */
} }
/** @pred optimizer_set_parameter(+Name,+Value,+Parameters) /** @pred lbfgs_set_parameter(+Name,+Value,+Parameters)
Set the parameter Name to Value. Only possible while the optimizer Set the parameter Name to Value. Only possible while the lbfgs
is not running. is not running.
*/ */
static YAP_Bool optimizer_set_parameter( void ) { static YAP_Bool lbfgs_set_parameter( void ) {
YAP_Term t1 = YAP_ARG1; YAP_Term t1 = YAP_ARG1;
YAP_Term t2 = YAP_ARG2; YAP_Term t2 = YAP_ARG2;
lbfgs_parameter_t *param = (lbfgs_parameter_t *) YAP_IntOfTerm(YAP_ARG3); lbfgs_parameter_t *param = (lbfgs_parameter_t *) YAP_IntOfTerm(YAP_ARG3);
/* if (optimizer_status != OPTIMIZER_STATUS_NONE && optimizer_status != OPTIMIZER_STATUS_INITIALIZED){ */ /* if (lbfgs_status != LBFGS_STATUS_NONE && lbfgs_status != LBFGS_STATUS_INITIALIZED){ */
/* printf("ERROR: Optimizer is running right now. Please wait till it is finished.\n"); */ /* printf("ERROR: Lbfgs is running right now. Please wait till it is finished.\n"); */
/* return FALSE; */ /* return FALSE; */
/* } */ /* } */
@ -430,11 +535,11 @@ static YAP_Bool optimizer_set_parameter( void ) {
} }
/** @pred optimizer_get_parameter(+Name,-Value)</h3> /** @pred lbfgs_get_parameter(+Name,-Value)</h3>
Get the current Value for Name Get the current Value for Name
*/ */
static YAP_Bool optimizer_get_parameter( void ) { static YAP_Bool lbfgs_get_parameter( void ) {
YAP_Term t1 = YAP_ARG1; YAP_Term t1 = YAP_ARG1;
YAP_Term t2 = YAP_ARG2; YAP_Term t2 = YAP_ARG2;
lbfgs_parameter_t *param = (lbfgs_parameter_t *) YAP_IntOfTerm(YAP_ARG3); lbfgs_parameter_t *param = (lbfgs_parameter_t *) YAP_IntOfTerm(YAP_ARG3);
@ -465,7 +570,7 @@ static YAP_Bool optimizer_get_parameter( void ) {
return YAP_Unify(t2,YAP_MkFloatTerm(param->max_step)); return YAP_Unify(t2,YAP_MkFloatTerm(param->max_step));
} else if ((strcmp(name, "ftol") == 0)) { } else if ((strcmp(name, "ftol") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param->ftol)); return YAP_Unify(t2,YAP_MkFloatTerm(param->ftol));
} else if ((strcmp(name, "gtol") == 0)) { } else if ((strcmp(name, "gtol") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param->gtol)); return YAP_Unify(t2,YAP_MkFloatTerm(param->gtol));
} else if ((strcmp(name, "xtol") == 0)) { } else if ((strcmp(name, "xtol") == 0)) {
return YAP_Unify(t2,YAP_MkFloatTerm(param->xtol)); return YAP_Unify(t2,YAP_MkFloatTerm(param->xtol));
@ -487,7 +592,7 @@ static YAP_Bool optimizer_get_parameter( void ) {
X_API void init_lbfgs_predicates( void ) X_API void init_lbfgs_predicates( void )
{ {
fevaluate = YAP_MkFunctor(YAP_LookupAtom("evaluate"), 5); fevaluate = YAP_MkFunctor(YAP_LookupAtom("evaluate"), 6);
fprogress = YAP_MkFunctor(YAP_LookupAtom("progress"), 10); fprogress = YAP_MkFunctor(YAP_LookupAtom("progress"), 10);
fmodule = YAP_MkFunctor(YAP_LookupAtom(":"), 2); fmodule = YAP_MkFunctor(YAP_LookupAtom(":"), 2);
ffloats = YAP_MkFunctor(YAP_LookupAtom("floats"), 1); ffloats = YAP_MkFunctor(YAP_LookupAtom("floats"), 1);
@ -497,10 +602,12 @@ X_API void init_lbfgs_predicates( void )
// lbfgs_parameter_init(&param); // lbfgs_parameter_init(&param);
YAP_UserCPredicate("optimizer_reserve_memory",optimizer_initialize,4); YAP_UserCPredicate("lbfgs_grab",lbfgs_grab,2);
YAP_UserCPredicate("optimizer_run",optimizer_run,6); YAP_UserCPredicate("lbfgs",p_lbfgs, 5);
YAP_UserCPredicate("optimizer_free_memory",optimizer_finalize,3); YAP_UserCPredicate("lbfgs_release",lbfgs_release,1);
YAP_UserCPredicate("optimizer_set_parameter",optimizer_set_parameter,3); YAP_UserCPredicate("lbfgs_defaults",lbfgs_parameters,1);
YAP_UserCPredicate("optimizer_get_parameter",optimizer_get_parameter,3); YAP_UserCPredicate("lbfgs_release_parameters",lbfgs_release_parameters,1);
YAP_UserCPredicate("lbfgs_set_parameter",lbfgs_set_parameter,3);
YAP_UserCPredicate("lbfgs_get_parameter",lbfgs_get_parameter,3);
} }