DFS implements using Python

depth-first-search-python.ipynb

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 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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   "source": [
    "class Node:\n",
    "    \n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        self.visited = False\n",
    "        self.neighbors = []\n",
    "        self.predecessor = None  # use in shortest path\n",
    "        \n",
    "    def __repr__(self):\n",
    "        return 'Node(name={})'.format(self.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DFS:\n",
    "    \"\"\"\n",
    "    For BFS, use queue; For DFS, use stack / recursion(os stack)\n",
    "    \"\"\"\n",
    "    def __init__(self, start):\n",
    "        self.start = start\n",
    "        \n",
    "    def traversal(self):\n",
    "        interface = self.stack()\n",
    "        interface(self.start)\n",
    "        return self.result\n",
    "        \n",
    "    def stack(self):\n",
    "        self.result = []\n",
    "        def interface(node):\n",
    "            self.result.append(node)            \n",
    "            node.visited = True\n",
    "            for n in node.neighbors:\n",
    "                if not n.visited:\n",
    "                    interface(n) \n",
    "        return interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image](https://storage.googleapis.com/ssivart/super9-blog/level-order-traversal.png)\n",
    "[圖片來源](http://alrightchiu.github.io/SecondRound/graph-breadth-first-searchbfsguang-du-you-xian-sou-xun.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "na = Node('A')\n",
    "nb = Node('B')\n",
    "nc = Node('C')\n",
    "nd = Node('D')\n",
    "ne = Node('E')\n",
    "nf = Node('F')\n",
    "\n",
    "na.neighbors = [nb, nc]\n",
    "nb.neighbors = [nd, ne]\n",
    "nc.neighbors = [nf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs = DFS(na)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Node(name=A)\n",
      "Node(name=B)\n",
      "Node(name=D)\n",
      "Node(name=E)\n",
      "Node(name=C)\n",
      "Node(name=F)\n"
     ]
    }
   ],
   "source": [
    "for node in dfs.traversal():\n",
    "    print(node)"
   ]
  }
 ],
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