1
0
Fork 0
Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍 https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
This repo is archived. You can view files and clone it, but cannot push or open issues/pull-requests.
PyCTBN/main_package/classes/conditional_intensity_matri...

29 lines
1.0 KiB

import numpy as np
class ConditionalIntensityMatrix:
def __init__(self, dimension, state_residence_times, state_transition_matrix):
self.state_residence_times = state_residence_times
self.state_transition_matrix = state_transition_matrix
#self.cim = np.zeros(shape=(dimension, dimension), dtype=float)
self.cim = self.state_transition_matrix.astype(np.float)
def update_state_transition_count(self, element_indx):
#print(element_indx)
#self.state_transition_matrix[element_indx[0]][element_indx[1]] += 1
self.state_transition_matrix[element_indx] += 1
def update_state_residence_time_for_state(self, state, time):
#print("Time updating In state", state, time)
self.state_residence_times[state] += time
def compute_cim_coefficients(self):
np.fill_diagonal(self.cim, self.cim.diagonal() * -1)
self.cim = ((self.cim.T + 1) / (self.state_residence_times + 1)).T
def __repr__(self):
return 'CIM:\n' + str(self.cim)