14 정수론 및 조합론.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f148468",
   "metadata": {},
   "source": [
    "# [5086 배수와 약수](https://www.acmicpc.net/problem/5086)\n",
    "- `문제` 4 × 3 = 12이다. 이 식을 통해 다음과 같은 사실을 알 수 있다. 3은 12의 약수이고, 12는 3의 배수이다. 4도 12의 약수이고, 12는 4의 배수이다. 두 수가 주어졌을 때, 다음 3가지 중 어떤 관계인지 구하는 프로그램을 작성하시오. \n",
    "    1. 첫 번째 숫자가 두 번째 숫자의 약수이다.\n",
    "    2. 첫 번째 숫자가 두 번째 숫자의 배수이다.\n",
    "    3. 첫 번째 숫자가 두 번째 숫자의 약수와 배수 모두 아니다.\n",
    "    \n",
    "\n",
    "- `입력` 입력은 여러 테스트 케이스로 이루어져 있다. 각 테스트 케이스는 10,000이 넘지않는 두 자연수로 이루어져 있다. 마지막 줄에는 0이 2개 주어진다. 두 수가 같은 경우는 없다.\n",
    "    - ex) 8 16 `\\n` 32 4 `\\n` 17 5 `\\n` 0 0\n",
    "\n",
    "\n",
    "- `출력` 각 테스트 케이스마다 첫 번째 숫자가 두 번째 숫자의 약수라면 factor를, 배수라면 multiple을, 둘 다 아니라면 neither를 출력한다.\n",
    "    - ex) factor `\\n` multiple `\\n` neither"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "94a34ee3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T01:57:38.128322Z",
     "start_time": "2022-05-10T01:57:27.376747Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 16\n",
      "factor\n",
      "32 4\n",
      "multiple\n",
      "17 5\n",
      "neither\n",
      "0 0\n"
     ]
    }
   ],
   "source": [
    "# SOL 1)\n",
    "# --- import sys : 64 ms\n",
    "# --- import sys + pypy3 : 108 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "while True:\n",
    "    A, B = map(int, input().split())\n",
    "    if (A == 0) and (B == 0):\n",
    "        break\n",
    "    if (A < B) and (B % A == 0):\n",
    "        print('factor')\n",
    "    elif (A > B) and (A % B == 0):\n",
    "        print('multiple')\n",
    "    else:\n",
    "        print('neither')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bbb9641",
   "metadata": {},
   "source": [
    "# [1037 약수](https://www.acmicpc.net/problem/1037)\n",
    "- `문제` 양수 A가 N의 진짜 약수가 되려면, N이 A의 배수이고, A가 1과 N이 아니어야 한다. 어떤 수 N의 진짜 약수가 모두 주어질 때, N을 구하는 프로그램을 작성하시오.\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에 N의 진짜 약수의 개수가 주어진다. 이 개수는 50보다 작거나 같은 자연수이다. 둘째 줄에는 N의 진짜 약수가 주어진다. 1,000,000보다 작거나 같고, 2보다 크거나 같은 자연수이고, 중복되지 않는다.\n",
    "    - ex) 2 `\\n` 4 2\n",
    "    - ex) 1 `\\n` 2\n",
    "    - ex) 6 `\\n` 3 4 2 12 6 8\n",
    "    - ex) 14 `\\n` 14 26456 2 28 13228 3307 7 23149 8 6614 46298 56 4 92596\n",
    "\n",
    "\n",
    "- `출력` 첫째 줄에 N을 출력한다. N은 항상 32비트 부호있는 정수로 표현할 수 있다.\n",
    "    - ex) 8\n",
    "    - ex) 4\n",
    "    - ex) 24\n",
    "    - ex) 185192"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9048f30f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T02:23:45.730597Z",
     "start_time": "2022-05-10T02:23:39.749380Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n",
      "14 26456 2 28 13228 3307 7 23149 8 6614 46298 56 4 92596\n",
      "185192\n"
     ]
    }
   ],
   "source": [
    "# SOL 1) \n",
    "# --- import sys : 68 ms\n",
    "# --- import sys + pypy3 : 120 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "N = int(input())\n",
    "N_lst = sorted(list(map(int, input().split())))\n",
    "\n",
    "if len(N_lst) == 1:\n",
    "    ori_num = N_lst[0]**2\n",
    "else:\n",
    "    ori_num = N_lst[0]*N_lst[-1]\n",
    "\n",
    "print(ori_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "08e632fa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T02:28:57.110181Z",
     "start_time": "2022-05-10T02:28:50.008794Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n",
      "14 26456 2 28 13228 3307 7 23149 8 6614 46298 56 4 92596\n",
      "185192\n"
     ]
    }
   ],
   "source": [
    "# SOL 2)\n",
    "# --- import sys : 68 ms\n",
    "# --- import sys + pypy3 : 120 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "N = int(input())\n",
    "N_lst = list(map(int, input().split()))\n",
    "N_lst.sort()\n",
    "print(N_lst[0]*N_lst[-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7609d64b",
   "metadata": {},
   "source": [
    "# [2609 최대공약수와 최소공배수](https://www.acmicpc.net/problem/2609)\n",
    "- `문제` 두 개의 자연수를 입력받아 최대 공약수와 최소 공배수를 출력하는 프로그램을 작성하시오.\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에는 두 개의 자연수가 주어진다. 이 둘은 10,000이하의 자연수이며 사이에 한 칸의 공백이 주어진다.\n",
    "    - ex) 24 18\n",
    "\n",
    "\n",
    "- `출력` 첫째 줄에는 입력으로 주어진 두 수의 최대공약수를, 둘째 줄에는 입력으로 주어진 두 수의 최소 공배수를 출력한다.\n",
    "    - ex) 6 `\\n` 72"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "821b9657",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T02:52:38.321196Z",
     "start_time": "2022-05-10T02:52:34.903251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 18\n",
      "6\n",
      "72\n"
     ]
    }
   ],
   "source": [
    "# SOL 1)\n",
    "# --- import sys : 68 ms\n",
    "# --- import sys + pypy3 : 116 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "A, B = map(int, input().split())\n",
    "A_nums = list(range(1, A+1))\n",
    "B_nums = list(range(1, B+1))\n",
    "\n",
    "# 최대공약수 (greatest)\n",
    "A_prime = []\n",
    "for i in A_nums:\n",
    "    if A % i == 0:\n",
    "        A_prime.append(i)\n",
    "\n",
    "greatest = 0 \n",
    "for j in B_nums:\n",
    "    if (j in A_prime) and (B % j == 0):\n",
    "        greatest = j\n",
    "\n",
    "# 최소공배수 (least)\n",
    "least = A * B / greatest\n",
    "\n",
    "print(int(greatest))\n",
    "print(int(least))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba297dae",
   "metadata": {},
   "source": [
    "# [1934 최소공배수](https://www.acmicpc.net/problem/1934)\n",
    "- `문제` 두 자연수 A와 B에 대해서, A의 배수이면서 B의 배수인 자연수를 A와 B의 공배수라고 한다. 이런 공배수 중에서 가장 작은 수를 최소공배수라고 한다. 예를 들어, 6과 15의 공배수는 30, 60, 90등이 있으며, 최소 공배수는 30이다. 두 자연수 A와 B가 주어졌을 때, A와 B의 최소공배수를 구하는 프로그램을 작성하시오.\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에 테스트 케이스의 개수 T(1 ≤ T ≤ 1,000)가 주어진다. 둘째 줄부터 T개의 줄에 걸쳐서 A와 B가 주어진다. (1 ≤ A, B ≤ 45,000)\n",
    "    - ex) 3 `\\n` 1 45000 `\\n` 6 10 `\\n` 13 17\n",
    "\n",
    "\n",
    "- `출력` 첫째 줄부터 T개의 줄에 A와 B의 최소공배수를 입력받은 순서대로 한 줄에 하나씩 출력한다.\n",
    "    - ex) 45000 `\\n` 30 `\\n` 221"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81e55484",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T03:08:43.541067Z",
     "start_time": "2022-05-10T03:08:36.317250Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "1 45000\n",
      "6 10\n",
      "13 17\n",
      "45000\n",
      "30\n",
      "221\n"
     ]
    }
   ],
   "source": [
    "# SOL 1) \n",
    "# --- import sys : 시간 초과\n",
    "# --- import sys + pypy3 : 1744 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "T = int(input())\n",
    "\n",
    "least_lst = []\n",
    "for _ in range(T):\n",
    "    A, B = map(int, input().split())\n",
    "    A_nums = list(range(1, A+1))\n",
    "    B_nums = list(range(1, B+1))\n",
    "\n",
    "    # 최대공약수 (greatest)\n",
    "    A_prime = []\n",
    "    for i in A_nums:\n",
    "        if A % i == 0:\n",
    "            A_prime.append(i)\n",
    "\n",
    "    greatest = 0 \n",
    "    for j in B_nums:\n",
    "        if (j in A_prime) and (B % j == 0):\n",
    "            greatest = j\n",
    "    \n",
    "    # 최소공배수 (least)\n",
    "    least_lst.append(int(A * B / greatest))\n",
    "\n",
    "print(*least_lst, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "19da56f1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T04:35:56.082518Z",
     "start_time": "2022-05-10T04:35:48.037800Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "1 45000\n",
      "6 10\n",
      "13 17\n",
      "45000\n",
      "30\n",
      "221\n"
     ]
    }
   ],
   "source": [
    "# SOL 2) 유클리드 알고리즘 (자연수 2개의 최대공약수를 구하는 알고리즘)\n",
    "# --- import sys : 68 ms\n",
    "# --- import sys + pypy3 : 120 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "def GCD(a, b):\n",
    "    while b > 0:\n",
    "        a, b = b, a % b\n",
    "    return a\n",
    "\n",
    "T = int(input())\n",
    "LCM_lst = []\n",
    "\n",
    "for _ in range(T):\n",
    "    A, B = map(int, input().split())\n",
    "    LCM = (A * B) // GCD(A, B)\n",
    "    LCM_lst.append(LCM)\n",
    "\n",
    "print(*LCM_lst, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d6d8fdde",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T04:52:21.812998Z",
     "start_time": "2022-05-10T04:52:09.581780Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "1 45000\n",
      "6 10\n",
      "13 17\n",
      "45000\n",
      "30\n",
      "221\n"
     ]
    }
   ],
   "source": [
    "# SOL 3) 유클리드 알고리즘 - 재귀 함수로 표현\n",
    "# --- import sys : 72 ms\n",
    "# --- import sys + pypy3 : 132 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "def GCD(a, b):\n",
    "    if a % b == 0:\n",
    "        return b\n",
    "    else:\n",
    "        return GCD(b, a % b)\n",
    "\n",
    "T = int(input())\n",
    "LCM_lst = []\n",
    "\n",
    "for _ in range(T):\n",
    "    A, B = map(int, input().split())\n",
    "    LCM = (A * B) // GCD(A, B)\n",
    "    LCM_lst.append(LCM)\n",
    "\n",
    "print(*LCM_lst, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9703207b",
   "metadata": {},
   "source": [
    "# [2981 검문](https://www.acmicpc.net/problem/2981)\n",
    "- `문제` 트럭을 타고 이동하던 상근이는 경찰의 검문을 받게 되었다. 경찰은 상근이가 운반하던 화물을 하나하나 모두 확인할 것이기 때문에, 검문하는데 엄청나게 오랜 시간이 걸린다. 상근이는 시간을 때우기 위해서 수학 게임을 하기로 했다. 먼저 근처에 보이는 숫자 N개를 종이에 적는다. 그 다음, 종이에 적은 수를 M으로 나누었을 때, 나머지가 모두 같게 되는 M을 모두 찾으려고 한다. M은 1보다 커야 한다. N개의 수가 주어졌을 때, 가능한 M을 모두 찾는 프로그램을 작성하시오.\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에 종이에 적은 수의 개수 N이 주어진다. (2 ≤ N ≤ 100) 다음 줄부터 N개 줄에는 종이에 적은 수가 하나씩 주어진다. 이 수는 모두 1보다 크거나 같고, 1,000,000,000보다 작거나 같은 자연수이다. 같은 수가 두 번 이상 주어지지 않는다. 항상 M이 하나 이상 존재하는 경우만 입력으로 주어진다.\n",
    "    - ex) 3 `\\n` 6 `\\n` 34 `\\n` 38\n",
    "    - ex) 5 `\\n` 5 `\\n` 17 `\\n` 23 `\\n` 14 `\\n` 83\n",
    "\n",
    "\n",
    "- `출력` \n",
    "    - ex) 2 4\n",
    "    - ex) 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ea60e41d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T05:13:43.443709Z",
     "start_time": "2022-05-10T05:13:37.691242Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "5\n",
      "17\n",
      "23\n",
      "14\n",
      "83\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# TRY 1) 시간 초과\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "N = int(input())\n",
    "N_lst = [int(input()) for _ in range(N)]\n",
    "M_lst = []\n",
    "\n",
    "for i in range(2, min(N_lst)+1):\n",
    "    temp = []\n",
    "    for j in range(len(N_lst)):\n",
    "        temp.append((N_lst[j] % i))\n",
    "        \n",
    "    if temp.count(temp[0]) == len(temp):\n",
    "        M_lst.append(i)\n",
    "\n",
    "print(*M_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aeb6cfff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T05:28:47.806296Z",
     "start_time": "2022-05-10T05:28:41.173563Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "5\n",
      "17\n",
      "23\n",
      "14\n",
      "83\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# TRY 2) 시간 초과\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "N = int(input())\n",
    "N_lst = [int(input()) for _ in range(N)]\n",
    "M_lst = []\n",
    "\n",
    "for i in range(2, min(N_lst)+1):\n",
    "    temp = list(map(lambda x:x%i, N_lst))\n",
    "    \n",
    "    if temp.count(temp[0]) == len(temp):\n",
    "        M_lst.append(i)\n",
    "\n",
    "print(*M_lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d3b62a4c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T06:19:24.244956Z",
     "start_time": "2022-05-10T06:19:10.774235Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "5\n",
      "17\n",
      "23\n",
      "14\n",
      "83\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# SOL 1) \n",
    "# --- import sys : 68 ms\n",
    "# --- import sys + pypy3 : 112 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "N = int(input())\n",
    "N_lst = [int(input()) for _ in range(N)]\n",
    "\n",
    "# 최대공약수 구하는 함수\n",
    "def GCD(a, b):\n",
    "    while b > 0:\n",
    "        a, b = b, a % b\n",
    "    return a\n",
    "\n",
    "# N개의 수의 간격을 각각 구함\n",
    "diff_nums = []\n",
    "for i in range(len(N_lst)-1, 0, -1):\n",
    "    diff_nums.append(N_lst[i]-N_lst[i-1])\n",
    "\n",
    "# 위에서 구한 diff_nums에 대해 최대공약수를 구함\n",
    "gcd = diff_nums[0]\n",
    "for i in range(1, len(diff_nums)):\n",
    "    gcd = GCD(gcd, diff_nums[i])\n",
    "\n",
    "# 최대공약수에 대해 1을 제외한 소수를 구함 \n",
    "result = []\n",
    "for i in range(2, int(gcd**0.5)+1):\n",
    "    if gcd % i == 0:\n",
    "        result.append(i)\n",
    "        result.append(gcd // i)\n",
    "\n",
    "result.append(gcd)\n",
    "result = list(set(result))\n",
    "result.sort()\n",
    "print(*result)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "69e2722c",
   "metadata": {},
   "source": [
    "# [3036 링](https://www.acmicpc.net/problem/3036)\n",
    "- `문제` 상근이는 창고에서 링 N개를 발견했다. 상근이는 각각의 링이 앞에 있는 링과 뒤에 있는 링과 접하도록 바닥에 내려놓았다.![image.png](attachment:image.png) 상근이는 첫 번째 링을 돌리기 시작했고, 나머지 링도 같이 돌아간다는 사실을 발견했다. 나머지 링은 첫 번째 링 보다 빠르게 돌아가기도 했고, 느리게 돌아가기도 했다. 이렇게 링을 돌리다 보니 첫 번째 링을 한 바퀴 돌리면, 나머지 링은 몇 바퀴 도는지 궁금해졌다. 링의 반지름이 주어진다. 이때, 첫 번째 링을 한 바퀴 돌리면, 나머지 링은 몇 바퀴 돌아가는지 구하는 프로그램을 작성하시오.\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에 링의 개수 N이 주어진다. (3 ≤ N ≤ 100) 다음 줄에는 링의 반지름이 상근이가 바닥에 놓은 순서대로 주어진다. 반지름은 1과 1000를 포함하는 사이의 자연수이다.\n",
    "    - ex) 3 `\\n` 8 4 2\n",
    "    - ex) 4 `\\n` 12 3 8 4\n",
    "    - ex) 4 `\\n` 300 1 1 300\n",
    "\n",
    "- `출력` 출력은 총 N-1줄을 해야 한다. 첫 번째 링을 제외한 각각의 링에 대해서, 첫 번째 링을 한 바퀴 돌리면 그 링은 몇 바퀴 도는지 기약 분수 형태 A/B로 출력한다.\n",
    "    - ex) 2/1 `\\n` 4/1\n",
    "    - ex) 4/1 `\\n` 3/2 `\\n` 3/1\n",
    "    - ex) 300/1 `\\n` 300/1 `\\n` 1/1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "309571fd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T06:53:30.435703Z",
     "start_time": "2022-05-10T06:53:26.138794Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "300 1 1 300\n",
      "300/1\n",
      "300/1\n",
      "1/1\n"
     ]
    }
   ],
   "source": [
    "# SOL 1)\n",
    "# --- import sys : 68 ms\n",
    "# --- import sys + pypy3 : 108 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "N = int(input())\n",
    "N_lst = list(map(int, input().split()))\n",
    "\n",
    "def GCD(a, b):\n",
    "    while b > 0:\n",
    "        a, b = b, a % b\n",
    "    return a\n",
    "\n",
    "result = []\n",
    "for i in range(1, len(N_lst)):\n",
    "    first = N_lst[0]\n",
    "    temp = N_lst[i]\n",
    "    if first % temp == 0:\n",
    "        result.append(f'{int(first/temp)}/1')\n",
    "    else:\n",
    "        gcd = GCD(first, temp)\n",
    "        result.append(f'{int(first/gcd)}/{int(temp/gcd)}')\n",
    "print(*result, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b092b20e",
   "metadata": {},
   "source": [
    "# [11050 이항 계수 1](https://www.acmicpc.net/problem/11050)\n",
    "- `문제` 자연수 N과 정수 K가 주어졌을 때 이항 계수 N Combination K를 구하는 프로그램을 작성하시오.\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에 N과 K가 주어진다. (1 ≤ N ≤ 10, 0 ≤ K ≤ N)\n",
    "    - ex) 5 2\n",
    "\n",
    "\n",
    "- `출력` N Combination K를 출력한다.\n",
    "    - ex) 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12f23fcf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T07:36:09.232227Z",
     "start_time": "2022-05-10T07:36:07.927449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 0\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# SOL 1)\n",
    "# --- import sys : 72 ms\n",
    "# --- import sys + pypy3 : 128 ms\n",
    "\n",
    "import math\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "N, K = map(int, input().split())\n",
    "N_list = list(range(1, N+1))[-K:-1]\n",
    "mul = N\n",
    "for i in N_list:\n",
    "    mul *= i\n",
    "\n",
    "if K == 0:\n",
    "    print(1)\n",
    "else:\n",
    "    print(int(mul/math.factorial(K)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "74a51ed5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T07:40:15.516092Z",
     "start_time": "2022-05-10T07:40:14.034722Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 2\n",
      "45\n"
     ]
    }
   ],
   "source": [
    "# SOL 2)\n",
    "# --- import sys : 72 ms\n",
    "# --- import sys + pypy3 : 104 ms\n",
    "\n",
    "import math\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "N, K = map(int, input().split())\n",
    "num = math.factorial(N) / (math.factorial(K)*(math.factorial(N-K)))\n",
    "print(int(num))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6016f7b6",
   "metadata": {},
   "source": [
    "# [11051 이항 계수 2](https://www.acmicpc.net/problem/11051)\n",
    "- `문제` 자연수 N과 정수 K가 주어졌을 때 이항 계수 N Combination K를 10,007로 나눈 나머지를 구하는 프로그램을 작성하시오.\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에 N과 K가 주어진다. (1 ≤ N ≤ 1,000, 0 ≤ K ≤ N)\n",
    "    - ex) 5 2\n",
    "\n",
    "\n",
    "- `출력` N Combination K를 10,007로 나눈 나머지를 출력한다.\n",
    "    - ex) 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "311f5d04",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T07:54:56.973683Z",
     "start_time": "2022-05-10T07:54:55.548943Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13 5\n",
      "1287\n"
     ]
    }
   ],
   "source": [
    "# SOL 1) num에 유의..\n",
    "# --- import sys : 88 ms\n",
    "# --- import sys + pypy3 : 116 ms\n",
    "\n",
    "import math\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "N, K = map(int, input().split())\n",
    "num = math.factorial(N) // (math.factorial(K)*math.factorial(N-K))\n",
    "print(num % 10007)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "acde0813",
   "metadata": {},
   "source": [
    "# [1010 다리 놓기](https://www.acmicpc.net/problem/1010)\n",
    "- `문제` 재원이는 한 도시의 시장이 되었다. 이 도시에는 도시를 동쪽과 서쪽으로 나누는 큰 일직선 모양의 강이 흐르고 있다. 하지만 재원이는 다리가 없어서 시민들이 강을 건너는데 큰 불편을 겪고 있음을 알고 다리를 짓기로 결심하였다. 강 주변에서 다리를 짓기에 적합한 곳을 사이트라고 한다. 재원이는 강 주변을 면밀히 조사해 본 결과 강의 서쪽에는 N개의 사이트가 있고 동쪽에는 M개의 사이트가 있다는 것을 알았다. (N ≤ M) 재원이는 서쪽의 사이트와 동쪽의 사이트를 다리로 연결하려고 한다. (이때 한 사이트에는 최대 한 개의 다리만 연결될 수 있다.) 재원이는 다리를 최대한 많이 지으려고 하기 때문에 서쪽의 사이트 개수만큼 (N개) 다리를 지으려고 한다. 다리끼리는 서로 겹쳐질 수 없다고 할 때 다리를 지을 수 있는 경우의 수를 구하는 프로그램을 작성하라.\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "- `입력` 입력의 첫 줄에는 테스트 케이스의 개수 T가 주어진다. 그 다음 줄부터 각각의 테스트케이스에 대해 강의 서쪽과 동쪽에 있는 사이트의 개수 정수 N, M (0 < N ≤ M < 30)이 주어진다.\n",
    "    - ex) 3 `\\n` 2 2 `\\n` 1 5 `\\n` 13 29\n",
    "\n",
    "\n",
    "- `출력` 각 테스트 케이스에 대해 주어진 조건하에 다리를 지을 수 있는 경우의 수를 출력한다.\n",
    "    - ex) 1 `\\n` 5 `\\n` 67863915"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b75e897",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T08:48:55.355915Z",
     "start_time": "2022-05-10T08:48:46.027621Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "2 2\n",
      "1\n",
      "1 5\n",
      "5\n",
      "13 29\n",
      "67863915\n"
     ]
    }
   ],
   "source": [
    "# SOL 1)\n",
    "# --- import sys : 68 ms\n",
    "# --- import sys + pypy3 : 120 ms\n",
    "\n",
    "import math\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "\n",
    "T = int(input())\n",
    "for _ in range(T):\n",
    "    N, M = map(int, input().split())\n",
    "    num = math.factorial(M) // (math.factorial(N)*math.factorial(M-N))\n",
    "    print(num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a8d14a2",
   "metadata": {},
   "source": [
    "# [9375 패션왕 신해빈](https://www.acmicpc.net/problem/9375)\n",
    "- `문제` 해빈이는 패션에 매우 민감해서 한번 입었던 옷들의 조합을 절대 다시 입지 않는다. 예를 들어 오늘 해빈이가 안경, 코트, 상의, 신발을 입었다면, 다음날은 바지를 추가로 입거나 안경대신 렌즈를 착용하거나 해야한다. 해빈이가 가진 의상들이 주어졌을때 과연 해빈이는 알몸이 아닌 상태로 며칠동안 밖에 돌아다닐 수 있을까?\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에 테스트 케이스가 주어진다. 테스트 케이스는 최대 100이다. 각 테스트 케이스의 첫째 줄에는 해빈이가 가진 의상의 수 n(0 ≤ n ≤ 30)이 주어진다. 다음 n개에는 해빈이가 가진 의상의 이름과 의상의 종류가 공백으로 구분되어 주어진다. 같은 종류의 의상은 하나만 입을 수 있다. 모든 문자열은 1이상 20이하의 알파벳 소문자로 이루어져있으며 같은 이름을 가진 의상은 존재하지 않는다.\n",
    "    - ex) 2 `\\n` 3 `\\n` hat headgear `\\n` sunglasses eyewear `\\n` turban headgear `\\n` 3 `\\n` mask face `\\n` sunglasses face `\\n` makeup face\n",
    "\n",
    "\n",
    "- `출력` 각 테스트 케이스에 대해 해빈이가 알몸이 아닌 상태로 의상을 입을 수 있는 경우를 출력하시오.\n",
    "    - ex) 5 `\\n` 3\n",
    "  \n",
    "  \n",
    "- `힌트` 첫 번째 테스트 케이스는 headgear에 해당하는 의상이 hat, turban이며 eyewear에 해당하는 의상이 sunglasses이므로   (hat), (turban), (sunglasses), (hat,sunglasses), (turban,sunglasses)로 총 5가지 이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b730d14e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T09:36:31.367244Z",
     "start_time": "2022-05-10T09:36:14.330319Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n",
      "h aa\n",
      "g dd\n",
      "w aa\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({'aa': 2, 'dd': 1})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TRY 1) 귀납적으로 하려니 너무 복잡하다..^_ㅠ\n",
    "\n",
    "from collections import Counter\n",
    "from itertools import combinations\n",
    "\n",
    "T = int(input())\n",
    "for _ in range(T):\n",
    "    \n",
    "    # 각 의상 종류별 개수를 담은 dict 생성\n",
    "    n = int(input())\n",
    "    kind_lst = []\n",
    "    \n",
    "    for _ in range(n):\n",
    "        name, kind = input().split()\n",
    "        kind_lst.append(kind)\n",
    "    \n",
    "    dic = Counter(kind_lst)\n",
    "    cnt = 0\n",
    "    \n",
    "    # k가지 중 1가지\n",
    "    for i in dic.values():\n",
    "        cnt += i\n",
    "    \n",
    "    # k가지 중 2가지\n",
    "    temp = list(combinations(dic.values(), 2))\n",
    "    ...\n",
    "    \n",
    "    # k가지 중 k가지.. -> 너무 복잡하다!\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fb81a05a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-10T09:55:23.523330Z",
     "start_time": "2022-05-10T09:54:52.188162Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "hat headgear\n",
      "sunglasses eyewear\n",
      "turban headgear\n",
      "5\n",
      "3\n",
      "mask face\n",
      "sunglasses face\n",
      "makeup face\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# SOL 1) 안 입는 경우도 경우의 수에 넣어서 냅다 곱해주고 모두 안 입는 경우 빼기 !! (뿌듯..)\n",
    "# --- import sys : 92 ms\n",
    "# --- import sys + pypy3 : 128 ms\n",
    "\n",
    "import sys\n",
    "input = sys.stdin.readline\n",
    "from collections import Counter\n",
    "\n",
    "T = int(input())\n",
    "for _ in range(T):\n",
    "    \n",
    "    # 각 의상 종류별 개수를 담은 dict 생성\n",
    "    n = int(input())\n",
    "    kind_lst = []\n",
    "    \n",
    "    for _ in range(n):\n",
    "        name, kind = input().split()\n",
    "        kind_lst.append(kind)\n",
    "    \n",
    "    dic = Counter(kind_lst)\n",
    "    cnt = 1\n",
    "    for i in dic.values():\n",
    "        cnt *= i+1\n",
    "        \n",
    "    print(cnt-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66892d58",
   "metadata": {},
   "source": [
    "# [1676 팩토리얼 0의 개수](https://www.acmicpc.net/problem/1676)\n",
    "- `문제` N!에서 뒤에서부터 처음 0이 아닌 숫자가 나올 때까지 0의 개수를 구하는 프로그램을 작성하시오.\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에 N이 주어진다. (0 ≤ N ≤ 500)\n",
    "    - ex) 10 / 3\n",
    "\n",
    "\n",
    "- `출력` 첫째 줄에 구한 0의 개수를 출력한다.\n",
    "    - ex) 2 / 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8d7ffc78",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-23T01:25:14.119534Z",
     "start_time": "2022-05-23T01:25:11.757479Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SOL 1)\n",
    "# --- import sys : 68 ms\n",
    "# --- import sys + pypy3 : 116 ms\n",
    "\n",
    "N = int(input())\n",
    "fac = 1\n",
    "while N > 0:\n",
    "    fac *= N\n",
    "    N -= 1\n",
    "\n",
    "cnt = 0\n",
    "for i in reversed(str(fac)):\n",
    "    if i == '0':\n",
    "        cnt += 1\n",
    "    else:\n",
    "        print(cnt)\n",
    "        break\n",
    "cnt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d26f1f3a",
   "metadata": {},
   "source": [
    "# [2004 조합 0의 개수](https://www.acmicpc.net/problem/2004)\n",
    "- `문제` n Combination m의 끝자리 0의 개수를 출력하는 프로그램을 작성하시오.\n",
    "\n",
    "\n",
    "- `입력` 첫째 줄에 정수 n, m (0 <= m <= n <= 2,000,000,000, n != 0)이 들어온다.\n",
    "    - ex) 25 12\n",
    "\n",
    "\n",
    "- `출력` 첫째 줄에 n Combination m의 끝자리 0의 개수를 출력한다.\n",
    "    - ex) 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8105cf17",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-23T01:32:53.407324Z",
     "start_time": "2022-05-23T01:32:50.735332Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 12\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# TRY 1) 시간 초과\n",
    "\n",
    "n, m = map(int, input().split())\n",
    "com = 1\n",
    "while m > 0:\n",
    "    com *= n\n",
    "    com /= m\n",
    "    n -= 1\n",
    "    m -= 1\n",
    "\n",
    "cnt = 0\n",
    "for i in reversed(str(int(com))):\n",
    "    if i == '0': cnt += 1\n",
    "    else:\n",
    "        print(cnt)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "50a46c49",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-23T01:36:28.243525Z",
     "start_time": "2022-05-23T01:36:25.377363Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 12\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# TRY 2) 애초에 조합 계산을 다 하면 시간 초과..\n",
    "\n",
    "from math import comb\n",
    "\n",
    "n, m = map(int, input().split())\n",
    "comb = comb(n, m)\n",
    "\n",
    "cnt = 0\n",
    "for i in reversed(str(int(comb))):\n",
    "    if i == '0': cnt += 1\n",
    "    else:\n",
    "        print(cnt)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0fd7bb0e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-05-23T02:00:05.273747Z",
     "start_time": "2022-05-23T02:00:02.041139Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 12\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# SOL 1)\n",
    "# --- import sys : 68 ms\n",
    "# --- import sys + pypy3 : 112 ms\n",
    "\n",
    "def CNT(N, num):\n",
    "    cnt = 0 \n",
    "    divN = num\n",
    "    while N >= divN:\n",
    "        cnt += (N // divN)\n",
    "        divN *= num\n",
    "    return cnt\n",
    "\n",
    "m, n = map(int, input().split())\n",
    "print(min(CNT(m, 2)-CNT(n, 2)-CNT(m-n, 2), CNT(m, 5)-CNT(n, 5)-CNT(m-n, 5)))"
   ]
  }
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