Sesion1_AprendizajeNoSupervisado.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlqcVPrvPV2q",
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      },
      "source": [
        "## Bienvenidos a al ambiente de ejecución de código Colab!!!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LmIh93thx69V"
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      "source": [
        "En el presente ejemplo comenzaremos con un dataset **público** utilizado para la formación de **Machine Learning** denominado Iris dataset que se basa en describir las longitudes del sépalo y pétalo de tres tipos de flores: Setosa, virginica y versicolor. Este dataset es uno de los más básicos y usados para introducirnos en el mundo de la ciencia de datos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTigmeodVxKw",
        "colab_type": "text"
      },
      "source": [
        "Podemos ver una breve historia de este dataset [acá](https://es.wikipedia.org/wiki/Conjunto_de_datos_flor_iris)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6VkelaG7xqbY",
        "colab": {}
      },
      "source": [
        "#Importamos librerías útiles\n",
        "import pandas as pd #Es una librería para manipulación de datos\n",
        "import seaborn as sns  #Es una librería para poder hacer nuestros gráficos rápidos y atractivos"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "GnbvxtyiCnAh",
        "colab": {}
      },
      "source": [
        "#Asignamos la variable iris \n",
        "iris = pd.read_csv('https://raw.githubusercontent.com/javalillo13689/hackspace/master/iris.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CA2hL-oQCnL-",
        "outputId": "bfcda1ca-1325-4dcb-be21-c92e2f9a4266",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "#Mostramos los primeros cinco (5) registros del dataset iris con la instrucción head. Este número se muestra por defecto\n",
        "iris.tail(3) "
      ],
      "execution_count": 0,
      "outputs": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>largo_sepalo</th>\n",
              "      <th>ancho_sepalo</th>\n",
              "      <th>largo_petalo</th>\n",
              "      <th>ancho_petalo</th>\n",
              "      <th>tipo_flor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>7.9</td>\n",
              "      <td>3.8</td>\n",
              "      <td>6.4</td>\n",
              "      <td>2.0</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>6.4</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.6</td>\n",
              "      <td>2.2</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.8</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "    largo_sepalo  ancho_sepalo  largo_petalo  ancho_petalo  tipo_flor\n",
              "97           7.9           3.8           6.4           2.0  virginica\n",
              "98           6.4           2.8           5.6           2.2  virginica\n",
              "99           6.3           2.8           5.1           1.5  virginica"
            ]
          },
          "metadata": {
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          "execution_count": 32
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      "cell_type": "markdown",
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      "source": [
        "Como resultado hemos obtenido 5 filas con las observaciones de las características de los pétalos y el sépalo.\n",
        "    Hay una columna a la izquierda (**en negrita**) que hace referencia a la posición de cada fila. A esto se le llama index (índice) y nos servirá para ubicar más rápidos nuestros datos. \n",
        "Hay una columna a la derecha denominada *species* que asigna el tipo de flor de cada observación. Esta columna es importante porque será la **variable objetivo** que vamos a predecir o tratar de acercarnos usando el algoritmo de aprendizaje supervisado: KNN (que nos formará clusters)"
      ]
    },
    {
      "cell_type": "code",
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        "id": "UOnsPcwEPV3E",
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      },
      "source": [
        "iris.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>largo_sepalo</th>\n",
              "      <th>ancho_sepalo</th>\n",
              "      <th>largo_petalo</th>\n",
              "      <th>ancho_petalo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>100.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.914000</td>\n",
              "      <td>3.041000</td>\n",
              "      <td>3.851000</td>\n",
              "      <td>1.21300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.856469</td>\n",
              "      <td>0.439489</td>\n",
              "      <td>1.785378</td>\n",
              "      <td>0.74558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.10000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.175000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.37500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.850000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>1.40000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.500000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.80000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.50000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "       largo_sepalo  ancho_sepalo  largo_petalo  ancho_petalo\n",
              "count    100.000000    100.000000    100.000000     100.00000\n",
              "mean       5.914000      3.041000      3.851000       1.21300\n",
              "std        0.856469      0.439489      1.785378       0.74558\n",
              "min        4.300000      2.000000      1.000000       0.10000\n",
              "25%        5.175000      2.800000      1.600000       0.37500\n",
              "50%        5.850000      3.000000      4.500000       1.40000\n",
              "75%        6.500000      3.300000      5.100000       1.80000\n",
              "max        7.900000      4.400000      6.900000       2.50000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
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      "metadata": {
        "id": "jGl2dcgFPV3H",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "iris.info()\n",
        "#float, object, integer, hay más..."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100 entries, 0 to 99\n",
            "Data columns (total 5 columns):\n",
            "largo_sepalo    100 non-null float64\n",
            "ancho_sepalo    100 non-null float64\n",
            "largo_petalo    100 non-null float64\n",
            "ancho_petalo    100 non-null float64\n",
            "tipo_flor       100 non-null object\n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 4.0+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sJZ8urIc8X8e",
        "colab_type": "text"
      },
      "source": [
        "#Gráficando en Python"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fCiNCQY8ilQ",
        "colab_type": "text"
      },
      "source": [
        "La primera gráfica que realizaremos hoy en clase será la **gráfica de dispersión** que requiere declarar antes **dos variables: x, y**\n",
        "\n",
        "Para ello define dos columnas del dataset iris para graficar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-pX6evBPV3L",
        "colab_type": "code",
        "outputId": "3ebc3e3a-3065-48c3-c5b7-da1241bd98a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "x =iris['ancho_petalo']\n",
        "y= iris['largo_petalo']\n",
        "sns.scatterplot(x, y,data=iris)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e9da44e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VgX3AuTm5qAE4Efgw8JSZPRE89hlgIeT+s29k7LF/9lrWQEQkx7KQrhERkYQo\nyIuI5JiCvIhIjinIi4jkmIK8iEiOKciLiOSYgry0DDNbVG9534TbXWZm72vguJVmdk8afZLWoSAv\nkrxlVCf3iKROQV6mDDP7BzPrDTZcWBs8tsfM/jxYte9RM5sfPD7fzO4MHn/SzP5T8DJtZnZj8Br3\nmVlHcPyy4PytwXmzJ+jHFjO7OtjU4YfB9HPMbIZVNwT5VzP7gZmdGSxX8XngnOD4c8zseDN7JDjm\n+2a2uEYbbw3GuzXo19KY305pEQryMpX8gbsvB7qpLvcwB5gBPBqs2vcQcGFw7DXAg8HjxwFPB48f\nBXzZ3d8BvAqcFTx+C/A/gtU+n6K61MBE3uLuy6huaPO14LE/pbqu0vHAe4H1VJcr+DOqG90sc/fb\ngWeB33L3Y4PnvlDj9T8H/CDoz2eC/omE1vS1a0RCuNTMVgXfH0E1YA8CI3nsXuCU4PuTgPMB3H0Y\neC24Ov+/7v7EmOMXmdmhwCx3fzB4/Gbgm5P05bbgtR8ys0PMbBZwKnCGmf1xcMx0gnVJxjkUuNnM\njgKcYN2acd5F8A+Qu99vZnPM7JBWW5ZXDp6CvEwJZrYS+G3gBHd/w8y2UA2iQ2MWcBpm8t/pgTHf\nDwMdEbs0ftEnp7pd3Vnuvn3sE2b2m+OOvQp4wN1XBZtHbInYB5FJKV0jU8WhwO4gwB9Nde/PiXwP\nuBjAzNqCq/Wa3P01YLeZ/Vbw0IeBB+sdHzgneO13UV0K9zWqq4l+YsxSsccGx/ZT3dNz7FhG1ke/\noM7r/zPwoeB1VgI/11W8RKEgL1PFZqBoZs8Afwk8OsnxlwHvNbOnqKZllkxy/EeA9Wa2lWo1zOcn\nOf6XZvYDYAPwseCxq6imXraa2dPBzwAPAEtGbrwC64C/CM6v95fHZ4HlQX/+kjf3UxAJRUsNi4QU\npIr+2N17mt0XkcnoSl5EJMd041WkDjP7MtXdfMa62t1XNqE7IpEoXSMikmNK14iI5JiCvIhIjinI\ni4jkmIK8iEiO/X+bOV4tKcD7vwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "iGfLwrP39bXj"
      },
      "source": [
        "La segunda gráfica que realizaremos hoy en clase será la **gráfica de distribución** que requiere declarar antes **solo una variable**\n",
        "\n",
        "Para ello escoge una columna del dataset iris para graficar"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3siCBk-DPV3N",
        "colab_type": "code",
        "outputId": "4a739c5c-b802-4253-e8aa-f27e370eef0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        }
      },
      "source": [
        "#El parámetro bins te permite variar el número de intervalos en la gráfica\n",
        "sns.distplot(iris[\"ancho_petalo\"],bins=5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e9abf2cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FZK+IrBvm5RTwyr46okIDuUpntygbmpMZR1iQg23lOoVxrLn1/31jzHpg/TnX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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qpdBMuoEATU0"
      },
      "source": [
        "La tercera gráfica que realizaremos hoy en clase será **pairplot** qu no requiere **declarar ninguna variable**.\n",
        "Además puedes dibujar todas las relaciones entre todas las variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0RFgyy0wK93Z",
        "outputId": "87d285a8-f1a2-4400-987d-23efdf6f1256",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        }
      },
      "source": [
        "#El parámetro hue le da nombre a la leyenda del gráfico y el size permite limitar el tamaño del gráfico (mientras más pequeño es más facil ver)\n",
        "sns.pairplot(iris, hue='tipo_flor', size=2.0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f6e9a58a128>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RwcwOQkLaTZUwZymaI4eyfz/KyjMfxKdZsesB3O8sRjv9F7Dws/B4JL3Vpvqd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QPVwO2L1w3vrjJ++vK57ye+XYqRZddzUHBMxL6wrXU/GaY0JqQNPGPNp8NnTWbmTWgddHNcadl0\nWnM43iP5d9suPLobizY4awS71cTs0mxe3VPHTxbNHnSbFIMgWq3TrJ71zVXwyIXB22PRbilGJ7HU\nQh6ohq5CkQDGV7IXD9HqW/ZB93lxuupx616smpl8mwOtyxnb00PFyNNYAdVbYN53wm6WUvJO0x5m\nZExEi2FW7KFWM90+jbLsOMqkhWtW1kS+0NrEC3kWPm3dwbG5cwd9rlMm57HmnQPsr29namGorYwi\niZithuXKnMsNoX5/v7WxP4t/fDFALWQg6lgTMr7YC9FMathWxI+KFJGIQeOn+7yUN+0z6lWu+zJL\nX7qS8uZ96BtuUtqLVGX7U4CAo88Ou3l/Zw1Hup2cmBVbFYr9TmPCzqQhJnvOzEksaKnBhIldTR+G\n3ae5u4EnK37P/bt/wgf1r0U81ymTjdl+r+2pG1KbFMOAvcDwV3v5x7BmUbDf2gjV1FaMELFq8SKM\nNbotP3R8adqH3mP2r1DEg0r2ItFXg3PDTuN30eygb2VOVz0r3rgx4E5e3VHNijcMLz+gV3vRWZ+M\n/4GiP1LC9r/DhJMgPbye7W3nTgBOyJoS0yk/dVqwmQc/OcNPY1YZ6VIy1eJgZ9N7Idsbuo7wPx9f\nx5s16/msbQ9/+uROXj70ZNhzFWalMTk/nX/vqR1SmxTDgKsRnroiWJ/11BXG+s768NqtznqcXU5W\nvL4iONa8vgJnlzNJ/xHFgES5nkFEGGucXQ1hxpcbcbrUeKKIH5XsRcOvwcmdZPzu9/jdrXvD1x3s\na+ehtBepQ9UH0PQZTF0YcZfXGj+mzOYg35oV0yn3NliYlN016MkZfpoyS9HRONNno7rzQNCs3Iau\nI/zvjhtw+dq5fOoKvjvjJ8zIPol/HHiIQx37w57v1Cl5fPiZk8PNrqE1TJFYoumzotVCHWK9W0US\niEeLF2asiTi+6OrJniJ+VLI3BKyaOVCGxk9pRinWzj7ftqN5+SlGli1rDDPbo8JbruzvrGFb637O\nyJ0V0+naugWVLRam5g49ofKZrLRkFLOk2YlVs/H8wceQUlLrquJ/d9xAp7eNS6dcS7F9EprQOK/0\n61hNafzjwB/Dnu8L0wuRwLqth4bcNkUCieb3GWWbv95tX1SN0xQnXm/XfkQcXzSl2VPEj0r2hkC+\nvTBMvd17yd+61thhIC8/xcjR6YRdz8LUBUbCF4Z/1GzChMaZecfGdMo99UbQnprblZAm1uVMZarz\nE84sOIftTe9w365b+O9t1+DydnDplOUU23tn9dnNGXzOcR47m95nf+uukHMVZduYPSGbp7ccQtdj\nK4moGAGiaYFVTe2xRbzerv0IP76sJN+uxhNF/KivCENAM5mZnjeDtReuCZ6Nu/heowybmo2bOmz7\nK3i7YcaXwm5u8rTzzJFNnJwzjRxLRkyn3F1vxaLpTMpJTLJXmzuNmdWbWCQK6Sr8IruaNzMhfQrn\nTbiUbGteyP4n5Z/Je/X/ZuORdUzNPi5k+zmzivjDxk95ZXctFx5fkpA2KobIQH6fEbZpwPS86axd\ntFbNxh0txOvt2v/wcOOLmo2rGCSq1wwRzWTGkdmv0oIywkwtfF748E9GebT88LNsHzz4Ai5fN0uK\nw9fKDceOWiuTc7oxJ2i8rc8+Cp8wMbFhB/NnX8H84i9H3d9qsnFc3qlsbniDS4++lhxr8KST06cW\n8MzWQ/z+9XIuOK4YEYOVjGIEiOb3qWpqjy3i8XYNd3i48UWhGATD+rVQCDFJCLFRCLFbCLFLCHH9\ncH5ezKg6hOOLXeuMiRnHfTXs5rcad/Bk9VucXXACE22xVZ2o6zBR2WJhVkFHwprpM1lpyJ5MWe3m\nmI+Zk38WPunl7ZoNIdtMmuDiORPZVd3Kkx9WJaydihhQMUYBqh8oUobhfgfgBf5TSjkbOB34f0KI\n5Nr6qzqE4wtdh7f/F3KPgkmfC9l8oLOWW/c++v/ZO+8wqaqzgf/eO2Vndhe2MLQFEQsWRGxIVEwU\nSzSo2DERFTAqil+iaEKiwY4NQxENYomCiIZYsWtUbIgQioJBARttaQvswu7OTrvn++POzM7szmyf\n3Znl/J5nn5255cyZueee89630svdmWHdf9ngZpdsygLgsM4tJ+wBbOrUl4LyjeTtaVhgRWFWF3rn\nHsInm+cRTBCl98s+HvoVdeSeN1exesueFu2rJgl6jtGAHgeatCKlwp5SarNSaln49R7gW6BHKj+z\nXhqa+0jTPvh2Hmz/Dg6/yKpLGkN50MsN/3scQ4Tr9z2bLMPR4GYXb8qiW64fT3bLpkHYWGg9C+27\neWGDzzmq04mUBXayfMentfYZIow+6QCy7AaXPLGQT9bocZ5y9ByjAT0ONGlFq3n3ikhv4ChgUY3t\n14jIEhFZsn17K9wEug5hRtAi4yLghfdvg4Le0PtXcbtMZfK31c/ys3cr1/YagsfZscHNbq8wWLXN\nSb/O5U3rVx1UZXVke8d9OWDjJ9DA6gj75R5KvtPD/M2vJtzvyc3ijnMOI9thY8TTi7lg+gKe+3Id\npZWZNeZbfa5oKnqOaTXSekzocaBJI1pF2BORXOBl4EalVFzFeKXUE0qpAUqpAZ07t0JIeTNzH2la\nhxYZF188AmUbYOA1YNjidj25/j0+2vE1w7r/kkNz92lUs+//YKVuObYoNWbRH7oOIK9iM0XbVzTo\neBGDIwoH8f3ulawvX5vwmK4dXTxwYX8uP25ftu3xMf61bzj23g+4/vllGWPebfW5oqnoOabVSOsx\noceBJo1IubAnIg4sQW+OUuqVVH9evTQz95EmQ9j6P/j079D7ROjWP27XpztW8o91b3J8/iGc7jmq\nUc36gvDhD276dq6kwJWaTPYbPf2ocuRy2I9vNPicfgW/IMtw8daG2UmPcdgMhhzenYkX9ue+8w/n\ntEO78tG32zhz6qfc8spKyn06M3+LoOcYDehxoEkrUpp6RaxcD/8EvlVKTU7lZzWYZuY+0mQAVWXw\n4khwZsPAa+N2/VCxORqQMaLnaY1ORzLvu1z2+G38qldpC3Y4HtOws7poEEese4/u279mc+cj6j3H\nZXNztOckFm57j5/3fEfvDsmrgIgI+3ly2M+Tw/lH9eC15Zv41+L1fLZ2O1MuOZJje+tEvc1CzzEa\n0ONAk1aketQNAi4HThGRr8J/dScPaw3qqXmryWD8FTD3Mtj5A/zyT+DOj+7aUrWT0SsfwS42rt/3\n7EaXHdq028Zr3+ZwZNc97NdCVTOSsbboOMpdBRy38kkcgcoGnXNMp5PJsXdg9vd/J2gGGnROB5eD\ny4/vze3n9CUQMhk2YyH3v/MtvmCoOd3X6DlGA3ocaNKGVEfjfq6UEqVUf6XUkeG/t1P5mZq9mF3r\nYNZQ+PlzOOEG6F6tEVvn3cbvVzzM7mAlY/c7r1EBGQA7Kg3u+7SALHuIIQfuaOme18I07Pz3wPPp\nULmVk5dOwh6sX7jMsrk4rWgYGyq+54Ufp6FUw8ukHdKtI/ef35/Bh3Th8U9+ZOijC/ji+5LmfAWN\nRqPRpAm6goYms1HKSq3y9Quw+ElA4KS/wr4nAFAWqOSVLQuYsf5tDAzG7ncevRpRW7IqKHy+zsWc\nFbn4Q8I1RxWT72odrdf2vP1Yuv85HPPD65z1+V9ZeujlbOp8JKpGsEksB3bsx0DPaXy25Q3KA2Vc\nvN91eFwNy8Dvdtq4+pf7c8y+BTz9+U9c+tQijtu/kIuP2YdTDulCQY52LNdoNJpMRAt7msxizxYr\nnYpvD1SVQskaqNxh5dDbdxAcM5Jp279kxYqH2RUo54eKzYQw6ddhX67ocQoeZ169H7FoYxYf/ehm\nh9fGxjI7ISXs07GKS/puo0tOw8yjLcVP3QbgzerI0T+8yWmL78Nvd1PaoReVrgJ+6HkSG7oNrHXO\niV2HkGVz8cW2d1m+41O6Z/fm4LwjufSAGxv0mUf3KqDfsDzeX7WF/6zays0vfg1Az3w33fNd5Gc7\nybIb3HZ2X7p2dLXo99VoNBpNyyONMfWkGhHZDqxrwKEeIN1sTHtbn0qUUmemqO04GjEuGkI6XqeW\npC2/X6aOiYaSLmMn0/rRKuOijjGRLr9XW5NOv0NrjYl84FKl1HQRKQKmKaUuasH2/whcBywD/gMM\nUEr9X0u135KklbDXUERkiVJqQFv3Ixbdp8ygvf8m7f37tSXp8tvqfjSOTOlnqtkbf4dwMYc3lVL9\nUtT+d8BpSqmNIjKSRgh7ImJXSrVavisdGqTRaDQajaY98gBwQDgTyIsi8g2AiIwUkXki8rGIrBWR\nOyIniMhNIvJN+C+p74uIzAD2B94RkbE19vUWkY9EZIWIfCgivcLbZ4rIDBFZBExMxRdOhvbZ02g0\nGo1G0x75K9BPKXVkRMsXs28g0A+oBP4rIm8BChgF/AIQYJGIfKKUWl6zYaXUtSJyJjBYKVUS1uxF\neASYpZSaJSJXAtOA88L7egInKKVaNb9Vpmr2nmjrDiRA9ykzaO+/SXv/fm1Juvy2uh+NI1P6mWr0\n7xDPf5RSO5RSXuAV4MTw36tKqQqlVHl4+y+b0PbxwPPh17PD7UZ4sbUFPchQYU8plXaDVvcpM2jv\nv0l7/35tSbr8trofjSNT+plq9O9Qi5oBC60VwFDRSp8TR0YKexqNRqPRaDT1sAfokGTf6SJSKCJu\nLBPrAuAz4DwRyRaRHOD88LbG8gXw2/Dr4U1so0XRPnsajUaj0WjaHUqpHSKyIByY8W2N3YuBl7F8\n6J5TSi0BK4givA/gqUT+eg3gD8AzIvJnYDuWH2CbkpGpVzQajUaj0WiaQmPTpLQHtBlXo9FoNBqN\nph2jNXsajUaj0Wg0CRCRTsCHCXadqpTa0dr9aSpa2NNoNBqNRqNpx2gzrkaj0Wg0Gk07Rgt7Go1G\no9FoNO0YLexpNBqNRqPRtGO0sKfRaDQajUbTAERkpIgUtXU/GosW9jQajUaj0WgaxkhAC3sajUaj\n0Wg0qcIXDB2/aZf3i3U7Kn7atMv7hS8YOr457YlIjoi8JSJfi8g3InKJiBwjIp+IyFIReU9EuovI\nRcAAYI6IfCUibhE5VUSWi8hKEXlaRLLCbT4gIqtEZIWI/D287RwRWRQ+/gMR6dr8X6OB31GnXtFo\nNBqNRpMJ+IKh49dsLX/9uueWejbu8tKzwM1jlx1TclDX3KFZdtvCprQpIhcCZyqlrg6/zwPeAc5V\nSm0XkUuAM5RSV4rIx8CflFJLRMQFrMXKubdGRJ4FlgGzserjHqKUUiKSr5QqFZECoDS87SrgUKXU\nzc39TRqC1uxpNBqNRqPJCEr2+CdFBD2Ajbu8XPfcUk/JHv+kZjS7EjhdRB4UkV8C+wD9gP+IyFfA\neKwaujU5GPhJKbUm/H4W8CugDKgC/ikiFwCV4f09gfdEZCXwZ+CwZvS5UWhhT6PRaDQaTUYQNM3u\nEUEvwsZdXoKm2b2pbYaFtaOxhL4JwIXA/5RSR4b/DldK/boR7QWBgcBLwNnAu+FdjwCPKqUOB0YD\nrqb2ubGklbB35plnKkD/ZcZfq6HHRcb8tRp6TGTUX6ugx0RG/TUZu2Fs7lngjtvWs8CN3TA2N7XN\ncHRtpVLqOeAh4BdAZxE5PrzfISIRLdweoEP49Wqgt4gcGH5/OfCJiOQCeUqpt4GxwBHh/XnApvDr\nEU3tb1NIK2GvpKSkrbugSUP0uNDURI8JTU30mNg78HRw3vzYZceURAS+iM+ep4OzOb5vhwOLwybb\nO4DbgYuAB0Xka+Ar4ITwsTOBGeFjBRgFvBg2zZrADCxh8E0RWQF8DtwUPvfO8LFLgVYdsPbW/DCN\nRqPRaDSappJlty08qGvu0LnXHD8paJrd7Yax2dPBeXNTgzMAlFLvAe8l2PWrBMe+DLwcs+lD4Kga\nh23GMuPWPHceMK+p/WwOWtjTaDQajUaTMWTZbQt7FLhPqP9ITYS0MuNqNBpNKvmppAKdbkqj0ext\naGEvwzGVSYm3hOLyYkq8JZjKbOsutVv0b53ZLPl5J4P//jEvLN7Q1l3RaNIOPb+1b7Swl8GYymTt\nrrUMf2s4Z7x8BsPfGs7aXWv1TZoC9G+d+azeugeAj77b2sY90WjSCz2/tX+0sJfB7KzayR8/+iPF\nvT8/XwAAIABJREFUFcUAFFcU88eP/sjOqp1t3LP2h/6tM5/q3FzSpv3QaNINPb+1f7Swl8H4Q/7o\nzRmhuKIYf8jfRj1qv+jfOvPZWW5dq217qtq4JxpNeqHnt/aPjsbNYJw2J4N7DubcPueS58yjzF/G\nvLXzcNqcbd21dofT5qQopyhuQizKKcJld1HiLcEf8uO0OSl0FWJIap+hTGWys2pnq35me6DcHwSg\nzBto455oNK1HXfNFZB+QcH7Ta0nTEZG7gU+VUh808ryTsWrvnt2S/dHCXoZiKhOHOLj2yGsZO38s\nxRXFFOUUMWXwFPKceW3dvXZHoauQaadMi5o6inKKmHH6DLZXbo/bNu2UafQp6AOQEoEs4luT6DO1\nwFc35VWWsLcn/F+jae8kmi8eHvwwha5C7DZ7dP7yuD1MGDSB8QvGx80rha7Ctv4KaY2ICCBK1XZu\nVErd3kp9sIfLs9WJFvYykMgN7DAcUUEPLLX72PljmfWbWXTL6dbGvWxfGGLQp6APc86aExXgUDD8\nP8Nr+bnMGTKHHVU7UiKQJfOtmXPWHDxuT7O/Z3um3BcR9rRmT7N3kGi+uGH+DYwbOA6n4WTClxMo\nriimuKKYqcumMv648eyXtx8uuyu9LQZB3/GUb5uEGeyOYd9MbpebsWc1OamyiDwAbFBK/SP8/k6g\nHMvBdxiQBbyqlLpDRHpjJWBeBBwDDBGRu4ABWKXgnlZKTRGRmcCbSqmXRORY4GEgB/ABpwIB4LHw\neUHgJqXU/Br9KgSeBvYHKoFrlFIrwv07ILx9PfC7+r5jml5JTV1EbmBDjIR+FgFTL2apwBADj9tD\nUW4RHrcHv5nYz6UqVJUyZ2ftW9N0Ipq9QEjhC4bauDcaTepJNl/kOfNw291x+1aUrGDMh2Oi81xa\nC3rbvn2dmUOOZ9qRvZk5xHof9B3fjFbnYgl1EYYB24E+WJUwjgSOEZFIRY0+wHSl1GGAB+ihlOqn\nlDoceCa2YRFxhtu/QSl1BHAa4AWuB1T4nN8Bs0TEVaNfdwHLlVL9gVuBZ2P29QVOU0rVK+iBFvYy\nksgNHFIhinKK4vYV5RThMBxt1LO9i4gfXyxFOUVJhfCWEMiSfab2ramfPb7qhyBtytXsDSSbL8r8\nZZT5yzJzLinfNol/X+6hdL31vnQ9/PtyD+XbJjW1SaXUcqCLiBSJyBHALqx6ub8GlgPLgEOwhDyA\ndUqpL8OvfwT2F5FHRORMYHeN5g8GNiul/hv+rN1hs+uJwHPhbd8B64CDapx7IjA7fMxHQCcR6Rje\n97pSyksD0WbcDCRyA8/6ZhaTT57MTR/fFDUXTj9tOjaxUVxerJ33U0wiP75pp0zDZXelzNm50FXI\njNNnsHHPRtx2N96gl54demrfmgZQ4QuR7bRR6Q+xpyqIJzerrbuk0aSURPNFXlYeDy5+ECAz/fTM\nYPeooBehdL21vXm8CFwEdMPSxO0L3K+Uejz2oLAZtyLyXim1KywgngFci6UVvLKZfWkIFfUfUo0W\n9tKcRJFUsUIGwGOnPYbDcOCyudjp28llb1+mnfdbgVp+fIYTwzCoClbx1BlP8dDih5i/cX6LT6L+\nkD/qaxNpW1M3SikqfEG657mo9Hu1356mXVFXxG3N+eL+X94PQIm3BE+2hzlD5uA3Myiy37BvJr9X\n7ziBL7+Xtb15zAWexDLLnoSl2btHROYopcpFpAeWn10cIuIB/Eqpl0VkNWFtXQyrge4icqxS6r8i\n0gHLjPsZMBz4SEQOAnqFj401R0eOuSccpVuilNptxYU0Di3spTF1RV7WDBYodBVq5/02IOLfkizq\nbfxx4zEMo8UmUX2Nm0YgpAiaivxsJxt2eaP+expNplPXOpFovrjls1t45sxnMke4q0lul5sZNvv1\nqCk3vxcMm11Cbpebm9OsUup/YUFsk1JqM7BZRA4FFoaFq3LgMqCmw28P4BmR6A95S412/SJyCfCI\niLixBL3TgOnAYyKyEitAY6RSyldDkLsTeFpEVmAFaIxo6vfTwl4aU9/CXnNx1877bUeyqLeWFsL0\nNW4akYCMXJc15VX6dYCGpn1Q1zqRbL4AMvfh0J61kC6HDmXk2y0WjRshHCwR+/5hrCjamvSLOeZr\n4OgEbY2Mef1f4LgE7YxKcN7HwMfh1zuB8xIcc2fCL1AHWthLU0xl4g/5uffEeynzl/HJ+k84qddJ\n5Dnz8If8mMqs9USWLPFv2jvcZjAR84k34GXcwHE8vfJpVpSsAJovhCUyzehr3DT8QSsNVocsa8rz\nBrSwp2kfJBLoPG5PdO5pifki7RK527MWkr/PCW3Xgcwjw/S3ewcRtfyod0cx6r1RzFs7j0sOuYSJ\niycy6r1RjHp3VMIi1RFfvkiEVcY43GYoscXDh7w6hImLJ/KHo/9Af09/oHlCWLLC5PlZ+foaNwFf\nWNjLjQh7WrOnaSfUjLjt7+nPjUffyKh3R/HnT/7MhEETmjVfJJuLaq4/mvRGlFKp/QCRfOApLLWn\nAq5USiVUtw4YMEAtWbIkpf1pLZrzJFTiLeHuL+6OlkErcBUw5oMxtZ7OEpkIW/EJrNWqyafruCjx\nljD8reG1rsu4geOYuHhiwuCY2OtjiIGBkdCnL1nbc86aE/XPTJun7GrSdkz8XFLByX//mMuP25fZ\nX67jznP6MnLQfinsoSaGVhkX6TpPpBpTmazbvS4acVvoKuS6D66Lzh39Pf259ohrm5wsua65qBmm\n4FabKzQWrbFCPAy8q5Q6BDgC+LYVPrNNae6TkGmaXNr30qgmb7dvd4P9tGom/k0TIaBdkswf5qCC\ng5hz1pyEgl7suBjxzgh+3P0jd39xd63xUZdvnr7GjaemZq9Sm3E17YhIxO2o90ZR5itr0WTJ2k+4\nfZDSVUJE8oBfAf8EKypFKVWays9MB5I5zDa0goKpTO5YcEf0/J2+nZmZ/LKdkyxhaeTpemfVTorL\niynxlkQ1ejXHxR0L7uDcPufWGh86eXLLEvHZy86yIWgzrqb9UHNeqWu9MJVJibckbl6qD0OMpMnj\nNZlDqq/WflglR54RkeUi8pSI5MQeICLXiMgSEVmyffv2FHendaj5JNTf059xA8fhDXgp8ZYQNINJ\nbzhTmfhNPx63h6mDp/LMGc/QwdGBe0+8d6/y00qXcVHX5JjMRzI/K5/i8mJ2+3bjD/ktzWx5cZ2l\ni2o+KWv/y9o0Z0xEonGdNgOXw9DRuO2EdJknWotE81HNeeXplU9z16C7Es5LNS1O63avo6SybuHP\nwKjV3l2D7sLQLv8ZRaqjce1YIcl/UEotEpGHgb8Ct0UOUEo9ATwBls9FivvTKsRGTPb39OcPR/8h\nqqkb3HMw1x55LWPnj02Y+Hhn1U5KvCXcePSNcZnNJ580mVm/mYWpzHTz00oJ6TAu6spfZYhRO6ly\n+Lrs9u2mzFcWV9lk8smT6ZLdJWFkXKRsUazWLlnb7fma10dzxkREs+ewGWTZbVrYayekwzzRWiSb\njzq7O8fNKytKVvD8queZdeYsTMykeVg9bg8llSVcu+DaOpPwG4bB86ueZ9zAceQ58yjzl/H8que5\n/YTb2+R3aC1EpAiYppS6qJHnPQVMVkqtquOYa4FKpdSzyY5paVK9cmwENiqlFoXfv0SCfDTtjVit\nzJWHXxlnkj23z7nM+GoG4waO45kznmHcwHG8+f2bbKvcFtX+CBIV9MDS/tz0yU3Yxa79tFqRhpjj\nE/nPVQYro4Je5LybPr6JoBmspa2bfPJkCrIKmHH6jFpaO+2b13L4osKekOUw8Pp1UmVNZpFsPvKb\n/loRt5f3vRyn3Rk3d/hD8RajuwfdzexVs+OEv8g6FKvlK3QVcv1R10d9yCcunsj1R13f7q0MSqni\nRIKeiNSpJFNKXVWXoBc+ZkZrCnqQYs2eUmqLiGwQkYOVUquBU4E6f4T2QKxWxhvwxmlyumV349K+\nl0YFwMiCf9+X90VLaz1++uOJHWJN7RDbmjTVMTlkhhKeF1RBnDYnE06cgMftYeOejdy36D5KvCW6\n5FmKiZhxtWZPk6kkm48CZoCpy6bGad6mLpvKQyc9FHesy+6qZTG6a9Bd7KjaARBngaqp5Us3K4M/\n5D9+h3fHpKAKdreLfXMnd6ebnTZnk5Mqi8gDwAal1D/C7+/EqpgxUinVT0RGAhcAuYBNRAYDjwKn\nABuwyqg9rZR6SUQ+Bv6klFoiIuVYQapnY1XOOFcptTXSvlLq7yJyIDAD6IxVneNiYCswDygAHMB4\npdS8pn4/aJ2kyn8A5oiIE/iRBBmj2yt2sWMzbLxx3huEVIhZ38zCZXcl1PqMGziO+RvnU1xRzIY9\nG3Ti3DSgIQmMI/6XgVDAqk9sd2EzbAnPs4udq/5zFeMGjmP85+Pj9uuSZ6klotmz2wyy7IZOqqzJ\nOJLNRw7DYbn+zL8xbnvN9cI0TWavmh0nFC7YsIC7B92NIGwq34TH7aG4orhWtaaIlSEd8If8x39f\n+v3rY+eP9YQF095TBk95/cD8A4c2Q+CbC0wF/hF+PwwYDYyMOeZooL9SaqeIXAT0BvoCXbCyjDyd\noN0c4Eul1N9EZCJwNTChxjFzgAeUUq+KiAvL4uoHzg/XwfUAX4rI66oZufJSLuwppb4CBqT6c9IJ\nU5kUlxfX8tuaMngKuY7cpE76EWZ8PYOpg6dy4/wb456y2rvaPN2ImONr+shErkPQDLJm15o4/8vJ\nJ09m9Y7VTBk8JW771MFTsYktLiAjFp3KILVEhD2nTciy6wANTeaRbD7q5OrE9NOmU1xejNvuxhv0\nUpRbRH5Wftz5pjLjrEqDew5m9BGjozlcI5q+R5Y9woqSFWk7J+3w7pgUEfTAmjvHzh/rmXnmzEnd\nc7s3qaqGUmq5iHQJ++l1BnZhaexi+U+4fBnAicCLSikT2CIi85M07QfeDL9eCpweuzNci7eHUurV\ncD+qwtsdwH0i8ivAxKq/2xXY0pTvB40U9sLauYPCb1crpQJN/eD2TCThbU0N3tj5Y/nnGf9M6qQf\nocRbQtecrmmlNt8bqc98UeItiQp0UK2lfeLXTxAIBRh/3Pjo5Ou0ORGRuIAMrbltPfyxmj2HjUrt\ns6fJMJLNR6W+Usp8ZUz4ckJUaLv/l/ez2787TkFQM6XXuX3OrbVG3bHgDsYNHMeN829M2zkpqILd\nk7jJdG9m0y8CFwHdsDR9NaloQpuBGG1ciIbLXMOxhM5jlFIBEfkZcDXh86M0WHoQkZOBtVhqzunA\nmrDUqalBJPFtogEZUqFazrSTT57MvLXzou8jYfLaOb/tqStIIhAKJLzGBgbXf3g9Yz4cw6j3RjHm\nwzGM+WAMANNOmca8tfMSpkbQmtvU4YuJxnXaDZ1nT5ORJJqPqoJV3PLZLXFC2y2f3UJVsCru3KAK\nxs1XySwMec68tJ6T7GLfnCjvn13sm5vZ9Fzgt1gC34v1HLsAuFBEDBHpCpzclA9USu0BNorIeQAi\nkiUi2UAesC0s6A0G9m1K+7E0RrM3Cfh1ONACETkIeAE4prmdaG84bU6qglUJtTdBM8jUZVN55sxn\nLFOvv4y5383l3D7ncsVhV1CUW0S3nG5auMsAHDZHwmtsM2wJJ9GqUBV9Cvpw+wm3Y5rmXpVKp63x\nx0TjurQZV9OOMJWZcL6pmTMvkhw5cmwyC0NRbnVZxnSckzq5O908ZfCUWJ89pgyeUtLJ3enm5rSr\nlPpf2Ky6SSm1WUR613H4y1QHnG4AlgFldRxfF5cDj4vI3ViBHhdj+fG9ISIrgSXAd01sO0pjhD1H\nRNADUEqtCduVNTUodBVSFaxi8smTa+Vam/XNLEq8JRhi8LfP/xa90V794dVovcF0vME08ZjKxGVz\n1fLNm3zyZARJ6kgdIRKZm64TansjGo1rWNG4OkBD015w2VxJA8KKy4uj84zL7mLCoAnRaNx5a+fV\nWqMeHvww9rozi7Q5Tptz4YH5Bw6deebMFovGjaCUOjzm9c9Av/DrmcDMmH2miPxJKVUuIp2AxcDK\n8L6TY47LjXn9Elb6OZRSd8ZsX4sV1VuT45v7fWJpzFVdEk4W+Fz4/XAsiVNTA0MMumZ3RSnF46c/\njiEGQTPIzG9msmjLIqadMg2P21On878mfYlNbvqLbr/gsdMew2E4sBt23HY3ITNUSwicMngKnVyd\n6kzSrEkd/qCJzRAMI5JnTwt7mvZBobt24MaUwVO4b1F1Oq9pp0zjgPwD8GR74nyJ87LymDNkDn7T\nT0iFeGjxQ3HnpOvc5LQ5FzY1GKMFeVNE8gEncI9SqsnBE62BNDSSV0SygOuxolAAPgOmK6V8LdWZ\nAQMGqCVL2of8uKViCyPeGRGtonHl4VdSmFVIt5xudM3piiFGtF5qhgZhSGt9ULqNixJvCcPfGl7r\nSTo2dUo0JYtppWTxuD2U+krrPS/DSdsxcc+bq5izaB3PjBzIq8s38e8lG1gz4Tc47Rlzv2UyrTIu\n0m2eaE1i1xJDjGje1giReSZSSaPmmtOQOa2FabW5QmPRYM1eWKibHP7ba4nLq2Zz4MnqhL1qJwT9\nYHdCdmcwDJRScfmMnl75NCtKVvD2+W9H20qn3EWahhOb3PT8A85nRL8R2MSGP+QnaAaxG3bshp1u\nOd2SnhchXdMbtDf8QROHzRLsssICntcf0sKeJq2ppRBw5mN4d9Rab2LXkuLy4jhBD6x5xhv0srNq\nZ0KlQrK5yRu06rlnmCJCk4B6r56IrBSRFcn+WqOT6UIkr9qId0Yw5NUhjHhnBGvK1hL84h8wtR88\ndRpsW4UZClLqK40rL/OHo//A4J6DCZgB1u5am7DgtCYzcBpWYMb5B5zPJYdcwpgPxnDOa+cw6t1R\nrNm1hqCZOK2H03CSKIrMaaRfeoP2hi8Yqhb2HGFhT/vtadKYiLvI8LeGc8bLZzD8reGs3bUG882b\n4tYbzPi1JNk8s2bXmnAbtdefSMLmxpyjySwaIqqfDZxTx99egalMtldur5VXbcZXM9h+/DUUX/Ue\nJUMexPz4fnZ6t0cTIkeOu2PBHfzp2D8x85uZVn3Viq1QugHKt9a6WTWtg6lMSrwltWpB1neMoRQT\nBk1gZL+RCXMplnhLrGtaHn+NDcOolXZnwqAJGIZ+Yk41lmbPshy57DYAnWtPk9YkrIX78Vh2Hj3c\nOqB0Pfzrd7B7U9w6UnOeGdxzMI+f/jget4dxA8fxj+X/iKvvDfH13IFoguWnVz6dsCa4JvOo14yr\nlFrXGh1JZ4JmkM0VmwmZIcYNHBc1yfb39OfSvpcy8r3f43F7uPaIa+n1mwnYzGBClXh5oJxXf3gV\nAP/uTfDEqZDfC377AnToBgFvnGpekzpigyySBUskO6bA2ZE3fniDq/tfnbRWpbnzR3aWrcOflYPT\nV0Fhx32oynI3qIalpuXxJTDj6vQrmnQmqdtHhxj3kNL1ULqO4Jr3KTl+NAFlYjNsLNu6jHEDx3FI\nwSF4g1427NmA2+7GaTi5qv9VmGbttCzReu5BL2t2rYlW0oh+rnY3yWga7LMnIscBjwCHYkWf2IAK\npVTHFPUtLTCVyfe7vueG+TfUKilz5eFXcseCO/C4PXFFpKefOj1xlQxfWfS1s3ybtSPydHbWJJhz\ncbXw16WvFvhSSMKn5hr1aXd6Ex/z3G9mc84B5/Dz7p8Tp1gRB2t9m/jj8onVQuLxd9HZ2b1BNSw1\nLY8/aGI3LM1exE9Pm3E16YyRJIWTkRWz5Ob3IujIZs3RlzD2vSvjUkDN/W4uB+UfxK6qXXHVNSYM\nmoBI7fiIiN9fibeEiYsn6go/7YzGSBOPAr/DqqLhBq6iumhwu2Vn1c6ooAfVJtlIdG1xRXFU6Isc\nM+PrGQnNdTaxWQv/cXdQ+Omk6g8pXQ/5+0LPAdXCX+X2Vv+uexMNCZbwB72JNXcqyPgF45nx9Qzu\nPfHeuOt8/y/vxyYGf1x4R7yQuPAOTGDaCffoyhltQFWMz57LETHjamFPk4aEXUAMqFVp565Bd2EE\nwwkw8nvB0Ecpyc5nbA13kps+vomR/UaiUNG8epF94xeMJ2Amr3SayKSr56nMp1HZE5VS34uITSkV\nAp4RkeXALanpWnqQTCg4IO8AssJOrTXLzqwoWcHUZVN54tdPYCqTLRVbmLpsKg/88gHmnDmTwjf/\nhLExJkVAfi8oXQen3A4f3Q0bl1jRVpqUEXFIruvp1RkWzmseY5ohiiuK8bg9OG3OuLxVdsOOL+RL\nLEiqEH1MG3OOGldt3jVtGAqdiCDFJI7G1T57mjTDNK2gi3/9DmPIgzy/4d04t4/nVz3P7QP/Ajd8\nDSVr4KO7CV78z4TzjcPmAEWDqmvEUl9NcE1m0hhhr1JEnMBXIjIR2EzjNIMZSTKhwAZ0/vBeph13\nB9uC3lrHlHhL+L70eyYunsi4geMo8ZbgNux4srvCybfAlhVhjZ71dMZHd0P5NjjjPnjvVhCxbnxt\nyk0JkafXupJaFxpOph1/V1RLFzHHusLRuFcefiV//uTPtcbGzDNnJhYkFRgvjcRTur66I/m9YNQ7\n0KFIX+sU4gtUB2hk2bVmT5MGmKZlwYlNo1K53bLslK6n8NNJXH/mPfzxy7vi5p9CZYAtC3K7Qfk2\nbIY9aQWNZCUdXTZXnV3TacHaH40R9i7HEu7+DxgL7ANckIpOpROJhIKHB0+l+8cPYV/+HH22f0fn\nIQ8xdfDUaARurF9fcUUxhVmFTDt5MoWGC/YUQ3YnGPmWJex5d1Vr8wByOlvC39vjYPCt2ncvRTTk\n6dVwF9LH64nXxGV5IFz9pDJQmfCp2aYU0wbdyx8X/K16kh50L4XeMuuax1K6Hso2grdUX+sU4guZ\ndMiyprtI6hUt7GnajBgNXvSh/5I54OoYnSOMjUvo8+5tzPnVzfi7HIpzx48UOvIx3roZVr8FB58F\nV7yOTWzcNeiuqCtRZP2xkbi6xrRTplHo1ibZvY3GCHvnKaUeBqqAuwBE5Abg4VR0LF1IKBSYYPz0\nqbV/4xIKyzaR//W/mPXrf7A5sIedvp3RSKainCK6Z3emyzdvYvQaCC+OsG7m4S/CWzfHL/75vcCV\nD69fbwl/W1fCVR9Abtc2+vbtm3qfXg0Do3B/PK4O1tN3fnWkdJ+CPmyr2JLYgVqF6PPWX5gz+Fb8\nnQ/GaXdRqMAIVIZN9jWuecV2eOVqfa1TiC8QojDbMtHHJlXWaNqEGA0eYP2fOxxGvBE3Rxgbl+B5\n+y8w4k3ouA98cKcl6IH1f+tKjN+/x/Ornq9t7v3FLdokq4nSmCs+IsG2kS3Uj1ahIXnVEmEo8ARD\nFAX8eHxe60b57QvWTdlzAHTpi3HcdXQpKyY7FGDi4olRQW/aCffQZe4IjPwe1YIewCcPwrnTrTag\n2pwbEfTAOlb77qWOBLnwah0iUGKzUWy3UWKzYYZ96wwx6KJsTDtpUrwj8+ApFJoKY/B4PIaDomAA\njxnCcBeC4YDzZtS+5gum6mudYvyh2nn2dDSups0I+hNr+RFrTojMEWHtHcoEhwuKjowebvYcYOV2\nFWHMkdfFJfG//ohrKXR3Bqofaotyi/C4PVrQ20upV7MnIr8DLgX2E5HXY3Z1BDImy2JD8qolPjGB\nuv3c6Vb07NXzYXcxzD4PStdj5Peiz0UzmTPgb/id2Thd+RRWllkSdU7n+Jt74xL48E4YGS6fJmKZ\nbmsGbth1uHtKSHRda6S8qW/MGKEAfZbNZc4pj+K3OXAiFL5zK8Z3b1aPk1eusnwxf/sCdCwChxuG\nvwzenZZGL2LC19c6pcTm2TMMwWETbcbVtB12Z2Itf8ALix63fLc79YGQD54dWj1HDZsNgLn2PdZG\n/PkWFjO452Ce+vVT2ACnYafQ3RnD1qj4S007pyEi/hfAJOC78P/I303AGanrWsuSLK9avVnBE6nb\n542Bku8gWGWp3mP2GS+NxBOooqiqAs8rozFeGgkn/cXy0zv4LLjkOctcN2YRnDXFEvI69rAc9Aff\nGq/1+e0LltlQ0/Ikuq41Ut7UO2ZsdoyDfo3n2QsoKl6J55mzLUEv0t68MXB+eOKefx/4y6GyBL54\nGII+KxAnIujpa51SAjHCHljaPR2Nq2kzsjtXW4egWsu/8BE4ZTx4DgZnNsy9LH6O+vfl0P8Sdv7q\n5mjgBsD8jfO56v2rcDrceHK7a0FPU4uGVtBYBxwvIvsCfZRSH4iIGyvf3p4U97FFaHIR+mTqdkc2\nhAKJ9+Xvay30EH5CO9B6YjtpnHWzxkbhfvxAdSBGl76WIFijyLUmBSS7rjGm1HrHTChgaWfPuA+6\nHJq4vT1bLKFu6KNQucPy0xz6KKz8t3Weu8AaLx176GudQnxBE7utOr9NlsPQmj1N22EY1fN9wAsI\nlG2A48aAGYAXLoHzHks8pygTf7fDKV7YhPVMs9fS4NVFRK4GXgIeD2/qCbyWik6lgmSFnuvNCh5R\nt8eS3wsClWBzJN5XssZ6fcrt1kI/7SirfmFE0APr/+v/B0f+rlqjZBiWg37+PtZ/vfinjmTXNcaU\nWu+YsTstE+3cyyy/v0TteXdVX2u7u/r1QWda5712ndWOvtYpQymFP2jijNXsOWxa2NO0LZH53plr\nuQO9dh3s/LFam+fdlXhOMew4He6mrWeavZbGrDDXA4OA3QBKqbVAl1R0KhU0OSt4InX7udPBcwjY\nXVa4fOy+YbPhqxdg0I3Woh4R7hzZiZ/S3AXaOb8tSHRda5hS6x0zsW0YtsQBNwumWu9L14N/T/Vr\nd4E237YSgZBCAfYYYS/LblCpzbiadMAMwavXxK8HYM0dscEakfUlt6uucqFpNI0x7PuUUv5ITT0R\nsQMqJb1KAU0OQY+o23//AQS9IDZw5lhPYrPOgtwuVl3bwgOsJ7TsTnDOFPBXxgt3kae0mg65ke3a\nOb91iTWjJDGb1ztmYtvwV1rBGGfcB56DrIoosfkT83tZJt3I6/x9rfO0qT7l+ENWlLUj1ozeeB1M\nAAAgAElEQVRrt1GhNXuadCAU41ISu05sXGLNIWdNsoI1xADDCYZdp1TRNJrGjIxPRORWwC0ipwMv\nAm+kplupockh6IYBHbpCQW/LtypQaTnbn3GftX/OxVZErgA2e1g1nx2vgl8wNbHm56sXtHanrWiA\n2TzpmImkbdm9yXrvyK426c4bY2W4L99m7YtogxdMrdbmdeyhTfWthC+cYsWhNXua1qYB6Z3iXEpq\navPKt1lrw2d/h4f7wzO/jgaR6ZQqmsbQGM3eX4HfAyuB0cDbwFMNOVFEbMASYJNS6uzGdrLNqFnO\nxt0Jtn8Xn67jvBmgQpAVTrxbuqH62N++UH1s+Tbo0N3SEIb8VhSu2CwtoNbutA2x19fmtEyxAW/9\nwTGxaVtyu1jR1p0PtUz6c4dbT+SLHofLXoGqUvCVAwoueMp6CNDXu1XxBSOavRhhz2FQujt5MXiN\nptk0IL0TEHYHeR7+dWn13HFFOMtZ0GdF6C5/znqvXX40TaTBwp5SyhSRWcAiLPPtaqVUQ824NwDf\nYuXmywwS3ahXvF47Xcdr18IFT0DVbvj3FfE3dedDdHRtupIsf+KHd1bnxUtWviyStiW3ixWEE/HN\njCRAFbGCdF67Nt6MqytktAn+YG0zrpV6RZtxNSmkIkl6p99/YFmKIijTSrh+1iTLQhCotIS8HE91\njr0I2uVH00QaE417FvADMA14FPheRH7TgPN6AmfRQC1gm5BI1R6bh63nAMtka4bg3H/A5a9ZtW0v\nec5a8HM6xwdjRG5q7w4dXZuuJMufOOjG+Jx7icZG0G9d96H/AHuWNTZ6DrDKFz071Iq67VAUb8bV\npvo2I7Fmz6bNuJrUEvTGC2qRdSRQac0loaD1v2wDlK23qirNPMtyC3r+YsvKUDMAUM8jmibSGDPu\nJGCwUup7ABE5AHgLeKee86YC44AOiXaKyDXANQC9evVKdEhqSaZqz+5ULehFtDe5XeDUO+GNP8Zr\ng8RWb842TeNI+bhIlmfPXVD9OtnY6FhkjYPnL47PmRgJyAj5dc7EFNDUMRHR7MXm2XPpPHvtgjZf\nP+pCbNXBFrHrSMQKkCjvamQOKV1vBXktnG5ZCwy7nkc0zaIxo2ZPRNAL8yP1JFQWkbOBbUqppcmO\nUUo9oZQaoJQa0LlzGzyxJKukoELWDRmrvRk83tL+1NQGmcF6c7ZpGkfKx0WyPHveXdbEPPxFq0LK\nnmJLyIfqseGvqD0OXv8/SysYue46Z2KL09Qx4QtaQp0zLkDDRtBUUUFQk5m0+fpRFw53dVDeoBur\ny6CNfAtOvT1x3tVBN1rvI3NR1Frg1POIplk0ZuQsEZG3RWSkiIzAisT9r4hcICIXJDlnEDBURH4G\n/gWcIiLPNa/LLUwyDY/YrBuybL0lzNmzrIU70bG+PbXzIWl1e3qTLH/imnfhNw9Z2yKRtmdOtARA\nCGv8gonHQU5nfd3TkERmXJfDeq399jQpw11opVga/jJ07Qen32VlX5h5lhW4lcyykChHp7YSaZpJ\nY8y4LmArcFL4/XascmnnYAVsvFLzBKXULcAtACJyMvAnpdRlzehvy5OsILWI5WD71s3VavbLXk18\nbG4XK9J21DuglFa3ZwI18+xFonEHj4cd38df93Onw6l3wKxzohnsE46Djj102bM0JKLZc9TQ7AFU\n+IPkZTvapF+avQDf7ng3kKGPQsVWa21JNIfk97ICNWrm6NRWIk0zafCqpJQaVcfflSJySyo7mjKS\nVVIIBWqb6v5zuxWUUfPYDkXQsTvk9dRmu0wi1tTaoasV/WYGE5vqOxZVX+/cblYm+1qZ7btZbgF1\n5dTStDq+QIJo3LBmT/vtaVJGIhehiKk2UXWMS+ZYUbmd+kBO1+rt2lqgaQEao9mrj4uB+5PtVEp9\nDHzcgp/XMiSrpFC2vraaffVbMORBGPGmlc1ca/DaHyqU3KwfW/Giaz9LkxsKWDWSc7pCyer6c2pp\nWp1IBY2aPnugzbiaFFJXEFgkn17EGmQG4b2/WWtMRPA7a5I1d+g1RtMCtOQIkvoPSRNqptOA2s70\ndndiB/5QwFrUHW6twcskGpLJHpJfd0c4GXJEc+fdYWl0C/ezNLpVOxMH+oSz3Wvajqhmz17bZ69C\np1/RpIq6gsDye8HgW605xO60gjBWv2UdU7reSs4esTzoNUbTArTkKMqMOrmRdBpPnQZT+1n/t62q\nvfjnJDDvnv8EvHK15c+1u1ib6TKFhl5zSHzdI6l46moj2VO8dqxuc+ry2dOaPU3KcBXWdve45Dno\nMcCyEkS0/nru0LQCLWnGzQzNXrJUKzWrG0TMu6PesZJg7voZ/jO+2ml27nBdESFTaOg1h+Rm/fra\nSBboox2r2xxfggoaWXat2dOkmIqt8MlEK92Ku8DS6H38IAyZCLndq4/Tc4emFWhJYe/FFmwrNZim\nlZW8oU9RhmGp2Xf9ZGU1b8g5mvSjJZ6c62sjEuhTKzm3dqxuaxKnXrE0ezpAQ5MyQgHLNBsxz0Y4\n497493ru0LQCDRb2wmXPHgFOxDLZfgbcoJTaCKCUui8lPWwpIqa8PcWNe4oyDHDm6ievTKYxT87J\nqmbkdK67jWQaQe1v0+b4ApZAZzdiNHuRaFyf1uxpUoTNkXjOsNVI9aPnDk0r0JjR9AzwOtAdKMJK\nqvxMKjqVEiJmuE8ebHwC5KR+XPrJKyNIll4n0fVLZq41bPW3oatmpCW+oInTZiASk3ol7LNXGdCa\nPU2KcGTDsGdrpGh61tpeEz13aFJMY8y4nZVSscLdTBG5saU7lDIiZrjS9VbCyogfRf6+9SfC1U9e\nmU1jrl8yc23Aq8dAhuILmjjs8S7FDptgCFT6tLCnSRH+CvjvU3Dpi9bDohmChY/ASX+F7MK27p1m\nL6Mxwt4OEbkMeCH8/nfAjpbvUoqINeVtXAJzL7PeX/VBwxbsyJOXJjNp6PWry+Srx0BG4guG4nLs\nAYgILodN++xpUofdCT99CstjKoTm97LKcGo0rUxj1BJXAsOALcBm4CJgVCo6lRIaY8rT7L3ocdLu\n8AXNuOCMCC67jUodjatJFXou0aQRDdbsKaXWAUNT2JfUok2xmoagx0m7I6mw5zC0Zk+TOvRcokkj\nGhONOy3B5jJgiVJqXst1KYVoM5ymIehx0q7wBcy4HHsRshxas6dJMXou0aQJjXnEcAFHAmvDf/2B\nnsDvRWRqCvqm0Wg0zcYXDCXU7GXZtWZPo9HsHTQmQKM/MEgpFQIQkcewcu2dCKxMQd80Go2m2fiC\nJvZEmj0t7Gk0mr2Exmj2CoDcmPc5QGFY+PO1aK80Go2mhagKJNPsaTOuRqPZO2iMZm8i8JWIfIxV\nB/dXwH0ikgN8kIK+aTQaTbOp9IcozK5dLSXLYVBZpjV7Go2m/dMgYU+s1PPvA28DA8Obb1VKFYdf\n/zkFfdNoNJpm4/UHycpz1dqeZbdRoculaTSavYAGCXtKKSUibyulDgcyI/JWo9FosDR7WeHyaLG4\nHAZeXS5No9HsBTTGZ2+ZiBybsp5oNBpNCvAGQrgciX32qgImIVO1Qa80Go2m9WiMz94vgOEisg6o\nwPLbU0qp/inpmUaj0TQT01RUBUyy7ImTKoMlDOZmNWYqTDG71sFnf7den3on5HRq0+5oNJrMpzEz\n3Bkp64VGo9GkgIiZNpEZN7Kt0hdMH2GvfDs8MwQqtoMKwtb/wZXvgy1N+qfRaDKSBptxlVLrwiXT\nvICK+dNoNJq0JJJHLyuBGTei2UurXHsf3AHlW+A3D8KgG2HTUljxr7bulUajyXAaLOyJyFARWQv8\nBHwC/Ay8k6J+aTQaTbPx+pNr9lzhbRXpkmtv41L4ag70PRc6HQj7nWz9/3wKKP1crdFomk5jAjTu\nAY4D1iil9gNOBb5MSa80Go2mBagMWIKcK4HPXkTb500HzZ5S8O5fwV0I/S+xtonAwUNgx/ew8b9t\n2z+NRpPRNEbYCyildgCGiBhKqfnAgBT1S6PRaJpNha8uM25Es5cGwt63r8PGxXDUZeDIrt7e+0Sw\nOeGbV9qubxqNJuNpjLBXKiK5wKfAHBF5GCsqNykiso+IzBeRVSLyPxG5oTmd1Wg0msawuyoAQLaz\ndoBDJELX29Zm3KAPPrgT8veFA06N3+fIhm79YfXb2pSr0WiaTGOEvXOxgjPGAu8CPwDn1HNOELhZ\nKdUXywR8vYj0bUpHNRqNprHs9lrCXk6CaNuoZs/Xxpq9j+6BnT/CgCvBqO1byD4DoXQdbF/d+n3T\naDTtggbH8yulYrV4sxp4zmZgc/j1HhH5FugBrGpMJ1sC01TsqPDjD4Zw2m10ynFiGNLa3dBoaqHH\nZuqICnvORKlXwtG4bVVFwzThs0nwxSOWb16PYxIf13MgMB3WvAtdDmnVLrZH9P2m2RupV9gTkT0k\nTrESSarcsSEfJCK9gaOARTW2XwNcA9CrV6+GNNVoTFOxeusern52CRt3eelZ4ObJKwZwcNcO+iZP\nU1pjXKQDemw2nKaMibIGaPbaxIxbsQNevQa+/wD2HwwDfp/82ByPZeL96RM48ca4XUopft79M18U\nf8FX275i3e51GGLQv3N/Ljv0Mnp1bL/3DjR+TOj7TbO3Uq8ZVynVQSnVMcFfh0YIernAy8CNSqnd\nNdp/Qik1QCk1oHPnzk37FvWwo8IfvbkBNu7ycvWzS9hR4U/J52maT2uMi3RAj82G05QxUeYNkGU3\ncNhqT3XOsGav1c24xV/BjBPhx0/guOvhxJvAnlX3Od36w/qFELTGxfbK7Ty6/FHOfPlMhr42lAcW\nP8CSrUuwGTZMZfLSmpc4b955vLK2fQd2NHZM6PtNs7eS8rTsIuLAEvTmKKXaZObxB0PRmzvCxl1e\n/MEQ2/f4aqnzg0GTbeU+AiETh82gS24WhiFa9a9pMSKmpEp/MOnYrO9cfzCEiGATMAxDj8kElHkD\nCbV6AIYIWXYjWmWjVdjwX5h9nhV4MeQhK49eQ+h2OHz3BmxayrzANu5bdB/eoJfDPIdx6r6n0q9T\nPzpnVws7pb5S/rnyn9zxxR2YyuSigy5K0RfKLJKtBd5AiE27KnHYDeyG4PWHcDttBE1FIGjqOV+T\n8aRU2BMRAf4JfKuUmpzKz6oLp91GzwJ33E3es8BNyFScP31BnDr/QE8Oq7eVc+1zS6PbZ1x2DNlO\nG1c8vVir/jXNJtaUdNvZfROOTUeCvHA1z42MxQcv7M+sL35i7OkH6zFZg50VgTpLobkcNip8rWTG\nLd8Gc4dDVgc4437LPNtQuh0OCM9+/TgP7VrKIYWHMKLvCLrmdE14eH5WPjccfQOPLH+ECV9O4MD8\nAzmyy5Et8z0yGIfdSHi/FZd6+e0TX9KzwM1DF/Xn1WWbOP/oHvz5pRV6zte0C1Kt2RsEXA6sFJGv\nwttuVUq9neLPjWKaCoVi7jXH4Q2Y7KkKIEC3PBfeQIhnRh7Lk5/+yL+XbuTqZ5cw95rjooIeWE99\n1z63lHvO7VdL9f/qmEF07lCP+UWzVxKrfYvVEDjsBiio8AW57ey+fLhqK49eehS7KgJkO21U+kMU\n5jiwJ1lQEpmh/vLyCm47u68ekwnYUualMMeZdL/LYbReUuW3bgbvLhjy98YJegBZHfi8y378fedS\njul2DKP7j8Zu1D192w07o/uP5u6Fd/OnT/7Ey0NfJi8rrxlfIPOxG8Ls3x+L3bARUgqbCEEzxJYy\nHwCdc7OoCpiMGXwgP5VU0Dk3i427vHrO12Q8KRX2lFKfYwVytAkRLciU/6xmxAn78ZeXq5/SHrqo\nPxPfXc32ch/Thx8NwL+XbiRoqoRq/uwa0Xz1mdo0ey+x2rfOuVmMO/PgOA1B7NibdPER2ES4bd43\n0f2TLj6CQNBM2HYyM1S+26HHZAI2l1VxxD75Sfdn2W2tUy7t+w+txMlHXQ6F+zf69MpQFXflCgf4\nAlx96BX1CnoRsh3ZjD5iNPcvup8JX07goZMeavRnty8U5T6T6577b/R+e+yyYyjKd3HUPvn86YyD\n49aJBy/sz9/fW83yDaX6/tJkNCn32WsrTFOxZXdV1FQWuYHBWhz//JKlDRk9eymPfrSW8WcfxrBj\n98FuCKN/2Zuje3ci3+2g1Bvg5aUbahVL71ngxpmg3qZGE6t9u+3svlFBD2qPvZtf/Jopw47ktrP7\nku92EAiZGCJUBU227/HV8iMNmop/XXMcSikMkej4LPUGGjwm95bUE75giB0VfjrVodnLshu17u0W\nJxSEd8ZBhyI47IImNTFj3TtsUQFml+zAVfI9e3oc1eBz98vbj6EHDuWVta8weJ/BDNl/SJP6kMlU\nj3mT62pYbq57bilzrzmOycOO4P53vo3ei6XeALO++IlrTz6A0bOX6jlfk9G0G2EvdgFz2A18gRC+\noIrTesQS2X7UPvmMGXwgP2wrJ9tpo7i0inOO7Bnns/fYZceQ57ZHfT0ifnwFbkcbfVtNOpIo6KKu\nsQeW2cjlMLjnzVXRsTVl2BHc+K+v2F7u4/HLj6F7XhabSn3RRSqiHXzgne+imum3vt7Ek1cMqFOw\nifRxb0k9sWGn9bt7cpOb3bLsRup99la+aNW3PflWsDV+zthStZPZmz7ixLyDOeLnjWzeuLxRwh7A\nb3r/hq+3fc2ERRM4uuvRdMvp1uh+ZCqmqfh5RwXrdlSyf+echPdj0FT4gqFaFqAHL+xPR5c9ep/U\nd39pNOlKYypopC2RBez86QsY9OB8Lpj+BbsqA5SU++hZ4I5qPWKJbB935sF4/SFum/cNlzzxJeW+\nYC2fveueW0pxaRW3nd2Xudccx21n92Xah2vYFc7hpdHEjsENOyuj462usQfwx1P7cN2cZXHjbey/\nv+bmXx/Exl1eRs9eSoWvtjbizy+t4NqTD2DjLi9j5ixjxKD9GySw7U2pJ/5XXAbAvp2ykx6Tk2Vn\nV2UK7+NQED550DLd9jq+SU08s/EDlFKc230QFQW96LhpWaPbsBk2rjr8KvwhP7cvuB21F5VeK/X6\n2bq7itvmfYPdkIT3o90QsrMctSxAf3l5BYU5Tl4dM6hdPhBp9h7ahbCXaAEbM2cZSikevLA/Ly/d\nwIMX9o/e5BHNyIyPf6BbnivOzJbttCV88hNg9OylXPLEl4yevZT3V23T/huaKLFj0GYID11kjbcZ\nH/8QfQ3xY69ngZvenuyE461bniv62lSJ/Ugj2sGIZqIh1JWGqL3xyZrt5Dht9KixuMeS53awo9yX\nuk6s/Dfs+gmOuBSk8YJCib+Ml7Z8zvEFh9LJ2ZGyzgeRs30Ntqrd9Z9cg645XRl28DAWbl7Iv1b/\nq9HnZypefyg6x9sNYfrwo+Pux+nDj8ZuCCHTTHhvhExF5w5ZWtDTZDTtwoybbAEzRJj47ndMHnYE\n5b4gz4w8lnJfkJCp2KfAzaRhR2CIxJ0b0cTUDM2vuZRq/w1NLLFj0BDhgXe+i/r+mErxwAWHU5Rv\npXiwGwZTLjmSn0oqcNoSp4KwhQWDngVubGFtRM1jItrBngVufthWToUvWK/2IVkaovY2ljfsrOSN\nr4s56aDO2I3kz7R5bge7q4L4giGyWvo3iGr1DoB9ftGkJp7d+CFBM8RZXQYAUNblEHqsfp+Om5az\n64CTGt3eyT1P5qttXzFpyST6e/pzmOewJvUrkwjFPCzt8QV5buE6nhl5LDZDCJmKJz/9kWtO2h+l\nSHwvaiFP0w5oF5q9yAIWS2QxXL6hlDXbyrluzjJOn/Ip50//gotmLKSk3E+5L8jmsqq4c5NpYvYt\ndMdte/yyY7AZlvlOo4kdg6XeANvLfVFN8O+eXMRfX1nJ2m3l/PWVlWQ5DKbP/55RM/+LQiUcb1t2\nV0Vfl1cFamkjYrWDD17Yn2kfruXqZ5ewuczL9j2+pOOyU46TJ68YENdWe/RFuvftbzFEOO/IHnUe\nl5dtaUd3lKfAjL1iLuz6uclavbJAJXOLP+PY/IPomlUAQEX+PgQdbvI2LGlSl0SEK/tdSQdnB67/\n8Ho2lW9qUjuZhMtRfW9uKaviix93cPqUTzll0iecPuVTvvhxByXlfp789MeEWr/srHaxTGr2ciSd\nfDcGDBiglixp3CRmmoqSCh+VvhA/lVQw7cO1bC/3MeOyY5j24RreX7WNX/ftwh9OqfaN6lngZuao\nY6PlkvZ4g4yODcgYfjRl3gAOm0GpN8CMj3/g0UuPwmE3an1Oe3VubwCt9oWbMi5am5rpVu4c2ped\nMbnz9il0k+20UeELRfM6Agw7pifXnLQ/G3Z6o8f2KHDhC5iUlPspzHFw5+urGNg7n8tP2A/TVDhs\nBobAzzsqo+Nz+YZSAOZecxw3v/h1neMyhdG4aTEmSsp9DLz3A87uX8TvBtZdL3XJup1Men8N864f\nVGeKlkYTCsKjA0AMOHtqk4S9J9a/wyM/v8FdfYazj7u6OsYBi2eSs2cLK4bPaVK7AJvKN3H/ovvp\n6OzIU2c8xb4d921SOw2kVcZFsjERG6DRs8CFL6hqJc33+kOYSpGbZScvHBUfDGv9bjitDz0Kkvt9\naprEXrdgtjUZbcZNFFn4+GXH0D3fRccsB3cO7cc1v/Kyo8LP7IXruO3svnTKcVKU58JuM1BKETQV\nhTkO5l5zHGA9+d75+je8v2pb9HNizVyX/XNRnJpfJ9rUABiGcHDXDrw6ZhCmaVJS4Y/Lnffk5QNw\n5dgY9viXcePnix93cOtZh5DttBMMl+ezGUKVPYQnNwunQ3j00qNqCWXb9/i4+cWvE5p260sAaxjS\nrsfrf1ZtxVRwwgGd6j22W0fLN/LHkvKWFfZWzLV89U65rUkCWVXIz3Ob5nN4h95xgh7A7i4HU7h5\nBa7SDVQV1C3MJqNHbg/GHTuOyUsnM+KdEcw4fQaHFB7SpLYyAV/Q5LZ533Db2X15eemGuPQqb3y1\nkStO2I+QqfhhewXjX/sm+vDUs8DNn+3t93fR7D1ktH46UWDG6OeWEjKtBc1uE2vBtBms3VbOPW+u\nIifLTrc8N106uuia56ZHQTbd87PpUWD9devoYuzpByc0c+1Nzu2axhMRogzDYPTs+OjZq2cvsTQF\nCUyoHV1OivLd9OqUQ/d8a2z26pRD144uTDOxoJDIHPvghZZpN/KZe+u4XLZuFx3ddnoV1q+N6Zbn\nwm4Iq7eUt1wHQkH4NFz3tufAJjXx2taF7AqUM6TzgFr7yrocDEDe+sXN6mavjr34y7F/QaEY8c4I\nFm1e1Kz20pXYdSLf7WD7nniT/eKfI4JdNt3yXGwPB+y0VxcHzd5JRmv2kglfpmkm1fjlu+s2WcVq\naGqaufYW53ZN80g2LgNBM+nYqkl9+fBix6k3EOKHbeXRTP+wd4/LFZvK2K9TDtIAjZrdMNinMJuv\nNuxqwQ40T6sXVCFmbviAA7K7c1BObZ9Df3YhFf/P3pnHR1Wd//997izJJIGEEPawqbigggoqiFXi\nUsCNtu6CC9aFUqtYf1prrVvRVqyCVFH0q+K+1KpYF3BDtCgqKCKigKCyQ0IWyDrLPb8/7kwySWaS\nmWQmM5l53q9XXpm5y7nP3PPcM8+c8znPye1H/saP2Dn8rHaZ2ienDzcdfROzVsxi6ntTuevYu5gw\neEK7ykw2gp9HU+uQK9q4nLYW235B6Ox06p69cBMzfJoWe/xaI9BD069bVqMp9+kibhfaRzi/dNpt\nYX2rKZHkwwuUVZjnkh4JP7UeHz/srGRwQU7E5xzaL5flP5WxtzYG+fZi0Ku3YMcyttbt5pQeI8MG\nrGV9h5Ozcw3OvTvbYy0A+Zn53HjUjeyTuw83fHQDT695ut1lJhPBz6PP1CFXtAmkLor0+RSEzkan\nDvbCBV86TF6y9g5rBf/yW/qnIkm0KYQkFj8KopEMiF828N32Pfi0Zp+C7IjPGd4/D6+pWfrD7vYb\n8OWT/rx657epV6/aV8cDP/+X/bL6cljX8GvolvYdDkD+Dx+21dJGZDuyuW7EdYzoOYKZX8zkkVWP\nxKTcZCD4eXTYjLC97oKQynTqYdxw3e67q9xxG25NdXG70H5iMRwUrWRA/NJi9VZr1YzBPSIP9vbv\nlUOW08aHa3cx/pB2LCNWUw6L74Reh7a5V++Jze9S4t7D5f3HtzgMXZfTg73d96Hnt6+zY/hZYLS/\nbXPYHEwdPpXHVj/Gv776Fz7t43fDf9fuchNN0+dRpDhCOtKpe/YgdLe7DLcKiaa9w0Hiw21j9dY9\ndM20R3Wf7IbBIf1yWbx2V/uWEVsyE6pL4cjftqlX79u9m3h080JG5R3IkOy+rR6/c/CxZFTuJG9T\n7CZWBJZVO6bvMcxdOZcHVz6YEkurBZ7HPrkuea6EtKRT9+yFQ4S2QmdHfLhtfLO1gkEFkU3OCOaw\n/nl8/mMpa3fu5cDeXaO/8KZlsGwu7D/e0utFyV5vDX/+fj5d7VlM6js2onPK+xxKXVY+/b6YT/mA\no2PSuwdgKINLD7kUQxk8/PXD+Ewffzj8D1Hf02REnishXen0PXvhEKGt0NkRH46OWo+PdTv3MjgK\nvV6A4YVWjr3F3xdHf+E92+HfUyCnF4ycEvXpVd5arvn2YTbV7OKy/uPItmdGdJ42bGweehpZuzfS\nY80bUV+3JQxlcMnBl3Bcv+N49JtHmf3l7JTo4QN5roT0JGWDPUEQ0ovPfyzFa2r279Ul6nPzs50M\n6p7Fh2t3tX5wMOWb4OmJUFsGRTeBI7qVFr6r3MzklfewouIHftv/lxyU0z+q88v6Dqeix/4M+OQh\ncratiurc1jCUwUUHX8TYwrE8vvpxpr43le2V22N6DUEQOgYJ9gRBSAle+2orTrvBwX3bMAwLHNa/\nG8t/KmNzaXXrB+/dAUvvh4ePhYotUPRXyA8/ezaYck8l75es5I9rHuW8L/9BiXsP0wdPZFS3NqzU\noBQbRl5IXVY+B7xxA31WPIO9JnY5Aw1lcOHQC5l80GSW71jOKa+ewi1Lb+GTrZ+wxwSb7GsAACAA\nSURBVL0nZtcRBCG+pKRmTxCE9EFrzW+fXM4H3+/i1EP7kNHGmZUnHdSTt1dv51cPLmXmWcM48aBe\nDTtXPGklS67bA5XFULnD2t73cDhqKuQ2T35c5qnkz9/Pp9Z0U+fzUGO6KXZXsMdrBZM5tkzG9TiC\nCT2OJCfCodtQ+JzZfPeLPzB45UsUfjGfwi/mU5fTE4+rG76MHNadcle79HxKKU4YcALDewznzY1v\n8taPb/HqD68C0MPVg26Z3chx5JBhy+DsA87m5IEnt/lagiDEB5VMOgylVDHwcwSHFgAlcTYnWtLN\nphKt9fg4ld2IKPwiEpKxnmJJIj9fZ/WJSEkW3+lsdnSIX7TgE8lyvxJNMt2HDmsrBIukCvYiRSm1\nXGvdfNHIBCI2dQ5S/Z6k+udLJMlyb8WO6OgsdsYbuQ/pjWj2BEEQBEEQUhgJ9gRBEARBEFKYzhrs\nJePCjWJT5yDV70mqf75Ekiz3VuyIjs5iZ7yR+5DGdErNniAIgiAIghAZnbVnTxAEQRAEQYgACfYE\nQRAEQRBSGAn2BEEQBEEQUhgJ9gRBEARBEFIYCfYEQRAEQRBSGAn2BEEQBEEQUhgJ9gRBEARBEFIY\nCfYEQRAEQRBSGAn2BEEQBEEQUhgJ9gRBEARBEFIYCfYEQRAEQRBSGAn2BEEQBEEQUhgJ9gRBEARB\nEFIYCfYEQRAEQRBSGAn2BEEQBEEQUpikCvbGjx+vAfnrHH8dhvhFp/nrMMQnOtVfhyA+0an+hA4m\nqYK9kpKSRJsgJCHiF0JTxCeEpohPCEJ4kirYEwRBEARBEGKLBHuCIAiCIAgpjAR7giAIQlpR6/Hx\n5qrtmKbIx4T0wJ5oA1IVU5uU1pbi9rlx2pzkZ+ZjqMhj6/aeLyQ3kdSv+IDQXgI+ZJomJiamNsWX\ngPvfX89DH25g/pQjGXtAz0SbIwhxR4K9OGBqk/Vl67n6g6vZVrWNvtl9mXPCHIZ0GxJRA9ve84Xk\nJpL6FR8Q2kvAhx786kEuGHoBty69VXzJz6bSagB27qlNsCWC0DGk55MeZ0prS+u/pAG2VW3j6g+u\nprS2tEPOF5KbSOpXfEBoLwEfmjhkYn2gB+JLQH3yj7213sTaIQgdhAR7ccDtc9c3rAG2VW3D7XN3\nyPlCchNJ/YoPCO0l4EO5zlzxpSZof7RX7fYl2BJB6Bgk2IsSU5uU1JSwrXIbJTUlmNpsdozT5qRv\ndt9G2/pm98Vpc0Z0jfaeL3QckfhDUyKpX/EBIRTR+FvAhyrcFeJLTajzWPetqk569oT0QIK9KAho\nYCa9OYlx/xnHpDcnsb5sfbMGNz8znzknzKlvYAMamfzM/Iiu097zhY4hUn9oSiT1Kz4gNCVafwv4\n0IL1C7h9zO3iS0EEevSq3BLsCemB0jp5pp6PHDlSL1++PNFmhKWkpoRJb05qNCTSN7svz576LAWu\ngkbHpsFsXNVRF0pWv4jGH5qSorNx094n4klb/C1JZuN2iF9E4xMTH/gfX2+p4DeH9+O+cw+Ls2VC\nCDqsrRAsZDZuFESjozKU0eoXfku093wh/rRHVxdJ/YoPCMG0xd/Eh0JTJT17QpohwV4UBDQwTX9Z\nh9O+RNoz054enE7Y+9NpaXqvM+2ZUfkDgNf0UlJTgsfnwWFzUOAqwG7IYyi0TtP2Z1jBMKYOn1qv\n4wsMy7a3PUiHNqXaH+RV1ckEDSE9SK0nOM5Eo6OKVF/TVt1Xe88VoiPUvS6uLubhkx+OWAvlNb2s\nK1vHxW9fzCmvnsLFb1/MurJ1eE3pXRBaJ7j9GVYwjOlHTGfGshlMeGUCk96cxM97fm53e5AubUqt\nf4JGrVeCPSE9EM1elET6qzdSfU17dF/tOTcGpJU+K+y9PuVZUETUC7KjagcXv31xszKenPAkvbN7\nx/0zdABp5ROJILj9mbJwSiNfmnviXGYsm9Gu9iBObUrSafYOvnUhVXU+hhXm8vpVx8bZMiEEotnr\nYGT8KEoi1cBEqq9pj+5LcrF1HGHvtemmb07fMGc1xuPzhCzDY3piZqeQ2gTan22V25r5ksvuand7\nkC5tisdrdXK4vanVYykI4ZBgL044bU6KCouYOGQiuc5cKtwVLFi/AEMZmNqs7/2JVgfY9BptPVeI\njnD3OtOeSUlNSbOevVA9wA6bI2QZDsMRlS3poKkSWiaUPxrKYO6Jc3HZXVS4K3j8m8cpqSlp1uaE\nIuBTQMq3KVpr3D4ryKuTYE9IE+QbIk7kZeQx9bCpzPx8JlMWTWHm5zO5cviVPPvts400MO3Jp5aX\nkcesolmNzp1VNIu8jLz4fbA0JVQ9PXzywxRXFzfTN3lNb0jdU/fM7iHrK5rhsXTRVAkt09QfiwqL\ncNldzFg2o769mX7EdO45/h7uWnZXiz4S7FPXL7meGWNmpHROPo+vQbokPXtCuiCavTgRTvtyw1E3\nMPPzmY00MG3tqSmpKeGOT+5o1nt4yzG3iGYvDjStJzRMeqt5HT854cmQ2rxnT32WvIw8azau6cFh\nRD8bN8E6zdZIO59IJMH+aCgjpM/ddsxtXPHuFS36SFOfCszyHZw7mEx7Zix6jpNKs1dV5+XgWxcB\n0KNLBl/85aR4myY0RzR7HYwM48aJcNqXwDqVwRqYtubCcvvcLN6ymMVbFjfafqPvxrYZLbRI03oK\npZsKaPDC6Z7shr1dkzHSRVMltE6wP4bzxYBEoCUfaepTq0pWMe39aSw6c1Ey/ICIOcG9edKzJ6QL\nEuzFiXAarwp3BUWFRRjKYFvlNgxlYGBgGEaLeq9QqysYykh5fU0yE66OHYaDSw66hF/t/ytsyoZP\n+3ht3WuN6qWtvbmi0xSa0lJbEJj80ze7L06juY+kYzvi8ev1MuyGBHtC2iCavTgRSk9339j72LF3\nB1MPm8rFb1/MuP+M4+K3L2bjno3c8ckdLeq9gvU2AY3NXcvukjUvE0hLmskJ+05g2nvTOP2105n2\n3jQm7DuBro6uQPt0d7JmrhBMwJee/fZZ7ht7X7P2pntmd4oKi5gxZgZ7PXulHaFhUobLYasP/AQh\n1RHNXpwIp6f789F/5pKFl4TV8rWk9woMqQRrbIYVDOPSQy8lPyOfPjl96JnVs6NmZqa9PivaOp4/\nfj59cvq0W3eXxLNx094nOpqAL91w1A0sWL+gmS+efcDZ9M3pyy1Lb6GkpiRR7UhSafY2Fldywr1L\n6JObyfaKWjbcdQo2QyRkHYzc8A5GhnHjRDg93fVHXd+ilq8lvVdw2YFjVpWsYvri6QAsOnNRsnzp\npwVh6/jI0HUcWCmjvbo7We9UCBDwpVxnbkhfvOjgiyirLWNVyar645ueC+nVjgTSrrgcNuu918Tl\ntCXSJEGIO3EN9pRS/YGngF6ABh7RWt8fz2vGA1OblNaUUuurxVAGmfZM8jLyWmwQw2mr7MoeVssX\n0Hu1pp8JLjv4F7mhDLyml/K68mTs9emUtNSLFraOjdB1bDfsMdFIJXHPnhAHGs26xQBlbcu0Z+I0\nnI3aj6Y+VeOtwW26698HfCzgh0+Nf4rSulKWbFrC8QOOr29HWsvL15kJJFTOlGBPSCOifpqVUjlK\nqZwID/cC12mthwKjgN8rpYZGe81EUq+vemsSE16ZwJSFU9hQtoGf9/zcosYqlLbq9jG389ya55rp\nvG4fczsL1i9gzglzKHAVtKrJCpRdVFjEH474AzM/n8lFCy/irmV3sa5sneRgixGtaetynbkhNXvd\nMruF3N49s3u7NVKSZy+9aFrfFy+8mJ/2/MQ/PvsHG8o2UOurZc4Jc1iwfgF3HntnI5+aMWYG+Rn5\nPP7N4418LFDmxW9fzEULL2Lm5zM598BzWbB+ARctvIiL3744pX3K7bPWw83yB3h1PlkfV0h9Itbs\nKaUOxeqly8caby8GLtZar474YkotAB7QWr8ban8y6nDC6atuHnUzB3U/qMXhNFOb7KrexfbK7ZTW\nlfL4N4+zqmQVRYVF3DTqpvpfz22djburelcjfd/sotnM/HxmR+VgS3l9Vmvauh1VO7hr2V3NdFI3\njbqJ7pndKakpwWt6sRt2ClwFVLgr2q2Rkjx7FsnYVsSD1vJ13jzqZg4uOBhTm5imiYmJ1/RaIxC2\nTFBQ661t1I60VGZgKDfGPpVUmr1PN+zm/EeX8Yv9Cvj4hxL+96ciCrtldYCFQhCi2etgohnGnQf8\nUWu9GEApNRZ4BDgmkpOVUoOAw4HPmmy/ArgCYMCAAVGY0zGE01e57K5WNVaBwO2ihRc12r54y2Ju\n1DeGXVM1Ek1WoOxg2wK6v6a2dsYcbMngF61p6zw+T2jNnnk9DpuDPjl9wpbXVo1UOufZSwaf6Gha\ny9fpsruo9dZGvD5za2UGv+8MPtUWn6jX7DkbhnEFIdWJZhg3OxDoAWitPwSyIznRP+z7H2C61npP\n8D6t9SNa65Fa65E9evSIwpyOIaDLCiaghQkEXOEI1sXMLprNsIJh9edHm8PK1CYlNSVsq9xGSU0J\npjab2RbQ7TS1tTPmy0oGvwhX906bs77HrqiwiNlFs3li3BPMLppNUWFR2LVuWyovFjalOsngEx1N\nuPoOPOuBYdlAmxCO4PYjoBkNVWbw+87gU23xiUBwV6/Zk/QrQhoQTbC3USn1V6XUIP/fzcDG1k5S\nSjmwAr1ntdavtNXQRBFKezdjzAxyM3JbXHMylC7mD0f8gaLCoqhzWIXTaeVl5DWybcH6Bc20Yqmc\nLyvehMtpl+vMZV3ZOj74+YNm6x9PPWwq3TO7R1VeNPUjefbSi3Da3wXrF3Df2PuYvWI2E16Z0KJ2\ns2n7cdeyu0JqShesX1D/PpV9yiM9e0IaEo1mrxtwO3Csf9PHwG1a67IWzlHAk0Cp1np6a9dIVh1O\nYDZuja8GU5vsrNrJ/V/ez6qSVWG1LeF0MU9OeDLqHFYt6bTyM/Mb6fvyMvI6ajZuWuizQuknA1rJ\ngG4qGv1cLGbSJvFs3LTwiY4mUN+13lq8phebsmEog7s/v7uRhCCatihYN9wB7UZSafZe+2or019c\nySXHDGL+Jz/x8tTRjByUmoFtEiOavQ4mYs2eP6i7OsryxwAXAt8opVb6t92ktX4rynISiqEMCrIK\n2Fa5jVNeOaXRvnDalnC6mLakNGhJpxVK35cEQv2UIdT99fg8jXRTwbSmdYpFjjzJs5deBOp7W+U2\nTn/tdACeGPdEM61oNG1RKN1wuvhUqDx7gpDqtBrsKaX+i5UjLyRa6zNa2Pc/UiiCj2Zd0liuYSrr\noSYXDpujxdxmUi9CPAhuB6LxPWk/GuP2Ng726kSzJ6QBkfTs/TPuVnQSAvqZqz+4mm1V2yxtS9Fs\n8k0wfV5K3eX1vW0GBk+Mf4I9dXsoqysjw5ZBd1d30ESU+Dh4qM5hOLi/6H6uWXxNw3VTWFOTTIQa\nMi1wFTD3pLlU1FUwq2gW1y6+tr5e7i+6H4/PQ2lNKXW+OrymF4fNQYGrwEqq7PNSWlOM2/TiNOzk\nu3pg2GQhGyE0ps9LeW0ZtdqDz/Qxf/x8nlvzHI9/8zgzxszg5qU3U+AqYOrwqQzoOgCv6WVX1S7s\nNruVikWbKKWYXTSb6Yun1/vp7KLZKZ88ORwBzV6mX7PnkZ49IQ1o9VtGa72kIwzpDBjKYEi3ITx7\nyrO4PVU4S34g/9WrIKc360/6C1d/2PClH0igfMHQC3j+u+e5cOiF3PTxTZTUlDCraBYPr3yYxVsW\n1wduQ7oNqW90A4Lq4KDynuPvYcaxM+iV1Yssexb5rqTRaaUsoephzglz2DdvX7yml/mr53PZsMu4\nedTNuOwuarw1+Ewfr//wOscWHssfP/xj/XmzimYxJHc/Npb/0MhP5oydxZBu+0vAJzTD9Hn5ee/P\nlNSUcvPSmxsFapcccgnKUDx/6vPsrN7ZKJC789g7cdlczFs1jwuGXsCtS2+lwFXAzaNuZkDXAeyq\n3sWMZTMoqSlp1vakA0179mQ2rpAORPyEK6WGKKVeVkqtUUptDPzF07hkxFAGBaaPvk+cRsHTv8HY\nspzSIybVf4GDpZ25demtTBwysf7/zUtv5tJDL2Vb1TauXXwtE4dMrD/26g+uprS2tP4apbWl9QFG\n4Jjrl1zPHvceLn/nclCkVeOcKELVw9UfXE1JTQlXf3A1E4dM5Pol1zPt/WlMWTSFae9P449L/sip\n+55aH+gFzrt28bWU1O5u5idXf3gtpTXFCfuMQvJSWlPMlspt9YEeWD4zffF0PNpjpV3BrA/0Avv/\n8r+/UFpXWt/+bKvaxqqSVUx7fxpXvHMFe9x7WFWyKmTbkw54RLMnpCHRRAxPAA9hLYFWhLWaxjPx\nMCrp8bqhfFP9W3dWfouJT4P/B+8LPjbcAuWhyusMyU5TgXD10NoEjabJrgPbvaY39IQO0xufDyB0\natymF5fdFdoHTY91TAtJ38P5Z2dMnhxLAsFdhsNo9F4QUplogj2X1vp9rHQtP2utbwNOjY9Z8SFU\nYuI2YXdCXkO2dmd1aYuJT4P/B+8LPjZYLN1aItV0FVZ3NOHqoekEjab7wyWttRv20AmRDRnCFSwa\nJT827NR4a0L7oLKD2TyxemB/jbcmrH92xuTJsaTOZ+KwKeyGNXfQI8O4QhoQTbBXp5QygPVKqauU\nUr8GcuJkV8yJ6QLyWT3gvOfrA778L59lztjGSUoDiU8D/2eMmVG/IHmzBKZjZ5HvzKsvvqVEqjIx\no+PIy8gLmXy2e2b3+sXnbx9ze7OE229ueJP7xt7X7LyCzPxmfjJn7CzyXemxGoTQMs2SH3/2d/rl\n9GPGmBmNfGb22Pso+OQh2LWGfGdes7bizmPvJD8jP6R/plPy5HB4vBq7YeCwWV9/ddKzJ6QB0SRV\nPhL4DsgD/gZ0BWZqrT9r8cQoiGei1JgvIG+aUF1sDenanZiu7s1m4/q0DxQoFE7DSa2vFieKvE8e\nonzQKNxZ+TirS8n/8lmM0+6DnF4NxQfNAg2UZxiGJNDtQEpqSrjjkzuYOGQiuc5cKtwVLFi/gFuO\nuaU+mXVg8XlTm/XLpNV6a8lWdurMOrxoHD4fBcufwn7M7y0/Sd3ZuCnvE/EkXPLjv466GY/24jO9\nOEwfBUv+if2rZ6wfm5e9h5ndo1lbETwbN+CfHZx0PZikSqp882vfsGDlNh44/wgufuJzbhh/ANPG\n7tcBFgpBpExKts5CNN8yg7TWXwCVwBQApdTZQMyCvXgS8wXkDaNRcGYQYVLS8s3wyf0UfHJ/4+3j\n/9G4eEmcm3DcPjeLtyxulrz2Rt+NLddP+WaYfWDz7UddjmGzU5DTJw7WCp2dFpMfe30w+9DGJ5Rv\nAm/oxOotke7tisercdiM+mFc0ewJ6UA0wd6fgX9HsC0piVti0SY9fGT1sALBcNsDer+gCR7kDbC2\nBxebvEtipQ0t+ky4+gWrp/fA0yg9YlLj3tvgOm7pfCEtCetvGlDKaidyesKY6eDqBp5qcLhCliXt\nR3jcfs2eYSgMJZo9IT2IZAWNCcApQD+l1JygXV2xZuZ2CkImRG6vXsU0YdcaeOF8K3jLG2Bp+Xoc\nCMXfN9/ec2iD3q/pvqwG3Va4/G7plg8r0YT1GWde6HrvORQMA9PVvVnexTknzWKIq7slkg3nN/7z\nhfQkpL+Nvp38Fy+E7F5wwb+hqhgWTGvsN678Rn4j7UfLuH0mNn+vnsNmSM+ekBa0qtlTSg0HDgPu\nAG4J2rUXWOxfMzcmxFuHE/Nfu5U74f9Oat5LN+VteGJC8+2XvWcN/bbSqxNzfWF8SAt9VkifqSoO\nXe/++m21/sL5TcA/Oi9p4RPxpN7fvLU4d60lf/FdGFv8n/OSN+G137XqN0nYfiSVZu+Kp5azdude\n/vGbYVz+1HLOPKIft088pAMsFIIQzV4HE8kKGl8DXyulnvMfP0BrvTbulsWBmOvgmuTbA6z3Pk/o\n7V6/PrCJ3q8pMdcXCm0mpM+Eq3d//bZaf62cL6Qv9f5Wvhme/k3zAyLwG2k/WsbtM+v1eg6bkhU0\nhLQgGs3eeKx1cp3AYKXUYcAdWusz4mJZMhGqJw5AqdDaLMOO+fsvKM1w4dY+nD4P+V88ZQ3hVe5s\nrs9qUr7TkIXLO4KI1qkNVfdBukuzcCSlx12HO6cnTpuN/KoSnJrw2qu9O0EZEek2hTSgqX9l5kHl\nLjC9cM0qWPUirF8EY6Zj5g2k/LfvUtulJ6b2kVmxlfxl8xprQYmjPjlF8PhM7P72124YuL2RZaQQ\nhM5MNGOYtwFHAeUAWuuVwOA42JRcBPRV/3cSzD7E+r9rDZRuxFzzButPuolJq+5n3PuXM2nV/aw/\n6Sa8xetZj5tJ71zGuNdOZ9IHV7F+xHmYXz/fcL5phi0/v7q8We6sdMyHFU9Mn5f1ZeuYtPASxr16\nCpMWXsL6snWYviAZari6d3WH857HPPA01o//m1X/705h0qIprC//gbwP7mTO6Mb5zeaMvY/8L5+B\nx06yAv5fPdyQmDuEblNIA5r61zevwq7vYP4pMOcwePI0GDIOTp2F+fUL/OyuYINdMeW9K5nw+q+Y\ntPxO1p/8F0xX90bFhsrTKe1HA25vg2bPLj17QpoQTZ69ZVrrUUqpr7TWh/u3rdJaD4uVMUmpwwmn\nrzr1Xkq6D2bSB1c1+wX95PgnuHjhlOaamRMeoGDOyMY6mzDlm5cvptQgmWfTdWp9VknldiYtvKR5\nHY2f35AapSVtXVYPSqp2MGlRiHoedg35H91LadFNuPMH4Sxea/X4nvBXmHu0VcbEB6G2ArJ7QG4h\ndOmbCpMzOrVPdDhN/euar+HJ00O3NdrLd1ldmLFsRkRavCSbjZtUmr2JD/wPgBsnHMSf/vM1B/Xp\nyrwLR8bbPKExotnrYKIZxv1WKXUBYFNKDQGuBj6Jj1lJRDh9lSMLt2EPs26lL7RmJrAsVrDOJkz5\nhqeGgrz+sfwkQhDuSNapbUlbZxi4dZh6zsrH2LKcgqd/Y4nqnzvf2nnSbQ1lKANenGy9n746FQI9\nIVqa+pc2w7c1djsue+j2JpQWT/J0hsftM8lyWm2xXWbjCmlCNN8wfwAOBuqA54E9wPR4GNXhmKb1\nK7t8s/XfDHr4m6yDC1jvPdU4TW/odSsNW+g1ULWGc5+By/1Jeit3Wf8vX2xtLxzZUL5Sje0QYooz\nknVqw9W93cqz51Rh6rm6tOHYLr2tuj3gVLA5G17XlDUuT0gPgtuaQO48gMMng2EP7W/ZPXFm9wq7\nTq5o8aLD7W2YoGE3FB6faPaE1CfiYE9rXa21/gtwIlCktf6L1ro2fqZ1EOF0WYFAq8k6uPX6qm77\nkP/FU8w5/t5m2qyCjctCbs+v2QOLboJHi+DtP0HFVkuf82iRtf2EW6xA4IwH4K0bGtshxJR8V4/W\n16kNV/eu7ta6pJ881LyeR99O/kf3+odq58KrV1p1e/z18NHMhtfrFlrHnPO0VZ6Q+jRta966war/\n0VfDyMvg7RutZz/Y3855Gt67jfz/XEahM7fZOrmixYset9fEbgueoCFtrJD6RLs27uNAF/+mCuBS\nrfWKWBmTEB1OJDnPws3GrS7GRFGqvbh9dThNL/nfvIrR8yDMggMozczBrb04lZ18DIzHTm64zrnP\nWF/8Ta97wb/h9d/DluXJnnut0+uz2jwbt9qfZ2/cXZhfv9AwG9vrJt9wYGT3gtIfYMndVj2CVZfj\n7rKGbvMGwIV+Mf7K5+H0Wclax9HS6X0iroRqaw44FSbcbf3oK99k9e6PmW5pObv2tQLAtW8CYBaO\npHzc36ntVoipTTJtmeS7kk7LG4qk0uyNuut9DuzdhSuP35e/v/0dSikW/H5MB1goBCGavQ4mGs3e\nY8A0rfXHAEqpY4EngJhN0EgIkeQ8C5cXL6eXtSZu+WaYfVijXQZQMH11w6/08s2Nr+PqFvq61SUN\nAYLkXosrEa1TG6ruAz7j6obx/RsUfP9G4/1XLYdnz268zX98/eu9Oxo0exPubvuHEDoPodqatW/C\nuDsbtm9Z3uAXV39VH+gBGFuWk//YyZbGU/S8bcbtM7HbGlbQqHZ3moWgBKHNRBPs+QKBHoDW+n9K\nqc7/lLS0Vm2gV8c0LfG06QFls9ajDF6iyO60fqEfdr71hV5TZvXYOFzWr3mvu0GfE7hOTVno69aU\nNbdDiD/RrFUb8JlwdWjPaLlum76WOk5dgv1KKaudqNrZeH1bW5g2SNkkH2Mc8PiCh3GVDOMKaUE0\n/f9LlFLzlFJjlVLHK6XmAh8qpY5QSh0RLwPjTiu6LP57LZRuhCfGw/3DreGWXd9Z2wJ6Old3OP4G\na1h2/qnW/xNvgb3bm+tzAtdZ+Tyc/VRzfc7K5xvbIbnX4k9rus2mBHxm0+fN63DiXGv4duLc5tuX\nzm7+Wuo4dWnqV09MsNqFk2c0tBXL50Pd3ub+cs7TVmLlptvPe078pZ14glbQsNsMybMnpAXR9OwN\n9/+/tcn2wwENnBATizoaw7AWoL/svea6rBfOt3RWr01t+HVdvslaiPzUeyGzizXEV7MbXrqw8TEV\nm+DN6xq2BYZjLvg3uPeCs4uVfuOSt6ztdqcVNJ4+yxrWa613SYgdgboOrr8Xzg+vlwz4zNFXWBNt\nxt0FBftD+c/w/m3WUFzx95aPdB8CjiwwbHDWfKsXJ/Ba6ji1CeVXTduFw86H586GnJ6WHwV6+3we\nePUKOPFWuPA1q61wuCC7p/hLO3F7TRz+nj2H9OwJaULEwZ7Wuqil/Uqpi7XWT7bfpATQii4rXO6r\nFnPlObJC63NGT7N+0Qdoqr9JDaF+56Ita9UahtXzsvZN6++SNxvr9Lb4dXtXr4QuUqdpSSTtQqB9\nKd/UoNUDy5+2LLeSLIPVTnTpHX+bUxyfqTE1jXv2JNgT0oBoevZa4xqgcwZ7oQinyyocCcf/yfqF\nHciFF0qzB6LJ6yy0pNsMh2laedEuX2x9gTuzQpdhxPIREzoVofzKsMGkf1s+iAHIYgAAIABJREFU\nU1NmaYHDtRPBM3MDbY306rWLQGBXr9mT5dKENCGWLUdqTaUO6LJWPt+wjmnhSDjxNmsY5sEjLQ3O\nrjWQmd9cs5dVYOlrRJOX/ITTbYarm4AWK5Aj8bmzrdnWZz/ZvL5zpDcmbWnqVwecagV5b17X0E4Y\nDkva0chvnrL0oCfcYh3z+LiGtkbybraLQGAX6NlzSFJlIU2IOM9eqwUp9aXWul0TNZIud1bT2bja\nZzXSTX+FT3nbaoybbr98sXVOQAvo6m7p+yKZ8Zn8pFZOtWhm44bLzXj6HGvGdvchVo9eTm+wO+Jr\nd3KRWj4RC5rOxg3VTvz2XWuf121pOm0OcFc15N4LPjZ58262RNLk2SveW8eRd77HpWMGcfLQ3vx7\n+WZe+WorP/79FJRKrf6KJEdudgcTyzGm1Ku8plq+prnywHrv84Te7qlpng+r8zXU6UG4XIqhCKfx\nszng6V9JHjShgWC/aqn9aOov7qrodaRCq3jqe/YCw7iGf7vGaU+9rzBBCBDLbqWlMSwrOQhex7Ji\nCxC0lmWAvAHWl3y4NVSFzkdwve/dCVUljddNDrdmbkDfKfUuNMU0G6+FGyCcv7S0LrPQZho0ew1r\n4wKi2xNSnoiDPaVUrlJqllJquf/vXqVUbmC/1vqq+JiYIELlyKrcCb9+pLm2K6d3dJovIXlpWu+P\nnWTlVXz5kob8e67uzev7jAcsPabUu9CUgE+9dUPotW9DrY0crY5UiIimPXtOu/W/zuNLmE2C0BFE\nM4z7OLAaOMf//kKs5dJ+E2ujkoJQObJevgQmPmjlTyvY38p7FdB2hcrV13n1eOlLqHpfMK1hXdtA\n/r3g+lbKWu3g9FlS70Jzgn2qaqflS9k9IDMP3r8j9NrI0qbEhUAPnsPfsxcI9qrdPkKE3IKQMkQT\n7O2rtT4z6P3tSqmVsTYoaQiny1KGlT9t+urGDXQ0mi8heQlX78Hr2nrdUt9C5AT7VPDat5f4czSG\nWxtZfCzmNB3GzfQHe7XSsyekONEEezVKqWO11v8DUEqNAWpaOkEp9ThwGrBLa31I282MEY1m1/pA\n6/C/mMOtd9ult5Uny7Bbw7qpNcM2dQk12xaabwtX74G8Z8f/yfKdvTutnGmeGql3wSLYx5zZDSth\n2DMb59ZbOhsqdzVoPCWHXodRH+z573WG3QZYPXuCkMpEE+xNBZ7y6/QUUApc0so584EHgKfaYlxM\nCehmFt8FR18Jr19l/doOaGF6Dm3c2AbWuw0sgxbIf/W/WfDjR1buvU8ftI5ZMtP6hR6uLCGxBOo+\nMJSWNwAmvwre2sbbznveGp4PVe/r37VyLC6Y1rB94lxrebTKXVLv6U6wjw0+Do68DF66yFoG7eQZ\nDUukBfzGkQUf32tp+N66AYpuEv/pAOr8wV5g+DbD0TCMKwipTMQti9b6a631cGAYcKjW+nCt9det\nnPMRVlCYeAK6mcPObwj0oGEd1OrixseHWu/2pYtg//HW69emWmW9dKH1v6WyhMQSSodXtjH0eriV\nO0PX+/DzGgK9wPYF06wVDqTehWAfG/0Hy2fKN1n+8eoVzf0mK99qNz64w/qhKP7TIdT4h2vrgz3/\n/xqPN2E2CUJHEHHPnlIqAzgTGATYAwkotdZ3tMcApdQVwBUAAwYMaOXodtDaWrdN81dFot0KlBXY\nFq4sIWpi6heRrl1cvsla7zbkdl9kWj4hbnRYW9EWgn3MsDW8Dtfe7NnaeC1c8Z82Ea1PBLR5GbbG\nw7g1bkm9IqQ20YwZLAAmAl6gKuivXWitH9Faj9Raj+zRI45pBZqudRtMqPxVLeVSC34t693GhZj6\nRai69FSHrl/DHt32YH+Qeo8rHdZWtIVgHzN9Da/DtTee6ubbxH+iJlqfqA3Ts1ftlp49IbWJJtgr\n1Fqfq7WeqbW+N/AXN8tiTfBat01zXYXKXxUqz9XEuZa4Ojivmqx3m/yEqstu+4TOY5bT26rTUGvc\ntuQPUu/pTbCPffovS+eZN8Dyj4lzG/vNuc9Ya2dLDr0Op8bdJNhz+Hv2ZDaukOJEvDauUuoR4F9a\n62+iuoBSg4A3IpmNG/f1LqOZjRt8fGDNysDsy0BeNcNI59m4nWsd1Ehn4xoG+LxQucOaSWlzWIGe\nzR7eH9Kr3luic/lErAk3G9fhsvb7PA1rZNeWW76jfWB3WXn3Utd/kmZt3Ic+3MDdC79n/pQjybDb\nqPX4mDL/C26ccCBTj9+3I8wULGRtug6mVc2eUuobQPuPnaKU2gjUYVWW1loPa+Hc54GxQIFSagtw\nq9b6sVgY3iaizVsV6fGSCyv5CVeXobbZ7JBbGHkZggAh/CM//LHZBXE3R2hOYBjXYWu8gkaNzMYV\nUpxIJmic1tbCtdbnt/VcQRAEQYgltR4fGXYDwz/B0FCKDLshw7hCytPquIHW+met9c9AH6A06H0Z\n0DveBgqCIAhCLKjx+HDaGn/tZTlt7KnxJMgiQegYohGJPARUBr2v9G8TBEEQhKSn1uPD6Wj8tdcl\n00FpVUPamwcX/8Dj//uxo00ThLgSzQoaSgfN5tBam0qpaM4XBEEQhIRR4zGb9ex1ybRTVm0Fe+XV\nbu5ZtBaAKWMGEcgnKwidnWh69jYqpa5WSjn8f9cAG+NlmCAIgiDEkhq3rz63XoCcDHt9z94PuxoG\nr7aWt7j0uyB0KqIJ9qYCxwBbgS3A0fgzlwuCIAhCslPr8dXPxA0QPIy7uawh2fWWMgn2hNQh4mFY\nrfUu4Lw42iIIgiAIcaOyzlufSDlA10w7FTUefKZm0+6GAK+ksq6jzWuMafpzuspQstB+olkbtwdw\nOf61cQPbtdaXxt4sQRAEQYgte2o99MjJaLQt1+XA1LC7so5NpdXYDYXX1JTsTVCwZ5qweAYse8ha\nW/msx2HAqMTYIqQM0a6Nmwu8B7wZ9CcIgiAISc/eWi9ZzsY9e726ZgLwY0kVm8uq2adHNoaC3UEz\ndDuU92+Hj++Fvodb75/+NZT9lBhbhJQhmtm0WVrrP8XNEkEQBEGII5W1XlzOxl97fXIbgr1Nu6sZ\n0iuHnXvqEjOMu2U5LL0fhoyD0VdBVTG8/ntYeCOc/0LH2yOkDNH07L2hlDolbpYIgiAIQpzw+Exq\nPL5mPXsFORk4bIo12/ewc08tPbtkkutyULw3AT17H/4DMnPhyMssrV5OTzj4N7D2bdi5puPtEVKG\naIK9a7ACvhql1B6l1F6l1J54GSYIgiAIsWJvrRegWbBnGIrBBdm8sWo7GujVNYOcDDvl1R0c7O34\nBn54Fw46HRyuhu0HnAq2DPj8kY61R0gpopmN20UplQ8MATLjZ1LHYZqa3VVu3F4fTruN7tlODENm\nPgnxR3wvscj9Tz927qkFoFuWs9m+/Xt1Yd3O7QD07JJJl0w7xR09QePj+8CRBQee2nh7ZlcYOBrW\nvAYTZoK9uf2C0BrRzMa9DKt3rxBYCYwCPgFOjI9p8cU0NWt37uXyp5azpayGwm4uHr1oJAf06iKN\nvhBXxPcSi9z/9GRHhRXs5Wc3D5YO6t2VN1ZZwd6ggiy6ZNpZt3Nvxxm3e4MVzB38a3DmNN8/+HjY\n+CFseB8OmBBxsR7Tg4GBzbC1frCQ0kQ7jHsk8LPWugg4HKiIi1UdwO4qd31jD1YCzcufWp64GVhC\n2iC+l1jk/qcn2/3BXvcQwd7w/nkc2i+XUw7tQ4bdRpdMBxU1HkxTNzs2LnwyBwwbHDQx9P6+h0NG\nV/jm360W5TE9PL3maU5/9XSOePoIRjwzgnP+ew7zvp7H7prdMTZc6CxEE+zVaq1rAZRSGVrr74ED\n4mNW/HF7fc0ypG8pq8Ht9SXIIiFdEN9LLHL/05Mde2oxFORmOZrtsxmKm045iAtHDQSsJdRMbeXl\nizt7tsPK52DfEyErP/Qxhh0GjIZ1C8FTG7aove69XP7O5cz8YiZOm5Mz9j2DXw78JT7t44GVDzDh\nlQk8991zmNqM04cRkpVoUq9sUUrlAa8B7yqlyoCf42NW/HHabRR2czVq9Au7uXDapbtbiC/ie4lF\n7n96sqOihrwsJ3aj9T6OLpnWV2NplZu8EBq/mLJsLpheOPjMlo8beAysXwQ/LoH9xzXb7TW9XLv4\nWlbuWsllh17GMX2PabR/e+V2nv/+ef7++d9ZsXMFd/3iLjJsGc3KEVKTaCZo/Nr/8jal1GKsBMsL\n42JVnDFNjUbzzG+P5seSKua8v57iyjoevWgk3bOdeDw+dlXW4TU1dkPRMycDm80QQbcQE7pnO3n0\nopH1Q4lX/mIQFx4zmGq3l23lJj1zMrA3Waw91IQC09TsqqzD4zNx2IyQ54UinSYnBH/WrAyDGrfG\n4zN5/vJR7Kio5a63vmv07Aupy/aK2pB6vVAEgr2y6jj37FUWwxf/B4N+AV37tHxs72HgyIbvXg8Z\n7D32zWN8tuMzphwypVmgB9Anpw/XjriWRT8t4qV1L1FRV8GDJz0oAV+aEE3PXj1a6yWxNqSjCCXO\nfmjSEdR6TDLsBl6vj7XFVfzumRUN+yePIM9l5/xHPxNBt9BuDENxQK8uvDptDArN9j11nPfIsnrf\nenjyCA7s1aU+cAs5oeDCkTjsikue+CLseaFIp8kJwZ/1mH26c+Hogfzu2S/rP/c9Zw1j5tnDKK/y\nkBFBkCx0bnZU1NItxBBuKLpkWseVxVvH+b/7wFsHwy9o/VibAwpHWjn3fF6wNXx9b9qziYe+foij\nex/NL/r9ImwRSinGDx5PF2cXHlv9GDd+dCP/PP6fMoEjDUi7Fi6UOPt3z35JabWbix7/nOIqd32g\nV7//mRXUebUIuoWYYRiKHl0ycPt0M3+b+swKdgVl7w85oeDp5WwurWnxvFCk0+SE4M96+XH71Ad6\nYH3u619exZbSmvpnPxXvgdCA1bMXWS9WTkagZy+OPrHrO6tXb98TILdfZOcMGA3Vu2Hzskab566c\ni03ZOO/A8yIqZky/MZx3wHm8t+k9Hvr6oWgtFzohaRfshRNn57kcbCmrwWvqkPubdnqIoFuIBR6f\nGdLfvL4GAXU4n22aHLbpeaFIp8kJwZ/VZqiw9zDw7KfiPRAs9tZ6qKzztmEYN07BXk0ZvDzFSp58\nxMWRn9dvBNic8N1/6zetL1vPWz++xYkDTyQ3Izfiok4eeDLH9juWeavmsWRzpx2sEyIkZYM909QU\n761ja1k1xXvr6qfQB8TZwRR2c1Fe46Gwmwu7oULuVwrmXTiCF68YxbwLR/DLoT1F0C20G4fNCOlv\nNkPV+204n+2b5+Lda4/jnBGF9dvstpYf6XBlpaIvB39WDSE/d7XbR362U57nFCeQUDlSXabLYcNm\nqNhr9so3wbKH4dEToGQ9HHc9uPIiP9/hstKwfP8GaOs77V9f/YtMeyYTBkWefw+sId3JB01mYNeB\n/PnjP7Nl75aozhc6FykZ7AW0Or+eu5Qxdy/m13OXsnbnXkxT14vjAw1/YTcXd585jP+s2MxDk0fQ\n3eXkockjGu1/ePIIfKbmb2+s4dxHlvG3N9Zw9Yn7080Vmf5DEMLRI7u5v82ddAT3v7e+3m+7uRzN\nfPaes4Zx9fNfMWX+F0wePZArfzGIhyePoGdOy8NUofw/VScnBD7rL4f2xFCah5vc53vOGkbPrhnc\ns+h7eZ5TnO0tJFQOhVKKrpn22Gn2Nn8Bj42D2YfCwj9ZgdrJM6DPYdGX1X8UVGyB7StZXbKaxZsX\nM27gOHJCJWNuBafNybTh0/BpH/9vyf/D4+uAVDNCQmjTBI1kJ5wu6dVpY+jRJYMDenXhxStGUVLp\nJstpo9bj48wR/fnX++u47YxDOLBnDi9eMap+Nm6mw2Dig58000cFyhOEtlJe6+WNlVt44pIjcdgN\nvD7NI0s28NIK61d2wG8DEzpq3F42FFcxc+FavtpcDsC0Z7/kxStG0atLZquzcYMnh6T6bNzAZ731\n9INZt7OSZRuKeerSo7AZCkMpyqrd7Knx8M6aXazZvlee5xSmpdUzwpGTYY/NMO66RfDCBVYP3hEX\nWylUukao0QtF/6NA2eC7N7jft4kuji6cPOjkNhfXI6sHUw6ZwoMrH+TeFfdy41E3tt02IWlJyWCv\nNV2SYSi8pmbig0ubnXvzqSYOh41+3bLqt20tq04bnZPQsbi9PuZ9/BPzPv6JF68YxbmPNBZeB/ws\nMKHj591epsz/otkxPlNHlHYFGiaHpAOBZz3Laau/z8G8eMUoQJ7nVCcQ7IVaFzccXTLt7R/G3bMN\nXr4Uug2CX84IvRRatGTmQu9D+GLdayzLcnPOAefgsrtaP68FRvQawckDT+bZ755lZK+RnDTwpPbb\nKSQVKRPsBefTUkpx5S8GccSg7vTNzSTTYcNus3ouft5dhcNQuOwGL08dze4qN+u272H8oX2wGQqb\noXC7vZRUe+rzl2U6jZBJWJVSmKZOyV4RIXqCfTDTaeD2aNw+E5fDhs+0XjttBjZDUePx1fvWU1NG\nsG/PrnhNzf/+VERFjYfKWi8en0mG3YbX1Gwvr8HpUPWa0qa+aLcZFO+tS/neutYIrgOH3cBmUN+T\n98IVoxjY3YVp0tBr7zR499rjqHb75HlOYbbvqaVrph1nFCl2cjIc7G5ldnurLLoJfG447k+xCfT8\n6MKjmbNlAd0cBZzQ/4SYlHn2/mezoXwDf136Vw7IP4D+XfrHpFwhOUgJzV5Tjd5tr6/mtMMK+c+K\nzeyp9XLPou/ZVl7LuY8s4/h7PuScR5axoaSKO9/8ji9/2s3Yg3oxZf4XnHDvEs59ZBlri6t4culG\n69h5n7K9vI6nLj2qmc7vttdX12sBhfQm2Aeveu4r1u2o5Ox5nzL9hZVsKLZeH3/Ph5w971M2FFcy\n/YWVnDPvU3ZU1NGnW3a9b573yDL21Hj4x9vfc+Mr3+A1Taa/sJKz533Kuh2VvLJiC3MnHRFCU2qG\n1KimE03bgd/M/YR1Oyp5dcUWXE4bi7/bQfFed/29PveRZWwrr2Pp+l1MfHAp58z7NC3vWzqwo6KW\nblHqUvOznWyvqEXrNvrD1i/h21fhkLNaT5gcJR90zWNlZgaTMwtx2mKjt7UbdqYOn4pG8/+W/D/c\nPklFlEqkRLDXVKN35oj+/O6ZFZw5oj9/+s+q+v9Nc2xNHbsvZ40cEDKv3lkjB9S/n/rMClwOGy9d\nOZqXp47mr6cN5Z+L1vLOml0pm6NMiI5gH5w6dl8rh1uT19DY97aU1XDl0yvY0iRfXvD+pq/379OV\nZz79mflTjmLJ9WN56crRFHbLrE/4HSgjHf0ylFb3+pdXMf7QPkz1P9PTmuTa+90zKzhhaJ/69+l4\n39KBrWU1dI8wx16Anl0zqHb7KG2rP3z6gLXixdBfte38MHhMH7O2f8AgH5yz/ceYll3gKuDSQy5l\nze41/HP5P2NatpBYUiLYa6rRC+TNavo/mMD2cPm3bEFDOVvKavD4TLTWnPXwp1z59Ip6cbxofQRo\n7IPB/taS7wVeh8qXF7y/6euXVmzhpPuWYDcUffNcVNWlT+68lgin1Q084+Ge9eCem3S8b+nA1vIa\nCnKi6wHr2SUTgE2l1dFfsHwTfPsaDPklOLNaPz4Kntr6Pj/X7OLirMHkFa/DtXtjTMs/vOfh/HLg\nL3n+++d5fcPrMS1bSBydPtgzTY1SipenjmbehSM4vH9efc68pv+DCWz3mTrkPl/QUE5AE5VOOcqE\n6Aj2jWB/a8n3Aq+r3b4W94d7HfA78UuLcPch8IyHe9aVUo3ep9t9S3UqaqyEytFOSurV1Tp+Y3FV\n9Bdd9jAoYOgZ0Z/bAj9W72TuT28wout+7DP4JHw2J72+eSWm1wA4a/+zGJo/lFuW3sKn2z6NeflC\nx9Opg72ARueceZ9y1sOf8rc31vD/xh3Alz/t5qHJI/jPis31OfTuPnNYsxxbD3+4gZeXb2qW5+yh\nySN4efmm+veB/GXplKNMiI5g31i3fU+9Tz384QbuOSu07xV2czHvwhEU5rvC7g/3OtjvxC8tQt2H\ne84axqMfbWTWOcN5efmmZnrHhyaP4IM12+vfp+N9S3W2+ntzC1rJQdmUvrkuXA4bX20ui+6CtRXw\n5ZMw8FjI7hHduS0V63Pz5++fwGHYmNyvCJ8zm939R9J9/fs4qkpidh2w9HvTDptG35y+XLv4WtaW\nro1p+ULHo9osPo0DI0eO1MuXL4/4+OK9dfx67tJmMxNfunI0PbKdFFe5UWhMDbUeHw67DZ9pojVU\n1nnJdtrJdBjkZBpU1ppW+gqbQUGWg5JqD16fid1m0DMno9Gi9IHZfuk86xHrd2uHEK1fJIpg37j9\nv99y5oj+5LkcmNrqfe7dNZMtZdVk2G306ppR71s+n0lJtRuvqXEYCofdoMbtn02qlOW7NgOnQ1Hr\nNkP6XZL4ZcJ9orSqjq83V5DltOHxmdgMhcNm0LNrBrv21NGvW2aj2bhZGQY1bo3WOt2f53jSITc0\nnE+8u2Ynlz+1nL9NPIT9ekY3I/bvb39HtdvHe388PvKTls6Bd/8Kp82G7vtFdb1waK3567qnWbBz\nGX8YeDqH5+4LQEbVbg754G52DzmRn4quj8m1gimtLeXOz+7Eruw8NeEpCrsUxqpoecg6mE6deiWc\nRkdrjcNho2+e9Qt+a1k1J973UbPzX7xiFIXdXOS6MsltkqaorzP0rUmnHGVCdDTkwqvinTW7eGfN\nrkb7P7jueCY/9jkAS/9UVO+fdrtBv4wIH8Xslq+d7tS4fc3yEAIhcxgGnv/gnJpC6rG1zNLcRavZ\nAzi8fzee/PQn1u/cy5BeXVo/wVsHy+ZC70NjGujN+vE1Fuxcxhk9j64P9ADqsruzc59j6b32HYqH\nnkpVr6ExuWaA/Mx8rj3iWu7+4m4uXXQpT45/kj45sZ1ZLHQMcR/GVUqNV0qtVUr9oJSKaWruSLVK\n4Y6rdvtEnyPEnHDr3QZ0oKILix8tPeuhtkk9pD5bympw2gxy27Ac3qh98jEU/PfrbZGd8PXzsHc7\nHHJ21NcKhdaaf/30X57Y8i5F3YcxsdeoZsds3/9k3K489nnv7xjuNugLW6GwSyHXjbiOiroKLl10\nKTurdsb8GkL8iWuwp5SyAQ8CE4ChwPlKqZj99IhUqxROyzOwe5boc4SY0zMno9k6rHMnHcGjH20U\nXVicCdcmDOyeJc9/mvJDcSV98jIbTcSJlLwsJ0P7dmXB19taz7fnrYOP74OC/aHv4W20tgGP6eO2\n9c/y6OaFHJd/CJP6FoX8DD6Hi40jJpNRuZP93vkbKg7r2w7KHcS1I65ld+1upiyawpa9W2J+DSG+\nxFWzp5QaDdymtR7nf/9nAK3130Md3xZtVqRaJdPUlFTVUesxsSlwOW3kuUSf0w4Srs9KZrxek12V\ndfW6zyynQVVdyus8k8InQrUJgDz/iSOhmr3Rf3+ffXrkcFVR24ZVF3+/i0c+3sjrV41hWGFe+AOX\n3AOLZ8BJd0C/I9p0rQAVnmpu+O4xPin/jtN7Hs2veo1qNVgt+PkzBq98kbKBo9h40l8wHe1bQi0U\nP5T/wP1f3k+mLZN5J8/jgPwD2lqUPHgdTLyHcfsBm4Peb/Fvq0cpdYVSarlSanlxcXHUFwholfp1\ny6JHl4ywjbdhKHp2yWRAfhb9umWRnx3+WCHxtNcvEo3dbtA3z8WA7tn0zXORl9W6jwotE6lPhGoT\n5PlPTVrzidIqN9sraunfre2Bz5GD87Ebile+3Br+oJ1r4ON7YOCYdgd6Kyp+4Kwv7+Sz8rVMKTyZ\nX/ceHVGvZMnAo/lp2JnkbfqMA1+/Duee7e2yIxT75e3HjUfdiEYz+a3JvLr+1bavMCJ0KAlPvaK1\nfkRrPVJrPbJHj9hNUxc6N+IXQlPEJ4SmtOYT//vBSklycN+ubb5GToadkYO68epXW6n1hEi4vXsD\nPHeOtVrGUVe2+To768q5bd2zXPr1LEyt+fN+5/CL/IOjKqN48Bh+OHIKmWWbOOSly+n19csobzvX\n921Cv5x+3DzqZgbnDuaWT27hmsXX8GNFbFfyEGJPvGfjbgWCV1Mu9G8TBEEQhLjh9Zm8+Pkmcl0O\n9imILuVKU8YN7c2yjaXc9+46rjxuH7pnaCj+Hr5/A5Y9BMqAk++ArPyIytNas9dXw+aaYtZUbubj\n0tV8tHs1ACcWHMaveo3G1cY1b8v7HMLqousZ9PW/GfDpw/RZ+SK79ytiT/+RVOcPxuvKQ9scAUOg\nLVrGjDyuG3kdi35axH83/JcPN3/IqD6jOL7/8QwrGEa/Lv3IdeZiM2QCVLIQ72DvC2CIUmowVpB3\nHnBBnK8pCIIgpDm3vP4tSzfs5rfHDibT2b6gY1j/PE48sCePfLSRDz9ewqLMm1DaBygYMBpG/Q66\n9G61nBtWz2PJ7q+p8bnRNAx/5ju6MK7XkZzY4wh6ZrSgC4wQnevix+OuIbt4HT3XvU/Pb/9L76CV\nNkybA2X62HbM7ygefk6br3PGvmdwfOHxvLfpPT7Z9gmfbm9YbcNQBln2LEb2Hsm/TvhXuz6P0H7i\nnlRZKXUKMBuwAY9rre9s4dhi4OcIii0AYpsyvP2km00lWuvxcSq7EVH4RSQkYz3FkkR+vs7qE5GS\nLL7T2ezoEL9owSeS5X4lmmS6Dx3WVggWSbWCRqQopZZrrUcm2o5gxKbOQarfk1T/fIkkWe6t2BEd\nncXOeCP3Ib1J+AQNQRAEQRAEIX5IsCcIgiAIgpDCdNZg75FEGxACsalzkOr3JNU/XyJJlnsrdkRH\nZ7Ez3sh9SGM6pWZPEARBEARBiIzO2rMnCIIgCIIgRIAEe4IgCIIgCClMpwv2lFI2pdRXSqk3Em1L\nAKVUnlLqZaXU90qp75RSo5PApmuVUt8qpVYrpZ5XSmUm2qZEoZTqr5RarJRa478n1yTapniRjM9H\nZyESP1FKjVVKVSilVvr/bomjPT8ppb7xX2d5iP1KKTVHKfWDUmqVUqoQ5pttAAAMBElEQVR9i7KG\ntuGAoM+6Uim1Ryk1vckxHXZPokEpNV4ptdZ/f25MtD2JIp3aPyE88V5BIx5cA3wHtH2xw9hzP7BQ\na32WUsoJZCXSGKVUP+BqYKjWukYp9RLW6iXzE2lXAvEC12mtv1RKdQFWKKXe1VqvSbRhcSAZn4/O\nQqR+8rHW+rQOsqlIax0uEe4EYIj/72jgIf//mKG1XgscBtYPCayVkF4NcWhH3pNW8dv6IHAysAX4\nQin1eoo+862RTu2fEIZO1bOnlCoETgX+L9G2BFBK5QLHAY8BaK3dWuvyxFoFWIG8Syllxwo+tyXY\nnoShtd6utf7S/3ovVjDUL7FWxZ5kfD46E53QTyYCT2mLZUCeUqpPHK93IrBBa93RK5e0haOAH7TW\nG7XWbuAFrPuVdnRCvxbiQKcK9rCWXbsBMBNtSBCDgWLgCf/w2f8ppbITaZDWeivwT2ATsB2o0Fq/\nk0ibkgWl1CDgcOCzxFoSF5Lx+eiUtOIno5VSXyul3lZKHRxHMzTwjlJqhVLqihD7+wGbg95vIb5f\n4ucBz4fZ11H3JFI6+t50ClK8/RNaoNMEe0qp04BdWusVibalCXbgCOAhrfXhQBWQUH2IUqob1q/Y\nwUBfIFspNTmRNiUDSqkc4D/AdK31nkTbE0uS+PnodLTiJ18CA7XWw4F/Aa/F0ZRjtdZHYA3X/l4p\ndVwcr9UifnnKGcC/Q+zuyHsitJFUbv+E1uk0wR4wBjhDKfUTVpf8CUqpZxJrEmD9YtyitQ78UnoZ\nK/hLJCcBP2qti7XWHuAV4JgE25RQlFIOrIbuWa31K4m2Jw4k6/PRqWjNT7TWe7TWlf7XbwEOpVRB\nPGzx99Cjtd6FpZM7qskhW4H+Qe8L/dviwQTgS631zhB2dtg9iYKOvDdJTxq0f0IrdJpgT2v9Z611\nodZ6ENZwwgda64T3VmmtdwCblVIH+DedCCRa+LoJGKWUylJKKb9N3yXYpoThvwePAd9pre9LtD3x\nIFmfj85EJH6ilOrtPw6l1FFYbejuONiS7RfT45eF/BJY3eSw14GL/LNyR2HJNbbH2hY/5xNmCLej\n7kmUfAEMUUoN9vdKnod1v9KOdGj/hNbpjLNxk5E/AM/6G5WNwJREGqO1/kwp9TLW8IoX+Ir0Xipn\nDHAh8I1SaqV/203+XghBCBDST4ABAFrrh4GzgN8ppbxADXCejs8yRL2AV/0xlB14Tmu9UCk1NciW\nt4BTgB+AauLU7viDzZOBK4O2BdvRUfckYrTWXqXUVcAiwAY8rrX+NpE2JRBp/wRZLk0QBEEQBCGV\n6TTDuIIgCIIgCEL0SLAnCIIgCIKQwkiwJwiCIAiCkMJIsCcIgiAIgpDCSLAnCIIgCIKQwkiwJwiC\nIAiCkMKkTbCnlKpMtA3xQCn1oVJqZKLtSDeUUoOUUk2T3CYdSqmfkmA1g05FZ2kr/D54QYTHJb2v\npiqJuv9KqcOUUqdEcNxYpdQbHWGTkDjSJtiLBn9Gerk3giC0SILbikFAq8GekLYchpV0WxDSL9hT\nSuUopd5XSn2plPpGKTXRv32QUmqtUuoprGWJ+iulfquUWqeU+lwp9ahS6oGgYz9QSq3ylzWgheud\nrZRarZT6Win1kX+bTSl1j1LqC38ZV/q3j1VKfaSUetNvy8OBLxKl1ENKqeVKqW+VUreHudb5/s+0\nWil1d2zvXGqhlHpNKbXCfz+v8G+rVErd6a+rZUqpXv7tvZRSr/q3f62UCqwzbPP7xbdKqXeUUi7/\n8Yf5z1/lP69bC3ZcrZRa4z/2Bf+2bKXU436/+yrIRy9RSi3w9+auV0rd2tLnCXGtP/p9Y7VSanpM\nbmQKk4C2Yr7/mV/uL+s0//aQ7QXwD+AXSqmVSqlr/df62G/vl0F+GnyNTKXUE/7P85VSqijW9y3V\nSKK24kOl1P3++l6trKXpQrYXylrN6Q7gXP/x5yqljlJKfeo/5hPVsMRn8DXy/Z93ld+uYTG+nUKi\n0FqnxR9Q6f9vB7r6XxdgLTWksH4lm8Ao/76+wE9APuAAPgYe8O/7L3Cx//WlwGstXPcboJ//dZ7/\n/xXAzf7XGcByYDAwFqgF9sFa4udd4Cz/cfn+/zbgQ2CY//2HwEi/vZuAHv7P+AHwq0Tf92T9C7qf\nLqwv7O6ABk73b58ZVEcvAtOD7n+u31+8wGH+7S8Bk/2vVwHH+1/fAcxuwY5tQEYT/7grqKw8YB2Q\nDVwCbPfbGrB7ZLjP43//k9/PR/h9MRvIAb4FDk90PSTjXwLbivnAQqwf4UOALUAmLbcXbwSdnwVk\n+l8PAZb7Xw8CVvtfX4e1dBjA/2/vfGO2nsI4/vmqNkIx3mE0ao2ZmRHzIJYXVrbMEkMys9UmZsvY\nGDbDCy/8y5+pkYkXkhg2Rp4SVo+Eh03NJs1aIySUyNPXi+s8ut3dfzT13HfPfX22ezv7Ped3zvU7\nv+v3Pedc5zw7YwnNOLDVbd7OvzbSiqXA3JI+t+KdNtKLORX3jwCGlvQEYFFJ/+NHwKPAXSV9AfBp\nq9s/f3vn13GRPUKs75PUC7wDHEWcQwmw3vaKkj4DWGb7J9s7gIUVZZwFvFDSzwFdDer7AJgv6Xri\n44c41Hya4pzClYR4jC5/67H9te0+4uDx/rIvk7SaOOf2JODEqnpOB5ba3mT7L+B5QhCS2two6TNg\nBXAM0f5/Av17Vz4mRBpC9J4AsN1ne0u5vs72p5X5JY0kBm3LyvVnafweeolzla8iOgQI/7it+MdS\nosPvjwi9bftH278DL7PLP2o9TyVdwGLbW23/Vu49p4FdycBrBcCLtnfa/oo4Z3ssjfWikmHAXEmf\nFxuqNYJS/wIA22uA9cCYJjZ1Ou2iFRB9ArbfA0ZIOozGelHJSGChYv/gg0Q/Uk0X4afYfhc4QtKI\nJjYl+wFDW21AC7iSiH6dZnuHpG+IjwNg696uzPYMSeOAicDHkk4jOpFZtt+qzCtpPDFj/FcRkkYB\ns4HTbW+WNL/C5mQPKe08ATjL9jZJS4n23GG7v/37aP59/FGR7iNm/nvKRELgLwZul3Qy4R+X2l5b\nZfc4avvHeGo/T/L/GFCtKOz2fmmsF5XcDHwHnEJEB7fvIxs7hjbTCqjvH/X0opJ7gG7bl0g6jhgY\nJh1CJ0b2RgLfF/E+Hzi2Tr6PgPMkHS5pKHBpxd8+BC4v6SuJZZuaSDre9krbdwKbiJnhW8BMScNK\nnjGSDi63nCFplGKv3lTgfSL8vhXYUvaGXFSjqp5i75GShgBXAMtq5EvCBzYX8R4LnNkk/xJgJvyz\nf2pkvYxlJr9ZUn/U7GrqvIfyjo+x3Q3cWuw6hPCPWZJU8p1acduFZV/NQcBkInL8X55nOTBZ0vDi\na5fQwG8TYIC1ojBF0gGSjie2c6ylvl78ChxaZe9G2zsJvxvC7iwvdiBpDBEBWlsjXxK0hVZUMLWU\n3QVsKWXU04ta/rGhpKfXKb/SP8YDP9j+pYlNyX5AJ0b2ngdeK0sdq4A1tTLZ3iDpPmIQ9VPJ1x+S\nnwU8I+kWYgB3bYP6HpA0mph9LQE+I5bujgNWlw90E9FxQ3Qcc4ATgG5i6W2npE+KDd8SHXy1vRsl\n3VbuEfCG7VebN0dH8iYwQ9KXREe3okn+m4CnJF1HzMpnEnvn6nEN8KSk4cRSXD3/GAIsKB2CgEds\n/yzpHuAhoLcMCNcBk8o9PcAi4Ghgge1VxZcbPo/t1SUi3FMuzbP9SZPn7nQGWisg9tD1EBO8Gba3\nS5pHbb3oBfrKEuN84HFgkaRphI/Xij4+DjxRnukvYLrtP2rkS4J20Yp+tpe+YBixBxQiYldLL7rZ\ntbx7P7G38FlJdwBv1Cn/buDpsnVhW7EvGQRoVyQ6qUbSIbZ/K7P1xcTG5sX7sL7xwGzbk5rlTToP\nSdOJf8i4odW2JP9mb2hFGYy/bvulfWFjsn9TlpBn217ValuS/Y9OXMbdE+4us6IviNnSKy22J0mS\n9iS1IkmStiUje3sJSbcDU6ouL7R9byvsSdoLSY8BZ1ddftj2M62wJ2kdqRVJI1Irkn1BDvaSJEmS\nJEkGMbmMmyRJkiRJMojJwV6SJEmSJMkgJgd7SZIkSZIkg5gc7CVJkiRJkgxi/gbzcIwCXDimqgAA\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 660.75x576 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "0l544dAdz7Zi"
      },
      "source": [
        "# Tratamiento de la Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "POiGyV5d0S61"
      },
      "source": [
        "Identificamos como **variable objetivo** (target) como 'species'. Las demás variables serán las **predictoras** (features) para el modelo. También podemos obtener estadísticos básicos de estas variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "k8XRz2q3bkpo",
        "colab": {}
      },
      "source": [
        "#Creamos una variable y_iris para guardar la columna species del dataset iris\n",
        "y_iris = iris['tipo_flor']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OojkhBcw0DgC",
        "colab": {}
      },
      "source": [
        "#Creamos una variable X_iris para guardar el resultado de eliminar la columna species del dataset iris\n",
        "X_iris = iris.drop('tipo_flor', axis=1) \n",
        "#axis = 1 es para las columnas, axis = 0 es para las filas."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9JkThrAT0Pwi",
        "outputId": "30745f44-c44a-4227-ec29-e3e51e521b27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Para visualizar el tamaño del dataset utilizamos el método shape, como resultado obtenemos el números de filas y columnas (en este orden)\n",
        "X_iris.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_BUDr5Ek0LA0"
      },
      "source": [
        "Estos descriptivos verifican que las cuatro variables almacenan **150 observaciones**, todas ellas con datos numéricos. Para este ejemplo no contamos con datos *categóricos* (no numéricos) y tampoco aparecen vacíos o errores. Sobre el tratamiento de este tipo de datos profundizaremos en las siguientes sesiones de clase"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y8nS8JjwPV3t",
        "colab_type": "text"
      },
      "source": [
        "## Haremos PCA para poder ver que una clusterización te agrupa de manera intuitiva los targets.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "T8kynYVM5npv"
      },
      "source": [
        "Para aplicar el modelo de *clustering* vamos a realizar una **reducción de dimensionalidad** donde las variables predictoras se reducen a solo dos (2) PCA -que son las iniciales de *Principal Component Analysis*- de esta manera vamos a visualizar el dataset iris en función de solo dos *nuevas variables predictoras* denominadas PCA1 y PCA2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JA7LzYPC5KyD",
        "colab": {}
      },
      "source": [
        "#Importamos la librería PCA\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "#Creamos  una variable feature_pca para asignar PCA, definimos el valor de n_components para limitar a dos las variables finales\n",
        "features_pca = PCA(n_components=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "b_LAI87nHAge",
        "colab": {}
      },
      "source": [
        "#Adaptamos la variable creada feature_pca con la data guardada en X_iris\n",
        "features_pca.fit(X_iris)\n",
        "\n",
        "#Creamos una variable X_2D para guardar este último cambio\n",
        "X_2D = features_pca.transform(X_iris)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IJHHr0ePV35",
        "colab_type": "code",
        "outputId": "779faec7-9b8b-4967-f887-a04ccc61a664",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print(X_2D)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-2.79388723  0.31773453]\n",
            " [-2.82498165 -0.16977798]\n",
            " [-3.00183191 -0.14737925]\n",
            " [-2.85862741 -0.32040471]\n",
            " [-2.8394125   0.32023224]\n",
            " [-2.39061534  0.72672501]\n",
            " [-2.9354961  -0.10383377]\n",
            " [-2.73648245  0.16211238]\n",
            " [-3.00121703 -0.57943909]\n",
            " [-2.78237359 -0.10491582]\n",
            " [-2.6143375   0.64462947]\n",
            " [-2.72460294  0.00898794]\n",
            " [-2.89656827 -0.22573135]\n",
            " [-3.33876799 -0.51282425]\n",
            " [-2.74959173  1.17935649]\n",
            " [-2.49416027  1.31690471]\n",
            " [-2.73381426  0.79633825]\n",
            " [-2.75926072  0.30582733]\n",
            " [-2.30579637  0.87185563]\n",
            " [-2.69915647  0.49949885]\n",
            " [-2.41704256  0.39874802]\n",
            " [-2.6559648   0.41723338]\n",
            " [-3.33045184  0.11840322]\n",
            " [-2.4154782   0.08908645]\n",
            " [-2.46720375 -0.04322199]\n",
            " [-2.61642209 -0.13672404]\n",
            " [-2.58142971  0.12089467]\n",
            " [-2.6711274   0.36819179]\n",
            " [-2.74836197  0.31523682]\n",
            " [-2.74443273 -0.19958918]\n",
            " [-2.69890746 -0.20208689]\n",
            " [-2.519389    0.40974024]\n",
            " [ 1.18095943  0.67881484]\n",
            " [ 0.82222584  0.29455087]\n",
            " [ 1.35879045  0.49388218]\n",
            " [ 0.06841964 -0.83858777]\n",
            " [ 0.97924631  0.06357502]\n",
            " [ 0.5285127  -0.4380918 ]\n",
            " [ 0.98292654  0.25033476]\n",
            " [-0.86638379 -1.01784812]\n",
            " [ 0.93838824  0.22560826]\n",
            " [-0.12789454 -0.75524024]\n",
            " [-0.62356359 -1.26622728]\n",
            " [ 0.39715643 -0.13325858]\n",
            " [ 0.15790581 -0.53392162]\n",
            " [ 0.87401394 -0.14300508]\n",
            " [-0.28921012 -0.2789643 ]\n",
            " [ 0.82124508  0.4570848 ]\n",
            " [ 0.54367529 -0.38905021]\n",
            " [ 0.12695953 -0.33525468]\n",
            " [ 0.83395721 -0.54475303]\n",
            " [-0.06680331 -0.58879294]\n",
            " [ 0.99870401 -0.13268348]\n",
            " [ 0.24735449 -0.07963299]\n",
            " [ 1.18842076 -0.33543087]\n",
            " [ 0.81332608 -0.18954896]\n",
            " [ 0.60706884  0.14209705]\n",
            " [ 0.79285013  0.31886595]\n",
            " [ 1.22709958  0.24425729]\n",
            " [ 1.44848814  0.24658506]\n",
            " [ 0.70008088 -0.18796622]\n",
            " [-0.41623379 -0.36905366]\n",
            " [-0.18099799 -0.70960847]\n",
            " [-0.30142423 -0.68029796]\n",
            " [ 0.02461309 -0.32426245]\n",
            " [ 1.26663608 -0.44500983]\n",
            " [ 2.40996158 -0.08307305]\n",
            " [ 1.29659539 -0.61645256]\n",
            " [ 2.50703216  0.31376874]\n",
            " [ 1.85863774 -0.21154253]\n",
            " [ 2.23409829 -0.08789854]\n",
            " [ 3.2924308   0.5312484 ]\n",
            " [ 0.39703335 -1.23967995]\n",
            " [ 2.82883692  0.34523996]\n",
            " [ 2.21233827 -0.25633999]\n",
            " [ 2.8027068   0.72134355]\n",
            " [ 1.54711686  0.19845558]\n",
            " [ 1.68995549 -0.24409579]\n",
            " [ 2.05295292  0.17980029]\n",
            " [ 1.22559238 -0.81953357]\n",
            " [ 1.46116278 -0.60563028]\n",
            " [ 1.78563573  0.0600668 ]\n",
            " [ 1.83819307  0.01194019]\n",
            " [ 3.38129587  1.13266467]\n",
            " [ 3.69030374  0.24165154]\n",
            " [ 1.18903564 -0.76749071]\n",
            " [ 2.31363519  0.32975639]\n",
            " [ 1.07713707 -0.658916  ]\n",
            " [ 3.39769444  0.45289631]\n",
            " [ 1.27516996 -0.23043592]\n",
            " [ 2.16189679  0.28820793]\n",
            " [ 2.50878215  0.54066415]\n",
            " [ 1.14384497 -0.2105349 ]\n",
            " [ 1.17555427 -0.15508222]\n",
            " [ 2.00804254 -0.24976184]\n",
            " [ 2.28505999  0.45856861]\n",
            " [ 2.73738924  0.36564167]\n",
            " [ 3.12856388  1.34441013]\n",
            " [ 2.04266905 -0.26166904]\n",
            " [ 1.33432474 -0.15916266]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "M0Mj75DOCK3R"
      },
      "source": [
        "Añadimos al dataset iris las dos dimensiones ya obtenidas: PCA 1 y PCA 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "PlfmIqB4CBYV",
        "colab": {}
      },
      "source": [
        "#Añadimmos una columna denominada PCA1 al dataset iris usando la columna 0 de la variable X_2D, es decir la primera columna contando de izquierda a derecha\n",
        "iris['PCA1'] = X_2D[:, 0]\n",
        "\n",
        "#Añadimmos una columna denominada PCA2 al dataset iris usando la columna 1 de la variable X_2D, es decir la segunda columna contando de izquierda a derecha\n",
        "iris['PCA2'] = X_2D[:, 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2FS9Kn24zp8w"
      },
      "source": [
        "# Selección y preparación del modelo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eonVeo5O0BiQ"
      },
      "source": [
        "Ahora vamos a seleccionar un **modelo de clustering o agrupamiento** denominado *Kmeans*. Este modelo lo vamos a adaptar con las nuevas dos dimensiones que obtuvimos a partir de X_iris (PCA1, PCA2). Ambas variables están guardadas en X_2D"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "tpNu_u9lJSRc",
        "colab": {}
      },
      "source": [
        "#Importamos la librería KMeans\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "#Creamos una variable kmeans para guardar el modelo Kmeans\n",
        "#Definimos los parámetros n_clusters para limitar solo tres agrupamientos, también podemos definir el máximo de iteraciones con max_iter\n",
        "kmeans = KMeans(n_clusters=3, max_iter=600)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZjdyAlZnKpTB",
        "colab": {}
      },
      "source": [
        "#Perfilamos el modelo kmeans con los datos guardados en X_2D\n",
        "kmeans = kmeans.fit(X_iris)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F3cjoBo-Kpgw",
        "colab": {}
      },
      "source": [
        "#Creamos una variable predicciones para obtener las predicciones usando el modelo kmeans a partir de los datos guardados en X_2D\n",
        "predicciones = kmeans.predict(X_iris)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CSzFkytOPV4c",
        "colab_type": "code",
        "outputId": "f8abe0a4-8ac2-4ca3-e101-5730b7b4135f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "predicciones"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       2, 1, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 1,\n",
              "       2, 1, 2, 2, 1, 1, 2, 2, 2, 2, 2, 1], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "XqE8cak9HIJi",
        "outputId": "f420032e-c128-4a18-9290-8a10aa13db51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "x =iris['PCA1']\n",
        "y= iris['PCA2']\n",
        "sns.scatterplot(x, y,hue='tipo_flor',data=iris)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e92b972e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+Fzf0voGhLYeyMXsjewv3ohGaOk+vLdVeVcHiQeAy4DXgEyHEZ3X55EKI84AX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\n+2vNSf5U7dAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0Gk_iVxC8kj",
        "colab_type": "code",
        "outputId": "fd106752-59e7-4bf5-da58-109dd051d4f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "iris['predicciones'] = predicciones\n",
        "sns.scatterplot(x = 'PCA1', y = 'PCA2', hue = 'predicciones',data = iris)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6e901f0550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sdhtF44dQuXp/WvHsQQXojp5PN+PzeXG7XYTDEa676QpOmD2dLz5bzdbN29GE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\ns0IuFmZmVuj/0jVqS2UYSGMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_e-5dGaEp68j",
        "colab_type": "text"
      },
      "source": [
        "# KModes #\n",
        " Es usado para el clustering (agrupamiento) de variables categoricas ( tipo texto ). Define los clusters basados en el numero de categorias coincidentes con el centroide o centro de masa de los datos punteados asociado."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HZw_p5CYOmnb",
        "colab_type": "code",
        "outputId": "98e02e38-4181-4b1e-fda7-50b439b3a949",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 159
        }
      },
      "source": [
        "#Instalamos el modulo de kmodes\n",
        "pip install kmodes "
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting kmodes\n",
            "  Downloading https://files.pythonhosted.org/packages/79/c0/f7d8a0eb41ac6f302b4bc100f91b6e0f2558425ccfefaa0ec0430f77ee97/kmodes-0.10.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.6/dist-packages (from kmodes) (1.17.4)\n",
            "Requirement already satisfied: scipy>=0.13.3 in /usr/local/lib/python3.6/dist-packages (from kmodes) (1.3.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from kmodes) (0.14.0)\n",
            "Requirement already satisfied: scikit-learn>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from kmodes) (0.21.3)\n",
            "Installing collected packages: kmodes\n",
            "Successfully installed kmodes-0.10.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPvL-8ExOpS7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Importamos las librerias necesarias\n",
        "#Numpy y panda para manipulacion de dataframes y uso de arrays\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "#KModes para usar la implementacion del algoritmo en si\n",
        "from kmodes.kmodes import KModes"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVRizKBOOrS9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#La instancia km de KModes la establecemos con los siguientes parametros:\n",
        "#n_clusters : numero entero de clusters a determinar\n",
        "#init       : tipo de metodo para inicializar : 'Huang' , 'Cao' , 'random' (aleatorio)\n",
        "#n_init     : numero entero de veces que correra el algoritmo cambiando el centroide\n",
        "#             de las cuales se desplegara la mejor salida en terminos de costo\n",
        "km=KModes(n_clusters=3,init='Huang',n_init=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sS9ZIAmuO3d2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Leeemos el archivo people.csv \n",
        "X_test=pd.read_csv('https://raw.githubusercontent.com/javalillo13689/hackspace/master/people.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-skngGYvkYbA",
        "colab_type": "code",
        "outputId": "4a81df7f-4bc5-4388-b4e2-ad96f2bb9d1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        }
      },
      "source": [
        "#Mostramos las primeras 3 filas de la data\n",
        "X_test.head(3)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SEXO</th>\n",
              "      <th>UNIVERSIDAD</th>\n",
              "      <th>PERFIL</th>\n",
              "      <th>CARRERA</th>\n",
              "      <th>POSICION</th>\n",
              "      <th>ESPECIALIZACION_DATA SCIENCE</th>\n",
              "      <th>DIA_PREFERIDO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F</td>\n",
              "      <td>ULIMA</td>\n",
              "      <td>JOVEN</td>\n",
              "      <td>ADMINISTRACION</td>\n",
              "      <td>ANALISTA</td>\n",
              "      <td>NO</td>\n",
              "      <td>SABADO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>ADULTO</td>\n",
              "      <td>ADMINISTRACION</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NO</td>\n",
              "      <td>VIERNES</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>ADULTO</td>\n",
              "      <td>ADMINISTRACION</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>NO</td>\n",
              "      <td>VIERNES</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  SEXO UNIVERSIDAD  PERFIL  ...  POSICION ESPECIALIZACION_DATA SCIENCE DIA_PREFERIDO\n",
              "0    F       ULIMA   JOVEN  ...  ANALISTA                           NO        SABADO\n",
              "1    M       OTROS  ADULTO  ...       NaN                           NO       VIERNES\n",
              "2    M       OTROS  ADULTO  ...     OTROS                           NO       VIERNES\n",
              "\n",
              "[3 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BwysSH-PGSe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Dado que observamos en la fila 2 en la columna posicion un valor NaN procedemos a eliminarlo\n",
        "#en este caso ya que son datos perdidos o vacios que nos llevarian al error\n",
        "\n",
        "#Para elimianar las filas con valores nulos usamos el metodo dropna\n",
        "#inplace=True nos dice que incluya los cambios en la tabla X_test , de otro modo solo lo muestra\n",
        "#más no lo modifica\n",
        "X_test.dropna(inplace=True)\n",
        "#Debido a la eliminación de las filas con valores nulos el indice se encuentra desordenado\n",
        "#Para reiniciar los indices y mantener el orden de estos\n",
        "X_test.reset_index(drop=True,inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnigGcEcPNjg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Renombramos la data como data_kmodes(No obligatorio, se puede seguir usando X_test)\n",
        "data_kmodes=X_test\n",
        "#Mediante el metodo fit_predict ingresamos el dataframe data_kmodes para obtener\n",
        "#los valores del cluster al cual pertenece cada fila\n",
        "clusters=km.fit_predict(data_kmodes)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eq_2fbyPUKq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#A nuestro dataframe le agregamos una columna mas llamada cluster a la cual le asignamos\n",
        "#los valores obtenidos en la celda anterior\n",
        "X_test['cluster']=clusters"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "djpaVki9PVek",
        "colab_type": "code",
        "outputId": "c54eae40-ccf0-43a3-ac9d-3bc7ae3130be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "#Visualizamos a la izquierda del dataframe que se ha añido la columna cluster con\n",
        "#valores del 0 al 2 que seria el identificador para cada cluster\n",
        "display(X_test)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SEXO</th>\n",
              "      <th>UNIVERSIDAD</th>\n",
              "      <th>PERFIL</th>\n",
              "      <th>CARRERA</th>\n",
              "      <th>POSICION</th>\n",
              "      <th>ESPECIALIZACION_DATA SCIENCE</th>\n",
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              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>F</td>\n",
              "      <td>ULIMA</td>\n",
              "      <td>JOVEN</td>\n",
              "      <td>ADMINISTRACION</td>\n",
              "      <td>ANALISTA</td>\n",
              "      <td>NO</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>M</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>ADULTO</td>\n",
              "      <td>ADMINISTRACION</td>\n",
              "      <td>OTROS</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>M</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>ADULTO</td>\n",
              "      <td>ADMINISTRACION</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>NO</td>\n",
              "      <td>VIERNES</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F</td>\n",
              "      <td>SAN MARCOS</td>\n",
              "      <td>JOVEN</td>\n",
              "      <td>CIENCIAS PURAS</td>\n",
              "      <td>ESTUDIANTE</td>\n",
              "      <td>NO</td>\n",
              "      <td>SABADO</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>SAN MARCOS</td>\n",
              "      <td>JOVEN</td>\n",
              "      <td>CIENCIAS PURAS</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>NO</td>\n",
              "      <td>SABADO</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>M</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>ADULTO</td>\n",
              "      <td>OTRA INGENIERIA</td>\n",
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              "      <td>NO</td>\n",
              "      <td>SABADO</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>F</td>\n",
              "      <td>PUCP</td>\n",
              "      <td>JOVEN</td>\n",
              "      <td>OTRA INGENIERIA</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>NO</td>\n",
              "      <td>VIERNES</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>M</td>\n",
              "      <td>UNI</td>\n",
              "      <td>JOVEN</td>\n",
              "      <td>OTRA INGENIERIA</td>\n",
              "      <td>LIDER</td>\n",
              "      <td>NO</td>\n",
              "      <td>VIERNES</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>M</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>JOVEN</td>\n",
              "      <td>OTRA INGENIERIA</td>\n",
              "      <td>ESTUDIANTE</td>\n",
              "      <td>NO</td>\n",
              "      <td>VIERNES</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>M</td>\n",
              "      <td>SAN MARCOS</td>\n",
              "      <td>JOVEN</td>\n",
              "      <td>OTRA INGENIERIA</td>\n",
              "      <td>OTROS</td>\n",
              "      <td>NO</td>\n",
              "      <td>VIERNES</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>115 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    SEXO UNIVERSIDAD  ... DIA_PREFERIDO cluster\n",
              "0      F       ULIMA  ...        SABADO       1\n",
              "1      M       OTROS  ...       VIERNES       0\n",
              "2      M       OTROS  ...       VIERNES       0\n",
              "3      F  SAN MARCOS  ...        SABADO       1\n",
              "4      M  SAN MARCOS  ...        SABADO       1\n",
              "..   ...         ...  ...           ...     ...\n",
              "110    M       OTROS  ...        SABADO       0\n",
              "111    F        PUCP  ...       VIERNES       0\n",
              "112    M         UNI  ...       VIERNES       0\n",
              "113    M       OTROS  ...       VIERNES       0\n",
              "114    M  SAN MARCOS  ...       VIERNES       0\n",
              "\n",
              "[115 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4o_EQm93rRt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}