euclidean_distance.py

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((0, 0), (2, 2))
    2.8284271247461903
    >>> euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8])
    8.0
    """
    the_sum = 0
    for i in range(len(vector_1)):
        the_sum += (vector_2[i] - vector_1[i]) ** 2
    return the_sum ** (1 / 2)


if __name__ == "__main__":
    point = np.array([2, 2])
    point_2 = np.array([0, 0])
    print(euclidean_distance(point, point_2))