CoursMagistral_1.ipynb

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   "source": [
    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#ALGO1-:-Introduction-à-l'algorithmique\" data-toc-modified-id=\"ALGO1-:-Introduction-à-l'algorithmique-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span><a href=\"https://perso.crans.org/besson/teach/info1_algo1_2019/\" target=\"_blank\">ALGO1 : Introduction à l'algorithmique</a></a></div><div class=\"lev1 toc-item\"><a href=\"#Cours-Magistral-1\" data-toc-modified-id=\"Cours-Magistral-1-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Cours Magistral 1</a></div><div class=\"lev2 toc-item\"><a href=\"#Listes-simplement-chaînées\" data-toc-modified-id=\"Listes-simplement-chaînées-21\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Listes simplement chaînées</a></div><div class=\"lev3 toc-item\"><a href=\"#pop/push-pour-une-structure-de-pile-(FILO)\" data-toc-modified-id=\"pop/push-pour-une-structure-de-pile-(FILO)-211\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span><code>pop</code>/<code>push</code> pour une structure de pile (FILO)</a></div><div class=\"lev3 toc-item\"><a href=\"#add/remove-pour-une-structure-de-file-(FIFO)\" data-toc-modified-id=\"add/remove-pour-une-structure-de-file-(FIFO)-212\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span><code>add</code>/<code>remove</code> pour une structure de file (FIFO)</a></div><div class=\"lev2 toc-item\"><a href=\"#Listes-doublement-chaînées\" data-toc-modified-id=\"Listes-doublement-chaînées-22\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Listes doublement chaînées</a></div><div class=\"lev3 toc-item\"><a href=\"#Exemple\" data-toc-modified-id=\"Exemple-221\"><span class=\"toc-item-num\">2.2.1&nbsp;&nbsp;</span>Exemple</a></div><div class=\"lev2 toc-item\"><a href=\"#Implémenter-une-file-d'attente-avec-deux-piles\" data-toc-modified-id=\"Implémenter-une-file-d'attente-avec-deux-piles-23\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Implémenter une file d'attente avec deux piles</a></div><div class=\"lev2 toc-item\"><a href=\"#File-de-priorité\" data-toc-modified-id=\"File-de-priorité-24\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>File de priorité</a></div><div class=\"lev2 toc-item\"><a href=\"#Implémentation-d'un-tas-binaire\" data-toc-modified-id=\"Implémentation-d'un-tas-binaire-25\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Implémentation d'un tas binaire</a></div><div class=\"lev3 toc-item\"><a href=\"#Naïve-:-tableau-trié-!\" data-toc-modified-id=\"Naïve-:-tableau-trié-!-251\"><span class=\"toc-item-num\">2.5.1&nbsp;&nbsp;</span>Naïve : tableau trié !</a></div><div class=\"lev3 toc-item\"><a href=\"#Intelligente-:-tas-binaire-min-équilibré\" data-toc-modified-id=\"Intelligente-:-tas-binaire-min-équilibré-252\"><span class=\"toc-item-num\">2.5.2&nbsp;&nbsp;</span>Intelligente : tas binaire min équilibré</a></div><div class=\"lev2 toc-item\"><a href=\"#Tri-par-tas\" data-toc-modified-id=\"Tri-par-tas-26\"><span class=\"toc-item-num\">2.6&nbsp;&nbsp;</span>Tri par tas</a></div><div class=\"lev3 toc-item\"><a href=\"#Remarques\" data-toc-modified-id=\"Remarques-261\"><span class=\"toc-item-num\">2.6.1&nbsp;&nbsp;</span>Remarques</a></div><div class=\"lev3 toc-item\"><a href=\"#Tri-par-tas\" data-toc-modified-id=\"Tri-par-tas-262\"><span class=\"toc-item-num\">2.6.2&nbsp;&nbsp;</span>Tri par tas</a></div><div class=\"lev2 toc-item\"><a href=\"#Test-numérique-de-l'efficacité-du-tri-par-tas\" data-toc-modified-id=\"Test-numérique-de-l'efficacité-du-tri-par-tas-27\"><span class=\"toc-item-num\">2.7&nbsp;&nbsp;</span>Test numérique de l'efficacité du tri par tas</a></div><div class=\"lev2 toc-item\"><a href=\"#Évaluation-numérique-de-la-complexité-des-opérations-du-tas-binaire\" data-toc-modified-id=\"Évaluation-numérique-de-la-complexité-des-opérations-du-tas-binaire-28\"><span class=\"toc-item-num\">2.8&nbsp;&nbsp;</span>Évaluation numérique de la complexité des opérations du tas binaire</a></div><div class=\"lev2 toc-item\"><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-29\"><span class=\"toc-item-num\">2.9&nbsp;&nbsp;</span>Conclusion</a></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [ALGO1 : Introduction à l'algorithmique](https://perso.crans.org/besson/teach/info1_algo1_2019/)\n",
    "\n",
    "- [Page du cours](https://perso.crans.org/besson/teach/info1_algo1_2019/) : https://perso.crans.org/besson/teach/info1_algo1_2019/\n",
    "- Magistère d'Informatique de Rennes - ENS Rennes - Année 2019/2020\n",
    "- Intervenants :\n",
    "  + Cours : [Lilian Besson](https://perso.crans.org/besson/)\n",
    "  + Travaux dirigés : [Raphaël Truffet](http://perso.eleves.ens-rennes.fr/people/Raphael.Truffet/)\n",
    "- Références :\n",
    "  + [Open Data Structures](http://opendatastructures.org/ods-python.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cours Magistral 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listes simplement chaînées\n",
    "![figures/CM1_SimplyLinkedList.png](figures/CM1_SimplyLinkedList.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On utilise une petite classe qui va encapsuler la donnée courante, et le pointeur vers la liste suivante."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ListNode:\n",
    "    def __init__(self, data, link=None):\n",
    "        self.data = data\n",
    "        self.link = link\n",
    "    \n",
    "    def __str__(self):\n",
    "        return \"[{}|.-]->{}\".format(str(self.data), \"\" if self.link is None else str(self.link))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0|.-]->\n",
      "[1|.-]->[0|.-]->\n"
     ]
    }
   ],
   "source": [
    "example_node = ListNode(0)\n",
    "print(example_node)\n",
    "\n",
    "example_node2 = ListNode(1, link=example_node)\n",
    "print(example_node2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut parcourir $i$ fois cette structure linéaire :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "def traverse(one_node, i):\n",
    "    assert i >= 0\n",
    "    if i == 0:\n",
    "        return one_node.data\n",
    "    else:\n",
    "        return traverse(one_node.link, i-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
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     "data": {
      "text/plain": [
       "[0]"
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     "execution_count": 121,
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     "data": {
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   "source": [
    "[ traverse(example_node, 0) ]\n",
    "# traverse(example_node, 1)\n",
    "\n",
    "[ traverse(example_node2, 1), traverse(example_node2, 0) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On implémente les opérations push/pop et add/remove :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
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   "source": [
    "class LinkedList:\n",
    "    def __init__(self):\n",
    "        self._head = None\n",
    "        self._tail = None\n",
    "        self._length = 0\n",
    "\n",
    "    def __len__(self):\n",
    "        return self._length\n",
    "\n",
    "    def isempty(self):\n",
    "        return len(self) == 0\n",
    "\n",
    "    # Methods push/pop for Stack (FIFO) data structure\n",
    "\n",
    "    def _addfirst(self, item):\n",
    "        self._head = ListNode(item, self._head)\n",
    "        # if it has only one element, we make it loop\n",
    "        if self._tail is None:\n",
    "            self._tail = self._head\n",
    "        # but the structure knows it has only element: length = 1\n",
    "        self._length += 1\n",
    "    \n",
    "    def push(self, item):\n",
    "        \"\"\" Insert a new element as the new head, in O(1) time.\"\"\"\n",
    "        self._addfirst(item)\n",
    "\n",
    "    def _removefirst(self):\n",
    "        item = self._head.data        # get the current head data\n",
    "        self._head = self._head.link  # compress the head\n",
    "        if self._head is None:        # if link was None, then list is now empty\n",
    "            self._tail = None  \n",
    "        self._length -= 1             # remove one element\n",
    "        return item\n",
    "    \n",
    "    def pop(self):\n",
    "        \"\"\" Get and remove the head, in O(1) time.\"\"\"\n",
    "        return self._removefirst()\n",
    "    \n",
    "    # Methods add/remove for Queue (FILO) data structure\n",
    "\n",
    "    def _addlast(self, item):\n",
    "        if self._head is None:  # if list is empty, just add at the beginning\n",
    "            self._addfirst(item)\n",
    "        else:  # or create new element, and change tail\n",
    "            self._tail.link = ListNode(item)\n",
    "            self._tail = self._tail.link\n",
    "            self._length += 1\n",
    "    \n",
    "    def add(self, item):\n",
    "        \"\"\" Insert a new element at the end of the list, in O(n) time.\"\"\"\n",
    "        self._addlast(item)\n",
    "    \n",
    "    remove = pop\n",
    "\n",
    "    def removelast(self):\n",
    "        if self._head is self._tail:\n",
    "            return self._removefirst()\n",
    "        else:\n",
    "            currentnode = self._head\n",
    "            while currentnode.link is not self._tail:\n",
    "                currentnode = currentnode.link\n",
    "            item = self._tail.data\n",
    "            self._tail = currentnode\n",
    "            self._tail.link = None\n",
    "            self._length -= 1\n",
    "            return item\n",
    "\n",
    "    # Access to i-th element, in O(i)\n",
    "    def __getitem__(self, index):\n",
    "        if not (0 <= index < len(self)): raise IndexError\n",
    "        return traverse(self._head, index)\n",
    "    \n",
    "    def items(self):\n",
    "        n = len(self)\n",
    "        return [ self[i] for i in range(len(self)) ]\n",
    "    \n",
    "    # Method to print the list    \n",
    "    def __str__(self) -> str:\n",
    "        if self.isempty(): return \"[]\"\n",
    "        return str(self._head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deux exemples, que l'on visualise encore mieux sur [PythonTutor.com](http://pythontutor.com/live.html#mode=edit)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `pop`/`push` pour une structure de pile (FILO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[0|.-]->\n",
      "[1|.-]->[0|.-]->\n",
      "[2|.-]->[1|.-]->[0|.-]->\n",
      "[3|.-]->[2|.-]->[1|.-]->[0|.-]->\n",
      "[3, 2, 1, 0]\n",
      "0th value is = 3\n",
      "1th value is = 2\n",
      "2th value is = 1\n",
      "3th value is = 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2|.-]->[1|.-]->[0|.-]->\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1|.-]->[0|.-]->\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0|.-]->\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "example_list = LinkedList()\n",
    "print(example_list)\n",
    "\n",
    "example_list.push(0)\n",
    "print(example_list)\n",
    "\n",
    "example_list.push(1)\n",
    "print(example_list)\n",
    "\n",
    "example_list.push(2)\n",
    "print(example_list)\n",
    "\n",
    "example_list.push(3)\n",
    "print(example_list)\n",
    "\n",
    "print(example_list.items())\n",
    "for i in range(len(example_list)):\n",
    "    print(\"{}th value is = {}\".format(i, example_list[i]))\n",
    "\n",
    "example_list.pop()\n",
    "print(example_list)\n",
    "\n",
    "example_list.pop()\n",
    "print(example_list)\n",
    "\n",
    "example_list.pop()\n",
    "print(example_list)\n",
    "\n",
    "example_list.pop()\n",
    "print(example_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `add`/`remove` pour une structure de file (FIFO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[0|.-]->\n",
      "[0|.-]->[1|.-]->\n",
      "[0|.-]->[1|.-]->[2|.-]->\n",
      "[0|.-]->[1|.-]->[2|.-]->[3|.-]->\n",
      "[0, 1, 2, 3]\n",
      "0th value is = 0\n",
      "1th value is = 1\n",
      "2th value is = 2\n",
      "3th value is = 3\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1|.-]->[2|.-]->[3|.-]->\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2|.-]->[3|.-]->\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3|.-]->\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "example_list = LinkedList()\n",
    "print(example_list)\n",
    "\n",
    "example_list.add(0)\n",
    "print(example_list)\n",
    "\n",
    "example_list.add(1)\n",
    "print(example_list)\n",
    "\n",
    "example_list.add(2)\n",
    "print(example_list)\n",
    "\n",
    "example_list.add(3)\n",
    "print(example_list)\n",
    "print(example_list.items())\n",
    "for i in range(len(example_list)):\n",
    "    print(\"{}th value is = {}\".format(i, example_list[i]))\n",
    "\n",
    "example_list.remove()\n",
    "print(example_list)\n",
    "\n",
    "example_list.remove()\n",
    "print(example_list)\n",
    "\n",
    "example_list.remove()\n",
    "print(example_list)\n",
    "\n",
    "example_list.remove()\n",
    "print(example_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Listes doublement chaînées\n",
    "![figures/CM1_DoublyLinkedList.png](figures/CM1_DoublyLinkedList.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On utilise une petite classe qui va encapsuler la donnée courante, et les deux pointeurs vers les listes suivante et précédente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ListNodeDoublyLinked:\n",
    "    def __init__(self, data, prev = None, link = None):\n",
    "        self.data = data\n",
    "        self.prev = prev\n",
    "        self.link = link\n",
    "        if prev is not None:\n",
    "            self.prev.link = self\n",
    "        if link is not None:\n",
    "            self.link.prev = self\n",
    "    \n",
    "    def __str__(self):\n",
    "        return \"[{}]{}\".format(str(self.data), \"\" if self.link is None else \"<->{}\".format(str(self.link)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "code_folding": [
     57
    ]
   },
   "outputs": [],
   "source": [
    "class DoublyLinkedList:\n",
    "    def __init__(self):\n",
    "        self._head = None\n",
    "        self._tail = None\n",
    "        self._length = 0\n",
    "    \n",
    "    def isempty(self):\n",
    "        return self._length == 0\n",
    "\n",
    "    def __len__(self):\n",
    "        return self._length\n",
    "\n",
    "    # Add an element, in O(1)\n",
    "    \n",
    "    def _addbetween(self, item, before, after):\n",
    "        node = ListNodeDoublyLinked(item, before, after)\n",
    "        if after is self._head:\n",
    "            self._head = node\n",
    "        if before is self._tail:\n",
    "            self._tail = node\n",
    "        self._length += 1\n",
    "\n",
    "    def addfirst(self, item):\n",
    "        \"\"\" Insert a new element as the beginning of the list, in O(1) time.\"\"\"\n",
    "        self._addbetween(item, None, self._head)\n",
    "\n",
    "    def addlast(self, item):\n",
    "        \"\"\" Insert a new element as the end of the list, in O(1) time.\"\"\"\n",
    "        self._addbetween(item, self._tail, None)\n",
    "\n",
    "    # Remove an element, in O(1)\n",
    "\n",
    "    def _remove(self, node):\n",
    "        before, after = node.prev, node.link\n",
    "        if node is self._head:\n",
    "            self._head = after\n",
    "        else:\n",
    "            before.link = after\n",
    "        if node is self._tail:\n",
    "            self._tail = before\n",
    "        else:\n",
    "            after.prev = before\n",
    "        self._length -= 1\n",
    "        return node.data\n",
    "\n",
    "    def removefirst(self):\n",
    "        \"\"\" Remove and return the beginning of the list, in O(1) time.\"\"\"\n",
    "        return self._remove(self._head)\n",
    "\n",
    "    def removelast(self):\n",
    "        \"\"\" Remove and return the end of the list, in O(1) time.\"\"\"\n",
    "        return self._remove(self._tail)\n",
    "    \n",
    "    # Access to i-th element, in O(i)\n",
    "\n",
    "\n",
    "    def __iadd__(self, other):\n",
    "        if other._head is None: return\n",
    "        if self._head is None:\n",
    "            self._head = other._head\n",
    "        else:\n",
    "            self._tail.link = other._head\n",
    "            other._head.prev = self._tail\n",
    "        self._tail = other._tail\n",
    "        self._length = self._length + other._length\n",
    "\n",
    "        # Clean up the other list.\n",
    "        other.__init__()\n",
    "        return self\n",
    "    \n",
    "    # Access to i-th element, in O(i)\n",
    "    def __getitem__(self, index):\n",
    "        if not (0 <= index < len(self)): raise IndexError\n",
    "        return traverse(self._head, index)\n",
    "    \n",
    "    def items(self):\n",
    "        n = len(self)\n",
    "        return [ self[i] for i in range(len(self)) ]\n",
    "\n",
    "    # Method to print the list    \n",
    "    def __str__(self) -> str:\n",
    "        if self.isempty(): return \"[]\"\n",
    "        return str(self._head)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un exemple, que l'on visualise encore mieux sur [PythonTutor.com](http://pythontutor.com/live.html#mode=edit)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exemple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n",
      "[0]\n",
      "[1]<->[0]\n",
      "[2]<->[1]<->[0]\n",
      "[2]<->[1]<->[0]<->[100]\n",
      "[2]<->[1]<->[0]<->[100]<->[101]\n",
      "[2]<->[1]<->[0]<->[100]<->[101]<->[102]\n",
      "[2, 1, 0, 100, 101, 102]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]<->[0]<->[100]<->[101]<->[102]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]<->[0]<->[100]<->[101]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]<->[100]<->[101]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]<->[100]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[]\n"
     ]
    }
   ],
   "source": [
    "example_list = DoublyLinkedList()\n",
    "print(example_list)\n",
    "\n",
    "example_list.addfirst(0)\n",
    "print(example_list)\n",
    "\n",
    "example_list.addfirst(1)\n",
    "print(example_list)\n",
    "\n",
    "example_list.addfirst(2)\n",
    "print(example_list)\n",
    "\n",
    "example_list.addlast(100)\n",
    "print(example_list)\n",
    "\n",
    "example_list.addlast(101)\n",
    "print(example_list)\n",
    "\n",
    "example_list.addlast(102)\n",
    "print(example_list)\n",
    "\n",
    "print(list(example_list))\n",
    "\n",
    "example_list.removefirst()\n",
    "print(example_list)\n",
    "\n",
    "example_list.removelast()\n",
    "print(example_list)\n",
    "\n",
    "example_list.removefirst()\n",
    "print(example_list)\n",
    "\n",
    "example_list.removelast()\n",
    "print(example_list)\n",
    "\n",
    "example_list.removefirst()\n",
    "print(example_list)\n",
    "\n",
    "example_list.removelast()\n",
    "print(example_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implémenter une file d'attente avec deux piles\n",
    "- On va utiliser deux piles (des `list` de Python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://github.com/jilljenn/tryalgo/blob/master/tryalgo/our_queue.py\n",
    "class Queue:\n",
    "    \"\"\"A FIFO queue\n",
    "    \n",
    "    - Complexity:\n",
    "        + all operators in amortized constant time,\n",
    "        + except __str__ which is linear\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.in_stack  = [ ]  # tail\n",
    "        self.out_stack = [ ]  # head\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.in_stack) + len(self.out_stack)\n",
    "\n",
    "    def push(self, obj):\n",
    "        self.in_stack.append(obj)\n",
    "\n",
    "    def pop(self):\n",
    "        if not self.out_stack:    # head is empty\n",
    "            self.out_stack = self.in_stack[::-1]\n",
    "            self.in_stack = []\n",
    "        return self.out_stack.pop()\n",
    "\n",
    "    def __str__(self):\n",
    "        return str(self.out_stack[::-1] + self.in_stack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "[0, 1]\n",
      "[0, 1, 2]\n",
      "[0, 1, 2, 3]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3]\n"
     ]
    }
   ],
   "source": [
    "queue = Queue()\n",
    "\n",
    "queue.push(0)\n",
    "print(queue)\n",
    "\n",
    "queue.push(1)\n",
    "print(queue)\n",
    "\n",
    "queue.push(2)\n",
    "print(queue)\n",
    "\n",
    "queue.push(3)\n",
    "print(queue)\n",
    "\n",
    "queue.pop()\n",
    "print(queue)\n",
    "\n",
    "queue.pop()\n",
    "print(queue)\n",
    "\n",
    "queue.pop()\n",
    "print(queue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File de priorité\n",
    "A propos…"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implémentation d'un tas binaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naïve : tableau trié !\n",
    "\n",
    "On conserve le tableau trié en insérant chaque nouvel élément par une insertion à sa bonne position (avec des inversions locales), comme dans le tri par tas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "def swap(array, i, j):\n",
    "    array[i], array[j] = array[j], array[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OurNaiveHeap:\n",
    "    \"\"\" min naive heap\n",
    "    * heap: is the actual heap, containing the sorted value\n",
    "    * n: size of the heap\n",
    "\n",
    "    Complexity: init O(n^2), len O(1),\n",
    "                other operations O(n) in all cases\n",
    "    \"\"\"\n",
    "    def __init__(self, items=None):\n",
    "        self.heap = []  # index 0 will be ignored\n",
    "        if items is not None:\n",
    "            for x in items:\n",
    "                self.push(x)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.heap)\n",
    "\n",
    "    def push(self, x):\n",
    "        \"\"\"Insert new element x in the heap.\"\"\"\n",
    "        # add a new element\n",
    "        self.heap.append(x)\n",
    "        # then insert it, from the end, to its correct location\n",
    "        position = len(self) - 1\n",
    "        while position > 0 and self.heap[position - 1] > self.heap[position]:\n",
    "            swap(self.heap, position - 1, position)\n",
    "            position -= 1\n",
    "\n",
    "    def pop(self):\n",
    "        \"\"\"Remove and return smallest element\"\"\"\n",
    "        # move heap[0] to heap[n] and copy heap[1:n] to heap[0:n-1]\n",
    "        for position in range(len(self) - 1):\n",
    "            swap(self.heap, position, position + 1)\n",
    "        smallest_element = self.heap.pop()    # remove last element\n",
    "        return smallest_element"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intelligente : tas binaire min équilibré"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "class OurHeap:\n",
    "    \"\"\" min heap\n",
    "    * heap: is the actual heap, heap[1] = index of the smallest element\n",
    "    * rank: inverse of heap with rank[x]=i iff heap[i]=x\n",
    "    * n: size of the heap\n",
    "    :complexity: init O(n log n), len O(1),\n",
    "                other operations O(log n) in expectation\n",
    "                and O(n) in worst case, due to the usage of a dictionary\n",
    "    \"\"\"\n",
    "    def __init__(self, items=None):\n",
    "        self.heap = [None]  # index 0 will be ignored\n",
    "        if items is not None:\n",
    "            for x in items:\n",
    "                self.push(x)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.heap) - 1\n",
    "\n",
    "    def push(self, x):\n",
    "        \"\"\"Insert new element x in the heap.\"\"\"\n",
    "        i = len(self.heap)\n",
    "        self.heap.append(x)    # add a new leaf\n",
    "        self.up(i)             # maintain heap order\n",
    "\n",
    "    def pop(self):\n",
    "        \"\"\"Remove and return smallest element\"\"\"\n",
    "        root = self.heap[1]\n",
    "        x = self.heap.pop()    # remove last leaf\n",
    "        if self:               # if heap is not empty\n",
    "            self.heap[1] = x   # put last leaf to root\n",
    "            self.down(1)       # maintain heap order\n",
    "        return root\n",
    "\n",
    "    def up(self, i):\n",
    "        \"\"\"The value of heap[i] has decreased. Maintain heap invariant.\"\"\"\n",
    "        x = self.heap[i]\n",
    "        while i > 1 and x < self.heap[i // 2]:\n",
    "            self.heap[i] = self.heap[i // 2]\n",
    "            i //= 2\n",
    "        self.heap[i] = x       # insertion index found\n",
    "\n",
    "    def down(self, i):\n",
    "        \"\"\"the value of heap[i] has increased. Maintain heap invariant.\"\"\"\n",
    "        x = self.heap[i]\n",
    "        n = len(self.heap)\n",
    "        while True:\n",
    "            left = 2 * i       # climb down the tree\n",
    "            right = left + 1\n",
    "            if (right < n and self.heap[right] < x and\n",
    "                    self.heap[right] < self.heap[left]):\n",
    "                self.heap[i] = self.heap[right]\n",
    "                i = right\n",
    "            elif left < n and self.heap[left] < x:\n",
    "                self.heap[i] = self.heap[left]\n",
    "                i = left\n",
    "            else:\n",
    "                self.heap[i] = x   # insertion index found\n",
    "                return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tri par tas\n",
    "Dès que l'on a une implémentation d'un tas (min), on peut facilement trier un tableau `T` de la façon suivante :\n",
    "\n",
    "- Entrée : un tableau `T` de taille `n`\n",
    "- Créer tas `mon_tas`\n",
    "- Pour chaque valeur `T[i]` dans le tableau `T` :\n",
    "  + entasser `T[i]` dans `mon_tas`\n",
    "- Créer un tableau `T_trie` de même taille que `T` (`n`)\n",
    "- Initialiser `i = 0`\n",
    "- Tant que `mon_tas` n'est pas vide :\n",
    "  + extraire le minimum du tas : `nouveau_min_du_tas <- extraireMin(mon_tas)`\n",
    "  + placer ce minimum à la `i`ème position dans le nouveau tableau : `T_trie[i] = nouvea_min_du_tas`\n",
    "  + `i += 1`\n",
    "- Sortie : Le tableau `T_trie` est le tableau `T` trié par ordre croissant.\n",
    "\n",
    "### Remarques\n",
    "- L'avantage du tri par tas est que l'on peut aussi effectuer toutes ces opérations *en place* (i.e., en utilisant le tableau `T` et pas de mémoire supplémentaire).\n",
    "- On utilise un tri max pour trier en ordre décroissant, ou alors on renverse juste le tableau `T_trie` à la fin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tri par tas\n",
    "L'algorithme est indépendent de la structure de tas que l'on utilise !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "def heapSort(array, heapStructure=OurHeap):\n",
    "    n = len(array)\n",
    "    heap = heapStructure()\n",
    "    for i in range(n):\n",
    "        heap.push(array[i])\n",
    "    sorted_array = [ None ] * n  # taille n\n",
    "    i = 0\n",
    "    while heap:  # while not empty\n",
    "        sorted_array[i] = heap.pop()\n",
    "        i += 1\n",
    "    return sorted_array\n",
    "\n",
    "def insertionSort(array):\n",
    "    return heapSort(array, heapStructure=OurNaiveHeap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 10, 19]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[9, 10, 19]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[9, 10, 19]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "example_array = [10, 9, 19]\n",
    "\n",
    "sorted(example_array)\n",
    "heapSort(example_array)\n",
    "insertionSort(example_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "989 µs ± 72.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "23.8 ms ± 1.33 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "3.85 s ± 577 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "example_array = list(range(2019)) + list(range(2019))  # twice the numbers from 0 to 2018\n",
    "import random\n",
    "random.shuffle(example_array)\n",
    "\n",
    "%timeit sorted(example_array)\n",
    "%timeit heapSort(example_array)\n",
    "%timeit insertionSort(example_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test numérique de l'efficacité du tri par tas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.figsize'] = (10, 7)\n",
    "mpl.rcParams['figure.dpi'] = 120\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(context=\"notebook\", style=\"whitegrid\", palette=\"hls\", font=\"sans-serif\", font_scale=1.1)\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "random.seed(1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On va générer des tableaux aléatoires :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_array_of_int(max_int=10000, length=1000):\n",
    "    return [ random.randint(0, max_int) for _ in range(length) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14, 3, 0, 2, 18, 1, 2, 3, 11, 7]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_array_of_int(max_int=20, length=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut facilement mesurer le temps d'exécution d'une fontion de tri, sur des tableaux aléatoires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "import timeit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from tqdm import tqdm_notebook\n",
    "except ImportError:\n",
    "    def tqdm_notebook(iterator, *args, **kwargs): return iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_a_sort_function(sort_function, sort_function_name, values_n, number=1000, max_int=1000000):\n",
    "    return [ timeit.timeit(\"{}(random_array_of_int(max_int={}, length={}))\".format(sort_function_name, max_int, n),\n",
    "              globals={\n",
    "                  'random_array_of_int': random_array_of_int,\n",
    "                  sort_function_name: sort_function,\n",
    "              },\n",
    "              number=number,\n",
    "    ) for n in tqdm_notebook(values_n) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparons notre tri par tas avec la fonction `sorted()` de Python :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "small_values_n = [10, 100, 500] + list(range(1000, 5000, 1000))\n",
    "big_values_n = list(range(6000, 100000, 4000))\n",
    "# very_big_values_n = list(range(100000, 5000000, 100000))\n",
    "\n",
    "values_n = small_values_n + big_values_n #+ very_big_values_n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "45fe9e7d93944723b2392f995dc88b1b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=31), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times_sorted = time_a_sort_function(sorted, \"sorted\", values_n, number=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57699109400844beaf042996c6c346e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=31), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times_heapSort = time_a_sort_function(heapSort, \"heapSort\", values_n, number=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1c9c259aec34644a25ef9a36ec4de32",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=7), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times_insertionSort = time_a_sort_function(insertionSort, \"insertionSort\", small_values_n, number=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x840 with 0 Axes>"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, \"Taille du tableau d'entrée $n$\")"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Temps en secondes')"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Comparaison des tris builtin, par tas ou par insertion')"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff4f3b1c9e8>]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff4f3b1cac8>]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff4f3ab5470>]"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff4f3ab5828>"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.xlabel(\"Taille du tableau d'entrée $n$\")\n",
    "plt.ylabel(\"Temps en secondes\")\n",
    "plt.title(\"Comparaison des tris builtin, par tas ou par insertion\")\n",
    "plt.plot(values_n, times_sorted, \"d-\", label=\"Builtin\", lw=5, ms=12)\n",
    "plt.plot(values_n, times_heapSort, \"o-\", label=\"Par tas\", lw=5, ms=12)\n",
    "plt.plot(small_values_n, times_insertionSort, \">-\", label=\"Par insertion\", lw=5, ms=12)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attention : Trier des nombres tout petit peut être effectué en temps linéaire (*bin sort*) :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133 µs ± 9.34 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "1.29 ms ± 34.9 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "1.25 ms ± 53.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "13.3 ms ± 912 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sorted(random_array_of_int(max_int=10, length=100))\n",
    "%timeit sorted(random_array_of_int(max_int=10, length=1000))\n",
    "%timeit sorted(random_array_of_int(max_int=10, length=10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "142 µs ± 16.7 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n",
      "1.32 ms ± 74 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n",
      "12.8 ms ± 307 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sorted(random_array_of_int(max_int=1000, length=100))\n",
    "%timeit sorted(random_array_of_int(max_int=1000, length=1000))\n",
    "%timeit sorted(random_array_of_int(max_int=1000, length=10000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Évaluation numérique de la complexité des opérations du tas binaire\n",
    "\n",
    "On peut évaluer, sur des exemples, la complexité des opérations d'ajout et d'extraction du maximum que l'on a implémenté dans notre structure de tas binaire.\n",
    "On a montré qu'elles sont toutes les deux en $\\mathcal{O}(\\log(n))$, mais peut-on le vérifier empiriquement ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "def times_push_and_pop(values_n, number1=100, number2=100, max_int=1000_000):\n",
    "    # create a random array for each value of n\n",
    "    times_push = np.array([ np.mean([\n",
    "        timeit.timeit(\n",
    "            \"heap.push(random.randint(-{}, {}))\".format(max_int, max_int),\n",
    "            globals={\n",
    "                'random_array_of_int': random_array_of_int,\n",
    "                'heap': heap,\n",
    "                'random': random,\n",
    "            },\n",
    "            number=number1,\n",
    "        ) / number1 for _ in range(number2)\n",
    "        for heap in [ OurHeap(random_array_of_int(max_int=max_int, length=n)) ]\n",
    "        ])\n",
    "       for n in tqdm_notebook(values_n, desc=\"push\")\n",
    "    ])\n",
    "    times_push_and_pop = np.array([  np.mean([\n",
    "        timeit.timeit(\n",
    "        \"heap.push(random.randint(-{}, {})); heap.pop()\".format(max_int, max_int),\n",
    "        globals={\n",
    "            'random_array_of_int': random_array_of_int,\n",
    "            'heap': heap,\n",
    "            'random': random,\n",
    "        },\n",
    "        number=number1,\n",
    "        ) / number1 for _ in range(number2)\n",
    "        for heap in [ OurHeap(random_array_of_int(max_int=max_int, length=n)) ]\n",
    "        ])\n",
    "       for n in tqdm_notebook(values_n, desc=\"push & pop\")\n",
    "    ])\n",
    "    times_pop = times_push_and_pop - times_push\n",
    "    return times_push, times_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1033dfcaae944bc18f573e3367e61f8f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='push', max=3, style=ProgressStyle(description_width='initial'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e8d26ea04f44785ad0fe34fd01df556",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='push & pop', max=3, style=ProgressStyle(description_width='in…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(array([2.49903648e-06, 2.33068655e-06, 2.83064253e-06]),\n",
       " array([1.84623170e-06, 3.63986017e-06, 5.05909610e-06]))"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times_push_and_pop([10, 100, 1000], number1=100, number2=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualisation_temps_push_and_pop(values_n, **kwargs):\n",
    "    times_push, times_pop = times_push_and_pop(values_n, **kwargs)\n",
    "    plt.figure()\n",
    "    plt.xlabel(\"Taille du tableau d'entrée $n$\")\n",
    "    plt.ylabel(\"Temps en micro-secondes\")\n",
    "    plt.title(\"Temps des opérations ajouterMax et retirerMax\")\n",
    "    plt.plot(values_n, 1e6 * times_push, \"d-\", label=\"ajouterMax\", lw=5, ms=12)\n",
    "    plt.plot(values_n, 1e6 * times_pop, \"o-\", label=\"retirerMax\", lw=5, ms=12)\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='push', max=17, style=ProgressStyle(description_width='initial…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='push & pop', max=17, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualisation_temps_push_and_pop(\n",
    "    [\n",
    "        100, 500, 1000, 2000, 3000, 4000, 5000,\n",
    "        10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000,\n",
    "        #110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000,\n",
    "        #300000, 400000, 500000, 600000, 700000, 800000, 900000,\n",
    "        #1000000, 2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000,\n",
    "    ],\n",
    "    number1=100, number2=1000,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abff3ab82dea482ca3b9aef53f1b300a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='push', max=34, style=ProgressStyle(description_width='initial…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8db7eee37064de0804b022c7204dba1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='push & pop', max=34, style=ProgressStyle(description_width='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x840 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualisation_temps_push_and_pop(\n",
    "    [\n",
    "        100, 500, 1000, 2000, 3000, 4000, 5000,\n",
    "        10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000,\n",
    "        110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000,\n",
    "        300000, 400000, 500000, 600000, 700000, 800000, 900000,\n",
    "        #1000000, 2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000,\n",
    "    ],\n",
    "    number1=20, number2=100,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "C'est bon pour aujourd'hui !"
   ]
  }
 ],
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
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  "toc": {
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    "top": "120px",
    "width": "251.25px"
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    "python": {
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     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
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