import pandas as pd
+from sklearn.linear_model import LinearRegression, Ridge, Lasso
+from sklearn.model_selection import train_test_split
+
+# 读取数据
+data = pd.DataFrame(
+ [[7, 26, 6, 60, 78.5],
+ [1, 29, 15, 52, 74.3],
+ [11, 56, 8, 20, 104.3],
+ [11, 31, 8, 47, 87.6],
+ [7, 52, 6, 33, 95.9],
+ [11, 55, 9, 22, 109.2],
+ [3, 71, 17, 6, 102.7],
+ [1, 31, 22, 44, 72.5],
+ [2, 54, 18, 22, 93.1],
+ [21, 47, 4, 26, 115.9],
+ [1, 40, 23, 34, 83.8],
+ [11, 66, 9, 12, 113.3],
+ [10, 68, 8, 12, 109.4]],
+ columns=['X1', 'X2', 'X3', 'X4', 'Y']
+)
+
+# 将数据分为训练集和测试集
+seed = 37 # 以学号后两位为随机种子
+train_data, test_data = train_test_split(data, test_size=0.2, random_state=seed)
+
+# 线性回归
+linreg = LinearRegression()
+linreg.fit(train_data[['X1', 'X2', 'X3', 'X4']], train_data['Y'])
+
+print('=====线性回归=====')
+print('W:', linreg.coef_)
+print('b:', linreg.intercept_)
+print('Train R^2 Score:', linreg.score(train_data[['X1', 'X2', 'X3', 'X4']], train_data['Y']))
+print('Test R^2 Score:', linreg.score(test_data[['X1', 'X2', 'X3', 'X4']], test_data['Y']))
+
+# 岭回归
+ridge = Ridge(alpha=0.1)
+ridge.fit(train_data[['X1', 'X2', 'X3', 'X4']], train_data['Y'])
+
+print('\n=====岭回归=====')
+print('W:', ridge.coef_)
+print('b:', ridge.intercept_)
+print('Train R^2 Score:', ridge.score(train_data[['X1', 'X2', 'X3', 'X4']], train_data['Y']))
+print('Test R^2 Score:', ridge.score(test_data[['X1', 'X2', 'X3', 'X4']], test_data['Y']))
+
+# Lasso回归
+lasso = Lasso(alpha=0.1)
+lasso.fit(train_data[['X1', 'X2', 'X3', 'X4']], train_data['Y'])
+
+print('\n=====Lasso回归=====')
+print('W:', lasso.coef_)
+print('b:', lasso.intercept_)
+print('Train R^2 Score:', lasso.score(train_data[['X1', 'X2', 'X3', 'X4']], train_data['Y']))
+print('Test R^2 Score:', lasso.score(test_data[['X1', 'X2', 'X3', 'X4']], test_data['Y']))
+
+