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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 Superseded by reCTBN.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/structure_graph/conditional_intensity_matri...

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import numpy as np
import sys
sys.path.append('../')
class ConditionalIntensityMatrix:
"""Abstracts the Conditional Intesity matrix of a node as aggregation of the state residence times vector
and state transition matrix and the actual CIM matrix.
:param state_residence_times: state residence times vector
:type state_residence_times: numpy.array
:param state_transition_matrix: the transitions count matrix
:type state_transition_matrix: numpy.ndArray
:_cim: the actual cim of the node
"""
def __init__(self, state_residence_times: np.array, state_transition_matrix: np.array):
"""Constructor Method
"""
self._state_residence_times = state_residence_times
self._state_transition_matrix = state_transition_matrix
self._cim = self.state_transition_matrix.astype(np.float64)
def compute_cim_coefficients(self):
"""Compute the coefficients of the matrix _cim by using the following equality q_xx' = M[x, x'] / T[x].
The class member ``_cim`` will contain the computed cim
"""
np.fill_diagonal(self._cim, self._cim.diagonal() * -1)
self._cim = ((self._cim.T + 1) / (self._state_residence_times + 1)).T
@property
def state_residence_times(self):
return self._state_residence_times
@property
def state_transition_matrix(self):
return self._state_transition_matrix
@property
def cim(self):
return self._cim
def __repr__(self):
return 'CIM:\n' + str(self.cim)