Random_Forest.ipynb

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        "<a href=\"https://colab.research.google.com/github/KiraFitzge/IWS2023/blob/main/Independent_Work.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Data Processing\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from numpy import mean\n",
        "\n",
        "# Modelling\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, ConfusionMatrixDisplay\n",
        "from sklearn.model_selection import RandomizedSearchCV, train_test_split\n",
        "from scipy.stats import randint\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Tree Visualisation\n",
        "from sklearn.tree import export_graphviz\n",
        "from IPython.display import Image\n",
        "import graphviz\n",
        "\n",
        "# Graphing\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# for data balancing\n",
        "from collections import Counter\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "\n",
        "# Cross-validation\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Cleaning\n",
        "best = pd.read_csv('BEST.csv')\n",
        "best = best.drop(['PARTICIPANT.NUMBER', 'GENDER', 'HANDEDNESS'], axis = 1)\n",
        "\n",
        "best['MAXIMUM.EDUCATIONAL.LEVEL'] = best['MAXIMUM.EDUCATIONAL.LEVEL'].map({'High School':0,'University':1,'Postgraduate':2, 'Professional Training':3})\n",
        "\n",
        "best.head(10)\n",
        "\n",
        "# print(best['MAXIMUM.EDUCATIONAL.LEVEL'].unique())\n",
        "# Create future datasets - untangle english and other languages, as well as look at\n",
        "# the best number of features to look at"
      ],
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              "   AGE.IN.YEARS  MAXIMUM.EDUCATIONAL.LEVEL  PERCENTAGE.EXPOSED.SPANISH  \\\n",
              "0            22                          0                          40   \n",
              "1            21                          1                          70   \n",
              "2            30                          2                          20   \n",
              "3            21                          1                          90   \n",
              "4            39                          3                          50   \n",
              "5            20                          1                         100   \n",
              "6            23                          1                          60   \n",
              "7            24                          1                          50   \n",
              "8            36                          2                          30   \n",
              "9            25                          1                          30   \n",
              "\n",
              "   PERCENTAGE.EXPOSED.BASQUE  PERCENTAGE.EXPOSED.ENGLISH  \\\n",
              "0                         40                          20   \n",
              "1                         20                          10   \n",
              "2                         70                          10   \n",
              "3                         10                           0   \n",
              "4                         40                          10   \n",
              "5                          0                           0   \n",
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              "7                         30                          10   \n",
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              "9                         60                          10   \n",
              "\n",
              "   SELF.PERCEIVED.LEVEL.SPANISH  SELF.PERCEIVED.LEVEL.BASQUE  \\\n",
              "0                            10                            9   \n",
              "1                            10                            9   \n",
              "2                             9                           10   \n",
              "3                             9                            7   \n",
              "4                             9                            8   \n",
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              "6                            10                           10   \n",
              "7                             9                            9   \n",
              "8                             9                            9   \n",
              "9                             8                           10   \n",
              "\n",
              "   SELF.PERCEIVED.LEVEL.ENGLISH  INTERVIEW.MARK.SPANISH  \\\n",
              "0                             5                       5   \n",
              "1                             7                       5   \n",
              "2                             5                       5   \n",
              "3                             3                       5   \n",
              "4                             7                       5   \n",
              "5                             2                       5   \n",
              "6                             7                       5   \n",
              "7                             7                       5   \n",
              "8                             7                       5   \n",
              "9                             7                       5   \n",
              "\n",
              "   INTERVIEW.MARK.BASQUE  INTERVIEW.MARK.ENGLISH  AGE.OF.ACQUISITION.SPANISH  \\\n",
              "0                      5                       3                           0   \n",
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              "7                      5                       4                           0   \n",
              "8                      5                       4                           5   \n",
              "9                      5                       3                           6   \n",
              "\n",
              "   AGE.OF.ACQUISITION.BASQUE  AGE.OF.ACQUISITION.ENGLISH  \\\n",
              "0                          3                           5   \n",
              "1                          0                           5   \n",
              "2                          0                           8   \n",
              "3                          3                           3   \n",
              "4                          0                          12   \n",
              "5                          6                           6   \n",
              "6                          0                           6   \n",
              "7                          0                           8   \n",
              "8                          0                          11   \n",
              "9                          0                           6   \n",
              "\n",
              "   PICTURE.NAMING.TEST.SPANISH  PICTURE.NAMING.TEST.BASQUE  \\\n",
              "0                           63                          52   \n",
              "1                           65                          57   \n",
              "2                           65                          65   \n",
              "3                           65                          45   \n",
              "4                           65                          64   \n",
              "5                           65                          57   \n",
              "6                           64                          64   \n",
              "7                           65                          63   \n",
              "8                           65                          65   \n",
              "9                           62                          65   \n",
              "\n",
              "   PICTURE.NAMING.TEST.ENGLISH  LEXTALE.TEST.SPANISH  LEXTALE.TEST.BASQUE  \\\n",
              "0                           33                 88.33                   89   \n",
              "1                           59                 85.00                   92   \n",
              "2                           50                 93.33                   96   \n",
              "3                           39                 92.50                   88   \n",
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              "5                           31                 90.83                   83   \n",
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              "7                           56                 98.33                   98   \n",
              "8                           60                 98.33                   96   \n",
              "9                           50                 94.17                   97   \n",
              "\n",
              "   LEXTALE.TEST.ENGLISH  \n",
              "0                 58.75  \n",
              "1                 72.50  \n",
              "2                 78.75  \n",
              "3                 67.50  \n",
              "4                 60.00  \n",
              "5                 61.25  \n",
              "6                 66.25  \n",
              "7                 76.25  \n",
              "8                 75.00  \n",
              "9                 71.25  "
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              "          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": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create initial binary mapping of early vs. late stage\n",
        "def earlyClassifier1(n):\n",
        "  if n <= 1:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier2(n):\n",
        "  if n <= 2:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier3(n):\n",
        "  if n <= 3:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier4(n):\n",
        "  if n <= 4:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier5(n):\n",
        "  if n <= 5:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier6(n):\n",
        "  if n <= 6:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier7(n):\n",
        "  if n <= 7:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier8(n):\n",
        "  if n <= 8:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier9(n):\n",
        "  if n <= 9:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "def earlyClassifier10(n):\n",
        "  if n <= 10:\n",
        "    return 0\n",
        "  return 1\n",
        "\n",
        "binaryAcquisition1 = list(map(earlyClassifier1, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition2 = list(map(earlyClassifier2, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition3 = list(map(earlyClassifier3, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition4 = list(map(earlyClassifier4, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition5 = list(map(earlyClassifier5, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition6 = list(map(earlyClassifier6, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition7 = list(map(earlyClassifier7, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition8 = list(map(earlyClassifier8, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition9 = list(map(earlyClassifier9, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "binaryAcquisition10 = list(map(earlyClassifier10, list(best['AGE.OF.ACQUISITION.ENGLISH'])))\n",
        "\n",
        "\n",
        "#best['AGE.OF.ACQUISITION.BASQUE'] = list(map(earlyClassifier, list(best['AGE.OF.ACQUISITION.BASQUE'])))\n",
        "#best['AGE.OF.ACQUISITION.SPANISH'] = list(map(earlyClassifier, list(best['AGE.OF.ACQUISITION.SPANISH'])))\n",
        "\n",
        "#binaryAcquisition = best['AGE.OF.ACQUISITION.ENGLISH']\n",
        "#binaryAcquisitionBasque = best['AGE.OF.ACQUISITION.BASQUE']\n",
        "#binaryAcquisitionSpanish = best['AGE.OF.ACQUISITION.SPANISH']\n",
        "\n",
        "#allVariables = best.drop(['AGE.OF.ACQUISITION.ENGLISH'], axis = 1)\n",
        "\n",
        "# Create an English only dataframe \n",
        "EnglishOnly = pd.DataFrame()\n",
        "EnglishOnly['AGE'] = best['AGE.IN.YEARS']\n",
        "EnglishOnly['EDUCATION'] = best['MAXIMUM.EDUCATIONAL.LEVEL']\n",
        "EnglishOnly['EXPOSURE'] = best['PERCENTAGE.EXPOSED.ENGLISH']\n",
        "EnglishOnly['SELF.PERCEIVED'] = best['SELF.PERCEIVED.LEVEL.ENGLISH']\n",
        "EnglishOnly['INTERVIEW'] = best['INTERVIEW.MARK.ENGLISH']\n",
        "EnglishOnly['PICTURE.NAMING'] = best['PICTURE.NAMING.TEST.ENGLISH']\n",
        "EnglishOnly['LEXTALE.TEST'] = best['LEXTALE.TEST.ENGLISH']\n",
        "\n",
        "oversample = RandomOverSampler(sampling_strategy='minority')\n",
        "# fit and apply the transform\n",
        "X_over1, y_over1 = oversample.fit_resample(EnglishOnly, binaryAcquisition1)\n",
        "X_over2, y_over2 = oversample.fit_resample(EnglishOnly, binaryAcquisition2)\n",
        "X_over3, y_over3 = oversample.fit_resample(EnglishOnly, binaryAcquisition3)\n",
        "X_over4, y_over4 = oversample.fit_resample(EnglishOnly, binaryAcquisition4)\n",
        "X_over5, y_over5 = oversample.fit_resample(EnglishOnly, binaryAcquisition5)\n",
        "X_over6, y_over6 = oversample.fit_resample(EnglishOnly, binaryAcquisition6)\n",
        "X_over7, y_over7 = oversample.fit_resample(EnglishOnly, binaryAcquisition7)\n",
        "X_over8, y_over8 = oversample.fit_resample(EnglishOnly, binaryAcquisition8)\n",
        "X_over9, y_over9 = oversample.fit_resample(EnglishOnly, binaryAcquisition9)\n",
        "X_over10, y_over10 = oversample.fit_resample(EnglishOnly, binaryAcquisition10)\n",
        "\n",
        "#print(Counter(y_over))\n",
        "\n",
        "#print(sum(binaryAcquisition6))\n",
        "#print(sum(y_over))\n",
        "\n",
        "X_train1, X_test1, y_train1, y_test1 = train_test_split(X_over1, y_over1, test_size=0.2, random_state = 1, stratify = y_over1)\n",
        "X_train2, X_test2, y_train2, y_test2 = train_test_split(X_over2, y_over2, test_size=0.2, random_state = 1, stratify = y_over2)\n",
        "X_train3, X_test3, y_train3, y_test3 = train_test_split(X_over3, y_over3, test_size=0.2, random_state = 1, stratify = y_over3)\n",
        "X_train4, X_test4, y_train4, y_test4 = train_test_split(X_over4, y_over4, test_size=0.2, random_state = 1, stratify = y_over4)\n",
        "X_train5, X_test5, y_train5, y_test5 = train_test_split(X_over5, y_over5, test_size=0.2, random_state = 1, stratify = y_over5)\n",
        "X_train6, X_test6, y_train6, y_test6 = train_test_split(X_over6, y_over6, test_size=0.2, random_state = 1, stratify = y_over6)\n",
        "X_train7, X_test7, y_train7, y_test7 = train_test_split(X_over7, y_over7, test_size=0.2, random_state = 1, stratify = y_over7)\n",
        "X_train8, X_test8, y_train8, y_test8 = train_test_split(X_over8, y_over8, test_size=0.2, random_state = 1, stratify = y_over8)\n",
        "X_train9, X_test9, y_train9, y_test9 = train_test_split(X_over9, y_over9, test_size=0.2, random_state = 1, stratify = y_over9)\n",
        "X_train10, X_test10, y_train10, y_test10 = train_test_split(X_over10, y_over10, test_size=0.2, random_state = 1, stratify = y_over10)\n",
        "\n",
        "#allVariables.head()\n"
      ],
      "metadata": {
        "id": "4hklxC_vcAC-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "param_grid =  {'n_estimators': randint(50,500),\n",
        "              'max_depth': randint(1,20)}\n",
        "\n",
        "# Create a random forest classifier\n",
        "rf = RandomForestClassifier()\n",
        "cv = KFold(n_splits=5, random_state=1, shuffle=True)\n",
        "\n",
        "# Use random search to find the best hyperparameters\n",
        "rand_search = RandomizedSearchCV(rf, \n",
        "                                 param_distributions = param_grid, \n",
        "                                 n_iter=10, \n",
        "                                 cv=5)\n",
        "\n",
        "# Fit the random search object to the data\n",
        "rf.fit(X_train1, y_train1)\n",
        "y_pred1n = rf.predict(X_test1)\n",
        "y_pred1nK = cross_val_predict(rf, X_test1, y_test1, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train1, y_train1)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred1noK = best_rf.predict(X_test1)\n",
        "y_pred1 = cross_val_predict(best_rf, X_test1, y_test1, cv=cv)\n",
        "\n",
        "\n",
        "rf.fit(X_train2, y_train2)\n",
        "y_pred2n = rf.predict(X_test2)\n",
        "y_pred2nK = cross_val_predict(rf, X_test2, y_test2, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train2, y_train2)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred2noK = best_rf.predict(X_test2)\n",
        "y_pred2 = cross_val_predict(best_rf, X_test2, y_test2, cv=cv)\n",
        "\n",
        "\n",
        "rf.fit(X_train3, y_train3)\n",
        "y_pred3n = rf.predict(X_test3)\n",
        "y_pred3nK = cross_val_predict(rf, X_test3, y_test3, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train3, y_train3)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred3noK = best_rf.predict(X_test3)\n",
        "y_pred3 = cross_val_predict(best_rf, X_test3, y_test3, cv=cv)\n",
        "\n",
        "rf.fit(X_train4, y_train4)\n",
        "y_pred4n = rf.predict(X_test4)\n",
        "y_pred4nK = cross_val_predict(rf, X_test4, y_test4, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train4, y_train4)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred4noK = best_rf.predict(X_test4)\n",
        "y_pred4 = cross_val_predict(best_rf, X_test4, y_test4, cv=cv)\n",
        "\n",
        "\n",
        "rf.fit(X_train5, y_train5)\n",
        "y_pred5n = rf.predict(X_test5)\n",
        "y_pred5nK = cross_val_predict(rf, X_test5, y_test5, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train5, y_train5)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred5noK = best_rf.predict(X_test5)\n",
        "y_pred5 = cross_val_predict(best_rf, X_test5, y_test5, cv=cv)\n",
        "\n",
        "rf.fit(X_train6, y_train6)\n",
        "y_pred6n = rf.predict(X_test6)\n",
        "y_pred6nK = cross_val_predict(rf, X_test6, y_test6, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train6, y_train6)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred6noK = best_rf.predict(X_test6)\n",
        "y_pred6 = cross_val_predict(best_rf, X_test6, y_test6, cv=cv)\n",
        "\n",
        "\n",
        "rf.fit(X_train7, y_train7)\n",
        "y_pred7n = rf.predict(X_test7)\n",
        "y_pred7nK = cross_val_predict(rf, X_test7, y_test7, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train7, y_train7)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred7noK = best_rf.predict(X_test7)\n",
        "y_pred7 = cross_val_predict(best_rf, X_test7, y_test7, cv=cv)\n",
        "\n",
        "\n",
        "rf.fit(X_train8, y_train8)\n",
        "y_pred8n = rf.predict(X_test8)\n",
        "y_pred8nK = cross_val_predict(rf, X_test8, y_test8, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train8, y_train8)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred8noK = best_rf.predict(X_test8)\n",
        "y_pred8 = cross_val_predict(best_rf, X_test8, y_test8, cv=cv)\n",
        "\n",
        "rf.fit(X_train9, y_train9)\n",
        "y_pred9n = rf.predict(X_test9)\n",
        "y_pred9nK = cross_val_predict(rf, X_test9, y_test9, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train9, y_train9)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred9noK = best_rf.predict(X_test9)\n",
        "y_pred9 = cross_val_predict(best_rf, X_test9, y_test9, cv=cv)\n",
        "\n",
        "\n",
        "rf.fit(X_train10, y_train10)\n",
        "y_pred10n = rf.predict(X_test10)\n",
        "y_pred10nK = cross_val_predict(rf, X_test10, y_test10, cv=cv)\n",
        "\n",
        "rand_search.fit(X_train10, y_train10)\n",
        "best_rf = rand_search.best_estimator_\n",
        "y_pred10noK = best_rf.predict(X_test10)\n",
        "y_pred10 = cross_val_predict(best_rf, X_test10, y_test10, cv=cv)\n",
        "\n",
        "# Print the best hyperparameters\n",
        "# #print('Best hyperparameters:',  rand_search.best_params_)"
      ],
      "metadata": {
        "id": "KH-jtyJzi18C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating Performance of the Models\n",
        "\n",
        "# With hyperparameter tuning and kfold validation - REDUNDANT\n",
        "accuracy1 = accuracy_score(y_test1, y_pred1)\n",
        "precision1 = precision_score(y_test1, y_pred1)\n",
        "recall1 = recall_score(y_test1, y_pred1)\n",
        "\n",
        "accuracy2 = accuracy_score(y_test2, y_pred2)\n",
        "precision2 = precision_score(y_test2, y_pred2)\n",
        "recall2 = recall_score(y_test2, y_pred2)\n",
        "\n",
        "accuracy3 = accuracy_score(y_test3, y_pred3)\n",
        "precision3 = precision_score(y_test3, y_pred3)\n",
        "recall3 = recall_score(y_test3, y_pred3)\n",
        "\n",
        "accuracy4 = accuracy_score(y_test4, y_pred4)\n",
        "precision4 = precision_score(y_test4, y_pred4)\n",
        "recall4 = recall_score(y_test4, y_pred4)\n",
        "\n",
        "accuracy5 = accuracy_score(y_test5, y_pred5)\n",
        "precision5 = precision_score(y_test5, y_pred5)\n",
        "recall5 = recall_score(y_test5, y_pred5)\n",
        "\n",
        "accuracy6 = accuracy_score(y_test6, y_pred6)\n",
        "precision6 = precision_score(y_test6, y_pred6)\n",
        "recall6 = recall_score(y_test6, y_pred6)\n",
        "\n",
        "accuracy7 = accuracy_score(y_test7, y_pred7)\n",
        "precision7 = precision_score(y_test7, y_pred7)\n",
        "recall7 = recall_score(y_test7, y_pred7)\n",
        "\n",
        "accuracy8 = accuracy_score(y_test8, y_pred8)\n",
        "precision8 = precision_score(y_test8, y_pred8)\n",
        "recall8 = recall_score(y_test8, y_pred8)\n",
        "\n",
        "accuracy9 = accuracy_score(y_test9, y_pred9)\n",
        "precision9 = precision_score(y_test9, y_pred9)\n",
        "recall9 = recall_score(y_test9, y_pred9)\n",
        "\n",
        "accuracy10 = accuracy_score(y_test10, y_pred10)\n",
        "precision10 = precision_score(y_test10, y_pred10)\n",
        "recall10 = recall_score(y_test10, y_pred10)\n",
        "\n",
        "x_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "accuracies = [accuracy1, accuracy2, accuracy3, accuracy4, accuracy5, accuracy6, accuracy7, accuracy8, accuracy9, accuracy10]\n",
        "precisions = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
        "recalls = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"Accuracy:\", accuracies[i])\n",
        "  print(\"Precision:\", precisions[i])\n",
        "  print(\"Recall:\", recalls[i])\n",
        "  print(\"----------\")\n",
        "\n",
        "plt.plot(x_values, accuracies, label = \"Accuracy\")\n",
        "plt.plot(x_values, precisions, label = \"Precision\")\n",
        "plt.plot(x_values, recalls, label = \"Recall\")\n",
        "plt.legend()\n",
        "plt.xlabel(\"Binary Cutoff\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.title(\"Results with Optimization and Cross Fold Validation\")\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QwEYQ_bKsD2C",
        "outputId": "fddef920-846b-45ee-9a34-82e12153f3d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.980544747081712\n",
            "Precision: 1.0\n",
            "Recall: 0.9612403100775194\n",
            "----------\n",
            "Accuracy: 0.96875\n",
            "Precision: 1.0\n",
            "Recall: 0.9375\n",
            "----------\n",
            "Accuracy: 0.7434782608695653\n",
            "Precision: 0.8043478260869565\n",
            "Recall: 0.6434782608695652\n",
            "----------\n",
            "Accuracy: 0.7285714285714285\n",
            "Precision: 0.7790697674418605\n",
            "Recall: 0.638095238095238\n",
            "----------\n",
            "Accuracy: 0.6890243902439024\n",
            "Precision: 0.7183098591549296\n",
            "Recall: 0.6219512195121951\n",
            "----------\n",
            "Accuracy: 0.7317073170731707\n",
            "Precision: 0.7317073170731707\n",
            "Recall: 0.7317073170731707\n",
            "----------\n",
            "Accuracy: 0.7754010695187166\n",
            "Precision: 0.782608695652174\n",
            "Recall: 0.7659574468085106\n",
            "----------\n",
            "Accuracy: 0.8075117370892019\n",
            "Precision: 0.8173076923076923\n",
            "Recall: 0.794392523364486\n",
            "----------\n",
            "Accuracy: 0.827433628318584\n",
            "Precision: 0.8245614035087719\n",
            "Recall: 0.831858407079646\n",
            "----------\n",
            "Accuracy: 0.9291666666666667\n",
            "Precision: 0.912\n",
            "Recall: 0.95\n",
            "----------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ncXFxJCQk0KVLl3xj79Wrl8EduGbNmuHo6Kh/n5ycHHbt2sXQoUMNPteNGjWiT58+hcaS+/1RlLs/ABMmTDC4jn/++YeoqCimT59uUAs3YMAA/P399Z8/GxsbLC0t2bNnD3FxcfmeO/f/7rZt2wqsPXoQLy8vdu/ebfB47bXXgKJ/ne+1Y8cOatasyeOPP67fZmtrq79TWhgzMzNGjx5NcHCwwbDS6tWr8fDwoGfPngBYWVnp75Dm5OQQExOjH8Iv6vd37vW+8MILBttnzpyZZ997v55ZWVnExMTg5+eHs7Nzif5f1ahRgzFjxui3WVhY8MILL5CcnMxff/1lsP+oUaMMRhqM+ZlY0UkCVMksWrSI3bt3s3HjRvr37090dDRWVlb610NDQ1EUhbfffht3d3eDx5w5cwD049zz5s0jPj6eBg0aEBAQwKuvvsrJkyfL/BpGjRpFp06dmDx5Mh4eHowePZr169cXmgyZmZnRoUMH9u3bB6gJUJcuXejcuTM5OTkcOnSIs2fPEhsbW+IE6N5fWHB3iPFBvxjg7i+qgqbT3vv6/b/Y6tevb/Cxr68vWq22RFNa778OJycnADw9PfPdXtD13WvKlClcunSJn3/+Oc/MmBUrVtCsWTN9XZm7uzvbt28nISGhWNdw7do1tFotfn5+Bttr1KiBs7Mz165dM9h+/zWD+vUz5tq2bdtG+/btsba2xsXFRT9cl1/shb3P7du3SUtLy/N1BWjYsGGhsTg6OgKFfz/dz9vb2+Dj3M9Pfu/p7++vf93KyooPPviAX3/9FQ8PD7p27cqHH35IRESEfv9u3brx2GOP8e677+Lm5saQIUP4/vvvjaq1AbCzs6NXr14Gj8aNG+vjLMrX+f5r9PPzy1NjY8znOVdukXPurLobN26wb98+Ro8erU/wdTodn3zyCfXr18fKygo3Nzfc3d05efJkkb+/c6/3/mHs/GJOS0vjnXfewdPT0+B94+PjS/T/qn79+nmGvHOHzAr7f2XMz8SKThKgSqZt27b06tWLxx57jK1bt9K0aVPGjh1LcnIygD6JeOWVV/L8pZX7yP0B07VrVy5dusSyZcto2rQpS5YsoWXLlixZskT/fg9q3JWTk1Psa7CxsWHv3r38/vvv+hlRo0aN4tFHHy30vJ07d+bvv/8mPT1dnwA5OzvTtGlT9u3bp0+OSpoAPWhGlaIoDzzGycmJmjVrFppEnjx5ktq1a+t/wT1IaTTKe9B1FOf6cn322WesWbOGxYsX07x5c4PXVq1axcSJE/H19WXp0qXs3LmT3bt388gjjxTpbl9+jP18FPfacuvIrK2t+eqrr9ixYwe7d+9m7Nix+R5bks+hMfz9/QG1jqgo7r1bUFQzZ87kwoULLFiwAGtra95++20aNWqkL5DPbRIaHBzMjBkzuHnzJk899RStWrXS/wwqKVM1iGzVqhX+/v6sWbMGgDVr1qAoisHsr/fff59Zs2bRtWtXVq1axa5du9i9ezdNmjQp8fd3QZ5//nnee+89Ro4cyfr16/ntt9/YvXs3rq6uZfq+9yrr73dTkASoEjMzM2PBggXcunWLL7/8EkA/tdrCwiLPX1q5j3vvPLi4uDBp0iTWrFnD9evXadasmUEH0mrVquXbx6egv8ZyFfSDTKvV0rNnTxYuXMjZs2d57733+OOPPwxmC+WnS5cuZGZmsmbNGm7evKlPdLp27apPgBo0aFBokWlZ/ZAdOHAgV65cYf/+/fm+vm/fPq5evaqfuXOv+4ciQ0ND0el0BjPUTN09eN++fbzyyivMnDkz32nBGzduxMfHh02bNvHkk0/Sp08fevXqladPVVGuo169euh0ujyfn8jISOLj40ut99FPP/2EtbU1u3bt4qmnnqJfv355ir6Lwt3dHRsbm3yHmM+fP1/o8Q0aNKBhw4Zs2bKlRMlF7ucnv/c8f/58ns+fr68vL7/8Mr/99hunT58mMzOT//3vfwb7tG/fnvfee49//vmHH3/8kTNnzrB27dpix5gbZ3G/zvXq1ePSpUt5fhkb83m+17hx4zh9+jQnT55k9erV1K9fnzZt2uhf37hxIz169GDp0qWMHj2a3r1706tXr2L1Osu93vtn8OYX88aNG5kwYQL/+9//ePzxx3n00Ufp3Llznvct6v+rixcv5kmgcptSlmZPsYpKEqBKrnv37rRt25ZPP/2U9PR0qlevTvfu3fn2228JDw/Ps//t27f1z+/vampvb4+fn5/B7WxfX1/OnTtncNyJEycKnbEF6u1uIM9/0tjY2Dz75t5JKOxWert27bCwsOCDDz7AxcVFP6unS5cuHDp0iL/++suouz92dnbFvnVckFdffRUbGxueffbZPJ/f2NhYpk6diq2trb5FwL0WLVpk8PEXX3wBqL2f7o3bVI0lw8PDGTlyJJ07d+ajjz7Kd5/cvxLv/UV0+PBhgoODDfaztbUF8n5v5Kd///4AeRpXLly4ECDfWTPFYWZmhkajMbgLefXq1WJ33jYzM6NPnz5s3ryZsLAw/faQkBB27dpl1DneffddYmJimDx5MtnZ2Xle/+2339i2bVuB52jdujXVq1fnm2++Mfj/9euvvxISEqL//KWmpuZJVH19fXFwcNAfFxcXlyfJMPb/bmFK8nXu378/t27dMmhZkNuOoihyk/p33nmH48eP50nyzczM8lz/hg0bitWNP/f/9eeff26wPb8Grfm97xdffJHnjvmDfubmp3///kRERLBu3Tr9tuzsbL744gvs7e3p1q2bMZdRqck0+IfAq6++yogRI1i+fDlTp05l0aJFdO7cmYCAAKZMmYKPjw+RkZEEBwdz48YNfc+Kxo0b0717d1q1aoWLiwv//PMPGzduNJgG+tRTT7Fw4UL69OnD008/TVRUFN988w1NmjTJU8R7v1atWgHqtPPRo0djYWHBoEGDmDdvHnv37mXAgAHUq1ePqKgovvrqK+rUqUPnzp0LPKetrS2tWrXi0KFD+h5AoN4BSklJISUlxagEqFWrVqxbt45Zs2bRpk0b7O3tGTRoUKHHFaZ+/fqsWLGCcePGERAQwNNPP423tzdXr15l6dKlREdHs2bNmnynr1+5coXBgwfTt29fgoOD9dNjAwMDDeL+/fffWbhwIbVq1cLb21tfwFzWXnjhBW7fvs1rr72W56/9Zs2a0axZMwYOHMimTZsYNmwYAwYM4MqVK3zzzTc0btzY4C6GjY0NjRs3Zt26dTRo0AAXFxeaNm1K06ZN87xvYGAgEyZM4LvvviM+Pp5u3bpx5MgRVqxYwdChQ+nRo0epXN+AAQNYuHAhffv2ZezYsURFRbFo0SL8/PyKXRv37rvvsnPnTrp06cL06dP1v2CaNGli1DlHjRrFqVOneO+99/j3338ZM2YM9erVIyYmhp07dxIUFFRoJ+jcPxgmTZpEt27dGDNmjH4avJeXFy+99BIAFy5coGfPnowcOZLGjRtjbm7Ozz//TGRkJKNHjwbU+q6vvvqKYcOG4evrS1JSEosXL8bR0VGfwBRXSb7OU6ZM4csvv2T8+PEcPXqUmjVrsnLlSn2ibSxvb286duzIli1bAPIkQAMHDmTevHlMmjSJjh07curUKX788cdiNTVt3rw5Y8aM4auvviIhIYGOHTsSFBSUb7+xgQMHsnLlSpycnGjcuDHBwcH8/vvveervmjdvjpmZGR988AEJCQlYWVnxyCOP6NtU3OuZZ57h22+/ZeLEiRw9ehQvLy82btzIgQMH+PTTT4tcfF8pmWLqmSi63GnO+U0/zcnJUXx9fRVfX1/9dORLly4p48ePV2rUqKFYWFgotWvXVgYOHKhs3LhRf9z//d//KW3btlWcnZ0VGxsbxd/fX3nvvfeUzMxMg/OvWrVK8fHxUSwtLZXmzZsru3btMmoavKIoyvz585XatWsrWq1WP007KChIGTJkiFKrVi3F0tJSqVWrljJmzBjlwoULRn0uXn31VQVQPvjgA4PtuVNeL126ZLA9v2nwycnJytixYxVnZ2cF0F9L7r4bNmwwOEd+01oLcvLkSWXMmDFKzZo1FQsLC6VGjRrKmDFj8p1Gnzs99uzZs8rjjz+uODg4KNWqVVNmzJhhMG1ZURTl3LlzSteuXRUbGxuDKcQPmgafX5sDQHnuuefyvb57p0DfP233QdOY7/2663Q65f3331fq1aunWFlZKS1atFC2bduW7/fLwYMHlVatWimWlpYG58hv+n1WVpby7rvvKt7e3oqFhYXi6empzJ4926DVQ0HX/KB2DvdbunSpUr9+fcXKykrx9/dXvv/++3zjye9zmPv+97cp+Ouvv/TX6ePjo3zzzTeFthi4X+7/merVqyvm5uaKu7u7MmjQIGXLli36fR70vZtr3bp1SosWLRQrKyvFxcVFGTdunHLjxg3969HR0cpzzz2n+Pv7K3Z2doqTk5PSrl07g6nlx44dU8aMGaPUrVtXsbKyUqpXr64MHDhQ+eeffwq9hm7duilNmjQpcB9jv875fT2vXbumDB48WLG1tVXc3NyUF198Udm5c6dR0+DvtWjRIgVQ2rZtm+e19PR05eWXX1Zq1qyp2NjYKJ06dVKCg4PzxGPMNHhFUZS0tDTlhRdeUFxdXRU7Oztl0KBByvXr1/P8LI2Li1MmTZqkuLm5Kfb29kqfPn2Uc+fO5fv9tnjxYsXHx0ffaiH32vP7nEVGRurPa2lpqQQEBOT5GZffz4Zc+f3Mr0w0ilKJK5iEeAjMnTuXd999l9u3b+sbRgohhChbUgMkhBBCiCpHEiAhhBBCVDmSAAkhhBCiypEaICGEEEJUOXIHSAghhBBVjiRAQgghhKhypBFiPnQ6Hbdu3cLBwcHkSw8IIYQQwjiKopCUlEStWrXyLPR6P0mA8nHr1q08q2ULIYQQonK4fv06derUKXAfSYDykdsC/Pr164Wu2C2EEEKIiiExMRFPT0+jlvKQBCgfucNejo6OkgAJIYQQlYwx5StSBC2EEEKIKkcSICGEEEJUOZIACSGEEKLKkQRICCGEEFWOJEBCCCGEqHIkARJCCCFElSMJkBBCCCGqHEmAhBBCCFHlSAIkhBBCiCpHEiAhhBBCVDkmTYD27t3LoEGDqFWrFhqNhs2bNxd6zJ49e2jZsiVWVlb4+fmxfPnyPPssWrQILy8vrK2tadeuHUeOHCn94IUQQghRaZk0AUpJSSEwMJBFixYZtf+VK1cYMGAAPXr04Pjx48ycOZPJkyeza9cu/T7r1q1j1qxZzJkzh2PHjhEYGEifPn2Iiooqq8sQQgghRCWjURRFMXUQoC5c9vPPPzN06NAH7vOf//yH7du3c/r0af220aNHEx8fz86dOwFo164dbdq04csvvwRAp9Ph6enJ888/z+uvv25ULImJiTg5OZGQkFC6i6GmJ6iPh4HWHBxqghELzgkhhBDloSi/vyvVavDBwcH06tXLYFufPn2YOXMmAJmZmRw9epTZs2frX9dqtfTq1Yvg4OAHnjcjI4OMjAz9x4mJiaUbeK6/l0LQu2VzblPoMAP6vGfqKIQQQogiq1QJUEREBB4eHgbbPDw8SExMJC0tjbi4OHJycvLd59y5cw8874IFC3j33XJITLTmYG5d9u9T1hQFcjLgxBp4dD5opZZeCCFE5VKpEqCyMnv2bGbNmqX/ODExEU9Pz9J/o04vqI/KLicLPvSB1Bi49S/UaWXqiIQQQogiqVR/uteoUYPIyEiDbZGRkTg6OmJjY4ObmxtmZmb57lOjRo0HntfKygpHR0eDhyiAmQX49lCfX/zNtLEIIYQQxVCpEqAOHToQFBRksG337t106NABAEtLS1q1amWwj06nIygoSL+PKCV+j6r/SgIkhBCiEjJpApScnMzx48c5fvw4oE5zP378OGFhYYA6NDV+/Hj9/lOnTuXy5cu89tprnDt3jq+++or169fz0ksv6feZNWsWixcvZsWKFYSEhDBt2jRSUlKYNGlSuV7bQ8/vTjH6rWOQfNu0sQghhBBFZNIaoH/++YcePXroP86tw5kwYQLLly8nPDxcnwwBeHt7s337dl566SU+++wz6tSpw5IlS+jTp49+n1GjRnH79m3eeecdIiIiaN68OTt37sxTGC1KyLEm1GgGESfhUhAEjjZ1REIIIYTRKkwfoIqkzPoAPWyC5sO+j6HpY/D4MlNHI4QQoooryu/vSlUDJCqY+nfqgEKDICfbtLEIIYQQRSAJkCi+2q3B2hnS4+HmP6aORgghhDCaJECi+MzMwa+n+vzibtPGIoQQQhSBJECiZOr3Vv+V6fBCCCEqEUmARMn43rkDFHESEsNNG4sQQghhJEmARMnYu0Otlurz0N9NG4sQQghhJEmARMnlDoOFSh2QEEKIykESIFFyuQnQpT/VhVKFEEKICk4SoHL00PacrNUCbF0hIxGuHzZ1NEIIIUShJAEqR7+cDKfLh3/w3OpjLN57mcOXY0jJeAgaCGq1d9cGk9lgQgghKgGTrgVW1Zy4Hs/12DSux6ax/aQ6Y0qrgfrVHQj0dCLQ05nAOs40rOGAhVkly03r94aT6+Di7/DoPFNHI4QQQhRI1gLLR1mtBZaYnsWpGwmcuBHPievxnLieQERiep79rMy1NK7lSGAdZzUxquOMl6sdWq2m1GIpdamx8JEvKDp46Qw41TF1REIIIaqYovz+ljtA5cjR2oJOfm508nPTb4tMTOfE9XhO3pMYJaZn829YPP+Gxd9zrDnN7iREzeo409zTGQ9HaxNcxQPYuqhLY9w4onaFbj3J1BEJIYQQDyQJkIl5OFrTu0kNejepAaiF0ldjUtU7RHcSojO3EklMz2Z/aDT7Q6PvOdbqzl0idegsoI4TTjYWproUdRhMEiAhhBCVgAyB5aOshsCKKytHx/mIJPUu0Z3E6EJkErp8vnI+bnYEejrTrI5aU9S4piPWFmblE+it4/BdN7C0h9cug7lV+byvEEIIQdF+f0sClI+KlgDlJzUzm9M3Ezl5I57jd4bQwmJT8+xnrtXgX9NBHTa7c7fIr7o9ZmVRT6TTwUJ/SI6E8VvAp3vpv4cQQgjxAFIDVAXYWprT1tuFtt4u+m2xKZmcuBHPyet364liUjI5fTOR0zcTWX047M6xZjSt7URgnbszz+pUs0GjKWFSlDsd/viP6jCYJEBCCCEqKLkDlI/KcAfIGIqicDM+TT90dvx6PKdvJpCSmZNnXxc7SwLr3C2wblbHCVf7YgxhnfkZNkwEtwYw4++SX4QQQghhJBkCK6GHJQHKT45O4dLtZH0t0ckbCYSEJ5KVk/fboE41G/1U/GZ1nAmo7YSdVSE3DdPi4UMfUHLgxRNQzatMrkMIIYS4nyRAJfQwJ0D5Sc/KISQ80aDI+tLtlDz7aTXgV91eP/NsULNaONnmM+vs+/5w7QD0/xjaTimHKxBCCCGkBkgUkbWFGS3qVqNF3Wr6bYnpWZy+kcDxe2qKwhPSuRCZzIXIZDYcvcHBS9F8Na5V3hPWf1RNgC7ulgRICCFEhSQJkMiXo7UFHf3c6HhP08aoxHRO3EjgQGg0yw9eZd/FaHJ0St4ZZX6Pwu9z4cpeyEoHiwrUsFEIIYRAFkMVRVDd0ZpHG3vw1oBGOFiZk5SeTUh4Yt4dPZqAQy3IToNr+8s/UCGEEKIQkgCJIjM309LmzvT7Q5dj8u6g0ajDYKAOgwkhhBAVjCRAolja+xSQAIG6LAbAxd/KKSIhhBDCeJIAiWJp7+MKwOErseTkuyZHN9BaQOxliLlUztEJIYQQBZMEqBydjTnLmG1j2BK6hYycDFOHUyJNajkVXAdk5QD1OqjPZRhMCCFEBSMJUDlad34dp2NO89aBt3h0w6N8evRTbiXfMnVYxWKm1eiX4ZBhMCGEEJWNJEDl6MWWL/JiyxepYVeDuIw4lp5eSr9N/Xjxjxc5FH6IytaTMncYLPhSIQnQ1f2QmbexohBCCGEqkgCVIxdrFyYHTObX4b/yaY9PaVezHTpFxx/X/2DKb1MYsmUIq0NWk5yZbOpQjZKbAB15UB2QWwNwqgs5GXBlXzlHJ4QQQjyYJEAmYK41p2fdnizpvYQtQ7Ywxn8Mtua2XEm4woIjC+i5oSfvHXqPy/GXTR1qgRrXclTrgDKyOXsrnzogg+nwMgwmhBCi4pAEyMR8nH14o90bBI0I4o12b+Dt5E1qdiprz69lyJYhTP5tMkHXgsjWZZs61DyKVAcUuhsq2RCfEEKIh5ckQBWEvaU9Y/zHsGXIFhb3XkzPuj3RarQcDj/MzD0z6bepH0tOLSE2PdbUoRrIHQZ7YALk3QXMrCA+DKIvlGNkQgghxIOZPAFatGgRXl5eWFtb065dO44cOfLAfbOyspg3bx6+vr5YW1sTGBjIzp07DfaZO3cuGo3G4OHv71/Wl1FqNBoN7Wu259Men7Jz+E4mB0ymmlU1IlIi+OzYZ/Ta0Is397/JqdunTB0qYEQdkKUdeHVSn8swmBBCiArCpAnQunXrmDVrFnPmzOHYsWMEBgbSp08foqKi8t3/rbfe4ttvv+WLL77g7NmzTJ06lWHDhvHvv/8a7NekSRPCw8P1j/37K+d6VDXta/JiyxfZPWI373V+jwC3ALJ0WWy9tJWxO8YyZtsYtl7aatKeQoXWAYFMhxdCCFHhaBQTzr1u164dbdq04csvvwRAp9Ph6enJ888/z+uvv55n/1q1avHmm2/y3HPP6bc99thj2NjYsGrVKkC9A7R582aOHz9e7LgSExNxcnIiISEBR0fHYp+nLJy6fYq159fy65VfydJlAVDNqhqPNXiMkQ1GUtO+ZrnH9PTyvwk6F8Wb/RsxpatP3h1iLsEXLdXO0P+5ojZJFEIIUSXlph0ajabUz12U398muwOUmZnJ0aNH6dWr191gtFp69epFcHBwvsdkZGRgbW1tsM3GxibPHZ6LFy9Sq1YtfHx8GDduHGFhYQXGkpGRQWJiosGjogpwD+C9zu/x+4jfDXoKLTm1hL6b+pqkp1ChdUCuvuDiA7osuPxXucUlhBCi4jkccZhxO8ax98Zek8ZhsgQoOjqanJwcPDw8DLZ7eHgQERGR7zF9+vRh4cKFXLx4EZ1Ox+7du9m0aRPh4eH6fdq1a8fy5cvZuXMnX3/9NVeuXKFLly4kJSU9MJYFCxbg5OSkf3h6epbORZYhY3oKrTm3hpSssm9AWGgdEICfTIcXQggBS04u4VT0KQ7cPGDSOExeBF0Un332GfXr18ff3x9LS0tmzJjBpEmT0GrvXka/fv0YMWIEzZo1o0+fPuzYsYP4+HjWr1//wPPOnj2bhIQE/eP69evlcTml4t6eQpuHbGZ0w9H6nkLvH36/XHoKFa0OSKbDCyFEVXXi9gkORxzGXGPOxCYTTRqLyRIgNzc3zMzMiIyMNNgeGRlJjRo18j3G3d2dzZs3k5KSwrVr1zh37hz29vb4+ORTd3KHs7MzDRo0IDQ09IH7WFlZ4ejoaPCojHydfXmz/ZsGPYVSslIMewqFlX5PIaP6AXl1AnMbSLoFUWdL9f2FEEJUDktOLQFgkO8gk9Ss3stkCZClpSWtWrUiKChIv02n0xEUFESHDh0KPNba2pratWuTnZ3NTz/9xJAhQx64b3JyMpcuXaJmTdN+osvT/T2FHvF85G5PoT9n0n9T/1LvKVRoHZCFDXh3VZ/LMJgQQlQ5F+IusOf6HjRoeKrpU6YOx7RDYLNmzWLx4sWsWLGCkJAQpk2bRkpKCpMmTQJg/PjxzJ49W7//4cOH2bRpE5cvX2bfvn307dsXnU7Ha6+9pt/nlVde4a+//uLq1ascPHiQYcOGYWZmxpgxY8r9+kwtt6fQZ498ZtBTKDwl3KCn0Ono0yV+L6PqgPTLYuwu8fsJIYSoXHLv/vT26o2Xk5dpgwHMTfnmo0aN4vbt27zzzjtERETQvHlzdu7cqS+MDgsLM6jvSU9P56233uLy5cvY29vTv39/Vq5cibOzs36fGzduMGbMGGJiYnB3d6dz584cOnQId3f38r68CiW3p9DUwKnsurqLNSFrOB1zmq2XtrL10lYC3AIY7T+aPl59sDKzKvL5G9dyxMHanKR0tQ4ooI5T3p387sz4CzsEafFg41yiaxJCCFE5hCWGsevqLgAmB0w2cTQqk/YBqqgqch+g0lTaPYUmr/ib30MK6AcE8GUbdUmMESugydASXoEQQojKYO7Bufx08Se61O7CV72+KrP3qRR9gITpFdZTaOafM4vUUyh3GCz4QXVAYDgbTAghxEMvMiWSLZe2ADCl2RQTR3OXJEDCsKdQ909pV0PtKRQUFsSU36YwdMtQo3oK5SZAf1+JJTtHl/9OucNgobtB94B9hBBCPDRWnF1Bti6bVh6taFG9hanD0ZMESOiZa83pWa8nS/oY9hS6nHBZ31Po/cPvczkh/55CjWreqQPKyOZs+AP6AdXrCBZ2kBwJESfL8GqEEEKYWlx6HBsvbATgmYBnTByNIUmARL7u7Sk0u+1svBy9SMlKYc25NQzZrPYUOhd7zuAYM62GdoX1AzK3Ap/u6vNQGQYTQoiH2aqQVaRlp9HYtTEdahXc4qa8SQIkCmRvac/YRmPZOnRrnp5CH//zcZ797/YDKqDHkEyHF0KIh15yZjJrQtYAMCVgSpksfloSkgAJo9zbU2hp76UAnI05m6dA2qg6oNwE6MbfkFp6zRiFEEJUHOvOryMpKwlvJ28eqfuIqcPJQxIgUWSB7oGYa81JykziVsotg9eMqgNyqgPVG4Oig0t/lEPEQgghylN6djo/nP0BUPv+aDUVL92oeBGJCs/CzAI/Zz8AzsUUow4IZBhMCCEeYj+H/kxseiy17WvTz7ufqcPJlyRAolj8XfwBCIkNyfOacXVAd/oByXR4IYR4qGTpsvj+9PcATGoyCQuthYkjyp8kQKJYchOg+2eCgZF1QJ7twMoRUmPg1r9lFqcQQojytf3ydsJTwnG1dmVo/aGmDueBJAESxdLIpREAITF57wAZVQdkZnF3OrysDi+EEA+FHF0OS0+pE2UmNJmQ79qSKRnZ/H420uhVBsqKJECiWBq6NESDhqi0KGLSDGt9jK8DumcYTAghRKUXFBbE1cSrOFg6MLLhyHz3+XbvZSb/8A+vbjRtM1xJgESx2FnYUc+xHlDwMFiBdUC5y2LcPAbJt0s9RiGEEOVHURSWnFoCwLhG47CzsMuzT0RCOt/tvQTAI/7VyzW++0kCJIrNmELoAuuAHGtCjWaAApeCyipMIYQQ5eDArQOExIZgY27DOP9x+e6zcPd50rN0tKzrTL+mNco5QkOSAIliK6gQulFNRxwLqwOCe6bDSx2QEEJUZotPLgZgRIMROFs753k9JDyRDUdvAPDmgMYm7wwtCZAottxC6PwSIDOthrbeucNgxtQBBYEup9RjFEIIUfaORh7lWNQxLLQWTGgyId99Fvx6DkWBAQE1aVWvWjlHmJckQKLY/F3VO0DXEq+RkpWS5/X2PmohdPClAhKg2q3B2hnS4+HGP2UQpRBCiLK2+JR692eo31Cq2+at7fnrwm32XriNhZmG1/o2LO/w8iUJkCg2F2sX/Tf6+djzeV7X1wFdjXtwHZCZOfj1VJ/LMJgQQlQ6Z2POcuDmAbQaLZOaTsrzeo5OYcEOtVb0yfZe1HPNWxxtCpIAiRLR9wPKpxA6tw4oOSObM7cKqAPykzogIYSorHJnfvXz7oeng2ee1386eoNzEUk4Wpvz/CN+5R3eA0kCJEqkoEJoo+uAcqfDR5yEpIhSj1EIIUTZuJxwmd+v/Q7A002fzvN6amY2H/+mjhA8/0h9qtlZlmt8BZEESJRIQYXQcLcOqMAEyN4darVUn4f+XqrxCSGEKDvLTi1DQaGHZw/qV6uf5/XFe68QlZRBnWo2jO9YzwQRPpgkQKJEcguhQ+NDyczJzPO6UXVAcHc2mAyDCSFEpXAr+RbbL28HYErAlDyvRyWl8+2dpof/6euPlblZucZXGEmARInUsquFo6Uj2bpsQuND87xudB1Qbj+gS39CTlYZRSuEEKK0fH/6e7KVbNrXbE+Ae0Ce1z/ZfZHUzByaezozsFlNE0RYMEmARIloNJrS6QdUqwXYukJGIlw/UiaxCiGEKB3RadFsurgJyP/uz4XIJNb9HQbAmwMambzpYX4kARIlpl8SI5+V4cHIOiCt2d1iaBkGE0KICm3l2ZVk6jJp5t6MNjXa5Hl9wY4QdAr0aeJBGy8XE0RYOEmARInl1gE9uBC6qHVAsjq8EEJUVAkZCaw7vw5Q7/7cf3dn/8Vo/jx/G3Othv/09c97AkWBxPDyCLVAkgCJEssdAjsfd56cfJazMLoOyPcR0Ggh6gwk3CircIUQQpTA2nNrSclKoX61+nSt09XgtRydwnt3mh4+0b4ePu72eU9waiN83gIOf1se4T6QJECixLwcvbA2syYtO42wpLA8rxtdB2Troi6NATIdXgghKqDUrFRWhawCYHLTyWg1hmnEz//eJCQ8EQcrc17omXdaPCkxsPM/kJ0G6QX8QVwOJAESJWamNaNBtQZACfsBgQyDCSFEBfbTxZ+Iz4inrkNd+nj1MXgtLTOH/91pevjcI3645Nf0cOfrkBoD1ZtApxfLI+QHkgRIlAp9IXQ+S2IAdPA1tg7oznT4y3sgO6M0QxRCCFECmTmZLD+9HICnmj6Fmdawr8+yA1cIT0intrMNEzt65T3Bxd1war1a6jDkCzA3bVdoSYBEqdAXQsfkfweoUQ1HnGwsCq8DqtEM7KpDZjKEBZdFqEIIIYph66WtRKVFUd22OoN8Bxm8djspg6/+VHvBvdqnIdYW9zU9zEiCX2aqz9tPh9qtyiHigkkCJErFvb2AFEXJ87pWq6GttzHT4bV37wLJMJgQQlQI2bpslp1eBsDEJhOxNDO8e/NZ0AVSMnMIqO3E4MBaeU8QNA8Sb4BzPejxRnmEXChJgESpqF+tPmYaM+Iy4ohMjcx3n9zp8MGF1gFJAiSEEBXJb1d/43rSdZytnHms/mMGr4VGJbHmyHUA3ujfCK32vqaHYYfhyGL1+aDPwNKuPEIulCRAolRYmVnh7eQNFF4I/feV2ILrgHx6gMYMos9D3NXSDlUIIUQR6BQdi0+pCcwTjZ7A1sLW4PX//nqOHJ1Cr0Ye+npPvewM2Po8oEDzJ8C3RzlFXTiTJ0CLFi3Cy8sLa2tr2rVrx5EjD14GISsri3nz5uHr64u1tTWBgYHs3LmzROcUpSd3GOxBhdC5dUApmTmcLqgOyMYZPNupz+UukBBCmNTeG3sJjQ/FzsKO0f6jDV4LvhTD7yFRmGk1vN4vn6aHez9W/5i1qw59/q+cIjaOSROgdevWMWvWLObMmcOxY8cIDAykT58+REVF5bv/W2+9xbfffssXX3zB2bNnmTp1KsOGDePff/8t9jlF6SlsSQyj64BAhsGEEKICUBSFxSfVuz+jG47GycpJ/5pOp/D+naaHY9vWxa/6fU0PI8/A/oXq8/4fgU21conZWCZNgBYuXMiUKVOYNGkSjRs35ptvvsHW1pZly5blu//KlSt544036N+/Pz4+PkybNo3+/fvzv//9r9jnFKWnkeuDF0XNlVsHZHQ/oCt7ISu9VOITQghRNEcijnAy+iRWZlY80fgJg9e2nrjFqZsJ2FuZ82Kv+5oe6nLUoS9dNvgPhMZDyjFq45gsAcrMzOTo0aP06tXrbjBaLb169SI4OP/pzxkZGVhbWxtss7GxYf/+/cU+Z+55ExMTDR6i6HLvAIWnhBOfHp/vPkbXAXk0AYdaarfQa/tLO1QhhBBGyK39GV5/OG42bvrt6Vk5fLRLbXo4rbsvbvZWhgce/gZuHgUrJ+j/Mchq8HdFR0eTk5ODh4eHwXYPDw8iIiLyPaZPnz4sXLiQixcvotPp2L17N5s2bSI8PLzY5wRYsGABTk5O+oenp2cJr65qcrB0oI59HQDOxRXcD6jQOiCNBurnrg4vw2BCCFHeTt4+yeHww5hrzJnYZKLBa98fuMrN+DRqOlnzVCdvwwPjrsIfd+p9es8Dx5rlEm9RmbwIuig+++wz6tevj7+/P5aWlsyYMYNJkyah1ZbsMmbPnk1CQoL+cf369VKKuOrRD4M9oCFi0eqAcpfF+K3U4hNCCGGcJaeWADDAZwC17O/29olJvtv08JXeDbGxvKfpoaLALy9CVip4dYGWE8o15qIwWQLk5uaGmZkZkZGGPWMiIyOpUaNGvse4u7uzefNmUlJSuHbtGufOncPe3h4fH59inxPAysoKR0dHg4consKWxIAi1AF5dwOtBcRehphLpRajEEKIgl2Mu8if1/9Eg4anA542eO3zoIskZWTTuKYjw1rUNjzw+Gp1KSNza7XnTwUc+splsgTI0tKSVq1aERQUpN+m0+kICgqiQ4cOBR5rbW1N7dq1yc7O5qeffmLIkCElPqcoHbkJUMGF0EbWAVk7Qr07XzcZBhNCiHKz9PRSAB6t96i+xxvA5dvJ/Hg4DIC3BtzX9DA5Cnbd6fLcfTa4+pZbvMVh0iGwWbNmsXjxYlasWEFISAjTpk0jJSWFSZMmATB+/Hhmz56t3//w4cNs2rSJy5cvs2/fPvr27YtOp+O1114z+pyibOX2ArqaeJW07LT89zG2DgjAL3c6vAyDCSFEebieeJ1fr/wKwOSAyQavfbDzHNk6hUf8q9PRz83wwB2vQno81AyEDjPKKdriMzflm48aNYrbt2/zzjvvEBERQfPmzdm5c6e+iDksLMygvic9PZ233nqLy5cvY29vT//+/Vm5ciXOzs5Gn1OULXdbd1ytXYlJj+FC3AUC3QPz7JNbB7T7bCSHLsfQ3NP5wSes3xt2vw1X90NmKljaPnhfIYQQJbbszDJ0io7OtTvr6zoBjlyJZdeZSLQamH1/08OQbXB2s9rFf/CXYGbS9MIoJo9wxowZzJiRf6a4Z88eg4+7devG2bNnS3ROUfb8Xf05cPMA52LO5ZsAgVoHlJsATe1WwG1S94bgVBcSwuDqPmjQp4yiFkIIEZkSyZbQLQBMCZii367TKby3Xf39O6pNXep7ONw9KC0etr+sPu/0AtRsVl7hlkilmgUmKofClsQA6HCnELrQOiCN5p6u0DIMJoQQZemHsz+QpcuiZfWWtPRoqd++7VQ4J24kYGtpxkuP3tf0cPc7kBwBLr7Q7T/lHHHxSQIkSp0xhdD+NRyMrwO6NwFSlNIKUwghxD3i0+PZcGEDAFOa3b37k56Vwwe/qj/Pp3bzpbrDPQ2Jr+yDYyvU54O/AAubcou3pCQBEqUu9w7QhbgLZOmy8t1Hq9XQzth+QN5dwcwS4sMg+kKpxiqEEEL147kfSctOo5FLIzrV6qTf/kOw2vTQw9GKyV3uaXqYlQa/vKA+bzUJvDpRmUgCJEpdHYc62FvYk6XL4nL85Qful9sPKPhSIQmQpR14dVafy3R4IYQodcmZyfwY8iOg3v3R3OnfE5eSyZd/qE0PX+7dEFvLe0qH9yxQ+7Q51IRH3y33mEtKEiBR6rQaLQ1dGgLGLYz6z9VYsgqqAwLpCi2EEGVo/YX1JGUm4e3kTc+6PfXbv/gjlMT0bPxrOPBYyzp3D7h1HA5+qT4fsBCsnahsJAESZSJ3GMzoOqCbCQWfMDcBunYQMpJKK0whhKjy0rPT+eHMDwA83fRptBo1NbgancLKQ1cBeKN/I8xymx7mZMHWGaDkQJPh4N/fFGGXmCRAokwYsySGYR1QbMEndPWFat6gy4LLf5VanEIIUdVtDt1MTHoMNe1q0t/nbjLz4a5zZOUodG3gTtcG7ncPOPgFRJwCm2rQ70MTRFw6JAESZSI3ATofex6d8uDhLaPXBYO7d4FCpQ5ICCFKQ5Yui+9Pfw/ApKaTsNBaAHD0Wiw7TkWg1cAb/e9pehgdCnv+qz7vswDs3e8/ZaUhCZAoEz7OPlhqLUnOSuZm0s0H7le8OqDdMh1eCCFKwa9XfuVWyi1crV0Z5jcMAEVReG+7evd+RCtP/GvcWSBcp1NnfeVkgO8jEDjaVGGXCkmARJmw0FrgV80PKHgYrEh1QF6dwNwGEm9CVOEdwYUQQjyYTtGx5NQSAMY3GY+1udrf59fTERwLi8fGwoxZvRvcPeDYcrh2ACzsYOCnFXqld2NIAiTKjDGF0EWqA7KwAe8u6nOZDSaEECUSFBbElYQrOFg6MLLBSAAys3X8907Tw2e6+uDheKfpYeIt2D1Hfd7zbahWzxQhlypJgESZMaYQGopZB3Tx9xLFJoQQVZmiKCw+uRiAsf5jsbe0B2DloWuExabi7mDFM119cndW1/rKSITaraHtM6YKu1RJAiTKjDFLYkAR64D8eqn/hgVDeiFDZkIIIfJ18NZBQmJDsDG3YVyjcQAkpGbxedBFAGY92gA7qztND8/8DOd3gNYChnwJWjNThV2qJAESZaZBtQZo0BCdFk10WvQD9ytSHZCLN7g1UPtPXPqzlCMWQoiqYfEp9e7P4w0ep5p1NQC+/PMiCWlZNPCwZ0SrO00PU2Ph19fU511ehuqNTBFumZAESJQZWwtbvJy8AAiJKaV+QAB+uYujynR4IYQoqmORxzgaeRRzrTkTGk8AICwmlRUHrwEwu38jzM3upAe73oSU2+DuD11mmSrkMiEJkChTuYXQhdUBdfAtSh3QnQQoVKbDCyFEUeXO/BriOwQPOw9AbXqYmaOjs58b3XObHoYGwYnVgEZd6d3cykQRlw1JgESZMmYmGBSxDqheR3UaZnIkRJwslTiFEKIqOBd7jn0396HVaHm66dMA/BsWx7aT4Wg0MLu/v7oQakYybJupHtTuWfBsa7qgy4gkQKJM+bvemQlWwBAYQEMPB5xtjawDMrcCn+7qc5kOL4QQRsud+dXXqy+ejp4oisL7O9Sfz4+1rEOTWncWNf3zPYgPA6e68Mjbpgq3TEkCJMpU7h2gG8k3SMp88CKmRa4Dqn9nNpjUAQkhhFGuJFxh9zX1Z+bTAerdn11nIvn7ahzWFlpezm16eOMfOPS1+nzQJ2Blb4pwy5wkQKJMOVk5UdOuJmD8MFiwMXVAuYXQN/5WZykIIYQo0LLTy1BQ6O7ZnQbVGtxpeqje/Znc2YeaTjaQnQlbnwcUaDb6buuRh5AkQKLMlUk/IGdPqN4YFB1c+qNU4hRCiIdVeHI42y5tA2BywGQAVh++xtWYVNzsLZna3Vfdcf8n6lJDtm7Qd4Gpwi0XkgCJMmdsIXRuHVBqZg6nCqsDgruzwWQYTAghCrT8zHKylWza1WhHoHsgCWlZfHan6eHMXg2wtzKHqBDY+5F6QL8PwNbFhBGXPUmARJkzdkkMwzqgIgyDhf6urlIshBAij5i0GH66+BMAU5pNAeDrPZeIS83C192O0W08QZejDn3psqBBX2j6mClDLheSAIky18hVvQN0Of4yGTkZBe57d10wI+p66rYHSwdIjYbwf0scpxBCPIxWnl1JRk4Gzdya0bZGW27EpbLswBUA3shtenhksVpTaekAAxZW+pXejSEJkChzHrYeOFs5k6PkEBoXWuC+RaoDMrMA3x7qcxkGE0KIPBIzE1l7fi2g1v5oNBo+3nWezGwdHXxcecS/ujrdPWieesCjc8GptukCLkeSAIkyp9FojB4GK3odUO7q8NIPSAgh7rf23FpSslLwc/ajm2c3Tt6IZ/PxWwC8OaARGoBfZkJWCtTtCK2eMmW45UoSIFEujC2ELnod0J0pmjePQcqDF1wVQoiqJjUrlVVnVwF37v6g4b3t6h+hw1rUpmltJzi5Di4FgZkVDP4ctFUnLag6VypMKrcOqLCO0FDEOiDHmlAjAFDUdWuEEEIAsOniJuIy4qhjX4c+Xn34PSSKw1disTTX8kqfhpB8G3a+ru7c7TVwq2/agMuZJECiXOQOgV2Iu0COLqfAfYtUBwQyDCaEEPfJysni+zPfA/BUwFMoipYFd5oePt3Zm9rONrDzP5AWBx4B0OlFU4ZrEpIAiXJRz7EeNuY2pOekczXxaoH7FrsOKPR3dSqnEEJUcb9c/oWo1Ciq21RniO8Q1v59ncu3U3Cxs2Rad184vxNO/wQaLQz5Qp1UUsVIAiTKhVajpWG1hkAZ9AOq3RqsnSA9Xl3DRgghqrBsXTZLTy0FYEKTCWRkafh09wUAZvaqjyNpsH2WunOHGVCrhalCNSlJgES50S+JEVNwITRAh6LUAZmZg29P9XmoTIcXQlRtu6/tJiwpDGcrZx5v8Djf/HWJmJRMfNzsGNO2Lvw+FxJvQjVv6D7b1OGajCRAotzkFkIXNhMMoL2v1AEJIURRKYrC4lOLARjXaBzxKRqW7FObHv6nnz8WNw7BP+rdIQZ/Dpa2pgrV5EyeAC1atAgvLy+sra1p164dR44cKXD/Tz/9lIYNG2JjY4OnpycvvfQS6enp+tfnzp2LRqMxePj7+5f1ZQgj3NsLSFGUAvdtUN2BakWpA8qdDh9+ApIiShqqEEJUSntv7OVi3EVszW0Z4z+Gj387T0a2jrZeLvRu4HRnpXeg5Xjw7mraYE3MpAnQunXrmDVrFnPmzOHYsWMEBgbSp08foqKi8t1/9erVvP7668yZM4eQkBCWLl3KunXreOONNwz2a9KkCeHh4frH/v37y+NyRCH8nP0w15iTmJlIeEp4gfuqdUC5w2BG1AHZu98dxw79vaShCiFEpaMoCt+d+g6AUf6juB4NP/97E4A3BjRCs/cjiAkF+xrw6HxThlohmDQBWrhwIVOmTGHSpEk0btyYb775BltbW5YtW5bv/gcPHqRTp06MHTsWLy8vevfuzZgxY/LcNTI3N6dGjRr6h5ubW3lcjiiEpZklvs6+QOGF0ADtfdRC6OBLRiRAcM8wmNQBCSGqnn8i/+Hk7ZNYmVkxvtF43t8RgqLA4MBaNLe4Dgc+U3cc8DHYOJs01orAZAlQZmYmR48epVevXneD0Wrp1asXwcHB+R7TsWNHjh49qk94Ll++zI4dO+jfv7/BfhcvXqRWrVr4+Pgwbtw4wsLCCowlIyODxMREg4coG/pC6CLVAcUVrQ7o0p+Qk1XsGIUQojL67qR692eY3zBOhek4eCkGSzMtrz7qC1tmgC4bGg2GRoNMHGnFYLIEKDo6mpycHDw8PAy2e3h4EBGRfw3H2LFjmTdvHp07d8bCwgJfX1+6d+9uMATWrl07li9fzs6dO/n666+5cuUKXbp0ISkp6YGxLFiwACcnJ/3D09OzdC5S5KEvhDZiJlhuHVBaVg4nbxhRB1SrBdi6QkYCXC+4lkwIIR4mp26f4lD4Icw15jzZaALv71Dvsk/q5IXn+eUQflxtF9L/Y5PGWZGYvAi6KPbs2cP777/PV199xbFjx9i0aRPbt29n/vy7Y5n9+vVjxIgRNGvWjD59+rBjxw7i4+NZv379A887e/ZsEhIS9I/r16+Xx+VUScYuigrFqAPSmt2dDi+zwYQQVciSU0sA6O/Tn/3ndFyMSsbZ1oIZzc3gz/fVnXq/Bw4eBZylajFZAuTm5oaZmRmRkZEG2yMjI6lRo0a+x7z99ts8+eSTTJ48mYCAAIYNG8b777/PggUL0OnyHyJxdnamQYMGhIaGPjAWKysrHB0dDR6ibOQ2Q4xMjSQ2vfAeP7l1QEYlQGDYFVoIIaqA0LhQ/rj+Bxo0jGkwgYV3mh6+0MMPh90vQ3YaeHeDFk+YONKKxWQJkKWlJa1atSIo6O4CljqdjqCgIDp06JDvMampqWjvW6nWzMwM4IHTqpOTk7l06RI1a9YspchFSdhb2lPPsR5g3DBYkeuA/HoCGog8DQk3SxKqEEJUCktPq319etXrxW/HFaKTM6jnast4631wZS+Y28Cgz0CjMXGkFYtJh8BmzZrF4sWLWbFiBSEhIUybNo2UlBQmTZoEwPjx45k9+26XykGDBvH111+zdu1arly5wu7du3n77bcZNGiQPhF65ZVX+Ouvv7h69SoHDx5k2LBhmJmZMWbMGJNco8irKMNgRa4DsnWBOm3U59IVWgjxkLuedJ1fr/wKwFDvJ/lu32UA5nRzwfz3t9WdHnkTXLxNFWKFZW7KNx81ahS3b9/mnXfeISIigubNm7Nz5059YXRYWJjBHZ+33noLjUbDW2+9xc2bN3F3d2fQoEG89957+n1u3LjBmDFjiImJwd3dnc6dO3Po0CHc3d3L/fpE/vxd/Nl1dZdRM8Fy64B2nong0OUYWtWrVvgb1O8NN46o0+FbTSx5wEIIUUEtP72cHCWHTrU7se1vLelZOlrVq0aPKx+pE0JqtYB200wdZoWkUQpryVsFJSYm4uTkREJCgtQDlYEDNw8w9fepeDl68cuwXwrdf/mBK8z95Sxd6rux8ul2hb/BrX/hu+5gaQ+vXQFzy5IHLYQQFUxUahR9f+pLli6Ld9ss4uWVSSgK/NE/EZ8/poLWHJ75C2o0NXWo5aYov78r1Sww8XDIHQK7lniN1KzUQvcvch1QjUCwqw6ZyRCWf08pIYSo7H448wNZuixaVm/Jz8FWKAo83sQenyNz1B06zaxSyU9RSQIkyp2rjSvVbaqjoHA+7nyh+xe5DkirhfqPqs9lOrwQ4iEUnx7P+gtqe5d2LiPYdzEaCzMNc63WQHIkuNaHrq+aOMqKTRIgYRL+rncKoWPKoB8Q3JMASSG0EOLhs/rcatKy02hYzZ/NBx0AeKfJbezPrlF3GPwFWFibMMKKTxIgYRJFWRIDoINvERMgnx6gMYPo8xB3rVgxCiFERZSSlcKPIT8C0MRuKBcik6luncPYqIXqDm0mQ73828mIuyQBEibRyOXOkhhGJkDtfYpYB2TjDJ53CqZlOrwQ4iGy4fwGEjMTqefgxY5D6gznJXV/wyz+KjjWhp5zTBtgJSEJkDCJ3DtAF+MvkmXEwqX1q9vjYmdpfB0QyDCYEOKhk5GTwYqzKwCoZzaQ20lZPOp0g4Dr6h0hBn4C1jJ72RiSAAmTqG1fGwdLB7J12VxKuFTo/modUDGXxbj8F2SlFzdUIYSoMDZf3Ex0WjTVbWrw59E6mJPN/6yXolF00PRxaNDH1CFWGpIACZPQaDR3O0IbUQgNd4fBjE6APJqAQy11HZxr+4sVpxBCVBRZuiy+P/M9AO66PqRmwlyX33FMOA82LtDvAxNHWLlIAiRMJrcOyJglMcCwDigz24g6II0G6vdSn1+UxVGFEJXb+vPruZl8EyfLahw56Yev5iZj09eqL/b7AOzcTBtgJSMJkDCZos4Eu7cO6NTNeOPeJHcYTPoBCSEqqfj0eF7f9zr/PfJfAOzTH0HRmfGN0w9odZng9ygEjDBxlJWPJEDCZHLvAJ2PPY9OKfyOjmEdUKxxb+LdDbQWEHsJYgqvNRJCiIokKCyIoVuGsv3ydrQaLb1rjebc+ZaMNw+ifvopdcmfgZ/ISu/FUKIEKDMzk/Pnz5OdnV1a8YgqxMvJCyszK1KzUwlLDDPqmCLXAVk7Qt326nOZDSaEqCTi0uN4be9rzPxzJjHpMfg4+bCi70rOnu1KTeJ5w3KdumPPOeDsadpgK6liJUCpqak8/fTT2Nra0qRJE8LC1F9ezz//PP/9739LNUDx8DLXmtOgWgOgeP2AjKoDgrvDYNIPSAhRCfx+7XeGbhnKr1d+RavR8nTTp/mg/TI2BWsJCU/gv9bfY6VLVXudtZls6nArrWIlQLNnz+bEiRPs2bMHa+u7rbZ79erFunXrSi048fDTzwQzshC6RHVAV/ZBZuGLrwohhCnEpcfx6l+v8tKel4hNj8XLwYcRNT/kj+DW9P30ED8EX2OwNphuHAMzS3W5C61UshRXsT5zmzdv5ssvv6Rz585o7hl3bNKkCZcuSZ2FMF5RC6GLVQfk3hCc6kJOBlzdV6w4hRCiLP129TeGbhnKzqs70aClek5/Tv/zFEuCsjl5IwEzLTzuk81H9ncaHnZ9Vf3ZJorNvDgH3b59m+rVq+fZnpKSYpAQCVGYe5fEUBTFqO+f9j6u/Ho6gkOXY3iuh1/hb5I7Hf6fZepsMGkUJoSoIGLTY5kf/H/8HqYO0VtkuNAoPJBGmUmM166kqX08vhYxOGWEo7mVph5UvTF0mmm6oB8SxUqAWrduzfbt23n++ecB9L+0lixZQocOsgCbMF79avUx05gRmx5LVGoUHnYehR5zfx2QpbkRNzLr976bACmKzJgQQpS/zFSID4P4a+hir7Lu8l6+yDhNklaHmaLwdHwiz8aHYclxsMg95s4DQKMF90Yw/FswtzTNNTxEipUAvf/++/Tr14+zZ8+SnZ3NZ599xtmzZzl48CB//fVXaccoHmLW5tZ4O3kTGh/KudhzRiVAuXVAsSmZnLoZT6t6LoW/kXdXdcw8PgyiL4J7g1KIXggh7pGTBQk3IP4axF3L+29KFAAxWi3vubmw284WtFA/M5P5t2NokplFto0biosXmmr1wLke3PuvYx1JfEpRsRKgzp07c+LECRYsWEBAQAC//fYbLVu2JDg4mICAgNKOUTzk/F38CY0PJSQ2hG6e3QrdX6vV0N7HhR2nIjh0Oda4BMjSDrw6w6U/1LtAkgAJIYpKp4PkiLzJTXyY+jzxBhTQ00wBNts685GbI0lmoFWgW44/zzQYTqNBgeBSD3NLu/K7niquyAlQVlYWzz77LG+//TaLFy8ui5hEFePv4s+2y9uMLoQGdRhMTYCMrAMCtVtqbgLUcUYxoxVCPLQUBdLiIO5q/ndx4q+rkykKYmYFznXJcPDkcrYrh+McOBznwFWtHTc8/gFH9edcTRsfPu7+Ps2qNyn76xL5KnICZGFhwU8//cTbb79dFvGIKqixa2PA+JlgUII6oF2z4dpByEgGK/tixSuEqMQykvV1OPkOU2UmFXy8xgycat8zPOWlH6ZKtq3Fr1d0bD0ZwYHz0egUAAVLp5PY1twMmmTMNOY802wKUwKmYGFmUfB7iTJVrCGwoUOHsnnzZl566aXSjkdUQQ1d1KmcN5NvkpCRgJOVU6HHFKsOyNUXqnlD3BW48hf4Dyhp6EKIii4nC458B6d/Uu/spBrRRd7eI2/9jb4Opzbck7hkZOew5/xttuy7SVDIaTLuadAaUE+DuftmQlMOkQM0rNaQ/+v8f/r2H8K0ipUA1a9fn3nz5nHgwAFatWqFnZ3hmOULL7xQKsGJqsHR0pHa9rW5mXyTc7HnaFezXaHHaDTFqAPSaNS7QEe+VYfBJAES4uEWdhi2vQRRZwy3Wzvdl9h43fNxXbCwKfC0Op3CoSsxbD1+ix2nwklMv7sclK+7HUMCa+FU/TSLzy4kISUBc405zzR7hskBk+WuTwVSrARo6dKlODs7c/ToUY4ePWrwmkajkQRIFFkjl0ZFSoDgbh1Q8KUi1AHVf/ROArRbpsML8bBKjYXf58CxH9SPbapBjzfBs62a6Ng4F/mUiqJw5lYiW47f5JcT4UQkputfq+FozeDmtRgcWAuPapn836H/449//wDUGsf/6/R/+jvdouIoVgJ05cqV0o5DVHH+Lv78Hva70UtiwD11QNdija8D8uoM5taQeBOiQsCjcXFDFkJUNIoCx1fD7rfvDnW1eAJ6zQM712Kd8lpMCluP32Lz8Ztcup2i3+5obU7/gJoMaV6btt4uaDWw/cp2nt2ygMTMRMy15jzb7FmeDngaC63c9amIipUA3UtRFADpAC1KpJHrnY7QMcYXQt9bB3TyRjytvYwYBrOwUXsCXfxNfUgCJMTDISoEts2CsIPqx+6NYOAnUK/ozXmjkzPYduIWW07c4t+weP12K3MtvRp5MLh5Lbo3dMfK3AyA26m3mXdoHnuu7wHUO9rzO82Xuz4VXLEToB9++IGPPvqIixcvAtCgQQNeffVVnnzyyVILTlQduUWBVxKvkJadho15wWPwcH8dUIxxCRCodUAXf1OHwTrPLEHUQgiTy0yFvR/CwS9Alw0WttDtP9DhOYNi5cIkZ2Tz25kINh+/xYHQaHLUKVxoNdDJz40hzWvTp4kHDtZ3z6koCtsub2PBkQUkZSZhrjVnWuA0JjWdJHd9KoFiJUALFy7k7bffZsaMGXTq1AmA/fv3M3XqVKKjo2V2mCgydxt3XKxdiE2P5WLcRZq5NzPquLv9gGKZ8YiRb+bXS/03LBjSE9SCSCFE5XN+J+x4FRLC1I8b9od+H6iFzEbIzNbx14XbbDl+k99DIknPujuDK9DTmSGBtRgYWJPqDtZ5jo1KjWJe8Dz+uqGuftDIpRH/1/n/aFBNmqxWFsVKgL744gu+/vprxo8fr982ePBgmjRpwty5cyUBEkWm0Who5NKIA7cOcC72XJESIChiHZCLN7jWh5iLcHkPNB5SgsiFEOUu/jrsfB3ObVM/dqwD/T80amanTqdw5GosW+7M4EpIy9K/5uNmx5DmtRncvBbebvl3ZFYUhV8u/8J/j/yXpMwkLLQWTAucxsSmE+WuTyVTrAQoPDycjh075tnesWNHwsPDSxyUqJr8Xfw5cOtAkQqhi1UHBOowWMxFdShMEiAhKoecLDj0Nez5L2SlgNYc2k9Xh7wKaGyqKAoh4UlsOX6TrSduEZ5wdwZXdQcrBgXWYmjz2jSt7VhgPWtkSiTzDs1j7429ADRxbcL8TvOpX61+6V2jKDfFSoD8/PxYv349b7zxhsH2devWUb++fCOI4vF3VeuAilIIXfw6oEfh0CKZDi9EZXF/Tx/P9jBwIXgUvJTE6ZsJzN92lsNXYvXbHKzM6RdQgyHNa9PexxUzbcH//xVFYculLXx45EOSstS7PtObT2dik4mYa0s8l0iYSLG+cu+++y6jRo1i7969+hqgAwcOEBQUxPr160s1QFF1NHJRZ4JdjL9Iti7b6B8sxaoDqtcRLOwgORIiTkLNwGJGLYQoU/n19Hl0PjQfB9oHD3lHJabz8W/n2XD0BooClmZaHvGvztAWtejesDrWFmZGvX1ESgTvBr/L/pv7AWjq2pT/6/x/+Dr7lvjShGkVKwF67LHHOHz4MJ988gmbN28GoFGjRhw5coQWLVqUZnyiCvF08MTOwo6UrBSuJFwx+rZyseqAzK3Apxuc36HeBZIESIiKRVHgxBr47a0i9fRJz8ph6f4rfPVnKCmZOQAMbV6L1/r6U8u58Nmld99eYXPoZj78+0OSs5Kx0FrwXPPnmNBkgtz1eUgY8Zsif61atWLVqlX6btCrVq0qVvKzaNEivLy8sLa2pl27dhw5cqTA/T/99FMaNmyIjY0Nnp6evPTSS6SnpxvsU9RziopBq9HSsJraN6OodUCudpakZ+k4eSPe+Des/6j6776FsHO2uhCiEML0os7B8gGweZqa/Lg3gkk7YciiByY/iqKw7eQtev7vLz7adZ6UzByaezqzaXpHPh3dokjJT0RKBNOCpvHOwXdIzkqmmVszNgzawNMBT0vy8xApVgK0Y8cOdu3alWf7rl27+PXXX40+z7p165g1axZz5szh2LFjBAYG0qdPH6KiovLdf/Xq1bz++uvMmTOHkJAQli5dyrp16wxqkYp6TlGx5DZEDIkxPgFS64DUH4qHLhux0GGupo9D7VZqMeWhr+Dz5rBhItz4pwgRCyFKTWYq/D4XvukE1w6AuQ30ehem7iuwoeHJG/GM+CaYGav/5WZ8GjWdrPlsdHM2TetIy7rVjH57RVHYdHETw7YM48DNA1hqLZnVahY/9PtBhrweQsVKgF5//XVycnLybFcUhddff93o8yxcuJApU6YwadIkGjduzDfffIOtrS3Lli3Ld/+DBw/SqVMnxo4di5eXF71792bMmDEGd3iKek5RseQ2RDwXa3whNEB7H7X4+dDl2EL2vIe1I0wOgid+Ap8eoOjgzM+wpCcs7QMhv4Au7/e5EKIMnN8JX7WD/Z+oDQ0b9IPnDqvNSh/Q0DAiIZ1Z648z+MsD/HMtDhsLM17q1YA/Xu7OkOa10RZS3Hyv8ORwpv4+lTkH56h3fdybsWHwBiY1nYSZ1rh6IVG5FOte3sWLF2ncOO8SAv7+/oSGhhp1jszMTI4ePcrs2bP127RaLb169SI4ODjfYzp27MiqVas4cuQIbdu25fLly+zYsUPffbo45xQVS24h9PnY8yiKYvQSK8WqAwJ19pdfL/URcRqCF8GpDXD9EKw7BNW81Wm2LcaBZf59QYQQJZBwA379T5F6+qRl5rB432W+3nOJtCz1j5ThLWvzWh9/ajjlbVpYEEVR+OniT3z8z8ekZKVgZWbF8y2e54lGT0ji85ArVgLk5OTE5cuX8fLyMtgeGhqKnZ1xvySio6PJycnBw8PDYLuHhwfnzuX/1//YsWOJjo6mc+fOKIpCdnY2U6dO1Q+BFeecABkZGWRkZOg/TkxMNOoaROnzcfbBQmtBUlYSN5Jv4OngadRxfnfqgGKK2g/oXjWawrCvoec78Pdi+HspxF2BX1+FP9+D1k9Bu2fBoUbRzy2EMJSTBYe/gT8XqMPQGjN1+YoCevooisLWE7f44Ndz3LrTy6dVvWq8M7AxgZ7ORQ7hVvIt5h6cS3C4+gdyoHsg8zvNx9vJu9iXJSqPYg2BDRkyhJkzZ3Lp0iX9ttDQUF5++WUGDx5casHdb8+ePbz//vt89dVXHDt2jE2bNrF9+3bmz59fovMuWLAAJycn/cPT07hfuqL0WWgt8HP2A4o2DFbsOqD8ONZUk6BZZ6H/x+pdoPR42L8QPmkKP09T7xYJIYon7DB8202d4ZWVovb0mboPes9/YPLzb1gcw78+yItrj3MrIZ3azjZ8MaYFG6d2KHLyoygKGy5sYPjW4QSHB2NlZsUrrV9hRd8VkvxUIcVKgD788EPs7Ozw9/fH29sbb29v/P39cXV15eOPPzbqHG5ubpiZmREZGWmwPTIykho18v8L++233+bJJ59k8uTJBAQEMGzYMN5//30WLFiATqcr1jkBZs+eTUJCgv5x/fp1o65BlI3iFELD3Tqg4JImQLks7aDtFHj+KIxaBXU7gC4LTqxWizR/GAoXf1en6wohCpcaC1tfgGW91YaGNtVg8Bcw6dcHNjS8FZ/GzLX/Muyrg/wbFo+tpRmv9G5A0MvdGBRYy+hhcv35km/xzO5nmBc8j5SsFFpUb8HGQRuZ0GSCDHlVMcUeAjt48CC7d+/mxIkT2NjYEBgYSJcuXYw+h6WlJa1atSIoKIihQ4cCoNPpCAoKYsaMGfkek5qaiva+xldmZuo3rKIoxTongJWVFVZWVkbHLspW8Quh1TtAR6/FkZGdg5V5Kf0w05pBo0Hq48ZRCP4Czm6By3+qD/dG6q37ZiPV/kJCCEP59fRp/gQ8+uCePqmZ2Xz712W+3XuJ9CwdGg083rIOr/ZpSHXHotX5AOgUHRsvbOR///yP1OxUrM2seaHlC4z1HyuJTxVVpAQoODiYmJgYBg4ciEajoXfv3oSHhzNnzhxSU1MZOnQoX3zxhdHJxKxZs5gwYQKtW7embdu2fPrpp6SkpDBp0iQAxo8fT+3atVmwYAEAgwYNYuHChbRo0YJ27doRGhrK22+/zaBBg/SJUGHnFBVfbiF0URMgwzqgBNoUpw6oMHVawYjlas+gw9+o3Wlvh8DWGRA0T71j1PrpAhu1CVGlRJ2D7S/DNbWTMu6N1CUs6uVdTxLUxUo3H7/JhzvPE5Go1vm09XLh7YGNCajjVKwQwhLDmBc8j8MRhwFoWb0l8zrNo55jvWKdTzwcipQAzZs3j+7duzNw4EAATp06xZQpU5gwYQKNGjXio48+olatWsydO9eo840aNYrbt2/zzjvvEBERQfPmzdm5c6e+iDksLMzgjs9bb72FRqPhrbfe4ubNm7i7uzNo0CDee+89o88pKr4G1RqgQcPttNtEp0XjZuNm1HG5dUDbT4Vz6FJM2SRAuarVg74L1ILNYz+oyVDiTbVYet9CaD4G2j8Hbn5lF4MQFVlmKuz9CA5+rk5rN7eB7v9R/1+YW+Z7yNFrscz75SwnbiQAUKeaDW/2b0TfpjWKPNQFcCPpBt+d/I6tl7aSo+RgbWbNiy1fZGyjsWg1xe4DLB4SGkUxvoChZs2a/PLLL7Ru3RqAN998k7/++ov9+9XMfsOGDcyZM4ezZ8+WTbTlJDExEScnJxISEnB0dDR1OFXSoJ8HcTXxKl/3+prOtTsbfdzK4Ku8veUMnfxc+XFy+zKM8D45WXBmszo8Fn7izkYNNOgLHWdAvU6y4KqoOi7sgh2vQHyY+nGDftDvA/UPh3zciEvlg53n+eXELQDsrcx5rocfkzp5Gb1m171uJt9k8cnFbAndQraSDUCn2p14o+0b1HWsW7xrEpVCUX5/F+kOUFxcnMGdlL/++ot+/frpP27Tpo0UEItS0cilEVcTr3Iu9lyREqAyqwMqjJkFNBsBAY/D1f1qP6ELv9591GwOHZ+HxkMe2NRNiEqviD19UjKy+XrPJRbvu0xGtlrnM6q1J7N6N6C6Q9HrfMKTw/nu1HdsDt1Mtk5NfDrW6si0wGk0r968uFclHlJFSoA8PDy4cuUKnp6eZGZmcuzYMd59913960lJSVhYyA93UXKNXBvx69VfizwTrFzqgAqi0YB3F/URfVFNhE6sgfDj8NPTsHsOtJ8KLceDdfHqGYSocIrY00enU/jp2A0+2nWeqCS1B1t7H7XOp0mtov+/iEiJYMmpJfx08Sd94tO+ZnumN59Oi+qyQLfIX5ESoP79+/P666/zwQcfsHnzZmxtbQ1mfp08eRJfX1kvRZRc7kywoiyKCuVcB1QYt/ow6FN45C34Zxkc+Q4Sb6gzYfZ8oCZB7aeCs9ySF5XY9SOw7SWIvNMby7MdDPzkgdPaj1yJZf62s5y6qdb51HO15Y3+jejd2KPIdT6RKZH6xCdLlwVA2xptmd58Oq08WhX/mkSVUKQEaP78+QwfPpxu3bphb2/PihUrsLS8W8y2bNkyevfuXepBiqondybY9aTrJGUm4WDpYPSx7X1c1AToSgzPU7+sQjSenRt0ew06vgCn1qt3hW6fg0OL1L+aGw9R64Rqyw9sUYmkxqoLlx5boX5sU02d1t78CdDmLTC+HpvKf389x/ZT4QA4WJnzfE8/JnT0KvJQdVRqFEtPLWXjhY1k6jIBaO3RmunNp9OmRpsSXZaoOoqUALm5ubF3714SEhKwt7fXTz3PtWHDBuzt8+/iKURROFs7U8OuBhEpEZyPPU/rGq2NPtZkdUCFsbBW7/o0fwIuBcHBL+DKX3Bmk/qo2wE6zICG/dTeQ0JURIoCJ9bCb2/e09Nn3J2ePnlnbCalZ/HVnkss3X+FzGwdWg2MbluXWY82wM2+aH2zotOiWXpqKRsubCAjRx06a1m9Jc81f462NduW+NJE1VLsRoj5cXEx4XCDeOj4u/gTkRLBudhzRUqA/Krb42ZvSXSyieqACqPVQv1H1Uf4STj0FZzaCGHB6sPFR12AtflYWYBVVCy3z8O2Wff09PGHAQvBq1OeXXN0ChuPXuejXReITlaTlU5+rrw9sDH+NYo2uzY6LZrvT3/PuvPr9IlPc/fmTG8+nfY12xdrirwQxUqAhCgPjVwasef6nmLVAbXzcWX7yQpQB1SYms1g2Dfq2mNHvlNrhWIvq1OIcxdgbfuMLMAqTEvf0+cLdTmYQnr6BF+KYf62s5wNVxeW9naz483+jejZqHqRkpXY9Fi+P/09a8+tJT1HbYrYzL0ZzwU+R4daHSTxESUiCZCosIq7JAaow2DbT1agOqDCONaCXnOhyytwfLVaHxR3Ffb9T/2lEzBCnVXzgMJSIcpMnp4+faHfh/n29LkWk8L7O0LYdUZdj9HB2pwXe9ZnfAcvLM2NbzwYlx7H92fUxCctOw2AALcApjefTqdanSTxEaVCEiBRYeUWQl+Ov0xmTiaWZvl3j81PhzsLo1a4OqDCWNlDu2egzdNwbjsEfwnXD8PxH9WHTw+1YNq3pzRWFGUrO0NduPTkWvVjxzpqM0P/AXm+9xLTs1j0RyjfH7hKZo4OM62Gce3qMrNXA1zsjP9/G58ez4qzK1gdsprU7FQAmrg2YXrz6XSp3UUSH1GqJAESFVYNuxo4WTmRkJHAxfiLNHE1/u6Hr3sFrwMqjNYMGg9WH9f/VjtMh/xydwHW6o3VO0IBI2QBVlH60hNh3RNqkb7GDDpMh26v5+npk6NTWPt3GAt/u0BMijobq0t9N94e2JgGHsbP3EzISGDFmRWsPrealKwUQP0DaHrz6XSr000SH1EmJAESFZZGo8HfxZ/D4Yc5F3OuSAlQpaoDKoxnG/D8QR0SO3RnAdaos7DlOcMFWG0r8TWKiiMpEn58HCJOgqU9jFoJvo/k2e1AaDTzt53lXEQSAD7udrw9oDHdG7obnbAkZCSw8uxKfgz5keSsZEAd+p4WOI0enj0k8RFlShIgUaE1cmnE4fDDRS6Ehrt1QMGXY3i+ZyWoAypMNS/o91/o/jocXQ6Hv4WkW/DH/8He/0HnmWrnXfmlIYor5hKsGq4m27Zu8MRGqGXYSflKdArvbQ/h9xC1zsfJxoKXetVnXPt6WJgZV+eTlJnEqrOrWHl2JUlZagLVoFoDpgdOp0fdHrJQqSgXkgCJCq0khdCVtg6oMDbOarLTfjqc3awWSUechD0LQNFBjzdMHKColG79C6seh9RoNdl+YhO43u3sn5CWxRdBF1kRfJWsHAUzrYYn29djZq/6ONsaV+eTnJnMqpBV/HD2B5Iy1cTHz9mP6c2n07NuT0l8RLmSBEhUaI1c1ULoC3EXyNHlYFaEBoH31gGduJ5AW++HbIjI3BKajVTrgI58B7++Bn99oHbkbT/N1NGJyuTSH7DuSchMhhoBMO4ncFAXvs7O0bHm7+ss/O08canqchM9Grrz5oBG+FU3rs4nJSuFH0N+ZMWZFSRmqlPjfZ18mdp8Kr3r9ZbER5iEJECiQqvnUA8bcxvSstO4lngNH2cfo481qAO6HPPwJUC5NBpo9yykJ6i9g3a+DtbO0HyMqSMTlcGpjfDzVLW/j3dXGPUjWKuNCoMvxTBn62kuRKr1OfWr2/PWwMZ0a+Bu1KlTs1JZfW41y88sJyFDXfvL28mbaYHT6F2vd5H+oBGitEkCJCo0M60ZDao14MTtE4TEhhQpAYJ7+gFdjuGFh6EOqCBdX4W0OLWz9Jbn1NXm/fubOipRkQUvgl13hkybDINh34K5FTqdwld7Qvnf7gsoClSztWDWow0Y07Yu5kbU+aRmpbL2/FqWn15OXEYcAF6OXkwNnEpfr76S+IgKQRIgUeH5u/hz4vYJzsWeY4DPgCId+9DWAeVHo4He70FaPJxYDRsmwhM/gXcXU0cmKhpFgd/nwIHP1I/bTYU+C0CrJSE1i1nrjxN0LgqAEa3q8NaAxjjZWhR62rTsNNadW8f3Z74nNj0WgHqO9Xi22bP09+4viY+oUCQBEhVebkPE4swEe+jrgO6n1cLgLyA9Hs7vgDVjYOIveWbyiCosJwu2Pg8n1qgf95wDnV8CjYbTNxOY9uNRrsemYWmuZf6QJoxqU7fQU6Znp7P+/HqWnl6qT3w8HTyZGjiV/t79MdfKrxpR8ch3pajw/F3vzgRTFKVIvUGqTB3QvczM4fHv1V4uV/fBqsdg0k5wb2DqyISpZabA+gkQulttcDj4C2gxDoD1/1zn7c2nycjW4eliw9fjWtG0dv4LX+dKz05n44WNLD29lOi0aABq29fm2WbPMsh3kCQ+okKT705R4dV3ro+5xpyEjAQiUiKoaV+zSMdXqTqgXBbWMHo1rBgE4cdh5TB4aic4e5o6MmEqKTGwegTcPKouZjpyBTToQ3pWDnO3nmHt39cBeMS/Op+MbF7gkFdGToaa+Jxayu2024Ca+DzT7BkG+Q7CQlv4cJkQpiYJkKjwLM0s8XH24ULcBUJiQ4qcAFWpOqB7WTuqNUDL+kLMxbtJkJ2bqSMT5S3umtrgMCZUbZMwdj14tuV6bCrTfjzK6ZuJaDTw8qMNmN7dD602/7usmTmZ/HTxJ5acWkJUqlojVNOuJs80e4YhvkOwMJPER1QekgCJSsHfxZ8LcRc4F3uOR+rmbctfELUOyIro5IyqUQd0Lzs3GL8ZlvZRk6BVw2HCNv00Z1EFRJxWh0GTI9QFTZ/cBO4N+fNcFDPXHSchLYtqthZ8PqYFXernP709KyeLn0N/5ruT3xGZqnaA9rD14JlmzzDMb5gkPqJSku5TolIoSSG0RqOh/Z27QIcux5RqXJWCUx01CbJ1g/ATamF0VpqpoxLl4ep++L6fmvxUbwyTd5Pj2oCFv51n0vK/SUjLItDTmW0vdMk3+cnKyWLDhQ0M+HkA8w/NJzI1kuq21Xmz3ZvsGL6DkQ1HSvIjKi1JgESlUJIlMUCtA4IqmgABuNVXh8MsHeDaftgwSZ0NJB5eZ7fAyuGQkQh1O8KkHcSauTHx+yN8/kcoAE+0r8v6Z9tT29kmz+EHbx1k+NbhzAueR3hKONVtqjO77Wx2DN/BaP/RWJoZt/yFEBWVDIGJSiE3AYpIiSA+PR5na+ciHZ+bAFW5OqB71WoOY9eqwyEXfoUtM2Do1+rUefFw+XsJbH8FUMB/IDy2hBMRGUz/cT8349OwttDy/rAAhresk+fQyJRIPvrnI3Zd3QWAq7UrU5pN4fEGj2NlZlXOFyJE2ZGffKJSsLe0p66D2o+keP2A7HCztyIjW8eJ6wmlHV7l4dUZRixXp0CfXAu7ZqtN8cTDQVHgj/dg+8uAAq0mooxYwY/HohjxTTA349PwcrVl83Od8iQ/WbosVpxZweDNg9l1dRdajZYnGj3BL8N+YVyjcZL8iIeOJECi0ijJMFiVrwO6V8N+6p0fgMPfwF8fmjYeUTpysuGXF2Hvna9nt9dJ6/0/Xv7pNG/+fJrMHB29G3uw9fnO+NcwLII/FnmMkb+M5ON/PiY1O5VA90DWDVzHf9r+BwdL4xY8FaKykQRIVBq5K8OHxBT9DhDcHQYLvlTFEyCAwFHQ9wP1+Z734fB3po1HlExWGqwfD8dWgEYLAxZyNeAFhn19kE3HbqLVwOx+/nz7ZCscre8WLcekxfDW/reYsHMCofGhOFs5M6/jPH7o94P+Dw4hHlZSAyQqjdwfyMUZAoO7CdCxsDjSs3KwtqiCdUD3aj9VXTJjzwL49VWwcYZmI00dlSiqtDh1Zl9YMJhZwWNL+E1py8tf7CcpIxs3e0u+GNOSDr6u+kNydDlsvLCRz/79jKTMJAAeq/8YM1vOLHJ9nRCVlSRAotLITYCuJV4jNSsVWwvbIh2fWwek9gOKp52Pa+EHPey6/QdSY+HIt/DzVHUF+QZ9TB2VMFbCTbWo/XYIWDmRPepH/nfBna/3HAWgdb1qLBrXEg9Ha/0hZ6LPMP/QfM7EnAHUFhNvtn+TQPdAk1yCEKYiQ2Ci0nCzccPdxh0FhQtxF4p8vGEdUGxph1c5aTTQ97/QbBQoOeowyrWDpo5KGOP2eVjaW01+HGoSO2oLT/5uwdd7LgHwVCdv1jzTXp/8JGQk8H+H/o8x28dwJuYM9hb2zG47mzUD1kjyI6okSYBEpVJaw2BVvhD6XlotDFkEDfpBdjqsHqU2TBQVV9hhNflJvAGu9TnVdz391sYQfDkGW0szvhzbgncGNcbCTIuiKGwJ3cLgzYNZd34dCgoDfQbyy7BfGNtoLGbaKj4ULKosSYBEpVJaDRFz64DEHWYWMOJ7qNdJbZy3cjhEh5o6KpGf87/CD0MgPR6ldmtWN/2OYT/eIDIxA193O7bO6MTAZrUAuBh3kYk7J/LWgbeITY/F18mXZX2WsaDLAtxsZE04UbVJAiQqlZLOBDPsBxRfipE9BCxsYMwaqNEMUqNh5VC1xkRUHP+ugrXjIDuNbN9HecV2Pm/sCidbpzCgWU22zOiMX3UHUrJS+PjvjxnxywiORR3DxtyGl1q9xIZBG2hTo42pr0KICqFCJECLFi3Cy8sLa2tr2rVrx5EjRx64b/fu3dFoNHkeAwYM0O8zceLEPK/37du3PC5FlLHcO0Ch8aFk6Yq+lIPUARXC2gme2ASufpBwXV1BPkWGC01OUWDvx7DlOVBySGw4goFR0/jpVBzmWg3vDGzMl2NaYGdpxq6ruxi8eTArzq4gR8mhV91ebBmyhaeaPiXrdglxD5MnQOvWrWPWrFnMmTOHY8eOERgYSJ8+fYiKisp3/02bNhEeHq5/nD59GjMzM0aMGGGwX9++fQ32W7NmTXlcjihjdezr4GDhQJYui8vxl4t1DqkDKoS9Ozy5GRxrQ/R5+PExyEgydVRVl04Hv/4H/pgPwKWGU+gQ8hjnbqfj4WjF2mfa81Rnb64lXuPZ3c/yyl+vEJUaRR37OnzV8ys+6fEJNe1rmvgihKh4TJ4ALVy4kClTpjBp0iQaN27MN998g62tLcuWLct3fxcXF2rUqKF/7N69G1tb2zwJkJWVlcF+1apVK4/LEWVMo9HQ0KUhUHr9gEQ+nD3VJMjWFW79C2vHQla6qaOqerIz4Ken1DYFwC7PmfQ80YOUTB3tfVzY9nwXmtax5ct/v2T41uEEhwdjqbVkWuA0fh7yM13qdDHxBQhRcZk0AcrMzOTo0aP06tVLv02r1dKrVy+Cg4ONOsfSpUsZPXo0dnZ2Btv37NlD9erVadiwIdOmTSMm5sF/7WdkZJCYmGjwEBVXbh1QcQuhfd3tcHeQOqBCuTeAcRvB0h6u7IWfnlaXWxDlIz0RfnwczvyMorXgE6f/8OzFtgBM7ebLqqfbcTb+EEO3DOXbk9+SpcuiU+1O/DzkZ6Y3n461uXUhbyBE1WbSBCg6OpqcnBw8PDwMtnt4eBAREVHo8UeOHOH06dNMnjzZYHvfvn354YcfCAoK4oMPPuCvv/6iX79+5OTk/9f+ggULcHJy0j88PT2Lf1GizDVyKVkhtFoHlDsMJnVABardUi2MNrOCc9vglxfUIRlRtpIiYXl/uLKXHHM7nmM2n0UG4mBlzrdPtmJ8F0dm/TWTGX/M4GbyTTxsPfik+yd83fNr6jrWNXX0QlQKJh8CK4mlS5cSEBBA27ZtDbaPHj2awYMHExAQwNChQ9m2bRt///03e/bsyfc8s2fPJiEhQf+4fv16OUQviuveqfA6pXi/jGVh1CLw7qpOkdeYwfEfYffbsoJ8WYq5BEsfhYhTpFq4MCztTXak+tPQw4GfnmvHdd02hmwewp/X/8RcY86kJpPYOnQrver1QqPRmDp6ISoNky6F4ebmhpmZGZGRkQbbIyMjqVGjRoHHpqSksHbtWubNm1fo+/j4+ODm5kZoaCg9e/bM87qVlRVWVlZFC16YjLeTN1ZmVqRmp3I96Tr1HOsV+RyyLlgR+Q+AIV/C5mkQ/CXYVIOur5g6qofPzWPw4whIjSbKvBaPJ79KmOLBsBa1GdYhlVcOjudKwhUAWnu05s12b+JXzc/EQQtROZn0DpClpSWtWrUiKChIv02n0xEUFESHDh0KPHbDhg1kZGTwxBNPFPo+N27cICYmhpo1ZSbEw8Bca0595/pA8QuhfdykDqjImo+FPgvU53/Mh7+XmDaeh01oECwfCKnRXND60j/5bcK1NfjPwFpY1FjNc38+y5WEK7hYu/B+5/dZ1meZJD9ClIDJh8BmzZrF4sWLWbFiBSEhIUybNo2UlBQmTZoEwPjx45k9e3ae45YuXcrQoUNxdTVc0DI5OZlXX32VQ4cOcfXqVYKCghgyZAh+fn706SOLPD4s/F3vDIPFFK8QWuqAiqnDdOj6mvp8+ytwaqNp43lYnFwPq0dCVgoHlQCGpb6BhZMbkwfcZEXYNH69+itajZYx/mP4ZdgvDPIdJMNdQpSQyVeDHzVqFLdv3+add94hIiKC5s2bs3PnTn1hdFhYGFqtYZ52/vx59u/fz2+//ZbnfGZmZpw8eZIVK1YQHx9PrVq16N27N/Pnz5dhrodIbiF0cWeCgVoH9MuJW3z9Vyj/Xo+js58bHX3d8K/hgFYrv1weqMcbkBYHfy+Gn59VmyfWf9TUUVVeB7+E394EYGtOB17OmkZA/RQU129ZdVFd9DfALYC32r9FY9fGpoxUiIeKRlGkmvF+iYmJODk5kZCQgKOjo6nDEfk4efsk43aMw8XahT0j9xTrr+GopHSGf3WQG3FpBttd7Szp6OdGJ19XOvm54eliW1phPzx0Ovj5GTi1AcxtYPxmqNve1FFVLjod/P4OHPwCgGXZfZmvDKNF4CEupqllAY6WjsxsNZPH6j+GVmPyG/ZCVHhF+f0tCVA+JAGq+NKy02i/uj06RUfQiCCq21Yv1nl0OoXzkUkcCI3mQGg0h6/Ekppp2C6hnqstnfzc6OTrRkdfV6rZWZbGJVR+OVlqg8SLv4GVE0zaDjUCTB1V5ZCTpS5rcXIdAAuyRrHapTa2NXaRmq123R7mN4yZrWbiYu1iykiFqFQkASohSYAqh6Gbh3Ip4RKLei6ia52upXLOzGwdx6/H6xOif6/Hk6O7+19Eo4EmtRzp5OtGJz832ni5YGNZhWeQZabCquEQFgx21eGpneDqa+qoKraMZJT149FcCiJb0TJNO4pjdW6SaX4VgAbVGvBW+7doUb2FaeMUohKSBKiEJAGqHF7f9zrbL29nRvMZPBv4bJm8R1J6FkeuxHIgNIYDodGcjzRcE8vSTEvLes509lMTooDaTpibVbGhirR4dfZS5ClwrgtP7QLHWqaOqmJKiSZ75eOYR/xLFNaMdu7E7WqhgIKdhR3PNX+OMf5jMNeavDxTiEpJEqASkgSoclhxZgUf//Mxver24pMen5TLe0YlpRN8KYb9F9U7RLcSDNfHcrAyp72v652EyBVfd/uqMVsnOQqW9YHYy+DuD5N+BVsZujEQd5WM5UOxTLjCejsXFri4k2Ou1p/18+rHK21eKfZQrhBCJQlQCUkCVDkcDj/M5N8mU9u+Njsf21nu768oCldjUtkfGs2Bi9EcvBRNYrrhWlkejlZ08nPT3yHycHyI12eKuwbL+kLSLajdGsZvASt7U0dVMUScIu37odzSxfOOqwcnbdRhUy9HL95s/ybta0oBuRClQRKgEpIEqHJIyEig89rOABwYcwBHS9N+rXJ0CmduJagJUWg0f1+NIzPbcKkOv+r2d6bbu9Le1xVHawsTRVtGos7B933VafI+3WHsejCv2u0nMkP/InnNGJY7WvCDkwM5Gg1WZlY82+xZJjSZgKWZFNULUVokASohSYAqj74/9eVm8k2W9VlGmxptTB2OgfSsHI5ei2N/aDQHQ6M5eTPBYAktrQYCPZ31BdUt6zljZf4QFFTfOAorBkFWCjQaDCOWg/YhuK5iuH1oHUf3zuJ/ro5EmKt1Pd3rdOf1dq9T2762iaMT4uEjCVAJSQJUecz8cyZBYUG80voVJjSZYOpwChSfmsmhyzF3EqIYLkenGLxubaGljZeLfriscU3HytuQ8fIedU2rnExo8SQM/kKdQleF/LXlXdbeWsl+WxsAXC09mNv5Lbp7djdtYEI8xIry+1umGohKzd/Fn6CwoBJ1hC4vzraW9G1ak75N1TXpbsan6afbHwiNITo5g30Xo9l3MRqAarYWdLxzd6iznxt1XStRQ0af7vD4Mlg/Hv5dqS6e2nu+qaMqe8lR5Fw7yKcHvma1xU0ybW0wUzSMaDCBWW2nY2NuY+oIhRB3SAIkKrXSWBLDVGo72zCytScjW3uiKAoXIpP1w2WHLscQl5rF9lPhbD8VDkCdajb6u0MdfV1xta/gtTWNBql3frY8Bwc/V2eFdX7J1FGVHkWBuCtwLRjCDqr/xl7isoUFK2rXQNFoaJDjwvtDltLQVRYtFaKikSGwfMgQWOURmRJJr429MNOYcWjsIazNH45ZVlk56ir1ucNlx8LiyNYZ/ldtVNORzn6udPRzo523C7aWFfTvmXvWumLgp9B6kknDKTZdDkSeUZs+hgWrCU9yhOEuaHjGvQ6H7TU0t2rID6M2VI02CEJUEFIDVEKSAFUeiqLQfX13YtNjWd1/NQHuD+dSDCkZ2XcaMkazPzSacxGGDRktzDT0a1qTdwc3qZhLdQTNg33/AzTq0FjT4aaOqHDZGXDz2N27O9ePQEaC4T5aC5RaLflX48+iy9U5Yu4APkvQoGHT4E34VZM7P0KUJ6kBElWGRqPB38Wfg7cOEhIb8tAmQHZW5vTwr04Pf7VR3u2kDA5eUu8O7Q+N5mZ8GltP3OLQ5Rg+GhFItwbuJo74Po+8rU6N/2cZbHoGrB3Br5epozKUnqAmOdcOqnd4bh6DnAzDfSwdwLMt1OsAdTsQ5xzArJ/P8+f52wB4+68lWgf9vPtJ8iNEBScJkKj0chOgylgHVFzuDlYMaV6bIc1roygKJ24k8PL641y6ncKEZUeY2NGL1/v5Y21RQaafazTQ/2N12Ywzm2Ddk2qjRM+2pospKeJOsnNIvcsTeQYUw75N2LlD3Q5Qr6P6r0dTMFN/bB4Li2PG1/9wKyEdK3MtzzxqxvKrxzHTmDEtcJoJLkgIURSSAIlKrzIXQpcGjUZDc09ntj3fhf/+GsKK4GssP3iV/aHRfDqqOU1rO5k6RJXWDIZ9CxmJEPo7/Pi4umSGR5Oyf29FUZfpyL27c+2gWsB8v2red5Odeh3BxSfP9H1FUVi6/wr//fUc2ToFbzc7vhrXkk9OvQLAIN9BeDl5lf01CSFKRBIgUen5u/gDcCHuAtm67Cq7kKSNpRnvDmlKD//qvLrxJKFRyQz76gCzHm3IM119MKsIPYXMLWHkSlg5FK4fhpXD1MVTXbxL9310ORBx6m6yE3YIUqLu20kDNZpC3Y76IS0cahR42oS0LF7beIJdZyIBGNisJguGB3A+/gTB4cGYa82ZGji1dK9FCFEmquZvCvFQqetYF1tzW1KzU7macLXK1150b1idXTO7MnvTSXadieSDnef483wUC0cGUqdaBeglZGkLY9fdWUH+tJoMPbWr0OSjQFlpeQuWMw0LxTGzgtqtoG579e6OZ1uwNv7u2KkbCUxffZTrsWlYmml5e2AjnmhfD4Av/v0CgOF+w6XDsxCVhCRAotLTarT4u/hzLOoYIbEhVT4BAnCxs+SbJ1qx4egN3t16hiNXYun36T7mDW3C0Oa1TT8126YaPLFJXUE+7op6J2jiduNXkE+LV+8g5Q5p3fpX7Tp9LytH8Gx35+5OR6jVAiyK3iZBURRWHbrG/G0hZObo8HSx4auxrQiooyZPB28d5FjUMSy1lkxpNqXI5xdCmIYkQOKhkJsAnYs9xyDfQaYOp0LQaDSMbO1Je29XXlp/nKPX4nhp3QmCQqJ4b2gATrYmXojVwQPGb4alfSDqLKwepX5saZd338Rbd5OdsENqwTL3dfCwr3E32anXAao3LvEaZMkZ2bz+00m2nVSbUfZu7MFHIwJxslE/d4qisOjfRQCMbDiSGnYluIslhChXkgCJh0JuHVBIbIiJI6l46rrasu6Z9ny95xKfBV1k28lw/rkax/9GBtLJz820wVXzgid/hu/7wY0jsO4JGLMW4sMMC5bjr+U91tVPHc7KTXiqeZfqemMh4Yk89+MxLkenYK7V8Ho/f57u7G1w92zvjb2cjD6JjbkNTwc8XWrvLYQoe5IAiYdCI9e7M8EURTH9EE8FY26m5fme9enawJ2X1h3ncnQK45Yc5unO3rzap6Fpp8t7NIZxG+GHIXDpD/hvPchOM9xHo4UaAYYFy/bVyyQcRVHY8M8N3t5ymoxsHTWdrPlybEta1atmsJ9O0bHouHr3Z7T/aNxsTJxMCiGKRBIg8VDwdfLFXGtOUmYSN5NvUsehjqlDqpACPZ3Z9kJn3tsewo+Hw1i6/wr7L0bz6ejmNKppwq7nnm1g9I+weqSa/JhbQ+3Wd5Mdz7Zg5VDmYaRmZvP25jP8dOwGAN0burNwZHNc8umuHRQWREhsCLbmtkxqUkmX9xCiCpMESDwULMwsqO9cn5DYEM7FnpMEqAC2lua8NyyAno2q89rGk5yPTGLIlwd4tU9Dnu7sjdZU0+V9e8DUA5AeDzUDwbx8F3sNjUpi+o/HuBCZjFYDL/duyLRuvvl+PnJ0OXx1/CsAnmz8JNWsq+XZRwhRsWlNHYAQpUXqgIrmEX8Pds7sSq9GHmTm6HhvRwjjlhzmVnxa4QeXFfcG6t2eck5+Nv97k8FfHuBCZDLuDlasntKe53r4PTAZ3Hl1J6HxoThYOjC+yfhyjVUIUTokARIPjdwEqKp2hC4ON3srFo9vxYLhAdhYmBF8OYa+n+5l64lbpg6tXKRn5TB70ylmrjtOamYOnfxc2fFCF9r7uD7wmGxdNl+f+BqAiU0m4mgpCyYLURlJAiQeGvpC6BhJgIpCo9Ewpm1ddrzYhUBPZxLTs3lhzb+8uPZfEtKyTB1embkSncKwrw6y5kgYGg282LM+PzzVDneHgu8+/XLpF64lXsPZyplxjcaVU7RCiNImCZB4aDSs1hANGqLSoohJizF1OJWOt5sdG6d24MWe9THTathy/Bb9Pt1L8KWH73O5/WQ4g77YT0h4Iq52lvzwVFteerRBocuFZOVk8e3JbwF4uunT2Fnk07NICFEpSAIkHhq2FrbUc1SXJpBhsOKxMNPy0qMN2DC1A/VcbbmVkM7YJYdYsCOEjOwcU4dXYhnZOczdeobnVh8jOSObNl7V2P5CF7rUdzfq+J9Df+Zm8k3cbNwY5T+qjKMVQpQlSYDEQ0UKoUtHy7rV2PFCF0a38URR4Nu9lxm66CDnI5IKP7iCuh6byshvgll+8CoAU7v5smZKe2o4Gbc8RkZOhv7uz+SAydiY25RVqEKIciAJkHio3NsQUZSMnZU5/32sGd892QoXO0tCwhMZ9OV+lu6/gk6nFH6CCmT32UgGfL6PEzcScLKxYNnE1rzezx9zM+N/BG44v4Go1Cg8bD14vMHjZRitEKI8SAIkHioyE6z09W5Sg50zu9CjoTuZ2TrmbzvL+GVHiEhIN3VohcrK0bFgRwhTfviHxPRsmns6s/2Fzjzi71Gk86RmpbLk1BIAng18Fiuz8p2mL4QofZIAiYdKIxf1DtC1xGskZyabOJqHR3UHa5ZNbMP8oU2xttCyPzSaPp/uZcepcFOH9kDhCWmM/u4Q3+69DMBTnbxZ/2wH6lSzLfK51p5fS0x6DLXtazPUb2gpRyqEMAVJgMRDpZp1NTxs1b/uz8edN3E0DxeNRsOT7eux/YUuBNR2IiEti+k/HmPW+uMkpVes6fJ/XbjNgM/3c/RaHA5W5nzzREveGdQYS/Oi/8hLzkzm+9PfAzAtcBoWWovSDlcIYQIVIgFatGgRXl5eWFtb065dO44cOfLAfbt3745Go8nzGDBggH4fRVF45513qFmzJjY2NvTq1YuLFy+Wx6WICiD3LpAMg5UNX3d7Nk3vyIwefmg1sOnYTfp9to8jV2JNHRo5OoX//Xaeid8fITYlkya1HNn2Qmf6Nq1Z7HOuCllFfEY8Xo5eDPAZUPgBQohKweQJ0Lp165g1a9b/t3fnYVVV6wPHv2eAwwyCzCJgDuAETtcQzbGcS7McMzS1fl6w1Ky0yW5ppqmlmZqKqJmVlpqZWUrmlCGKmJQKKjiCOAUCMp79+8M4N65oosAGzvt5nv08svfaa7+bLZyXtdZei6lTpxIXF0dQUBDdu3cnPT291PLr168nNTXVtCUkJKDT6XjyySdNZWbNmsX8+fNZvHgxMTEx2Nra0r17d3Jzq/6YBXH/Alz+ehPsirwJVlEsdFomdW/E2udC8HG25ty1Gwxaso9ZW4+RX2hUJab0zFyGLfuVj346gaLAUw/W5eux7fB1ufe5ejLyMlj1+yoA/h38b/RaWT5RiJpC9QRo7ty5jBkzhpEjR9K4cWMWL16MjY0Ny5cvL7W8s7MzHh4epm3btm3Y2NiYEiBFUfjwww95/fXXeeyxx2jevDmrVq3iwoULbNy4sRLvTKhFBkJXntZ+zmx5vgNPtqqDosDCn0/y+KK9nEiv3Nflfzl5mV7z9/DrqavYWOqYNziYaf2aYWWhu696V/6+kusF16nvVJ/uft3LKVohRFWgagKUn5/PwYMH6datm2mfVqulW7du7Nu3767qiIyMZPDgwdja3vwrLzk5mbS0tBJ1Ojo60rZt27uuU1RvxV1gJ/88SX5RvsrR1Hz2Vha8/2QQi59qiZONBQnnM+k9fw+r9qWgKBX7urzRqPBRdBJPLYvhclYejdzt2RTRnseCve+77mu51/js6GcARARHoNWo/veiEKIcqfoTffnyZYqKinB3L/lKqru7O2lpaf94/v79+0lISGD06NGmfcXnlaXOvLw8MjMzS2yi+vK09cTB0oFCpZATf55QOxyz0aOpJz+Mf4iHGrqSV2jkzW9+Z0RULOmZFdP1fCUrjxErYpmzLRGjAk+2qsPG8FDqu9mVS/1RCVHkFOYQ6BxIl7pdyqVOIUTVUa3/pImMjKRZs2b861//uq96ZsyYgaOjo2nz8fEppwiFGjQajQyEVom7gxUrR7bhP482waDXsjPxEt0/3MXWhH/+g6YsDqRcpff8PexKvISVhZZZTzTn/SeDsLa8vy6vYpdvXObzY58DENEiAo3mzmuECSGqH1UToNq1a6PT6bh48WKJ/RcvXsTDw+OO52ZnZ/PFF18watSoEvuLzytLnVOmTCEjI8O0nT17tqy3IqoY05IYMhC60mk0GsLa+bF5XHsaezpwLaeA/1t9kJe/OkxWXuF91a0oCkt2nWTQkl9Jy8ylnqstG8NDGdi6fP9oWXZkGblFuTR3bU4H7w7lWrcQompQNQGytLSkVatWREdHm/YZjUaio6MJCQm547nr1q0jLy+Pp556qsR+f39/PDw8StSZmZlJTEzMbes0GAw4ODiU2ET1VvwmmLQAqaeBuz0bw0MZ2+kBNBpYe+Acvebt5uDpe3td/s+cfMasOsC7W45RZFR4NMiLTRHtCfAo35/XtOw01h5fC8C4FuOk9UeIGkr1LrCJEyeydOlSVq5cydGjRxk7dizZ2dmMHDkSgKeffpopU6bccl5kZCT9+vXDxcWlxH6NRsP48eOZNm0amzZt4siRIzz99NN4eXnRr1+/yrglUQU0dm4M3JwMschY/Vcxr64s9Vpe6RHAF2MexNvJmjNXc3hy8T7m/nicgqK7f10+/uyf9J6/h+1H07HUa5nevynzBgdjZyj/19KX/LaEAmMBrd1b09ajbbnXL4SoGlSf1GLQoEFcunSJN998k7S0NIKDg9m6datpEPOZM2fQakvmacePH2fPnj38+OOPpdb58ssvk52dzbPPPsuff/5J+/bt2bp1K1ZWd7fqs6j+fB18sdZbc6PwBmeun8Hf0V/tkMxa23oufD++A2998zvrD51n/k8n2Jl4iQ8GBVPP9faDlhVFYcUvKby75SgFRQq+LjZ8PLQlTb0dKyTOs9fPsiFpAyBjf4So6TRKRb+nWg1lZmbi6OhIRkaGdIdVY8O2DOO3S78x66FZ9PTvqXY44i/fHr7AaxuOkJlbiLWFjtd6BzKsbd1bko3M3AJe+eo3vv9rAHXPph7MfKI5DlYVtxTF63te55uT39DOqx2fPPxJhV1HCFExyvL5rXoXmBAVpfhNsKNXZSB0VdI3yIsfJjxEaH0XbhQU8frGBEatPMCl63mmMgnnM+j70R6+T0jDQqdhat/GLBzWskKTn+SMZL499S1wc94fIUTNJgmQqLHkTbCqy9PRmk+facvrvQOx1Gv56Vg6PT7cxfY/LrIm5gyPL/qF01dy8HayZu1zIYwM9a/w7qhFhxdhVIx0qtOJZq7NKvRaQgj1qT4GSIiK8ve5gBRFkfEcVYxWq2F0h3q0b1Cb8V/EcyztOqNXHTAd7xrgxpyBQTjZWFZ4LEnXktiavBWA8BbhFX49IYT6pAVI1Fj1a9VHp9HxZ96fXMy5+M8nCFUEeDjwTUQozz5UD40GdFoNk3sGsPTp1pWS/AAsjF+IgsLDvg+bWg6FEDWbtACJGsugM1DPqR5J15I4euUoHrZ3nlxTqMeg1/Fqr0D6t/BGr9XQwN2+0q79x5U/2H5mOxo0hAdL648Q5kISIFGjBToHknQtiWNXj9G5bme1wxH/INCz8t+6/Dj+YwB61evFA04PVPr1hTqMRiP5+bJYcnVjYWGBTlc+S95IAiRqtADnADad3CRvgolSxafHs+vcLnQaHWODxqodjqgk+fn5JCcnYzTe/WScoupwcnLCw8Pjvsd1SgIkarTi8RyyJIYoTXHrz6MPPIqvg6/K0YjKoCgKqamp6HQ6fHx8bploV1RdiqKQk5NDeno6AJ6envdVnyRAokYrToBSs1P5M/dPnKyc1A1IVBmxabH8mvoreq2e54KeUzscUUkKCwvJycnBy8sLGxsbtcMRZWRtbQ1Aeno6bm5u99UdJqmvqNHsLe2pY1cHgGPXpBVI3KQoCgsOLQBgQIMBeNt5qxyRqCxFRTfXBrS0rJw3DEX5K05cCwoK7qseSYBEjRfo8td8QFckARI37buwj7j0OCy1loxpNkbtcIQKZF6w6qu8np0kQKLGkyUxxN8pisKC+JutPwMbDcTd1l3liIQQapAESNR4MhBa/N3Oczs5cvkI1nprRjUbpXY4QgiVSAIkarziLrDkjGRyCnJUjkaoyagYTW9+DQkYQm3r2ipHJETZ7Nu3D51OR+/evdUOpdqTBEjUeLWta1PbujYKConXEtUOR6ho++ntHLt6DFsLW0Y2Gal2OEKUWWRkJOPGjWPXrl1cuHBBtThqwiSSkgAJsyDdYKLIWMTC+IUADG88XKZEEMBfc8vkF6qyKYpSplizsrL48ssvGTt2LL1792bFihUljn/77be0adMGKysrateuTf/+/U3H8vLyeOWVV/Dx8cFgMFC/fn0iIyMBWLFiBU5OTiXq2rhxY4nBxm+99RbBwcEsW7YMf39/rKysANi6dSvt27fHyckJFxcX+vTpw8mTJ0vUde7cOYYMGYKzszO2tra0bt2amJgYUlJS0Gq1HDhwoET5Dz/8EF9f3wqfqFLmARJmIdA5kD3n90gCZMa+T/mekxkncbB0YHjj4WqHI6qIGwVFNH7zB1Wu/cfb3bGxvPuP4bVr1xIQEECjRo146qmnGD9+PFOmTEGj0fDdd9/Rv39/XnvtNVatWkV+fj5btmwxnfv000+zb98+5s+fT1BQEMnJyVy+fLlM8Z44cYKvv/6a9evXm+bfyc7OZuLEiTRv3pysrCzefPNN+vfvT3x8PFqtlqysLDp27Ii3tzebNm3Cw8ODuLg4jEYjfn5+dOvWjaioKFq3bm26TlRUFCNGjKjwSSolARJmobgFSN4EM0+FxkIWxS8CYESTEThYVv6aY0Lcr8jISJ566ikAevToQUZGBjt37qRTp05Mnz6dwYMH85///MdUPigoCIDExETWrl3Ltm3b6NatGwD16tUr8/Xz8/NZtWoVrq6upn0DBgwoUWb58uW4urryxx9/0LRpU9asWcOlS5eIjY3F2dkZgPr165vKjx49mv/7v/9j7ty5GAwG4uLiOHLkCN98802Z4ysrSYCEWSh+FT7pWhIFxgIstBYqRyQq07cnv+XM9TPUMtRiWOAwtcMRVYi1hY4/3u6u2rXv1vHjx9m/fz8bNmwAQK/XM2jQICIjI+nUqRPx8fGMGVP6nFbx8fHodDo6dux4X/H6+vqWSH4AkpKSePPNN4mJieHy5cumbqszZ87QtGlT4uPjadGihSn5+V/9+vUjPDycDRs2MHjwYFasWEHnzp3x8/O7r1jvhiRAwix423tjZ2FHVkEWp/48RSPnRmqHJCpJQVEBiw8vBmBUs1HYWMjyB+K/NBpNmbqh1BIZGUlhYSFeXl6mfYqiYDAYWLBggWmJiNLc6RiAVqu9ZTxSabMs29ra3rKvb9+++Pr6snTpUry8vDAajTRt2tQ0SPqfrm1pacnTTz9NVFQUjz/+OGvWrGHevHl3PKe8yCBoYRa0Gq0p6ZFxQOZlfdJ6LmRfwNXalYGNBqodjhBlVlhYyKpVq5gzZw7x8fGm7fDhw3h5efH555/TvHlzoqOjSz2/WbNmGI1Gdu7cWepxV1dXrl+/TnZ2tmlffHz8P8Z15coVjh8/zuuvv07Xrl0JDAzk2rVrJco0b96c+Ph4rl69ett6Ro8ezfbt21m4cCGFhYU8/vjj/3jt8iAJkDAbxd1gkgCZj9zCXJb8tgSA0c1GY62/81+jQlRFmzdv5tq1a4waNYqmTZuW2AYMGEBkZCRTp07l888/Z+rUqRw9epQjR44wc+ZMAPz8/AgLC+OZZ55h48aNJCcn8/PPP7N27VoA2rZti42NDa+++ionT55kzZo1t7xhVppatWrh4uLCkiVLOHHiBD/99BMTJ04sUWbIkCF4eHjQr18/9u7dy6lTp/j666/Zt2+fqUxgYCAPPvggr7zyCkOGDPnHVqPyIgmQMBvFEyLKQGjzsS5xHek30vGw9eCJhk+oHY4Q9yQyMpJu3brh6Oh4y7EBAwZw4MABnJ2dWbduHZs2bSI4OJguXbqwf/9+U7lFixbxxBNP8O9//5uAgADGjBljavFxdnZm9erVbNmyhWbNmvH555/z1ltv/WNcWq2WL774goMHD9K0aVMmTJjA+++/X6KMpaUlP/74I25ubvTq1YtmzZrx3nvv3bKK+6hRo8jPz+eZZ565h+/QvdEoZZ2IwAxkZmbi6OhIRkYGDg7ytkhNkXgtkQGbBmBnYcfeIXvRaiT/r8lyCnLoub4nV3OvMjVkqiRAAoDc3FySk5NLzGUj1PfOO++wbt06fvvtt38se6dnWJbPb/kEEGbD39EfS60lWQVZnL9+Xu1wRAX7/NjnXM29Sh27OjxW/zG1wxFClCIrK4uEhAQWLFjAuHHjKvXakgAJs2GhtaBBrQaAdIPVdFn5WUT9HgXA2OCxMu2BEFVUREQErVq1olOnTpXa/QWSAAkzIxMimodPj35KRl4Gfg5+9PaXRSOFqKpWrFhBXl4eX3755S3jgiqaJEDCrBS/CSYJUM2VkZfBqt9XARAeHI5OW7m/VIUQ1YMkQMKsBLj8tSjqFXkVvqZa+ftKsgqyaFCrAY/4PaJ2OEKIKkoSIGFWGtZqiFaj5UruFS7lXFI7HFHOruZeZfXR1cDN1h95008IcTvy20GYFWu9NX4OfoB0g9VEy48s50bhDRq7NKaLTxe1wxFCVGGSAAmzUzwQWmaErlku5Vzii+NfABARHIFGo1E5IiFEVSYJkDA7siRGzbTsyDLyivIIcg2ivXd7tcMRotrTaDRs3Lix3MtWFZIACbNTPBD66BXpAqspUrNSWZe4DoBxLcZJ64+ocUaMGIFGo0Gj0WBpaUn9+vV5++23KSwsrLBrpqam0rNnz3IvW1WongB9/PHH+Pn5YWVlRdu2bUusXVKaP//8k/DwcDw9PTEYDDRs2JAtW7aYjr/11lum/yTFW0BAQEXfhqhGiluAzmWd43r+dZWjEeVhyZElFBgLaOPRhraebdUOR4gK0aNHD1JTU0lKSuLFF1/krbfeumXtLYD8/PxyuZ6HhwcGg6Hcy1YVqiZAX375JRMnTmTq1KnExcURFBRE9+7dSU9PL7V8fn4+Dz/8MCkpKXz11VccP36cpUuX4u3tXaJckyZNSE1NNW179uypjNsR1YSjwREvWy9AusFqgrPXz7IxaSNwc+yPEDWVwWDAw8MDX19fxo4dS7du3di0aRMjRoygX79+TJ8+HS8vLxo1agTA2bNnGThwIE5OTjg7O/PYY4+RkpJSos7ly5fTpEkTDAYDnp6eRET892fo791a+fn5RERE4OnpiZWVFb6+vsyYMaPUsgBHjhyhS5cuWFtb4+LiwrPPPktWVpbpeHHMs2fPxtPTExcXF8LDwykoKCj/b9xt6CvtSqWYO3cuY8aMYeTIkQAsXryY7777juXLlzN58uRbyi9fvpyrV6/yyy+/YGFxc2p7Pz+/W8rp9Xo8PDwqNHZRvQU4B3Ah+wLHrh6jjUcbtcMR92Hx4cUUKoWEeoXS0r2l2uGI6kZRoCBHnWtb2MB9dNdaW1tz5coVAKKjo3FwcGDbtm0AFBQU0L17d0JCQti9ezd6vZ5p06bRo0cPfvvtNywtLVm0aBETJ07kvffeo2fPnmRkZLB3795SrzV//nw2bdrE2rVrqVu3LmfPnuXs2bOlls3OzjZdOzY2lvT0dEaPHk1ERAQrVqwwlduxYweenp7s2LGDEydOMGjQIIKDgxkzZsw9f0/KQrUEKD8/n4MHDzJlyhTTPq1WS7du3di3b1+p52zatImQkBDCw8P55ptvcHV1ZejQobzyyislptBOSkrCy8sLKysrQkJCmDFjBnXr1r1tLHl5eeTl5Zm+zszMLIc7FFVZgEsAP539SVqAqrlTGafYfGozABEtpPVH3IOCHHjXS51rv3oBLG3LfJqiKERHR/PDDz8wbtw4Ll26hK2tLcuWLcPS0hKA1atXYzQaWbZsmWlMXFRUFE5OTvz888888sgjTJs2jRdffJEXXnjBVHebNqX/QXjmzBkaNGhA+/bt0Wg0+Pr63ja+NWvWkJuby6pVq7C1vXl/CxYsoG/fvsycORN3d3cAatWqxYIFC9DpdAQEBNC7d2+io6MrLQFSrQvs8uXLFBUVmb4Rxdzd3UlLSyv1nFOnTvHVV19RVFTEli1beOONN5gzZw7Tpk0zlWnbti0rVqxg69atLFq0iOTkZDp06MD167cf6zFjxgwcHR1Nm4+PT/ncpKiyiscB/XHlD5UjEfdjcfxijIqRTj6daFq7qdrhCFGhNm/ejJ2dHVZWVvTs2ZNBgwbx1ltvAdCsWTNT8gNw+PBhTpw4gb29PXZ2dtjZ2eHs7Exubi4nT54kPT2dCxcu0LVr17u69ogRI4iPj6dRo0Y8//zz/Pjjj7cte/ToUYKCgkzJD0BoaChGo5Hjx4+b9jVp0qRE44Wnp+dth8BUBFW7wMrKaDTi5ubGkiVL0Ol0tGrVivPnz/P+++8zdepUgBKj0Js3b07btm3x9fVl7dq1jBo1qtR6p0yZwsSJE01fZ2ZmShJUwxXPBZSckUxuYS5WeiuVIxJllXgtke9Tvgdk7I+4DxY2N1ti1Lp2GXTu3JlFixZhaWmJl5cXev1/P8L/nmwAZGVl0apVKz777LNb6nF1dUWrLVv7R8uWLUlOTub7779n+/btDBw4kG7duvHVV1+VqZ6/Kx7KUkyj0WA0Gu+5vrJSLQGqXbs2Op2Oixcvlth/8eLF247f8fT0xMLCokTGGBgYSFpaGvn5+SWy32JOTk40bNiQEydO3DYWg8FQ7Uavi/vjbuNOLUMtruVd48SfJ6T1oBpaGL8QgEd8H6GRcyOVoxHVlkZzT91QarC1taV+/fp3VbZly5Z8+eWXuLm54eDgUGoZPz8/oqOj6dy5813V6eDgwKBBgxg0aBBPPPEEPXr04OrVqzg7O5coFxgYyIoVK8jOzjYlZnv37kWr1ZoGaFcFqnWBWVpa0qpVK6Kjo037jEYj0dHRhISElHpOaGgoJ06cKJEhJiYm4unpWWryAzez4JMnT+Lp6Vm+NyCqNY1GY2oFkiUxqp/fr/xO9JlotBot4cHhaocjRJUzbNgwateuzWOPPcbu3btJTk7m559/5vnnn+fcuXPAzWlj5syZw/z580lKSiIuLo6PPvqo1Prmzp3L559/zrFjx0hMTGTdunV4eHjg5ORU6rWtrKwICwsjISGBHTt2MG7cOIYPH37LsBc1qfoa/MSJE1m6dCkrV67k6NGjjB07luzsbNNbYU8//XSJQdJjx47l6tWrvPDCCyQmJvLdd9/x7rvvEh7+31+AkyZNYufOnaSkpPDLL7/Qv39/dDodQ4YMqfT7E1Vb8YSIBy8e5PKNyxQZi1SOSNytjw99DEAv/17Uc6qncjRCVD02Njbs2rWLunXr8vjjjxMYGMioUaPIzc01tQiFhYXx4YcfsnDhQpo0aUKfPn1ISkoqtT57e3tmzZpF69atadOmDSkpKWzZsqXUrjQbGxt++OEHrl69Sps2bXjiiSfo2rUrCxYsqNB7LiuNoiiKmgEsWLCA999/n7S0NIKDg5k/fz5t296cyKxTp074+fmVeG1u3759TJgwgfj4eLy9vRk1alSJt8AGDx7Mrl27uHLlCq6urrRv357p06fzwAMP3HVMmZmZODo6kpGRcdumQ1H9fZ/8PS/vetn0tVajxdnKmdrWtU2bq7UrLtYupn8X77cpY9+9KD/x6fEM/344Oo2OTf02Udfh9m94CvG/cnNzSU5Oxt/fHysrGftXHd3pGZbl81v1BKgqkgTIPGQXZDNhxwQSryVyNfcqCnf/o2Cjt8HVxhUXq7+SIxvXUhOnWoZa6LS6f65Q3LXRP44mJjWGxxs8zn/a/UftcEQ1IwlQ9VdeCVC1egtMiPJka2HLkkeWAFBoLORa7jUu37jMpRuXuHLjCpduXOLyjcu3bDcKb5BTmMPpzNOczjx9x2voNLpbWpVMSVJx0mRVm9o2tbHWW1fGbVdrsWmxxKTGoNfqea75c2qHI4SoxiQBEgLQa/W42rjiauNKIIG3LacoCjmFOVzK+Ss5yr3M5ZzLpSZO13KvUaQUcenGJS7duPSPMdha2Jpajoq72/7+7+KtllUttBrVl/GrdIqisODQzTEEAxoMwMtOpcnrhBA1giRAQpSBRqPB1sIWW0db/Bz97li2wFhgalUq3kyJ0/9suUW5ZBdkk12QTUpmyh3r1Wl0uFi53EyO/tb11rBWQ0K8QnCwrJndtr9c+IW49DgMOgPPNn9W7XCEENWcJEBCVBALrQVuNm642bjdsZyiKGQXZN+2y+3vrUtXc69SpBSRfiOd9Bvpt7zCr9PoCHINokOdDrT3bk+jWo1M0+BXZ39v/RnYaOA/fk+FEOKfSAIkhMo0Gg12lnbYWdrh7+h/x7IFxgKu3rh6S3KUnpPOwYsHOZVxirj0OOLS45gXNw9Xa1fae7envXd7QrxCsLe0r6S7Kl8/n/2ZhCsJWOutGdW09BndhRCiLCQBEqIasdBa4G7rjrtt6ZOJnc86z97ze9l9bjcxaTFcunGJDSc2sOHEBnQaHcFuwbT3bk8H7w40rNWwWrQOGRUjH8ffnPdnaMBQXKxdVI5ICFETSAIkRA3ibefNwEYDGdhoIPlF+Ry8eJDd53ez5/wekjOSOXjxIAcvHmRe3DzcbNxMrUMPej5YZVuHtp3exvFrx7GzsGNk05FqhyOEqCEkARKihrLUWRLiFUKIVwgvt3mZc9fPsef8Hvac38P+tP2k56SzPmk965PWo9foTa1D7b3bV5nWoSJjkWnNr+GNh+NocFQ5IiFETSEJkBBmoo59HQYHDGZwwGDyivJutg6du9k6lJKZwoGLBzhw8QAfxn2Im40bHbw7mFqH7CztVIl5S/IWTmWcwsHSgeGNh6sSgxDiJo1Gw4YNG+jXrx8pKSn4+/tz6NAhgoOD1Q7tnkgCJIQZMugMtPNqRzuvdrzCK5y9fva/rUOpN1uHvk76mq+Tvkav0dPCvYWpdaiBU4NKaR0qMBaw+PBiAEY2HVllu+iEqAwjRoxg5cqVAOj1eurUqcOTTz7J22+/LTNa3yNJgIQQ+Nj7MCRgCEMChpBXlMeBtAOmhCglM4XYtFhi02L54OAHuNu4mwZSP+j1ILYWthUS07cnv+XM9TM4WzkzNGBohVxDiOqkR48eREVFUVBQwMGDBwkLC0Oj0TBz5ky1Q6uWzG86WSHEHRl0BkK9Q3nlX6/wbf9v2dJ/C1P+NYUO3h2w0llxMeciXyd9zfifx9P+i/aM+mEUUQlRJF1LoryWFswvyje1/jzT9BlZfFYIwGAw4OHhgY+PD/369aNbt25s27YNAKPRyIwZM/D398fa2pqgoCC++uqrEuf//vvv9OnTBwcHB+zt7enQoQMnT54EIDY2locffpjatWvj6OhIx44diYuLq/R7rEzSAiSEuCMfBx+GOgxlaOBQcgtzOXDxZuvQ7nO7OXP9DPvT9rM/bT9zD87Fw9ajxJtl99o6tD5pPanZqbhauzKo0aByviMh/ktRFG4U3lDl2tZ663vuTk5ISOCXX37B19cXgBkzZrB69WoWL15MgwYN2LVrF0899RSurq507NiR8+fP89BDD9GpUyd++uknHBwc2Lt3L4WFhQBcv36dsLAwPvroIxRFYc6cOfTq1YukpCTs7Wtm97MkQEKIu2altzIlOJP/NZkzmWfYfX43u8/v5kDaAdKy0/gq8Su+SvwKvVZPK7dWN7vL6nSgnmO9u/pln1uYy9LflgIwpvkYrPQyvkFUnBuFN2i7pq0q144ZGlOm1s3NmzdjZ2dHYWEheXl5aLVaFixYQF5eHu+++y7bt28nJCQEgHr16rFnzx4++eQTOnbsyMcff4yjoyNffPEFFhYWADRs2NBUd5cuXUpca8mSJTg5ObFz50769OlTDndb9UgCJIS4Z3Ud6jLMYRjDAoeRW5hLbFrszdah87s5e/0sMWkxxKTFMOfgHDxtPU1jh9p6tr3tL/61x9eSfiMdT1tPBjQYUMl3JETV1blzZxYtWkR2djYffPABer2eAQMG8Pvvv5OTk8PDDz9conx+fj4tWrQAID4+ng4dOpiSn/918eJFXn/9dX7++WfS09MpKioiJyeHM2fOVPh9qUUSICFEubDSW9GhTgc61OnAFKZwOvO0qassNi2W1OxU1iWuY13iOiy0FrR0b0kH7w508O6Av6M/Go2GnIIcIhMiAXiu+XNY6ixVvitR01nrrYkZGqPatcvC1taW+vXrA7B8+XKCgoKIjIykadOmAHz33Xd4e3uXOMdgMNy8lvWdrxUWFsaVK1eYN28evr6+GAwGQkJCyM/PL1OM1YkkQEKICuHr4Iuvgy/DAodxo/AGsWmxpnmHzmWdIyY1hpjUGGYfmI2XrRftvdtTqBRyNfcqPvY+PFr/UbVvQZgBjUZTLQfZa7VaXn31VSZOnEhiYiIGg4EzZ87QsWPHUss3b96clStXUlBQUGor0N69e1m4cCG9evUC4OzZs1y+fLlC70FtkgAJISqctd6ah+o8xEN1HkJRFE5nnjYt0XEg7QAXsi+wNnGtqfzYoLFYaEtvqhdC3PTkk0/y0ksv8cknnzBp0iQmTJiA0Wikffv2ZGRksHfvXhwcHAgLCyMiIoKPPvqIwYMHM2XKFBwdHfn111/517/+RaNGjWjQoAGffvoprVu3JjMzk5deeukfW42qO0mAhBCVSqPR4Ofoh5+jH8MbDyenIIcDFw+w69wufk39lbr2denl30vtMIWo8vR6PREREcyaNYvk5GRcXV2ZMWMGp06dwsnJiZYtW/Lqq68C4OLiwk8//cRLL71Ex44d0el0BAcHExoaCkBkZCTPPvssLVu2xMfHh3fffZdJkyapeXsVTqOU18QdNUhmZiaOjo5kZGTg4OCgdjhCCCHKSW5uLsnJyfj7+8sMytXUnZ5hWT6/ZSJEIYQQQpgdSYCEEEIIYXYkARJCCCGE2ZEESAghhBBmRxIgIYQQQpgdSYCEEEKYHXkBuvoqr2cnCZAQQgizodPpAGr0Eg81XU5ODsBt1zW7WzIRohBCCLOh1+uxsbHh0qVLWFhYoNVKO0B1oSgKOTk5pKen4+TkZEpm75UkQEIIIcyGRqPB09OT5ORkTp8+rXY44h44OTnh4eFx3/VIAiSEEMKsWFpa0qBBA+kGq4YsLCzuu+WnmCRAQgghzI5Wq5WlMMycdH4KIYQQwuxIAiSEEEIIsyMJkBBCCCHMjowBKkXxJEuZmZkqRyKEEEKIu1X8uX03kyVKAlSK69evA+Dj46NyJEIIIYQoq+vXr+Po6HjHMhpF5gO/hdFo5MKFC9jb26PRaNQOp0rKzMzEx8eHs2fP4uDgoHY4Zk+eR9Uiz6NqkedRtVTk81AUhevXr+Pl5fWPk1xKC1AptFotderUUTuMasHBwUF+oVQh8jyqFnkeVYs8j6qlop7HP7X8FJNB0EIIIYQwO5IACSGEEMLsSAIk7onBYGDq1KkYDAa1QxHI86hq5HlULfI8qpaq8jxkELQQQgghzI60AAkhhBDC7EgCJIQQQgizIwmQEEIIIcyOJEBCCCGEMDuSAIm7NmPGDNq0aYO9vT1ubm7069eP48ePqx2W+Mt7772HRqNh/Pjxaodi1s6fP89TTz2Fi4sL1tbWNGvWjAMHDqgdllkqKirijTfewN/fH2trax544AHeeeedu1onSty/Xbt20bdvX7y8vNBoNGzcuLHEcUVRePPNN/H09MTa2ppu3bqRlJRUafFJAiTu2s6dOwkPD+fXX39l27ZtFBQU8Mgjj5Cdna12aGYvNjaWTz75hObNm6sdilm7du0aoaGhWFhY8P333/PHH38wZ84catWqpXZoZmnmzJksWrSIBQsWcPToUWbOnMmsWbP46KOP1A7NLGRnZxMUFMTHH39c6vFZs2Yxf/58Fi9eTExMDLa2tnTv3p3c3NxKiU9egxf37NKlS7i5ubFz504eeughtcMxW1lZWbRs2ZKFCxcybdo0goOD+fDDD9UOyyxNnjyZvXv3snv3brVDEUCfPn1wd3cnMjLStG/AgAFYW1uzevVqFSMzPxqNhg0bNtCvXz/gZuuPl5cXL774IpMmTQIgIyMDd3d3VqxYweDBgys8JmkBEvcsIyMDAGdnZ5UjMW/h4eH07t2bbt26qR2K2du0aROtW7fmySefxM3NjRYtWrB06VK1wzJb7dq1Izo6msTERAAOHz7Mnj176Nmzp8qRieTkZNLS0kr83nJ0dKRt27bs27evUmKQxVDFPTEajYwfP57Q0FCaNm2qdjhm64svviAuLo7Y2Fi1QxHAqVOnWLRoERMnTuTVV18lNjaW559/HktLS8LCwtQOz+xMnjyZzMxMAgIC0Ol0FBUVMX36dIYNG6Z2aGYvLS0NAHd39xL73d3dTccqmiRA4p6Eh4eTkJDAnj171A7FbJ09e5YXXniBbdu2YWVlpXY4gpt/GLRu3Zp3330XgBYtWpCQkMDixYslAVLB2rVr+eyzz1izZg1NmjQhPj6e8ePH4+XlJc9DSBeYKLuIiAg2b97Mjh07qFOnjtrhmK2DBw+Snp5Oy5Yt0ev16PV6du7cyfz589Hr9RQVFakdotnx9PSkcePGJfYFBgZy5swZlSIyby+99BKTJ09m8ODBNGvWjOHDhzNhwgRmzJihdmhmz8PDA4CLFy+W2H/x4kXTsYomCZC4a4qiEBERwYYNG/jpp5/w9/dXOySz1rVrV44cOUJ8fLxpa926NcOGDSM+Ph6dTqd2iGYnNDT0lqkhEhMT8fX1VSki85aTk4NWW/JjTqfTYTQaVYpIFPP398fDw4Po6GjTvszMTGJiYggJCamUGKQLTNy18PBw1qxZwzfffIO9vb2pn9bR0RFra2uVozM/9vb2t4y/srW1xcXFRcZlqWTChAm0a9eOd999l4EDB7J//36WLFnCkiVL1A7NLPXt25fp06dTt25dmjRpwqFDh5g7dy7PPPOM2qGZhaysLE6cOGH6Ojk5mfj4eJydnalbty7jx49n2rRpNGjQAH9/f9544w28vLxMb4pVOEWIuwSUukVFRakdmvhLx44dlRdeeEHtMMzat99+qzRt2lQxGAxKQECAsmTJErVDMluZmZnKCy+8oNStW1exsrJS6tWrp7z22mtKXl6e2qGZhR07dpT6mREWFqYoiqIYjUbljTfeUNzd3RWDwaB07dpVOX78eKXFJ/MACSGEEMLsyBggIYQQQpgdSYCEEEIIYXYkARJCCCGE2ZEESAghhBBmRxIgIYQQQpgdSYCEEEIIYXYkARJCCCGE2ZEESAhR4VJSUtBoNMTHx6sdSpWVlpbGww8/jK2tLU5OTrfdJ4QoH5IACSHuy4gRI9BoNKbNxcWFHj168Ntvv5nK+Pj4kJqaWqWX6MjPz2fWrFkEBQVhY2ND7dq1CQ0NJSoqioKCgruuR6PRsHHjxjJf/4MPPiA1NZX4+HgSExNvu08IUT4kARJC3LcePXqQmppKamoq0dHR6PV6+vTpYzqu0+nw8PBAr6/Y5Qfz8/Pv+bzu3bvz3nvv8eyzz/LLL7+wf/9+wsPD+eijj/j999/LOdJbnTx5klatWtGgQQPc3Nxuu08IUU4qbdENIUSNFBYWpjz22GMl9u3evVsBlPT0dEVRFCU5OVkBlEOHDimK8t81grZv3660atVKsba2VkJCQpRjx46Z6jhx4oTy6KOPKm5uboqtra3SunVrZdu2bSWu4+vrq7z99tvK8OHDFXt7eyUsLEzp3LmzEh4eXqJcenq6YmFhoWzfvr3Ue5g5c6ai1WqVuLi4W47l5+crWVlZput98MEHJY4HBQUpU6dONR3nb2se+fr6msotXLhQqVevnmJhYaE0bNhQWbVqVYn74H/WSiptnxCi/EgLkBCiXGVlZbF69Wrq16+Pi4vLHcu+9tprzJkzhwMHDqDX60us0p2VlUWvXr2Ijo7m0KFD9OjRg759+3LmzJkSdcyePZugoCAOHTrEG2+8wejRo1mzZg15eXmmMqtXr8bb25suXbqUGsdnn31Gt27daNGixS3HLCwssLW1vat7j42NBSAqKorU1FTT1xs2bOCFF17gxRdfJCEhgeeee46RI0eyY8cO03k9evRg4MCBpKamMm/evFL3CSHKT8W2RwshzMLmzZuxs7MDIDs7G09PTzZv3oxWe+e/saZPn07Hjh0BmDx5Mr179yY3NxcrKyuCgoIICgoylX3nnXfYsGEDmzZtIiIiwrS/S5cuvPjii6avvb29iYiI4JtvvmHgwIEArFixwjRWqTRJSUl06tTpnu7971xdXQFwcnLCw8PDtH/27NmMGDGCf//73wBMnDiRX3/9ldmzZ9O5c2dcXV0xGAxYW1uXOK+0fUKI8iEtQEKI+9a5c2fi4+OJj49n//79dO/enZ49e3L69Ok7nte8eXPTvz09PQFIT08HbrYATZo0icDAQJycnLCzs+Po0aO3tAC1bt26xNdWVlYMHz6c5cuXAxAXF0dCQgIjRoy4bRyKotz1vd6Lo0ePEhoaWmJfaGgoR48erdDrCiFuT1qAhBD3zdbWlvr165u+XrZsGY6OjixdupRp06bd9jwLCwvTv4tbZ4xGIwCTJk1i27ZtzJ49m/r162Ntbc0TTzxxy0Dn0rqnRo8eTXBwMOfOnSMqKoouXbrg6+t72zgaNmzIsWPH/vE+tVrtLclSWd4QE0JUHdICJIQodxqNBq1Wy40bN+65jr179zJixAj69+9Ps2bN8PDwICUl5a7ObdasGa1bt2bp0qWsWbOmxNii0gwdOpTt27dz6NChW44VFBSQnZ0N3OziSk1NNR3LzMwkOTm5RHkLCwuKiopK7AsMDGTv3r233F/jxo3v6n6EEOVPEiAhxH3Ly8sjLS2NtLQ0jh49yrhx48jKyqJv3773XGeDBg1Yv3498fHxHD58mKFDh5pah+7G6NGjee+991AUhf79+9+x7Pjx4wkNDaVr1658/PHHHD58mFOnTrF27VoefPBBkpKSgJvjjT799FN2797NkSNHCAsLQ6fTlajLz8+P6Oho0tLSuHbtGgAvvfQSK1asYNGiRSQlJTF37lzWr1/PpEmTyvhdEUKUF0mAhBD3bevWrXh6euLp6Unbtm2JjY1l3bp19zWweO7cudSqVYt27drRt29funfvTsuWLe/6/CFDhqDX6xkyZAhWVlZ3LGswGNi2bRsvv/wyn3zyCQ8++CBt2rRh/vz5PP/886YJHKdMmULHjh3p06cPvXv3pl+/fjzwwAMl6pozZw7btm3Dx8fH9FZZv379mDdvHrNnz6ZJkyZ88sknREVFlcvAayHEvdEoFT36TwghVJCSksIDDzxAbGxsmRInIYR5kARICFGjFBQUcOXKFSZNmkRycvItY2+EEAKkC0wIUcPs3bsXT09PYmNjWbx4sdrhCCGqKGkBEkIIIYTZkRYgIYQQQpgdSYCEEEIIYXYkARJCCCGE2ZEESAghhBBmRxIgIYQQQpgdSYCEEEIIYXYkARJCCCGE2ZEESAghhBBmRxIgIYQQQpid/wfyygx+KQSSKAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Displaying the results of Random Forest Classifier\n",
        "# With hyperparameter tuning only\n",
        "\n",
        "accuracy1 = accuracy_score(y_test1, y_pred1noK)\n",
        "precision1 = precision_score(y_test1, y_pred1noK)\n",
        "recall1 = recall_score(y_test1, y_pred1noK)\n",
        "\n",
        "accuracy2 = accuracy_score(y_test2, y_pred2noK)\n",
        "precision2 = precision_score(y_test2, y_pred2noK)\n",
        "recall2 = recall_score(y_test2, y_pred2noK)\n",
        "\n",
        "accuracy3 = accuracy_score(y_test3, y_pred3noK)\n",
        "precision3 = precision_score(y_test3, y_pred3noK)\n",
        "recall3 = recall_score(y_test3, y_pred3noK)\n",
        "\n",
        "accuracy4 = accuracy_score(y_test4, y_pred4noK)\n",
        "precision4 = precision_score(y_test4, y_pred4noK)\n",
        "recall4 = recall_score(y_test4, y_pred4noK)\n",
        "\n",
        "accuracy5 = accuracy_score(y_test5, y_pred5noK)\n",
        "precision5 = precision_score(y_test5, y_pred5noK)\n",
        "recall5 = recall_score(y_test5, y_pred5noK)\n",
        "\n",
        "accuracy6 = accuracy_score(y_test6, y_pred6noK)\n",
        "precision6 = precision_score(y_test6, y_pred6noK)\n",
        "recall6 = recall_score(y_test6, y_pred6noK)\n",
        "\n",
        "accuracy7 = accuracy_score(y_test7, y_pred7noK)\n",
        "precision7 = precision_score(y_test7, y_pred7noK)\n",
        "recall7 = recall_score(y_test7, y_pred7noK)\n",
        "\n",
        "accuracy8 = accuracy_score(y_test8, y_pred8noK)\n",
        "precision8 = precision_score(y_test8, y_pred8noK)\n",
        "recall8 = recall_score(y_test8, y_pred8noK)\n",
        "\n",
        "accuracy9 = accuracy_score(y_test9, y_pred9noK)\n",
        "precision9 = precision_score(y_test9, y_pred9noK)\n",
        "recall9 = recall_score(y_test9, y_pred9noK)\n",
        "\n",
        "accuracy10 = accuracy_score(y_test10, y_pred10noK)\n",
        "precision10 = precision_score(y_test10, y_pred10noK)\n",
        "recall10 = recall_score(y_test10, y_pred10noK)\n",
        "\n",
        "x_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "accuracies = [accuracy1, accuracy2, accuracy3, accuracy4, accuracy5, accuracy6, accuracy7, accuracy8, accuracy9, accuracy10]\n",
        "precisions = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
        "recalls = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"Accuracy:\", accuracies[i])\n",
        "  print(\"Precision:\", precisions[i])\n",
        "  print(\"Recall:\", recalls[i])\n",
        "  print(\"----------\")\n",
        "\n",
        "plt.plot(x_values, accuracies, label = \"Accuracy\")\n",
        "plt.plot(x_values, precisions, label = \"Precision\")\n",
        "plt.plot(x_values, recalls, label = \"Recall\")\n",
        "plt.legend()\n",
        "plt.xlabel(\"Binary Cutoff Definition\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.title(\"Results with Optimization\")\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9iCZtXVJIOgm",
        "outputId": "082871b0-0edc-4980-a8e6-fab0694b946b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n",
            "Precision: 1.0\n",
            "Recall: 1.0\n",
            "----------\n",
            "Accuracy: 1.0\n",
            "Precision: 1.0\n",
            "Recall: 1.0\n",
            "----------\n",
            "Accuracy: 0.9608695652173913\n",
            "Precision: 1.0\n",
            "Recall: 0.9217391304347826\n",
            "----------\n",
            "Accuracy: 0.9095238095238095\n",
            "Precision: 1.0\n",
            "Recall: 0.819047619047619\n",
            "----------\n",
            "Accuracy: 0.8048780487804879\n",
            "Precision: 0.8472222222222222\n",
            "Recall: 0.7439024390243902\n",
            "----------\n",
            "Accuracy: 0.8109756097560976\n",
            "Precision: 0.7865168539325843\n",
            "Recall: 0.8536585365853658\n",
            "----------\n",
            "Accuracy: 0.8770053475935828\n",
            "Precision: 0.8380952380952381\n",
            "Recall: 0.9361702127659575\n",
            "----------\n",
            "Accuracy: 0.9577464788732394\n",
            "Precision: 0.9224137931034483\n",
            "Recall: 1.0\n",
            "----------\n",
            "Accuracy: 0.9601769911504425\n",
            "Precision: 0.9262295081967213\n",
            "Recall: 1.0\n",
            "----------\n",
            "Accuracy: 0.9791666666666666\n",
            "Precision: 0.96\n",
            "Recall: 1.0\n",
            "----------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the performance of the models, no optimization and kfold cross validation\n",
        "\n",
        "accuracy1 = accuracy_score(y_test1, y_pred1nK)\n",
        "precision1 = precision_score(y_test1, y_pred1nK)\n",
        "recall1 = recall_score(y_test1, y_pred1nK)\n",
        "\n",
        "accuracy2 = accuracy_score(y_test2, y_pred2nK)\n",
        "precision2 = precision_score(y_test2, y_pred2nK)\n",
        "recall2 = recall_score(y_test2, y_pred2nK)\n",
        "\n",
        "accuracy3 = accuracy_score(y_test3, y_pred3nK)\n",
        "precision3 = precision_score(y_test3, y_pred3nK)\n",
        "recall3 = recall_score(y_test3, y_pred3nK)\n",
        "\n",
        "accuracy4 = accuracy_score(y_test4, y_pred4nK)\n",
        "precision4 = precision_score(y_test4, y_pred4nK)\n",
        "recall4 = recall_score(y_test4, y_pred4nK)\n",
        "\n",
        "accuracy5 = accuracy_score(y_test5, y_pred5nK)\n",
        "precision5 = precision_score(y_test5, y_pred5nK)\n",
        "recall5 = recall_score(y_test5, y_pred5nK)\n",
        "\n",
        "accuracy6 = accuracy_score(y_test6, y_pred6nK)\n",
        "precision6 = precision_score(y_test6, y_pred6nK)\n",
        "recall6 = recall_score(y_test6, y_pred6nK)\n",
        "\n",
        "accuracy7 = accuracy_score(y_test7, y_pred7nK)\n",
        "precision7 = precision_score(y_test7, y_pred7nK)\n",
        "recall7 = recall_score(y_test7, y_pred7nK)\n",
        "\n",
        "accuracy8 = accuracy_score(y_test8, y_pred8nK)\n",
        "precision8 = precision_score(y_test8, y_pred8nK)\n",
        "recall8 = recall_score(y_test8, y_pred8nK)\n",
        "\n",
        "accuracy9 = accuracy_score(y_test9, y_pred9nK)\n",
        "precision9 = precision_score(y_test9, y_pred9nK)\n",
        "recall9 = recall_score(y_test9, y_pred9nK)\n",
        "\n",
        "accuracy10 = accuracy_score(y_test10, y_pred10nK)\n",
        "precision10 = precision_score(y_test10, y_pred10nK)\n",
        "recall10 = recall_score(y_test10, y_pred10nK)\n",
        "\n",
        "x_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "accuracies = [accuracy1, accuracy2, accuracy3, accuracy4, accuracy5, accuracy6, accuracy7, accuracy8, accuracy9, accuracy10]\n",
        "precisions = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
        "recalls = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"Accuracy:\", accuracies[i])\n",
        "  print(\"Precision:\", precisions[i])\n",
        "  print(\"Recall:\", recalls[i])\n",
        "  print(\"----------\")\n",
        "\n",
        "plt.plot(x_values, accuracies, label = \"Accuracy\")\n",
        "plt.plot(x_values, precisions, label = \"Precision\")\n",
        "plt.plot(x_values, recalls, label = \"Recall\")\n",
        "plt.legend()\n",
        "plt.xlabel(\"Binary Cutoff Definition\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.title(\"Results with No Optimization\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fsdJlQ_p8G4V",
        "outputId": "6849463a-3c19-4b5d-8b87-58ca9a9e21ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9883268482490273\n",
            "Precision: 1.0\n",
            "Recall: 0.9767441860465116\n",
            "----------\n",
            "Accuracy: 0.97265625\n",
            "Precision: 1.0\n",
            "Recall: 0.9453125\n",
            "----------\n",
            "Accuracy: 0.782608695652174\n",
            "Precision: 0.8421052631578947\n",
            "Recall: 0.6956521739130435\n",
            "----------\n",
            "Accuracy: 0.6714285714285714\n",
            "Precision: 0.7045454545454546\n",
            "Recall: 0.5904761904761905\n",
            "----------\n",
            "Accuracy: 0.676829268292683\n",
            "Precision: 0.704225352112676\n",
            "Recall: 0.6097560975609756\n",
            "----------\n",
            "Accuracy: 0.6524390243902439\n",
            "Precision: 0.6582278481012658\n",
            "Recall: 0.6341463414634146\n",
            "----------\n",
            "Accuracy: 0.7807486631016043\n",
            "Precision: 0.7789473684210526\n",
            "Recall: 0.7872340425531915\n",
            "----------\n",
            "Accuracy: 0.8169014084507042\n",
            "Precision: 0.8090909090909091\n",
            "Recall: 0.8317757009345794\n",
            "----------\n",
            "Accuracy: 0.8451327433628318\n",
            "Precision: 0.8305084745762712\n",
            "Recall: 0.8672566371681416\n",
            "----------\n",
            "Accuracy: 0.9458333333333333\n",
            "Precision: 0.9212598425196851\n",
            "Recall: 0.975\n",
            "----------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the performance of the model, with no optimization - REDUNDANT\n",
        "\n",
        "accuracy1 = accuracy_score(y_test1, y_pred1n)\n",
        "precision1 = precision_score(y_test1, y_pred1n)\n",
        "recall1 = recall_score(y_test1, y_pred1n)\n",
        "\n",
        "accuracy2 = accuracy_score(y_test2, y_pred2n)\n",
        "precision2 = precision_score(y_test2, y_pred2n)\n",
        "recall2 = recall_score(y_test2, y_pred2n)\n",
        "\n",
        "accuracy3 = accuracy_score(y_test3, y_pred3n)\n",
        "precision3 = precision_score(y_test3, y_pred3n)\n",
        "recall3 = recall_score(y_test3, y_pred3n)\n",
        "\n",
        "accuracy4 = accuracy_score(y_test4, y_pred4n)\n",
        "precision4 = precision_score(y_test4, y_pred4n)\n",
        "recall4 = recall_score(y_test4, y_pred4n)\n",
        "\n",
        "accuracy5 = accuracy_score(y_test5, y_pred5n)\n",
        "precision5 = precision_score(y_test5, y_pred5n)\n",
        "recall5 = recall_score(y_test5, y_pred5n)\n",
        "\n",
        "accuracy6 = accuracy_score(y_test6, y_pred6n)\n",
        "precision6 = precision_score(y_test6, y_pred6n)\n",
        "recall6 = recall_score(y_test6, y_pred6n)\n",
        "\n",
        "accuracy7 = accuracy_score(y_test7, y_pred7n)\n",
        "precision7 = precision_score(y_test7, y_pred7n)\n",
        "recall7 = recall_score(y_test7, y_pred7n)\n",
        "\n",
        "accuracy8 = accuracy_score(y_test8, y_pred8n)\n",
        "precision8 = precision_score(y_test8, y_pred8n)\n",
        "recall8 = recall_score(y_test8, y_pred8n)\n",
        "\n",
        "accuracy9 = accuracy_score(y_test9, y_pred9n)\n",
        "precision9 = precision_score(y_test9, y_pred9n)\n",
        "recall9 = recall_score(y_test9, y_pred9n)\n",
        "\n",
        "accuracy10 = accuracy_score(y_test10, y_pred10n)\n",
        "precision10 = precision_score(y_test10, y_pred10n)\n",
        "recall10 = recall_score(y_test10, y_pred10n)\n",
        "\n",
        "x_values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
        "accuracies = [accuracy1, accuracy2, accuracy3, accuracy4, accuracy5, accuracy6, accuracy7, accuracy8, accuracy9, accuracy10]\n",
        "precisions = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n",
        "recalls = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"Accuracy:\", accuracies[i])\n",
        "  print(\"Precision:\", precisions[i])\n",
        "  print(\"Recall:\", recalls[i])\n",
        "  print(\"----------\")\n",
        "\n",
        "plt.plot(x_values, accuracies, label = \"Accuracy\")\n",
        "plt.plot(x_values, precisions, label = \"Precision\")\n",
        "plt.plot(x_values, recalls, label = \"Recall\")\n",
        "plt.legend()\n",
        "plt.xlabel(\"Binary Cutoff\")\n",
        "plt.ylabel(\"Score\")\n",
        "plt.title(\"Results with No Optimization\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "32lzf6PGuMnO",
        "outputId": "dd41bd8c-7285-4a34-97f0-3976ed223fd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 1.0\n",
            "Precision: 1.0\n",
            "Recall: 1.0\n",
            "----------\n",
            "Accuracy: 1.0\n",
            "Precision: 1.0\n",
            "Recall: 1.0\n",
            "----------\n",
            "Accuracy: 0.9652173913043478\n",
            "Precision: 1.0\n",
            "Recall: 0.9304347826086956\n",
            "----------\n",
            "Accuracy: 0.9142857142857143\n",
            "Precision: 0.967741935483871\n",
            "Recall: 0.8571428571428571\n",
            "----------\n",
            "Accuracy: 0.8048780487804879\n",
            "Precision: 0.8289473684210527\n",
            "Recall: 0.7682926829268293\n",
            "----------\n",
            "Accuracy: 0.8048780487804879\n",
            "Precision: 0.7717391304347826\n",
            "Recall: 0.8658536585365854\n",
            "----------\n",
            "Accuracy: 0.8877005347593583\n",
            "Precision: 0.8543689320388349\n",
            "Recall: 0.9361702127659575\n",
            "----------\n",
            "Accuracy: 0.9436619718309859\n",
            "Precision: 0.8991596638655462\n",
            "Recall: 1.0\n",
            "----------\n",
            "Accuracy: 0.9601769911504425\n",
            "Precision: 0.9262295081967213\n",
            "Recall: 1.0\n",
            "----------\n",
            "Accuracy: 0.9791666666666666\n",
            "Precision: 0.96\n",
            "Recall: 1.0\n",
            "----------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizations for random forest classifier\n",
        "\n",
        "X_train5, X_test5, y_train5, y_test5 = train_test_split(X_over5, y_over5, test_size=0.2, random_state = 1, stratify = y_over5)\n",
        "cv = KFold(n_splits=5, random_state=1, shuffle=True)\n",
        "\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train5, y_train5)\n",
        "y_pred5 = cross_val_predict(rf, X_test5, y_test5, cv=cv)\n",
        "accuracy = accuracy_score(y_test5, y_pred5)\n",
        "print(\"Accuracy:\", accuracy)\n",
        "\n",
        "cm = confusion_matrix(y_test5, y_pred5)\n",
        "ConfusionMatrixDisplay(confusion_matrix=cm).plot()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "feature_importances = pd.Series(rf.feature_importances_, index=X_train5.columns).sort_values(ascending=False)\n",
        "feature_importances.plot.bar()\n",
        "plt.show()\n",
        "\n",
        "param_grid =  {'n_estimators': randint(50,500),\n",
        "              'max_depth': randint(1,20)}\n",
        "\n",
        "# Create a based model\n",
        "# Instantiate the grid search model\n",
        "rand_search = RandomizedSearchCV(rf, \n",
        "                                 param_distributions = param_grid, \n",
        "                                 n_iter=15, \n",
        "                                 cv=5)\n",
        "\n",
        "# Fit the random search object to the data\n",
        "rand_search.fit(X_train5, y_train5)\n",
        "best_rf = rand_search.best_estimator_\n",
        "# Fit the random search object to the data\n",
        "y_pred5 = best_rf.predict(X_test5)\n",
        "accuracy = accuracy_score(y_test5, y_pred5)\n",
        "print(\"Confusion Matrix for Random Forest\")\n",
        "print(\"Accuracy:\", accuracy)\n",
        "cm = confusion_matrix(y_test5, y_pred5)\n",
        "ConfusionMatrixDisplay(confusion_matrix=cm).plot()\n",
        "plt.show()\n",
        "\n",
        "\n",
        "feature_importances = pd.Series(best_rf.feature_importances_, index=X_train5.columns).sort_values(ascending=False)\n",
        "print(feature_importances)\n",
        "feature_importances.plot.bar()\n",
        "plt.xlabel(\"Feature\")\n",
        "plt.ylabel(\"Importance\")\n",
        "plt.title(\"Feature Importance with Random Forest\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7TB0SkevCJFJ",
        "outputId": "6fe148c4-7439-489e-8468-39d1107dd013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6524390243902439\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix for Random Forest\n",
            "Accuracy: 0.7987804878048781\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AGE               0.275763\n",
            "PICTURE.NAMING    0.207567\n",
            "LEXTALE.TEST      0.193676\n",
            "SELF.PERCEIVED    0.120917\n",
            "EXPOSURE          0.070924\n",
            "INTERVIEW         0.066216\n",
            "EDUCATION         0.064938\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}