Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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69 lines
1.7 KiB
69 lines
1.7 KiB
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import sample_path as sp
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import networkx as nx
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import os
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class NetworkGraph():
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"""
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Rappresenta un grafo dinamico con la seguente struttura:
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:sample_path: le traiettorie/a da cui costruire il grafo
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:graph: la struttura dinamica che definisce il grafo
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"""
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def __init__(self, sample_path):
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self.sample_path = sample_path
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self.graph = nx.DiGraph()
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def init_graph(self):
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self.sample_path.build_trajectories()
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self.sample_path.build_structure()
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self.add_nodes(self.sample_path.structure.list_of_node_ids())
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self.add_edges(self.sample_path.structure.list_of_edge_ids())
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def add_nodes(self, list_of_node_ids):
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for indx, id in enumerate(list_of_node_ids):
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#print(indx, id)
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self.graph.add_node(id)
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nx.set_node_attributes(self.graph, {id:indx}, 'indx')
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#for node in list(self.graph.nodes):
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#print(node)
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def add_edges(self, list_of_edges):
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self.graph.add_edges_from(list_of_edges)
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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_node(self, node_id):
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to_find = nd.Node(node_id)
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for node in self.graph.nodes:
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if node == to_find:
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return node
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######Veloci Tests#######
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os.getcwd()
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os.chdir('..')
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path = os.getcwd() + '/data'
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s1 = sp.SamplePath(path)
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g1 = NetworkGraph(s1)
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g1.init_graph()
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print(g1.graph.number_of_nodes())
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print(g1.graph.number_of_edges())
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print(nx.get_node_attributes(g1.graph, 'indx')['X'])
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for node in g1.get_parents_by_id('X'):
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print(g1.sample_path.structure.get_node_indx(node))
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print(node)
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