在Python中，如何将numpy数组与矩阵的每一行进行比较，以选择与向量最相似的行？

import numpy as np

array = np.array([1, 2, 3])  # numpy数组
matrix = np.array([[4, 5, 6], [7, 8, 9], [10, 11, 12]])  # 矩阵

similarity = np.dot(matrix, array) / (np.linalg.norm(matrix, axis=1) * np.linalg.norm(array))

most_similar_row = np.argmax(similarity)

import numpy as np

array = np.array([1, 2, 3])  # numpy数组
matrix = np.array([[4, 5, 6], [7, 8, 9], [10, 11, 12]])  # 矩阵

similarity = np.dot(matrix, array) / (np.linalg.norm(matrix, axis=1) * np.linalg.norm(array))
most_similar_row = np.argmax(similarity)

print("最相似的行索引：", most_similar_row)
print("最相似的行：", matrix[most_similar_row])