Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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298 lines
13 KiB
298 lines
13 KiB
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import networkx as nx
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import numpy as np
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import typing
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class NetworkGraph:
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"""
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Abstracts the infos contained in the Structure class in the form of a directed graph.
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Has the task of creating all the necessary filtering structures for parameters estimation
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:graph_struct: the Structure object from which infos about the net will be extracted
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:graph: directed graph
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:nodes_labels: the symbolic names of the variables
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:nodes_indexes: the indexes of the nodes
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:nodes_values: the cardinalites of the nodes
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:aggregated_info_about_nodes_parents: a structure that contains all the necessary infos about every parents of every
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node in the net
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:_fancy_indexing: the indexes of every parent of every node in the net
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:_time_scalar_indexing_structure: the indexing structure for state res time estimation
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:_transition_scalar_indexing_structure: the indexing structure for transition computation
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:_time_filtering: the columns filtering structure used in the computation of the state res times
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:_transition_filtering: the columns filtering structure used in the computation of the transition from one state to another
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:self._p_combs_structure: all the possibile parents states combination for every node in the net
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"""
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def __init__(self, graph_struct):
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self.graph_struct = graph_struct
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self.graph = nx.DiGraph()
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self._nodes_indexes = self.graph_struct.nodes_indexes
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self._nodes_labels = self.graph_struct.nodes_labels
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self._nodes_values = self.graph_struct.nodes_values
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self.aggregated_info_about_nodes_parents = None
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self._fancy_indexing = None
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self._time_scalar_indexing_structure = None
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self._transition_scalar_indexing_structure = None
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self._time_filtering = None
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self._transition_filtering = None
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self._p_combs_structure = None
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def init_graph(self):
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self.add_nodes(self._nodes_labels)
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self.add_edges(self.graph_struct.edges)
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self.aggregated_info_about_nodes_parents = self.get_ord_set_of_par_of_all_nodes()
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self._fancy_indexing = self.build_fancy_indexing_structure(0)
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self.build_scalar_indexing_structures()
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self.build_time_columns_filtering_structure()
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self.build_transition_columns_filtering_structure()
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self._p_combs_structure = self.build_p_combs_structure()
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def fast_init(self, node_id: str):
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"""
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Initializes all the necessary structures for parameters estimation of the node identified by the label node_id
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Parameters:
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node_id: the label of the node
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Returns:
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void
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"""
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self.add_nodes(self._nodes_labels)
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self.add_edges(self.graph_struct.edges)
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self.aggregated_info_about_nodes_parents = self.get_ordered_by_indx_set_of_parents(node_id)
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self._fancy_indexing = self.aggregated_info_about_nodes_parents[1]
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p_indxs = self._fancy_indexing
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p_vals = self.aggregated_info_about_nodes_parents[2]
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self._time_scalar_indexing_structure = self.build_time_scalar_indexing_structure_for_a_node(node_id,
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p_vals)
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self._transition_scalar_indexing_structure = self.build_transition_scalar_indexing_structure_for_a_node(node_id,
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p_vals)
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node_indx = self.get_node_indx(node_id)
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self._time_filtering = self.build_time_columns_filtering_for_a_node(node_indx, p_indxs)
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self._transition_filtering = self.build_transition_filtering_for_a_node(node_indx, p_indxs)
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self._p_combs_structure = self.build_p_comb_structure_for_a_node(p_vals)
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def add_nodes(self, list_of_nodes: typing.List):
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"""
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Adds the nodes to the graph contained in the list of nodes list_of_nodes.
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Sets all the properties that identify a nodes (index, positional index, cardinality)
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Parameters:
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list_of_nodes: the nodes to add to graph
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Returns:
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void
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"""
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nodes_indxs = self._nodes_indexes
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nodes_vals = self.graph_struct.nodes_values
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pos = 0
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for id, node_indx, node_val in zip(list_of_nodes, nodes_indxs, nodes_vals):
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self.graph.add_node(id, indx=node_indx, val=node_val, pos_indx=pos)
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pos += 1
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def add_edges(self, list_of_edges: typing.List):
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"""
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Add the edges to the graph contained in the list list_of_edges.
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Parameters:
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list_of_edges
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Returns:
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void
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"""
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self.graph.add_edges_from(list_of_edges)
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def get_ordered_by_indx_set_of_parents(self, node: str):
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"""
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Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely
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(parents_labels, parents_indexes, parents_cardinalities).
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N.B. The parent set is sorted using the list of sorted nodes nodes
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Parameters:
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node: the label of the node
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Returns:
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a tuple containing all the parent set infos
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"""
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parents = self.get_parents_by_id(node)
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nodes = self._nodes_labels
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d = {v: i for i, v in enumerate(nodes)}
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sorted_parents = sorted(parents, key=lambda v: d[v])
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get_node_indx = self.get_node_indx
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p_indxes = [get_node_indx(node) for node in sorted_parents]
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p_values = [self.get_states_number(node) for node in sorted_parents]
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return (sorted_parents, p_indxes, p_values)
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def get_ord_set_of_par_of_all_nodes(self):
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get_ordered_by_indx_set_of_parents = self.get_ordered_by_indx_set_of_parents
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result = [get_ordered_by_indx_set_of_parents(node) for node in self._nodes_labels]
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return result
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def get_ordered_by_indx_parents_values_for_all_nodes(self):
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pars_values = [i[2] for i in self.aggregated_info_about_nodes_parents]
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return pars_values
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def build_fancy_indexing_structure(self, start_indx):
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if start_indx > 0:
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pass
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else:
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fancy_indx = [i[1] for i in self.aggregated_info_about_nodes_parents]
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return fancy_indx
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def build_time_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List) -> np.ndarray:
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"""
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Builds an indexing structure for the computation of state residence times values.
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Parameters:
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node_id: the node label
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parents_vals: the caridinalites of the node's parents
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Returns:
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a numpy array.
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"""
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T_vector = np.array([self.get_states_number(node_id)])
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T_vector = np.append(T_vector, parents_vals)
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T_vector = T_vector.cumprod().astype(np.int)
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return T_vector
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def build_transition_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List) -> np.ndarray:
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"""
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Builds an indexing structure for the computation of state transitions values.
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Parameters:
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node_id: the node label
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parents_vals: the caridinalites of the node's parents
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Returns:
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a numpy array.
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"""
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node_states_number = self.get_states_number(node_id)
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M_vector = np.array([node_states_number,
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node_states_number])
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M_vector = np.append(M_vector, parents_vals)
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M_vector = M_vector.cumprod().astype(np.int)
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return M_vector
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def build_time_columns_filtering_for_a_node(self, node_indx: int, p_indxs: typing.List) -> np.ndarray:
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"""
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Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs in the dataset.
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This structute will be used in the computation of the state res times.
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Parameters:
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node_indx: the index of the node
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p_indxs: the indexes of the node's parents
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Returns:
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a numpy array
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"""
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return np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int)
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def build_transition_filtering_for_a_node(self, node_indx, p_indxs) -> np.ndarray:
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"""
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Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs in the dataset.
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This structute will be used in the computation of the state transitions values.
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Parameters:
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node_indx: the index of the node
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p_indxs: the indexes of the node's parents
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Returns:
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a numpy array
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"""
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nodes_number = self.graph_struct.total_variables_number
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return np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
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def build_p_comb_structure_for_a_node(self, parents_values: typing.List) -> np.ndarray:
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"""
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Builds the combinatory structure that contains the combinations of all the values contained in parents_values.
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Parameters:
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parents_values: the cardinalities of the nodes
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Returns:
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a numpy matrix containinga grid of the combinations
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"""
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tmp = []
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for val in parents_values:
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tmp.append([x for x in range(val)])
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if len(parents_values) > 0:
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parents_comb = np.array(np.meshgrid(*tmp)).T.reshape(-1, len(parents_values))
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if len(parents_values) > 1:
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tmp_comb = parents_comb[:, 1].copy()
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parents_comb[:, 1] = parents_comb[:, 0].copy()
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parents_comb[:, 0] = tmp_comb
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else:
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parents_comb = np.array([[]], dtype=np.int)
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return parents_comb
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def build_time_columns_filtering_structure(self):
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nodes_indxs = self._nodes_indexes
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self._time_filtering = [np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int)
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for node_indx, p_indxs in zip(nodes_indxs, self._fancy_indexing)]
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def build_transition_columns_filtering_structure(self):
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nodes_number = self.graph_struct.total_variables_number
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nodes_indxs = self._nodes_indexes
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self._transition_filtering = [np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
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for node_indx, p_indxs in zip(nodes_indxs,
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self._fancy_indexing)]
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def build_scalar_indexing_structures(self):
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parents_values_for_all_nodes = self.get_ordered_by_indx_parents_values_for_all_nodes()
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build_transition_scalar_indexing_structure_for_a_node = self.build_transition_scalar_indexing_structure_for_a_node
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build_time_scalar_indexing_structure_for_a_node = self.build_time_scalar_indexing_structure_for_a_node
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aggr = [(build_transition_scalar_indexing_structure_for_a_node(node_id, p_vals),
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build_time_scalar_indexing_structure_for_a_node(node_id, p_vals))
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for node_id, p_vals in
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zip(self._nodes_labels,
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parents_values_for_all_nodes)]
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self._transition_scalar_indexing_structure = [i[0] for i in aggr]
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self._time_scalar_indexing_structure = [i[1] for i in aggr]
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def build_p_combs_structure(self):
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parents_values_for_all_nodes = self.get_ordered_by_indx_parents_values_for_all_nodes()
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p_combs_struct = [self.build_p_comb_structure_for_a_node(p_vals) for p_vals in parents_values_for_all_nodes]
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return p_combs_struct
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def get_parents_by_id(self, node_id):
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return list(self.graph.predecessors(node_id))
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def get_states_number(self, node_id):
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return self.graph.nodes[node_id]['val']
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def get_node_indx(self, node_id):
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return nx.get_node_attributes(self.graph, 'indx')[node_id]
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def get_positional_node_indx(self, node_id):
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return self.graph.nodes[node_id]['pos_indx']
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@property
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def nodes(self):
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return self._nodes_labels
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@property
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def edges(self):
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return list(self.graph.edges)
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@property
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def nodes_indexes(self):
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return self._nodes_indexes
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@property
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def nodes_values(self):
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return self._nodes_values
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@property
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def time_scalar_indexing_strucure(self):
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return self._time_scalar_indexing_structure
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@property
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def time_filtering(self):
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return self._time_filtering
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@property
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def transition_scalar_indexing_structure(self):
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return self._transition_scalar_indexing_structure
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@property
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def transition_filtering(self):
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return self._transition_filtering
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@property
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def p_combs(self):
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return self._p_combs_structure
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