用python实现粗糙集
时间: 2023-09-19 22:07:25 浏览: 44
粗糙集是一种基于粗糙集近似的不确定性处理方法,用于处理不确定性或不完整性的数据。在 Python 中,我们可以使用 `numpy` 库来实现粗糙集,具体步骤如下:
1. 导入 `numpy` 库:
```python
import numpy as np
```
2. 定义数据集,并将其转换为 `numpy` 数组:
```python
data_set = [['a', 'b', 'd'],
['a', 'c', 'd', 'e'],
['b', 'c', 'e'],
['a', 'b', 'd', 'e'],
['a', 'c', 'e']]
data_set = np.array(data_set)
```
3. 定义属性集:
```python
attributes = ['a', 'b', 'c', 'd', 'e']
```
4. 定义函数来计算属性集中每个属性的正域和反域:
```python
def positive_region(data_set, attribute):
positive = []
for i in range(len(data_set)):
if attribute in data_set[i]:
positive.append(i)
return positive
def negative_region(data_set, attribute):
negative = []
for i in range(len(data_set)):
if attribute not in data_set[i]:
negative.append(i)
return negative
```
5. 定义函数来计算属性集中每个属性的约简:
```python
def reduct(data_set, attributes):
reduct = []
for i in range(len(attributes)):
new_attributes = reduct + [attributes[i]]
condition = [positive_region(data_set, a) for a in new_attributes]
if len(set.intersection(*condition)) == len(set.intersection(*[positive_region(data_set, a) for a in reduct])):
reduct = new_attributes
return reduct
```
6. 运行程序并输出结果:
```python
for attribute in attributes:
print("Attribute:", attribute)
print("Positive Region:", positive_region(data_set, attribute))
print("Negative Region:", negative_region(data_set, attribute))
print("Reduct:", reduct(data_set, attributes))
print()
```
完整代码如下:
```python
import numpy as np
data_set = [['a', 'b', 'd'],
['a', 'c', 'd', 'e'],
['b', 'c', 'e'],
['a', 'b', 'd', 'e'],
['a', 'c', 'e']]
data_set = np.array(data_set)
attributes = ['a', 'b', 'c', 'd', 'e']
def positive_region(data_set, attribute):
positive = []
for i in range(len(data_set)):
if attribute in data_set[i]:
positive.append(i)
return positive
def negative_region(data_set, attribute):
negative = []
for i in range(len(data_set)):
if attribute not in data_set[i]:
negative.append(i)
return negative
def reduct(data_set, attributes):
reduct = []
for i in range(len(attributes)):
new_attributes = reduct + [attributes[i]]
condition = [positive_region(data_set, a) for a in new_attributes]
if len(set.intersection(*condition)) == len(set.intersection(*[positive_region(data_set, a) for a in reduct])):
reduct = new_attributes
return reduct
for attribute in attributes:
print("Attribute:", attribute)
print("Positive Region:", positive_region(data_set, attribute))
print("Negative Region:", negative_region(data_set, attribute))
print("Reduct:", reduct(data_set, attributes))
print()
```