add helloworld.md

data/1.AI初阶/1.预备知识/1.AI简介/helloworld.md

+# 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)
+```
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