Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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87 lines
2.9 KiB
87 lines
2.9 KiB
import typing as ty
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import numpy as np
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class Structure:
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"""
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Contains all the infos about the network structure(nodes names, nodes caridinalites, edges...)
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:nodes_labels_list: the symbolic names of the variables
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:nodes_indexes_arr: the indexes of the nodes
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:nodes_vals_arr: the cardinalites of the nodes
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:edges_list: the edges of the network
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:total_variables_number: the total number of variables in the net
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"""
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def __init__(self, nodes_label_list: ty.List, node_indexes_arr: np.ndarray, nodes_vals_arr: np.ndarray,
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edges_list: ty.List, total_variables_number: int):
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self._nodes_labels_list = nodes_label_list
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self._nodes_indexes_arr = node_indexes_arr
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self._nodes_vals_arr = nodes_vals_arr
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self._edges_list = edges_list
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self._total_variables_number = total_variables_number
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@property
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def edges(self):
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#records = self.structure_frame.to_records(index=False)
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#edges_list = list(records)
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return self._edges_list
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@property
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def nodes_labels(self):
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return self._nodes_labels_list
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@property
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def nodes_indexes(self) -> np.ndarray:
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return self._nodes_indexes_arr
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@property
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def nodes_values(self) -> np.ndarray:
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return self._nodes_vals_arr
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@property
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def total_variables_number(self):
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return self._total_variables_number
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def get_node_id(self, node_indx: int) -> str:
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return self._nodes_labels_list[node_indx]
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def clean_structure_edges(self):
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self._edges_list = list()
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def add_edge(self,edge: tuple):
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self._edges_list.append(tuple)
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print(self._edges_list)
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def remove_edge(self,edge: tuple):
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self._edges_list.remove(tuple)
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def contains_edge(self,edge:tuple) -> bool:
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return edge in self._edges_list
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def get_node_indx(self, node_id: str) -> int:
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pos_indx = self._nodes_labels_list.index(node_id)
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return self._nodes_indexes_arr[pos_indx]
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def get_positional_node_indx(self, node_id: str) -> int:
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return self._nodes_labels_list.index(node_id)
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def get_states_number(self, node: str) -> int:
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pos_indx = self._nodes_labels_list.index(node)
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return self._nodes_vals_arr[pos_indx]
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def __repr__(self):
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return "Variables:\n" + str(self._nodes_labels_list) +"\nValues:\n"+ str(self._nodes_vals_arr) +\
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"\nEdges: \n" + str(self._edges_list)
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def __eq__(self, other):
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"""Overrides the default implementation"""
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if isinstance(other, Structure):
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return set(self._nodes_labels_list) == set(other._nodes_labels_list) and \
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np.array_equal(self._nodes_vals_arr, other._nodes_vals_arr) and \
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np.array_equal(self._nodes_indexes_arr, other._nodes_indexes_arr) and \
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self._edges_list == other._edges_list
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return NotImplemented
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