diff options
Diffstat (limited to 'beliefs/models/beliefupdate/BeliefUpdateNodeModel.py')
-rw-r--r-- | beliefs/models/beliefupdate/BeliefUpdateNodeModel.py | 91 |
1 files changed, 91 insertions, 0 deletions
diff --git a/beliefs/models/beliefupdate/BeliefUpdateNodeModel.py b/beliefs/models/beliefupdate/BeliefUpdateNodeModel.py new file mode 100644 index 0000000..d74eaa7 --- /dev/null +++ b/beliefs/models/beliefupdate/BeliefUpdateNodeModel.py @@ -0,0 +1,91 @@ +import copy +import numpy as np + +from beliefs.models.BayesianModel import BayesianModel +from beliefs.utils.edges_helper import EdgesHelper + + +class BeliefUpdateNodeModel(BayesianModel): + """ + A Bayesian model storing nodes (e.g. Node or BernoulliOrNode) implementing properties + and methods for Pearl's belief update algorithm. + + ref: "Fusion, Propagation, and Structuring in Belief Networks" + Artificial Intelligence 29 (1986) 241-288 + + """ + def __init__(self, nodes_dict): + """ + Input: + nodes_dict: dict + a dict key, value pair as {label_id: instance_of_node_class_or_subclass} + """ + super().__init__(edges=self._get_edges_from_nodes(nodes_dict.values()), + variables=list(nodes_dict.keys()), + cpds=[node.cpd for node in nodes_dict.values()]) + + self.nodes_dict = nodes_dict + + @classmethod + def from_edges(cls, edges, node_class): + """Create nodes from the same node class. + + Input: + edges: list of edge tuples of form ('parent', 'child') + node_class: the Node class or subclass from which to + create all the nodes from edges. + """ + edges_helper = EdgesHelper(edges) + nodes = edges_helper.create_nodes_from_edges(node_class) + nodes_dict = {node.label_id: node for node in nodes} + return cls(nodes_dict) + + @staticmethod + def _get_edges_from_nodes(nodes): + """ + Return list of all directed edges in nodes. + + Args: + nodes: an iterable of objects of the Node class or subclass + Returns: + edges: list of edge tuples + """ + edges = set() + for node in nodes: + if node.parents: + edge_tuples = zip(node.parents, [node.label_id]*len(node.parents)) + edges.update(edge_tuples) + return list(edges) + + def set_boundary_conditions(self): + """ + 1. Root nodes: if x is a node with no parents, set Pi(x) = prior + probability of x. + + 2. Leaf nodes: if x is a node with no children, set Lambda(x) + to an (unnormalized) unit vector, of length the cardinality of x. + """ + for root in self.get_roots(): + self.nodes_dict[root].pi_agg = self.nodes_dict[root].cpd.values + + for leaf in self.get_leaves(): + self.nodes_dict[leaf].lambda_agg = np.ones([self.nodes_dict[leaf].cardinality]) + + @property + def all_nodes_are_fully_initialized(self): + """ + 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_dict.values(): + if not node.is_fully_initialized: + return False + return True + + def copy(self): + """ + Returns a copy of the model. + """ + copy_nodes = copy.deepcopy(self.nodes_dict) + copy_model = self.__class__(nodes_dict=copy_nodes) + return copy_model |