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 Superseded by reCTBN.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