Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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124 lines
5.4 KiB
124 lines
5.4 KiB
import os
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import time as tm
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import network_graph as ng
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import sample_path as sp
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import amalgamated_cims as acims
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class ParametersEstimator:
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def __init__(self, sample_path, net_graph):
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self.sample_path = sample_path
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self.net_graph = net_graph
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self.fancy_indexing_structure = self.net_graph.build_fancy_indexing_structure(1)
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self.amalgamated_cims_struct = None
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def init_amalgamated_cims_struct(self):
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self.amalgamated_cims_struct = acims.AmalgamatedCims(self.net_graph.get_states_number_of_all_nodes_sorted(),
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self.net_graph.get_nodes(),
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self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes())
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def parameters_estimation(self):
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print("Starting computing")
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t0 = tm.time()
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for trajectory in self.sample_path.trajectories:
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#tr_length = trajectory.size()
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self.parameters_estimation_single_trajectory(trajectory.get_trajectory())
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#print("Finished Trajectory number", indx)
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t1 = tm.time() - t0
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print("Elapsed Time ", t1)
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def parameters_estimation_single_trajectory(self, trajectory):
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#t0 = tm.time()
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row_length = trajectory.shape[1]
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for indx, row in enumerate(trajectory[:-1]):
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self.compute_sufficient_statistics_for_row(trajectory[indx], trajectory[indx + 1], row_length)
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"""if trajectory[indx + 1][1] != -1:
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transition = self.find_transition(trajectory[indx], trajectory[indx + 1], row_length)
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which_node = transition[0]
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# print(which_node)
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which_matrix = self.which_matrix_to_update(row, transition[0])
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which_element = transition[1]
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self.amalgamated_cims_struct.update_state_transition_for_matrix(which_node, which_matrix, which_element)
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#changed_node = which_node
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time = self.compute_time_delta(trajectory[indx], trajectory[indx + 1])
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which_element = transition[1][0]
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(which_node, which_matrix, which_element,
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time)
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for node_indx in range(0, 3):
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if node_indx != transition[0]:
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# print(node)
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which_node = node_indx
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which_matrix = self.which_matrix_to_update(row, node_indx)
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which_element = row[node_indx + 1]
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(
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which_node, which_matrix, which_element, time)"""
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#t1 = tm.time() - t0
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#print("Elapsed Time ", t1)
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def compute_sufficient_statistics_for_row(self, current_row, next_row, row_length):
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#time = self.compute_time_delta(current_row, next_row)
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time = current_row[0]
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for indx in range(1, row_length):
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if current_row[indx] != next_row[indx] and next_row[indx] != -1:
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transition = [indx - 1, (current_row[indx], next_row[indx])]
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which_node = transition[0]
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which_matrix = self.which_matrix_to_update(current_row, transition[0])
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which_element = transition[1]
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self.amalgamated_cims_struct.update_state_transition_for_matrix(which_node, which_matrix, which_element)
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which_element = transition[1][0]
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(which_node, which_matrix,
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which_element,
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time)
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else:
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which_node = indx - 1
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which_matrix = self.which_matrix_to_update(current_row, which_node)
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which_element = current_row[indx]
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self.amalgamated_cims_struct.update_state_residence_time_for_matrix(
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which_node, which_matrix, which_element, time)
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def find_transition(self, current_row, next_row, row_length):
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for indx in range(1, row_length):
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if current_row[indx] != next_row[indx]:
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return [indx - 1, (current_row[indx], next_row[indx])]
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def compute_time_delta(self, current_row, next_row):
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return next_row[0] - current_row[0]
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def which_matrix_to_update(self, current_row, node_indx): # produce strutture {'X':1, 'Y':2} dove X e Y sono i parent di node_id
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return current_row[self.fancy_indexing_structure[node_indx]]
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# Simple Test #
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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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s1.build_trajectories()
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s1.build_structure()
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g1 = ng.NetworkGraph(s1.structure)
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g1.init_graph()
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pe = ParametersEstimator(s1, g1)
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pe.init_amalgamated_cims_struct()
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print(pe.amalgamated_cims_struct.get_set_of_cims(0).get_cims_number())
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print(pe.amalgamated_cims_struct.get_set_of_cims(1).get_cims_number())
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print(pe.amalgamated_cims_struct.get_set_of_cims(2).get_cims_number())
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#pe.parameters_estimation_single_trajectory(pe.sample_path.trajectories[0].get_trajectory())
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pe.parameters_estimation()
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for matrix in pe.amalgamated_cims_struct.get_set_of_cims(1).actual_cims:
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print(matrix.state_residence_times)
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print(matrix.state_transition_matrix)
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matrix.compute_cim_coefficients()
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print(matrix.cim)
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