3_Analisis_Discriminante_Lineal.ipynb

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        "<a href=\"https://colab.research.google.com/github/XavierCarrera/Tutorial-Machine-Learning-Clasificacion-Lineal/blob/main/3_Analisis_Discriminante_Lineal.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1OPmmZEUlJBi"
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      "source": [
        "# Introducción\n",
        "\n",
        "Como vimos en el apunte pasado, la clasificación logística nos sirve para para clasificar uno a uno los tipos de flores. Sin embargo, podemos utilizar algoritmos cono el análisis discriminante lineal para clasificar de un solo golpe todas nuestras flores."
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "%matplotlib inline\n",
        "plt.rcParams['figure.figsize'] = (20, 10)\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVPEcj_9_Kub"
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      "source": [
        "Para este caso en particular, no utilizaremos la matriz de indicadores para dividir las especies de plantas. Aunque no hay una relación ordinal entre ellas (como ya habíamos discutido antes), utilizaremos etiquetas de 0 a 2 para facilitar el trabajo del algoritmo de *Linear Discriminant Analysis* de *Scikit-Learn*. Para eso usamos el módulo *LabelEncoder*."
      ]
    },
    {
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        "id": "MFrR_1tbkxDr",
        "outputId": "dd3757fa-7cec-44e3-f361-f1f7e6d333f9"
      },
      "source": [
        "df = sns.load_dataset(\"iris\")\n",
        "df"
      ],
      "execution_count": 2,
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
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              "      <td>5.1</td>\n",
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              "      <td>setosa</td>\n",
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              "      <td>4.9</td>\n",
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              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>virginica</td>\n",
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              "    <tr>\n",
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              "      <td>virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
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            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width    species\n",
              "0             5.1          3.5           1.4          0.2     setosa\n",
              "1             4.9          3.0           1.4          0.2     setosa\n",
              "2             4.7          3.2           1.3          0.2     setosa\n",
              "3             4.6          3.1           1.5          0.2     setosa\n",
              "4             5.0          3.6           1.4          0.2     setosa\n",
              "..            ...          ...           ...          ...        ...\n",
              "145           6.7          3.0           5.2          2.3  virginica\n",
              "146           6.3          2.5           5.0          1.9  virginica\n",
              "147           6.5          3.0           5.2          2.0  virginica\n",
              "148           6.2          3.4           5.4          2.3  virginica\n",
              "149           5.9          3.0           5.1          1.8  virginica\n",
              "\n",
              "[150 rows x 5 columns]"
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      "source": [
        "le = LabelEncoder()\n",
        "df[\"species\"] = le.fit_transform(df[\"species\"])\n",
        "df"
      ],
      "execution_count": 3,
      "outputs": [
        {
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              "      <td>2</td>\n",
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              "<p>150 rows × 5 columns</p>\n",
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            "text/plain": [
              "     sepal_length  sepal_width  petal_length  petal_width  species\n",
              "0             5.1          3.5           1.4          0.2        0\n",
              "1             4.9          3.0           1.4          0.2        0\n",
              "2             4.7          3.2           1.3          0.2        0\n",
              "3             4.6          3.1           1.5          0.2        0\n",
              "4             5.0          3.6           1.4          0.2        0\n",
              "..            ...          ...           ...          ...      ...\n",
              "145           6.7          3.0           5.2          2.3        2\n",
              "146           6.3          2.5           5.0          1.9        2\n",
              "147           6.5          3.0           5.2          2.0        2\n",
              "148           6.2          3.4           5.4          2.3        2\n",
              "149           5.9          3.0           5.1          1.8        2\n",
              "\n",
              "[150 rows x 5 columns]"
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          "execution_count": 3
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4pTDTh8rlSiy"
      },
      "source": [
        "# Exploración de Datos\n",
        "\n",
        "Antes de seguir, veamos como se ven nuestros datos en un plano bidimensional. En este, trataremos de ubicar categorias al analizar el tamaño de los sépalos y luego el de los pétalos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "MuSrHcMZlQqr",
        "outputId": "bac13078-a562-4464-9076-6b4ecb728365"
      },
      "source": [
        "sns.scatterplot(data=df, x=\"sepal_length\", y=\"sepal_width\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4809e8ba58>"
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          "metadata": {
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          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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c2gYMrJHlZifwiIhIW604e+zo+rdpSiLvOQ7CMgUAgEEmSAIGV3OhE3hsSFutiKXFggrqg7znOAjLFAAABpggCRhYSX06kmp182vVasTkVEEV9V7ecxyEZQoAAINMkAQMrNVaPSZmj3SCj437+Zyv1QuurHfynuMgLFMAABhkbrYNDKx2msbq/oMxfs9D65deTU6V7gljec9xEJYpAAAMMkESMNDaaRorY5MRY5PrL5Qw8Mh7joOwTAEAYFC5tA0AAACATARJAAAAAGQiSAIAAAAgE0ESAAAAAJkIkgAAAADIRJAEAAAAQCaCJAAAAAAyESQBAAAAkIkgCQAAAIBMBEkAAAAAZCJIAgAAACATQRIAAAAAmQiSAAAAAMhkuOgCoJcqSRIjy82I5kIk9elYrdWjnaZFl7Wt5b1MN8a7+MKzURsdtw4BALYY59ww2ARJlEYlSWL01Ik4e+xopK1WJNVqTMweidX9Bx3Yfkh5L1PrEABga3O+Bri0jdIYWW52DmgREWmrFWePHV3/1xJ+KHkvU+sQAGBrc74GCJIoj+ZC54C2IW21IpYWCyqoBPJeptYhAMDW5nwNBp4gidJI6tORVKubX6tWIyanCqpo+8t7mVqHAABbm/M1QJBEaazW6jExe6RzYNu4Xvt8rV5wZdtX3svUOgQA2NqcrwFJmm7vO6KdPn266BLIoNFoxNzcXN/H6TxBYmkxYnIqznuCxOuW9zLdGG/H6nJcGK1Zh2SW136G8tAzdEvP0K2y9oxz7v4pa8/QP/3qmT179lz2PU9to1TaaRorY5MRY5PrLzigvW55L9ON8Rpv2hfNuTnrEABgi3HODYPNpW0AAAAAZCJIAgAAACATQRIAAAAAmQiSAAAAAMhEkAQAAABAJoIkAAAAADIRJAEAAACQiSAJAAAAgEwESQAAAABkIkgCAAAAIBNBEgAAAACZDOcxyIsvvhh33XVXXLx4MdbW1uLmm2+O9773vZs+85WvfCW+8IUvxPT0dEREvPvd745bbrklj/IAAAAAyCCXIGnHjh1x1113xZVXXhkXL16MI0eOxN/8m38z3va2t2363I//+I/Hr/7qr+ZRErBFDVUqUVs8ExeeeSrq0404N7Ur1trtvo+Xzs9FZWZn38eLiKgkSYwsNyOaC5HUp2O1Vo92mvZ1zDLbWJ4XX3g2aqPjlicAAPRRLkFSkiRx5ZVXRkTE2tparK2tRZIkeQwNbCNDlUqMPfH1WHzgnkhbrUiq1ajfcThWrruxL+FO3uNFrIceo6dOxNljRztjTsweidX9B4UfPwTLEwAA8pXbPZLa7XZ85CMfidtuuy2uu+66eOtb3/qKzzz66KPxz/7ZP4t777035ubm8ioN2CJqi2ei+VKoExGRtlrRfOCeqC2eKcV4EREjy81O6LEx5tljR9e/oUTXLE8AAMhXLt9IioioVCrxiU98IlZWVuK3f/u34/vf/3688Y1v7Lx//fXXxzve8Y7YsWNH/PEf/3Hcf//9cdddd73i5xw/fjyOHz8eERF33313NBqNvKbA6zA8PGxd8ZouPPNUJxDYkLZaEYsL0dh3zbYfLyLi4gvPXnLMHavL0XjTvr6MWWaWJ6+HYxPd0jN0S8/QLT1Dt4romdyCpA1jY2Nx4MCB+OY3v7kpSBofH+/8+ZZbbon/8B/+wyX//0OHDsWhQ4c6f/fNpe2h0WhYV7ym+nQjkmp1UzCQVKsRU9N96Z+8x4uIqI2OX3LMC6O1aNpGumZ58no4NtEtPUO39Azd0jN0q189s2fPnsu+l8ulbWfPno2VlZWIWH+C24kTJ2Lv3r2bPrO4uNj582OPPRZveMMb8igN2ELOTe2K+h2H18OciM49i5andpdivIiI1Vo9JmaPbBpzYvZInK/V+zZmmVmeAACQryRN+3830u9973tx//33R7vdjjRN4+1vf3u85z3viYcffjj27dsXN9xwQ/z+7/9+PPbYYzE0NBS1Wi1uu+22V4RNl3L69Ol+l08PSNbJauMparG4EDE1HctTu3N7alsy0+j7eBEve2rb0mLE5FSc95Sx12Vjee5YXY4LozXLk8wcm+iWnqFbeoZu6Rm6VcQ3knIJkvpJkLQ92CHSLT1Dt/QM3dIzdEvP0C09Q7f0DN0q7aVtAAAAAGx/giQAAAAAMhEkAQAAAJCJIAkAAACATARJAAAAAGQiSAIAAAAgE0ESAAAAAJkIkgAAAADIRJAEAAAAQCaCJAAAAAAyESQBAAAAkIkgCQAAAIBMBEkAAAAAZDJcdAHQS5UkiZHlZkRzIZL6dKzW6tFO06LL6qmhSiVqi2cinZ+LyszOODe1K9ba7aLL6pmN+V145qmoTzf6Pr8ieqbsfVr2HmX729gGL77wbNRGx0u3DdqvAQD9JEiiNCpJEqOnTsTZY0cjbbUiqVZjYvZIrO4/WJqT2aFKJcae+HosPnBPZ471Ow7HynU3luIX9bznV0TPlL1Py96jbH9l3wbt1wCAfnNpG6UxstzsnMRGRKStVpw9dnT9X0hLorZ4Jpov/YIesT7H5gP3RG3xTMGV9Ube8yuiZ8rep2XvUba/sm+D9msAQL8JkiiP5kLnJHZD2mpFLC0WVFDvpfNzl5xjOj9XUEW9lfv8iuiZkvdp2XuUEij5Nmi/BgD0myCJ0kjq05FUq5tfq1YjJqcKqqj3KjM7LznHZKZRUEW9lff8iuiZsvdp2XuU7a/s26D9GgDQb4IkSmO1Vo+J2SOdk9mNezScr9ULrqx3zk3tivodhzfNsX7H4Vie2l1wZb2R9/yK6Jmy92nZe5Ttr+zboP0aANBvSZpu77sgnj59uugSyKDRaMTcXP8vbek8NWZpMWJyKs6X8KkxL38iVjLTiOWp3aW6ifHG/GJxIWJquu/zK6Jnyt6nRfVoXvsZtr+NbXDH6nJcGK2Vbhu0X+sf+xm6pWfolp6hW/3qmT179lz2PU9to1TaaRorY5MRY5PrL5TwJHat3Y6lyZ0RkzvXXyhRiBTx1/Nr7LtmfYfY5/kV0TNl79Oy9yjb38Y22HjTvmjOzZVuG7RfAwD6yaVtAAAAAGQiSAIAAAAgE0ESAAAAAJl0dY+k//2//3d897vfjb/6q7/a9Pr73ve+nhYFAAAAwNaTOUj67Gc/G//zf/7POHDgQFRferwrAAAAAIMjc5D0yCOPxCc+8YloNBr9rAcAAACALSrzPZImJiZibGysn7UAAAAAsIW96jeSzpw50/nzz/7sz8axY8fi53/+52NycnLT53bv3t2f6gAAAADYMl41SJqdnX3Fa48//vgrXnv44Yd7VxEAAAAAW9KrBkkCIgAAAAA2ZL5H0u/8zu9c8vXPfe5zvaoFAAAAgC0sc5D0P/7H/7jk61/96ld7VgwAAAAAW9erXtoWEfHlL385IiLW1tY6f97w/PPPx/j4eH8qAwZSJUliZLkZF194Nmqj47Faq0c7Tfs+XjQXIqlP9308gDIaqlSitngm0vm5qMzsjHNTu2Kt3S66LDKy/gDoxmsGSX/yJ38SEREXL17s/HnD5ORk/ON//I/7UxkwcCpJEqOnTsTZY0cjbbUiqVZjYvZIrO4/2JdwJ+/xAMpoqFKJsSe+HosP3NPZl9bvOBwr190ojNgGrD8AupWkabbflv7Tf/pP8Yu/+Iv9rqdrp0+fLroEMmg0GjE3N1d0GWxxYytLce7wbZG2Wp3Xkmo1xu95KFbGJrf9ePSX/Qzd0jO9Mbn0Qiz+i19/xb506t98KpYmdxZYWe+VsWcGaf0VoYw9Q3/pGbrVr57Zs2fPZd971Xsktdvtzn/vfe97N/395f8B9ERzYdOJbESs/31psRzjAZRQOj93yX1pOu8Xoe3A+gOgW696adsv/dIvZfohDz/8cE+KAQZbUp+OpFp9xb+KxuRUKcYDKKPKzM5L7kuTmUaBVZGV9QdAt141SLrvvvs6f3788cfja1/7Wvz8z/9856tT//W//te46aab+l4kMBhWa/WYmD3yynsW1eoRfbhnUd7jAZTRualdUb/jcDR/4B47y1O7I3xzfcuz/gDoVuZ7JP3Gb/xG3H333TE2NtZ5bXl5OT72sY/FJz/5yb4V+FrcI2l7cK0vWW08RW3H6nJcGK3F+bye2ra0GDE51ffx6B/7GbqlZ3rn5U/9SmYasTy1u5Q3ai5rzwzK+itCWXuG/tEzdKuIeyS95lPbNqyurkar1doUJL344ouxurr6+qoDeJl2msbK2GQ03rQvmnNzff9m0MZ4sXFzbSESQNfW2u31GzNv3JxZCLGtWH8AdCNzkPTOd74z/tW/+lfxMz/zMzEzMxPz8/Pxh3/4h/HOd76zn/UBAAAAsEVkDpI+8IEPxFVXXRV/9md/FouLi1Gv1+Pv/J2/E4cOHepnfQAAAABsEZmDpEqlEu9617viXe96Vz/rAQAAAGCLetUg6atf/Wr85E/+ZEREfPnLX77s537qp36qt1UBAAAAsOW8apD0p3/6p50g6U/+5E8u+zlBEgAAAED5vWqQ9LGPfazz57vuuqvvxQAAAACwdVWyfvBLX/pSfO973+tnLQAAAABsYZlvtv3000/HH/zBH8T58+fjmmuuiWuvvTauvfba+NEf/dFIkqSfNQIAAACwBWQOkv7JP/knERHx/PPPx5NPPhlPPvlk/Jf/8l8iIuJzn/tcX4oDAAAAYOvIHCRFRJw+fTqefPLJOHnyZJw6dSquvvrquPbaa/tVGwAAAABbSOYg6R/+w38YV155Zdx8883xzne+M37t134tRkZG+lkbAAAAAFtI5iDp+uuvj6eeeir+/M//PFZWVmJ5eTmuvfbamJ6e7md9AAAAAGwRmYOk22+/PSIims1mfPvb344nn3wyHnrooRgfH49PfvKTfSsQAAAAgK2hq3skPfPMM517JH3729+OarUab3nLW/pVG31QSZIYWW5GNBciqU/Haq0e7TQtuqyeKWJ+Q5VK1BbPRDo/F5WZnXFualestdt9HbPMNtbhxReejdroeOl6FHilsh+b6L28j72OTb1VxDaf95hl7xn7bRhsmYOkX/mVX4nR0dG45ppr4oYbbohf/uVfjquuuqqftdFjlSSJ0VMn4uyxo5G2WpFUqzExeyRW9x8sxY6/iPkNVSox9sTXY/GBezpj1u84HCvX3ShM+iGUvUeBV7Ld0628j716tLeKWJ55j1n2nin7/IDXlqRptq39+eefj127dr3qZx555JH4iZ/4iZ4UltXp06dzHW87G1tZinOHb4u01eq8llSrMX7PQ7EyNtnXsRuNRszNzfV1jCLmN7n0Qiz+i19/xZhT/+ZTsTS5sy9jllmRPcr2l8d+ht4r+7GJ3sv72OvY1FtFLM+8xyx7z5R9fkVzbKJb/eqZPXv2XPa9StYf8lohUkTEZz7zmaw/jiI0Fzbt8CNi/e9LiwUV1GMFzC+dn7vkmOm8nf8Ppew9CryS7Z4u5X7s1aO9VcTyzHvMsvdM2ecHvKbMQVIWGb/cREGS+nQk1erm16rViMmpgirqrSLmV5nZeckxk5lG38Yss7L3KPBKtnu6lfexV4/2VhHLM+8xy94zZZ8f8Np6GiQlSdLLH0ePrdbqMTF7pLPj37ie+XytXnBlvVHE/M5N7Yr6HYc3jVm/43AsT+3u25hlVvYeBV7Jdk+38j726tHeKmJ55j1m2Xum7PMDXlvmeyRl8cEPfjA+//nP9+rHZeIeSd3pPGFhaTFicirO5/SEhbyu9S1ifi9/ckwy04jlqd1utP06bKzDHavLcWG0lluPsv25p8D2VfZjE72X97HXsam3itjm8x6z7D1T1H57EDg20a0i7pGU+altlEM7TddvgrdxI7yS7fCLmN9au71+c8+NG3wKkV6XjXXYeNO+aM7Nla5HgVcq+7GJ3sv72OvY1FtFbPN5j1n2nrHfhsHW00vbGg33hQEAAAAoq1f9RtKZM2cy/ZDdu9evSb/33ntff0UAAAAAbEmvGiTNzs5m+iEPP/xwT4oBAAAAYOt61SBJQAQAAADAhp7eIwkAAACA8sr81La1tbX4b//tv8WTTz4Z586d2/Tev/yX/7LnhQEAAACwtWT+RtLnP//5OH78eFx77bXx9NNPx0033RRLS9jV/ngAACAASURBVEtx4MCBftYHAAAAwBaROUh69NFH45//838et956awwNDcWtt94aH/nIR+LkyZP9rA8AAACALSJzkPTiiy/GzMxMRERcccUV0Wq1Yu/evfHd7363X7UBAAAAsIVkvkfS3r174//8n/8Tb3nLW+LNb35z/Of//J9jZGQkpqen+1kfAAAAAFtE5m8kfehDH4pKZf3jH/zgB+OZZ56Jb3zjG/Frv/ZrfSsOAAAAgK0j8zeS3vKWt3T+fPXVV8dv/uZv9qUgAAAAALamzEFSRMS3vvWteOSRR2JxcTGmpqbiHe94R1x33XX9qg0AAACALSTzpW1f/OIX49/9u38XtVotfuzHfizGx8fj2LFj8cUvfrGf9UFXKkkSYytLMfZ/n4naylJUkqToknou7zkOVSoxufRCTDz97agvzcVQJfNuAwD6YhCO93karlRiavFMTH7nZEwtPh/DJTzWD8IcAfKS+RtJf/AHfxBHjhyJN77xjZ3XfvInfzL+9b/+1/FzP/dzfSkOulFJkhg9dSLOHjsaaasVSbUaE7NHYnX/wWinadHl9UTecxyqVGLsia/H4gP3dMar33E4Vq67Mdba7Z6PBwCvZRCO93karlRi9MSjMf/gx//6WH/7R2P14E1xsSTH+kGYI0Ceuorir7rqqk1/3717d0+LgddjZLnZOamMiEhbrTh77GiMLDcLrqx38p5jbfFMNF8KkTbGaz5wT9QWz/RlPAB4LYNwvM/T+Pxz0XwpYIl46Vj/4MdjfP65givrnUGYI0CeMgdJv/ALvxAPPvhgPPfcc/Hiiy/G6dOn49Of/nS8973vjXa73fkPCtNc6JwgbEhbrYilxYIK6oOc55jOz11yvHR+ri/jAcBrGoTjfY7alznWt0t0rB+EOQLkKfOlbZ/5zGciIuJP//RPN73+yCOPxL//9/++8/eHH364R6VBd5L6dCTV6qYThaRajZicKrCq3sp7jpWZnZccL5lp9GU8AHgtg3C8z9PljvWVEh3rB2GOAHnKHCTdd999/awDXrfVWj0mZo+88p4JtXpESe6ZkPccz03tivodhzuXt23cI2l5aneEbyACUIBBON7n6dzMVVG//aOdS7827h90bubq0hzrB2GOAHlK0rS7I2673Y6lpaWYmtoa/+pz+vTpoksgg0ajEXNz/f/6cCVJ1u+RsLQYMTkV52v10t14M+85DlUqUVs8E+n8XCQzjVie2p3Ljbbz6hnKQ8/QLT2zfRV1vC9rzwxXKjE+/1y05+eiMtOIczNXl+4m1EXNsaw9Q//oGbrVr57Zs2fPZd/L/I2klZWVeOihh+JrX/taDA8Pxxe+8IV47LHH4jvf+U784i/+Yk8KhdernaaxMjYZMTa5/kLJQqSI/Oe41m7H0uTOiMmdLxVQrhNLALafQTje5+liux2LU7sjpl56kE4Jj/WDMEeAvGS+2fZnPvOZGB0djU996lMxPLyeP73tbW+LP/uzP+tbcQAAAABsHZm/kfTEE0/Epz/96U6IFBExMTERS0tLfSkMAAAAgK0l8zeSRkdH49y5c5tem5ub2zL3SgIAAACgvzIHSbfcckvce++98a1vfSvSNI2/+Iu/iPvvvz9++qd/up/1AQAAALBFZL607e/9vb8XV1xxRXz2s5+NtbW1eOCBB+Knf/qn4+/+3b/bz/oAAAAA2CIyB0knT56MG264IW699dZYXFyM3/u934vvfve7sbS0FPV6vZ81AgAAALAFZL607bOf/WxUKusf/93f/d1YW1uLJEni05/+dN+KAwAAAGDryPyNpIWFhWg0GrG2thbf/OY344EHHojh4eH4R//oH/WzPgAAAAC2iMxB0sjISDSbzfjLv/zL+JEf+ZG48sor4+LFi3Hx4sV+1gcAAADAFpE5SHr3u98dH/vYx+LixYvxoQ99KCIinnrqqdi7d2+/agMAAABgC8kcJP39v//348Ybb4xKpRJXXXVVRERMT0/H7bff3rfiAAAAANg6MgdJERF79ux51b9fzosvvhh33XVXXLx4MdbW1uLmm2+O9773vZs+c+HChbjvvvvi6aefjvHx8fjwhz8cu3bt6qY8AAAAAPpo6Ld+67d+q9+DVCqV+Imf+Im49dZb45Zbbon/+B//Y/zIj/xIzMzMdD5z/PjxWF1djd/8zd+MK6+8Mv7oj/4o3v72t7/mzz537lw/S++7SpLE6MpSXPHCc1EdqsTF6kikJRpvw+joaKyuruYwUvkNVSox0Xw+qqe/HyOVJC6M1iJN+7cWi+rR5Lnvx3CkufUovVP2/UzZt8EiFLVM89rP5D0/em+4UonJhf8v1v7fb8doEnFxbDzaJdru7df6J69j06D8TjEI9Azd6lfPjI+PX/a9rr6R9MNKkiSuvPLKiIhYW1uLtbW1SJJk02cee+yx+IVf+IWIiLj55pvjd37ndyJN01d8rkwqSRKjp07E2WNHI221IqlWY2L2SKzuP9iXk5O8x6P3hiqVGHvi67H4wD2ddVi/43CsXHdjrLXbPR9Pj9Ktsq/Dsm+DRSj7Ms17fvTecKUSoycejfkHP/7X6/D2j8bqwZvioh7t2iDs1/LmfI1u6Rler0peA7Xb7fjIRz4St912W1x33XXx1re+ddP7CwsLnW8oDQ0Nxejo6Lb/ttFrGVludjamiIi01Yqzx47GyHKzFOPRe7XFM9F86UQvYn0dNh+4J2qLZ/oynh6lW2Vfh2XfBotQ9mWa9/zovfH556L5UogU8dI6fPDjMT7/XF/GK3uPDsJ+LW/O1+iWnuH1yuUbSRHrl7d94hOfiJWVlfjt3/7t+P73vx9vfOMbu/45x48fj+PHj0dExN133x2NRqPXpebm4gvPdjamDWmrFTtWl6Pxpn3bfryXGx4e3tbraqu48MxTl1yHsbgQjX3X9Hy8QepReqPs+5myb4NFKPsyzXt+9N6F75y85DpsL8xH460Hej5e2Xt0EPZrL5fHscn5WrnoGbpVxO/auQVJG8bGxuLAgQPxzW9+c1OQND09HfPz8zEzMxNra2uxurp6yWvyDh06FIcOHer8fW5uLpe6+6E2Oh5Jtbppo0qq1bgwWotmH+aV93gv12g0tvW62irq041LrsOYmu7L8h2kHqU3yr6fKfs2WISyL9O850fvTc3svOQ6rEzP6NEfwiDs114uj2OT87Vy0TN0q18982oPV8vl0razZ8/GyspKRKw/we3EiROxd+/eTZ+5/vrr4ytf+UpERHzta1+LAwcOlPr+SBERq7V6TMweWT9YR3SuFT1fq5diPHrv3NSuqN9xeNM6rN9xOJandvdlPD1Kt8q+Dsu+DRah7Ms07/nRe+dmror67R/dvA5v/2icm7m6L+OVvUcHYb+WN+drdEvP8HolaQ6PDfne974X999/f7Tb7UjTNN7+9rfHe97znnj44Ydj3759ccMNN8SLL74Y9913XzzzzDNRq9Xiwx/+cOze/doHsNOnT/e7/L6qJMn6taFLixGTU3G+Vu/7U0DyHG+DbyT1zlClErXFM5HOz0Uy04jlqd19vWFrUT26Y3U5LozWcutReqfs+5myb4NFKGqZ5rWfyXt+9N5wpRLj889Fe2E+KtMzcW7m6r7caHtD3tu9/Vr/5HVsGpTfKQaBnqFbRXwjKZcgqZ+2e5A0KARJdEvP0C09Q7f0DN3SM3RLz9AtPUO3SntpGwAAAADbnyAJAAAAgEwESQAAAABkIkgCAAAAIBNBEgAAAACZCJIAAAAAyESQBAAAAEAmgiQAAAAAMhEkAQAAAJCJIAkAAACATARJAAAAAGQiSAIAAAAgE0ESAAAAAJkMF10A+aokSYwsNyOaC5HUp2O1Vo92mhZdFsC2kfd+1H679zaW6cUXno3a6Hjp1mERPTMIc8xT2ecH3bJNwNYiSBoglSSJ0VMn4uyxo5G2WpFUqzExeyRW9x+0IwbIIO/9qP1275V9HRbRM4MwxzyVfX7QLdsEbD0ubRsgI8vNzg44IiJtteLssaPr6T4Arynv/aj9du+VfR0W0TODMMc8lX1+0C3bBGw9gqRB0lzo7IA3pK1WxNJiQQUBbDN570ftt3uv7OuwiJ4ZhDnmqezzg27ZJmDLESQNkKQ+HUm1uvm1ajVicqqgigC2l7z3o/bbvVf2dVhEzwzCHPNU9vlBt2wTsPUIkgbIaq0eE7NHOjvijeuLz9fqBVcGsD3kvR+13+69sq/DInpmEOaYp7LPD7plm4CtJ0nT7X2HstOnTxddwrbSeeLB0mLE5FScz+mJB41GI+bm5vo+DuWhZ+hWXj2T9360qP12mW0s0x2ry3FhtFa6dVhEzwzCHCPKu5+hf5zP9MYgbRN6hm71q2f27Nlz2fc8tW3AtNM0VsYmI8Ym118o6Q4YoF/y3o/ab/fexjJtvGlfNOfmSrcOi+iZQZhjnso+P+iWbQK2Fpe2AQAAAJCJIAkAAACATARJAAAAAGQiSAIAAAAgE0ESAAAAAJkIkgAAAADIRJAEAAAAQCaCJAAAAAAyESQBAAAAkIkgCQAAAIBMBEkAAAAAZCJIAgAAACCT4aILAIDtpJIkMbLcjGguRFKfjtVaPdpp2rfxhiqVqC2eiXR+LiozO+Pc1K5Ya7f7Nl5E/nMsu7zXYRHrr+zbxcb8Lr7wbNRGx20Tr5N9DNuBPu0ty7NcBEkAkFElSWL01Ik4e+xopK1WJNVqTMweidX9B/tyMjRUqcTYE1+PxQfu6YxXv+NwrFx3Y99+ac57jmWX9zosYv2VfbuwTfSW5cl2oE97y/IsH5e2AUBGI8vNzklQRETaasXZY0fX/4WtD2qLZ6L50i/LG+M1H7gnaotn+jJeRP5zLLu812ER66/s24VtorcsT7YDfdpblmf5CJIAIKvmQuckaEPaakUsLfZluHR+7pLjpfNzfRkvInKfY9nlvg6LWH9l3y5sE71lebId6NPesjxLR5AEABkl9elIqtXNr1WrEZNTfRmvMrPzkuMlM42+jBeR/xzLLu91WMT6K/t2YZvoLcuT7UCf9pblWT6CJADIaLVWj4nZI52ToY1r/M/X6n0Z79zUrqjfcXjTePU7Dsfy1O6+jBeR/xzLLu91WMT6K/t2YZvoLcuT7UCf9pblWT5Jmm7vu1udPn266BLIoNFoxNxcHy/FoHT0DN3Kq2c6Tx1ZWoyYnIrzOT6dKplpxPLU7vye2pbTHIuSV8/kvQ6LWH9l3y425rdjdTkujNZKu03kZVD2MRHOZ7azovq0rD0zSNt93vrVM3v27Lnse57aBgBdaKdprIxNRoxNrr/Q55OgtXY7liZ3RkzufKmA/oZIEfnPsezyXodFrL+ybxcb82u8aV805+ZsE6+TfQzbgT7tLcuzXFzaBgAAAEAmgiQAAAAAMhEkAQAAAJCJIAkAAACATARJAAAAAGQiSAIAAAAgE0ESAAAAAJkIkgAAAADIRJAEAAAAQCaCJAAAAAAyESQBAAAAkIkgCQAAAIBMBEkAAAAAZDJcdAEAg6aSJDGy3IxoLkRSn47VWj3aaVp0WWQ0COtvR6US43PPxdrCCzE0vTPONa6OC+120WX1zMY6vPjCs1EbHS/lOsxb3tvFIGyHALBVCZIAclRJkhg9dSLOHjsaaasVSbUaE7NHYnX/Qb8EbQODsP52VCoxcuLRmHvw45051m//aMTBm0oRJg3COsxb3svUOgSAYrm0DSBHI8vNzi8/ERFpqxVnjx1d/5d1trxBWH/jc89F86UQKWJ9js0HPx7jc88VXFlvDMI6zFvey9Q6BIBiCZIA8tRc6PzysyFttSKWFgsqiK4MwPpbW3jhknNcW5grqKIeG4B1mLu8l6l1CACFEiQB5CipT0dSrW5+rVqNmJwqqCK6MQjrb2h65yXnODTdKKii3hqEdZi3vJepdQgAxRIkAeRotVaPidkjnV+CNu7tcb5WL7gyshiE9XeucXXUb//opjnWb/9onGtcXXBlvTEI6zBveS9T6xAAipWk6fa+K+Hp06eLLoEMGo1GzM2V5LIIclHmnuk8bWhpMWJyKs572lBP5NUzg7D+/vqpbXMxNN0o7VPbdqwux4XRWinXYd7y3i6K2g7LfGyiP/QM3dIzdKtfPbNnz57LvuepbQA5a6dprIxNRoxNrr/gF9htZRDW34V2Oxamd0dM715/oUQhUsRfr8PGm/ZFc26ulOswb3lvF4OwHQLAVuXSNgAAAAAyESQBAAAAkIkgCQAAAIBMBEkAAAAAZCJIAgAAACATQRIAAAAAmQiSAAAAAMhEkAQAAABAJoIkAAAAADIRJAEAAACQiSAJAAAAgEwESQAAAABkIkgCAAAAIJPhogsAgNejkiQxstyMiy88G7XR8Vit1aOdpkWX1TMb84vmQiT16VzmV8SYZWZ5AjDoHAvLRZAEwLZVSZIYPXUizh47GmmrFUm1GhOzR2J1/8FSnJwUMb+yL9O8WZ4ADDrHwvJxaRsA29bIcrNzUhIRkbZacfbY0fV/8SqBIuZX9mWaN8sTgEHnWFg+giQAtq/mQuekZEPaakUsLRZUUI8VMb+yL9O8WZ4ADDrHwtIRJAGwbSX16Uiq1c2vVasRk1MFVdRbRcyv7Ms0b5YnAIPOsbB8BEkAbFurtXpMzB7pnJxsXHN/vlYvuLLeKGJ+ZV+mebM8ARh0joXlk6Tp9r671enTp4sugQwajUbMzc0VXQbbiJ4hq42ngOxYXY4Lo7U4X7KngHSecrK0GDE5lcv8ihizCHntZwZleQ4Cxya6pWfoVll7xrGwf/rVM3v27Lnse57aBsC21k7TWBmbjMab9kVzbi6iZCclG/OLscn1F3KYXxFjlpnlCcCgcywsF5e2AQAAAJCJIAkAAACATARJAAAAAGQiSAIAAAAgE0ESAAAAAJkIkgAAAADIRJAEAAAAQCaCJAAAAAAyESQBAAAAkIkgCQAAAIBMBEkAAAAAZDKcxyBzc3Nx//33R7PZjCRJ4tChQ3Hrrbdu+szJkyfj4x//eOzatSsiIm666aZ4z3vek0d5AAAAAGSQS5A0NDQU/+Af/IN485vfHOfPn48777wzDh48GG94wxs2fe6aa66JO++8M4+SgIwqSRIjy82I5kIk9elYrdWjnaZFl9UzZZ/fINhYhxdfeDZqo+N9X4d594weZTsYqlSitngm0vm5qMzsjHNTu2Kt3S66LDKynwGgG7kESVNTUzE1NRURESMjI7F3795YWFh4RZAEbC2VJInRUyfi7LGjkbZakVSrMTF7JFb3HyzFCWbZ5zcI8l6HZR8PfhhDlUqMPfH1WHzgnk6f1u84HCvX3ShM2gbsZwDoVu73SHr++efjmWeeibe85S2veO8v/uIv4iMf+Uj823/7b+Mv//Iv8y4N+AEjy83OiWVERNpqxdljR9f/1bIEyj6/QZD3Oiz7ePDDqC2eieZLIVLEep82H7gnaotnCq6MLOxnAOhWLt9I2vBXf/VXce+998aHPvShGB0d3fTej/7oj8anPvWpuPLKK+Pxxx+PT3ziE3Hs2LFX/Izjx4/H8ePHIyLi7rvvjkajkUvtvD7Dw8PW1TZ08YVnOyeWG9JWK3asLkfjTfv6OnYePVPk/OiNvNdh2ccbNI5NvXHhmacu2aexuBCNfdcUVFV/lLFn7Gf6q4w9Q3/pGbpVRM/kFiRdvHgx7r333vhbf+tvxU033fSK918eLP3Yj/1YfPazn42zZ8/GxMTEps8dOnQoDh061Pn73Nxc/4qmZxqNhnW1DdVGxyOpVjedYCbValwYrUWzz+szj54pcn70Rt7rsOzjDRrHpt6oTzcu2acxNV265VvGnrGf6a8y9gz9pWfoVr96Zs+ePZd9L5dL29I0jQcffDD27t0bP/uzP3vJzzSbzUhfug77O9/5TrTb7RgfH8+jPOAyVmv1mJg9sv4LQUTnvgnna/WCK+uNss9vEOS9Dss+Hvwwzk3tivodhzf1af2Ow7E8tbvgysjCfgaAbiVp2v+76D311FNx5MiReOMb3xhJkkRExC/90i91UrN3vetd8Ud/9Efx3//7f4+hoaG44oor4pd/+Zdj//79r/mzT58+3dfa6Q3J+vbVeZLL0mLE5FScz+lJLnn1TFHzo3c21uGO1eW4MFrr+zrMu2f0aP84NvXOy5/alsw0YnlqdylvtF3WnrGf6Z+y9gz9o2foVhHfSMolSOonQdL2YIdIt/QM3dIzdEvP0C09Q7f0DN3SM3SrtJe2AQAAALD9CZIAAAAAyESQBAAAAEAmgiQAAAAAMhEkAQAAAJCJIAkAAACATARJAAAAAGQiSAIAAAAgE0ESAAAAAJkIkgAAAADIRJAEAAAAQCaCJAAAAAAyESQBAAAAkIkgCXhVlSSJsZWlGPu/z0RtZSkqSVJ0SbDJUKUSk0svxIU/fyTqS3MxVHFoAwCAfhkuugBg66okSYyeOhFnjx2NtNWKpFqNidkjsbr/YLTTtOjyIIYqlRh74uux+MA9nR6t33E4Vq67Mdba7aLLAwCA0vHPtsBljSw3OyFSRETaasXZY0djZLlZcGWwrrZ4JpovhUgR6z3afOCeqC2eKbgyAAAoJ0EScHnNhc4v6BvSVitiabGggmCzdH7ukj2azs8VVBEAwP/f3r0HWV3X/wN/nl3clWVhr5KJV8i7oaZ5yxovaJraH5aONZlOGQ1oRDml40ymVpZ3IzF1mGhsLMua0UbzmkNMmiZKo6SjWFgiGSwLGCyswJ7vH/w4Pza5fNZgz7o8Hn/t+VzOee3w+rz38Dyf9/vA4CZIAjap1NyaUn1972319UlTS5Uqgt5q2nbaaI+W2tqrVBEAAAxugiRgk7oamzNi0uWV/6ivXyNpZWNzlSuDdf7TMjLNEy7p1aPNEy7J8pb3VbkyAAAYnCy2DWxST7mcrn3HZvg109ZNZ2tqSVdjs4W2GTDW9vRkxQePSMv3bk2WdCYtrVne8j4LbQMAwDYiSAI2q6dczophTcmwpnUbhEgMMGt7erKsaae0j9k/HR0diRAJAAC2GVPbAAAAAChEkAQAAABAIYIkAAAAAAoRJAEAAABQiCAJAAAAgEIESQAAAAAUIkgCAAAAoBBBEgAAAACFCJIAAAAAKESQBAAAAEAhgiQAAAAAChEkAQAAAFDIkGoXwOBWUypl6PKlWbNofhobhqersTk95XK1y2IA0zP0lZ6B6lt/HWZpZ0rNra5DABjEBElsMzWlUhpefj5vTbkq5e7ulOrrM2LS5enad6w3l2yUnqGv9AxUn+sQALYvpraxzQxdvrTypjJJyt3deWvKVes+sYSN0DP0lZ6B6nMdAsD2RZDEtrO0s/Kmcr1yd3eybEmVCmLA0zP0lZ6B6nMdAsB2RZDENlNqbk2pvr73tvr6pKmlShUx0OkZ+krPQPW5DgFg+yJIYpvpamzOiEmXV95crl8zYWVjc5UrY6DSM/SVnoHqcx0CwPalVC6/t1dBXLBgQbVLYDPWf4vLDl3Ls7qhMSt9iwtboGfoKz3Du9Xe3p6Ojo5qlzEoVL61bdmSpKll0F6Heoa+0jP0lZ6hr7ZVz+yyyy6b3Odb29imesrlrBjWlPY9xmRpR0cyCN9UsnXpGfpKz0D1rb8OM6xp3QbXIQAMWqa2AQAAAFCIIAkAAACAQgRJAAAAABQiSAIAAACgEEESAAAAAIUIkgAAAAAoRJAEAAAAQCGCJAAAAAAKESQBAAAAUIggCQAAAIBCBEkAAAAAFCJIAgAAAKAQQRIAAAAAhQiSAIDtSm1NTZqWLcrqZ/6Y5mUdqa0ZXG+HakqlDFuxLMPemJfGFctSUypVuyQAYBAZUu0CAAD6S21NTYa98Ocs+fE1KXd3p1Rfn+YJl2TFB4/I2p6eapf3P6spldLw8vN5a8pVld9vxKTL07Xv2PSUy9UuDwAYBAbXR3AAAJvRuOTfWfr/QqQkKXd3Z+mPr0njkn9XubKtY+jypZUQKVn3+7015aoMXb60ypUBAIOFIAkA2G6UF3dUQpbKtu7ulBd3VKmirWxp50Z/vyxbUqWCAIDBRpAEAGw3atp2Sqm+vte2Un19Sm3tVapo6yo1t27090tTS5UqAgAGG0ESALDd+E/LyDRPuKQStqxfI2l5y/uqXNnW0dXYnBGTLu/1+42YdHlWNjZXuTIAYLCw2DYAsN1Y29OTFR88Ii3fuzVZ0pm0tGZ5y/sGxULbSdJTLqdr37EZfs20ddPZmlrS1dhsoW0AYKsRJAEA25W1PT1Z1rRT2sfsn46OjmSQhEjr9ZTLWTGsKRnWtG6DEAkA2IpMbQMAAACgEEESAAAAAIUIkgAAAAAoRJAEAAAAQCGCJAAAAAAKESQBAAAAUIggCQAAAIBCBEkAAAAAFCJIAgAAAKAQQRIAAAAAhQiSAAAAAChEkAQAAABAIYIkAAAAAAoRJAEAAABQiCAJAAAAgEIESQAAAAAUIkgCAAAAoBBBEgAAAACFCJIAAAAAKESQBAAAAEAhgiQAAAAAChEkAQAAAFCIIAkAAACAQgRJAAAAABQiSAIAAACgEEESAAAAAIUIkgAAAAAoRJAEAAAAQCGCJAAAAAAKESQBAAAAUIggCQAAAIBCBEkAAAAAFDKkP16ko6MjU6dOzdKlS1MqlTJu3Lh84hOf6HVMuVzO9OnTM3v27NTX12fixIkZPXp0f5QHAAAAQAH9EiTV1tbm3HPPzejRo7Ny5cpceumlGTt2bHbdddfKMbNnz86bb76ZKVOmZO7cuZk2bVquvvrq/igPGEBqSqUMXb40axbNT2PD8HQ1NqenXK52WQAAAKSfgqSWlpa0tLQkSYYOHZpRo0als7OzV5A0a9asfOxjH0upVMo+++yTFStWZMmSJZXzgMGvplRKw8vP560pV6Xc3Z1SfX1GTLo8XfuOFSYBAAAMAP2+RtLChQszb968fOADH+i1vbOzM+3t7ZXHbW1t6ezs7O/yfXbeDwAADtZJREFUgCoaunxpJURKknJ3d96aclWGLl9a5coAAABI+umOpPVWrVqVG264Ieeff34aGhre1XM89thjeeyxx5IkP/jBD3qFTwxcQ4YM8W/FFq1ZNL8SIq1X7u7ODl3L077HmCpVxXuFcYa+0jP0lZ6hr/QMfaVn6Ktq9Ey/BUlr1qzJDTfckI9+9KM58sgj37G/tbU1HR0dlceLFy9Oa2vrO44bN25cxo0bV3m84TkMXO3t7f6t2KLGhuEp1df3CpNK9fVZ3dCYpfqHLTDO0Fd6hr7SM/SVnqGv9Ax9ta16Zpdddtnkvn6Z2lYul3Pbbbdl1KhROf300zd6zOGHH56ZM2emXC7nlVdeSUNDg/WRYDvT1dicEZMuT6m+PkkqayStbGyucmUAAAAk/XRH0ssvv5yZM2dm9913zze+8Y0kyWc+85lKanbyySfn0EMPzXPPPZdJkyalrq4uEydO7I/SgAGkp1xO175jM/yaadmha3lWNzT61jYAAIABpF+CpP322y+/+tWvNntMqVTKBRdc0B/lAANYT7mcFcOa0r7HmHXT2YRIAAAAA0a/f2sbAAAAAO9NgiQAAAAAChEkAQAAAFCIIAkAAACAQgRJAAAAABQiSAIAAACgEEESAAAAAIUIkgAAAAAoRJAEAAAAQCGCJAAAAAAKESQBAAAAUIggCQAAAIBCBEkAAAAAFCJIAgAAAKAQQRIAAAAAhQiSAAAAAChEkAQAAABAIYIkAAAAAAoRJAEAAABQiCAJAAAAgEJK5XK5XO0iAAAAABj43JFEv7j00kurXQLvMXqGvtIz9JWeoa/0DH2lZ+grPUNfVaNnBEkAAAAAFCJIAgAAAKCQ2iuuuOKKahfB9mH06NHVLoH3GD1DX+kZ+krP0Fd6hr7SM/SVnqGv+rtnLLYNAAAAQCGmtgEAAABQyJBqF8Dg09PTk0svvTStra3vWEF+xowZ+dnPfpbW1tYkySmnnJITTzyxGmUyQFx44YXZcccdU1NTk9ra2vzgBz/otb9cLmf69OmZPXt26uvrM3HiRLf7bue21DN//etfc+2112bkyJFJkiOPPDKf/vSnq1EqA8SKFSty22235fXXX0+pVMqECROyzz77VPYbZ/hvW+oZ4wwbWrBgQW666abK44ULF+bss8/OaaedVtlmnGFDRXrGOMOG7r///jz++OMplUrZbbfdMnHixNTV1VX2r169Orfcckv+/ve/Z/jw4Zk8eXKld7YFQRJb3e9+97uMGjUqK1eu3Oj+Y445Jl/84hf7uSoGsm9/+9sZMWLERvfNnj07b775ZqZMmZK5c+dm2rRpufrqq/u5QgaazfVMkuy///6+PpeK6dOn55BDDsnFF1+cNWvWpLu7u9d+4wz/bUs9kxhn+P922WWXXHfddUnWfaD65S9/OUcccUSvY4wzbKhIzyTGGdbp7OzMgw8+mJtuuil1dXW58cYb8+STT+a4446rHPP4449n2LBh+dGPfpQnnngid911V772ta9ts5pMbWOrWrx4cZ577jl3GbHVzJo1Kx/72MdSKpWyzz77ZMWKFVmyZEm1ywLeI7q6uvLSSy/lhBNOSJIMGTIkw4YN63WMcYYNFekZ2JQXXnghO++8c3baaade240zbMqmegY21NPTk7fffjtr167N22+/nZaWll77Z82aVQmWjjrqqMyZMyfbcjlsdySxVf30pz/N5z73uU3ejZQkTz/9dF566aW8//3vz3nnnZf29vZ+rJCB6Hvf+16S5KSTTsq4ceN67evs7OzVI21tbens7HzH4Mn2ZXM9kySvvPJKvvGNb6SlpSXnnntudtttt/4ukQFi4cKFGTFiRG699db84x//yOjRo3P++ednxx13rBxjnGFDRXomMc6wcU888UQ+8pGPvGO7cYZN2VTPJMYZ1mltbc0ZZ5yRCRMmpK6uLgcffHAOPvjgXsd0dnamra0tSVJbW5uGhob85z//2ewd/P8LdySx1Tz77LNpamra7Hzvww47LFOnTs3111+fsWPHZurUqf1YIQPRd77znVxzzTW57LLL8vDDD+fFF1+sdkkMcFvqmb322iu33nprrrvuupxyyimVW8fZPq1duzbz5s3LySefnGuvvTb19fW59957q10WA1iRnjHOsDFr1qzJs88+m6OOOqrapfAesbmeMc6w3vLly/PMM89k6tSpuf3227Nq1arMnDmzqjUJkthqXn755cyaNSsXXnhhbr755syZMydTpkzpdczw4cOzww47JElOPPHE/P3vf69GqQwg6xdeb2pqyoc//OG8+uqr79jf0dFRebx48eLKOWyfttQzDQ0NlTsHPvShD2Xt2rV56623+r1OBoa2tra0tbVl7733TrLudu958+b1OsY4w4aK9Ixxho2ZPXt29tprrzQ3N79jn3GGjdlczxhnWO+FF17IyJEjM2LEiAwZMiRHHnlkXnnllV7HtLa2ZvHixUnWfSDS1dWV4cOHb7OaBElsNZ/97Gdz2223ZerUqZk8eXIOOuigTJo0qdcxG84FnzVrVnbdddf+LpMBZNWqVZVpkKtWrcrzzz+f3Xffvdcxhx9+eGbOnJlyuZxXXnklDQ0NbgPfjhXpmaVLl1bmhL/66qvp6enZpn9IGdiam5vT1taWBQsWJFn3Zuy///YYZ9hQkZ4xzrAxm5uiZJxhYzbXM8YZ1mtvb8/cuXPT3d2dcrmcF154IaNGjep1zGGHHZYZM2YkSZ566qkceOCBKZVK26wmaySxzf3yl7/MmDFjcvjhh+fBBx/MrFmzUltbm8bGxkycOLHa5VFFy5Yty/XXX59kXXJ+7LHH5pBDDskjjzySJDn55JNz6KGH5rnnnsukSZNSV1enZ7ZzRXrmqaeeyiOPPJLa2trU1dVl8uTJ2/QPKQPfF77whUyZMiVr1qzJyJEjM3HiROMMm7WlnjHO8N/Wf7gxfvz4yjbjDJuzpZ4xzrDe3nvvnaOOOiqXXHJJamtrs+eee2bcuHG9/p99wgkn5JZbbslXvvKVNDY2ZvLkydu0plJ5Wy7lDQAAAMCgYWobAAAAAIUIkgAAAAAoRJAEAAAAQCGCJAAAAAAKESQBAAAAUIggCQBgKzj77LPz5ptvbvaYqVOn5u677+6ninq78MIL8/zzz1fltQGAwUOQBAAwyFQzsAIABjdBEgAAAACFDKl2AQAA28K9996bBx98MCtXrkxLS0suuOCCHHjggfntb3+b3//+91mxYkUOOuigjB8/Po2NjVm4cGEuuuiijB8/Pvfcc0/K5XJOP/30fPKTn0ySvPrqq5k+fXreeOON1NXV5cgjj8x5552XIUPe/dupZ599NnfffXcWLVqUXXfdNV/60peyxx57JFk3Fe3jH/94Zs6cmUWLFuWQQw7JhRdemLq6uiTJfffdlwceeCClUilnn312br/99kyZMiVz5szJH//4xyTJAw88kAMPPDCXXnppkuS1117LnXfeudHnAwAoQpAEAAw6CxYsyMMPP5zvf//7aW1tzcKFC9PT05OHHnoozzzzTK644oqMGDEi06dPz7Rp0zJ58uTKuXPmzMkPf/jDLFy4MFdeeWX23HPPjB07NjU1NTnvvPMyZsyYLF68ON///vfz8MMP57TTTntXNc6bNy8//vGPc8kll2TMmDGZOXNmrr322tx8883ZYYcdkiR/+tOfctlll6Wuri7f+ta3MmPGjJx88sn5y1/+kvvvvz+XX355Ro4cmdtvv73yvOPGjcvLL7+ctra2nHPOOb1ec1PPBwBQlKltAMCgU1NTk9WrV2f+/PlZs2ZNRo4cmZ133jmPPvpozjnnnLS1tWWHHXbIWWedlaeffjpr166tnHvWWWdlxx13zO67757jjz8+TzzxRJJk9OjR2WeffVJbW5uRI0dm3LhxefHFF991jY899ljGjRuXvffeOzU1NTnuuOMyZMiQzJ07t3LMqaeemtbW1jQ2Nuawww7La6+9liR58sknc/zxx2e33XZLfX19zj777EKvuannAwAoyh1JAMCgs/POO+f888/PPffck/nz5+fggw/O5z//+SxatCjXX399SqVS5diamposW7as8ritra3yc3t7e/75z38mWXeX05133pm//e1vefvtt7N27dqMHj36XdfY0dGRP/zhD3nooYcq29asWZPOzs7K4+bm5srPdXV1lX1LlizJmDFjNlrz5mzq+QAAihIkAQCD0rHHHptjjz02XV1dueOOO3LXXXelra0tEyZMyH777feO4xcuXJgkWbx4cUaNGpVkXdjT0tKSJJk2bVr23HPPfPWrX83QoUPzwAMP5KmnnnrX9bW1teXMM8/MmWee2edzW1pasnjx4srjDX9O0isoAwDYmkxtAwAGnQULFmTOnDlZvXp16urqUldXl1KplJNOOqmyuHWSvPXWW3nmmWd6nfub3/wm3d3def311zNjxowcc8wxSZKVK1emoaEhO+64Y95444088sgj/1ONJ554Yh599NHMnTs35XI5q1atynPPPZeVK1du8dyjjz46M2bMyPz589Pd3Z1f//rXvfY3NTXl3//+9/9UHwDAxrgjCQAYdFavXp277rorb7zxRmpra7Pvvvtm/Pjxlald3/3ud7NkyZI0NTXl6KOPzoc//OHKuQcccEAmTZqUnp6enHHGGTn44IOTJOeee27uuOOO3Hfffdlrr71yzDHHZM6cOe+6xjFjxuTLX/5yfvKTn+Rf//pX6urqst9++2X//fff4rmHHnpoTj311Fx55ZWpqanJpz71qcycObPyDXInnHBCbrzxxpx//vk54IAD8s1vfvNd1wkAsKFSuVwuV7sIAIBqW7hwYS666KL84he/SG1tbbXL6ZP58+fn4osvzs9//vP3XO0AwHuLqW0AAO9Bf/7zn7N69eosX748d911Vw477DAhEgCwzZnaBgCwFX3961+vrMG0ofHjx+ejH/3oVnudRx99NFOnTk1NTU0OOOCAXHDBBVvtuQEANsXUNgAAAAAKMbUNAAAAgEIESQAAAAAUIkgCAAAAoBBBEgAAAACFCJIAAAAAKESQBAAAAEAh/we3tBwfjppT/QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "OVTtBLIWlE-i",
        "outputId": "01839fcb-ddc1-4537-d4f9-e20721e4c184"
      },
      "source": [
        "sns.scatterplot(data=df, x=\"petal_length\", y=\"petal_width\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4809e8b080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rP44oLHN_1a3"
      },
      "source": [
        "Como podemos ver, al analizar el tamaño de los pétalos podemos crear categorias lineales más claras. Es decir, podemos ubicar espacialmente diferentes de cateogrias. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YAoc-6swnfy7"
      },
      "source": [
        "# Análisis de Discriminación Lineal\n",
        "\n",
        "Dado el descubrimiento anterior, usaremos los datos de tamaño de pétalos para entrenar un pequeño modelo de análisis de discriminación lineal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cmgasp94oAT-"
      },
      "source": [
        "X = df[[\"petal_length\", \"petal_width\"]]\n",
        "y = df[\"species\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hj-ti2GrDnxK",
        "outputId": "38b99127-2a69-481c-e21e-b6428bed7c71"
      },
      "source": [
        "clf = LinearDiscriminantAnalysis().fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtFjnJD4B2zQ"
      },
      "source": [
        "Como podemos ver, en Anális de Discriminación Lineal es muy poderoso ya que nos ayudó a acertar en un 100% de los casos si la flor pertenecía a una categoría o la otra.\n",
        "\n",
        "Aunque como siempre, debemos ser escépticos de tales niveles de precisión ya que podríamos estar cayendo en [overfitting](https://es.wikipedia.org/wiki/Sobreajuste).\n",
        "\n",
        "Finalmente, podemos visualizar nuestros resultados graficando con el plano anterior. Como podemos ver, esta herramienta ha encontrado el punto de quiebre que clasifica a cada tipo de flor. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "FVWHeknvE3Xs",
        "outputId": "02e3a565-f122-4c3a-adc4-932a44dce60e"
      },
      "source": [
        "y_labels = clf.predict(X)\n",
        "plt.scatter(df[\"petal_length\"], df[\"petal_width\"], c = y_labels, s = 30)\n",
        "plt.xlabel(\"Longitud del Pétalo\")\n",
        "plt.ylabel(\"Anchura del Pétalo\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Anchura del Pétalo')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}