def solve_problem(self, target_variable, maximize=False):
"""
Solve the linear programming problem
"""
target_column = self.var_corr[target_variable]
c = [0.0] * self.variable_count
if maximize:
c[target_column] = -1.0
else:
c[target_column] = 1.0
sol = lp_solver_factory.apply(c,
self.Aub,
self.bub,
self.Aeq,
self.beq,
variant=DEFAULT_LP_SOLVER_VARIANT)
parameters_points = {
"maximize": maximize,
"return_when_none": True,
"var_corr": self.var_corr
}
return lp_solver_factory.get_points_from_sol(
sol,
parameters=parameters_points,
variant=DEFAULT_LP_SOLVER_VARIANT)
def __compute_exact_heuristic_new_version(sync_net, a_matrix, h_cvx, g_matrix,
cost_vec, incidence_matrix, marking,
fin_vec):
m_vec = incidence_matrix.encode_marking(marking)
b_term = [i - j for i, j in zip(fin_vec, m_vec)]
b_term = np.matrix([x * 1.0 for x in b_term]).transpose()
if DEFAULT_LP_SOLVER_VARIANT == lp_solver_factory.CVXOPT_SOLVER_CUSTOM_ALIGN:
b_term = matrix(b_term)
parameters_solving = {"solver": "glpk"}
sol = lp_solver_factory.apply(cost_vec,
g_matrix,
h_cvx,
a_matrix,
b_term,
parameters=parameters_solving,
variant=DEFAULT_LP_SOLVER_VARIANT)
prim_obj = lp_solver_factory.get_prim_obj_from_sol(
sol, variant=DEFAULT_LP_SOLVER_VARIANT)
points = lp_solver_factory.get_points_from_sol(
sol, variant=DEFAULT_LP_SOLVER_VARIANT)
prim_obj = prim_obj if prim_obj is not None else sys.maxsize
points = points if points is not None else [0.0] * len(
sync_net.transitions)
return prim_obj, points
def __compute_exact_heuristic(sync_net, incidence_matrix, marking, cost_vec,
fin_vec):
"""
Computes an exact heuristic using an LP based on the marking equation.
Parameters
----------
:param sync_net: synchronous product net
:param incidence_matrix: incidence matrix
:param marking: marking to start from
:param cost_vec: cost vector
:param fin_vec: marking to reach
Returns
-------
:return: h: heuristic value, x: solution vector
"""
m_vec = incidence_matrix.encode_marking(marking)
g_matrix = -np.eye(len(sync_net.transitions))
h_cvx = np.zeros(len(sync_net.transitions))
a_matrix = incidence_matrix.a_matrix
b_term = [i - j for i, j in zip(fin_vec, m_vec)]
cost_vec = [x * 1.0 for x in cost_vec]
a_matrix = np.asmatrix(a_matrix).astype(np.float64)
h_cvx = np.matrix([x * 1.0 for x in h_cvx]).transpose()
b_term = np.matrix([x * 1.0 for x in b_term]).transpose()
parameters_solving = {"solver": "glpk"}
sol = lp_solver_factory.apply(cost_vec,
g_matrix,
h_cvx,
a_matrix,
b_term,
parameters=parameters_solving,
variant=DEFAULT_LP_SOLVER_VARIANT)
prim_obj = lp_solver_factory.get_prim_obj_from_sol(
sol, variant=DEFAULT_LP_SOLVER_VARIANT)
points = lp_solver_factory.get_points_from_sol(
sol, variant=DEFAULT_LP_SOLVER_VARIANT)
prim_obj = prim_obj if prim_obj is not None else sys.maxsize
points = points if points is not None else [0.0] * len(
sync_net.transitions)
return prim_obj, points
def __compute_exact_heuristic_new_version(sync_net,
a_matrix,
h_cvx,
g_matrix,
cost_vec,
incidence_matrix,
marking,
fin_vec,
variant,
use_cvxopt=False):
m_vec = incidence_matrix.encode_marking(marking)
b_term = [i - j for i, j in zip(fin_vec, m_vec)]
b_term = np.matrix([x * 1.0 for x in b_term]).transpose()
if use_cvxopt:
# not available in the latest version of PM4Py
from cvxopt import matrix
b_term = matrix(b_term)
parameters_solving = {"solver": "glpk"}
sol = lp_solver_factory.apply(cost_vec,
g_matrix,
h_cvx,
a_matrix,
b_term,
parameters=parameters_solving,
variant=variant)
prim_obj = lp_solver_factory.get_prim_obj_from_sol(sol, variant=variant)
points = lp_solver_factory.get_points_from_sol(sol, variant=variant)
prim_obj = prim_obj if prim_obj is not None else sys.maxsize
points = points if points is not None else [0.0] * len(
sync_net.transitions)
return prim_obj, points