Practica 7 (nueva) - Ejercicio 4.ipynb

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    "![4.png](./img/practica7/4.png)"
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   "source": [
    "**(a)**\n",
    "> **Estimadores** de:\n",
    "> * Momentos\n",
    "> * Máxima Verosimilitud\n",
    "> * 90-percentil\n",
    ">\n",
    "> https://imgur.com/gallery/mIRQp0C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(b) (c)**\n",
    "\n",
    "> * Insesgados?\n",
    "> * Consistentes?\n",
    ">\n",
    "> https://imgur.com/gallery/f2SJ96Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**(d)**"
   ]
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    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>39.08</li>\n",
       "\t<li>45.27</li>\n",
       "\t<li>26.27</li>\n",
       "\t<li>14.77</li>\n",
       "\t<li>65.84</li>\n",
       "\t<li>49.64</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 39.08\n",
       "\\item 45.27\n",
       "\\item 26.27\n",
       "\\item 14.77\n",
       "\\item 65.84\n",
       "\\item 49.64\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 39.08\n",
       "2. 45.27\n",
       "3. 26.27\n",
       "4. 14.77\n",
       "5. 65.84\n",
       "6. 49.64\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 39.08 45.27 26.27 14.77 65.84 49.64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X <- c( 39.08, 45.27,  26.27, 14.77, 65.84,\n",
    "        49.64,  0.80,  66.58, 69.60, 32.42,\n",
    "       228.36, 64.79,   9.38,  3.86, 37.18,\n",
    "       104.75,  3.64, 104.19,  8.17,  8.36)\n",
    "head(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-27T15:25:02.537470Z",
     "start_time": "2019-10-27T15:25:02.497Z"
    }
   },
   "outputs": [],
   "source": [
    "# Estimador de Momentos\n",
    "estimadorMomentos <- function(x){\n",
    "    return (1/mean(x))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T00:56:52.417451Z",
     "start_time": "2019-10-26T00:56:52.394Z"
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   "outputs": [
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      "text/plain": [
       "[1] 0.02034691"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_m <- estimadorMomentos(X)\n",
    "theta_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T00:58:00.256785Z",
     "start_time": "2019-10-26T00:58:00.240Z"
    }
   },
   "outputs": [],
   "source": [
    "# Estimador de Maxima Verosimilitud (igual al de momentos)\n",
    "estimadorMV <- function(x){\n",
    "    return (1/mean(x))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T00:58:03.784899Z",
     "start_time": "2019-10-26T00:58:03.767Z"
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   "outputs": [
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       "[1] 0.02034691"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "theta_MV <- estimadorMV(X)\n",
    "theta_MV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Percentil 90** : usando modelo\n",
    "\n",
    "Lo escribo porque estuvo bueno:\n",
    "\n",
    "Si\n",
    "\n",
    "$$X_i \\sim \\mathcal{E}(\\theta) $$\n",
    "\n",
    "Con densidad (PDF):\n",
    "\n",
    "$$f_X(x) = \\theta e^{-\\theta x}$$\n",
    "\n",
    "Y Distribución (CDF): \n",
    "\n",
    "$$F(X < k) = 1-e^{-\\theta k}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quiero que esta probabilidad sea del 90%\n",
    "\n",
    "$$1-e^{-\\theta k} = 0.9$$\n",
    "\n",
    "\n",
    "$$e^{-\\theta k} = 0.1$$\n",
    "\n",
    "\n",
    "$$log(e^{-\\theta k}) = log(0.1)$$\n",
    "\n",
    "\n",
    "$$-\\theta k = log(0.1)$$\n",
    "\n",
    "\n",
    "$$k = - \\frac {log(0.1)}{\\theta}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Donde por procedimiento *Plug-in*, podemos utilizar el estimador de Momentos o M.V. de $\\theta$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\hat k_m = - \\frac {log(0.1)}{\\theta_m}$$\n",
    "\n",
    "\n",
    "$$\\hat k_m = - \\frac {log(0.1)}{\\frac 1 {\\bar X}}$$\n",
    "\n",
    "| Estimador de 90 percentil |\n",
    "|---|\n",
    "|$$\\huge \\hat k_m = - log(0.1) \\ \\bar X$$|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:38:57.242599Z",
     "start_time": "2019-10-26T02:38:57.220Z"
    }
   },
   "outputs": [
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       "113.166300857925"
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       "[1] 113.1663"
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percentil 90 usando modelo\n",
    "estimadorPercentil90 <- function(theta_hat){\n",
    "    return (-log(0.1)/theta_hat)\n",
    "}\n",
    "\n",
    "k_hat <- estimadorPercentil90(theta_m)\n",
    "k_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Percentil 90** : Sin modelo\n",
    "\n",
    "Usando el hecho de que ***si ordeno los datos puedo extraer el número en la posición correspondiente al 90% del total***.\n",
    "\n",
    "* Ej: Si hay **10 elementos ordenados**, **el noveno** corresponderá al **90 percentil**, pues es mayor o igual que el 90% de los datos.\n",
    "\n",
    "**Observación:** En caso de **no** obtener un valor **entero**, debe redondearse **hacia arriba**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:38:58.254070Z",
     "start_time": "2019-10-26T02:38:58.236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0.8</li>\n",
       "\t<li>3.64</li>\n",
       "\t<li>3.86</li>\n",
       "\t<li>8.17</li>\n",
       "\t<li>8.36</li>\n",
       "\t<li>9.38</li>\n",
       "\t<li>14.77</li>\n",
       "\t<li>26.27</li>\n",
       "\t<li>32.42</li>\n",
       "\t<li>37.18</li>\n",
       "\t<li>39.08</li>\n",
       "\t<li>45.27</li>\n",
       "\t<li>49.64</li>\n",
       "\t<li>64.79</li>\n",
       "\t<li>65.84</li>\n",
       "\t<li>66.58</li>\n",
       "\t<li>69.6</li>\n",
       "\t<li>104.19</li>\n",
       "\t<li>104.75</li>\n",
       "\t<li>228.36</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.8\n",
       "\\item 3.64\n",
       "\\item 3.86\n",
       "\\item 8.17\n",
       "\\item 8.36\n",
       "\\item 9.38\n",
       "\\item 14.77\n",
       "\\item 26.27\n",
       "\\item 32.42\n",
       "\\item 37.18\n",
       "\\item 39.08\n",
       "\\item 45.27\n",
       "\\item 49.64\n",
       "\\item 64.79\n",
       "\\item 65.84\n",
       "\\item 66.58\n",
       "\\item 69.6\n",
       "\\item 104.19\n",
       "\\item 104.75\n",
       "\\item 228.36\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.8\n",
       "2. 3.64\n",
       "3. 3.86\n",
       "4. 8.17\n",
       "5. 8.36\n",
       "6. 9.38\n",
       "7. 14.77\n",
       "8. 26.27\n",
       "9. 32.42\n",
       "10. 37.18\n",
       "11. 39.08\n",
       "12. 45.27\n",
       "13. 49.64\n",
       "14. 64.79\n",
       "15. 65.84\n",
       "16. 66.58\n",
       "17. 69.6\n",
       "18. 104.19\n",
       "19. 104.75\n",
       "20. 228.36\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]   0.80   3.64   3.86   8.17   8.36   9.38  14.77  26.27  32.42  37.18\n",
       "[11]  39.08  45.27  49.64  64.79  65.84  66.58  69.60 104.19 104.75 228.36"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Percentil 90\n",
    "sort(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:38:58.428006Z",
     "start_time": "2019-10-26T02:38:58.399Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "20"
      ],
      "text/latex": [
       "20"
      ],
      "text/markdown": [
       "20"
      ],
      "text/plain": [
       "[1] 20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cantidad de datos\n",
    "length(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:38:58.595657Z",
     "start_time": "2019-10-26T02:38:58.566Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "18"
      ],
      "text/latex": [
       "18"
      ],
      "text/markdown": [
       "18"
      ],
      "text/plain": [
       "[1] 18"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 90% de los datos (redondeo hacia arriba)\n",
    "ceiling(length(X)*0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:38:58.757663Z",
     "start_time": "2019-10-26T02:38:58.741Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "104.19"
      ],
      "text/latex": [
       "104.19"
      ],
      "text/markdown": [
       "104.19"
      ],
      "text/plain": [
       "[1] 104.19"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Elemento en el índice que corresponde al 90 percentil\n",
    "sort(X)[ceiling(length(X)*0.9)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo que es aceptablemente cercano al valor calculado usando el modelo Exponencial:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:38:59.213632Z",
     "start_time": "2019-10-26T02:38:59.195Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "113.166300857925"
      ],
      "text/latex": [
       "113.166300857925"
      ],
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       "113.166300857925"
      ],
      "text/plain": [
       "[1] 113.1663"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Usando datos de la materia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:39:00.361161Z",
     "start_time": "2019-10-26T02:39:00.334Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>lamparas</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>4.1889362</td></tr>\n",
       "\t<tr><td>0.4398173</td></tr>\n",
       "\t<tr><td>1.3663653</td></tr>\n",
       "\t<tr><td>8.3793087</td></tr>\n",
       "\t<tr><td>1.7733742</td></tr>\n",
       "\t<tr><td>5.9061234</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       " lamparas\\\\\n",
       "\\hline\n",
       "\t 4.1889362\\\\\n",
       "\t 0.4398173\\\\\n",
       "\t 1.3663653\\\\\n",
       "\t 8.3793087\\\\\n",
       "\t 1.7733742\\\\\n",
       "\t 5.9061234\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| lamparas |\n",
       "|---|\n",
       "| 4.1889362 |\n",
       "| 0.4398173 |\n",
       "| 1.3663653 |\n",
       "| 8.3793087 |\n",
       "| 1.7733742 |\n",
       "| 5.9061234 |\n",
       "\n"
      ],
      "text/plain": [
       "  lamparas \n",
       "1 4.1889362\n",
       "2 0.4398173\n",
       "3 1.3663653\n",
       "4 8.3793087\n",
       "5 1.7733742\n",
       "6 5.9061234"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_n5 <- read.csv(\"./data/practica7/datos_Practica7_ej4_n_5.csv\")\n",
    "data_n50 <- read.csv(\"./data/practica7/datos_Practica7_ej4_n_50.csv\")\n",
    "data_n100 <- read.csv(\"./data/practica7/datos_Practica7_ej4_n_100.csv\")\n",
    "head(data_n100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:40:22.242009Z",
     "start_time": "2019-10-26T02:40:22.213Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.309639670780193"
      ],
      "text/latex": [
       "0.309639670780193"
      ],
      "text/markdown": [
       "0.309639670780193"
      ],
      "text/plain": [
       "[1] 0.3096397"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.223066146270974"
      ],
      "text/latex": [
       "0.223066146270974"
      ],
      "text/markdown": [
       "0.223066146270974"
      ],
      "text/plain": [
       "[1] 0.2230661"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "0.263471823512416"
      ],
      "text/latex": [
       "0.263471823512416"
      ],
      "text/markdown": [
       "0.263471823512416"
      ],
      "text/plain": [
       "[1] 0.2634718"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta_m_n5 <- estimadorMomentos(data_n5$lamparas)\n",
    "theta_m_n50 <- estimadorMomentos(data_n50$lamparas)\n",
    "theta_m_n100 <- estimadorMomentos(data_n100$lamparas)\n",
    "theta_m_n5\n",
    "theta_m_n50\n",
    "theta_m_n100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:41:48.819304Z",
     "start_time": "2019-10-26T02:41:48.788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "7.43633749251919"
      ],
      "text/latex": [
       "7.43633749251919"
      ],
      "text/markdown": [
       "7.43633749251919"
      ],
      "text/plain": [
       "[1] 7.436337"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "10.3224318503128"
      ],
      "text/latex": [
       "10.3224318503128"
      ],
      "text/markdown": [
       "10.3224318503128"
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      "text/plain": [
       "[1] 10.32243"
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     "metadata": {},
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     "data": {
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       "8.73939786918254"
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       "8.73939786918254"
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      "text/markdown": [
       "8.73939786918254"
      ],
      "text/plain": [
       "[1] 8.739398"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 90 percentil con modelo\n",
    "k_hat_n5 <- estimadorPercentil90(theta_m_n5)\n",
    "k_hat_n50 <- estimadorPercentil90(theta_m_n50)\n",
    "k_hat_n100 <- estimadorPercentil90(theta_m_n100)\n",
    "k_hat_n5\n",
    "k_hat_n50\n",
    "k_hat_n100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:43:23.810458Z",
     "start_time": "2019-10-26T02:43:23.774Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "8.37930867770567"
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      "text/latex": [
       "8.37930867770567"
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      "text/markdown": [
       "8.37930867770567"
      ],
      "text/plain": [
       "[1] 8.379309"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "9.61394720034703"
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      ],
      "text/plain": [
       "[1] 9.613947"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "8.37930867770567"
      ],
      "text/latex": [
       "8.37930867770567"
      ],
      "text/markdown": [
       "8.37930867770567"
      ],
      "text/plain": [
       "[1] 8.379309"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 90 percentil sin modelo\n",
    "sort(data_n5$lamparas)[ceiling(length(data_n5$lamparas)*0.9)]\n",
    "sort(data_n50$lamparas)[ceiling(length(data_n50$lamparas)*0.9)]\n",
    "sort(data_n100$lamparas)[ceiling(length(data_n100$lamparas)*0.9)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-26T02:47:05.569296Z",
     "start_time": "2019-10-26T02:47:05.478Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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97cv39/c3NzMzOz/v37b9my5dq1a8pt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mTYMHPoWkJKSuTvnDD4PvmXm84sHDDyzuG6EBgJTzMCEAACAASURBVPAELbfdqaiu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\ngg==",
      "text/plain": [
       "Plot with title “Histogram of data_n100$lamparas”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(2,2))\n",
    "plot(data_n100$lamparas, col=\"lightblue\", cex=1.5)\n",
    "points(data_n50$lamparas, col=\"red\", cex=1.25)\n",
    "points(data_n5$lamparas, col=\"blue\")\n",
    "\n",
    "hist(data_n100$lamparas, col=\"lightblue\")\n",
    "\n",
    "boxplot(data_n100$lamparas, col=\"lightblue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}