完成图和图的遍历markdown 和代码

Author: PegasusWang

docs/07_哈希表/hashtable.md

@@ -70,7 +70,7 @@ for number in to_insert:
     i = 1
     while index in inserted_index_set:   # 如果计算发现已经占用，继续计算得到下一个可用槽的位置
         print('\th({number}) = {number} % M = {index} collision'.format(number=number, index=index))
-        index = (first_index +  i*i) % M
+        index = (first_index +  i*i) % M   # 根据二次方探查的公式重新计算下一个需要插入的位置
         i += 1
     else:
         print('h({number}) = {number} % M = {index}'.format(number=number, index=index))

docs/18_图与图的遍历/graph.md

@@ -93,17 +93,67 @@ def bfs(graph, start):
             for node in graph[cur_node]:
                 search_queue.push(node)
 
-
-print(list(bfs(GRAPH, 'A')))  # 输出 ['A', 'B', 'F', 'C', 'I', 'G', 'E', 'D', 'H']
+print('bfs:')
+bfs(GRAPH, 'A')
+"""
+bfs:
+A
+B
+F
+C
+I
+G
+E
+D
+H
+"""
 ```
 
 ![](./bfs.png)
 
 ### DFS
+深度优先搜索(DFS)是每遇到一个节点，如果没有被访问过，就直接去访问它的邻居节点，不断加深。代码其实很简单：
+
+```
+DFS_SEARCHED = set()
+
+
+def dfs(graph, start):
+    if start not in DFS_SEARCHED:
+        print(start)
+        DFS_SEARCHED.add(start)
+    for node in graph[start]:
+        if node not in DFS_SEARCHED:
+            dfs(graph, node)
+
+
+print('dfs:')
+dfs(GRAPH, 'A')
+
+"""
+DFS_SEARCHED = set()
+
+
+def dfs(graph, start):
+    if start not in DFS_SEARCHED:
+        print(start)
+        DFS_SEARCHED.add(start)
+    for node in graph[start]:
+        if node not in DFS_SEARCHED:
+            dfs(graph, node)   # 递归访问邻居节点
+
+
+print('dfs:')
+dfs(GRAPH, 'A')
+"""
+```
 
 
 # 思考题
+- DFS 中我们使用到了递归，请你用栈来改写这个函数？
 
 # 延伸阅读
+图的算法还有很多，这里就不一一列举了，感兴趣的读者可以继续阅读一下材料。
+
 - [数据结构之图](https://www.zybuluo.com/guoxs/note/249812)
 - 《算法图解》第六章

docs/18_图与图的遍历/graph.py

@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+
+from collections import deque
+
+
+GRAPH = {
+    'A': ['B', 'F'],
+    'B': ['C', 'I', 'G'],
+    'C': ['B', 'I', 'D'],
+    'D': ['C', 'I', 'G', 'H', 'E'],
+    'E': ['D', 'H', 'F'],
+    'F': ['A', 'G', 'E'],
+    'G': ['B', 'F', 'H', 'D'],
+    'H': ['G', 'D', 'E'],
+    'I': ['B', 'C', 'D'],
+}
+
+
+class Queue(object):
+    def __init__(self):
+        self._deque = deque()
+
+    def push(self, value):
+        return self._deque.append(value)
+
+    def pop(self):
+        return self._deque.popleft()
+
+    def __len__(self):
+        return len(self._deque)
+
+
+def bfs(graph, start):
+    search_queue = Queue()
+    search_queue.push(start)
+    searched = set()
+    while search_queue:   # 队列不为空就继续
+        cur_node = search_queue.pop()
+        if cur_node not in searched:
+            print(cur_node)
+            searched.add(cur_node)
+            for node in graph[cur_node]:
+                search_queue.push(node)
+
+
+print('bfs:')
+bfs(GRAPH, 'A')
+
+
+DFS_SEARCHED = set()
+
+
+def dfs(graph, start):
+    if start not in DFS_SEARCHED:
+        print(start)
+        DFS_SEARCHED.add(start)
+    for node in graph[start]:
+        if node not in DFS_SEARCHED:
+            dfs(graph, node)
+
+
+print('dfs:')
+dfs(GRAPH, 'A')
+
+
+class Stack(object):
+    def __init__(self):
+        self._deque = deque()
+
+    def push(self, value):
+        return self._deque.append(value)
+
+    def pop(self):
+        return self._deque.pop()
+
+    def __len__(self):
+        return len(self._deque)
+
+
+def dfs_use_stack(graph, start):
+    stack = Stack()
+    stack.push(start)
+    searched = set()
+    while stack:
+        cur_node = stack.pop()
+        if cur_node not in searched:
+            print(cur_node)
+            searched.add(cur_node)
+            # 请读者思考这里我为啥加了 reversed，其实不加上是可以的，你看下代码输出
+            for node in reversed(graph[cur_node]):
+                stack.push(node)
+
+
+print('dfs_use_stack:')
+dfs_use_stack(GRAPH, 'A')