def setUp(self):
self.partitions = []
graph = nx.Graph()
nx.add_star(graph, range(20))
nx.add_path(graph, range(10))
self.partitions.append(
NxPartitionGraphBased(
graph, representation={node: node % 6
for node in graph}))
graph = nx.DiGraph()
nx.add_star(graph, range(20))
nx.add_path(graph, range(10))
self.partitions.append(
NxPartitionGraphBased(
graph, representation={node: node % 6
for node in graph}))
self.uniform_icl_ex = IntegratedCompleteLikelihoodExact(
is_directed=False,
hyperprior=IntegratedCompleteLikelihoodExact.UNIFORM_HYPERPRIOR)
self.jeffrey_icl_ex = IntegratedCompleteLikelihoodExact(
is_directed=False,
hyperprior=IntegratedCompleteLikelihoodExact.JEFFREY_HYPERPRIOR)
def setUp(self):
self.partitions = []
# ring graph with 4 nodes
graph = nx.DiGraph()
nx.add_path(graph, [0, 1, 2, 3, 0])
self.partitions.append(NxPartitionGraphBased(graph, representation={node: node % 2 for node in graph}))
# node counts 2, 2
# edge matrix
# 0 2
# 2 0
# Ring Graph with selfloop
graph = nx.DiGraph()
nx.add_path(graph, [0, 1, 2, 3, 0, 0])
self.partitions.append(NxPartitionGraphBased(graph, representation={node: node % 2 for node in graph}))
# node counts 2, 2
# edge matrix
# 1 2
# 2 0
# graph with 3 groups
graph = nx.DiGraph()
nx.add_path(graph, [0, 1, 2, 3, 0, 4, 5, 6, 4])
self.partitions.append(NxPartitionGraphBased(graph, representation={0: 0, 1: 1, 2: 1, 3: 1, 4: 2, 5: 2, 6: 2}))
# node counts 1, 3, 3
# edge matrix
# 0 1 1
# 1 2 0
# 0 0 3
graph = nx.DiGraph(nx.karate_club_graph())
self.partitions.append(NxPartitionGraphBased(graph, representation={node: node % 5 for node in graph}))
self._replace_likelihood = TraditionalUnnormalizedLogLikelyhood(is_directed=True)
def setUp(self):
self.partitions = []
graph = nx.Graph()
nx.add_star(graph, range(20))
nx.add_path(graph, range(10))
self.partitions.append(NxPartitionGraphBased(graph, representation={node: node % 6 for node in graph}))
graph = nx.DiGraph()
nx.add_star(graph, range(20))
nx.add_path(graph, range(10))
self.partitions.append(NxPartitionGraphBased(graph, representation={node: node % 6 for node in graph}))
self.non_degree_corrected = NewmanReinertNonDegreeCorrected(is_directed=False)
self.degree_corrected = NewmanReinertDegreeCorrected(is_directed=False)