Source code for pm4py.objects.petri_net.utils.consumption_matrix

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Implements the consumption matrix as explained in the following paper:

van Dongen, Boudewijn F. "Efficiently computing alignments." International Conference on Business Process Management.
Springer, Cham, 2018.

from typing import Dict

import numpy as np

from pm4py.objects.petri_net.obj import PetriNet

[docs]class ConsumptionMatrix(object): def __init__(self, net: PetriNet): """ Constructor Parameters -------------- net Petri net """ self.__place_indices = {} self.__transition_indices = {} self.__C = None places = sorted([x for x in net.places], key=lambda x: (, id(x))) transitions = sorted([x for x in net.transitions], key=lambda x: (, id(x))) for p in places: self.__place_indices[p] = len(self.__place_indices) for t in transitions: self.__transition_indices[t] = len(self.__transition_indices) self.__compute_C_matrix(net) def __compute_C_matrix(self, net: PetriNet): """ Builds the C matrix Parameters --------------- net Petri net """ inv_indices = {y: x for x, y in self.__transition_indices.items()} inv_indices = [inv_indices[i] for i in range(len(inv_indices))] self.__C = [[0 for i in range(len(self.__transition_indices))] for j in range(len(self.__place_indices))] for p in net.places: outgoing_trans = [ for a in p.out_arcs] self.__C[self.__place_indices[p]] = [-1 if inv_indices[i] in outgoing_trans else 0 for i in range(len(inv_indices))] def __get_transition_indices(self) -> Dict[PetriNet.Transition, int]: """ Gets the transitions in the order in which they have been inserted in the consumption matrix Returns ------------- trans_indices Dictionary associating to each transition an incremental number """ return self.__transition_indices def __get_place_indices(self) -> Dict[PetriNet.Place, int]: """ Gets the places in the order in which they have been inserted in the consumption matrix Returns ------------- place_indices Dictionary associating to each place an incremental number """ return self.__place_indices def __get_c_matrix(self) -> np.ndarray: """ Gets the Numpy representation of the consumption matrix Returns ------------- C C matrix """ return self.__C places = property(__get_place_indices) transitions = property(__get_transition_indices) c_matrix = property(__get_c_matrix)
[docs]def construct(net: PetriNet) -> ConsumptionMatrix: """ Construct a consumption matrix given a Petri net Parameters ---------------- net Petri net Returns --------------- cons_mat Consumption matrix object """ return ConsumptionMatrix(net)