|
| 1 | +from typing import Iterable, Union |
| 2 | + |
| 3 | +import numpy as np |
| 4 | + |
| 5 | +Vector = Union[Iterable[float], Iterable[int], np.ndarray] |
| 6 | +VectorOut = Union[np.float64, int, float] |
| 7 | + |
| 8 | + |
| 9 | +def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut: |
| 10 | + """ |
| 11 | + Calculate the distance between the two endpoints of two vectors. |
| 12 | + A vector is defined as a list, tuple, or numpy 1D array. |
| 13 | + >>> euclidean_distance((0, 0), (2, 2)) |
| 14 | + 2.8284271247461903 |
| 15 | + >>> euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2])) |
| 16 | + 3.4641016151377544 |
| 17 | + >>> euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8])) |
| 18 | + 8.0 |
| 19 | + >>> euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8]) |
| 20 | + 8.0 |
| 21 | + """ |
| 22 | + return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2)) |
| 23 | + |
| 24 | + |
| 25 | +def euclidean_distance_no_np(vector_1: Vector, vector_2: Vector) -> VectorOut: |
| 26 | + """ |
| 27 | + Calculate the distance between the two endpoints of two vectors without numpy. |
| 28 | + A vector is defined as a list, tuple, or numpy 1D array. |
| 29 | + >>> euclidean_distance_no_np((0, 0), (2, 2)) |
| 30 | + 2.8284271247461903 |
| 31 | + >>> euclidean_distance_no_np([1, 2, 3, 4], [5, 6, 7, 8]) |
| 32 | + 8.0 |
| 33 | + """ |
| 34 | + return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2) |
| 35 | + |
| 36 | + |
| 37 | +if __name__ == "__main__": |
| 38 | + |
| 39 | + def benchmark() -> None: |
| 40 | + """ |
| 41 | + Benchmarks |
| 42 | + """ |
| 43 | + from timeit import timeit |
| 44 | + |
| 45 | + print("Without Numpy") |
| 46 | + print( |
| 47 | + timeit( |
| 48 | + "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])", |
| 49 | + number=10000, |
| 50 | + globals=globals(), |
| 51 | + ) |
| 52 | + ) |
| 53 | + print("With Numpy") |
| 54 | + print( |
| 55 | + timeit( |
| 56 | + "euclidean_distance([1, 2, 3], [4, 5, 6])", |
| 57 | + number=10000, |
| 58 | + globals=globals(), |
| 59 | + ) |
| 60 | + ) |
| 61 | + |
| 62 | + benchmark() |
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