def build_join_tree(self, triangulatedGraph):
# The Triangulated Graph is really a graph of cliques.
cliques = triangulatedGraph.cliques
# We start by creating a forest of trees, one for each clique.
forest = [JoinTree(clique) for clique in cliques]
sepsetHeap = self.create_sepset_priority_queue(cliques)
# Join n - 1 sepsets together forming (hopefully) a single tree.
for n in range(len(forest) - 1):
while sepsetHeap:
sepset = heapq.heappop(sepsetHeap)
# Find out which tree each clique is from
joinTreeX = GraphUtilities.getTree(forest, sepset.cliqueX)
joinTreeY = GraphUtilities.getTree(forest, sepset.cliqueY)
if not joinTreeX == joinTreeY:
# If the cliques are on different trees, then join to make a larger one.
joinTreeX.merge(sepset, joinTreeY)
forest.remove(joinTreeY)
break
if len(forest) > 1:
raise BadTreeStructure(
"Inference on a forest of Junction Trees is not yet supported")
else:
tree = forest[0]
tree.init_clique_potentials(self.bnet.nodes)
return tree
def global_propagation(self):
self.joinTree.initialized = False
# Arbitrarily pick a clique to be the root node, could be OPTIMIZED
startClique = self.joinTree.nodes.pop()
# Seems very awkward, but with sets there is no
# way that I know of to get an element without popping it off.
self.joinTree.nodes.add(startClique)
GraphUtilities.unmark_all_nodes(self.joinTree)
# We use 0 to denote that there was no prevCluster
self.collect_evidence(0, startClique, 0, True)
GraphUtilities.unmark_all_nodes(self.joinTree)
self.distribute_evidence(startClique)
def global_propagation(self):
self.joinTree.initialized = False
# Arbitrarily pick a clique to be the root node, could be OPTIMIZED
startClique = self.joinTree.nodes.pop()
# Seems very awkward, but with sets there is no
# way that I know of to get an element without popping it off.
self.joinTree.nodes.add(startClique)
GraphUtilities.unmark_all_nodes(self.joinTree)
# We use 0 to denote that there was no prevCluster
self.collect_evidence(0, startClique, 0, True)
GraphUtilities.unmark_all_nodes(self.joinTree)
self.distribute_evidence(startClique)
def __init__(self, moral):
Graph.__init__(self, moral.nodes)
heap = GraphUtilities.ClusterBinaryHeap()
# Copy the graph so that we can destroy the copy as we insert it into heap.
# Deep copy isn't working, need to trace down bug but for now use hack.
for i, node in enumerate(moral.deep_copy_nodes()):
heap.insert(node)
inducedCliques = []
nodes = list(self.nodes)
# Want nodes in their index order
nodes.sort
for (node, edges) in heap:
realnode = nodes[node.index]
for edge in edges:
# We need to make sure we reference the nodes in the actual graph,
# not the copied ones that were inserted into the heap.
node1 = nodes[edge[0].index]
node2 = nodes[edge[1].index]
self.connect_nodes(node1, node2)
clique = Clique(realnode.neighbors.union([realnode]))
# We only add clique to inducedCliques if is not contained in a previously added clique
GraphUtilities.addClique(inducedCliques, clique)
self.cliques = inducedCliques
def build_join_tree (self, triangulatedGraph):
# The Triangulated Graph is really a graph of cliques.
cliques = triangulatedGraph.cliques
# We start by creating a forest of trees, one for each clique.
forest = [JoinTree(clique) for clique in cliques]
sepsetHeap = self.create_sepset_priority_queue(cliques)
# Join n - 1 sepsets together forming (hopefully) a single tree.
for n in range(len(forest) - 1):
while sepsetHeap:
sepset = heapq.heappop(sepsetHeap)
# Find out which tree each clique is from
joinTreeX = GraphUtilities.getTree(forest, sepset.cliqueX)
joinTreeY = GraphUtilities.getTree(forest, sepset.cliqueY)
if not joinTreeX == joinTreeY:
# If the cliques are on different trees, then join to make a larger one.
joinTreeX.merge(sepset, joinTreeY)
forest.remove(joinTreeY)
break
if len(forest) > 1:
raise BadTreeStructure("Inference on a forest of Junction Trees is not yet supported")
else:
tree = forest[0]
tree.init_clique_potentials(self.bnet.nodes)
return tree