欧几里德距离(python3，sklearn)：高效地计算最接近的对及其对应的距离

from sklearn.metrics.pairwise import euclidean_distances
import numpy as np

# 示例数据
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])

# 计算所有点对的欧几里得距离
distances = euclidean_distances(X)

# 初始化最小距离和对应的点对索引
min_distance = np.inf
min_pair = (None, None)

# 遍历距离矩阵，找到最小距离及其对应的点对
for i in range(distances.shape[0]):
    for j in range(i+1, distances.shape[1]):
        if distances[i, j] < min_distance:
            min_distance = distances[i, j]
            min_pair = (i, j)

print(f"最接近的对是 {min_pair}，对应的距离是 {min_distance}")

from sklearn.neighbors import KDTree

# 示例数据
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])

# 构建KD树
tree = KDTree(X)

# 查询最近的点对
distances, indices = tree.query(X, k=2)

# 初始化最小距离和对应的点对索引
min_distance = np.inf
min_pair = (None, None)

# 遍历查询结果，找到最小距离及其对应的点对
for i in range(len(X)):
    if distances[i, 1] < min_distance:
        min_distance = distances[i, 1]
        min_pair = (i, indices[i, 1])

print(f"最接近的对是 {min_pair}，对应的距离是 {min_distance}")