SU-2015-0-SciPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sveučilište u Zagrebu<br>\n",
    "Fakultet elektrotehnike i računarstva\n",
    "\n",
    "# Strojno učenje\n",
    "\n",
    "<a href=\"http://www.fer.unizg.hr/predmet/su\">http://www.fer.unizg.hr/predmet/su</a>\n",
    "\n",
    "Ak. god. 2015./2016.\n",
    "\n",
    "# Bilježnica 0: Uvod u SciPy\n",
    "\n",
    "(c) 2015 Jan Šnajder\n",
    "\n",
    "<i>Verzija: 0.5 (2015-10-15) </i>\n",
    "\n",
    "<p style=\"color:red\">NEPOTPUNO</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. SciPy stack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Glavni paketi (*core packages*):\n",
    "* **Python**\n",
    "* **NumPy**\n",
    "* **biblioteka SciPy**\n",
    "* **IPython**\n",
    "* **Matplotlib**\n",
    "* SymPy \n",
    "* **pandas**\n",
    "* nose\n",
    "\n",
    "Dodatni paketi:\n",
    "* Cython\n",
    "* SciKits paketi: **scikit-learn**, scikit-multilearn, scikit-image, ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. IPython notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ćelije se evaluiraju sa SHIFT+ENTER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Markdown tekst s **posebnim formatiranjem** i kodom u $\\LaTeX$-u: $f(\\mathbf{x}) = \\sum_{i=1}^n \\ln \\frac{P(x)P(y)}{P(x, y)}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%quickref"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više: https://ipython.org/ipython-doc/3/interactive/tutorial.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1. Varijable i vrijednosti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x + 1) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x += 1; x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "?x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'x' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-401b30e3b8b5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'x' is not defined"
     ]
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X=7; varijabla_s_vrlo_dugackim_imenom = 747"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x=1; y=-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x==y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "(x==y)==False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x!=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x==y or (x>0 and not y>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "z = 42 if x==y else 66"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "moj_string = 'ringe ringe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "'hopa' + ' ' + \"cupa\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "moj_string += ' raja'; moj_string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "len(moj_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print \"X=%0.2f y=%d, s='%s'\" % (x, y, moj_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "1/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "1/2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "1/float(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "round(0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2. Matematičke funkcije"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "math.sqrt(68)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "math.exp(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "math.log(_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "math.log(100, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više: https://docs.python.org/2/library/math.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3. Lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs = [5, 6, 2, 3]   # Stvara listu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[0]  # Zero-based indeksiranje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[-1]  # Negativni indeksi broje od kraja liste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[1] = 100  # Ažuriranje liste\n",
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[1] = 'foo'  # Liste mogu biti heterogene\n",
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[3] = [1,2]\n",
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs.append(x)  # Dodaje na kraj\n",
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs + [77, 88]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs.extend([77, 88]); xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs.pop()  # Skida zadnji element liste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[:-2]  # Sve osim zadnja dva"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs[0:2] = [1,2]\n",
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "range(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "range(1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "range(0, 51, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for x in range(5):\n",
    "    print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for x in xs: print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for ix, x in enumerate(range(0, 51, 5)):\n",
    "  print ix, x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs = []\n",
    "for x in range(10):\n",
    "    xs.append(x ** 2)\n",
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[x ** 2 for x in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[x ** 2 for x in range(10) if x % 2 == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[(x, x ** 2) for x in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zip([1, 2, 3], [4, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zip(*[(1, 4), (2, 5), (3, 6)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs, ys = zip(*[(1, 4), (2, 5), (3, 6)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "map(lambda x : x + 1, xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "[ x + 1 for x in xs ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ys = []\n",
    "for x in xs :\n",
    "    ys.append(x + 1)\n",
    "ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sum(ys)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4. Rječnik (mapa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d = {'zagreb' : 790017, 'split' : 178102, 'rijeka' : 128624}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178102"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['split']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'osijek'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-b6288821d7a0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'osijek'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m: 'osijek'"
     ]
    }
   ],
   "source": [
    "d['osijek']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.get('osijek', 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'osijek': 108048, 'rijeka': 128624, 'split': 178102, 'zagreb': 790017}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['osijek'] = 108048; d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'rijeka' in d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'osijek': 108048, 'rijeka': 128624, 'split': 178102, 'zagreb': 790200}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d['zagreb'] = 790200; d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'osijek': 108048, 'split': 178102, 'zagreb': 790200}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del d['rijeka']; d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iteriranje po rječniku:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grad osijek ima 108048 stanovnika\n",
      "Grad split ima 178102 stanovnika\n",
      "Grad zagreb ima 790200 stanovnika\n"
     ]
    }
   ],
   "source": [
    "for grad in d:\n",
    "    print 'Grad %s ima %d stanovnika' % (grad, d[grad])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iteriranje po ključevima i po vrijednostima:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grad osijek ima 108048 stanovnika\n",
      "Grad split ima 178102 stanovnika\n",
      "Grad zagreb ima 790200 stanovnika\n"
     ]
    }
   ],
   "source": [
    "for grad, stanovnici in d.iteritems():\n",
    "    print 'Grad %s ima %d stanovnika' % (grad, stanovnici)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ugniježđeni rječnici:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120240"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2 = {'zagreb' : {'trešnjevka' : 120240, 'centar' : 145302}}\n",
    "d2 ['zagreb']['trešnjevka']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5. Funkcije"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def inc(x): return x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negativno\n",
      "nula\n",
      "pozitivno\n"
     ]
    }
   ],
   "source": [
    "def sign(x):\n",
    "    if x > 0:\n",
    "        return 'pozitivno'\n",
    "    elif x < 0:\n",
    "        return 'negativno'\n",
    "    else:\n",
    "        return 'nula'\n",
    "\n",
    "for x in [-1, 0, 1]:\n",
    "    print sign(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podrazumijevani argumenti:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def broj_stanovnika(grad, godina=2015):\n",
    "    if grad in d:\n",
    "        return d[grad] + round((godina - 2015) * 10000 * (-1.2))\n",
    "    else: \n",
    "        raise ValueError('Nepoznat neki grad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "790200.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "broj_stanovnika('zagreb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "730200.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "broj_stanovnika('zagreb', godina=2020)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Nepoznat neki grad",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-33-ed4c8aabd867>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mbroj_stanovnika\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'zadar'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-30-4ea6caac6059>\u001b[0m in \u001b[0;36mbroj_stanovnika\u001b[1;34m(grad, godina)\u001b[0m\n\u001b[0;32m      3\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mgrad\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgodina\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m2015\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;36m10000\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Nepoznat neki grad'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: Nepoznat neki grad"
     ]
    }
   ],
   "source": [
    "broj_stanovnika('zadar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6. Klase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class RegistarStanovnika:\n",
    "\n",
    "    # Konstruktor\n",
    "    def __init__(self, drzava, d):\n",
    "        self.drzava = drzava  # Varijabla instance (drugačija za svaku instancu)\n",
    "        self.d = d  \n",
    "        \n",
    "    prirast = -1.2  # Varijabla klase (dijele ju sve instance)\n",
    "\n",
    "    # Metoda\n",
    "    def broj_stanovnika(self, grad, godina=2015):\n",
    "        if grad in self.d:\n",
    "            return self.d[grad] + round((godina - 2015) * 10000 * self.prirast)\n",
    "        else: \n",
    "            raise ValueError('Nepoznat neki grad')\n",
    "    \n",
    "    def ukupno_stanovnika(self):\n",
    "        return sum(self.d.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reg = RegistarStanovnika('Hrvatska', {'zagreb' : 790017, 'split' : 178102, 'rijeka' : 128624})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178102.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.broj_stanovnika('split')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1096743"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.ukupno_stanovnika()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "?np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.10.0.post2'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1. Polja"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jednodimenzijsko polje (polje ranga 1):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.array([1, 2, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n"
     ]
    }
   ],
   "source": [
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.array([1, 2, 3], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 100.,    2.,    3.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0] = 100; a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', 'a', '2'], \n",
       "      dtype='|S21')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,'a',2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matrica (dvodimenzijsko polje, polje ranga 2):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m = np.array([[1,2,3],[4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "print m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 6])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[1][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m2 = np.array([[1,2,3],[4,5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 2, 3] [4, 5]]\n"
     ]
    }
   ],
   "source": [
    "print m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Izrezivanje (engl. slicing):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "print m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 5])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[0,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "m[1,:2] = [77, 78]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [77, 78,  6]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uočiti razliku:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 77])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[:,0]  # daje polje ranga 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [77]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[:,0:1]  # daje polje ranga 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trodimenzijsko polje (tenzor ranga 3):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t = np.array([[[1,2],[3,4]],[[4,5],[6,7]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 2)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[0,1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[0,:,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2. Stvaranje polja"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 1.],\n",
       "       [ 1.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 55.,  55.,  55.,  55.,  55.],\n",
       "       [ 55.,  55.,  55.,  55.,  55.],\n",
       "       [ 55.,  55.,  55.,  55.,  55.],\n",
       "       [ 55.,  55.,  55.,  55.,  55.],\n",
       "       [ 55.,  55.,  55.,  55.,  55.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((5,5), 55)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  1.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  1.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.34294388,  0.2407478 ,  0.54200906,  0.85047566],\n",
       "       [ 0.84029626,  0.60056098,  0.84275663,  0.40206247],\n",
       "       [ 0.59492988,  0.69943282,  0.67430892,  0.7806004 ],\n",
       "       [ 0.07980246,  0.92217663,  0.35797981,  0.67351464]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1, 10, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.  ,   3.25,   5.5 ,   7.75,  10.  ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.        ,   1.18367347,   1.36734694,   1.55102041,\n",
       "         1.73469388,   1.91836735,   2.10204082,   2.28571429,\n",
       "         2.46938776,   2.65306122,   2.83673469,   3.02040816,\n",
       "         3.20408163,   3.3877551 ,   3.57142857,   3.75510204,\n",
       "         3.93877551,   4.12244898,   4.30612245,   4.48979592,\n",
       "         4.67346939,   4.85714286,   5.04081633,   5.2244898 ,\n",
       "         5.40816327,   5.59183673,   5.7755102 ,   5.95918367,\n",
       "         6.14285714,   6.32653061,   6.51020408,   6.69387755,\n",
       "         6.87755102,   7.06122449,   7.24489796,   7.42857143,\n",
       "         7.6122449 ,   7.79591837,   7.97959184,   8.16326531,\n",
       "         8.34693878,   8.53061224,   8.71428571,   8.89795918,\n",
       "         9.08163265,   9.26530612,   9.44897959,   9.63265306,\n",
       "         9.81632653,  10.        ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više: http://docs.scipy.org/doc/numpy/reference/routines.array-creation.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3. Napredno indeksiranje"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeksiranje poljem brojeva:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2], [3, 4], [5, 6]]); a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[[0,2]]   # Nije isto kao a[0,2] !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 5])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[[0,1,2], [0,1,0]]   # Isto kao: np.array([a[0,0], a[1,1], a[2,0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indeksiranje Booleovim poljem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False],\n",
       "       [ True,  True],\n",
       "       [ True,  True]], dtype=bool)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_ix = a > 2\n",
    "bool_ix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5, 6])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[bool_ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5, 6])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a > 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više: http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4. Širenje i naslagivanje"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Širenje (eng. <i>broadcasting</i>):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.array([[1, 2], [3, 4]])\n",
    "v = np.array([1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4],\n",
       "       [4, 6]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 5.,  5.,  5.],\n",
       "        [ 5.,  5.,  5.]],\n",
       "\n",
       "       [[ 5.,  5.,  5.],\n",
       "        [ 5.,  5.,  5.]]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,2,3)) * 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Naslagivanje (engl. <i>stacking</i>):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [1, 2]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack([v, v])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack([x, x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack((v, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 1, 2])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((v, v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 1, 2],\n",
       "       [3, 4, 3, 4]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((x, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "all the input arrays must have same number of dimensions",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-6d17658301dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/numpy/core/shape_base.pyc\u001b[0m in \u001b[0;36mhstack\u001b[1;34m(tup)\u001b[0m\n\u001b[0;32m    276\u001b[0m     \u001b[1;31m# As a special case, dimension 0 of 1-dimensional arrays is \"horizontal\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    277\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0marrs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 278\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    279\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    280\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_nx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: all the input arrays must have same number of dimensions"
     ]
    }
   ],
   "source": [
    "np.hstack((v, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 2],\n",
       "       [2, 3, 4]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.column_stack((v, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1],\n",
       "        [2, 2]],\n",
       "\n",
       "       [[3, 3],\n",
       "        [4, 4]]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dstack((x, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 2)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preoblikovanje polja:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2],\n",
       "       [ 3, 77],\n",
       "       [78,  6]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.array([[ 1,  2,  3], [77, 78,  6]])\n",
    "m.reshape(3, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.4. Operacije s poljima (vektorske i matrične operacije)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "y = np.array([[5,6],[7,8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n",
      "[[5 6]\n",
      " [7 8]]\n"
     ]
    }
   ],
   "source": [
    "print x; print y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operacije \"po elementima\" (element-wise):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6,  8],\n",
       "       [10, 12]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4, -4],\n",
       "       [-4, -4]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5,  1. ],\n",
       "       [ 1.5,  2. ]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x / 2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x/2.0).dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5, 12],\n",
       "       [21, 32]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x * y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x.dtype='float64'\n",
    "y.dtype='float64'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.2       ,  0.33333333],\n",
       "       [ 0.42857143,  0.5       ]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x / y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2.22275875e-162,   3.14345557e-162],\n",
       "       [  3.84993109e-162,   4.44551750e-162]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vektorske/matrične operacije:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "y = np.array([[5,6],[7,8]])\n",
    "v = np.array([1,2])\n",
    "w = np.array([5,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Skalarni (unutarnji, dot) umnožak vektora:\n",
    "$\n",
    "\\begin{pmatrix}\n",
    "1 & 2 \\\\\n",
    "\\end{pmatrix}\n",
    "\\cdot\n",
    "\\begin{pmatrix}\n",
    "5\\\\\n",
    "3\\\\\n",
    "\\end{pmatrix}\n",
    "= 11\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n",
      "11\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "print v.dot(w)\n",
    "print w.dot(v)\n",
    "print np.dot(v, w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Umnožak matrice i vektora:\n",
    "$\n",
    "\\begin{pmatrix}\n",
    "1 & 2 \\\\\n",
    "3 & 4 \\\\\n",
    "\\end{pmatrix}\n",
    "\\cdot\n",
    "\\begin{pmatrix}\n",
    "1\\\\\n",
    "2\\\\\n",
    "\\end{pmatrix}\n",
    "=\n",
    "\\begin{pmatrix}\n",
    "5\\\\\n",
    "11\\\\\n",
    "\\end{pmatrix}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 11])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dot(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 11])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(x, v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Umnožak vektora i matrice:\n",
    "$\n",
    "\\begin{pmatrix}\n",
    "1 & 2\\\\\n",
    "\\end{pmatrix}\n",
    "\\cdot\n",
    "\\begin{pmatrix}\n",
    "1 & 2 \\\\\n",
    "3 & 4 \\\\\n",
    "\\end{pmatrix}\n",
    "=\n",
    "\\begin{pmatrix}\n",
    "7 & 10\\\\\n",
    "\\end{pmatrix}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7, 10])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.dot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7, 10])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(v,x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primijetite da nema razlike između vektor-stupca i vektor-retka."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Umnožak matrice i matrice:\n",
    "$\n",
    "\\begin{pmatrix}\n",
    "1 & 2\\\\\n",
    "3 & 4\\\\\n",
    "\\end{pmatrix}\n",
    "\\cdot\n",
    "\\begin{pmatrix}\n",
    "5 & 6 \\\\\n",
    "7 & 7 \\\\\n",
    "\\end{pmatrix}\n",
    "=\n",
    "\\begin{pmatrix}\n",
    "19 & 22\\\\\n",
    "43 & 50\\\\\n",
    "\\end{pmatrix}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19, 22],\n",
       "       [43, 50]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19, 22],\n",
       "       [43, 50]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vanjski umnožak vektora: \n",
    "$\n",
    "\\begin{pmatrix}\n",
    "1\\\\\n",
    "2\\\\\n",
    "\\end{pmatrix}\n",
    "\\times\n",
    "\\begin{pmatrix}\n",
    "5 \\\\ \n",
    "3 \\\\\n",
    "\\end{pmatrix}\n",
    "=\n",
    "\\begin{pmatrix}\n",
    "1\\\\\n",
    "2\\\\\n",
    "\\end{pmatrix}\n",
    "\\cdot\n",
    "\\begin{pmatrix}\n",
    "5 & 3\\\\ \n",
    "\\end{pmatrix}\n",
    "=\n",
    "\\begin{pmatrix}\n",
    "5 & 3 \\\\\n",
    "10 & 6 \\\\\n",
    "\\end{pmatrix}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  3],\n",
       "       [10,  6]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.outer(v, w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ostale operacije:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.array([0, 2, 4, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.5. Statističke funkcije"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.88042383,  0.22280293,  0.00769093,  0.9631947 ,  0.82314693,\n",
       "        0.6021121 ,  0.42227832,  0.54826309,  0.10995267,  0.27862066])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.random(10); x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.41554955347505657"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.28811051704517238"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09738450671168114"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31206490785040403"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1, 2, np.nan])\n",
    "np.mean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nanmean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95550376524614955"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ptp(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "X = np.array([[1,2],[3,4]])\n",
    "print X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  3.])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(X, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5,  0.5],\n",
       "       [ 0.5,  0.5]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cov(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.45896982,  0.98801242,  0.97315322, ...,  0.2251306 ,\n",
       "        0.28813426,  0.77343545])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.random(10000); x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1028, 1024, 1029, 1022, 1014,  953, 1029,  960,  971,  970]),\n",
       " array([  8.52564207e-05,   1.00051953e-01,   2.00018649e-01,\n",
       "          2.99985345e-01,   3.99952041e-01,   4.99918737e-01,\n",
       "          5.99885433e-01,   6.99852129e-01,   7.99818825e-01,\n",
       "          8.99785521e-01,   9.99752217e-01]))"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više: http://docs.scipy.org/doc/numpy/reference/routines.statistics.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6. Druge često korištene funkcije"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]]); x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 6])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 7])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [2, 4]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.diagonal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.trace()  # == x.sum(x.diagonal())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aplikacija funkcije na polje:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 7])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.apply_along_axis(sum, 1, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.apply_along_axis(len, 1, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Većina ugrađenih funkcija su *vektorizirane*, tj. moguće ih je primijeniti na cijelo polje tako da provode operaciju nad pojedinačnim elementima polja. Npr.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [1, 1]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sign(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.69314718],\n",
       "       [ 1.09861229,  1.38629436]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Isto vrijedi i za korisnički definirane funkcije koje su definirane pomoći vektoriziranih ugrađenih funkcija:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def inc(x) : return x + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [4, 5]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inc(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Složenije funkcije treba eksplicitno vektorizirati pomoću [`numpy.vectorize`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.vectorize.html) (ili jednostavno aplicirati funkciju u `for` petlji, što funkcija `vectorize` zapravo i radi)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Permutacije:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(0,10); x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 3, 4, 0, 2, 7, 9, 8, 1, 6])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.permutation(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 9, 4, 7, 8, 0, 6, 5, 3])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.shuffle(x); x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 9, 4, 7, 8, 0, 6, 5, 3])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više: http://docs.scipy.org/doc/numpy/reference/routines.sort.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.7. Konverzija lista <-> polje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [1, 2, 3]\n",
    "a = np.array(l); a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = [[1, 2, 3], [4,5,6]]\n",
    "a = np.array(l); a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([1, 2, 3]), array([4, 5, 6])]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6]]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. SciPy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.16.0'"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SciPy importa NumPy. Npr.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = sp.array([1,2,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iz biblioteke SciPy interesantni su nam moduli [`scipy.linalg`](http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html) i [`scipy.stats`](http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1. SciPy.linalg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy import linalg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inverz matrice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 6],\n",
       "       [7, 8]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-4. ,  3. ],\n",
       "       [ 3.5, -2.5]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_inv = linalg.inv(y); y_inv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.00000000e+00,   3.55271368e-15],\n",
       "       [  0.00000000e+00,   1.00000000e+00]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.dot(y, y_inv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determinanta:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.0000000000000036"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.det(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Euklidska norma ($l_2$-norma) vektora: $\\|\\mathbf{x}\\|_2 = \\sqrt{\\sum_i x_i^2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 3])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.8309518948453007"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.norm(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Općenita $p$-norma: $\\|\\mathbf{x}\\|_p = \\big(\\sum_i |x_i|^p\\big)^{1/p}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.norm(w, ord=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.norm(w, ord=sp.inf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više: http://docs.scipy.org/doc/scipy/reference/tutorial/linalg.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2. SciPy.stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<scipy.stats._continuous_distns.norm_gen at 0x7f970ba55e10>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3989422804014327"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.pdf(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "xs = sp.linspace(-2, 2, 10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.05399097,  0.11897819,  0.21519246,  0.31944801,  0.38921247,\n",
       "        0.38921247,  0.31944801,  0.21519246,  0.11897819,  0.05399097])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.pdf(xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.0647588 ,  0.08817395,  0.11427077,  0.14095594,  0.16549503,\n",
       "        0.18494385,  0.19671986,  0.19916355,  0.19192205,  0.17603266])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.pdf(xs, loc=1, scale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uzorkovanje iz normalne distribucije:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.58147381,  3.71386122,  1.24616214,  1.79738483,  2.69992991,\n",
       "       -0.01069313,  3.30819964,  1.69645732,  1.09588046, -1.44891004])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.norm.rvs(loc=1, scale=2, size=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Zamrzavanje\" distribucije:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "normal = stats.norm(1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.0647588 ,  0.08817395,  0.11427077,  0.14095594,  0.16549503,\n",
       "        0.18494385,  0.19671986,  0.19916355,  0.19192205,  0.17603266])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal.pdf(xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.20691566,  0.9434288 , -3.30867649, -1.24524492, -0.95381916])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal.rvs(size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multivarijatna Gaussova distribucija:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "?stats.multivariate_normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mean    = sp.array([1.0, 3.0])\n",
    "cov     = sp.array([[2.0, 0.3], [0.5, 0.7]])\n",
    "mnormal = stats.multivariate_normal(mean, cov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.9244062489471738e-05"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnormal.pdf([1, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.32295504,  2.7257843 ],\n",
       "       [-0.19250187,  3.91415511],\n",
       "       [ 1.37357915,  2.90579424],\n",
       "       [-1.37198427,  3.02935659],\n",
       "       [ 1.56503442,  3.5666907 ]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)   # Radi reproducibilnosti rezultata\n",
    "mnormal.rvs(size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Koeficijent korelacije:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x, y = np.random.random((2, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.45606998,  0.78517596,  0.19967378,  0.51423444,  0.59241457,\n",
       "        0.04645041,  0.60754485,  0.17052412,  0.06505159,  0.94888554])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.30346130585985159, 0.39400530307438952)"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.pearsonr(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više: http://docs.scipy.org/doc/scipy/reference/tutorial/stats.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`matplotlib` sadrži više modula: [`pyplot`](http://matplotlib.org/api/pyplot_api.html#module-matplotlib.pyplot), `image`, `matplot3d`, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4.3'"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matplotlib.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: pylab import has clobbered these variables: ['linalg', 'cov', 'normal', 'mean']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pylab` kombinira `pyplot` i `numpy`. Gornja naredba (ipython magic) osigurava da pplotovi budu renderirani direktno u bilježnicu, umjesto da otvoaraju zaseban prozor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1. Funkcija `plot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97054c7490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1,2,3,4,5], [4,5,5,7,3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff188674ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([4,5,5,7,3]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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K4BeAu1eq2dXQ7/fXuoVO7HN61kOPYJ/Ttl76XKqxyzJVdXKweR5wLvDMSMlO4J5B7cPA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\nGmS4S1KDDHdJapDhLkkN+j/eTewHw92UIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1886c2ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([4,5,5,7,3], 'ro');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def f(x) : return x**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   0.        ,    2.04081633,    4.08163265,    6.12244898,\n",
       "          8.16326531,   10.20408163,   12.24489796,   14.28571429,\n",
       "         16.32653061,   18.36734694,   20.40816327,   22.44897959,\n",
       "         24.48979592,   26.53061224,   28.57142857,   30.6122449 ,\n",
       "         32.65306122,   34.69387755,   36.73469388,   38.7755102 ,\n",
       "         40.81632653,   42.85714286,   44.89795918,   46.93877551,\n",
       "         48.97959184,   51.02040816,   53.06122449,   55.10204082,\n",
       "         57.14285714,   59.18367347,   61.2244898 ,   63.26530612,\n",
       "         65.30612245,   67.34693878,   69.3877551 ,   71.42857143,\n",
       "         73.46938776,   75.51020408,   77.55102041,   79.59183673,\n",
       "         81.63265306,   83.67346939,   85.71428571,   87.75510204,\n",
       "         89.79591837,   91.83673469,   93.87755102,   95.91836735,\n",
       "         97.95918367,  100.        ])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs = linspace(0,100); xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.00000000e+00,   4.16493128e+00,   1.66597251e+01,\n",
       "         3.74843815e+01,   6.66389005e+01,   1.04123282e+02,\n",
       "         1.49937526e+02,   2.04081633e+02,   2.66555602e+02,\n",
       "         3.37359434e+02,   4.16493128e+02,   5.03956685e+02,\n",
       "         5.99750104e+02,   7.03873386e+02,   8.16326531e+02,\n",
       "         9.37109538e+02,   1.06622241e+03,   1.20366514e+03,\n",
       "         1.34943773e+03,   1.50354019e+03,   1.66597251e+03,\n",
       "         1.83673469e+03,   2.01582674e+03,   2.20324865e+03,\n",
       "         2.39900042e+03,   2.60308205e+03,   2.81549354e+03,\n",
       "         3.03623490e+03,   3.26530612e+03,   3.50270721e+03,\n",
       "         3.74843815e+03,   4.00249896e+03,   4.26488963e+03,\n",
       "         4.53561016e+03,   4.81466056e+03,   5.10204082e+03,\n",
       "         5.39775094e+03,   5.70179092e+03,   6.01416077e+03,\n",
       "         6.33486047e+03,   6.66389005e+03,   7.00124948e+03,\n",
       "         7.34693878e+03,   7.70095793e+03,   8.06330696e+03,\n",
       "         8.43398584e+03,   8.81299459e+03,   9.20033319e+03,\n",
       "         9.59600167e+03,   1.00000000e+04])"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MFppZlzw+F4Ojfx/zzexJMzsgX86FmT1iZmVmNj9h3y7fe3SulkSfp1339PoZ\nHQ7RALkHgUKgPdDHzPJpgcsK4EZ3Px44Dbguev+DgGJ3bwtMjx7ngxuAhVTf4Zav52EkMNXd2wEn\nAYvIw3NhZkcB1wAd3f1EwmXpy8ifc/Eo4bMx0U7fu5m1B3oTPkcLgVFmttvP/4wOBxIGyLl7BbB1\ngFxecPdV7v5+tP0NYWBgC6A7MC46bBzQM54K08fMWgIXAg8DW+++yMfzcAhwlrs/AqHPzt3XkYfn\nAlhP+APqoOjGloMIN7Xkxblw9zeANdvt3tV77wFMcPeKaMDxUsLn6y5lejjsbIBci5hqiVX0V1IH\n4G2gqbuXRT8qA5rGVFY6DQduBqoS9uXjeWgNfGlmj5rZe2Y2xswakIfnwt2/Bu4DPiGEwlp3LyYP\nz0WCXb335oTPz632+Fma6eGg3nLAzA4GpgA3uPuGxJ9Fk07l9Hkys4uAL9x9HtWthu/Jh/MQ2R/o\nCIxy947ARra7bJIv58LMjgZ+DRxF+PA72MyuSDwmX87FzuzFe9/tecn0cFgJtEp43Irvp1/OM7O6\nhGB43N2Lot1lZnZE9PNmwBdx1ZcmpwPdzewfwATgHDN7nPw7DxD+/1/h7nOjx5MJYbEqD89FZ+At\nd1/t7pXAM8CPyM9zsdWu/k1s/1naMtq3S5keDu8AbczsKDOrR+hQeT7mmtLGzAwYCyx09xEJP3oe\n6Bdt9wOKtn9uLnH3Ie7eyt1bEzocZ7j7leTZeYDQDwV8amZto13nAQuAF8izc0HoiD/NzOpH/1bO\nI9ywkI/nYqtd/Zt4HrjMzOqZWWugDWHA8S5l/DgHM+tG9VoPY939zphLShszOxN4HfiQ6ibgYMJ/\n1InAkcByoJe7r42jxnQzs7OB37h7dzNrQh6eBzM7mdAxXw/4GLiK8O8jH8/F7wgfglXAe8AAoCF5\ncC7MbAJwNnAYoX/hj8Bz7OK9m9kQ4GqgknCJ+uXdvn6mh4OIiKRfpl9WEhGRGCgcRERkBwoHERHZ\ngcJBRER2oHAQEZEdKBxERGQHCgcREdmBwkFERHbw/5lE6RqxNDErAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9705456610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xs, f(xs));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff170f16350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xs, f(xs), 'bo');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J9gEHgWPAjV1dgRvpTGU9nc5U1tfMVJI2lOUjaVl9l5VGybKS1m3puMItt3S2XbOgGTJI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\nJPUwHCRJPf4fnGdXqIa5Nn8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff170e04890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xs, f(xs), 'r+');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IIEceGXY/lG+nJiwRkRT//CdcdBGMHRuWi8/l2euaiR4pgYhIdXnrLTjttNC8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\nJ7UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97053a81d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xs, 1 - f(xs), 'b', xs, f(xs)/2 - 1000, 'r--');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V3FX86cw/0axOs6DLCivlcgzEH9PYbwiwxF/+BLjSzKqZWQsgFkh0zm0GMs2s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\nxBx5CUAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9705377c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xs, f(xs), label='f(x)')\n",
    "plt.plot(xs, 1 - f(xs), label='1-f(x)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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guwGrVXWdquYCY4FiQwhVNUdV5wHH/xUHPNbEv92HdtP0maalJ/nDh93EzWvW\nuOVq1WxWpgpoUrMJTWs2ZXb2bK9DCU6jRi7JV/OXOv7+e5sNKoQCJfg0YGOR5Wz/umBU5FgTJ6Z8\nP4VODTuRkpxy4saCWZmmF6mE+PbbhZM4m5PSt03f2GimKdC5s6stVNAcN306DB1qNWtCoFKA7RX5\nCQd9bGZm5rHH6enppKenV+C0Jpp8suoT+rYpZbKHzExXdqDAY4+5q3lTIdd3vJ6NezYG3jGaXHml\nmw1quP9W3ZtvQqtW8Ne/ehpWNPH5fPh8vnIdU2YvGhHpAWSqaoZ/+T4gX1WfKGHfB4F9qvrP8hxr\nvWjiV77mk/Z0GjNunMHpdU4vvvGtt9zMPwVuvtlNFGE3VRPX8fPsgpsZ6vrrvYspioWiF808oI2I\ntBCRVGAQ8FFp56vAsSYOLdiygJqVa56Y3L/6yiX0Apdc4ioMWnJPbCLw8svQu3fhut/8Br780ruY\nYlyZCV5VjwLDgM+AZcC7qpolIkNFZCiAiDQUkY3AXcBfRGSDiNQo7dhwvhgTXdbsWsOgnx43SCkr\nyzXD5Ppvunbo4PpCp5TQRm8ST0oKjBvnfi/A/Z5cdZVN+XeSbKCTiZzt26FHj8JZmRo1gtmzoWlT\nb+My0WfjRujeHbZsccvNmsGsWda7qggrF2yix9690Ldv8Sn3Pv7YkrspWdOm8MknUKOGW96wwfWR\n37fP27hijCV4E34Ffd3nz3fLSUkwdix06eJtXHHuvaXvcc/n93gdxsnr3Bnee8+VMQD49ls32jU3\nwMA5c4wleBNe+fnw61/DlCmF60aOdFdjJqy6p3XnzYVvcvjoYa9DOXl9+hQvRDZ5sut9VVDSwpTJ\nErwJH1VX+vfddwvXPfyw6wpnwq55reZ0aNAhtgY9leSWW4r3h3/nHVdi2O7dBWQJ3oTcu9+9y7zN\n89zAlRdeKNwwbBjcf793gSWgIR2H8Nait7wOo+IeesiNbi3w/PPw6KPexRMjrBeNCSlVpc0Lbfji\nyC9oee8jhRsGDnRXXgXtqSYi9hzeQ7NnmrH6jtWcVu00r8OpmLw8V2J43LjCdS+/XDzxJxDrRWMi\nbmb2TC5fdJAWwx8rXPmzn7kRiZbcI65m5Zr8ov0vWLh1odehVFxysittcfHFhetuvbV4wjfF2BW8\nCakX/3Ip//f4FJLz/DfBOnWCadOgZk1vAzPxY+9ed9Ewb55bTk11FSkvvdTbuCIsmCt4S/AmZI5M\nmoj260ejoViTAAARLklEQVTlPP+Ktm1dcm/QwNO4TBzKyYELL4QVK9xylSpuXEXRq/s4ZwneRI7P\nR17GJSQf9vdRbt3aJfc0qxBtwmTDBpfkN2xwy1WrwqRJkCDVaK0N3kTG11/D5ZcXJvdmzWDqVEvu\nJryaNXOF6wpmhDp4EC67DGbM8DauKGIJ3lTM3LmuBMH+/W65cWNX/a95c2/jMomhVSuX5Bs3dssH\nDrjfx2++8TauKGEJ3py8b76Bn/8c9uxxy/Xruyv31q29jcuUaPmO5fzzm396HUbonX66u6ho2NAt\n79sHGRmukF2CswRvTs6XX7o67gUTZdet65L7GWd4G5cpVZ2qdfj79L+z9/Ber0MJvbZt3e9fvXpu\nee9e9/uZ4M01luBN+X3ySfFmmXr13B9X+/bexmXKVL96fXo278n4rPFehxIe7dq5C4+6dd3ynj2u\n6+Tkyd7G5SFL8KZ8xo2DAQNchUiAtDQW/3cEe89o5W1cJihDzoqT0gWlad/etckXdM09eBD69XOT\nyiQgS/AmeKNHw6BBheVaW7bkiG8qGfPuZNPeTd7GZoJyxU+uYPG2xazdtdbrUMKnQwfXNFNwoz83\n15U4GDXK27g8YAneBOe551zZ34IyrWecATNmMHrPDNrXb88Zp1nbeyyoXKkyv+3yW0bOH+l1KOHV\npg3873+F94Ty810V06ef9jauCAs40ElEMoBngWTgNVV9ooR9ngf6AAeAG1R1gX/9OmAPkAfkqmq3\nEo61gU7RLC8P/vAHV72vQMeO8MUX5NatTdsRbRk9YDQXNLvAuxhNuew7so/U5FRSk1O9DiX8cnJc\nj5pvvy1c96c/wSOPuIlnYliFBzqJSDIwAsgA2gGDReTM4/bpC5yuqm2AW4CXimxWIF1VO5eU3E2U\n278frr66eHI/91zXxlm/PmOWjKFFrRaW3GNMjdQaiZHcwXUA+PJLN+K1wOOPu6bGAwe8iytCAn2E\ndQNWq+o6Vc0FxgL9j9unH/AWgKrOBmqJSNHiI2V+wpgotW0bXHQRTJhQuO6aa1xvmTp1yMvP49EZ\nj/JArwe8i9GYYJx6qutJc9llhevGjXMlDbZu9SysSAiU4NOAjUWWs/3rgt1HgSkiMk9Ebq5IoCaC\nsrKgRw83SrXAvfe6mZmqVgUgSZIYc9UYejXv5VGQxpRDtWrw4Ydwxx2F6+bOhe7dYckS7+IKs0AJ\nPtjG8dKu0i9Q1c649vnficiFpexnosUnn8B558G6dW45KQlefBGefLJYm6WI0DWtKyL2D5qJEZUq\nuc4CL7xQ+Lu8YQOcfz58+qm3sYVJpQDbNwFNiyw3xV2hl7VPE/86VHWz/3uOiHyAa/I5YWhZZmbm\nscfp6emkJ0g1uKiSl+emRfv73wvXVavmrtptguy49b8N/2N29mzuPu9ur0OJnGHDXDmNQYPciNe9\ne93v+MMPw/DhUXvz1efz4fP5yneQqpb6hfsAWAO0AFKBhcCZx+3TF5jkf9wDmOV/XA04xf+4OvA1\ncEkJ51DjsZwc1UsuUXXTGLuvJk1U583zOjITZht+3KC1H6+tOftzvA4l8pYsUW3WrPjvfd++qjt2\neB1ZUPy5s8wcXuZHlaoeBYYBnwHLgHdVNUtEhorIUP8+k4DvRWQ1MBK4zX94Q2CGiCwEZgMfq+rn\n5fv4MWE3dy6cfTZ8XuSt6d3bdSs7+2zv4jIR0fTUpgz86UCemfmM16FEXvv2riDZBUV6gU2aBF26\nxE+hskCfAOH+wq7gvZGfr/qvf6mmpha/grn/ftWjR0s5JF//8fU/9GDuwQgHa8Jp7a61WueJOrrz\nwE6vQ/HGkSOqf/xj8b+DlBTVZ591fydRiopewZs4tXmzKxZ2221w5Ihbd+qprkvkI4+UOjn2xJUT\nGbNkDJWTK0cwWBNuLWq1YMAZA3hu1nNeh+KNlBR44gn3+1+rlluXmwt33unGgeTkeBtfBViCTzTv\nvuv+NS1aYa9jRzeBcb9+pR52MPcgw6cMJzM903rOxKH7LriPb7K/KfivOjH16wcLFsA55xSu++AD\nV6UyRouV2ZysieKHH+B3v4OxY4uvv+sud9Xu799emjsn38nWfVsZe83YMvczsUtV7cMbXKXUe+6B\nESOKrx840K0rqDnvMZt027gWxQkTXHLfvLlwfbNm8OabbrRqAFO/n8oNE25g0f8tok7VOuGL1Zho\n8tlnrkBZdpGe4fXqwUsvuaYbj9mk24lu1SrX1j5gQPHkfuONbvReEMkdYMr3UxjVb5Qld5NYLr0U\nvvsObrqpcF1OjivZMWAAfP+9d7EFya7g49GBA/Doo/DUU4U3UcFdfbz6KvQ/vpyQMaZMkybBzTcX\nv1CqXNk15dx3H1SvHvGQ7Ao+0ajC+PFw5pmuXb0guYvArbfC8uWW3E1Qfjz0I+OWjfM6jOjRty8s\nXer++y1w+LD7O2vbFt55x/39RRlL8PFAFb74whUIu/pqV1+jQPfubjDTv/4FdayJxQTnSN4Rbv/0\ndr7Z+I3XoUSPWrXg9dfhm2+K97TZtAmuu86VJC5adz4KWBNNrJs+Hf76V/e9qNNOc317b7ghamtr\nmOg2YfkE7v78bhb+30JqpNbwOpzokp8Pb73lmme2bStc36ABrF/vmm/CzJpo4pWqu4q49FLo1at4\ncq9c2Q3QWLHC3Rw6ieT+8ryX+W77dyEM2MSi/mf0p2fzntw44UaO5h/1OpzokpTkmmtWrnTt8Ckp\nbv3DD0ckuQfLruBjSW6um6jg2Wdhzpzi2ypVcl26/vxnaNLkpE/xt2l/4z/f/YepQ6bS+JTGFQzY\nxLpDRw8x4N0B1EitwTtXvUNKcorXIUWnlStd98l//KPUkeChZv3g48XOnfDKK64u+6ZNxbclJcGQ\nIfDAA9Cy5UmfQlV54KsH+GD5B0wdMpUGNRoEPsgkhMNHD/Pqt69yW9fbSBL7pz9aWIKPZXl5bi7J\n0aPh/ffh4MHi2ytXhl/+0tWvbtu2QqdSVe6beh+frv6UKddPoV716BipZ4wpXTAJPtCEHybSli51\nSf3f/y7e57ZAgwZuVOrQoVC/fkhO+b8N/+OL77/gyyFfUrda3ZA8pzHGe3YF7zVVWLgQJk50hY0W\nLix5v86d3c3TQYPCchPnSN4RUpNTQ/68xpjwsCv4aHXoEEybBh995BL7xo0l71evnutfO2QIdOrk\nBiyFiSV3Ux5H84+S8e8Mft/991zR9gqvwzGlsCv4SDh0yM0Q4/O5r5kz3Si4klSu7EabDhkCl1xS\n2P0qRPLy80hOisxdfhPfvlz7JUM/HspZDc7i+T7PW6+rCLObrF5QhbVrYf58V2N99myYNav0hA5u\nhNxll7l61Jde6ibfCINPV33K7yf/nqlDptL01KaBDzAmgIO5B3lkxiOMnD+Sv1/0d245+xbraRMh\nluDDbc8eV98lKwuWLXPDlOfPh127Ah/bpo2byb1fPzj//JBfqRfI13y+XPslL859kcXbFvPSZS9x\nSetLwnIuk7iWbl/KLR/fwkPpD9G7VW+vw0kIIUnwIpIBPAskA6+p6hMl7PM80Ac4ANygqgvKcWz0\nJnhV1wd93Tp3VV7wfdUql9SP75Nelp/8BNLT3VevXtA4/P/Ozlg/gyEfDuHUyqfy2y6/5abON1Et\npVrYz2sSU77m29V7BFU4wYtIMrAC6A1sAuYCg1U1q8g+fYFhqtpXRLoDz6lqj2CO9R8f2QSvCvv3\nw44d7mvnTvd961bXLXHLFve1ebNL4Pv3l/8ctWu7YkRnn42vShXSb7kFGjUK/WsJYNu+bWTvyaZL\noy5hm6nH5/ORnp4elueOBvH8+iL12rbv3847S97hgmYXcFaDsyI2Gjae3zsITS+absBqVV3nf8Kx\nQH+gaJLuB7wFoKqzRaSWiDQEWgZxbOSMHAl/+5tL5kVrpFdESoq7Mj/zTPfVvr1L7C1bHuvx4svM\nJD3EyV1V2bZ/G8t3LGfmxpks2raI/1z9nxOSeIMaDcI+IjXe/4ji+fVF6rXtPbyXrJwsRi0Yxbof\n13FO43M4r8l59GnThwuaXRC288bzexesQAk+DSjahy8b6B7EPmlA4yCOjZz8/JIHDgVSvbpL2AVf\nLVpAq1Yuobdq5WrAVJCqcjjvMAdzD7Ln8B52HdpF+/rtqZR04nN3GdmF5TuWUyO1BqfXOZ0eTXpw\nbbtrydd8ksV6x5jo07pOa0ZeMRJwdeZnZc/im43fsGjrohIT/KKti5iZPZPaVWpTp2odald13+tX\nr29VLcspUHYKtu0k+mfqPe20Yw8PpSax55RU9tZIZW+NFPbUSKVrlyuo2qyla0pp3Nh9b9SIX/nu\n4IdDu1BygZXASnSvMqbJGOqWkNyvfu9qcvbnkK/55Gs+679dz8RXJjL5uskllgBo9Vwr1v24jpTk\nFKpWqkrNyjWpXbU2U4dM5bRqp52w/9hrxtL4lMb2i25iUq0qtcg4PYOM0zNK3Wf34d0s2LKAXYd2\n8cPBH459/2X7X/LIxY+csP8bC97g0f89SkpSCpWSKlEpqRIpySnUzq5d4vO/v+x9Xp7/MoIgIog/\nfQ04YwBDzxl6wv4fLv+Q17597YT1/dv25+azbw72pXsiUBt8DyBTVTP8y/cB+UVvlorIy4BPVcf6\nl5cDvXBNNGUe618fpXdYjTEmulW0DX4e0EZEWgCbgUHA4OP2+QgYBoz1fyD8qKrbRGRnEMcGDNAY\nY8zJKTPBq+pRERkGfIbr6jhKVbNEZKh/+0hVnSQifUVkNbAfuLGsY8P5YowxxhTyfKCTMcaY8Iia\nUQkicruIZInIdyJywoCoeCAid4tIvojEzezXIvKU/31bJCLjRSQ8dRYiTEQyRGS5iKwSkeFexxNK\nItJURL4SkaX+v7c7vI4pHEQkWUQWiMhEr2MJJX9X9HH+v7tl/qbxEkVFgheRi3D96TuqanvgHx6H\nFHIi0hT4ObDe61hC7HPgp6p6Fq6b0X0ex1Nh/kF6I4AMoB0wWETO9DaqkMoF7lLVnwI9gN/F2esr\n8HtgGcH3BowVzwGTVPVMoCNljC2KigQP3Ao8pqq5AKqa43E84fA08Eevgwg1Vf1CVfP9i7OBk58Q\nNnocG+Dn/50sGKQXF1R1q6ou9D/eh0sQcVUKUkSaAH2B14iFbtxB8v+HfKGqvg7uXqeq7i5t/2hJ\n8G2AniIyS0R8InKO1wGFkoj0B7JVdbHXsYTZTcAkr4MIgdIG78Udfy+3zrgP53jyDHAvkB9oxxjT\nEsgRkTdE5FsReVVESi0wFbEJP0TkC6BhCZv+7I+jtr+GTVfgPaBVpGILhQCv7z6gaAnHmLqiKOO1\n3a+qE/37/Bk4oqrvRDS48Ii3f+lLJCI1gHHA7/1X8nFBRC4HtqvqAhFJ9zqeEKsEdMHV/5orIs8C\nfwIeKG3niFDVn5e2TURuBcb795vrvxFZV1V3Riq+iirt9YlIe9yn7iJ/rZgmwHwR6aaq2yMY4kkr\n670DEJEbcP8OXxyRgMJvE1C0YH5T3FV83BCRFOB94N+q+qHX8YTYeUA/fyHEKkBNERmtqkM8jisU\nsnGtAXP9y+NwCb5E0dJE8yHwMwAR+QmQGkvJvSyq+p2qNlDVlqraEvcGdYmV5B6IvyT0vUB/VT3k\ndTwhcmyAn4ik4gbpfeRxTCEj7kpjFLBMVZ/1Op5QU9X7VbWp/+/tF8CXcZLcUdWtwEZ/ngRXrXdp\naftHy5ysrwOvi8gS4AgQF29GKeLt3/8XgFTgC/9/KDNV9TZvQ6qYBBikdz7wK2CxiCzwr7tPVSd7\nGFM4xdvf3O3AGP/Fxxr8g0tLYgOdjDEmTkVLE40xxpgQswRvjDFxyhK8McbEKUvwxhgTpyzBG2NM\nnLIEb4wxccoSvDHGxClL8MYYE6f+H8fQmnhkhJU9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97054416d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = linspace(-5,5)\n",
    "plt.plot(xs, stats.norm.pdf(xs), 'g--');\n",
    "plt.plot(xs, stats.norm.pdf(xs, loc=1, scale=2), 'r', linewidth=3);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2. Funkcija `scatter`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEACAYAAAB8nvebAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f970526a250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([0, 1, 2, 0], [4, 5, 2, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEACAYAAAB8nvebAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff170e26310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter([0,1,2,0], [4, 5, 2, 1], s=200, marker='s');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.96563203,  0.80839735,  0.30461377,  0.09767211,  0.68423303,\n",
       "        0.44015249,  0.12203823,  0.49517691,  0.03438852,  0.9093204 ])"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/OX2ITJ2MZ40N+iGcSI0VNVMUlUA8SjSUY8VANaURzZPECfM5c+JyuXj288+y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\nz9siSTSVW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asrM8BVVze/JlDW47h1vYWj8tFFG7EPdg+y0bCRKpONno4OGuNxHjr/KtYN\nEuf9YwheHZHQh3BXri/pURBNphgekvngHR8hEjmZs1DM9om69+/H5PcjpjKT2apBJOF207Zjx5Ke\nhyv4r3PguoL/RLjdbv527z/wb1/4AmsCAaxypmxTbjPTtnxmCObjlS3866teTq+I4XdJbHJlMvws\nJVGWFcZGopT7rOy5OhOEfnnwIJ/QNFokKZeN+Xw+3KpKQtPojkYJyiquSIT7fvMb7n3nO2maJbQF\nM8M3zz77LD3PPrukvsClomVFC/2d/YynxwmdkImFdmOqcCKnkmiiVhD05agMITDUG4jLGpp8M/5X\nfoih3IAqqtj0QCVcuDDF8eMQCr0fu30TgqBDlhV0GjTYy9CJ4A36GQp7qXKSk3KQJPOcKegsG2Vy\nsjrnF2uxWNi6tY3Ozm5CoQZstlpEUU88fh69/iVaWu6eXkQyRusPPPDfeeihJxkcDOZsB/ORf+ye\nPXdhsVg4df+pOWKCxZCVnrjznjszJb877uDhhx8mVT5GeXsVjdEEL50exONciVBEQE/RVCIpmaAo\n4myqJBKJUFW1MScDAhDFygVW4mcraTK0YT1h3IzRnKvRFcfl2jlewZXA/5+Cy/nAut1uPvY3f5Nr\nthbbNts0jZ04+Y1Xw5Ow4CNadHewa0OmbPPzY8fYkk7TYDLlzNEBRqameHEqyckxG2ntWkQyC8hI\napQ/euwEH99Qza6WujmlH30qxW++/33uaW5e0DlrKX2B+aSFs1LC5mCSwLlRQieqMTrdKOEw8UAK\nncmAoigICCQnk8jjKWw2CXXChwQ41HKmvGUMd/WhVolUGjQUUeDIYQm0TRgMy9E0kVQyjZgUaLA6\nkaYzXoexirhs5pzvOOZAAKY/8+wp6Hw2yuDgjF+s0+lk+/YN9PT00dv7AtHoKHr9IdavvxGf70X0\n+tPTbKFMz6ClpSVnO1go0uafcyxQUkwwi1KGOzabjfKKcgzlmcUlOhbBbN9KyuZiMBzGomk5/zIZ\niE2zxGoqKohNxogn4rkF8Nlnn+XZZ3tIJLbQ0PBumptNOb3/ZDyOnEzyH/9xls7O7/DXf/2HuFyu\nnPJoMBik79QJUl4v7YIwL6PsChaHK6WeNxBLMQm/nO/97MgItfE4n9q8GZNeT7fPl6PnlRvNtOWN\nyEdSKb751FPcY7PhC4eR165lRUsL/cEgf/3rbuSJdlbaVqMXZprCsqryUirGsqo0dmMne7bWF5R+\n/uG551hpNnPnDTcs6rN0Dg4yum3bvG5ckFlA7/m7e+b46eZLCdfabPT1B+jo2oBsXo+q04gNyeAA\ndHoMmhmdqlJlN1BuNeVsMAECgZdpvaqTbtlHIBEi4bVhGPswJpPI1GQbRq7GqTdRYTLngn4+hsMD\nVK5Oc+s7bwagv/9FPvjBOLfffmvBcaXVNf1o2gmamgy0tq7A6XRSXm4vmSRkE4oZKZDSxy5kxL5t\nwzZ23TrzLGafr1/9+CEG7OdprLcz0BvlXM97EBwtGO12JJMJpn+3s2WZg2NB1laspWVlC2fPPkE4\n/Buam+/B5VpRoPfvVtWcpHdM0zgfHyapf4pVrWuw2bYQi5noPXECt+Kj2tLLzc3GgmQjkU5zeHQ0\n1xu4HDvHNxMuttRzJfC/QcinQBbLyi/XA5v98Wf58vbycmKxGKEHH+R9TU0Lvr5rcJDRQ4d4X1kZ\npwMBbJs2YXK52Hugl1h0J5YJDbdprvDV6UQCW20tFimMqj3G3u0ZEbZIKsW9P/sZ927ZwspFnB8K\n+wIL7YIe/MmDdEzMGODEYjGOHjiQkRI2Z5goZ88GOHVqFzb7SsYmohgndUSNMkabyLgSZ1mZE0cR\nT9dg8Cxr1z6JuUpg6EIfhzurWGf5nxgkP72TVqpsu9HN4xHgi0dQygb5wAduwWAw0Nf3GJ/+dHVJ\nWutSAvflQu6ceUbss8+Z/+ymFYUfxI/StKYsd1/tjhbCqRRBUaSitpZUKkU0EkFRlMzEsc2GGlK5\nfsX11NfX89xz/4DZvJIbbrhzziKdb94TTCT4bX8QX9SPzTXItnd9jvNHjuS+27SaYjRyJqf0mp9s\nnJ+c5FFVfcMYZb+ruFLj/x3CUkzCL5UCWYwvH4lE+OaPf1xS2TIf2alMWVHwiyLLPR4ePuUlkd5G\nnWslQ5Pncg5K+bAASVmm3lHHYGgb+3q6+Fh7C4eHh6kFltXWLvozLOTGlY/ZBjjzef2mkgplcRNb\nGhvo8HkRDCmqEhpJWYZ5zLw9VitHQmBOrmdluZXKqnrGo08iCDJQ/HWKqpIQJayGBnw+H1VVFQtO\nQb8Rsw4LYaFzzn52I6kUPzx4jHRaxWIR0bQJRGEVFr2eYDjM+WPHcIgiZkHASKbk49MgNaFjxFCL\nzWZgdLSfLVvunKP3n4+YLNM5EEIntNLoNDAZ+QZdTz3OWocb1zQFVC8aqHesZzLu4r7Ox9i7Xcpl\n/qUYZVdQHFe0et4A5EzCF+Asw/QDO81+uVzIKVuOji588DRGolFcjY2kgP0XYiiqyvnAUcZ0Xs5G\nTpNSEyVfW2VdRYc3RSSV4sjICM3V1UsWfJvPjavguGlqq3pKpfe1XsbP91I7K0M2GDSi4VG0cdjq\naaDMZKLBaCc8FaNckognEihFtP41bQKzWcCg06HEjDi1KpRwGB0GGp2VRJLHS15XRJYxO53o9Xbi\n8eS8U9AXg0gkQldXF/v2Pc2+fU/T1dW1aJ2gpWD2s2szGNjmamR0OJKRvxZPEkvFGZyaQheN0qyq\n1IgCVUYjlUYjtUYjVWkda9M6Jo4cYf+vvoMsV1Jbu2xmkTab55y3ZyJEWm3ELDnQiSb0wjosU6dJ\nJudKU7jMdSTS29jXM1Tw7xurqujp6HhD7stbDVcy/suMxZqE52PjAsbjF4P5lC3z4bBYOJtIINjt\nNNTV8dXOo7w8aMMiGRGoRVEbiMX7OJXoYrnZSYt5JRadnRhgm3ZZknRG0uo6njr3OqckiTsrSrsh\nXQyKWQze+8l7+cEPf8DwKy8z2ExB81obhRrFyzXVH8BhzJSpaiUTF0YFZJOGRQdRWS4o9yhKClE8\nicdjZXIyTnXUgU8yIssy0WiUloqrGAo/T1z2YJYaC64vLmeYLDUVFcRiI4TDXsrLDxedgl4qLsZt\n62JR6tnd1dTCwaNDRGwpauoSHDl5gnJ9HeWigB6RZFpBkzQEQSCeVDAEBW6wOUioKk+MHUexriEW\nizHp9bKqyPOdUhS8QQWbYeYzpJUynILCRMBHr8mPrGYWAItkwWNtnk42OrijNZXrVy1l5/h2x5XA\nf5mxGJPw2XgjHtissuVCQzNhWeYpQeAv1q7lvoNjnJ24EbP+HZSZZnoODsM1hAM+emKvM5h8ievt\nrfgFB8utGfqdrCiMx0w8GUvxe5/8JJHHHlvy9RYT5Voo6LmcKp9a1k6DzTnTvLaZadvi4dFyLx3e\nCziM6wGQNI3r9FZO+BOkbAqCOV1Q7olGe6mri+AbFTEN6PnTdRv4kSLR44NEMMhqWx1bG26gc+AX\nhBJbsRmvQtMkItNB39PQgCimiUR+i9F4hD17PnfJwbi/v5/77ntkyW5bF4tSz67bYmFP21bu6+4k\npJ/ApD6NIfVeJFMlADpNQ5YVEjLoAwJbjU4sOh0JdYIa8QQRbQUnjh6lUlULavpZ+KJRVM2DKMw0\nzOV0gjG5j0AywLB8DVZDZrHVmEAUnqTRWYlR31wgJgeL3zm+3XEl8F9mLMYkvBjeiAd2UWqZO3Zw\nw7p1/MO/P0+d/aNUWiyMRwvr2Hq9Hkd5FYm4g2jMzZOTP6PWfR2jkQgxTcMviqjV1dz60Y+yc+dO\nvvnEE4vqL2SRL8qVze5PnznNz3/2KrCD1tY/w2IprAmn0wkOHfwF5wde5X/fVl/w4wfY1VLHwaEO\nJuMuXOaMa4lDp2O7VEZXMMRwMk3AlUCQIJkcR5N/gzOoss3czK4NLZj0ep4424Ou6irGnBVMRSK4\nVSMrXdcwGDrKYGQ/cW0tJlsjNqeTROIkiUQ3tbXn+OpX915yEM43bcnq989GMbetS1ls5nt2lzmd\n3LtuC3/xq0fRxwcZ0R5jOHELZqkeVdYhJNOskGy0mMxIgsJg4gwm3QE+Vyfwj2Nj+Pv7qbcXl/uO\nywpCnjBeIh3BnzhNmrXY9bvR9OU4TTOUYVW7GW/gOHHllxwbzTTGQ7GMh3BvKETNNCV5KWb0bzdc\nCfxvcSxGLfP//J9/Z4CbqcOFWZLRmKvMmGVrYFiLP/YukjUTyKvWYjObWe7xMDy8j6am6iU5Z2Vx\nZGyMqg0bePRXj9JxtIOIMcKxFwIIwvswSCJDnQdorG6kZUULlulmnl5von7ZOxgdCHBf53MFjT6Y\nzlK31nNf52MMhrZh0dUgA06djlqDmebKKgQhzUj4DAZdB390zSp2Ll9e4AS1rdHAg929rFz7Djwe\nT45z3shmmvUq4COdTgAyZnM1smxlx45WmuZhMy02GM02bZkPxSQi3giMRiK8S2fiGs3BqBbiqfBj\nnJTdoK2jzFiLw2DFJ0+iF06yrSzGrgozJtHNd30nGU9sIiEtLBYnKwn6A+cRhDjlug+iCkaUWceI\nghGztBFfNMX9v72Pv2o4z/Lp906Ew+z7znd45qnfMhVzotdf/YaWx96suBL4LzMWMgkvhfn0xy8H\nSrE5IpEIR46Mcv2Ouzn1ymGqFQXwoWoNBVtvRVVzZY2mVbtJp79DQ2MNBoONaDTI5OQLDA1tZ9++\np7F6PDwviosW5fq1LOO7cBQ0qN5ZzcnnezHb3o3Dk/EYVhUV74SXoa4htl6zNWdF6PF46DPXEU3d\nyL6eg3ysvaXgvZc5nezdLrGvp4sDvQlGUk4CqodzisJqbQiz7jQfWW9gV8uGovpDNzd5+PaZ3yJJ\n78dgqM+Zq89gde6/MhIJvy1Z119KrX7GtOXd8967fORPyF5sRrvQsxuKxTAnk+iTSdYrCtcLkJT6\nOZa+wCRmlJSEx2Fhc00N5aaZHdr1tjCPRfuIpItn/GZJl0s2JuJjJJUQbmsratJCStMwzpqZSKTT\n+IJBTGkby807If08N7odyIrCmCxzctxMX5+HqL2V9nfcnHte4PKXx96suOTALwj/P3tvHt3Wfd55\nf+4G4F6AAEiCICnuFKmVsizJlhUzkRnX8RYnabykTjt247ZJ+047adNpc97TdDLqNG3f+LzT47aZ\nzoy7N81bZ+I6bprIW5zKsmhLsiXZFrVSC0ESFAmSILFe4K7vHyApLiBFWU6bOPqeo3Mk8RK4uACe\n+/s9y+cr3A08AUjAX7qu+9VFP/854IuAAGSA/8t13eXbI37MdSWT8HIaz+V4MZnkp+Jj7N374r/p\nlnTWIKS6upZtPT0M9PdjTh9nODNCyFPK4S6eylQUhenpDQwOvkkuF+XkyX1Eoz6+970Zk3VngkIh\nypcuvMOD1WPUaBq5YmndVqV56IpGkQSBo2NjvOS6xMMQ2q5R1VCFoRvE3jEIVF8OqqIkEowG0X06\nvUd66dnVg6Zpcyhh+2KR/TFjQaFvVhFN49Gtndy/0eBfenvpO/cyLWub+djm9cv6Ac9qMJPh5z97\nJ33nv8vwcLIsIiGXS3H69MvAyzzw4M2cO3cOn8+3pC/+anL1IyMjS0xbrqRyiIjltNwk+ZU+u4l8\nnpGpKXaYJo0eD6Ig4LguAUGgoioIglDq7x8aQm1qwjcz/7ErKPMDu5chu45tTuOSPH/U70cUEph2\nHZP583jlc0S0BymgkMrnaZu3UzBtm0QqRTU2tjTJJq2T/alD3F9jcy6V4p/yESL+h9hR38BYNkvv\nCy/QsnEjPp8Pr6oSjUbf0/TYj6uuKfALgiABXwPuAOLAG4IgfMd13VPzDrsA7HZdNzVzk3gSeN+W\n3K8m1TGRz7O3P863Tk6Rqbmdwlzg/Lfbks43CNE0jU1bt7KmpYUXXzyEbnvweEJ4FIXKeVOZAKZZ\nweHDb6EoOwgGU3zgA/8ZTbt8nun0TRwc/Cu+cPIQIW89tVIUnyDguJNY4jEq6+GeT91HVBEYNo7O\ncWQSFxM49mZEaWlAVoMq6UKa/vP9bN2yFZiHEs50LCn0zVfA42HLunV8J5nkN7s2cPMV3Mrn8MCP\nPMKDsASRUCgUGBx+i/HpHxDdYNO0rZr96f3s27cP+ZnLk7DAVefqu7tbEcX6Fc+vnGYREculk5aD\n5s1HH9Ru28aRY8eWfHYn8nkOnj1LhygiStLcXIdh28g+H+LMY4W8XjymSWJoiPrWVhRFwZIkblvn\n45Xs67w9ZrC1dgeyePn99UgSjRUWb419D5e3aQ79MopURVbWsXw+DNtGnXn8aV0n5LrYpGnxmfhl\nH+PWZnqTBzigC3gCn8AnRzk5OkoylSJqGFi5HEpVFVnXZUAUqWppobWzk2Tyh58e+1HVta74dwLn\nXNcdABAE4SngE8Bc4Hdd9/V5xx8CVpf4/THWalopB1MpHu8dJpG7hbTWRlPrVsxiaXrZq7ZSVXU7\n+/cf/3fZkobDYe66axe9vX1YljwDDpsf9E3GxibRtDzh8EG6u39uQdBPpQbp7f0mlnUHdQ2fxbLO\nIlTLJJJJTMtC8X0IwZvn1SNnyHtO03F/x9zv5tPGHDa6nPxVfmIXY2xcX0ITa5pGV3c3B54/Qe/Q\nO9x4BeTv5598kn3f+AbGVeKBS9z3UkA9c+Y833ruW1Td7HDr7i600MI0kWVY7D+7n8NPHGZt9Zar\nztW/+eZLwNUH/kJhmpdfPsI//dORJemkQuG7MPEOj9QF+Y3W1mWheSeAftNc8tl9pb+fTygKB30+\n4pkMHa6L6zgUBYHAor58VVEIFYtMT04SqKmhD/A1NfG1X/91Hv/iHs5eeh1N2Y448z477iRB3ztU\n+QbwSY/hkxtKHWeyTGNTE+OXLhEqFtFkGb1QQBMKCAzQqcrYjkPeDPJm0SambSDkW8uxgQGaHId1\nHg+CohA3DBoCASRJwnIcRmIxjsXjbLhlO/v3v3pN6bEfV11r4G8Ahub9exi4ZYXjfxF47yaVfkR1\npVbKiXyeP3o1xnDmDobNABWCi9XXN8csmb8yESL3/FC3pOUMQoAF4LBY7CCOE5nzh52cHMEwjrF5\nc5BbbnlsQdDP5yfo7f0mgnA/qtrMRCJBYtQiPvA2wRoBQQErC8PTAm/1SQihC0RuixCpX91rk2Rp\nzhx+NuceCoXo3LaNZP0ET4yOXtHrt7Oz8135AgcCATo6OviH7/4D7R9rmTRNjQAAIABJREFUXpZ2\nKXtkGrsaGVVH+Ys/+y533/7Qql4blHL1x48/TUXF0JUPnqdUapBjx75HJvMRtm69d0GaKJ/P8+YP\nIKKHeS59iC01uSVY7fmT5H+XSvH1XI6P5PPsqKvDchz6YzF+IxQiXSwSz+cZMAxqZBl/KFQWzxxQ\nFOKpFCe9XqRwmI0/9VNs376dx//6a/z9V75CzehR6rQgHkmiUlXoim7g6ZMC/8+BJCOZIUxvDbXN\nraUUTWsr05OTjCZGkcxxDGmQm/wWeVNiQhCw/H7kpiaM8c2cjo+xEaiabdUVBDTXJZfLEQwGkUWR\n5mCQgK5z6tBRqttbfyL7/q818K8asCMIwoeBXwC6y/18z549c3/v6emhp6fnGk/t31crtVJ+qz/O\na8luZAJsUFW8soxpuxRxURWJ9kCADkFgJBZjIC5T3b7lh7YlLWcQMitN09i6dRMbN5YoirpexLIs\njh4dobXVS3f3f8LjWbhS6u9/Bcu6FY+ngaFz58jrGTxaBYIQoL5WQJoxBXFcl6ExhxF9G9/5xvd5\n4LP3EAqF0IIeXHdyxXOeNYefL48ny8ce+iRdXV1X9Pq9Fl/gvS/tpVBXoLHhyhtXq2hheTdwMTbE\n1i1XnuKGUq6+ouJWMpl/xbI+uao8fz4/wauv/gOi2MOWLfchywvTZAP9/bQ6Is01N5HU65fgDuZr\nbVUVd+Xz9G/fzmggwBP79yNcukRlOk3CdVkjy3x9Jud+r9+/xIhlVpIoMmaaPJvLEbrpJm6bcdRq\nbm7mV/7wD3ll715OzXwvLMfhrdFRBvUslncMUR3BtSaYnJxAVeuRJAXFl0cNDVHjSXBDZRhVlpFn\nUpBO5hKKIpNIK7Q6zuWgP3tNAcs0F/xflaqWzHNGCgsIqj/q2rdvH/v27bvmx7nWwB8Hmub9u4nS\nqn+BBEG4AfgL4G7Xdcs2q88P/O8XlQswuq5z5smXCfjX4885nEm6OG54ro/ZRUcUErSEJDqrgwQs\ni75zOi++eJz777/rPd+SBgIBdu/uZP/+IwsMQubL4/HMra6Hh4cBnbVrtywJ+oaRJRbrx+u9k0sX\nBygWsngrJBRFolCIksuNEAyWvpSiIBDwKFQozUzGFH7w6g/48Ac+jFkwySRfwzTW41X9+MN+JKW8\n4cesTFOf4+JcDf/malk55awfV1I+beALNRAbvZyaWo283nrq6moYHV3+PZmv/v5XyGTWl3XmMgxj\nwcRslbqQrVRO22trefXoUR584gnuuv9+/vrJJwkbBmY0SpOq8pteL3/f28v/GR7mDtel3etFnsdy\n0h2Hw4bB/2dZFFtb+cJ//a8Ldk+LvxfDAwO88eZJjo+quD4T1b8Nn6ug60ny+TNEoxrt7Y1Yxlq0\nfoeGmd2KbdukMknymddByzM5Pc2uUNOS17Oc6v1+3hk9ia6v7qb8o6DFi+Lf+73fe1ePc62B/02g\nUxCEVmAE+Bng0/MPEAShGXgG+A+u6567xuf7sdT8AHPw4EFSk2uYHjXR5U4CnsiCtkkAx22aM/fo\nbgrS6kicO6/y1FNPzcHP3ksTinIGIcspkTiLLL9JZ+eXyvysD8fZQHoqh1vUEVUBZS5oa5jmwg2i\nIkvIhhePs5nYG8/x7KlnCXeGsWqLjA4eQVE7EIYEQpUhquurUXyllaZruqi+y3nl1XJxrnWgZznr\nx5UkSUtTU6vRzTdv4cCB1674nhhGlrNnj1BRcQedna1Lfj5rpjO/k2Yx7iBrGPQlEiTzBlDqvGrR\n9bkUyIYNG9D6+lg7r870+Tvv5LsnTvDfT5ygIZ1mLeADJoBjgkChooKGtja2/dIvLVufCgQCrFmz\nhn/4h1cpFO5i+/YdeL3fIxYbJxjsBtpwHItsdoSzZ4dob68m77qYpsn05CR6KkW6eJbuikGMoSwV\nxXr0qUZMnw+fqs6loEzAU2aGQJEkgs44yWTiSm/H+07XFPhd17UEQfg14AVK7Zx/5bruKUEQfnnm\n5/8b+DJQCfxPobQqMF3X3Xltp/2jpasJKKdOneVcf4FW7Qb8nqVESQBRkObMPV6NnWSTZjI0fon+\nb7zEhrbSl+i9NKFYziBkvmadnVz322zZ0rMgrz9rqHHu3GlSqQiFVALHsfB5V/54+f0KzoRLwQzi\n1hvkb8jT1NGEf62fgX/uBSGKXFNDajpF5nSGpo4mFJ+CqItEoyXD82TyPD7faytycd4r3k1yKjnH\nsV+NZtNWgtK8JDW1khxngtbWRm69decV35O+vm9g2xof+tCOueG2+Srq+lztaFazbKUDg+dI5Fz2\nxwwsZ/OCYutE/jCbn95LR0dH2f7+iKbxmZtv5sGtWzkcj9M3OopuGKgeD5+qq2NnQwPfHxmhboVh\ntnLTyZ2dtxGP/w26XoeqrkUUZYLBZnQ9QH9/H55CAW8iQZXrIgjTaOprPNJYRe+UwKsTp9DEm7AL\nBbLFIv5QCEEUyQsClf6l7l6mXUQTTxGtuvKu6v2ma+7jd133OeC5Rf/3v+f9/ZeAX7rW5/lR1LsJ\nKC+99CqKc+OyQX++FEFjKlXFmN5PmyRR5/HywXmrp/fSvrC5uZk9ex67orNTNPpRnnqqROrM5/Mz\nxd8kjhMhk5GZnHRxiiq2YGLnbFRVnMnr51GUhQHIcUqMF8fv4qsRsWRrrgjXdE+Boee+TTHVgzfc\njuWDoXNDVEeqWVu3FkGwGR7unbMYXC5o/1vzbuYr2hZFlE5gm5tX/TuL01ZXek86OvIEArsXDCmt\nRnkzwNcOD1MXeIC6wPoF7ZUAjruZd47b7NnzN/zqr963bH9/wOPh9rY2bm9rW/D/8zEcy6ncdLKm\nReju/hl6e79JOn0rgcAORNGHqlaRTDYyMXmaZjdHRk7gE/fxxSaXyMxqvkYc5Q0jRo3UjuLYGNPT\nyH4/amVl2QL0WO4sN9Y5qGVooe93XZ/cfZd6NwElm81y8XwSVTau+Pi2bZNLpaiWwoxbKtVKEglr\nwTHvFdN/VpFIZEHb4mWDkDpaW3cxMDBAfCTOpdEfkMu1ceFCCtdtJRBYhyjKiOI0yeQAklCBK6kU\nCgbF4jTBoIkgjOP3L9xuT07reKISxfwUgmjjCi6mVSrC+ap9tH7CZPKdF0id1XDtzRRMP+45jU27\nDMbGnltiMbhY7zXvZiXrx3LyqB5abvDQ9/1+1ObVuZEtTlut9J50dd3B/v2v8fTTPgwjSyLRRz5f\n2nVqWpBotAuvqpJdZHKUN02Oj+XprPoAjcEtZc/DEBQamj6AKBb5H//jGXYs09+/nI6OjdG5e/ey\nabRy08mzO0ddL9LUtJuJibdJpfYBmxGECKnpBGbxLc55jvBpLcdn6ks3u78fTfP0RAVJez2T9iDD\n9g48hLDcUQJOkQ+0LF1kJfU4Pnk/69aEqfghTsz/qOp64H8XercB5dy5c/jUDyAJZ3DcIqKwvBlI\nQdfxui5e2UPODKI7b9Ma6ih77HttQjG/JjFr2feXz/zlnGVfOhjj0L5voUg/TXXYg6q6iCL4/V0I\nPI/j7kIQPMiyhm3LTE2doaGhiCRdXlnZtkNKL+CpdwhwFlsQMFIWbsXlIKVUKNR1K1RtNZg8tY+K\ncYE1uTV87nO3s3PnfVfMzb/XvJuuri7kZ2QsY/V5/tZtUc68UMI+XEljY6dIpZ5BVW7ixb17F9Rx\nlitEi6JDf//3eOutAzjOBgShdNNy3VFEcR8NDW2kHJMOJziX5++fTGM7MtFA+ULofFMej8fD8PCt\n6EI/r/l8q8ZwvObz8dhMJ085zU6My7KvtHM8309sNIajOggzO0NXieL6QoTVDOGgi5vpoy6q8/Nb\nGxkeGuKVVIrvJwOYzl1sUFs5Wshwh5LmYPH76M6HQNjAhGNxYGianY0uRdsmU8wzVThPyHuA3/lQ\nMy/YNvevsCt5v+p64H8XercBxe+XMcxKBFElnj5MyLcTv6IssfNzHAerUECb2Z4WnGHaPNNEy7hM\nzeqHwfQfHBzk8Scfp1BXWOBzO3ouhaIOoPiypKwUmeEMTfVN+HwBgqG1TObfAfemmUfRgaXtmbmc\niauBqQ9T1T4N9RVMZQTMuMl0cfryl990EXWRTRs20fnRThLvJNA0bVWF3PeadxMIBNi9bTf7z162\nfrySMokMn33sTs6fWR77kEpN8s7RfyE//k1+YZOf+pdKjW9XquMMDg7y7W8fIZFooqbmUWR54Tk7\nToF4/Ajp7Hc46xTZFFmLYdtcnNbRPCNE/dvKnvOlXI5gQw2JxFHy+TS2XeT55w/z5S//As/87d+u\niPkuN/xWTrMT46lUit4jvVgBi0B7AFFa9F2wHTKTWcaHCqz1bKDGW0nY9ybbd+7kN/b2kTLvoFmK\n4JguriQRN/x0KusZE95m0j2EYG9gMBWiP/kOlSoo0iB1AY0qdQtf3tdH623rKRQK1we4rmtlvZuA\nUlGxgb/6m79GDg5zKf8h8lVRzMmXSBkVKIXGJQbepmmiUPLT1O0hROEVFLW0Rd97/iwAVV5tgWn6\ne830n5iY4PEnH0fcKC7oWTd0g9FzIm077iZ+6lkcpxvXt5GhS0O0NrZSV3cnycQTWGYlCAEEYZBw\nuJFsNo9tO3N9/KZlYzkTeOV/JdxhMyqK1FXV8pHujzA9PT1XDFV9Jb7KbJui6Bfn/GJhee7M/BXl\narUa3s1i68eVlIwn8Y36eOQ3HgGWYh8A0ukBBk9+k7tqLH757o00Lrq5L1fHmd11er0Ps2lTkVgs\nSTC4MHiJoo9gsBtBqOb14T8j5PHjopEzpuiK1uKRlua24+lRDk+fQLQ8iENbZnYQGpnMev7bf/sG\nd999E2fdHK8eO8YGy0KzbS5OTTFcLDAkSazfeQuf/oVfWFWtpFAo0HeuFyEiEFxmUTPLacoXcpw7\nO0lFZSndeSg+RUP4AbY1bCKRy1E0Taoti6GJCbYrlazztDBuZHkhOYRX8ODXBJqCCbqbfxaPpJLU\ndfrsW5nKB9iz5ycP2HY98F+lrjaglFY0h5guVtF54yjBooVKB/hheuw72PYHmXbXk8kWafKH8Mky\njm2DWyBt9QOvIIguAz6dv9KPkRIKmJaFM+GinvBwX/067u/YSETT3lOm/3KDSrMsHS3YSFOXxujF\nI6QS38cqrOPc+SZqo7UEw1Ukx/8AuJ1QaBeyHEDXa0hMDeDVJBzHYCp9AVd9kabuLEXZQXC9tK9p\nn0trXEkTExMrcmfSwSoM486rft2zvJvlNGv9+PiTjzM8NUzdurolaR+zaDLWP4Zv1McXP1ce+zA1\nlSGVSnHg6Wf58u4GbljGsW1xHeeh3/otRkdH+dbT3+Ns/yaam7y0tFQTj59A1wOo6tKbUUXFBoo1\nD/D69L8StiqRhKN0Vi+cJjZtm/6pAV4fP0qo5gHC4VsRxfmf8WY0LcvRo1P4fK/x6cce48UfvMjh\nk4ewWy08lT4qKys5YwzylT//yhyraLlVv88nc+LUK2T9HWimhpk28S/iQc2Xx++hEHIZzFzEK8P+\nmDFTlJYW3CwbQiH6hoaY0nXO5n0ExHvx+mvw+EQyxp9j2jajuTRDsjxH7kwm237igG3XA/9Vaj7U\n7ErK5/P0HimtaPx6M/7KOKJ0gmDFzYxlItQ2eMmn3yaVOkBRWs+5VIR6NYBlXkJyjtHgqyDmNjCh\nvUHLTh9j9jgR10XzAB7IBPL8RfI1vv3SCf7olqsPcstppUGlfNrAtkOMjo2SyqZwg03I/gj29BDp\nkX6E6SA1NQ1E/B9nMtmHXjyBpW/AKErARSq0KUThBGH/KBN1GSYFlXxBplatp3Nt+YGi+XJyDsVC\nkb/Zs4dbCwU+WiblULAsvvbGMV4arKG29uar6ngxTZPTp0/hUioyV1UunZdobm5mz2/sYe9Le9n/\n/f1ztY/Z85OTM5C2h8tjH2Z3E//093/Pp8PhskF/trd+1mDEEUUm4gP84hd+nrobG3mjdwq14gYS\nA28i6iI11TWMjx3HNNtm2EoLr0kweBOJwrfJho7hkboYzLloQgqAvOsy4mQZyZ8l2vifqKjYUPba\nSJKXxsZuYjE/v/Ibv8vWB8LseOwmHNshcTFR+mzgIDTYvDzyMoefOMwXP7ew22y2ZvT8q88zMH4a\nb/Az5PQc5EAYFwgFQlRXlQiw8yUrCqLqMD51CN1ci+VsXtKJBBDy+djW2sq+C0OMWI1o+DBtG48h\nMm2sZd/kKdo23cm2zsveDv9WfgY/Sroe+H+I6j/fjxWw8Kt+pi7lSAwk8AZckoPvUL3mBiYvjRCq\n3ER1xEHPj5BOjRIwNVorw9gTN3DYNEl6L1ARHEMec7mhRkPzzlsReWG95nJazfAff/BtHly/k1vf\ngw6FlQaV8rkcl8YvodRoSCGJYqGAZdj4KtYgR2wCgkrOAj1noclbKOgJKvzDyJ4RWmt7WdsgEQ0r\n2K6X75zOkhnP47oVbLtrW9k+9PkyiybWiEXfhb182udj7TIdJj5Z5oNN9bw+mKSvt5dtPT1XfOzZ\nAuOJM3tJVpzkuPwWMBPI59E2ZwN5JBLh0U8/yv0fK02fzqafKsOVqxoKW87fdiKf55X+fvpjMTY4\nDhFBYMww+KvCGGOtDmYgQMRXhxbaSbiuhM12bIfxyXFcr0s0mGB8fHABW8l184jiBHV1a3nggXbe\nfLMRVb2J4oxTVUBVCY31ko1/fNmg77p5VDVAPp/nxPkRHH8PU6OHyP3rRWLvGDj25jm4nutOIkon\nqG6a5vee+D2++jtfJRKJzNWMMpUZUt4UnmgSY+J11OoOlGADgughpc+vGV3ecfj9fkYLQ7Q1pjgz\nmZqbOSgnWRSxCNBc3cqgXsRXV4ctiijFDbTfaLNx49Ylv/Ne+Bn8OOl64L9KLQc1WyzDMDg/dJ6i\nt0g8dhE9/yo57SxKQGEq8bdMjn+KytoN5FVI6Vk0TwP+apfEkIHhapw2x3BCSQTP9/F35hh2REbO\nF2nRfHTWqmi+0g1AFgS6In4oZPnm8SPcfxUm78tpuUGlfD7P2fgp8FYiedeRS6Xwui6aJCEIAgUf\nqLZMdZVGSi0Q759ms+PH0tsZcsbBLpAuyiRHi+g6BNJ+qtMqGztqGDh+nOrq6hUD9Fj/GJWOxocd\n54qdJV3RKJVqP35jBwP9/WzauvTLPqvZAqOh5tCaLrL1Y1vxqJdXk/Npm4tXsFeLfZhVOX/bwVSK\nb/b2cqtl8dFAAJ8oMmGaPFVM0LHVy/YKmZPZDG/vP4xk3jz3e/P9CqYmEuzefSvZbBZdLxmUq2qA\naLSd0VFoaUly4sRJ6uo+NpeuNIwsR49eJBD4RNlztW0TUZwgGm3n1JlTpcWMt5PDzz7PmnV3E6q9\nYQlG27FvZXLwDPHsXr7+j1/nkU8/wu8+/rucjg8yGfeQm2qjYHVg6T6MKQ+ichhfdQVqbQeO7Jur\nGc2u/I3cKFrlm2jVJtl0ccVrO+vhW7RdKmtqiNSUbpCplB9ZLj9IdzV+Bu8HXQ/8V6mVoGbzdeHC\nBYbHz2IrQ1h6FsdowNJvwDYEPJUJcsPfRy+eoiKwk6YNm7Gx0dN5xpURCjUmgnoJRX2Bpm2j1IRL\nbZ9Om0ssUSA+YNDdGCQUuPz21VfKTGgOB158kdbPfQ6YN1E8VSp8lktbXI36z/ejtClIZ0+SnerA\nLyko0sKPkOO6pItFJs08StjFSAk0exWi/ik+Gb0Zw3FQvB7aGuto2lnJ/3viNURToMk1VwzQyXgS\naVAiPJ1jxwrToLMKeDzsbvHwgwvDJGMejI3lWTnz03FiYZSWrd4FQR/AwYEwDKYG+dUv/Sq/+nO/\nygc/+MFrWhku9redyOf5Zm8v9wsCa+flrPemJym02jQGS5+1Fo+HVN7hVCJBuM5ckBKZ9SuIDcXm\n/AoWS1XVJWymWdTGwpz+ZeVyl2hpKd1oY6MxvGu8DB5P4br3IClqWe8EUfIQjG5BVML8ryf/nn37\n9/HKGwOIgTtQQq0YFQ5WsYAoitiZAAg7KExcwpjuJdixGdsTYDI5SU11JbnkWWTvfu74xQ0ceXGM\nQrKIswLITzdtDNtDwSNSX315Z+C6E6jq8gujK9V33k+6HvivUquBmuXzeQ699SJ64QSSczsItfgb\nhlCjs3376/BW3kQmdo7U5NMUjxyhruUmxkfGEYNjCMIAwY1xjOo4wXnBXZQFgmtkdM2m93SantYQ\nmk9Ct0xyHom6DSFe++53+eCdd7L/9f3sP1Ym/1wmbbFY5QaVDMMgNhoj1B5iYk0c68wgSuW6uZ/b\nroOeNxl1bSSfiC4WUSsFzuR1RvR+dgbi3LPjviVEyC92dfN4Xy9Og824dXFJgM5N5zjz6hmcuMi2\nzhsJTL2Btcrui5Lhei++jHdZVs5sOk4hg+zdT+euyxOo5frLcxU5/uDrf8D6H6y/4nW8Gr3S38+t\nlrUg6Gdtm/1OirrqhfMe1RUymj3B9OQkNYt2eIv9CubLcSaorKzj5pu3LWAz5fPpuf7/xdL1JLI8\nRGfnNhKJBI7qMHUpi2O3ICsCZnFwxdclKzXEx9uIJQ5QtelRPKHS+Yq5IqZuIqkSTGaxR08h2CEs\naxfJ48/jb2oiUcjgI0XbjT46d7WhhTRa2jczkbeYHDtMXWAXvsUYC9tmPJ9n2nVpbmqauzHato4o\nniYavWPF8/1J0fXA/y50JahZ38nDJFNvI8gPIEg1CJxCrV14qWXNR+XGLoxUhPzoP1IwjhDeISFG\nXGrDtYzJIlI+QKFYwLUtvJKEQKm3XQ1LpJstzozlaa/zkBJFos1NJMaKyP1Jfvsrv03oxtCC3vtZ\nrZS2mFW5QaXZL72Li1yfxTvwCqZehaJGsByHVLGAXXAJNnmxcFAFEU0UyfoT+NR9TK8Z48tHXub/\nvmH3AhZ8cyjEnm097B3o5+unTvC2/Da1bbUUsgWGjk2SOC1RE76d5sYbOXxwHPFilv5L/exu8XBv\nZ0NZtPCsZg3Xv/D8PxMfqqSu7qcX7NIMw+Bi/CyER3DdA3Q/3DhnqrJcf3mgMkDu1RxV3VXsH1r5\nOq6k+fybrGHQH4vx0UU7iL5cDqvGRZYuoy5MF6LRAAHvGaantlNVU7OgE6acXwEsRUHMZzM5jgEs\nvI62bZLLXUKWh+ju7kLTNOLxOI7okBqz8agRivmVg75ZMBk6N4TtM7HT9yL7o3M/U3wKpEtPq0QV\nxICNOzZNoMqPkWnHW/U91BaLrTffSltH6WZsFk3CRpjfeeJ3+e9/+Ce8/Mox1qob51hEKcti0DAw\nNC+KJWIYBoqiIEkSudxRWlo6lxBl52v2xviToNUTp65rTrNQM8d5huHhXiyrMPczwzA4eeYlROU2\nXEsFThHqAMm7TJtaqA5P+G7GE2lqb4ug1WtoAQ3bsvDJEoFwGNvnI23b5C2Lgl36I1a5nMro5PwV\n1Le24vP6Sj3fEzHMtiKNXY1li7OzJiHiRpHHn3yciYmJJcfMDiqNnr1cy9B1vbTizeWoUAWau1Pg\nPoue6me6mMEuuGgBBUkWcVwXwTEpZC8iyP/MhpZx1jV4KbQUeLyvl4mZTpW566lpPLppK7+97hbu\narqD20K3kXmtirD+y3zi7qe44/bfYt26O4hGb6K+4kPUBT7L/tgu9uy7yGAqteJ71RwK8di2Km7Y\ncpLR0ScYGHiWwcEDDA4e4O23/5a8/de0bTtMz2faCNWWbkjz0z/BaHDBUJHkkXCqHZLx5Nx1/IOv\n/QHPP/88L+7dy4t793Lw4EGy2eyK59XV1cXpGf5NXyLBBsfBt2iQL2mbiPNa7W3XKQHHKoO0teqI\nuYvkcrklj13Or2AxCmKWzbR79yj5/F4ymSOkUoOkUoNMT58mlztES0uWnp5tC7qiCpkCrhtFECVg\nEsW7POdm8tIklpbH1k2Qb8JMX2bii5KIT/Vhz3gxS5oEYbAtm4q2ddh6NWqDijUPUzLWP8bubbtp\nbW3lv/7hl9je4+Csi5Bdu5YzfpU+yaCwxoNbL5K1LxBPDnFu4ByDQweBV+jsvG3Zc718Y/zJmOK9\nvuJ/l1oOajYyEiM5dRI1uAHXd45AuxfZt/xldhwHy4lStKqIX4jjtbxUb6qmf6ofJJBECb8/gKNp\nmIaJ45S+KIooobbbOATmtrODiRQ0QEW04ornX9VQxfDUMHtf2sujn16KeVhuUMkyTRQBfCGZ1p4U\nF058C+t8JWJ6C2J1HYWUiGEmEJ23qGmaQPHq+At+FAECAZF8k8XegX4e3bQ0B60pCmvq69l3YIi2\nti8s2U3Ncmdk0UNjcAtJvWpFU5G5c/Z4+OSD984Nds3mcU+dzlJlR1h708LnmU3/LDdUJGgCelYn\nn88zOjHExdhJfF89yUNtpXbU1ZBT53sz5/J5IosImuWUNUzUcMnxqrNT5fTFfeSm2wkGV4bALUcw\nneUA3XlnN5/73FfRtCyS5J0rBi9OFamqiqU7gIrjFBDEk/jD95R9Ttu0SU2lEILj4GxClMLY5vTC\nxwuoFCeK2JKN5JGQghKFoQJa7QybaexfYc3Ma5gZhrv34Xvnzv1LX3qE//Jf/pI33g7gqW2ntq0W\nURJLnP5kntToCIIwil54BV9wO4ODbyGKpV3WLMtodgewWrT3+0XXA/81qBxA65lnXiU42EDbtjWM\nJxVSVgp53mV2rAJmOo5ZyGEYJq4LghEBYRO5U89TG40wcvIEhXQeT5VNaCa9Kwoi3kXOQoVAAb1Y\nWkXlCgbj8TybPlCLd5W0wdrOWva/vJ/7P7YU87B4UEnRFFzTXfCJcWUXI5AlXJen+sYpxJlCryEV\nEX0FajWF6X5QFYlZs7baej/7D8W439g4N3U8qwnH4fipGIXCB8viMKLRKAOiiDXDl1+Nqch8SuTi\nDhwXk75zfQuOn61lBNpXDgC5fI5j+/bRZFnc3h5hcjLHjXV1c69pNeTUWW/mingcIZcjbZWuUZUi\n0uX3UyUpODMLd90ySYki9VXV2LaNZem0rZ/g+MV/4NLAx4msuRFP3Te3AAAfK0lEQVTFM2PmM+NX\nMIvSvhLBtK6ujgcf/CD790+taPwSjUYRigKu62LoxwnX1SAp5T9ruekcruZimzqC2zSXppwvSZYI\nVYVIJVNYtlXaFauUdgZCNVYGFFFhuG94yTAclBzi5Oo4DbsmmBx6g+SldaSzCrl8Dtsdp2CeQtKa\nEZUKLgwfZOhinsb6G/H5fHMso5aWTiKR5iuivd9vuh743wPNDyiH3ziMGhSRFInqqmoywxlM2USk\ngD52jsJkBsfegGWuQQJEktjmCRxRR5pWuO3GJjyShJma4uREgiqfB4+81ERisU5fnCQSVMkEvGyM\nRq94PIDiVbCqlsc8zB9Uevnwy+SH8jj1DvaUgWuBPmLh9ys07QyiaJdTWbYrE08WMS0HcUIg2qGQ\ntg08soKiSFgRh75Egl3zctC6afK26zIwYNDUtKPs+Xo8HqpaWhiJxWieWY0vNhVZrOUokdlslpH4\nCINHRijkLLSgh2hblMRkqZaxmBkzX2baZGT6LLsaKqiaOQ8rklvwmlZLTpXXbuFPv3ea0OQtrFcb\nEQUBh0lk4QQ7KxyMjMNktEBOkaiqr2c6OYk+nUJzXUTTYOOGBKmxv+Pi8WfxqLfgDzRjjlvotSZj\nY+evSDCd1WrMeDweD411a3j75Gn8VW9R3fDh5a+RYYICVsbG6/VhWgXEMtFG9siEI2H0rE4hVcC2\nbQrJAghFpAsF3GqX3TvLD8PtfWkvQrtAd9c2xkfH+dcXv40Hm2CFD09QwipanP/HGJb+s3iCOzF1\nidEJH+vXrsfn82GaGU6efBGP54958snP/8RM7cL1wP+eqyJQgWuV8ruKotBU38TFi2+RGTmFKNyG\n4NmI47p4fSAJIq7rYju7cLOHkeLDmHkbLaTQvaaJ4f5pRsaSNNVHkMSlNQI3D2pAIZnU4aKLtsZD\nZUvLqi3+YCn7ZrHmDyo9/seP89rIa0zosC0UILfZoN+XXBD0ofS6VJ+P8Qt5Nmsakgx557IZhqgJ\nTBUX5qCPjo3haWlBGNyyYptsa2cnx+JxArpOlarOmYr0JYYX3EigPCVyvoeCrq9l8J0exi+pCMIU\nonQCb20Su9Fe9vltwyZ3Icu29hBV83ZW5V4TLE9OnY/1vv2jn+D1f3kOp1gkACg0YzldvDgxQMx+\nganpKTq7opyOxZAdC1WWcQSRrEfh1pY1eNol8gWTk7EXOHvJoOdD9/DwgzuBaqxCgaOHD1/RsW21\nZjx10QLHj/8N1Y2PoPhWnoi2DRtZUJGVNKJQ2iGWkyRLBMIBtAqN3GiOCrsCM69z18Y7+NPf/9Oy\n5zw6OsrTLzyNtlPjeN9xTp0/hdalEa0qHWtmTAb+2SHY/nHMjEph8jSiWINueOi/kKE+GsbjmWbT\npo1EIl/iG994js7Ozp+Y4H898L/HuvHGLsS/fg7HLq0aJYqIU3G84sOYbg1mwURwHVxBxHIdsEGT\nArhuBwHfQ/T2PkdPj4mmKdzTvI5nz59gRJimoa4KUby8XbZNBxJgKjZOTOZD4Wa+pcXZ1nll7AFc\nZp/Hh4Y4Ov3GFSdOA4EAn/+Pnyf5RJILNSJCbgLFXt4L16crpIcEIs3ygtx0Oc0G6NaNW+gfXvmL\np2kaXd3d9PX20pROsyYQQBSqmdIvzh2zHCWynIdCvvA2MT1GMLoVx76V+NnDpE9/G/8DBXzVS29A\n6QtpNAlaqlaPgVhMTi2H9V67fTvChQt4JAnLNBGAZqUFr7GeV47+CX3WSUKNEopXBEwyBRvb8lKZ\nHKczXI3mU2ivCaPFsmT7+tifTnOjLC9hGK1Ud1iNGc9HP9pJV9fH+cd/SpJW0qhBFT2jl1b4gOJR\n8FX4MLIGbtalbcNOLh5+huqGuyg4MmbRRPGW38GKkogiKag+Bc0Z4stf+rMln8dZ5MPT33uak85J\nKjIVJKeSZIoZhIMCqlxBRVUF+SEDU78PraYebyVoaxzM9AiFMQOfoFFd0073B7rnFknDw6nryIbr\nevfauXMn9TXfIJVIEq6PMBl/B9fdTahyI7ZtMz0xgSrK4IIkCCheCaNgIErT+Dw1WFYP/f0vsHWr\nQl0gwH1tG3juwjku5TOoIQXJUwr+2UGDNckgH6pspr42xHOFAi3t4VWhCQb6+0nGYkQcB/F8lojr\nZfT8hRUDQzab5dy5c+zquIlze89y0JyiM6LgLrIUsE2H3CUDeUjizpvWcSY5QoVl0xUuoSQMw2Zs\nMMspeYKCZZHI6YzX1fHo7/4ufX2nV3WNQ6EQ23p6GOjv52AshpvJcGI8QYVXYsJxOD3zOh6b9zqW\n81DoXNtJ/GAc3aejBlVC0RtIXXIZem4vrZ8wUSouByk9oWOeN1m/JrDAwxbAybtUBsrnu1VFoUXX\neeqpp1izppmXX36NWOwGtmy5zMOf3cmEBGEufZQqFOifFsDbAxfieCIWgaBEUXRwVJm6YIBYLsXQ\ncJoOuwrvkExHTmZHYYz2cJht27cvOI/V1B2uZPwye+M6c+aPeev4KLGTxZKHwuznoAhCRqAp0kTU\nFwU7RzA6hD84Tk11ieJatIp4VA+CuDDvb5s2ZtLE9Q3xucfuo3XRoN58TLi2UaMiV4HX62X6lIE9\n2ozrbCZvhpksgJs0kH2TOPox1NoOJG8F3iovrusSVoJkigsHta4jG67rmhQIBPiZhz7E1568QNYr\nkhobx6t+CgDLslAlCW3emL5p2tj5FNtqA6SyWdzqRmIxjY0bbTweicZgiJ2NLUxXhskmxlELJk7G\nInTOx6c7djBRWUnl7t381u7dfOXPv7KiSUgqlSqtlC2LdYEAru0ypov83M4bCHg8c4Hhfx04QNe9\n96J5PKRSKc6dOoURi7FVEGgVRX5NjvDd4SleeOss6aiBsd7CK8u4eRAnBFqqwrRuDZNyDMQ1axBq\norw+eIniYJxkKosuWGTDNjlHJlAbpdEXZF/vPqKVURxnaXtpOWmaxqatWzE2buTtt0fx3HEn+oYN\n1FVWzhVy52s5DwVN0+je0U3vkV7ShTRqWEWRo9jF25h850XqupVSeieWQ47LbNixjvBYbMFjmKaN\nPCHS1b60tjKRz7O3P87/OTGFMVagth4OHZrG51tLPH6YlpYqOjtbl+xkwl4vvYkh3GqX6lwjTqGZ\n2kvDnL9QIBuFyho/hayFm4fisMWldJZb3Ap+UdNoDgY5NDyM0dW1IO13NY5tK6Eo8vk8UmUcJ3SM\nhto7EdUmbLcUxBVZQfWp5CanmT77Nqb3IHf+ygaO7n0N14jS2tjKZHKS1FQKV3EvN5Q7YE6a1Kte\ndt08xSOPLFx5L8aEnz18FiNlMHxAxMh+Ek91G4I0g3eYMLAm21E8DRQmT2NMv0qwYzOyVpriFSVx\nyazDdWTDdV2zPvWp+zh06E/pPfwmxVwHHl/py+fY9uXPueNiGjZOPs8a/yTb6iOYdiW9iSGy4jou\nXTpKS0sYgEpFIdDcQt3OD3D6jdOIF+FTv/kga9evX5CeWckkJJ/P09fby8Z5K8rh4TS7q1rmiqJZ\nw2B0cpL82bP07d/PlvXrGT97lirbZkLTsNva2NDZSUTTuG/dOgYnbuHze/+Fg4UJmpurifp9VK73\nYSoixx2DqpYWdnZ2YmZM9p+aINupYvskWpR6OtdvmuPszw6VicdECoUolnX3qrHXgmBTX5/kFz77\na1dl8zdfoVCInl09pSndWAyloJB1axh/Q0FRJlEyCi3tLXR+spNLiTiMLfz9sUu5BddxVoOpFI/3\nDlOwdlOl1uHW3ojXO4mm9RAOb8VxLGKxEeLxY3R3dy3YyRx6+yi6qqM6Ao4rUnA3MeGL07W+Ec3y\n4douOKUaT3S3n0MnB/H2ZVg7gyiIOEuHuGZ1LY5tswE4tCvEfXfU0n/wTWLvvAYzkDbXhFx+ElE+\nwbrbcoxdGMEW2+l+uJHep55Fn9pNNLKemkgNuVxuzmrTyhVw3WF27Zziy1/+TNlC7nxMuIjI2AEv\nbsXHkcJ+hHnoEMGVcCUV25LwqNuwjQjpc98mtH4bmApKUMF13CWzDteRDdd1TYpEIvz+7/8Sn/3s\nl8m83UkxqYNHxDBNhKINBmDYeAWdav8ot7VUoikKKAo99a0cHIkzcqwXVy8VDceyWUxzDG+/ykPb\nHuLez5fP0a5kEjLQ30+TZc0F/WRSxzckc++2Uk1gPiDsy9XVDE5Pc+b4cR6uqaFKVSk4DkdiMf4m\nHudnurtpDoVojkT4ywd/hl9/bS8nQwW0XTeA10tAVeds+/KpPL3P9SJ3yYR9YdwJl+5dH1yQkpod\nKktWJrlw+h3kgQN0dKxutH41/der8VDQNI2tW7aycf1GBgcHOfz2YXT3Ztr8g3R9rGuO3+PNLPSw\nXXwdZzWRz/N47zCi8NM0Bhs4PT1NQFUX4BFEUSYYbEbXA/T29tHTUyKUdmzcyJlLFwjWVpJOTSM6\nDorYRHukgY2NS+F0hm3jKEWGPSXMQ0CS0ARhjr5ZTu/WsW1xAN56VycbdxskLg6jZ0p1FrVCIdrW\niUf1cHp/kIsvXKR1dysf/NkmLh49SOyd/XM0T8G2KaTiyMXTfO6x+3jkkQfKphkXY8JTCRM3dzti\nTYS5ntd5EkQBy7RQPAqSpwlL/xD5S8fwOJvxr/WTTa48YPd+1/XJ3R+Smpub+cxnPsr6dTK11eNU\nSDk0x0Qp5Kiwk9T6BtheO8kd7dWE5uFnNUWhI1zNL6/fwa8EbuIhcxPbp1v41I0P88SXn+DRTz+6\n7PZ8tvfeOeUw3DeMZZSmHg3DIBmLsSYQwDRthgfTOKdcvtjVTUTTFgDCuoNBfKJIsVCgLpUiNLOK\n9Yki3cEg9wsC3+y9PH0b0TT+6OaP0H7ew2T/OC0tbTQ2Ns6lGPqP9lOsK2JiloL+ju5l6xBVDVXU\n3RpkNPGPJJPnr3iNLw8mLT+RWTpu9R4KHo+Hjo4OPnrHRwlILRRz9oLWzmg0yoQoohfNJddxvvb2\nx2dW+g1zHrbRZdpsVbUKy2qiv38AKOExhIBAuDJMoKKCCq8Xv0dBLtPZBSUaZa0EbrSEeViN5ju2\nrVZzAXjdQqyBR/XQuKmRzlva6LyljcZNjXM3yrW3rKUuWsfOwE7Sb6QJhRQ2f9hH46Y3qa77HvXB\nl3j4oyp7n/0zvvCFXy772V6MCTd0g/gph+r6G3CyzuyIyJwExQVBB6k0CQwgeTeij6aoCHqRFGlu\n1mG+SsiGKw8/vh90fcX/Q1RbWxPt7aOsWfMBEokEmUyOM2+e4cYKP2sqIniW6XJx3EnWVGjsamws\n9bd7VR5++OFVrczKmYRM5CZwh9IMSw7yhMjuqhbu3dY5F6wWA8IM2yaVydAhy+RyuQUTrGtVlVvT\naV7p7+eBGZJmcyjEE7vu4j8fP8qQOIRQKyD6RcyiyYmDJ9Bu1GhRW+js6rxi8bllWwvWwHmKxacY\nHu5Ztq1wNYNJ16JQKMS2zduor5hg9PujC2B3Vkbj5OvjPNS0acF1nFXWMOYcoqDkYVs102araUFc\ndynW2++vJxY7xMaNxhweA0oGJAYgu5Ooavnp3qJpogmAClNTpdRJ3nUJXGGQ72od21byaVhOildB\nbpS5YcMN/OyDP3vZu6Br9d4FizHhsy5wNQ31ZE7p6I6O63ERZqafpYCEIIzjEsWd2Z05lgLGJny+\nNLZlI+oLb8TzWUY/CbrmwC8Iwt3AE4AE/KXrul8tc8yfAvcAeeAzruseu9bn/XHQLMJZFO+ey7W6\nRgExFls26Jt2EVk8QVe0lDpYbgBpJS02CXn5pRfxpg0+2NRMV3t0QS66HCAskcsRcV1UQcAyzSWP\nv93v509iMbIbL0/fNoVC/GxjG8GPPYqmaUxNT3H61GmSm5Ns/amtq54tULwKvlYfn7xxB4nE6LJt\nhasdTILVeygslseT5aFPfvIy6mFm3kHoFHhTeIZun68sKqIvkZhziErqOkOyPNdmG412IYr7cJzC\nAgyyJCk4ToREIrHgsfx+P0nXJCz0EY36V3XeszuM9lUO8q1Wy/k0XEmzsyLv1rtgsfJpA0GoRvEp\ntHS2cOadMxTsAp4qD4IkIMgCcqWOMZ7G9Xmx8haCIeAPtuASJ5fM0VK3cN7lOrLhKiQIggR8DbgD\niANvCILwHdd1T8075l6gw3XdTkEQbgH+J/D+L5tTHuG8eABpscZyZ9nd4iHg8ZQdQLra59+1axfp\nZBLt4sCSASegLCCsaJqstC5XJYkNztLp24gooheL7Lr9dgBc1+W4fPyqBsqgFChc3Cu2Fa5Wq/VQ\nmK/FNMvFAWvTpk188/HHuXV4mB2L7B+TeQPHCTOYLnm7dnVfTm95PAFaWjqJxY4QDC7EIwiChq4X\nUVW1hMcAJElCFCYJ1k7h8ZQP5F5FIeuCT4dKSVmww1hJE45D3Xvg2PbDVjlM+Kx8AR+dWzrpP9VP\ncaCIGBDBM5PuKV7AKWhooRrUiIpZkCjmi6iGTGfX5ZrMciyj97OuNce/Ezjnuu6A67om8BSw2Mbn\n48DfAbiuewgIC4JQe43P+2Oje++9DZ/vtbmc9Wzb3inXZTCdxnIuf6CTehyfvJ8Pt0bpHR7mGcdZ\nMID0bhWsqmLCKf/FSa8ACDMppRrKKSIIZFYoHr5Xmg2699zzEe655yPs2rXrqldlszfg0dEjq/6d\nK60Am5ubeWzPHkZ37+aJ0VGeHRjgwOAgBwYHeT2R4GShQLalhW09PUs8fzs7b0OWX0PXy9cxotEo\noi7i2A56Ok645i2EDi/JZa531O9nzAYhAWtEkSFZpvUKg3yzDKOroVGuFIBXkpNzqAy/+xtMV1cX\nclKeq1lpQQ/uPCMWrUJj/Q3rCdWG8MpeNFFDUzQqgg7hmlEUTw5wMPRLyHnfXJ3JNHWGh3txnGd+\naCnDH1Vda+BvAIbm/Xt45v+udEx5s9T3ocohnGfb9rItLRzMZulLjvHmyGvE019na7PLNzIZRnfv\n5rE9e66a815O8xHAq1FpBemSFwT8/tWlF6C0gqyYt4L89woU5bT4BrySVls0jkQiPPDoo/zaE09Q\n9yu/gv7QQ+gPPUTLww+y/ua1bNq6tWxNQ9MidHf/DK77DOl0L45TwnqXfG29JSZOTS1j5w7hus9y\n239oY/tHesouFoBS2rDoIZCFIVFcsMNYTu8mhbg4AK9GZtFETl7dDWaxFmPCo21RROkEjm3MHePz\n+WhvaacmUoMkS9imTbAqSE2zB8VzllziAIp9kM2dnaRSxxkYeJaxsT9h9+5R9ux57D35nv046Vpz\n/OXhG0u1eEm55Pf27Nkz9/eenh56enre9Un9qGm5UfhAJShakUzmIJvXVXDzTZ+hsbX1muwRy2k+\nArh7UbonqGmMugvfjqjfT59lsTUSWRazMOG61M1LVc2nYM6qnKHLlfReBIpyWi2L5t0UjRengrLZ\nLAcPfm3F1FIo1ExPz2P097/y/7d3rzFylXUcx7//ZZfetttaG7sUSreUbSmkICVihVrWUOOCiZKa\naMQr+IIYETAaAX2zITGor6ohUYNIaFSIsY1WaRuqsi5WLtn0Qu+0lZaW0pZhWaDb3ba7+/fFnJkO\ns3M5M7M7Z5bz+ySbntl5Zua30/P858w553kOhw+vYnDwcgYGTnLmzCIOHeqmqXEbc4a3MO8zs9LX\nCcgcrTxzeDh9AZKTbw9w6vgk3m1pYdrChSO+YWQrdxdiqgDnGyuSS2oO/UrX5+xTledefSGHt++j\n6SOL020aGhpontVM44RGTved5sr5VzJh4gQmzZvE6dN7aG29hqVLJwL9Ze0yrAWdnZ10dnZW/Dzm\nHrZ253iw2VKgw93bg9sPAsOZB3jN7NdAp7s/FdzeC9zk7icy2nglOcaT1HVwz++znjrqhT6XRCLB\n4x0drKyre9+Fyk+dPcsjGzdyX3Bxb4Ce/n6ee/NNFjc2clmOi5r3Dw3xi74+7m5vTx/c3Xz0KMeX\nLx8xIGj1k6vpeit8oTi68yjLP7w85zUCRkPmJG35DxrfVPHX/tWr19DV1VxwmuOUs2dPsXPnH2ht\nPcaKFcvS60RPT096ioLmBeevppaaZ6nvnffoPdJLY6KJh77/EFOmTEkedxgYGHHcAUbOYVTOVm4i\nkaBjVQd1i+pGjBXJ1vN6D8N7hum4L/8I4VJkTtnQNKuJ//zxCGa3MakpuZNhaHCIvp4+6k/Vc+N1\nN6Y/AHt6DjI8vJaOjnAnA4wnZoa7F7+YQ/bjKiz89cA+4GbgGPAS8OUcB3fvdvdbgw+KVe6+NOt5\nYlP4o/Taa6/lLAxrtm+n+fBhrp8yhTf6+jhSX8/8JUs4uGVLcqRv1kHoze++y/G5c9Oncx7s6WHt\n8HDOKQCiLBSFjPUHcCKRoKPjcerqVuad5jilUGFKTUqW8/rJPSOvn5xIJPj3+vXs7+riisHB9CRt\nmXMY5ZukLazMApz5gZRy7sw5Tuw/kZ5DfzR3o2S+H711vez67ymGzi1n4sTLqT87kbnNc2md35re\nh5/5De6DuDsnksIfvPAtnD+d8zF3f9jM7gJw998EbR4B2oE+4A5335L1HCr8VZKrMPQODPD01q20\n1dWxeMECWlqTHSdzbp/ZjcmJyQ7297PWnTva2pjS0BBqCzLKQhGl988GWnzXUqG/O/1BFZxSWuwc\n+FT71Hn6Uz8U7pz5sEr9QBptqb/v0OFDdHfv5pV97zF16g1MmHBRMscof4OrVZEV/tGgwl992YWh\n78wZdm3YQNvQ0Pu+DaRm83zj1Vc53tdHd309yxYv5oLJk0vagoy6UESlWruWolLqB9KY56jyLtSo\nqfBLxQrtJtjhzoUtLcxftIhp06aVvQVZK4Wi2uJamGRsqfDLqBnr3QQiMjpU+EVEYqbcwq/ZOUVE\nYkaFX0QkZlT4RURiRoVfRCRmVPhFRGJGhV9EJGZU+EVEYkaFX0QkZlT4RURiRoVfRCRmVPhFRGJG\nhV9EJGZU+EVEYkaFX0QkZlT4RURiRoVfRCRmVPhFRGJGhV9EJGZU+EVEYqbswm9mM8xsk5m9YmbP\nmNn0HG3mmNmzZrbLzHaa2T2VxRURkUpVssX/ALDJ3RcA/wxuZzsHfM/drwKWAt8xs0UVvGZN6uzs\njDpCRZQ/WsofnfGcvRKVFP7PAU8Ey08At2U3cPfj7r4tWD4F7AFmV/CaNWm8rzzKHy3lj854zl6J\nSgr/LHc/ESyfAGYVamxmLcC1wIsVvKaIiFSovtCdZrYJaM5x148zb7i7m5kXeJ5G4M/AvcGWv4iI\nRMTc89brwg802wu0uftxM7sIeNbdr8jRrgH4O7DB3Vflea7yQoiIxJy7W6mPKbjFX8Q64BvAz4J/\n/5LdwMwMeAzYna/oQ3nBRUSkPJVs8c8A/gRcChwCvujuvWY2G3jU3T9rZsuALuBlIPVCD7r7xoqT\ni4hIWcou/CIiMj5FMnJ3vA7+MrN2M9trZvvN7P48bX4Z3L/dzK6tdsZCiuU3s68EuV82s81mdnUU\nOfMJ8/4H7T5mZoNmtrKa+QoJue60mdnWYH3vrHLEgkKsOzPNbKOZbQvyfzOCmDmZ2e/M7ISZ7SjQ\nppb7bcH8ZfVbd6/6D/Bz4IfB8v3AT3O0aQY+Giw3AvuARVHkDTJcABwAWoAGYFt2HuBWYH2w/HHg\nhajylpn/E8C0YLl9vOXPaPcvkicUfCHq3CW899OBXcAlwe2ZUecuMX8H8HAqO/AWUB919iDPJ0me\nSr4jz/01229D5i+530Y1V894HPx1PXDA3Q+5+zngKeDzWW3Sf5e7vwhMN7OC4xuqqGh+d3/e3d8J\nbr4IXFLljIWEef8Bvkvy1OE3qxmuiDDZbwfWuPtRAHdPVDljIWHyvwE0BctNwFvuPljFjHm5+3PA\n2wWa1HK/LZq/nH4bVeEfj4O/LgaOZNw+GvyuWJtaKZ5h8mf6FrB+TBOVpmh+M7uYZEH6VfCrWjmA\nFea9bwVmBLs3u83sa1VLV1yY/I8CV5nZMWA7cG+Vso2GWu63pQrVbys5nbOgD+Dgr7BFJPvU1Fop\nPqFzmNmngDuBG8cuTsnC5F8FPBCsU8bI/4uohMneACwBbgYmA8+b2Qvuvn9Mk4UTJv+PgG3u3mZm\n84FNZnaNu783xtlGS63229BK6bdjVvjd/dP57gsOVDT7+cFfJ/O0awDWAL939xHjBKrsdWBOxu05\nJLcMCrW5JPhdLQiTn+DA0KNAu7sX+npcbWHyXwc8laz5zARuMbNz7r6uOhHzCpP9CJBw936g38y6\ngGuAWij8YfLfAPwEwN0PmtmrwEKguyoJK1PL/TaUUvttVLt6UoO/oMLBX1XUDbSaWYuZXQh8ieTf\nkWkd8HUAM1sK9Gbs0opa0fxmdimwFviqux+IIGMhRfO7+2XuPs/d55H8lvjtGij6EG7d+SuwzMwu\nMLPJJA8y7q5yznzC5N8LrAAI9o8vBP5X1ZTlq+V+W1RZ/Taio9QzgH8ArwDPANOD388Gng6WlwHD\nJM8g2Br8tEd8dP0WkmcXHSA5EA3gLuCujDaPBPdvB5ZEmbfU/MBvSZ6NkXq/X4o6c6nvf0bbx4GV\nUWcucd35Ackze3YA90SducR1Zybwt2C93wHcHnXmjOxPAseAsyS/Wd05zvptwfzl9FsN4BIRiRld\nelFEJGZU+EVEYkaFX0QkZlT4RURiRoVfRCRmVPhFRGJGhV9EJGZU+EVEYub/4HewX30WdU8AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97051f04d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in 'rgb':\n",
    "  plt.scatter(sp.random.random(100), sp.random.random(100), s=200, alpha=0.5, marker='o', c=c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3. Grafikon konture i gustoće"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2.,  3.,  4.,  5.])"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(1,5,5); x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X, Y = np.meshgrid(x, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.,  3.,  4.,  5.],\n",
       "       [ 1.,  2.,  3.,  4.,  5.],\n",
       "       [ 1.,  2.,  3.,  4.,  5.],\n",
       "       [ 1.,  2.,  3.,  4.,  5.],\n",
       "       [ 1.,  2.,  3.,  4.,  5.]])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  1.,  1.],\n",
       "       [ 2.,  2.,  2.,  2.,  2.],\n",
       "       [ 3.,  3.,  3.,  3.,  3.],\n",
       "       [ 4.,  4.,  4.,  4.,  4.],\n",
       "       [ 5.,  5.,  5.,  5.,  5.]])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 11.,  21.,  31.,  41.,  51.],\n",
       "       [ 12.,  22.,  32.,  42.,  52.],\n",
       "       [ 13.,  23.,  33.,  43.,  53.],\n",
       "       [ 14.,  24.,  34.,  44.,  54.],\n",
       "       [ 15.,  25.,  35.,  45.,  55.]])"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z = 10 * X + Y\n",
    "Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XLXDsv4AdVfXt3v6vgPdW1aG+cRa+JK1AVfVfQA80NYLnvR+4Gfh2kquAF/qLfaXhJEkr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9705153190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(X, Y, Z, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.pcolormesh, http://matplotlib.org/users/colormaps.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "mnormal = stats.multivariate_normal([0, 1], [[1, 1], [0.2, 3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.055730458106194758"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnormal.pdf([1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-1, 1)\n",
    "y = np.linspace(-2, 2)\n",
    "X, Y = np.meshgrid(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 50)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 50, 2)"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shape(XY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.01492383,  0.01541297,  0.01589129, ...,  0.01095132,\n",
       "         0.01044738,  0.00994981],\n",
       "       [ 0.01610377,  0.01663533,  0.01715544, ...,  0.01194288,\n",
       "         0.01139588,  0.01085559],\n",
       "       [ 0.01733793,  0.01791426,  0.01847852, ...,  0.01299494,\n",
       "         0.01240254,  0.01181718],\n",
       "       ..., \n",
       "       [ 0.04679266,  0.04884039,  0.05089173, ...,  0.05646055,\n",
       "         0.0544354 ,  0.05239434],\n",
       "       [ 0.04542256,  0.04742101,  0.04942386, ...,  0.05539039,\n",
       "         0.05341564,  0.0514244 ],\n",
       "       [ 0.04399343,  0.04593934,  0.04789039, ...,  0.0542183 ,\n",
       "         0.05229712,  0.05035891]])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnormal.pdf(XY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2IGyYid82ErhN90sVVC0nm4xtIrl7Tf/P2+4VfavV+TrnHSQPAtgB4GGSu/vP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\nZmYd44XfzKxj/j9wMK3Apnab6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97055ed910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pcolormesh(X, Y, mnormal.pdf(XY))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff16c6e56d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(X, Y, mnormal.pdf(XY));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff16c6e5e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(X, Y, mnormal.pdf(XY), levels=[0,0.06, 0.07]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mcow7bw9oXdSu+RDevxI6na1N5KYoLcmhP+GtC+CCJ2HojXpH0yyVbCSP9+jK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\nG2NMmLHAb4wxYcYCvzHGhJn/B8vOxK6rf7C+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97051e60d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contour(X, Y, mnormal.pdf(XY));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x = linspace(-10,10)\n",
    "X, Y = np.meshgrid(x, x)\n",
    "Z = X*3 + Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f97050db850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contour(X, Y, Z);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FOC3pod73D0n6zrAHl1Lyw9g+JulJSe/X1kVUL0fE/RHxiu3+RVRXxUVUk1i2fVhbY4c3\nJH2m5uNplIi4avthST+QdJukZyLipzUfVpPNSnrOtrS11qxExJl6D6lZbD8r6eOS3t+7+PTLkk5K\n+rbteUnnJf3p0OdgHQWAfNU9rgEAlIhFHgAyxiIPABljkQeAjLHIA0DGWOQBIGMs8gCQMRZ5AMjY\n/wPjyQyR58X4lwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff170e50050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contour(X, Y, Z, levels=[0]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kombinacija više grafikona:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xffRBvftffujB3d8q/vszMzur9uiKBb2/VTO7y91/amZ3S1or40nrPHLpntmdk/RZM7vN\nzD4k6V5J/LW9S/Gm6din9h+Y8W4vS7rXzO4xs9vU/kP7uYr3KWpm9j4z+83i8nZJe8R7axDnJD1W\nXH5M0j+X8aTRFXo/ZrZP0t9KulPtr717xd0/4e6vmlnna+9uiq+962XOzO5Xe6zwhqQvVLw/0XH3\nm2b2F5IWJG2R9LS7/7ji3YrdtKSzZia115N5d1+sdpfiYmbPSvq4pDuLr+38qqSnJD1nZgclXZD0\n6VK2xboHAGmo88gFANCFBR0AEsGCDgCJYEEHgESwoANAIljQASARLOgAkAgWdABIxP8DCSlM0fgu\nv8IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff170e26bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contour(X, Y, Z, levels=[0])\n",
    "plt.scatter([-5,-3,2,5], [4, 5, 2, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4. Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "x = stats.norm.rvs(size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dSW5JclLfNc0myd8luber9YtJXtJ3TbNJ8ntJ7knyZJJX9V3PTOvhprkkn05yIMldfdcy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\nQwx1SWqIoS5JDTHUJakhhrokNeT/AXKLAr1mn+6VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff16c6cd190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Više-manje istovjetno s:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Su7t/7+8leX/fNc0lyYuS7O3e49NJPtp3TceT5KQk+5Lc1nctx5PkYJLvdnXeO9+6fV3Q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\nEENdkhry/86mDEk0+L8NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff1886bb050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist, bins = np.histogram(x)\n",
    "centers = (bins[:-1] + bins[1:]) / 2\n",
    "plt.bar(centers, hist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5. Podgrafikoni"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'0.17.0'"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.15.2'"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}