Merge branch 'dev/function' into 'master'

dev(hansbug): add java function problems

See merge request !4

README.md

@@ -18,7 +18,7 @@ git clone git@codechina.csdn.net:csdn/skill_tree_java.git
 然后进入 skill_tree_java 目录，执行下列命令安装依赖：
 
 ```shell
-pip install -r requrirements.txt
+pip install -r requirements.txt
 ```
 
 然后执行下列命令安装钩子：

data/1.Java初阶/9.控制执行流程/5.函数/config.json

@@ -8,7 +8,8 @@
   "export": [
     "call.json",
     "parser.json",
-    "permutation.json"
+    "permutation.json",
+    "override.json"
   ],
   "keywords_must": [
     "函数",

data/1.Java初阶/9.控制执行流程/5.函数/override.json

+{
+  "type": "code_options",
+  "author": "HansBug",
+  "source": "override.md",
+  "notebook_enable": false,
+  "exercise_id": "0c7313f92ebf4b299f56ff9e9e80b6a7"
+}
\ No newline at end of file

data/1.Java初阶/9.控制执行流程/5.函数/override.md

+# 函数的重写
+
+我们需要编写一个函数`gaussian`，使其生成服从正态分布$N\left(\mu, \sigma^2\right)$的随机数。其中$\mu$为数学期望，$\sigma^2$为方差，关于正态分布可以参考[百度百科](https://baike.baidu.com/item/%E6%AD%A3%E6%80%81%E5%88%86%E5%B8%83/829892)。现已有函数如下：
+
+```java
+import java.util.Random;
+
+public class TestMain {
+    private static final Random random = new Random();
+    private static double gaussian(double mu, double sigma2) {
+        return Math.sqrt(sigma2) * random.nextGaussian() + mu;
+    }
+}
+```
+
+但上述函数仍然需要我们传入$\mu$和$\sigma^2$的值，这会在实际使用这一函数的时候带来麻烦。
+
+为了解决这一问题，我们需要对函数进行重写，使之能快速产生：
+
+1. 数学期望$\mu = 0$的正态分布随机数
+2. 服从标准正态分布（$\mu = 0$，$\sigma^2 = 1$）的随机数
+
+以达成简化`gaussian`函数使用的效果，以下选项正确的是？
+
+## 答案
+
+```java
+import java.util.Random;
+
+public class TestMain {
+    private static final Random random = new Random();
+
+    private static double gaussian() {
+        return gaussian(0, 1);
+    }
+
+    private static double gaussian(double sigma2) {
+        return gaussian(0, sigma2);
+    }
+
+    private static double gaussian(double mu, double sigma2) {
+        return Math.sqrt(sigma2) * random.nextGaussian() + mu;
+    }
+}
+```
+
+## 选项
+
+### Python味的默认值，然而Java并不支持
+
+```java
+import java.util.Random;
+
+public class TestMain {
+    private static final Random random = new Random();
+    private static double gaussian(double mu = 0, double sigma2 = 1) {
+        return Math.sqrt(sigma2) * random.nextGaussian() + mu;
+    }
+}
+```
+
+### 用法正确，但不符合题目要求
+
+```java
+import java.util.Random;
+
+public class TestMain {
+    private static final Random random = new Random();
+
+    private static double gaussian() {
+        return gaussian(0, 1);
+    }
+
+    private static double gaussian(double mu) {
+        return gaussian(mu, 1);
+    }
+
+    private static double gaussian(double mu, double sigma2) {
+        return Math.sqrt(sigma2) * random.nextGaussian() + mu;
+    }
+}
+```
+
+### Python味的默认值，Java并不支持，就一个也不成
+
+```java
+import java.util.Random;
+
+public class TestMain {
+    private static final Random random = new Random();
+
+    private static double gaussian() {
+        return gaussian(0, 1);
+    }
+
+    private static double gaussian(double mu, double sigma2 = 1) {
+        return Math.sqrt(sigma2) * random.nextGaussian() + mu;
+    }
+}
+```
+
+### 函数重写时的标识符必须可区分
+
+```java
+import java.util.Random;
+
+public class TestMain {
+    private static final Random random = new Random();
+
+    private static double gaussian() {
+        return gaussian(0, 1);
+    }
+
+    private static double gaussian(double sigma2) {
+        return gaussian(0, sigma2);
+    }
+
+    private static double gaussian(double mu, double sigma2) {
+        return Math.sqrt(sigma2) * random.nextGaussian() + mu;
+    }
+
+    private static double gaussian(double sigma2, double mu) {
+        return Math.sqrt(sigma2) * random.nextGaussian() + mu;
+    }
+}
+```
+