Python:提高类中计算欧几里得距离的速度

import numpy as np

def euclidean_distance_numpy(point1, point2):
    return np.sqrt(np.sum((point1 - point2) ** 2))

# 示例
point1 = np.array([1, 2, 3])
point2 = np.array([4, 5, 6])
print(euclidean_distance_numpy(point1, point2))

# 安装Cython
# pip install cython

# 创建一个Cython文件，例如euclidean.pyx
def euclidean_distance_cython(double[:] point1, double[:] point2):
    cdef double distance = 0.0
    for i in range(point1.shape[0]):
        distance += (point1[i] - point2[i]) ** 2
    return distance ** 0.5

# 创建一个setup.py文件
from setuptools import setup
from Cython.Build import cythonize

setup(
    ext_modules=cythonize("euclidean.pyx")
)

# 编译Cython代码
# python setup.py build_ext --inplace

# 使用Cython加速的函数
import euclidean

point1 = [1, 2, 3]
point2 = [4, 5, 6]
print(euclidean.euclidean_distance_cython(point1, point2))

import numba

@numba.jit(nopython=True)
def euclidean_distance_numba(point1, point2):
    distance = 0.0
    for i in range(len(point1)):
        distance += (point1[i] - point2[i]) ** 2
    return distance ** 0.5

# 示例
point1 = [1, 2, 3]
point2 = [4, 5, 6]
print(euclidean_distance_numba(point1, point2))