pm4py.algo.discovery.inductive.variants.im_d.data_structures package
Submodules
pm4py.algo.discovery.inductive.variants.im_d.data_structures.subtree module
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/>.
- class pm4py.algo.discovery.inductive.variants.im_d.data_structures.subtree.SubtreeDFGBased(dfg, master_dfg, initial_dfg, activities, counts, rec_depth, noise_threshold=0, initial_start_activities=None, initial_end_activities=None)[source]
Bases:
object
- check_sa_ea_for_each_branch(conn_components)[source]
Checks if each branch of the parallel cut has a start and an end node of the subgraph
- Parameters
conn_components – Parallel cut
- Returns
True if each branch of the parallel cut has a start and an end node
- Return type
boolean
- detect_cut(second_iteration=False)[source]
Detect generally a cut in the graph (applying all the algorithms)
- detect_loop_cut(conn_components, this_nx_graph, strongly_connected_components)[source]
Detect loop cut
- Parameters
conn_components – Connected components of the graph
this_nx_graph – NX graph calculated on the DFG
strongly_connected_components – Strongly connected components
- detect_parallel_cut(orig_conn_components, this_nx_graph, strongly_connected_components)[source]
Detects parallel cut
- Parameters
orig_conn_components – Connected components of the graph
this_nx_graph – NX graph calculated on the DFG
strongly_connected_components – Strongly connected components
- detect_sequential_cut(conn_components, this_nx_graph, strongly_connected_components)[source]
Detect sequential cut in DFG graph
- Parameters
conn_components – Connected components of the graph
this_nx_graph – NX graph calculated on the DFG
strongly_connected_components – Strongly connected components
- detect_xor_cut(conn_components, this_nx_graph, strongly_connected_components)[source]
Detects XOR cut
- Parameters
conn_components – Connected components
this_nx_graph – NX graph calculated on the DFG
strongly_connected_components – Strongly connected components
- initialize_tree(dfg, initial_dfg, activities, second_iteration=False)[source]
Initialize the tree
- Parameters
dfg – Directly follows graph of this subtree
initial_dfg – Referral directly follows graph that should be taken in account adding hidden/loop transitions
activities – Activities of this subtree
second_iteration – Boolean that indicates if we are executing this method for the second time
Module contents
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/>.