K_neighbors.ipynb

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        "id": "PvGp_baAZqe-"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset=pd.read_csv('/content/iris/Iris.csv')"
      ],
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        "id": "6g_F5t2sa1la"
      },
      "execution_count": 3,
      "outputs": []
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        "dataset.shape"
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              "(150, 6)"
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          "execution_count": 4
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        "dataset.head(5)"
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              "   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"
            ],
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              "      <td>Iris-setosa</td>\n",
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            "text/plain": [
              "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
              "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
              "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
              "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
              "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
              "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
              "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
              "max    150.000000       7.900000      4.400000       6.900000      2.500000"
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              "      <th>mean</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.843333</td>\n",
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              "      <td>3.758667</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>38.250000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
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              "      <th>50%</th>\n",
              "      <td>75.500000</td>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>112.750000</td>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
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              "      <th>max</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.groupby('Species').size()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BgDiCzJPbXDg",
        "outputId": "423eeaea-a3a9-4e42-d439-3f0810bd5679"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Species\n",
              "Iris-setosa        50\n",
              "Iris-versicolor    50\n",
              "Iris-virginica     50\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from os import X_OK\n",
        "feature_columns = ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm','PetalWidthCm']\n",
        "X = dataset[feature_columns].values\n",
        "y = dataset['Species'].values\n"
      ],
      "metadata": {
        "id": "awLfm4oCbtfz"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n",
        "     "
      ],
      "metadata": {
        "id": "sJZsvmh-fsnp"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "# Instantiate learning model (k = 3)\n",
        "classifier = KNeighborsClassifier(n_neighbors=3)\n",
        "\n",
        "# Fitting the model\n",
        "classifier.fit(X_train, y_train)\n",
        "\n",
        "# Predicting the Test set results\n",
        "y_pred = classifier.predict(X_test)"
      ],
      "metadata": {
        "id": "XwGmyrMqgJ0n"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZoBeurzgSoc",
        "outputId": "f0e7a6e2-df84-4f72-fb13-64a831a2a578"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[15,  0,  0],\n",
              "       [ 0, 11,  0],\n",
              "       [ 0,  0, 12]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)*100\n",
        "print('Accuracy of our model is equal ' + str(round(accuracy, 2)) + ' %.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jeIjxQLgXt0",
        "outputId": "e65843f1-a115-4b32-e8ed-ae68b76e77ad"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of our model is equal 100.0 %.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating list of K for KNN\n",
        "k_list = list(range(1,50,2))\n",
        "# creating list of cv scores\n",
        "cv_scores = []\n",
        "\n",
        "# perform 10-fold cross validation\n",
        "for k in k_list:\n",
        "    knn = KNeighborsClassifier(n_neighbors=k)\n",
        "    scores = cross_val_score(knn, X_train, y_train, cv=10, scoring='accuracy')\n",
        "    cv_scores.append(scores.mean())"
      ],
      "metadata": {
        "id": "IlD28k9AgnJ_"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# changing to misclassification error\n",
        "MSE = [1 - x for x in cv_scores]\n",
        "\n",
        "plt.figure()\n",
        "plt.figure(figsize=(15,10))\n",
        "plt.title('The optimal number of neighbors', fontsize=20, fontweight='bold')\n",
        "plt.xlabel('Number of Neighbors K', fontsize=15)\n",
        "plt.ylabel('Misclassification Error', fontsize=15)\n",
        "sns.set_style(\"whitegrid\")\n",
        "plt.plot(k_list, MSE)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "Mj50dA6Sh_sh",
        "outputId": "e131de35-121d-4a82-9d6b-d049561dc566"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 1 Axes>"
            ],
            "image/png": 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LrNqawPpvAFBVFHAAAAAAgEo5cDJT5/IK1cTdRZ1a+ZodBwDqDQo4AAAAAECllKz/1ifcX86s/wYAlUYBBwAAAAColE3n13+LiWT9NwCoCgo4AAAAAMAlWa2GtiSUbMDA+m8AUBUUcAAAAACAS/o1OVNnswvk6eqsbsF+ZscBgHqFAg4AAAAAcEmbz99+2jvMX67O/CkJAFXBT00AAAAAwCWVrP/G7acAUHUUcAAAAACAizIMQ5vizm/AQAEHAFVGAQcAAAAAuKj4lCylnMuTm4uTuoc2NTsOANQ7FHAAAAAAgIsquf20R2hTebg6m5wGAOofCjgAAAAAwEWVbMDA7acAYB8KOAAAAADARW1mAwYAqBYKOAAAAABAhY6eydbxszlycbKod5i/2XEAoF6igAMAAAAAVKhk9lvXYD95ubmYnAYA6icKOAAAAABAhTbFp0qSYiK5/RQA7EUBBwAAAACoEBswAED1UcABAAAAAGw6lZGrhNRsWSxS7zAKOACwFwUcAAAAAMCmTednv3Vu5Ss/T1eT0wBA/UUBBwAAAACwafP59d+iuf0UAKqFAg4AAAAAYNOmuJL13wJNTgIA9RsFHAAAAACgnNRzeTqUfE4SM+AAoLoo4AAAAAAA5WxJSJMktW/howBvN5PTAED9RgEHAAAAAChnE+u/AYDDUMABAAAAAMrZfH4H1GjWfwOAaqOAAwAAAACUkZ5ToP0nMyRJMcyAA4Bqo4ADAAAAAJSx7cgZGYYUHuilFr4eZscBgHqPAg4AAAAAUMam87efxnD7KQA4BAUcAAAAAKCMTXEl679x+ykAOAIFHAAAAACgVFZeofYeT5dEAQcAjkIBBwAAAAAotT0xTYVWQ8FNPRUa4GV2HABoECjgAAAAAAClNsdz+ykAOBoFHAAAAACg1G8bMFDAAYCjUMABAAAAACRJuQVF2nn0rCRmwAGAI1HAAQAAAAAkSbuOnlV+oVVBPu6KCPI2Ow4ANBgUcAAAAAAASb+t/xYTGSCLxWJyGgBoOCjgAAAAAACSWP8NAGoKBRwAAAAAQAVFVm07kiZJiokINDkNADQsFHAAAAAAAO05nq6cgiI19XJVu+Y+ZscBgAaFAg4AAAAAULr+W9/wADk5sf4bADgSBRwAAAAA4LcNGFj/DQAcjgIOAAAAABq5IquhLaUFHOu/AYCjUcABAAAAQCN34GSGMvMK5ePuos6tfc2OAwANDgUcAAAAADRym87PfusT7i9n1n8DAIejgAMAAACARm5zfKokKZr13wCgRlDAAQAAAEAjZhjG7zZgYP03AKgJFHAAAAAA0IgdSj6ntOwCebg6qVuwn9lxAKBBooADAAAAgEasZP233mH+cnPhT0QAqAn8dAUAAACARmxT3Pn138K5/RQAaopdBdzu3bu1d+9eR2cBAAAAANSi36//xgYMAFBz7CrgevTooYcfftjRWQAAAAAAtehIaraSM/Pk5uyknm2amh0HABosuwq4gIAAtWrVytFZAAAAAAC1aFN88e2n3UP95OHqbHIaAGi47Crg+vXrpz179jg6CwAAAACgFpVswBATwfpvAFCT7CrgZs6cqYMHD2r27NmOzgMAAAAAqCWb4lj/DQBqg4s9Tzpw4IAmTJig//u//9OHH36osWPHqk2bNvLw8LA5/o477qhWSAAAAACAYx1Ly9bxszlydrKoV5i/2XEAoEGzq4C76667ZLFYZBiGdu3apV27dslisZQbZxiGLBYLBRwAAAAA1DFbEopnv3UN9pOPu11/GgIAKsmun7JPP/20zcINAAAAAFA/lNx+GsPtpwBQ4+wq4GbNmuXgGAAAAACA2rQ5ngIOAGqLXZswAAAAAADqr+SMXMWlZMlikfqEUcABQE2r9o3+x48f17p163T8+HFJUnBwsAYMGKDg4OBqhwMAAAAAR9oUl6oZX+7Rk2M6aWTnFmbHMc3m8+u/dWzpKz8vV5PTAEDDZ3cBd/r0aU2ZMkVLliyR1Wot8z6LxaJx48Zp7ty5atasWbVDAgAAAEB1GYahWd/sV1xKlmZ9s09DOjSTq3PjvCmI208BoHbZVcClp6dr8ODBOnjwoDw9PTVq1CiFh4fLYrEoISFB3333nT7//HPt3r1bGzdulJ+fn6NzAwAAAECVrDiQrAMnMyRJx9Jy9NWO47q5T6jJqczBBgwAULvsKuBefPFFHTx4UDfffLPNWW4pKSmaOnWqPvvsM7300kt6/vnnHRIWAAAAAOxhGIbmrDwkSQpu6qnjZ3P0xurDurFXiJydLCanq11pWfk6eCpTkhRNAQcAtcKu+dZLlixRaGioPvzwQ5u3mAYFBemDDz5QaGiovvjii2qHBAAAAIDqWHsoRbuOpcvD1Ukf39tPTb1cFZ+Spf/uPmF2tFpXsv5bVHMfBfq4m5wGABoHuwq4I0eOaMCAAXJ1rXixTldXVw0YMECJiYl2hwMAAACA6jIMQ3NWFM9++2N0mNoEeunuARGSpLkrY2W1GmbGq3Ul678x+w0Aao9dBZynp6dSUlIuOS4lJUWenp72XEKSNG/ePIWHh8vDw0MxMTHavHnzRcd//vnn6tixozw8PNStWzctW7aszPtPnTqlu+66S61bt5aXl5dGjx6tQ4cO2Z0PAAAAQN23IS5VW4+kyc3FSfcPiZQk3TkgXE08XHQo+ZyW70syOWHtYgMGAKh9dhVwvXv31po1a7R169YKx2zbtk2rV69Wnz597Ar26aefavr06Zo5c6a2b9+u7t2768orr1RycrLN8evXr9ett96qu+++Wzt27ND111+v66+/Xnv37pVU/K9e119/veLi4vT1119rx44dCgsL08iRI5WVlWVXRgAAAAB139yVsZKk8X1C1cLXQ5Lk6+GqiZeHS5LmrIyVYTSOWXAZuQXadyJdkhQTEWhyGgBoPOwq4KZNm6aCggKNGDFCTz31lA4cOKCcnBzl5OTol19+0cyZMzVy5EgVFRVp2rRpdgV75ZVXdO+992rixInq3Lmz5s+fLy8vLy1YsMDm+Ndee02jR4/WY489pk6dOunvf/+7evXqpblz50qSDh06pI0bN+rNN99U37591aFDB7355pvKycnRxx9/bFdGAAAAAHXbtiNntP5wqlycLHpgaNsy75s4IELebs46cDJDKw7Y/of+hmbbkTRZDSks0Est/TzMjgMAjYZdBdyYMWP03HPPKSsrS88//7y6du0qHx8f+fj4qEuXLnr22WeVmZmpZ599VldddVWVz5+fn69t27Zp5MiRvwV1ctLIkSO1YcMGm8/ZsGFDmfGSdOWVV5aOz8vLkyR5ePz2S8bJyUnu7u76+eefbZ4zLy9PGRkZZd4AAAAA1B+vryie/TauV4iCm5ZdHsff200T+odJkuasPNQoZsFtiju//ls4t58CQG2yq4CTpBkzZmjjxo2aMGGCwsPD5ebmJjc3N4WHh+v222/Xhg0bNGPGDLvOnZKSoqKiIrVo0aLM8RYtWigpyfb6DElJSRcd37FjR7Vp00YzZsxQWlqa8vPz9dJLL+nYsWM6efKkzXO+8MIL8vPzK30LDQ216+MBAAAAUPt2HT2rNb+elrOTRZOHtbU55t5BkfJwddKuY+lae+jS61zXd5vjUyWxAQMA1DYXe560dOlSubq66qqrrtJ7773n6Ew1wtXVVV9++aXuvvtuBQQEyNnZWSNHjtRVV11V4b90zZgxQ9OnTy99nJGRQQkHAAAA1BNzzq/9dl331goL9LY5JsjHXX+MDtOCdfGas+KQBrcLksViqc2YtSYnv0i7jxWv/9YvkvXfAKA22TUD7oYbbtDrr7/u6CylgoKC5OzsrFOnTpU5furUKbVs2dLmc1q2bHnJ8b1799bOnTt19uxZnTx5UsuXL1dqaqoiIyNtntPd3V2+vr5l3gAAAADUfftPZOjHA6dksUiTh0VddOz9QyLl5uKkrUfStCEutZYS1r7tiWkqtBpq5eehEH/PSz8BAOAwdhVwzZo1k7+/v6OzlHJzc1Pv3r21YsWK0mNWq1UrVqxQ//79bT6nf//+ZcZL0g8//GBzvJ+fn5o1a6ZDhw5p69atuu666xz7AQAAAAAw1bxVxbPfxnZrpajmPhcd28LXQ+P7FN/pUrJjakO0Kb54/beYiIAGO8sPAOoquwq4oUOHavPmzTW6SOn06dP1zjvv6L333tOBAwf04IMPKisrSxMnTpQk3XHHHWXWmHvkkUe0fPlyzZ49W7/88otmzZqlrVu3aurUqaVjPv/8c61evVpxcXH6+uuvdcUVV+j666/XqFGjauzjAAAAAFC7YpMztWxv8TrPU4dffPZbifuHRMrFyaL1h1O17ciZmoxnmk1xJeu/cfspANQ2uwq4v//970pJSdG0adOUm5vr6EySpPHjx+vll1/W008/rR49emjnzp1avnx56UYLiYmJZTZPuPzyy/XRRx/p7bffVvfu3bV48WJ99dVX6tq1a+mYkydP6vbbb1fHjh318MMP6/bbb9fHH39cI/kBAAAAmGPuylgZhjSqcwt1bFm5ZWRC/L00rleIpN92Tm1I8gqLtOPoWUlswAAAZrAYdkxje+aZZ/TLL7/o008/VbNmzTRy5Ei1adNGHh4e5S9gseipp55ySFizZWRkyM/PT+np6awHBwAAANRB8SlZGjF7tayG9M3UgeoW4lfp5x5JzdLw2WtUZDX09ZQB6h7atOaC1rItCWd08/wNCvJx05a/jOQWVABwkMp2RXbtgjpr1ixZLBYZhqHk5GR99NFHFY5tSAUcAAAAgLrtzdWxshrSsA7NqlS+SVJYoLeu695aX+44rjkrY/XunX1qKGXt++32U9Z/AwAz2FXALViwgB/aAAAAAOqUo2ey9eX245Kkh0a0s+sck4dFacnO4/rxwCntP5Ghzq0bxp0vv23AwPpvAGAGuwq4G2+8URaLRU2aNHF0HgAAAACwy/w1h1VoNTQwKki92vjbdY6o5j4a262V/rv7pOatitW823o5OGXtKyiyatuRNEms/wYAZrFrEwZ/f392DgUAAABQZySl5+rzrcckVX7n04qUPH/Z3pOKTc6sdjaz7TuRoez8Ivl5uqpDCyZRAIAZ7CrgfH19FRkZ6egsAAAAAGCX+WsOK7/IqujwAPWLrN5tlh1b+mpU5xYyjOIdVeu7zfHF67/1DQ+QkxNLCQGAGewq4Hr27KnDhw87OgsAAAAAVNnpzDx9vDlRkvTQiOrNfivx0PDiNeSW7jqh+JQsh5zTLJviStZ/4/ZTADCLXQXc448/ri1btmjx4sWOzgMAAAAAVfLuT3HKK7SqR2hTDYwKcsg5u4X4aViHZrIaxTur1ldFVkObE84XcJEUcABgFrs2YfD09NQ999yj8ePH6+qrr9Y111yjNm3ayMPDw+b4wYMHVyskAAAAANhyJitfH2w8Ikl6eESULBbH3WI5dXg7rTp4Wl9uP66HhrdTaICXw85dW35JylBmbqG83ZzVuVXD2NEVAOojuwq4oUOHymKxyDAMffPNN/rvf/970fFFRUV2hQMAAACAi1nwc7yy84vUpbWvhnVo7tBz9w7z14CoQK2LTdX8NYf13A3dHHr+2rA5vnj2W+/wALk423UDFADAAewq4O644w6H/ssSAAAAAFRVek6B3lufIEl6aLhjZ7+VeGh4O62LTdXnW4/poeHt1NLP9l0/dVVJAcf6bwBgLrsKuEWLFjk4BgAAAABUzXvrE5SZV6gOLZpoVOeWNXKNfpGBig4P0OaEM5q/5rBmXdulRq5TEwzDoIADgDqCOcgAAAAA6p1zeYVasC5ekjRleJScnGruDp2SnVU/3pyo05l5NXYdRzt8+pxSs/Ll7uKky0Kamh0HABq1Gi3gjh07pt27d9fkJQAAAAA0Qh9sOKKz2QWKDPLW2G6tavRaA6OC1CO0qfIKrXr3p7gavZYjbYwrnv3Wq42/3FyYewEAZqrUT2FnZ2fdfffdNt934403au7cuTbf99RTT6lnz572pwMAAACAC2TnF5YWYZOHRcm5Bme/SZLFYtFDw4tnwX2w8YjOZOXX6PUcpfT200huPwUAs1WqgDMMQ4Zh2HzfV199pe3btzs0FAAAAABU5KNNiUrNyldogKeu69G6Vq45vGNzdWntq+z8Ii34Ob5Wrlkdv1//LZr13wDAdMxDBgAAAFBv5BYU6e2152e/DY2Sq3Pt/Enz+1lw761PUHpOQa1c116JZ7KVlJErV2eLeob6mx0HABo9CjgAAAAA9cbnW48qOTNPrf08NK5XSK1ee1TnlurQooky8wr13vqEWr12VW06P/ute0hTebo5m5wGAEABBwAAAKBeyC+06s3VhyVJDwxtW+sbCzg5WTTl/Cy4BevidS6vsFavXxWb4rj9FADqEgo4AAAAAPXCl9uP6UR6rpo1cdcf+oSakmFst1aKDPLW2ewCfbDhiCkZKmNzQqokKSYy0OQkAACJAg4AAABAPVBYZNUb52e/3T84Uh6u5txW6exk0eRhxbPg3v0pTtn5dW8W3ImzOTp6JkdOFql3GOu/AUBd4FLZgbGxsXr//fer9L7Y2Fj7kwEAAADAeUt3nVDimWwFeLvpjzFtTM1yXY/Wem3Frzp6JkcfbUrUPYMiTc1zoZLdT7sG+8nHvdJ/8gEAalClfxqvW7dO69ats/m+n3/+2eb7DMOQxWKxPx0AAACARq/IamjuquJ/3L9nUIS83MwtlVydnTR5aJRmfLlHb6+N04R+YabNyLOlZAOGGNZ/A4A6o1K/ue68886azgEAAAAANi3bc1Jxp7Pk5+mqO/qHmx1HkjSuV4jmrDikE+m5+nzrUd1eR3JJ0qb44vXfoiNY/w0A6opKFXALFy6s6RwAAAAAUI7VamjuyuLZb5MGRNSZWyrdXJz0wNC2evrrfXpz9WGN79um1ndlteV0Zp7iTmfJYpGiw5kBBwB1hfm/IQAAAACgAj8cOKWDpzLl4+6iuy4PNztOGX/oE6pmTdx1Ij1XX24/ZnYcSdKWhOLbTzu0aCI/L1eT0wAASlDAAQAAAKiTDMPQnJWHJEl3Xh5W5wolD1dn3T+4eAOGN1YfVmGR1eRE0qa44ttPWf8NAOoWCjgAAAAAddLqg6e193iGvNycdffAurXTaIk/xrRRgLebEs9ka+muE2bH+W0DhkjWfwOAuoQCDgAAAECdYxiGXj8/+21CvzAFeLuZnMg2LzcX3TMoQpI0d1WsiqyGaVnOZufr4KlMSVJf1n8DgDqFAg4AAABAnbMuNlU7Es/K3cWptOCqq27vFyY/T1fFnc7Ssj0nTcuxJSFNhiG1beatZk3cTcsBACiPAg4AAABAnVOy9tut0W3UvImHyWkuromHqyYOCJckzV0ZK6tJs+A2xxev/xYdwe2nAFDXUMABAAAAqFM2x5/RpvgzcnN20v1D6ubabxeaeHmEfNxddPBUpr7ff8qUDKXrv7EBAwDUORRwAAAAAOqUktlvN/UJUSs/T5PTVI6fl6vuvDxMkjR31SEZRu3OgjuXV6i9x9MlSdEUcABQ51DAAQAAAKgzdiSm6adDKXJxsujBIW3NjlMldw+MlJebs/Yez9Dqg6dr9dpbE87IakihAZ5q3bR+lJYA0JjYXcDt379fd911lyIjI+Xp6SlnZ2ebby4uLo7MCwAAAKABm7MyVpJ0Q89ghQZ4mZymagK83TShX/EsuNdX1u4suM2lt5+y/hsA1EV2tWMbNmzQyJEjlZOTI0kKCAhQy5YtHRoMAAAAQOOy93i6Vv6SLCeLNHlYlNlx7HLPoAi9tz5BOxLPal1sqga2C6qV65YUcNx+CgB1k10F3IwZM5STk6M//elP+utf/6qAAH7IAwAAAKieuednv13TvbUigrxNTmOf5k08dGt0Gy1an6DXVx6qlQIuJ79Iu46dlcQGDABQV9l1C+rWrVvVo0cPvfLKK5RvAAAAAKrtYFKmlu9LksUiTa2ns99K3D8kUm7OTsW7ucal1vj1dhxNU0GRoZa+HmpTz27bBYDGwq4Czs3NTR07dnR0FgAAAACN1NxVxbPfruraUu1aNDE5TfW08vPUTX1CJP32cdWk399+arFYavx6AICqs6uAGzhwoPbv3+/oLAAAAAAaocOnz+m/u09IkqYOa2dyGsd4cEhbOTtZ9NOhFO1ITKvRa22KO78BQyR3JwFAXWVXAff8888rNjZW8+bNc3QeAAAAAI3MG6sOyzCkkZ2aq3NrX7PjOERogJdu6Bks6bedXWtCfqFV288XfKz/BgB1l12bMGzfvl0TJ07Uww8/rM8++0xXXHGFQkJC5ORku8+74447qhUSAAAAQMOUmJqtr3YelyQ9NLxhzH4rMWVYlL7cfkwrf0nW3uPp6hrs5/Br7D52VnmFVgV6u6ltMx+Hnx8A4Bh2FXB33XWXLBaLDMPQTz/9pJ9++snmWgOGYchisVDAAQAAALDpzTWxKrIaGty+mbqHNjU7jkNFBHnrmu6t9fXOE5q7Mlbzb+/t8GtsYv03AKgX7Crgnn76aX64AwAAAKiW42dztHjbMUnSw8Pr986nFZk6LEpLd53Q8n1JOpiUqQ4tHbvBxO83YAAA1F12FXCzZs1ycAwAAAAAjc1baw6roMhQ/8hA9QlvmAVSuxZNdFXXllq2J0lzV8Vqzq09HXbuwiKrtiac34AhItBh5wUAOJ5dmzAAAAAAQHUkZ+Tqky1HJUkPjWiYs99KlOzs+t/dJ3T49DmHnXf/yQxl5RfJ18PF4TPrAACOZdcMuN87fvy41q1bp+PHixdODQ4O1oABAxQcHFztcAAAAAAaprfXxim/0KreYf7qH9mwZ291bu2rkZ2a68cDyZq3Klav/KGHQ867Ka549lvf8AA5O7FEEADUZXYXcKdPn9aUKVO0ZMkSWa3WMu+zWCwaN26c5s6dq2bNmlU7JAAAAICGI/Vcnv6zKVGS9NDwqEaxvvRDw9vpxwPJ+nrnCf1pRHu1CfSq9jlLNmCIiWyYt+8CQENiVwGXnp6uwYMH6+DBg/L09NSoUaMUHh4ui8WihIQEfffdd/r888+1e/dubdy4UX5+jt9uGwAAAED99O7P8copKNJlIX4a0r5x/IN999CmGty+mdb+elpvronVCzdeVq3zWa2GtiSUbMDQsGcQAkBDYFcB9+KLL+rgwYO6+eabbc5yS0lJ0dSpU/XZZ5/ppZde0vPPP++QsAAAAADqt7PZ+Xp/fYKk4llhjWH2W4mHh0dp7a+ntXjbMU0d3k7BTT3tPtfBU5lKzymQl5uzurb2dWBKAEBNsGsThiVLlig0NFQffvihzVtMg4KC9MEHHyg0NFRffPFFtUMCAAAAaBgWrktQVn6ROrUqXhetMekTHqD+kYEqKDL01prD1TrX5vO3n/YO85eLM3vrAUBdZ9dP6iNHjmjAgAFydXWtcIyrq6sGDBigxMREu8MBAAAAaDgycwu0cF28JGnqsMax9tuFHhpevOPrJ1uOKjkj1+7zbIpPlSTFRLD+GwDUB3YVcJ6enkpJSbnkuJSUFHl62j+tGgAAAEDD8f6GI8rILVRUcx9d1bWl2XFM0b9toHqH+Su/0Kq318bZdQ7DMEpnwMU08B1kAaChsKuA6927t9asWaOtW7dWOGbbtm1avXq1+vTpY3c4AAAAAA1DVl6h3v2puHCaOixKTk6Nb/abJFksltJZcP/ZlKjUc3lVPkdcSpZSzuXL3cVJl4Ww4R0A1Ad2FXDTpk1TQUGBRowYoaeeekoHDhxQTk6OcnJy9Msvv2jmzJkaOXKkioqKNG3aNEdnBgAAAFDP/GfTEaVlFyg80EtXX9bK7DimGtK+mS4L8VNOQZHe/Tm+ys/fFFc8+61nm6Zyd3F2dDwAQA2wq4AbM2aMnnvuOWVlZen5559X165d5ePjIx8fH3Xp0kXPPvusMjMz9eyzz+qqq65ydGYAAAAA9UhuQZHeXltcNE0eFtXoNw0ongXXTpL0/voEnc3Or9LzN59f/y06gttPAaC+sPs334wZM7Rx40ZNmDBB4eHhcnNzk5ubm8LDw3X77bdrw4YNmjFjhiOzAgAAAKiHPtmcqJRzeQpu6qkbegabHadOGNmpuTq2bKKs/CItWJdQ6ecZhqFNJeu/sQEDANQbLtV5cp8+ffTee+85KgsAAACABiavsEjz1xSv/fbg0LZybeSz30qUzIKb8tF2LVoXr3sGRcjXw/WSzzuWlqOT6blycbKoVxv/WkgKAHAEfvsBAAAAqDGLtx1TUkauWvp66OY+IWbHqVOu6tpSUc19lJFbqA82HKnUc0pmv10W4idPN9Z/A4D6ggIOAAAAQI0oKLLqzdWHJUn3D4lkw4ALODlZNHVY8Y6o7/4Up6y8wks+Z1Nc8fpvMZGs/wYA9UmlbkGNjIyUxWLRjz/+qIiICEVGRlb6AhaLRYcPH7Y7IAAAAID66asdx3UsLUdBPm66NbqN2XHqpKsva6VXf/xVCanZ+s+mI7pvcNuLjt+cUDwDLpr13wCgXqlUAZeQkCBJKigoKPMYAAAAAGwpshp64/zst3sHRcrDldlvtrg4O2ny0Cj93xe79fbaeN3RP7zCz1VSeq6OpGbLySL1CWP9NwCoTyp1C6rVapXValX79u3LPK7sGwAAAIDG5b+7Tyg+JUv+Xq6a0C/M7Dh12g29ghXc1FMp5/L0yebECsdtii++/bRLaz81qcSGDQCAuoM14AAAAAA4lNVqaO7KWEnS3QMj5O1eqRtvGi1XZyc9OLT41tP5a+KUV1hkc9zmeG4/BYD6yq4C7plnntHSpUsvOe6bb77RM888Y88lAAAAANRT3+1L0qHkc/L1cNEdl4ebHadeuLlPiFr6eigpI1eLtx2zOaZkB9QYCjgAqHfsKuBmzZqlr7766pLjli5dqr/97W/2XAIAAABAPWQYhuacn/1214AI+XKrZKW4uzjr/iHFm929ufqwCorKLuWTci5PscnnJEl9wyngAKC+qdFbUIuKiuTkxF2uAAAAQGOx4kCy9p/MkLebsyYNCDc7Tr1ya3QbBfm46VhajpbsOF7mfVvOz37r0KKJ/L3dzIgHAKiGGm3H9u3bJ39/ducBAAAAGoPi2W+HJEm39w9XUy+KoqrwcHXWvYOKZ8G9sSpWhb+bBVd6+2kks98AoD6q9GqokyZNKvP4559/LnesRGFhoQ4ePKitW7fq+uuvr1ZAAAAAAPXD2kMp2nUsXR6uTrpnUITZceqlCf3CNH/NYSWkZuvbPSd1XY9gSWzAAAD1XaULuEWLFpX+t8ViUWxsrGJjYy/6nMsuu0z//Oc/7Q4HAAAAoH4wDENzVhTPfrstJkxBPu4mJ6qfvN1ddPfACL38/a+auzJW11zWWpm5hTqQlCGJAg4A6qtKF3CrVq2SVPyLdfjw4Ro9erQef/xxm2Pd3NzUunVrhYWFOSYlAAAAgDptY9wZbT2SJjcXJ90/ONLsOPXaHZeH6+21cTqUfE7L9yXJ3cVJhiFFBnmreRMPs+MBAOxQ6QJuyJAhpf995513atCgQWWOAQAAAGi8StZ+u6VvqJr7UhJVh6+Hq+4aEKHXVxzSnJWxGhgVKInZbwBQn1W6gPu9hQsXOjoHAAAAgHpq25EzWn84Va7OFt0/pK3ZcRqESQPC9e+f4nTgZIYSU7MksQEDANRnNboLKgAAAICG7/UVxWtDj+sVouCmnianaRiaernp9v7hkqSs/CJJUnREoImJAADVYXcBl52drWeffVZ9+/ZV06ZN5ezsbPPNxcWuSXYAAAAA6oHdx85qza+n5exk0eShUWbHaVDuGRQhD9fiP9lC/D0pNwGgHrOrHUtPT9egQYO0b98+OTs7y83NTYZhqFWrVkpKSpJhGJLEJgwAAABAAzdnZfHst+t6tFabQC+T0zQsQT7umhATpnd/jtfAqCCz4wAAqsGuGXAvvvii9u7dq/vuu08ZGRm66aabZLFYdPz4cWVlZWnRokVq2bKlYmJiFBcX5+jMAAAAAOqAAycz9MP+U7JYpCnDmP1WE/5vdEfNvrm7Hh/d0ewoAIBqsKuA++qrr9S6dWu9/vrr8vDwkMViKX2fh4eH7rjjDv34449asmSJZs+e7bCwAAAAAOqOuednv43t1kptm/mYnKZhcnNx0rjeIfL3djM7CgCgGuwq4I4cOaJevXrJ1dW1+CROxacpKCgoHdO5c2cNGTJEixYtqn5KAAAAAHVKbHKmlu09KUmaOpzZbwAAXIxdBZyHh4c8PDxKH/v6+kqSkpKSyowLCAhQfHx8NeIBAAAAqIvmrTosw5Cu7NJCHVv6mh0HAIA6za4CLjQ0VEePHi193LFj8XoEa9asKT1WWFioLVu2KDCQrbIBAACAhiQhJUtf7zwuSXpoeDuT0wAAUPfZVcANGjRIu3fvVmZmpiTpmmuukYuLix5++GHNnz9f33zzjW666SYlJCRoyJAhDg0MAAAAwFxvrI6V1ZCGd2yursF+ZscBAKDOs6uAu+WWW3TZZZdp/fr1kqTWrVvrhRde0NmzZzVlyhRdf/31Wrp0qVq0aKGXXnrJoYEBAAAAmOfomWx9ub149htrvwEAUDku9jxp4MCB2rBhQ5lj06dP14ABA7RkyRKlpaWpffv2mjhxogICAhwSFAAAAID55q85rEKroYFRQerVxt/sOAAA1At2FXAViYmJUUxMjCNPCQAAAKCOSErP1edbj0mSHmL2GwAAlWbXLagAAAAAGp+31h5WfpFV0REBiolkszUAACrLrgLuyy+/VK9evbRixYoKx/z444/q1auXvv76a7vDAQAAAKgbTmfm6aNNiZKkh9n5FACAKrGrgFu4cKGOHDmigQMHVjhm0KBBSkhI0IIFC+wOBwAAAKBuePenOOUVWtUjtKkGRDH7DQCAqrCrgNu1a5e6d+8ud3f3Cse4u7urR48e2rlzp73ZAAAAANQBZ7Ly9cHGI5Kkh0dEyWKxmJwIAID6xa4CLjk5Wa1bt77kuFatWik5OdmeSwAAAACoIxb8HK/s/CJ1DfbVsA7NzY4DAEC9Y1cB17RpUyUmJl5y3NGjR+Xj42PPJSRJ8+bNU3h4uDw8PBQTE6PNmzdfdPznn3+ujh07ysPDQ926ddOyZcvKvP/cuXOaOnWqQkJC5Onpqc6dO2v+/Pl25wMAAAAauvScAr23PkGSNHVYO2a/AQBgB7sKuOjoaG3YsEF79uypcMyePXu0YcMG9e3b165gn376qaZPn66ZM2dq+/bt6t69u6688soKZ9StX79et956q+6++27t2LFD119/va6//nrt3bu3dMz06dO1fPlyffjhhzpw4ID+9Kc/aerUqVq6dKldGQEAAICG7r31CcrMK1SHFk00qnMLs+MAAFAv2VXATZ48WUVFRRo7dqwWL15c7v2LFy/W2LFjZbVaNXnyZLuCvfLKK7r33ns1ceLE0plqXl5eFW7q8Nprr2n06NF67LHH1KlTJ/39739Xr169NHfu3NIx69ev15133qmhQ4cqPDxc9913n7p3717hzLq8vDxlZGSUeQMAAAAai3N5hVqwLl6SNGV4lJycmP0GAIA97CrgRo8erWnTpunYsWMaP368AgMD1bdvX/Xt21eBgYEaP368jh07poceekhXX311lc+fn5+vbdu2aeTIkb8FdXLSyJEjtWHDBpvP2bBhQ5nxknTllVeWGX/55Zdr6dKlOn78uAzD0KpVq/Trr79q1KhRNs/5wgsvyM/Pr/QtNDS0yh8LAAAAUF99sOGIzmYXKLKZt8Z2a2V2HAAA6i27CjhJmj17tt5//3116NBBaWlp2rZtm7Zt26a0tDR17NhR7733nl599VW7zp2SkqKioiK1aFF2inuLFi2UlJRk8zlJSUmXHD9nzhx17txZISEhcnNz0+jRozVv3jwNHjzY5jlnzJih9PT00rejR4/a9fEAAAAA9U12fqHe/SlOkjRlaJScmf0GAIDdXKrz5AkTJmjChAk6efJkaTkVGhqqVq3q5r+OzZkzRxs3btTSpUsVFhamtWvXasqUKWrdunW52XOS5O7uLnd3dxOSAgAAAOb6ePNRpWblq02Al67r0drsOAAA1GvVKuBKtGrVyqGlW1BQkJydnXXq1Kkyx0+dOqWWLVvafE7Lli0vOj4nJ0dPPvmklixZorFjx0qSLrvsMu3cuVMvv/yyzQIOAAAAaIxyC4r01prDkqTJQ9vKxdnuG2cAAICqcQtqTXJzc1Pv3r21YsWK0mNWq1UrVqxQ//79bT6nf//+ZcZL0g8//FA6vqCgQAUFBXJyKvshOzs7y2q1OvgjAAAAAOqvz7ceVXJmnlr7eejGXiFmxwEAoN6r1Ay4SZMmyWKx6Pnnn1eLFi00adKkSl/AYrHo3//+d5WDTZ8+XXfeeaf69Omj6Ohovfrqq8rKytLEiRMlSXfccYeCg4P1wgsvSJIeeeQRDRkyRLNnz9bYsWP1ySefaOvWrXr77bclSb6+vhoyZIgee+wxeXp6KiwsTGvWrNH777+vV155pcr5AAAAgIYov9CqN1cXz357YGhbubnUyX+zBwCgXrEYhmFcapCTk5MsFosOHDig9u3bl5tFdtELWCwqKiqyK9zcuXP1z3/+U0lJSerRo4def/11xcTESJKGDh2q8PBwLVq0qHT8559/rr/+9a9KSEhQu3bt9I9//ENjxowpfX9SUpJmzJih77//XmfOnFFYWJjuu+8+TZs2TRbLpReVzcjIkJ+fn9LT0+Xr62vXxwQAAADUZZ9sTtQTX+5R8ybuWvt/w+Th6mx2JAAA6qzKdkWVKuDWrFkjSYqJiZGHh0fp48oaMmRIlcbXVRRwAAAAaMgKi6waPnuNEs9k669jO+meQZFmRwIAoE6rbFdUqVtQIyIi5OPjIw8PD0kNp1ADAAAA8Julu04o8Uy2Ar3d9MeYNmbHAQCgwajUvaQRERF67LHHSh9PmjRJCxYsqLFQAAAAAGpXkdXQ3FWxkqS7B0XIy61S/1YPAAAqoVIFnGEYZXYKXbRokX7++ecaCwUAAACgdi3bc1Jxp7Pk5+mqO/qHmx0HAIAGpVIFnJ+fn44ePVrTWQAAAACYwGo1NHdl8ey3SQMi5OPO7DcAABypUr9Z+/btq5UrV2rixImKiIiQJO3cuVPPPPPMJZ9rsVj01FNPVS8lAAAAgBrz/f5TOngqU03cXXTXgHCz4wAA0OBUahfU7du365prrtHJkyerfgGLRUVFRXaFq2vYBRUAAAANjWEYumbuz9p7PENTh0Xpz1d2MDsSAAD1hkN3Qe3Vq5cOHjyoLVu26OjRo7rrrrs0cOBA3X333Q4LDAAAAKD2rT54WnuPZ8jLzVmTBkaYHQcAgAap0os7+Pj4aNiwYZKku+66S1FRUbrzzjtrLBgAAACAmmUYhl5feUiSNKFfmAK83UxOBABAw2TX6qrx8fHy8fFxdBYAAAAAtWhdbKp2JJ6Vu4uT7hnE7DcAAGqKXQVcWFiYo3MAAAAAqGUls99ujW6j5k08TE4DAEDDVakC7v3335ck3XDDDWrSpEnp48q64447qp4MAAAAQI3ZFJeqzfFn5ObspAeGtDU7DgAADVqldkF1cnKSxWLRgQMH1L59+9LHl2IYBrugAgAAAHXQ7f/epJ8Opei2mDZ67oZuZscBAKBecuguqE8//bQsFouCgoLKPAYAAABQ/+xITNNPh1Lk4mRh9hsAALWgUjPgUIwZcAAAAGgIJi3aopW/JOvm3iH6583dzY4DAEC9VdmuyKkWMwEAAAAw2d7j6Vr5S7KcLNKUYVFmxwEAoFGwaxfUi1m+fLn27t2r0NBQ3XjjjXJ1dXX0JQAAAADYac75nU+v7d5a4UHeJqcBAKBxsGsG3BtvvKHIyEitW7euzPE//OEPGjt2rB5//HH98Y9/1KBBg5Sbm+uQoAAAAACq52BSpr7bd0oWZr8BAFCr7CrglixZouzsbPXv37/02PLly7V48WIFBwfriSeeUHR0tLZs2aJ33nnHYWEBAAAA2G/uqlhJ0lVdW6pdiyYmpwEAoPGw6xbUgwcPqmvXrnJy+q2/++STT2SxWLR48WJFR0crNzdXYWFh+vDDD/XQQw85LDAAAACAqjt8+pz+u/uEJGnqsHYmpwEAoHGxawbc6dOn1bJlyzLH1qxZo9DQUEVHR0uSPDw8dPnllys+Pr76KQEAAABUy7xVsTIMaWSnFurcuuJd2gAAgOPZVcD5+fkpJSWl9HF8fLyOHDmioUOHlhnn7e2trKysagUEAAAAUD2Jqdn6emfx7LeHR7D2GwAAtc2uAi4qKkpr165VYmKiJOntt9+WxWLR6NGjy4w7duxYuZlyAAAAAGrXm2tiVWQ1NKR9M10W0tTsOAAANDp2FXAPPvigcnNzddlll6l37976xz/+oWbNmunqq68uHZOTk6OtW7eqc+fODgsLAAAAoGqOn83R4m3HJEkPDWf2GwAAZrCrgLvtttv06KOPKi8vTzt27FBwcLA+/vhj+fj4lI757LPPlJ2drREjRjgsLAAAAICqeWvNYRUUGeofGag+4QFmxwEAoFGyGIZh2PvkvLw8ZWRkqFmzZuXed/ToUZ05c0Zt27YtU8zVZxkZGfLz81N6erp8fVm4FgAAAHVbckauBv5jlfILrfro3hhd3jbI7EgAADQole2KXKpzEXd3d5vlmySFhoYqNDS0OqcHAAAAUA1vrY1TfqFVfcL81T8y0Ow4AAA0WtUq4C5UWFiod999V3v37lWbNm107733yt/f35GXAAAAAFAJqefy9J9NRyRJD41oJ4vFYnIiAAAaL7vWgHvmmWfk7OystWvXlh6zWq0aOnSopkyZojfeeEMzZsxQ3759dfbsWUdlBQAAAFBJ7/4cr9wCqy4L8dPgdtx6CgCAmewq4H744QeFhIRo8ODBpccWL16s9evXq1u3bnrrrbd03XXXKS4uTvPmzXNYWAAAAACXdjY7X++vT5AkPTSc2W8AAJjNrgIuLi5OnTp1KnPsyy+/lMVi0ccff6x7771XX3zxhUJDQ7V48WKHBAUAAABQOQvWJSgrv0idWvlqZKfmZscBAKDRs6uAS01NVVBQ2Wnsa9asUbt27UqLOYvFor59+yoxMbH6KQEAAABUSkZugRaui5ckPTQ8itlvAADUAXYVcEFBQTp+/Hjp4/379+vUqVMaOnRomXFubm7Kz8+vVkAAAAAAlffBhiPKzC1UVHMfje7S0uw4AABAdhZwnTp10rp167Rjxw5J0iuvvCKLxaIxY8aUGZeQkKBWrVpVPyUAAACAS8rKK9S7P8VJkqYOi5KTE7PfAACoC+wq4KZNm6bCwkL17dtXQUFBWrhwoSIiIjR69OjSMenp6dq2bZu6d+/usLAAAAAAKvafTUeUll2g8EAvXX0Z/xAOAEBdYVcBN2bMGM2ZM0fBwcHKycnRgAEDtGTJErm5uZWOef/991VQUKARI0Y4LCwAAAAA23ILivT22uK13yYPi5KLs13/Vx8AANQAi2EYRk2cOCcnR/n5+fLx8ZGzs3NNXKLWZWRkyM/PT+np6fL19TU7DgAAAFBq4bp4/e2b/Qrx99SqPw+VKwUcAAA1rrJdkUtNBfD09JSnp2dNnR4AAADAeXmFRXprTfHabw8ObUv5BgBAHcNvZgAAAKCeW7ztmJIyctXS10M39Q4xOw4AALiA3TPgDMPQf/7zH3399dc6dOiQMjMzZetuVovFosOHD1crJAAAAADbCoqsenN18f/fvn9IpNxdGsbyLwAANCR2FXD5+fkaO3asVq5cabN0k4qLtxpaXg4AAADAeUt2HNextBwF+bjr1ug2ZscBAAA22HUL6uzZs7VixQpdffXVOnTokG6//XZZLBbl5eXpwIEDmjVrlry9vfXYY4/JarU6OjMAAAAASYVFVr2xKlaSdN/gCHm4MvsNAIC6yK4ZcJ9++qkCAgL00UcfydvbW05OxT2eq6urOnTooKefflrDhg3TsGHD1KFDB02aNMmhoQEAAABI/919Ugmp2fL3ctVtMWFmxwEAABWwawZcbGysoqOj5e3tXXyS8wVcUVFR6ZhBgwZpwIABeuONNxwQEwAAAMDvWa2G5p6f/Xb3wAh5u9u9vDMAAKhhdhVwzs7O8vPzK31cUsSdPn26zLjg4GAdPHiwGvEAAAAA2LJ8X5Jik8/J18NFd1webnYcAABwEXYVcMHBwTp27Fjp46ioKEnSxo0by4zbvXu3fHx8qhEPAAAAwIUMw9CclcWz3+4aECFfD1eTEwEAgIuxq4Dr16+f9u7dq7y8PEnSmDFjJEl/+tOftHz5cu3Zs0cPPfSQDhw4oJiYGMelBQAAAKAfDyTrwMkMebs5a9KAcLPjAACAS7CrgBs3bpw8PDz0/fffSyqeAfenP/1JiYmJGjt2rHr06KF58+bJy8tL//jHPxwaGAAAAGjMime/HZIk3XF5uJp6uZmcCAAAXIpdK7WOHTtWJ0+eLHNs9uzZ6tu3r7766iulpaWpffv2evjhh9WuXTuHBAUAAAAgrT2Uot3H0uXh6qS7B0aYHQcAAFSCQ7dKuuWWW3TLLbc48pQAAAAAzjMMQ3NWFM9+uy0mTEE+7iYnAgAAlWHXLagAAAAAat+GuFRtPZImNxcn3T840uw4AACgkijgAAAAgHpizorinU9v6Ruq5r4eJqcBAACVValbUJ2dne2+gMViUWFhod3PBwAAACBtTTijDXGpcnW26P4hbc2OAwAAqqBSBVxoaKgsFktNZwEAAABQgddXFs9+G9crRMFNPU1OAwAAqqJSBVxCQkINxwAAAABQkV1Hz2rtr6fl7GTR5KFRZscBAABVxBpwAAAAQB035/zst+t6tFabQC+T0wAAgKqigAMAAADqsP0nMvTjgVOyWKQpw5j9BgBAfWRXAffll1+qV69eWrFiRYVjfvzxR/Xq1Utff/213eEAAACAxm7uqkOSpKsva622zXxMTgMAAOxhVwG3cOFCHTlyRAMHDqxwzKBBg5SQkKAFCxbYHQ4AAABozA6dytT/9iZJkqYMY+dTAADqK7sKuF27dql79+5yd3evcIy7u7t69OihnTt32psNAAAAaNTmrYqVYUhXdmmhji19zY4DAADsZFcBl5ycrNatW19yXKtWrZScnGzPJQAAAIBGLT4lS0t3nZAkPTS8nclpAABAddhVwDVt2lSJiYmXHHf06FH5+LBOBQAAAFBVb6yKldWQhndsrq7BfmbHAQAA1WBXARcdHa0NGzZoz549FY7Zs2ePNmzYoL59+9odDgAAAGiMjp7J1pIdxyVJU4ez8ykAAPWdXQXc5MmTVVRUpLFjx2rx4sXl3r948WKNHTtWVqtVkydPrnZIAAAAoDF5c81hFVoNDYwKUq82/mbHAQAA1eRiz5NGjx6tadOm6V//+pfGjx+vpk2bKjIyUpIUFxens2fPyjAMPfzww7r66qsdGhgAAKChyS0o0uHT59SlNbcZQjqZnqPFW49Jkh5i9hsAAA2CXTPgJGn27Nl6//331aFDB6WlpWnbtm3atm2b0tLS1LFjR7333nt69dVXHRgVAACgYXrii90a+/rP+mzrUbOjoA74YMMR5RdZFR0RoJjIQLPjAAAAB7BrBlyJCRMmaMKECTp58qSOHi3+P4yhoaFq1aqVQ8IBAAA0dIdOZerr8ztdvr7ikG7oGSxXZ7v/jRQNwLrYFEnSLX1DTU4CAAAcpVoFXIlWrVpRugEAANhh7qpYGUbxfx9Ly9GSHcf1hz4UL43VubxC7T2RIUnMfgMAoAFx6D+vFhYWav78+Zo6dapeeuklpaWlOfL0AAAADUp8Spa+OT/77YaewZKkN1bFqrDIamYsmGj7kTQVWQ0FN/VUcFNPs+MAAAAHsauAe+aZZ+Ts7Ky1a9eWHrNarRo6dKimTJmiN954Q08++aT69u2rs2fPOiorAABAg/LGqlhZDWlEx+Z69vqu8vdyVUJqtr7dc9LsaDDJpvhUSVJMZIDJSQAAgCPZVcD98MMPCgkJ0eDBg0uPLV68WOvXr1e3bt301ltv6brrrlNcXJzmzZvnsLAAAAANxdEz2Vqy47gkaerwKHm7u+jugRGSpLkrY2W1GmbGg0k2x5+RJMVEUMABANCQ2FXAxcXFqVOnTmWOffnll7JYLPr4449177336osvvlBoaKgWL17skKAAAAANyZtrDqvQamhQuyD1bOMvSbrj8nA18XDRoeRzWr4vyeSEqG25BUXadTRdkhQTwfpvAAA0JHYVcKmpqQoKCipzbM2aNWrXrl1pMWexWNS3b18lJiZWPyUAAEADcjI9R4u3HpMkPTS8XelxXw9XTbw8XJI0Z2WsDINZcI3JjsSzyi+yqnkTd4UFepkdBwAAOJBdBVxQUJCOHz9e+nj//v06deqUhg4dWmacm5ub8vPzqxUQAACgoXlrTZzyi6yKiQhQ9AW3Gk4aGCFvN2cdOJmhHw8km5QQZii5/TQ6IkAWi8XkNAAAwJHsKuA6deqkdevWaceOHZKkV155RRaLRWPGjCkzLiEhQa1atap+SgAAgAYiOTNXH28uvkPg4RHtyr2/qZebbu8fLkmas/IQs+Aakd82YOD2UwAAGhq7Crhp06apsLBQffv2VVBQkBYuXKiIiAiNHj26dEx6erq2bdum7t27OywsAABAfffuT/HKK7SqV5umuryt7aLlnkER8nB10u5j6Vp7KKWWE8IM+YVWbU9Mk8QGDAAANER2FXBjxozRnDlzFBwcrJycHA0YMEBLliyRm5tb6Zj3339fBQUFGjFihMPCAgAA1GdnsvL14cYjkorXfqvoNsMgH3f9MTpMkjRnBbPgGoM9x88qt8CqAG83tWvuY3YcAADgYHYVcJI0ZcoUHTlyRFlZWVq7dq26detW5v333HOP0tLSdO+991Y7JAAAQEPw75/jlJ1fpG7BfhraodlFx94/JFJuLk7aeiRNG+JSaykhzLLp/PpvfcP9Wf8NAIAGyO4C7lI8PT3l5+cnZ2fnmroEAABAvZGeXaD31hfPfps6POqSJUsLXw+N7xMqSZqzIrbG88FcJRswxESw/hsAAA1RjRVwAAAA+M3C9fE6l1eoji2b6IpOLSr1nAeGtpWrs0Ub4lK1NeFMDSeEWQqLrNqaULz+24W74gIAgIbBpTKDhg8fLovFovfee08hISEaPnx4pS9gsVi0YsUKuwMCAADUd5m5BVq4LkFS8ew3J6fK3WIY3NRT43qF6JMtRzVnZazemxRdgylhlgMnM3Uur1BNPFzUqZWv2XEAAEANqFQBt3r1alksFmVnZ5c+rizWsAAAAI3dBxuPKD2nQJHNvHVV11ZVeu6DQ9vq823HtObX09p19Ky6hzatmZAwzab44jX++oYHyLmS5SwAAKhfKlXAxcfHS5KCg4PLPAYAAMDFZecX6t2fiv+/09RhUVUuWMICvXVd99b6csdxzVkZq3fv7FMTMWGikg0YuP0UAICGq1IFXFhY2EUfAwAAwLaPNiXqTFa+wgK9dG331nadY/KwKC3ZeVw/Hjil/Scy1Lk1tyk2FFaroS0JJRswUMABANBQsQkDAABADcktKNJba+MkSZOHtpWLs33/1yuquY/Gdiu+dXXuqkMOywfz/ZqcqbPZBfJ0dVbXYD+z4wAAgBpCAQcAAFBDPtt6VKcz8xTc1FM39Ayp1rmmDo+SJP1vb5IOncp0RDzUAZvP337aO8xfrnYWtAAAoO6r1C2okZGRdl/AYrHo8OHDdj8fAACgPsovtGr+6uL/D/TAkEi5uVSvXOnY0lejOrfQ9/tPad6qWL16S09HxITJNsVx+ykAAI1BpQq4hIQEWSwWGYZR5QuwCyoAAGiMvth+TCfSc9W8ibtu7hPqkHM+NLydvt9/Skt3ndAjI9srIsjbIeeFOQzDYAMGAAAaiUoVcCV69+6tCRMm6LrrrpOnp2dNZQIAAKjXCoqsemN1rCTp/iFt5eHq7JDzdgvx07AOzbTq4Gm9sSpW/7y5u0POC3PEp2Qp5Vye3Fyc1D20qdlxAABADarUvRCffPKJrr76au3evVvTp09X9/9v787DoyzMvu//ZrLvgQQSIJCFIPsiWwgiOwKuKK0WqeJaF0BbWq22d9U+9nmwtvq2Aoq4V4tQVLRabzc2FULCqqAIhCQQIAshZCfrXO8fk4ykBAgwk2sm8/0cR45DZ66ZOSeTi2TOOZfBg/XII49o9+7d6ty5s2JiYs76daGWLFmihIQEBQYGKiUlRRkZGWc9ftWqVerTp48CAwM1cOBAffzxx82ut1gsLX795S9/ueAYAQAA/tsHO48qt/ikokP9dfPIHk697/mTekmSVu84otziKqfeN9pWU/XbkO6RTkvSAgAA99SqBNyNN96oDz74QHl5eVq0aJH69++vN954Q1dccYW6d++uhx56SDt37nRqYCtXrtSCBQv0+OOPa/v27Ro8eLCmTp2qwsLCFo/ftGmTZs2apTvvvFM7duzQjBkzNGPGDO3evdtxTF5eXrOvV199VRaLRTNnznRq7AAAwHs12Aw9v85e/XbX5UkK8nduYmVojw4akxytepuhpRuYs+vJmhYwMP8NAID2z2JcyGA3SdnZ2Xrrrbe0fPly7d27VxaLRX379tUtt9yim2++Wd27X9ysk5SUFI0YMUKLFy+WJNlsNnXv3l3z58/XI488ctrxN910kyorK/XRRx85Lhs1apSGDBmipUuXtvgYM2bMUHl5udasWdOqmMrKyhQREaHS0lKFh4dfwLMCAADt3b+/OaoH3t6hyGA/ff3biQoNOK+JH62yOeu4frZss/x9rPry4QmKjQh0+mPAtQzD0GVPrdXR0mq9dWeKxvSKNjskAABwAVqbK7rgdVyJiYn6wx/+oD179ig9PV3z58/X8ePH9bvf/U4jRoy40LuVJNXW1mrbtm2aPHnyj4FarZo8ebLS0tJavE1aWlqz4yVp6tSpZzy+oKBA//nPf3TnnXeeMY6amhqVlZU1+wIAADgTm83Q4rX7JUl3XJbokuSbJI1KitLIhI6qbbBRBeehDp84qaOl1fK1WjQ0PtLscAAAgItdcALuVPHx8UpKSlLXrl1lGIZsNttF3V9RUZEaGhpOmx8XExOj/Pz8Fm+Tn59/Xse/8cYbCgsL0w033HDGOBYuXKiIiAjH18VW9QEAgPbts+/zta+gQmEBvpozOsGljzV/UrIk6e2MQyosr3bpY8H5mtpPB8ZFKNjfNYlaAADgPi44AVdVVaW33npL06dPV1xcnH71q18pOztbd999t9577z1nxugSr776qmbPnq3AwDO3bDz66KMqLS11fOXm5rZhhAAAwJMYhqFFa+2z3267LEERQX4ufbwxydEa0j1SNfU2vfxVtksfC86Xnn1ckjSS+W8AAHiF8/q4zWaz6dNPP9Vbb72lf//736qqqpK/v7+uvfZa/fznP9eVV14pf3//iw4qOjpaPj4+KigoaHZ5QUGBYmNjW7xNbGxsq4//6quvtHfvXq1cufKscQQEBCggIOA8owcAAN5o3d5CfXe0TMH+PrrjskSXP57FYtEDk5J1x+tb9dbmg7p3XE91DLn4v8PQNljAAACAd2lVBVx6eroeeOABdenSRVdddZVWrFih4cOHa9myZcrPz9c777yjGTNmOCX5Jkn+/v4aNmxYs+UINptNa9asUWpqaou3SU1NPW2Zwueff97i8a+88oqGDRumwYMHOyVeAADg3QzD0HNr7NVvt4yKV4c2SoRN6N1Z/buGq6q2Qa9+TRWcpygoq1bO8SpZLNLwBBJwAAB4g1ZVwKWmpspisWjgwIH6zW9+o5tvvlndunVzaWALFizQnDlzNHz4cI0cOVJ/+9vfVFlZqdtvv12SdOutt6pbt25auHChJOnBBx/UuHHj9MwzzziShFu3btWyZcua3W9ZWZlWrVqlZ555xqXxAwAA7/F1ZpF25pYo0M+quy5ParPHtVgsmj8xWfe+tV1vbMrR3WOTXN76iouX3lj91q9LuMIDeb0AAPAG5zUD7vvvv9fvf/97JSYmyt/fv1VfF9rCedNNN+mvf/2rHnvsMQ0ZMkQ7d+7UJ5984li0cOjQIeXl5TmOHz16tJYvX65ly5Zp8ODBeuedd/T+++9rwIABze53xYoVMgxDs2bNuqC4AAAA/tuixuq3WSN7qFNY246vuKJfrHrHhKm8pl6vb8xp08fGhclonP+WkhhlciQAAKCtWAzDMM51kNV6cctSL3YrqrsoKytTRESESktLFR4ebnY4AADADWzOOq6fLdssfx+rvnx4gmIjzrzgyVX+/c1RPfD2DkUE+enr305QGFVVbm3Ksxu0v7BCS38+TNMGtDzfGAAAeIbW5opalVmz2WwX9QUAANBeLVq7X5J044g4U5JvknTVwC5Kig5R6ck6vbX5kCkxoHWOV9Rof2GFJDagAgDgTS6utA0AAMCLbTt4Qhszj8vXatG943qaFoeP1aL7JyRLkl7+KktVtfWmxYKz25Jjn/92SUwoW2sBAPAiJOAAAAAu0OLG6rcbhnZTXIdgU2O5bkhXde8YpOOVtVqeThWcu2pawED1GwAA3oUEHAAAwAXYdbhU6/Yek9Ui3T8+2exw5OdjdcSx7MssVdc1mBwRWpLRmIBjAQMAAN6FBBwAAMAFaJr9dt2QbkqIDjE5GruZQ+PUNSJQheU1+tfWXLPDwX8pPVmn7/PKJFEBBwCAtyEBBwAAcJ725JXps+8LZLFIcyeYN/vtv/n7WnXveHs8S9cfUG09y7DcybaDxTIMKSEqWDHh5izsAAAA5iABBwAAcJ6WrMuUJF05oIuSO4eZHE1zNw7vrk5hATpaWq33th82OxycIj2L9lMAALwVCTgAAIDzkFlYof/sypMkzZto/uy3/xbo56N7xiZJkp5ff0D1DVTBuQsWMAAA4L1IwAEAAJyH59dlyjCkKf1i1LdLuNnhtOjmlB7qGOKvQ8VV+mDnUbPDgaTKmnrtPlIqSUpJIgEHAIC3IQEHAADQSgePV+qDb+wJrQcm9jI5mjML9vfVXZcnSrK3yzbYDJMjwvZDJ1RvM9QtMkhxHYLNDgcAALQxEnAAAACt9Py6A2qwGRrfu5MGxkWYHc5Z3ZqaoIggP2UVVTpaZmGeDNpPAQDwaiTgAAAAWuFIyUm927jUYL4bV781CQ3w1R2XNVbBrc2UjSo4U/24gIEEHAAA3ogEHAAAQCssXX9A9TZDo3tGaVh8B7PDaZXbRicoNMBXewvK9dn3BWaH47Wq6xq0M7dEEhVwAAB4KxJwAAAA51BQVq2VW3MleUb1W5OIYD/NGR0vSVq0dr8Mgyo4M3yTW6LaBps6hQUoMTrE7HAAAIAJSMABAACcw4sbslRbb9OIhA4a5WEbLO8ck6Rgfx99d7RM6/YWmh2OV0o/Zf6bxWIxORoAAGAGEnAAAABnUVRRo+UZByXZq988LYHSMcRfPx9lr4J7bk0mVXAmaFrAwPw3AAC8Fwk4AACAs3j5q2xV19k0uHukLu8VbXY4F+SuyxMV4GvVztwSbcw8bnY4XqWuwaZtB09IklISo0yOBgAAmIUEHAAAwBmcqKzVm2k5kqT5E5I9rvqtSeewQM0a2UOS9Nza/SZH4112HSnVyboGRQb7qVfnULPDAQAAJiEBBwAAcAavbcxWZW2D+nUJ16S+nc0O56LcMy5J/j5WZWQXKz2LKri20tR+OjKho6xWz0zgAgCAi0cCDgAAoAVl1XV6bVOOJGn+RM+tfmvSJSJIPxkeJ0latDbT5Gi8R1OycyTz3wAA8Gok4AAAAFrwxsYclVfX65KYUE3tH2t2OE5x37ie8rVa9HVmkbYfOmF2OO1eg83Q1hzmvwEAABJwAAAAp6msqdcrG7MlSXMnJLeb1sHuHYN1/aXdJEmLqYJzuT15ZSqvqVdogK/6dQ03OxwAAGAiEnAAAAD/5a3NB1VSVafE6BBdPair2eE41f0TkmW1SGt/KNTuI6Vmh9OupTfOfxue0EE+7SSJCwAALgwJOAAAgFOcrG3QS19lSZLuH9+z3SVOEqNDdM1ge1JxERtRXSoj2z7/jfZTAABAAg4AAOAUb2ccUlFFreI6BGlGY7tmezNvQrIsFunT7wr0Q36Z2eG0Szab8eMGVBYwAADg9UjAAQAANKqua9CLXx6QJN0/Pll+Pu3zT6VeMWGaPsC+WIJZcK6ReaxCJ6rqFOhn1cBuEWaHAwAATNY+/6oEAAC4AKu2HVZBWY26RARq5rD2Wf3WZN6EXpKk/+zK04FjFSZH0/6kZ9nbT4fFd5C/L39yAwDg7fhrAAAAQFJdg01L19ur3+4Zm6QAXx+TI3Ktfl3DNblvZxmGtGQdVXDO1rSAYWQC898AAAAJOAAAAEnS6u1HdKTkpKJDA/SzkT3MDqdNzJ9or4L7YOdRHTpeZXI07Ydh/Dj/LSWJ+W8AAIAEHAAAgOobbFqy3l4Fds/YJAX6te/qtyaDu0dq7CWd1GAz9Px6quCcJed4lQrLa+TvY9WQ7pFmhwMAANwACTgAAOD1Pvz2qA4er1LHEH/NHuUd1W9NHpiYLEl6d/thHSk5aXI07UNGtn3+2+DuEV6TzAUAAGdHAg4AAHi1Bpvh2AR655hEBfv7mhxR2xqe0FGpSVGqazD04oYDZofTLqRnNbafJjL/DQAA2JGAAwAAXu2T3fk6cKxS4YG+ujU13uxwTDG/sQpuxZZcFZZVmxyN53MsYEhk/hsAALAjAQcAALyWzWZo0dr9kqTbL0tUWKCfyRGZI7VnlIbFd1BtvU0vfplldjge7fCJKh0pOSkfq0XD4juYHQ4AAHATJOAAAIDX+mJPgX7IL1dogK/uuCzR7HBMY7FYHFVw/0w/qKKKGpMj8lxN208HdItQSIB3tTMDAIAzIwEHAAC8kmEYWtQ4++3W1HhFBHtn9VuTcZd00qC4CFXX2fTyV9lmh+OxmhJwo2g/BQAApyABBwAAvNL6fce060ipgvx8dOcY761+a2KvguslSXozLUclVbUmR+SZmP8GAABaQgIOAAB4HcMwtGiNffbb7JQeigoNMDki9zC5b2f1iQ1TZW2DXt2YY3Y4HqewrFrZRZWyWOzbZQEAAJqQgAMAAF4n7cBxbT9UIn9fq34xNsnscNzGqVVwr23MVll1nckReZaMHHv1W9/YcEUEeXdLMwAAaI4EHAAA8DrPNW4+nTWiuzqHB5ocjXuZPiBWyZ1DVV5dr39syjE7HI+SnkX7KQAAaBkJOAAA4FW25BRrc1ax/HwsumdcT7PDcTtWq0XzJtg3or7ydbYqa+pNjshzOBYwJJGAAwAAzZGAAwAAXuW5xtlvPxnWXV0jg0yOxj1dPaiLEqKCdaKqTm9tPmh2OB6huLJWewvKJUkjmP8GAAD+Cwk4AADgNXbmluir/UXysVp0H9VvZ+TrY9X94+1VcC99laXqugaTI3J/WxrnvyV3DmWpBwAAOA0JOAAA4DUWN85+mzGkm3pEBZscjXu7fmg3dYsMUlFFrd7OOGR2OG6vqf00hflvAACgBSTgAACAV/juaKm+2FMoq0WaO4Hqt3Px87HqvvH279OLG7JUU08V3NmkZx+XxAIGAADQMhJwAADAKyxemylJunpQVyV1CjU5Gs/w0+Fxig0PVH5ZtVZtPWx2OG6rrLpO3x8tkySlJEaZHA0AAHBHJOAAAEC7t6+gXP+7O1+SNG9issnReI4AXx/dMy5JkvTC+gOqa7CZHJF72pZzQjZDio8KVmxEoNnhAAAAN0QCDgAAtHtL1tmr36b1j9UlMWEmR+NZfjaih6JD/XWk5KRW7zhidjhuKb1x/ttItp8CAIAzIAEHAADateyiSn34zVFJVL9diCB/H919ub0K7vl1maqnCu40GY3z31KSaD8FAAAtIwEHAADatSXrMmUzpEl9OmtAtwizw/FIPx8Vrw7Bfso5XqWPvs0zOxy3UlVbr28Pl0piAyoAADgzEnAAAKDdyi2ucrRNzp/Uy+RoPFdIgK/uHJMoSVq8LlM2m2FyRO5jx6ES1dsMdY0IVFyHILPDAQAAbooEHAAAaLeeX39ADTZDl/eK1pDukWaH49FuHZ2gsEBfZRZWOBZaQErPsrefjkzsKIvFYnI0AADAXZGAAwAA7VJe6Um9sy1XkjR/ItVvFys80E+3j06QJC1au1+GQRWcdMoChkTmvwEAgDMjAQcAANqlFzdkqa7BUEpiR41kNpdT3DEmUSH+Pvohv1xf7Ck0OxzT1dQ3aEduiSQpJYmfMQAAcGYk4AAAQLtTWF6ttzMOSZIeYPab00QG++uW1ARJVMFJ0je5paqttyk6NEBJ0SFmhwMAANyYr9kBAAAA97dkXaaWrMtUg4cM37cZhuoaDA3tEanRPWkNdKa7Lk/U65uy9e3hUm3Yd0zje3c2OyTTZGTb57+lMP8NAACcAwk4AABwVgVl1fr7mv2qrbeZHcp5sVqk30ztTWLEyaJDAzQ7JV6vfJ2tRWszNe6STl77Pf5x/hvtpwAA4OxIwAEAgLNa9mWWauttGhbfQc/NutTscFot1N9XEcF+ZofRLv1ibJLe3HxQ2w6eUFrWcY3uGW12SG2ursGmbQdPSCIBBwAAzo0EHAAAOKOiihr9M/2gJPsstW6RQSZHBHcQEx6om4Z315ubD2rRmkyvTMB9d7RMVbUNigjyU++YMLPDAQAAbo4lDAAA4Ixe/ipb1XU2DYqL0Nhe3pdkwZndO76n/HwsSss6rq05xWaH0+bSs+zz30YkdJTV6p0tuAAAoPVIwAEAgBadqKzVm2k5kqT5E3t57ZwvtKxbZJBmDo2TJD23NtPkaNpeRuP8t1FJtJ8CAIBzIwEHAABa9NrGbFXWNqhvl3BN7uu9my5xZvePT5aP1aIv9x3TztwSs8NpMw02Qxk5LGAAAACtxww4L7czt0QWSYO7R5odCtzA7iOljo1uniKpU4gm9CYxADhbWXWdXtuUI0maPzGZ6je0qEdUsK4b0lXvbT+ixWsz9fKc4WaH1CZ+yC9TeXW9QgN81a9LuNnhAAAAD0ACzsv9/OV01dbbtObX49S9Y7DZ4cBEJVW1mrVss8pr6s0O5bx9/qux6sUAbMCp/rEpR+XV9erVOVTT+seaHQ7c2P3jk7V6xxF9sadA3x8tU7+u7T8h1dR+Oiy+g3x9aCgBAADnRgLOy13aI1Jf7S/S0g0H9H+vH2h2ODDRqxtzVF5Tr26RQRqe0MHscFpl28ETOnzipL7aX0QCDnCiypp6vfJ1tiRp3sRkBszjrJI7h+qqgV300bd5Wrxuv56fPczskFwuPYv2UwAAcH5IwHm5eROS9dX+Iq3aeljzJ/ZSbESg2SHBBGXVdXp9o/3N9u+v6qsrB3YxOaLWeWH9Af35kx+UlnVcd4xJNDscoN14a/NBnaiqU0JUsK7ykH8PYK55E5P10bd5+t/d+dpfUN6uPxQxjB/nv7GAAQAAtBY1814uJSlKIxM7qrbBphe/PGB2ODDJm2kHVVZdr2QPazVL7RklSUrPOq4Gm2FyNED7cLK2QS99lSVJun9CMu11aJU+seGa2j9GhiEtXte+N6IeOFah4spaBfhaNbBbpNnhAAAAD8Ff1dADE3tJkpanH9Kx8hqTo0Fbq6yp18uNb7bnTfCsVrMBXcMVGuCrsup67ckrMzscoF1YseWQiipqFdchSNdf2s3scOBB5jf+PfHhN0eVXVRpcjSus7mx/XRojw7y9+VPaQAA0Dr81QBdlhylId0jVVNvcyRi4D3+mf5jq9nVgzyr1czXx+qYv5N24LjJ0QCer6a+QS9usP8euG98T/lR/YbzMKBbhCb07iSbIT3fjqvgmhYwpNB+CgAAzgN/WUMWi0UPTEqWJL25+aCKK2tNjghtpbquQcu+tM9+89RWs9QkexvqpgNFJkcCeL5VWw8rv6xaseGB+smwOLPDgQeaP8leBbd6xxHlFleZHI3zGYah9Gz7Bz4sYAAAAOfD895twyUm9O6sAd3CVVXboNcah/Gj/VuRcUhFFTUe3WrWNAduS84J1TfYTI4G8Fx1DTa9sN4+C/SecUkK8PUxOSJ4oqE9OmhMcrTqbYZe2ND+ZsseKq5SQVmN/HwsGtrDMzaGAwAA90ACDpLsVXDzJtg/tX59Y45KT9aZHBFcraa+QUvbQatZ3y7hCg/0VUVNvXYdKTU7HMBjrd5+REdKTio6NECzRvYwOxx4sPkT7VX172w9rLzSkyZH41zpje2ng+MiFehHkhoAALSeZ77jhktc0S9GvWPCVF5Trzc25ZgdDlzsnW3to9XMx2rRqMY21LQs5sABF6K+wabn19tndv1ibCKJBVyUZhvWN7Sv2bLpjQsYaD8FAADniwQcHKxWi+Y2fmr96sZsVdTUmxwRXKW9tZo1taGyiAG4MB99m6ec41XqEOyn2SnxZoeDdqCpCu7tjEMqLK82ORrnycix/55JafzgBwAAoLVIwKGZqwZ2UVKnEJVU1enNtINmhwMXWb3jiA6faD+tZk0JuK05J1Rbzxw44HzYbIYWN26svOvyJIUE+JocEdqDMcnRp2xYbx+zZY+WnFRu8Un5WC0aFs/8NwAAcH5IwKEZH6tFc8fbP7V++assnaxtMDkiOFuDzdDz69pXq9klncPUMcRfJ+sa9O3hErPDATzK/+7OV2ZhhcIDfXVLKtVvcI5TN6y/1U42rGc0zn8b0DVcoSSqAQDAeSIBh9NcN6SrenQM1vHKWi3POGR2OHCyj7492u5azaxWi0Yl2efx0IYKtJ7NZmjR2v2SpNsuS1R4oJ/JEaE9OXXD+itfe/4suKYFDMx/AwAAF4IEHE7j62PV/eN7SpJe3HBA1XVUwbUXNpuhxWvt1W93jklsV61mqSxiAM7bmh8K9UN+uUL8fXTHZQlmh4N25tQN629sOqjSKs/esJ6ebf/9MjKR+W8AAOD8kYBDi24YGqeuEYEqLK/Rqq25ZocDJ/nku3ztb2w1u3V0gtnhOJVjDtzBEySNgVYwjB+r324dnaDIYH+TI0J71LRhvaKmXq978Ib1Y+U1yjpWKYtFGplABRwAADh/JODQIn9fq+5trIJ7Yf0BBtu3A/Y32/bqt/bYatazU6g6hQWott6mHYdKzA4HcHsb9h3Tt4dLFehn1V1jEs0OB+3Uf29YL6/2zCq4pvlvvWPCFBHcvn5/AgCAtkECDmd04/Du6hwWoKOl1Xpv+2Gzw8FF+mJPofbklbXbVjOLxUIbKtBKpybkZ6fEKyo0wOSI0J41bVgvPVmnNzd75ob1jMb201FJtJ8CAIALQwIOZxTo56NfjE2SJD2//oDqG6iC81SGYWixF7SaNbWhbmYRA3BWaVnHte3gCfn7WnVP47/zgKs037CeraraepMjOn8sYAAAABeLBBzO6uaUHuoY4q9DxVX69zdHzQ4HF+jL/UX6prHV7M523GrWVAG3I/eETtYyBw44k0Vr7NVvPxvRXZ3DA02OBt6gacN6cWWtlqd71ob1kqpa7S0olySNYP4bAAC4QCTgcFbB/r6663J7wmbxukw12AyTI8L5MgxDi9bYq99mp8Qruh23msVHBatLRKDqGgxtO3jC7HAAt7Q1p1hpWcfl52PRveN6mh0OvESzDetfZnnUspwtOSdkGFLPTiHqFNZ+f4cCAADXIgGHc7o1NUERQX7KOlapj3flmR0OzlNa1nFt9ZJWs+Zz4IpMjgZwT881zn77ybA4dY0MMjkaeJOmDevHymv0Lw/asJ7eOFd0ZCLz3wAAwIUjAYdzCg3w1R2XNVbBrc2UjSo4j+JtrWajGufApTEHDjjNztwSfbnvmHysFt03LtnscOBlTt2wvtSDNqxn5Njnv41Kov0UAABcOBJwaJXbLktQWICv9haU6/M9BWaHg1Y6tdXsHi9pNWuqgPv2cKkqajxv0DfgSosbq9+uG9JVPaKCTY4G3ujUDevvesCG9Yqaeu0+UiqJ+W8AAODikIBDq0QE+enW0fGSpEVr98swqILzBIsa32zPHBqnbl7Sata9Y7DiOgSp3mZoS2PVAgDp+6Nl+mJPgSwWae4Eqt9gjuYb1jNV5+Yb1rfmFMtmSN07BtGyDQAALopbJ+CWLFmihIQEBQYGKiUlRRkZGWc9ftWqVerTp48CAwM1cOBAffzxx6cds2fPHl177bWKiIhQSEiIRowYoUOHPGsbl1nuHJOkYH8f7T5SpvV7j5kdDs7hm9wSbWhsNbt/vHe92W6qgttMGyrgsHidfRnL1YO6qmenUJOjgTe7OaWHokL8lVt8Uv/e6d4b1jOy7R/kpDD/DQAAXCS3TcCtXLlSCxYs0OOPP67t27dr8ODBmjp1qgoLC1s8ftOmTZo1a5buvPNO7dixQzNmzNCMGTO0e/duxzEHDhzQmDFj1KdPH61fv17ffvut/vCHPygwsP3PxXKGjiH++vkoexXcc1TBub1FXtxqNjq5aREDCThAkvYXlOt/d+dLkuZR/QaTBfv76s7GDetL3HzDenpjAm5kIu2nAADg4rhtAu7ZZ5/V3Xffrdtvv139+vXT0qVLFRwcrFdffbXF4//+979r2rRpeuihh9S3b189+eSTGjp0qBYvXuw45ve//72uvPJKPf3007r00kvVs2dPXXvttercuXNbPS2Pd9fliQrwtWrHoRJtzCS54a68vdUsNSlakrT7SKnKqutMjgYw3+J1mTIMaVr/WPWODTM7HODHDetFlfqPm25YP1nboG8Pl0iSRlEBBwAALpJbJuBqa2u1bds2TZ482XGZ1WrV5MmTlZaW1uJt0tLSmh0vSVOnTnUcb7PZ9J///EeXXHKJpk6dqs6dOyslJUXvv//+GeOoqalRWVlZsy9v1zksULNG9pBknwUH97Rknb36zVtbzWIjApUYHSKbIWVkMQcO3i27qFIffmNv85s30fsS8nBPzTes73fLDes7ck+orsFQbHigundk/hsAALg4bpmAKyoqUkNDg2JiYppdHhMTo/z8/BZvk5+ff9bjCwsLVVFRoaeeekrTpk3TZ599puuvv1433HCDNmzY0OJ9Lly4UBEREY6v7t27O+HZeb57xiXJ38eq9Oxix2wUuI/MwnJ9vNteTTB3gndsPm3JqCTaUAFJen5dpmyGNLFPZw3oFmF2OIBD04b1fQUV+uz7lv++M1N61o/tpxaLxeRoAACAp3PLBJwr2Gz2LVvXXXedfvWrX2nIkCF65JFHdPXVV2vp0qUt3ubRRx9VaWmp4ys3N7ctQ3ZbXSKC9JPhcZKognNHi9faW82m9o9Rn9hws8MxTWrPxgQcixjgxXKLq7R6xxFJ0nyq3+BmIoL8NGd0giT73FJ3my3rWMCQxPw3AABw8dwyARcdHS0fHx8VFBQ0u7ygoECxsbEt3iY2Nvasx0dHR8vX11f9+vVrdkzfvn3PuAU1ICBA4eHhzb5gd9+4nvK1WvTV/iLtOHTC7HDQKLuoUv9ubDWbP7GXydGYa1TjG6Y9+WU6UVlrcjSAOV7YcED1NkOX94rWpT06mB0OcJo7xiQq2N9H3x0t07q9LS/aMkNNfYO2N/59k8ICBgAA4ARumYDz9/fXsGHDtGbNGsdlNptNa9asUWpqaou3SU1NbXa8JH3++eeO4/39/TVixAjt3bu32TH79u1TfHy8k59B+9e9Y7Cuv7SbpB+3bcJ8tJr9qHNYoJI7h8owpPRsquDgffJKT+qdrYclkZCH+2q2YX2N+1TB7Tpcqpp6m6JC/L1ylioAAHA+t0zASdKCBQv00ksv6Y033tCePXt03333qbKyUrfffrsk6dZbb9Wjjz7qOP7BBx/UJ598omeeeUY//PCDnnjiCW3dulXz5s1zHPPQQw9p5cqVeumll5SZmanFixfrww8/1P3339/mz689uH9CsqwWae0Phdp9pNTscLzeqa1mDFq3S02iDRXe68UNWaptsGlkYkeNpIIHbqxpw/rO3BJ9nVlkdjiSpPRs5r8BAADnctsE3E033aS//vWveuyxxzRkyBDt3LlTn3zyiWPRwqFDh5SX9+Pa+tGjR2v58uVatmyZBg8erHfeeUfvv/++BgwY4Djm+uuv19KlS/X0009r4MCBevnll/Xuu+9qzJgxbf782oPE6BBdM7irJPvcMZhraWOr2ZjkaA2l1UySNLonixjgnQrLq/V2hn28wgNUv8HNNduwvsY9/p5oSsDRfgoAAJzFYrhLrb8HKCsrU0REhEpLS5kH12h/Qbmu+NuXMgzp01+OVe/YMLND8kr5pdUa+/Q61TbYtPIXo5TSWPnl7YorazX0yc8lSVv/Z7KiQwNMjghoG//v4z1a9mWWLu0RqffuG00FD9zeqb/HVvxilGOTtRnqG2wa/MfPVFnboI8fuFz9uvI3HwAAOLPW5orctgIOnqFXTJimD7Avuli8zj0+tfZGSzcccLSakXz7UccQf/VpTApvpgoOXqK4slZvbT4oyV79RvINniA2IlA/bdywbnZV/XdHy1RZ26DwQF8+WAQAAE5DAg4Xbd4Ee3vTR98e1YFjFSZH431oNTu71J7MgYN3eeXrLFXVNmhAt3CN793J7HCAVru3ccP615lFjg2kZsg4Zf6bj5UENgAAcA4ScLho/bqGa3LfGBmG9Py6A2aH43Ve+SpbNfU2XdojUpclU/323xyLGKiAgxcorarTG5vs1W/zJlD9Bs/SbMP6mv2mxXHqAgYAAABnIQEHp5jfuHXz/Z1HdOh4lcnReI/iylq92dhqNn9iMm+2W5CSGCWLRco6VqmCsmqzwwFc6vVNOaqoqVfvmDBd0S/G7HCA8za3ccP6ur3HtOtw229Yt9kMbclpWsDAh1oAAMB5SMDBKQZ3j9TYSzqpwWbohQ3Mgmsrr36d7Wg1m9C7s9nhuKWIYD/1bxygzRw4tGfl1XV6dWO2JGnexGRZaZ2DB0qIDtG1jRvWF61t+yq4vQXlKj1Zp2B/H8fvDgAAAGcgAQeneaCxCu6dbYd1pOSkydG0f6Un6/TGphxJtJqdS1Mb6qZMEnBov97cfFClJ+uU1ClEVw7sYnY4wAWbOyFZFov02fcF+iG/rE0fO73xg5ph8R3k68OfyQAAwHn4ywJOMzyho1KTolTXYOjFDcyCc7XXN+aonFazVnEsYqACDu1UVW29Xv6qsfptQjKD4+HRmm1Yb+ONqBmN7aej2CgOAACcjAQcnGr+JHsV3IotuSpk3pbLVNTU02p2HkYk2DfZHSquojoT7dLy9EMqrqxVj47BjvY9wJM1bVj/z648ZRa2zYZ1wzCabUAFAABwJhJwcKrUpCgNi++g2nqbln2ZZXY47dababSanY+wQD8N7BYhSUo7QBUc2pfqugbHv7f3j+9J2xzaheYb1tumCu7AsUoVVdQqwNeqQXERbfKYAADAe/BXOpzKYrE4NqL+M/2QjlfUmBxR+2NvNbO/2Z47nlaz1nK0oZKAQzvzr625KiyvUbfIIN0wNM7scACneaCxqv6Db47q4PFKlz9eU/XbpT0iFeDr4/LHAwAA3oUEHJxu3CWdNCguQifrGvTy19lmh9PuLE8/pOONrWbXDaHVrLWaFjFszjouwzBMjgZwjtp6m5aut8/cvHdckvx9+bWO9mNQXKTGNW1YX+/62bLp2fYPaEYmMv8NAAA4H3+pw+nsVXD22S3/2JSjkqpakyNqP2g1u3DDEzrIz8eiIyUnlVvMHDi0D+9uP6yjpdXqHBagnw7vbnY4gNM1VcG9u921G9YNw1B6VuMCBua/AQAAF+DdO1xict/O6tslXJW1DXp1Y47Z4bQbTa1mXSMCaTU7T8H+vhocFylJSssqMjcYwAnqG2x6fr19NtYvxiYp0I+WObQ/w+J/3LC+1IVVcIdPnFR+WbX8fCy6tEcHlz0OAADwXiTg4BIWi0XzJtg/tX59Y7bKqutMjsjzNWs1G9+TVrMLwBw4tCcf7Dyq3OKTigrx1+yUeLPDAVymacP6yq25KnDRhvXNWfbfC4PiIhXkTzIbAAA4H+/g4TLTB8QquXOoyqrr9WbaQbPD8XjvndJqdiOtZhekaQ7cpgPMgYNna7AZWtK4GfKuy5NIGKBdS02K0vDGDesvbnDNhvWmBQwjaT8FAAAuQgIOLmO1/lgF9/JXWaqsqTc5Is9lbzWzV7/RanbhhsZ3kL+PVYXlNcoqcv1GPcBV/rMrT1lFlYoM9tMtqVS/oX2zWCyaP8k+W3Z5xkEVuWDDejoJOAAA4GIk4OBSVw/qooSoYJ2oqtM/06mCu1Af7DyqQ8VVigrx180pPcwOx2MF+vloaHykJNpQ4blsNkOL1+6XJN1xWaJCA3xNjghwvbG9ojU4LkLVdTa9/JVzN6znlZ7UoeIqWS3S8HjmvwEAANcgAQeX8vWx6v7GKrhlX2aruq7B5Ig8z6mtZndenqhgf95sX4zUpGhJUloWCTh4ps++L9C+ggqFBfhqzugEs8MB2oTFYtG8xg3rb6bl6ESl8zasN7Wf9u8aobBAP6fdLwAAwKlIwMHlrr+0m7pFBqmookYrMg6ZHY7H+bix1SwiyE+3piaYHY7Ha1rEkJ7FHDh4HsMwtKix+m3O6ARFBJEsgPc4dcP6axudVwXX1H6aQvspAABwIRJwcDk/H6vuG99TkrR0Q5Zq6qmCay17q5m9+o1WM+cY3D1CgX5WFVXUan9hhdnhAOdl3d5CfXe0TMH+PrpjTKLZ4QBtymKxaP5Ee1X9a5tynLZhnQUMAACgLZCAQ5v46fA4xYYHKr+sWu9sO2x2OB7js+8LtLegXGEBvrrtsgSzw2kXAnx9NDze/iaLOXDwJIZh6Lk19oT8LaPi1THE3+SIgLY3rX+senUOVXl1vd7YmHPR91dUUaPMxg9jRiSQgAMAAK5DAg5tIsDXR/eMS5IkvbD+gOoabCZH5P5oNXOdpjZUEnDwJF9nFmlnbokC/ay66/Iks8MBTGG1WjSvsQrulY3ZF71hfUtj9Vuf2DB1IKkNAABciAQc2szPRvRQdKi/Dp84qdU7jpgdjtuj1cx1RiXZE3Cbs4/LZmMOHDzDosZ29Fkje6hTWIDJ0QDmuWqgfcN6SVWd3tp8cRvW02k/BQAAbYQEHNpMkL+P7m6s2nh+XaYaSHyc0amtZj+n1czpBsVFKNjfRyVVddqTX2Z2OMA5pWcdV0Z2sfx9rLpnbE+zwwFMdeqG9Ze+ytLJ2gufLfvjAoYop8QGAABwJiTg0KZ+PipeHYL9lHO8Sh99e9TscNzWxszj2plbogBfq+66nOo3Z/PzsTqqHWhDhSdoqn776fA4xUYEmhwNYL7rL+2muA5BKqqo1dsXuGG9tKpOPzR+CDMisYMzwwMAADgNCTi0qZAAX93Z2E65eG0m7X9n8Fzj7LdZI3uocxhvtl0htakNNYsEHNzb9kMn9HVmkXytFsdGacDbnbph/cUvD6i67vyr4LbkFMswpKToEH7XAgAAlyMBhzZ36+gEhQX6an9hhT75Lt/scNxOs1azcQxad5WmRQzp2cW0Q8OtLVpjT8jfMLSb4joEmxwN4D5+MixOXSICVVBWc0Eb1jNyGttPk5j/BgAAXI8EHNpceKCfbh+dIMneVmUYJD9O1dRq9pPhceoSEWRyNO1X/64RCgv0VXl1vb47Wmp2OECLdh8p1bq9x2S1SPePTzY7HMCtBPj66J6xF75hnQUMAACgLZGAgynuGJOoEH8f7ckr05o9hWaH4zaatZqNo9XMlXysFqUwBw5ublFjO/q1g7sqITrE5GgA9/OzkT0UHRqgIyUntXp76zesV9TUa/cR+4cvLGAAAABtgQQcTBEZ7K9bUhMk2d9gUgVnt7ix+u36S7upe0dazVxtVOMcuDTmwMEN/ZBfpk+/K5DFIs2bSPUb0JJAPx/9Yqx9tuyS9Zmqb2UV3PaDJ9RgMxTXIUhdI6k2BwAArkcCDqa56/JEBfpZ9c3hUn25v8jscEy3+0ip1v5QKKtFmjuBN9ttoWkO3Jbs4vNuXQJcrSkhf+WALkruHGZyNID7mp1i37B+8HiVPmzlhvX0bPsHL7SfAgCAtkICDqaJDg3Q7JR4SfYh495eBUerWdvrGxuuyGA/VdY2aNcR5sDBfWQWVug/u/IkUf0GnEtIgK/uutw+C661G9YzGue/jaL9FAAAtBEScDDVL8Ymyd/Xqq0HT3h1G+CprWZUv7UdK3Pg4KaeX58pw5Cm9ItR3y7hZocDuL1bU+MVHuirA8cq9b+7z75hvbquQd/k2j90oQIOAAC0FRJwMFVMeKBuGt5d0o/tVt5oyboDkqTpA2LVK4ZWs7Y0ume0JBJwcB+Hjlfpg532Nrr5VL8BrRIW6KfbLrPPglu0dv9Zq+B2HCpRbYNNMeEBio9i3ioAAGgbJOBgunvH95Sfj0WbDhzXtoPFZofT5g4cq9BHjTNr5k3oZXI03qdpDtzWg8WqqW8wORrAXv3WYDM07pJOGhQXaXY4gMe447IEhfj76If8cn2xp+CMxzW1n45MjJLFYmmr8AAAgJcjAQfTdYsM0syhcZKk59Z4XxXcknX2VrPJfWPUryutZm2tV+dQRYf6q7rO5mhJAsxypOSk3t1+WJL0wCSq34DzERnsr1tHJ0iSFq3NPONsWRYwAAAAM5CAg1u4f3yyfKwWbdh3TN/klpgdTpuh1cx8FotFKUn2KjjaUGG2FzccUF2DodE9ozQsnuQAcL7uGpOoID8f7TpSqg37jp12fW29TdsPnZAkjSIBBwAA2hAJOLiFHlHBum5wV0n2T629RVOr2dhLOmlw90izw/FaqU0JuKwikyOBNyssq9aKLbmSpPkTaUcHLkRUaIBmp/SQ1HIV3K4jpaqus6ljiL+SO4eaESIAAPBSJODgNu6fkCyLRfpiT4G+P1pmdjgu16zVjOo3UzXNgdt+qETVdcyBgzle/DJLtfU2DY/voFFJVOYAF6ppw/q2gydOq2x2tJ8mdGT+GwAAaFMk4OA2kjuH6qqBXSTZ56K1d02tZqlJURqewJttMyVFh6hzWECz1iSgLRVV1Oif6QclSfMn9SIxAFyEzuGB+tkI+4b159bub3bdjwsY+L0LAADaFgk4uJV5jZVgH+/OU2ZhucnRuE6zVjMGrZvOYrE4quCYAwczvPxVtqrrbBocF6GxvaLNDgfwePeOs29Y35xVrC059qRbfYNNW3PsH7KQgAMAAG2NBBzcSp/YcE3tHyPDkBa341lwp7aaNc0fg7lGk4CDSUqqavVmWo4k++w3qt+Ai9c1Mkg/GWbfsN40W3ZPXrkqauoVFuirvl3YOg4AANoWCTi4nXkT7MPH//3NUWUXVZocjfOd2mo2b2Iyb7bdRGqSverom8MlqqqtNzkaeJNXN+aosrZB/bqEa1LfzmaHA7Qb942zb1j/ct8x7cwtccx/G5HQUT5WfvcCAIC2RQIObmdgXIQm9O4kmyE93w5nwTW1mg2Ki9C4SzqZHQ4ade8YpG6RQaprMBwtSoCrlVXX6bWN2ZKk+STkAafqERWs64bYN6wvXrtf6Y3z31JoPwUAACYgAQe3NH+SvQpu9Y4jyi2uMjka56HVzH1ZLBaNamwHTsuiDRVt4x+bclReXa9enUM1tX+s2eEA7c5cx4b1Qn21/5gk5r8BAABzkICDWxrao4PGJEer3mZo6YYDZofjNE2tZn27hGsyrWZuh0UMaEuVNfV65Wt79du8icmy0hIHOF3PTqG6epC9Cq66zqZgfx8N6BZhclQAAMAb+ZodAHAm8ycm6+vMIq3aeljzJ/ZSbESg2SFdFFrN3F9TAm7XkVJV1NQrNMC7/4lcs6dAH32bJ8MwzA6lXcorrdaJqjolRoc4EgQAnG/ehGR9+M1RSdKw+A7y8+HzZwAA0Pa8+90l3FpKUpRGJnRURk6xlm44oCeu7W92SBelqdUsuXOoptFq5pa6RQapR8dgHSqu0pbsYk3o471ViiVVtXrg7R2qrG0wO5R2b+6EZAbCAy7UOzZMVw6M1ce78nV5r2izwwEAAF6KBBzc2vxJybrllQy9nXFI90/oqc5hnlkF16zVbAKtZu4sNSlKh4qrlJZ13KsTcK81tkv37BSiWSN7mB1Ou9UpLEDXDqb6DXC1v/xksKb2j9X0AV3MDgUAAHgpEnBwa2OSozWke6R25pbola+y9eiVfc0O6YL8M/2gTlTVKSEqWFcP4o9/d5baM0ort+Zq04Eis0MxTfkp7dILpvTWVfzMAvBwIQG+um5IN7PDAAAAXowhGHBrFotFD0xKliS9ufmgiitrTY7o/FXXNWjZl/Zkxv0TkuXL7Bm31jQH7rujZSqtqjM5GnP8I+2gyhrbpacPoF0aAAAAAC4WmQC4vQm9O6t/13BV1Tbo1cY2Tk/ydsYhFVXUqFtkkK6/lE/f3V1MeKCSOoXIMKT0bO/bhlpZU6+Xv8qSRLs0AAAAADgLCTi4PYvFovkT7VVwb2zKUelJz6lKqqlv0Isb7MmM+8b3ZPOah0hNslfBpWV5XwKOdmkAAAAAcD5mwMEjXNEvVr1jwrS3oFxLNxzQLaPizQ6pVf7zbZ7yy6oVGx6onw6PMzsctFJqzyj9M/2Q0g54VwKuWbv0eNqlAQAAAMBZSMDBI1itFs2dmKwH3t6hF9Yf0AvrD5gd0nm5Z1ySAnx9zA4DrTSqsQLuh/xyFVfWqmOIv8kRtY0Vp7ZLD6VdGgAAAACchQQcPMZVA7vonW2HtdnD2gL7dQnXrJE9zA4D5yE6NECXxIRqX0GF0rOOa/rA9t+KWVPfoKW0SwMAAACAS5CAg8fwsVr0jztGmh0GvERqUpT2FVQozUsScO9sO0y7NAAAAAC4CCUOANCC1J6Nixi8YA5cXYPN0dZNuzQAAAAAOB8JOABoQUpilCwWaX9hhQrLq80Ox6Xe33FEh0+cVHSov342gnZpAAAAAHA2EnAA0IIOIf7qGxsuSdqcVWxyNK7TYDP0fGP1292XJynIn+o3AAAAAHA2EnAAcAbe0Ib60bdHlV1UqQ7Bfvr5qHizwwEAAACAdokEHACcQWqSPQHnaZt3W8tmM7R4baYk6c4xiQoJYC8PAAAAALgCCTgAOIORSR1ltUjZRZXKL21/c+A++S5f+wsrFBboq1tHJ5gdDgAAAAC0WyTgAOAMwgP9NKBbhCQpLavI5GicyzAMLWqsfrt9dILCA/1MjggAAAAA2i8ScABwFk1tqO1tDtyaPYXak1emEH8f3TEm0exwAAAAAKBdIwEHAGcxqmkRQzuaA2evftsvSbolNUGRwf4mRwQAAAAA7RsJOAA4ixEJHeVjtSi3+KRyi6vMDscpvtxfpG8OlyrQz6q7Lqf6DQAAAABcjQQcAJxFaICvBsc1zYHz/Co4wzC0aI29+m12SryiQwNMjggAAAAA2j8ScABwDqmNbaib28EcuLSs49p68IT8fa36xdgks8MBAAAAAK9AAg4AziE1KVqSPXllGIbJ0VycxY2bT28a3l0x4YEmRwMAAAAA3oEEHACcw7D4DvLzsSivtFoHj3vuHLhtB4u16cBx+flYdO/4nmaHAwAAAABegwQcAJxDkL+PLu3eQZJnz4F7bo29+m3m0Dh1iwwyORoAAAAA8B4k4ACgFUY1zoFL89A5cN/klmjDvmPysVp0//hks8MBAAAAAK9CAg4AWiE1qTEB56Fz4BY1zn67bnBX9YgKNjkaAAAAAPAuJOAAoBUu7REpf1+rjpXX6MCxSrPDOS/fHy3TF3sKZLFI90+g+g0AAAAA2hoJOABohUA/Hw2P98w5cEvW2avfrhrYRcmdQ02OBgAAAAC8Dwk4AGglRxvqgSKTI2m9zMJyfbw7T5I0byLVbwAAAABgBhJwANBKqY2LGDZnFctm84w5cIvXZsowpKn9Y9QnNtzscAAAAADAK5GAA4BWGhQXqSA/HxVX1mpfYbnZ4ZxTdlGl/v3NUUnSvAm9TI4GAAAAALwXCTgAaCV/X6uGJzTOgTvg/nPgXlifKZshTejdSQPjIswOBwAAAAC8Fgk4ADgPTW2o7p6Ayy2u0nvbj0iS5k+i+g0AAAAAzEQCDgDOQ9MihvRs954Dt3TDAdXbDI1JjtbQHh3MDgcAAAAAvBoJOAA4DwO7RSg0wFelJ+v0fV6Z2eG0KL+0Wqu2HpbE5lMAAAAAcAck4ADgPPj6WDWicQ7c5iz3bENduuGAahtsGpnQUaMaK/YAAAAAAOYhAQcA52l0z2hJ0iY3nAN3rLxGb2cckiTNn0T1GwAAAAC4AxJwAHCemhYxZGQXq77BZnI0zb38VZZq6m0a0j1SY5KjzQ4HAAAAACAScABw3vp2CVd4oK8qauq1+6j7zIErrqzVm5sPSpIemJQsi8VickQAAAAAAIkEHACcNx+rRSmNs9XS3KgN9dWvs1VV26D+XcM1oXdns8MBAAAAADQiAQcAFyC1KQHnJosYSk/W6Y1NOZKk+ROpfgMAAAAAd+LWCbglS5YoISFBgYGBSklJUUZGxlmPX7Vqlfr06aPAwEANHDhQH3/8cbPrb7vtNlkslmZf06ZNc+VTANBONc2B25pTrDo3mAP3xqYcldfUq3dMmK7oF2t2OAAAAACAU7htAm7lypVasGCBHn/8cW3fvl2DBw/W1KlTVVhY2OLxmzZt0qxZs3TnnXdqx44dmjFjhmbMmKHdu3c3O27atGnKy8tzfL399ttt8XQAtDO9Y8LUIdhPVbUN+vZwiamxVNTU69WN2ZKkuROTZbVS/QYAAAAA7sRtE3DPPvus7r77bt1+++3q16+fli5dquDgYL366qstHv/3v/9d06ZN00MPPaS+ffvqySef1NChQ7V48eJmxwUEBCg2Ntbx1aFDh7Z4OgDaGavVolFuMgfuzbSDKqmqU1J0iK4a2MXUWAAAAAAAp3PLBFxtba22bdumyZMnOy6zWq2aPHmy0tLSWrxNWlpas+MlaerUqacdv379enXu3Fm9e/fWfffdp+PHz/zGuaamRmVlZc2+AKDJ6J7mz4Grqq3Xy19lSZLun5AsH6rfAAAAAMDtuGUCrqioSA0NDYqJiWl2eUxMjPLz81u8TX5+/jmPnzZtmv7xj39ozZo1+vOf/6wNGzZo+vTpamhoaPE+Fy5cqIiICMdX9+7dL/KZAWhPfpwDd0I19S3/O+Jqy9MP6Xhlrbp3DNJ1Q7qaEgMAAAAA4Ox8zQ6gLf3sZz9z/PfAgQM1aNAg9ezZU+vXr9ekSZNOO/7RRx/VggULHP9fVlZGEg6AQ89OoeoUFqBj5TXacajE0ZLaVqrrGrTsy8bqt/HJ8vNxy89UAAAAAMDrueW7tejoaPn4+KigoKDZ5QUFBYqNbXm7X2xs7HkdL0lJSUmKjo5WZmZmi9cHBAQoPDy82RcANLFYzJ0Dt2prrgrLa9Q1IlAzh8a1+eMDAAAAAFrHLRNw/v7+GjZsmNasWeO4zGazac2aNUpNTW3xNqmpqc2Ol6TPP//8jMdL0uHDh3X8+HF16cLQcgAXJjXJnDlwtfU2vbD+gCTp3vE95e/rlv+cAwAAAADkpgk4SVqwYIFeeuklvfHGG9qzZ4/uu+8+VVZW6vbbb5ck3XrrrXr00Ucdxz/44IP65JNP9Mwzz+iHH37QE088oa1bt2revHmSpIqKCj300EPavHmzcnJytGbNGl133XVKTk7W1KlTTXmOADxf0xy4nYdKVF3XdnPg3tt+WEdLq9UpLEA3Dqc1HgAAAADcmdvOgLvpppt07NgxPfbYY8rPz9eQIUP0ySefOBYtHDp0SFbrj/nD0aNHa/ny5fqf//kf/e53v1OvXr30/vvva8CAAZIkHx8fffvtt3rjjTdUUlKirl276oorrtCTTz6pgIAAU54jAM+XEBWs2PBA5ZdVa9vBE7osOdrlj1nfYNPzjdVv94xNUqCfj8sfEwAAAABw4SyGYRhmB+EpysrKFBERodLSUubBAXD41cqdWr3jiOZNSNZvpvZ2+eO9t/2wFvzrG3UM8dfXv52gYH+3/SwFAAAAANq11uaK3LYFFQA8RVvOgWuwGVq8zr445q7LE0m+AQAAAIAHIAEHABepaQ7cN7klqqypd+ljfbwrT1nHKhUR5KdbRsW79LEAAAAAAM5BAg4ALlL3jsGK6xCkepuhLTnFLnscm83Q4rX26rfbL0tQWKCfyx4LAAAAAOA8JOAAwAnaog31s+8LtLegXKEBvrp9dKLLHgcAAAAA4Fwk4ADACZraUDcfcE0CzjAMLV63X5I0Z3S8IoKpfgMAAAAAT0ECDgCcoCkBt+tIqcqq65x+/+v3HtPuI2UK9vfRnWOSnH7/AAAAAADXIQEHAE7QJSJICVHBshnSlmznzoEzDEPPrbVXv/18VLw6hvg79f4BAAAAAK5FAg4AnKSpCi7NyW2oGzOPa8ehEgX4WnXX5cx+AwAAAABPQwIOAJxklIsWMTRVv80a2UOdwwKdet8AAAAAANcjAQcATtK0CfX7vDKVVNU65T7Ts44rI7tY/j5W3TOO2W8AAAAA4IlIwAGAk3QOD1Ry51AZhpTupDlwi9dlSpJ+MjxOXSKCnHKfAAAAAIC2RQIOAJyoqQrOGXPgdhw6oa/2F8nHatF943pe9P0BAAAAAMxBAg4AnMiZixgWrbVXv11/aTd17xh80fcHAAAAADAHCTgAcKKmRQx7C8p1vKLmgu9n95FSrf2hUFaLNHdCsrPCAwAAAACYgAQcADhRxxB/9YkNkyRtzrrwOXCLG6vfrhncVYnRIU6JDQAAAABgDhJwAOBkTVVwaVlFF3T7vfnl+uS7fFks0jyq3wAAAADA45GAAwAnu9g5cE2bT6cPiFWvmDCnxQUAAAAAMAcJOABwslGJUbJYpAPHKlVYVn1etz1wrEIffXtUErPfAAAAAKC9IAEHAE4WEeyn/l3DJUlpWedXBbdkXaYMQ5rct7P6d41wRXgAAAAAgDZGAg4AXCA16fzbUA8dr9IHO+3Vb/Mn9nJJXAAAAACAtkcCDgBcwDEH7jwq4F7YkKkGm6Gxl3TS4O6RLooMAAAAANDWSMABgAuMSOgoH6tFB49X6WjJyXMef6TkpN7Zdli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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# finding best k\n",
        "best_k = k_list[MSE.index(min(MSE))]\n",
        "print(\"The optimal number of neighbors is %d.\" % best_k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PD5lStCglUfO",
        "outputId": "06afd62d-a612-48bf-b7a3-b8dd0ef32620"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The optimal number of neighbors is 11.\n"
          ]
        }
      ]
    }
  ]
}