def combine_adj_matrix(starting_node: int, graph1: Matrix, graph2: Matrix,
target_nodes: List[int]) -> Matrix:
if graph1.equals(graph2):
return graph1
elif graph1.subset(graph2):
return graph1
elif graph2.subset(graph1):
return graph2
out = Matrix(graph1.size())
def does_lead_to_target(curr_node, visited: Set[int]):
if curr_node not in target_nodes:
if curr_node not in visited:
all_outgoing = set(
graph1.get_connected_lst(curr_node) +
graph2.get_connected_lst(curr_node))
visited.add(curr_node)
for outgoing in all_outgoing:
if outgoing not in visited:
# out.add_edge(curr_node, outgoing)
if does_lead_to_target(outgoing, visited):
return True
return False
else:
return True
def traverse(curr_node, visited: Set[int]):
# Do nothing if we already visited the node
if curr_node not in visited:
# Get all outgoing edges
all_outgoing = set(
graph1.get_connected_lst(curr_node) +
graph2.get_connected_lst(curr_node))
visited.add(curr_node)
for outgoing in all_outgoing:
# Make sure we don't go in circles, make sure to prune any extraneous edges
if outgoing not in visited and does_lead_to_target(
outgoing, deepcopy(visited)):
out.add_edge(curr_node, outgoing)
traverse(outgoing, visited)
traverse(starting_node, set())
return out
def test_create_matrix_size(self):
m = Matrix(size = 3)
assert m.size() == 3