iris_data_multi_logistic_regression.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "H0stwsckI-bv",
        "iKgkFkHhJAOU",
        "6-1ruSoeJCOd",
        "LRld-2IsLmjm",
        "AWbRI02o1DMt",
        "Deqtdn8LTzzK"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn import preprocessing\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import datasets\n",
        "\n",
        "#membaca csv\n",
        "df = pd.read_csv('/content/Iris.csv')\n",
        "\n",
        "#Untuk memberi tampilan yang menarik\n",
        "class color:\n",
        "   PURPLE = '\\033[95m'\n",
        "   CYAN = '\\033[96m'\n",
        "   DARKCYAN = '\\033[36m'\n",
        "   BLUE = '\\033[94m'\n",
        "   GREEN = '\\033[92m'\n",
        "   YELLOW = '\\033[93m'\n",
        "   RED = '\\033[91m'\n",
        "   BOLD = '\\033[1m'\n",
        "   UNDERLINE = '\\033[4m'\n",
        "   GREYBACKBLACK =  '\\033[0;30;47m'\n",
        "   END = '\\033[0m'\n",
        "\n",
        "print(color.BOLD + color.GREYBACKBLACK + \"Berikut adalah dataset yang akan kita gunakan\"+color.END)\n",
        "display(df.head())\n",
        "print(\"\")\n",
        "\n",
        "#mengubah kategori species menjadi sebuah integer\n",
        "df.Species = pd.Categorical(df.Species)\n",
        "df['SpeciesCode'] = df.Species.cat.codes\n",
        "\n",
        "#megambil kolom Panjang Petal, Lebar Petal, dan Species\n",
        "cdf = df[['PetalLengthCm', 'PetalWidthCm', 'SpeciesCode']]\n",
        "\n",
        "#ubah value dari feature/kolom data frame menjadi array 2D untuk selanjutnya kita olah, set sebagai x\n",
        "X = cdf[['PetalLengthCm', 'PetalWidthCm']]\n",
        "Y = cdf['SpeciesCode']\n",
        "\n",
        "\n",
        "#split data\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n",
        "\n",
        "#Mencari Nilai Terbaik\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "grid={'multi_class':['multinomial'],\"C\":np.arange(0.1,1,0.1)}\n",
        "LR=LogisticRegression()\n",
        "LR_cv=GridSearchCV(LR,grid,cv=3,scoring='accuracy').fit(X_train.values, Y_train.values)\n",
        "print(color.BOLD + color.DARKCYAN + \"Akan dipilih parameter terbaik untuk model\"+color.END)\n",
        "print(\"Parameter (C) Terbaik: \",LR_cv.best_params_)\n",
        "print(\"Nilai Rata-Rata (Score) Terbaik: \",LR_cv.best_score_)\n",
        "print(\"Akan dipilih Parameter (C) yaitu 0.8\")\n",
        "print(\"\")\n",
        "\n",
        "MLR = LogisticRegression(C = 0.8, multi_class='multinomial').fit(X_train.values, Y_train.values)\n",
        "#prediksikan nilai y berdasarkan x pada data uji\n",
        "yhat = MLR.predict(X_test.values)\n",
        "\n",
        "from sklearn.metrics import classification_report,accuracy_score\n",
        "print(color.BOLD+color.BLUE +\"Berikut adalah hasil pengukuran klasifikasi\"+color.END)\n",
        "print (classification_report(Y_test, yhat))\n",
        "print(\"Terlihat akurasi yang dihasilkan dengan parameter yang telah dicapai sebelumnya adalah 0.98 (cukup tinggi).\")\n",
        "print(\"\")\n",
        "\n",
        "#Membuat plot berwarna sebagai background dengan contour plot dan meshgrid\n",
        "h=0.01\n",
        "xx, yy = np.meshgrid(np.arange(0, 7.5, h), np.arange(0, 3.5, h)) #sumbu x dan y\n",
        "Z = MLR.predict(np.c_[xx.ravel(), yy.ravel()]) #level dari graf\n",
        "cmap = 'Paired' #jenis warna cmap yang digunakan adalah Paired\n",
        "Z = Z.reshape(xx.shape) #mengubah dimensi dari Z\n",
        "cs = plt.contourf(xx, yy, Z, cmap=cmap, alpha=0.2, levels=3) #menampilkan plot\n",
        "proxy = [plt.Rectangle((0,0),1,1,fc = pc.get_facecolor()[0]) for pc in cs.collections] #membuat legend contour plot\n",
        "scat1=plt.scatter(X[Y==0].iloc[:,0], X[Y==0].iloc[:,1], c='brown', marker=11) #scatter plot untuk kategori 1\n",
        "scat2=plt.scatter(X[Y==1].iloc[:,0], X[Y==1].iloc[:,1], c='red', marker='+') #scatter plot untuk kategori 2\n",
        "scat3=plt.scatter(X[Y==2].iloc[:,0], X[Y==2].iloc[:,1], c='green', marker=10) #scatter plot untuk kategori 3\n",
        "\n",
        "#menampilkan legend sebanyak dua bagian yaitu untuk scatter dan contour secara terpisah\n",
        "legend1=plt.legend((scat1,scat2,scat3),[\"Iris-Sentosa\",\"Iris-Versicolor\",\"Iris-Virginica\"],loc='lower right')\n",
        "plt.gca().add_artist(legend1)\n",
        "legend2=plt.legend(proxy, [\"Iris Sentosa (prediction)\", \"Iris Versicolor (prediction)\", \"Iris Virginica (prediction)\"])\n",
        "plt.gca().add_artist(legend2)\n",
        "\n",
        "#pemberian nama sumbu x dan y serta judul graf\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.ylabel(\"Petal Width\")\n",
        "plt.title('Decision Boundaries')\n",
        "\n",
        "#menampilkan plot\n",
        "print(color.BOLD + color.RED + \"Berikut adalah grafik Decision Boundaries dari data yang telah diklasifikasikan\"+color.END)\n",
        "plt.show()\n",
        "\n",
        "print(\"-------------------------------------------------\")\n",
        "print(color.BOLD + color.GREYBACKBLACK + \"Berikut adalah jawaban untuk soal pada tugas\"+color.END)\n",
        "print(\"\")\n",
        "Pdata = [[4,2],[2.4,3],[5.6,3.3]]\n",
        "for i in range(len(Pdata)):\n",
        "  print(color.BOLD + \"Nomor {0}\".format(i+1)+color.END)\n",
        "  print(\"Petal Length = {0} cm dan Petal Width = {1}\".format(Pdata[i][0],Pdata[i][1]))\n",
        "  spec=MLR.predict([Pdata[i]])\n",
        "  if spec==[0]:\n",
        "    specpre=\"Iris-setosa\"\n",
        "  elif spec==[1]:\n",
        "    specpre=\"Iris-versicolor\"\n",
        "  else :\n",
        "    specpre=\"Iris-virginica\"\n",
        "  print(\"Adalah spesies \"+color.BOLD+color.DARKCYAN+\"{0}\".format(specpre)+color.END)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4KTNtpnLJQmN",
        "outputId": "97f81854-3554-4c36-f28b-edb38d2b3398"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m\u001b[0;30;47mBerikut adalah dataset yang akan kita gunakan\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
              "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
              "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
              "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
              "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
              "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-56fc6889-7994-4300-9ea2-efd9dbd3588c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56fc6889-7994-4300-9ea2-efd9dbd3588c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-56fc6889-7994-4300-9ea2-efd9dbd3588c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-56fc6889-7994-4300-9ea2-efd9dbd3588c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\u001b[1m\u001b[36mAkan dipilih parameter terbaik untuk model\u001b[0m\n",
            "Parameter (C) Terbaik:  {'C': 0.8, 'multi_class': 'multinomial'}\n",
            "Nilai Rata-Rata (Score) Terbaik:  0.9714285714285714\n",
            "Akan dipilih Parameter (C) yaitu 0.8\n",
            "\n",
            "\u001b[1m\u001b[94mBerikut adalah hasil pengukuran klasifikasi\u001b[0m\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        14\n",
            "           1       1.00      0.94      0.97        18\n",
            "           2       0.93      1.00      0.96        13\n",
            "\n",
            "    accuracy                           0.98        45\n",
            "   macro avg       0.98      0.98      0.98        45\n",
            "weighted avg       0.98      0.98      0.98        45\n",
            "\n",
            "Terlihat akurasi yang dihasilkan dengan parameter yang telah dicapai sebelumnya adalah 0.98 (cukup tinggi).\n",
            "\n",
            "\u001b[1m\u001b[91mBerikut adalah grafik Decision Boundaries dari data yang telah diklasifikasikan\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3hUxfrA8e+bRmihKFEgYIMQEJIAIRSpIopeBVREFAWRIlZU5HrtiHrVH3blKtgRpYhdsUvvCb03qSJESkILJNn5/bGbJWWzu9lsTd7P8+zj7tk558xucOfMzHveEWMMSimlKq6wQFdAKaVUYGlDoJRSFZw2BEopVcFpQ6CUUhWcNgRKKVXBaUOglFIVnDYEKiSJyI8iMsiNcsdE5EJ/1ClQRGSHiFxWhv3L/XeknIsIdAVU+SUiO4BzgFwgD1gPTAImGmMsZTm2MeZKN8tVK8t5SlLgs+UBOcBCYIQxZrcvzudLvvqOVOjQHoHytWuMMdWB84AXgIeB9wNbJa+5xvYjWhfYD7wZ4PqUiojohaACtCFQfmKMyTTGfAvcCAwSkeYAIlJJRF4SkV0isl9E3hGRyvn7iUhvEVkpIlkisk1Eetq2zxaRobbnjURkjohkisg/IjKtwP5GRBrZntcQkUkikiEiO0XkcREJs713m4jMt9XlsIj8KSLu9jqygRlAswLndXauMSIyuUDZ8231jCjw2Z4RkQUiclREfhGRswuUv9V2zIMi8ljBuohIqogsEpEjIrJPRN4Skagi38fdIrIF2OLgOyrx7yEiZ4vI97ZjHxKRefmfSYU2/SMqvzLGLAX2AJ1sm14A4oFkoBFQH3gSrD9qWIeSRgM1gc7ADgeHfQb4BagFxFHylfmbQA3gQqALMBAYXOD9tsAm4Gzg/4D3RURcfSYRqYK1gVtcinO5crOtfCwQBTxkO1cz4G3gVqAecBbWz5wvD3jA9hnaA92Bu4ocuw/Wz9qM4kr8ewCjsP7t6mAdFnsU0Bw15YA2BCoQ/gJq235khwMPGGMOGWOOAv8F+tvKDQE+MMb8aoyxGGP2GmM2OjheDtahp3rGmGxjzPyiBUQk3HbcR4wxR40xO4CXsf6g5ttpjHnXGJMHfIx1yOccJ5/jaxE5AmQCPYBxpTiXKx8aYzYbY04C07H+MAP0Bb43xsw1xpwCngDs8y3GmHRjzGJjTK7tvBOwNkQFPW/7vk8W3OjG3yMH63dynjEmxxgzz2iysnJBGwIVCPWBQ1ivLKsA6bbhhiPAT7btAA2AbW4c79+AAEtFZJ2I3O6gzNlAJLCzwLadtrrk+zv/iTHmhO2ps4nUPsaYmkA0cA8wR0TOdfNcrvxd4PmJAvWoB9gnpI0xx4GD+a9FJN42fPO3iGRh/SE/m8JKmtB29fcYB2wFfhGR7SLyn1J8HhXEtCFQfiUibbD+IM4H/gFOAhcbY2raHjUKRLHsBi5ydUxjzN/GmGHGmHrAHcD/8se8C/iHMz2HfA2BvWX7RGCMyTPGfIl1WKajG+c6jvUHN9+5pTjdPqwNJGAfljqrwPtvAxuBxsaYGKzDN0WHt0q6inf697D1bkYZYy4EegEPikj3UtRdBSltCJRfiEiMiFwNTAUmG2PW2EJI3wVeFZFYW7n6InKFbbf3gcEi0l1EwmzvJTg49g0ikj9OfhjrD12h8FTbcM904DkRqS4i5wEPApMpI7HqjXWOYoMb51oJdBaRhiJSA3ikFKebAVwtIh1tk8BjKfz/cXUgCzhm+67udPfArv4eInK1bWJesA6H5VHke1ahSRsC5WvfichRrFf3jwGvUHjS9GGsww2LbUMZvwFNwD6xPBh4FesPzxwKX2XnawMsEZFjwLfASGPMdgfl7sV6Nb4da4/kM+CDMn62Y1h/eJ8DBhlj1rk6lzHmV2AasBpIB75394S2499tO94+rA3fngJFHsI60XwU64/6tKLHcKHEvwfQ2Pb6GLAI+J8xZlYpj6+CkOhcj1JKVWzaI1BKqQpOGwKllKrgtCFQSqkKThsCpZSq4EIu6VSt2meZeg0aBroaqgzC8nKpFBFufZF3HCpVDWyFlKoA0tNX/WOMqePovZBrCOo1aMjUmbMDXQ1VBtGZh2hUx3bPWNZSAOSi1ADWSKnyTyR2Z0nv6dCQCqwYawNgti0NcEWUqri0IVCBZ2sMyNgQ2HooVUFpQ6CCQ0wqJuuo9gyUCoCQmyNQ5VhMqnXOIGMD1Gka6NqUysmTp9m+/RB5eZp6RwVWeHgYF15Ym8qVo1wXttGGQAWX7EoYjiIOYxuC1/bthzj77DrUqVOHsDCXa9ko5RMWiyEjI4Pt2zO4+GL3k9rq0JAKLrFJQOhNHuflWbQRUAEXFibUqVOn1D1TbQhU8IlJtfYMQqwx0EZABQNP/h1qQ6CCU4j2DJQKRdoQqOAVwvcYLNtx2KsPd1SpUvKqmi1btna77lOnTqdp04tp0qQpF13UmJdeesXtfQv655+DvPji/3m0b2kMGTKMn3762SfHzv9Od+zYSc+eVzkt+8wzz3L06DH76y5duvHPPwed7FGyO+64k+++c3uZijLThkAFtxBuDIJBTk4uACtWpLtV/tSp09x333388MP3bNq0gbVr13D55T08OvfBg4d47733PdrXXfv3HyAtLY2ePa9wXdgm/zspjfPPP4+ffprptMw770zg2LEzDcGcObM4++yznOxRslGjHvRLI5pPGwIV/GI0/URpzJz5IykpqXTv3oPGjeOBM1e2O3fuIiUllYSEZjRu3ISff/6l0L6ZmZnk5uZyzjnnAFC5cjSJiS0A+OuvfVxxxZU0b55I8+aJ/Prr7wCMGvUQ/frdSGpqO+LiGvLcc8/bto9i9+7dJCQ0Y8SIu7BYDCNG3EXjxk2Ij0/g/fc/cFqnW24ZSPPmiTRqFM8DD4xy+FknT57MZZedWTa5fv0G3HnnXcTHJ5CYmMS6desB6Nu3HwMG3EpiYjJ33XUX69dvoFOnLlx8cQtSUlJZuXIVABs3biY5uRXx8QmMHHm//bibNm2hcWPrQm25uXnccced9s/x3HPP89xzz3PgwAG6dOlK27bt7XXZt+9vAMaMGUvjxk1o3LgJzzzzrP2YF17YiP79b6ZRo3g6duzM8eMnAIiPb8zhw4fZvbvg4nO+ow2BCg35k8d697Fb1q1bx/jx49mxo/CKnR9++BGXXdadjRvXs2HDetq2bVvo/djYOvTo0YMGDRpyzTW9eeedifYIlBEj7mTUqFGsXbuar776kjvuuMO+35YtW5kzZzbLli1l3LhxnDp1mpdffpkGDRqwceN63nnnf3zyySesWbOGDRvW88cfv/P440+wc+euEuv0yisvs3btajZu3MCCBQtYsmRZsc+5cOFCUlLaFNpWo0YNNm/eyB13jOCee+61b//rr79Yvjydd9+dyJAhQ3n77f+xbt0axo0bx4gR1qWd7777boYNG8bmzRupW7euw+/2lVdeZefOnaxfv47NmzcybNhQHnvsEWJjY5kzZzZLliwqVH7+/AV89tlnpKenkZa2jI8/nsTChYsB2LVrFyNHjmTr1s3ExMQwadIn9v1atGjBH3/84bAO3qb3EajQEJsEB1aF5D0GgZCYmEhCQnyx7e3atWX48DvIycmhb98baN++bbEy06ZNYenSZfz000+8/vrr/Pbbb8yYMZ358+ezefNme7ljx46RmZkFwBVXXE7lytFUrnwutWvXZu/evcWOO2/efPr160dERDhxcfVp164dCxYsLLFOH388iQ8//JDc3FwyMjJYs2Y1bdsW/tHfv/8A55wTW2jboEG3ATB06BAee+wx+/brr7+OiIhwMjOzWLFiBX373mB/7/Tp0wCkp6czc+YPANxxxx2MHftMsc/x+++/c+eddxIZaf35jI11/g9y9uzZXHXVVcTEVAfg6qv/xaxZs+jbty/169e3f96WLVvy559/2veLja3Dnj3Fv0df0IZAhY78xmDbUs1W6kKVKlUcbu/Z8wrmzp3Dl19+yeDBg7n33nu5++47i5VLTW1Damobhg0bRqNGjQEwxrB8eTpVqlQuVr5SpUr25+Hh4aUah3dUp+7du/Pmm2+Snp5GnTpn07dvP06ezC62b3R0NNnZhbeLiMPn1apZf4gtFgvVq1dn48b1DusTFua/gZKoqDN3/0ZEhJOdfdL+Ojv7VIl/R2/ToSEVWjSstEw2b95CXFwcDz74AIMGDWT58uWF3s/MzGLmzB/tr5cuXUa9evUA6NSpEy+88IL9vUWLljg9V82aNTh+/Lj9defOnZgxYwa5uXn89dc+lixZQqdOHR3W6ciRI1SuXJnatWuzZ89eZs2a5fAcTZrEs2nT5kLbJk2aBMAHH3xIy5Yti+1Tq1ZN4uLi+PDDjwDr3biLF1v/PbVu3do+wf3uu+85POdll13GO++8Y2/sDhzIAKBq1ar2HlJB3bpdysyZMzl69BhZWUf54YeZdOvWrcTvLd/WrVtJSkp0Wc4btEegQo8tJ1Ew9wzanF8r0FVw6JdffuH1198gIiKCqlWrMnny5ELvG2N48cX/4+6776ZSpWiqVKnCBx9YfxgnTHiHoUOHER+fQF5eHu3atXM4tJTvnHNiSUlJoXHjJnTv3p3//W88CxcuomnTZogIzzwzlgYN4njrrfHF6pSQEE+LFi246KJG1K1bl9atHYe/9urVi7fffocHHhhp33bkyGHi4xOIiopi2rSpDvebMmUKw4YN44UXXiQ3N5frrruWdu1SGT9+PP379+fVV1/lyit7Otz3/vtHsnnzJhISmhIREcFttw3ikUf+w6BBg7jyyp7Exp5TaJ7gkkvac/PNN9OqlfUzDBx4Kx06tGPTpi0lfnenTp1mx44ddOrUqcQy3iTGGL+cyFsuTmppdGGa0FZoYZqyyFqKxFQPigR1q1f/ZY+uUf7VunUbfv75J84++yzq129AWtoy6tZ1P89OMPrkk8mkp6fz2muverT/6tVrSEysV2ibSGy6MSbFUXkdGlKhy5a6WiOJKraXXnqJbdu2uy4YQnJzc3n88cf9dj4dGlKhLT9bKaGXulp5R7duXezP9+7dHcCaeM/gwbf59XzaI1B+l12jNlszjrku6I7YJGtjkHXUO8dTqgLShkCFPo0kUqpMtCFQ5YPmJFLKY9oQqPJDGwOlPKINgSpf8hPUBTiSyLLvb68+3OGNNNQPPTSae+65r9C2RYuWcOGFjdzavyTupHEuSWpqO+bNm1+m82uqaue0IVDljy2sVHsGpU9DPWjQIL7++utC2yZP/oTrr7+uVOcryp00zt6Sm5tX6LWmqnZNGwJVPlXg1NVlSUPdokVzatSowaxZc+zbvvnmWwYNGlRi6uaiKZ5//PEnEhKakZDQjKZNL+bIkUyXaZwBvv32e5o2vZj4+AT69bvRYW6hiRPfIz4+gcaNm3DXXXfbt1epUo3hw0fQpElT/vijcDoKTVXtms8aAhGJFpGlIrJKRNaJyNMOylQSkWkislVElojI+b6qj6qAQnDdY2/xNA01QN++1/Ppp58C8Mcfs6hZsybNm19cYupmKJziedy4l3jzzTfZuHE9ixcvomrVqoWO7yiN84kTJxk+fDjTp09j8+aN5Obm8tJLLxXab8eOnTz11FPMnj2L9evXsXz5CiZPttbz5MmTtGvXlk2bNnD55ZcV2k9TVbvmyx7BKeBSY0wSkAz0FJF2RcoMAQ4bYxoBrwIv+rA+qqKpwGGlztJQf/bZFEaNeohly9KoWbNGsTIDBw7k+++/Jy/Pwqeffkbfvn0LpW5OSGjGnXfeyYEDB+z75Kd4tp6jHaNGjeK5557n4MGD9nTN+X7//XdGjBhRKI3z6tVriIuLo0WL5gDcdtvgYvMCCxcupH37dtSrV5fIyAj69+/PnDnWnkt4eDgDBw50+F24SlW9YsWKYp/D2edNT09n6NAhAIXWZHD1GZ0pmKq6Ro0Ye6pqwC+pqn3WEBir/MGwSNujaGKj3sDHtuczgO5SMG+sUmUVk1ohewau0lDXr1+fwYMHM37828XKXHTRhcTFxfHjjz/xww8/MHDgrYVSN+c/tm/fat8nP8UzwH//+yzvvfcuJ06coGPHTvYhFV+KioqyN0RFlTVVtaPPG8hU1Xl5Z+YvvJWq2qefRkTCRWQlcAD41RhTNG9tfWA3gDEmF8gEis2ciMhwEUkTkbTDBz2bYVcVWAXuGRTlKg11vn79+vHQQw/RoEEDLrzwAqepm4tav34DqalteO65Z0hMTGTt2rWF3neUxjkxsQV79+61j9dPmjSJzp0LZ9685JJLWLx4Cfv2/U1ubh7Tp093K52zpqp2zacNgTEmzxiTDMQBqSLS3MPjTDTGpBhjUmqd5dkMu6rg/HyPQVjdc7368JZffvmFpk2tk7hffPElo0ePdlju1ltvYevWrfTr18++bcqUKXzwwYc0adKUxo3j+eKLGQ73/b//G2efJI2MjOT6668v9P7994+kQYM4EhKa0qRJU95//32qVKnMhAkT6Nv3BuLjEwgLE0aNKrxO8XnnNWTMmDF06dKVpk2bkZycxM033+TyM/fq1cs+hJQvP1X1+PHjefPNNxzuV9LnHT9+PBMmTCA+PoG9ex1P1Dr6jIA9VXX+ZHG+gqmqW7dOsaeqdsabqar9loZaRJ4EThhjXiqw7WdgjDFmkYhEAH8DdYyTSmka6vLBa6moS8tHqas1DXVwq2ipqoMmDbWI1BGRmrbnlYEewMYixb4FBtme9wX+cNYIqPLFa4nnSiM/dbWqUDRVtXO+TENdF/hYRMKxNjjTjTHfi8hYIM0Y8y3wPvCJiGwFDgH9fVgfFUSya9QmOvNQgE5unTwOlkVtlO9pqmrnfNYQGGNWA8VmYYwxTxZ4ng3c4Ks6KOVQbBIcWGVdx8B5VJ9SFYLeWawqJo0kUspOGwJVcWm2UqUAbQhURaeNgVLaECjlk9TVOxd49+EGX6eh9jSVtDuplu+//0G++ea7Uh+7qOPHT9CmTdtiGUi94c03xzNo0GAAxo172eFd2fk2bdrChAnv2l/PmzefwYOHeHTe7OxTpKSkepQR1V3aECgFZ8JKA7yOgbd5Mw11SWmWXf1AuZNq+bXXXqF372vcqqMzb775Jr179yox3YQjnjQao0eP4u677yzx/W3btjJ16lT7606dOvLhh++X+jwA0dGV6Nq1C++//4FH+7tDGwKl8mVXKhf3GPgqDXXBNMtvvjme7t170K5dBzp0uISjR49x1VVXc9FFjbn88p4kJibbk8blp1p2llK5b99+fPSRNe3D3LnzaNmyNU2aNCUxMcmexjolJZVmzZrTrFlzfvvNccbNadOmc8MN/Qp9D127XsoFF1zIgAG3kpdnsX8fBdNWv/32BBITk0hIaMbNN99ibxzeeOMtzj//QhITk1iw4EzPbNSoh3jqqTEArFu3ng4dOtKkSVOaNWvO+vUbeOSRR0lLSyMhoRljxz7DzJk/0q2bNRX2gQMZ9OhxOfHxCSQltWTJkmX2Y/brdyOpqe2Ii2toT89t/X5uYMqUKe7/IyglbQiUyhebVG4S1PkiDXVRa9eu5dtvv2HZsiW89NJL1KxZk23btvDf/z7H+vXrHdbLWUplsA6D3HzzAF5//TU2bdrAnDlzqFq1KvXq1WXu3DmsX7+W6dOncf/99xc7dnb2KXbv3k2TJo3t29asWcPbb7/Nli1b2LFjB598MhkonLa6Tp2z+fzzz1m2bBkbN64nPDycCRMmsnPnLp5//nkWLlzA0qVL2bx5c7FzAtx0083ceecINm3aQFraMho0aMDzz/+XlJQUNm5cz5NPPlGo/L///TBJSUls3ryRZ599hkGDBtnf27JlK3PmzGbZsqWMGzeOU6dOA5CS0prVq1c7PL83aEOgVEHlJKzU22moHenUqZM9vfLChQu5+eabAeuPVnx88XOD85TKAKtXryY2NtaecK5WrZpERkZw+vRpBgy4hfj4BPr2vYGtW7cWO/bff/9N9erVC21r0aIFTZs2ISIinH79+jFv3jygcNrqn376ibVr15KUlExCQjPmz5/H9u3bmDdvnj3tdXR0pWI5kwCOHMlk//793HrrLQBUqVKZ6tWdp05ZsmQJQ4YMBeCaa67myJEjHD58BIArrricypWjqVv3XGrXrs3evdYU0xER4URGRnLkSKbTY3tKGwKliioHqau9nYbakaILzrjDWUplZ/773/8SGxvLhg3rWbNmNbm5xferWrUqp0+fLrStaFb7/NcF01ZbLIZ+/frZ003/+ed2Xn658KI4/lKpUiX78/Dw8ELzLzk5OVSuXNkn59WGQClHyknPoChP01C70r59e/vk6PLlK0ocRnElMTGRAwcOMHeu9cr9yJFMcnJyyczMom7duoSHh/H22++Ql1d8grdOnbPJy8vjxImT9m1r1qxh48bN5OVZmDFjBp06dSy235VX9uS7775j796/AOsY/ubNW+jUqROLFy9h//4DnDp1mq+++qrYvjVr1uDcc88tsFJaNkePHiMmJqbQ2sQFtWvXjg8+sE78zpz5I7Vq1aJWrZpOv5f9+w9Qs2ZNKlWKclrOU77MNaRUaItJhayl1kii0uYkOu8S39SpjH755Rdef/0NIiIiqFq1KpMnT3ZY7tZbb+HRRx/l+eefd/h+UQ89ZJ3ovOiixlx00UU0atSIWrVqlbp+0dGV+OyzT7n33vvIzs4mOjqauXPncP/9I7nuuuuZMmUK3btfWuKVcZcunfn119/sEUjNmzdnxIgR7Ny5gw4dLuGWW24ptk+rVi158skn6N79MiwWC5GRkbz55htcemk3/vOf/9C2bTtiYqpz8cWOs+h/+ulkhg4dxtixzxAZGcmMGTNo06YN4eHhNGnSlAEDbiYl5UzSzxdffIEBAwYQH59A5cqV+eijj1x+Lz/++BM9evRw4xv0jN/SUHuLpqEuPwKWiro0DqyC6FMuE9RV9DTUubl5nD59mipVKrN+/QZ69Licbdu2Eh1dyfXOXrRgwSJeeuklvvrqC2bO/JFx415i1qzf/VoHX7j88p68/PJL9qU8XSltGmrtEaiA2ppxLLgbg/whoqylmqDOiWPHjtGxYydycnIwxvD666/5vREA6wIvaWldfXJDWaBkZ5+id+/ebjcCntAegQqokOgVwJmewUWpDt+u6D0CFVyCZmEapcoVNyaPLZbQuqhS5ZMn/w61IVDKXU4S1IWHh5GRkaGNgQooi8WQkZFBeHjpftp1jkCp0ighkujCC2uzfXsG+/fvD2DllLJelFx4Ye1S7aMNgVKlFZOKyVoKWUvtcwaVK0dx8cWhvRi6qrh0aEgpT/gidbVSAaINgVKeyk9drVSI04ZAqTIqb2kogtGuzD3c/s1IdmXuKdM+nhynItCGQKmyKAcJ6oKdMYbbvr6XD1dOYfDX9+HOvU+O9vHkOBWFzxoCEWkgIrNEZL2IrBORkQ7KdBWRTBFZaXs86av6KOUz5TRBXbCYkP4xs3ZYF4X5Y8d8JqZP8mgfT45TUfjszmIRqQvUNcYsF5HqQDrQxxizvkCZrsBDxpir3T2u3llcvoTMncXuyLI2BCXdfaxKb8eRXVz8v06czMnGYBCEypHRrLtrHufXbOj2PpUiohCE7NxTbh+nvAnIncXGmH3GmOW250eBDUB9X51PqYBzcsOZKj1jDLd/M9L+4w1gMGTnnmLIN/c7HNpxts/J3Gy3j1PR+OU+AhE5H2gJLHHwdnsRWQX8hbV3sM7B/sOB4QB16zfwXUWVKquypK5WheSZPJqeHU+1qKq0qpto356+bzUNY+qTZ/KIkAiX+1iMhUmrpgPCwKQbCJMwl8epaHyedE5EqgFzgOeMMV8WeS8GsBhjjonIVcDrxpjGjo6TT4eGypfozEMA5Wd4CNxOXR3qdmXuYczscYzpOpqGNeK8euxFu5cx6Ot7+LjPW7Rv0Marx66oApZ0TkQigS+AT4s2AgDGmCxjzDHb85lApIic7cs6qeCSXaN0t8KHhNikcn+PgS8jcCwWC1d9dhNbDv3JVZ/djMVi8dqxlWO+jBoS4H1ggzHmlRLKnGsrh4ik2upz0Fd1UsqvynFYqS8jcG756k6OZGcBcCQ7k1u/ustrx1aO+bJHcAlwK3BpgfDQq0RkhIiMsJXpC6y1zRG8AfQ3OnOjyotyGla648guRv3yFIJ1IXhBePCXJ9lxZFeZjz1/12KmrC28NvBna79k/q7FZT62KpkuTKMCrlyFkDpSjsJKjTF0n3Qdc3YuwmLODNmESRhdz+vAbwO/wNbJLzWLxcJZ4+LtvYGCakbX4ODoTYSF6T2wntKlKpUKpHIUSeRJJI+7TltOU796PSqFRZFQ50zMyIaMLdSpejanLaeJDosu82dQxWmPQAVcue8R5CtHPQMVenSpSqWCQTlLXe1OUjdvJX5zZx9vlfGnYKmPDg0p5U+2RW2kTqArUjb54aOzdixg55Hd/DbwC4BC2369dYbLMu7MKTg6V9F9vFXGn4KpPtojUCoAQj2SyJ2kbrd+dZdXEr+5s4+3yvhTMNVH5whUwFWYOYKC8u8+DsH5AneTuuXn9QE8TvzmTtI5b5Xxp0DUR+cIlAo2IXqPQWmSuhXaz4PEb+4knfNWGX8KtvqAzhEoFTi2sFKzbWnI9AzcSeoG8OOWP9iVuYeGNepzZePuAKVO/OZOqCrglTL+TDrnyxBcT+nQkAq4Cjk0VJCGlRbjKqGdLxPeeUuw1VFvKFMqmOXfcKYA19E0wRRtU5JQqGNBOkegVDDIT1BXTu4xKAtX0TTBFG1TklCoY0HaECgVDCpA6mp3uEpo58uEd94SCnUsShsCpYJJOU5d7YqraBqLxRJ00TZFBWNEkDt0jkCpYBKbFHKRRN7iKprmtOV00EXbFBWMEUHu0KghFXAVPmrIkXIcSVQ0msZRdI07ZVwd15+CLULIkTJFDYnIdcCLQCwgtocxxsR4tZZKqTPKUerqgopG07iTj8hRmWDKIxRqEUKOuDNH8H9AL2NMDWNMjDGmujYCytu2ZhwLdBWCT/7kcTmKJPIkH5GjMq6O688onVCLEHLE5dCQiCwwxlzip/q4pABIu8IAACAASURBVEND5ZMOD5UgPydRTPWQ7xk4yq/jTj6iomWCKY9QsOUwcsajXEMicp1tWChNRKaJyE3522zblVK+Vk7CSkuKpilUphQ5i4Ihj1CoRgg54myO4JoCz08Alxd4bYAvfVIjpZRDoRxJVDSaxmIsLvMRQfGcRWESFjR5hEI1QsgRd4aGLjHGLHC1zV90aKh80qEhN/gxdbWnkTyhED1TWp5+pmD7Lsqaa+hNoJUb25RSvuSnewz8ufpYsPM0IijUIomczRG0F5FRQB0RebDAYwwQ7urAItJARGaJyHoRWSciIx2UERF5Q0S2ishqEdHGRSln/LDusT9XHwt2nn6mUPsuShwaEpEuQFdgBPBOgbeOAt8ZY7Y4PbBIXaCuMWa5iFQH0oE+xpj1BcpcBdwLXAW0BV43xrR1dlwdGiqfdGiolHx0w5mjKJjoiEoYDKdyT3t19bFg52lEULBGEnkUNWSMmWOMeRpoZ4x5usDjFVeNgG3/fcaY5bbnR4ENQP0ixXoDk4zVYqCmrQFRSjkT4/2hoZKiYE7mZnsU7ROK0TP5PI0ICtVIohLnCETkO6zRQQ7Htowxvdw9iYicD7QElhR5qz6wu8DrPbZt+4rsPxwYDlC3fgN3T6tU+WZLUOetXkHJq499DhgGJvUDvLP6WLDzNCIoVCOJXA0NAVwHnAtMtr2+CdhvjHnArROIVAPmAM8ZY74s8t73wAvGmPm2178DDxtj0ko6ng4NlU86NOQhP+YkKhoFs2j3MgZ9fQ8f93mL9g3auLVPoAVbffzJo6ghY8wc687ycpGdvxOREn+oC59YIoEvgE+LNgI2e4GCl/hxtm1KKXfEpMKBVX6PJPrlls+56rObOJKdxVWf3czB0ZsICwtzuk+gI2eCrT7BxJ1cQ1VF5ML8FyJyAVDV1U5i/YbfBzYYY14podi3wEBb9FA7INMYs6+EskopR2KTAHy6jkHRKJj2H1zJkewsAI5kZ3LrV3e53CfQkTPBVp9g4s4NZT2BicB2rJlHzwPuMMb87GK/jsA8YA1gsW1+FGgIYIx5x9ZYvAX0xHr38mBnw0KgQ0PllQ4NeYEfI4mKThgDzBv8LR0btitxn0BGzgRbfQLB2dCQW+sRiEglIMH2cqMx5pQX61cq2hCUT9oQeEnWUq8mqDPG0H3SdczZuQiLsTgtWzO6BgdHb0JEHO4TJmF0Pa+D34dkSvoMgapPoHg0RyAilxpj/nCQYO4iEaGEMX+lPJJdozZbM7QxKLOYVEzWUqSOdw5XNAom15LLxPRPyMzO4qwqtWlc+wJEhA0ZW6hT9WxOW04TERYRVJEzoRrJ40/OooaeNsY8JSIfOnjbGGNu923VHNMeQfmlvQIvKUepq5X3eHRDGfCViIgxZrCDR0AaAaWUGzxIXb1o9zLi32zLot3LHL52ZFfmHm7/ZiS7MveUqbqOzuXq2O6c29P6eetzhRJnDcF7wEER+VVEnhaRy22pIpRSIcKdSCKLxcJVn93ElkN/ctVnN5Obm1votcVSfG4gPxTzw5VTGPz1fR7fMVv03BaLxeWx3Tm3p/Xz1ucKNc5STKRgjet/DjgF3AdsFZFVIvI/P9VPKeUpWxoKV43BLV/dWSgUtNFbqX4LDS167lu/usvlsd05d0VJFuct7kYNVQXaAZcAA4EwY8yFzvfyDZ0jKL90jsBHnISVzt+1mE4f9rImk3ESOOOL0FD7uYuICo8iJy/H4bHdOXd5SxbnLZ4uVXmziLwlIvOx3vjVA+s9AR0D1QgopTxQQupqi8XCNROvLd4IOLg2vGbKrfZhG28kVbNYLFwz5RaH753OO+3w2BaLxeW5K1qyOG9xFjM1AdiENQX1XGPMZv9USSnldbawUrLOpKI4bTlN/VNRVLLkkLAvFwuQ3kA4KYboyMqk1E0kLCzMJ6Ghpy2nqV+9HpXCokio0xiw/hgv+2sl4RLOyHbDiAiLKHTs05bTLs8Nni1dWdFDTJ2Fj4YDSUAH26MJ1qygi4BFxpg//FXJgnRoqPzSoaGy23V0D2OWjWNMm9E0rO4gqVpWCTmJuvax/nf2176toAoYT9cjyDPGLDfGvGWMuRnr4jE/AYOBX31TVaWUp4wx3Pb7vXy0cQqD/ygh4sWWulqpgpzNESSKyAgRmSQiW4FlQEes6xU7XUVMKeV/E9Z9zOy/rBEvs/bOZ+J6BxEvJSWom/21695A1z5neg6qXHF2H8FHQDPgR+BSY0xDY0x/Y8zrrhLDKaX8a0fWLh5a+BRim/UVhFELnmRH1q7ihWNStWegCnG2HoEuJK9UCDDGcPsfJUS8zLqf33o5SKoWmwRZS91bxyC/FzBnYeHXOp9QbpTfaXClKog8k0fTWvFUi6xKqzpnIl6WZ6ymQTUnES8xqdZ7DDI2aE6iCk4bAhVUtmYc08ihUooIi2B8lxc92zm7EoajCE4ag/wr/yDqCVTkJSd9QRsCFTSya9QmOvNQoKtRseRPHnsxdbWv6ZKT3udsPYLvcHiPoZUxpvi94UqpwOpju2r/2slVu6Mytsljp6mri/YEAtRDcJQP6I6UQX6tQ3njrEfwkt9qoZQKrNgkOLDKOkwUxD2DHUd2MeqXp+zLZQrCg788yRWNupWLfECB4ixqaI4/K6KUKgPbVb4stEb2GEdX/a7KuBtJFKAoIlf5gHSIyHMu5whEpDHwPNZ7CqLzt2viOaXKIVskkVthpX5W0fMB+ZLLNNS27KNPAa8C12BNMRFmjHnS99UrTnMNlW+ab6iMPJ0jKMpB6uqikTq7rriSMRfsZsyLP2nkTgjwdKnKfJWNMb9jbTR2GmPGAP/yZgWVUkGmSOrqoit3WSwWbmu6hQ/rHqhQK3mVV+40BKdEJAzYIiL3iMi1gMtLNhH5QEQOiMjaEt7vKiKZIrLS9ghID0OpoNWnz5mrd3ctXGh9lPU4jRpBq5vt6x4XjdS59au7mFUry/66oqzkVV650xCMBKpgXaqyNXAL1lXKXPkI6OmizDxjTLLtMdaNYyql/OzPVd/ZI3Xyfbb2y0J5jR785Ul2HHGQ10iFBHdmVs43xiwDjmGdH0BEbgCWONvJGDNXRM4vawWVqnDciQAqKjbWuo/tpbG9pkOHM8dp1MhaJiur5OMWKWNpdRO39z1OdkNjj9TJp5E75Yc7DcEjwOdubPNEexFZBfwFPGSMWeeokIgMB4YD1K3fwAunVUq5I0+g6aFwqrVvSavaF2Fq1eXHLb+zK3MvDWvEcWXjSwmTMI3cCXHOVii7EutiNP2AaQXeigGaGWNcxpbZegTfG2OaO3gvBrAYY46JyFXA68aYxq6OqVFD5ZtGDRXgTk+gqPyewIEDJR/HnePaegZs3Xpmm4NIIhU6PI0a+gtIA7KB9AKPb4ErylopY0yWMeaY7flMIFJEzi7rcZVSPmKLJPLHOga7Mvdw+zcj2ZW5x+fnUu7dRxCJdQipoTFmU6kO7rxHcC6w3xhjRCQVmAGcZ1xUSHsE5Vt+0jntFQSxLBc5icrIGEP3Sdcxa8cCLj2/o847eElZ7yPoCazEul4xIpIsIt+6PqlMwbrQfRMR2SMiQ2xLX46wFekLrLXNEbwB9HfVCKjyL7tG7UBXwTc8CeF0R6NGZ4Zx3D23o31KU7/sStawUts9Bt7mKKmc8i13ZnXGAKnAbABjzEoRucDVTsaYm1y8/xbwlhvnV0oFEx+mrtakcoHhTkOQY4zJLNI10yt3pdzhSSioO4qGgjqa3C167rp1ra/z8s7sc/w4tG3rWf3yU1d7afJYk8oFjjsNwToRuRkItyWguw9Y6GIfpVR5V5p1j92gSeUCx53J4irAY8Dltk0/A88aY7J9XDeHdLK4/CuXIaTe6gkU5agn4OrcTnoPHtVPw0pDgrPJYmcrlEUDI4BGwBqgvTEm1zdVVEqFLFvqajKcrHusgpqzftbHQA4wD7gSaArc749KKRW0PL1yLlreNl7Pvn3W/zq6Si+6zVGZ5kUis92pn6PeQ1l7KjGpmKyl1tBS7RmEHGcNQTNjTAsAEXkf8P1dJEqp0KU9g5DlrCHIyX9ijMnV2XpVoXkr+qdo5E7RZHGNGkFWFsTEnIkIclSmaLSPo4Rya9dC8+bej1hyxtYzCOZ1j1Vxzm4oSxKRLNvjKJCY/1xEsvxVQaVU6PFHGgrlPc4Wrw/3Z0WUCmq2q+gyX1Xb5gRMkTkCR/cBFN3msEyR+jiqn196AgXFpMKBVUG57rFyzJ0UE0opVTr5dx9rzyAk6N0ZSpWGp1fVRa/K27Yt/L6jSJ6iEUHO7hXI56h+Rbf5q4egk8chQ3sESinfiUnFZB3VnkGQ0x6BCjrZNWqzNaOc3F1cNNrIneUi3YlQ8iSKyVd5j1zRnkHQ0x6BUsr3fJy6WpWNy1xDwUZzDVUM5S7fkCfLRXqrjDf28YYDqyD6lEYSBUhZF6ZRSqmyi02yp65WwUXnCFToC9QVbkncqY87dfVWGW/s4y1eTl2tvEN7BEop/4qxNgDaMwge2iNQoStQUTClqU8g8v2EAo0kCiraI1BKBYbtHgMVeNojUKHLW/l/vCWY8v2Eivx1j2Oqa88ggLQhUEoFTn5OIk1dHVA+awhE5APgauCAMaa5g/cFeB24CjgB3GaMWe6r+qhyzNOrbHfW+/VkTWBP8v1oj0EjiQLIl3MEHwE9nbx/JdDY9hgOvO3DuiilgplGEgWUz3oExpi5InK+kyK9gUnGemvzYhGpKSJ1jTH7fFUnpQD7Vb4934+T9YKdlvFGvh93cg9VFLZIIu0Z+F8go4bqA7sLvN5j21aMiAwXkTQRSTt88KBfKqeUCgBbz0BzEvlXSEwWG2MmAhPBmmsowNVRoc7Jil+lKuNJ1FIJ+1TonkBRtnWPydKegb8EskewF2hQ4HWcbZtSqqLTnoFfBbJH8C1wj4hMBdoCmTo/oPzKnRW/PF0VrLT7aE+guOxKGI5qWKkf+KxHICJTgEVAExHZIyJDRGSEiIywFZkJbAe2Au8Cd/mqLkqpEKTrHvuNL6OGbnLxvgHu9tX5lVLlQEwqHFilkUQ+prmGlFLBTXsGPqcNgQpaWzOOBboKKljoDWc+pQ2BCkrZNWoHugoq2Ggkkc9oQ6CUCh2autonQuKGMqVU8MnJy2NvZibZOTn+PbGlHqzeDWEC4ZH+PXcIiI6OJC6uBpGR4W7vow2BUsojezMzqV6zJufVqoU1mbAf5Z4EsSDRVf173iBnjOHgwcPs2XOECy5wf3hVh4aUUh7JzsmhdiAaAYCIygCY7OP+P3cQExHOOqsW2dml66VpQ6CU8lhAGoF84dbegDYGhXnyN9GGQCkVurQx8AptCJRSIat6rVr2xoCc7ELvdejQ2e3jnDhxggEDBtKiRUuaN0+mY8euHDvm2X0sX3/9DevXr/do30DRyWKlVOgLr4rJO45EQm5uLhERESxcONft3V9//U3OOecc1qyZBMCmTZuIjPQsIunrr7/l6quvolmzZh7tHwjaI1BKhbzZc+bQuftV9Lq6F82atgCgWrVaAOzbt4/OnS8lOTmF5s2TmTdvfrH99+37m/r169lfN2nShEqVKgEwefKnpKZ2IDk5hTvuuIu8vDz78R977AmSklrTrl1H9u/fz8KFi/j22+8ZPfoRkpNT2LZtGytXrqRdu44kJrbi2mv7cvjwYQDeeOMtmjVLJDGxFf37DwBg6dJltG/fiZYt29ChQ2c2bdrkuy+tAG0IlFLlwvKVq3ht3ItsWpNWaPtnn03liit6sHJlGqtWpZOcnFRs39tvH8SLL75E+/adePzxJ9myZQsAGzZsYNq0z1mwYA4rV6YRHh7Op59+BsDx48dp164tq1al07lzR9599306dGhPr15XM27c86xcmcZFF13EwIG38+KL/2X16uW0aNGcp59+FoAXXhjHihXLWL16Oe+8Mx6AhIQmzJs3ixUrljF27FM8+ugTvvzK7HRoSCnlU5bcXNJffJET+/dTu2lT+/ZDGzZQ5ZxzaP3ww4RFlP2nKLVNGy5o1BTyjheaPG7TJoXbbx9OTk4Offr0Ijk5udi+ycnJbN++iV9++ZXffvuDNm06sGjRXH7/fRbp6Sto06Y9ACdPniQ21rpAQlRUFFdf/S8AWrduxa+//l7suJmZmRw5kkmXLtb5ikGDbuWGG6yJmRMTWzBgwED69OlFnz697eUHDbqdLVu2IiLk+OlmPW0IlFI+JeHhZP35JwfS0/lrboFxexHOSUlBwt2/A9aZqlVtk8bhVSHvTEPQuXMn5s79nR9++JHbbhvKgw+OpHr16vYr8/fem0BKSmuqVavGddddy3XXXUtYWBgzZ/5EVFQUgwbdwvPPP1fsfJGRkfZQzfDwcHJzc0tV3x9++Ia5c+fx3Xc/8NxzL7BmzQqeeGIM3bp15auvZrBjxw66du3h4bdROjo0pJTyKRGh7dNPE16pEuTHuIsQXqkSbZ9+2jf3IhQIK925cyfnnHMOw4YNYejQ21m+fCXXXtuHlSvTWLkyjZSU1ixYsNA+dn/69GnWr9/Aeec1pHv3bsyY8RUHDhwA4NChQ+zcudPpqatXr87Ro9aIoxo1alCrVk37vMQnn3xKly6dsVgs7N69m27duvLii/8lMzOLY8eOkZmZaZ+r+OijSd7/XkqgDYEKWtk1amsq6nKiWv36tBw1CoyxbjCGVg89RNV69Zzv6AWzf/uNpKTWtGzZhmnTPmfkyHuKldm2bRtdunSnRYuWtGzZhpSUVlx//XU0a9aMZ58dw+WXX0ViYit69LiSffv+dnq+/v37MW7cy7Rs2YZt27bx8cfvM3r0f0hMbMXKlat48snHyMvL45ZbbrOf77777qZmzZr8+98P8cgjj9OyZZtS9zDKQkz+HyZEXJzU0kydOTvQ1VB+Ep15iEZ1qgW6GsqBjfv3k9CkidvljTH8MWwYB9LSOKdNG7pNnOj7O5PzcxKFh0NktG/PFUQ2bNhE06bnFNomEptujElxVF57BEopvxAR2j3zDBf07k3bsWP9k54iorLtHoM8358rhOlkcYgzebn8Nf5VcjIOULnxmauzk1s2EVknlnp3P4CE659ZBYeqdevS7umn/X9iE4bJPq7ZSkugvxChLiycU7t3cnz1Co4uWXBmuwhVk1pBmHciMpQKaRGV7WGl2hgUp0NDIU5EiBv1GBJVOCJDoioRN+rRwGaHVCqYaIK6Evm0IRCRniKySUS2ish/HLx/m4hkiMhK22OoL+tTXkWdW5e6d9xbKCKj7h33EXVO3cBWTKlgo42BQz5rCEQkHBgPXAk0A24SEUdZmKYZY5Jtj/d8VZ/yrva/+liHgoCqya2p/a/eAa6RUkFKG4NifNkjSAW2GmO2G2NOA1MB/XXyEREhbvTj1LriX8Q99JgOCakKoXqtWiW+d0lnJ2moC6SuHjx4KBMmvFvo7a+//oYrr7ymTHVLS0vnvvse8Gjf889vzD///FOm85eGLxuC+sDuAq/32LYVdb2IrBaRGSLSwIf1KfeiYs8lbtRjRMWeG+iqKOXYZZdZHz6UfyPWgrku0lDbwkpvuulGpk6dXuitqVOnc9NN/Up1vqJSUlrzxhuvunWMsjDGYLFYynSMQE8Wfwecb4xJBH4FPnZUSESGi0iaiKQdPnjQrxUMNSYvl71vjGPHE6PZP+k9+2PHE6PZ+8Y4TJ7/7lZUyl9mz5lD527d6H3ttVycmAic6S3s27ePLpdeSsuUFFokJzNvfuE01Jde0paNGzeyb98+wJpV9Lff/qBPn96kpy+nS5futG7dliuu+Je9TNeul3H//aNISWnH66+/yeefz6B582SSklrTufOl1jrNnsPVV/cB4NixYwwePJQWLVqSmNiKL774EoApU6baF8N5+OFHHH62V155jebNk2nePJnXXnsDgB07dtCkycUMHDiY5s2T2b17t8N93eXL8NG9QMEr/DjbNjtjTMFf9feA/3N0IGPMRGAiWO8s9m41yxkNJ1XByNYLENtVusnvFfz2m9dOsXzFCtasWMEFF1xQaPtnU6dyeY8ePPbII+Tl5XHixIkzb4ZXJdyc5Lre1zB9+gxGjryX7777nq5dO1O5cmXuvfd+vvnmC+rUqcO0adN57LEn+eAD6zDS6dOnSUtbDECLFi35+ecfqF+/PkeOHClWt2eeeY4aNWqwZs0KAA4fPsxff/3Fww8/Rnr6YmrVqsXll1/F119/Y89ECpCevpwPP/yYJUsWYIyhbdtL6NKlE7Vq1WLLlq18/PEHtGvXtszfnS97BMuAxiJygYhEAf2BbwsWEJGCYS29gA0+rE+FoOGkqqJKbdOmWCMA0CYlhY8mTWLM2LGsWbOG6tWrFy4QUZmb+l3P1M+mAvnDQjeyadMm1q5dR48eV5KcnMKzzz7Pnj1nrmVvvPEG+/NLLunAbbcN5d1337cvXFPQb7/9wd13j7C/rlWrFsuWpdG1a2fq1KlDREQEAwbcxNy5hXsr8+cv4Npre1O1alVbdtQ+zJtnvcA777zzvNIIgA97BMaYXBG5B/gZCAc+MMasE5GxQJox5lvgPhHpBeQCh4DbfFWfiiQ/nPSvN8ZZN2g4qQo025W/L3oC+expqIvo3KkTc37/nR9+/JHBQ4fywEhrGuqxz1rTUL87YQIdOl7Kvr+HsyptGQsXLmbq1E/ZsmULF1/cjEWL5rk83zvvjGfJkqX88MNMWrduR3r6Yq9/vuLnr+K1Y/l0jsAYM9MYE2+MucgY85xt25O2RgBjzCPGmIuNMUnGmG7GmI2+rE9FUp7CSTUDqSoLexrqIUMYcvvtLF+5kmv79GFFWhor0tJIad0aEaHfDf0YNOQOrry8O9HR0TRp0oSMjH9YtMj6o56Tk8O6descnmPbtm20bZvK2LFjqFPn7GJj9j16dGf8+Hfsrw8fPkxqahvmzJnHP//8Q15eHlOmTKNLl06F9uvUqSNff/0tJ06c4Pjx43z11Td06nSJl7+hwE8WKx8pL+Gk2TVqB7oKypt++80nvQFnZs+ZQ3Lr1rRq04bpn3/OyHuKp6EGuOnGG1m1ei39+10POdlERUUxY8YUHn74UZKSWpOcnMLChY6v9EeP/o990rdDh/YkJRVeDvPxxx/l8OHD9gnlWbNmU7duXV544Vm6detBUlJrWrduSe/evQrt16pVS267bSCpqR1o2/YShg69nZYtW3rniylA01AHCXeSx1lOn2LjgOuwZJ8kovZZth0h958DEBbG2X1vQiIiOLp0MTkZ+4mscw7V27RDwsIKHQcIqUR1moo6OJU2DXXIKAepq0ubhjp4/m+v6NyJ9omKxnLiOFgs5B7YX3j/vDz+mTa58KYjh8nesrH4cUAji5QqSURlyD2JyctDIgNdGf/QoaEg4U60T3h4OA0eGev4ABGRZ/ajyDBQkeNoZJFSLkRUtqeurgi0IQgi7iSPq9nlUqLjEwrtFx3flHp33X9mPww1uhVY9NrBcTRRnVIuRFQGKkZOIm0Igow70T4Xvv4uhNn+dGHhXPj6xGL7xT38lMvjlKfIIqV8ooIkqNOGIMi4E+0THh5OwyeeI6xKFRo+8Szh4eHF9gsLC3N5nPISWaSUT1WAxkCjhoKE5XQ2W+8eQm7mESo1OA8AY8nj5Lo1AITXiUUQ8o5mYbJPQmQUZ1/Xj7DIqJCOCHKHRg0FJ0+ihnZl7mLMvGcY0+kJGtZo6KOa+Uhe6KxupovXh6qIKHIOZpB35DAn1qzkxJqV9kYAIC/jALkZ+62NAEDOaf6ZNpkDkz/g6NKFnNqzyxrtY4s+Orp0IQcmf2B/FCqjVAAYY7jt+yF8tPpjBn8/FG9chHqchrqA24cOZcK7RdJQf/MNV11zDWnp6dz3gC2VdP7kcU62y2N26OD63EOH3sH69evdqqOvaUMQJMLCwjj/6XGl31EjglSImLDiXWbvnAPArJ2zmbjCN+tQuZ2G2qb/jTcybXrhNNTTpk+nf79+pLRuzRuv2lJJR1S2p64uKfV0voULXZ/7vfcm0KyZo7W6/E8bgiBStUVS4WgfoEa3y4tFCUUUXG9AI4JUCNhxZAcP/f5vxBbaLAijfh/NjiM7vHL8sqSh7n7ppWzctKlwGuo//qBP797MnjOHa/pYU0mPGTuWgbfdRsduV3DrzbeQkZFBjx5XcvHFSQwdegfnndfIvphMtWrWc8+ePYeuXS+jb98bSUhozoABA+09oa5dLyMtLR2An376mVatUklKak337lcAsHTpMtq370TLlm3o0KEzmzZt8sp35Yg2BEEm7uGnCKtmHQ8Pq1aduIefLBYl1Pij6RoRpEKGMYbbfxhGdm42BuuPoMGQnZvNkB+Ge2WICKxpqF975RU2FRluyU9DvSItjZXp6SQXSf8QHh7Odddey/QZMwD47vvv6dq5MzExMcXOsX7DBn79+Vc+m/Q+Y554iksv7cq6davo2/c6du3a5bBeK1as5LXXXmb9+tVs3/4nCxYsLPR+RkYGw4bdyRdfTGPVqnQ+/3wKAAkJTZg3bxYrVixj7NinePTRJzz8ZlwLrVnDCiAsLIzzn32ZPeOeIW70E4TZGoCGTzxn3xYREUHc6Mc58Mn7xN46xGlEkLMySvlDnsmj6VkJVIusRqtzz+TJWf73chrENCDP5BEhZf8pcpaGesjw4eTk5NCnVy+Sk5OLlbnpxhsZ/fDDjLz3XqZNn84tAwY4PMc1V19N5crW+wsWLFrMl/db8xb17HkFtUqYr0hNbUNcXBwAyclJ7Nixg44dzySOW7x4CZ07d7TXvXZta36tzMxMBg26nS1btiIi5OTkuPtVlJo2BD7gTt4gk5dbKEooLy+XU+vX2koKUqUy2x++D06dKnTsXU8XXsXo8M8/nHkRFm7tOeTl2oeFal3fn8M/fR/ykUVbM45p5FCIigiLYHzPN31+njKloW7fnn1//82qVatYhsIj4wAACnpJREFUuHgxUz791I1z2HrpOdlOcxJVqlTJ/jw8PJzc3OLrFTjyxBNj6NatK199NYMdO3bQtWsP1zt5KLj+by8v3MkbJGHkHMzAcuwYJ44cLnIAgym4ipK7LHnWRwGHv5ha/NyEVq6h7Bq1ic48FOhqqBC1c+dO4uLiGDZkCKdOnWL5ypW89vLLXGsb+8/Xr29fbhsyhCuvuILoaNfJ5jp06MD0L3/g4Qfv4pefv+Pw4aL/H7unXbu23HXXffz5559ccMEFHDp0iNq1a5OZmUn9+vUA+OijSR4d2106R+AD7kTueBwl5FmFNLJIVVilS0O9mv433ujWcZ96/HF+/e03WrS+hM+//IZzzz2n+OpnbqhTpw4TJ/6P667rR1JSa2680Tos9e9/P8QjjzxOy5ZtXEYplZXeUOZDB7//6swqYUC9+/7NWVcXvgrZ9fxTZM761f5aKkVjTp2JU46IPdeaZtpiKfE8UikaiYrEcvRoicdxdG536hcs9Kay4FNu01C76dSpU4SHhxMREcGiBXO4a+QDrFy1PNDVAvSGsqDiTuRO0Sih+C9+KvR+44+m0/jrwgt5nDejcJn4L34ifur3xbZpZJFSvrNr1y5S27cnuXVrRj70HyaOfz1k01BoQ+BD7uTyyY8Siqofx/nPvkRUVBT17n8YwsOpd//DREREEB0dTcyl1tjimEuvICYmhrP63QLAWf1uISoqisjISGJvvxNEiL39TqKiojTXkFI+1LhxY5YvW8bK9HSWLlpEm7adQjZ1tQ4NqZCgQ0PBZ+P+/TSJj9cLiKLyrA1BoPISGWPYuHGzrlAWbNwJJ3UUrll0v9xT2RyaMRWMBcIjEFtYmjlxHCIiaDr9eyKqFb8JRilfiI6M5ODhw5xVq5Y2BgWFV4W845hs/yepM8Zw8OBhoqNLt7SaNgT+4E44aWn2A8jLxZwoEEmQmwvRVbxfd6VKUL9GDfYeOcI/GRmBrkpwspy2/jcyyq+njY6OJC6uRqn20YbAD/LDNTcPG4A5fcp6s5cb4ZoO9ytB3XtHERGhf07lP5Hh4ZxvuwtWlSBrKXJRaqBr4ZJPJ4tFpKeIbBKRrSLyHwfvVxKRabb3l4jI+b6sTyB5mgiu2H5AWJFY5YjYczn7muu9XmelVBllV8JsWwoZGwJdE6d81hCISDgwHrgSaAbcJCJFc64OAQ4bYxoBrwIv+qo+wcDTcM2i+xUNFW380XRHuymlAi02CWJSMVlHXZcNIF/2CFKBrcaY7caY08BUoOgvX2/gY9vzGUB3KcezTp6GaxbdLzIysliIqVIquJltSwNdhRL5LHxURPoCPY0xQ22vbwXaGmPuKVBmra3MHtvrbbYy/xQ51nBguO1lc2AtoeVs4B+XpYJLqNU51OoLWmd/CLX6gu/qfJ4xpo6jN0LiUtIYMxGYCCAiaSXFwgYrrbPvhVp9QevsD6FWXwhMnX05NLQXaFDgdZxtm8MyIhIB1AAO+rBOSimlivBlQ7AMaCwiF4hIFNAf+LZImW+BQbbnfYE/TKjd6qyUUiHOZ0NDxphcEbkH+BkIBz4wxqwTkbFAmjHmW+B94BMR2QocwtpYuDLRV3X2Ia2z74VafUHr7A+hVl8IQJ1DLteQUkop79Lso0opVcFpQ6CUUhVcSDUErlJWBBsR+UBEDtjulwh6ItJARGaJyHoRWSciIwNdJ1dEJFpElorIKludnw50ndwhIuEiskJEvnddOvBEZIeIrBGRlSKSFuj6uENEaorIDBHZKCIbRKR9oOvkjIg0sX2/+Y8sEbnfL+cOlTkCW8qKzUAPYA/WqKSbjDHrA1oxJ0SkM3AMmGSMaR7o+rgiInWBusaY5SJSHUgH+gT5dyxAVWPMMRGJBOYDI40xiwNcNadE5EEgBYgxxlwd6Pq4IiI7gJSiN3sGMxH5GJhnjHnPFrlYxRhzJND1coft924v1htsd/r6fKHUI3AnZUVQMcbMxRoNFRKMMfuMMcttz48CG4D6ga2Vc8bqmO1lpO0R1Fc3IhIH/At4L9B1Ka9EpAbQGWtkIsaY06HSCNh0B7b5oxGA0GoI6gO7C7zeQ5D/SIUyWybYlsCSwNbENdswy0rgAPCrMSbY6/wa8G/AEuiKlIIBfhGRdFvKl2B3AZABfGgbgntPRAKzZJhn+gNT/HWyUGoIlJ+ISDXgC+B+Y0xWoOvjijEmzxiTjPXu9VQRCdphOBG5GjhgjEkPdF1KqaMxphXWbMJ324Y9g1kE0Ap42xjTEjgOBP28IoBtGKsX8Lm/zhlKDYE7KStUGdnG2b8APjXGfBno+pSGres/C+gZ6Lo4cQnQyzbmPhX4//buJ8SqMozj+PcXE2T/LDAiiBKsFrMaNwaFJGVCbTIkJCiwWtQid7WwKCPBwKBN7kQhSIuUJiJiAjNDmqAoqeyPCiU0QbYIxEAMhqfF+0xdp/tv/jjnnjm/z2bmvvc95324zPDc8573PO/dkt6sNqTeIuK3/PkHMEqZqh1kE8BEy9XhAUpiqIP7gK8j4vRCDVinRNBPyQqbg7zxuhv4MSJeqzqefki6TtI1+fsSymKCn6qNqrOI2BIRN0bEcsrf8KGIeKTisLqSdEUuHiCnV9Yx4BWAI+J34FdJU5uE3wMM7KKHaR5mAaeFoCbVR6FzyYqKw+pK0lvAGmCZpAlga0Tsrjaqru4EHgW+yzl3gOci4sMKY+rlBuCNXGVxCfBORNRiSWaNXA+M5v4ZQ8C+iBirNqS+bAb25hfHn4HHKo6np0y09wJPLui4dVk+amZmF0edpobMzOwicCIwM2s4JwIzs4ZzIjAzazgnAjOzhnMisEVL0mRWcTwmab+ky7v0HZF0fx/nXNOuYmin9rmStF7ScMvrw5JqtRm7DT4nAlvMzkXESFZ+/Rt4qkvfEaBnIqjAemC4Zy+zOXAisKY4AtyST8nuyT0Mjkp6IB84ehnYmFcQGyWtkvR59hlveUK1p3ZjZPsmSe9KGpN0UtKOlmOekHQij9klaaekOyg1Z17NuFZk94ey3wlJq+fvI7Kmqs2TxWazJWmIUr9lDHieUtbh8SxN8QVwEHiRUm//6TzmamB1PtG+FtgObOhzyP+NIelgvjdCqep6Hjgu6XVgEniBUgvnLHAI+CYixiW9D3wQEQcyLoChiFiVU1lbgbWz/nDMcCKwxW1JS6mMI5Q6SuOUom/PZPtlwE1tjl1KKV1xK6UE86UzGHddlzE+jogzAJJ+AG4GlgGfRsSf2b4fuK3L+aeKAX4FLJ9BXGZtORHYYnYuy1P/KwvrbYiI49Pab5927Dbgk4h4MPdmODyDcbuNcb6laZLZ/Q9OnWO2x5tdwPcIrGk+AjZnQkDSymw/C1zV0m8p/5U53zRPY3TyJXCXpGtzGqt1Cmp6XGbzzonAmmYbZZrnW0nf52so+xgMT90sBnYAr0g6ysy/dXcao62s9b+dcr/iM+AUcCbffht4Nm86r2h/BrO5cfVRswEg6cqI+CuvCEYpZdZHq47LmsFXBGaD4aW8sX0M+AV4r+J4rEF8RWBm1nC+IjAzazgnAjOzhnMiMDNrOCcCM7OGcyIwM2u4fwAoCxppswHowQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------------------------------\n",
            "\u001b[1m\u001b[0;30;47mBerikut adalah jawaban untuk soal pada tugas\u001b[0m\n",
            "\n",
            "\u001b[1mNomor 1\u001b[0m\n",
            "Petal Length = 4 cm dan Petal Width = 2\n",
            "Adalah spesies \u001b[1m\u001b[36mIris-versicolor\u001b[0m\n",
            "\u001b[1mNomor 2\u001b[0m\n",
            "Petal Length = 2.4 cm dan Petal Width = 3\n",
            "Adalah spesies \u001b[1m\u001b[36mIris-setosa\u001b[0m\n",
            "\u001b[1mNomor 3\u001b[0m\n",
            "Petal Length = 5.6 cm dan Petal Width = 3.3\n",
            "Adalah spesies \u001b[1m\u001b[36mIris-virginica\u001b[0m\n"
          ]
        }
      ]
    }
  ]
}