计算欧氏距离【python】

奥特曼在思考

调用 numpy内库 计算欧氏距离

# 样本数据
import numpy as np

coords1 = [1, 2, 3]
coords2 = [4, 5, 6]
np_c1 = np.array(coords1)
np_c2 = np.array(coords2)
# NumPy 内建函数
coords =  np.linalg.norm(np_c1 - np_c2)
print(coords)

构建for循环计算欧式距离

import numpy as np

# 样本数据
coords1 = [1, 2, 3]
coords2 = [4, 5, 6]
np_c1 = np.array(coords1)
np_c2 = np.array(coords2)
dist = 0
for (x, y) in zip(coords1, coords2):
    dist += (x - y) ** 2
coords = dist**0.5
print(coords)

生成器计算

import numpy as np

# 样本数据
coords1 = [1, 2, 3]
coords2 = [4, 5, 6]
np_c1 = np.array(coords1)
np_c2 = np.array(coords2)
coords = sum((x - y) ** 2 for x, y in zip(coords1, coords2)) ** 0.5
print(coords)

numpy函数解决

import numpy as np

# 样本数据
coords1 = [1, 2, 3]
coords2 = [4, 5, 6]
np_c1 = np.array(coords1)
np_c2 = np.array(coords2)
coords = np.sqrt(np.sum((np_c1 - np_c2)**2))
print(coords)