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Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍 https://github.com/madlabunimib/PyCTBN
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PyCTBN/main_package/classes/set_of_cims.py

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import numpy as np
import conditional_intensity_matrix as cim
class SetOfCims:
def __init__(self, node_id, ordered_parent_set, value_type):
self.node_id = node_id
self.ordered_parent_set = ordered_parent_set
self.value = value_type
self.actual_cims = None
self.build_actual_cims_structure()
def build_actual_cims_structure(self):
cims_number = self.value**len(self.ordered_parent_set)
self.actual_cims = np.empty(cims_number, dtype=cim.ConditionalIntensityMatrix)
for indx, matrix in enumerate(self.actual_cims):
self.actual_cims[indx] = cim.ConditionalIntensityMatrix(self.value)
def update_state_transition(self, dict_of_indexes, element_indx_tuple):
matrix_indx = self.indexes_converter(dict_of_indexes)
#print("Converted Indx SRT")
#print(matrix_indx)
self.actual_cims[matrix_indx].update_state_transition_count(element_indx_tuple)
def update_state_residence_time(self, which_matrix, which_element, time):
matrix_indx = self.indexes_converter(which_matrix)
#print("Converted Indx")
#print(matrix_indx)
#print("Updating Time for variable ",self.node_id)
self.actual_cims[matrix_indx].update_state_residence_time_for_state(which_element, time)
def get_cims_number(self):
return len(self.actual_cims)
def indexes_converter(self, dict_of_indexes): # Si aspetta oggetti del tipo {X:1, Y:1, Z:0}
#print(dict_of_indexes)
if not dict_of_indexes:
return 0
else:
literal_index = ""
for node in self.ordered_parent_set:
literal_index = literal_index + str(dict_of_indexes[node])
#print(literal_index)
return int(literal_index, self.value)
"""sofc = SetOfCims('W', ['X','Y', 'Z'], 2)
sofc.build_actual_cims_structure()
print(sofc.actual_cims)
print(sofc.indexes_converter({'X':1, 'Y':1, 'Z':0}))"""