Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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36 lines
1.1 KiB
36 lines
1.1 KiB
4 years ago
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import numpy as np
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import conditional_intensity_matrix as cim
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class SetOfCims:
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def __init__(self, node_id, ordered_parent_set, value_type):
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self.node_id = node_id
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self.ordered_parent_set = ordered_parent_set
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self.value = value_type
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self.actual_cims = None
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def build_actual_cims_structure(self):
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cims_number = self.value**len(self.ordered_parent_set)
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self.actual_cims = np.empty(cims_number, dtype=cim.ConditionalIntensityMatrix)
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for indx, matrix in enumerate(self.actual_cims):
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self.actual_cims[indx] = cim.ConditionalIntensityMatrix(self.value)
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def indexes_converter(self, dict_of_indexes): # Si aspetta oggetti del tipo {X:1, Y:1, Z:0}
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literal_index = ""
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for node in self.ordered_parent_set:
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literal_index = literal_index + str(dict_of_indexes[node])
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return int(literal_index, self.value)
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sofc = SetOfCims('W', ['X','Y', 'Z'], 2)
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#sofc.build_actual_cims_structure(sofc.ordered_parent_set, sofc.value)
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sofc.build_actual_cims_structure()
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print(sofc.actual_cims)
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print(sofc.indexes_converter({'X':1, 'Y':1, 'Z':0}))
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