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k_closest_stars.py
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k_closest_stars.py
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import functools
import heapq
import math
from test_framework import generic_test
from test_framework.test_utils import enable_executor_hook
class Star:
def __init__(self, x, y, z):
self.x, self.y, self.z = x, y, z
@property
def distance(self):
return math.sqrt(self.x**2 + self.y**2 + self.z**2)
def __lt__(self, rhs):
return self.distance < rhs.distance
def __repr__(self):
return str(self.distance)
def __str__(self):
return self.__repr__()
def __eq__(self, rhs):
return math.isclose(self.distance, rhs.distance)
def find_closest_k_stars(stars, k):
# max_heap to store the closest k stars seen so far.
max_heap = []
for star in stars:
# Add each star to the max-heap. If the max-heap size exceeds k, remove
# the maximum element from the max-heap.
# As python has only min-heap, insert tuple (negative of distance, star)
# to sort in reversed distance order.
heapq.heappush(max_heap, (-star.distance, star))
if len(max_heap) == k + 1:
heapq.heappop(max_heap)
# Iteratively extract from the max-heap, which yields the stars sorted
# according from furthest to closest.
return [s[1] for s in heapq.nlargest(k, max_heap)]
def comp(expected_output, output):
if len(output) != len(expected_output):
return False
return all(
math.isclose(s.distance, d)
for s, d in zip(sorted(output), expected_output))
@enable_executor_hook
def find_closest_k_stars_wrapper(executor, stars, k):
stars = [Star(*a) for a in stars]
return executor.run(
functools.partial(find_closest_k_stars, iter(stars), k))
if __name__ == '__main__':
exit(
generic_test.generic_test_main("k_closest_stars.py",
"k_closest_stars.tsv",
find_closest_k_stars_wrapper, comp))