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from __future__ import annotations

import typing

from collections.abc import Iterable

import numpy as np

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

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

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.

>>> 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

"""

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()

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