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pip install -r requirement.txt
{
    // ...

    "children": [
        {
            "AI简史": {
                "keywords": [
                    "AI起源",
                    "人工智能简史"
                ],
                "children": []
            }
        }
    ],
}
{
    // ...
    "export": [
        "helloworld.json",
        // ...
    ]
}
{
    "type": "code_options",
    "author": "幻灰龙",
    "source": "helloworld.md",
    "notebook_enable": true
}
# Hello World

HelloWorld, 请阅读如下代码：

```python
import numpy as np

def test():
    X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
    y = np.dot(X, np.array([1, 2])) + 3

    // TODO(选择选项中的代码填充此处)

    y_predict = reg.predict(np.array([[3, 5]]))
    print(y_predict)

if __name__ == '__main__':
    test()
```

若将以下选项中的代码分别填充到上述代码中**TODO**处，哪个选项不是线性模型？

## template

```java
import numpy as np

def test():
    X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
    y = np.dot(X, np.array([1, 2])) + 3

    // 下面的 code 占位符会被替换成答案和选项代码
    $code

    y_predict = reg.predict(np.array([[3, 5]]))
    print(y_predict)

if __name__ == '__main__':
    test()
```


## 答案

```python
from sklearn import svm
reg = svm.SVC(kernel='rbf').fit(X, y)
```

## 选项

### 使用 LinearRegression

```python
from sklearn.linear_model import LinearRegression
reg = LinearRegression().fit(X, y)
```

### 使用岭回归

```python
from sklearn.linear_model import Ridge
reg = Ridge(alpha=0.1)
```

### 使用拉索算法

```python
from sklearn.linear_model import Lasso
reg = Lasso(alpha=0.1).fit(X, y)
```