def find_nearest_to(self, point: Point):
set_a = set(self.graph.get_points())
point_current = set_a.pop()
num_current_distance = point.distance_to(point_current)
list_neighbors = self.graph.get_neighbours_list(point_current)
while len(list_neighbors) != 0:
# Get shortest distance among neighbors
list_distances = list(
map(lambda x: x[1].distance_to(point), list_neighbors))
num_new_distance = min(list_distances)
if num_current_distance < num_new_distance:
return point_current
# Update current distance
num_current_distance = num_new_distance
# Update point_current
index_of_new_distance = list_distances.index(num_new_distance)
point_current = list_neighbors[index_of_new_distance][1]
# Update set_a
for neighbor in list_neighbors:
set_a.discard(neighbor)
# Update list_neighbors
filter_only_a_marked_points = filter(
lambda x: x[1] in set_a,
self.graph.get_neighbours_list(point_current))
list_neighbors = list(filter_only_a_marked_points)
return point_current
def find_nearest_to(self, point: Point):
current = self.cache
temp = None
minimal = point.distance_to(current)
cluster_set = {x for x in self.graph.get_points()}
cluster_set.discard(current)
while True:
the_smallest = float('inf')
neighbours = self.graph.get_neighbours(current)
cluster_set.difference_update(neighbours)
for neighbour in neighbours:
dist = neighbour.distance_to(point)
if dist < the_smallest:
the_smallest = dist
temp = neighbour
# noinspection PyChainedComparisons
if the_smallest < minimal and len(
cluster_set) != 0 and minimal != 0:
minimal = the_smallest
current = temp
else:
break
self.cache = current
return current
pass
def find_nearest_to(self, point: Point):
clusters = list(self.graph.graph.keys())
dif = float('inf')
nearest = None
for cluster in clusters:
dist = point.distance_to(cluster)
if dist < dif:
dif = dist
nearest = cluster
return nearest
pass
