This repository has been archived on 2023-08-20. You can view files and clone it, but cannot push or open issues or pull requests.
yap-6.3/packages/yap-lbfgs/lbfgs.pl
Vitor Santos Costa bfb4cef8f9 fixes
2013-07-29 17:55:51 -05:00

122 lines
5.0 KiB
Prolog

%%% -*- Mode: Prolog; -*-
% This file is part of YAP-LBFGS.
% Copyright (C) 2009 Bernd Gutmann
%
% YAP-LBFGS is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% YAP-LBFGS is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with YAP-LBFGS. If not, see <http://www.gnu.org/licenses/>.
:- module(lbfgs,[optimizer_initialize/3,
optimizer_initialize/4,
optimizer_run/2,
optimizer_get_x/2,
optimizer_set_x/2,
optimizer_get_g/2,
optimizer_set_g/2,
optimizer_finalize/0,
optimizer_set_parameter/2,
optimizer_get_parameter/2,
optimizer_parameters/0]).
% switch on all the checks to reduce bug searching time
% :- yap_flag(unknown,error).
% :- style_check(single_var).
:- dynamic initialized/0.
:- load_foreign_files(['yap_lbfgs'],[],'init_lbfgs_predicates').
optimizer_initialize(N,Call_Evaluate,Call_Progress) :-
optimizer_initialize(N,user,Call_Evaluate,Call_Progress).
optimizer_initialize(N,Module,Call_Evaluate,Call_Progress) :-
\+ initialized,
integer(N),
N>0,
% check whether there are such call back functions
current_module(Module),
current_predicate(Module:Call_Evaluate/3),
current_predicate(Module:Call_Progress/8),
optimizer_reserve_memory(N),
% install call back predicates in the user module which call
% the predicates given by the arguments
EvalGoal =.. [Call_Evaluate,E1,E2,E3],
ProgressGoal =.. [Call_Progress,P1,P2,P3,P4,P5,P6,P7,P8],
retractall( user:'$lbfgs_callback_evaluate'(_E1,_E2,_E3) ),
retractall( user:'$lbfgs_callback_progress'(_P1,_P2,_P3,_P4,_P5,_P6,_P7,_P8) ),
assert( (user:'$lbfgs_callback_evaluate'(E1,E2,E3) :- Module:EvalGoal, !) ),
assert( (user:'$lbfgs_callback_progress'(P1,P2,P3,P4,P5,P6,P7,P8) :- Module:ProgressGoal, !) ),
assert(initialized).
optimizer_finalize :-
initialized,
optimizer_free_memory,
retractall(user:'$lbfgs_callback_evaluate'(_,_,_)),
retractall(user:'$lbfgs_callback_progress'(_,_,_,_,_,_,_,_)),
retractall(initialized).
optimizer_parameters :-
optimizer_get_parameter(m,M),
optimizer_get_parameter(epsilon,Epsilon),
optimizer_get_parameter(past,Past),
optimizer_get_parameter(delta,Delta),
optimizer_get_parameter(max_iterations,Max_Iterations),
optimizer_get_parameter(linesearch,Linesearch),
optimizer_get_parameter(max_linesearch,Max_Linesearch),
optimizer_get_parameter(min_step,Min_Step),
optimizer_get_parameter(max_step,Max_Step),
optimizer_get_parameter(ftol,Ftol),
optimizer_get_parameter(gtol,Gtol),
optimizer_get_parameter(xtol,Xtol),
optimizer_get_parameter(orthantwise_c,Orthantwise_C),
optimizer_get_parameter(orthantwise_start,Orthantwise_Start),
optimizer_get_parameter(orthantwise_end,Orthantwise_End),
format('/******************************************************************************************~n',[]),
print_param('Name','Value','Description','Type'),
format('******************************************************************************************~n',[]),
print_param(m,M,'The number of corrections to approximate the inverse hessian matrix.',int),
print_param(epsilon,Epsilon,'Epsilon for convergence test.',float),
print_param(past,Past,'Distance for delta-based convergence test.',int),
print_param(delta,Delta,'Delta for convergence test.',float),
print_param(max_iterations,Max_Iterations,'The maximum number of iterations',int),
print_param(linesearch,Linesearch,'The line search algorithm.',int),
print_param(max_linesearch,Max_Linesearch,'The maximum number of trials for the line search.',int),
print_param(min_step,Min_Step,'The minimum step of the line search routine.',float),
print_param(max_step,Max_Step,'The maximum step of the line search.',float),
print_param(ftol,Ftol,'A parameter to control the accuracy of the line search routine.',float),
print_param(gtol,Gtol,'A parameter to control the accuracy of the line search routine.',float),
print_param(xtol,Xtol,'The machine precision for floating-point values.',float),
print_param(orthantwise_c,Orthantwise_C,'Coefficient for the L1 norm of variables',float),
print_param(orthantwise_start,Orthantwise_Start,'Start index for computing the L1 norm of the variables.',int),
print_param(orthantwise_end,Orthantwise_End,'End index for computing the L1 norm of the variables.',int),
format('******************************************************************************************/~n',[]),
format(' use optimizer_set_paramater(Name,Value) to change parameters~n',[]),
format(' use optimizer_get_parameter(Name,Value) to see current parameters~n',[]),
format(' use optimizer_parameters to print this overview~2n',[]).
print_param(Name,Value,Text,Dom) :-
format(user,'~w~10+~w~19+~w~15+~w~30+~n',[Dom,Name,Value,Text]).