Source code for pm4py.util.lp.variants.cvxopt_solver_custom_align_arm

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py 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.

    PM4Py 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 PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
import sys

from cvxopt import blas
from cvxopt import glpk


[docs]def custom_solve_lp(c, G, h, A, b): status, x, z, y = glpk.lp(c, G, h, A, b) if status == 'optimal': pcost = blas.dot(c, x) else: pcost = None return {'status': status, 'x': x, 'primal objective': pcost}
[docs]def apply(c, Aub, bub, Aeq, beq, parameters=None): """ Gets the overall solution of the problem Parameters ------------ c c parameter of the algorithm Aub A_ub parameter of the algorithm bub b_ub parameter of the algorithm Aeq A_eq parameter of the algorithm beq b_eq parameter of the algorithm parameters Possible parameters of the algorithm Returns ------------- sol Solution of the LP problem by the given algorithm """ sol = custom_solve_lp(c, Aub, bub, Aeq, beq) return sol
[docs]def get_prim_obj_from_sol(sol, parameters=None): """ Gets the primal objective from the solution of the LP problem Parameters ------------- sol Solution of the ILP problem by the given algorithm parameters Possible parameters of the algorithm Returns ------------- prim_obj Primal objective """ return sol["primal objective"]
[docs]def get_points_from_sol(sol, parameters=None): """ Gets the points from the solution Parameters ------------- sol Solution of the LP problem by the given algorithm parameters Possible parameters of the algorithm Returns ------------- points Point of the solution """ if parameters is None: parameters = {} maximize = parameters["maximize"] if "maximize" in parameters else False return_when_none = parameters["return_when_none"] if "return_when_none" in parameters else False var_corr = parameters["var_corr"] if "var_corr" in parameters else {} if sol and 'x' in sol and sol['x'] is not None: return list(sol['x']) else: if return_when_none: if maximize: return [sys.float_info.max] * len(list(var_corr.keys())) return [sys.float_info.min] * len(list(var_corr.keys()))