Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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68 lines
2.1 KiB
68 lines
2.1 KiB
import numpy as np
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import dynamic_graph as dg
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import sample_path as sp
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import priority_queue as pq
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class RateMatrix():
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"""
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Rappresenta la matrice Q di una generica CTMC costruita a partire dalle informazioni contenute nel grafo dinamico
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"""
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def __init__(self, graph, dim):
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self.graph = graph
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self.matrix = np.zeros(shape=(dim,dim))
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self.pr_queue = pq.PriorityQueue()
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def build_matrix(self):
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root = self.graph.get_root_node()
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root.color = dg.node.Color.GRAY
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self.pr_queue.enqueue(root)
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while not self.pr_queue.is_empty():
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n = self.pr_queue.dequeue()
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adjacency_list = self.graph.get_neighbours(n)
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#print(adjacency_list)
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time = self.graph.graph[n.state_id]["Time"]
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sum_of_qs = 0.0
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for nd, weight in adjacency_list.items():
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sum_of_qs += self.calculate_off_diagonal_element_and_fill_matrix(n.node_id, nd.node_id, weight, time)
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if self.graph.graph[nd.state_id]["Node"].color == dg.node.Color.WHITE:
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self.graph.graph[nd.state_id]["Node"].color = dg.node.Color.GRAY
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self.pr_queue.enqueue(nd)
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n.color = dg.node.Color.BLACK
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self.calculate_diagonal_element_and_fill_matrix(sum_of_qs, n.node_id)
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def calculate_off_diagonal_element_and_fill_matrix(self, start_node, arrival_node, weight, time):
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q = weight / time
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self.matrix[start_node][arrival_node] = q
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return q
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def calculate_diagonal_element_and_fill_matrix(self, sum_of_qs, start_node):
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self.matrix[start_node][start_node] = -sum_of_qs
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# A Simple Test #
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s1 = sp.SamplePath()
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s1.build_trajectories()
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print(s1.get_number_trajectories())
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g1 = dg.DynamicGraph(s1)
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g1.build_graph()
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print(g1.graph)
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#print(g1.states_number)
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Q = RateMatrix(g1, g1.states_number)
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#print(Q.matrix)
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Q.build_matrix()
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print(Q.matrix)
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non_zero_values = 0
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val = 0.0
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for coeff in Q.matrix[0][1:]:
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if(coeff != 0):
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non_zero_values += 1
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val += coeff
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print(non_zero_values == len(Q.graph.graph["222"]["Arcs"].keys())) |