384. Shuffle an Array.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shuffle a set of numbers without duplicates.\n",
    "\n",
    "Example:\n",
    "\n",
    "// Init an array with set 1, 2, and 3.\n",
    "int[] nums = {1,2,3};\n",
    "Solution solution = new Solution(nums);\n",
    "\n",
    "// Shuffle the array [1,2,3] and return its result. Any permutation of [1,2,3] must equally likely to be returned.\n",
    "solution.shuffle();\n",
    "\n",
    "// Resets the array back to its original configuration [1,2,3].\n",
    "solution.reset();\n",
    "\n",
    "// Returns the random shuffling of array [1,2,3].\n",
    "solution.shuffle();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "class Solution(object):\n",
    "\n",
    "    def __init__(self, nums):\n",
    "        \"\"\"\n",
    "        :type nums: List[int]\n",
    "        \"\"\"\n",
    "        self.nums=nums\n",
    "\n",
    "    def reset(self):\n",
    "        \"\"\"\n",
    "        Resets the array to its original configuration and return it.\n",
    "        :rtype: List[int]\n",
    "        \"\"\"\n",
    "        return self.nums\n",
    "\n",
    "    def shuffle(self):\n",
    "        \"\"\"\n",
    "        Returns a random shuffling of the array.\n",
    "        :rtype: List[int]\n",
    "        \"\"\"\n",
    "        res=self.nums[:]\n",
    "        for i in range(1,len(res)):\n",
    "            j=random.randint(0,i)\n",
    "            if i!=j:\n",
    "                t=res[i]\n",
    "                res[i]=res[j]\n",
    "                res[j]=t\n",
    "        return res\n",
    "\n",
    "\n",
    "# Your Solution object will be instantiated and called as such:\n",
    "# obj = Solution(nums)\n",
    "# param_1 = obj.reset()\n",
    "# param_2 = obj.shuffle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "class Solution(object):\n",
    "\n",
    "    def __init__(self, nums):\n",
    "        \"\"\"\n",
    "        :type nums: List[int]\n",
    "        \"\"\"\n",
    "        self.nums=nums\n",
    "\n",
    "    def reset(self):\n",
    "        \"\"\"\n",
    "        Resets the array to its original configuration and return it.\n",
    "        :rtype: List[int]\n",
    "        \"\"\"\n",
    "        return self.nums\n",
    "\n",
    "    def shuffle(self):\n",
    "        \"\"\"\n",
    "        Returns a random shuffling of the array.\n",
    "        :rtype: List[int]\n",
    "        \"\"\"\n",
    "        res=[]\n",
    "        for n in self.nums:\n",
    "            res.append(n)\n",
    "        \n",
    "        for i in range(1,len(self.nums)):\n",
    "            j=random.randint(0,i)\n",
    "            if i!=j:\n",
    "                t=self.nums[i]\n",
    "                self.nums[i]=self.nums[j]\n",
    "                self.nums[j]=t\n",
    "            \n",
    "        return res\n",
    "\n",
    "\n",
    "# Your Solution object will be instantiated and called as such:\n",
    "# obj = Solution(nums)\n",
    "# param_1 = obj.reset()\n",
    "# param_2 = obj.shuffle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "s=Solution([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 2, 5, 3, 1]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "res=[[] for _ in range(5)]\n",
    "for i in range(10000):\n",
    "    t=s.shuffle()\n",
    "    for j in range(5):\n",
    "        res[j].append(t[j])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a=[[1,1,2,2,3,3],[1,2,2],[3,3,1,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 1941.,     0.,  2016.,     0.,     0.,  2057.,     0.,  1945.,\n",
       "             0.,  2041.]),\n",
       "  array([ 2040.,     0.,  1999.,     0.,     0.,  1979.,     0.,  1999.,\n",
       "             0.,  1983.]),\n",
       "  array([ 2010.,     0.,  1953.,     0.,     0.,  2039.,     0.,  1988.,\n",
       "             0.,  2010.]),\n",
       "  array([ 2027.,     0.,  2029.,     0.,     0.,  1947.,     0.,  2016.,\n",
       "             0.,  1981.]),\n",
       "  array([ 1982.,     0.,  2003.,     0.,     0.,  1978.,     0.,  2052.,\n",
       "             0.,  1985.])],\n",
       " array([ 1. ,  1.4,  1.8,  2.2,  2.6,  3. ,  3.4,  3.8,  4.2,  4.6,  5. ]),\n",
       " <a list of 5 Lists of Patches objects>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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o/3yssTEzt0fEkw01kiRpDOt4yMjM6xr++fuIuAf4I3A8cFunn6/Z4sWLmTFjBgADAwPA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b42438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(res)"
   ]
  },
  {
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