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66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
from __future__ import annotations
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import typing
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from collections.abc import Iterable
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import numpy as np
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Vector = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
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VectorOut = typing.Union[np.float64, int, float] # noqa: UP007
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def euclidean_distance(vector_1: Vector, vector_2: Vector) -> VectorOut:
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"""
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Calculate the distance between the two endpoints of two vectors.
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A vector is defined as a list, tuple, or numpy 1D array.
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>>> euclidean_distance((0, 0), (2, 2))
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2.8284271247461903
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>>> euclidean_distance(np.array([0, 0, 0]), np.array([2, 2, 2]))
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3.4641016151377544
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>>> euclidean_distance(np.array([1, 2, 3, 4]), np.array([5, 6, 7, 8]))
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8.0
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>>> euclidean_distance([1, 2, 3, 4], [5, 6, 7, 8])
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8.0
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"""
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return np.sqrt(np.sum((np.asarray(vector_1) - np.asarray(vector_2)) ** 2))
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def euclidean_distance_no_np(vector_1: Vector, vector_2: Vector) -> VectorOut:
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"""
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Calculate the distance between the two endpoints of two vectors without numpy.
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A vector is defined as a list, tuple, or numpy 1D array.
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>>> euclidean_distance_no_np((0, 0), (2, 2))
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2.8284271247461903
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>>> euclidean_distance_no_np([1, 2, 3, 4], [5, 6, 7, 8])
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8.0
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"""
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return sum((v1 - v2) ** 2 for v1, v2 in zip(vector_1, vector_2)) ** (1 / 2)
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if __name__ == "__main__":
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def benchmark() -> None:
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"""
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Benchmarks
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"""
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from timeit import timeit
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print("Without Numpy")
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print(
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timeit(
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"euclidean_distance_no_np([1, 2, 3], [4, 5, 6])",
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number=10000,
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globals=globals(),
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)
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)
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print("With Numpy")
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print(
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timeit(
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"euclidean_distance([1, 2, 3], [4, 5, 6])",
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number=10000,
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globals=globals(),
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)
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)
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benchmark()
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