Lab_7_Packages_para_ML.ipynb

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    "colab": {
      "name": "Lab_7 Packages para ML.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyP17xJjdZAoQpLtPROG8rX7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RubenQuispe/Machine-Learning-con-Python-001/blob/master/Lab_7_Packages_para_ML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mtszfz-aU1yM"
      },
      "source": [
        "# Packages para Machine Learning part1\n",
        "\n",
        "## Mg. Rubén Quispe\n",
        "Este tutorial está diseñado para proporcionar una vista panorámica del panorama de los paquetes de ML. El objetivo no es dar una explicación detallada de todas las características de cada paquete, sino demostrar el propósito de algunos paquetes ML ampliamente utilizados. Para obtener más detalles, remitimos al lector a la documentación de los paquetes y otros tutoriales en línea.\n",
        "\n",
        "Veremos secciones de Jupyter, Numpy y Matplotlib.\n",
        "# Jupyter\n",
        "[Jupyter](https://jupyter.org/) no es estrictamente hablando un paquete ML. Proporciona un front-end de navegador conectado a una instancia de IPython que permite REPL para pruebas rápidas, permite crear documentos que entrelazan código, salida, imágenes y texto. Esto es excelente para la creación de prototipos, demostraciones y tutoriales, pero terrible para la codificación real."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDF2hDAoU5nK",
        "outputId": "10f48a51-be25-444a-9c19-f79960cc636c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "12*4"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xo731PoIXBxv"
      },
      "source": [
        "def tokenize(text):\n",
        "  return text.split(\" \")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJgEO6k9XKni",
        "outputId": "6dc3d25d-45b7-4be7-8fc9-413751928c47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "text =  \"In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley\"\n",
        "print(tokenize(text))"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['In', 'a', 'shocking', 'finding,', 'scientist', 'discovered', 'a', 'herd', 'of', 'unicorns', 'living', 'in', 'a', 'remote,', 'previously', 'unexplored', 'valley']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPzXhPdVXePR"
      },
      "source": [
        "# Numpy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NhnbF-ViXftJ"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QO1cIi3vXuGD"
      },
      "source": [
        "[Numpy](https://numpy.org/) está diseñado para manejar grandes matrices multidimensionales y permitir cálculos eficientes con ellas. En la parte posterior, ejecuta código C precompilado que es mucho más rápido que, digamos, un bucle for de Python\n",
        "\n",
        "En el tutorial de Numpy, hemos cubierto los conceptos básicos de Numpy, matrices numpy, operaciones basadas en elementos, operaciones con matrices y generación de matrices aleatorias. En esta sección, cubriremos la indexación, la división y la transmisión, que son conceptos útiles que se reutilizarán en Pandas y PyTorch.\n",
        "\n",
        "# Indexing y slicing\n",
        "Las matrices Numpy se pueden indexar y dividir como matrices de Python normales"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2pVd_VuXigZ"
      },
      "source": [
        "a_py = [1,2,3,4,5,6,7,8,9]\n",
        "a_np = np.array(a_np)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80-LDceoaB3G",
        "outputId": "6251bca0-4e82-48a4-998f-7057b01e9470",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print (a_py[3:7:2], a_np[3:7:2])\n",
        "print(a_py[2:-1:2], a_np[2:-1:2])\n",
        "print(a_py[::-1], a_np[::-1])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4, 6] [4 6]\n",
            "[3, 5, 7] [3 5 7]\n",
            "[9, 8, 7, 6, 5, 4, 3, 2, 1] [9 8 7 6 5 4 3 2 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hN2S1aLhbGSk"
      },
      "source": [
        "Pero también puede usar matrices para indexar otras matrices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGj4QJYEabYp",
        "outputId": "0cf8cb8a-6e03-4335-88a7-682bdd2f2fcc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "idx= np.array([7,2])\n",
        "a_np[idx]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8, 3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qyj0WmP_bT5Z"
      },
      "source": [
        "# a_py[idx]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXowPyBrcALf"
      },
      "source": [
        "Lo que permite consultar, volver a indexar e incluso ordenar de manera conveniente"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjgbUuiabee5",
        "outputId": "98d0c1a5-7dee-45e0-83e4-001c32b4f8f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "ages = np.random.randint(low=30, high=60, size=10)\n",
        "heights = np.random.randint(low=150, high=210, size=10)\n",
        "\n",
        "print(ages)\n",
        "print(heights)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[41 56 36 47 31 48 41 39 44 30]\n",
            "[153 204 161 193 170 198 179 167 186 169]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFzbSJb0cTlw",
        "outputId": "fd7b9a6a-9d6d-4bc2-9094-e3408df0e41e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(ages < 50)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[ True False  True  True  True  True  True  True  True  True]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHS4Vh2TcbtQ",
        "outputId": "3f05473b-4706-444f-d707-c3ec56074e61",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(heights[ages < 50])\n",
        "print(ages[ages < 50])"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[153 161 193 170 198 179 167 186 169]\n",
            "[41 36 47 31 48 41 39 44 30]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uy8EMQmQc84k",
        "outputId": "92cd2c7f-e397-47c9-f92d-4e7e28da9fb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "shuffled_idx = np.random.permutation(10)\n",
        "print(shuffled_idx)\n",
        "print(ages[shuffled_idx])\n",
        "print(heights[shuffled_idx])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[9 8 0 5 7 6 3 4 2 1]\n",
            "[30 44 41 48 39 41 47 31 36 56]\n",
            "[169 186 153 198 167 179 193 170 161 204]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyDWq09Zc83Z",
        "outputId": "9f4f52dd-765e-4b43-cdbd-e27be8cf2030",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "sorted_idx = np.argsort(ages)\n",
        "print(sorted_idx)\n",
        "print(ages[sorted_idx])\n",
        "print(heights[sorted_idx])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[9 4 2 7 0 6 8 3 5 1]\n",
            "[30 31 36 39 41 41 44 47 48 56]\n",
            "[169 170 161 167 153 179 186 193 198 204]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqqnLVVtdO75"
      },
      "source": [
        "\n",
        "# Broadcasting \n",
        "Cuando se le pide a Numpy que realice una operación entre arreglos de diferentes tamaños, \"broadcasts\" el más pequeño al más grande."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SNFswg2KdGCq",
        "outputId": "5e0df3b7-3e76-46be-db8f-c287a6bf8b26",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "a = np.array([4, 5, 6])\n",
        "b = np.array([2, 2, 2])\n",
        "a * b"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8, 10, 12])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSOZiILGdtOS",
        "outputId": "0b433946-2832-472b-9e9b-ee273bc1ccf2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "a = np.array([4, 5, 6])\n",
        "b = 2\n",
        "a * b"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 8, 10, 12])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WF927alieEWZ"
      },
      "source": [
        "Los dos fragmentos de código anteriores son equivalentes, pero el segundo es más fácil de leer y también más eficiente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ywn2V9gXg8go"
      },
      "source": [
        "a = np.arange(10).reshape(1,10)\n",
        "b = np.arange(12).reshape(12,1)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ghn1e3Og_QN",
        "outputId": "4b068913-27d0-4c0c-c981-56a3c4bc1cff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "print(a)\n",
        "print(b)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0 1 2 3 4 5 6 7 8 9]]\n",
            "[[ 0]\n",
            " [ 1]\n",
            " [ 2]\n",
            " [ 3]\n",
            " [ 4]\n",
            " [ 5]\n",
            " [ 6]\n",
            " [ 7]\n",
            " [ 8]\n",
            " [ 9]\n",
            " [10]\n",
            " [11]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pdRHBpLWhCR5",
        "outputId": "62ed65a5-1859-43b1-ffc3-59b8f6c578e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "print(a*b)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0  0  0  0  0  0  0  0  0  0]\n",
            " [ 0  1  2  3  4  5  6  7  8  9]\n",
            " [ 0  2  4  6  8 10 12 14 16 18]\n",
            " [ 0  3  6  9 12 15 18 21 24 27]\n",
            " [ 0  4  8 12 16 20 24 28 32 36]\n",
            " [ 0  5 10 15 20 25 30 35 40 45]\n",
            " [ 0  6 12 18 24 30 36 42 48 54]\n",
            " [ 0  7 14 21 28 35 42 49 56 63]\n",
            " [ 0  8 16 24 32 40 48 56 64 72]\n",
            " [ 0  9 18 27 36 45 54 63 72 81]\n",
            " [ 0 10 20 30 40 50 60 70 80 90]\n",
            " [ 0 11 22 33 44 55 66 77 88 99]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXSWBV_hiFL5"
      },
      "source": [
        "# Matplotlib\n",
        "[Matplotlib](https://matplotlib.org/) es la librería de referencia para producir gráficos con Python. Viene con dos API: una similar a MATLAB que mucha gente ha aprendido a usar y amar, y una API orientada a objetos que recomendamos usar.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjWGbvoxhIcC"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1FXcLs6iPWG"
      },
      "source": [
        "plt.rcParams['figure.figsize'] = [10, 7]"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JINzvr8iUJy",
        "outputId": "e6fecba6-d1c2-4d43-90b1-ed553cf04d24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "x = np.linspace(-2*np.pi, 2*np.pi, 400)\n",
        "y = np.tanh(x)\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(x,y)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa4685ab9b0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UyflezHajUAA"
      },
      "source": [
        "Puede trazar varias subparcelas en la misma figura o varias funciones en la misma subtrama"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dH_ODhX2jMYT",
        "outputId": "2959bffb-7ec2-4bdd-c6ad-aa255c9b35c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "x = np.linspace(0, 2*np.pi, 400)\n",
        "y1 = np.tanh(x)\n",
        "y2 = np.cos(x**2)\n",
        "fig, axes = plt.subplots(1, 2, sharey=True)\n",
        "axes[1].plot(x, y1)\n",
        "axes[1].plot(x, -y1)\n",
        "axes[0].plot(x, y2)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa467d139e8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EGkJMjA5lOmKcc5RMG8Wc989VNFSUavFFTtkUo0StaTQpwREzVBQM9bIly8T6ZqVq4ac/9Rj+8pHj+Pl/cyU++3N3YPNEsjtgCSKrkBCLwRuu24iJgo6/2n8y7aOkxplLlTVBfaBxU/HCSnpCbLFk1nu2ADRuGCbs0q1ULYz5I8mJBN6zYjlwOeqOWMHQYgkmQcnPcI34wgmIJ8RqvvAq6CrymkqOGAHA+57x9o8/jIcPL+C333YTPvjm6xK9xUwQWYfqK2KQ11W89eYt+OL+Eyj54en1xtlLVWybWlvhMTfmCaD5FB2xxbJZH0sC3v9eOU1OnqoTjstRMp26Izbq/zuxUpP3nsL9KhoNR6xixR99Nhwxtb6jL9ZosikjltfVWMF/Yjh45UIJ7/iTR7BatfGp9+7DXVfPpn0kgkgd+s+QmLzlps2o2S6++fJ82kdJhdNL3R2x+RQdseWqXXekBKJOIilERYNwxIQQkzE6FAjnquGIyR1NFnOalFuTzaNJLyNGo8n1zNELJdz7iUdQtRx8/ufvIBFGED4kxGLymiumMVM08NVnz6Z9lL5TtRwsV21sGFt7w2mioENXGS6splfvsVxp3F4UjOc16cH5y97TF3nCERupCzF571kyxQjRfw9DjeVcrX1uYzQZ69akf6acpiCn02hyPXNsoYSf+OOHYTkuPve+O3DDlom0j0QQmYGEWExUheH7r9+Ir794HjV7ff2gETUQs22EGGMMM8VcqmH95WobIZawIyZKTMXqIXH7sCRBKAmE+yXcthFDQ1nGaLLWcMQKevyMWNVyUNBVMMYoI7aOmV+p4d2ffBSm4+KzP3cHrt00nvaRCCJTkBCTwJtu3ITVmo2HDi2kfZS+Ut+p2BSIb2ZuLGUhVrHrtxYF4/n4OxS7UTIvHxsqCsOIoaIs0xEToXr/1mRBkiMmxpAFXZV2a1I4a3ldQdWm0eR6Y7Vm472fehTnlqv45E+9Bq/aNJb2kQgic5AQk8DrrprFWE5bd+PJhZInsmZH2wux2VEjNSFm2i4qlrPGERvLa9Ja6NtRaQq8C0YMrS7QZCCeVXfEdFXKrcnWugkAqMTJiJlO3Vkr0Ghy3eG4HL/0uSfxwpkV/NFP7sFtO6bSPhJBZBISYhIwNAXfdc0s/uXgvPSdglmm4Yi1b8GeHc3hwko6GbEVf/w43i6sn6AjVm4qMRWM5uSE6VvfQ4icEcMTYnG3GAihlNdVGKoCVWGxHbG8rtSfWbWcdfX/j/XO/3rwJXztxfP4r2+9Ad9z7Ya0j0MQmYWEmCTu2j2HM5eqOHKhlPZR+obIiM10csT80WQaa46E67Xm1mRex0rVTkwQiBoJEaQXv5ZZ3VCzLhd74kJANWZGUfR+5TUFjLHYTlu1ZTTpcsBySIitB/7x6dP4g38+jHfs24533b4j7eMQRKYhISaJu3Z7V7H/9dD6adm/UKrBUBWMdehPmykasF2OlRSWPQvXqzUjNpbXYDouagnllSqm99zm0WQxp0pdeC1qIHK+2yQjzwV4Qk5TWH3Rck5XY/05VSwHeU0IMbX+HsRwc/RCCb/ypaexZ+cUfuOtN4IxlvaRCCLTkBCTxI6ZEWyfLuBbB9ePEFtYNTFdNDp+o02iVT4ojRqJyx2xoiTR0ol64P0yISan+V4gbucKkVOQsBcS8ByxnNb4lpDTlFg3gauWWxdgOSHEKCc21NRsBx/43BMwNAW//85bYWj0I4YgekH/L5HIXbvn8MjhBdjO+rgddrFkdhxLAg0htlTuvxBrtM9f7ogl0evVTKUlvyXOIPP9qpYLhQG66glgMQaNeyGgajuXZdtyuhLLEbMct37GvP8DuUalrkPNb3/1JTx7ahm//WM30e5IgggICTGJ3LV7Fis1G0+dvJT2UfrCwmoNM6Ptg/oAMOkv2Za58Doojfb5y5eRFyWJlk6ULQe6yi7bnVeUHNavWp5gEk5kwVD8j8cTOc0OFuA5bnGEk2m7dUdEPDdOQSyRbR46dAF/9u2jePedO/HGGzalfRyCGBhIiEnkzqtmAACPHFkffWILJRMzHTrEgCZHrNL/m5OtfV4CIcxkCqNmmisbBAXJexarttMyQpQz9qvZLaNJPd5o0nTcuiDN02hyqCnVbPyXv3kau2aL+D/ffF3axyGIgWL9balOkOmigavminji2GLaR+kLC6vdhZhYuJ2OI3Z5+7xACLOkFlBXTOeyG5MAkDdUqWWmtVbnyg/tx72AULWcepYLEBkxOY6YEHgmlboOJR994CWcuFjBF3/+zsv+3SQIojfkiElmz84pPHF8cej7ksqmjYrldB1NppsRs6EwXObwAI0bhkk5YuWmygZBXlNh2q60Go+qfbkQk+WIVZt6v8Rz42bExJ+/QUJsaHn82EV8+uFX8J47d2Lfrum0j0MQAwcJMcns2TmFxbKFo0PeJybKXLs5YnldRU5TUrk1uVqzUcxpa250ioxYko5Y62hSiCZZlRlV6/LRpKyx35rRpKbUO8uiYNqN0aQQYrV1cpFlvWA7Ln7tb5/F5vE8fuXua9M+DkEMJCTEJLNnp7fG4/EhH0+KcaMYP3ZioqCn4oiVTXvNWBJo7GeUuYS7mYplX9YhBjRGh7KC6jXbXTNCBOLfSKxZrbcm4zlipu3CEEJMJUdsGPnso8fx4tkV/Npbrl+TxyQIIhgkxCRz5ewoxvManjg+3EJMiCtxM7ITkyN6ahmxVkEENDliCdVXlM02o0nJQfWq5dTrIJqfHydY7z1XriNmORw6ZcSGloXVGv7nAy/htVfN4M2vpluSBBEVEmKSURSG23ZODb0jJm5CBnLEUrg1uVpr74iJsWFijliHW5OAPCHW6lwJxy1ufUWttUcsRlifcw7TaThiYkRp0WhyaPif//tllE0Hv/HWG6g9nyBiQEIsAfbsmMLB86upOEH9QjhirbscW5koGLhU6f+Ko7Jptx2VKArzlmQnVehqrXXiZAklQatzJctxq1puva1fPDeqEBM7JQ0K6w8lh86v4guPHce77tiJqzeOpX0cghhoSIglwC07JsE58Nyp4S12FSKztxDTcamchiO2tkZCUMxpyRW6mg4KLe+bk1xm2upcaQqDwiQs/bad+v5KIN6KI9N3voyWsL5JjthQ8NEHXsSIoeED37s77aMQxMBDQiwBbtgyAQB47vRyyidJjksVC3ld6dkZlF5GzMZorv3Ziobcpvtm2t6a9F2mOHmrZrwG/Mb/dRljnnsluVk/p6mwHA4nQu2GcL7IERs+nji+iAeeO4ef+64ru9bXEAQRDBJiCTBdNLBlIo9nTw+vI7ZUNjFZ6B7UBzxHrGQ6fc8GdRpNAt5uxiTqKzjnKJtrb02K8H5cx0rQuhMS8MaIUhyxlmZ9IJp4Ev976y23JmVVeBDpwDnH//jKi5gdNfCz37Ur7eMQxFAgRYgxxu5mjL3EGDvEGPvVNp//GGPsgP/Xy4yxpabPOU2fu0/GebLA9VsmhtoRWypbPYP6QHrt+qJHrB2ydz8KTMeFy9Hm1qTsjJizpqg2rymxnu+4HC5vCCagcdMxSvZsjSNG9RVDwcOHF/Cdoxfx/u/ZTXUVBCGJ2P9PYoypAP4AwPcDOAngMcbYfZzz58VrOOf/oen1HwBwa9MjKpzzW+KeI2vcsGUcX3vxnO+QDN83rEsVC+M98mHA5e36s30aY9iOi6rl1qsqWhkxNCwlkFur+Dcx14T1JTXfA54jUWtp1ge8HFqc5wuBpLfZYRnFxTLrjph3m05RGDSF0a3JAed3v34QG8ZyuHffjrSPQhBDgwxHbB+AQ5zzI5xzE8DnAdzT5fXvAPA5Ce+baW7cOgHOgRfOrKR9lES4VLEwGUCIjee916xU++eIlX1BUuyUEcupidRXlP1ndmrWlxHWNx0XnGOtEIu7F7IlXC+eCUTrJxPCrtm5MzSFHLEB5rFXLuKRIxfx86+/ivZJEoREZAixrQBONP3+pP+xNTDGdgLYBeDrTR/OM8b2M8YeYYz9cKc3YYy9z3/d/vn5eQnHTpYbtowDAJ4b0pxY0NHkWN5zpVYTqotoR8l/r64ZsQTOI4RW62iy0SMWX4SIZ6wZTSbgiImxYhQXq3U0KX69Xm9NDtr3r3b87tcOYqZo4J3khhGEVPod1r8XwJc4580/MXZyzvcCeCeA32GMXdXuCznnn+Cc7+Wc752bm+vHWWOxeSKP6aKB504NZ05sqWL2bNUHgFFfiK1UsyPEikYyjlhjNNlaXxE9a9WKuHmZW+O6KbFuTVp1R6xRzKnXc13hb022hvW9Z69fR2zQvn+18tSJJXzr4AX87HddueY/NAiCiIcMIXYKwPam32/zP9aOe9EyluScn/L/fgTAN3B5fmxgYYzhhi3jQ3lzsmo5qFpuzw4xAPV2+9W+CjHHf+/2PzBGcsncmix3yIjlNAWMyamvEOPHfIsjZmhqrIXa7R0sT5RFcbHqz1NpNDkMfOJbRzCW1/Bv79yZ9lEIYuiQIcQeA3A1Y2wXY8yAJ7bW3H5kjF0LYArAw00fm2KM5fxfzwJ4HYDnW792ULlu8zgOnl+N1MOUZZYDlrkCwJjIiKUwmux0SWI0p8FyuHRRIMRda36GMYacpkjJiAlXrfU94rpN7RysOGuJ6mF97XJHLI5YJNLh5GIZX332LN65b0fbtWEEQcQjthDjnNsA3g/gAQAvAPgi5/w5xtiHGWNvbXrpvQA+zzlvViXXAdjPGHsKwD8D+K3m25aDztUbRmHaLo4tlNI+ilSWKmLhdzYdMZFH6/RDQzhWJcniUIikdsvGvQyXvIzYGiGmMZgxesRqbRysuhCLcmuygyMW5VlEunz6oVcAAO957RWpnoMghhUp/3nDOb8fwP0tH/tQy+//a5uvewjAq2WcIYtc4+9ge/ncKq6cG035NPIQeyaDFLqq/m7Hvt6aNMWtyU4ZMe/jJdPGVLH3P0PY9229NSk+JiMjJkpbW8P6hqrU9ztGwWrnYMVYSyTOsubWJDliA8VqzcbnHz2BN796M7ZMFtI+DkEMJdSsnyC7N3ji6+C54aqwCLpnUjCa0/p6a1K8V7FDqHjEz46VJQf2havULszsNd/HFyG1jo5YvNFkvW6iJVwPIJLAMx3vz5bC+oPNFx87gZWajZ+5i1r0CSIpSIglSDGnYetkAS+fX037KFIRGTFRTdGLsbzW14xY/fZiB0dMOFYV2ULMau9WiY9JccTqGbHWsH48t0mIrWZHLFZGrFN9BQmxgcF1Of7ikWPYs3MKt2yfTPs4BDG0kBBLmGs2jg6dIybGjEGF2Ghe72tGTITiW28WCvK6vKb7Zqr1EtNOGTF5o8m1YX01niPmO1iXZ8S8W5PRwvq+sGu9NUmjyYHh4SMLOHqhhH97B92UJIgkISGWMNdsHMOR+RLsIfoBJDrBxI3IXozltL5mxCqWA0NVoKndhZiMW4zNiLGh0UYASsuIdSh0jT+aXCuc9Bj7Ids5YpoSL8dG9JfPfOcYpkZ03H3jprSPQhBDDQmxhLl64xhMx8UrC+W0jyKNlZqNnKa0FRzt6HdGrGI6a0Z3zchewi2o2Q50lUFV2JrP5fV4S7mb38N7Xqsjxvz1R9GETn3FkbSw/lrBqKtsqP6DZJg5v1zF/37uHN62ZxutMyKIhCEhljDXbBy+wP5KNdjCb8FYXuvvaNJ0ui5aLyQ0mqzZbtuxJCBxNCnC+trasD4QLVgPNCoqZNdXtDps9pB16g0rX9x/ArbL8Q5aZ0QQiUNCLGHEzcmXzw1PYH+5agfOhwHemqO+hvUtp+salqQyYjW7sxPn3ZqUF9bPtQnrA9Hcq+ava+eIRbo1abtQlcvdQU1lFNYfAFyX43OPnsDrds8MVe0OQWQVEmIJM2Jo2DZVwKH54RFiK1U7cD4M8DJiqzUbbp/ckIrldB2nFBLMiHV2xBRUTBmjyQ4ZsRh5LqC5Wb9512T0FUeW4172LADQFQW2S0Is6zx8ZAGnlir4ideQG0YQ/YCEWB/YNVvEKxeGp11/pWphPKQjxjlQlix8OlG1HBS6ZsSEIyZXFFRtt211hXhPGbsmTduFoSlg7HKRY/gCMKoQaxeu15Xo9RU1271szAl4jphNYf3M87+skgMAACAASURBVNdPnMRYXsMbr9+Y9lEIYl1AQqwPXDlbxNELpchB6qyxEnI0KdyzfuXEymb30aQQS/IdMafjBQZZo0nLWStwgKbRZFQh1mbXpKIwaAqLJMQsx62LQ4GuKpGeRfSPUs3GV589ix+8aTOF9AmiT5AQ6wO7ZotYrdmYX62lfRQpLFcsjOWCjybr+yZr/amwqJgOCnpnoago3hJuGQ5VMzXbRa7DD6+8psJyeOxbg+1GfkDzGDHaP1O73ZDec6NVTpi2C6N1NKkyqq/IOF999izKpoMfu21b2kchiHUDCbE+sMsPvL5yYTgqLMI6YqP+a5f75IhVe4T1AW8NkXRHzHa6lMj6lRkxw+qeEGvf3A80+sCiPFdTGBRlrXiK1CPmuGvcQU2ljFjW+esnTmLnzAj27JxK+ygEsW4gIdYHds0UAQBHLwx+YN9yXFQsJ3RYH+jfaLLSIyMGeA5VIvUVnRwx/+NxXTjT5m2FWOxbk/Za4SSeGz2s3+KuKZ4jNiwj+mHj1FIFDx9ZwI/eum1NBpEgiOQgIdYHtk4VoKsMR4YgsL9ab9UPnxFb6WdGrEe+pWCo8gtdrc5h/bhCSWC1cZoAb8UREOfWZHuBp6tK5B6x1nOK5zvUJZZJ/u7JU+Ac+NHbtqZ9FIJYV5AQ6wOqwrBzZjhuTq5EEGIj/piwbPbREetS6Ap4ozzZo8mq7XQWYjHrJQSdMmJxw/o1u/3I09CiBezNNsJOqy8RJyGWNTjn+JsnTmLfrmlsnx5J+zgEsa4gIdYnrpjxbk4OOsv1hd/BR5NFfzRZNpOvr3BcDtN2Azpi/esREwWstYQyYg3HLdo/k+W0d/Oih/XX3iCtLxGnnFjmePHsCg7Pl3DPLVvSPgpBrDtIiPWJK+eKeGWh3LdS06QQjliYHjHhiJX64IgJcVUw0sqIJeuItXOagKZbkxHD+qbd6TZm1IwYXyPsNP8iAHWJZY/7nzkDhQFvuoEWfBNEvyEh1id2zRZh2i5OX6qkfZRYrPiOWJhdkzlNgaowlGvJO2LCdUslI2Y7a3ZACoQ7FNsRa1OUCjTdmoyYQeucPYvWI2a2GXXq/vNp8Xe24Jzjy0+fwZ1XzWB2NJf2cQhi3UFCrE/smhU3Jwd7PBklI8YYw4ih9tkR636+vC4/I9bVEYuZ4RJYjgtda5MRixnWbyecgOglrGYbwSia+uNeWCDk8sKZFRy5UMJbXk1jSYJIAxJifeIKv8LilYXB7hKLkhEDgKKh9cURE+KqlyOW1+WOJl0/m9YprB/XsRL0zIjFaNZv54jpqhLpmZ5gXLviCKDRZNb48jOnoSoMb7qBVhoRRBqQEOsTG8ZyMDQFJy8OthCL4ogBwEiuP45YfTTZKyMmWYgJgdUxrK9J6hFzODSlmxCL3qzf1hHTFJgRhFP7XZP+aJLC+pmBc477nzmLO6+cwQyNJQkiFUiI9QlFYdg2VcDxgRdiFvK60vaHdjeKhtaXW5MV/z167ckr6HIzYjVLCLF+9Ih1qa+IkRFrd3ZDZZF6xNqdU6w8ovqK7PD8mWUcvVDCW27anPZRCGLdQkKsj2yfGsGJxUEXYnbosSTgheNLtT5mxHqOJuVmxGq+E5X0rclOo0k9psgxu4w8oy79XtMjpij1zxHZ4MtPn/HHknRbkiDSgoRYH9kxPYITFwf91mS4PZOCoqH2xxHzxdVIj7B+QVfhuFyaKBC3IXvdmowtxDoVr6rxbmVaNm97GzNqWN9uM0LVyBHLHF991htLTheNtI9CEOsWEmJ9ZPt0AZcqFi5VrLSPEpnlqhXJERvJaX1p1g9aXyFGl7JcsZ6OmKT6ik49YowxGBGD9d5z14brgeiFrpa7tpdMnJvqK7LB4flVHLlQwhsppE8QqUJCrI/s8FeHnBjgnNhK1Q5V5irotyOWDxDWByAtsF+1eoX15Y0mjTbFq4C/oDtGfUUnRyxK7sx2eN0Ba34WANgDXmo8LPzT8+cAAN93HQkxgkgTEmJ9ZNvUMAgxK9JocsTQ+pMRC+mIVU1Zo0nfEetDWL/TRQlDUyKvODI7XQKIUOjKOYftcqgdRpPUI5YN/umFc7h+8zi2ThbSPgpBrGtIiPWRHTO+EBvgwP5K1cZYLvxospjzHDHOk3VDgvaIic9XI9Y9tNLz1mTMDJegXT9X83tEdcQ8p01Oj5jjO1660uKIKWI0SY5Y2iys1vD4sUW84XpywwgibUiI9ZHxvI6Jgj7QFRbLMRwx2+WJuyFl04GhKvXOqk7k/SyXrNFkPazfQQDGzXABntNkdciIAYCusei3Jrv0iIV1xMTosfV/g0ahKzliafPPL83D5cD301iSIFKHhFifGeSbk5bjomq5kcL6RX/xd9Lt+lXL6RiYb0Y4YhVJubVeYX3Ac8tqMRw4IXA6ZsTiOmJdwvphnEwh3DqF9S3KiKXOg8+fxabxPG7cOp72UQhi3UNCrM9sny4MbEZMZLxGozhiOe9rkm7Xr1pOz7EkAOTqo0m59RWdwvpAvDA90CxwOmXE1EijT9ft7LSJUWsYp02MHrXW0aSor5D0Z05Eo2o5+JeXL+AN128AY+1FPUEQ/YOEWJ/ZPjWCk4sVuAPoCqwKIZbrLXRaGRGOWMI3J7st3m5GtiMmRpydMmKABCFm+9mrrmH9COWr/sqh9o6Y6P4K/lzxPJVWHGWShw8voGI5eAONJQkiE5AQ6zPbpkdgOi7Or9TSPkpohIgq5qLUV/iOWMI3J6uW07FUtRmREYszKmym4Yj1EGIx8lHiazuF9XOqEmnXpBCHncL6QDgh1jmsT4WuWeDBF86haKi486qZtI9CEARIiPWdbf5V8VNLg5cTE45YsUdrfTuy5ojJKlitv6+4NdllLGqoSv11URBiSHaPmBBGrZku72Phazfqo8kWYUeFrunDOcc3X5rHa3fPdh2jEwTRP6QIMcbY3Yyxlxhjhxhjv9rm8z/FGJtnjB3w//rZps+9hzF20P/rPTLOk2W2+ELs9AAKMeFmRXLEcv1xxGp2MEdM/BCSJsR8JyrfxRHL6fEcsV4ZMV2Ndmuy7oi1+XMz1PAZsU5hfVpxlD6H51dxaqmC737VXNpHIQjCJ/xP1BYYYyqAPwDw/QBOAniMMXYf5/z5lpd+gXP+/pavnQbw6wD2AuAAHve/djHuubLKlsk8gEEXYtEzYjIXbbejarn1sWM3hGtWk1hfobC1LlAzcesreof1ozpi7YUT4FViAOE2AtTrK5T2jphFGbHU+MZL8wCA119DQowgsoIMR2wfgEOc8yOccxPA5wHcE/Br3wTgQc75RV98PQjgbglnyixjeR3jeW0ghdiqXz0xGssRS3o0GdQRkzyatN2eo564YX2zZ1hfjeS4ia/pVF8BhAzr+69tXXEkblFSoWt6fPPleezeMFrf8kEQRPrIEGJbAZxo+v1J/2Ot/Bhj7GnG2JcYY9tDfi0YY+9jjO1njO2fn5+XcOz02DJZwKmlatrHCI1Y2h1lNNnIiCUd1g+YEZPUdC/wClG7VwEYmoqahNFku1VEQHTHrVE30VmIhXLEOtRXqAoDY+szI5aF718V08F3jl4kN4wgMka/wvr/AOAKzvlN8FyvT4d9AOf8E5zzvZzzvXNzg/2NZOtkYUAdsThh/f45YkFCyIyx2AWrzXi7Gru/b05TYo1Cg4wmowjLTg6WeGbza4LQqVmfMQZdUWCuQ0csC9+/Hjm6ANN2SYgRRMaQIcROAdje9Ptt/sfqcM4XOOeir+FPAewJ+rXDiOeIDZ4QK9VsKAyBMlitqIonfPrhiAU9nyeM5Dli3aorAIn1FZ2EWIQF3UBDOLVz9KKE9YXj1VpfAXhibz06Ylngmy/NI68r2LdrOu2jEATRhAwh9hiAqxljuxhjBoB7AdzX/ALG2Oam374VwAv+rx8A8EbG2BRjbArAG/2PDTVbJgu4VLHqDtOgUKo5KOa0yG3cxZyWeLN+zQrmiAFe1YTM0WS7jNVl7xc7rN89I6arSiSR081pi5IR6+SIAd640h7AMuNh4F9enscdV8503IdKEEQ6xBZinHMbwPvhCagXAHyRc/4cY+zDjLG3+i/7RcbYc4yxpwD8IoCf8r/2IoD/Bk/MPQbgw/7Hhhpxc/LMgLliqzU7UlBfMGKoye+aDNgjBsTf/dhMsIxY3Gb9zsWrgCd8olRD1EeTbTNi/q1JCWF9wPsziOLaEfE4vlDGkQslGksSRAaJXV8BAJzz+wHc3/KxDzX9+oMAPtjhaz8J4JMyzjEobJtqlLpevXEs5dMEp2zakYL6gqKRrCPGOfdHhAEdsZjCqJlOS7ObiTuarDtXHcP6DJbrgnMeyrW0AxS6htkPWX9eG2GnKSTE0uCbB6m2giCyCjXrp0Cj1HWwbk6u1hwUjehjjZGcmmizvhgzBs+ISRxNOm5Hp6rxfvEyab0yYpqqgPPGiqGgiN2PbUeJvjgLM04Uz1M7ZsRoNNlvvn1wHlsnC9g1W0z7KARBtEBCLAU2jOWhKmzgbk6WahIcsQRzcfU1Q4EzYtFuGbZ97wAZsfiOmCdgOgm++gqhkELM6lA34X0sSo9Yd4fNooxYX3FcjkeOXMTrds9EzncSBJEcJMRSQFUYNo3n150QGzGSdcSqYs1QqFuTMjNiPYSYqsJxeWjHShBkxREQLs/V/Nz2ha7hS1idLmF9nW5N9p3nTy/jUsXCa6+aTfsoBEG0gYRYSmwdwAqLuGH9Yk5LdjQZ0hEzJI4mLSdYfQUQrhy19T2A9k6T93GxVDvkaLKbI1Z32SKE9Ts4bJQR6y8PHb4AAHjtVTMpn4QgiHaQEEuJzZN5nLk0WBmxsulE2jMpKBhqoj1ikRyxftZX1NcqRROjQsDpHd6nsVQ7miPWtr5CCb+ou9FL1t4Ro6Xf/eWhwwvYvWEUG8bzaR+FIIg2kBBLiU3jeZxdroLzwfmhtFqzI7XqC4qGmmizfuiMmOxm/V6jydiOWLCMWNjnN3q/ujhiYXrEutRXaKoSyl0j4mHaLh49ehGvIzeMIDILCbGU2Dieh2m7WCxbaR8lEJbjwrTdmBkxDRXLiZyR6kV4R0yV2qzfMyMWc9F4t5Ef0JTnCntrskuPWJRbk93C/7rKYNmD8x8fg85TJ5dQsRzcSfkwgsgsJMRSYtOENyY4OyDjSXHbMdatSX+sWZEUkG8lzVuTQXrExGgy6s1Jy3HBWPtaCCBaC753ns5Om16/NRm+vqJ9WF+BRY5Y3/jXQxfAGHDnleSIEURWISGWEhv9vMa55cEQYmId02isjJgn4soJVVhUrSgZMTmiMFB9RcTRocB0PNetUwVBlKoJoNcokV32mkDPc7vVYVCPWD956PACbtwygYkRPe2jEATRARJiKVF3xAZEiInbjrFGk3rCjpgdNiPW512TeszRpM275tAMLXywHuiRERNh/TCFrl12YnprmMgR6wdl08aTxxfx2t3khhFEliEhlhIbxnJgbHBGk6sSRpMFI1khFsURM2039oUJznmwsL7q/fPHqa/ots9SjxCsF88F2q8kYoxBVRicEONEu8sI1SAh1jf2v7IIy+HUH0YQGYeEWEroqoKZYm5gRpP1jFiMW5MF4Ygl1CUW2hGL6VAJHJeD8863GQV6hDFfM5bT/UKAGE2GzaDZDofCAKVD9izsONFyeVtRB/grjqhZvy88dHgBusrwmium0j4KQRBdICGWIpsmcgMzmmyE9eP1iAHJCbHwjph3nrhCzOzSTN+MHjOsb/YQYmI0GTaD1Uvg6aoSLqzvuB0vFGiKQhmxPvHQ4Qu4dfsURmL8xxNBEMlDQixFNo3nB2g06YmcOM36hcxlxOIVrArEqDHpsL7l8K7vETWsbzm8u9OmspDN+rxt3gwQha40mkya5aqFZ09dwp3UH0YQmYeEWIpsHM8PzGhSNOLH3TXpPStZR6zXqiFBXYjF7BKrN973HE2Gr4Joxg6YEQsf1nc7CidArCUKt2uy8z5Myoj1g8dfWYTLgduvnE77KARB9ICEWIpsGs9jsWzVBUSWadRXRBdi+T44YoaqdMw6tZLzzxN1VCgIOpoUn48qRHqPEKOuOOJty1ybnxuuvsLtWDqrqVRf0Q++c/QidJXh1u2UDyOIrENCLEU2+hUW55drKZ+kN6WaDYUFd5vaITJiSQnPquXUA/hBkO2I9fqzEUIpekas+wixfmsyZGFqL6ctbMC+26hTVxUK6/eBx165iJu2Tdb/P0cQRHYhIZYim8YHp0usVHNQzGkdy0SDkPRosma7gfNhQPPKoZgZMeGI9ayviOmI2d0rMupLv0OuEOrptCnhxom203nUqSrh8mZEeCqmg6dPLmHfLhpLEsQgQEIsRQap1HW1ZscaSwJAXku4vsJyAt+YBJrD+n3OiMXpEdM6C+G60AspdCy3c7geCD9OtFzeeQ2TQvUVSfPkCa8/bN8VJMQIYhAgIZYi9TVHA3BzsmzasYL6gNdTldeVRDNiYUansuorrD7VV/TsEYso9GzH7dj7BfiVEyELXTs9T1UUcI7EFr8TwKNHL4IxYA/1hxHEQEBCLEXG8xoKujogjpgTW4gBXoVFkj1i4kJAEBoZsXjnqQWsr2iE6aOJkN4ZsYgrjrrUTYjnhusR6/y8+u5KGk8mxmOvXMT1m8cxnqf9kgQxCJAQSxHGGDZN5AdCiJVqNooSgr8FXc2MI5aX1KwftEdMuERxVhx1y4jpsUaT3Z22UI5Yl+eJ25TkiCWDabt4/Ngi5cMIYoAgIZYyG8dzAzGaLNXijyYB7+ZkdhwxSc36drCwvqKwWIWmQXdNhg7r2y6MLs9VlZCOmOtC75ARE9mxqK4g0Z1nT19C1XIpH0YQAwQJsZTZND4YjpiMsD7gC7GMOGKymvWFqOjliAHxCk0tu3tGTFUYGItQX+G6PXvEwjhY3Zv1vfchRywZHj16EQDwGnLECGJgICGWMhsn8ji/XAPn2f7BVDadWHsmBUlmxGp2REcsbo+Y4/3z9HLEgPB7Gy9/H14P/Hd7ftjLAN2EEyD2Q4asr+gY1o+3+JzozmNHL+KquSJmR3NpH4UgiICQEEuZTeN5mI6LiyUz7aN0ZVXaaFJDObFC15COWJ8zYoAnlKK+X6+MGOCJwbDN9bbbu7E/3Giy+65J8RpCLo7L8egrF7FvF+2XJIhBgoRYyogKi/Mr2W3XtxwXpu2iaMi4NamgmqAjFqrQVZW79LtXj5j3nsllxADvVmLY59sO77iSCAhfX9FtZZLqf5zWHMnnpbMrWKnauJ3GkgQxUJAQS5kNY94IIctCrFSLv/BbMGJoKFt27Oe0o2q5oQpdFYXBiOFQCcwwGTEtRkasR48YEG30aTpu15Fn2EJXx+0sGIXgo/oK+Tx6dAEA5cMIYtAgIZYyc74Qm8+wEGss/I6fEcvrKipmMj+Ea7ZTX+QdlJym9G3XJOC5cFGEGOe86w5Hga5Ec8Q63XIEfHEXqtC1S32FSvUVSfH48SVsmchj62Qh7aMQBBECEmIpM1d3xLJ7c1LshpRV6JrE0m/OueeIhVxKntOVvo4mdVWBGbJeAgh+M1PXwgXrAbEbsluzftgVR53rKzSqr0iMJ44t4rad1KZPEIMGCbGUGTE0jOa0gXDE5IwmVZRNW/otUXFTMKwjJmc06UBVWMf9is1EHU2Kr+mZEQvZ+QV4ha7dnquFHHfaTuddkyI7Ro6YXM5cquDUUgV7SYgRxMBBQiwDzI3lMi3ESvXRpJweMZdH37fYiaoVfDzYTE5XJeya5IGqK4DoYf2GEAuSEYvgiPXoEQsd1u9wTlWsYaKMmFQeP7YIANizk/JhBDFokBDLAHNjuYEI649IWnEEQHqXmBgvRsmImRJGk0GC+oAYTYYXIWaCQqxX9szrEQvZrN8jrE+OmFweP7aIgq7i2s1jaR+FIIiQkBDLAHNjOVzIsBBbrXlCRZYjBkB6u74I3IfOiGnxR5O1kEIsmiPmZ8R6CjEWuqOr9+qkcC6e06W+QqP6ikR44tgibt4+ESinSBBEtpDy/1rG2N2MsZcYY4cYY7/a5vP/kTH2PGPsacbY1xhjO5s+5zDGDvh/3SfjPIPGhow7YmVTXkYse46YKuXWZODRpKbU6y7CYPlisVsDvvf58I5btwJW8Z5hxJ3VzRFTqb5CNhXTwXOnl7GH8mEEMZDEFmKMMRXAHwD4AQDXA3gHY+z6lpc9CWAv5/wmAF8C8NtNn6twzm/x/3pr3PMMInNjOazW7LrgyRqrkjNiQOMmpiyqUR0xGbcmneCOWNT6iqAZsbDP55zDcTs7WIBXwuq4PPAFC7vLyqRGjxg5YrJ4+uQSbJeTECOIAUWGI7YPwCHO+RHOuQng8wDuaX4B5/yfOedl/7ePANgm4X2Hhjl/L9yFlWyuOSrVbKgKCx2Eb4dwxGRXWMTJiMUO64dwxMKO+QRBM2Kh3Svfnes6mgwhnjjnsF1eb9Bfcz4aTUpnvx/Uv3U7CTGCGERkCLGtAE40/f6k/7FO/AyArzT9Ps8Y288Ye4Qx9sOdvogx9j7/dfvn5+fjnThjbKivOcpml1ip5mDEUMFY73qGXoxkzRHT4t+aDOOI6apSHzOGodEj1v1/g7CXAYI4beIGZBDxJMRaxx6xeqHr+hpNJvn964lji7hqroipoiH1uQRB9Ie+JjsZY+8CsBfAR5s+vJNzvhfAOwH8DmPsqnZfyzn/BOd8L+d879zcXB9O2z+EI5bVCovVmi1lLAl4zfpAAmH9WI5Y/FuTvfq9BLqmRKruCF5fEc4RE+KqW6GrHqJyotfz1muha1LfvzjnePz4Io0lCWKAkSHETgHY3vT7bf7HLoMx9gYAvwbgrZzzuuLgnJ/y/34EwDcA3CrhTAPFhvFs75ssm7aUoD7QyIjJHk3WHbEQuyYBPyMmI6wfIiMWpb7CCtjeH/ZWphBXXQtdxWgygHjq9TyV6iukcuRCCUtli4QYQQwwMoTYYwCuZoztYowZAO4FcNntR8bYrQD+GJ4IO9/08SnGWM7/9SyA1wF4XsKZBorpEQOqwjLsiDnShFhSo8m6I6ZFuDUpZTQZ7H0NLfxSbvEeQDAhFqrzSzhYXcL6jdFk7z8np/689kJMnJ/C+nKgIleCGHxi/3TlnNuMsfcDeACACuCTnPPnGGMfBrCfc34fvFHkKIC/8nNGx/0bktcB+GPGmAtPFP4W53zdCTFFYZgdNTIrxEo1W8rCbyC5+orIjpik0WTSYf0wPWJhRp/iLN3qKxqjyeCOWMdm/bq7tr4yYknxxLFFTI7ouHK2mPZRCIKIiBSbg3N+P4D7Wz72oaZfv6HD1z0E4NUyzjDoeO36WQ3r25gujkh5VmIZMSuqI+bdmuScR76M4DliATNiqgLb5XBdDiXAbkqBEC56gLB+GJEjhFg3gde46RgiI9YjrE+OmBz2H1vEbTumQv27RBBEtqAa5owwN5rD/Go2HTGZYf2cpkBhCThidjRHzNAUcB4vPB7OEfNeF3bXYuD6CiXkgm5XhOu7F7oCwf6Meof1g4s6ojtLZROHzq9SPowgBhwSYhlhw1ge55ezKcTKpoOipNEkYwwFXU1sxVGUjBgAVGOMJ62Qha4AQgf2A48mtXCjz/posuvSb5Hr6v3cXmF9csTk8eTxJQDAbTtIiBHEIENCLCPMjeWwUDIzeZtstSbv1iQAFAxNfo+Y7UBXWT2DFJScHk0YNRNu6Xe0+obA9RVKuFuTdoBC1zC3JnuF/2nptzweP7YIVWG4eftE2kchCCIGJMQywobxHByXY7GcrXZ9y3Fh2i5GDZlCTJHfrG+5od0wILpD1YzXIxZ016R3xrCB/YYQ650Rc3lwoWP3CNeLZzafIdjzutdXkCMWnwMnlnDtpjGMSPz/JkEQ/YeEWEYQpa5ZG0+W/D2TIzIdMV1NICPmhM6HAQ1HLE6FRS1Us74nRMIKP/F6vcf7NPJcwZ5v2gEcsRDjxF4Om04rjqTguhxPnVjCLdsn0z4KQRAxISGWEebG/Hb9jAX2Gwu/5WTEAH80mRlHzPuaqI4Y5xyW4yIX2BEL7i41EzQjZoTs6bLd3iNPVQku7sTzOu2aVBQGxoLlzYjOHLmwipWaTUKMIIYAEmIZYcOYv29yOVsVFiLLJTUjpiuoJuCI5aI4YppwxKKdx3Y5OEeoXZNAchmxuiMWUFj2qptofs8g4876EvFuz1MUGk3G5Ak/qH/rDhJiBDHokBDLCFl3xGQKsRFDQ9mypT0PiO6IxQ3rmwFXDwn0yLcmXSgMPS8jhMlzNb+u69LvKGH9Hg4b1VfE48CJJYzlNVw5O5r2UQiCiAkJsYxQMFSMGCoWVrMV1i/VR5NyM2LVmPsdW6lFzIiJUV7UjJgQVKEzYiGFiOkEuxAQpgUfCNYjFkbcWT3C+oAn7MgRi8eB40u4edskFbkSxBBAQixDzI7msJAxR6we1jfkZcTyCYT1PUcsSlg/Xkas3kwfskcsdEbM5oFKY+uiKeA/TyBHLERY36mPJrs/j+orolMxHbx0boXyYQQxJJAQyxAzowYWStlyxFZrnmCS6oglUF/h3ZqMXl8RNSMmnLSgzfrRw/puzxuTQNOC7oBheCuIcFLk1VcAXpA/ziaD9c4zpy7BcTkJMYIYEkiIZYiZYi5zi7/LpvyMWFLN+tEcsZijyZCOWNgMl8By3J4dYgBg1OsxAo4mQyz9DpIRswKF/xkcujUZmQMnFgEAt1BQnyCGAhJiGWJuLIuOWDIZsYrlgHN5rkgttiMWMyMWOqwf7p89eEYspCMWaNdk8GcGKYj1wvrkiEXlwIklbJsqYNbvHiQIYrAhIZYhZoo5XCyZcDOUnynVbKgKi+Q2dSJvqOA8XolqK9WUHLHQGTEtWljfcoJlxLSQjptwxLqNJvV6j5gsR4zqK+Jw4DgVuRLEMEFCLEPMjBpwXI6lipX2UeqUag6KhgrGPIq5ngAAIABJREFU5N3OKvjOlcycWFRHLBez0DX8rclwYXqBFXCNUthdlvWwfpfz1x2xIBmxerN+d0eMwvrROL9cxelLVRJiBDFEkBDLEGLUkKWbk7IXfgMNISYzJxbfEYt2lqg9YtHC+r3FcPgesd4OVqgVRwHrK8L+8xMeT56gIleCGDZIiGWImVEDQLZKXctmAkLMr8KQVWHBOUfNdlJZ+l0LPZqMJsRCZ8QCOmJBHCy9fmsyxK5Jqq9IhAMnlqApDDdsmUj7KARBSIKEWIZoOGLZCeyv1hzpQkwIJlmOmOVwuByRCl0VhUFXWfSMWNSwfoQVR0GEmHC2gmbQbNcF69HYX3fEwuya7FVfQUIsEgeOL+H6LeORxvAEQWQTEmIZIoujyVLNlrrwG2g4YrIyYmKsGMURE18XOSPWr0LXgGF9cY6gjpjl8K7uFdAQd0HEU6CwvkL1FVFwXI6nT1JQnyCGDRJiGWKyoENhwIUMOWKlmo2ikVBGzJTzw1isS4riiAGeeImbEQvuiIVbyi0I2iNWF00hdk32ei5jzA/YywvrU6FreA6dX0XJdEiIEcSQQUIsQygKw3Qxh4VSdhyxQQjrx3fEFNQi7r4Me2tSVRgYi7Br0na7dnMJwob1bSfYc7WA3V9BR52UEQtPvciVhBhBDBUkxDLG7KiRKUesbDooSh9Nev/ayRJiwhHLxXDEwgojQdgeMcYYdDX8+1mOG27XZNDRpMsDOW26GmwtUbBRJ/WIReHAiSVMFHTsmi2mfRSCICRCQixjzI7mcCFDGbEkHDERNK5KujWZpiNWC1lfAQA5VYEVslnfcoIKpnCjSdtx67sku6GpLFCzvuO6XasrAOGuUUYsLE8eX8LN2yeldvoRBJE+JMQyxsyokZlbk5bjwrRdjCaVEZPsiMXJiEV1xMTXhekw0zUl4q7JJJr1eU/hBHguVlBHrNtY0jsjjSbDUqrZePncCo0lCWIIISGWMbLkiJVq8hd+A009YpnJiKl9C+sDnmsVrdA1wK3JkKNJM/DIM5iLZbu9BaMn6sgRC8Mzpy7B5cCtJMQIYuggIZYxZkYNlE0HZdNO+yj1hd+yM2J5TW6hay2uI6YqkesrLMeFpjAoPVygZqJkxEw7mGAK0/nlvS6gI6ayYM36Du9aXSGeRY5YOA74jfo3kxAjiKGDhFjGmC1mp9S17Asl2Y6Y4i8Rz0yPmK5ELnQ1A+6AbCaK8AuaEQtbX2G7wTJiekAXyztn9+epSjBRRzQ4cHwJO2dGMF000j4KQRCSISGWMWbHvG+0WRhPriY0mgS88WRWMmI5LbojZtpu4BuTAu8GYjIZMcYYDDV4c31ggacGr6/omRELWIVBNHjyxCLlwwhiSCEhljFmMuSIiYzYaBJCTFflO2IR174YmhrdEXPCCzFDCxZ8F3DOYbu9nSaBprLAhbG2G7RHTAl0a9J2e486NZXqK8Jw5lIF55ZrJMQIYkghIZYxxOLvLJS61sP6km9NAp4Qq0SsjGil7oiFFESCeI5YsNVDzYQN6wvRFlTw6SGETphajGBLv91AK5OCiDrC48BxLx9GQowghhMSYhlD7JvMQqnrak1kxOQvGM7rqrywfmxHLMaKowiOmB4yIyZEWxDBJF4X9DJAmFqMQI5YgPC/pihwaDQZmAMnlmCoCq7fMp72UQiCSAASYhkjr6sYzWmZyIiJm5tJZcRkjSZlOGLRVxw5oR0xI2SPWEOIhXDEwtyaDHDjM+h+SG802cMRUxkscsQC8+SJJVy3ZTzyZRSCILINCbEMkpVS19WEM2Iye8RUhQXKOrXD0BTUoha6Rgzrh6mvMEMKMS3gGBHw6zcC9ogFqZzwbmF2F3beAnFyxIJgOy6eOXmJ+sMIYoghIZZBslLqWqrZUP2qCdnIHE1WLTeyGwZ4tRem7YLz8OLAcngEIcZCrTiqZ8RCOGJBhZ4dcNekpgRz2awADptO9RWBefncKiqWg1t3kBAjiGFFyk9YxtjdjLGXGGOHGGO/2ubzOcbYF/zPf4cxdkXT5z7of/wlxtibZJxn0JkpZsMRK9UcFA01kd12MkeTNduJnA8DGuuJoqw58nrEwv35hK2vEDcgdS1gRiygaAL8cH1ARyxwWL9nj5gCzkGuWABEkSsF9QlieIktxBhjKoA/APADAK4H8A7G2PUtL/sZAIuc890APgbgf/hfez2AewHcAOBuAH/oP29dM5MRR2y1ZicylgSAgq5I7RGL54h5XxulwqLmuDBCZnfC7rYMnRHTgvd0eQ6WvPoKxw22axIA3ZwMwIETi5guGtgxPZL2UQiCSAgZjtg+AIc450c45yaAzwO4p+U19wD4tP/rLwH4PubZLPcA+DznvMY5PwrgkP+8dc3cqIGLZTN1x6Bs2hhJTIjJzIi5chyxCEIs6OqhZoyQjljojJgSXOh5tyblFboGqcMQo0sqde2B62D5lSfx3ZvMRFxpgiCygQwhthXAiabfn/Q/1vY1nHMbwCUAMwG/dt0xM5oD58BiOd3x5GrNSeTGJADkDZkZMSdWjs2I4YiZdvj39kaTSWbEgjtiQQpYvWcqgW46BlmZJC4HUE6sOyvLF/Hx1V/C27Rvp30UgiASZGDC+oyx9zHG9jPG9s/Pz6d9nEQRpa5pjydLNRujCXSIAZ4jVrNduBJ+GMd3xLyvjeKIBS1EbSZ6j1jwsH5Qx81bWh5kNBl0xVGQHrFwi8mHgSjfv3KjMyiPX4lblEMJn44giDSRIcROAdje9Ptt/sfavoYxpgGYALAQ8GsBAJzzT3DO93LO987NzUk4dnYRpa5pB/ZLNTuRVn3AE2IAUI1YpNpM1XJiZcQajlj4s0Sqr9CCF64CTWH9gIJPC7Fr0g5461ML6OIF6SUTGbK0R+/9JMr3L0NTMLLrdoycPwBEuNFLEMRgIEOIPQbgasbYLsaYAS98f1/La+4D8B7/128D8HXudQXcB+Be/1blLgBXA3hUwpkGmtmMOGKJhvUNT4jJGE/KyohFKXWNtGvSd6yC1mXUM2IB38dQWWC3yXPEgq04Ctas37uXTK+H9Ulc9GTrHqB0Hrh0ovdrCYIYSGL/lOWc24yx9wN4AIAK4JOc8+cYYx8GsJ9zfh+APwPwF4yxQwAuwhNr8F/3RQDPA7AB/ALnXE5waICZ9hd/Xyyl64iVTQcjCY0m875wkhHYr1kO8mO5yF9vxKyvMNSQtybVRn1DkHxW2IyYpgQbTYpl4oGXfgcJ6wfoJVP9USiF9QOwba/395P7gckd6Z6FIIhEkGJ3cM7vB3B/y8c+1PTrKoC3d/jajwD4iIxzDAuTBR0KS1+Irdbs5ML6YjQpQ4hJyohFdcSC9nsJhLNlORxBmi/CZsSC3nAUjpQe0BELIu6C1FfoVF8RnI03AloeOPU4cOOPpn0agiASYGDC+usJRWGYGjGwkKIQsxwXpu1iNOGMWMWM/8O4JikjZjrhRCHnHKbtIheyvkIIqjAVE97XBRN8RsBmfSHWAjliarA2/CDhfyHUaDQZAFUHNt/sOWIEQQwlJMQyynTRwMUUw/qlWnILv4EmISbBEavaLnK6hELXkI5YfWQYOiPG/K8P9n6mnYwjZoYQeKqiwHF5z1ybTT1i8tm6FzhzAHCstE9CEEQCkBDLKNNFI9XRZJILvwGgYHj/6knLiIVst28makZMvD7K0m8geF1GWMGnq8Fa8O0QI089oIvlBMicCcdsPd2ajMW2PYBdBc49l/ZJCIJIABJiGWVm1MBCKb1bk2X/NmPiYX0JtyZTc8RCOlUC8fowXV9h3idoT5kQVUEuDNRLWHu4WJbb+xamKhxByogFY6sf2D9F40mCGEZIiGWUrDhiSY8m44b1bceF4/J64D4K9bB+nxwxQ4sqxAIu/Q6R5wK8JeFBngl0F0/e6BI9M2I6OWLhmNwBFOeAk4+nfRKCIBKAhFhGmS7msFSxUvthVUp8NCknI1b1nZ98DEesXuga8izCdQq7a7IxmgzZIxY4IxasvqIR1g/giPkul9XFaRPv2et5IqwfZt/muoYxzxU7+VjaJyEIIgFIiGWUmaKR6r7Jelg/8VuT8YSYEE/xHLFoGTGxmzK8IxZOiFi+YAs8mlQYLKd3sD7MyDPIfkjxHw29RpPCXSNHLATb9gALB4HKYtonIQhCMiTEMsp00WvXT2s8uVrzBE5Sjlhe0oojKY6YGvXWZDxHLMxoUmHo2c/V+vxe40lxCSDIyFMPcNMzaB2GSrcmw1PPiT2R7jkIgpAOCbGMMuMLsbT2TZZNzxFLKqyf0xQwBlQz4IgpCoOhKnWHKyhmREcs/K1JN9SFgKDBenGzMtjS797PFPmx3vUVwYQi0cTW2wAwr9iVIIihgoRYRpkeTdsRSzYjxhhDQVfjZ8Ss+I4Y4ImpoMJIELu+ImiPmOOGct2CBOuBhiMWJCMmtgF0q8VojCZ71FfUR5OUEQtMfgKYvYaKXQliCCEhllEao8l0KixKNRuqwur5qSSQIcRqdnxHzPt6pf6soEQN6+eaVhwFwXLcwAu/gabRZw9hGaVHrNuZ62H9HiNULcCziDZs2+tVWARcFk8QxGBAQiyjTI34o8mUHLFSzUHRUMFYuD2KYcjrauwVR8IRi9MjBsRzxMKIJCBCRszu3VbfjKYGK19tZMRChPW7iKegtzDFsyisH5Kte4DyArD4StonIQhCIiTEMoquKpgo6KmOJpMaSwoKhhq7R0yuIxYxIxY6rB/y1mTIjFjQDJoYXQYrdO097qxnzno261N9RSS2icA+5cQIYpggIZZhpovpLf4um3ZiZa6CLGXEcpoa3hHzXx92fBs6rO/ySBmxXo6YcLACFboGCOvbAesrNKqviMaGGwCtQDkxghgySIhlmDQXf6/WHIz0Q4jFvTUpyREz4mTEIjbrBw3rW3Y0R6yX42QHLGBtfo0dpL6i14qjgHsriRZUDdhyC606Ioghg4RYhklzzVGpZmM0oeoKQd6QENaX5ogpoQtdw+6AFAQN0ze/j66FyIgpwYSY5UboEesinoL+eTSqMGg0GZqte4AzTwN2euvPCIKQCwmxDDOT4miyVLMTa9UXFHQlMxkxQ1NCF7pGr68Id2vQDJkRE839PRd0h1haHkQ8OQGXiAe9TEC0YdtrAKcGnHsm7ZMQBCEJEmIZZrpoYLFswk3hB9ZqzcZofj1lxPpX6Bp6NBm20DWgIxY0XO+9Jkh9hfe5XhsANBpNRkcE9mkBOEEMDSTEMsx00YDjcixXrb6/d79uTWYpIxY2rF+LemsyoFASWE64sH4Q0dT8eT3A6qTG2qTetyaDjiYprB+B8a3A6CbKiRHEEEFCLMPMjKbXJVaqJX9rMi/JEVNYsJxTN3Ka2rdCV0Vh0BQWsr4i+D+fETqsH2Q02XvcGTSsT/UVMWDMc8Xo5iRBDA0kxDLMdDEHoP9rjmq2A8vhyTtiupwesZwWv3g2aqGrrjIoAZdxN6Orwd/PDHlrUgvgXnmfD7HiKIC4swOuOFIUBsbIEYvM1j3AxcNA+WLaJyEIQgIkxDJMWou/V6vJ7pkUFHQVlsNjOSNVy42dDwOiZ8TCumECQ1MSXHEUbDRZv2wQIiPWLdcVpg5DVxTKiEWlXuz6RLrnIAhCCiTEMkxj32R/hVip5rlUiRe6Gl6uK44rJhyxuEQtdA0b1BfoanDhFzYjFrxHLNgo0XtN71uTYeowVIVRfUVUttwKgAEnH0v7JARBSICEWIZJa/H3aq0/jlhe9wRUnJyYLEfMiOqIRRRihppcRqwerO/hiAkh1OuWo/fM3i6bI25hBmjq11RGjlhUcmPAhusosE8QQwIJsQyT11UUDbXvYf1+C7FqjMXfVcupPycOotCV8+DiwHRiCDFNSWzXpHC4etVjWK63TDxIvi5I7ixofYU4Yy+hSHRh6x5v52SIf18JgsgmJMQyzvRo/9v1S74QKybcrF+Q4YjZbuhdj+0QgiqMKxYnI5ZkWD+MIxbEvQKabzr2vjUZqCBWpYxYLLbtBSqLwMUjaZ+EIIiYkBDLONPFXN+FmHDExpIudDW8f/3iCLGa5SAnyREDwgmxmu3CiJhPC+eI8VDOW2OM2MMRc3jgkWcQcdcoiA3miDk9bnUSXdgqil1pPEkQgw4JsYwzUzT6f2uy7oj1KSMWo9S1arvSRpMAQgX244wmw4X1w2XEtIBh/TAjT9WvnOha6Boi/K/SaDIeG64D9CLlxAhiCCAhlnHSWPxd6pMQE6PJasgi1WZqloO8hNGkuHkZptTVtB3kYtVX9BZirsthuzzcrsl6nqvXaJIHcq8EutK9ciPMyiSdRpPxUFTv9iQ5YgQx8JAQyzgzvhALEyKPS90RS3rpt6iviOOISRpNGlEcsVi3JoNlxKyAa4Oaqa846vF8yw2eERPP7VpfEdYRo9FkPLbtBc4+A1jVtE9CEEQMSIhlnOmiAdNx6+KoH6xWbYwYaqDbb3GQEta3XEmOWISwfqzRJAtU6CpeE2rXpAjWB3DEQo08le6VE44bIqxPo8n4bN8HuBZw5kDaJyEIIgbJWh5EbJpLXcfyel/es2Qmv/AbkCPEarac+orIjljCo0nhaoURTIwxX+jJy4h5Z+h+ZuGWBdHvn/nZ2wONMIkubNvn/f3Ed4Add6R7FoIgIkPfCTNOGou/V6r9EWJ5Q0JYX9qKI5ER689oMmh9hRA+YVYcAV6paq/mesvhocSQN5rs7GKF6SWbGc1hotCf/7AYWkbngOmrgBOPpn0SgiBiQEIs49QXf/fx5mSpZice1AeawvoRHTHOOaqSHLGc3ueMmF8g2/M9nPAZMe/1vUefthvyNqai1DNrbZ8XopeMkMT224Hjj1CxK0EMMPRdM+PMpLBvslRz+uKI6aoCTWGRR5NeEz7kFLqqIiMW4tZknGb9wI5Y8P2NzfQaIwL+rckQOUC9hyNmu+GeR0hg+z6gfIGKXQligIn1E4wxNs0Ye5AxdtD/+1Sb19zCGHuYMfYcY+xpxthPNH3uU4yxo4yxA/5ft8Q5zzAiMmJ9HU32yREDPFesEnHFkRgjpuWI1WI26wfKiEV2xJSeYXjLcUOOJpWePWJh6jAICWy/3fs7jScJYmCJayX8KoCvcc6vBvA1//etlAG8m3N+A4C7AfwOY2yy6fP/mXN+i/8XXf9pYcRQkdOUvi7+LtVsjCa83kiQN9TIjpgYaUqpr1Aj3JqMsV7J0II5YqYdTYhpAcP6YW9j9uoRowB+n5m7FsiNe4F9giAGkrjfNe8B8Gn/158G8MOtL+Ccv8w5P+j/+jSA8wDmYr7vuoEx5rXr93U0aWM04fVGgoKuRs6I1SzfEZNRX6GHK3TlnMdu1g9WX+H9M4Z13nRV6V1f4YYsdFW7XwCwHQ6dRpP9RVGAba8hR4wgBpi4P8E2cs7P+L8+C2BjtxczxvYBMAAcbvrwR/yR5ccYY7kuX/s+xth+xtj++fn5mMceLPq9+Lv/o8l4jpiU+go13GjSdjk4Dy+Q6u/nh/V7FfVaIRZpN6OrrHehq8PDF7p2EXe2y6HSaLItiX7/2nEHcP55oHpJ7nMJgugLPb8LM8b+iTH2bJu/7ml+Hfd+onT8Ls0Y2wzgLwC8l3MufkJ8EMC1AF4DYBrAf+n09ZzzT3DO93LO987NrS9DrZ+Lvy3HhWm7GE24VV8QbzTp/WskI6wvMmJBR5NCsEUP64vF3L1zXED4sL6mdM9zAd4txzDP9VYcdWvWd6HTrcm2JPr9a/s+AJzWHRHEgNLzpy3n/A2dPscYO8cY28w5P+MLrfMdXjcO4MsAfo1z/kjTs4WbVmOM/X8A/lOo068TZooGDp9f7ct7iT2T/RtNKpGFmBgjynTE+ibEtMZi7m7PMCP2iOla79GnN5oM54h1cwwprJ8SW/cATPHGk7u/L+3TEAQRkrj/+XofgPf4v34PgL9vfQFjzADwtwD+nHP+pZbPbfb/zuDly56NeZ6hpJ+Lv1eq/Vn4LYiTEROOmJRbkyFXHAmBFCcjBvQehYrxYuiMmNI7rG/aIXvEeuTObJdDJUes/+TGgI03UGCfIAaUuN81fwvA9zPGDgJ4g/97MMb2Msb+1H/NjwP4NwB+qk1NxWcYY88AeAbALIDfjHmeoWS6aKBiObEa6INSMn1HrF9CzJCREYv/w58xFvgmI9DkiMWorwDQu+srxP7GZnq14HvPDjdK1JXuS7/DFsQSEtl+uzeadJP/HkEQxP/f3pkHSXKWd/r5Muvqqr6me1pzz2ikEUJIAiHNSkjySiCEETKBhBfbgI1lGwKHr/WxtgE7Yhc71mGZsDn+sL3GAiwbG60Ww4rlkkEc5rYGEOhCaEbnjDT39PRVR1bVt39kZlV1d515VGZWv09ERXVXV2e+3Rp985vfewWLr79ttdangHVeuNb6APA25+OPAh/t8P03+Ln/RmG2MUuszM5MPtR7LZWGK8RyaR81YgGmJgGyptF312Q5oNRkLwfOa41Y2jRYtLovih80ldhrJMagA2KFANl1Fdx/h120v/XSqKMRBGEAJI+QAGaGOF1/qZy81GQQxfpgF+wP6oh5niPWpyPmdY5YusfwVffeg1w31WNIrMwRi5DGYFdJTwpC0pBTMwEMc/H3ctkWRRNDnCPmNTUZZLE+2OJoWDVizWL9/sZXDHofe3xFH8X6g6w4MlSPXZPiiEXG9G4Y3yrzxAQhgYgQSwDDXPy9VLaAITpizviKXvO02tEo1k8FlJpMm4N3TZre7t13sb7HFUd2YX0fjtgAAq+XI2YN2IUpBIhS9hgLccQEIXHIqZkAhpuatF2moc0RS5vUddNhGoTmiqNg/hjbi7j7c+eCGl/R6+f2WiOW6SGatNZYA07CT5s9VhzV6jJZP0p2XQVnnoLFo1FHIgjCAIgQSwCTuRRpUw0pNenWiA1n1+SYk1YseVj8XXaFWIA1Yv2nJu17ex9fYQuWXo5Yxasj1mN8Rc3pxhzEwepVd1ara0wRYtGx5xr7+elvRhuHIAgDIUIsASil2JTPDGXx91K5Si5tDC3F5NZ3lfp0olopOUu37TF0/rEdseGMr+i3WN+t8/KUmuxaWO8KsQG6Jo0eqckBi/+FgNn6YsiMixAThIQhp2ZCGNZQ16VydWijKwDGMvYfQS8F+2WrFlihPgzmiAU1vqKfGjHTUAM7TZkeoyYaKc9B5oj1Gl8x4BJxIWDMlJ2eFCEmCIlChFhCmB3PDC01OVQh5ggpL7PESlY9kGGuLl4cMa9p0X4HuloD7oN0sQvruwkx12kbbI5Y16XfAy4RF0Jgz9Vw/GFYOR11JIIg9ImcmglhWIu/l0rVoXVMQjM16UmIVQN2xFJm3wNdgxpf0atYv+JxkXa61zoi574D7Zo0DGp13bHDtVqvy/iKqNlzrf38zLe7v08QhNggQiwhzBYyQxpfMVwh1izW9+KI1QIr1AdvA1391oj1LNavdl8K3oleaUSrPrgj5r63U+2ZLP2OAdsvBzMLz0h6UhCSggixhDBTyLBYrvbt2HhluVJlYqg1Yt4dsXK1HqgjNtBA14BqxHoPdPVWAJ82DbRudkeupeGIDeC2ue5Zp85JKdaPAekc7NwvdWKCkCDk1EwI7nT9M8tWqPcZdmrSX41YLbBhruDREfNZI9ZrbplXRyzVcK86iSYvXZPdHTEZXxETdl8Nzz0A5aWoIxEEoQ9EiCUEd/H3yaVwR1gslWvR1Ih5Sk3WAxvmCvaU/EFWHCmF55qoXmk+F6umPRXru3VlnYXY4KlVVzx2agKwpGsyHuy5BnQNDsu6I0FIAiLEEsLsuLPmKOSC/aWyNbQ9k9BMTXpZ/F0KZXxF/5P1M6b3GWb9FuuXq3UyHly/fuq5YMBifeeanTonqx4bC4SA2XUlKFPSk4KQEOTUTAjumqNTIQ51rdbqlKw6hSGtNwJ/qcmyM9A1KDLOENR6l27D1nt7TUtC07HqZ45YxuP4CujmXrldk4Ms/e7sstXrmroe7HpCSGQnYNtL4OlvRR2JIAh9IEIsIWx2Fn+fCrFzctlJD44P0RFrpia9rTgK2hGD/vZeVmr+RKBhqJ6djeAIMQ/3aUzu7+heOV2TAxXrq1Xfu+p67qR+qRGLB3uugcP3QzX8bRyCIPhDhFhCmBxLkTJUqKnJJWfP5PiQ9kwCmIYikzI8zhELfqAr0FedmJua9EO6jwGylaq3TsRGsX6H6zfniA0y0LVz12S1PvhcMiFE9lwDtTIc+V7UkQiC0AM5NROCUoqZQiZUR2yxZHdkTuTSod2jHWNp03uNWKBdk/a1+qkT89rN2EomZYTmiPUaNeFlmXi6S9dkowtTHLF4sPtq+/npr0cbhyAIPREhliBmCuGuOVos2Y7YMIv1wRZig3ZNaq3tga4BOmLZPvc/uu/xK8TSptFXsb4XR8ytK6tUe6QmvThibYRYTVKT8SI/A1suhSf/PepIBEHogQixBLF5PBtqsX5kjljGHDg1adXs4vBAHbHUAKlJj05VK/Zuy94DXT05YkZ3R6yRSvRQI2a1S016WJkkhMze6+CZ74BVijoSQRC6IKdmgpgpZEKtEYvKEculBxdibvow2F2TAzpiPkVHJtXbEavUvN0n3WNyv5el325hfztHzMvKJCFkzrverhN79jtRRyIIQhdEiCWI2fFwa8QWIktNGgPXiJUsW8AEWqw/iCMWSGpSdSymd7GqXge6dp+s76W4vtk1uf6atUaNmBwpsWH31fY8MUlPCkKskVMzQcwWMiyVq54K2/uhkZrMRpCaHLBGzP0dZAN1xJxi/T5+v+Wat0GrrfRTrO81Beo6Yu3cK7AFHnhc+t1mJIaXuWRCyOQmYcfl8ORXo45EEIQuiBBLEGFP118sVUkZKlCXqR/GfKQmAx3o2ue0ewhwfEWvrkmv4yt6OGKucBrk2o26szbXrIojFk/2Xm+PsCgtRB2JIAgdkFMt5W0HAAAgAElEQVQzQbjT9cMTYvZ6I69re7zipUasmZoMoVjf6keI1XyLwEw/c8S8OmLuQNdOqUkP4yZSXdYmWR7mkglDYO919t5JWXckCLFFhFiC2Dwe7uLvxVJ16B2T4MwRGzA1GUax/kCOWBBdkz2K9bXW3ov1zV7F+oPXiKW7zCaT8RUxZddVYGalTkwQYowIsQQxUwg/NTnsQn2wa8RKfRTIt9JwxAJMTTZqxPod6BpAarJbjVitrtEaT/dpLujuVKxvC6dBru2KrPYrjmR8RSxJ52D3VVInJggxRk7NBDHrOGJhdU66qclh42Wgq1usH4ojNqSuyYxpNIrm297DnX7vZ9dkx2J9D0u/u6Q7G+MwxBGLH3uvh2MPwfLJqCMRBKENIsQSxEQ2RdpUoU3Xjyo16daIad19uGkrriMWxmT9oY2v6JGadEWan9RkJ1FpeUglNl22No6YW3Mmjlj82Hu9/SzpSUGIJXJqJgilFLOFLKdCrRGLJjUJ/Qkgl4YjFsJk/b4cscAm63e+V7lm/4xeHLHGqImOxfp1UoYaqDGja9ekk5o0xRGLH9tfCpkJEWKCEFNEiCWMMKfrL5QsJiMq1gcGSk+WnDqufCb41GQvQViva6yaDmCyvuruiNVcR8zDQNdU5zQi2K7WoB2O6S5dk152VwpDwkzBuddKnZggxBQRYgljdjzDyRCEWL2uWSpH44i5c8sGGWHhirZckELMdMdXdI/DFU9BLP3uVqzvumVe7tP4WTqlJmuDzydLdema9LK7Uhgi570cTj8Bp5+MOhJBENYgp2bCmC1kOB3C4u/lShWth7/eCJoF94MIMTc1ORZgsb5SikzKoNzHtHvwP0y2V2rSFWleBrpmeswR8yTEjC6OmFtzJo5YPNl3o/186L5o4xAEYR0ixBLG7Hg2lK7J5sLvZKQmi1aNlKE8iZRuZFNGz4GufpyqVtI9Vhw17uPhZzQMRcpQXQe6DjrzqzFHrEtqUuaIxZTZfTC9Gw6KEBOEuOHrbxKl1IxS6gtKqced500d3ldTSj3gPD7V8vpepdR3lFIHlVL/WymV8RPPRmCmkGGlUgt83+RiRAu/oVmsP8jPVKzUA3XDXLI9OhnBn0BqJWMaWDXdsVvUz/gKcFYodXDcKh4cMdNQKNU+NenHvROGgFK2K/bEV6EaTo2pIAje8HtqvhO4T2t9AXCf83k7ilrry5zH61pe/wvgfVrrfcAZ4K0+4xl53On6QY+waCz8jtIRG0SIWbVA68NcsilzaI5Yr0n+7qyvrEdxYy8V7yDyqnVPqdW00f6abmpShFiM2XcjWMvw7LejjkQQhBb8npq3AHc6H98J3NrvNyq7b/4G4ONevn+j4k7XD3qERZSOWM5L16RVC8UR67V2CJoF8FmfozN6DV0NwhELslgf7BqwduMrmo6YpCZjy97rwEjBwS9GHYkgCC34FWJbtNbPOx8fBbZ0eF9OKXVAKfVtpZQrtmaBea111fn8MLCj042UUm93rnHgxIkTPsNOLrMhOWILjiM2GWFqctCuybBSk726Jt0VSH6L9V3R0nHoas1fCjTbpQbN60DaTnVn7s/gVTSOOrE4v7ITsPtqqRMThJjR89RUSn1RKfVQm8ctre/TdqFLp9Hoe7TW+4E3A+9XSp0/aKBa6w9qrfdrrffPzc0N+u0jw2whnDVHcSjWH6hGLLTUZP+OmN/1Sr1mfTXEjUchljZVF5GnPblXadNoTOVfez3wXzc3qsTm/Nr3Snvd0cLzvd8rCMJQ6Hlqaq1v1Fpf0uZxD3BMKbUNwHk+3uEaR5znJ4CvAC8FTgHTSinXgtkJHPH9E404s+Pu4u/RSU26QmxlwK7JsQDXG7lk+uiadAWj3/VKmR5riCquuEl5S/dlujliHjcD9E5NihCLNTLGQhBih99T81PAbc7HtwH3rH2DUmqTUirrfLwZuBZ4xHHQvgy8odv3C6spZEwyKSMER8zCNFQo6b5e5LPe5oiFk5o0ezti7p7LkIv1m92Z3n7Orl2TVY81YobRdnyFVatjKFlxFHu2XALjW6ROTBBihF8hdjvwKqXU48CNzucopfYrpe5w3nMRcEAp9QNs4XW71voR52vvAH5PKXUQu2bsQz7jGXmUUmwuZELomrSn6g+yezAoMqZBylCslAesEQshNZlJGY0asE6465X8pib7GboKkPboiKXNzmlWq+axa9JUbVOTXsZhCBGgFOx7FRz8EtSsqKMRBAHwlYfSWp8CXtnm9QPA25yPvwlc2uH7nwCu9BPDRmRmPBNC16QVSVoSbHGZz5gsV6q93+ywUqn5FkLt6Gega1COWLpXatLnvLJMKgRHzDTapyar/ndvCkPiwtfAAx+Fp78J510fdTSCsOGRkzOBzBaygS/+XixVmcgOv1DfpZBNDeSIjcT4ih7F+pbP8RWZLrssLY81Ymmz/Rwxq1aXjsmkcP4rwMzCY5+LOhJBEBAhlkhmCxlOhtA1GZUjBgzsiBVDqxHrv1g/57NY33WkOs36qvgcX9FNVHp1xNKm6jhZX2aIJYRMwV4C/thnocNWB0EQhocIsQQyO54J3BFbKFmRjK5wKWRTfXdNaq1tIRZSjdiwHbGwUpNpU2FVOw2L1R6L9VXbYn2pEUsYF74G5p+G449GHYkgbHjk5EwgM4UsRavGygAOUi8WS1UmxyJ2xMr9/Tzlah2t/RfLt8NecTScga7ZHkLMqtVJGQrDYydiJmV2mVFW8xR/qkO606pJjViieMFN9vNjn402DkEQRIglkcZ0/QDTk2eLFtNj0e1cL2T6d8Tc1GA+ooGuJatOxjQ8CyQXN7XZMTXpMX3okjZVlxVHXge6qsZeyVXX8xmrMGQmt8H2y6VOTBBigJycCaQxXT+g9KRVq7NUrjI1Fl1qMp9N9V0j5s4bC6tY36pp6m3EhkvZo5u0Fje1GbRYal4/rGL9DjViHsdsCBHxwpvhyAFYPBp1JIKwoREhlkCCnq6/ULTnCU1FmJosZMy+uybd5eBh1Ii54qibK1au1skGIAJdMddpblm5Wifjow6t0xyxel1TrXurEct0GBIrNWIJ5MKb7ecffz7aOARhgyMnZwJxHbGgOifPOkJsOh9dajKfGdwRC6NGzHWJunVOlqyAHbEO97JqdTI+HLGMaWB1EE2AN0esQ+rWEiGWPM55EUzvhh99JupIBGFDIydnAtnsOGInAxrqerbhiEXZNWmyUqmh+2inL4WYmuzlUtlfq/veMwnNXZXdasS8iCWXTqLJz1iMbAdHTIr1E4hScNHr4NCXoTgfdTSCsGGRkzOBjGVMxrMpTi4G44jNu0IsH2GNWCZFra47ipJWihX7PeGkJruLI7AdrJzP0RXQFEKdRJ9flynjDF9dK25dl8yLyOu0SFzmiCWUi18PdUuK9gUhQkSIJZTN4xlOBOSILcTEEQP66pwMs1jfFXfdFpCXq7VAHDHDUD06G/05Yp2Wirufexvo2qFGTLomk8mOK2BqFzz8yagjEYQNi5ycCWVuIsuJxVIg15pfiV6I5TN2o0A/s8TCrBFzxV2xiyAsW94WZrfDnlvWXoiVAxhfAaxbSeQOefWSSuy0v1JWHCUUpeBFt8ChL0l6UhAiQk7OhDI3kQ28WD9SRyzTvyNWCrFr0hV3pR6OWFAiMJsyunZN+hF8rtBaK5wqNft+XoSTO95jLVIjlmAu/mknPSnDXQUhCuTkTChz41lOLAaTmpxfsShkzEhTS/ms44j10TkZZmrSFVjdUpOlQB0xo2Nqslyt+xJ8rtBaW9NVaThiXga62g0A6+rOpEYsuey4HKZ2S3pSECJChFhCmZvIcrZode3u65ezRSvS0RXQ4oj1MUss1BqxhiPWbY5YzfeeSZds2uy44qjsc0xGZ0fMe7F+tiHu2gkxOU4SiVJw8a1O9+SZqKMRhA2HnJwJZW7CHWHhPz15tmgxGWFaElpqxPpxxCrB7Hpsh7t2qFtqsmTVG+/zS7fUZMXn4NhOxfqWr2J91faaUqyfcC6+1U5P/kjSk4IwbOTkTCjuLLEg0pNni5VIp+pDa9dkf6nJsbTpe9djO/rumgzKEeuRmgzCEVubmmyMr/A4Wb/1Go1r+lzHJETM9stheg88+H+ijkQQNhwixBKK64gFI8SiXfgNrV2TvVOTy+VqQ7gFjTsfrHux/nC6Jv1O8E93SE2WXUfMU7F++xVQkppMOErBi38OnvwqLDwXdTSCsKGQkzOhBCnE5lesSDsmYTBHbKVSawi3oOnliGmtKVkBdk2me3VN+k9NBumINVKTLeLOz+5KIUa85I2g6/DDu6OORBA2FHJyJpTZQnBrjs4WrUin6oPtRCnVvyOWD2F0BTTrzkodxmhU65q6Dq4+rXtqsuarFi3dmNwfXLF+u7ozq+79ekKMmD0fdl0FP/gY9LFqTBCEYJCTM6FkUgab8mnfjljJqlGu1iN3xAxDkU+bfTtihWw4jphSilzaoNSlbguCGyab6SDEanWNVdMBOWLrOxzBX41YqyPmXl9qxEaAl7wJTvwInvt+1JEIwoZBhFiCsafr+xNicRjm6pLPpljuY6DrciU8RwzsERadJuu7tWNBrDgCp0asTWrSFTp+7tNxfEXVT43Y+nSnm+qU1OQIcPHrwczCD+6KOhJB2DDIyZlgNo9nfe+bdIXYdMSpSbBnia30seJopVyjEFKNGNhCrFOxvuteBZqabFOs74ozX8X6KXfF0drUpHcHq5GaXOWIiRAbGcam4YU3292T1WA2dwiC0B05ORNMEI5YHPZMuuQzAzhiIXVNgp127FSsXw54z2WnGrGm4PORmuzhiGXNwa/drhOz4iPVKcSQl7wZiqfh8X+LOhJB2BDIyZlg3DVHa9fNDEKcUpOF7AA1YiE6Yrkujpg7cT8wRyzdPjXZSIEGMb6i00DXlA9HrNamRszD9YQYcv4NML4Fvv9PUUciCBsCEWIJZm4iS9Gq9eUidaKRmox4jhg4jli/XZOhOmJGxxVHzZRhsI7YWjEdRFNAtk0asfXz4Ir1JTU5UpgpuPwX4cf3wvwzUUcjCCOPnJwJprHmyEd6cn7FrgOJgyOWz/R2xKq1OuVqPdwasUyX1GQARfStZFMGWttjMVbdJwDnLd1psn6tjlJgethM0K4TsyLF+qPH5bfZQ16/949RRyIII4+cnAmmMdTVR8H+QtFCKZjIRbviCPpzxFYcgRR512Rgjph9nbV1Yg3nzU/XZCdHrFYnYxoo5UGINdKdzd+Pn3EYQkyZ3gUX/KQtxGpW1NEIwkgjJ2eCCWK6/nzRYjKXDmVv46D0UyPmCqSw5oiBXbdV6jDtPvgaMWfo6hoHLohi/U6OWKVa9yya3JEXVrXpiDXniMlxMlLs/xVYOgaPySJwQQgTOTkTTBCLv08vV5gpRF8fBv11TS474y3CdsQ6TdYvWsHe3xV0HR0xX6lJZx1Rm4GuXqfguwKu3CLuqo0asejFvBAg+26EqV1w/4eijkQQRhoRYglmUz6DaaiREWKFjEmlWl/n4LSyUnFTk+HOEetUIxb0/TumJi3/tWhKKTKm0bZY36t71a5Yv+JjibgQYwzTdsWe/CocezjqaARhZJGTM8GYhmK2kPEtxDbl4yHE8k66caWLK+Y6YoUQHbFuXZNuanQsoPtnGo7Y6p+5FFB3Zja1XohZNe3dEWs3Wd9x3KRGbAS54pcgnYdv/03UkQjCyCInZ8KZm/A3Xf/0coXZGDliQNc6sYYjFWKNmOuItZvPVqwE2yzQSE1a7R0xP0u/wXbU1oo82xHzlkaUyfobjPwMXPZm+OHdsHQ86mgEYSSRkzPhnDOR5dhCydP3aq05s1JhZjweQswVV8td1hwtV8J3xLLp9ulCsLs206YKTHR07pr0X6zvfv/aa1dq3lOTpqEw1FpHTGrERpqrfs3unLz/jqgjEYSRxNffJkqpGaXUF5RSjzvPm9q85xVKqQdaHiWl1K3O1/5BKfVky9cu8xPPRmTr1JhnIbZYrmLVNDMxSU1OOEJssdTFESsPxxED2k7XL1Zqja8HQaNrsrq2a9J/sb77/euEWLXu67qZNelOmSM24mzeBy+4yRZiVjHqaARh5PB7cr4TuE9rfQFwn/P5KrTWX9ZaX6a1vgy4AVgBWpeY/YH7da31Az7j2XBsncxxcqnSdk1OL04v2cNc41Ks784y6ybEhuGIufVf7Qr2VyrVwOrDoHdq0q8Qy6SMdaMxLB+OGNiCq1xtUyMmxfqjyzW/BSunZMCrIISA35PzFuBO5+M7gVt7vP8NwOe01is+7ys4bJ2yR1gcXxi8TuzUsiPEYpKanMjZ0/27OmJD6Jp067LaFewXrXqg9+6WmjQNRcqny2TvsmwzR8yHaMqmjA6pSRFiI8u518Lua+AbH4Cq95pUQRDW4/fk3KK1ft75+Ciwpcf73wh8bM1rf6aU+qFS6n1KqWynb1RKvV0pdUApdeDEiRM+Qh4ttk6NAXhKT55xhFhcivWbjljnSd7L5SppU4Xqvripx3bT9YuVarCpycYS7fWpySCGxtqpyfXDYn2lJteMxJAasd6MxPl13e/DwhF44F+ijkQQRoqep7FS6otKqYfaPG5pfZ+2W8zWt5k1r7MNuBS4t+XldwEvBP4TMAO8o9P3a60/qLXer7XePzc31yvsDcPWyRwAz58dXIiddoRYXMZX9JOaXKnUQnXDoLlou31qshboMNnmZP31jpifhd+N67epEStZNV/XTq9xxCriiPVkJM6v82+AHVfA198ra48EIUB6npxa6xu11pe0edwDHHMEliu0uvU3/yzwSa114/9grfXz2qYMfAS40t+Ps/HYOmULMS+O2Gln4fdsTFKThUwKpXo7YmHWh0FTiLUr1l+p1AKuEWt/r5IVlCNmthV5vh2x1tRkVVYcbQiUguv+EOafgR/cFXU0gjAy+D05PwXc5nx8G3BPl/e+iTVpyRYRp7Dryx7yGc+GYzKXYixtctSjI5ZLG6E7TP1iGIrxbIqFHo5YkEKoHYVM5zEaxYAdsUaHZpsasSCEWK7NHLFy1acjZhpUVu2atOvZzBjsKxVC5gWvhu2Xw1duB8tbt7YgCKvxe9LfDrxKKfU4cKPzOUqp/UqpxtAZpdS5wC7gq2u+/5+VUg8CDwKbgf/pM54Nh1KKrVM5nvfgiJ1aqsRmdIXLZC7dNTW5ULKYHEuHGkMh6w6WbVMjZgU8vsIRW2vr0cpW3fcMMfv65rqmg5IVwPiKNalJqQ/bICgFN74bFg7D/X8fdTSCMBL4skK01qeAV7Z5/QDwtpbPnwJ2tHnfDX7uL9hsncxxzIMjFqdhri4TuVTX1ORCqcpUyEJs3JlRttTGEbMdueAcRMNQzkqlNsX6PqfqgztZf/1C8awPMWnPEWvGW/ZZcyYkjPOut+vFvvZXcPkvQm4q6ogEIdFIUccIsHUq56lY/+RSmdlCx0bVSLCFWGdHbLFkNYr6w6LbhP9ipRpoahLaLxkPKjW5tmtSa03JqpPzWSPmzg6D4GIVEsSN74biGfj6+6OORBASj5yeI8CWyRzHF0vU6x2bVttyfKHMORNxE2JpFsudHbHFUpXJXLiOWN5xd9YKMa01K1awNWLgCLG1qcnAuiZXzxFzU4r+HbHmNe3GAnHENhTbXgKX/gx866/h9JNRRyMIiUaE2AiwbSqHVdONLsh+qNc1J5fKzMVOiHV3xBaKFpMhO2KGoShkTJbK68WR1gTeLJDLtHPEgpsjVqnWGwvMmzss/UzWV6vGV4gjtkF51Z+CkYJ7/zjqSAQh0cjpOQJscWaJDdI5eWalQrWuY+iIdRZi5WqNcrUeemoSoJBNsVJZHYfrWgVZrO9eb/34ioCK9Ru7LOvOdZ0dlr4cMXOVI1au1gOpZxMSxuR2uP4P4LHPwONfiDoaQUgscnqOANumBhdiJ5bsNSVzE7lQYvLKRC7NYslqODituAIt7K5JsAv21xbrr1jueqVh1IjVAtkesHaFUhA7LDNrdk2WqzVykprcmLzs12F2H3zuHTLOQhA8IkJsBHCHuh4dYISFu5vynMn4OWJWTa/r9IOmEBuGI5bPmutqxIqOQxZk16R9vfU1YsVKPZAUaGOpuFOw7z77qT/LpNakJi1xxDYsqSy85j1w+hD8+3uijkYQEomcniPA5vEsKUPx3Hyx7+85seg4YuNxE2K227XQZoSFO9ZiIhu+I1bIpFheUyPWWDgecGoylzYprpn1VaxUA7lPY0tAxU1NBu+IlapSrL+h2fdKuOzn7Q7K5x6IOhpBSBwixEYA01Bsnx7jyABC7LgrxGJWIzbZZd/kQnG4qcnlNTViDSEWQmqytUYsyO7M5uR+1xGzBZQfRyyXWR1v2eeAWGEEePWfQWEO7vkNqPbfNCQIggixkWHH9BiHzwzmiBUyJoVsPNYbuXRb/N1wxIZUrL8uNemIj6C7JteOr2h2Z/r/Occyqyf3l91ifR/CKeeMxGjtxBQhtsEZ2wSvfR8cewi+envU0QhCopDTc0TYuWmMw2dW+n7/8cUS50zGq1AfmqnJdtP1h1kjVsim1o2vaHRNBj2+Im2sKtYP0nkbS9u/K/f6QYyvcN20RgOAz92Vwojwwpvhpb8AX3svPLF2m50gCJ0QITYi7NyU59hCed2C506cWCzHrj4MaAxrddOQrbh1Y8NITRYy64v1mzViwQrBtXPE3LEZQYzJcEWjKyLdlKIf4TSWXuOyiSMmuLzmPbD5AvjE22H5ZNTRCEIikNNzRNi5aQyA5+b765w8sVhmLmYdkwCb8rbIOtNmOO1CqYpSMB5w12I7CtkURatGrWVbQbNrMlj3J59OUanWqTqdiEE6b66Yc4VeMQAhlltTd1ay/O2uFEaITAHe8GF7/dEn3g71/v5hKAgbGRFiI8IOR4j1k57UWnN0ocSWmM0QA5jO20vIzyy3EWJFi/FMCsNQocfhLv5uHeoaVrF+IWtfz51TFmxqcrUjFsS1W102rbU4YsJqtl4KN78HDt0H9/1J1NEIQuyR03NEcB2xI30U7M+vWKxUag3xFicyKYOJbKrtuqbFUnUoaUmg0cTQOsJiqVzFUGEIsdVLxleCdMQyq0VeEG6bO6qiZNWxahqt/dWcCSPIFb8E+98K3/gAPPjxqKMRhFgjp+eIsHUyh2movjon3TEXO6bj54gBbCpk2jpiiyVrKIX60HSpWqfrL5aqjGdTKBWsI+cKO1eIFa2q83oQXZPuHLE1jpiv1KRTI2bVAhkQK4woN90Oe661R1o8/a2ooxGE2CJCbERImQbbpnI8c7p3atId/Lp9On6OGNhC7PTK+q7JhWEKscz61ORiqdro6gyS8TXuW5CpyVyqKZoAVqwqGdMgZXr/X99Nd5atWiBdmMKIksrAz/4TTO2Cj/0cHHsk6ogEIZbI6TlC7N1c4KlTyz3f13TE4inEZvLpDo5YOEKoHW66cLUjFo4QdJ2vdanJAFymlGmQMZvjMYqVmu+UZ2uxvpvqlMn6QlsKs/CWT0BqDD76X+DM01FHJAixQ4TYCHHubIEnTy63XZjdynPzRbIpg5lCZkiRDcamQobTbYTY/IrFpvxwYl7rUoEtysIQYuNrRF8x4KaA1l2WKxX/E/ubxfr1hsDLZ0WICR2Y3g2/8K9grcA//BScfjLqiAQhVogQGyH2bi6wWKq2FTGtPDdfYsf0WOC1TkExk8+0HV9xernCTGFYjtjqui1o1oiFdS/XCXNXKwVRIwarJ/cXAxBiuUaxfq3x+ykMYaSIkGC2XgK3fQoqS/APr4VTh6KOSBBigwixEWLv5gIAT57snp48PF+MbX0Y2I7YSqW2ap9hsVKjaNXYNCQXb61LBW5qMnghuDYNulSqkjJUoyjeL2MtA2NXKlXfAi+XadadBdnhKYw4214Ct/0/2xn78KvhyHejjkgQYoEIsRGiXyH23HwxtvVhQCNl2uqKuR/PDCk16Y7JWGhZtbRUrjIeQmqysGZmmV0LF1x3Zi5tNgTTSpA1Yi1CTBwxoS+2Xgq/ci+k8/CRn4IffSbqiAQhckSIjRA7N42RMlRXIVayapxYLLMtpqMrgEYdWGuK1f14WI5YLm2STRmcbeneXCiFUyPmjpJwd1sG7bxNtCwwL1rBpiZd8Sg1YkLfzL0A3vZF2PIiuOvn7VljPepaBWGUESE2QqRMg90z+a6dk+54C9c9iyMNR2y5KYJcITbMBoPpfJp5R4iVqzUq1ToTIdSIGYYi37LbcjFgwTeeS7FYtn+OIIr106YibSqWK7XQtg0II874OXDbp+FFr4Mv/Hf42Jtg5XTUUQlCJIgQGzH2bi5w8PhSx68fcr52/tz4sEIaGLcgv11qclhdkwDTYxnmi/Z9XWdsOqT7j2dTLDpp0MVysE0BE7kUi6VmR+aYz6XlSinGHZfNFY9BNRYIG4hMHn7mTntR+MEvwt9dB09/M+qoBGHoiBAbMV64bYInTiw3Jp6v5dAJW4jF2RFzxVarEDu5ZH88O0RHbKrFEXNXLoXlyE3n05wtOkIs4Hlp49kUS44QW65UG12avq7piDtxxARfKAVX/Sq89V4wTPjIa+Azvw/lxagjE4ShIUJsxLho2yTVuubxY+1dsUMnltk+lWsUiMeR6XwGQ8GJxXLjteMLJTKmwXR+OOMrAKbHmuLITY2Gdf/psUyLELOYDDA1OZFLs1iqorVmoWgxGYDIG8+mG0IsYxqkfUzqFwR2XAG/9k142a/D/XfA31wNj9wjtWPChkBOzxHjhVsnAXj0+YW2Xz90Yonzz4lvWhLANBRzE1mOni01Xju+WOacyexQZ59N59MNV851xsJyxFrdt8VSsN2ZE7kUlVqd08sV6hqmAlicPpFLsVS2WKlUZXSFEAyZAtz05/DWf4PsBNz9i7ZDJmMuhBFHhNiIsXdzgVza4NHn11v7WmsOHV+KdabCjbcAAA0dSURBVH2Yy9apMY4uNIXYsYUS50xkhxrDdD7DmZXVjlhYNWpTjvumtQ58gr97LXe1VSBCLJtiqVxluVyjIEJMCJJdV8Kvfg1e+344dRD+/ga7mF8EmTCiiBAbMUxDceGWCX50dL0jdmyhzHKlxvlz8a0Pc9k2mVvniG2ZHO7IjdlChkq1zlK52th9GV5q0hZiy5UatboOJH3o4hb+Hz5jC7HJAISYWyNWtKrkY5zmFhKKmYL9vwy/9T14+bvsIv6/vwH+6fXw2Oeh3r4GVhCSiAixEeSibZM8/NwC9frq+opHHXG275yJKMIaiK1Tq4XYsYXS0IWYe79jCyXOrFgUMmZoy62n82lWKjWed1yr2fHg3D+38P/wGXt0yeSYf+HkNgCcLYazCF0QAMhNwsvfCb/zINz4bjj2CHzs5+ADl8HX/goWno86QkHwjQixEeSKPZs4W7T48fHV6ckfPnsWpeDSnVMRRdY/26ZyLJarLJQszq5YLJaqbB/yEFo3FXpsocTJpXKg4mgtbrrQ7WrdPB5cCnStIxZEatKeTVbl1FJlqJ2swgYlNwk/8bvwuw/ZIy9mzoX7/hTee5G9u/LAh2H5ZNRRCoIn5J+yI8jV588C8K1DpxrF+wA/PDzP+XPjoSyuDpo9s3b69KmWLQHua8PiHMcRO75Q5rn5YqhC0J1P9uNjrhALTvS5ou6JE/bvMqgasUq1zrGFEi9OgLAXRgQzDRffaj9OHoSHPg4Pfhw+/bvwmf9md1/uu9F+bH+pPRJDEGJO/P9GFgZm56Y8u2bG+NahU/zytXsBqNU19z91mpsu2RpxdP1x3lxzb6bhdErumc0PNYYtk01H7Mh8sSFww8AVeT88fBaAuQAbE7ZM2df+8THbIQ1CiLl1ZmdWLGYKw22iEAQANu+z05bXvwOOPQSPftoeDPuV2+Erfw65adh1lV38v/tlsP1ye4isIMQMEWIjytXnzXLvw8eo1TWmoXjkuQUWSlWu3bc56tD6YvdMHqVsFydtqsZrw2Q8myKfMTl8psixhRI7Q1yUvt259g8OzwPBjsmYyKYYS5scXyyTTRmBOKLbppq/C0lNCpGilL1MfOul8Ip32auSDn0JnvgKPPsf8Pi9zvsMmN0HWy6GLZfYj7kLYWqX3RwgCBHh60+fUupngHcDFwFXaq0PdHjfTcAHABO4Q2t9u/P6XuAuYBb4LvAWrXWl3TWEwfjPF8xx94HD3P/UaV523ixfePQYhoJrzk+GEMulTXbP5Hns6CKGATumx4a+RkcpxXlzBb5+8CR13RRLYXDORI6UoTixWGZTPh3ogFSlFFsmszx1aoU9s/lAZrHtaPldDHP/pyD0JD8Dl77BfoAtzA4fgCMH4OhDcOR78PAnm+83UjC9GzbthZnzYNMemNjmPLbaz+KkCSHi92+2h4CfBv6u0xuUUibw18CrgMPA/UqpT2mtHwH+Anif1voupdT/At4K/K3PmATgxou2UMiY3PUfz7B/zyb+7/ePcM35mwNNeYXNFXs28ZXHTmAaimtCTAt240XbJrn7wGEAzgtx/pppKLZO5Th8psiFW4Pvat0ymeOpUyucG1Cd3Y5NLUIswMYCQQic/Ay84Cfth0vprN2BeepxOP0knH4CzjwJh++Hcpth2LkpGN8K+VkY2+Q8pls+3mQ3FKQL9mBa95HO289SqyZ0wZcQ01o/CvT6F/aVwEGt9RPOe+8CblFKPQrcALzZed+d2O6aCLEAGMuYvPmq3Xzo609Ssuo8c3qFP7r5hVGHNRAvO2+WT3zvCAD7z52JJIaLtjWbHV6yK9yi9D2zeQ6fKa5qsAiK3TN5vvPk6cDmoLXWmV24Jf7jUARhFbkp2HO1/WhFayjNw+IxWHwOFo/C4vPN5+I8zD8Nzz8AxTNgrfR3v1SuKczMjP1IOc9m1m5C6PSaYYIywTCcZ3PNc7vXjebn7scoO42L8/e1+7Fq/ZzV7+v4WrtrrH2N9dfoh4Ec+wHeG8Z191wTiMgeRq5nB/Bsy+eHgauw05HzWutqy+s7hhDPhuE3X3EBX/3xCT7/8FFuungrr744GYX6Lq+5ZCt/ee9j1OqaWy/bHkkMN1+6jb/+8iFedt5MaDPEXH71uvP5xsFTXPeC4NPH//WVF/CNgycDbdZ47Yu3MTWWDjVlKwhDRammw3VOH/9wtUq2cCvO2y6btQyVZaisQGXJFmqV5ebDKkKtDLUK1Cyolu3nypL9WrXS/Jr7vnoddM0eYtv6LETPu45A1n+mROkeS1WVUl8E2p3ef6y1vsd5z1eA329XI6aUegNwk9b6bc7nb8EWYu8Gvq213ue8vgv4nNb6kg5xvB14O8Du3buvePrpp/v5+TY8JavGs6dXOH9uHMMY3p7GoFgsWVRrmk0R1iHV65qa1kNZbH0q5HllSUUp9V2t9f6o4/CKnF9C4HQSaGtf13VnE4FevURd65bXdP+vNa7R+hptXlt7jX4YYMn7QAvhQ7ru7qv7bvTodob1vILW+sb+o2rLEWBXy+c7nddOAdNKqZTjirmvd4rjg8AHAfbv3z/If4ENTS5tckGCU0cTAa768YphKIxBLHAfiAgbTeT8EgLHMADDTmEKiWYYk/XvBy5QSu1VSmWANwKf0rYV92XAaW3hNuCeIcQjCIIgCIIQC3wJMaXU65VSh4Grgc8ope51Xt+ulPosgON2/SZwL/AocLfW+mHnEu8Afk8pdRC7ZuxDfuIRBEEQBEFIEn67Jj8JfLLN688BN7d8/lngs23e9wR2V6UgCIIgCMKGQ5Z+C4IgCIIgRIQIMUEQBEEQhIgQISYIgiAIghARIsQEQRAEQRAiQoSYIAiCIAhCRIgQEwRBEARBiAgRYoIgCIIgCBEhQkwQBEEQBCEiRIgJgiAIgiBEhAgxQRAEQRCEiBAhJgiCIAiCEBEixARBEARBECJCaa2jjmFglFIngKf7fPtm4GSI4XhBYupN3OIBialfwohpj9Z6LuBrRsKA5xfE779x3OIBialf4hZT3OKB8GLqeIYlUogNglLqgNZ6f9RxtCIx9SZu8YDE1C9xjCnJxO33Gbd4QGLql7jFFLd4IJqYJDUpCIIgCIIQESLEBEEQBEEQImIjCLEPRh1AGySm3sQtHpCY+iWOMSWZuP0+4xYPSEz9EreY4hYPRBDTyNeICYIgCIIgxJWN4IgJgiAIgiDEEhFigiAIgiAIETHSQkwpdZNS6jGl1EGl1DtjEM+HlVLHlVIPRR0LgFJql1Lqy0qpR5RSDyulfjsGMeWUUv+hlPqBE9OfRB0TgFLKVEp9Xyn16ahjAVBKPaWUelAp9YBS6kDU8QAopaaVUh9XSv1IKfWoUurqqGNKMnJ+9SZuZ1hczy+QM6yPeCI7v0a2RkwpZQI/Bl4FHAbuB96ktX4kwpiuA5aAf9RaXxJVHC3xbAO2aa2/p5SaAL4L3Brx70gBBa31klIqDXwd+G2t9bejismJ6/eA/cCk1vq1UcbixPMUsF9rHZthiEqpO4Gvaa3vUEplgLzWej7quJKInF/9EbczLK7nF8gZ1osoz69RdsSuBA5qrZ/QWleAu4BbogxIa/3vwOkoY2hFa/281vp7zseLwKPAjohj0lrrJefTtPOI9F8LSqmdwE8Bd0QZR5xRSk0B1wEfAtBaV0SE+ULOrz6I2xkWx/ML5AzrRdTn1ygLsR3Asy2fHyZikRFnlFLnAi8FvhNtJA0L/QHgOPAFrXXUMb0f+EOgHnEcrWjg35RS31VKvT3qYIC9wAngI0764w6lVCHqoBKMnF8DEpczLIbnF8gZ1otIz69RFmJCnyilxoF/BX5Ha70QdTxa65rW+jJgJ3ClUiqyNIhS6rXAca31d6OKoQM/obW+HHgN8BtO2ihKUsDlwN9qrV8KLAOR1zUJG4M4nWFxOr9AzrA+ifT8GmUhdgTY1fL5Tuc1oQWnjuFfgX/WWn8i6nhacazhLwM3RRjGtcDrnHqGu4AblFIfjTAeALTWR5zn48AnsVNZUXIYONzyr/+PYx9sgjfk/OqTuJ5hMTm/QM6wfoj0/BplIXY/cIFSaq9TePdG4FMRxxQrnMLSDwGPaq3fG3U8AEqpOaXUtPPxGHax8o+iikdr/S6t9U6t9bnYf4a+pLX+hajiAVBKFZzCZBz7/CeBSDvZtNZHgWeVUhc6L70SiKywfASQ86sP4naGxe38AjnD+iHq8ys1rBsNG611VSn1m8C9gAl8WGv9cJQxKaU+Brwc2KyUOgz8D631hyIM6VrgLcCDTk0DwB9prT8bYUzbgDudrjEDuFtrHYt26xixBfik/XcQKeBftNafjzYkAH4L+GdHODwB/HLE8SQWOb/6Jm5nmJxf/RHHMyyy82tkx1cIgiAIgiDEnVFOTQqCIAiCIMQaEWKCIAiCIAgRIUJMEARBEAQhIkSICYIgCIIgRIQIMUEQBEEQhIgQISYIgiAIghARIsQEQRAEQRAi4v8DXfKROKsgqfsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FRU5m12kkH1B"
      },
      "source": [
        "Matplotlib también viene con muchas opciones diferentes para personalizar, los colores, las etiquetas, los ejes, etc. Por ejemplo, vea esto [Introducción a matplotlib](https://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb)\n",
        "# Scikit- Learn \n",
        "[Scikit learn](https://scikit-learn.org/stable/) incluye una serie de funciones y utilidades para iniciar su viaje en el aprendizaje automático.\n",
        "\n",
        "## Un ejemplo de juguete"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pa2py0ykbHg"
      },
      "source": [
        "from sklearn.datasets import make_blobs"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-3OZ3XBlZzv",
        "outputId": "40d9ba26-8d11-4729-babe-d25f279b2e21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "X, y = make_blobs(n_samples=1000, centers=2, random_state=0)\n",
        "X[:5], y[:5]"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[0.4666179 , 3.86571303],\n",
              "        [2.84382807, 3.32650945],\n",
              "        [0.61121486, 2.51245978],\n",
              "        [3.81653365, 1.65175932],\n",
              "        [1.28097244, 0.62827388]]), array([0, 0, 0, 1, 1]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exOFPPBUliEQ",
        "outputId": "79b700de-d149-456c-f1fb-9b2c11803dbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "for label in [0, 1]:\n",
        "    mask = (y == label)\n",
        "    ax.scatter(X[mask, 0], X[mask, 1])"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vfaf38calpYx"
      },
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t2_WDWYfl2zp"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-zJL0pIl4rJ",
        "outputId": "a118aea9-6112-40b1-afa6-04a877803feb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "\n",
        "fig, ax = plt.subplots()\n",
        "for label in [0, 1]:\n",
        "    mask = (y_train == label)\n",
        "    ax.scatter(X_train[mask, 0], X_train[mask, 1])\n",
        "for label in [0, 1]:\n",
        "    mask = (y_test == label)\n",
        "    ax.scatter(X_test[mask, 0], X_test[mask, 1])"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hTXwhWtCmKB-"
      },
      "source": [
        "Sklearn usa una API uniforme y muy consistente, lo que facilita el cambio de algoritmos Por ejemplo, entrenar y predecir con un perceptrón."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GduiIPlGl7bw"
      },
      "source": [
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cXKAT9_ymP0C"
      },
      "source": [
        "clf = Perceptron(max_iter=40, random_state=0)\n",
        "# clf = LinearSVC(max_iter=40, random_state=0)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ESs2eJcomUyR",
        "outputId": "bc181868-bb55-440d-dd56-d8ba74c2ed44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "print('Test accuracy: %.4f' % accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 0.9400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATLLG0uhmn76"
      },
      "source": [
        "theta = clf.coef_[0]\n",
        "theta_0 = clf.intercept_"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sgbprvxmqea",
        "outputId": "93b86dd7-d279-4401-eb26-ed955bbecfe4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "for label in [0, 1]:\n",
        "    mask = (y_train == label)\n",
        "    ax.scatter(X_train[mask, 0], X_train[mask, 1])\n",
        "for label in [0, 1]:\n",
        "    mask = (y_test == label)\n",
        "    ax.scatter(X_test[mask, 0], X_test[mask, 1])\n",
        "x_bnd = np.linspace(X[:, 0].min() - 1, X[:, 0].max() + 1,  400)\n",
        "y_bnd = - x_bnd * (theta[0] /theta[1]) - (theta_0 / theta[1])\n",
        "ax.plot(x_bnd, y_bnd)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fa45a4f5908>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mV8GVpN1m866"
      },
      "source": [
        "# Otro ejemplo de juguete"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6UrIPqVkm-8x",
        "outputId": "3a00dc0d-52a1-4d2e-89c0-b2bc1f5d87cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        }
      },
      "source": [
        "X, y = make_blobs(n_samples=500, centers=3, random_state=7)\n",
        "y[y==2] = 0\n",
        "fig, ax = plt.subplots()\n",
        "for label in [0, 1]:\n",
        "    mask = (y == label)\n",
        "    ax.scatter(X[mask, 0], X[mask, 1])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayegYVxWnMbu"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjN-cXfjnM5i",
        "outputId": "1cd1381c-2d8f-4705-d59d-b3b531003d4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "# clf = SVC(kernel=\"linear\", random_state=0)\n",
        "clf = SVC(kernel=\"rbf\", random_state=0)\n",
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
              "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='rbf',\n",
              "    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n",
              "    verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjV85hXynPK5",
        "outputId": "f463cf6b-8d1d-4087-fc66-689c56e2d5ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_pred = clf.predict(X_test)\n",
        "\n",
        "print('Test accuracy: %.4f' % accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvyrD-kknWlZ",
        "outputId": "f5691206-ab94-4682-e464-8582afe7ff0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "\n",
        "x_min = X[:, 0].min()\n",
        "x_max = X[:, 0].max()\n",
        "y_min = X[:, 1].min()\n",
        "y_max = X[:, 1].max()\n",
        "\n",
        "XX, YY = np.mgrid[x_min:x_max:200j, y_min:y_max:200j]\n",
        "Z = clf.decision_function(np.c_[XX.ravel(), YY.ravel()])\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "for label in [0, 1]:\n",
        "    mask = (y == label)\n",
        "    ax.scatter(X[mask, 0], X[mask, 1])\n",
        "    \n",
        "Z = Z.reshape(XX.shape)\n",
        "ax.contour(XX, YY, Z, colors=\"black\",\n",
        "    linestyles=['--', '-', '--'], levels=[-.5, 0, .5])"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.contour.QuadContourSet at 0x7fa45bc87208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tJPHT4sknpEn"
      },
      "source": [
        "# Clasificar Digitos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7VlVrmvndZo"
      },
      "source": [
        "\n",
        "# from sklearn.datasets import load_breast_cancer\n",
        "# breast_cancer = load_breast_cancer()\n",
        "# X, y = breast_cancer.data, breast_cancer.target\n",
        "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPsPxd-4nwst"
      },
      "source": [
        "from sklearn.datasets import load_digits"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ADObAm47nymi"
      },
      "source": [
        "digits = load_digits()\n",
        "X, y = digits.data, digits.target\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IMgeCUI8n05J",
        "outputId": "098dfc35-c712-4f54-efd0-4797a957fa93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.matshow(digits.images[0])"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fa45a4db390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAQ29i6qn3jV",
        "outputId": "b0e5598f-e44a-43e0-bfe3-e5198f478c54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1437, 64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaFfuVUPn_Cy"
      },
      "source": [
        "clf = Perceptron(max_iter=40, random_state=0)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WuDAtisXoDdR",
        "outputId": "2b094804-91c9-4d7e-ffbf-4d36b6962ff3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "print('Accuracy: %.4f' % accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9389\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "no--UmxloNlz"
      },
      "source": [
        "clf = LinearSVC(C=1, random_state=0)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9b4jlq3koXd1",
        "outputId": "564c3cd2-afd5-41a7-dbdd-3334262030bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "print('Accuracy: %.4f' % accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9333\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVU5zCe_oZ-r"
      },
      "source": [
        "\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tZOI0i7loemB",
        "outputId": "40d1fda9-8b79-4c6b-9a45-731affff90f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "confusion_matrix(y_test, clf.predict(X_test))"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[27,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0, 29,  0,  0,  0,  0,  1,  0,  5,  0],\n",
              "       [ 0,  1, 33,  2,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0, 29,  0,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  0, 30,  0,  0,  0,  0,  0],\n",
              "       [ 0,  0,  0,  1,  0, 38,  1,  0,  0,  0],\n",
              "       [ 0,  1,  0,  0,  0,  0, 43,  0,  0,  0],\n",
              "       [ 0,  1,  0,  0,  1,  0,  0, 37,  0,  0],\n",
              "       [ 0,  2,  1,  0,  0,  0,  0,  0, 35,  1],\n",
              "       [ 0,  0,  0,  3,  0,  1,  0,  0,  2, 35]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZBbH6aNFot5b"
      },
      "source": [
        "Scikit-learn también incluye utilidades para calcular rápidamente una puntuación de validación cruzada .."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qwfrotdWog9y",
        "outputId": "8681895e-4db2-4d6c-863f-18ac3caec059",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "clf = LinearSVC(C=1, random_state=0)\n",
        "from sklearn.model_selection import cross_val_score\n",
        "scores =  cross_val_score(clf, X_train, y_train, cv=5)\n",
        "print(\"Mean: %.4f, Std: %.4f\" % (np.mean(scores), np.std(scores)))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Mean: 0.9436, Std: 0.0097\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AByqgiRAownh",
        "outputId": "14694564-4e6b-47ad-8bf0-27fba6d48db6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "\n",
        "clf = LinearSVC(C=0.1, random_state=0)\n",
        "scores =  cross_val_score(clf, X_train, y_train, cv=5)\n",
        "print(\"Mean: %.4f, Std: %.4f\" % (np.mean(scores), np.std(scores)))"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Mean: 0.9555, Std: 0.0068\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B0CBPqP2pOSa"
      },
      "source": [
        "... o para realizar una búsqueda en la cuadrícula"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0X3hNTNFo9QS"
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiMITEG8pNsJ"
      },
      "source": [
        "clf = LinearSVC(random_state=0)\n",
        "param_grid = {'C': 10. ** np.arange(-6, 4)}\n",
        "grid_search = GridSearchCV(clf, param_grid=param_grid, cv=5, verbose=3, return_train_score=True)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "057M-2A9pjdZ",
        "outputId": "1ca12735-1c9f-4a20-92e9-24620fcd0734",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "grid_search.fit(X_train, y_train);"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
            "[CV] C=1e-06 .........................................................\n",
            "[CV] ......... C=1e-06, score=(train=0.883, test=0.844), total=   0.0s\n",
            "[CV] C=1e-06 .........................................................\n",
            "[CV] ......... C=1e-06, score=(train=0.880, test=0.865), total=   0.0s\n",
            "[CV] C=1e-06 .........................................................\n",
            "[CV] ......... C=1e-06, score=(train=0.869, test=0.909), total=   0.0s\n",
            "[CV] C=1e-06 .........................................................\n",
            "[CV] ......... C=1e-06, score=(train=0.880, test=0.840), total=   0.0s\n",
            "[CV] C=1e-06 .........................................................\n",
            "[CV] ......... C=1e-06, score=(train=0.873, test=0.899), total=   0.0s\n",
            "[CV] C=1e-05 .........................................................\n",
            "[CV] ......... C=1e-05, score=(train=0.929, test=0.906), total=   0.0s\n",
            "[CV] C=1e-05 .........................................................\n",
            "[CV] ......... C=1e-05, score=(train=0.930, test=0.917), total=   0.0s\n",
            "[CV] C=1e-05 .........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
            "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.1s remaining:    0.0s\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ......... C=1e-05, score=(train=0.923, test=0.944), total=   0.0s\n",
            "[CV] C=1e-05 .........................................................\n",
            "[CV] ......... C=1e-05, score=(train=0.933, test=0.906), total=   0.0s\n",
            "[CV] C=1e-05 .........................................................\n",
            "[CV] ......... C=1e-05, score=(train=0.925, test=0.937), total=   0.0s\n",
            "[CV] C=0.0001 ........................................................\n",
            "[CV] ........ C=0.0001, score=(train=0.963, test=0.938), total=   0.0s\n",
            "[CV] C=0.0001 ........................................................\n",
            "[CV] ........ C=0.0001, score=(train=0.964, test=0.941), total=   0.0s\n",
            "[CV] C=0.0001 ........................................................\n",
            "[CV] ........ C=0.0001, score=(train=0.960, test=0.969), total=   0.0s\n",
            "[CV] C=0.0001 ........................................................\n",
            "[CV] ........ C=0.0001, score=(train=0.962, test=0.937), total=   0.0s\n",
            "[CV] C=0.0001 ........................................................\n",
            "[CV] ........ C=0.0001, score=(train=0.957, test=0.969), total=   0.0s\n",
            "[CV] C=0.001 .........................................................\n",
            "[CV] ......... C=0.001, score=(train=0.983, test=0.962), total=   0.0s\n",
            "[CV] C=0.001 .........................................................\n",
            "[CV] ......... C=0.001, score=(train=0.983, test=0.965), total=   0.0s\n",
            "[CV] C=0.001 .........................................................\n",
            "[CV] ......... C=0.001, score=(train=0.983, test=0.969), total=   0.0s\n",
            "[CV] C=0.001 .........................................................\n",
            "[CV] ......... C=0.001, score=(train=0.981, test=0.962), total=   0.0s\n",
            "[CV] C=0.001 .........................................................\n",
            "[CV] ......... C=0.001, score=(train=0.982, test=0.979), total=   0.0s\n",
            "[CV] C=0.01 ..........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .......... C=0.01, score=(train=0.992, test=0.958), total=   0.1s\n",
            "[CV] C=0.01 ..........................................................\n",
            "[CV] .......... C=0.01, score=(train=0.994, test=0.962), total=   0.1s\n",
            "[CV] C=0.01 ..........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .......... C=0.01, score=(train=0.996, test=0.972), total=   0.1s\n",
            "[CV] C=0.01 ..........................................................\n",
            "[CV] .......... C=0.01, score=(train=0.993, test=0.962), total=   0.1s\n",
            "[CV] C=0.01 ..........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .......... C=0.01, score=(train=0.994, test=0.972), total=   0.1s\n",
            "[CV] C=0.1 ...........................................................\n",
            "[CV] ........... C=0.1, score=(train=0.998, test=0.951), total=   0.1s\n",
            "[CV] C=0.1 ...........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ........... C=0.1, score=(train=0.997, test=0.969), total=   0.1s\n",
            "[CV] C=0.1 ...........................................................\n",
            "[CV] ........... C=0.1, score=(train=0.997, test=0.951), total=   0.1s\n",
            "[CV] C=0.1 ...........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ........... C=0.1, score=(train=0.990, test=0.955), total=   0.2s\n",
            "[CV] C=0.1 ...........................................................\n",
            "[CV] ........... C=0.1, score=(train=0.996, test=0.951), total=   0.1s\n",
            "[CV] C=1.0 ...........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ........... C=1.0, score=(train=0.996, test=0.941), total=   0.1s\n",
            "[CV] C=1.0 ...........................................................\n",
            "[CV] ........... C=1.0, score=(train=0.974, test=0.955), total=   0.1s\n",
            "[CV] C=1.0 ...........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ........... C=1.0, score=(train=0.992, test=0.927), total=   0.1s\n",
            "[CV] C=1.0 ...........................................................\n",
            "[CV] ........... C=1.0, score=(train=0.983, test=0.944), total=   0.1s\n",
            "[CV] C=1.0 ...........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ........... C=1.0, score=(train=0.990, test=0.951), total=   0.1s\n",
            "[CV] C=10.0 ..........................................................\n",
            "[CV] .......... C=10.0, score=(train=0.996, test=0.934), total=   0.1s\n",
            "[CV] C=10.0 ..........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .......... C=10.0, score=(train=0.988, test=0.955), total=   0.1s\n",
            "[CV] C=10.0 ..........................................................\n",
            "[CV] .......... C=10.0, score=(train=0.995, test=0.934), total=   0.1s\n",
            "[CV] C=10.0 ..........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] .......... C=10.0, score=(train=0.983, test=0.927), total=   0.1s\n",
            "[CV] C=10.0 ..........................................................\n",
            "[CV] .......... C=10.0, score=(train=0.994, test=0.941), total=   0.1s\n",
            "[CV] C=100.0 .........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ......... C=100.0, score=(train=0.996, test=0.934), total=   0.1s\n",
            "[CV] C=100.0 .........................................................\n",
            "[CV] ......... C=100.0, score=(train=0.995, test=0.969), total=   0.1s\n",
            "[CV] C=100.0 .........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ......... C=100.0, score=(train=0.997, test=0.934), total=   0.1s\n",
            "[CV] C=100.0 .........................................................\n",
            "[CV] ......... C=100.0, score=(train=0.957, test=0.913), total=   0.1s\n",
            "[CV] C=100.0 .........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ......... C=100.0, score=(train=0.989, test=0.941), total=   0.1s\n",
            "[CV] C=1000.0 ........................................................\n",
            "[CV] ........ C=1000.0, score=(train=0.997, test=0.941), total=   0.1s\n",
            "[CV] C=1000.0 ........................................................\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[CV] ........ C=1000.0, score=(train=0.984, test=0.958), total=   0.1s\n",
            "[CV] C=1000.0 ........................................................\n",
            "[CV] ........ C=1000.0, score=(train=0.997, test=0.934), total=   0.1s\n",
            "[CV] C=1000.0 ........................................................\n",
            "[CV] ........ C=1000.0, score=(train=0.989, test=0.944), total=   0.1s\n",
            "[CV] C=1000.0 ........................................................\n",
            "[CV] ........ C=1000.0, score=(train=0.989, test=0.951), total=   0.1s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n",
            "[Parallel(n_jobs=1)]: Done  50 out of  50 | elapsed:    4.8s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6nqwRORvplVg",
        "outputId": "ce32fff9-c9c5-43e5-c355-5075260bef86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "print(grid_search.best_params_)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'C': 0.001}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txWkqVCwpv9Q",
        "outputId": "e955d3d8-bb63-4e4d-80fb-dbbcea372266",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(grid_search.best_score_)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9672981997677119\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x0hB89u6pyHx",
        "outputId": "9a898f0f-faff-4d66-f67c-dfa4e9bd40d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "\n",
        "y_pred = grid_search.predict(X_test)\n",
        "print('Accuracy: %.4f' % accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9639\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G09o9rE-qCPQ"
      },
      "source": [
        "¡Y muchas más funciones! Solo hemos analizado algunos modelos de clasificación y algunas características de selección de modelos, pero sklearn también se puede usar para regresiones.\n",
        "# Pandas\n",
        "[Pandas](https://pandas.pydata.org/) es una librería que proporciona un conjunto de herramientas para el análisis de datos (Biblioteca de análisis de datos de Python). \n",
        "Los dataframe de Pandas se pueden crear importando un archivo CSV (o TSV, JSON, SQL, etc.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8VDSY5Yp0aB"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"train_2.csv\")"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KE5vOHOqvDj"
      },
      "source": [
        "Los detaframe de Pandas también se pueden crear directamente desde un diccionario de matrices."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9oIex4bpqpw7",
        "outputId": "1921df89-d295-4651-e944-949a03cb4f55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 462
        }
      },
      "source": [
        "print(grid_search.cv_results_)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'mean_fit_time': array([0.02222934, 0.02340851, 0.02317324, 0.04224839, 0.13977137,\n",
            "       0.13774352, 0.13476987, 0.13284068, 0.13460026, 0.13372598]), 'std_fit_time': array([0.00125214, 0.00218207, 0.00219904, 0.00149231, 0.00433516,\n",
            "       0.00596258, 0.00516357, 0.00128343, 0.00452831, 0.00179095]), 'mean_score_time': array([0.00099659, 0.00097833, 0.00087051, 0.00124054, 0.00097542,\n",
            "       0.00093064, 0.00104675, 0.00092797, 0.00109682, 0.00100908]), 'std_score_time': array([5.63229265e-05, 3.93997360e-05, 7.23827812e-05, 8.82045242e-04,\n",
            "       2.33482885e-04, 1.37754665e-04, 2.02189110e-04, 1.07784122e-04,\n",
            "       1.37023088e-04, 2.19040047e-04]), 'param_C': masked_array(data=[1e-06, 1e-05, 0.0001, 0.001, 0.01, 0.1, 1.0, 10.0,\n",
            "                   100.0, 1000.0],\n",
            "             mask=[False, False, False, False, False, False, False, False,\n",
            "                   False, False],\n",
            "       fill_value='?',\n",
            "            dtype=object), 'params': [{'C': 1e-06}, {'C': 1e-05}, {'C': 0.0001}, {'C': 0.001}, {'C': 0.01}, {'C': 0.1}, {'C': 1.0}, {'C': 10.0}, {'C': 100.0}, {'C': 1000.0}], 'split0_test_score': array([0.84375   , 0.90625   , 0.9375    , 0.96180556, 0.95833333,\n",
            "       0.95138889, 0.94097222, 0.93402778, 0.93402778, 0.94097222]), 'split1_test_score': array([0.86458333, 0.91666667, 0.94097222, 0.96527778, 0.96180556,\n",
            "       0.96875   , 0.95486111, 0.95486111, 0.96875   , 0.95833333]), 'split2_test_score': array([0.90940767, 0.94425087, 0.96864111, 0.96864111, 0.97212544,\n",
            "       0.95121951, 0.92682927, 0.93379791, 0.93379791, 0.93379791]), 'split3_test_score': array([0.83972125, 0.90592334, 0.93728223, 0.96167247, 0.96167247,\n",
            "       0.95470383, 0.94425087, 0.92682927, 0.91289199, 0.94425087]), 'split4_test_score': array([0.8989547 , 0.93728223, 0.96864111, 0.97909408, 0.97212544,\n",
            "       0.95121951, 0.95121951, 0.94076655, 0.94076655, 0.95121951]), 'mean_test_score': array([0.87128339, 0.92207462, 0.95060734, 0.9672982 , 0.96521245,\n",
            "       0.95545635, 0.9436266 , 0.93805652, 0.93804684, 0.94571477]), 'std_test_score': array([0.02834893, 0.01589708, 0.01478263, 0.00643189, 0.00577995,\n",
            "       0.0067784 , 0.0097309 , 0.00948848, 0.01798362, 0.0084401 ]), 'rank_test_score': array([10,  9,  4,  1,  2,  3,  6,  7,  8,  5], dtype=int32), 'split0_train_score': array([0.88250653, 0.92863359, 0.96344648, 0.98259356, 0.9921671 ,\n",
            "       0.99825936, 0.99564839, 0.99564839, 0.99564839, 0.99651871]), 'split1_train_score': array([0.87989556, 0.93037424, 0.9643168 , 0.98259356, 0.99390775,\n",
            "       0.99651871, 0.97389034, 0.98781549, 0.99477807, 0.9843342 ]), 'split2_train_score': array([0.86869565, 0.9226087 , 0.96      , 0.9826087 , 0.99565217,\n",
            "       0.9973913 , 0.99217391, 0.99478261, 0.99652174, 0.99652174]), 'split3_train_score': array([0.88      , 0.93304348, 0.96173913, 0.98086957, 0.99304348,\n",
            "       0.99043478, 0.98347826, 0.98347826, 0.95652174, 0.98869565]), 'split4_train_score': array([0.87304348, 0.92521739, 0.9573913 , 0.98173913, 0.99391304,\n",
            "       0.99565217, 0.98956522, 0.99391304, 0.98869565, 0.98869565]), 'mean_train_score': array([0.87682824, 0.92797548, 0.96137874, 0.9820809 , 0.99373671,\n",
            "       0.99565127, 0.98695122, 0.99112756, 0.98643312, 0.99095319]), 'std_train_score': array([0.0051415 , 0.00369543, 0.00248347, 0.00069114, 0.0011553 ,\n",
            "       0.00274933, 0.0076472 , 0.00471249, 0.01520624, 0.00481638])}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGdETOL1q5Eh",
        "outputId": "233d7f46-71cf-4fd8-e375-15284232dd5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "source": [
        "df = pd.DataFrame(grid_search.cv_results_)\n",
        "df"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_C</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.022229</td>\n",
              "      <td>0.001252</td>\n",
              "      <td>0.000997</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>1e-06</td>\n",
              "      <td>{'C': 1e-06}</td>\n",
              "      <td>0.843750</td>\n",
              "      <td>0.864583</td>\n",
              "      <td>0.909408</td>\n",
              "      <td>0.839721</td>\n",
              "      <td>0.898955</td>\n",
              "      <td>0.871283</td>\n",
              "      <td>0.028349</td>\n",
              "      <td>10</td>\n",
              "      <td>0.882507</td>\n",
              "      <td>0.879896</td>\n",
              "      <td>0.868696</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.873043</td>\n",
              "      <td>0.876828</td>\n",
              "      <td>0.005141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.023409</td>\n",
              "      <td>0.002182</td>\n",
              "      <td>0.000978</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>1e-05</td>\n",
              "      <td>{'C': 1e-05}</td>\n",
              "      <td>0.906250</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.944251</td>\n",
              "      <td>0.905923</td>\n",
              "      <td>0.937282</td>\n",
              "      <td>0.922075</td>\n",
              "      <td>0.015897</td>\n",
              "      <td>9</td>\n",
              "      <td>0.928634</td>\n",
              "      <td>0.930374</td>\n",
              "      <td>0.922609</td>\n",
              "      <td>0.933043</td>\n",
              "      <td>0.925217</td>\n",
              "      <td>0.927975</td>\n",
              "      <td>0.003695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.023173</td>\n",
              "      <td>0.002199</td>\n",
              "      <td>0.000871</td>\n",
              "      <td>0.000072</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>{'C': 0.0001}</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.940972</td>\n",
              "      <td>0.968641</td>\n",
              "      <td>0.937282</td>\n",
              "      <td>0.968641</td>\n",
              "      <td>0.950607</td>\n",
              "      <td>0.014783</td>\n",
              "      <td>4</td>\n",
              "      <td>0.963446</td>\n",
              "      <td>0.964317</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.961739</td>\n",
              "      <td>0.957391</td>\n",
              "      <td>0.961379</td>\n",
              "      <td>0.002483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.042248</td>\n",
              "      <td>0.001492</td>\n",
              "      <td>0.001241</td>\n",
              "      <td>0.000882</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'C': 0.001}</td>\n",
              "      <td>0.961806</td>\n",
              "      <td>0.965278</td>\n",
              "      <td>0.968641</td>\n",
              "      <td>0.961672</td>\n",
              "      <td>0.979094</td>\n",
              "      <td>0.967298</td>\n",
              "      <td>0.006432</td>\n",
              "      <td>1</td>\n",
              "      <td>0.982594</td>\n",
              "      <td>0.982594</td>\n",
              "      <td>0.982609</td>\n",
              "      <td>0.980870</td>\n",
              "      <td>0.981739</td>\n",
              "      <td>0.982081</td>\n",
              "      <td>0.000691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.139771</td>\n",
              "      <td>0.004335</td>\n",
              "      <td>0.000975</td>\n",
              "      <td>0.000233</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'C': 0.01}</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.961806</td>\n",
              "      <td>0.972125</td>\n",
              "      <td>0.961672</td>\n",
              "      <td>0.972125</td>\n",
              "      <td>0.965212</td>\n",
              "      <td>0.005780</td>\n",
              "      <td>2</td>\n",
              "      <td>0.992167</td>\n",
              "      <td>0.993908</td>\n",
              "      <td>0.995652</td>\n",
              "      <td>0.993043</td>\n",
              "      <td>0.993913</td>\n",
              "      <td>0.993737</td>\n",
              "      <td>0.001155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.137744</td>\n",
              "      <td>0.005963</td>\n",
              "      <td>0.000931</td>\n",
              "      <td>0.000138</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'C': 0.1}</td>\n",
              "      <td>0.951389</td>\n",
              "      <td>0.968750</td>\n",
              "      <td>0.951220</td>\n",
              "      <td>0.954704</td>\n",
              "      <td>0.951220</td>\n",
              "      <td>0.955456</td>\n",
              "      <td>0.006778</td>\n",
              "      <td>3</td>\n",
              "      <td>0.998259</td>\n",
              "      <td>0.996519</td>\n",
              "      <td>0.997391</td>\n",
              "      <td>0.990435</td>\n",
              "      <td>0.995652</td>\n",
              "      <td>0.995651</td>\n",
              "      <td>0.002749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.134770</td>\n",
              "      <td>0.005164</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000202</td>\n",
              "      <td>1</td>\n",
              "      <td>{'C': 1.0}</td>\n",
              "      <td>0.940972</td>\n",
              "      <td>0.954861</td>\n",
              "      <td>0.926829</td>\n",
              "      <td>0.944251</td>\n",
              "      <td>0.951220</td>\n",
              "      <td>0.943627</td>\n",
              "      <td>0.009731</td>\n",
              "      <td>6</td>\n",
              "      <td>0.995648</td>\n",
              "      <td>0.973890</td>\n",
              "      <td>0.992174</td>\n",
              "      <td>0.983478</td>\n",
              "      <td>0.989565</td>\n",
              "      <td>0.986951</td>\n",
              "      <td>0.007647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.132841</td>\n",
              "      <td>0.001283</td>\n",
              "      <td>0.000928</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>10</td>\n",
              "      <td>{'C': 10.0}</td>\n",
              "      <td>0.934028</td>\n",
              "      <td>0.954861</td>\n",
              "      <td>0.933798</td>\n",
              "      <td>0.926829</td>\n",
              "      <td>0.940767</td>\n",
              "      <td>0.938057</td>\n",
              "      <td>0.009488</td>\n",
              "      <td>7</td>\n",
              "      <td>0.995648</td>\n",
              "      <td>0.987815</td>\n",
              "      <td>0.994783</td>\n",
              "      <td>0.983478</td>\n",
              "      <td>0.993913</td>\n",
              "      <td>0.991128</td>\n",
              "      <td>0.004712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.134600</td>\n",
              "      <td>0.004528</td>\n",
              "      <td>0.001097</td>\n",
              "      <td>0.000137</td>\n",
              "      <td>100</td>\n",
              "      <td>{'C': 100.0}</td>\n",
              "      <td>0.934028</td>\n",
              "      <td>0.968750</td>\n",
              "      <td>0.933798</td>\n",
              "      <td>0.912892</td>\n",
              "      <td>0.940767</td>\n",
              "      <td>0.938047</td>\n",
              "      <td>0.017984</td>\n",
              "      <td>8</td>\n",
              "      <td>0.995648</td>\n",
              "      <td>0.994778</td>\n",
              "      <td>0.996522</td>\n",
              "      <td>0.956522</td>\n",
              "      <td>0.988696</td>\n",
              "      <td>0.986433</td>\n",
              "      <td>0.015206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.133726</td>\n",
              "      <td>0.001791</td>\n",
              "      <td>0.001009</td>\n",
              "      <td>0.000219</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'C': 1000.0}</td>\n",
              "      <td>0.940972</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.933798</td>\n",
              "      <td>0.944251</td>\n",
              "      <td>0.951220</td>\n",
              "      <td>0.945715</td>\n",
              "      <td>0.008440</td>\n",
              "      <td>5</td>\n",
              "      <td>0.996519</td>\n",
              "      <td>0.984334</td>\n",
              "      <td>0.996522</td>\n",
              "      <td>0.988696</td>\n",
              "      <td>0.988696</td>\n",
              "      <td>0.990953</td>\n",
              "      <td>0.004816</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "0       0.022229      0.001252  ...          0.876828         0.005141\n",
              "1       0.023409      0.002182  ...          0.927975         0.003695\n",
              "2       0.023173      0.002199  ...          0.961379         0.002483\n",
              "3       0.042248      0.001492  ...          0.982081         0.000691\n",
              "4       0.139771      0.004335  ...          0.993737         0.001155\n",
              "5       0.137744      0.005963  ...          0.995651         0.002749\n",
              "6       0.134770      0.005164  ...          0.986951         0.007647\n",
              "7       0.132841      0.001283  ...          0.991128         0.004712\n",
              "8       0.134600      0.004528  ...          0.986433         0.015206\n",
              "9       0.133726      0.001791  ...          0.990953         0.004816\n",
              "\n",
              "[10 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k59w-OZeq9GQ",
        "outputId": "5b52ffca-52e3-42ee-f9bc-3c2f79aed1e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        }
      },
      "source": [
        "df = pd.DataFrame(grid_search.cv_results_)\n",
        "df"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_C</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
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              "      <th>std_train_score</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.022229</td>\n",
              "      <td>0.001252</td>\n",
              "      <td>0.000997</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>1e-06</td>\n",
              "      <td>{'C': 1e-06}</td>\n",
              "      <td>0.843750</td>\n",
              "      <td>0.864583</td>\n",
              "      <td>0.909408</td>\n",
              "      <td>0.839721</td>\n",
              "      <td>0.898955</td>\n",
              "      <td>0.871283</td>\n",
              "      <td>0.028349</td>\n",
              "      <td>10</td>\n",
              "      <td>0.882507</td>\n",
              "      <td>0.879896</td>\n",
              "      <td>0.868696</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.873043</td>\n",
              "      <td>0.876828</td>\n",
              "      <td>0.005141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.023409</td>\n",
              "      <td>0.002182</td>\n",
              "      <td>0.000978</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>1e-05</td>\n",
              "      <td>{'C': 1e-05}</td>\n",
              "      <td>0.906250</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.944251</td>\n",
              "      <td>0.905923</td>\n",
              "      <td>0.937282</td>\n",
              "      <td>0.922075</td>\n",
              "      <td>0.015897</td>\n",
              "      <td>9</td>\n",
              "      <td>0.928634</td>\n",
              "      <td>0.930374</td>\n",
              "      <td>0.922609</td>\n",
              "      <td>0.933043</td>\n",
              "      <td>0.925217</td>\n",
              "      <td>0.927975</td>\n",
              "      <td>0.003695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.023173</td>\n",
              "      <td>0.002199</td>\n",
              "      <td>0.000871</td>\n",
              "      <td>0.000072</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>{'C': 0.0001}</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.940972</td>\n",
              "      <td>0.968641</td>\n",
              "      <td>0.937282</td>\n",
              "      <td>0.968641</td>\n",
              "      <td>0.950607</td>\n",
              "      <td>0.014783</td>\n",
              "      <td>4</td>\n",
              "      <td>0.963446</td>\n",
              "      <td>0.964317</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.961739</td>\n",
              "      <td>0.957391</td>\n",
              "      <td>0.961379</td>\n",
              "      <td>0.002483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.042248</td>\n",
              "      <td>0.001492</td>\n",
              "      <td>0.001241</td>\n",
              "      <td>0.000882</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'C': 0.001}</td>\n",
              "      <td>0.961806</td>\n",
              "      <td>0.965278</td>\n",
              "      <td>0.968641</td>\n",
              "      <td>0.961672</td>\n",
              "      <td>0.979094</td>\n",
              "      <td>0.967298</td>\n",
              "      <td>0.006432</td>\n",
              "      <td>1</td>\n",
              "      <td>0.982594</td>\n",
              "      <td>0.982594</td>\n",
              "      <td>0.982609</td>\n",
              "      <td>0.980870</td>\n",
              "      <td>0.981739</td>\n",
              "      <td>0.982081</td>\n",
              "      <td>0.000691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.139771</td>\n",
              "      <td>0.004335</td>\n",
              "      <td>0.000975</td>\n",
              "      <td>0.000233</td>\n",
              "      <td>0.01</td>\n",
              "      <td>{'C': 0.01}</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.961806</td>\n",
              "      <td>0.972125</td>\n",
              "      <td>0.961672</td>\n",
              "      <td>0.972125</td>\n",
              "      <td>0.965212</td>\n",
              "      <td>0.005780</td>\n",
              "      <td>2</td>\n",
              "      <td>0.992167</td>\n",
              "      <td>0.993908</td>\n",
              "      <td>0.995652</td>\n",
              "      <td>0.993043</td>\n",
              "      <td>0.993913</td>\n",
              "      <td>0.993737</td>\n",
              "      <td>0.001155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.137744</td>\n",
              "      <td>0.005963</td>\n",
              "      <td>0.000931</td>\n",
              "      <td>0.000138</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'C': 0.1}</td>\n",
              "      <td>0.951389</td>\n",
              "      <td>0.968750</td>\n",
              "      <td>0.951220</td>\n",
              "      <td>0.954704</td>\n",
              "      <td>0.951220</td>\n",
              "      <td>0.955456</td>\n",
              "      <td>0.006778</td>\n",
              "      <td>3</td>\n",
              "      <td>0.998259</td>\n",
              "      <td>0.996519</td>\n",
              "      <td>0.997391</td>\n",
              "      <td>0.990435</td>\n",
              "      <td>0.995652</td>\n",
              "      <td>0.995651</td>\n",
              "      <td>0.002749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.134770</td>\n",
              "      <td>0.005164</td>\n",
              "      <td>0.001047</td>\n",
              "      <td>0.000202</td>\n",
              "      <td>1</td>\n",
              "      <td>{'C': 1.0}</td>\n",
              "      <td>0.940972</td>\n",
              "      <td>0.954861</td>\n",
              "      <td>0.926829</td>\n",
              "      <td>0.944251</td>\n",
              "      <td>0.951220</td>\n",
              "      <td>0.943627</td>\n",
              "      <td>0.009731</td>\n",
              "      <td>6</td>\n",
              "      <td>0.995648</td>\n",
              "      <td>0.973890</td>\n",
              "      <td>0.992174</td>\n",
              "      <td>0.983478</td>\n",
              "      <td>0.989565</td>\n",
              "      <td>0.986951</td>\n",
              "      <td>0.007647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.132841</td>\n",
              "      <td>0.001283</td>\n",
              "      <td>0.000928</td>\n",
              "      <td>0.000108</td>\n",
              "      <td>10</td>\n",
              "      <td>{'C': 10.0}</td>\n",
              "      <td>0.934028</td>\n",
              "      <td>0.954861</td>\n",
              "      <td>0.933798</td>\n",
              "      <td>0.926829</td>\n",
              "      <td>0.940767</td>\n",
              "      <td>0.938057</td>\n",
              "      <td>0.009488</td>\n",
              "      <td>7</td>\n",
              "      <td>0.995648</td>\n",
              "      <td>0.987815</td>\n",
              "      <td>0.994783</td>\n",
              "      <td>0.983478</td>\n",
              "      <td>0.993913</td>\n",
              "      <td>0.991128</td>\n",
              "      <td>0.004712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.134600</td>\n",
              "      <td>0.004528</td>\n",
              "      <td>0.001097</td>\n",
              "      <td>0.000137</td>\n",
              "      <td>100</td>\n",
              "      <td>{'C': 100.0}</td>\n",
              "      <td>0.934028</td>\n",
              "      <td>0.968750</td>\n",
              "      <td>0.933798</td>\n",
              "      <td>0.912892</td>\n",
              "      <td>0.940767</td>\n",
              "      <td>0.938047</td>\n",
              "      <td>0.017984</td>\n",
              "      <td>8</td>\n",
              "      <td>0.995648</td>\n",
              "      <td>0.994778</td>\n",
              "      <td>0.996522</td>\n",
              "      <td>0.956522</td>\n",
              "      <td>0.988696</td>\n",
              "      <td>0.986433</td>\n",
              "      <td>0.015206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.133726</td>\n",
              "      <td>0.001791</td>\n",
              "      <td>0.001009</td>\n",
              "      <td>0.000219</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'C': 1000.0}</td>\n",
              "      <td>0.940972</td>\n",
              "      <td>0.958333</td>\n",
              "      <td>0.933798</td>\n",
              "      <td>0.944251</td>\n",
              "      <td>0.951220</td>\n",
              "      <td>0.945715</td>\n",
              "      <td>0.008440</td>\n",
              "      <td>5</td>\n",
              "      <td>0.996519</td>\n",
              "      <td>0.984334</td>\n",
              "      <td>0.996522</td>\n",
              "      <td>0.988696</td>\n",
              "      <td>0.988696</td>\n",
              "      <td>0.990953</td>\n",
              "      <td>0.004816</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "0       0.022229      0.001252  ...          0.876828         0.005141\n",
              "1       0.023409      0.002182  ...          0.927975         0.003695\n",
              "2       0.023173      0.002199  ...          0.961379         0.002483\n",
              "3       0.042248      0.001492  ...          0.982081         0.000691\n",
              "4       0.139771      0.004335  ...          0.993737         0.001155\n",
              "5       0.137744      0.005963  ...          0.995651         0.002749\n",
              "6       0.134770      0.005164  ...          0.986951         0.007647\n",
              "7       0.132841      0.001283  ...          0.991128         0.004712\n",
              "8       0.134600      0.004528  ...          0.986433         0.015206\n",
              "9       0.133726      0.001791  ...          0.990953         0.004816\n",
              "\n",
              "[10 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9JVqn1XrbCZ",
        "outputId": "c17b477a-0d9b-46ed-9891-616bd142d905",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "df[df['param_C'] < 0.01]"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_C</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "      <th>split0_train_score</th>\n",
              "      <th>split1_train_score</th>\n",
              "      <th>split2_train_score</th>\n",
              "      <th>split3_train_score</th>\n",
              "      <th>split4_train_score</th>\n",
              "      <th>mean_train_score</th>\n",
              "      <th>std_train_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.022229</td>\n",
              "      <td>0.001252</td>\n",
              "      <td>0.000997</td>\n",
              "      <td>0.000056</td>\n",
              "      <td>1e-06</td>\n",
              "      <td>{'C': 1e-06}</td>\n",
              "      <td>0.843750</td>\n",
              "      <td>0.864583</td>\n",
              "      <td>0.909408</td>\n",
              "      <td>0.839721</td>\n",
              "      <td>0.898955</td>\n",
              "      <td>0.871283</td>\n",
              "      <td>0.028349</td>\n",
              "      <td>10</td>\n",
              "      <td>0.882507</td>\n",
              "      <td>0.879896</td>\n",
              "      <td>0.868696</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.873043</td>\n",
              "      <td>0.876828</td>\n",
              "      <td>0.005141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.023409</td>\n",
              "      <td>0.002182</td>\n",
              "      <td>0.000978</td>\n",
              "      <td>0.000039</td>\n",
              "      <td>1e-05</td>\n",
              "      <td>{'C': 1e-05}</td>\n",
              "      <td>0.906250</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.944251</td>\n",
              "      <td>0.905923</td>\n",
              "      <td>0.937282</td>\n",
              "      <td>0.922075</td>\n",
              "      <td>0.015897</td>\n",
              "      <td>9</td>\n",
              "      <td>0.928634</td>\n",
              "      <td>0.930374</td>\n",
              "      <td>0.922609</td>\n",
              "      <td>0.933043</td>\n",
              "      <td>0.925217</td>\n",
              "      <td>0.927975</td>\n",
              "      <td>0.003695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.023173</td>\n",
              "      <td>0.002199</td>\n",
              "      <td>0.000871</td>\n",
              "      <td>0.000072</td>\n",
              "      <td>0.0001</td>\n",
              "      <td>{'C': 0.0001}</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.940972</td>\n",
              "      <td>0.968641</td>\n",
              "      <td>0.937282</td>\n",
              "      <td>0.968641</td>\n",
              "      <td>0.950607</td>\n",
              "      <td>0.014783</td>\n",
              "      <td>4</td>\n",
              "      <td>0.963446</td>\n",
              "      <td>0.964317</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.961739</td>\n",
              "      <td>0.957391</td>\n",
              "      <td>0.961379</td>\n",
              "      <td>0.002483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.042248</td>\n",
              "      <td>0.001492</td>\n",
              "      <td>0.001241</td>\n",
              "      <td>0.000882</td>\n",
              "      <td>0.001</td>\n",
              "      <td>{'C': 0.001}</td>\n",
              "      <td>0.961806</td>\n",
              "      <td>0.965278</td>\n",
              "      <td>0.968641</td>\n",
              "      <td>0.961672</td>\n",
              "      <td>0.979094</td>\n",
              "      <td>0.967298</td>\n",
              "      <td>0.006432</td>\n",
              "      <td>1</td>\n",
              "      <td>0.982594</td>\n",
              "      <td>0.982594</td>\n",
              "      <td>0.982609</td>\n",
              "      <td>0.980870</td>\n",
              "      <td>0.981739</td>\n",
              "      <td>0.982081</td>\n",
              "      <td>0.000691</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean_fit_time  std_fit_time  ...  mean_train_score  std_train_score\n",
              "0       0.022229      0.001252  ...          0.876828         0.005141\n",
              "1       0.023409      0.002182  ...          0.927975         0.003695\n",
              "2       0.023173      0.002199  ...          0.961379         0.002483\n",
              "3       0.042248      0.001492  ...          0.982081         0.000691\n",
              "\n",
              "[4 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2l8a8v8reS6",
        "outputId": "bb3fd5ad-afbd-4817-da54-a7478d8588b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(df['mean_test_score'], label=\"validation error\")\n",
        "ax.plot(df['mean_train_score'], label=\"training error\")\n",
        "ax.set_xticklabels(df['param_C'])\n",
        "ax.set_xlabel(\"C\")\n",
        "ax.set_ylabel(\"Accuracy\")\n",
        "ax.legend(loc='best');"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s2VS7KwTsa9R"
      },
      "source": [
        "# otros Packages\n",
        "1. [Plotly](https://plotly.com/) y [Seaborn](https://plotly.com/): otras dos bibliotecas de trazado \n",
        "2. [Scipy](https://www.scipy.org/): una biblioteca científica construida sobre Numpy\n",
        "3. [Scrapy](https://www.scipy.org/): una biblioteca de rastreo web \n",
        "4. [pdb](https://docs.python.org/3/library/pdb.html): un depurador para Python (no específico de ML pero muy útil)\n",
        "5. [tqdm](https://github.com/tqdm/tqdm): una barra de progreso (no específica de ML)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TSOIUI76rg_R"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}