Coursera Stanford ML Python wiki.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python tutorial\n",
    "\n",
    "### This tutorial loosely follows the topics covered in the Octave tutorial in week 2 of the course"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The modules needed to run this tutorial are imported below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "from numpy.random import randn\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "import scipy.misc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elementary arithmetic operations\n",
    "##### Python is capable of working like a calculator with some caveats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5+6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5*8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Beware: integer division rounds the result down! You can implicitly convert to a float by adding a '.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1./2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exponents use the '**' operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2**6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logical operations\n",
    "##### Every object has a boolean value returned from bool(). The following elements are false:\n",
    "\n",
    "* None\n",
    "* False\n",
    "* 0\n",
    "* Empty collections: “”, (), [], {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 and 0 # AND"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 or 0 # OR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 != 0 # XOR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool([]) and True # False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='foo'\n",
    "b='bar'\n",
    "bool(a) != bool(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=None\n",
    "bool(a) != bool(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Python variables and types\n",
    "### Displaying variables\n",
    "##### Variables are displayed on the console by typing the variable name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=3\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from math import pi\n",
    "b=pi\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "floating point numbers are formatted in two ways:\n",
    "\n",
    "The 'old' way (pre-python 2.7):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1416\n"
     ]
    }
   ],
   "source": [
    "print '%1.4f'%b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The 'new' way (python 2.7+):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1416\n"
     ]
    }
   ],
   "source": [
    "print '{:1.5}'.format(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy basics \n",
    "### Vectors and matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.array([[1,2],[3,4],[5,6]]) # 3x2 numpy matrix\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v=[1,2,3]   # ordinary python list\n",
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v=np.array([1,2,3]) # numpy array\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Use `np.arange(start, stop, increment)` to generate a sequence of floats in a numpy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v=np.arange(1,2,0.1)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Use `tolist()` to convert a numpy array to a python list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 1.1,\n",
       " 1.2000000000000002,\n",
       " 1.3000000000000003,\n",
       " 1.4000000000000004,\n",
       " 1.5000000000000004,\n",
       " 1.6000000000000005,\n",
       " 1.7000000000000006,\n",
       " 1.8000000000000007,\n",
       " 1.9000000000000008]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The `range()` built-in function generates integer sequences in a `list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v=range(1,6)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### numpy's `linspace` function generates a non-integer sequence with a specific number of elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1. ,  1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v=np.linspace(1,2,11)\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comprehensions\n",
    "### list comprehensions\n",
    "##### List comprehensions allow you to create iterative code without using a loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 9]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v=[1,2,3]\n",
    "[e**2 for e in v]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 9]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[e**2 for e in v if e%2 !=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, -1, 9]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[e**2 if e%2 != 0 else -1 for e in v]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### dictionary comprehensions\n",
    "##### Dictionary comprehensions allow to generate dictionaries without a loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 'a', 2: 'b', 3: 'c'}"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'a':1, 'b':2, 'c':3}   \n",
    "{v: k for k, v in d.items()}   # swap keys and values\n",
    "{1: 'a', 2: 'b', 3: 'c'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set comprehension\n",
    "\n",
    "##### Set comrehensions generate sets in a similar way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1, 4}"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{x**2 for x in [1, 1, 2]}\n",
    "set([1, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Special matrix functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.],\n",
       "       [ 1.,  1.],\n",
       "       [ 1.,  1.]])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ones=np.ones((3,2))\n",
    "ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  3.],\n",
       "       [ 3.,  3.],\n",
       "       [ 3.,  3.]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.],\n",
       "       [ 0.,  0.],\n",
       "       [ 0.,  0.]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Generate an array of uniform random numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.24673352,  0.95515221],\n",
       "       [ 0.15973648,  0.63851934],\n",
       "       [ 0.29663561,  0.24061361]])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(3,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Generate an array of normal random numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.77346713,  0.41712293],\n",
       "       [ 0.05755083,  0.77414929],\n",
       "       [ 0.05536786, -0.31576823]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id=np.eye(3)\n",
    "id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.,  0.,  0.],\n",
       "       [ 0.,  3.,  0.],\n",
       "       [ 0.,  0.,  3.]])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3*id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving data around\n",
    "### shape and size of a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.68547607,  0.30289257],\n",
       "       [ 0.426697  ,  0.64895812],\n",
       "       [ 0.77105183,  0.72787418]])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.random.rand(3,2)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading files in python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Reading the contents of a simple text file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"> Anyone knows how much it costs to host a web portal ?\\n>\\nWell, it depends on how many visitors you're expecting.\\nThis can be anywhere from less than 10 bucks a month to a couple of $100. \\nYou should checkout http://www.rackspace.com/ or perhaps Amazon EC2 \\nif youre running something big..\\n\\nTo unsubscribe yourself from this mailing list, send an email to:\\ngroupname-unsubscribe@egroups.com\\n\\n\""
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file=open('ex6/emailSample1.txt', 'r')\n",
    "file_contents=file.read()\n",
    "file_contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Loading image files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10e29c7d0>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wdXWsGxHGuulIE9ZMwNaGdENGClUl8/GVjRInOhMWBbfALVHDzt7v12zk+fZ3PmylX1Ng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eENImd1jVLobM9+t0HyG5Y5MT0lptMCdtIxtn2+KhjH+Ib+7oD5h+/oes9ShGIDWU7kZBDx+O0W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18KiFTbCF2x3t73n6ZhVtVe6v7Tl8LxHS+7q1M/jYQcOWelh5bw9GdyzAG91/Cyh6uCvg/G6k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\nHOhAB+IgCA50oAMB/wtuZhs2CYTL0gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c9ca550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = scipy.misc.imread('ex7/bird_small.png')\n",
    "plt.imshow(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Loading the contents of a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  6.1101,  17.592 ],\n",
       "       [  5.5277,   9.1302],\n",
       "       [  8.5186,  13.662 ],\n",
       "       [  7.0032,  11.854 ],\n",
       "       [  5.8598,   6.8233]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.loadtxt('ex0.csv', delimiter=',')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Loading a Matlab formatted file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'X': array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        ..., \n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "        [ 0.,  0.,  0., ...,  0.,  0.,  0.]]),\n",
       " '__globals__': [],\n",
       " '__header__': 'MATLAB 5.0 MAT-file, Platform: GLNXA64, Created on: Sun Oct 16 13:09:09 2011',\n",
       " '__version__': '1.0',\n",
       " 'y': array([[10],\n",
       "        [10],\n",
       "        [10],\n",
       "        ..., \n",
       "        [ 9],\n",
       "        [ 9],\n",
       "        [ 9]], dtype=uint8)}"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = scipy.io.loadmat('ex3/ex3data1.mat')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating matrices\n",
    "### Indexing and Slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `a[start:end]` -  items start through end-1\n",
    "#### `a[start:]` - items start through the rest of the array\n",
    "#### `a[:end]` -  items from the beginning through end-1\n",
    "#### `a[:]` - a copy of the whole array\n",
    "##### There is also the step value, which can be used with any of the above:\n",
    "#### `a[start:end:step]` - start through not past end, by step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1:7:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Negative indices\n",
    "##### `a[-1]` - last item in the array\n",
    "##### `a[-2:]` - last two items in the array\n",
    "##### `a[:-2]` - everything except the last two items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2d matrices are accessed in the row, column order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[0,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean indexing\n",
    "\n",
    "#### Index selection can be done by filtering elements with boolean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['The', 'quick', 'brown'],\n",
       "       ['fox', 'jumped', 'over'],\n",
       "       ['the', 'lazy', 'dog']], \n",
       "      dtype='|S6')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = np.array(['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog']).reshape((3,3))\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True, False, False],\n",
       "       [ True, False, False],\n",
       "       [False,  True,  True]], dtype=bool)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand = np.random.randn(3,3)>0\n",
    "rand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['The', 'fox', 'lazy', 'dog'], \n",
       "      dtype='|S6')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[rand]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flattening\n",
    "\n",
    "### Reshaping from a higher dimensional to one dimensional order is called flattening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(9).reshape((3,3))\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The `flatten()` function returns a copy of the array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### flattening can be done columnwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 6, 1, 4, 7, 2, 5, 8])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.flatten(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### the `ravel()` function doesn't return a copy of the underlying data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector assignments\n",
    "\n",
    "### Python doesn't create copies of underlying data on assignment statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### create a reference to some elements in the array and reassign them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6, 7])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice=arr[4:8]\n",
    "slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5, -5, -5, -5])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice[:]=-5\n",
    "slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5, 50, -5, -5])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice[1]=50\n",
    "slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3, -5, 50, -5, -5,  8,  9])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### now create a copy of the array explicitly and reassign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3, -5, 50, -5, -5,  8,  9])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_copy=arr.copy()\n",
    "arr_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3, 20, 20, 20, 20,  8,  9])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_copy[4:8]=20\n",
    "arr_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The original array is unchanged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3, -5, 50, -5, -5,  8,  9])"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Horizontal and vertical concatenation\n",
    "### There are two ways to concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mat = np.array(['The', 'quick', 'brown', 'fox'])\n",
    "mat2 = np.array(['jumped', 'over', 'the', 'lazy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Method 1: Use stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['The', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy'], \n",
       "      dtype='|S6')"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((mat,mat2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['The', 'quick', 'brown', 'fox'],\n",
       "       ['jumped', 'over', 'the', 'lazy']], \n",
       "      dtype='|S6')"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack((mat,mat2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['The', 'jumped'],\n",
       "       ['quick', 'over'],\n",
       "       ['brown', 'the'],\n",
       "       ['fox', 'lazy']], \n",
       "      dtype='|S6')"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.column_stack((mat,mat2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Method 2: Use the `concatenate()` function applied to an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(12).reshape((3, 4))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7,  4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11,  8,  9, 10, 11]])"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((arr,arr), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((arr,arr), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(5)\n",
    "np.concatenate((arr,arr), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix multiplication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 30,  36,  42],\n",
       "       [ 66,  81,  96],\n",
       "       [102, 126, 150]])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=np.array([[1,2,3], [4,5,6], [7,8,9]])\n",
    "y=np.array([[1,2,3], [4,5,6], [7,8,9]])\n",
    "np.dot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Matrix multiplication is done using the `dot()` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 30,  36,  42],\n",
       "       [ 66,  81,  96],\n",
       "       [102, 126, 150]])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Element-wise multiplication using the '*' operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  9],\n",
       "       [16, 25, 36],\n",
       "       [49, 64, 81]])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x*y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Element-wise squaring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  9],\n",
       "       [16, 25, 36],\n",
       "       [49, 64, 81]])"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Element-wise reciprical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.5       ,  0.33333333],\n",
       "       [ 0.25      ,  0.2       ,  0.16666667],\n",
       "       [ 0.14285714,  0.125     ,  0.11111111]])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1./x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Element-wise logarithms/exponents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.69314718,  1.09861229],\n",
       "       [ 1.38629436,  1.60943791,  1.79175947],\n",
       "       [ 1.94591015,  2.07944154,  2.19722458]])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.71828183e+00,   7.38905610e+00,   2.00855369e+01],\n",
       "       [  5.45981500e+01,   1.48413159e+02,   4.03428793e+02],\n",
       "       [  1.09663316e+03,   2.98095799e+03,   8.10308393e+03]])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Element-wise addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3,  4],\n",
       "       [ 5,  6,  7],\n",
       "       [ 8,  9, 10]])"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1+x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transpose of a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4, 7],\n",
       "       [2, 5, 8],\n",
       "       [3, 6, 9]])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maximum and minimum of matrix values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sum and product of all elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 15, 18])"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 15, 24])"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "362880"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.product(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 28,  80, 162])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.product(x,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  6, 120, 504])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.product(x,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inverse and pseudo-inverse of a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5,  0. ,  0. ],\n",
       "       [ 0. ,  0.5,  0. ],\n",
       "       [ 0. ,  0. ,  0.5]])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=2*np.eye(3)\n",
    "np.linalg.inv(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5,  0. ,  0. ],\n",
       "       [ 0. ,  0.5,  0. ],\n",
       "       [ 0. ,  0. ,  0.5]])"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.pinv(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting data with matplotlib\n",
    "\n",
    "### Creating/clearing figures|\n",
    "##### Plots reside within figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x10e390390>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MHlyccAzlyJFHsWLFRr777j7ASlFRFXb7MZSWHgqo2O1jWLPmHtxud6Ru7D33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\nP/wz2Y2seo40AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e10a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(np.random.randn(500), np.random.randn(500), np.random.randn(500), marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cB6vVbrtarTa5Kk2JSHorcAtwWNLVmdtuA47k6P9mZpZTqzMsNwB7Ja2iMdB/NSKelPSw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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e448790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2, sharex=True, sharey=True)\n",
    "color = (e for e in ['r', 'g', 'k', 'b'])\n",
    "for i in range(2):\n",
    "    for j in range(2):\n",
    "        axes[i, j].hist(np.random.randn(500), bins=50, color=color.next(), alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Line color, labels, title and legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c964e50>]"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y7qYPB7rGQDUjThg+/D2qV69H+/Z9qV//NG677V6OHDkSa7X84tVsnIDeTrVu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\nAP8PnpPsHLmG9e8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e7c8950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,2)\n",
    "axes[0,0].plot(randn(50).cumsum(), 'k--')\n",
    "axes[0,1].hist(randn(100), bins=20, color='r', alpha=0.3)\n",
    "axes[1,1].scatter(np.arange(30), np.arange(30) + 3 * randn(30), np.arange(30))\n",
    "axes[1,0].plot(randn(1000).cumsum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Control statements\n",
    "### For loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "b\n",
      "e\n"
     ]
    }
   ],
   "source": [
    "li = ['a', 'b', 'e']\n",
    "for e in li:\n",
    "    print e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 a\n",
      "1 b\n",
      "2 e\n"
     ]
    }
   ],
   "source": [
    "d = enumerate(li)\n",
    "for k,v in d:\n",
    "    print k,v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### While loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The count is: 0\n",
      "The count is: 1\n",
      "The count is: 2\n",
      "The count is: 3\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "while (count <= 3):\n",
    "   print 'The count is:', count\n",
    "   count += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### break statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 is a prime number\n",
      "4 equals 2 * 2\n",
      "5 is a prime number\n",
      "5 is a prime number\n",
      "5 is a prime number\n",
      "6 equals 2 * 3\n",
      "7 is a prime number\n",
      "7 is a prime number\n",
      "7 is a prime number\n",
      "7 is a prime number\n",
      "7 is a prime number\n",
      "8 equals 2 * 4\n",
      "9 is a prime number\n",
      "9 equals 3 * 3\n"
     ]
    }
   ],
   "source": [
    "for n in range(2, 10):\n",
    "    for x in range(2, n):\n",
    "        if n % x == 0:\n",
    "            print n, 'equals', x, '*', n/x\n",
    "            break\n",
    "        else:\n",
    "        # loop fell through without finding a factor\n",
    "            print n, 'is a prime number'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### if-elif-else statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 - Got a true expression value\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "var = 100\n",
    "if var == 200:\n",
    "   print \"1 - Got a true expression value\"\n",
    "   print var\n",
    "elif var == 150:\n",
    "   print \"2 - Got a true expression value\"\n",
    "   print var\n",
    "elif var == 100:\n",
    "   print \"3 - Got a true expression value\"\n",
    "   print var\n",
    "else:\n",
    "   print \"4 - Got a false expression value\"\n",
    "   print var"
   ]
  }
 ],
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