def greedy_routing_annealing():
A = Graph.from_saved('test/data/jazz')
adj, vert = A.adj, A.vert
num_targets, num_rounds, num_tries = 100, 10000
distance = geometry.hyperboloid_distance
vertex_list = list(vert)
for _ in tqdm(range(num_rounds)):
verts = random.sample(vertex_list, k=num_targets+1)
source, targets = verts[0], verts[1:]
neighbours = adj[source]
r_source, theta_source = vert[source]['r'], vert[source]['theta']
targets_coords = {target: (vert[target]['r'], vert[target]['theta']) for target in targets}
neighbours_coords = {neighbour: (vert[neighbour]['r'], vert[neighbour]['theta']) for neighbour in neighbours}
source_dist = {target: distance(target_x, target_y, r_source, theta_source) for target, (target_x, target_y) in targets_coords.items()}
closest_neighbour_dist = {}
for target, (target_x, target_y) in targets_coords.items():
closest_dist = math.inf
for neighbour, (neigh_x, neigh_y) in neighbours_coords.items():
neigh_dist = distance(target_x, target_y, neigh_x, neigh_y)
if neigh_dist < closest_dist:
closest_dist = neigh_dist
closest_neighbour_dist[target] = closest_dist
good = sum([closest_neighbour_dist[target] < source_dist[target] for target in targets])
#r_source_new, theta_source_new = normalized_random_angular_move(r_source, theta_source)
r_source_new, theta_source_new = normalized_random_move(r_source, theta_source)
source_dist_new = {target: distance(target_x, target_y, r_source_new, theta_source_new) for target, (target_x, target_y) in targets_coords.items()}
good_new = sum([closest_neighbour_dist[target] < source_dist_new[target] for target in targets])
if good < good_new:
A.vert[source]['r'] = r_source_new
A.vert[source]['theta'] = theta_source_new
print(utils.greedy_routing_score(A.adj, A.vert, representation='hyperboloid'))
# 0.30126134440855257
A.draw()
