diff options
Diffstat (limited to 'beliefs/models/BayesianModel.py')
| -rw-r--r-- | beliefs/models/BayesianModel.py | 24 |
1 files changed, 12 insertions, 12 deletions
diff --git a/beliefs/models/BayesianModel.py b/beliefs/models/BayesianModel.py index bdfd037..6257a57 100644 --- a/beliefs/models/BayesianModel.py +++ b/beliefs/models/BayesianModel.py @@ -12,18 +12,18 @@ class BayesianModel(DirectedGraph): Bayesian model stores nodes and edges described by conditional probability distributions. """ - def __init__(self, edges, nodes=None): + def __init__(self, edges, nodes_dict=None): """ Input: edges: list of edge tuples of form ('parent', 'child') nodes: (optional) dict a dict key, value pair as {label_id: instance_of_node_class_or_subclass} """ - if nodes is not None: - super().__init__(edges, nodes.keys()) + if nodes_dict is not None: + super().__init__(edges, nodes_dict.keys()) else: super().__init__(edges) - self.nodes = nodes + self.nodes_dict = nodes_dict @classmethod def from_node_class(cls, edges, node_class): @@ -57,10 +57,10 @@ class BayesianModel(DirectedGraph): to an (unnormalized) unit vector, of length the cardinality of x. """ for root in self.get_roots(): - self.nodes[root].pi_agg = self.nodes[root].cpd.values + self.nodes_dict[root].pi_agg = self.nodes_dict[root].cpd.values for leaf in self.get_leaves(): - self.nodes[leaf].lambda_agg = np.ones([self.nodes[leaf].cardinality]) + self.nodes_dict[leaf].lambda_agg = np.ones([self.nodes_dict[leaf].cardinality]) @property def all_nodes_are_fully_initialized(self): @@ -68,7 +68,7 @@ class BayesianModel(DirectedGraph): Returns True if, for all nodes in the model, all lambda and pi messages and lambda_agg and pi_agg are not None, else False. """ - for node in self.nodes.values(): + for node in self.nodes_dict.values(): if not node.is_fully_initialized: return False return True @@ -77,8 +77,8 @@ class BayesianModel(DirectedGraph): """ Returns a copy of the model. """ - copy_edges = self.edges().copy() - copy_nodes = copy.deepcopy(self.nodes) + copy_edges = list(self.edges()).copy() + copy_nodes = copy.deepcopy(self.nodes_dict) copy_model = self.__class__(edges=copy_edges, nodes=copy_nodes) return copy_model @@ -90,7 +90,7 @@ class BayesianModel(DirectedGraph): RETURNS set of strings (labels) """ - return {label for label, node in self.nodes.items() if is_kronecker_delta(node.belief)} + return {label for label, node in self.nodes_dict.items() if is_kronecker_delta(node.belief)} def get_unobserved_variables_in_definite_state(self, observed=set()): """ @@ -105,9 +105,9 @@ class BayesianModel(DirectedGraph): # Assert that beliefs of directly observed vars are kronecker deltas for label in observed: - assert is_kronecker_delta(self.nodes[label].belief), \ + assert is_kronecker_delta(self.nodes_dict[label].belief), \ ("Observed label has belief {} but should be kronecker delta" - .format(self.nodes[label].belief)) + .format(self.nodes_dict[label].belief)) vars_in_definite_state = self.get_variables_in_definite_state() assert observed <= vars_in_definite_state, \ |