邻域粗糙集python代码实例

以下是基于邻域粗糙集的Python代码实例：

import numpy as np

# 定义邻域半径
radius = 2

# 定义属性集
attributes = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# 定义决策集
decisions = np.array([0, 1, 0])

# 定义邻域函数
def neighborhood(x, y):
    return np.linalg.norm(x - y) <= radius

# 定义邻域粗糙集函数
def neighborhood_rough_set(attributes, decisions):
    n = len(attributes)
    lower_approximation = set(range(n))
    for i in range(n):
        for j in range(n):
            if neighborhood(attributes[i], attributes[j]) and decisions[i] != decisions[j]:
                lower_approximation.discard(i)
                break
    return lower_approximation

# 输出邻域粗糙集
print(neighborhood_rough_set(attributes, decisions))

该代码实现了基于邻域粗糙集的属性约简，其中定义了邻域半径、属性集、决策集和邻域函数，并通过邻域函数计算出邻域粗糙集。