2. Power Method ( 2.0 ).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ranking via Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Team List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ['Sunrisers Hyderabad', 'Royal Challengers Bangalore', 'Rising Pune Supergiants', 'Mumbai Indians', 'Kolkata Knight Riders', 'Kings XI Punjab', 'Gujarat Lions', 'Delhi Daredevils']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('C:\\\\Users\\\\HP\\\\Desktop\\\\ipl2017 maths assignment.csv', )\n",
    "teamsl = list(data)\n",
    "teams = []\n",
    "for x in range(1,9):\n",
    "    teams.append(teamsl[x])\n",
    "print(\"\\n\", teams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Unnamed: 0  Sunrisers Hyderabad  \\\n",
      "0          Sunrisers Hyderabad                    0   \n",
      "1  Royal Challengers Bangalore                    0   \n",
      "2      Rising Pune Supergiants                    2   \n",
      "3               Mumbai Indians                    1   \n",
      "4        Kolkata Knight Riders                    1   \n",
      "5              Kings XI Punjab                    0   \n",
      "6                Gujarat Lions                    0   \n",
      "7             Delhi Daredevils                    1   \n",
      "\n",
      "   Royal Challengers Bangalore  Rising Pune Supergiants  Mumbai Indians  \\\n",
      "0                            1                        0               1   \n",
      "1                            0                        0               0   \n",
      "2                            2                        0               1   \n",
      "3                            2                        0               0   \n",
      "4                            2                        1               0   \n",
      "5                            2                        1               1   \n",
      "6                            1                        1               0   \n",
      "7                            0                        2               0   \n",
      "\n",
      "   Kolkata Knight Riders  Kings XI Punjab  Gujarat Lions  Delhi Daredevils  \n",
      "0                      1                2              2                 1  \n",
      "1                      0                0              1                 2  \n",
      "2                      1                1              1                 0  \n",
      "3                      2                1              2                 2  \n",
      "4                      0                1              1                 2  \n",
      "5                      1                0              1                 1  \n",
      "6                      1                1              0                 0  \n",
      "7                      0                1              2                 0   \n",
      "\n",
      "\n",
      "\n",
      "[[0 1 0 1 1 2 2 1]\n",
      " [0 0 0 0 0 0 1 2]\n",
      " [2 2 0 1 1 1 1 0]\n",
      " [1 2 0 0 2 1 2 2]\n",
      " [1 2 1 0 0 1 1 2]\n",
      " [0 2 1 1 1 0 1 1]\n",
      " [0 1 1 0 1 1 0 0]\n",
      " [1 0 2 0 0 1 2 0]]\n"
     ]
    }
   ],
   "source": [
    "mat = pd.read_csv('C:\\\\Users\\\\HP\\\\Desktop\\\\ipl2017 maths assignment.csv')\n",
    "print(mat, \"\\n\\n\\n\")\n",
    "\n",
    "n = 8\n",
    "\n",
    "mat = np.matrix(mat)\n",
    "mat2 = []\n",
    "for x in range(8):\n",
    "    mat2.append([])\n",
    "    for y in range(1, 9):\n",
    "        mat2[x].append(mat[x , y])\n",
    "        \n",
    "mat = np.matrix(mat2)\n",
    "print(mat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding Dominant Eigen Value and Eigen Vector via Power Method  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      " Iteration Number:  1 \n",
      "\n",
      "Eigen Value:  10.0\n",
      "Eigen Vector: \n",
      " [[0.8]\n",
      " [0.3]\n",
      " [0.8]\n",
      " [1. ]\n",
      " [0.8]\n",
      " [0.7]\n",
      " [0.4]\n",
      " [0.6]]\n",
      "\n",
      "\n",
      " Iteration Number:  2 \n",
      "\n",
      "Eigen Value:  5.7\n",
      "Eigen Vector: \n",
      " [[0.85964912]\n",
      " [0.28070175]\n",
      " [0.89473684]\n",
      " [1.        ]\n",
      " [0.78947368]\n",
      " [0.73684211]\n",
      " [0.45614035]\n",
      " [0.68421053]]\n",
      "\n",
      "\n",
      " Iteration Number:  3 \n",
      "\n",
      "Eigen Value:  6.017543859649123\n",
      "Eigen Vector: \n",
      " [[0.85422741]\n",
      " [0.303207  ]\n",
      " [0.87463557]\n",
      " [1.        ]\n",
      " [0.81049563]\n",
      " [0.72886297]\n",
      " [0.44897959]\n",
      " [0.71428571]]\n",
      "\n",
      "\n",
      " Iteration Number:  4 \n",
      "\n",
      "Eigen Value:  6.137026239067055\n",
      "Eigen Vector: \n",
      " [[0.84465558]\n",
      " [0.30593824]\n",
      " [0.86413302]\n",
      " [1.        ]\n",
      " [0.80522565]\n",
      " [0.72589074]\n",
      " [0.44275534]\n",
      " [0.68931116]]\n",
      "\n",
      "\n",
      " Iteration Number:  5 \n",
      "\n",
      "Eigen Value:  6.0570071258907365\n",
      "Eigen Vector: \n",
      " [[0.84823529]\n",
      " [0.30070588]\n",
      " [0.87090196]\n",
      " [1.        ]\n",
      " [0.80368627]\n",
      " [0.72862745]\n",
      " [0.44596078]\n",
      " [0.69082353]]\n",
      "\n",
      "\n",
      " Iteration Number:  6 \n",
      "\n",
      "Eigen Value:  6.059215686274509\n",
      "Eigen Vector: \n",
      " [[0.84901948]\n",
      " [0.30162449]\n",
      " [0.87076565]\n",
      " [1.        ]\n",
      " [0.80485405]\n",
      " [0.72827649]\n",
      " [0.44624943]\n",
      " [0.69490648]]\n",
      "\n",
      "\n",
      " Iteration Number:  7 \n",
      "\n",
      "Eigen Value:  6.072564882531875\n",
      "Eigen Vector: \n",
      " [[0.84814851]\n",
      " [0.30235369]\n",
      " [0.86959432]\n",
      " [1.        ]\n",
      " [0.80482845]\n",
      " [0.72786783]\n",
      " [0.44553179]\n",
      " [0.69350039]]\n",
      "\n",
      "\n",
      " Iteration Number:  8 \n",
      "\n",
      "Eigen Value:  6.068444971181065\n",
      "Eigen Vector: \n",
      " [[0.84823736]\n",
      " [0.30197729]\n",
      " [0.86994815]\n",
      " [1.        ]\n",
      " [0.80462963]\n",
      " [0.7280551 ]\n",
      " [0.44568984]\n",
      " [0.69313779]]\n",
      "\n",
      "\n",
      " Iteration Number:  9 \n",
      "\n",
      "Eigen Value:  6.06716156303325\n",
      "Eigen Vector: \n",
      " [[0.84837606]\n",
      " [0.30194769]\n",
      " [0.87006153]\n",
      " [1.        ]\n",
      " [0.80468611]\n",
      " [0.72807687]\n",
      " [0.4457785 ]\n",
      " [0.69349866]]\n",
      "\n",
      "\n",
      " Iteration Number:  10 \n",
      "\n",
      "Eigen Value:  6.068274844321348\n",
      "Eigen Vector: \n",
      " [[0.8483207 ]\n",
      " [0.30202584]\n",
      " [0.86996537]\n",
      " [1.        ]\n",
      " [0.8047074 ]\n",
      " [0.72803561]\n",
      " [0.44572342]\n",
      " [0.69346447]]\n",
      "\n",
      "\n",
      " Iteration Number:  11 \n",
      "\n",
      "Eigen Value:  6.068198565951688\n",
      "Eigen Vector: \n",
      " [[0.84831037]\n",
      " [0.30200929]\n",
      " [0.86997145]\n",
      " [1.        ]\n",
      " [0.80469116]\n",
      " [0.72804347]\n",
      " [0.44572276]\n",
      " [0.69340742]]\n",
      "\n",
      "\n",
      " Iteration Number:  12 \n",
      "\n",
      "Eigen Value:  6.068015089177378\n",
      "Eigen Vector: \n",
      " [[0.84832359]\n",
      " [0.30199951]\n",
      " [0.86998741]\n",
      " [1.        ]\n",
      " [0.80469171]\n",
      " [0.72804884]\n",
      " [0.44573313]\n",
      " [0.69342976]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "unit = np.ones((n,1))\n",
    "m = np.matrix(unit)\n",
    "\n",
    "b = True\n",
    "itr = 0\n",
    "while b:\n",
    "    itr += 1\n",
    "    y = 0\n",
    "    p = mat@m\n",
    "    x = p.max()\n",
    "    p = p / x\n",
    "\n",
    "    for i in range(n):\n",
    "        if round(p.item(i), 4) == round(m.item(i), 4):\n",
    "            y += 1\n",
    "\n",
    "    print(\"\\n\\n Iteration Number: \", itr, \"\\n\")\n",
    "\n",
    "    if(y == n):\n",
    "         b=False\n",
    "        \n",
    "    print(\"Eigen Value: \",x)\n",
    "    print(\"Eigen Vector: \\n\",p)        \n",
    "    \n",
    "    m = p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mumbai Indians \n",
      "\n",
      "Rising Pune Supergiants \n",
      "\n",
      "Sunrisers Hyderabad \n",
      "\n",
      "Kolkata Knight Riders \n",
      "\n",
      "Kings XI Punjab \n",
      "\n",
      "Delhi Daredevils \n",
      "\n",
      "Gujarat Lions \n",
      "\n",
      "Royal Challengers Bangalore \n",
      "\n"
     ]
    }
   ],
   "source": [
    "rank = []\n",
    "rankus = []\n",
    "for i in range(n):\n",
    "    rank.append(p.item(i))\n",
    "    rankus.append(p.item(i))\n",
    "rank.sort()\n",
    "\n",
    "for i in range(n):\n",
    "    print(teams[rankus.index(rank[-(i+1)])], \"\\n\")\n",
    "\n",
    "del rank\n",
    "del rankus"
   ]
  }
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