{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wCEAAUDBAgICAgICAgICAgGCAgICAgICAgICAgICAgICAgI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\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA/9k=\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/bxe2T-V8XRs\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x108307e48>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('bxe2T-V8XRs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import numpy as np\n",
    "X = np.array(([3, 5], [5, 1], [10, 2]), dtype=float)\n",
    "y = np.array(([75], [82], [93]), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.,   5.],\n",
       "       [  5.,   1.],\n",
       "       [ 10.,   2.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 75.],\n",
       "       [ 82.],\n",
       "       [ 93.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.3,  1. ],\n",
       "       [ 0.5,  0.2],\n",
       "       [ 1. ,  0.4]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X/np.amax(X, axis=0)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.75],\n",
       "       [ 0.82],\n",
       "       [ 0.93]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y/100\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}