euclidean_distance.py

from __future__ importannotations

importtyping
fromcollections.abcimportIterable

importnumpyasnp

Vector=typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
VectorOut=typing.Union[np.float64, int, float] # noqa: UP007


defeuclidean_distance(vector_1: Vector, vector_2: Vector) ->VectorOut:
"""
 Calculate the distance between the two endpoints of two vectors.
 A vector is defined as a list, tuple, or numpy 1D array.
 >>> float(euclidean_distance((0, 0), (2, 2)))
 2.8284271247461903
 >>> float(euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2])))
 3.4641016151377544
 >>> float(euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])))
 8.0
 >>> float(euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8]))
 8.0
 """
returnnp.sqrt(np.sum((np.asarray(vector_1) -np.asarray(vector_2)) **2))


defeuclidean_distance_no_np(vector_1: Vector, vector_2: Vector) ->VectorOut:
"""
 Calculate the distance between the two endpoints of two vectors without numpy.
 A vector is defined as a list, tuple, or numpy 1D array.
 >>> euclidean_distance_no_np((0, 0), (2, 2))
 2.8284271247461903
 >>> euclidean_distance_no_np([1, 2, 3, 4], [5, 6, 7, 8])
 8.0
 """
returnsum((v1-v2) **2forv1, v2inzip(vector_1, vector_2)) ** (1/2)


if__name__=="__main__":

defbenchmark() ->None:
"""
 Benchmarks
 """
fromtimeitimporttimeit

print("Without Numpy")
print(
timeit(
"euclidean_distance_no_np([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
 )
 )
print("With Numpy")
print(
timeit(
"euclidean_distance([1, 2, 3], [4, 5, 6])",
number=10000,
globals=globals(),
 )
 )

benchmark()