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Co-authored-by: Christian Clauss <cclauss@me.com>

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Co-authored-by: Christian Clauss <cclauss@me.com>

Co-authored-by: Christian Clauss <cclauss@me.com>
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from typing import Iterable, Union
import numpy as np
Vector = Union[Iterable[float], Iterable[int], np.ndarray]
VectorOut = Union[np.float64, int, float]
def euclidean_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.
>>> euclidean_distance((0, 0), (2, 2))
2.8284271247461903
>>> euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2]))
3.4641016151377544
>>> euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]))
8.0
>>> euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8])
8.0
"""
return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))
def euclidean_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
"""
return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2)
if __name__ == "__main__":
def benchmark() -> None:
"""
Benchmarks
"""
from timeit import timeit
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()