L1,L2正则化代码

# L1正则
import numpy as np
from sklearn.linear_model import Lasso
from sklearn.linear_model import SGDRegressor

X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

lasso_reg = Lasso(alpha=0.15)
lasso_reg.fit(X, y)
print(lasso_reg.predict(1.5))

sgd_reg = SGDRegressor(penalty='l1')
sgd_reg.fit(X, y.ravel())
print(sgd_reg.predict(1.5))

# L2正则
import numpy as np
from sklearn.linear_model import Ridge
from sklearn.linear_model import SGDRegressor

X = 2 * np.random.rand(100, 1)
y = 4 + 3 * X + np.random.randn(100, 1)

#两种方式第一种岭回归
ridge_reg = Ridge(alpha=1, solver='auto')
ridge_reg.fit(X, y)
print(ridge_reg.predict(1.5))#预测1.5的值
#第二种 使用随机梯度下降中L2正则
sgd_reg = SGDRegressor(penalty='l2')
sgd_reg.fit(X, y.ravel())
print(sgd_reg.predict(1.5))

 # elastic_net函数
 import numpy as np
 from sklearn.linear_model import ElasticNet
 from sklearn.linear_model import SGDRegressor

 X = 2 * np.random.rand(100, 1)
 y = 4 + 3 * X + np.random.randn(100, 1)
 #两种方式实现Elastic_net
 elastic_net = ElasticNet(alpha=0.1, l1_ratio=0.5)
 elastic_net.fit(X, y)
 print(elastic_net.predict(1.5))

 sgd_reg = SGDRegressor(penalty='elasticnet')
 sgd_reg.fit(X, y.ravel())
 print(sgd_reg.predict(1.5))