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 Superseded by reCTBN.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
1.1 KiB

import numpy as np
class ConditionalIntensityMatrix:
def __init__(self, dimension):
self.state_residence_times = np.zeros(shape=dimension)
self.state_transition_matrix = np.zeros(shape=(dimension, dimension), dtype=int)
self.cim = np.zeros(shape=(dimension, dimension), dtype=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):
for i, row in enumerate(self.state_transition_matrix):
row_sum = 0.0
for j, elem in enumerate(row):
rate_coefficient = elem / self.state_residence_times[i]
self.cim[i][j] = rate_coefficient
row_sum = row_sum + rate_coefficient
self.cim[i][i] = -1 * row_sum