SU-2016-LAB0-Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sveučilište u Zagrebu<br>\n",
    "Fakultet elektrotehnike i računarstva\n",
    "\n",
    "## Strojno učenje 2016./2017.\n",
    "\n",
    "<a href=\"http://www.fer.unizg.hr/predmet/su\">http://www.fer.unizg.hr/predmet/su</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Laboratorijska vježba 0: Uvod u Python\n",
    "\n",
    "(c) 2015-2016 Domagoj Alagić\n",
    "\n",
    "<i>Verzija: 0.1</i> <br/>\n",
    "<i>Zadnji put ažurirano: 7. listopada 2016.</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(a)__ Kreirajte dvije liste: $a = [2, 4, 5, 6, 8, 13]$ i $b = [1, 3, 10, 13, 15]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 4, 5, 6, 8, 13)\n"
     ]
    }
   ],
   "source": [
    "a=(2,4,5,6,8,13)\n",
    "print(a)\n",
    "b=(1,3,10,13,15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(b)__ Napišite kôd koji konkatenira liste $a$ i $b$ u listu $c$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "c=a+b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(c)__ Sortirajte listu $c$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 8, 10, 13, 13, 15]\n"
     ]
    }
   ],
   "source": [
    "c=sorted(c)\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(d)__ Izbacite duplikate iz liste c:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5, 6, 8, 10, 13, 15]\n"
     ]
    }
   ],
   "source": [
    "c=list(set(c))\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(e)__ Dohvatite i ispišite (pazite na indeksiranje od 0):\n",
    "* treći element,\n",
    "* zadnja dva elementa,\n",
    "* sve elemente između trećeg i šestog (uključujući treći element, isključujući šesti)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "[1, 2, 3, 4, 5, 6, 8, 10]\n",
      "[3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "print(c[2])\n",
    "print(c[0:len(c)-2])\n",
    "print(c[2:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(f)__ Generirajte interval brojeva `ran` s elementima iz $[2, 20]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]\n"
     ]
    }
   ],
   "source": [
    "ran=range(2,21)\n",
    "print(ran)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(g)__ Ispišite sve generirane brojeve iz intervala skupa s njihovim indeksima u listi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 2\n",
      "1 3\n",
      "2 4\n",
      "3 5\n",
      "4 6\n",
      "5 7\n",
      "6 8\n",
      "7 9\n",
      "8 10\n",
      "9 11\n",
      "10 12\n",
      "11 13\n",
      "12 14\n",
      "13 15\n",
      "14 16\n",
      "15 17\n",
      "16 18\n",
      "17 19\n",
      "18 20\n"
     ]
    }
   ],
   "source": [
    "for ind, val in enumerate(ran):\n",
    "    print ind, val"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(h)__ Korištenjem sažetog zapisa liste (engl. _list comprehension_) transformirajte listu `ran` tako da sve elemente zamijenite njihovim prirodnim logaritmom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.69314718055994529, 1.0986122886681098, 1.3862943611198906, 1.6094379124341003, 1.791759469228055, 1.9459101490553132, 2.0794415416798357, 2.1972245773362196, 2.3025850929940459, 2.3978952727983707, 2.4849066497880004, 2.5649493574615367, 2.6390573296152584, 2.7080502011022101, 2.7725887222397811, 2.8332133440562162, 2.8903717578961645, 2.9444389791664403, 2.9957322735539909]\n"
     ]
    }
   ],
   "source": [
    "ran_ln = [np.log(elem) for elem in ran]\n",
    "print ran_ln"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(a)__ Napišite funkciju `make_3sg_form(word)` koja vraća glagol u trećem licu jednine za dani glagol u infinitivu, npr. _try_ -> _tries_. Slijedite sljedeća pravila:\n",
    "* Ako glagol završava na _y_, odbaci _y_ i dodaj _ies_.\n",
    "* Ako glagol završava na _o_, _ch_, _s_, _sh_, _x_ ili _z_, dodaj _es_.\n",
    "* Inače samo dodaj _s_.\n",
    "\n",
    "NB: Znakovni niz je zapravo polje znakova tako da vrijede \"trikovi\" za indeksiranje polja.\n",
    "\n",
    "__(b)__ Isprobajte implementiranu funkciju za _fix_, _brush_, _cry_, _play_ i _echo_. Primjetite da je ovo složen problem i da tri jednostavna pravila neće uspjeti pokriti sve slučajeve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fix -> fixes\n",
      "brush -> brushes\n",
      "jump -> jumps\n",
      "cry -> cries\n",
      "echo -> echoes\n"
     ]
    }
   ],
   "source": [
    "def make_3sg_form(word):\n",
    "    ret_word = word\n",
    "    if ret_word.endswith(\"y\"):\n",
    "        ret_word = ret_word[0:-1] + \"ies\"\n",
    "    elif ret_word.endswith(\"o\") or ret_word.endswith(\"x\") or ret_word.endswith(\"s\") or \\\n",
    "        ret_word.endswith(\"ch\") or ret_word.endswith(\"sh\"):\n",
    "        ret_word += \"es\"\n",
    "    else:\n",
    "        ret_word += \"s\"\n",
    "    return ret_word\n",
    "    \n",
    "print \"fix\", \"->\", make_3sg_form(\"fix\")\n",
    "print \"brush\", \"->\", make_3sg_form(\"brush\")\n",
    "print \"jump\", \"->\", make_3sg_form(\"jump\")\n",
    "print \"cry\", \"->\", make_3sg_form(\"cry\")\n",
    "print \"echo\", \"->\", make_3sg_form(\"echo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Napišite funkciju `filterShortWords (word_list, min_length)` koja prima listu riječi i minimalnu dopuštenu duljinu riječi te vraća __novu__ listu riječi koja sadrži samo one riječi čija je duljina veća od minimalne dopuštene. Obavezno provjerite da je dana duljina riječi pozitivan broj veći od 1. Ako nije, bacite iznimku. U slučaju da korisnik nije zadao duljinu, funkcija radi jednako kao da je korisnik zadao duljinu od dva znaka (koristite podrazumijevane argumente, engl. _default arguments_). \n",
    "\n",
    "Zadatak riješite na dva načina: \n",
    "\n",
    "* __(a)__ korištenjem `for` petlji,\n",
    "* __(b)__ korištenjem sažetog zapisa liste,\n",
    "* __(c)__ korištenjem metode `filter` u kombinaciji s lambda-izrazom. \n",
    "\n",
    "__(d)__ Pokažite na primjeru da su izlazi implementiranih funkcija jednaki.\n",
    "\n",
    "NB: Podrazumijevani elementi (ili grupa istih) uvijek dolaze na kraju liste argumenata u definiciji funkcije."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filterShortWordsLoop (word_list, min_length):\n",
    "    if(min_length<1):\n",
    "        raise ValueError(\"Length must be >= 1.\")\n",
    "    filt=[]\n",
    "    for word in word_list:\n",
    "        if(len(word)>=min_length):\n",
    "            filt.append(word)\n",
    "    return filt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filterShortWordsLambda(word_list, min_length=2):\n",
    "    if min_length < 1:\n",
    "        raise ValueError(\"Length must be >= 1.\")\n",
    "    return filter(lambda elem: len(elem) >= min_length, word_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filterShortWordsCompr(word_list, min_length=2):\n",
    "    if min_length < 1:\n",
    "        raise ValueError(\"Length must be >= 1.\")\n",
    "    return [elem for elem in word_list if len(elem) >= min_length]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sam', 'mali', 'Mate', 'mame', 'tate']\n",
      "['sam', 'mali', 'Mate', 'mame', 'tate']\n",
      "['sam', 'mali', 'Mate', 'mame', 'tate']\n"
     ]
    }
   ],
   "source": [
    "word_list = [\"Ja\", \"sam\", \"mali\", \"Mate\", \"od\", \"mame\", \"i\", \"tate\"]\n",
    "print filterShortWordsLoop(word_list, min_length=3)\n",
    "print filterShortWordsLambda(word_list, min_length=3)\n",
    "print filterShortWordsCompr(word_list, min_length=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dana vam je mapa koja za ključeve sadrži nazive programskih jezika, a za vrijednosti liste programskih paradigmi koje određeni jezik pokriva."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "langs_paradigms = {\n",
    "                  \"Java\" : [\"object-oriented\", \"imperative\"],\n",
    "                  \"Haskell\" : [\"functional\"],\n",
    "                  \"Lisp\" : [\"functional\", \"procedural\"]\n",
    "                  }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Napišite razred `ProgLanguageRepository` uzimajući u obzir sljedeće stvari:\n",
    "* Konstruktor ovog razreda prima početnu mapu koja sadrži programske jezike i njihove paradigme.\n",
    "* Dana mapa sprema se kao član klase imena `data`.\n",
    "* Razred sadrži metodu `count` koja vraća broj programskih jezika dostupnih u repozitoriju.\n",
    "* Razred sadrži metodu `get_langs_paradigms (lang)` koja za dano ime programskog jezika vraća listu njegovih paradigmi ili vraća `None` ako taj jezik ne postoji u repozitoriju.\n",
    "* Razred sadrži metodu `add_lang_paradigm (lang, paradigm)` koja danom programskom jeziku dodaje paradigmu (ako je već nema).\n",
    "* Razred sadrži metodu `remove_lang_paradigm (lang, paradigm)` koja danom programskom jeziku miče danu paradigmu (ako je ima).\n",
    "* Razred ima nadjačanu metodu za znakovnu reprezentaciju koja samo ispisuje znakovnu reprezentaciju člana `data`.\n",
    "\n",
    "Pokažite na par primjera da vaša implementacija radi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class ProgLanguageRepository:\n",
    "    \n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "        \n",
    "    def count():\n",
    "        return len(data)\n",
    "    \n",
    "    def get_langs_paradigms(self, lang):\n",
    "        if lang in self.data:\n",
    "            return self.data[lang]\n",
    "        else:\n",
    "            return None\n",
    "    \n",
    "    def add(self, lang, paradigm):\n",
    "        if paradigm not in self.data[lang]:\n",
    "            self.data[lang].append(paradigm)\n",
    "        \n",
    "    def remove(self, lang, paradigm):\n",
    "        if paradigm in self.data[lang]:\n",
    "            self.data[lang].remove(paradigm)\n",
    "        \n",
    "    def __str__(self):\n",
    "        return self.data.__str__()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Lisp': ['functional', 'procedural'], 'Haskell': ['functional', 'object-oriented'], 'Java': ['object-oriented', 'imperative']}\n"
     ]
    }
   ],
   "source": [
    "prog_repo = ProgLanguageRepository(langs_paradigms)\n",
    "print prog_repo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['functional', 'procedural']\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print prog_repo.get_langs_paradigms(\"Lisp\")\n",
    "print prog_repo.get_langs_paradigms(\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Lisp': ['functional', 'procedural'], 'Haskell': ['functional', 'object-oriented'], 'Java': ['object-oriented', 'imperative']}\n"
     ]
    }
   ],
   "source": [
    "prog_repo.add(\"Haskell\", \"object-oriented\")\n",
    "prog_repo.add(\"Haskell\", \"object-oriented\")\n",
    "print prog_repo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Lisp': ['functional', 'procedural'], 'Haskell': ['functional'], 'Java': ['object-oriented', 'imperative']}\n"
     ]
    }
   ],
   "source": [
    "prog_repo.remove(\"Haskell\", \"object-oriented\")\n",
    "prog_repo.remove(\"Haskell\", \"object-oriented\")\n",
    "print prog_repo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(a)__ Napišite kôd koji stvara sljedeća tri vektor-stupca, jedan vektor-redak i kvadratnu matricu ranga 2 (koristite `numpy` za cijeli zadatak):\n",
    "\n",
    "$ x_{1} = \\begin{pmatrix}\n",
    "1\\\\\n",
    "2\\\\\n",
    "\\end{pmatrix}$,\n",
    "$ x_{2} = \\begin{pmatrix}\n",
    "3\\\\\n",
    "5\\\\\n",
    "\\end{pmatrix}$,\n",
    "$ x_{3} = \\begin{pmatrix}\n",
    "-3\\\\\n",
    "4\\\\\n",
    "\\end{pmatrix}$,\n",
    "$ x_{4} = \\begin{pmatrix}\n",
    "4\\\\\n",
    "0\\\\\n",
    "\\end{pmatrix}^T$,\n",
    "$ A = \\begin{pmatrix}\n",
    "5 & 3 \\\\\n",
    "10 & 6 \\\\\n",
    "\\end{pmatrix}\n",
    "$\n",
    "\n",
    "__(b)__ Zatim napišite naredbe koje stvaraju dvije nove matrice: matricu $C$ dimenzija $2\\times3$ koja se dobije tako da se vektor-stupci poredaju horizontalno jedan kraj drugog te matricu $D$ dimenzija $3\\times2$ koja se dobije tako da se matrica $A$ postavi ispod danog vektor-retka."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]]\n",
      "[[3]\n",
      " [5]]\n",
      "[[-3]\n",
      " [ 4]]\n",
      "[4 0]\n",
      "[[ 5  3]\n",
      " [10  6]]\n",
      "C:\n",
      "[[ 1  3 -3]\n",
      " [ 2  5  4]]\n",
      "D:\n",
      "[[ 4  0]\n",
      " [ 5  3]\n",
      " [10  6]]\n"
     ]
    }
   ],
   "source": [
    "x1 = np.array([[1],[2]])\n",
    "print(x1)\n",
    "x2 = np.array([[3],[5]])\n",
    "print(x2)\n",
    "x3 = np.array([[-3],[4]])\n",
    "print(x3)\n",
    "x4 = np.array([4,0])\n",
    "print(x4)\n",
    "A = np.array([[5,3],[10,6]])\n",
    "print(A)\n",
    "C = np.hstack([x1,x2,x3])\n",
    "print(\"C:\")\n",
    "print(C)\n",
    "D = np.vstack([x4,A])\n",
    "print(\"D:\")\n",
    "print(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(c)__ Izračunajte umnožak matrica $C$ i $D$ te ga pohranite u matricu $U$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-11  -9]\n",
      " [ 73  39]]\n"
     ]
    }
   ],
   "source": [
    "U = C.dot(D)\n",
    "print U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(d)__ Nadalje, napišite kôd koji izračunava inverz matrice $U$ (spremiti u matricu $I$) kojeg zatim transponira (i sprema u matricu $R$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.17105263  0.03947368]\n",
      " [-0.32017544 -0.04824561]]\n",
      "[[ 0.17105263 -0.32017544]\n",
      " [ 0.03947368 -0.04824561]]\n"
     ]
    }
   ],
   "source": [
    "I = np.linalg.inv(U)\n",
    "print I\n",
    "R = np.transpose(I)\n",
    "print R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(e)__ Izračunajte sumu svih elemenata u matrici $D$ (spremite u varijablu `D_total`), ali i sume po zasebnim dimenzijama (`D_x`, `D_y`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n",
      "[19  9]\n",
      "[ 4  8 16]\n"
     ]
    }
   ],
   "source": [
    "D_total = np.sum(D[:])\n",
    "print D_total\n",
    "D_x = np.sum(D, axis=0) # po x osi\n",
    "print D_x\n",
    "D_y = np.sum(D, axis=1) # po y osi\n",
    "print D_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(f)__ Ispišite determinantu matrice $R$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00438596491228\n"
     ]
    }
   ],
   "source": [
    "print np.linalg.det(R)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(g)__ Isprobajte vrijedi li $R\\cdot R^{-1} = I$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  0.]\n",
      " [ 0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "RI = np.dot(R, np.linalg.inv(R))\n",
    "print RI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(h)__ Stvorite vektor-stupac $n$ dimenzija $4\\times 1$ čiji su svi elementi 0 (bez ručnog pisanja svih elemenata)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.]\n",
      " [ 0.]\n",
      " [ 0.]\n",
      " [ 0.]]\n"
     ]
    }
   ],
   "source": [
    "n = np.zeros([4, 1])\n",
    "print n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(a)__ Napišite kôd koji iscrtava sljedeće funkcije:\n",
    "\n",
    "$f_{1}(x) = \\sqrt{(1-(|x|-1)^2)}$ i $f_{2}(x) = -3 \\cdot \\sqrt{(1-\\sqrt{\\frac{|x|}{2}})}$\n",
    "\n",
    "pritom definirajući domenu funkcija kao $x \\in [-2, 2]$ (u 1000 točaka). Graf funkcije $f_{1}(x)$ prikažite plavom bojom, a graf funkcije $f_{2}(x)$ crvenom. Iscrtajte oba grafa na istoj slici tako da su vidljive točke iz intervala $x \\in [-3, 3]$ i $y \\in [-3, 1.5]$.\n",
    "\n",
    "__(b)__ Ostavite oznake osi jednostavno kao $x$ i $y$. Iscrtajte legendu u donjem desnom kutu tako da je naziv funkcije $f_{1}(x)$ _bolja polovica_, a naziv funkcije $f_{2}(x)$ _dobra polovica_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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lJRJRfvrJSkoffLC1kBx8cNARSSgMGmSrNw8ZAhUqWAnxoC1daq1xVava7A0lJLFhwADY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\nB1O1ztC778X7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9098b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# funkcije\n",
    "def f_1(x):\n",
    "    return np.sqrt((1 - (np.abs(x) - 1)**2))\n",
    "def f_2(x):\n",
    "    return -3 * np.sqrt(1 - np.sqrt((np.abs(x)/2)))\n",
    "\n",
    "# domena u 1000 točaka\n",
    "x = np.linspace(-2, 2, 1000)\n",
    "# iscrtavanje prvog grafa plavom linijom\n",
    "plt.plot(x, f_1(x), 'b-', label=\"bolja polovica\")\n",
    "# iscrtavanje drugog grafa crvenom linijom\n",
    "plt.plot(x, f_2(x), 'r-', label=\"dobra polovica\")\n",
    "# postavljanje ograničenja prikaza\n",
    "plt.xlim([-3, 3])\n",
    "plt.ylim([-3, 1.5])\n",
    "# postavljanje oznaka osi\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "# iscrtavanje legende\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(a)__ Učitajte skup podataka _Iris_. Ovaj skup podataka sadrži 150 instanci, 50 za svaku od tri klase (_Iris setosa_, _Iris virginica_ i _Iris versicolor_). Svaka instanca, to jest vektor, sadrži četiri značajke: širinu i dužinu latice te širinu i dužinu čašićnog listića. U polju _iris.data_ nalaze se instance, dok se u _iris.target_ nalaze oznake instanci (njihove stvarne klase).  Ispišite dimenzije polja instanci te polja njihovih stvarnih oznaka."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150L, 4L)\n",
      "(150L,)\n"
     ]
    }
   ],
   "source": [
    "# učitavanje dataseta\n",
    "iris = datasets.load_iris()\n",
    "print iris.data.shape\n",
    "print iris.target.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(b)__ Budući da ne možemo vizualizirati 4-dimenzijske podatke, iz svake instance izdvojite samo prve dvije značajke. Koristite indeksiranje poljem brojeva. Ispišite dimenzije dobivenog polja instanci."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150L, 2L)\n"
     ]
    }
   ],
   "source": [
    "data_2dim = iris.data[:, 0:2]\n",
    "print data_2dim.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(c)__ Logičkim indeksiranjem razdvojite primjere za svaku od tri klase. Ispišite dimenzije dobivenih polja."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50L, 4L)\n",
      "(50L, 4L)\n",
      "(50L, 4L)\n"
     ]
    }
   ],
   "source": [
    "class_1_samples = iris.data[iris.target == 0]\n",
    "class_2_samples = iris.data[iris.target == 1]\n",
    "class_3_samples = iris.data[iris.target == 2]\n",
    "\n",
    "print class_1_samples.shape\n",
    "print class_2_samples.shape\n",
    "print class_3_samples.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(d)__ Napišite kôd koji iscrtava četiri zasebna grafa (četiri podgrafa u 2x2 mreži), pri čemu tri grafa prikazuju instance određene klase (točke u 2-dimenzijskom prostoru), a četvrti ih prikazuje sve (u različitim bojama). Osigurajte da svi grafovi prikazuju isto područje grafa (npr. $x \\in [4,9]$ i $y \\in [1.5, 5]$).\n",
    "\n",
    "Proučite kako se postavlja veličina glavnog okvira (u koji se smještaju podgrafovi) te ju postavite na 10x8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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XVDvOnjiSJEnKA6/sdLHj/WuGKfWtYfprmppi63T/mnJXUeY7Dqj5NSVJkqRO45WdLpa1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\nERERkVNisUNERERERE7p/wFse93rNH3E5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9169c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "# class 1\n",
    "plt.subplot(221)\n",
    "plt.scatter(class_1_samples[:,0], class_1_samples[:,1], c='r')\n",
    "plt.xlim([4, 9])\n",
    "plt.ylim([1.5, 5])\n",
    "# class 2\n",
    "plt.subplot(222)\n",
    "plt.scatter(class_2_samples[:,0], class_2_samples[:,1], c='g')\n",
    "plt.xlim([4, 9])\n",
    "plt.ylim([1.5, 5])\n",
    "# class 3\n",
    "plt.subplot(223)\n",
    "plt.scatter(class_3_samples[:,0], class_3_samples[:,1], c='b')\n",
    "plt.xlim([4, 9])\n",
    "plt.ylim([1.5, 5])\n",
    "# all three classes\n",
    "plt.subplot(224)\n",
    "plt.scatter(data_2dim[:,0], data_2dim[:,1], c=iris.target, cmap=plt.cm.Set1)\n",
    "plt.xlim([4, 9])\n",
    "plt.ylim([1.5, 5])\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(a)__ Napišite kôd koji u petlji iscrtava grafove normalnih distribucija definiranih s parametarima $\\mu$ i $\\sigma$:\n",
    "\n",
    "* $\\mu = 3$\n",
    "* $\\sigma \\in [1, 5]$ (korak 1)\n",
    "\n",
    "Dakle, generirat ćete pet grafova normalnih distribucija: $\\mathcal{N}_{1}(\\mu = 3, \\sigma = 1)$, $\\mathcal{N}_{2}(\\mu = 3, \\sigma = 2)$, ... , $\\mathcal{N}_{1}(\\mu = 3, \\sigma = 5)$.\n",
    "\n",
    "Grafove iscrtajte na istoj slici tako da su vidljive točke iz intervala $x \\in [-15, 15]$ i $y \\in [0, 0.5]$. Za svaki graf upotrijebite drugu boju. Za domenu koristite $[-50, 50]$ (uzorkujte 1000 točaka). Također napravite legendu u gornjem lijevom kutu koji će sve grafove nazvati onako kako su i u zadatku \n",
    "definirani ($\\LaTeX$ kôd radi unutar znakovnih nizova koji definiraju oznaku unutar legende), npr. $\\mathcal{N}_{1}(\\mu = 3, \\sigma = 5)$.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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In5+f0XTQ0tJSzp49y+DBg61eq6X1VlVV4ebmZpM6hBl++gm6dTPvmQ3V3Xsv\nfPaZ9WsSohlzipDQHH377beMHj3a8NrFxYXZs2fz+uuvs2rVKuLi4li+fLnZ1+vWrRsFBQUm9yUm\nJlJVVUVAQIBhW0JCAo888giVlZVERkaycOFCw77qzfe1qa35/sqVK1y+fNkotOTn5wPQu3fvOq9Z\nXl6Or6+v0bbt27czfvx4wwd3dU1Z77Vr1+jcuXOd9xE29NNPug/7hujbF375BW7f1q2zIISQkOBo\nbt68Sdu2bYmJieHtt9822rdgwQIiIiJYsWIF7u7uNbob6hIYGEhubi5arbbGegpxcXGA7gO4ffv2\nnDlzhkOHDhEWFkZ8fHyNLofGNt8PGDCAsWPHGgWPmJgYNBoNwcHBAGzdupWVK1dy6tQpo8GUI0aM\nYOnSpYb3kZ2dzebNm9m/f3+t92uKevXy8/NrhBjRhDIzYcCAhp3bty9otXD+vO4JkkIICQmOZN++\nfSxcuJDhw4fTr18/2rRpY7Tf29ubxYsXs3r1aubPn2/RtRVFYejQoaSlpdUY3BcXF8ewYcMIDQ0l\nICAAjUbDtm3biIyM5PLly6xZs6bR7616LTt37iQ8PNzQrVJcXExCQoIhEKiqSnl5OcnJyUycONFw\nbteuXVm3bh2zZ8/G19eXvLw8YmNjbTajwdx69VJSUmzW7SHqoaq6loSpUxt2vn4cQ2amhAQh7pCQ\n4EA6duxIq1atuHjxIp988onJY5YuXcrt27dZsGCBxdcPDg4mJibGKCSUl5dz4sQJDh8+XGN2w+OP\nP27xPczl4+PDu+++W2etQUFBHDlypMa+KVOmmD1d0lrqq1cvKSmJzZs3N0FFooaCAigqatigRQA/\nP3B3/3WGhBBCQoIjGTNmTL1PcXR3dyc8PNzkvi1btnD48GG8vLzw8PAgKCjIaP/48ePZuHEjt27d\nMrRSJCcn4+7ubnIWg70dPXrUYVaINEdOTg6qqtKnTx97l+Kc9B/uDR2ToJ8GKTMchDCQFRdbkJkz\nZxIfH8/+/ftrBAS9V155hdWrVxtep6en89RTTxlmUTiKyspKTp8+bdNuBGvbsGGDYfaDsAP9h3tj\nQppMgxTCiIQEJzN48GAGDRrEN998A8CcOXP48MMP7VxVTTk5OYSEhNi7DLOlpaVx+/ZtRo0aZe9S\nnFdmJtxzD7Rv3/Br3HuvhAQh7iLdDU6otlYGR9KjRw97l2CRgwcPGlaAFHby889QzxTaevXpo7tO\nZaVlT5GE0GHrAAAgAElEQVQUooWSlgQhrGDZsmUO12XjdM6fh7sW2WqQnj2hqgrueiy4EM5MQoIQ\nomWwVkjQX0sIISFBCNEC3L4NOTmNDwn6bi4JCUIAEhKEEC3BxYu61RIbGxI8PKBLF93yzEIICQlC\niBZA/5t/Y0OC/hrSkiAEICFBCNES6D/Ua3nIl0UkJAhhICFBCNH8nT8Pvr66ZZUbS0KCEAYSEoQQ\nzd/589Crl3Wu1aMHZGfrpkIK4eQkJAghmj9rTH/U69lTt5iSrJUghIQEIUQLYO2QoL+mEE5OQoIQ\nonnTr5FgraW8Za0EIQwkJAghmrecHN0aCdYKCe3aQefOEhKEQEKCU4qPjycxMdHeZQhhHRcu6L5b\nY/qjXvfuugWahHByEhJagKqqKmJjYwkLCyMrK6vOYzMzMzlw4ADDhw9vouqcw8svv0xpaam9y3BO\n2dm67927W++a3bv/Gj6EcGISEpqBuLi4OvdPnTqVyZMnExkZiZeXV53HLl++nL/+9a/WLK9BSktL\neeuttwgLC2PJkiVMnjyZM2fO2Lsss82ePZukpCTD6wULFhAaGmrHipzYhQvg7a3rJrAWjUZaEoRA\nQoLD27FjB6NHj+Z8Lf2jX3/9NRqNhqysLK5evYq3t3et10pKSqJLly506NDBRtWab9myZSQkJPDq\nq6+ydu1aevfuzZgxYyguLrZ3afX69ttv+eijj6ioqDBs02g0BAQEsG/fPjtW5qSys63bigDSkiDE\nHRISHFxGRgaKorB3716T+2NiYoiIiKBnz564uNT9xxkZGcmzzz5rizItpqoql+6ahx4QEEBBQQHp\n6el2rKp+5eXlfP311yiKUmNfSEgI77zzjh2qcnIXLlg/JGg0cP06lJRY97pCNDMSEhzc+fPnmTBh\nArt3766xT6vVUlFRgbuZS9EmJCQwaNAga5fYIBs2bCAlJcXw+ty5c7Rr147AwEA7VlW/jRs3smDB\nAlRVrbHP09MTLy+vZtVt0iJcuGDdQYugCwmgmzkhhBOTkNAMTJs2jRMnTpCtH6B1x/Hjxxk6dKhZ\n18jMzKRz5860atXK5P6SkhJ69OhBYWGhYVtUVBRz585teOFmKioqIjo6mqioKLO7Qq5fv05ISAhe\nXl64uLgYvlxdXfnoo49sUueZM2fo2rUrXbp0qfWYYcOGcejQIZvcX9TCVt0NIF0OwumZ/sRoYarK\nqihLL7PpPTwCPXD1cLXqNfPy8vD39+fJJ5/Ezc2NXbt2sWTJEsP+r776ipkzZ5p1rZycnDo/3OLj\n41FV1WhMw549exgyZEjD30A9ysvLefPNN0lISGDx4sVMmTLFrPOKi4sZNWoU48aNIz4+nlOnThES\nEkJSUhI+Pj50t/YHBrrukZ07d/LGG2/UedyDDz7Ijh07rH5/UYviYl23gLX/zP38dN9l8KJwcg0K\nCYqizAf+G7gHSANeUFX1ZC3HDgfeBAIBD+AX4H1VVd9tUMUNUJZeRuqgVJveY1DqIDoMtO6AwKNH\njzJu3Dg6duzIpEmT2Llzp1FIyM7ORqNvFr3j4MGDFBYWMn36dKPtV69exdPTs857VZ8WeezYMRYt\nWlTjWK1Wy9SpUykvLweo0fSu769XVRUvLy+io6NN3tPd3Z2VK1cCEBQURGJiIjExMbXWqPfSSy8x\nZMgQ1q5dC0D//v1Zv349Fy5c4KGHHrJJvVu2bDErkHl7e3Pu3Ll6jxNWYos1EkD3NMkuXaQlQTg9\ni0OCoih/BiKA2cAJIBT4UlGUAFVV802cUgpsAE7f+e9HgQ8URSlRVdU27cLVeAR6MCjVtn3xHoEe\nVr9mYmIiERERAAQHB/PEE08Yuhhu3LhRY7rje++9x+eff86MGTNqXEur1dZ5r7i4OJ5++mnD64yM\nDIqKinj44YdrHOvi4lLrQMqGmjdvHmPHjmX79u11Dq4sLCzk448/JiMjw2h7RUUFlZWVJs9pbL0X\nL17k5s2b9O3bt95jO3XqRFFRUYPvJSyk/xC3QeuRTIMUomEtCaHoWgK2ASiKMhf4d+B54K3qB6uq\nego4ddemvyuKMhUYATRJSHD1cLX6b/m2ptVquXnzJq6uui6MCRMm0K1bNyIjIxk6dChxcXGMHDnS\n6Jx58+aRl5dn8no+Pj5cv37d5L6ysjJSUlLYuHGjYduxY8fo378/Hh7WDz+5ubkMHDiQuXPnEhYW\nBoD/nd8Ek5OT6wwJiYmJ+Pn50fOuh/mUlpZy9uxZBg8ebPVaAb744gtOnDjB888/D2Bokfjb3/5G\nbGws69atMxxbVVWFm5ubTeoQJly4AIrya/eANcmqi0JYFhIURWkNDAJe129TVVVVFOUwMMzMazx0\n59gVltzb2Xz77beMHj3a8NrFxYXZs2fz+uuvs2rVKuLi4li+fLnZ1+vWrRsFBQUm9yUmJlJVVUVA\nQIBhW0JCAo888giVlZVERkaycOFCw77qzfe1qa35/sqVK1y+fNkotOTn6xqhevfuXec1y8vL8fX1\nNdq2fft2xo8fbwga1TW23lmzZjFr1izD619++YXo6GiWLVvGiBEjjI69du0anTt3rvM+woqys6Fb\nN2jd2vrX1mignoXMhGjpLG1J8AFcgdxq23OB++o6UVGUC0DnO+evUlX1Ewvv7RRu3rxJ27ZtiYmJ\n4e233zbat2DBAiIiIlixYgXu7u71rq54t8DAQHJzc9FqtTXWU9Cv6FheXk779u05c+YMhw4dIiws\njPj4+BpdDo1tvh8wYABjx441Ch4xMTFoNBqCg4MB2Lp1KytXruTUqVNGgylHjBjB0qVLDe8jOzub\nzZs3s3///lrvZ+3uEX23RlVVVY19+fn5NUKMsCFbrJGgJ90NQjTp7IZHgfbAw8CbiqJkqqq6q64T\nQkNDawy2mzZtGvfdV2ceabb27dvHwoULGT58OP369aNNmzZG+729vVm8eDGrV69m/vz5Fl1bURSG\nDh1KWlpajcF9cXFxDBs2jNDQUAICAtBoNGzbto3IyEguX77MmjVrGv3eqteyc+dOwsPDDd0qxcXF\nJCQkGAKBqqqUl5eTnJzMxIkTDed27dqVdevWMXv2bHx9fcnLyyM2NtYmMxpMee2119i1axeKojB7\n9mzGjx/Phg0bDPtTUlJs1u0hTMjJsU1XA+jCx7VrUFpq3SWfhbCi6OjoGq2fVh0Xpaqq2V9Aa6AC\neLLa9q3AXguuswI4W8f+gYCampqqmpKamqrWtb+5OnLkiNqrVy/10UcfVW/evGnymFu3bqkrVqxQ\nL1++bHL/qlWr1KioKJP7vvjiC3X58uU1rtemTRs1ISGhccXbwI0bN9S9e/fauwyLPP7442pmZma9\nx7XUv8NN7re/VdUXXrDNtb/9VlVBVdPTbXN9IWxE/+8LMFC14DPe1JdFLQmqqlYoipIKPAbEAii6\n+WOPAestuJQrYN4ygU5kzJgx9T7F0d3dnfDwcJP7tmzZwuHDh/Hy8sLDw4OgoCCj/ePHj2fjxo3c\nunXL0EqRnJyMu7u7yVkM9nb06FGHWSHSHDk5OaiqSp8+fexdivO4ePHX1RGtTd86dfEitNDWSyHq\n05AVF98GQhRFmaEoSiCwGd36B1sBFEV5Q1GUKP3BiqL8p6IokxRF6XvnayawGNje+PLF3WbOnEl8\nfDz79++vERD0XnnlFVavXm14nZ6ezlNPPWWYReEoKisrOX36dJN1I1jDhg0bDLM1RBMoKYEbN2zX\n3aAfWyJrJQgnZvGYBFVVdyuK4gOsAbqim944XlXVq3cOuQe4+192F+ANoCdQCZwDlqiq+kEj6hYN\nNHjwYM6fP88333zD73//e+bMmcOcOXPsXVYNOTk5hISE2LsMs6WlpXH79m1GjRpl71Kch/65CrYK\nCW3aQOfOMnhROLUGDVxUVTUSiKxlX3C11xuBjaaOFfZRWyuDI+nRo4e9S7DIwYMHDStAiiZi65AA\n8sho4fSc4tkNQtjasmXL7F2C82mKkCDTIIWTk6dACiGap4sXoVMnsMGqoAay6qJwchIShBDNky3X\nSNDTaKS7QTg1CQlCiOapKULC3QsqCeGEJCQIIZqnpmpJAOlyEE5LQoIQonmSkCCEzUlIEEI0P5WV\ncOWK7VZb1NOHEAkJwklJSBBCND9XroBWa/uWBP2CSjJ4UTgpCQlCiOanKdZI0JMFlYQTk5AghGh+\nmjIk+PnBpUu2v48QDkhCghCi+cnJATc38PGx/b18fX8NJUI4GQkJQojm5+JF3W/4imL7e/n5SUgQ\nTktCghOKj48nMTHR3mUI0XBNMf1Rz88P8vLg9u2muZ8QDkRCQgtQVVVFbGwsYWFhZGVl1XlsZmYm\nBw4cYPjw4U1UXctw28QHRHl5ueG/X375ZUplVb6m09QhAeDy5aa5nxAORJ4C2QzExcUxcuTIWvdP\nnTqV2NhYOnXqRGhoaJ3XWr58OVu2bLF2iRYrLS1l06ZNFBcXc+vWLX766SfCw8N54IEH7F2aSZ9+\n+imvvvoqI0eOpEOHDmRkZNCvXz/WrVsHwIIFCwgNDeWDDz6wc6VOIicHBg5smnvpQ0JODjSzR5gL\n0VjSkuDgduzYwejRozl//rzJ/V9//TUajYasrCyuXr2Kt7d3rddKSkqiS5cudOjQwUbVmm/ZsmUk\nJCTw6quvsnbtWnr37s2YMWMoLi62d2kmqarK7du32bVrF4cOHWLixImGgACg0WgICAhg3759dqzS\nSaiqfVoSZFyCcEISEhxcRkYGiqKwd+9ek/tjYmKIiIigZ8+euLjU/ccZGRnJs88+a4syLaaqKpfu\nmlYWEBBAQUEB6enpdqyqbtu3b6ekpISMjAxeeOGFGvtDQkJ455137FCZkykqgrIy26+2qOflBW3b\nSkgQTklCgoM7f/48EyZMYPfu3TX2abVaKioqcHd3N+taCQkJDBo0yNolNsiGDRtISUkxvD537hzt\n2rUjMDDQjlU1jqenJ15eXpw5c8bepbRs+iWSm6olQVF00yBlrQThhCQkNAPTpk3jxIkTZGdnG20/\nfvw4Q4cONesamZmZdO7cmVatTA9DKSkpoUePHhQWFhq2RUVFMXfu3IYXbqaioiKio6OJiooyuyvk\n+vXrhISE4OXlhYuLi+HL1dWVjz76yCZ1fvXVV7zxxhusWbOGZ555hoKCghrHDBs2jEOHDtnk/uKO\nplxISU+mQQon5RQDF6uqyigrs20ztodHIK6uHla9Zl5eHv7+/jz55JO4ubmxa9culixZYtj/1Vdf\nMXPmTLOulZOTQ5cuXWrdHx8fj6qqRmMa9uzZw5AhQxr+BupRXl7Om2++SUJCAosXL2bKlClmnVdc\nXMyoUaMYN24c8fHxnDp1ipCQEJKSkvDx8aF79+5Wr9XV1ZW+ffvyl7/8BYA1a9YwZcoU4uLijI57\n8MEH2bFjh9XvL+6i/7D29W26e0pIEE7KKUJCWVk6qam2bWYfNCiVDh2sO9r66NGjjBs3jo4dOzJp\n0iR27txpFBKys7PRVOuXPXjwIIWFhUyfPt1o+9WrV/H09KzzXtWnRR47doxFixbVOFar1TJ16lTD\nFEBVVY32K3cWuFFVFS8vL6Kjo03e093dnZUrVwIQFBREYmIiMTExtdao99JLLzFkyBDWrl0LQP/+\n/Vm/fj0XLlzgoYceskm91X+eQUFBrFq1isTERKOfm7e3N+fOnav3PYhGyMnRPXTJza3p7unnBydP\nNt39hHAQThESPDwCGTQo1eb3sLbExEQiIiIACA4O5oknnjB0Mdy4cQMvLy+j49977z0+//xzZsyY\nUeNaWq22znvFxcXx9NNPG15nZGRQVFTEww8/XONYFxeXWgdSNtS8efMYO3Ys27dvr3NwZWFhIR9/\n/DEZGRlG2ysqKqisrDR5ji3qbdeuHQAnTpwwCgmdOnWiqKjIqvcS1eTkNN2gRT19S4KqNs0qj0I4\nCKcICa6uHlb/Ld/WtFotN2/exNXVFYAJEybQrVs3IiMjGTp0qMm1E+bNm0deXp7J6/n4+HD9+nWT\n+8rKykhJSWHjxo2GbceOHaN///54eFi3CwUgNzeXgQMHMnfuXMLCwgDw9/cHIDk5uc6QkJiYiJ+f\nHz179jRsKy0t5ezZswwePNjqtYJuvMYDDzzAwoULefHFFw3bgBpjPKqqqnBryt9wnZF+Seam5OcH\nN2/C9evQqVPT3lsIO3KKkNAcffvtt4wePdrw2sXFhdmzZ/P666+zatUq4uLiWL58udnX69atm8mB\ndqD74K2qqiIgIMCwLSEhgUceeYTKykoiIyNZuHChYV/15vva1NZ8f+XKFS5fvmwUWvLz8wHo3bt3\nndcsLy/Ht1pf9Pbt2xk/frwhaFTX2HpdXFxQFIW+ffsatmVmZqIoitGfEcC1a9fo3LlznfcRjZST\nA2YO2LWau9dKkJAgnIiEBAdz8+ZN2rZtS0xMDG+//bbRvgULFhAREcGKFStwd3ev0d1Ql8DAQHJz\nc9FqtTXWU9APvisvL6d9+/acOXOGQ4cOERYWRnx8fI0uh8Y23w8YMICxY8caBY+YmBg0Gg3BwcEA\nbN26lZUrV3Lq1CmjwZQjRoxg6dKlhveRnZ3N5s2b2b9/f633a2y9Hh4ezJo1ixEjRhi2ffrpp8yY\nMYP+/fsbHZufn18jxAgru3Sp6VsS9H+mOTngoKuCCmELEhIcyL59+1i4cCHDhw+nX79+tGnTxmi/\nt7c3ixcvZvXq1cyfP9+iayuKwtChQ0lLS6sxuC8uLo5hw4YRGhpKQEAAGo2Gbdu2ERkZyeXLl1mz\nZk2j31v1Wnbu3El4eLihW6W4uJiEhARDIFBVlfLycpKTk5k4caLh3K5du7Ju3Tpmz56Nr68veXl5\nxMbG2mRGw90WLVpEeHg4ZWVllJSUEBAQwCuvvFLjuJSUFJt1ewh0D1nKy2vamQ3w6/1krQThZCQk\nOJCOHTvSqlUrLl68yCeffGLymKVLl3L79m0WLFhg8fWDg4OJiYkxCgnl5eWcOHGCw4cP15jd8Pjj\nj1t8D3P5+Pjw7rvv1llrUFAQR44cqbFvypQpZk+XtJb27dvzt7/9rd7jkpKS2Lx5cxNU5KT0D1lq\n6pYENzfdjAqZBimcjIQEBzJmzJh6n+Lo7u5OeHi4yX1btmzh8OHDeHl54eHhQVBQkNH+8ePHs3Hj\nRm7dumVopUhOTsbd3d3kLAZ7O3r0qMOsEGmOnJwcVFWlT58+9i6l5bLHQkp6slaCcEKy4mILMnPm\nTOLj49m/f3+NgKD3yiuvsHr1asPr9PR0nnrqKcMsCkdRWVnJ6dOnbd6NYE0bNmwwzNYQNiIhQYgm\nJSHByQwePJhBgwbxzTffADBnzhw+/PBDO1dVU05ODiEhIfYuw2xpaWncvn2bUaNG2buUli0nB9q0\nsc8MAwkJwglJd4MTqq2VwZH06NHD3iVY5ODBg4YVIIUN6R8RbY8FjXx9JSQIpyMtCUJYwbJlyxyu\ny6ZF0ocEe/Dz082sqKiwz/2FsAMJCUKI5sPeIUFV4coV+9xfCDuQkCCEaD7ssZCS3t2rLgrhJCQk\nCCGaB1XVfUDba0VLCQnCCUlIEEI0D9ev6x6yZK+WBG9vcHeXkCCcioQEIUTzYM81EkA3o0KmQQon\nIyFBCNE82DskgEyDFE6nWa+TcPbsWXuXIESDyN/dBtB/ONvzKZvSkiCcTLMMCT4+Pnh4eDB9+nR7\nlyJEg3l4eODj42PvMpqPnBzdQ5bc3OxXg58ffPed/e4vRBNrliHB39+fs2fPkp+fb+9ShGgwHx8f\n/P397V1G82HPNRL0/Px00zBV1T6rPgrRxJplSABdUJB/YIVwIo4SEkpL4cYN8PS0by1CNAEZuCiE\naB7suZCSnqyVIJyMhAQhRPNgz4WU9CQkCCcjIUEI4fgqKnQPV7J3S0K3brrvEhKEk5CQIIRwfJcv\n6wYL2jsktGkDv/mNhAThNCQkCCEcnyMspKQnayUIJyIhQQjh+BwtJFy6ZO8qhGgSEhKEEI4vJ0f3\ncCVvb3tXIi0JwqlISBBCOD79GgmOsICRhAThRCQkCCEcnyMspKTn6wu5uVBZae9KhLC5BoUERVHm\nK4rys6IoNxVFSVYUZUgdx05RFOUrRVHyFEUpUhTlmKIo4xpeshDC6Vy6ZP81EvT8/ECrhStX7F2J\nEDZncUhQFOXPQATwCvAQkAZ8qShKbU+qGQl8BfwBGAh8A+xXFOXBBlUshHA+jtSSIAsqCSfSkJaE\nUOB9VVW3qaqaDswFyoDnTR2sqmqoqqrrVFVNVVX1nKqqK4CfgCcaXLUQwnmoqoQEIezEopCgKEpr\nYBBwRL9NVVUVOAwMM/MaCtABKLTk3kIIJ1VUBGVljhMSfHx0j6uWkCCcgKUtCT6AK5BbbXsucI+Z\n11gCtAN2W3hvIYQzcqQ1EkA3w8LXV9ZKEE6hSR8VrSjK00AY8KSqqvn1HR8aGopntcexTps2jWnT\nptmoQiGEw2lESKjSVvHluS+J+SGG7y5/R8HNAtxc3XigywM81usxpg+YjnfbBqy9INMghYOIjo4m\nOjraaFtRUZHVrq/oegvMPFjX3VAGTFVVNfau7VsBT1VVp9Rx7n8AHwFBqqp+Uc99BgKpqampDBw4\n0Oz6hBAt0CefwPPPw61bugWVzKCqKvt/3M+LX77IuWvn+G3n3/Jo90e5p/09lFaUkpabxtHzR3F1\nceXFh19k+YjltHNrZ35Nf/oTFBTAkSP1HytEE/vuu+8YNGgQwCBVVb9rzLUsaklQVbVCUZRU4DEg\nFgxjDB4D1td2nqIo09AFhD/XFxCEEMJITo5uHICZAaH0dilzDsxh5792Mq7POKKnRjPYdzBKtYWY\n8krzWH98PeuOrePT7z9lz5/28OA9Zk668vOD06ctfSdCNDsNmd3wNhCiKMoMRVECgc2AB7AVQFGU\nNxRFidIffKeLIQpYDJxUFKXrna+Oja5eCNHyWTCz4UrJFYZ/PJy96XvZMWUHXzzzBUP8htQICABd\n2nUhfEw4/5r3Lzq6d2TYlmH8b/r/mleTdDcIJ2FxSFBVdTfw38Aa4J/AAGC8qqpX7xxyD9D9rlNC\n0A123ARcuuvr3YaXLYRwGmYupHTxxkVGfjKSq2VXOT7rOM8MeMZkOKju3t/cS+LziUy8dyJBnwXx\n2fef1V+Tnx+UlMCNG+a8AyGarQYNXFRVNRKIrGVfcLXXv2/IPYQQAtD9xv7QQ3Uecv3WdSbsmEB5\nVTlxf4mjj3cfi27h0dqDT4M+5bl9zzHt82l4tvFkXJ86Foa9e62EjtIoKloueXaDEMKx1dPdUFFV\nwR93/ZFLxZf4cvqXFgcEvVYurYiaHMW4PuMI2h3Ev3L/VfvBsqCScBISEoQQjquiQvcwpTpCwrIj\ny4jPjmfff+wj0CewUbdr5dKKXUG76N2pN3/c/UdulNfSnaDv/pC1EkQLJyFBCOG4rlzRLctcS0jY\nl76PiKQI1o5dy8geI61yyw7uHYj5Uwx5pXnMip2FyWnibdtCp07SkiBaPAkJQgjHVcdCSrklucyK\nncWUwCksGrrIqrft692XLU9u4bMfPmNb2jbTB8kMB+EEJCQIIRxXLSFBVVVmH5iNq4sr709636xZ\nDJYK+m0Qzw54lkVfLOLijYs1D5CQIJyAhAQhhOO6eFG3iNJvfmO0eff3u4nNiOX9Se/TuV1nm93+\nfyb8D+3d2jP3wNya3Q4SEoQTkJAghHBc2dng7697qNIdRbeK+K8v/4up909lcuBkm96+U9tObPjD\nBv7vp/9jX/o+453du8OFCza9vxD2JiFBCOG4LlzQfRjfJeybMEpul/DuhKZZj21y4GQmBUxi4RcL\nKb1d+usOf3/dwMry8iapQwh7kJAghHBc+paEOzLyM4g8GcnKkSvRdNQ0SQmKorB+wnrySvOISIr4\ndYc+vEiXg2jBJCQIIRzXhQtGIWHZkWVoOmp4YegLTVpGr069WPhvC3kr8S1yS3J1G/V1ZWc3aS1C\nNCUJCUIIx3T7Nly+bPiNPTE7kb3pe3ltzGu0adWmyctZPmI5bq5urD66WrdB35Ig4xJECyYhQQjh\nmHJydAsp+fujqipL/rGEgd0GMq3/NLuU06ltJ1aMWMEHqR+QkZ8BHh66WRfSkiBaMAkJQgjHpP8N\nvXt39pzdQ9LFJNaOXYuLYr9/tub/23w0HTUsO7JMt8HfX0KCaNEkJAghHNOdD98qP1+Wf72cP/T9\nA2N6jbFrSW1ateG1Ma+xN30vSReSdCFBuhtECyYhQQjhmC5cAG9vdv9ykB8LfuTV379q74oAmNZ/\nGr/t/FtejXtVNy5BWhJECyYhQQjhmLKz0XbXEB4fzsR7JzLId5C9KwLARXFhxYgVHMo8RIqfAr/8\nohs7IUQLJCFBCOGYLlxgz4DW/HD1B8JGhtm7GiN/7vdn7vW+l9daJ0NJCRQV2bskIWxCQoIQwiGp\n2b8Q7n+ex3s/zsOah+1djhFXF1eWj1jOvpKTnO6KjEsQLZaEBCGEQ9rfKou01gUO14qg90z/Z+jZ\nvjuvjUDGJYgWS0KCEMLhqEVFhA8uY2TbQEb2GGnvckxq7dqal0cs47N+kJ51wt7lCGETEhKEEA7n\n6Hd7OOkHy+6bZe9S6vSXgc/TrcyVdVf/196lCGETEhKEEA4n4vRmHsiF8b8LsncpdXJv5c4LlzRs\nV/716zMdhGhBJCQIIRxKen46B66f4MVkBcXPz97l1GuOMoTWWth0cpO9SxHC6iQkCCEcyjtJ73CP\n2o6nC3yhVSt7l1OvTpq+PP9jOyJPRlJWUWbvcoSwKgkJQgiHkVeax7bT23jhWgDumh72Lsc8/v78\n19dlXLt1jW1p2+xdjRBWJSFBCOEwIk9G4qK4MPcHj18fxezounend34Vf+z5B95JfgetqrV3RUJY\njYQEIYRDuFV5i8iTkQT/Lhjvn6/oHp7UHNypc3GXyfxY8CMHfjxg54KEsB4JCUIIh7D7+91cLbvK\nwoS3kRoAACAASURBVCELdCsYNpeWhDsh4eEbHXlY8zDrj6+3c0FCWI+EBCGEQ9h0chPj+4wnQNsJ\nbt9uPi0Jnp7Qvj1kZ/PCv73AkZ+P8MPVH+xdlRBWISFBCGF3J3NOciLnBPOHzP91iePmEhIURVdr\ndjZBvw2ia7uubDoh0yFFyyAhQQhhd5tObqKHZw8m3jvx15DQXLobQBcSLlzAzdWNOYPmEJUWRdEt\neTKkaP4kJAgh7Cq/LJ9Pz3zKvMHzcHVx1Y1HaNsWfvMbe5dmvu7dDeFmzuA5lFeVE5UWZeeihGg8\nCQlCCLv6+J8fAzBz4Ezdhp9/hh49dM34zYW/P/zyCwC+HXyZev9UNp7YKNMhRbMnIUEIYTdV2ire\nS3mP/3jgP/Dx8NFt/Pln6NXLvoVZqlcvKCiAkhIAFvzbAn4q/Il/nPuHnQsTonEkJAgh7ObgTwc5\nf/28bsCiXnMNCaCrHRjefTi/u+d3bDy50Y5FCdF4jr8wuhCixdp0chNDfIcwxG+IboOqwvnz8Nxz\njbpuVRX8+COkpuqGCuTmQl4eVFRAmza6Lx8fCAjQffXrB506NeKGd4eE/v1RFIUFQxYQsj+ErGtZ\n9O7Uu1HvRwh7kZAghLCLnwp+4stzX7L1/239daO+yb4BLQkXLsDevRAbC8ePG1r+6dwZunTRfXd3\nh/x8uHlTFxp++UWXSxQFHnoIHn8cJkyAUaPAxZJ21nvu0SWPOy0JANP6T2PJP5YQeTKSdePWWfx+\nhHAEEhKEEHbxXsp7/Kbtb/jzA3/+daP+Q9bMkHDrFnz6KWzerAsGrVvDY49BWBgMHgwDB4KXV93n\nnzsHJ0/CkSOwbRu89ZZussJzz8Ff/gJ9+phRiKJAz55GIcGjtQezBs7iw+8+ZPXo1bRza2fWexLC\nkciYBCFEkyu9XcrH//yYWQNn0aZVm1936D9ke/as8/yCAvjrX3Uf5sHB4O0NO3fC1atw6BC89BKM\nGVN3QADdL//9+unCwPbtcOkSHDsGf/gDrF8P994Lf/oTpKWZ8aZ69TIKCQDzBs+j6FYR0WeizbiA\nEI5HQoIQosn9/V9/50b5DeYOnmu84+efoWPHWgcIlJbCa69B797w7rvw9NOQkQEHD+r+29OzcXUp\nCgwbBu+/D1eu6FooUlLgd7+DJ5+Es2frOLlXL8jKMt7UqRf/HvDvbDyxEVVVG1ecEHYgIUEI0aRU\nVWXTyU1MCphET6+exjv1MxuqrZGgqrBjB/TtC2vW6FoPsrLgf/5HN/DQFtq2hdmzdQMgt22D77+H\nAQPgxRehyNRiivqWhGphYP6Q+aTlpnHswjHbFCqEDUlIEEI0qWMXjpGWm2Y87VHPxPTHc+dg/Hh4\n9lkYOVLXcvDuu7rBiE2hVSvdvb//XhdQPvhA1w3x2WfVDuzdW9fUkZ9vtHlcn3H09e7LppPyPAfR\n/EhIEEI0qU0nN3Gv972M7TO25s67QoKqwoYN0L+/7rf5//s/2LWr3uEKNtOmDSxbpgspI0fqxipM\nm6YbHwHUWCtBz0VxYd7gecT8EENuSW7TFi1EI0lIEEI0mSslV4j5IYb/HPKfuCjV/vnRanVzEnv1\n4upVeOIJWLgQZs6EM2dg4kT71Fydn5+uFeHvf4cvv4QHHoDDh6k1JAAE/y6YVi6t+PC7D5u2WCEa\nSUKCEKLJfJj6Ia1dW/OX3/2l5s6LF+H2bb4pGcKAAbopjQcO6FoT2rdv8lLrpCi6VoQzZ3TjFMaN\ng9X/40WVp3eNwYsAndp24pn+z7A5ZTOV2ko7VCxEw0hIEEI0iUptJf+/vfMOr6JY//hnTk9OeicJ\nvUmTXgVBFFBREL0qioAN+73+7A0v2K4F+7WhXlHRK+q1oYCI0jsEpfcSSkhvJ+ckp87vjwk9kApJ\nYD7PM8+e7Jmdnd3s2f3uO++87+SUyYzqMIoI24lzE+WOnbzB/zHo6Z60awfr1sHQobXQ0UqQmKhm\nVkycCM88A5fLX8jeWPaQwr097uWA4wA/bfnpzHZSo6kGWiRoNJozwk9bfuKA40CZDosuF4x+MpkH\neYMH7w/w66/QoEEtdLIKGI3wz3/Cb7/Bn+629PjuUTZtOrFep4RO9GnYRzswauoVWiRoNJozwjur\n3qFvo750TOh4zPqMDBgwAL5PacxXsf/gldeMmOphLNhLLoFV4z4ixFdA797w668n1rm3+73M2zOP\nTVllqAiNpg6iRYJGozntbMzcyPw980+wImzZAr16qbwLi/s9ychOW2qphzVD426xLPH14MIL/Awd\nCu+/f+z317S5hjh7HO+teq92OqjRVBItEjQazWnnvVXvEW+P5+o2Vx9et2gR9OkDdrtyUuySM0dF\nS6rPtGxJKEX8+K9N/P3vcM89ylfhUHwlq8nKHV3u4LO1n1HoLqzdvmo0FUCLBI1Gc1opdBfy+brP\nuaPrHViMFkA5+w0erMIdL14MjRpK2LGj/ouE0v4bd+/gjTfgX/9STo3/+Iea4QlwZ7c7KfYWM3Xt\n1Nrrp0ZTQaokEoQQ9wohdgshioUQy4UQ3U9RN0EI8aUQYqsQwi+EeL3q3dVoNPWNqWunUuwt5s6u\ndwIqxsDw4SqK4syZpUmY0tOV92J9FwmxsRAaCjt2IIQKvjR5Mrz3Htx0E3g8kByWzPDzhvPuqnd1\nPgdNnafSIkEIcT3wGjAB6AysBWYLIWJOsokVyASeA/6qYj81Gk095FCehhFtRpAUlsSUKTBypIpW\n+O23KoohoKwIUP9FghDqGLZvP7zqjjvgm2/gu+9UkiiXSzkwbs7ezPw982uvrxpNBaiKJeEBYLKU\n8nMp5RbgLsAF3FpWZSllqpTyASnlF4AehNNoziH+2P0Hm7M3c1/3+3j7bbj1Vhg3TqVlNpuPqrhj\nh3rANmtWa32tMVq0OCJ6SrnmGmU1WbwYrrgCesReRJuYNno6pKbOUymRIIQwA12BPw6tk8pe9jvQ\nu2a7ptFo6jtvrXiLjvEdWfHNhdx/Pzz8sPL4Nxx/59mxA5KTjzIt1GNatjxBJABcfDHMmgUrV8KV\nVwpua/8PftzyI/sL99dCJzWailFZS0IMYASODymWASTUSI80Gs1ZwY7cHczYNoPW2yfz2GOC8ePh\nlVdOyAKt2Lr1tOV8llLiL/bjzfXiyfbgzffic/gI+AKnZX+0aKHmdLpcJ3zVr5+Kn7B6Nfz49O3Y\nZDSTV08+Pf3QaGqAOh2y5IEHHiA8PPyYdTfccAM33HBDLfVIo9FUlHdXvktQyuN883NPnnhCpVku\nUyCACpgwYECl9yGlxJvlpXh7Ma5tLoq3FePa7sJz0IMnw4M304u/yA8n8Q802A2YIkyYY8xYk63Y\nGtqwNbFhb2/H3t6OtZEVcdJOn4TWrdVy+3bo2PGEr/v2VULh0ktNhOctZrJpCE/3f/rwzA+NpjJ8\n9dVXfPXVV8esKygoqLH2RWW8a0uHG1zANVLK6Uet/xQIl1KOKGf7ecCfUsoHy6nXBUhJSUmhS5cu\nFe6fRqOpGzjcDuKufYaSn1/l0UfhpZdOIRB8PhUs4bXX4L77Ttmur8iHY5WDwhWFFC5XxZvhVV8K\nsDa0EtQyCGuyFUu8BXOsGVO4CUOQAWOQEQwg/RL84Hf58eX78OX78GZ6ce93U7KvhJJdJfgdfgCM\noUbs7eyEdgsl/MJwIi6MwBJfzsM8Nxeio2HaNLj++pNWW7oUBg/x44xawiffpHNLz+tO3a5GU0HW\nrFlD165dAbpKKddUp61KWRKklF4hRApwMTAdQCiZfTHwdnU6otFozh7G/TOFkp9f5Y77CnnppbCT\nCwSAPXvU3MDzzjvhKxmQFP1VRO6sXHJm5VC4vBD86uEd2iOUBrc3ILRzKEGtgwhqHqSEQDWRUuLe\n58a5wYlzg5OidUXkzMrhwDsHAAhqHUTEhRFEXR5F1OAojMHH7TMqCuLilHXkFPTpA3N+M9L3ou48\neOt6blwDVmu1u6/R1ChVGW54Hfi0VCysRM12CAY+BRBCvAgkSinHHtpACNEREEAIEFv6t0dKubl6\n3ddoNHWNKZ8G+PqVAbS8fAYfvD301AIBjjxMS0VCwBcgf24+mV9nkjMjB2+GF2OIkchLImn1bivC\nLgjD3saOMFZyGKCCCCGwNbJha2Qj+vLow+vdB9zkL8qnYGEBeXPzOPjRQQw2A5GDIokZHkP0ldFY\n4ixHjqUckQDQuzf884OVTBzXg0uHF/Dbz+HHzvrQaGqZSosEKeU3pTERngXiUbEPhkgps0qrJAAN\nj9vsT46MCnYBbgRSgbNgvpNGoznE99/D7bcJ6DqZ/7zXrnyBALBlCzLYTmFqCJkvbyfzm0y8mV5s\nzW0kjE4g6vIowi8Ix2Cp3QCx1iQr8SPjiR8ZD4Bru4vsn7LJ+SmHrXdsBQFRQ6JIGJNAdMt2GFcv\nrVC7T47uw7+X3MbCTz9h7Fg1PdRYfYOIRlMjVMlxUUr5HlBmhhIp5S1lrNPhnzWas5zfflOBkuJ7\nLiR+9Ef0bbSq3G082R4yvhWk+adQ3PcvLEkW4m+KJ+6GOEK7hlbeafAMEtwymEYPN6LRw43wZHnI\n+i6LjM8z2DRyE0bbNcR5Q2mwPJ+wXhGnbMdsNPPYzR14wjGKr7+eRnCw4MMPy5gmqtHUAvoy1Gg0\n1WbJErjqKrhggJODl1zC/b3vO+kDXkpJ/uJ8Nt20iWVJy9i1qiOhDQroOLcjvff2psVrLQjrFlan\nBcLxWGItJN2VRJelXeixrQfJIyS5/s6s6f0XKT1SSJ+aTsB98imX47qOI6jjTIY+8j/+8x944IEj\nSaE0mtpEiwSNRlMt/vwThg6Fnj3hvLufJiY0gpHtR55QL+ALkPl1Jmt6rOGvfn9RuLyQpi80pXfE\nONrelk7kRZEIQ/0RBicjuGUwTf/Vgl6Mov1ED6ZIE1vGbGFZw2XsGr8Ld7r7hG0ibBHc3vl2Fkfe\nyZv/dvP22zB+fC10XqM5Di0SNBpNldmyRSVqatUKpkzL4fPNk7m3+73YTEciJ/qdfva/vZ+VLVcq\nU3y4kQ6zOtBzW08a3RyMJW/PkdgCZwuNGiGCrMSEb6Lj7I702NKDuBviOPD2AZY3Wc62e7ZRvKv4\nmE3u73U/Be4CjD0+4tVXVQbJl1+upf5rNKXU6WBKGo2m7pKaCoMGqdl+s2bB+xvfJyAD3Nv9XgB8\nDh8H3j3Avlf34cv3EXd9HO2+a0dol9AjjWzYoJbt2tXCEZxGDAYlfDZtAiC4dTAt32pJk2eakPZe\nGvvf3E/ah2nEXR9HoycaEdI+hCYRTbi27bW8sfwNtj1wN4WFRh5/HMLC4O67a/dwNOcuWiRoNJpK\nk54Ol1wCFgvMmQP28BL+vfLf3NzxZiIDkaS+lMq+V/fhL/TT4PYGNHqsEbbGZeRlWL9eNdKy5Zk/\niNNNhw7q+I7CHGGm8ZONSf6/ZA5+cpB9k/ax+vzVxF0fR5NnmvBQ74fo8XEPftr6ExMnXk1BAdx7\nrxIKo0bVzmFozm20SNBoNJUiNxcGD1apCRYvhgYN4KOUqRTkFzBu9TiW37z8iDh4ohG2hqdI2rR+\nPbRpw1kZHKBDB/jhBwgETpiqYAw2knxfMol3JpL+aTqpz6aysu1KEsYmcMV5V/Daste4us3VvP46\nFBbC2LEQGqpSTWs0ZxItEjQaTYUpKoLLL4e0NFi4EJo2Bb/Pz9I3lvLtrG9xFDqUOHiyHHFwiPXr\n1cO0CgQCXrzeLDyeDDyeDLzeTLzeHPx+B36/A5/Pgd9fhJRupPSXFh8QQAgLBoMFIawYDFaMxmBM\npohjitkcj9WaiMXSAKMxqPIdPP98dcL27DlpCmyD2UDiuETiR8dzcPJBUv+VyoNTH+SHzj+wrOMy\nenftzUcfgcMB110HM2aobJIazZlCiwSNRlMhSkpg+HA1zD5vHrRpI8n+JYe1D65l7PaxGIYa6PZG\nN4JbBleswUBA+SSMOHnKF5+vEJdrM07nJoqLd1JSsudw8XjSOD5zk8Fgx2QKxWg8UgwGK0IYEcKE\nwWABDEjpwecrJBBwI6Ubv9+Jz1eAz5eH3+84oR8mUyRWaxI2W3OCg1sSFNSSoKAWBAW1xGpNQogy\nfMAPiZ/1608qEg5htBlJvj+ZBrc3YN/b+xjy/BCK+hSx+/HdNHykIV98YWL4cHX+f/8devUq7+Rq\nNDWDFgkajaZcvF6Vq2jpUhU0qaW/kL8G7KRgYQGprVKZ/dRsvn7+68o1mpqq3rQ7dEBKP07nZhyO\nVTidG3A6N+JybcLt3ldaWWC1JmOzNSEoqBmRkQOxWhthtTbAbI7HYonDbI7DaKyA9aIcAgEffn8B\nHk8GbncaHk8abncabvd+iot3kJX1AyUlewCVBMpgCMJub0dISKfS0hm7/XxMDRqoPA7r1qmnewUw\n2o00eaIJv/X6jdX/XM2NL9/IwY8O0vS5pnz3bQKXXi647DJYsEAZKjSa040WCRqN5pQEAnDLLWoG\nw0+feYj9dBdrPknH3t5O4D8Bbtt7GzNGzahwe1JK3O69FG7+AMedUBj5DI5F1xIIOAGBzdYMu70t\n8fGjCA5uh93eluDg8zAaK2ihqCYGgwmDIRqzORq7vW2ZdQIBDyUleygu3oHLtQ2ncx0Ox2rS0z9D\nSi8gCApqQegEE+F5PxJeNBy7vR1CVCze8ui+o5lw1QRsY2zcNf8utt6+Ffvb+/nvs8256tkoBg+G\nRYvOTn9PTd2iUqmizxQ6VbRGUzeQEu65Bz6eHOD7m9OI+H43wiBo+kJTEu9IZOAXAyl0F7J63OpT\nRlgsKdlDfv78w8Xt3guANVMQ1vYaQsN6EBrandDQrphMoWW2Ux8IBDy4XJtxOP6kqOhPCtd/TVF4\nBtIIRmMYYWG9CQ+/gIiICwkL6106/FE2ry97ncd+f4ztf99O1NYodjy0g8IlhYRcEsXDO1uw2x/M\n4sXQ8PhMOZpznppMFa1FgkajOSmPPw6zXs7nleTtWA84aTCuAU1faIolxsLivYvpN6Uf31/3PSPa\nHOtX4PFkkZs7m7y8OUeJAkFISGciIgYQEdGfsIf/g2VXnvKAPFv56CP8999J4e5ZFJaspqBgCQUF\nS/H7CzAY7ERE9Ccy8hIiIweVWhqOCC2nx0mTt1TshPeGvoeUkqzvstj16C5K9ruZGZTMr3GN+X2J\nibi4WjxGTZ2jJkWCHm7QaDRl8tqTbkwv7+QtMglNCqXlD10I6xZ2+PsXFr1A+7j2DD9vOFL6KSxc\nRW7uLHJzZ+FwrAYkISGdiI39GxERAwgP74fZfFSyoyUPqakSZzMdO2IslkTujSKy+1MASBmgqOgv\n8vLmkJf3O7t2PYGUD2KxNCAychDR0VcQFTUEuyWMB3s9yMQFE3mq31MkhSUR97c4oq+IZt+kfQz9\n115678rgiR7NefXPOCIj639Ia03dQ4sEjUZzDAFPgG+v30+bH1MxBRto/U5rEsYmHJNXYdWBVczb\n9StfXf4gW7fcTE7OTHy+HEymSCIjB5OUdC+RkUOwWhPK3klBAezYAept5+zl/PPBZIKUFOjeHQAh\nDISGdiE0tAuNGj2G319MQcFi8vLmkJv7KxkZnyOEmYiIAVzbcDBT7DYmLZ3Em5e+CaiZEE2ebkLC\nmATWjNvB6Dmb+bFJGlfMbklsr5DaPFrNWYgWCRqN5jC5c3JJGbudmIPF7OmUxJi5TTBHHgl05PXm\nk5s7g7/WPcz0CwQWx+sUBdqTmHgX0dGXExraA4OhAreVNaUW0G7dTtOR1BFsNmjfHlavPmkVozGI\nqKhBREUNonnzVygu3kNOzs/k5PzMgdQn+bCzl13Ot9mwzULTpFuw29uophvb6PNbe5a/k4vpHztY\n33s1De5KpMW/mh7zP9NoqoMWCRqNhpLUEnY8uIPs77PZRDh7r2rHq9+HIAR4PNlkZ39PVtb35OfP\nRUovXi8UR42kb4dnCQ6ugov96tVgt599iZ3Kols3WLWqwtWDgpqQnPx3kpP/js9XyL7071iw/E4a\nHniT7LRJBAe3Iy7uWmJjr8Vub0uv+6Ioad2NDy87wM0f7yHnf1k0e7EpDW5tcFZk1dTULjoLpEZz\nDuMv8bPnuT2sbLOSzPmFvCDasPKGTrz4NWRmfsm6dUNZtqwB27bdg5Remjd/jTf29+aDAx24sseX\nVRMIoMzvnTqBsWJTAus13brBxo1QXFx+3eMwmcJomnwL5viJXLVEEtf0Q0JCOrFv32usWtWOlSvb\nsXv3RLr32cqNPzZktOzBlrAoto3bxpqeayhcWXgaDkhzLqFFgkZzjpL9czar2q0i9dlUXJcm8Tdn\nJ7o+upInnryR5cvj2Lz5Jny+Alq0eIs+fQ7SqdMfbPG0YfrOZTw/8HkMZUUZrCgpKWe/P8IhunYF\nn08FVaoi9/e8n1BbNK/8uYS2bb+gT59M2rf/idDQLuzf/warVrUlIaELH33/Ho8XRDLr8s4EvAHW\n9FrD1nFb8WR7avCANOcSWiRoNOcYrh0u1l2xjg3DNmBrbsX1cTF/NH+F//6YzKWXDqO4eCONG/+T\nnj1306XLYpKS7sFiiUVKyZN/PEmv5F5c2erKqnfgXHFaPESHDiqB1Sn8EsrDbrEz/sLxTF03lU1Z\nmzAabcTEDKNNm6lccEEm7dp9j83WlPDwx/n22ySi+1/PryM20/StRDK/zWRlq5Uc+OAA0l/3prxr\n6jbaJ0GjOUfwO/2kvpjKvkn7MJ+fSczM5WRa/0eQYS/9IhvTosVdJCTcQEhI2QmXftzyI6vSVjF3\nzNyTBk6qEIcelme70+IhrFY1y6EaIgHgjq538Nqy1xg/dzzfX//94fUGg5XY2BHExo7A680jK+tb\nPJ6pWK1j2ea1kzB3OIHvBrL9XjcHPzpIy3dbEt4rvLpHpTlH0CJBoznLORSEZ8cT6/GcNwfrtHm4\nI1eSJcOYNXMkTucY3nyzDxbLyR/8Xr+Xp+Y+xaBmg7io6UXV69CSJRARAeedV7126hM9e8Iff1Sr\nCYvRwjMDnmHsj2NZsX8FPZN7nlDHbI4kMfEOEhPv4OuvdzNjxpdcc81Uwgf9F9OgeNxzB/Pn9ReT\ncElPmr3UDEvsySM+ajSghxs0mrOaoo1FpNz2CZtWjcXz7pXw0CsENQ3F4/mCq646yNatk3nzzQtO\nKRAA3l/9Pluyt/DKoFeq36nFi+GCC8BwDt1++vaFrVshK6tazYzqMIqO8R25/9f7CcjAKetef31T\nLrtsPFdfvYXp01cS2+Ba5OBf4LObSe9+LcvveZq972/RQxCaU3IO/Uo1mnOHoqxdrJn8f6xe25qi\nMbdjGbKFJq2eoFev3ezc+TtXXDGKAQOC+fZbsJTzMpntymbC/AmM6zKOTgmdqtcxvx+WL1ci4Vzi\n0PEuWVKtZowGI29d+hYrDqzgy3Vfllv/hhvgyy8Fb73VnRde+Dc9e6bRtu3XRHRLJHDPJHY16cyS\n167kwOKZ1MUQ/ZraRw83aDRnCX5/MVlZ37P3z8m4rIuhsZWQwqE0a3svkbH9EcLA1Klw880q7fNn\nnyl/uvL457x/IqXk+YHPV7+T69eDw3HuiYRGjVQmpiVL4KqrqtVU/yb9ubbttTz2+2Ncdd5VhFpP\nnRBr5EgQAkaNgpISG1OnXkdct+soKdlH6uoPSW82he2+oez6uRFJzW8jsfmt2GzJ1eqj5uxBWxI0\nmnqMlJLCwpVs3XoXSxYlsGXLTbi25xO66Fm6t91Lt+v+R1TcRQhh4IMPYMwYuPVWmDq1YgJhXcY6\nJqdMZkL/CcTaY6vf4SVL1I5LQxSfU/Ttq4ZaaoBJgyaRV5LHi4tfrFD966+H776D6dNh2DBwOsFm\na0jrvs/R76pUkvf+j0BKW/bue5Hlyxqzdu2lZGZ+SyDgrpH+auov2pKg0dRDPJ4sMjK+ID39E5zO\nDRic8QR+uJLg1Ktp9c+BRPSLOKb+q6/CI4/A/ffDG2+oN8vykFJy/6/30zKqJff2uLdmOr5kCXTp\nAkFBNdNefeKCC+B//1NBlap5/I0jGvNon0d5aclL3Nb5NppHNS93m+HDYdYsJRIGDYIZMyAyEgwm\nIy3GXEPjnGHs+Oc6MlKnUXDNbPKaXofJFEV8/CgSEm4lNLSaQ02aeom2JGg09QQp/eTkzGTDhr+x\nbFkSu3Y9BvsaYZgwCXHL17Tq/grdfx1xjEAIBODBB5VAGD++4gIB4LO1nzF/z3zeuvQtLMYa8IKX\nEubPhwsvrH5b9ZG+fcHrVT4ZNcBjfR8jISSBu2fcXWF/gosugrlzYds26N8fDh488p052kybd7vS\nZcLT2N+fAmM/xZJyFZnp35CS0pnVq7uwf/+/8XpzaqT/mvqBFgkaTR3H5drBrl1PsWxZY9avH0px\n8TYS3BOxPvAzzuseoUHH4fTa2ofEOxMRxiMKoKREOa69+Sa88w4891zFBUJGUQYPzn6Qm86/iSEt\nhtTMgWzcqJ5KgwbVTHv1jQ4dIDYW5sypkeaCzcF8MPQD5uyaw2drP6vwdt27w8KFkJurjBtbthz7\nfVj3MLos70Krpwfh/det+C7/L7FrP8JqaszOnQ+ydGkiGzdeR07Or0jpr5Fj0dRdtEjQaOogfr+L\n9PTP+fPPAaxc2ZIDB95VEfZCF2Ae/zkHh/TBFhlPt7+60fLtlidk/cvLgyFD1Bj0d9/BvZUcLfjH\nr//AaDDyxpA3au6g5sxRgYX69q25NusTBoMSSL/9VmNNXtbyMm46/yYenP0gGUUZFd6ubVs18hMU\nBH36wIIFx34vDILE2xPpub0nDf/RhOzHWuK47BGa7V5Ds6Yv4nJtZv36y1i2rDG7dj2Fy7W9xo5J\nU7fQIkGjqSMoJ8QVbN16J0uXJrBly1iEMNCmzRd0abQL/wt/Z3P3AO49btp9346Ov3ckpH3ICe3s\n3auewxs2qPg9I0ZUrh/Tt07nm43f8NalbxETHFNDR4cSCf36nZv+CIcYPFilyc7OrrEm3xjyA6GV\nkQAAIABJREFUBkaDkb/P+nultmvc+IiLyKBBypn1eExhJpq90IyeW3sSMTCCnbfkkD6sH80dC+jS\nZRUxMcM4cOBdVq5sxZ9/XsjBg1Pw+Ypq6Mg0dQEtEjSaWsbjyWTfvtdZtaoDa9b0Ijd3JsnJ/0fP\nnjtp3/Q3it7oRcp5G8j9NZeW77ak+4buxI6ILTM08uLFypzscsHSpeotsTLkFudyz4x7uLzl5dzQ\n/oYaOkLA7Vavq+fqUMMhLrlE+WZUM/ri0cQEx/D2pW/z7aZv+WHzD5XaNiJCOTOOHq1mvkycqLp3\nPLbGNtp+2ZbOyzpjDDay7uJ1pN5kJck/iT59DtKmzX8xGGxs3XpbqcC9lfz8xTr2wlmAnt2g0dQC\nfn8JOTk/k5ExldzcWYCBmJiraNHiNSIjL0F6BWnvp7HnueUEXAEaPtyQho82xBR68p/sRx+pYYU+\nfeDbb9Xwd2WQUjLu53G4vC4+GPpB9fIzHM/SpUq5nOsiISkJ2rWD2bPVvMQaYmT7kXyz6Rtu//l2\nuid1Jzms4nEOzGb4+GNo0QKefFL5KPznP2C3n1g3vFc4nZd0JuvbLHY9totV7VeRMCaBJhNHEN/x\nBkpK9pKe/hnp6VNIT59CUFBLEhJuISFhDFZrUo0dr+YMIqWscwXoAsiUlBSp0ZwtBAIBmZ+/WG7Z\ncodcuDBczpuHXL26h9y//x3p8WSrOr6APPj5Qbms2TI5zzBPbrl9iyw5UHLKdj0eKe+5R0pQS4+n\nav2bvHqyZCLyu03fVa2BU/F//ydlYqKUfn/Nt13feOghKePjpfT5arTZbGe2THotSfaf0l/6/FVr\n+5tvpLTbpWzfXspt205d11/il/ve3icXxy2W8y3z5bZ/bJPuDLeUUspAwC9zc+fJTZtGywULguS8\neQa5du1lMiPja+nzuarUN03FSUlJkYAEusjqPo+r28DpKFokaM4mXK4dcteuCXLZsmZy3jzk0qWN\n5M6dT0mnc8vhOgF/QKZ/lS6Xt14u5zFPrhu2TjrWO8ptOz1dyv79pTSbpZw8uep93JCxQdqet8m7\nfr6r6o2cjEBAykaNpLz33ppvuz6yZIm69S5aVONNz989XxqeMchn5z9b5TY2bJCyVSspw8OlnD69\n/Ppeh1fueX6PXBi2UC6wL5C7xu+S3nzvke+9BfLAgQ9lSkovOW8ecuHCMLl58y0yN/d3GQjUrFDS\nKGpSJAhZB8eMhBBdgJSUlBS6dOlS293RaCrNoZS96emfU1i4BKMxlNjYa4mPH01ExIUIodyBZECS\n/UM2uyfsxrXRRdTlUTR5pglh3cLK3cecOWosGVSMnqpOGnB5XfT6uBcBGWDVuFUEmWvYsTAlRaWF\n/uMPGDiwZtuujwQCkJyshhveqMHZI6VMmDeB5xc9z4KbF9C3UdUuioICGDsWfvpJxdeYOBGMxlNv\n4831svflvRx4+wDCKki+P5nk+5MxRx2ZeeNybScj40syMr6gpGQnFksi8fE3Ehc3ipCQjjU7xHUO\ns2bNGrp27QrQVUq5pjptaZGg0dQQPl8h2dnTycr6mtzc2UjpJypqMPHxY4iJGY7RGHy4rvRLsn7I\nYu8Leyn6q4jIwZE0eaYJ4b3Cy92P1wsTJsBLL6kh/s8/h/j4qvVZSsnI70byy7ZfWH7bcjrEd6ha\nQ6fiqafggw8gIwNM2g0KUM4jv/wCe/ZUPHhFBfEFfFz8+cVszd7KqnGraBjesErtBALqGhs/Xk1K\n+fxzNSOiPNxpbvZN2kfa5DSESZB0XxLJDyZjiTkSkEtKicOxkoyML8jM/BqvN4vg4HbEx48iPv5G\nbLYK7EhzUrRI0GjqCH6/i5ycX8jM/JqcnBlI6SYsrA9xcdcTG3stVmuDY+oH3AHSp6az75V9FG8v\nJuLiCJpMaHJCGOWTkZoKN94IK1bACy+oSIrVybj8zPxnmLhgIt9d9x1Xt7m66g2dDCnhvPOUN+WU\nKTXffn1l7ly4+GL1j+zRo8abz3Rm0uOjHkTYIlh862JCLCdOla0oCxYoi1VhIbz/vgrQVRE8GR72\nvb6PA+8eAAmJdyfS8OGGWBOsx9QLBLzk5c0hI+NLsrN/IBAoJiysF7Gx1xIb+zdstkZV7vu5Sk2K\nhFr3PyiroH0SNHUYv79EZmX9KDduHCkXLLCXOiB2k6mpk2RxcWqZ23gLvXLvq3vlksQlcp6YJ9df\nvV4WrCio8D4DASk/+kjKsDApGzeWcunS6h/HNxu+kUxEPrfgueo3djKWLlXj73PmnL591Ee8XikT\nEk6rn8ba9LUy5F8h8qppV0l/oHoOo3l5Uo4cqf6Vo0ZJmZ9f8W3dWW6586mdcmHYQjnfOl9uvm2z\nLNpQVGZdr9ch09O/kOvWDZfz51tLf1s95d69r8ri4j3VOoZzCe2ToNGcYXw+B7m5M8nO/pGcnBn4\n/Q7s9g7ExY0kNvY6goNblLldyd4S0t5PI21yGv4iP/Gj42n4SEPs55Uxv+wk7NkD48bB77/DLbfA\na6+pxDzVYdm+ZVz8+cVcdd5VfHn1l6dvLHjcOBVhcPfu6pk8zkYefxwmT4a0tNMWYOrnrT8zfNpw\nHu7zMK8MeqVabUkJX34J99yj4iu89x5ccUXFt/fmeTn44UH2v70fT5qHqEujSH4omciLI8u8/ny+\nwlIr3Tfk5v6KlG5CQ3sSF3fIwqCHJE6GHm7QaM4AHk8G2dk/k539A3l5vyOlh5CQTsTEjCA29m/Y\n7W3L3E5KSf6CfA78+wDZP2ZjDDHS4LYGJD+QjK2hrcL7DwTUjfjxxyEqSsVBGFIDaRRS0lIY+PlA\nOsZ3ZPZNs2veUfEQTickJMBDDynPN82xbN8OrVqpJ++NN5623by5/E0emP0Az130HOMvHF/t9vbs\ngbvuUqEerr0W3noLGjQod7PDBDwBMr/JZP9r+yn6qwj7+XaSH0gm7vo4jEFle0ceEgxZWd+SkzML\nKd2EhHQhJmYY0dHDtdPjcWiRoNGcBqSUuFybycmZSU7OTxQULAEE4eF9iYkZQUzMVQQFNTnp9n6X\nn4wvMzjw7wM41zsJbhNM0t+TiB8djymkcg57K1aotM4rVsDddysHsrDyJzyUy4bMDfT/tD8tolow\nZ/Qcwqw10OjJ+OQTuO02ZUVo0uT07ac+07+/mjYwd+5p3c0LC19g/LzxTBo0iYf7PFzt9qSEadPU\nNerxqOvzjjsqZyySUpI/L599r+8jd0YupkgTCWMTaHBng1Na2nw+Bzk5v5CTM52cnJn4/YVYrQ2J\njh5GTMyVREQMwGCwnnT7cwEtEjSaGsLvd5KXN4/c3Jnk5MzE7U7FYLAREXExsbEjiI4ehsVy8tCF\nUkocqxwc/OQgmV9l4nf4ib4ymqS/J53UjHoqDh6EJ56Azz6Djh3h7bdrLrPypqxNDPxsIA1CGzB3\nzFwig6o5ZnEqAgGV9bBZM/j559O3n/rOtGnKE3DNGujc+bTuavzc8byw6AXeHPIm9/e6v0bazM2F\nRx9VERq7doVXX4UBAyrfjmuHi4MfHiR9SjrebC/h/cNJvCuR2BGxGKwnVx6BgIf8/IXk5EwnO3s6\nbncqRmMIUVGXEhV1GZGRg7HZKh598mxBiwSNpoooa8EW8vLmkJMzk/z8+UjpxmZrRnT05URFXU5E\nxACMxlOb4D2ZHjK+yODgJwdxbXRhTbaScHMCCbcmENS08uZ7l0sJghdeUIkSX3gBbr+9/LnpFWVR\n6iKGTRtGw7CG/DHmD2LtlYzZXFl++QWuvBIWLTp3sz5WBJ8PWrdWCTemTTutu5JS8tjvjzFp6SSe\n7Pskzw98vsZM9IsXq1GllSuVn8Irr0CbNpVvJ+AOkPV9FmmT0yhYUIA5xkzcjXHE3xRPaLfQU/ZX\nSonTuZ7s7Onk5PyMw7EKkAQHtyMqaghRUUMID+9X7m/7bECLBI2mEpSU7CMv7w/y8/8gL28uHk8a\nQliIiOhPVNTlREdfRlBQq3JvmL4iHzk/55D5dSa5M3LBADEjYmhwawNlNTBW/oZbXKx8115+WSUG\nvO8++Oc/q++YeDT/2/Q/bvr+Jvo07MMP1/9AuK38WAzVQkpl/ggEVJpBzan54AMVN2HrVpVA4TQi\npeT1Za/z8JyHGX3+aD4e9jEWo6X8DSvUNnzzjbKE7d2rRO5TT0HDqoVpwLnZycGPD5L530w86R6C\nWgURf1M88aPiCWpW/oPe680hL+93cnNnk5s7G48nDYPBRnh4f6KiBhMRcREhIecjRA0p8TqEFgka\nzSlwu9MpKFhcKgr+oLh4OyAICelEZOTFRERcTHh4X0ym8ueO+11+cmYcEQaBkgChPUOJH6VuVkdH\nk6sMJSXw4YdqLDczU2XgGz9eWedrioAM8NLilxg/dzwj249kyvApWE1nYKz2kBXhl19g6NDTv7/6\nTkmJ+scPGAD//e8Z2eXXG75mzI9juKDhBXx1zVfEh1QxGlcZuN3w7rvKGuZwqBgLjz2mfDSrgvRL\n8ubmkfFFBlnfZRFwBgi7IIy4a+OIGRGDrVH5zsDKyrCRvDwlGPLzFyKlG5MpgvDwC4mIGEBExICz\nRjRokaDRlHJo+KCgYAkFBYspLFxCcfEOAIKCWpWKgoFERl6E2RxdoTa9OV5yf80l++dscn7OIeAK\nENI1hLjr44i9NpagJlU3V6anK8vB++9DVpa6gY4fX/MvkJnOTEb/MJo5O+cw/sLxTBwwEYM4A1MQ\nPR7li9CokZr6qD3OK8ann6r5rYsXwwUXnJFdLkxdyHXfXocQgi9GfMHFzS6u0faLitS1/uqrSghf\ne60SC9VxvfA7/WRPzybjiwzy5uQhvZKQriHEXBVD7IhYgtsGV2gIxe8vweFYSX7+fPLz51NYuIxA\noOSwaAgP70d4eG9CQrrUy+EJLRI05yxebz5FRWtwOFaVCoOl+Hw5gIGQkE6Eh19AeHhfwsMvqHBq\nWiklzo1Ocn7JIeeXHAqXFUIAQrqEEHtNLLHXxRLcIrj8hk5BSoqaKjZtmkrNO3YsPPAAtGxZrWbL\n5Ledv3HzjzfjC/j48uovGdT8DKZnnjRJzdn86y8lFjQVIxCAnj3VcsWKMxa+Or0onZu+v4m5u+fy\nVL+nGH/h+Bq3NpWUKA308stq+mSvXmrGzrXXVi88hK/QR87MHLJ/yCZ3Zi7+Ij9BLYOIvjKaqCFR\nhPcLP+mUyuMJBNwUFh4SDfMoLFxBIOBCCDMhIZ0IC+tNWFgvwsJ6Y7M1rvPTLbVI0JwT+P1OHI4/\ncThW4XCsxuFYVTp0AEZjCKGhPUsFQV/CwnpiMoVWuG33ATd58/LIn5dP3u95uPe6MdgNRA2KImpo\nFNGXR2NNrN7NMisLvvpKxbxPSVFx7++7T80KrEmfg0OkF6Xz4OwH+WrDVwxsOpAvRnxBg9BKTGCv\nLuvWKQe8e++F118/c/s9W1ixQlkRDmVUOkP4A35eWvwSExdMpEVUC94f+j4Dmgyo8f34fGqiy/vv\nq+RkUVFw881w663Qrl312vaX+Mn/I5+sH7LInZWLJ82DsAoiLowgakgUkYMjsbe3V/jhHgj4cDrX\nUVi4nMLCZRQWLj9sobRYEggN7UZISBdCQjoTGtoZq7VRnRIOWiRoziqklJSUpOJ0rsfpXEdRkVq6\nXFuBAAaDrfTH2J3Q0G6EhnYjOLj14UyKFcF9wE3B4oLDwqB4WzEA9vZ2IgZGEH15NOH9wzHaqjce\n6XTCzJkwdSrMmqWs7Zdfrm6GV1xxel4Q3T43k1MmM2H+BIzCyGuDX2NMxzFn9qbldKpXRCGUi7ut\n4kGjNEfxzDPw7LMwb17NzX2tIOsz1nP3jLtZsm8Jo88fzXMXPUfjiNMT1XD7duWTM2UK5ORA+/Yw\ncqQqzZtXr20pJa5NLnJ/yyXvtzzyF+QTKA5gjjMT3jec8H7hhPcNJ6RTCAZTxe8hHk8WhYUrKCxc\nhsORQlHRGrzeLABMpqjSe5QSDnZ7e4KDW9VavAYtEjT1Ein9lJSk4nJto7h4G07nplJhsB6/3wGA\nyRSB3X4+dnuHUjNfd4KD22EwVPzp6nf5caxxULi8EMcKtXTvdwMQ1DqIyIsiiRgYQUT/CCxx1ffs\nTk9Xb0g//aRCJ7vdKmfPmDEqG3BMTLV3USYev4cpf07h+UXPk+ZI45ZOt/DyJS8THVwx34saw+eD\n4cNh4UJYtkzd8TVVw+eDSy6B9evVzJDzzjujuw/IAFP+nMLjfzxOQUkBt3e5nSf7PUly2OmJNeB2\nK9eVadPU78fpVFnFr7xSiesuXaofzdtf4qdwSSF5c/MoWFyAY6WDQEkAg91AWK8wwvuEE9otlNCu\noVgSLRUW11JKPJ40HI4/KSpaQ1HRnzgca3C795bWMBIc3JLg4HbY7W2x29sRHNyuVDzUzIySk6FF\ngqZMvvrqK26oaIq204T64WRQXLyD4uJtuFxbS5fbKC7egZQeAISwEhzcqlQMKFFgt5+P1ZpUqTdg\nT5aHorVFONc6KVpbpD5vdIIfDMEGQruFEtYrjLCeYYT1DsPa4ERlX9nzVlSk/MvmzVOB8lavVjey\nvn3Vs3L48Oq/DZ2K9KJ0Pl7zMZNTJnOg8ADXt7+eCf0ncF7MmXugHD5nHo+yF3/9NcyYAYMHn7E+\n1EcqdK3l5anczA6HUp2nw3GlHIo8Rbyz8h0mLZ1EkaeI69pdxz3d7qFXcq/TZqFyOtUl9O23SjgU\nFkJcHLRu/RV33XUD/fpVfTrl0QTcARxrHBQsLqBgcQGFSwvxZnsBMMebCe0aSmiXUEK6hhDSIQRb\nE1ulpjd7vbk4nZtwuTbidKricm3C40kvrWHAZmtMUFCL40pzbLZmNeIoWesiQQhxL/AwkACsBf4u\npVx1ivoDgNeAdsBe4AUp5WenqK9FQhUYNmwY06dPP637kNKP251GSUkqJSV7cLtTD39Wy1SkdJfW\nFlitjQgObk1wcCuCgloRHNyaoKBW2GwNKzzVSPolJaklFG8vxrXNRfG2YlxbXTjXO/GkK9FhCDJg\n72AnpGMIIV1CCOsVhr29vULmxFOdNymVs9WqVcqKvnSp+uzzqXj1F12k8ikMHQrRp/EF3uV1MXP7\nTKZtmMZPW3/CbDAzqsMo7u91P+3jzvyb+7Bhw5g+ZYqKFjh/PnzxBVx33RnvR32jwr/R/fuVRSE7\nG374QYmGWqDQXcjk1ZN5f/X77M7fTaeETtzU4Sb+1vZvp20oAsDrVUapmTPhvfeG4XCoc9a4sRLj\n/fopP8+2bcFSzZdyKSXufW4cKQ4cKQ6K1hThSHHgzVTCwWAzENwmmOC2wdjb2bG3tRPUKghbE1uF\nHSPVMeWUiofNFBfvLH2RUiUQcB2uZ7UmY7M1x2ZrjNXaEJutIVZro8OfTaby45zUpEio9AipEOJ6\n1AP/DmAl8AAwWwjRSkqZXUb9JsAvwHvAjcAlwMdCiDQp5Zyqd11TkwQCHrzeLNzug3g8B/F40o76\nrIr6Ox3wH97OZIrGZmuMzdaE6Oihhz/bbM0ICmpRIVUsAxJPpgf3PjfuvW5K9pXg3uumeFcxxduK\nKd5ZjPQoMSusgqDmQQS1DCLhtgQlCjqGENQ8qErBjI4mPx82b1Zl0ybYuFFZCbJLr+pGjdSNafRo\nGDhQzfs+ncP+u/J28fuu35mzaw4zt8/E5XXROaEzkwZN4uZONxNhizh9Oz8VUqoxlg4dlOv67NlK\nLWlqjuRkpUhHjFDxEx55BJ5+GuwVzx5aE4RZw3jkgkd4qM9DzN4xmw/XfMhTc5/i4TkP0yOpB0Nb\nDuWSZpfQPbE7ZmPVYoaUhdmsXDIuvFD9Fj/+WI2+LFqkyrRp4PcrgdC+vRqW6NRJjc60bg1JSRX/\nbQohsDWyYWtkI3aEikQqpcST5sG50YlzkxPXRhfOTWoGlL/gyP3PkmghqHkQtmY2gpodWVqTrVga\nWDCYj7ykmM3RRET0IyLiWMGnrK/ppYLhaPGwnfz8ubjdaUDgcH2jMRSrtWFpScJiiS8tCZjN6rPP\nV1j1k3/8+amsJUEIsRxYIaW8v/RvAewD3pZSnpCLVAjxMnCZlPL8o9Z9BYRLKS8/yT60JaEKDBs2\njB9+mIbPV4jfX4DPV4jPV4DPl4PXm4PXm33CUn2Xjd9fdFxrhtILrwEWSwOs1sTDn222RqUqt/Ep\nAxL5i/14M714Mjx4Mj14M7x4Mj14MtRn90G3Egb73YdFACirgLWRFVsTG8GtgwlqGURwq2Cl3htW\nzvR3NF4vHDgAqakqIlxqqio//jgMi2U6aWmqnhDQtKl6S+nSRfkXdO+uTJ+niyxnFmsOrlElfQ2r\nDqwitSAVozDSPak7w1sP529t/0aLqNMbke+U+P3q1W7SJIYtWsT0Sy9Vd++kik011VTB2ufzqWml\nEyeqKTEPP6y8YE+Xo0sFKHQX8su2X/hu83f8sesPCtwFhFhC6JXci+6J3emW2I2O8R1pEtEEo6H6\ngYnKOmdOJ6xdq1Je/PmnWm7cqH7jAMHBSsQfKo0bq6GK5GRVwsKqJvAPiYfincUU7yqmZGeJWu5S\ny0PWBwAEmGPNWJOsWBItWJOsWBOVeDDHmFWJVktTlOkYQXE0gYCv9KVtHyUl+3C795V+3lv6ApeB\n15tBIFByeJtt2+DOO4EzbUkQQpiBrsC/Dq2TUkohxO9A75Ns1gv4/bh1s4E3KrPvswEpJVL6kNJL\nIFBCIFCM3+8iEHDh9xcTCLiOWndkefT3fr8Tn68Av7+wVAwcWebk5LFoUdlvGkKYMJtjMJmiMZtj\nMJuVBcBsPvK32RyD2ZSAyRePwR1FwKmClwQcAfzpfvxOVdzOAE6HD19+Jr68NHz5Pnx5PrU86nOg\nJHBCP0zRJixxFizxFqwNrIT1CFOCoKFNLRvZMEWZyonRrsIZFxQcW/Lz1TInRwVvycyEjIwjnzMz\n1TT0Q8TEqJuH2ayG1du0UcKgdevqzd8+Hl/AR5YziwxnBpnOTNIcaezK23VMyXBmAOrNrUuDLlzT\n5hr6N+lP/8b9T38Y5ZN23KdCBaekqNe3X35RFoTu3ZVJZeZMHSzpdGMyqTjHN9ygZj48+aSKdTxw\noBrn6tZNZQILrfj03+oSZg3jxg43cmOHG/EFfKw5uIa5u+eyfP9yPlv7GS8ufhEAi9FCi6gWtIpu\nRevo1iSHJZMYmni4xNvjqxyTwW6HPn1UOYTPpxKObtumLttDy8WLVeK0o9+HQ0KUWIiPV/eB6Gi1\nPPpzdLQSEyEhR4rZLNTDPslKxIUnWvJ8RT5KdpfgPuDGk+Y5ZulIcZAzPQdPhgfKeDc3hhsPiwZz\ntBljmBFTmAljqBFjqBFTWCOMoU2xhZmwH15nwhhpRNgE0lKEz5CFT2Th8y0HHqvSuT2eSlkShBAN\ngANAbynliqPWvwxcKKU8QSgIIbYCn0gpXz5q3WWoIYhgeWQA++ht+gBL7h4+iMSYqNK18qjvD/0p\nkag/hJCH6wmDLK3gV+tFoHQpgQAYAiACR7YTAcThOgEEEikCCEMABKXrAmBQbQgRQBh8CIMfDH6E\nwX/478PrjH6E8JUu/WD0YzAeMVNVBBkQSJ8F6TMjfWYCPgt4zQTcQQTcNqTHhjxq+cncFMZ2uwRZ\nYoMSGxTbECVB4AiBEhuGgED4wRAQGPwCIVHL0lNiCAgMgfJv+hKJ3yTxmSVei8RnDuAzgdci8Zok\nXhO4TeA1SUpMgmKzoNgEJSaBXwr80kDALwhIY+lS4A8Y8XpN+LwmvD4TPp/62+szlv5dut5rwuM1\nEQicXN/abMWE2F2Hi710GWp3EhFRSER4IeFhDiwWpfqnTlvI6JGl082O+j1IwCf9ePHjkz680n+4\n+Djqs/TjkT6csgSn341TunEG3DgDJTgDbgoDxSf0McYUSrIpiiRTFEmmSJqa42hjSSTJFHXyyIgV\n/a1WtF4goIYLXC5VSkqU0jqkqNLS1DpQqZ779VMPpjZteOCBB3jjjXNO51ebap+3vDw1t3bBAhWw\nyudT6xMT1RMvNlY93YKDVQkKUsViUd615ZUaIMubz86Sg6S6M0gtySS1JJ29niwyPfn4pO+YuhaD\niRBDEKGmYEKMQYQagwgxBmERZqwGMxZhZsk367n0xh6H15mFEQMGDAgMwoBRGDAItcYoBAIDxmO+\nEwT8RgoKg8gvsJOXH0xBgZ38AjuOIhtOpw2ny0qR04rTZcPjOfmwicnow2LxYbX6sFq92Cw+TGY/\nZmMAk8mPySQxmfwYjQHMxgBGox+TKYDJGMBsDiAMEiMBzH6JzSuxeCUWL1i8EqtXYvKAxQNmD5h8\nYPSC0VdavAKjr2KifI9I5UX5L4ALpJRLq/HvrLMi4Ubgy8ociEaj0Wg0mmMYJaWsVkKQyjouZqO8\n1o7PBhIPpJ9YHUrXl1W/sCyBUMpsYBSwByg5SR2NRqPRaDQnYgOaoJ6l1aJSIkFK6RVCpAAXA9Ph\nsOPixcDbJ9lsGXDZcesGl64/2X5ygDOTDk2j0Wg0mrOPag0zHKIqg1CvA+OEEGOEEOcBHwDBwKcA\nQogXhRBHx0D4AGgmhHhZCNFaCHEP8LfSdjQajUaj0dRRKh0nQUr5jRAiBngWNWzwFzBESplVWiUB\naHhU/T1CiKGo2Qz/APYDt0kpj5/xoNFoNBqNpg5RJ8MyazQajUajqX1qZs6LRqPRaDSasw4tEjQa\njUaj0ZRJnRIJQognhRBLhBBOIUTuSeoEjit+IcQ5nVmmguetoRBiRmmddCHEK0KcLGrPuYkQYk8Z\n19ajtd2vuoYQ4l4hxG4hRLEQYrkQontt96muIoSYUMY9a1Nt96uuIYToJ4SYLoQ4UHqOhpVR51kh\nRJoQwiWEmCOEqMUY5bVPeedMCDGljGtvZmX3U9ceEmbgG+D9cuqNRTlNJgANgB9Pc7/qOqc8b6Vi\nYCbKUbUX6vzdjHI+1RxBAuM59tr6d632qI5xVIK3CUBnVBbY2aXOzJqy2cCRayoB6FsygUhXAAAE\nZ0lEQVS73amT2FFO8PdQRtBiIcRjwH2oxII9ACfquqtmDsh6zSnPWSmzOPbaKydP+YlUenbD6URK\n+QyAEGJsOVULjppNcc5TgfM2BDgPuKg0U+d6IcTTwEtCiIlSHhcr9dymSF9bp+QBYLKU8nMAIcRd\nwFDgVuCEBG8aAHz6mjo1UspfgV/hcOyd47kfeE5K+UtpnTFABnAV6gXpnKMC5wzAXd1rr65ZEirK\nu0KILCHECiHELbXdmXpAL2D9cam8ZwPhQLva6VKd5XEhRLYQYo0Q4mEhRPXT2J0lHJXg7Y9D66Sa\nHnWqBG8aaFlqEt4phPhCCNGw/E00hxBCNEW9BR993RUCK9DXXXkMEEJkCCG2CCHeE0JElb/JsdQp\nS0IFeRqYC7hQkRvfE0LYpZTv1G636jQJKNV9NBlHfbf2zHanzvIWsAbIBfoAL6HOz8O12ak6RAxg\npOxrqfWZ7069YDlqaG8ravhqIrBQCNFeSumsxX7VJxJQ5vSyrruEM9+desMs4DtgN9AceBGYKYTo\nLSsR++C0iwQhxIucOmelBNpIKbdVpD0p5QtH/blWCGEHHgHOKpFQ0+ftXKUy51FK+eZR6zcIITzA\nZCHEE1JK70m212hOipTy6Nj5G4QQK4FU4DpgSu30SnMuIKU8ehhmoxBiPbATGADMq2g7Z8KS8Crl\n/xh2VaP9lcDTQgjzWXYjr8nzlg4c74Eef9R3ZzPVOY8rUb+RJsD2GuxTfaUqCd40RyGlLBBCbAPO\nac/8SpIOCNR1drQ1IR74s1Z6VA+RUu4WQmSjrr26IxJKkzXlnMZddAbyzjKBUNPnbRnwpBAi5ii/\nhMFAAXBWT8eq5nnsDASAzJrrUf2ligneNEchhAhB3aQ/r+2+1BdKH27pqOtsHYAQIgzoCbxbm32r\nTwghkoFo4GBltqtTPgmlDj1RQGPAKIToWPrVDimlUwhxBUo9LkelkB4MPME57lVd3nkDfkOJgaml\nU4kaAM8B75xt4qqqCCF6oW468wAHyifhdWCqlLKgNvtWx3gd+LRULKxEzXY4nOBNcyxCiEnAz6gh\nhiTgGcALfFWb/aprlA4bt0BZDEAlBewI5Eop9wFvAuOFEDuAPaj7137gp1robp3gVOestExA+SSk\nl9Z7GdhGZdNHSynrTEGZhf1llAtLvx+CciwrAApLP99e2/2u7VLeeSut0xD4BShCmexeBgy13fe6\nUlBWg2WlPy4nam77o4C5tvtW1wpqXvYeoLj0nHWr7T7V1YISA/tLz9Ve4L9A09ruV10rQH+U1e74\ne9gnR9WZCKShnNZnAy1qu9919ZwBNtT0yHTUC/UuVByd2MruRyd40mg0Go1GUyb1NU6CRqPRaDSa\n04wWCRqNRqPRaMpEiwSNRqPRaDRlokWCRqPRaDSaMtEiQaPRaDQaTZlokaDRaDQajaZMtEjQaDQa\njUZTJlokaDQajUajKRMtEjQajUaj0ZSJFgkajUaj0WjKRIsEjUaj0Wg0ZfL/z+QAWsTcSdIAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x907a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.linspace(-50, 50, 1000)\n",
    "sigmas = range(1, 6)\n",
    "\n",
    "for sigma, color in zip(sigmas, \"rgbmy\"):\n",
    "    n_dist = stats.norm.pdf(xs, loc=3, scale=sigma)\n",
    "    label = \"$\\mathcal{N}_{1}(\\mu = 3, \\sigma = \" + str(sigma) + \")$\"\n",
    "    plt.plot(xs, n_dist, color, label=label)\n",
    "\n",
    "plt.xlim([-15, 15])\n",
    "plt.ylim([0, 0.5])\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(b)__ Sada pretpostavite da imate zadanu distribuciju ocjena na nekom kolegiju koja se pokorava normalnoj distribuciji $\\mathcal{N}_{G}(\\mu = 62, \\sigma = 13)$. Najprije je iscrtajte (nebitno kako), ali osigurajte da je domena $[0, 100]$ (raspon mogućih bodova na kolegiju) također uzorkovana u 1000 točaka."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mZYz2Tc2sYRzHXgT0B07CX21pByw0syZx7ENEEmDTJpg508/dUL9+6Gok02jO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ecf978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xsg = np.linspace(0, 100, 1000)\n",
    "n_g = stats.norm.pdf(xsg, loc=62, scale=13)\n",
    "plt.plot(xsg, n_g, 'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__(c)__ Međutim, vi polažete kolegij naknadno na roku, a profesoru se zaista ne da ispravljati vaš ispit. Kako bi tome doskočio, profesor često koristi tradicionalnu metodu stubišta (https://www.linkedin.com/pulse/20140414044726-2259773-a-guide-to-grading-exams) koju možemo aproksimirati nasumičnim odabirom broja iz distribucije bodova na ispitu. Napišite kôd koji iz distribucije bodova $\\mathcal{N}_{G}$ dohvaća slučajnu vrijednost koja predstavlja vaš broj bodova (zaokružite bodove na cijeli broj)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Na ispitu sam osvojio 89 bodova.\n"
     ]
    }
   ],
   "source": [
    "points = int(stats.norm.rvs(loc=62, scale=13))\n",
    "print \"Na ispitu sam osvojio\", points, \"bodova.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iscrtajte konture 2-dimenzijske Gausove distribucije $\\mathcal{N} (\\vec{\\mu}, \\Sigma)$ sa sljedećim parametrima:\n",
    "\n",
    "* $\\vec{\\mu} = [1, 1]$\n",
    "* $\\Sigma = \\begin{pmatrix}\n",
    "1 & 1 \\\\\n",
    "0.5 & 3 \\\\\n",
    "\\end{pmatrix}$\n",
    "\n",
    "Za domenu uzmite $x_{1} \\in [-1, 2]$ i $y_{2} \\in [-2, 2]$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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OIIDNHFi92Ax8ev3NX0Fjcy7A6oU6yWpGOQwT9eldqBg59pzB17j9cy4qtVC4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x907aef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnormal = stats.multivariate_normal([1, 1], [[1, 1], [0.5, 3]])\n",
    "x_1 = np.linspace(-1, 2)\n",
    "x_2 = np.linspace(-2, 2)\n",
    "\n",
    "X1, X2 = np.meshgrid(x_1, x_2)\n",
    "X1X2 = np.dstack((X1, X2))\n",
    "\n",
    "plt.contourf(X1, X2, mnormal.pdf(X1X2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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
}