{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# My Ugly Solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n",
      "[1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "class ListNode:\n",
    "    def __init__(self, val=0, next=None):\n",
    "        self.val = val\n",
    "        self.next = next\n",
    "    \n",
    "    def __repr__(self):\n",
    "        cur = self\n",
    "        vals = []\n",
    "        while cur:\n",
    "            vals.append(cur.val)\n",
    "            cur = cur.next\n",
    "        return str(vals)\n",
    "        \n",
    "class List:\n",
    "    def __init__(self, vals):\n",
    "        if not vals:\n",
    "            raise ValueError('Must initialize List with a non-empty array. ')\n",
    "        cur = self.head = ListNode(vals[0])\n",
    "        for val in vals[1:]:\n",
    "            cur.next = ListNode(val)\n",
    "            cur = cur.next\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return str(self.head)\n",
    "    \n",
    "print(List([1, 2, 3]).head)\n",
    "print(List([1, 2, 3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [139. Word Break](https://leetcode.com/problems/word-break/description/)\n",
    "\n",
    "Medium\n",
    "\n",
    "Given a string s and a dictionary of strings wordDict, return true if s can be segmented into a space-separated sequence of one or more dictionary words.\n",
    "\n",
    "Note that the same word in the dictionary may be reused multiple times in the segmentation.\n",
    "\n",
    "    Example 1:\n",
    "    \n",
    "    Input: s = \"leetcode\", wordDict = [\"leet\",\"code\"]\n",
    "    Output: true\n",
    "    Explanation: Return true because \"leetcode\" can be segmented as \"leet code\".\n",
    "    \n",
    "    Example 2:\n",
    "    \n",
    "    Input: s = \"applepenapple\", wordDict = [\"apple\",\"pen\"]\n",
    "    Output: true\n",
    "    Explanation: Return true because \"applepenapple\" can be segmented as \"apple pen apple\".\n",
    "    Note that you are allowed to reuse a dictionary word.\n",
    "    \n",
    "    Example 3:\n",
    "    \n",
    "    Input: s = \"catsandog\", wordDict = [\"cats\",\"dog\",\"sand\",\"and\",\"cat\"]\n",
    "    Output: false\n",
    "\n",
    "     \n",
    "    Constraints:\n",
    "    \n",
    "    1 <= s.length <= 300\n",
    "    1 <= wordDict.length <= 1000\n",
    "    1 <= wordDict[i].length <= 20\n",
    "    s and wordDict[i] consist of only lowercase English letters.\n",
    "    All the strings of wordDict are unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 邏輯是對的但 TLE（Time Limit Exceeded），因為重覆子問題太多\n",
    "# 例如 s 可以拆成 'cats' + 'and' 再判斷 wordBreak('og', wordDict)\n",
    "#        或者拆成 'cat' + 'sand' 再判斷 wordBreak('og', wordDict)，出現重覆\n",
    "\n",
    "def wordBreak(s, wordDict):\n",
    "    matche_lens = [len(word) for word in wordDict if s.startswith(word)]\n",
    "    if not s:\n",
    "        return True\n",
    "    elif not matche_lens:\n",
    "        return False\n",
    "    else:\n",
    "        for match_len in matche_lens:\n",
    "            if wordBreak(s[match_len:], wordDict):\n",
    "                return True\n",
    "\n",
    "        return False\n",
    "\n",
    "s = \"catsandog\"\n",
    "wordDict = [\"cats\",\"dog\",\"sand\",\"and\",\"cat\"]\n",
    "wordBreak(s, wordDict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# memoized version by AI, accepted on leetcode\n",
    "\n",
    "def wordBreak(s, wordDict, memo=None):\n",
    "    if memo is None:\n",
    "        memo = {}\n",
    "\n",
    "    if s in memo:\n",
    "        return memo[s]\n",
    "\n",
    "    if not s:\n",
    "        return True\n",
    "\n",
    "    for word in wordDict:\n",
    "        if s.startswith(word):\n",
    "            if wordBreak(s[len(word):], wordDict, memo):\n",
    "                memo[s] = True\n",
    "                return True\n",
    "\n",
    "    memo[s] = False\n",
    "    return False\n",
    "\n",
    "s = \"catsandog\"\n",
    "wordDict = [\"cats\",\"dog\",\"sand\",\"and\",\"cat\"]\n",
    "wordBreak(s, wordDict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [238. Product of Array Except Self](https://leetcode.com/problems/product-of-array-except-self/description/)\n",
    "\n",
    "Medium\n",
    "\n",
    "Given an integer array nums, return an array answer such that answer[i] is equal to the product of all the elements of nums except nums[i].\n",
    "\n",
    "The product of any prefix or suffix of nums is guaranteed to fit in a 32-bit integer.\n",
    "\n",
    "You must write an algorithm that runs in O(n) time and without using the division operation.\n",
    "\n",
    "    Example 1:\n",
    "    \n",
    "    Input: nums = [1,2,3,4]\n",
    "    Output: [24,12,8,6]\n",
    "    Example 2:\n",
    "    \n",
    "    Input: nums = [-1,1,0,-3,3]\n",
    "    Output: [0,0,9,0,0]     \n",
    "    \n",
    "    Constraints:\n",
    "    \n",
    "    2 <= nums.length <= 105\n",
    "    -30 <= nums[i] <= 30\n",
    "    The input is generated such that answer[i] is guaranteed to fit in a 32-bit integer.\n",
    "     \n",
    "    Follow up: Can you solve the problem in O(1) extra space complexity? (The output array does not count as extra space for space complexity analysis.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[24, 12, 8, 6]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 算每一個數字的左邊和右邊 cumulative products 存在兩個 lists 裡最後相乘，直觀但多用了 O(n) memory\n",
    "\n",
    "def productExceptSelf(nums):\n",
    "\n",
    "    prefix_products = [1]\n",
    "    for n in nums[:-1]:\n",
    "        prefix_products.append(prefix_products[-1]*n)\n",
    "\n",
    "    suffix_products = [1]\n",
    "    for n in nums[::-1][:-1]:\n",
    "        suffix_products.append(suffix_products[-1]*n)\n",
    "\n",
    "    suffix_products = suffix_products[::-1]\n",
    "\n",
    "    return [p*s for p, s in zip(prefix_products, suffix_products)]\n",
    "\n",
    "\n",
    "nums = [1, 2, 3, 4]\n",
    "\n",
    "productExceptSelf(nums)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 20. Valid Parentheses\n",
    "\n",
    "Easy\n",
    "\n",
    "Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.\n",
    "\n",
    "An input string is valid if: 1. Open brackets must be closed by the same type of brackets, 2. Open brackets must be closed in the correct order, and 3. Every close bracket has a corresponding open bracket of the same type.\n",
    " \n",
    "    Example 1:\n",
    "    \n",
    "    Input: s = \"()\"\n",
    "    Output: true\n",
    "    \n",
    "    Example 2:\n",
    "    \n",
    "    Input: s = \"()[]{}\"\n",
    "    Output: true\n",
    "     \n",
    "    Constraints:\n",
    "    \n",
    "    1 <= s.length <= 104\n",
    "    s consists of parentheses only '()[]{}'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def isValid(s):\n",
    "    if not s:\n",
    "        return True\n",
    "    elif s[0] in ')]}':\n",
    "        return False\n",
    "\n",
    "    stack = []\n",
    "    for i, c in enumerate(s):\n",
    "        if c in '([{':\n",
    "            stack.append(c)\n",
    "        elif c == ')':\n",
    "            if stack[-1] == '(':\n",
    "                del stack[-1]\n",
    "                if len(stack) == 0:\n",
    "                    return isValid(s[i+1:])\n",
    "            else: \n",
    "                return False\n",
    "        elif c == ']':\n",
    "            if stack[-1] == '[':\n",
    "                del stack[-1]\n",
    "                if len(stack) == 0:\n",
    "                    return isValid(s[i+1:])\n",
    "            else: \n",
    "                return False\n",
    "        elif c == '}':\n",
    "            if stack[-1] == '{':\n",
    "                del stack[-1]\n",
    "                if len(stack) == 0:\n",
    "                    return isValid(s[i+1:])\n",
    "            else: \n",
    "                return False\n",
    "\n",
    "    return len(stack) == 0\n",
    "\n",
    "isValid('([)]')"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [138. Copy List with Random Pointer](https://leetcode.com/problems/copy-list-with-random-pointer/)\n",
    "\n",
    "Medium\n",
    "\n",
    "A linked list of length n is given such that each node contains an additional random pointer, which could point to any node in the list, or null.\n",
    "\n",
    "Construct a deep copy of the list. The deep copy should consist of exactly n brand new nodes, where each new node has its value set to the value of its corresponding original node. Both the next and random pointer of the new nodes should point to new nodes in the copied list such that the pointers in the original list and copied list represent the same list state. None of the pointers in the new list should point to nodes in the original list.\n",
    "\n",
    "For example, if there are two nodes X and Y in the original list, where X.random --> Y, then for the corresponding two nodes x and y in the copied list, x.random --> y.\n",
    "\n",
    "Return the head of the copied linked list.\n",
    "\n",
    "The linked list is represented in the input/output as a list of n nodes. Each node is represented as a pair of [val, random_index] where:\n",
    "\n",
    "    val: an integer representing Node.val\n",
    "    random_index: the index of the node (range from 0 to n-1) that the random pointer points to, or null if it does not point to any node.\n",
    "\n",
    "Your code will only be given the head of the original linked list.\n",
    "\n",
    "Example 1:\n",
    "\n",
    "![image138.png](attachment:1beeee2e-5c49-4ff7-bf4e-52379b031ce2.png)\n",
    "\n",
    "    Input: head = [[7,null],[13,0],[11,4],[10,2],[1,0]]\n",
    "    Output: [[7,null],[13,0],[11,4],[10,2],[1,0]]\n",
    "\n",
    "Constraints:\n",
    "\n",
    "    0 <= n <= 1000\n",
    "    -10000 <= Node.val <= 10000\n",
    "    Node.random is null or is pointing to some node in the linked list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 第一個迴圈裡做三件事：\n",
    "# 1. 記下當前 node 的 random 的 id（randIds）\n",
    "# 2. 記下當前 node 的 id 和 idx（id2idx）\n",
    "# 3. 複製當前 node 並放進一個 list 裡\n",
    "# \n",
    "# 第一個迴圈結束之後得到\n",
    "# randIds: [9788560, 140495150944320, 140495150946768, 140495150946240, 140495150944320]\n",
    "# id2idx: {140495150944320: 0, 140495150945808: 1, 140495150946240: 2, 140495150946432: 3, 140495150946768: 4}\n",
    "#\n",
    "# 第二個迴圈根據這些資訊 assign random\n",
    "\n",
    "def copyRandomList(head):\n",
    "    cur = head\n",
    "    copy_dummy = copy_cur = Node(0)\n",
    "\n",
    "    randIds = []\n",
    "    id2idx = {}\n",
    "    ptrs = []\n",
    "    i = 0\n",
    "    while cur:\n",
    "        randIds.append(id(cur.random))\n",
    "        id2idx[id(cur)] = i\n",
    "        \n",
    "        copy_cur.next = Node(x=cur.val)\n",
    "        cur = cur.next\n",
    "        copy_cur = copy_cur.next\n",
    "        ptrs.append(copy_cur)\n",
    "        i += 1\n",
    "\n",
    "    copy_cur = copy_dummy.next\n",
    "    i = 0\n",
    "    while copy_cur:\n",
    "        copy_cur.random = ptrs[id2idx[randIds[i]]] if randIds[i] in id2idx else None\n",
    "        copy_cur = copy_cur.next\n",
    "        i += 1\n",
    "\n",
    "    return copy_dummy.next"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 752. Open the Lock\n",
    "\n",
    "Medium\n",
    "\n",
    "You have a lock in front of you with 4 circular wheels. Each wheel has 10 slots: '0', '1', '2', '3', '4', '5', '6', '7', '8', '9'. The wheels can rotate freely and wrap around: for example we can turn '9' to be '0', or '0' to be '9'. Each move consists of turning one wheel one slot.\n",
    "\n",
    "The lock initially starts at '0000', a string representing the state of the 4 wheels.\n",
    "\n",
    "You are given a list of deadends dead ends, meaning if the lock displays any of these codes, the wheels of the lock will stop turning and you will be unable to open it.\n",
    "\n",
    "Given a target representing the value of the wheels that will unlock the lock, return the minimum total number of turns required to open the lock, or -1 if it is impossible.\n",
    " \n",
    "\n",
    "Example 1:\n",
    "\n",
    "    Input: deadends = [\"0201\",\"0101\",\"0102\",\"1212\",\"2002\"], target = \"0202\"\n",
    "    Output: 6\n",
    "    Explanation:\n",
    "    A sequence of valid moves would be \"0000\" -> \"1000\" -> \"1100\" -> \"1200\" -> \"1201\" -> \"1202\" -> \"0202\".\n",
    "    Note that a sequence like \"0000\" -> \"0001\" -> \"0002\" -> \"0102\" -> \"0202\" would be invalid,\n",
    "    because the wheels of the lock become stuck after the display becomes the dead end \"0102\".\n",
    " \n",
    "\n",
    "Constraints:\n",
    "\n",
    "    1 <= deadends.length <= 500\n",
    "    deadends[i].length == 4\n",
    "    target.length == 4\n",
    "    target will not be in the list deadends.\n",
    "    target and deadends[i] consist of digits only.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deadends = [\"0201\",\"0101\",\"0102\",\"1212\",\"2002\"]\n",
    "target = \"0202\"\n",
    "\n",
    "def openLock(deadends, target):\n",
    "    \n",
    "    if '0000' in deadends:\n",
    "        return -1\n",
    "    \n",
    "    digitNeighbors = {'0': ['1', '9'], \n",
    "        '1': ['2', '0'], '2': ['3', '1'], '3': ['4', '2'], \n",
    "        '4': ['5', '3'], '5': ['6', '4'], '6': ['7', '5'], \n",
    "        '7': ['8', '6'], '8': ['9', '7'], '9': ['0', '8']}\n",
    "\n",
    "    def getNeighbors(comb):\n",
    "        res = set([])\n",
    "        for i in range(len(comb)):\n",
    "            a, b = digitNeighbors[comb[i]]\n",
    "            c = list(comb)\n",
    "            c[i] = a\n",
    "            res.add(''.join(c))\n",
    "            c[i] = b\n",
    "            res.add(''.join(c))\n",
    "        return res\n",
    "\n",
    "    nsteps = {deadend: -1 for deadend in deadends}\n",
    "    rest = set('%04d'%i for i in range(10000)) - set(deadends)\n",
    "    combs = set(['0000'])\n",
    "    n = 0\n",
    "\n",
    "    while rest:\n",
    "        rest -= combs\n",
    "        for comb in combs:\n",
    "            nsteps[comb] = n\n",
    "        \n",
    "        n += 1\n",
    "        combs = set().union(*[getNeighbors(comb) for comb in combs]) & rest\n",
    "        \n",
    "        if target in nsteps:\n",
    "            return nsteps[target]\n",
    "        \n",
    "        if not combs:\n",
    "            return -1\n",
    "    \n",
    "openLock(deadends, target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 259. 3Sum Smaller\n",
    "\n",
    "Medium\n",
    "\n",
    "Given an array of n integers nums and an integer target, find the number of index triplets i, j, k with 0 <= i < j < k < n that satisfy the condition nums[i] + nums[j] + nums[k] < target.\n",
    "\n",
    "Follow up: Could you solve it in O(n^2) runtime?\n",
    "\n",
    "\n",
    "Example 1:\n",
    "\n",
    "    Input: nums = [-2,0,1,3], target = 2\n",
    "    Output: 2\n",
    "    Explanation: Because there are two triplets which sums are less than 2:\n",
    "    [-2,0,1]\n",
    "    [-2,0,3]\n",
    "\n",
    "Constraints:\n",
    "\n",
    "    n == nums.length\n",
    "    0 <= n <= 300\n",
    "    -100 <= nums[i] <= 100\n",
    "    -100 <= target <= 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# brute force O(n^3) solution accepted!\n",
    "\n",
    "nums = [-2,0,1,3]\n",
    "target = 2\n",
    "\n",
    "def threeSumSmaller(nums, target):\n",
    "    return sum([1 for k in range(len(nums)) for j in range(k) for i in range(j) if nums[i]+nums[j]+nums[k] < target])\n",
    "\n",
    "threeSumSmaller(nums, target)"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 588. Design In-Memory File System\n",
    "Hard\n",
    "\n",
    "Design an in-memory file system to simulate the following functions:\n",
    "\n",
    "* ```ls```: Given a path in string format. If it is a file path, return a list that only contains this file's name. If it is a directory path, return the list of file and directory names in this directory. Your output (file and directory names together) should in lexicographic order.\n",
    "* ```mkdir```: Given a directory path that does not exist, you should make a new directory according to the path. If the middle directories in the path don't exist either, you should create them as well. This function has void return type.\n",
    "* ```addContentToFile```: Given a file path and file content in string format. If the file doesn't exist, you need to create that file containing given content. If the file already exists, you need to append given content to original content. This function has void return type.\n",
    "* ```readContentFromFile```: Given a file path, return its content in string format.\n",
    "\n",
    "Example:\n",
    "\n",
    "    Input: \n",
    "    [\"FileSystem\",\"ls\",\"mkdir\",\"addContentToFile\",\"ls\",\"readContentFromFile\"]\n",
    "    [[],[\"/\"],[\"/a/b/c\"],[\"/a/b/c/d\",\"hello\"],[\"/\"],[\"/a/b/c/d\"]]\n",
    "\n",
    "    Output:\n",
    "    [null,[],null,null,[\"a\"],\"hello\"]\n",
    "\n",
    "    Explanation:\n",
    "\n",
    "![image588.png](attachment:af380930-7eff-4625-b38c-dcb77dc75f57.png)\n",
    "\n",
    "Note:\n",
    "\n",
    "1. You can assume all file or directory paths are absolute paths which begin with / and do not end with / except that the path is just \"/\".\n",
    "1. You can assume that all operations will be passed valid parameters and users will not attempt to retrieve file content or list a directory or file that does not exist.\n",
    "1. You can assume that all directory names and file names only contain lower-case letters, and same names won't exist in the same directory.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>args</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mkdir</td>\n",
       "      <td>[/m]</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ls</td>\n",
       "      <td>[/m]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mkdir</td>\n",
       "      <td>[/w]</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ls</td>\n",
       "      <td>[/]</td>\n",
       "      <td>[m, w]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ls</td>\n",
       "      <td>[/w]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ls</td>\n",
       "      <td>[/]</td>\n",
       "      <td>[m, w]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>addContentToFile</td>\n",
       "      <td>[/dycete, emer]</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ls</td>\n",
       "      <td>[/w]</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ls</td>\n",
       "      <td>[/]</td>\n",
       "      <td>[dycete, m, w]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ls</td>\n",
       "      <td>[/dycete]</td>\n",
       "      <td>[dycete]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             method             args          result\n",
       "0             mkdir             [/m]            None\n",
       "1                ls             [/m]              []\n",
       "2             mkdir             [/w]            None\n",
       "3                ls              [/]          [m, w]\n",
       "4                ls             [/w]              []\n",
       "5                ls              [/]          [m, w]\n",
       "6  addContentToFile  [/dycete, emer]            None\n",
       "7                ls             [/w]              []\n",
       "8                ls              [/]  [dycete, m, w]\n",
       "9                ls        [/dycete]        [dycete]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class FileSystem:\n",
    "    def __init__(self):\n",
    "        self.fs = {}\n",
    "        self.head = self.fs\n",
    "\n",
    "    def cd(self, path=None):\n",
    "        if path is None:\n",
    "            self.head = self.fs\n",
    "            return \n",
    "        for directory in path.split('/'): \n",
    "            if len(directory):\n",
    "                self.head = self.head[directory]\n",
    "        \n",
    "    def ls(self, path):\n",
    "        self.cd()\n",
    "        if path=='/':\n",
    "            return sorted(self.fs.keys())\n",
    "        directory = '/'.join(path.split('/')[:-1])\n",
    "        filename = path.split('/')[-1]\n",
    "        self.cd(directory)\n",
    "        if isinstance(self.head[filename], dict):\n",
    "            return sorted(self.head[filename].keys())\n",
    "        else:\n",
    "            return [filename]\n",
    "\n",
    "    def mkdir(self, path):\n",
    "        self.cd()\n",
    "        for directory in path.split('/'): \n",
    "            if directory == '': \n",
    "                continue    \n",
    "            if directory not in self.head:    \n",
    "                self.head[directory] = {} \n",
    "            self.cd(directory)\n",
    "        self.cd()\n",
    "\n",
    "    def addContentToFile(self, filePath, content):\n",
    "        directory = '/'.join(filePath.split('/')[:-1])\n",
    "        filename = filePath.split('/')[-1]\n",
    "        self.mkdir(directory)\n",
    "        self.cd(directory)\n",
    "        if filename in self.head:\n",
    "            self.head[filename] += content\n",
    "        else:\n",
    "            self.head[filename] = content\n",
    "        \n",
    "    def readContentFromFile(self, filePath):\n",
    "        self.cd()\n",
    "        self.cd(filePath)\n",
    "        return self.head\n",
    "\n",
    "    \n",
    "from pandas import DataFrame\n",
    "\n",
    "methods = [\"FileSystem\",\"mkdir\",\"ls\",\"mkdir\",\"ls\",\"ls\",\"ls\",\"addContentToFile\",\"ls\",\"ls\",\"ls\"]\n",
    "argList = [[],[\"/m\"],[\"/m\"],[\"/w\"],[\"/\"],[\"/w\"],[\"/\"],[\"/dycete\",\"emer\"],[\"/w\"],[\"/\"],[\"/dycete\"]]\n",
    "\n",
    "fs = FileSystem()\n",
    "DataFrame([(method, args, getattr(fs, method)(*args)) for method, args in zip(methods[1:], argList[1:])], columns=['method', 'args', 'result'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 460. LFU Cache\n",
    "\n",
    "Hard\n",
    "\n",
    "Design and implement a data structure for a Least Frequently Used (LFU) cache.\n",
    "\n",
    "Implement the LFUCache class:\n",
    "\n",
    "* ```LFUCache(int capacity)``` Initializes the object with the ```capacity``` of the data structure.\n",
    "* ```int get(int key)``` Gets the value of the ```key``` if the ```key``` exists in the cache. Otherwise, returns -1.\n",
    "* ```void put(int key, int value)``` Update the value of the ```key``` if present, or inserts the ```key``` if not already present. When the cache reaches its ```capacity```, it should invalidate and remove the least frequently used key before inserting a new item. For this problem, when there is a tie (i.e., two or more keys with the same frequency), the least recently used ```key``` would be invalidated.\n",
    "\n",
    "To determine the least frequently used key, a use counter is maintained for each key in the cache. The key with the smallest use counter is the least frequently used key.\n",
    "\n",
    "When a key is first inserted into the cache, its use counter is set to 1 (due to the put operation). The use counter for a key in the cache is incremented either a get or put operation is called on it.\n",
    "\n",
    " \n",
    "\n",
    "Example 1:\n",
    "\n",
    "    Input\n",
    "    [\"LFUCache\", \"put\", \"put\", \"get\", \"put\", \"get\", \"get\", \"put\", \"get\", \"get\", \"get\"]\n",
    "    [[2], [1, 1], [2, 2], [1], [3, 3], [2], [3], [4, 4], [1], [3], [4]]\n",
    "    Output\n",
    "    [null, null, null, 1, null, -1, 3, null, -1, 3, 4]\n",
    "\n",
    "    Explanation\n",
    "    // cnt(x) = the use counter for key x\n",
    "    // cache=[] will show the last used order for tiebreakers (leftmost element is  most recent)\n",
    "    LFUCache lfu = new LFUCache(2);\n",
    "    lfu.put(1, 1);   // cache=[1,_], cnt(1)=1\n",
    "    lfu.put(2, 2);   // cache=[2,1], cnt(2)=1, cnt(1)=1\n",
    "    lfu.get(1);      // return 1\n",
    "                     // cache=[1,2], cnt(2)=1, cnt(1)=2\n",
    "    lfu.put(3, 3);   // 2 is the LFU key because cnt(2)=1 is the smallest, invalidate 2.\n",
    "                     // cache=[3,1], cnt(3)=1, cnt(1)=2\n",
    "    lfu.get(2);      // return -1 (not found)\n",
    "    lfu.get(3);      // return 3\n",
    "                     // cache=[3,1], cnt(3)=2, cnt(1)=2\n",
    "    lfu.put(4, 4);   // Both 1 and 3 have the same cnt, but 1 is LRU, invalidate 1.\n",
    "                     // cache=[4,3], cnt(4)=1, cnt(3)=2\n",
    "    lfu.get(1);      // return -1 (not found)\n",
    "    lfu.get(3);      // return 3\n",
    "                     // cache=[3,4], cnt(4)=1, cnt(3)=3\n",
    "    lfu.get(4);      // return 4\n",
    "                     // cache=[3,4], cnt(4)=2, cnt(3)=3\n",
    "\n",
    "Constraints:\n",
    "\n",
    "    0 <= capacity, key, value <= 10^4\n",
    "    At most 10^5 calls will be made to get and put.\n",
    "\n",
    " \n",
    "Follow up: Could you do both operations in O(1) time complexity?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>args</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>put</td>\n",
       "      <td>[1, 1]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>put</td>\n",
       "      <td>[2, 2]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>get</td>\n",
       "      <td>[1]</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>put</td>\n",
       "      <td>[3, 3]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>get</td>\n",
       "      <td>[2]</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>get</td>\n",
       "      <td>[3]</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>put</td>\n",
       "      <td>[4, 4]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>get</td>\n",
       "      <td>[1]</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>get</td>\n",
       "      <td>[3]</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>get</td>\n",
       "      <td>[4]</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  method    args  result\n",
       "0    put  [1, 1]     NaN\n",
       "1    put  [2, 2]     NaN\n",
       "2    get     [1]    -1.0\n",
       "3    put  [3, 3]     NaN\n",
       "4    get     [2]    -1.0\n",
       "5    get     [3]     3.0\n",
       "6    put  [4, 4]     NaN\n",
       "7    get     [1]    -1.0\n",
       "8    get     [3]    -1.0\n",
       "9    get     [4]     4.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5% solution 因為做太多次 swap\n",
    "# 把 items 和對應的 freq 存在一個 doubly linked list 裡，然後用 hash table 存指標指向 nodes\n",
    "# head 指向頻率最低的 node（linked list 的最左邊，最先被刪除）\n",
    "# 每次 freq 遞增完之後要一路 swap 到同 freq 的最右邊，因為這個 node 變成 most recently used\n",
    "# 而且這裡的寫法是一步一步 swap 而非一次 swap 到最右邊，所以才這麼慢\n",
    "# 就算要一次 swap 到位，還是要花 O(n) 時間找到要 swap 的位子，還是快不起來。n 是同 freq 的 node 數\n",
    "\n",
    "# LeetCode 上最快的寫法是每一個 freq 都用一個 doubly linked list 來存，真正做到 O(1) update_freq\n",
    "\n",
    "class Node:\n",
    "    def __init__(self, key, value):\n",
    "        self.key = key\n",
    "        self.value = value\n",
    "        self.freq = 0\n",
    "        self.prev = None\n",
    "        self.next = None\n",
    "\n",
    "class LFUCache(dict):\n",
    "    def __init__(self, capacity):\n",
    "        self.capacity = capacity\n",
    "        self.head = None\n",
    "        \n",
    "    def update_freq(self, key):\n",
    "        '''\n",
    "        Increase freq of key by 1 and adjust note order in the linked list if necessary\n",
    "        This method assumes key is in the cache\n",
    "        '''\n",
    "        self[key].freq += 1\n",
    "        \n",
    "        while self[key].next and self[key].next.freq <= self[key].freq:\n",
    "            # swap self[key] and self[key].next\n",
    "\n",
    "            a = self[key]\n",
    "            b = self[key].next\n",
    "            prev_nbr = a.prev\n",
    "            next_nbr = b.next\n",
    "\n",
    "            if prev_nbr:\n",
    "                prev_nbr.next = b\n",
    "\n",
    "            if next_nbr:\n",
    "                next_nbr.prev = a\n",
    "\n",
    "            a.prev = b\n",
    "            b.next = a\n",
    "            a.next = next_nbr\n",
    "            b.prev = prev_nbr\n",
    "\n",
    "            if self.head.prev:\n",
    "                self.head = self.head.prev\n",
    "\n",
    "    def get(self, key):\n",
    "        if self.capacity == 0: \n",
    "            return -1\n",
    "        \n",
    "        if key in self:\n",
    "            self.update_freq(key)\n",
    "            return self[key].value\n",
    "        else:\n",
    "            return -1\n",
    "        \n",
    "    def put(self, key, value):\n",
    "        if self.capacity == 0: \n",
    "            return \n",
    "        \n",
    "        if key in self:\n",
    "            self[key].value = value\n",
    "            self.update_freq(key)\n",
    "        else:\n",
    "            if len(self) == self.capacity:\n",
    "                keyToDelete = self.head.key\n",
    "                self.head = self.head.next\n",
    "                if self.head:\n",
    "                    self.head.prev.next = None\n",
    "                    self.head.prev = None\n",
    "                del self[keyToDelete]\n",
    "                    \n",
    "            self[key] = Node(key, value)\n",
    "            self[key].next = self.head\n",
    "            if self.head:\n",
    "                self.head.prev = self[key]\n",
    "            self.head = self[key]    \n",
    "            self.update_freq(key)\n",
    "            \n",
    "    def print_list(self):\n",
    "        curr = self.head\n",
    "        while curr:\n",
    "            print(curr.prev, curr.key, curr.value, curr.next)\n",
    "            curr = curr.next\n",
    "        print()\n",
    "            \n",
    "from pandas import DataFrame\n",
    "\n",
    "methods = [\"LFUCache\",\"put\",\"put\",\"get\",\"put\",\"get\",\"get\",\"put\",\"get\",\"get\",\"get\"]\n",
    "argList = [[2],[1,1],[2,2],[1],[3,3],[2],[3],[4,4],[1],[3],[4]]\n",
    "\n",
    "fs = LFUCache(1)\n",
    "DataFrame([(method, args, getattr(fs, method)(*args)) for method, args in zip(methods[1:], argList[1:])], columns=['method', 'args', 'result'])"
   ]
  }
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