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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 Superseded by reCTBN.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
from numba import njit, int32
import conditional_intensity_matrix as cim
class SetOfCims:
"""
Rappresenta la struttura che aggrega tutte le CIM per la variabile di label node_id
:node_id: la label della varibile a cui fanno riferimento le CIM
:ordered_parent_set: il set dei parent della variabile node_id ordinata secondo la property indx
:value: il numero massimo di stati assumibili dalla variabile
:actual_cims: le CIM della varibile
"""
def __init__(self, node_id, parents_states_number, node_states_number):
self.node_id = node_id
self.parents_states_number = parents_states_number
self.node_states_number = node_states_number
self.actual_cims = []
self.state_residence_times = None
self.transition_matrices = None
self.build_actual_cims_structure()
def build_actual_cims_structure(self):
#cims_number = 1
#for state_number in self.parents_states_number:
#cims_number = cims_number * state_number
if not self.parents_states_number:
#self.actual_cims = np.empty(1, dtype=cim.ConditionalIntensityMatrix)
#self.actual_cims[0] = cim.ConditionalIntensityMatrix(self.node_states_number)
self.state_residence_times = np.zeros((1, self.node_states_number), dtype=np.float)
self.transition_matrices = np.zeros((1,self.node_states_number, self.node_states_number), dtype=np.int)
else:
#self.actual_cims = np.empty(self.parents_states_number, dtype=cim.ConditionalIntensityMatrix)
#self.build_actual_cims(self.actual_cims)
#for indx, matrix in enumerate(self.actual_cims):
#self.actual_cims[indx] = cim.ConditionalIntensityMatrix(self.node_states_number)
self.state_residence_times = \
np.zeros((np.prod(self.parents_states_number), self.node_states_number), dtype=np.float)
self.transition_matrices = np.zeros([np.prod(self.parents_states_number), self.node_states_number,
self.node_states_number], dtype=np.int)
def update_state_transition(self, indexes, element_indx_tuple):
#matrix_indx = self.indexes_converter(indexes)
#print(indexes)
if not indexes:
self.actual_cims[0].update_state_transition_count(element_indx_tuple)
else:
self.actual_cims[indexes].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)
if not which_matrix:
self.actual_cims[0].update_state_residence_time_for_state(which_element, time)
else:
#print(type(which_matrix))
#print(self.actual_cims[(2,2)])
self.actual_cims[which_matrix].update_state_residence_time_for_state(which_element, time)
def build_actual_cims(self, cim_structure):
for indx in range(len(cim_structure)):
if cim_structure[indx] is None:
cim_structure[indx] = cim.ConditionalIntensityMatrix(self.node_states_number)
else:
self.build_actual_cims(cim_structure[indx])
def get_cims_number(self):
return len(self.actual_cims)
def indexes_converter(self, indexes): # Si aspetta array del tipo [2,2] dove
assert len(indexes) == len(self.parents_states_number)
vector_index = 0
if not indexes:
return vector_index
else:
for indx, value in enumerate(indexes):
vector_index = vector_index*self.parents_states_number[indx] + indexes[indx]
return vector_index
def build_cims(self, state_res_times, transition_matrices):
for state_res_time_vector, transition_matrix in zip(state_res_times, transition_matrices):
#print(state_res_time_vector, transition_matrix)
cim_to_add = cim.ConditionalIntensityMatrix(self.node_states_number,
state_res_time_vector, transition_matrix)
cim_to_add.compute_cim_coefficients()
#print(cim_to_add)
self.actual_cims.append(cim_to_add)
self.transition_matrices = None
self.state_residence_times = None
def get_cims(self):
return self.actual_cims
def get_cim(self, index):
flat_index = self.indexes_converter(index)
return self.actual_cims[flat_index]
"""sofc = SetOfCims('Z', [3, 3], 3)
sofc.build_actual_cims_structure()
print(sofc.actual_cims)
print(sofc.actual_cims[0,0])
print(sofc.actual_cims[1,2])
#print(sofc.indexes_converter([]))"""