1 files changed, 0 insertions, 91 deletions
diff --git a/beliefs/models/beliefupdate/BeliefUpdateNodeModel.py b/beliefs/models/beliefupdate/BeliefUpdateNodeModel.py
deleted file mode 100644
index d74eaa7..0000000
--- a/beliefs/models/beliefupdate/BeliefUpdateNodeModel.py
+++ /dev/null
@@ -1,91 +0,0 @@
-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