diff --git a/anacycliques.ipynb b/anacycliques.ipynb
new file mode 100644
index 0000000..781334a
--- /dev/null
+++ b/anacycliques.ipynb
@@ -0,0 +1,377 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Anacycliques\n",
+    "\n",
+    "Trouver tous les anacycliques dans une liste de mots (issue de lexique.org par exemple). Les palindromes sont des cas particuliers d'anacycliques.  \n",
+    "Ex : 'été' est un palidrome  \n",
+    "'vu' et 'uv', 'tort' et 'trot' sont des anacycliques\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tester si deux mots sont des anacycliques est très simple en Python. Pour cela on peut utiliser les slices avec le troisième argument pour le pas.  \n",
+    "`word[::-1]` : un pas de `-1` permet d'inverser la chaîne de caractères."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "def is_anacyclique(word1, word2):\n",
+    "    \"\"\"\n",
+    "    Returns True if the given words are palindromes\n",
+    "    False if not\n",
+    "    \"\"\"\n",
+    "    if word1 == word2[::-1]:\n",
+    "        return True\n",
+    "    else:\n",
+    "        return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "is_anacyclique(\"tort\", \"trot\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb de mots : 142641\n"
+     ]
+    }
+   ],
+   "source": [
+    "words = []\n",
+    "with open('lexique381.ortho', 'r') as f:\n",
+    "    for line in f:\n",
+    "        line = line.rstrip()\n",
+    "        if len(line) != 1:\n",
+    "            words.append(line)\n",
+    "print(\"Nb de mots : {}\".format(len(words)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tester l'appartenance d'un élément à une liste de 142641 éléments est couteux en temps de traitement.  \n",
+    "Le temps du d'appartenance d'un élément à une liste est de 0(n) (voir [TimeComplexity](https://wiki.python.org/moin/TimeComplexity) sur le wiki Python).  \n",
+    "Ici comme la liste est déjà pas mal grande et que l'opération est répétée n fois, le temps de traitement est rhédibitoire."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Puisqu'on aime se compliquer la vie on peut essayer de réduite la taille de la liste en se limitant aux mots de même taille. Les anacycliques sont forcément de même taille."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def words_by_length(words):\n",
+    "    \"\"\"\n",
+    "    Receives a list of words sorted by length, returns a list of words for each length\n",
+    "    \"\"\"\n",
+    "    for length in range(len(words[0]), len(words[-1]) + 1):\n",
+    "        yield [word for word in words if len(word) == length]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "res = set()\n",
+    "sorted_words = sorted(words, key = lambda x:len(x))\n",
+    "for words_n in words_by_length(sorted_words):\n",
+    "    for word in words_n:\n",
+    "        acyclique = word[::-1]\n",
+    "        if acyclique in words_n:\n",
+    "            res.add(tuple([word, acyclique]))\n",
+    "        \n",
+    "for match in sorted(res):\n",
+    "    print(match)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "Malgré le découpage du lexique en mots de même taille et l'utilisation des générateurs, le temps de traitement reste important.  \n",
+    "Essayons avec un dictionnaire."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb de mots : 125627\n"
+     ]
+    }
+   ],
+   "source": [
+    "from collections import defaultdict\n",
+    "words_d = defaultdict()\n",
+    "with open('lexique381.ortho', 'r') as f:\n",
+    "    for line in f:\n",
+    "        line = line.rstrip()\n",
+    "        if len(line) != 1:\n",
+    "            words_d[line] = \"\"\n",
+    "print(\"Nb de mots : {}\".format(len(words_d.keys())))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On a une différence de taille d'avec la liste parce que le dictionnaire supprime les doublons (125627 contre 142641). Mais ça ne change pas vraiment l'ordre du volume des données."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('aa', 'aa')\n",
+      "('ada', 'ada')\n",
+      "('ados', 'soda')\n",
+      "('adulé', 'éluda')\n",
+      "('aga', 'aga')\n",
+      "('agas', 'saga')\n",
+      "('ah', 'ha')\n",
+      "('ail', 'lia')\n",
+      "('ailé', 'élia')\n",
+      "('air', 'ria')\n",
+      "('ako', 'oka')\n",
+      "('alla', 'alla')\n",
+      "('alloc', 'colla')\n",
+      "('amis', 'sima')\n",
+      "('an', 'na')\n",
+      "('ana', 'ana')\n",
+      "('anas', 'sana')\n",
+      "('angor', 'rogna')\n",
+      "('annoté', 'étonna')\n",
+      "('ara', 'ara')\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = set()\n",
+    "for word in words_d.keys():\n",
+    "    acyclique = word[::-1]\n",
+    "    if acyclique in words:\n",
+    "        res.add(tuple([word, acyclique]))\n",
+    "        \n",
+    "for match in sorted(res)[:20]:\n",
+    "    print(match)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Là c'est tout de suite BEAUCOUP plus rapide <small>(supprimez `[:20]` pour avoir tous les résultats)</small>. Mais pourquoi ?  \n",
+    "Parce que le test d'appartenance pour un dictionnaire est estimé à O(1) en moyenne (O(n) dans le pire des cas).  \n",
+    "idem pour les ensembles (`set`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nb de mots : 125627\n"
+     ]
+    }
+   ],
+   "source": [
+    "words_s = set()\n",
+    "with open('lexique381.ortho', 'r') as f:\n",
+    "    for line in f:\n",
+    "        line = line.rstrip()\n",
+    "        if len(line) != 1:\n",
+    "            words_s.add(line)\n",
+    "print(\"Nb de mots : {}\".format(len(words_s)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('aa', 'aa')\n",
+      "('ada', 'ada')\n",
+      "('ados', 'soda')\n",
+      "('adulé', 'éluda')\n",
+      "('aga', 'aga')\n",
+      "('agas', 'saga')\n",
+      "('ah', 'ha')\n",
+      "('ail', 'lia')\n",
+      "('ailé', 'élia')\n",
+      "('air', 'ria')\n",
+      "('ako', 'oka')\n",
+      "('alla', 'alla')\n",
+      "('alloc', 'colla')\n",
+      "('amis', 'sima')\n",
+      "('an', 'na')\n",
+      "('ana', 'ana')\n",
+      "('anas', 'sana')\n",
+      "('angor', 'rogna')\n",
+      "('annoté', 'étonna')\n",
+      "('ara', 'ara')\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = set()\n",
+    "for word in words_s:\n",
+    "    acyclique = word[::-1]\n",
+    "    if acyclique in words:\n",
+    "        res.add(tuple([word, acyclique]))\n",
+    "        \n",
+    "for match in sorted(res)[:20]:\n",
+    "    print(match)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Allez on va s'amuser à mesurer ça :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n",
+      "Wall time: 3.02 ms\n",
+      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
+      "Wall time: 7.39 µs\n",
+      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
+      "Wall time: 8.82 µs\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%time \"palindrome\" in words\n",
+    "%time \"palindrome\" in words_d\n",
+    "%time \"palindrome\" in words_s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Si vous êtes intéressés par l'implémentation des dictionnaires vous pourrez lire l'article suivant : [Python dictionary implementation] (http://www.laurentluce.com/posts/python-dictionary-implementation/)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2+"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/generateur.ipynb b/generateur.ipynb
index 85ce87b..0321d14 100644
--- a/generateur.ipynb
+++ b/generateur.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 45,
    "metadata": {
     "collapsed": true
    },
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
@@ -73,22 +73,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
-      "Wall time: 11.4 µs\n",
-      "CPU times: user 596 ms, sys: 4 ms, total: 600 ms\n",
-      "Wall time: 606 ms\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%time mots_a = with_a(mots)\n",
     "mots_big = mots * 1000000\n",
@@ -108,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 46,
    "metadata": {
     "collapsed": true
    },
@@ -125,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 47,
    "metadata": {
     "collapsed": false
    },
@@ -134,15 +123,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 0 ns, sys: 4 ms, total: 4 ms\n",
-      "Wall time: 7.09 ms\n",
       "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
-      "Wall time: 13.8 µs\n"
+      "Wall time: 76.8 µs\n",
+      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
+      "Wall time: 8.82 µs\n"
      ]
     }
    ],
    "source": [
-    "mots_big = mots * 1000\n",
+    "mots_big = mots * 100\n",
     "%time mots_a = with_a(mots_big)\n",
     "%time mots_a_gen = gen_with_a(mots_big)"
    ]
@@ -157,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 48,
    "metadata": {
     "collapsed": false
    },
@@ -167,13 +156,18 @@
      "output_type": "stream",
      "text": [
       "mots_a is a <class 'list'>\n",
-      "mots_a_gen is a <class 'generator'>\n"
+      "mots_a_gen is a <class 'generator'>\n",
+      "Taille de mots_a : 1672\n",
+      "Taille de mots_a_gen : 88\n"
      ]
     }
    ],
    "source": [
     "print(\"mots_a is a {}\".format(type(mots_a)))\n",
-    "print(\"mots_a_gen is a {}\".format(type(mots_a_gen)))"
+    "print(\"mots_a_gen is a {}\".format(type(mots_a_gen)))\n",
+    "import sys\n",
+    "print(\"Taille de mots_a : {}\".format(sys.getsizeof(mots_a)))\n",
+    "print(\"Taille de mots_a_gen : {}\".format(sys.getsizeof(mots_a_gen)))"
    ]
   },
   {
@@ -188,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 18,
    "metadata": {
     "collapsed": false
    },
@@ -197,8 +191,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n",
-      "Wall time: 568 µs\n"
+      "CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n",
+      "Wall time: 5.68 ms\n"
      ]
     }
    ],
@@ -224,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -232,16 +226,38 @@
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7f0eac423db0>"
+       "['chat', 'matin']"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "(mot for mots in mots if 'a' in mot)"
+    "[mot for mot in mots if 'a' in mot]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7fd2a44637d8>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(mot for mot in mots if 'a' in mot)"
    ]
   },
   {
@@ -259,18 +275,6 @@
    "display_name": "Python 3",
    "language": "python",
    "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.2+"
   }
  },
  "nbformat": 4,