{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cours D3\n",
    "\n",
    "## Cours Méthodes d'analyse non supervisées\n",
    "\n",
    "Exemple de clustering complete linkage :\n",
    "On prend 4 singletons avec leur matrice de dissimilarité."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.   0.3  0.4  0.8 ]\n",
      " [0.3  0.   0.5  0.8 ]\n",
      " [0.4  0.5  0.   0.45]\n",
      " [0.8  0.8  0.45 0.  ]]\n"
     ]
    }
   ],
   "source": [
    "D = np.array([[0, 0.3, 0.4, 0.7],[0, 0, 0.5, 0.8],[0, 0, 0, 0.45],[0, 0, 0, 0]])\n",
    "D += D.T\n",
    "print(D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On link les 2 clusters les plus proches (a,b) donc on prend le max de différence entre (a,b) et c et d."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.   0.5  0.8 ]\n",
      " [0.5  0.   0.45]\n",
      " [0.8  0.45 0.  ]]\n"
     ]
    }
   ],
   "source": [
    "D2 = np.array([[0, 0.5, 0.8],[0.5, 0, 0.45],[0.8, 0.45, 0]])\n",
    "print(D2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On coupe au saut le plus important sur le dendrogramme (on continu jusqu'a avoir K-clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.  0.8]\n",
      " [0.8 0. ]]\n"
     ]
    }
   ],
   "source": [
    "# (a,b,c) et d\n",
    "D3 = np.array([[0, 0.8],[0.8,0]])\n",
    "print(D3)"
   ]
  }
 ],
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