模糊神经森林python代码
时间: 2023-07-22 08:14:06 浏览: 41
以下是一个简单的模糊神经森林的Python代码示例:
```python
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
# 生成虚拟数据
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([4, 5, 6])
# 定义模糊隶属度函数
def fuzzy_membership(x, a, b, c):
if x < a or x > c:
return 0
elif x >= a and x <= b:
return (x - a) / (b - a)
elif x >= b and x <= c:
return (c - x) / (c - b)
# 将输入数据转换为模糊隶属度矩阵
X_fuzzy = np.zeros((X.shape[0], X.shape[1], 3))
for i in range(X.shape[0]):
for j in range(X.shape[1]):
X_fuzzy[i][j][0] = fuzzy_membership(X[i][j], 1, 2, 3)
X_fuzzy[i][j][1] = fuzzy_membership(X[i][j], 3, 4, 6)
X_fuzzy[i][j][2] = fuzzy_membership(X[i][j], 6, 8, 9)
# 训练模糊神经森林
fuzzy_forest = RandomForestRegressor(n_estimators=10)
fuzzy_forest.fit(X_fuzzy.reshape(X.shape[0], -1), y)
# 预测并计算均方误差
y_pred = fuzzy_forest.predict(X_fuzzy.reshape(X.shape[0], -1))
mse = mean_squared_error(y, y_pred)
print("均方误差:", mse)
```
在这个例子中,我们首先定义了一个模糊隶属度函数,然后将输入数据转换为模糊隶属度矩阵。然后,我们使用sklearn库中的随机森林回归器训练模糊神经森林,并使用训练后的模型进行预测和均方误差计算。