train_errors = [] test_errors = [] for lambda_val in lambdas: pipeline = Pipeline([ ('polynomial_features', PolynomialFeatures(degree=5)), ('ridge_regression', c.SGDLinearRegressor(batch_size=20, eta=0.01, tau_max=1000, epsilon=0.00001, lambda_=lambda_val, random_state=42))]) train_mse = 0 test_mse = 0 for i in range(10): X_train, y_train = c.generate_data(20) X_test, y_test = c.generate_data(1000) pipeline.fit(X_train.reshape(-1, 1), y_train) y_train_pred = pipeline.predict(X_train.reshape(-1, 1)) y_test_pred = pipeline.predict(X_test.reshape(-1, 1)) train_mse += mean_squared_error(y_train, y_train_pred) test_mse += mean_squared_error(y_test, y_test_pred) train_errors.append(train_mse / 10) test_errors.append(test_mse / 10)给这段代码加注释
时间: 2023-12-24 13:09:00 浏览: 86
下面是加了注释的代码:
```python
# 定义两个空列表,用于存储不同正则化参数下的训练误差和测试误差
train_errors = []
test_errors = []
# 遍历一系列正则化参数值
for lambda_val in lambdas:
# 定义管道对象,包含多项式特征转换和SGD线性回归模型
pipeline = Pipeline([
('polynomial_features', PolynomialFeatures(degree=5)),
('ridge_regression', c.SGDLinearRegressor(batch_size=20, eta=0.01, tau_max=1000,
epsilon=0.00001, lambda_=lambda_val, random_state=42))
])
# 定义训练误差和测试误差
train_mse = 0
test_mse = 0
# 对每个正则化参数值进行10次训练和测试
for i in range(10):
# 生成20个训练样本和对应标签,1000个测试样本和对应标签
X_train, y_train = c.generate_data(20)
X_test, y_test = c.generate_data(1000)
# 在训练集上拟合模型
pipeline.fit(X_train.reshape(-1, 1), y_train)
# 在训练集和测试集上进行预测
y_train_pred = pipeline.predict(X_train.reshape(-1, 1))
y_test_pred = pipeline.predict(X_test.reshape(-1, 1))
# 计算并累加训练误差和测试误差
train_mse += mean_squared_error(y_train, y_train_pred)
test_mse += mean_squared_error(y_test, y_test_pred)
# 将每个正则化参数值下的训练误差和测试误差除以10,并存储到对应的列表中
train_errors.append(train_mse / 10)
test_errors.append(test_mse / 10)
```
注释中解释了代码的每个部分的作用和功能,包括定义列表、遍历正则化参数、定义管道对象、生成数据、拟合模型、预测,以及计算训练误差和测试误差。通过注释,可以更加清晰地理解代码的功能和执行流程。
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