# Ideone机器学习实验模板
print("=" * 70)
print("             机器学习线性回归实验报告")
print("=" * 70)
 
# 1. 环境检查
try:
    import numpy as np
    from sklearn.linear_model import LinearRegression
    from sklearn.metrics import r2_score
    print("✅ 环境检查通过")
except ImportError as e:
    print(f"❌ 环境错误: {e}")
    exit()
 
# 2. 数据生成
np.random.seed(42)
X = np.random.normal(5, 2, 100)
y = 2.5 * X + 1.0 + np.random.normal(0, 1, 100)
 
print(f"\n📊 实验数据:")
print(f"• 样本数量: {len(X)}")
print(f"• 特征范围: {X.min():.1f} ~ {X.max():.1f}")
print(f"• 目标范围: {y.min():.1f} ~ {y.max():.1f}")
 
# 3. 模型训练
from sklearn.model_selection import train_test_split
X_reshaped = X.reshape(-1, 1)
X_train, X_test, y_train, y_test = train_test_split(X_reshaped, y, test_size=0.2)
 
model = LinearRegression()
model.fit(X_train, y_train)
 
# 4. 结果分析
y_pred = model.predict(X_test)
r2 = r2_score(y_test, y_pred)
 
print(f"\n📈 实验结果:")
print(f"• 回归方程: y = {model.coef_[0]:.3f}x + {model.intercept_:.3f}")
print(f"• 模型准确率: {r2:.1%}")
print(f"• 特征重要性: {model.coef_[0]:.3f}")
 
print(f"\n💡 结论:")
if r2 > 0.8:
    print("模型拟合优秀，可用于实际预测")
elif r2 > 0.6:
    print("模型拟合良好，具有参考价值")
else:
    print("模型需要进一步优化")
 
print("\n" + "=" * 70)
print("                 实验完成")
print("=" * 70)

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