python模糊综合评价代码
时间: 2023-07-22 19:28:22 浏览: 131
以下是一个简单的 Python 模糊综合评价代码示例:
```python
import numpy as np
import skfuzzy as fuzz
# 输入变量
quality = np.arange(0, 11, 1)
service = np.arange(0, 11, 1)
tip = np.arange(0, 26, 1)
# 模糊化
quality_low = fuzz.trimf(quality, [0, 0, 5])
quality_medium = fuzz.trimf(quality, [0, 5, 10])
quality_high = fuzz.trimf(quality, [5, 10, 10])
service_low = fuzz.trimf(service, [0, 0, 5])
service_medium = fuzz.trimf(service, [0, 5, 10])
service_high = fuzz.trimf(service, [5, 10, 10])
tip_low = fuzz.trimf(tip, [0, 0, 13])
tip_medium = fuzz.trimf(tip, [0, 13, 25])
tip_high = fuzz.trimf(tip, [13, 25, 25])
# 规则
rule1 = fuzz.relation_min(quality_low, service_high)
rule2 = fuzz.relation_min(quality_medium, service_medium)
rule3 = fuzz.relation_min(quality_high, service_low)
# 推理
tip_activation_low = fuzz.relation_min(rule1, tip_low)
tip_activation_medium = fuzz.relation_min(rule2, tip_medium)
tip_activation_high = fuzz.relation_min(rule3, tip_high)
# 反模糊化
tip0 = np.zeros_like(tip)
tip_activation = np.fmax(tip_activation_low, np.fmax(tip_activation_medium, tip_activation_high))
tip_result = fuzz.defuzz(tip, tip_activation, 'centroid')
print("Tip:", tip_result)
```
这个示例代码中,我们使用了模糊逻辑的概念,对餐厅的服务质量和服务水平进行了模糊化,然后通过一些规则进行推理,最后反模糊化得到了一个模糊的评价结果。
阅读全文