def optimize_removal(solver: GraphSolver, tree: Graph, orig_cost: float):
candidates = [
v for v in tree.nodes
if not solver.is_required(v) and is_leaf(tree, v)
]
while candidates:
# print(candidates)
# random.seed(0)
node = random.choice(candidates)
candidates.remove(node)
edge = (node, list(tree.neighbors(node))[0])
solver.unvisit(node)
new_cost = average_pairwise_distance(tree)
if new_cost < orig_cost:
# print('removed', node)
return optimize_removal(solver, tree, new_cost)
else:
solver.add_edge(edge)
return orig_cost
def optimize_removal_sorted(solver: GraphSolver, tree: Graph,
orig_cost: float):
candidates = [
v for v in tree.nodes
if not solver.is_required(v) and is_leaf(tree, v)
]
while candidates:
# print(candidates)
# random.seed(0)
candidates.sort(key=lambda x: closeness_centrality(solver.T, x))
node = candidates.pop(0)
edge = (node, list(tree.neighbors(node))[0])
solver.unvisit(node)
new_cost = average_pairwise_distance(tree)
if new_cost < orig_cost:
# print('removed', node)
return optimize_removal_sorted(solver, tree, new_cost)
else:
solver.add_edge(edge)
return orig_cost